title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Kernelized Reinforcement Learning with Order Optimal Regret Bounds | Accept (poster) | Summary: This work studies RL with kernel function approximation, specifically where it is assumed that the transition dynamics and reward function live in some RKHS. While there has been some work on RL with kernel function approximation, existing work provides bounds which are suboptimal. This work seeks to tighten t... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing helpful comments. We have addressed your questions below and hope that it will positively affect your evaluation of the paper. We would also like to highlight that we firmly disagree that our results contradict the lower bounds for linear case, as cl... | Summary: This paper proposes a reinforcement learning algorithm called $\pi$-KRVI that achieves order-optimal regret. The algorithm performs local kernelized optimistic least squares value iteration update - specifically, it partitions the state-action space so that each cell contains a small number of observations, an... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate your feedback and we are glad that you find the paper generally clear. We respond to all your comments in the weaknesses section (in order) and hope that it positively affects your evaluation of the paper.
- In the kernel based bandit setting, whic... | Summary: This paper presents an optimism-based online learning algorithm for RL with large state-action spaces (including continuous spaces). It proposes a (Gaussian) kernel-based function approximation + optimism (building on UCBVI) algorithm. It assumes that the reward and transition density functions are representab... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We appreciate your feedback and are glad that you mention the significance and novelty our results.
While we agree that numerical experiments are important for evaluating the practicality of RL algorithms, our main contribution in this paper is theoretical in n... | Summary: This paper theoretically studies the performance of a reinforcement learning algorithm under the assumption that the Q function is a member of RKHS with a known kernel. The authors provide cumulative regret bound on the iterative-least value iteration algorithm and specialise their results to the kernel with p... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We value your feedback and are pleased that you found the paper clearly written. We address your comments below and hope that it will positively impact your evaluation of the paper. We are open to further discussion if required.
In terms of contribution, we wou... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance | Accept (spotlight) | Summary: The paper proposes a probabilistic version of the canonization method to construct equivariant architectures from generic non-equivariant backbones.
This approach is inspired by the symmetrization solution, which involves averaging a non-equivariant function to obtain an equivariant one, but replaces the unifo... | Rebuttal 1:
Rebuttal: Q1. The proposed approach essentially averages over N > 1 samples and, therefore, is N times more expensive than canonicalization.
A1. The O(N) cost with N samples is a genuine weakness, and we will add clarification n the main text. Nevertheless, as the sampling is completely parallelizable and ... | Summary: The paper presents a method to learn to symmetrize a neural network using data. The method considers a learnable probability distribution over the group and uses group averaging to enforce (relaxed) equivariance w.r.t. the learned distribution.
Strengths: Over recent years there has been a growing interest i... | Rebuttal 1:
Rebuttal: Q1. The proposed method already exists.
A1. Since our approach parameterizes a distribution p(g|x) on a group G for symmetrization ϕ(x) = E_g[g⋅f(g-1⋅x)] and learns it from data, one may find some similarity to Augerino [1] and other approaches in [2] such as [3-7] that learn distributions over a... | Summary: The paper suggests a probabilistic approach to symmetrization, where an input conditional distribution is used to replace the untractable haar measure distribution of infinite groups. In turn, the paper identifies what are the conditions on the conditional distribution under which the symmetrization yields an ... | Rebuttal 1:
Rebuttal: Q1. The introduction states that [1] focuses on manually deriving smaller subsets, but it is not clear.
A1. We intended to describe that one needs to manually solve G equivariant set-valued frame, and it cannot be discovered from data. In contrast, the G equivariant distribution p(g|x) requires l... | Summary: This paper proposes a new symmetrization method for achieving equivariance for any base function. It absorbs previous methods with the same objective as special cases, including group averaging, frame averaging, and canonicalization. The method uses a learnable, equivariant map to generate the group representa... | Rebuttal 1:
Rebuttal: Q1. The superiority compared to canonicalization is not clear.
A1. Theoretically, we can show that there always exist certain input data that canonicalization fails to guarantee exact G equivariance, while PS guarantees G equivariance to all input data (as in Theorem 1).
To see this, let us reca... | Rebuttal 1:
Rebuttal: The common response PDF contains the following:
- Table 1, including additional results on $\textnormal{S}\_n$ invariant graph isomorphism learning with $\textnormal{S}\_n$ symmetrized GIN-ID base function.
- Table 2, including additional results on $\textnormal{S}\_n\times\textnormal{E}(3)$ equ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work presents a generic approach to symmetrize wide range of base models. Instead of relying on uniform average sampling, the approach introduces a trainable transformation to model the group equivariant distribution. The framework theoretically encompasses existing approaches such as group averaging, fra... | Rebuttal 1:
Rebuttal: Q1. A thorough discussion of advantages and disadvantages of symmetrization approaches would enhance our understanding.
A1. We will add related work section with added thorough comparison. A tentative discussion is in Table 3. Please note that we performed theoretical analysis in Section 2.4 and ... | null | null | null | null | null | null |
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning | Accept (poster) | Summary: This paper proposes a novel approach called Federated Feature Distillation (FedFed) to mitigate the data heterogeneity problem while preserving privacy. In particular, FedFed partitions data into performance-sensitive features and performance-robust features based on the information bottleneck method. Only per... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ezSw:
> Q1:Quantitative measurement is missing in the evaluation of model inversion attack in Sec.4.4, e.g., PSNR and FID.
**A1:** Thank you for your valuable suggestion. Accordingly, we employ PSNR and FID, used in [R1][R2], to provide quantitative results for the samples... | Summary: The paper proposes a method based on feature distillation to tackle data heterogeneity. The main contribution , as I see it, is in identifying performance-robust and performance-sensitive features and sharing the latter among clients to mitigate the impact of heterogeneity.
Strengths: - The method is simple (... | Rebuttal 1:
Rebuttal: ## Response to Reviewer wKGY:
> Q1. The evaluation is quite limited to vision datasets, which makes me unsure if it is going to generalize across modalities.
**A1:** Thanks for your insightful comments. In this work, we merely focus on the image modality, while omitting the results on other moda... | Summary: The paper proposes Federated Feature distillation, a method that addresses the tradeoff between privacy and model performance. It involves extracting performance-robust and performance-sensitive features from local data. The latter is shared among clients after applying differential privacy for privacy preserv... | Rebuttal 1:
Rebuttal: ## Response to Reviewer bAmN:
> Q1. Additional communication costs: As sharing globally shared data to all clients, it requires additional communication costs. F.7 doesn’t consider the partial participation of federated learning.
**A1:** Thank you for bringing this potentially confusing problem ... | Summary: The paper introduces a federated learning framework (FedFed) to tackle data heterogeneity by utilizing an information-sharing approach. The method partitions data into performance-sensitive features and performance-robust features, based on their contribution to model performance. The performance-sensitive fea... | Rebuttal 1:
Rebuttal: ## Response to Reviewer LGae:
> Q1: Some overheads are not quantified
**A1:** Thank you for your insightful comments. Accordingly, we add analysis about overheads in **Joint Response**.
> Q2: Test with different levels of heterogeneity and datasets with natural non-IID partitions.
**A2:** Thank... | Rebuttal 1:
Rebuttal: ## Response to All Reviewers:
We sincerely appreciate all reviewers' great efforts on review and comments on our work. We especially thank the nice words:
- timely and important problem (Reviewer #LGae)
- new and interesting high-level idea (Reviewer #xchj & #LGae) and reasonable and simple metho... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Authors propose method (Federated Feature distillation) to share partial features in the data to tackle data heterogeneity, while the privacy issue is not compromised too much. FedFed partitions data into performance-sensitive features and performance-robust features. The performance-sensitive features are glo... | Rebuttal 1:
Rebuttal: ## Response to Reviewer xchj:
> Q1: Adopt recent baselines, e.g., FedGen (ICML 2021)[R6]
**A1:** Following your constructive suggestion, we have compared FedFed with open-source mainstream information-sharing-based works that aim to mitigate data heterogeneity.
The results are reported in Table ... | null | null | null | null | null | null |
Modulate Your Spectrum in Self-Supervised Learning | Reject | Summary: Feature collapse is a major problem in contrastive-base self-supervised learning. To address this issue, the paper introduces the spectral transformation framework, which aims to mitigate the aforementioned problem. Generally speaking, the spectral transformation expands upon the extensively employed whitening... | Rebuttal 1:
Rebuttal: ## Response to Reviewer Fnki
We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific concerns and questions below.
**Concern 1:** The comparison between the proposed method and baseline is unfair. ... the baseline approach.
**Response:** Thanks f... | Summary: For self-supervised learning, this paper proposes a spectral transformation (ST) framework to modulate embedding, seeking functions other than whitening that can avoid dimensional collapse. The authors propose IterNorm with trace loss and provide a lot of theoretical and experimental analysis. The results show... | Rebuttal 1:
Rebuttal: ## Response to Reviewer SEtq
We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific concerns below.
**Concern 1:** The motivation for the proposed IterNorm with trace loss is unclear. The authors empirically observe that IterNorm suffers from sev... | Summary: This submission proposes a spectral transformation for redundancy-reduction-based (a.k.a. whitening-based) self-supervised learning (SSL).
Spectrum-domain modulation is helpful in preventing collapsing caused by representations' rank deficiency.
Since the whitening operation can be seen as a square-root transf... | Rebuttal 1:
Rebuttal: ## Response to Reviewer tQK6
We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific concerns and questions below.
**Concern 1:** The difference between existing whitening-based methods and power-function modulation with $p = 0.5$: Sec 3.2 empiric... | Summary: The paper studies the dimension collapse problem in image self-supervised learning. Previously, people use image space data augmentation/momentum/stop gradient and so on to address this problem. This paper proposes to address the problem via spectrum transformation (ST) in the feature space instead (aka balanc... | Rebuttal 1:
Rebuttal: ## Response to Reviewer Ckne
We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific concerns and questions below.
**Concern 1:** The major concern for this paper is that, it resembles quite a few similarities in the key ideas to a recent AAAI wor... | Rebuttal 1:
Rebuttal: ## Global Response to Reviewers
We thank all the reviewers for their detailed and constructive comments. We briefly highlight the merits recognized by reviewers as follows:
1. Great representation, for example, “the paper is well-organized with a clear story and justification” (TM8b), “the presen... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the dimensional collapse problem in self-supervised learning and proposes a framework to moderate the spectrum of embedding. Besides, a new spectral transformation variant, called IterNorm with trace loss (INTL), is proposed that can avoid collapse and modulate the spectrum of embedding to... | Rebuttal 1:
Rebuttal: ## Response to Reviewer QdWo
We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific questions below.
**Question 1:** How do you balance the MSE loss and IterNorm trace loss? Are there coefficients in the two loss terms? If there are, how do you s... | Summary: This paper tackles the dimension collapse problem in self-supervised learning. The authors propose spectral transformation which can be served as an alternative for the whitening function in avoiding dimensional collapse. Further, they propose a new instance of ST, called IterNorm with trace loss (INTL), and p... | Rebuttal 1:
Rebuttal: ## Response to Reviewer TM8b
We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific concerns below.
**Concern 1:** Comparing methods in the Experiment section: As the proposed method is claimed to be the general form of whitening function in SSL,... | null | null | null | null |
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL | Accept (poster) | Summary: The paper investigate the performance of a specific type of architecture, RNNs coupled with hypernetworks, in meta-reinforcement learning. Meta-RL aims to address the sample inefficiency of RL algorithms by learning to perform few-shot learning when given a distribution of related tasks for meta-training.
The... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate you noting the clarity and fair tuning. We address your comments below. If we have addressed these concerns, please do raise our score, and if not, please let us know what remains unclear:
* **1) “While the hyperparameter tuning was fairly done in terms ... | Summary: This paper explores how to approach the problem of meta-reinforcement learning by using hypernetworks. In particular, the authors propose to employ an RL2-like scheme, where instead of producing a vector $\phi$, the recurrent model outputs a set of neural network weights. They dub the method RNN+HN. They intro... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback. We especially appreciate you noting the results are quite convincing and that you are open to increasing the score if we adjust our claims or add additional experimentation. We have now done both, and would very much appreciate a corresponding score increase.... | Summary: This paper investigates the performance of recurrent neural networks in meta-RL. They suggested that a current neural network can achieve strong performance with hypernetwork. They compared this method with numerous baselines on several meta-RL benchmarks and found that the recurrent baselines along with hyper... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback and for noting the rigorous empirical experiments, elucidating analysis, and clear structure. We address each of your points in turn below. Please do adjust the score correspondingly, or let us know if anything remains unclear. Thank you!
* **1) “The authors... | Summary: The manuscript proposes to modify a common meta-RL baseline, in which a meta-learned RNN provides the policy network with an encoding of the trajectory. The proposed recurrent hypernetwork instead lets the RNN predict the weights and biases of the policy network. This is shown to be a strong model in compariso... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We especially appreciate your time understanding that this paper is not in your field. We appreciate you noting that the ablation studies answered many questions, that the proposed method is stronger on 6/7 of the environments, and that our hyperparameters optimization... | Rebuttal 1:
Rebuttal: In order to address reviewers requests, we have added additional experiments (environments and baselines), adjusted claims, and added details (in a table and text). Figures for new experiments are in an attached PDF, and specifics are below.
**TLDR:**
* We have removed claims to SOTA
* New experi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper is an empirical investigation that tests the performance of different variants of three methods for meta-RL: RNN that provides task information implicitly from previous trajectories on the same MDP, TI that trains a task representation with VAE manner, and VI that is the baseline Varibad [1]. On muj... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate you noting the many benchmarks that we compare against and the rigorous conclusion that the hypernetwork is critical. We address your concerns below. Specifically, we add experiments (by running RNN on more domains and adding an additional domain) and add... | null | null | null | null | null | null |
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor | Accept (poster) | Summary: The paper introduces the Blackwell discount factor MDPs.
The authors show that when the discount factor is larger than the Blackwell discount factor $\gamma_{bw}$, all discount-optimal policies become Blackwell- and average-optimal, and derive a general upper bound on $\gamma_{bw}$. The authors claim to provid... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing the paper and for your constructive comments. We answer your questions and remarks below.
*Motivation and applications of Blackwell optimality.* The motivation for Blackwell optimality mostly comes from some of the shortcomings of the two other return criteria... | Summary: This paper studied the a Blackwell optimal policy can be obtained from an average-optimal policy by introducing the Blackwell discount factor $\gamma_{bw}$. The author further showed that the discount factor can be upper bounded by the bit-size of an MDP instance.
Strengths: 1. The paper is well written and o... | Rebuttal 1:
Rebuttal:
Thank you for your review. We first answer the weaknesses.
1. Note that our bound on $\gamma_{\sf bw}$ leads to a *polynomial* algorithm; we refer to Theorem 4.7 for the complexity statement. Polynomial-time complexity is commonly understood as {\em tractable} empirical performances. Our bound r... | Summary: The paper studies how to reduce the computation of Blackwell-optimal policies to that of discount-optimal policies. The authors introduce the notion of "Blackwell discount factor", a value $\gamma_{bw} \in [0,1)$ s.t. any discount-optimal policy for $\gamma > \gamma_{bw}$ is also Blackwell optimal. They show t... | Rebuttal 1:
Rebuttal:
We thank you for your detailed comments. We will answer your questions below.
*Q1 - conservativeness of our bound on $\gamma_{\sf bw}$.* You are correct that our bound on $\gamma_{\sf bw}$ may be loose. In particular, it relies on Theorem 4.6 (separation between the different roots of a polynomi... | Summary: The paper proposed a new concept called Blackwell discount factor $\gamma_{bw}$, which enjoys good properties: any policy that is $\gamma_{bw}$-discount-optimal will be Blackwell optimal as well. If $\gamma_{bw}$ is known, then one can reduce the problem of find average-optimal policy to finding discount-optim... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing the paper and for your constructive comments. We will answer your questions and remarks below.
### Writing and motivations.
*Points 1-2-3*. We will make these two points more explicit in our final version. Thanks for the clarification.
*Point 4*. Thanks for ... | Rebuttal 1:
Rebuttal: Dear editorial teams
We would like to thank all the reviewers for their constructive feedback. We provide our answers to all reviewers individually.
We would like to note that we have extended Proposition 3.4 (shortcomings of existing definition of Blackwell optimality) and Proposition 4.3 (nee... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper defines the Blackwell discount factor: when the discount factor is larger than this factor, discount-optimal policies become Blackwell and average-optimal. An analytical solution for such factor is also derived. The results are also extended to the setting of sa-rectangular RMDPs.
Strengths: Motiva... | Rebuttal 1:
Rebuttal:
We thank you for your time reviewing the paper.
*As regards coarseness and Proposition 4.3:* Our goal is to obtain an upper bound on the Blackwell discount factor $\gamma_{\sf bw}$ for a large class of MDP instance. Proposition 4.3 shows that without any condition on the rewards $\boldsymbol{r}... | Summary: This paper studies a new class of objective for MDPs. Instead of aiming to find the policy which maximizes the long-term average reward, the authors propose a new approach to find a Blackwell optimal policy. A Blackwell optimal policy also maximizes the average reward, but Blackwell optimality has not been stu... | Rebuttal 1:
Rebuttal:
Thank you for your review and suggestions. We answer your remarks and questions below.
*Therefore, I am concerned that for most reasonably sized MDPs, we will need to find an optimal policy with a discount factor arbitrarily close to 1 which will still be challenging and may require more assumpt... | null | null | null | null |
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks | Accept (poster) | Summary: This paper views a prototype but interesting enough neural network architecture from Rademacher complexity, and uses Rademacher complexity to analyze the sample complexity used for correctly training the neural network. It reveals an approves an interesting result that the Rademacher complexity hence the sampl... | Rebuttal 1:
Rebuttal: Thanks for your comments. We will see whether we can incorporate experiments (say along the lines of Bartlett et al [2017]). However, it should be emphasized that our paper is theoretical and focuses on understanding the minimax optimal sample complexity of various predictor classes, similar to ma... | Summary: In this paper, the authors give sample complexity bounds for learning function classes of the form $g(x) = f(Wx)$, where $W$ is a matrix, and $f$ is a Lipshitz function. They sample complexity bounds are based on the Frobeneous norm of the matrix $W - W_0$, where $W_0$ is a fixed initialization matrix. The upp... | Rebuttal 1:
Rebuttal: Thanks for your comments, we will improve the presentation according to your suggestions.
- Show dependence on $n$: We would like to emphasize that our focus is on size-independent bounds, which do not depend in any manner whatsoever on $n$. This is in line with a huge previous literature in stati... | Summary: The paper generally studies the sample complexity of the functions of the form $f(Wx)$ and it particularly focuses on size-independent bounds. First it shows matching exponential lower and upper bounds for the case that the reference matrix $W_0=0$. Then, it is showed that one cannot obtain an upper bound in t... | Rebuttal 1:
Rebuttal: Thanks for your comments, we will fix the typos.
- Q1: To prove a lower bound in a size-independent setting, we are free to choose the size parameters $n,d$ as we wish (since the upper bounds should hold for any $n,d$). In particular, we may choose them to depend on $L,B,\epsilon$. It would be in... | Summary: The authors provide various sample complexity results for linear and nonlinear networks in initialization dependent and independent cases. Here is the summary of results.
* For the class of predictors $f(Wx)$, the fat-shattering dimension is characterized and gives a lower bound on sample complexity where the ... | Rebuttal 1:
Rebuttal: Thanks for your careful reading of the paper and the detailed comments, we will be happy to add clarifications and discussions as suggested.
First, a general comment relevant to most of the points raised: We would like to emphasize that our focus is on *size-independent* bounds, which do not depe... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Binarized Spectral Compressive Imaging | Accept (poster) | Summary: This paper presents a deep neural network with binarized operations for low-cost spectral compressive imaging of hyperspectral images (HSI). The network introduces a basic unit for model binarization which adaptively redistributes HSI representations before binarization activation and uses a scalable hyperboli... | Rebuttal 1:
Rebuttal:
### Response to Reviewer kC2s
Thanks for your valuable comments.
Code and models will be released to the public.
`Q-1:` The proposed binarization scheme is general, without specific design for HSI restoration. It lacks comparison to other BNNs or in other tasks.
`A-1:`... | Summary: This paper proposes a novel method, Binarized Spectral-Redistribution Network (BiSRNet), for efficient and practical HSI restoration from compressed measurement in snapshot compressive imaging (SCI) systems. This paper redesigns the base model and presents the basic unit (BiSR-Conv) for model binarization. Spe... | Rebuttal 1:
Rebuttal:
### Response to Reviewer ZUfp
Thanks for your valuable comments.
Code and models will be released to the public.
`Q-1:` How to derive the 3D mask? Why does it have an additional dimension?
`A-1:` The original coded aperture $\mathbf{M}^* \in \mathbb{R}^{H \times W}$ is ... | Summary: This work focuses on studying the binarized spectral compressive imaging reconstruction problem. A Binarized Spectral-Redistribution Network (BiSRNet) is proposed. The authors first design a basic U-Net as the base model to begin the binarization. Then a Binarized Spectral-Redistribution Convolutional (BiSR-Co... | Rebuttal 1:
Rebuttal:
### Response to Reviewer nobf
Thanks for your valuable comments.
Code and models will be release to the public.
`Q-1:` Why Sign(0) and Tanh(0) are defined as -1?
`A-1:` As explained in Line 150 -152 of the main paper, if strictly following the mathematical definition, ... | Summary: In this paper, binarized neural network is first utilized in hyperspectral image reconstruction. For model binarization, authors propose the Binarized Spectral-Redistribution Convolution (BiSR-Conv), which adaptively redistributes the HSI representations before binarizing activation. Since the Sign function is... | Rebuttal 1:
Rebuttal:
### Response to Reviewer aGHs
Thanks for your valuable comments.
Code and models will be released to the public.
`Q-1:` How does the 'full-precision information' affect the whole network? Does the 'full-precision information' play a dominant role in BiSR-Conv or not?
`A... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a Binarized Neural Network based approach known as BiSRNet for binarized HSI restoration. The main motivation of the paper stems from the fact that any CNN or transformer-based architectures are computationally heavy for devices with low computing power and hence need extremely fast and lig... | Rebuttal 1:
Rebuttal:
### Response to Reviewer F7DC
Thanks for your valuable comments.
Code and models will be released to the public.
`Q-1:` Are the redesigned conv modules only applicable to the HSI domain?
`A-1:` No. Although this work mainly studies the binarized spectral compressive ima... | null | null | null | null | null | null |
Learning Generalizable Agents via Saliency-guided Features Decorrelation | Accept (spotlight) | Summary: This paper introduces an original approach named Stochastic Gradient Feature Decorrelation (SGFD), which aims to amplify the generalization capabilities of reinforcement learning (RL) agents across various environmental variations. These variations can encompass task-irrelevant visual attributes such as backgr... | Rebuttal 1:
Rebuttal: We are particularly encouraged that the Reviewer NdCs finds our method novel and effective.
**Reply to the weakness**
>**W1. Firstly, the methodology requires several environments with variations to train their environment classifier, introducing an element of manual supervision into the learnin... | Summary: In visual-based Reinforcement Learning (RL), agents often struggle with generalization to environmental variations that were not observed during training that have changed task-irrelevant features and changed task-relevant features.
To achieve generalization in environmental variations, The authors popose a sa... | Rebuttal 1:
Rebuttal: We are encouraged that Reviewer SPDk finds our method novel and the idea well-grounded.
**Reply to the weakness**
>**W1. The proposed method use the saliency to calculate and discriminate changed features under environmental changes. Their experimental results and analysis include only one of tas... | Summary: This paper deals with a novel sample re-weighting method designed to enhance generalization in visual reinforcement learning tasks across environments with unseen task-irrelevant and task-relevant features. SGFD is composed of two core components: RFF and a saliency-guided model. The paper puts down results co... | Rebuttal 1:
Rebuttal: We are particularly encouraged that the Reviewer jEbK finds our method novel and effective.
**Reply to the weakness**
>**W1. Related work needs an overhaul in terms of gaps.**
Building on the work discussed in Section 2, we further complement related work that generalizes to novel environment c... | Summary: This paper propose SGFD, an novel approach aimed at improving the generalization ability of distinguishing task-irrelevant and task-relevant situations. SGFD leverages two core techniques: Random Fourier Functions (RFF) and the saliency map, to estimate the complex non-linear correlations in high-dimensional i... | Rebuttal 1:
Rebuttal:
Thanks for the reviewer's positive appraisal, insightful comment, and criticism of our paper.
**Reply to the weakness**
>**W1. Figure 2 is confusing.**
We split the original Figure 2 into two figures in the global response PDF: Figure 1 discusses the motivation for feature decorrelation, while ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We are very grateful to the reviewers for their valuable suggestions, which further improved our work. We provide three visualizations about our motivation, technique, and environmental setting with a submitted 1-page pdf.
- Figure 1: The motivation for feature decorrelation used... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a method to weight experiences in reinforcement learning so that the state features they depict are maximally decorrelated. The assumption is that learning with decorrelated features leads to better generalization to unseen task instances because the policy does not confound the role of dif... | Rebuttal 1:
Rebuttal: We are particularly encouraged that the reviewer finds our method novel and effective.
**Reply to the weakness**
>**W1. Explaining more the reasoning behind the design decisions.**
As suggested, we introduce an example to explain why feature decorrelation can promote generalization, as shown in ... | null | null | null | null | null | null |
Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator | Accept (poster) | Summary: This paper presents a novel zero-shot text-video generation method that leverages an LLM as a director to generate per-frame prompts fed into a pre-trained text-image model such as Stable Diffusion. In order to retain temporal coherence, the authors propose (1) a noise distribution consisting of an interpolati... | Rebuttal 1:
Rebuttal: > **Benchmark**: Quantitative results/evaluation are a little weak, given the relatively small sample size.
Thanks for pointing out this. The standard benchmarks for video generation, especially for zero-shot video generation, are still building up in the community. The videos in our benchmark a... | Summary: This paper proposes a novel approach to achieve Text-to-Video generation through Zero-Shot learning using LLMs and a diffusion-based image generation model. By using LLMs to generate detailed and varied descriptions for each frame of the video, the proposed method captures the changes in the video frames. The ... | Rebuttal 1:
Rebuttal: > **Comparion with different LLMs**: Lacking a comparison of the quality of video generation guided by different large language models. Only ChatGPT was used in the experiments.
Thanks for your valuable feedback. Per your advice, we conduct experiments using other LLMs (GPT-4 and Bard), and the r... | Summary: In this work, the authors propose a principled pipeline for text to video generation. Specifically, three techniques are proposed: (1) a LLM based per-frame prompt generation, so that the motion/dynamics of each frame can be better specified. (2) a noise joint sampling schedule, and a step-aware attention shif... | Rebuttal 1:
Rebuttal: > **Prior in LLM (part 1)**: To my honest opinion, directing comparing this work w/ many baselines is kind of unfair, since this involves strong prior in LLM, and this is the key factor of improvement in video generation.
As the reviewer pointed out, using LLMs introduces prior knowledge about ho... | Summary: The paper proposes a zero-shot text-to-video generating pipeline called Free-Bloom, which first use LLM to generate prompt sequence decribing frames in a video, then generate frames according the prompts. To enhancing coherence, the authors proposed joint noise sampling, step-aware attention shift and dual-pat... | Rebuttal 1:
Rebuttal: > **Insufficient ablation study (part 1)**: more cases and quantitative results should be provided.
Thanks for your valuable feedback. Per your advice, we conducted additional ablation studies and evaluated their performance in both human evaluation and automatic metrics. As presented in CLIP met... | Rebuttal 1:
Rebuttal: We thank all reviewers for engaging in the review process. Our code will be made public upon acceptance.
We are deeply encouraged by positive comments from the reviewers. We appreciate the recognition and endorsement of our proposed zero-shot pipeline, such as acknowledging its qualities as inte... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Kernel-Based Tests for Likelihood-Free Hypothesis Testing | Accept (poster) | Summary: This paper studies a problem in the area of likelihood-free testing, where one is given black-box access to samples $X\sim P_X$, $Y \sim P_Y$ of size n, as well as "real-world data" $Z \sim P_Z$ of size m. The authors propose a new model ("Mixed likelihood-free testing") where it is known that $P_Z = (1 - \nu)... | Rebuttal 1:
Rebuttal: We greatly appreciate the helpful comments in the review and will make sure to address them in the revision. In the following we will address some of the questions mentioned in the review.
1. $\textbf{“The upper and lower bounds in the theory are difficult to interpret.”} $
In terms of simple ex... | Summary: This paper introduces a new framework for likelihood-free hypothesis testing (LFHT), denotes as Mixed-LFHT or mLFHT. The setting of mLFHT is as follows: assume $n$ i.i.d. samples from a background disttribution $P_X$, and the same number of samples from a signal distribution $P_Y$. Also, assume we are given an... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments on our writing as well as the ‘minor comments’; our revision will incorporate all of the helpful suggestions. The page limit is indeed challenging, but we believe we are able to make the improvements necessary for good readability. Below, we address some more ... | Summary: The paper addresses a likelihood-free hypothesis testing (LFHT) scheme, where the data distribution is a mixture of two distributions. The null hypothesis is given by one of these distributions, whereas the alternative hypothesis is that the mixture coefficient is lower-bounded by some delta. The motivation f... | Rebuttal 1:
Rebuttal: We greatly appreciate the helpful comments in the review and will make sure to address them in the revision. In the following we will address some of the questions mentioned in the review.
1.$\textbf{”It seems that the applications for the proposed mixture LFHT are quite limited.”}$
Our motivati... | Summary: This paper proposes a test statistic that can be used for likelihood-free hypothesis testing [LFHT] (for distributions $P_X, P_Y$, given a sample from $P_Z$, decide if $P_Z = P_X$ versus $P_Z = P_Y$) and mixed likelihood-free hypothesis testing [MLFHT] (for $P_Z = (1 - \nu) P_X + \nu P_Y$, decide if $ \nu = 0 ... | Rebuttal 1:
Rebuttal: We greatly appreciate the helpful comments in the review and will make sure to address them in the revision. In the following we will address some of the questions mentioned in the review.
1. $\textbf{“The bounds are a little non-intuitive / roundabout.”}$
Please see our general response #1. We ... | Rebuttal 1:
Rebuttal: We greatly appreciate the valuable suggestions given by reviewers and will revise our manuscript accordingly. In this response, we would like to address several comments that appear in multiple reviews.
1. $\textbf{“Interpretation of the bounds and its dependence on kernel, base measure, hypothes... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Enhancing Robot Program Synthesis Through Environmental Context | Accept (poster) | Summary:
This paper introduces the Environmental-context Validated lAtent Program Synthesis framework (EVAPS), which builds upon the SED approach by utilizing a trail-eval-repair loop to enhance program evolution and improve generalization capabilities. EVAPS leverages partial environmental observations by initially o... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments and constructive suggestions. We summarise the issues pointed out and address them in the following:
> A gap between the title and experiment environment in which this work is evaluated
Thanks for pointing this out. In this work, we explore the topic "enhan... | Summary: The paper claims that global observation in robot program synthesis is not achievable, so it proposes to use partial observation. And it learns an observation embedding module and a semantic-grammatical alignment module to repair the candidate programs that can increase the accuracy and generalization of robot... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments and constructive suggestions. We summarise the issues pointed out and address them in the following:
---
> Choice of baseline
We conducted a review of robotic program synthesis works published in the recent 5 years and identified SED proposed in 2020 as the ... | Summary: This paper proposes EVAPS to enhance robotic program synthesis by integrating parietal environmental observations. Specifically, EVAPS utilizes both the environmental context leveraging module and the code symbol alignment module to improve its ability to rectify semantically erroneous program segments and gen... | Rebuttal 1:
Rebuttal: Thanks for acknowledging our contributions. We address your comments in the following.
> Limitation
Since our approach is based on self-supervised training, it relies on the quality of the data used for training.
However, data quality is a common issue in this field.
Moreover, we anticipate so... | Summary: The paper proposes Environmental-context Validated lAtent Program Synthesis framework (EVAPS). a program synthesis model that generates executable program for robotic programming, evaluated in the vizdoom environment. It initially obtains candidate programs using other available synthesizers, then performs pro... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We address your comments in the following.
> Writing of the paper can be improved
Thanks for your comments. We will proofread the manuscript carefully.
> Do the baselines use program repair? Does any of them rely on environmental observations?
In baseline... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes an approach for program synthesis in robotic domains where the environment context can provide valuable insight for what the correct program should be. The approach takes candidate programs, executes them to get a trace of observations, and then passes the program and trace through a combin... | Rebuttal 1:
Rebuttal: Thank you for your meticulous review of our paper and for acknowledging our contribution.
Please see below our responses to your comments.
---
> Can SOTA approaches on Karel be applied to Vizdoom?
> My main concern is that the gist of the idea seems very similar to work on execution-guided synt... | null | null | null | null | null | null |
The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium | Accept (poster) | Summary: This paper examines the connection between the notions of consensus and equilibrium in a multi-agent system where multiple interacting sub-populations coexist. They argue that consensus can be seen as an intricate component of intra-population stability, whereas equilibrium can be seen as encoding inter-popula... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide us with your valuable feedback, and also for recognizing our paper to be an “Excellent paper Excellent paper that brings together the concepts of consensus and equilibrium” with “Strong theory, interesting experiments.”
**Comment**: "don't forget the concl... | Summary: The authors defined a network population game, which is a multipartite network game where each partite is a population and agents in the same population do not interact with each other and only interact with agents from other populations. In this way, each population can be easily abstracted into a "super-agen... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide us with your valuable feedback.
**Comment**: “The justification for using the network population game is not sufficient…easier to study.”
**Response**: Network population games model scenarios which are characterized by the presence of multiple interacti... | Summary: This paper combines reaching consensus and convergence to equilibrium for network population games. Consider a network whose vertices correspond to a population. Edges between vertices (or populations) represent two-player sub-games between each pair of agents in these neighboring populations. The authors spec... | Rebuttal 1:
Rebuttal: Thank you for your time and for your constructive comments on our paper!
**Comment**: “There is no motivating example for the network population game formulation” and “Can you provide a motivating example justifying the network population model in practice (specifically the 2-player sub-games)?”
... | Summary: This paper examines the connection between the notions of consensus and equilibrium in a multi-agent system where multiple interacting sub-populations coexist, and it aims at answering below two central research questions in network (population) games scenario:
[1] Are there natural multi-agent learning ... | Rebuttal 1:
Rebuttal: Thank you! We sincerely appreciate your diligent and thoughtful comments on our paper.
**Comment**: “I'd like to see some discussions about the future research directions, and how this work could inspire/benefit other future research.”
**Response**: We appreciate your interest in future direct... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Principled Weight Initialisation for Input-Convex Neural Networks | Accept (poster) | Summary: The paper discusses a principled weight initialization strategy for Input-Convex Neural Networks (ICNNs). The authors propose a new theory that generalizes signal propagation theory to include weights without zero mean, and derive a principled initialization strategy for ICNNs from this theory. They demonstrat... | Rebuttal 1:
Rebuttal: We would like to thank you for your feedback. We hope to address all of your points in detail soon!
For now, we would like to ask for some further clarification on what ablation studies are missing.
This would help us to better address your concerns in our detailed response.
Thanks in advance!
... | Summary: The proposed method aims to solve the initialisation problem in an Input convex Neural Network (ICNN), where the weights are required to the non-negative. The commonly applied approach, setting the negative entries stamped from zero-mean Gaussian as zero, leads to varies the desired mean. By analysing the sign... | Rebuttal 1:
Rebuttal: We would like to thank you for your feedback. We hope to address all of your points in detail soon!
For now, we would like to ask for some further clarification on what quantities the reviewer would like to see in the comparison.
This would help us to better address your concerns in our detailed ... | Summary: This paper investigates the initialization for input-convex neural networks. They generalize the signal progagation theory by removing the assumption of centred weight distrubution. The experiments show that the proposed initiaization method is effective in a set of datasets.
Strengths: 1 This paper generaliz... | Rebuttal 1:
Rebuttal: ## Theoretical contribution
We do not entirely understand why our generalisation of signal propagation theory and the derivation of the initialisation for ICNNs would be considered a _”limited”_ theoretical contribution.
The initialisation for ICNNs is indeed _limited_ to ICNNs.
However, the gene... | Summary: NOTE: edited after author rebuttal and score has been updated.
This paper is related to input convex neural networks (ICNN). It analyzes the signal propagation through such a network and based on that proposes a new initialization scheme that allows the networks to be trained efficiently. It investigates the ... | Rebuttal 1:
Rebuttal: ## Limitations and Strengths of ICNNs
You are right that the ICNN family is strictly speaking less powerful than regular networks because they are constrained to have convex decision boundaries.
Note that it is possible to construct a universal approximation theorem using theorem 1 from (Yuille ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and suggestions.
The comments and concerns of each reviewer will be addressed individually and the paper will be updated correspondingly.
We did observe a common (potential) misunderstanding about our contribution in the reviews.
Some reviewers claim in t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
RETVec: Resilient and Efficient Text Vectorizer | Accept (poster) | Summary: This paper introduces RETVec, a resilient and multilingual text vectorizer designed for neural-based text processing. RETVec combines a unique character encoding with an optional small model to embed words into a 256-dimensional vector space. The RETVec embedding model is pre-trained using pair-wise metric lea... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and constructive feedback. Please find our responses below.
> **Q1:** Though the proposed method exhibits significant improvements on several evaluations, it does not outperform sentencepiece overall when used for training pre-trained language mod... | Summary: This paper introduces RETVec, a resilient and efficient text vectorizer designed for neural-based text processing. RETVec is a multilingual tool that combines a novel character encoding with a pre-trained embedding model to create a 256-dimensional vector space. The vectorizer is significantly more resilient t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and insightful comments. Please find our response below.
> **Q1:** The paper does not provide a detailed analysis of RETVec's limitations and potential failure cases. While the paper does mention some of the challenges faced during the development... | Summary: The paper introduces RETVec, a resilient, efficient, and multilingual text vectorizer designed for neural-based text processing. It addresses the limitations of existing approaches by combining a novel UTF-8 character encoder with a small model. RETVec does not require dataset pre-processing and accepts all va... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and constructive feedback. Please find our responses below.
> **Q1:** The paper does not provide detailed comparison results with other vectorizers on different languages and multilingual settings.
**A1:** We evaluated RETVec on the Amazon Multi... | null | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CorresNeRF: Image Correspondence Priors for Neural Radiance Fields | Accept (poster) | Summary: The submission #8302, entitled "CorresNeRF: Image Correspondence Priors for Neural Radiance Fields" proposes a novel set of losses to improve the quality of NeRF under challenging conditions. In particular, the developed strategy effectively deals with the problem of sparse images. To achieve these performance... | Rebuttal 1:
Rebuttal: ### Q1: Ablation study with an increasing number of images.
We thank the reviewer for the suggestion. We tested the robustness of CorresNeRF with varying input view counts. Specifically, we doubled the number of input views from 3 to 6 and then evaluated CorresNeRF's performance on the LLFF datas... | Summary: The paper presents NeRF regularization method for few view NeRF.
Strengths: 1. Using the state-of-the-art image matcher to regularize NeRF training is novel.
2. The paper is well-written and clear.
Weaknesses: 1. The paper proposes employing the cutting-edge image matcher to enhance NeRF training. However, a... | Rebuttal 1:
Rebuttal: ### Q1: Comparison with Neuris.
We have examined the Neuris method and believe its approach of utilizing normal and monocular depth priors complements CorresNeRF. Notably, CorresNeRF serves as a plug-and-play module applicable to any NeRF, provided reasonable image correspondences can be achieved... | Summary: This paper proposed CorresNeRF, a method that leverages image correspondence priors to improve NeRF training on sparse input views. The correspodence matching is computed by off-the-shelf methods. The authors augue that the introduced inexpensive image correspodence priors can be used to supervise training of ... | Rebuttal 1:
Rebuttal:
### Q1 (from weaknesses): Dependence on the quality of image correspondence.
We thank the reviewer for the suggestion. We employed image matching methods to obtain correspondences and subsequently introduced Gaussian noise to these correspondences. Specifically, we added Gaussian noise with stan... | Summary: The paper introduces CorresNeRF, a method that leverages image correspondence priors to improve the performance of Neural Radiance Fields (NeRF) in scenarios with sparse input views. The authors propose a plug-and-play module that incorporates correspondence priors into the training process by adding loss term... | Rebuttal 1:
Rebuttal: ### Q1: Experimental Evaluation: computational efficiency of CorresNeRF.
We thank the reviewer for the question. At inference time, CorresNeRF operates at exactly the same runtime as the baseline NeRF model. However, during training, CorresNeRF incurs additional runtime overheads due to the searc... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and questions. In this section, we provide a summary of our responses and present new experimental results. Specifically, we introduce new experiments that examine:
- The robustness of CorresNeRF with noisy correspondences
- The robustness ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes an approach for sparse-view NeRF reconstruction by using image correspondences as a prior. NeRF under the sparse-view regime is overparameterized and under constrained hence requiring a prior to optimize. This paper proposes to use image correspondences that are extracted across the differen... | Rebuttal 1:
Rebuttal: ### Q1: Performance on wide-camera baselines (e.g. ScanNet) in addition to forward-facing LLFF.
We appreciate the reviewer's inquiry. In our paper, we present evaluations of
CorresNeRF on the LLFF dataset with forward-facing cameras, as well as on the
DTU dataset where the cameras have a spherica... | null | null | null | null | null | null |
Parameter-efficient Tuning of Large-scale Multimodal Foundation Model | Accept (poster) | Summary: This paper proposes a novel approach to address the challenge of high learning costs when migrating large models to specific downstream tasks. The proposed method aims to reduce task complexity and improve the consistency across the different modal outputs of multimodal models.
The authors employ a LoRA-like ... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the novelty and effectiveness of our approach as well as the visualization demonstration. We will continue to improve based on your feedback, and we believe that our Aurora has a very positive impact on promoting the efficient transfer of multimodal large models i... | Summary: This paper aims to design a lightweight prompt tuning method (i.e. Aurora) for cross-modal transfer. The main idea follows the observation by LoRA [15] that most of the features are redundant and a low-rank ∆W can be learned to adapt the features. Different from LoRA, they adopt CP decomposition [47] to decomp... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the lightweight design, effectiveness, and comprehensive experiments of our method. We will continue to improve based on your feedback, and we believe that our Aurora has a very positive impact on promoting the efficient transfer of multimodal large models in the ... | Summary: This paper proposes a parameter efficient adaptation technique Aurora for multi-modal models. Particularly, the proposed method motivates their design by suggesting that the original pre-trained weight matrices have redundancies due to their high dimensional nature and the downstream task often requires low-di... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our design idea and comprehensive experiments. We will continue to improve based on your feedback, and we believe that our Aurora has a very positive impact on promoting the efficient transfer of multimodal large models in the community.
> **Confusing presentatio... | Summary: The paper addresses the problems of (i) transfer learning and (ii) reducing the multimodality gap in multimodal models.
To address (i) it presents a technique which can be viewed as a generalization of LoRA; instead of independently factoring representing matrices as a low rank representation, all the matrice... | Rebuttal 1:
Rebuttal: Thank you for your highly accurate summary of our work and recognition of our comprehensive experiments. We will continue to improve based on your feedback, and we believe that our Aurora has a very positive impact on promoting the efficient transfer of multimodal large models in the community.
>... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes AURORA, a method which uses mode approximation to boost the knowledge transfer in Vision Language models and enhances alignment between the modalities in a lightweight parameter efficient manner. In addition to this, the paper further proposes Context Enhancement module and Gated Query Tran... | Rebuttal 1:
Rebuttal: Thank you for your professional comments. We will continue to improve based on your feedback, and we believe that our Aurora has a very positive impact on promoting the efficient transfer of multimodal large models in the community.
> **Motivation not clear.** The motivation for our mode approxim... | null | null | null | null | null | null |
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models | Accept (poster) | Summary: This paper proposes a framework for human-object interaction (HOI) detection that leverages vision-language foundation models and large language models to achieve universal and flexible recognition of complex interactions in images. The framework, named UniHOI, consists of three main components: a visual HOI d... | Rebuttal 1:
Rebuttal: Dear Reviewer X6bU,
First and foremost, we'd like to express our gratitude for your comprehensive review and insightful comments. We acknowledge the concerns you've raised and will attempt to address them point by point:
**Ablations**: We appreciate the importance of ablation studies to establ... | Summary: In view of the limited scalability and the suboptimal zero-shot performance of current HOI detection methods, the authors propose a novel method for HOI detection based on VL foundation models.
With in-depth analysis and adaptation of HOI detectors, the foundation model is effectively adopted to reason about H... | Rebuttal 1:
Rebuttal: Dear Reviewer djfM,
We truly appreciate the detailed feedback. Herein, we provide a detailed response to each of your concerns:
**On Weaknesses**:
a. **Comparison with GEN-VLKT**: In response, we replace BLIP2 with CLIP and conducted experiments on V-COCO datasets:
|Method|${AP}^{1}_{role}$|${... | Summary: This paper investigates the problem of human-object interaction (HOI) detection. The authors introduced UniHOI, a method for universal HOI detection in an open-world setting. They also explored the universal interaction recognition with Vision-Language (VL) foundation models and large language models (LLMs), a... | Rebuttal 1:
Rebuttal:
Dear Reviewer WF8N,
We greatly appreciate your thoughtful review and the time you have taken to provide insights and feedback on our submission. We are encouraged by the positive aspects you've highlighted and grateful for the critical points you've raised. Here, we address the weaknesses mentio... | Summary: The paper addresses human-object interaction (HOI) detection task. The authors propose a new method named as UniHOI, achieved by prompting BLIP2 using human-object paired features, as well as linguistic semantics generated by a LLM. The proposed UniHOI demonstrates significant performance gain on HICO-DET and ... | Rebuttal 1:
Rebuttal: Dear Reviewer pXLy,
First and foremost, we extend our deepest gratitude for your insightful feedback and hope our clarifications address your concerns. We're eager to highlight the significance and potential of our work.
**1. Regarding BLIP2's Utilization**:
We agree with your statement about P... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a universal HOID pipeline, which utilizes decoded ho-pair feature as spatial prompts to prompt the VL foundation model with the aim to implement effective prompt-based learning on base VL model and extract HOI related features from it. It also proposes knowledge retrieval for HOID in open-c... | Rebuttal 1:
Rebuttal: Dear Reviewer WxZv,
First and foremost, we extend our deepest gratitude for your thorough review and insightful feedback. Your recognition of our method is truly appreciated. We concur with your perspective that utilizing $V_f$ exclusively for prediction would offer a more direct testament to the... | null | null | null | null | null | null |
Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection | Accept (poster) | Summary: This paper introduces a novel cross-modal BEV distillation approach, namely VCD, but adopts identical feature extractors for the teacher and the student model, which eases the distillation difficulties during the knowledge-transferring phase.
(1) It proposes a new multi-modal teacher VCD-E, with an image-bas... | Rebuttal 1:
Rebuttal: Dear Reviewer oCet,
We sincerely appreciate your valuable feedback. We will address your comments below.
**1. lacks some theoretical analysis**
Thanks for your suggestions. We have done some analysis on the motion misalignment during the process of temporal fusion in Supplementary Sec.B.1. We w... | Summary: This work presents vision-centric distillation, which utilizes multi-modalities for the expert and images for the student. It includes two distillation parts, namely Trajectory-based Distillation and Occupancy Reconstruction. Experiments prove the effectiveness of the proposed method.
Strengths: 1. The propos... | Rebuttal 1:
Rebuttal: Dear Reviewer bEge,
Thank you for the valuable and detailed comments. We will address your concerns below.
**1. The confusion about the definition of Occupancy and how to generate the occupancy annotation.**
Excuse for the confusion. The **definition** of occupancy used in the paper is to deter... | Summary: This paper aims to design a multi-modal expert teacher with little domain gap to distillate the LSS-based 3D object detector. Different from existential work, it just leverages the LiDAR depth information to design a teacher model instead of using a cumbersome LiDAR feature extractor. The proposed method shows... | Rebuttal 1:
Rebuttal: Dear Reviewer xwmh,
Thank you for the constructive and thoughtful feedback. We will address your concerns below.
**1. The latency of VCD-E.**
Thanks for your suggestions. As shown in the Table below, we add the FPS in the comparison with other multi-modal methods. The FPS is measured by a singl... | Summary: The paper presents an innovative approach for improving camera-only 3D object detection. It introduces a vision-centric multi-modal expert and a trajectory-based distillation module to address key challenges in the field. The framework includes an apprentice-friendly multi-modal expert and a fine-grained traje... | Rebuttal 1:
Rebuttal: Dear Reviewer KRDk,
Thank you for the thoughtful and valuable comments. We will address your concerns below.
**1. The repeated Reference[23] and Reference[24]**
It indeed confuses easily, while these are two different papers.
[23] Li, Yinhao, Han Bao, Zheng Ge, Jinrong Yang, Jianjian Sun, and ... | Rebuttal 1:
Rebuttal: Dear Reviewers and AC(s):
We extend our gratitude. Below, we address all review comments and incorporate accordingly into the revised manuscript of our work.
The attached PDF below includes a table and a figure. The table illustrates the number of frames used by different approaches, addressing ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper seeks to improve camera-only 3D object detection by distilling a multi-modal expert model. The authors start by developing a multi-modal expert with essentially the same architecture as the camera-only model - intended to reduce the domain gap. Then, they propose trajectory-based distillation as wel... | Rebuttal 1:
Rebuttal: Dear Reviewer LE1a,
Your acknowledgment of our approach is sincerely appreciated. We will address your comments below.
**1. Occupancy Reconstruction**
1.1. Details of Occupancy Reconstruction
The **annotations of occupancy** are generated by gathering depth scores from multi-camera pixels with... | null | null | null | null | null | null |
ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy | Accept (poster) | Summary: The authors work on temporal graph representation learning that faces two challenges: (1) the diversity of the evolving patterns and their time-varying nature are hard to model; (2) high computational cost for structure recognization with increasing numbers of nodes and edges.
The authors propose to overcome ... | Rebuttal 1:
Rebuttal: We appreciate your careful reading, encouraging remarks, and constructive feedback.
**1) The von Neumann entropy and the graph entropy in the thermodynamic temperature difference are defined on undirected graphs. However, the temporal network can be generically directed. How can the proposed met... | Summary: The paper presents a new framework called ESSEN (Evolution StateS awarE Network) to measure the evolution of temporal networks using von Neumann entropy and thermodynamic temperature difference. Existing methods struggle to handle the time-varying nature of these networks, hindering their performance on comple... | Rebuttal 1:
Rebuttal: Thank you for your careful reading, positive comments, and constructive feedback.
**1) What is Von Neumann Entropy?**
In the revised manuscript, we will add more details about the definition of von Neumann entropy. Von Neumann entropy is a concept in quantum information theory. In the quantum c... | Summary: The authors propose a novel method on performing inference tasks on dynamic network structures. Differentiating from previous literature, the proposed method capitalizes on the Von Neumann entropy, which provides a set of indicators about the structural symmetries.
In combination with a quadratic approximati... | Rebuttal 1:
Rebuttal: Thank you for the careful reading and helpful comments. Our anonymous code link is provided in the official comment for Area Chairs.
**1) Theoretical indication of the contribution of Von Neumann entropy for representations of dynamic networks.**
Von Neumann entropy has shown its efficacy for ne... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for dedicating their valuable time and providing insightful comments. We are greatly pleased to receive some positive reviews. Specifically, we appreciate that they find our work is novel (w7vF), well motivated (oGt7), inspiring (jRZR), well presented (jRZR), a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Statistical Knowledge Assessment for Large Language Models | Accept (poster) | Summary: This paper proposes a statistical approach called KaRR to assess the factual knowledge contained in Generative Language Models (GLMs). The authors use a large-scale assessment suite with 994,123 entities and 600 relations to evaluate 14 GLMs of various sizes. The results show that KaRR exhibits a strong correl... | Rebuttal 1:
Rebuttal: We really appreciate your effort in reviewing our paper and your acknowledgment of our paper’s contribution. We are very glad that you liked our statistical approach, the large-scale assessment suite, and the insightful findings.
Our response to your further comments is as follows:
**Q1 "Some of... | Summary: The paper introduces an automatic evaluation metric to assess the amount of factual knowledge kept by large language models (LLMs). This proposed metric considers various surface forms of factual knowledge presentation, allowing for an evaluation that not only measures the accuracy of the models in terms of fa... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer zX87 for the positive recommendation as well as the valuable suggestions.
We really appreciate your kind words that our metric is commendable and our analysis is comprehensive. Below we would like to give detailed responses to each of your comments.
**Q1 "The human cor... | Summary: The paper proposes KaRR, a statistical approach to assess factual knowledge for generative language models based on graphical models. An assessment suite is also proposed for future research. Experiments are conducted with 14 popular large language models and comprehensive analyses are also conducted to reveal... | Rebuttal 1:
Rebuttal:
We sincerely thank Reviewer adWh for the positive comments on our method and analyses as well as the valuable suggestions. We would like to give detailed responses to each of your comments.
**Q1. “The knowledge assessment focuses on entity-aware knowledge, which could be a relatively limited kno... | Summary: this paper proposes a statistical method to probe the knowledge in generative language models, which aims at connecting symbolic knowledge and GLM's text format generation. More specifically, the KaRR comprises two components with regard to specifying relation and subject entity. The authors also present the g... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer e9uD for your review and are grateful for the time you spent on our submission. We are also glad you think our research problem is important and our findings are interesting. Below we would like to give detailed responses to each of your comments.
**Q1 “This work only ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a framework for the quantitative assessment of the knowledge captured by large language models, which includes a proposed metric and a large set of relations. Given subject-relation-object triplets, a basic approach to quantify the model's knowledge would be to use the probability of the obj... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer bA4R for the positive feedback and we are grateful for the time you spent on our submission. We are also glad for the acknowledgment that the problem we are working on is realistic and that the method we propose is sound. We would like to provide comprehensive responses... | null | null | null | null | null | null |
PRODIGY: Enabling In-context Learning Over Graphs | Accept (spotlight) | Summary: This paper proposes a pretraining framework that enables in-context learning on graph classification tasks (maybe diverse graph machine learning tasks). Specifically, it proposes a prompt graph as a unified representation for diverse tasks, then it designs a graph neural network architecture over the prompt gr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback. Below, we clarify a number of important points raised by the reviewer.
> Re: Does the success of this approach hinge solely on the close similarity between the test and pre-trained data?
The reviewer has concerns on whether the success of... | Summary:
This paper introduces Prodigy, a method aimed at facilitating 'in-context learning' over graphs.
The key contribution of this work is the formulation of a 'prompt' that can be utilized for in-context learning with graphs. This 'prompt' is defined as a data graph which incorporates typical (input, output) exa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback. Below, we clarify a number of important points raised by the reviewer.
> Re: Difference from K-shot prediction.
The reviewer raises a concern on our difference from K-shot prediction. As in the related work section, most existing few-shot... | Summary: I have read the author’s rebuttal, I think I misunderstood the in-context learning mentioned in this paper and see the difference from other typical ICL works. I have no object to accept the paper if AC thinks it is enough contribution.
The paper introduces an in-context few-shot prompting approach for edge ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback. Below, we clarify a number of important points raised by the reviewer.
> Re: Novelty
The reviewer claims that the paper lacks a truly novel contribution. We respectfully disagree. The paper proposed one of the first frameworks that allow ... | Summary: This paper proposed a framework for graph in-context learning. The PRODIGY architecture consists of the prompt graph, task graph, and in-context learning pretraining objective. The PRODIGY can directly conduct downstream tasks without finetuning and shows strong performance on downstream classification tasks.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback. Below, we clarify a number of important points raised by the reviewer.
> Re: contrastive learning baselines
Thank you for the reference. The reviewer suggests we compare our method with more contrastive learning methods. Here we would lik... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Kernelized Cumulants: Beyond Kernel Mean Embeddings | Accept (spotlight) | Summary: This paper proposes kernelized cumulants to extend classical cumulants in $\mathbb{R}^d$ and shows that the kernelized cumulants provide a new set of all-purpose statistics and are computationally tractable.
This paper also show advantages of kernelized cumulants both theoretically and empirically.
Strengths... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and for the kind review. Below, we answer the questions in detail.
- ”more kernels, such as the neural tangent kernel, could be considered.”
Yes, essentially any kernel can be used and in our experiments we focused on the standard kernel ch... | Summary: This paper generalizes the notion of cumulants to Hilbert-space-valued random variables.
When these Hilbert spaces are RKHSs, the kernel trick applies so that computations can be performed with the kernel function.
It leads to higher-order two-sample and independence tests, which generalizes MMD and HSIC.
The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and for the kind review. Below, we answer the questions in detail.
- ”use the generating function”
We agree that this is a more intuitive way to introduce the concept, but it is not very instructive when it comes to
actually computing the s... | Summary: The authors introduce the kernelized cumulant and show that it can characterize distributions and statistical (in)dependence.
Strengths: 1. The kernelized cumulant provides a natural generalization of the popular maximum mean discrepancy (MMD) as well as the Hilbert-Schmidt independence criterion (HSIC).
2. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and for the kind review. Below, we answer the questions in detail.
- ”Is the independence criterion kernel dependent?”
Since we are working in a very general framework, the independence criterion works for a very wide family of kernels,
eve... | Summary: This papers revisits advances in cumulants on real data, by extending them to provide cumulants for random variables in an RKHS.
Strengths: The idea of this work is interesting, as the paper proposed to go beyond conventional kernel mean and kernel covariance. Moreover, a proposed kernel trick allow to obtain... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and for the kind review.
- ”Is there any other tests (or beyond test) where the proposed kernelized cumulants would be relevant?”
The proposed kernelized-cumulant based measures are general divergence and independence measures, hence can be... | Rebuttal 1:
Rebuttal: **References for the rebuttal:**
[1] Mikolaj Binkowski, Danica Sutherland, Michael Arbel, and Arthur Gretton. Demystifying MMD GANs. In International
Conference on Learning Representations (ICLR), 2018.
[2] Gustavo Camps-Valls, Joris M. Mooij, and Bernhard Sch ̈olkopf. Remote sensing feature sel... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper generalises the notion of kernel mean embeddings to higher-order cumulants. It proposes kernelled cumulants in the RKHS. While kernelled cumulants reside in tensor product space of the RKHS, the paper shows that Hilbert space metric between cumulants can be exactly computed using the kernel trick. Ba... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and for the kind review. Below, we answer the questions in detail.
- ”If a characteristic kernel is used wouldn’t mean embedding (MMD) suffice?”
This is true of course, a characteristic kernel is theoretically sufficient for 2-sample testin... | null | null | null | null | null | null |
An active learning framework for multi-group mean estimation | Accept (poster) | Summary: This manuscript studies a special type of bandit problem: instead of maximizing the rewards, the learner aims to minimize the L_p norm of the variance vector for the mean estimators of each arm. The motivation of this problem is multi-group mean estimation, where a small total variance is desired.
The author... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments on our paper.
Q1 (Lp norms):
The expression of the $p$-norm for $p \geq 1$ is $\|\boldsymbol{x}\|_p := (x_1^p + \ldots + x_n^p)^{1/p}$. The most frequent measures of error we find in the ML literature are $p = 1$ (absolute error), $p=2$ (squared error), and $p... | Summary: This paper focuses on the active learning algorithm for multi-group mean estimation. The authors focus on minimizing the $l_p$-norm of the variance vector. This paper proposes the variance-UCB algorithm to actively select which group to sample in each round. The sample complexities for $p<\infty$ and $p=\infty... | Rebuttal 1:
Rebuttal: We thank you for your time reviewing our paper, and for your supportive comments. Below we address the two questions raised in your review.
Q1 (Instance-dependent upper bound): Our primary focus in the analysis was dependence on $T$, which is the parameter that we envision growing large in most p... | Summary: This paper propose the Variance-UCB algorithm to sequentially learn the mean in a multigroup setting in order to minimize the variance over all mean estimates, and prove the regret of the algorithm is optimal for both finite and infinite p values.
Strengths: 1. The Variance-UCB algorithm in this paper automat... | Rebuttal 1:
Rebuttal: Thank you for your review and for your kind words about our paper. Thank you also for pointing out the typo; we will correct that.
1- Comparison with other algorithms:
For the case where the norm $p = +\infty$, two algorithms are known: [Antos et al. 2008, Carpentier et al. 2011]. [Carpentier ... | Summary:
This paper studies the mean estimation problem under the multi-armed bandit setting. Here, we have a group of populations (random variables) with unknown mean and standard deviations. The goal is to estimate the mean of each group on the fly and optimize the regret (measured by different kinds of norms). Thei... | Rebuttal 1:
Rebuttal: Thank you for your review of our work and for your kind words about our contributions to the bandits and statistical learning literature. Below we address the questions posed in your review.
Q1 (integrality gap): Indeed, our tightness results are with respect to the original problem, which implie... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper provides a general bandit-based active learning approach to the problem of learning the means of several disjoint groups so as to optimize for the variance of the resulting estimates as measured by the p-norm of the variance vector. An algorithm is proposed which samples based on a certain upper conf... | Rebuttal 1:
Rebuttal: Thank you for your review and for your kind words about our paper and results.
We will add a brief discussion about the sub-Gaussianity assumption in the contributions.
For the "relatively small weakness" in the experimental section, corresponding to Q2 and Q3, we will incorporate your feedback... | null | null | null | null | null | null |
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization | Accept (poster) | Summary: This paper addresses the finite-sum coupled compositional optimization (FCCO) scenario, relaxing the requirement of Lipschitz gradient for the involved functions. Instead, they consider weakly convex functions with certain monotonicity conditions. The paper introduces new algorithms and provides oracle complex... | Rebuttal 1:
Rebuttal: We thank reviewer FiRa for the detailed and insightful review. We will fix the minor issues in the future revision. Here we would like to address the remaining concerns.
**Q1**. L137: Regarding the "monotonic property" of a multivariate mapping $f$.
**Response**: We apologize for this confusion... | Summary: The paper considers a class of finite-sum coupled compositional optimization (FCCO) problems and a class of tri-level finite-sum coupled compositional optimization (TCCO) problems. Under the setting of non-smooth weakly-convex FCCO, the paper establishes the complexity of a single-loop algorithm with the tool ... | Rebuttal 1:
Rebuttal: We thank reviewer GoBV for the insightful review. Here we would like to address your concerns.
**Q1**: Additional applications other than the two-way partial AUC maximization should be provided.
**Response**: We would like to provide another important application of NSWC FCCO for regularized gr... | Summary: This paper handles the problems of non-smooth weakly-convex compositional optimization. The first problem, referred to as FCCO (Finite-sum coupled compositional minimization), is given by
$$ \min_{w \in \mathbb{R}^d} F(w) \triangleq \frac{1}{n} \sum_{i=1}^n f_i(\mathbb{E}_{\xi\sim \mathcal{D}_i}[g_i(w; \xi)])... | Rebuttal 1:
Rebuttal: We thank reviewer evM3 for the detailed insightful review. Here we would like to address your concerns.
**Q1**: Regarding mismatch between theoretical baselines and experimental baselines. It is unclear, if some theoretical baseline can be applied to TPAUC and perform better.
**Response**: (1) ... | Summary: The purpose of this paper is to introduce a new approach to solving a specific type of optimization problem called non-smooth weakly-convex finite-sum coupled compositional optimization (NSWC FCCO) problems. The authors specifically focus on a variation of FCCO problems where the outer function is weakly conve... | Rebuttal 1:
Rebuttal: We thank reviewer mWSr for the insightful review. Here we would like to address your concerns.
**Q1**: It seems that other multistage algorithms have already achieved the optimal rate.
**Response**: Thank you for acknowledging our new convergence analysis. However, there is some **misundersta... | Rebuttal 1:
Rebuttal: We thank all the reviewers from their insightful reviews. Please find Figure 3 and Table 5 in the attached pdf file.
Pdf: /pdf/7a8fc8565a6324afb450f3388248ace2870519c2.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The manuscript studies a class of non-smooth non-convex compositional optimization problems in which the objective function is given in a form of finite-sum composition where the functions are assumed to be weakly convex. The authors present stochastic approximation algorithms to solve this class of problems a... | Rebuttal 1:
Rebuttal: We thank reviewer SCt1 for the detailed and insightful review. Here we would like to address your concerns.
**Q1**. Difference from the existing works in the literature.
**Response**: We politely disagree with the reviewer that our work is incremental in light of existing works. There are fun... | null | null | null | null | null | null |
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks | Accept (poster) | Summary: The paper proposes two history encoders for RL in partially observable control tasks. The history encoders are designed with PID-inspired inductive bias. Specifically, the inductive bias comes from lifting the observation into features consisting of summation and difference of observation over the historical h... | Rebuttal 1:
Rebuttal: Thank you for your time and your thoughtful review! We are glad to hear that you were generally impressed with the experimental results. For the failure of HalfCheetah-V, we agree that the current iteration of the paper could benefit from more information. Please see the global response for what w... | Summary: The paper proposes a new, simplified architecture for reinforcement learning in the partially observable setting inspired by PID control. Through experiments on a number of tracking problems, and some locomotion environments, strong performance is obtained.
Strengths: - The exposition of the argument is very ... | Rebuttal 1:
Rebuttal: Thank you for your time and your review! For your note on computational savings, please refer to the global response.
While it is true the majority of the environment variants (10 out of the 18) are lower-dimensional tracking problems (either 3 or 6 dimensions), we do not believe that they are an... | Summary: This paper considers history encoding for deep RL POMDP control problems through a PID-inspired lens. Specifically, authors introduce PIDE, a method of directly using PID control which extends to multiple-input multiple-output problems, and GPIDE, a PID-inspired encoder architecture. GPIDE consists of a series... | Rebuttal 1:
Rebuttal: Thank you for your time and your thorough review! We were happy to see that you thought the paper was high-quality and that the method seems broadly applicable across RL tasks. We have touched on many of your points in the weakness section in the global response (particularly computational expense... | Summary: This paper proposes a new way to encode features in partially observable environments using PID. Experiments show superior results over several domains compared with recurrent and transformer encoders.
Strengths: The paper is easy to read overall.
Experiments show promising performance on multiple domains.
... | Rebuttal 1:
Rebuttal: Thank you for your time and your review! We will respond to each of your weaknesses in a corresponding list. Starting with the list of assumptions and technical details,
1. For your first point, you are correct that the reference value need not be the same throughout time. However, GPIDE can stil... | Rebuttal 1:
Rebuttal: Thank you to all of the reviewers for taking the time to read our paper and give thoughtful reviews. We value your feedback and hope to use it to strengthen our work. In this global comment we will address some points that were raised in multiple reviews.
### Performance of HalfCheetah-V
Some re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of learning from histories in partially observable MDPs where a key question is how to design an architecture that is general enough so as to work on a large set of problems, yet specific enough to be sample efficient.
Inspired by the success of PID in the more classical control... | Rebuttal 1:
Rebuttal: Thank you for your time and your thoughtful review! We greatly appreciate the feedback, and we are glad that you liked the paper.
* Your comments highlight a fascinating problem which is the tradeoff between the benefits of high capacity modeling and the benefits of useful inductive biases. As yo... | null | null | null | null | null | null |
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces | Accept (poster) | Summary: The paper investigates adversarial robustness of trained ReLU neural networks when data lies within a linear subspace. For the theoretical part, which is the bulk of the paper, the networks have two layers and only the first layer is trained. Then the key observation is that the assumption on the data causes... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and constructive comments.
Code for the experiments: We indeed report all the experimental details in Appendix E. We will publish the full code with the camera-ready version.
"Are you able to say how the theoretical results would change if the second... | Summary: This paper studies the vulnerability of two-layer ReLU networks under adversarial attack when the data is in a low-dimensional manifold. The paper also observes that adding L2 regularization in clean training also improves the adversarial robustness.
Strengths: The intuition of this paper is interesting, with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and helpful comments.
Weaknesses:
1) We emphasize that an analysis of the perturbations on the data manifold would require having some additional assumptions on the data, e.g. about its distribution. We believe that one of the major benefits of our w... | Summary: This paper focus on the data lies on a low dimensional data manifold (linear subspace P). There’re no additional assumptions on the number of data and their structure (orthogonality). The paper considers the perturbation on P^\orth space. The paper claims that standard gradient descent leads to non-robust neur... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and helpful comments.
Regarding the weaknesses:
1) We acknowledge in the paper that some of our proof techniques were also used in previous works about robustness. The main difference is that in this work we consider trained networks, and our results... | Summary: The paper shows that on two-layer neural networks trained using data which lie on a low dimensional linear subspace that standard gradient methods lead to non-robust neural networks, that networks which have large gradients in directions orthogonal to the data subspace, and are susceptible to small adversarial... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and constructive comments.
"The experiments are insufficient, the number of data points used to evaluate the proposed method is few.":
The experiments in the paper were done mostly for visualization purposes, with low dimensions and dataset size. We ... | Rebuttal 1:
Rebuttal: We thank the reviewers for the thorough reviews and constructive comments.
In the attached PDF we add two experiments, aiming at showing empirically the effects of small initialization and that real datasets approximately lie in a low dimensional subspace.
1) To show that real datasets approximat... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Exploration for Model-Based RL in Nonlinear Systems | Accept (spotlight) | Summary: This paper addresses the problem of exploration when learning to control non linear systems. In particular, they derive a task-driven exploration method which focuses on learning the system parameters that are relevant for the specific task that a controller is trying to achieve.
To derive the algorithm, the... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and will work to incorporate the feedback into the final version. We address specific comments below.
> *“However, it would be quite hard to implement the proposed algorithm...” “The notation in dynamicOED is hard to follow, the explanation h... | Summary: This paper considers the problem of task-relevant system identification. It starts with the motivation that not all parameters in the dynamics model are relevant to the given task; thus, the exploration should be placed to prioritize the identification of the relevant dynamics parameters, and thus eventually l... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and will work to incorporate the feedback into the final version. We address specific comments below.
> *“In the motivating example, the explanation of task-irrelevant or relevant parameters are confusing. Shouldn't $a_{3:12}$ be irrelevant t... | Summary: The paper proposes an active exploration algorithm for systems of the form $x_{k+1} = A \phi(x_k, u_k) + w_k$ with known features $\phi$ and unknown matrix $A$, where $w_k \sim \mathcal{N}(0,1)$. The problem is reduced to minimising $\mathrm{tr}(\mathcal{H}(A_\star)\check{\Lambda}_T^{-1})$ where $\mathcal{H}(A... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and will work to incorporate the feedback into the final version. We address specific comments below.
> *“The class of systems is quite restricted. If one needs to learn the non-linear features first, then one could just as well use the coll... | Summary: This paper tackles the challenge of controlling unknown nonlinear dynamical systems in reinforcement learning and control theory. The authors propose a novel algorithm, inspired by recent work in linear systems, focusing on the most critical parameters for learning a low-cost controller on the actual system. T... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and will work to incorporate the feedback into the final version. We address specific comments below.
> *"The regularity assumptions in 3.1 should be addressed clearly and the author could give a toy example to illustrate when these assumptio... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their feedback and will do our best to incorporate as much of it as possible into the final version. We address several key points of clarification below:
1. **Primary Contributions:** We want to emphasize that the primary contribution of this paper is the... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper seeks to quantify formally in the settings of nonlinear dynamical systems (a) which parameters are most relevant to learning a good controller and (b) the best exploration strategy to minimize uncertainty in such parameters. This paper draws theoretical insights where minimizing the controller loss i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and will work to incorporate the feedback into the final version. We address specific comments below.
> *Response to W1*
We refer the reviewer to points 1. and 3. of our response to all reviewers. As stated there our contribution is primaril... | Summary:
In this paper, the authors propose a method for learning an optimal controller that is able to focus the exploration so as to minimize the estimation error of relevant parameters. They show that the proposed method is indeed optimal with tight upper and lower bounds on the gap. Experiments show that the propo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and will work to incorporate the feedback into the final version. We address specific comments below.
> *"On the other hand, the experiment section is quite thin. However, as a largely theoretical contribution, this is acceptable."*
We refer... | null | null | null | null |
What Makes Good Examples for Visual In-Context Learning? | Accept (poster) | Summary: This paper studied in-context learning abilities of large vision models and finds downstream task performance to be highly sensitive to the choice of examples. They observe that the closer the in-context example is to the query, the better the performance. Since manually designing prompts would be time-intensi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and comments. We respond to the concerns below:
**Q1**: Semantically close to the test example is known from language and not that surprising.
**A1**: Yes, we acknowledge the parallels between some of our observations and those from the natural language dom... | Summary: The paper identifies that visual prompting is sensitive to the choice of input-output example(s). To address this, the authors propose a retrieval framework to better select the examples. The authors propose supervised and unsupervised retrieval approaches which significantly improve the performance compared t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and comments. We respond to the concerns below:
**Q1**: The assumption that we have a set of tagged examples to retrieve from. Why should we retrieve rather than training our tagged examples?
**A1**: It is essential to note that our method is fundamentally... | Summary: This paper investigates in-context learning for large vision models. Specifically, the authors propose to automatically retrieve prompt for vision models in two methods: (1) nearest example search with off-the-shelf model, (2) supervised prompt retrieval method.
Experimental results show that the proposed meth... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and comments. We respond to the concerns below:
**Q1**:Only studies a limited set of tasks (assign labels to pixels) with an inpaining model; to a wider visual tasks, like classification, or standard segmentation tasks (semantic and instance).
**A1**: Thank... | Summary: This paper aims to address the problem of vision in-context-learning that the performance highly depends on the choice of visual in-context examples. In the paper, the authors propose automatically retrieving prompts in unsupervised and supervised ways without reaching internal weights of large vision models. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and comments. We respond to the concerns below:
**Q1**:Whether this method can be generalized to more general "visual in-context learning" settings.
**A1**:Thanks for your suggestion. Please refer to the **General Response** for the detailed explanation.
*... | Rebuttal 1:
Rebuttal: **General Response**
Dear Reviewers,
We sincerely appreciate the time and insightful comments provided by all reviewers, which have been instrumental in enhancing our paper. We are encouraged by the positive feedback regarding the motivation behind our work (QAph) and our strong performance acr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Causal Effect Identification in Uncertain Causal Networks | Accept (poster) | Summary: This paper considers two problems: 1. Finding the most probable graph that makes a desired causal query identifiable. 2. Finding the graph with the highest aggregate probability over its edge-induced subgraphs makes a desired causal query identifiable. This paper shows that both problems reduce to a special co... | Rebuttal 1:
Rebuttal: We are grateful for the thoughtful review and for acknowledging the significance of our work. We have addressed the main points and questions below.
---
>Could you elaborate on the assumption of mutual independence of edges in the graph? How might the results change [...]?
In the case of violati... | Summary: This paper is about causal identification in a setup where the assumption of reliable knowledge of the causal graph is relaxed. The authors assign a probabilistic weight to the possible confounders. The combinatorial task of finding the most probable causal graph such that a given causal query is identifiable... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful and positive feedback. We have carefully considered their comments and concerns and addressed them in the following points:
---
> is it possible to implement the polynomial transformation in Proposition 4.2,
This transformation is indeed implemented in th... | Summary: The paper considers a setting in which we have a probabililty distribution over causal graphs. This paper considers the problem of finding the most probable subgraph in which a given query is identified, and then that subgraph with the highest sum of probabilities of its own subgraphs in which the query is ide... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable input. We are delighted that the reviewer has found our results clear, extensive, and sound. We address the main points and questions below.
---
>Can the authors point to any other literature besides citation [12] in which probability distributions over AD... | Summary: The paper studies the problem of causal identification in a setting which the structure of a causal graph (of interest) is probabilistically uncertain i.e., only known per a certain degree of belief or with a degree of confidence of a particular statistical set, asking the most likely subgraph in which causal ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable feedback. We also thank the reviewer for pointing out typos and minor issues.
**Questions**
>how about NP-completeness case? -Can the work of Eiter Lukasiewicz 2001 be any useful/connect for further results in your setting?
The EdgeID problem is ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors consider the problem of causal effect identification assuming the given causal graph G is probabilistic where each edge (directed or bi-directed) is associated with a measure of confidence. Under this setting, the objective is to identify a subgraph of G with the highest plausibility such that the ... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback of the reviewer. We will address the concerns raised in the weaknesses and questions section.
-----------------------------------------
**- Motivation & bounding the effect:**
We agree that in some cases, relying on partial identification and bounding the... | null | null | null | null | null | null |
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning | Accept (poster) | Summary: Inspired by Sharpness-aware minimization (SAM) technique, this paper proposes a new semi-supervised learning method FlatMatch. The main idea is in Eqn.(4), where FlatMatch picks both $\theta$ (current weight parameters) and $\tilde{\theta}$ (the parameters close to $\theta$, and maximize the loss on labeled da... | Rebuttal 1:
Rebuttal: R5: We thank Reviewer 9MiC for your feedback. Here we justify that our Fix label setting is a common SSL technique which is reasonably designed in our method.
*Q1*: **Whether our Fix label setting violates the motivation of SSL.**
*A1*: We want to stress that transforming unlabeled data to labe... | Summary: The paper focuses on the problem of semi-supervised learning (SSL). The authors first study the loss landscapes of labeled data and unlabeled data and find a generalization mismatch. Base on the findings, they propose FlatMatch to encourage consistent learning performance between labeled data and unlabeled dat... | Rebuttal 1:
Rebuttal: R4: We thank Reviewer Z9Pn for your helpful feedback. We have carefully addressed all your concerns as follows.
*Q1*: **Why faster convergence leads to sharper loss curves and how to compare the convergence speed between labeled data and unlabeled data.**
*A1*: Good point.
* Here we clarify th... | Summary: This paper focuses on Semi-supervised Learning (SSL) where the training data consists of scarce labeled data and a massive amount of unlabeled data. The authors argue that the propagation of label guidance from labeled data to unlabeled data is challenging. This can cause the learning process on labeled data t... | Rebuttal 1:
Rebuttal: **R3**: We thank Reviewer yYJy for your helpful comments. Here we carefully conducted experiments on large-scale datasets (ImageNet) as well as sophisticated models (ViT) and justified the efficiency and effectiveness of FlatMatch.
*Q1&Q2*: **Performance on ImageNet dataset and Vision Transforme... | Summary: The authors propose a semi-supervised learning (SSL) method that applies the sharpness-aware minimization (SAM) method for consistency regularization. Unlike FixMatch, it does not apply perturbation to the unlabeled training sample but evaluates the consistency between posterior distributions of predicted labe... | Rebuttal 1:
Rebuttal: **R2**: We thank Reviewer Y5aq for your constructive comments. We have carefully included several typical baselines with SAM optimizer and reformulated our objectives, derivatives, and optimization.
*Q1*: **Comparison to existing SSL methods with SAM optimizer.**
*A1*: Thanks for the advice. He... | Rebuttal 1:
Rebuttal: ***General Response***:
We thank the reviewers for their insightful and constructive reviews of our manuscript. Delightfully, we are glad that the reviewers found that:
* Our idea is **novel, interesting, and well-motivated**. (Reviewers 9rMs, Z9Pn, 9MiC)
* The presentation of our paper is **clear... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduced FlatMatch, a new SSL method which encourages consistent learning performance between labeled and unlabeled data and aims to boost the performance of SSL methods without being limited by insufficient label information. The paper analyzes the loss landscapes of labeled and unlabeled data an... | Rebuttal 1:
Rebuttal: **R1**: We thank Review 9rMs for your positive opinion. We have carefully conducted experiments on ImageNet, added efficiency comparison with FlexMatch, analyzed the convergence regarding training accuracy, and showed the loss curves on the final iteration.
*Q1*: **Result on ImageNet.**
*A1*: ... | null | null | null | null | null | null |
Exponential Hardness of Optimization from the Locality in Quantum Neural Networks | Reject | Summary: In this work, the authors characterize the problem of the Barren Plateau from different perspectives: (1) local unitary within a QNN on the cost function, particularly the randomness for the generic cost function; (2) quantum information theory; (3) the optimization methods during training. This work discusses... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer for their time and the helpful feedback. Here we respond to the comments and questions.
> $\textbf{Comment 1:}$ ``Some latest work on the Barren Plateau problem in the training of VQC should be included, such as Refs. [1], [2], and [3]. Ref. [1] aims at the QNN archi... | Summary: The paper examines the critical issue of trainability in quantum neural networks (QNNs) by adopting a perspective centered around the locality. Through extensive analysis, the authors convincingly demonstrate that the adjustment of local quantum gates within a diverse range of QNNs results in an exponential de... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and for acknowledging that our paper is technically sound and well-presented. Below is a detailed response to the questions raised by the reviewer.
> $\textbf{Comment 1:}$ ``The main weakness of this paper lies in its limited impact ...''
$\textbf{R... | Summary: This paper investigates the trainability of random quantum circuits from the perspective of their locality and demonstrates the variation range of the cost function via adjusting any local quantum gate vanishes exponentially in the number of qubits. This theorem unifies the restrictions on gradient-based and g... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of our work as technically solid and well written. We also thank the reviewer for the helpful feedback. A detailed response to the reviewer's comments and questions is provided below.
> $\textbf{Comment 1:}$ ``Although Line 66-73 provides the advan... | Summary: The paper proof a result on the range of possible values that the cost function of a variational quantum algorithm can take when one optimises over a given unitary that is before or after random gates that form unitary 2 designs. This quantity vanishes exponentially with the number of qubits. This generalises ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's positive assessment on the correctness and significance of our work. We also thank the reviewer for the very helpful feedback. Below is our point-by-point response to the comments and questions.
> $\textbf{Comment 1:}$ ``The paper does not comment on recommend... | Rebuttal 1:
Rebuttal: Dear PC,
We are grateful to the PCs for their efforts in shaping the conference's scientific program and to the reviewers for their dedicated time and efforts in reviewing our paper.
We thank reviewers J4bd and dzQA for recognizing our paper's sound technique and clear writing style, acknowledgi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Latent Space Translation via Semantic Alignment | Accept (poster) | Summary: This work proposed a method to translate learned representations between two pre-trained networks, using surprisingly simple transformations. The method is demonstrated on models with different architectures, trained on different modalities and across different tasks.
Strengths: 1. The paper is well written... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work, providing valuable feedback, and firmly pushing for its acceptance.
- **Figure 2 (Additional Details)**: In response to the concern about missing details, we will provide comprehensive information in the appendix. For the classification aspect (lef... | Summary: In this paper the authors propose to translate latent space using an effectively angle-preserving affine transformation learned using pairs as anchors. Experiments are conducted on a wide range of tasks showing the okay performance by the swap-in latent space of embeddings/features.
Strengths: **Strengths**
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and comments. We appreciate the opportunity to address the concerns they've raised and offer clarifications on the following points:
**Page Limit**: As per the **official 2023 Call for Paper**, submissions are indeed limited to **nine** content pages and **not... | Summary: This article proposes a method of latent space translation through semantic alignment, using the similarity of latent spaces learned by different neural models on semantically similar data. The method allows for direct conversion of learned representations between different pre-trained networks, and achieve ze... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable input, which has prompted us to further enhance the soundness of our work. Here, we provide deeper clarification and offer additional insights regarding:
**Additional Comparisons**: we will add several columns to our tables (Table 1 and Table 2): **i)** p... | Summary: This paper show the latent space of different pretrained models can be translated between each other with simple transformations by using anchors. By using this method, a variety of encoders architectures are able to be cross-stiched to different classifier heads or within autoencoding models.
Strengths: This... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful feedback, highlighting important aspects warranting clarification and enhancement in our work. In light of their review, we have identified these steps to improve our work:
- Emphasize the relationship with RelRep in the Preliminaries section as a... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback provided by the reviewers. We would like to address the relationship between our work and the concepts presented in "relative representations" (Moschella et al.). While we draw upon the foundational principles of Moschella et al., it is essential t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Demystifying the Optimal Performance of Multi-Class Classification | Accept (poster) | Summary: This paper tackles the important problem of performance estimation in a multi-class setting. The contributions come in the form of several Bayesian Error Rate estimators for different multi-class variants. Several theoretical guarantees are provided throughout the paper, as well as empirical results on synthet... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and effort put into reading and reviewing our manuscript.
Clarity 1. The focus of our paper is on multi-class single-label learning, where each data sample is associated with only one true class.
Our rationale behind representing $\mathbf{y}$ as a vector stem... | Summary: This paper studies the estimation of the Bayes error rate (BER) in the multiclass classification problem. BER is the best classification error (in terms of expectation) that can be achieved by the Bayes optimal classifier. First, this paper studies the soft labels case, where the direct extension of the estima... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their thorough reading of our manuscript and their effort in carefully reviewing it.
Weakness 1. We agree with the Reviewer that comparisons with SOTA BER bounds are performed only on synthetic datasets. However, this is not due to a limitation of our approach, but rathe... | Summary: This paper proposes a few methods for estimating Bayes error rates of multi-class classification in different scenarios. The proposed method generalize the previous soft-label approach for binary classification [Ishida et al., 2023] and also extends it for robustness to label noise and outliers. The first exte... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their thorough reading of our manuscript, and for the positive assessment of our contribution.
Weakness 1. We agree that the multi-class extension may seem straightforward, but we also feel that this is not a weakness. Indeed, it is just the starting point for further an... | Summary: This paper aims to design a tighter estimate for the Multi-class classification error. The paper analyzes several theoretical aspects of the suggested Bias Error rate estimator, including its consistency, unbiasedness, convergence rate, variance, and robustness. Moreover the authors utilize a denoising method... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and effort put into thoroughly reading and reviewing our manuscript.
Regarding the impact/applicability of our paper. We would like to first note that we did consider a complex classification task for the evaluation of our bounds using the MovieLens dataset. T... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
From Trainable Negative Depth to Edge Heterophily in Graphs | Accept (poster) | Summary: This paper tackles the problem of graph heterophily in training Graph Neural Networks. Through graph spectral analysis, the authors decompose the graph Laplacian matrix into eigengraphs. Dwelling on the observation that low/high-frequency eigengraphs correspond to homophily/heterophily, the authors propose to ... | Rebuttal 1:
Rebuttal: **Q1: line 187. I do not fully understand the negative depth explained for integer values. Since the proposed method only use either positive or negative depth, when does the model perform both positive message passing and negative message passing?**
**Response**: When the depth is negative, it d... | Summary: The paper proposes a new GCN that is capable of handling both homophilic and heterophilic networks. This method can be interpreted as generalizing the depth of the GCN, which is generally a natural number and a hyperparameter, to an arbitrary real number that is a trainable parameter. The first version of this... | Rebuttal 1:
Rebuttal: **Q1: The Squirrel and Chameleon datasets were recently found to have a lot of erroneous duplicates ("A critical look at the evaluation of GNNs under heterophily: are we really making progress?" by Platonov et al.). Is this paper using the revised datasets or the originals?**
**Response**: We use... | Summary: This paper shows a connection between the depth of a GCN and it's suitability for homophilic / heterophilic graphs, by analyzing graph spectra. It proposes to address heterophily with negative depth and presents a GNN architecture called TeDGCN which allows for trainable and negative depth. TeDGCN outperforms ... | Rebuttal 1:
Rebuttal: **Q1: On line 262 the split should be 60/20/20 right?**
**Response**: Thanks for your question. As we mentioned in line 254, we adopt the **semi-supervised** node classification task to evaluate TEDGCN and other baselines with the split 20/20/60\% for training/validation/testing set. The reas... | Summary: This paper proposes two algorithms to learn GNN with a trainable depth. At first, the authors exploit previous theoretical results about the correlation between frequency and zero crossings and the intuition that capturing heterophily needs more zero crossings to motivate adjusting the weights of eigen-graphs.... | Rebuttal 1:
Rebuttal: **Q1: In line 130, the cardinalities of examples are 1 and 2. As the considered graph is undirected, why not 2 and 4?**
**Response**: Thank you for raising this question. In undirected graph, we regard edge $(v\_{j}, v\_{k})$ and edge $(v\_{k}, v\_{j})$ as one edge. We will add a footnote her... | Rebuttal 1:
Rebuttal: **General Response for All Reviewers**:
We sincerely thank all reviewers for their valuable time and insightful feedbacks which are very helpful for further improving the quality of our paper. We are grateful that the reviewers appreciate the novelty of our work (`wZLL`:"a significant novel contr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser | Accept (poster) | Summary: This paper proposes a method called VALOR to assign segment level audio-visual (AV) event/object labels given weak labels at the video-level. It makes use of pseudo-labels obtained from unimodal pretrained models CLIP and CLAP to derive additional guidance for the AV model. The final model is trained using a c... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer uN7g**
We thank Reviewer uN7g for the constructive comments and suggestive remarks. Please see our responses below for each raised issue.
**Q1. Improvement of presentation flow. Sect. 2 and Sect. 3.1. The motivation behind adding Section 4.2.**
Sect. 2 (Prelim) defines ... | Summary: The paper aims to improve performance of audio-visual video parsing (AVVP) on the LLP dataset via a novel pseudo-labelling technique. This is because LLP contains only coarse video-level labels, and expects the model to match fine-grained, temporally dense labels during testing. This mismatch (due to the inten... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer t3gt**
We thank Reviewer t3gt for the positive comments and suggestive remarks. Please see our responses below for each raised issue.
**Q1. Possible to consider visual/audio-language models other than CLIP or CLAP? What about other datasets?**
A1: In Table 2 of the reb... | Summary: The paper proposes visual-audio label elaboration (VALOR) for weakly supervised audio-visual video parsing. It generates fine-grained temporal labels in audio and visual modalities by harnessing large-scale pretrained contrastive models CLIP and CLAP and providing explicit supervision to guide the learning of ... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer Qd1s**
We thank Reviewer Qd1s for the positive comments and suggestive remarks. Please see our responses below for each raised issue.
**Q1. Extension to datasets other than LLP?**
A1: In addition to audio-visual video parsing (AVVP), we also tackled the task of audio-vi... | Summary: The paper tackles the task of audio-visual event parsing, where the goal is to independently recognize the localize the events occurring in the visual and audio modality. The paper argues that modality independent processing can be crucial for this task compared to joint modeling of the two modalities. Consequ... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer JWv6**
We thank Reviewer JWv6 for the positive comments and suggestive remarks. Please see our responses below for each raised issue.
**Q1. How accurate are our pseudo labels (derived via CLIP/CLAP)?**
A1: In Table 7 of the supplementary material, we have evaluated the ... | Rebuttal 1:
Rebuttal: ## **General Response**
We sincerely appreciate the valuable time and insightful feedback provided by the reviewers. We are grateful for the opportunity to address the concerns raised by each reviewer, which fundamentally strengthens our work. The strengths pointed out by the reviewers include:
... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes methods for weakly-supervised audio-visual event learning. The method relies primarily on pre-trained audio-language (CLIP) and visual-language (CLAP) models to guide the learning process. These pre-trained models serve as teachers and provide pseudo-labels which are then used to compute lo... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer 82wA**
We thank Reviewer 82wA for the positive comments and suggestive remarks. Please see our responses below for each raised issue.
**Q1. Missing analysis on pre-trained audio/image-language models.**
**(1a) What info is extracted from the pre-trained single-modality... | Summary: In this work, the authors propose a unified weakly supervised audio-visual scene understanding framework for audio-visual video parsing and audio-visual event localization. Different from previous works, the proposed Visual-Audio Label Elaboration (VALOR) method is simple and effective. It leverages large-scal... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer veqt**
We thank Reviewer veqt for the positive comments and suggestive remarks. Please see our responses below for each raised issue.
**Q1. Selection of class-dependent thresholds in VALOR. What if other threshold choices?**
A1: For simplicity and fairness, we select ... | null | null | null | null |
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction | Accept (poster) | Summary: In their study on the Text2SQL task, the authors demonstrate the efficacy of breaking down the generation problem into sub-problems when utilizing LLMs to improve performance on the Spider dataset. The approach is simple yet effective on the Spider dataset. It yielded consistent improvements across three LLMs:... | Rebuttal 1:
Rebuttal: The first, second, and third weaknesses mentioned by the reviewer have been thoroughly addressed in the general response to all reviewers.
Regarding the weakness related to other LLMs beside OpenAI models, at the time of writing this paper, GPT-based models, PaLM, and the OPT model were the widel... | Summary: This paper addresses the task of text-to-SQL prediction using large language model (LLM) prompting. The authors propose DIN-SQL, a chain-of-thought (CoT) prompting method which decomposes the SQL prediction process into four substeps: schema linking, complexity classification, SQL prediction, and self-correcti... | Rebuttal 1:
Rebuttal: The first weakness raised in the review has been thoroughly addressed in the general response to all reviewers.
Regarding the second weakness concerning the applicability of our proposed approach to real-world scenarios with more complex SQL query structures, our current classification structure ... | Summary: The paper proposed to decompose the text-to-SQL reasoning into multiple steps and solve them with large language models. The authors began by conducting an error study of LLMs with few-shot learning and identified the common errors, such as "schema linking" and "JOIN". To address these common errors, they prop... | Rebuttal 1:
Rebuttal: The first weakness highlighted by the reviewer has been thoroughly addressed in the general response to all reviewers.
Regarding the second weakness raised in the review, we would like to clarify that our examples were chosen from the training set of Spider and were selected deliberately to inclu... | Summary: This paper proposes to improve few-shot prompting LLMs for text-to-SQL task. It first provides detailed error analysis on existing few-shot prompting LLM approaches, into six categories. Then the paper proposes a new approach to decompose the task into a few sub-tasks, solve each task individually, and compose... | Rebuttal 1:
Rebuttal: A detailed explanation of the novelties of our prompting method is provided in the general response to the reviewers.
The argument regarding marginal improvement over the Chain-of-thought method is invalid because the cited performance “decomposed COT prompting” is not for the chain-of-thought met... | Rebuttal 1:
Rebuttal: Our method draws inspiration from chain-of-thought and decomposed prompting techniques and brings valuable contributions to prompting across various domains, including text-to-SQL. These contributions are as follows:
1) Adaptive Prompting Based on Task Complexity: Our technique involves classifyin... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper's contributions are the following:
- The authors examined the common failure modes of doing text-to-SQL with few-shot prompting of LLMs: schema linking, JOIN, GROUP BY, nested queries and set operations, invalid SQL and miscellaneous.
- The authors propose a method to do text-to-SQL by decomposing th... | Rebuttal 1:
Rebuttal: Question: How is the intermediate representation obtained (ref Appendix A.5.2)?
Answer: For our few-shot examples, we used the intermediate representation from the NatSQL Github repo. The repo gives the intermediate representation for all queries in the training set of Spider.
Question: How do... | null | null | null | null | null | null |
Riemannian stochastic optimization methods avoid strict saddle points | Accept (poster) | Summary: The authors study saddle point avoidance of stochastic gradient algorithms formulated on Riemannian manifolds. They prove a number of results on saddle avoidance for which the Euclidean analogs, possibly under slightly different technical assumptions, are known. These results show that stochastic gradient desc... | Rebuttal 1:
Rebuttal: We are sincerely grateful for your encouraging remarks and thoughtful suggestions. We reply to your questions below, and we will revise our paper accordingly in the next revision opportunity.
> This paper would have been made significantly stronger with even a minimal empirical study to illustra... | Summary: This paper proves that under a rather general RRM scheme (akin to vanilla GD in euclidean space), strict saddles can be avoided when stochastic approaches are used.
Strengths: 1. Proves that Riemannian Optimization, much like its euclidean counterpart, can have its strict saddles easily avoided by using a sim... | Rebuttal 1:
Rebuttal: Thank you for your input, insightful questions, and positive evaluation. We address each of your questions in a point-by-point thread below, and we will revise our manuscript accordingly in the next revision opportunity.
>For unfamiliar readers, it is hard to gauge how limiting the "our assumptio... | Summary: The paper presents a focused study on the avoidance of saddle points for stochastic Riemannian optimization algorithms. To tackle this issue, the authors introduce the Riemannian Robbins-Monro (RRM) schemes to the context of Riemannian manifolds, where includes fundamental Riemannian stochastic optimization me... | Rebuttal 1:
Rebuttal: Thank you for your input and remarks. We reply to your questions below, and we will revise our manuscript accordingly in the next revision opportunity.
> It would be better if there are numerical experiments to validate the findings.
Done - please see the "global rebuttal" where we included a s... | Summary: This paper studies the problem of when Riemannian first-order optimization algorithms evade strict saddles points. The proof leverages a connection to the Euclidean case, to the continuous-time Riemannian gradient flow, and shows that the deviation from them is bounded. Intuitions are given in the main paper t... | Rebuttal 1:
Rebuttal: Thank you again for your input and remarks. We reply to your questions below, and we will revise our manuscript accordingly in the upcoming revision.
>For the definition of a stable manifold: why do we need positive eigenvalues lower bounded by c+>0? What goes bad if the positive eigenvalues coul... | Rebuttal 1:
Rebuttal: Dear AC, dear reviewers,
We are sincerely grateful for your time, input and positive evaluation.
To streamline our rebuttal, we reply to each reviewer's questions in a separate point-by-point thread below. We only include in this global rebuttal a pdf with two figures showing the avoidance of sa... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model | Accept (poster) | Summary: The paper attempts to improve our understanding on the source of a certain ability (the use of "greater than" in mathematical tasks) in LMs. To this end, the authors outlined a circuit in GPT-2 with interpretable structure and semantics, adding to the evidence that circuits are a useful way of understanding pr... | Rebuttal 1:
Rebuttal: Thank you for your review! Here are our responses:
> The conclusion might be limited to certain model type (auto-regressive LMs such as GPT series).
We agree that some of our conclusions, such as our specific circuit, are limited. Other findings, such as the way attention heads and MLPs work toge... | Summary: This work investigates in depth the mechanism of GPT-2 small to compute the "greater than" function. Specifically, the work isolates a portion of the computational units that are causally related to making plausible predictions for inputs similar to: "The war lasted from 1731 to 17__" The isolation of computat... | Rebuttal 1:
Rebuttal: Thank you for your careful review! We’ve answered your questions below, omitting the weaknesses, as they correspond roughly to Q1 and Q3. Regarding our contributions: please see the **Contributions** section of the general response, and Q1. We will revise our paper to clarify our contributions, an... | Summary: This paper explores how GPT-2 performs the ``greater-than'' operation by analyzing its circuit. The authors construct a template of the operation and define two scores to evaluate the performance of GPT-2. They first evaluate that the found circuit is indeed important for performing the greater-than operation,... | Rebuttal 1:
Rebuttal: Thank you for your critique! We hope to resolve your concerns by clarifying both the intent of our work and our contributions.
> The key contribution is not clear. …There is a limited contribution to the analysis.
We clarify our key contributions and analyses in the global response’s **Contribut... | Summary: This paper presents an analysis on how the greater-than operator is implemented in the weights of GPT-2 small. They do so by tasking the model with completing sentences of the form "[something, e.g., a war or time period] lasted from $y_1$ to $y_2$" where $y_1, y_2$ are years. The idea is that GPT often assign... | Rebuttal 1:
Rebuttal: Thanks for your attentive review! We answer your questions below; let us know how else we can improve the paper!
> I will say that I'm not 100% convinced by the argument at the end of Section 5, that this circuit is not using memorization and actually generalizes. [...]
Thanks for this comment—we... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful reviews. We're glad you found our problem and approach interesting (Nf2K,GMC8,BwzU,WeSY), our experiments extensive and scientifically sound (BwzU,WeSY), and our paper clear and well-written (eiM2,WeSY). Still, we want to address some key concerns: our c... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies and tries to explain how GPT-2 (small) could be computing the mathematical operation of "greater than". A "circuit" (or a subgraph of GPT-2 model's computation graph) is identified by iteratively "patching" individual components to find which components are most responsible for making the cor... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and thoughtful questions!
> The task formulation is constrained.
True—for why, see Problem Scope in the general response.
> Compute the correctness of the entire year string, w/o the XX prefix: how much probability mass does GPT-2 assign to the correct XX year t... | null | null | null | null | null | null |
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision | Accept (spotlight) | Summary: The paper proposes a new method to generate diversified, principle-guided synthetic data from LLM itself to ease requirement of large amount of annotated instruction-following data for supervised fine-tuning task for LLMs. The generation process follows four steps. First step is to generate adversarial topic-g... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and positive feedback on our work. Your questions/comments/suggestions are invaluable for the improvement of our method and the revision of the manuscript. Let us address each individual comment/question below.
> Concerning the quality of response ge... | Summary: This paper proposes Self-Align, a method for aligning a language model from scratch (without previous RLHF training) with fewer annotations. Self-Align works by using the LM to generate a set of example instructions/tasks, generating from the LM conditioned on the instruction + a human-written set of principle... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and positive feedback on our work. Your questions/comments/suggestions are invaluable for the improvement of our method and the revision of the manuscript. Let us address each individual comment/question below.
> Regarding the difference between Drom... | Summary: The authors use the self-instruct approach combined with principle-driven prompting to self-instruct a pre-trained LLM. The instruction/response generation generally refers to what self-instruct and Alpaca did. The principle-driven prompting can be treated as a SFT version of Constitutional AI. Self-instructed... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and positive feedback on our work. Your questions/comments/suggestions are invaluable for the improvement of our method and the revision of the manuscript. Let us address each individual comment/question below.
> On the potential performance ceiling ... | Summary: The authors study the problem of language model alignment and propose to leveraged hand-crafted prompts, principles, and examples to provide guidance, instead of relying on manually annotated human preference data. The authors make comparisons with various ai systems and the results demonstrate the effectivene... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and positive feedback on our work. Your questions/comments/suggestions are invaluable for the improvement of our method and the revision of the manuscript. Let us address each individual comment/question below.
> Regarding the dependence of our algor... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Paper presents a SFT approach for instruction fine-tuning with minimal supervision.
(1) First uses self-instruct approach to augment instructions;\
(2) Using human-written rules & in-context demonstrations for thought process of response, final responses are generated by foundation models, and then distilled ... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and positive feedback on our work. Your questions/comments/suggestions are invaluable for the improvement of our method and the revision of the manuscript. Let us address each individual comment/question below.
> the need for in-context examples to p... | null | null | null | null | null | null |
FlowPG: Action-constrained Policy Gradient with Normalizing Flows | Accept (poster) | Summary: Handling constraints in reinforcement learning is a fundamental problem that has application in areas such as robotics and resource allocation. A common solution is to incorporate a projection step to compute feasible actions, which involves a computationally expensive optimization solver in the loop. This can... | Rebuttal 1:
Rebuttal: 1. How to generate samples to cover the state space sufficiently? Refine flow during training?
Indeed, this is a good point. We note that our approach models the space of feasible actions for different states. Therefore, even if the feasible state space is large, but the relationship between diff... | Summary: This paper solves the problem of action-constraint reinforcement learning. The author utilizes the Flow model to learn a projection from the action to the latent variable, and then integrate DDPG to construct the FlowPG framework. Empirically FlowPG outperforms its competitors in both fewer constraint violatio... | Rebuttal 1:
Rebuttal: 1. Other constrained RL methods
In our paper, we consider the scenario where constraints are imposed on actions at each RL step, and these constraints have closed forms. Unlike the standard constrained MDP, we do not define cost functions for individual state-action pairs. Therefore, the direct a... | Summary: This paper provide a new method for ACRL, which incorporate normalized flow methods to alleviate the action violation problem. It achieves better results on MuJuCo compared with other methods.
Strengths: - Introduce normalized flow into action control -- which maps the original hard-to-control action space in... | Rebuttal 1:
Rebuttal: 1. Ablation study for PSDD and HMC
Please see the common response. (Sec. 2)
2. Comparison with recent ACRL algorithms + more domains
Please see the common response. (Sec. 3 and 4)
---
Rebuttal 2:
Comment: Thanks for the detailed explanation!
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Rebuttal Comment 2.1:
Comment: Thank you ve... | Summary: The paper introduces a novel action-constrained reinforcement learning (ACRL) algorithm called FlowPG, which utilizes a normalizing flow model to generate actions within the feasible action region. Experimental results demonstrate that FlowPG effectively handles action constraints and outperforms two existing ... | Rebuttal 1:
Rebuttal: 1. Motivation behind HMC and PSDD. Ablation study to justify the reason of using them
Please see the common response. (Sec. 2)
2. Convergence speed of FlowPG
For half cheetah and reacher, we shall highlight that our approach has a similar training curve as NFWPO. However, the key difference i... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful feedback and suggestions. We would like to address a few common questions as follows.
1. Rationale for using normalizing flows
Our reasons are:
High Accuracy: We conducted an ablation study comparing different generative models such as VAE and WGA... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training | Accept (spotlight) | Summary: This paper proposes TempBalance for temperature balancing based on the theory of heavy-tail self-regularization (HT-SR), which is a simple yet effective layer-wise policy applicable to general global temperature allocations in deep learning regularization. This paper proposes learning rate balancing across lay... | Rebuttal 1:
Rebuttal: ## Experiments on other areas
In Table 6 of the rebuttal PDF, we provide experiments of applying our method TempBalance (TB) to two different tasks: object detection and language modeling. In both tasks, TB consistently improves generalization, outperforming the baseline scheduler cosine annealing... | Summary: The paper proposes a layer-wise learning rate scheduler that adapts the learning rate to the eigenvalue distribution of the Weight-covariance matrix. As explained in the appendix, output and weight covariance matrices as well as the Hessian and Fischer-Information matrix are closely related, and they often sho... | Rebuttal 1:
Rebuttal: ## Does TempBalance (TB) boil down to addressing gradient excursions
We first summarize the reviewer's questions and our primary responses, with subsequent detailing of our experiment and supporting results.
1. **Does gradient excursion exist?**
We discovered that gradient explosion does exist, b... | Summary: This paper propose TempBalance, an adaptive lr schedule that assigns lr to each layer based on its heavy-tail characterization. The authors estimate PL_Alpha, the exponent of the power law distribution that fits the heavy tail part of the empirical spectral density, for the weight at each layer. They propose t... | Rebuttal 1:
Rebuttal: ## Other design of learning rate assignment?
Our selection of the linear interpolation design for learning rate assignment in our TempBalance (TB) method was based on its superior performance in our ablation study, as provided in rebuttal PDF Figure 16.
We evaluated three alternative learning rat... | Summary: The paper proposes a way of modulating the learning rate, independently for each layer, when training deep networks via gradient descent.
This modulation keeps the average learning rate (over all layers) on a predefined path (e.g., cosine decay), but "balances" it according to the relative training stage of ea... | Rebuttal 1:
Rebuttal: ## Experiments with non-image data
In Table 6 (b) of our rebuttal PDF, we present a new experiment using a language dataset. Our TempBalance (TB) method performs better than the baseline cosine annealing learning rate schedule (CAL) when both used the Adam optimizer for language modeling.
Here a... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for the constructive feedback, which helps us improve our paper. Please refer to the attached PDF for our new experiments and see below for our responses to each comment.
Pdf: /pdf/d9bed6d608d2baf0a2eb178d010ef651b37e4814.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Language-based Action Concept Spaces Improve Video Self-Supervised Learning | Accept (poster) | Summary: This paper proposes to transfer CLIP to the video domain for self-supervised learning. Textual features of video categories are used to obtain text classifiers and fixed during pre-training to obtain transferable information. Multiple complementary loss functions are designed for pre-training. Experimental res... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and address all concerns below.
1. `Using dataset labels for training:` We understand this shortcoming and run two new experiments that use no textual labels from datasets for our action concept spaces. We report these results in main rebuttal PDF (... | Summary: The paper proposes a new self-supervised approach to adapt image-level CLIP features to video. The key idea is to use a teacher-student self-supervised learning framework, and distill the knowledge in the action concept space, derived from text action concepts using the CLIP's text encoder. The resulting frame... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and address all suggestions below.
1. `Use dataset-agnostic action concepts:` We take the reviewers advice and develop two alternate strategies to construct action concepts in a dataset-agnostic way: GPT based category generation (LSS-B) and image V... | Summary: The paper introduces a language-tied self-supervised learning approach to adapt an image CLIP model to the video domain. The method employs two video-specific self-supervised learning objectives: concept distillation and concept alignment, for training the model. The authors showcase that the proposed method a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and address all concerns below.
1. `Experiments removing dataset-level label awareness:` We eliminate the need for dataset-level labels in 2 additional variants (GPT based category generation and image VQA based labels) and run experiments for these ... | Summary: The paper presents a method to adapt a vision-language model (CLIP) to represent videos.
The method extends the image encoder of CLIP to a video encoder via factored space-time attention.
The paper introduces a self-distillation-based objective to adapt the video encoder to train on unlabelled videos (no vid... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and address all comments below.
1. `Text-to-video retrieval:` We run experiments on MSR-VTT text-to-video retrieval benchmark to demonstrate how LSS improves over our baseline CLIP. The performance increase is significant and consistent to our prio... | Rebuttal 1:
Rebuttal: We thank all reviewers for positive comments: results show high transferability and generality of method (R-Cb1q); interesting and seems effective for transfer on known classes (R-WHFS); presents good zero-shot performance, holds the potential to offer effective solutions (R-mCow); tackles an impo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a novel language-based self-supervised learning framework (LSS) for video representation learning. It extends the self-distillation based SSL approaches like BYOL and SimSiam by replacing the randomly initialized project network by the text classifier defined by language embeddings extracted... | Rebuttal 1:
Rebuttal: We thank the reviewer for positive comments and helpful feedback. We address all concerns below.
1. `Pre-training without dataset labels:` As we also described in the main rebuttal, we run 2 additional experiments for variants of our method that use NO textual labels from any datasets. Results (L... | null | null | null | null | null | null |
Deep Discriminative to Kernel Generative Networks for Calibrated Inference | Reject | Summary: The paper presents an algorithm to learn an auxiliary model. The goal is to enhance the out-of-distribution calibration performance while maintaining the in-distribution performance of a base discriminative model. This base model is either based on a random forest or a multi-layer perceptron. The algorithm fol... | Rebuttal 1:
Rebuttal: - **[1] provides an interesting insight on OOD classification. Specifically, when training on object datasets like CIFAR-10 and CIFAR-100, neural models assign high likelihood also to SVHN data. What does it happen when using the proposed algorithm? Does it overcome the weaknesses of neural approa... | Summary: This paper tackles the ID and OOD problem by proposing Kernel Generative Forest and Kernel Generative Network for estimating the similarity $w_{rs}$ and in turn approximate the class-conditional density.
The main contributions include: 1) theoretical results of the convergence of the approximated class-condit... | Rebuttal 1:
Rebuttal: - **The proposed method is highly related to [4], but in the experimental part there is no comparison to any of the recent methods such as [4] (except for the "parent algorithms" RF and DN). At least MMC is also reported in [4]. The readers will also benefit if a detailed discussion of similarity ... | Summary: The paper proposes to improve OOD detection for deep discriminative models by replacing the affine function over the polytopes with a Gaussian kernel, leading to a method called kernel generative networks.
An estimation method is developed for the proposed method and some theoretical results on asymptotic conv... | Rebuttal 1:
Rebuttal: - **While the paper says the proposed method "results in better in- and out-of-distribution calibration", the results in Figure 3 show a contradicting or mixed results in different cases.**
We apologize for not being clear while describing the results in Figure 3. We will clarify the results in F... | Summary: The paper proposes a new method for confidence calibration in discriminative deep ReLU networks and random forests based on approximating the class-conditional density with Gaussian kernels.
Strengths: - The proposed method is conceptually simple and fairly novel, and it does not require retraining the parent... | Rebuttal 1:
Rebuttal: - **Writing has room for improvement.**
We will thoroughly go over the text with an editor to ensure every sentence is clear and concise.
- **It is unclear to me how the proposed method would be able to overcome the curse of dimensionality, as the number of polytopes can scale exponentially with... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to all the reviewers for their helpful suggestions and feedback, which we have incorporated to further improve our work. In particular, we pursued the suggestion from reviewer Quey to use geodesic distance and demonstrated its effectiveness with additional experimen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition | Accept (poster) | Summary: This study introduces Multiple-Input-Multiple-Output Neural Networks (MIMONets) that can process multiple inputs simultaneously, reducing computational cost. Two types of MIMONets, MIMOConv for CNNs and MIMOFormer for Transformers, are presented. MIMOConv can handle multiple image inputs with minimal accuracy ... | Rebuttal 1:
Rebuttal: We would like to thank you for your time and feedback. However, it is unclear how the sparse set of stated weaknesses led to a reject decision. In particular, weakness 2 applies to most innovations that are yet to be established. Weaknesses 1 and 3 coincide, are thoroughly addressed in the paper (... | Summary: The main content of the article is about MIMONets, which are multiple-input-multiple-output neural networks that exploit computation superposition. By using fixed-width distributed representations in vector-symbolic architectures, MIMONets can represent a variable number of inputs in a data structure and proce... | Rebuttal 1:
Rebuttal: >“The experimental datasets and network applications in the study are relatively limited. It would be beneficial to apply the method to more datasets...”
We value the reviewer's proposal to conduct additional experiments on other tasks. We addressed this by adding results on MNIST (see Figure R1 ... | Summary: This paper proposes a novel method named multiple-input-multiple-output (MIMO) neural networks, which aims to achieve simultaneous inference for several inputs together by mixing them into one input. To this end, the authors invent one method to first encode the inputs, whose output can be decoded to give sepa... | Rebuttal 1:
Rebuttal: >“The method is only verified on some small datasets like CIFAR […] it would be better if the author could provide more results on other tasks […]”
We appreciate the reviewer's suggestion to conduct additional experiments on other tasks. We addressed this by adding results on MNIST (see Figure R1... | Summary: UPDATE: scores updated based on rebuttal.
This paper proposes a method (MIMONets) for multiplexing multiple independent samples in superposition in such a way that one can train neural networks to simultaneously process those samples in training and inference. The method is adapted both for CNNs and Transform... | Rebuttal 1:
Rebuttal: >“[the authors] fail to mention some related work […] Please review that work and discuss”
Thank you for pointing out the DataMUX paper. Given its importance, we discuss key differences and compare against it empirically on additional benchmarks (see pdf) in the global response. In short, DataMUX... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. We are encouraged that (b2eJ) found our method well-written and well-grounded in previous works. We are pleased that (b2eJ, A964) appreciated our new theoretical analysis and experimental evaluation. We are glad that (zbz3, 3r5d) agree with the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions | Accept (poster) | Summary: This paper explores and proves some properties of KL divergence between multivariate Gaussian distributions. One of the motivations is that as a statistical distance, KL divergence does not satisfy the properties of a metric, that is, symmetry and triangle inequality. In spite of these issues, this paper propo... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We explain your concerns as follows.
**C1**: Theorem 1 and Theorem 3 hold when two conditions are satisfied. For example, for the mean, it requires $\mu_1=\mu_2$, which is too strong in practice.
**A1**: **These strong conditions are advantages rath... | Summary: Kullback-Leibler (KL) divergence is an important measure of distance between probability distributions with uses in statistics, information theory and many other fields. However, it is not a proper distance measure, since it is not symmetric and does not satisfy the triangle inequality in general. The authors ... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We explain your concerns as follows.
**C1**: due to the continuity of the KL divergence around epsilon=0, the results are not too surprising.
**A1**: The task of this work is to (1) *quantify* the approximate symmetry and (2) *find* a relaxed triang... | Summary: This paper investigates the properties of KL divergence between Gaussian distributions. The main theoretical contributions include two main theorems. The first one gives the supremum of reverse KL divergence between Gaussians when the forward KL divergence is bounded. The conditions when the supremum is attain... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We explain your concerns as follows.
C1: It is possible to make some equations tighter by introducing notations earlier. For example, notations in Equations (G.146)-(G.150) can be introduced earlier to make Equations (G.128)-(G.144) more concise.
A1... | Summary: In this paper, the authors look at the KL divergence between two multivarite Gaussian distributions. The KL divergence is an important distance function between two distributions. However, it lacks certain nice properties that other metric distance functions such as variation distance satisfies: namely, symmet... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments, especially for suggesting one more application of our theorems. Please see the *common rebuttal for all reviewers* for discussion about this application.
There is no weakness, question, or limitation contained in the comments. Thanks again for your v... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments.
Here we address one concern from Reviewer (8dDm) on the applications of our theory. We think Reviewer (rMwu) may be also interested in this point. So we put the answer in the common rebuttal.
Reviewer (rMwu) proposes no weakness or question. We thank Reviewer r... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors prove the following interesting mathematical properties of the Kullback-Leibler (KL) divergence between multivariate Gaussian distributions, while the KL divergence is not a proper distance (in sense that it is not symmetric) and does not satisfy the triangle inequality, but:
1. if $... | Rebuttal 1:
Rebuttal: Thanks for your careful and valuable comments. We explain your concerns as follows.
C1: The authors mention that they propose an unified OOD detection algorithm KLODS, but no detail about KLODS is given, it would be great if the authors could elaborate more on this.
A1: We will add more details... | null | null | null | null | null | null |
Linear Time Algorithms for k-means with Multi-Swap Local Search | Accept (poster) | Summary: This paper studies local-search algorithms for k-means clustering. The goal here is to obtain a local-search algorithm which (1) give a constant factor approximation and (2) run in time linear in the dataset. In the past literature, one can distinguish essentially 2 types of local-search algorithms for this pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Question 1: The authors actually propose 2 algorithms, first MLS for which they prove theoretical guarantees. And a second MLSP which they say is "more practical" but I do not s... | Summary: The authors study the well-known k-means problem: Given a set of points in the Euclidean space, compute k centers such that the sum of squared distances between points and their closest center is minimized. A constant-factor approximation to this problem is achieved by local search: Start with arbitrary k cent... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Q1: Regarding $n^t$ instead of $nk^t$ in L69**
Response: The swap size $O(nk^t)$ used in L69 is actually $O((nk)^t)$, which is a typo. Sorry for the confusion.
**Q2: Typos a... | Summary: Local search algorithm is a well studied technique for clustering problems. In the k-means problem, the simple swap heuristic states that start with an intiial chosen set of k centers and then at each local search step, check if swapping an existing center with a new one leads to decrease in cost. It is known... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Question 1: Please compare with [9] in terms of theoretical and experimental results**
Response: We thank the reviewer for raising this question. We are sorry for the confusio... | Summary: This paper proposes a multi-swap local search algorithm for the k-means problem with linear running time in the data size, while also achieves a better approximation ratio when compared with other local search algorithms that adopt single-swap strategy. To benefit more from such algorithm when handling large-s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive rating and the thoughtful comments. In the following we address the concerns.
**Question 1: Line 11-12, "which improves the current best result 509": It might be true for local search methods that take single-swap strategy, but It is unfair to call this the... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive feedback and valuable suggestions and we highly appreciate the effort paid by the reviewers to provide in-depth reviews that helped us to improve our work. In the following, we will address the reviewers' comments in detail with separate responses. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning | Accept (poster) | Summary: This paper proposed a mutual learning approach to learn a pair of Bayesian Neural Network(BNN). The posterior of BNN is approximated by Variational Inference using a Gaussian distribution with a diagonal covariance matrix. To make the BNN learn different perspective of the data, the author proposed to increase... | Rebuttal 1:
Rebuttal: ### Weaknesses
1. BNN model with variational inference may underperform deterministic model or BNN obtained by MCMC, the baseline only involves BNN model trained with (DML) or without(vanilla) mutual learning. Would the proposed method close the gap to some extent? I would expect the BNN model to... | Summary: The paper proposes a method to combine deep mutual learning with BNN to diversify the weight distributions of each BNN networks in a pair or ensemble, to improve performance.
Strengths: 1. AFAIK this is the first work combining mutual learning with BNN, so the authors can claim this point.
2. The paper is in ... | Rebuttal 1:
Rebuttal: ### Weaknesses
1. Some design choices are found to be "empirically" working well without too much discussion or hypothesis.
**Answer:** Please refer to the general response for the ablation studies on hyper parameters T, $\alpha$, and $\beta$.
2. Would be interesting to see how the model perf... | Summary: The paper titled addresses the challenge of improving the performance of Bayesian Neural Networks (BNNs) by leveraging the concept of mutual learning. BNNs provide a means for quantifying uncertainty in predictions through probability distributions of model parameters. However, BNNs often fall short in perform... | Rebuttal 1:
Rebuttal: ### Weaknesses
1. Limited variety in experimental validation: One weakness of the paper is that the proposed approach and its effectiveness are only verified through experiments conducted on Residual Neural Networks (ResNets). It would have been beneficial to include experiments on a diverse set o... | Summary: The paper focuses on improving the accuracy of BNNs by promoting diversity in both parameter space and feature space while training two peer BNNs with mutual learning between them. More specifically, they train two variational BNNs with a mean-field Gaussian variational loss for each along with a KL divergence... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. A few key details are unclear in the text, and importantly a deterministic baseline is missing.
**Answer:** We acknowledge the reviewer's comment. Accordingly, we present here the results of the deterministic baseline in the following table. The results indicate that our BNN mod... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for the constructive feedback.
## General response to the choice of hyper parameters
Regarding hyper parameters $T$, $\alpha$, $\beta$:
For the temperature $T$, we follow the seminal work [13] and [4]. This parameter $T$ controls the smoothness of the prediction ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a novel method for enhancing the performance of Bayesian Neural Networks (BNNs) by employing deep mutual learning. The proposed approach aims to enhance the diversity of both network parameter distributions and feature distributions, encouraging individual networks to capture unique charact... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. The previous studies mentioned in the paper utilize alignments on feature maps [4] or predictions [38], rather than diversifying them. In contrast, the proposed method diversifies both feature distributions and parameter distributions which is an opposite approach to the previous... | null | null | null | null | null | null |
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits | Accept (poster) | Summary: The paper addresses the problem of adversarial linear contextual bandits, where loss vectors are selected adversarially and the context for each round is drawn from a fixed distribution; traditional approaches require access to a simulator for generating free i.i.d. contexts or achieve a sub-optimal regret no ... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. We agree that empirical validation is important to prove the efficacy of the algorithm. It will be an important future work to experimentally compare our algorithm to other existing linear contextual bandit algorithms.
$\textbf{Q1:}$ How might these estimato... | Summary: The paper studies an adversarial linear bandit problem. At each round, the adversary selects a hidden loss vector $y_t$, the environment 'stochastically generates' an action set $A_t$, and the learner chooses an action in the action set. The learner is able to observe noisy loss $\ip{y_t}{a_t}$.
Strengths: Up... | Rebuttal 1:
Rebuttal: Thank you for raising concerns about the clarity of the setting. We will clarify them in the new version.
$\textbf{Q1:}$ In this work context vectors = actions, I have never seen this formulation in previous literature. The action set $A_t$ is generated from the distribution $D$. What exactly is... | Summary: Summary:
This paper presents a near-optimal computationally efficient simulator-free algorithm in contextual bandits setting with i.i.d contexts and adversarial reward functions. Most of the past work on this setting either requires a simulator that allows them to draw a large number of contexts from a distri... | Rebuttal 1:
Rebuttal: Thanks for providing suggestions on improving the readability of the paper. We will incorporate them in the final version.
$\textbf{Q1:}$ To control the term $||(\hat{\Sigma}_t - H_t) y_t||\_{\hat{\Sigma}_t^{-1}}$, did you need to use any kind of matrix concentration? If so, did you need control... | Summary: This paper studied the contextual bandits with i.i.d. contexts and adversarial linear loss functions without simulators and proposed a follow-the-regularized-leader with log-determinant barrier (Logdet-FTRL) algorithm with a carefully designed covariance matrix estimator that is computationally efficient give... | Rebuttal 1:
Rebuttal: We thank the reviewer for the support and valuable feedback. We have changed the typos you mentioned in our new version. | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds | Accept (poster) | Summary: Information-theoretic generalization bounds offer a new approach in generalization theory by providing complexity measures that depend on the data distribution and learning algorithm itself. In the past years, it has been observed that information-theoretic generalization bounds are not compatible with the cla... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Our responses follow.
>- The presentation of the paper needs significant improvement. Specifically the part on defining the additional structure is extremely vague. For instance, there are many variables that are not clearly defined. For instance, what is... | Summary: The paper proposes several new stability assumptions named the sample-conditioned hypothesis (SCH) stability. Based on these notions, the authors present new IOMI and CMI bounds to address the limitations of existing information-theoretic bounds in the context of stochastic convex optimization (SCO) problems.
... | Rebuttal 1:
Rebuttal: We thank you sincerely for your comments to our paper. Our responses follow.
>- It seems to me the most significant contribution ...
**Response.** Accurately estimating the order of the stability parameter is challenging in all stability-based bounds. However it is still possible to bound the pa... | Summary: This paper studies how to develop information-theoretic generalization error bounds under the assumption that the algorithm is uniformly stable under a certain loss. Typically, these kinds of bounds are based on properties of the loss with respect to the data (e.g. subgaussian or bounded), while in this case t... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments, and we appreciate your positive feedback on our paper. Our responses follow.
>- Could you clarify further the definitions in Definition 2.1? Also, could you expand in the reasoning of why some parameters are larger/smaller than the others?
**Re... | Summary: This work improves information-theoretic generalization gap bounds by doing more careful derivations. As a result, the derived bounds get a multiplicative factors that capture some notions of hypothesis stability, while the existing bounds usually have a multiplicative constant factors that depend on the loss ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments. Our responses follow.
>- Line 219: there might be no uniform convergence if the conditional mutual information terms vanish with different rates for different $\tilde{w}_i$ such that their supremum does not vanish.
**Response.** We agree that if t... | Rebuttal 1:
Rebuttal: # To all reviewers, particularly to Reviewer s7qd:
>- Reviewer s7qd has pointed out that our Definition 2.1 lacks clarity. We would like to address this concern by offering the following commentary:
We first note that the reason we introduce SCH stabilities in Definition 2.1 is that solely using... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Explore In-Context Learning for 3D Point Cloud Understanding | Accept (spotlight) | Summary: This paper proposes an in-text learning method for multi-task 3D shape analysis. It handles several tasks such as denoising, part segmentation, reconstruction and registration with a single pretrained masked point model (MPM). The authors also claim previous MPM methods introduce information leakage during pr... | Rebuttal 1:
Rebuttal: >**Q1**:
>
>(1) **Task definition**
>
>(2) **Information leakage**
>
>(3) **JS module**
**A1**:
  We are sorry that you were confused about our paper and thank you very much for your suggestions on our paper. We provide more detailed explanations:
  **1. More details of the task defin... | Summary: This paper introduces a novel framework, named Point-In-Context, designed explicitly for in-context learning 3D point clouds. The authors conduct extensive experiments to validate the versatility and adaptability of the proposed methods in handling a wide range of tasks.
Strengths: This paper explores an inte... | Rebuttal 1:
Rebuttal: >**Q1: In reality, it might be hard to choose a proper prompt for it and the performance may not be stable, and it kind of involves some extra tuning effort compared to using the model which is directly trained for that task.**
**A1**:
  The prompt can indeed affect the performance of our P... | Summary: This work is conducted toward in-context learning for 3D point cloud data. Similar to 2D in-context learning, the authors first define and construct the in-context learning 3D dataset covering reconstruction, denoising, registration, and part segmentation tasks. To avoid information leaking during masked point... | Rebuttal 1:
Rebuttal: Due to word limit, we omit the questions, answer order is consistent with the above questions.
**A1**:
  Seriously lacking data is a **common problem for 3D tasks**. With the advent of 3D sensors such as LiDAR and Kinect, 3D point clouds have gained increasing popularity and are widely used... | Summary: Inspired by in-context learning in NLP and 2D vision tasks, this paper aims to explore the in-context learning in the 3D point cloud. The authors present Point-In-Context, which is a 3D mask point modeling framework. Meanwhile, to handle the data leakage issues, the authors also present a simple solution, name... | Rebuttal 1:
Rebuttal: >**Q1: Are there any other solutions to replace joint sampling to handle the data leakage problems?**
**A1**:
  When we adopt the original MPM pre-training framework for our task, we find that model utilizes center point coordinates that should have been masked before position embedding. T... | Rebuttal 1:
Rebuttal:   We would like to thank the four reviewers for their suggestions, which make our paper more solid.
  We are grateful that the reviewers acknowledge our work. Here list some excerpts:
  **1.** As the reviewer **Uvps** and **GuqQ** said, Our work is the first to explore in-context ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Multi-Fidelity Multi-Armed Bandits Revisited | Accept (poster) | Summary: The authors consider the problem of multi-fidelity multi-armed bandits under fixed-confidence BAI and regret-minimization objectives. The best-arm identification algorithm is based on lower-upper confidence bound and associated cost-complexity. A novel definition of regret is introduced which captures the fac... | Rebuttal 1:
Rebuttal: We thank the review for providing positive feedback to
this work. | Summary: This paper studies the multi-fidelity multi-armed bandits problem where each arm has different fidelities and observation accuracy. There are many existing works in this problem. This paper studies both best arm identification with fixed confidence and regret minimization. There are some novel theoretical resu... | Rebuttal 1:
Rebuttal: We highly appreciate the reviewer for these instructive comments. Below, we answer weaknesses and questions point by point according to the reviewer's numerical label.
**Weaknesses**
> *Although this paper claims to be the first work to study the BAI task under the MF-MAB model, I do have a quest... | Summary: The paper studies the multi-fidelity multi-armed bandit problem where each arm can be pulled at different fidelities, providing better or worse estimate of the true mean, at a different cost. The paper studies best arm identification with fixed confidence, provides a lower bound on the cost complexity, and an ... | Rebuttal 1:
Rebuttal: > - *The proposed algorithm for BAI requires the estimates
$\tilde{\mu}\_1\^{(M)}, \tilde{\mu}\_2\^{(M)}$ which is not a realistic
assumption. ...*
> - *The cost upper bound for BAI depends on
$\tilde{m}\_k\^{\star}$ , which depend on
$\tilde{\mu}\_1\^{(M)}, \tilde{\mu}\_2\^{(M)}$... | Summary: This paper studys the problem of Multi-Fidelity Multi-Armed Bandits (MF-MAB) where each arm can be pulled at different fidelity with different rewards and costs. The main contribution of this paper includes derive the cost complxity lower bound for best arm idenfication with fixed confidence and a new definit... | Rebuttal 1:
Rebuttal: > *Why use the UCB given that UCB of an arm is
larger than that of another arm does not necessary mean that the
first arm is better? How about use LCB?*
We believe this question is about why we use UCB
indices in Line 4 of Algoithm 1 (LUCB framework) to pick top two
... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this work authors revisit the multi-fidelity multi armed bandits and provide results i.e., upper and lower bounds for cost complexity for best arm identification with fixed confidence objective using the Lower-upper confidence bound framework. Further, this work introduces 3 procedures for finding optimal f... | Rebuttal 1:
Rebuttal: > *This work assumes the reward distributions are on
bounded support which may not be the case always.*
Assuming the reward distribution is bounded is common
and standard in bandits literature [@auer2002finite]. With known
approaches in bandits, this assumption can be easily extended ... | Summary: This paper studies the the multi-fidelity multi-armed bandit setting where each arm is associated with a cost (fidelity) and observed reward. In this setting when the learner pulls an arm $k \in \mathcal{K}:=\{1, \ldots, K\}$ at fidelity $m \in \mathcal{M}:=\{1, \ldots, M\}$, it pays a cost of $\lambda^{(m)}$ ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for spending time reviewing this paper. Below, we answer weakness point by point according to numerical order.
1. Yes, this is a standard setting of multi-fidelity multi-armed bandits proposed by Kandasamy et al. [18, 20].
**On known error upper bound** We present... | null | null | null | null |
Successor-Predecessor Intrinsic Exploration | Accept (poster) | Summary: This paper is about exploration in Reinforcement Learning. Many existing works do not leverage retrospective information when computing intrinsic rewards. To this end, the paper introduces a method called SPIE where the intrinsic reward is calculated by combining successor and predecessor representations to ac... | Rebuttal 1:
Rebuttal: We thank the reviewers for their instructive comments. Please find below our replies to the raised concerns/questions.
- We thank the reviewers for pointing us to the related literature on constructing intrinsic reward based on episodic information and retrospective transitions. SPIE is similar t... | Summary: This paper introduces a new intrinsic reward (termed SPIE) for encouraging exploration in reinforcement learning. Unlike existing methods that mainly focus on prospective information, SPIE also incorporates retrospective information into the intrinsic reward. SPIE combines successor representation (SR) and pre... | Rebuttal 1:
Rebuttal: We thank the reviewers for their instructive comments. Please find below our replies to the raised concerns/questions.
- The retrospective information can be leveraged for identifying essential predictors for reward states, hence exploration using the retrospective information allows the agent to... | Summary: This paper proposes Successor-Predecessor Intrinsic Exploration (SPIE), an exploration framework that uses the successor representation (SR) and predecessor representation (PR) to formulate intrinsic motivation for exploration. Amongst the variations, two specific design of intrinsic rewards are highlighted: S... | Rebuttal 1:
Rebuttal: We thank the reviewer for their instructive comments. Please find below our replies to the raised concerns/questions.
- Thanks for pointing out the missing of explanations of variables and logics in the current manuscript. In responding to other reviewers' comments, we have modified our manuscrip... | Summary: The paper proposes to use both successor feature of s and predecessor feature of s' as intrinsic reward to improve RL exploration. Specifically, in the tabular case, the proposed intrinsic reward encourages the agent to visit states that are infrequently visited from other states, using successor representatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their instructive comments. Please find below our replies to the raised concerns/questions.
- The generalisation to continuous state space means that we can no longer enumerate all states for the computation and learning of the SR. Hence we adopt a natural extension of t... | Rebuttal 1:
Rebuttal: We thank all reviewers for their instructive comments for making the paper clearer and more rigorous. There are a number of questions/concerns that are shared by multiple reviewers, which here we provide summarised responses below. We have also attached a rebuttal letter containing additional expe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Disentangling Cognitive Diagnosis with Limited Exercise Labels | Accept (poster) | Summary: The dissociation-based cognitive diagnosis (DisenCD) model proposed in this paper addresses the cognitive diagnosis challenge of limited practice labels by using students' historical practice records to model their proficiency, practice difficulty, and practice label distribution. The model introduces novel mo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments, efforts, and time. We respond to each of your questions and concerns one-by-one as follows:
### Weaknesses
**W1-(1): Limited Generalizability: Although the effectiveness of the DisenCD model is demonstrated on three real-world datasets, the model'... | Summary: This paper presents an algorithm for cognitive diagnosis (CD) in limited data scenarios. CD aims at labeling questions to knowledge concepts.
As the CD process requires expert tagging, labelling questions to knowlege concepts in time intensive.
This paper addresses the labelling task from three factors: stud... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments, efforts, and time. We respond to each of your questions and concerns one-by-one as follows:
### Weaknesses
**W1: The authors utilized student interaction distribution to derive exercise relevance and knowledge concepts from exercise relevance.
One... | Summary: This paper focuses on performing cognitive diagnosis with limited exercise labels. To address the enormous cost of labeling exercises, in this paper, the authors proposed Disentanglement based Cognitive Diagnosis (DisenCD). Specifically, they first used students’ practiced records to model student proficiency,... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments, efforts, and time. We respond to each of your questions and concerns one-by-one as follows:
### Weaknesses
**W1 and W2 : The radar chart in Figure 1 has some mistakes...., 'The', …**
**A1 and A2**:
Very sorry for the mistake and typos. In fac... | Summary: This paper introduces an innovative approach called DisenCD, aimed at enhancing the performance of cognitive diagnosis when exercise labels are limited. The proposed method incorporates two newly developed modules: the group-based disentanglement module and the limited-labeled alignment module. These modules e... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments, efforts, and time. We respond to each of your questions and concerns one-by-one as follows:
### Weaknesses
**W1-Brief: Although DisenCD aims to enhance interpretability in cognitive diagnosis, the model itself may lack interpretability.**
**A1**:... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' thoughtful comments, efforts, and time. In the current global response, we have included an **attached rebuttal global file**. We will be **referencing some figures or tables when replying to each reviewer**. Thanks again to the reviewers for their efforts.
... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a method to perform cognitive diagnosis. The method leverages notions of disentanglement representation learning to achieve better interpretability while avoiding sacrificing performance of predicting the students' answers. Experiments were conducted on three popular datasets and quantitativ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful comments, efforts, and time. We respond to each of your questions and concerns one-by-one as follows:
### Questions
**Q1 & Weakness - Brief: Confusion on interpretability and DOA.**
**A1**: Thanks for your question. In cognitive diagnosis, different dimen... | null | null | null | null | null | null |
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations | Accept (poster) | Summary: This paper develops a form that facilitates direct optimization, use it to learn Maximum Manifold Capacity Representations (MMCRs), and demonstrate that these are competitive with state-of-the-art results on standard self-supervised learning (SSL) recognition benchmarks. Empirical analyses reveal important dif... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work. Please let us know if any questions arise during the discussion period and we will be happy to clarify. | Summary: This paper proposes a new SSL objective to directly optimize manifold capacity (num of categories represented in a linearly separable fashion). It is found that the resulting representations can support linear classification well. It is also shown that the representations are somewhat sample efficient (reasona... | Rebuttal 1:
Rebuttal: Thank you for your review of our work, our responses to the listed weaknesses are below:
- *[Limited evaluations, objective too specific?]*: We agree that evaluating on more tasks can strengthen the assessment of a learned representation. We have run additional evaluations of baseline models trai... | Summary: This paper proposes an alternative way (i.e., maximizing manifold capacity) to learn useful representation in a self-supervised manner. In particular, it maximizes the separability of the centroid manifold (i.e., negative samples), while the second term aims to minimize the nuclear norm among positive samples.... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our submission. Below we respond to each of the listed weaknesses:
- *"As shown in Line 138-141..."*: Many self-supervised learning methods share these core motivations, and [1] in particular demonstrates that the logarithm of the average pairwise Gaussian po... | Summary: The efficient coding hypothesis suggests sensory systems maximize mutual information between their inputs and the environment. A recent adaptation, "manifold capacity", calculates the number of linearly separable object categories, but is computationally intensive. The authors simplify this measure to directly... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our paper. We respond to each of the listed weaknesses below:
- *"Although the initial results look very promising..."*: We agree including a measure of uncertainty would help strengthen the paper. For ImageNet experiments repeated trainings of the linear cla... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their careful consideration of our submission. Their feedback has helped to identify several simple ways to improve our paper. Below we summarize some key revisions to be made in the case our work is accepted:
- The framing/introduction of our objective wi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors convert recent results in manifold perceptron capacity calculations to a practical self-supervised learning algorithm. They then (1) provide one theoretical result, (2) show their method is predictive of primate visual cortex recordings, and (3) characterize some empirical properties of their metho... | Rebuttal 1:
Rebuttal: Thank you for your very thorough review of our work! Below we address each point listed in the weaknesses:
- *"Section 2.1 ..."*: Apologies that this was not expressed more clearly: each of the manifolds is assumed to have the same dimensionality and radius, but each has its own centroid. We wi... | null | null | null | null | null | null |
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost | Accept (spotlight) | Summary: This paper proposes a computationally efficient algorithm for multinomial logistic bandit with improved regret and computation cost. The algorithm uses online mirror descent and new approximation to efficiently compute consistent estimator and construct optimistic reward. Experimental results show improved reg... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation of this paper and the insightful common! In the following, we will address your questions and will improve the paper according to your suggestions.
---
**Q1:** Discussion and experimental results on whether there is a trade-off to gaining efficiency would be... | Summary: This paper considers Multinomial Logistic bandits (the extension of the usual logistic bandit model to a setting where there are more than two possible outcomes - e.g. in advertising one picks an action in response to a context and may observe 'no click'/'click'/'save for later'/etc instead of just 'no click'/... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation and constructive comments for this paper! In the following, we will address your questions. We will further improve the paper according to your suggestions.
---
**Q1**: What do you see as the utility of the algorithm presented in Section 2.3 over OFUMlogB?
... | Summary: This paper considers a generalization of the (binary) logistic bandit problem to the multinomial setting, significantly improving the state of the art for that problem and even for the special binary. For the binary logistic bandit problem, many (earlier) algorithms proposed were not optimal; their regret bou... | Rebuttal 1:
Rebuttal: Many thanks for your great appreciation and brining the related work to us! In the following, we will address your questions. We will further improve the paper according to your suggestions.
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**Q1**: a slightly more detailed discussion of similarities (if any) with OFU based methods and analy... | Summary: This paper has addressed multinomial logistic bandits whose feedback has multiple choices. This paper improves the regret bound in terms of $K$ and reduces the computation cost into constant complexity with respect to $T$. In experiments, the results support that the proposed method is much faster than the pri... | Rebuttal 1:
Rebuttal: Thank you for the careful review. In the following, we will first highlight the technical contribution of the paper then address your questions. If your concerns have been properly addressed, please consider updating your score to this paper. Thanks!
---
**Q1:** novelty of analysis techniques
*... | Rebuttal 1:
Rebuttal: We would express our heartfelt thanks to all reviewers for their careful review and constructive feedback. After carefully considering the comments from reviewers XVWd, Rks7, 6tgu, and ojs3, we conduct additional experiments to support the effectiveness of our algorithm by a more suitable way of o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary:
This paper examines multinomial logistic bandits (MLogB), a problem where the learner's action $x_t$ produces feedback $y_t$ with $K+1$ possible outcomes. The probabilities of these outcomes are modeled using a logistic model. In real-world scenarios like online advertising, customers may provide various type... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We will address your questions below and make enhancements to the paper based on your valuable suggestions.
---
**Q1:** what is the reason the optimistic rule follows a linear model.
**A1:** We are grateful to the reviewer for highlighting the ambiguity i... | null | null | null | null | null | null |
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? | Accept (poster) | Summary: This paper investigates the effectiveness of GNNs on nodes with different structural patterns in real-world graphs and proposes a new method to identify the reasons for performance disparities. The authors found that GNNs tend to perform well on homophilic nodes within homophilic graphs, but struggle with the ... | Rebuttal 1:
Rebuttal: **Q2:** Such differences lead to performance disparity between nodes in majority and minority patterns.If randomly sample train and test data,the distribution of majority and minority patterns is same for train and test nodes.Thus,the disparity shown in example is not valid in practice
**R:** The... | Summary: This paper focuses on the performance disparity on homophily and heterophily nodes in node-level classification tasks. It claims that although GNNs have good performance on both pure homophily and pure heterophily graphs, GNNs cannot perform well when dealing with graphs with both these two types of nodes. The... | Rebuttal 1:
Rebuttal: **W1:** The paper identifies the problem of performance disparity but does not provide a solution to address it
**R:** Thanks for the great question revolving the contribution of this paper. We would like to first provide a more comprehensive understanding on the motivation and contribution of o... | Summary: This paper provides a rigorous analysis of the effect of structural disparity on the performance of GNNs. The proposed CSBM-S model and the application of PAC-Bayes analysis, among others, show the different effects of aggregation on the performance of nodes with different structural disparity. The analysis fu... | Rebuttal 1:
Rebuttal: **W1:** The conclusions hold in this paper share the same promise that the aggregation operation is neighborhood averaging, which may not generalize to a broad range of GCNs models.
**R:** Thanks for your great question pointing out the gap between our theoretical analysis and empirical result. ... | Summary: This work tries to understand the effectiveness of GNNs w.r.t different structural disparities within a graph. Previous studies have focussed on GNN's effectiveness on overall graphs, but here the authors try to understand GNN's effectiveness w.r.t structural patterns such as homophilic and heterophilic nodes ... | Rebuttal 1:
Rebuttal: **W1:** The work by itself is strong and would have been even stronger if presented with more understanding of the effectiveness of Deeper GNNs on doing comparatively well on minority nodes.
**Q1:** Is there a way to prove deeper GNNs improved discriminative ability on minority nodes?
**R:** Tha... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for their constructive reviews. The pdf file contains new experimental results for reviewers pgwY and GxBV.
With the help of reviewers, we find some typos and writing issues in our paper. We will correct those mistakes and add more explanations for better understanding i... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks | Accept (poster) | Summary: The manuscript studies a semi-supervised node classification task using graph convolutional networks (GCNs). Specifically, motivated by the growth of the input graph (data) size, this paper considers a federated learning setting in which training is performed in a distributed manner, by partitioning the underl... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments!
>**For weakness: marginal gain of accuracy compared to FedSage+**
We agree that FedSage+ often achieves good accuracies. However, the main contribution of FedGCN is that it requires 100X less communication cost by using a pre-training communication... | Summary: The authors propose a secure federated protocol over GCNs which are split across federated clients. To use the features of a node's neighbors present in other clients (of which both clients are mutually aware of an edge), the accumulation of neighbor features through the adjacency matrix is encrypted via some ... | Rebuttal 1:
Rebuttal: We highly appreciate your opinions especially "Algorithm is astoundingly simple", which is also our goal in designing the algorithm.
>**For weakness 1: the one neighbor case wasn't adequately addressed.**
We agree that there could be cases when the graph is heavily partitioned and also nodes hav... | Summary: The paper introduces a technique to facilitate the federated learning of graph convolution networks. The key idea is to send aggregated node features to each client before the federated learning. The approach involves transmitting these features to each client in an encrypted manner to ensure privacy.
Overall... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition that the method and proofs are interesting! We are very happy to elaborate on more technical details and proof sketches.
>**Technical details: why feature aggregations of only 1- and 2-hop neighbors of nodes are sufficient to evaluate the 2-layer GCN?**
Ba... | Summary: The paper presents FedGCN, a framework designed for federated training of graph convolutional networks (GCNs) specifically for semi-supervised node classification. The proposed method aim to communicate cross-client neighbor information just once before training initiates, diverging from previous methods that... | Rebuttal 1:
Rebuttal: Thank you for your helpful suggestions! We plan to incorporate them into our manuscript, as we detail below, and we believe that our reply will resolve your concerns.
>**For weakness 1: motivation is not clear**
“Keeping data where it is generated” refers to typical data constraints in federated... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SQ Lower Bounds for Learning Mixtures of Linear Classifiers | Accept (poster) | Summary: This paper studies the problem of learning mixtures of linear classifiers under Gaussian sampling. The paper provides a stastical query lower bound which demonstrates that known algorithms for the problem in the literature are essentially best possible, even for the special case of learning uniform mixtures. I... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and effort. We will address the typos pointed out in the revision.
The reviewer’s stated weakness of our work is the fact that it is “primarily a theoretical work”.
We respectfully point out that theoretical research in machine learning (“learning theory”... | Summary: A statistical query (SQ) algorithm is an algorithm that attempts to learn the data distribution $D$ by querying $f$ to the oracle who responses with $v$ such that $\lvert v - E_{x\sim D} f(x) \rvert$ is small. In this paper, the authors study the problem of finding an SQ lower bound for learning a mixture of l... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their effort and positive assessment of our work.
We start by addressing the reviewer’s concerns in the “Limitations” sections below:
1. “Also, am I correct in assuming that the result only applies when $r$ is already known. Is there anything we can do whe... | Summary: The paper provides statistical query lower bounds for learning mixture of linear classifier.
In the problem of learning mixture of linear classifier, there are $r$ linear classifier $v_1, \ldots ,v_r \in \mathbb{R}^{n}$. The input feature $x\in \mathbb{R}^{n}$ is draw from gaussian, the label $y = \mathsf{sig... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their effort and positive assessment of our work.
We respond to the reviewer’s questions below:
1. I have some concern with Lemma 3.5. In the statement, it is only required that $f\in L^2(\mathbb{R},\mathcal{N})$, but it seems not true for every such func... | Summary: The authors prove a lower bound for the number of queries needed in the statistical query model for learning a mixture of linear classifiers. The statistical query model essentially makes oracle queries with a polynomial f(x) and the oracle responds with a value v such that: |v-E[f(x)]\le t, for some threshold... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their effort and positive assessment of our work.
1. Regarding the reviewer’s point:
“The authors claim the lower bound is "qualitatively match" previous results by Chen et al. 2022. It would be good to reduce the Che et al result to the SQ model or vice ve... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in providing feedback. We are encouraged by the positive comments from reviewers (**Jk98**,**vDTe**,**Azis**) for the following:
(i) the importance of the problem we study (learning mixtures of linear classifiers)
and the tightness of our SQ lower ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors study the problem of learning mixture of linear classifiers with Gaussian covariates. Their primary result is a near-optimal SQ lower bound which applies even for the uniform mixture case. Moreover, as a purely mathematical result, they construct an efficient spherical design (under a stronger defi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and effort.
We start by addressing the concerns within the review in order:
1. Mathematical Contributions and Presentation.
Our main technical contribution is a novel construction of a spherical $t$-design, which leads to a nearly-optimal SQ lower bound ... | null | null | null | null | null | null |
FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation | Accept (poster) | Summary: The paper proposes FGPrompt that conditions the goal image embedding on the observation such that the agent can obtain goal-relevant visual cues during image-goal navigation.
To fuse the input and goal image embeddings, FGPrompt introduces two strategies: Mid Fusion by FiLM and Early fusion by encoding the con... | Rebuttal 1:
Rebuttal: > Q1. The paper motivates the necessity of fusing the input and goal images but its methodology is directly adopted from FiLM (Mid Fusion). It is unclear which part is novel in the proposed method and what we can learn from the novel part.
A1. We are sorry for the confusion. Nevertheless, **we h... | Summary: The paper proposes different early to middle fusion mechanisms to improve performance of ImageNav tasks thanks to the availability of higher-resolution information in early to intermediate visual encoder layers. The best proposed method (also the simplest in terms of implementation) just concatenates the targe... | Rebuttal 1:
Rebuttal: > Q1. I wish the method had also been applied to other task types, e.g. visual rearrangement. Showing good performance in a single task type is a bit limited, even if two variants (panoramic versus limited FOV) are considered.
A1. Thanks for your valuable suggestion. We conduct experiments on t... | Summary: This paper introduces FGPrompt (Fine-grained Goal Prompting) for the image-goal navigation task (ImageNav). Existing methods for ImageNav suffer from limitations in capturing detailed goal information and focusing on goal-relevant regions in observation images. FGPrompt tries out three different methods for go... | Rebuttal 1:
Rebuttal: > Q1. Mid fusion technique is more complicated, requires additional computation, and still performs worse than early fusion. I believe that the mid-fusion technique might generalize more on the instance imagenav task where the goal image is assumed to be coming from a camera with different paramet... | Summary: The authors offer a solution to the image-goal navigation task. The solution focuses on granular feature extraction from the goal image early on in the model pipeline, and usage of the goal image to inform whcih features in the observation the agent should attend to. The paper offers multiple mechanisms to do ... | Rebuttal 1:
Rebuttal: > Q1. The best performing method in the paper (Early Fusion), while having impressive performance, doesn't demonstrate a significantly novel method.
A1. Thanks for your comments. We would still like to point out the novelty and contribution of our early-fusion method. We empower the ImageNav agen... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers’ time and efforts in reviewing our paper and for the constructive feedback. In addition to the response to specific reviewers, here we would like to 1) thank reviewers for their acknowledgment of our work, 2) summarize our contributions, and 3) highlight the n... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph | Accept (poster) | Summary: This paper proposes TFLEX, a framework that reasons over temporal knowledge graphs (TKG). It takes a complex query about either an entity or a timestamp and a set of constraints as input. The query is then converted into a directed acyclic graph (DAG), which is a computation graph that projects the query into ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and detailed feedback. Below please find responses to individual comments/questions:
## Related Works
> TKGC works in the related work part.
see global rebuttal.
> The discussion on complex query embedding works is insufficient.
To begin with, we have ma... | Summary: The authors introduce an embedding-based method for answering complex, i.e., multi-hop, queries on temporal knowledge graphs. The Temporal Feature-Logic Embedding framework (TFLEX) uses fuzzy logic to model first-order logic operations on the entity and timestamp sets. The queries, with answers being either en... | Rebuttal 1:
Rebuttal:
We would like to express our sincere gratitude for the thoughtful feedback. Below, we address the comments and concerns in a comprehensive manner.
> Figure 1: The logical formulation and the representation of entities in the query example.
The example logical formulation is designed to correctl... | Summary: This paper proposes a method to learn on temporal knowledge graphs, using a combination of fuzzy logic with a temporal extension and node embeddings. To test the method, three new datasets were generated.
The choices made for the model seem logical and are over all well motivated in the paper (e.g. figure 2).... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful assessment of our paper. We would like to address the points raised in the review and provide clarification and additional information to enhance the overall understanding and appreciation of our work.
> To me, it is not clear why we would want up to n comp... | Summary: This paper studied the multi-hop logical reasoning problem on temporal knowledge graphs and proposed the first temporal complex query embedding framework named Temporal Feature-Logic Embedding framework(TFLEX).Firstly, they defined the task of multi-hop logical reasoning over TKGs. Secondly, they designed the ... | Rebuttal 1:
Rebuttal:
We appreciate the reviewer's time and effort in evaluating our manuscript. We have carefully considered the provided feedback and would like to address each point of concern:
> Lack of comparison with related work:
(see pdf in the global rebuttal section)
We acknowledge the reviewer's comment ... | Rebuttal 1:
Rebuttal:
Dear Reviewers,
We would like to express our sincere gratitude to the reviewers for their thoughtful and constructive feedback on our submission. We greatly appreciate the time and effort dedicated to evaluating our work, and we are excited to engage in this rebuttal process to address the raise... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Authors present new embedding framework called TFLEX to embed complex temporal queries over temporal knowledge graphs to perform multi-hop reasoning with time constraints on TKGs. Authors present the overall embedding framework using Fuzzy logic to model complex logical queries and extending fuzzy logic to inc... | Rebuttal 1:
Rebuttal:
We appreciate the reviewer's constructive feedback on our paper. We are pleased that the reviewer recognizes the merits of our work and acknowledges the contributions we have made to the field. We would like to address the concerns and questions below.
> Not sure if this framework can handle com... | null | null | null | null | null | null |
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts | Accept (spotlight) | Summary: The paper studies a variant of linear contextual bandits where a post-serving context is provided to the learner *after* the learner selects an arm. The reward is linear in the context and the post-serving context (but thus may not be linear in the original context on its own). The post-serving context is spec... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the thoughtful comments! You can find our responses to each question below.
**Re: weakness 1 \& Q1** For $\alpha=1/2$, our bound is tight, which achieves $1/\sqrt{T}$, which matches the lower bound. However, for other $\alpha$, it’s an interesting problem ... | Summary: This paper looks into contextual bandits in the scenario where only partial context is provided to the learner for making decisions. Specifically, this paper comes up with an environment setting where the full context consists of pre and post components which are revealed to the learner before and after she fi... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for the comprehensive and valuable comments! Below, you'll find our in-depth responses to the questions raised.
**Re: weakness 1 & 2** The reviewer is correct that there may be other hidden factors (both pre-serving or post-serving) that affect the reward. In this work... | Summary: The paper proposes a novel contextual bandit problem with post-serving contexts and introduces a new algorithm, poLinUCB, that achieves tight regret under standard assumptions. The authors demonstrate the effectiveness of their approach through both synthetic and real-world experiments, showing that poLinUCB c... | Rebuttal 1:
Rebuttal: Many thanks to the reviewer for the thoughtful observations and comments! Our detailed responses to the questions can be found below.
**Re: weakness 1 \& 2** We apologize for missing the conclusion and limitation section in the main paper. Due to space constraints, we had to move the conclusion... | Summary: This paper introduces and analyzes the problem of contextual multi-armed bandits with post-serving contexts, where additional reward-relevant information is revealed after the the algorithm makes its choice. It divides the traditional context into a pre-serving context which is known the the algorithm at decis... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the insightful comments! Please find our detailed responses to the questions below.
**Re: weakness 1 \& limitations** We apologize for missing the conclusion and limitation section in the main paper. Due to space constraints, we had to move the conclusions and... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We have added the figures to the accompanying PDF. We hope you find them useful and informative.
Best,
Authors
Pdf: /pdf/626febd9f47231d42cb0800583480407794dd0ec.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work considered a novel contextual bandit problem with post-serving contexts and designed a new algorithm, poLinUCB. With a generalized version of Elliptical Potential Lemma (EPL), they provided tight regret under standard assumptions. Empirically, they showed on synthetic and real-world datasets, the pro... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the thoughtful comments! Below please find our detailed responses to the questions below.
**Re: weakness 1, Q1 \& Q2** Our regret analysis can accomodate different values of $\alpha$ in Assumption 1, which we would view it as a strength rather than weakness. The r... | null | null | null | null | null | null |
Learning Cuts via Enumeration Oracles | Accept (poster) | Summary: The paper proposes an efficient algorithm for learning local cuts used in integer programming relaxations. The algorithm uses a variant of the Frank-Wolfe algorithm and employs a stopping criterion for reducing the number of iterations during the application of the Frank-Wolfe algorithm. The paper showcases th... | Rebuttal 1:
Rebuttal:
1. The paper lack a discussion about the relevant of the studied problem to the NeurIPS community. I am not sure how much the results would appeal to the broad NeurIPS audience. Please include a discussion about the relevance of the problem to NeurIPS audience. I appreciate the theoretical work,... | Summary: The authors propose a new method for generated lifted cuts from a smaller projected polytope that replaces the usually-expensive LP/IP-based separation routines with an optimization-based method that uses the Frank-Wolfe algorithm. The authors present theory showing that the proposed separation routine indeed ... | Rebuttal 1:
Rebuttal: Comments on weakness:
1. [...] E.g., how does the authors’ Frank-Wolfe based method compare to the usual pipelines of lifting for knapsack inequalities – in particular sequential up-lifting/down-lifting for minimal cover inequalities? [...] Also, how does the authors’ separation procedure itsel... | Summary: This paper presents a method for generating separating cutting planes for integer programming problems that is based on the Frank-Wolfe method for optimizing over polyhedron. It falls roughly within the existing local/Fenchel cut framework: given a point, it identifies the closest point within the feasible reg... | Rebuttal 1:
Rebuttal:
1. L19: "However, the by far most interesting case is the one we consider here" (IP vs. MIP). This is a subjective statement that I imagine many researchers in the MIP community would (strongly!) disagree with. I'd suggest softening or removing, as this is not really necessary to justify the rest... | Summary: * Context:
This paper deals with the subject of generating cuts for solving Integer Programs (MIP).
Rather than using a cut generating algorithm based on a formula (like Gomory cuts) to create hyperplanes separating the solution of the relaxed problem from the (integer) feasible domain, the papers attempts to ... | Rebuttal 1:
Rebuttal: Comments on weaknesses:
1. [..] What would be beneficial would be to have somewhere clearly "These are the requirements that you need for your problem to be solvable using this method" [..]
As we restrict the scope to IPs for this paper, we will only list the requirements for applying the meth... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you all for reviewing our paper. Your insights and suggestions have been incredibly valuable in refining our work and will undoubtedly lead to enhancing the quality and impact of our research. We appreciate your time and dedication to peer review and are gratefu... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies cut generation from operations research. While this is potentially a promising approach, I believe the paper falls out of scope of the conference. Optimization can be seen as part of machine learning machiery, but normally NeurIPS papers are expected to have some connection to machine learnin... | Rebuttal 1:
Rebuttal:
1. What is the motivation for submiting this paper to NeurIPS?
We recognize that our paper primarily falls under Operations Research, but it's crucial to note a significant distinction. Unlike many conventional cutting plane techniques in this field that hinge on fixed equations and formulas for... | Summary: This paper studies integer programming (IP) by proposing an alternative cutting-plane approach that makes use of the Frank-Wolfe (FW) algorithm for the separation sub-routine (i.e., separation of the target from the feasible set). The main idea is to use “local cuts” which aims at deriving the facets of $P_I$,... | Rebuttal 1:
Rebuttal:
1. How do you set $\epsilon$ in your algorithm?
Our algorithm sets $\epsilon$ to 1e-9, following the SCIP default. We will include this into the revised version of the paper.
2. Have you compared your algorithm numerically with other proposed methods for solving MKP or other popular IP proble... | null | null | null | null |
Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks | Accept (poster) | Summary:
This paper proposes the Softmax Output Approximation algorithm, which approximates the softmax output during the forward pass and reconstructs it during the backward pass. In attention-based models like Transformer, the softmax output activations consume a significant amount of memory. By approximating them, ... | Rebuttal 1:
Rebuttal: We deeply appreciate the constructive feedback and the clear guidelines provided by the reviewer to make our work better.
**Q1** Low accuracy on IMDb tasks
> We have conducted a new experiment with the IMDb task and obtained comparable performance to the baseline, i.e., baseline accuracy 0.876 v... | Summary: This paper tries to reduce the memory footprint while training Transformer models by only keeping a fraction of softmax output and estimate them back during back propagation.
Strengths: This is a very interesting and practical topic. With this tech, we can reduce the computation cost significantly while train... | Rebuttal 1:
Rebuttal: We wanted to take a moment to express our heartfelt gratitude for reviewing our paper. Received your positive assessment has given us renewed confidence in our work.
**Q1** Training profiling
> Thanks for a good question. We felt that the training profiling the reviewer mentioned means two aspect... | Summary: The paper proposes an effective scheme for compressing the backpropagation of Softmax, the main idea of which is to only retain the maximum and minimum m values of the output, with the middle part using linear interpolation. Impressively, this approximation scheme not only saves memory but also achieves better... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable and positive comments regarding our work, and the questions that helped us to refine our work. Here, we have provided a detailed response.
**Q1** The proportion of memory occupied by the softmax
> Thanks for asking a crucial question about our work. It... | Summary: This paper proposes to approximate softmax to improve memory efficiency for training attention models. During forward pass, first the softmax output is computed. Only the $m$ highest and $m$ lowest elements are stored along with sorted order from the the $n$ elements. Rest of the $n-2m$ entries are discarded t... | Rebuttal 1:
Rebuttal: ### Incorrect claims and/or missing justifications
**W1** Line 286-290
> Thanks for letting us know about the related works we missed. What we meant was that existing works on efficient Transformers do not explicitly try to save activation memory, unlike our method. Although they can save memory n... | Rebuttal 1:
Rebuttal: We want to extend our heartfelt thanks to all the reviewers for taking the time to review our research paper from diverse angles and offering constructive critiques. Your valuable insights have greatly enriched the quality of our research.
We have conducted new and re-experiments requested by the... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper is about approximating softmax function, by storing only a fraction of the entire softmax output in a memory.
The authors argue that by applying approximation on the softmax function, they were able to save memory usage of softmax activation up to 84%.
The motivation starts from the observation that... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have dedicated to providing insightful feedback on our paper, including the typo checking.
**Q1** Inverse of softmax
>We understand that Equation 5 may be confusing. In Equation 5, we can restore $z_i'$ (i.e., approximated softmax input) if we have $s_i'$ (... | Summary: This paper targets improving the memory efficiency of attention networks by reducing the activation storage of the softmax output. The authors propose to store only m highest and m lowest softmax output values, together with some auxiliary variables, and infer the missing part during backpropagation through in... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable and positive comments regarding our work, and the questions that helped us to refine our work.
**Q1** Comparison of our method with FlashAttention and Checkpointing
> Flash attention tries to speed up the attention mechanism by using different memory ... | null | null | null | null |
Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning | Accept (poster) | Summary: In this paper, the authors provide theoretical analysis on convergence and downstream performance of self supervised representation learning (SSL) approaches using tools from low-rank matrix completion.
In particular, (i) they relate an eigenproblem objective to SSL methods, (ii) find that SSL methods perfor... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback!
We would like to start the response by addressing the weaknesses pointed out by the reviewer. We first report additional experiments and details on the performance across small-scale datasets (CIFAR-10, CIFAR-100, and ImageNet-100), followed by... | Summary: Self-supervised learning methods can effectively leverage limited signals to converge towards meaningful representations, but how is it made possible? This paper tries to give a response. This paper establishes a connection between SSL and a matrix completion problem by showing that these are Lagrangian dual o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their perceptive feedback.
The review offers a very well-structured and observant summary of the submission. We would like to add an additional comment about the incoherence and the role of the projection head. We think there might be a typo in the following ---
*''A les... | Summary: This paper aims to provide a theoretical understanding of the recent successes of self-supervised learning methods by leveraging tools like Laplacian-based dimensionality reduction methods and low-rank matrix completion. The authors introduce an eigen-problem objective for spectral embeddings from graphs, whic... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback!
First, we would like to address the concern about the dependence on the incoherence between the backbone and the projection head.
We first would like to clarify that the underlying assumption for learning useful representations is that the sim... | Summary: The authors observe that self-supervised learning (SSL) attracts growing attention and that, by now, numerous corresponding loss functions have been proposed. They systemize these from the point of view of Laplace operators (on Riemannian manifolds) and low-rank matrix approximation. Indeed, for SimCLR, Barlow... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind and perceptive feedback.
We sincerely appreciate the reviewer’s high opinion of the strengths of this submission. We hope that additional experimentation results (attached as PDF, summarized in the global response) will only help reinforce this position. We wi... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time, effort, and considerate reviews!
While we will address each review individually, in this global response, we would like to summarize the main pieces of those individual responses.
First of all, we would like to present more experimental r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods | Accept (poster) | Summary: The paper presents a theoretical analysis of the sample complexity of the problem of learning a linear denoiser with a self-supervised learning loss, and verifies the bounds on a series of experiments with linear denoising. The paper also presents an empirical study of the gap between self-supervised learning ... | Rebuttal 1:
Rebuttal: Many thanks for the feedback. In the following we address the weaknesses and questions pointed out by the reviewer.
- **Weakness and limitations; on the connection of theory and practice, and our theory pertaining to a linear estimator:** We think our theory and empirical results for the linear es... | Summary: The work investigates the cost of self-supervised training by characterizing its sample complexity.
Strengths: 1. The paper is based on the given theory and carries out corresponding empirical research on self-supervised denoising and accelerated MRI.
2. The paper shows that a model trained with such self-sup... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. In the following we address the weaknesses in the order as pointed out by the reviewer.
- **Weakness 1, 'the theoretical approach of this paper seems to be similar to [1]':** We would like to point out that the theoretical approach of this paper is different... | Summary: The paper is an study on the sample complexity for image reconstruction in two types of methods, self-supervised and supervised. The authors studies the risk bounds for the case of self-supervised methods. They then evaluate the convergance rates in numerical and empirical experiments for two problems, denoisi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and their positive evaluation of our work. In the following we address the weakness and questions pointed out by the reviewer.
- **Weakness 1, reporting only the best runs:** For all empirical results on denoising and compressive sensing presented in Figure... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
Attached is a pdf containing experimental results on real-world camera image denoising as discussed in our response to reviewer 8mky (weakness 3), who asked if our results hold for real-world noise beyond the Gaussian setup studied so far in our paper.
The results demonstrate how... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Activity Grammars for Temporal Action Segmentation | Accept (poster) | Summary: This paper proposes a grammar-based activity segmentation method. The authors proposed a grammar induction algorithm, as well as an improvement over the existing activity grammar parser, the Generalized Earley Parser (GEP). The proposed model shows improvements over prior works on both grammar induction and ac... | Rebuttal 1:
Rebuttal: We thank reviewer rBwB for their meaningful comments.
### **[Comparison with SOTA]**
Following the reviewer's suggestion, we compare ours with the state-of-the-art methods in Table R3 where ours shows comparable or superior performance. Note that most of the refinement methods [A3, 9, A5, 5, A6, ... | Summary: This paper proposes a new grammar induction algorithm, an effective parser, and a grammar evaluation framework. They improve temporal action segmentation by extracting and handling context-free grammar with recursive rules. The assessment presents good generalization and discrimination capabilities of induced ... | Rebuttal 1:
Rebuttal: We thank reviewer FXdk for constructive comments.
### **[Comparison with SOTA]**
Following the reviewer's suggestion, we compare ours with the state-of-the-art methods in Table R3 where ours shows comparable or superior performance. Note that most of the refinement methods [A3, 9, A5, 5, A6, 15, ... | Summary: This paper addresses the challenge of temporal action segmentation by introducing an activity grammar to guide neural predictions. The proposed approach involves a grammar induction algorithm (KARI) to extract a powerful context-free grammar from action sequence data. Additionally, an efficient generalized par... | Rebuttal 1:
Rebuttal: We thank reviewer CeJ9 for their meaningful comments and suggestions.
### **[Scalability and computation cost of the proposed method]**
Following the reviewer's suggestion, we analyze the computational cost of the KARI and BEP.
Since KARI induces activity grammars based on the activity sequence... | Summary: This paper presents a grammar induction algorithm that takes as input sequences of frame-level predictions and outputs structured sequences of actions. The advantage of their approach is that it allows recursive rules, which enhances its generalization abilities.
Strengths: 1. The method is more flexible than... | Rebuttal 1:
Rebuttal: We thank reviewer YXrU for constructive comments and suggestions.
We will clearly revise Section 3.2 using simpler notations and more illustrations.
### **[Grammar illustration]**
Following the suggestion, we created Figure R1 with example sequences and KARI-induced grammar for a better underst... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions.
We are happy to see that the reviewers have given our work a positive evaluation, noting that "the method is more flexible than previously proposed grammar induction methods (YXrU)," "the idea of introducing the recursive r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Neural Fields with Hard Constraints of Arbitrary Differential Order | Accept (poster) | Summary: The authors propose a method to enforce hard constraint points on neural fields. Instead of a single black-box coordinate network predicting the field values, this work uses neural networks to learn basis functions which are then combined in a linear transformation. Given enough basis functions, the weights of... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback.
## Theoretical analysis
We greatly appreciate this suggestion. Please refer to the general response Q3.
## Summary of basis functions
Here, we summarize the properties of various basis functions.
| ... | Summary: A broad range of problems can be formulated as linearly constrained problems, e.g., learning material appearance, interpolatory reconstruction, solving linear PDE, etc. In order to solve linearly constrained optimization problems, this paper developed a novel hard constraint method that builds upon neural fiel... | Rebuttal 1:
Rebuttal: We greatly appreciate the positive comments and feedback.
We acknowledge the challenges posed by nonlinear problems, particularly in terms of convergence and the expensive computational graph of nonlinear solvers. Potentially, the latter could be addressed through the use of implicit layers. We ... | Summary: The paper presents a method for integrating hard constraints, represented by a linear operator, into neural field basis functions. This is achieved by learning kernel functions as basis functions at specific constraint points. Through experimentation, the paper provides evidence to show the effectiveness of th... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
## Details regarding the training procedure
We appreciate your suggestion to dedicate a separate section to explain the training process in detail. Please refer to the general response Q1 for training details.
## Regularization
The only regularization we r... | Summary: The authors look at enforcing hard constraints on neural fields. Here, the problem formulation is to take continuous coordinates as input and predict the solution on these points as output. The neural field is represented as a linear sum of basis functions, and specifically, variants of a neural kernel functio... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback.
## Comparison to soft constraint approaches
In our experiment on learning material appearance described in Sec. 4.2, we compared CNF with FFN [35] and SIREN [32], two representative soft constraint methods. Our approach surpasses them qualitatively and qu... | Rebuttal 1:
Rebuttal: We thank the reviewers for the detailed and constructive feedback. Below are our responses to the common questions:
# Q1. Training and inference details
We offer a thoroughly tested codebase that assists users in modeling challenging constraints using CNF. To ensure comprehensiveness, we will al... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow | Accept (poster) | Summary: This paper proposes to compute gradient flows of the KL divergence in the space of probability measures endowed by a generalization of the Wasserstein distance, which uses more general cost based on Young functions, and called Generalized Wasserstein gradient flows (GWGF).
Authors provide the forward Euler sc... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
### Weaknesses
`W1`: The class of Young functions investigated seems not very big.
A1: The choices of Young functions are quite delicate since it should strick a balance between acceleratin... | Summary: The paper's primary focus is the use of a generalized Wasserstein gradient flow of the KL divergence for solving the sampling problem in a particle-based variational inference framework, which they name Generalized Wasserstein Gradient Descent (GWG). Unlike the usual Wasserstein gradient flow of the KL, which ... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
#### Weaknesses
`W1`: The technical novelty appears to be limited, as the underlying analysis follows the framework presented by Balasubramanian et al.
A1: We indeed borrow some ideas from... | Summary: This paper proposes a general version to solve Wasserstein Gradient Flow for Particle-based VI. The authors show that this approach offers strong convergence guarantees and better performance over SVGD and other methods, as evidenced by extensive experiments on both simulated and real data sets. The paper also... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
#### Weaknesses
`W1`: The title Particle-based Variational Inference with Generalized Wasserstein Gradient Flow is too close to (Dong et al. 2023).
A1: Thanks for the suggestion! We will mo... | Summary: This paper proposes a novel particle-based variational inference framework based on generalized Wasserstein gradient flow of KL divergence, named generalized Wasserstein gradient descent (GWG). The strong convergence guarantee of the proposed algorithm is provided. The authors also provide an adaptive version... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable questions! We address your comments and questions as below.
### Weaknesses
`W1`: The description of main results (Theorem 2 and Theorem 3) are unclear. It is not specified how the vector-valued function $v\_k$ is learned. Is $v\_k$ the maximizer of e... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and will modify our paper accordingly in our revision. We address some of the common issues raised by the reviewers below.
**Justification for Assumption 1 and 2**
1. `Theoretical justification`
Given current particle distribution $\mu$, ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo | Accept (poster) | Summary: This paper propose a computational framework for sampling via Hamiltonian Monte Carlo via self-concordant barrier functions for the purposes of constrained sampling on suitably nice Riemannian manifolds, where the Riemannian metric defines the barrier function. They provide computational and theoretical guaran... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and their positive evaluation.
> What is this [x,y] or x : y notation in lines 101-102 (and other places)? I was not able to parse this...
We refer to the general response for more details about our presentation. We hope that these explanations clarify ou... | Summary: The paper proposes a new algorithm for sampling on a constrained space via a Hamiltonian Monte Carlo algorithm, which uses a Riemannian Manifold HMC algorithm, with the Hessian metric given by a self-concordant barrier. This generalizes and improves upon existing work in constrained sampling, while removing a ... | Rebuttal 1:
Rebuttal: Thank you for your comments, we appreciate your acknowledgment of the paper’s merits.
> No rigorous non-asymptotic theory was presented, although this is also true of Kook et al., likely because of the complexity of these algorithms.
> How difficult is it to establish any non-asymptotic converge... | Summary: This paper introduces a "involution checking step" in Riemannian Hamiltonian Monte Carlo methods that ensures time reversibility of the resulting Markov chain. The numerical experiments demonstrate the competitiveness of the proposed algorithm with regard to the method proposed in Kook et al. (2022a).
---
T... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission and for your constructive feedback.
> 1. How does this work compare with Kook et al. (2022b)?
We refer to the general response for a detailed comparison with [1]. In particular, we show that [1] suffers from the same shortcomings as [2] when... | Summary: The authors resolve an existing bias with naive manifold versions of HMC by adding an involution checking step, and therefore leading the new HMC algorithm to be unbiased.
I'm not super familiar with this problem, and I would like the authors to explain some of the technical details, as the proofs are quite ... | Rebuttal 1:
Rebuttal: Thank you for your comment and thoughtful questions.
### Answer to Question 1.
We refer to the general response for a discussion on the number of solutions of the implicit mapping $G_h$. We provide a one-dimensional example, where $G_h$ may admit 0,1 or 2 solutions for the implicit update of the... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and are encouraged by their positive feedback regarding the proposition, soundness, and clarity of our work. We provide detailed responses to each reviewer but summarize here their main feedback.
### 1. Comparison with [1] and counterexample
I... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning | Accept (oral) | Summary: This paper focuses on learning from demonstration via offline inverse reinforcement learning (IRL). Offline IRL suffers a similar problem as offline reinforcement learning (RL) and imitation learning (IL), where the policy cannot generalize well on unseen states and actions---this problem is known as distribut... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review of the paper and the valuable feedback. Below, we address the reviewer's comments in a point-by-point manner.
**Response to Weakness 1:** We thank the reviewer for this good question. As we discussed in Section. 6, we constructed an ensemble of estima... | Summary: This paper addressed the issue of covariate shift in offline imitation learning. The authors extended the uncertainty-regularized model-based offline RL to the imitation learning setting. The key idea is to first learn transition dynamics from samples, and then solve an optimization problem that jointly seeks ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review of the paper and the valuable feedback. Below, we address the reviewer's comments in a point-by-point manner.
**Response to Weakness 1:** We will address your comments to improve the readability of the paper.
**Response to Weakness 2:** We appreciat... | Summary: Offline inverse reinforcement learning (IRL) is a method for finding an unknown reward function optimized by an agent from demonstrations using only a finite dataset. The most common framework is maximum entropy (MaxEnt) IRL, which attempts to find a reward function that induces a policy which achieves the sam... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review of the paper and the valuable feedback. Below, we address the reviewer's comments in a point-by-point manner.
**Response to Weakness 1&Question 1:** We appreciate the reviewer’s comments. However, we do not agree that the proposed alternating optimiza... | Summary: In this paper the authors present a two level maximum likelihood based framework for offline inverse reinforcement learning, where both a world model and a reward model are learnt from expert demonstrations. In this two level algorithm the outer level or loop involves estimating the reward function, while the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review of the paper and the valuable feedback. Below, we address the reviewer's questions in a point-by-point manner.
**Response to Question 1:** We appreciate this question raised by the reviewer. This question indeed helps us find a typo in the term $P(s_t... | Rebuttal 1:
Rebuttal: We thank the detailed review and comments from all reviewers.
In the global response, we present a conclusion here and we will include it into our final version:
In this paper, we model the offline Inverse Reinforcement Learning (IRL) problem from a maximum likelihood estimation perspective. We ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper "Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning" proposes an innovative approach of offline inverse reinforcement learning. After a deep theoretical analysis of the inter-dependence between errors arising from dynamics modelin... | Rebuttal 1:
Rebuttal: We thank Reviewer 5szs for your positive comments and recognizing the importance of this work. Below, we address the reviewer's comments in a point-by-point manner.
**Our Response to Weakness 1:** We appreciate the reviewer for raising this insightful question. In our practical implementation, we... | null | null | null | null | null | null |
Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations | Accept (poster) | Summary: This paper theoretically analyzes the worst-case performance of DiskANN. It shows that DiskANN (with slow preprocessing) can provably solve the approximate nearest neighbor problem with a constant approximation ratio. It also provides empirical results for other algorithms, such as DiskANN with fast preprocess... | Rebuttal 1:
Rebuttal: Reviewer GZP3
W1: Theoretical analysis only shows the constant approximation factor, while the experiments mainly focus on Recall@5
A: We believe that our paper treats both measures (Recall@5 and approximation factor) in a fairly balanced way. In particular, for both of the three main algorithm... | Summary: This paper studies a specific class of graph-based similarity search algorithms, and establishes approximation upper bounds, and runtime lower bounds for this algorithm. In the process of establishing lower bounds, the paper also presents various point configurations that would be hard for these graph-based se... | Rebuttal 1:
Rebuttal: Reviewer EYFR
W1: [interpreting the worst-case upper-bounds] Interpreting the worst-case upper-bounds. One weakness of this paper is that I am unable to get an intuition of what the bound in Theorem 3.4 is telling us. One interpretation is that, as $i\to\infty$ (in the asymptotic range), we get ... | Summary: Nearest neighbour (NN) queries select for a given data point the closest point in a set and can be answered exactly in linear time by scanning the whole set. c-approximate Nearest Neighbour (ANN) queries ask for an arbitrary data point that has at most a c times larger distance than the NN. A popular approach ... | Rebuttal 1:
Rebuttal: Reviewer ZVW4
W1: It is not clear which point is meant to be the (exact) nearest neighbor (NN) in Figure 1. If "a" is the NN, then it would seem that "p0" would be an approximate nearest neighbor given the value
A: In our figure 1, q represents the query point and a represents the true nearest n... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their useful comments and feedback. We will fix the typos and presentation issues in the final version of the paper. In what follows we address the issues identified by the reviewers as weaknesses and/or listed as questions. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation | Accept (poster) | Summary: The paper presents UniT, a unified certified robust training against text adversarial perturbation. The paper identifies two frameworks of robust training: type (I) does random data augmentation over the text inputs, and type (II) adds Gaussian noise to the latent feature space. The paper unifies two types of ... | Rebuttal 1:
Rebuttal: We would like to sincerely express our thankfulness to the reviewer for the positive and insightful feedback on our paper. We are deeply appreciative of the acknowledgment of our paper on the novelty of loss design and the practical effectiveness of the proposed UniT framework. We take every comme... | Summary: This paper proposes a unified framework called UniT for certified robust training against text adversarial perturbation. UniT can train and certify in both discrete word space and continuous latent space by working in the word embedding space, without extra modules or loose bounds. It also introduces a decoupl... | Rebuttal 1:
Rebuttal: We genuinely thank the reviewer for the positive and constructive feedback on our paper. We thank the reviewer for recognizing the novelty and effectiveness of our solution. Each comment and question is of great importance to us, and we hope our response can address the raised concerns. Moreover, ... | Summary: This paper focuses on the certified robustness of language models. To improve the certified robustness, the authors propose a better robust training method that enables robust feature extraction and a larger prediction margin. Experiment results show the effectiveness of the proposed DR loss, leading to a bett... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the encouraging and constructive comments. We are deeply grateful to the reviewer for acknowledging the significance of our research and thinking highly of the novelty and effectiveness of the proposed DR loss. We take the posted comments and questions seriously... | Summary: This research paper discusses the vulnerability of Deep Neural Networks (DNNs) used in Natural Language Processing (NLP) tasks against adversarial attacks, specifically word-level adversarial perturbation (or synonym substitution). The authors delve into two existing training frameworks (Type I and Type II) fo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback and comments, and we appreciate the reviewer very much for the recognition on the novelty, improved performance, and meaningfulness of our solution. Taking the raised questions seriously, we answer each of them with utmost sincerity as foll... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you for your time and effort spent on our paper. We deeply appreciate all your constructive feedback for improving our paper. We have responded to every raised concern with utmost sincerity, deadly seriousness, and the hope that our response can address them. Pl... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a unified framework called UniT, aiming to solve the limitations of existing certified robust training pipelines against text adversarial perturbations. The main contribution is that it works in the word embedding space and provides stronger robustness guarantees without extra modules. Ad... | Rebuttal 1:
Rebuttal: We deeply thank the reviewer for the time spent reading our paper and for recognizing the reasonableness of the proposed unified framework and DR loss. The questions raised in the review are constructive for our paper. We understand that there is some misunderstanding, and we would like to address... | null | null | null | null | null | null |
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion | Accept (poster) | Summary: This paper presents an innovative paradigm shift in sequential recommendation systems. It departs from the traditional learning-to-classify approach, which includes negative sampling, to a learning-to-generate model, proposing a novel system called Diff4Rec, grounded on guided diffusion. This approach is predi... | Rebuttal 1:
Rebuttal: > 1. *The step to retrive recommendation list is needed to complete the recommendation task. Moreover, to retrieve top nearest items, how to encode properly the embedding space for items becomes also a relevant question.*
Thank you for highlighting this point. We recognize the importance of id... | Summary: This paper describes a new learning-to-generate paradigm for sequential recommendation based on diffusion models. The authors discuss the limitations of previous approaches in sequential recommendation, where a recommender model learns to classify user preferences based on positive and negative item samples. T... | Rebuttal 1:
Rebuttal: > 1. *Why Diff4Rec is superior to softmax-based approaches.*
We appreciate your comments, but respectfully emphasize the fundamental distinction between Diff4Rec and softmax-based approaches. Conceptually, Diff4Rec is a learning-to-generate paradigm that casts off negative sampling, while soft... | Summary: The paper presents a new approach Diff4Rec, a guided diffusion model for sequential recommendations. The paper could be divided in three main parts:
1. Problem formulation and method: It describes the sequential recommendation as oracle item generation and then explains how diffusion is applied.
2. Experiment... | Rebuttal 1:
Rebuttal: > 1. *The paper doesn't show the impact of negatives on the recommendations.*
Thank you for raising this point. We feel a little confused about the "impact of negatives", and try to analyze it from two distinct perspectives:
- **Negative Societal Impacts**: we would like to discuss the neg... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and positive feedback! We are encouraged that they find Diff4Rec introduces a transformative methodology in sequential recommendation (Reviewer $\color{Blue}\text{b6yV}$), achieves much superior performance compared to state-of-the-art baseline alternati... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Gradient Informed Proximal Policy Optimization | Accept (poster) | Summary: This paper studied the combined use of both the analytical policy gradient and the likelihood ratio policy gradient for training policy networks, based on the PPO algorithm. To make the combination feasible, a new alpha-policy is introduced and its approximation technique has been successfully developed. Besid... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments, we really appreciate them.
**Q1. What are the possibilities and potential limitations of using the proposed policy gradient combination technique on other algorithms, particularly off-policy algorithms?**
A1. We'd like to emphasize that **on-policy algorithm... | Summary: This paper introduces a novel policy learning method that adopts analytical gradients into the PPO framework without the necessity of estimating LR gradients. The authors introduce an adaptive α-policy that allows us to dynamically adjust the influence of analytical gradients by evaluating the variance and bia... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and support. Here we address your key concerns.
**Q1. In Section 5.1, Figure 8 is missing in main paper, although we may find the figure in Appendix. Also, the plots in Figure 3 are too small to distinguish the relative performance of different algorithms.**
... | Summary: This paper integrates analytical gradients with PPO. They introduce an \alpha-policy to control the bias and variance during the update of the policy. Results on some differentiable environments show the effectiveness of the proposed method.
Strengths: This paper study how to incorporate analytical gradient i... | Rebuttal 1:
Rebuttal: Thank you for your comments. We address some possible confusions below.
**Q1. How is PPO used in this paper? Eq. 4 is not PPO, and description about PPO, especially that related to availability of $\rho_{\pi_{\theta}}(s)$, is wrong.**
A1. Though the exact formulation is different, the **main ob... | Summary: In this manuscript, the author(s) combined the PPO framework with analytical gradients and proposed a policy learning method to learn better policy quickly. Through empirical experiments, the author(s) demonstrated that the proposed method was superior to baseline methods in several scenarios.
Strengths: #... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and support, we really appreciate them. Here we address some of your concerns.
**Q1. Where is Definition 5, and what does it mean by proof of Definition 5?**
A1. Sorry for the confusion, Definition 5 means Definition 4-1. We will fix it in the revised versio... | Rebuttal 1:
Rebuttal: We really appreciate all of the valuable comments from reviewers!
Here we address the major concerns, with a 1-page supplementary document. Our code will be released publicly, when the paper is published.
**Q1. This paper does not fully utilize the analytical gradients, because it does not cons... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a policy gradient algorithm based on Proximal Policy Optimization (PPO) algorithm. This new algorithm utilizes the analytical gradients from differential environments and achieves competitive performance in various scenarios including function optimization, classic control environments, a... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and support, we really appreciate them. Here we address some of your concerns.
**Q1. The analytical gradients are not fully utilized, because this paper didn't consider dynamically changing the PPO clip limit.**
A1. Please refer to A1 of global rebuttal at th... | null | null | null | null | null | null |
Directional diffusion models for graph representation learning | Accept (poster) | Summary: The authors present directional diffusion model (DDM) to learn graph / node representation. Compared to vanilla diffusion based representation learning techniques, DDM adds a batch based mean and variance, as well as preserving direction of diffusion. The authors then demonstrate that their model perform bette... | Rebuttal 1:
Rebuttal:
> A lot of citations by the paper, including some baselines like Infograph and GraphMAE, come from arxiv. Are these methods reliable?
>
We apologize for our problem, in section 5, the articles we compared and cited are all published in top-tier conferences, as follows： GIN-ICLR 201, DiffPool-NIP... | Summary: This paper offers a good study on anisotropic and directional structure learning of the diffusion model. The authors first conduct a statistical analysis emphasizing the importance of anisotropic structure in graph dataset and demonstrate the disadvantage of the vanilla diffusion model through signal-to-noise ... | Rebuttal 1:
Rebuttal: > Do you have proof of the effectiveness of your idea?
>
Indeed, in this paper, we didn’t provide some theoretical proof. However, we believe that the problem studied in this paper and our contributions are considerably interesting and extensible. Just as mentioned by Reviewer 5, for the graph l... | Summary: This paper presents a method named Directional Diffusion Model (DDM) for unsupervised representation learning, targeting applications in graph and node classification. The model's performance is evaluated on various benchmark datasets and compared to both unsupervised and supervised models. The results demonst... | Rebuttal 1:
Rebuttal: > The SVD visualizations could be more insightful. However, the methodology behind these visualizations remains unclear.
>
Thanks for your suggestions, Using the SVD decomposition to analyze the anisotropic is first proposed by [1] and has been widely used in NLP[1,2,3,4,5]. Specifically, we cal... | Summary: This study proposed adding directional noise on learning anisotropic graphs using diffusion models. The study adds new perspectives in exploring the anisotropic structures in graph data. The numerical results are promising to support the authors' ideas.
Strengths: I find this is an interesting paper - the rat... | Rebuttal 1:
Rebuttal: > For figure 3, does each dot represent a graph? Are they from simulated datasets?
>
Thanks for your question, Figure 3 shows a set of simulated data. Each sample point comes from an anisotropic (covariance is not equal to the identity matrix) normal distribution. As discussed in section 4, the ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes a class of diffusion models to improve the accuracy of graph representation learning. The model incorporates both data-dependent and anisotropic noise in the forward noising process, by scaling its magnitude and direction based on each coordinate of the data. This structured noise maintains ... | Rebuttal 1:
Rebuttal: > It would be great for the authors to comment and compare with other non-isotropic noise processes
>
With full respect, The existing literature you mentioned differs fundamentally from our approach. As pointed out by reviewer 6, our paper proposes that the ultimate distribution of the diffusion p... | Summary: The authors address the gap in unsupervised graph representation learning by exploring the use of diffusion models. They propose directional diffusion models that incorporate data-dependent, anisotropic, and directional noises in the forward diffusion process to better handle anisotropic structures in graphs. ... | Rebuttal 1:
Rebuttal:
> **missing releted work,** here are existing works in the intersection of graphs and diffusions are missing ....
First, with full respect, the three existing works you mentioned do not conflict with our statements. These three works you pointed out are devoted to exploring graph structure gener... | null | null | null | null |
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective | Accept (poster) | Summary: This paper aims to unify the assumptions on user behavior in a ranking called click model by formulating the ranking problem as a click-model agnostic Markov Decision Process (MDP). By doing so, the paper proposes to reduce the Off-Policy Learning to Rank (LTR) problem to a variant of offline RL, which does no... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for recognizing the advantages of our unified RL formulation, connecting LTR and RL solution, and our ablation study.
**Q:** The proposed method (CUOLR) itself is not a fundamentally new framework for offline RL, and the proposed state representation learning method se... | Summary: This paper presents a unified approach for off-policy learning to rank (LTR) that is adaptable to general click models. The authors formulate off-policy LTR as a Markov Decision Process (MDP) and leverage offline reinforcement learning (RL) techniques to optimize the ranking process. They propose the Click Mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our work.
**Q:** The synthetic dataset and the offline evaluation can give biased evaluation results of the algorithms.
**A:** First, same as previous off-policy learning to rank research (consistent with all baseline methods), we conduct the ... | Summary: The paper talks about how to model user behaviors with positional biases in an online search system. The paper proposes a unified RL framework to generalize three common types of positional bias models: Position-Based Modeling (PBM), CASCADE (each click depends on the previous click), and Dependent Click Model... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the originality of our unified formulation and solution.
**Q:** The paper made a rather limiting assumption that all users in the same search system follow a single pattern of positional bias. This may not be true, as different users may have di... | Summary: - Authors propose a unified off-policy reinforcement learning-based approach for learning to rank (LTR) problem that is adaptable to all general click models.
- Authors argue that a user's behavior in terms of clicks can be modeled by a Markov Decision Process (MDP), thereby allowing them to look at LTR from t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciating our motivation, presentation and experiments.
**Q:** Is this a Markov Decision Process? Given the authors' approach uses state representation, it is unclear to me how/if the Markovian property Pr(st|s0,a0 …st-1, at-1) = Pr(st| st-1, at-1) still holds?
*... | Rebuttal 1:
Rebuttal: We thank all the reviewers for your detailed comments and questions, which helped us to improve our paper. We appreciate that the reviewers generally agree our unified formulation is well-motivated, our click model-agnostic approach is novel and original, and experimental results are extensive and... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this work, the authors model the process of learning optimal ordering of ranked lists, called Learning to Rank (LTR) as a Markov decision process (MDP). This allows them to utilize techniques from reinforcement learning to solve for the policy that generates the optimal ordering. In practical applications o... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the advantages of our click model-agnostic solution.
**Q:** The paper extended prior formulations of LTR as an MDP to allow for using the transformers architecture, with the aim of performing policy learning without click model specific methods. In addition ... | null | null | null | null | null | null |
Indeterminate Probability Neural Network | Reject | Summary: This paper proposes a new probability theory named indeterminate probability theory and a new model name Indeterminate Probability Neural Network (IPNN). For the new probability theory, it is an extension of the classical probability theory, with which some intractable probability problems become tractable (an... | Rebuttal 1:
Rebuttal: Dear Reviewer #Xo5z,
Thank you for taking the time to review our paper! Please find detailed responses in the following.
**Q1: ...the authors claim CIPNN as the contribution of this paper.**
A1: The purpose of highlighting CIPNN is to demonstrate that our indeterminate probability theory is gen... | Summary: The main contribution is the proposal of the inference architecture which creates parallel softmax outputs (the authors call “splits”), which are combined into a joint soft-label space to make classification decisions under MAP rule; the joint label space can help with sub-classification tasks.
Strengths: The... | Rebuttal 1:
Rebuttal: Dear Reviewer #zXvC,
Thank you for taking the time to review our paper! Please find detailed responses in the following.
**Q1: ...which is a potential Bayesian neural network approach.**
A1. We respectfully disagree this point.
**Q2: The assumptions may not be very reasonable:...The assumption... | Summary: This paper proposes what I consider to be a type of neuro-symbolic AI model involving neural networks for multi-class classification problems; the authors refer to the model as an indeterminate probability neural network (IPNN). Frankly I did not fully understand the model, but the main intent appears to be a ... | Rebuttal 1:
Rebuttal: Dear Reviewer #1Vya,
Thank you very much for taking the time to review our paper again, we are glad to see you again here, and we can continue with more discussions. We hope we can answer your confusions this time.
**Q1: X_i is the true coin toss result, A_i the adult’s reading and Y_i is the ch... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank all of you for taking time to read our paper.
Our proposed indeterminate probability theory is far more important than our proposed IPNN model, let's firstly focus on the theory part here.
## 1. Analytical form of any general complex posterior is discovered by... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning | Accept (poster) | Summary: This paper investigates policy evaluation of distributional RL by leverage of Maximum entropy density estimations without explicitly considering Bellman operator and contraction mapping. They further derived the new generalization error bound in the maximum entropy PE process. For a practical algorithm, author... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review. We will answer proceeding by points:
*What is the representation issue in distributional RL? Is it just the representation of the value distribution? Note that people may also think distributional RL is also able to improve the representation of env... | Summary: This paper proposes two algorithms, distributional maximum entropy policy evaluation (D-Max-Ent PE) and D-Max-Ent Progressive Factorization.
The first algorithm applies the formulations of maximum entropy RL to the problem of distributional policy evaluation, while the second extends the first by adding a pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and for the constructive comments.
*I don't see distributional policy evaluation defined explicitly anywhere (though I think it's mostly covered by equation 3 and surrounding material?), unlike maxent RL which is defined in detail. For clarity this s... | Summary: The authors proposed a novel Maximum Entropy framework for policy evaluation in a distributional RL that can explicitly take into account the features used to represent the state space while evaluating a policy. Based on the framework, the authors developed D-Max-Ent Progressive Factorization algorithm balanc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the points outlined, in particular the ones about the readability of the figures.
*I see the experiments are conducted on a Grid task, will the proposed algorithm also applicable on continuous environments? Will the algorithm help with performance, training stability, sa... | Summary: This paper takes a maximum entropy based approach to learning the return distribution of a policy. This framework learns a distribution with maximum entropy subject to matching the expectation of a certain set of functions, referred to as the feature functions. They adapted a classical maximum entropy error bo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the important points outlined.
*The paper takes an RL agnostic approach, and learns an approximate return distribution without taking into account the underlying RL decision problem (i.e. Bellman operators aren't used, nor reward nor transition information, etc). This s... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a new framework called Distributional Maximum Entropy Policy Evaluation (D-Max-Ent PE) for policy evaluation in a distributional reinforcement learning setting. The framework considers the complexity of the representation used to evaluate a policy and incorporates it into a generalization-... | Rebuttal 1:
Rebuttal: *This paper lacks experiments in practical reinforcement learning tasks, such as Atari. The authors should conduct more experiments to demonstrate the efficiency of the proposed policy evaluation method.*
We thank the reviewer for this feedback. We remark that the main contribution of our work is... | null | null | null | null | null | null |
Towards the Universal Learning Principle for Graph Neural Networks | Reject | Summary: The authors propose a spectral GNN with geometrically decaying weight $\alpha^k$ and $P$-hop polynomial basis. They study the Lipschitzness and generalization bound of the model. Experiment results demonstrate the good performance of the proposed method over baselines
Strengths: (+) Interesting generalization... | Rebuttal 1:
Rebuttal: Thanks for your comments and detailed suggestions to improve our work.
In response to the points raised in your comments,
we give the following discussion.
1.To explain the necessity of $\alpha$,
we look back to the motivation of the proposed learning framework.
Consider graph filter $g(\... | Summary: This paper theoretically studies the criterion for the graph filter formed by power series using Lipschitz smoothness and then proposes a novel Adaptive Power GN architecture. Some convergence and generalization analyses are covered. Experiments also show the advantages of the proposed methods, with some ablat... | Rebuttal 1:
Rebuttal: We really appreciate your high approval and constructive suggestions for our work!
As you mentioned,
this work provides a comprehensive framework with generalization analysis to pursue deeper and more effective GNN.
Here we will address your concerns and state the revision plan.
1.Your understand... | Summary: This paper studies the polynomial filters in GNNs. Specfically, the authors propose a Adaptive Power GNN which employs exponentially decaying coefficients. A theoretically generalization analysis of the proposed framework is conducted. Experiments demostrate the proposed method can outperform some baselines ... | Rebuttal 1:
Rebuttal: We are thankful for your positive comment on our method.
But it seems that the reviewer misunderstood some important facts about our work.
We will further explain these points and dispel your concerns one by one.
1.It should be noted that the main topic of this paper is the design principle of gr... | Summary: In GNNs, designing a graph filter or propagation mechanism plays a critical role. Spectral-based GNNs formulate graph filters in the graph Fourier domain, and those filters are usually in the form of polynomials or power series. The manuscript argues that a well-defined graph filter should be convergent when r... | Rebuttal 1:
Rebuttal: We are thankful for your detailed and constructive suggestions. We carefully consider your meaningful questions and hope the following elaboration can answer your concern.
1.We discuss the essential difference of "infinite depth" in the mentioned works.
GCNII stacks multiple graph convolution... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a regularized learning framework for creating deep Graph Neural Networks (GNNs), including the Adaptive Power GNN (APGNN) that uses exponentially decaying weights to aggregate graph information of varying orders. The proposed multiple P-hop message passing strategy efficiently perceives high... | Rebuttal 1:
Rebuttal: We are grateful for your positive comments and meaningful suggestions.
We hope the following explanation can help you better understand our work.
1.Here we give the analysis of complexity.
Denote $N$ as the number of nodes,
$E$ as the set of edges,
$d$ as the hidden layer of MLP,
... | null | null | null | null | null | null |
Recovering Unbalanced Communities in the Stochastic Block Model with Application to Clustering with a Faulty Oracle | Accept (poster) | Summary: This paper studies a classic problem of recovering clusters in a random graph. Concretely, the authors consider the stochastic block model. Here there is an underlying graph on n nodes. The n nodes are partitioned into k unknown clusters. There is then an edge independently between any two nodes in the same cl... | Rebuttal 1:
Rebuttal: Thanks a lot for your thorough and detailed review! Please find the answers to the questions below.
### Run time comparison:
From our experiments, we find our algorithm to be much faster compared to the state-of-the-art algorithms of Ailon, Chen, and Xu (JLMR 2015).
For example, in experiment ... | Summary: This work studies stochastic block models where blocks/clusters can have different sizes. It proposed a simple SVD algorithm which recovers communities in this setting. The main technical improvement of this work is that the assumption is removed which requires there to be a ‘size interval’ where no clusters a... | Rebuttal 1:
Rebuttal: Thanks a lot for your thorough and detailed review! Please find the answers to your questions below.
### Run time comparison:
Regarding the asymptotic running time of Algorithm 1, a major contributing factor is the computation of the $(p-q)\sqrt{n}/\sqrt{p(1-q)}$ dimensional SVD projection, which... | Summary: The authors consider the problem of perfect recovery in a stochastic block model where the average degree is large and where the groups are not balanced. They provide an algorithm based on singular value decomposition to recover recursively the largest clusters. They provide a few numerical experiments illustr... | Rebuttal 1:
Rebuttal: We thank you for your efforts! We will definitely add a concluding section highlighting some future works that we think will be of interest.
---
Rebuttal Comment 1.1:
Comment: I thank the authors for their answers. | Summary: The paper deals with the problem of community detection for unbalanced community sizes. Specifically, the paper concentrates on the situation where both large (O(\sqrt{n})) and small communities exist in the network. The paper proposes a stepwise method of recovering the large clusters in the presence of small... | Rebuttal 1:
Rebuttal: We thank you for your detailed review. Please find our comments on the weakness and the answers to your question below.
### Knowledge of $p,q,k$:
Our algorithm necessitates knowledge of the parameters $p$ and $q$ but not the number $k$ of communities. However, it is worth noting that even the p... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Regression with Cost-based Rejection | Accept (poster) | Summary: Learning with rejection is an important machine learning problem. Most of the existing papers focus on the classification setting, i.e., classification with rejection and selective classification, and seldom works are targeting at the regression setting. This paper aims to investigate regression with cost-base... | Rebuttal 1:
Rebuttal: Thank you for your constructive review!
**Q1: The concept of "classification calibrated binary classification loss" is missing.**
A1: Thank you for pointing out this issue, which will be very helpful for us to improve our paper. We have rechecked our manuscript and added the concept to the revis... | Summary: The paper addresses problem of regression with the reject option. The authors propose the cost-based formulation of an optimal reject-option regression rule and they derive (Bayes) optimal strategy for the case the distribution is known. The authors further propose a surrogate loss to learn the reject-option r... | Rebuttal 1:
Rebuttal: Thank you so much for your valuable comments!
**Q1: The first contribution regarding the formulation of the cost-based reject option regression and the optimal solution.**
A1: We agree that our derived optimal solution of regression with cost-based rejection is quite similar to the result in [41... | Summary: This paper focuses on the problem of regression with rejection, specifically the approach of specifing a cost function and learn the pair of regressor and rejector at the same time. The paper prensents a concrete path to solving the problem. It first properly defines the problem and shows the Bayes optimal sol... | Rebuttal 1:
Rebuttal: Thank you so much for your valuable comments!
**Q1: There is no comparison with existing methods.**
A1: Since this question was repeatedly mentioned by reviewers, we think it is very important. We provide a detailed response to this question in Global Response, so please refer to **Global Respon... | Summary: The paper explores the framework of regression with rejection, in which the model can opt to refrain from making predictions on certain instances at specific costs, with the intention of avoiding critical mispredictions. The paper determines the Bayes optimal solution and introduces a theoretically grounded su... | Rebuttal 1:
Rebuttal: Thank you for your constructive review!
**Q1: While the regression with rejection setting represents a fresh concept in the literature, the technical approach seems to closely mirror the standard classification with rejection setting. This resemblance potentially limits the novelty of the paper. ... | Rebuttal 1:
Rebuttal: ## Global Response
We sincerely appreciate the thoughtful feedback and insightful comments from all reviewers to help improve our work. We are glad that our study was recognized by the reviewers (Reviewer 1UF6, Reviewer 4Fnw, Reviewer J2H6 and Reviewer mRfi). We are delighted that they found our ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels | Accept (poster) | Summary: This paper proposes a Label-Retrieval-Augmented (LRA) diffusion model for learning from noisy labels. The model leverages the neighbor consistency principle and incorporates pre-trained models to improve performance. The paper introduces the label-retrieval-augmented component, an accelerated label diffusion p... | Rebuttal 1:
Rebuttal: Q1: The paper mentions that the proposed model is flexible and general, but it does not provide a detailed discussion of the types of label noise that the model can effectively handle. For example, feature-dependent label noise. Since the comparison method [15] is a baseline that focuses on featur... | Summary: The paper focuses on learning from noisy labels using diffusion models. They reformulate the noisy label problem from a generative perspective. Specifically, the clean label is our target variable ($y_0$) and the noisy label is the diffused variable ($y_T$), which they call Label-Retrieval-Augmented (LRA) diff... | Rebuttal 1:
Rebuttal: Q1.1: It is important to explain why the noisy label process should be formulated as a diffusion process and to discuss in detail the advantages it offers. Since I think it is a novel approach, it is crucial to provide detailed explanations of the motivation behind it.\
A: Thank you for highlighti... | Summary: The paper proposes the application of denoising diffusion probabilistic models for modeling the true class probability distribution when training with noisy labels. In particular, the paper extends Classification and Regression Diffusion Models for this problem and uses pretrained self-supervised representatio... | Rebuttal 1:
Rebuttal: W1: The requirement of a pre-trained self-supervised image embedding network.\
A: We believe the requirement of a self-supervised embedding model like SimCLR is not necessarily a limitation, as it can be obtained for free by training on the same training dataset. Furthermore, the utilization of Si... | Summary: The paper proposes using a diffusion model to perform classification on a noisy-label dataset. The authors utilize diffusion to learn how to conditionally generate labels given an image. By combining recent work on classification diffusion models with label retrieval augmentations they demonstrate state-of-the... | Rebuttal 1:
Rebuttal: W1: The novelty of the paper is limited. The authors combine the existing classification diffusion model with the retrieval augmentation technique to capture the noisy label distribution. The methodologies for both are inherited almost unchanged from their respective papers and there is no signifi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. We are happy that the reviewers find our manuscript well-written, and demonstrate effective application of our method. A common issue raised by the reviewers is the incorporation of the CLIP feature which uses external data to train. We will addr... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers image classification with noisy labels. Rather than consider a single label for each image, it considers the label distribution within the set of neighboring examples (determined using a pre-trained feature extractor), modelling this distribution by a learned diffusion process that is cond... | Rebuttal 1:
Rebuttal: Q1: Real-world evaluation (WebVision, ILSVRC, Food-101N) does not include simple baselines using CLIP features (KNN classifier and linear probe). This is important because it lets us verify that the results do not simply reflect the strength of the CLIP features.\
A: We add the KNN and Linear Prob... | null | null | null | null | null | null |
Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability | Accept (poster) | Summary: Recursion in Recursion combines the efficiency characteristics of Binary Balanced Tree Recursive Neural Networks (BB-Tree RvNNs) and quality characteristics of Beam Tree RvNNs by applying the recursion in a hierarchical fashion.
Doing so is not straightforward and requires several "modifiers" such as Beam Ali... | Rebuttal 1:
Rebuttal: Thank you for the review.
**Re Weaknesses**
1. Please see the general response for the clarification on the complexity.
2. We provide figures and additional intuition on beam alignment in the general response.
3. Argument generalization was a recently proposed challenge, and currently, most ... | Summary: The paper studies a “recursion in recursion” (RIR) approach, the outer loop being a balanced k-tree recursive NN, and the inner loop a general recursive NN that is based on a beam tree. The goal is to obtain O(k*log_k(N)) time complexity. O(2*log_2(N)) is a special case of k=2 (for binary trees) and O(N) is th... | Rebuttal 1:
Rebuttal: Thank you for the review.
**W1:** We will add the Pareto frontier graphs. We currently show them in the General Response PDF.
-----
**W3:** At this point, we can suggest some speculative moves we can make to set up RIR-EBT-GRC in an LLM context.
First, we need to think about parameter scaling... | Summary: The authors propose a new tree-style RNN that combines the computational efficiency of tree RNNs with the computational power of more complex tree RNNs. The method makes each node perform a recursive computation, so there are logarithmically many nodes but they are more expressive than the standard tree networ... | Rebuttal 1:
Rebuttal: Thank you for the review.
**Re Ethics:**
We will not say too much about this given Ethics Reviewers have already checked it out. We didn’t provide any deanonymizing information about the work. While we haven’t cited the work or provided the anonymized copy at the moment (we will cite it in the ... | Summary: The paper proposes a novel framework - Recursion in Recursion (RIR) for tree recursive neural networks (Tree-RvNNs) so as to get around the issue of computational infeasibility of typical RvNN models (Beam tree RvNNs are $\mathcal{O}(n)$) while still being able to exhibit length generalization on simple arithm... | Rebuttal 1:
Rebuttal: Thank you for the review. We will take the formatting suggestions into account.
**Re Weaknesses:**
1. $k$ is a constant. It is the chunk size hyperparameter and is not made to depend on sequence length or the input. Yes, if we set $k$ to be very large (e.g., as large as the maximum input sequen... | Rebuttal 1:
Rebuttal: We thank all our reviewers for their insightful reviews and comments.
**On the complicacy of RIR:**
* **RIR Computational Complexity:** RIR-X is generally computationally more efficient than X both memory-wise and time-wise. For example, this can be observed in RIR-EBT-GRC vs EBT-GRC in Table 1.... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes Recursion in Recursion (RIR) to address the shortcomings that computationally efficient models like Binary Balanced Tree Recursive neural networks have in solving arithmetic tasks like ListOps. RIR also seeks to alleviate the computational burden brought forth by structure-aware ListOps-comp... | Rebuttal 1:
Rebuttal: Thank you for the review.
Please note the general response for clarification on the complexity of the approach.
We will fix the typo.
---
Rebuttal 2:
Title: Acknowledgement of rebuttal
Comment: I have read the authors' rebuttal and decided to keep my original score. | Summary: In this work, the authors try to combine the best of two worlds in proposing Recursion in Recursion, where outer recursion is K-array balanced binary tree and inner implements its cell function for recursive neural networks for sequential inputs. The proposed framework is tested on various logical inference an... | Rebuttal 1:
Rebuttal: Thank you for the review.
**Re Weaknesses:**
1. Our goal is not pure scalability. Our goals are (i) to extend RvNNs in a novel manner that achieves a balance between scalability and length-generalization capacities in structure-sensitive contexts (ii) to show that it has competitive results (if ... | Summary: This paper addresses the long-sequence modeling problem. A recursion in recursion strategy is proposed to balance the advantages between BB-Tree RvNNs and RvNN models. The idea is straightforward but achieves competitive performance on LRA tasks.
Strengths: 1. The paper is easy to follow.
Weaknesses: 1. Ther... | Rebuttal 1:
Rebuttal: Thank you for the review.
* (1.1) Many of the models you mention are already cited. SSM models are cited in [15,16,17,40], LongConv models are cited in [39,12], Linear RNN is cited in [34,30]. Top efficient Transformer based models are cited in [49,9] + indirectly efficient Transformers are refer... | Summary: This paper introduces the recursion-in-recursion (RIR) framework for balancing the tradeoffs between
1. sequential processing, which offers better inductive bias and stronger solutions for many types of symbolic processing and logical inference tasks, but can be very expensive
2. balanced tree recursion, which... | Rebuttal 1:
Rebuttal: Thank you for the review.
* Please check the general response for comments on the complexity of RIR.
* Regarding the selection of the hyperparameter $k$, a higher $k$ (chunk size) should be generally better in structure-sensitive tasks. $k=$infinite (or maximum input sequence length) for exampl... |
Arbitrarily Scalable Environment Generators via Neural Cellular Automata | Accept (poster) | Summary: The authors' goal is to optimize large floorplans for warehouses or manufacturing facilities, where robots need to drive during completion of tasks, while avoiding a congestion of the system.
Their innovation is to use the framework of neural cellular automata (NCA) to generate optimized patterns from an initi... | Rebuttal 1:
Rebuttal: 1. Why should a bigger warehouse plan be more difficult to optimize overall? I understand that the complexity of the search space grows, but why is simply tiling good solutions for small environments not mentioned as a (possibly) good baseline? After all, this is how the NCA environments seem to b... | Summary: In this work, the authors train neural cellular automata with a quality diversity evolutionary algorithm to grow the 2D environment of a multi-agent automated warehouse, manufacturing domain, and single agent maze domain. They show that the approach is able to generate environments that can scale to different ... | Rebuttal 1:
Rebuttal: 1. "We define a tile pattern as one possible arrangement of a 2- 2 grid in the environment." -> What about the distribution of global patterns? I assume they would not be captured this way?
We clarify that the environment entropy measure introduced in Section 4 only measures the local pattern... | Summary: This paper proposes a method for generating diverse sets of environments and solving arbitrarily large environments. The main difference of this method is that the individual in QD algorithms serves as an environment generator rather than an environment, making it capable of handling very large-scale environme... | Rebuttal 1:
Rebuttal: 1. Why are there only two lines in Figure 3-f?
Our method has a hyperparameter alpha that controls the weighting between throughput and similarity score in the objective function. We studied the effect of hyperparameter alpha in Section 6.1 in the warehouse domains. We observe that alpha = 5 ... | Summary: The authors have used the Covariance Matrix Adaptation MAP-Annealing algorithm to generate warehouse environments that are efficient for robots to roam around and do their task of moving packages from one place to the other. The authors have presented the idea very nicely in step by step manner, making the con... | Rebuttal 1:
Rebuttal: # Measure Space
Measure space is part of the QD problem definition and we adopt the definition from the DSAGE paper [47]. Specifically, a measure space is a bounded and discretized space defined by the user-specified diversity measure functions. The goal of the QD algorithm is then to simultaneous... | Rebuttal 1:
Rebuttal: We thank all reviewers for the detailed feedback. We appreciate that the reviewers find the combination of NCA, QD, and MILP novel and interesting (Reviewer YTho, v7gi) and the results comprehensive and promising (Reviewer fmHH).
## Role of MILP
Q3 of reviewer v7gi and Q3 of ShTu
The role of MIL... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The research paper addresses the problem of generating large environments to improve the throughput of multi-robot systems. While previous work has proposed Quality Diversity (QD) algorithms for optimizing small-scale environments in automated warehouses, these approaches fall short when replicating real-world... | Rebuttal 1:
Rebuttal: 1. Clarification on Novelty:
In prior environment generation methods [47], the size of the environments searched over and the size of the environments generated are the same. These methods involve thousands of agent simulations during the search. In large environments that we generate in our ... | null | null | null | null | null | null |
Certifiably Robust Graph Contrastive Learning | Accept (poster) | Summary: Inspired by the certifiable robustness of graph contrastive learning (GCL) is still remain unexplored, the authors develop the first certifiably robust framework in GCL by proposing a unified criteria to evaluate and certify the robustness of GCL. Specifically, the authors introduce a novel technique, RES (Ran... | Rebuttal 1:
Rebuttal: We thank reviewer A65y for recognizing the novelties, the clear presentation, the valid technique details and extensively conducted experiments. The following is our point-to-point response to the reviewer’s concerns and comments:
**Q1: Compare with some recent GCL baselines? E.g., RGCL [1] and G... | Summary: This paper introduces a certifiably robust graph contrastive learning method called RES (randomized Edgedrop Smoothing). This paper (1) first introduces the criteria of how to evaluate and certify robustness, (2) and then introduces RES to ensure certifiable robustness, (3) finally, proves that the certified r... | Rebuttal 1:
Rebuttal: We thank reviewer vCmM for recognizing the novelties, the valid theoretical and technique details and extensive experiments of this work. The following is our point-to-point response to the reviewer’s concerns and comments:
**W1: Why to use $\mathbf{v}^+$ instead of $\mathbf{v}$ in Eq. (3)**.
Th... | Summary: This study represents the first exploration of certifiable robustness in Graph Contrastive Learning (GCL). The authors propose a unified definition of robustness in GCL, addressing the existing ambiguity in quantifying its resilience to perturbations. The introduction of the Randomized Edgedrop Smoothing metho... | Rebuttal 1:
Rebuttal: We thank reviewer F8a7 for recognizing the novelties, the valid theoretical and technique details and extensive experiments of this work. The following is our point-to-point response to the reviewer’s concerns and comments:
**Q1: Clarify the operation of the evasion attack in the context of trans... | Summary: This paper studies the problem of certifiable robustness against adversarial perturbations in Graph Contrastive learning (GCL). This is an interesting paper where theoretical and empirical results are provided. The goal is to provide provable guarantee of robustness in the face of a limited budget adversarial ... | Rebuttal 1:
Rebuttal: We thank reviewer ssR8 for recognizing the novelties, the valid theoretical and technique details and extensive experiments of this work. The following is our point-to-point response to the reviewer’s concerns and comments:
**W1: The proposed work seems to rely on the latent classes in the downst... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful comments and constructive suggestions, which significantly helped us strengthen our paper. We are encouraged to find that the reviewers appreciate the novelty of this work, the valid theoretical and technique details and extensively conduct... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper is the first one to dive into the certifiably robust Graph Contrastive Learning (GCL) and proposes a certifiably robust GCL framework. It defines the certified robustness of GCL and then proposes Randomized EdgeDrop Smoothing (RES), which randomly drops each edge of the input sample with a certain p... | Rebuttal 1:
Rebuttal: We are grateful for your approval of the novelties, the valid theoretical and technique details and extensively conducted experiments of our work. We sincerely thank you for your time and thoughtful feedback. We will perform all the changes requested in the minor comments. Here, we hope to address... | null | null | null | null | null | null |
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data | Accept (poster) | Summary: This paper is about causal discovery with exchangeable data, thus relaxing the traditional iid assumption. This allows for causal structure identification by conditional independence tests. The result has application to multi-environment data.
Strengths: Despite my low confidence, I think the paper delivers s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the efforts and we greatly appreciate your positive feedback. From your comments, you clearly understood the paper.
> The identifiability result seems to hold for Markovian models only.
We thank the reviewer for the suggestion, however, Pearl points out in his classic... | Summary: Causal analogs to de Finetti's theorem are proven, showing that if in an exchangeable distribution certain conditional independences hold, the distribution can be seen as being generated by a DAG with a latent variable corresponding to each node, determining that node's causal mechanism. It is further shown th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough feedback, and we appreciate the kind words that causal de Finetti theorems “deserve to be commonly known in the community”. Thank you for pointing out the interesting reference Dawid 2021. We will discuss and cite [1] in an updated version.
Re ‘grouped data... | Summary: The authors describe how the combination of assumptions about exchangeability and specific statistical tests can enhance the causal implications that can be derived from observational data.
Strengths: The utility and implications of exchangeability for causal inference are well described by the authors. Under... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and are glad to find the reviewer appreciates the “utility and implications of exchangeability for causal inference” and considers our results as “clearly very useful”.
> That paper differs somewhat from this paper in goals and focus, but the current pap... | Summary: This paper examines a stronger notion of exchangeability which provides invariant causal structure, i.e., is capable of serving as the basis for causal reasoning. The main contribution is a theorem which states that for pairs of RV with certain conditional independence properties it is always possible to repre... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time and providing a thorough review and constructive feedback. We are glad you found the paper interesting as a simple, intuitive, thorough piece of work.
> The largest weakness I can see here is the lack of comprehensive empirical evaluations. The evaluation... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and effort in providing valuable feedback. We are glad to see all the reviewers have unanimous agreement that this paper delivers solid contributions to the community and some consider it to be “very insightful and deserve to be commonly known in the commu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Identifiability Guarantees for Causal Disentanglement from Soft Interventions | Accept (poster) | Summary: This paper focuses on the problem of causal discovery with unobserved causal variables. The authors show that under necessary assumptions, the latent causal model can be recovered by iteratively identifying the interventions that target the source nodes and removing these source nodes. Based on this finding, t... | Rebuttal 1:
Rebuttal: Thank you for the encouraging comments. We appreciate that you think the identifiability proof is well laid out and the experiment shows promising results. We would like to address your remaining questions as follows:
> **"It is better to briefly discuss the meaning of interventional faithfulness... | Summary: This paper studies the task of learning causal variables from high-dimensional observations. This is done under the assumption that single-node, soft interventions are available for all causal variables, as well as the observation function being a full row rank polynomial, e.g. linear. Based on prior work, the... | Rebuttal 1:
Rebuttal:
Thank you for the detailed review! We appreciate that reviewer found the application to real-world data encouraging. Due to character limits, we’d address the reviewer’s main points below.
> **“Assumption 2 is done without much of simplification...” | “$G$ ... is the sparsest within its transit... | Summary: This paper proves identifiability of causal disentaglement in latent space in the presence of interventional data. In more details, consider data X generated as X = f(U) where the distribution of U encodes a causal graph G (or a Bayesian network), identifiability is the question of whether f and U can be recov... | Rebuttal 1:
Rebuttal: Thank you for the thorough review! We appreciate your recognition of our theoretical result, and the appreciation of our illustrative examples. We’ve ran addition experiments as suggested and we’d like to address your detailed comments below:
> **”It's not mentioned anywhere in the introduction t... | Summary: This paper focuses on latent causal representation learning, with a specific focus on showcasing the identifiability of the causal structure among latent variables. The authors propose an approach based on assuming a full-row rank polynomial generator, soft interventions on each latent variable, a generalized ... | Rebuttal 1:
Rebuttal: Thank you for appreciating our results exploring soft interventions, and for your recognition of our contribution. We would like to clarify the following points in response to your comments:
> **”In line 111, the paper mentions, "We focus on the scenario where we have at least one intervention pe... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments and suggestions!
---
In this general response, we attached a pdf of the additional figures and tables that we will add to the manuscript. To summarize the PDF, it includes:
- A modified Figure 4, which now has a larger font with the proced... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Active Observing in Continuous-time Control | Accept (poster) | Summary: This paper tackles the issue of determining the optimal timing for observations in continuous-time control with costly observations.
The authors formulate the problem and develop theoretical findings to provide insights about why observing at irregular intervals is advantageous in certain environments.
They t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions!
## (A) Dependence on the accuracy of the learned model
We agree; allow us to clarify that it is standard in all offline model-based RL methods to learn an accurate dynamics model [Levine et al. 2020, Lutter et al., 2021].
We verified empiric... | Summary: The paper addresses the problem of continuous control with costly observations. The authors provide a formal definition of the observation problem and the control problem. The continue by proposing a scheme to how to do the two simultaneously in irregular intervals. Finally, the method is evaluated against ben... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions!
## (A) Add similar related work of event-based sampling
We agree that it is helpful for the reader to expand the related work section to include a discussion of the similar topic of event-based sampling. We now extend our related work section... | Summary: The paper addresses the problem of controlling continuous-time environments while actively deciding when to take costly observations in time, which is relevant to real-world scenarios such as medicine, low-power systems, and resource management. Existing approaches either rely on continuous-time control method... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions!
## (A) The optimal method remains open
We agree—in fact, this is the primary motivation for our paper, to first formalize this problem, theoretically contribute a property that an optimal method should have and lay the groundwork for the deve... | Summary: The authors present a continuous-time framework for "active observation", meaning deciding when to take observations in a control problem, assuming taking observations has a cost. They present two separated controllers, one for taking action and one for deciding when to observe. The controller for acting is an... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and suggestions!
## (A) Distributions of state and reward
We agree that a uniformly weighted mixture of Gaussians is not a Gaussian itself, rather it is possible to compute the mean and variance of the mixture to approximate it with a Gaussian, and we have no... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful comments and suggestions! We respond to each reviewer with an individual rebuttal and share all **additional new experimental** results here.
## (R1) Dependence on the accuracy of the learned dynamics model
To understand this further, we performed an *a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Individual Arbitrariness and Group Fairness | Accept (spotlight) | Summary: This paper explores the interaction of predictive multiplicity with fairness interventions, making the observation that models which receive common fairness interventions often result in increased predictive multiplicity within the Rashomon set. They back this up with some theoretical exploration and experimen... | Rebuttal 1:
Rebuttal: We thank Reviewer gGVC for the thoughtful comments and for appreciating the novelty and value of the work. We hope that the answers below address the points raised in the review.
---
- **Q1: “Is reducing predictive multiplicity ultimately a good thing?”**
--- We thank the Reviewer for t... | Summary: The authors discuss arbitrariness in automated decision-making, i.e. the fact that similarly trained classifiers may end up disagreeing on the most suitable label for a data point. In particular, the authors observe that arbitrariness can be worsened by fairness interventions, suggesting that increased group f... | Rebuttal 1:
Rebuttal: We thank Reviewer APJf for the thoughtful comments and for appreciating the novelty and value of the work. We hope that the answers below address the points raised in the review.
---
- **Q1: ‘’My biggest concern for the paper is a philosophical one: why is arbitrariness (under your definition) h... | Summary: The authors theoretically and empirically study predictive multiplicity as it relates to fairness and accuracy. They show in both ways that multiplicity increases as a result of bias mitigation.
Strengths: This is a problem that has not been studied and was worth studying because practitioners don't think ab... | Rebuttal 1:
Rebuttal: We thank Reviewer MNFT for the thoughtful review and for appreciating the novelty, complexity, significance, and positive prospects of the work!
---
**Q1: “Justify why arbitrariness is so bad under limited resources. If two people are exactly the same and right at the decision boundary, what oth... | Summary: This paper studies the effect of group fairness regularization constraints on a notion termed arbitrariness. Here, arbitrariness is defined to be the variability of the predictions, for a set of similarly accurate models, on individual samples. In this paper, they show that when a model is regularized to satis... | Rebuttal 1:
Rebuttal: We thank Reviewer 47CU for the thoughtful comments and for appreciating the novelty and value of the work. We hope that the answers below address the points raised in the review.
---
**Q1: Proposition 3.1 shows orthogonality of group fairness and predictive multiplicity. Can you extend equality ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort! We are glad our paper was positively received. In particular, we were encouraged that all reviewers recognized the novelty and impact of our work: “If it remains the case that arbitrariness is orthogonal to group fairness constraints, then that is ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Variational Gaussian Processes with Decoupled Conditionals | Accept (poster) | Summary: This paper addresses enhancing the performance of sparse approximate Gaussian processes, which has scalability with the dataset size but causes performance degradation.
The authors proposed a novel parametrization of conditionals as decoupled forms for training and testing ones, where lengthscales in kernel re... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will address each of your questions below.
> Originality is limited to the proposal of Eq.3.
We would greatly appreciate it if you can **concretely** point out the limitations on our novelty. We believe that referring to Eq (3) as our full novelty is reductive, a... | Summary: This paper studies sparse Gaussian processes which allow different kernel hyperparameters such as length scale to be used, for instance, for the variational posterior mean and covariance. They derive a variational approximation that enables training of models of this kind. This extra scalable approximation fle... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We will address each of your questions below.
> "Could move some RMSE tables into appendix and describe more about experiments."
Thanks for the suggestion. We plan to use the additional page of content to add additional description for the experiments. We a... | Summary: This paper develops a variational approximation for learning sparse Gaussian Processes. The central idea is to "decouple" the mean and covariance parameters. From my read, this decoupling simply means formulating two different kernel matrices (called $Q$ and $K$ in the paper), and then working those new parame... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We reply to each of your suggestions and questions below.
> Clarity: Key summary statements, to act as milestones, throughout would definitely help. This is especially true of Table 1, section 3.2 (Eqn 3), and section 3.3: broadly, the equations are presented... | Summary: This paper considers the problem of increasing the expressivity of sparse Gaussian processes without increasing the number of inducing points by considering a decoupled ELBO. In their setup, the gram matrices appearing in the predictive mean and covariance have different parameterizations. They consider two su... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will address each of your questions below.
> Typos/grammatical errors and inconsistencies in use of abbreviations.
Thank you for pointing this out. We will make sure to resolve all inconsistencies, for example changing DKL-DCPPGPR to PPGPR-DCDKL in 5.2.
> The no... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
State-Action Similarity-Based Representations for Off-Policy Evaluation | Accept (poster) | Summary: This paper introduces a new diffuse-metric for measuring behavioral similarity between state-action pairs for OPE, named ROPE. ROPE is used to learn state-action representations using available offline data. Theoretically, this metric can bound the OPE error. Empirically, ROPE boosts the data-efficiency of FQE... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the merits of our work and for your suggestions. We address the concerns from the Weaknesses and Questions sections below.
Weaknesses
- Re: general comment. Thank you for the suggestions. We address them in the questions section and will make the improvements for the c... | Summary: This paper introduces an OPE-tailored state-action behavioral similarity metric that acts as a new loss for representation learning that can be used to learn a encoder for the state-action features in place of the original features.
Strengths: -- Very well written paper
-- Interesting contribution to OPE
We... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work. We address your comments below.
Weaknesses:
- Re: easiness of representation learning vs. OPE. This is a very good point and, as far as we are aware, is an open question as to when is it easier to learn a representation and plug it into FQE vs. applying FQE di... | Summary: The paper introduces a method to enhance the data-efficiency of the fitted q-evaluation (FQE) algorithm in off-policy evaluation (OPE) for reinforcement learning. They propose using a learned encoder and an OPE-tailored state-action behavioral similarity metric to transform the fixed dataset, improving the rep... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their acknowledgement of the merits of our work, comments, and suggestions. Furthermore, thank you for providing an actionable suggestion for us to improve your evaluation of our work. Please also see our global response.
Weaknesses:
- Re: intuition behind data-effic... | Summary: Towards enhanced data-efficiency of the fitted q-evaluation (FQE) method, this work first proposes an OPE-tailored state-action behavioral similarity metric and then uses this metric and the fixed dataset to learn an encoder, which is used to transform the fixed dataset. Experiments on the OPE tasks illustrate... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their comments and suggestions. We answer concerns from the Weakness and Questions sections below. Please also see our global response.
Weaknesses:
- Re: general comment. We hope our response clarifies the details and we will make these improvements to the camera-rea... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for kind words regarding the merits of our work and helpful suggestions. Before responding to individual questions and comments, we wanted to briefly elaborate on the intuition for ROPE increasing data efficiency and how ROPE is tailored to the OPE setting. We also inclu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models | Accept (poster) | Summary: The paper considers causal inference in the context of additive noise models (ANMs). In particular the paper points out a possible problem in simulation benchmarks for this setup: if the weights of the causal network are not chosen appropriately, the simulation may result in datasets in which identifying the c... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and your suggestions on how to better present the strengths of our work.
We propose the following edits in response and individually answer your questions below.
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### Edit summary
* **Edit 1 (see Figure 2 rebuttal PDF)** We will include the $R^2$-sortab... | Summary: The paper introduces the issue of "$R^2$-sortability" for synthetically generated data used in the evaluation of causal structure learning methods. $R^2$-sortability is a generalization of varsortability, which is invariant to re-scaling (e.g., standardizing) the simulated variables. They show that, using typi... | Rebuttal 1:
Rebuttal: We thank you for your detailed review and for highlighting the significance of our contribution for the causal structure learning community. We propose to make the following edits in response to your review and answer your questions individually below.
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### Edit summary
* **Edit 1 (see Figur... | Summary: This paper is concerned with synthetic data generation for causal discovery. The paper explores the scale invariant pattern given by the coefficient of determination $R^2$ that potentially exists in synthetic data benchmarks. The authors present analysis in the case of linear ANMs. They also find out that prio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for pointing out the impact of our findings on the causal structure learning community. We propose to make the following changes in response to your review and answer your questions individually below.
---
### Edit summary
* **Edit 1** We will highlight ... | Summary: This paper proposes an interesting extension of the var-sortability approach proposed by Reisach et al. (2021), denoted R2-sortability. While var-sortability measures the agreement between the causal order and the order of increasing marginal variance, R2-sortability measures the agreement between causal order... | Rebuttal 1:
Rebuttal: We thank you for your thorough and positive review.
We propose to make the following edits in response to your review and answer your questions individually below.
---
### Edit summary
* **Edit 1** We will include DirectLiNGAM (Shimizu 2021) as an additional algorithm in our comparisons in Appe... | Rebuttal 1:
Rebuttal: Dear All,
We thank you for the review of our submission, valuable suggestions, and recognition of our work's significance and timeliness for the causal discovery community.
All reviews seem to agree on the good technical soundness (4x good)
and presentation (1x excellent, 3x good) of the overall ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition | Accept (poster) | Summary: This paper deals with the topic of facial expression recognition (FER). In particular, authors point out the problem of imbalanced class data since most FER data sets will have many more neutral or happy face images than images with other facial expressions. Authors propose an approach to address this proble... | Rebuttal 1:
Rebuttal: We are very exhilarated to receive your review. We sincerely thank you for your professional review, which gives us many instructions for polishing our paper. Thanks very much.
**Weaknesses:**
**1.** Thanks for your valuable suggestion. We carried out experiments to study the performance of usin... | Summary: This paper mainly focuses on solving the imbalanced problem in Facial Expression Recognition (FER). The goal of this paper is to enhance the performance on minor classes without compromising the performance on major classes. The contribution is two fold. Re-balanced attention consistency (RAC) module is propos... | Rebuttal 1:
Rebuttal: Thanks very much for your thorough review, which helps us a lot to improve our paper.
**Weaknesses:**
**1.** Yes, the knowledge pertains to attention regions on the feature map. The transformation invariant knowledge ensures the FER model to focus on the same region before and after the transfor... | Summary: This paper focuses on the imbalanced learning problem in facial expression recognition (FER). Unlike existing works in imbalanced learning for image classification, the proposed method addresses imbalanced FER from a novel perspective of label distribution learning. Specifically, the proposed method is motivat... | Rebuttal 1:
Rebuttal: We are thrilled to receive your review. We sincerely thank you for acknowledging the strengths of our work.
**Weaknesses:**
**1.** The re-balanced weight is inspired by the effective number of [5]. We visualize the re-balanced weight on the original RAF-DB according to different $\beta$ values. ... | Summary:
In the paper, the authors tackle the issue of imbalanced learning in the facial expression recognition task by introducing a fresh approach. Their proposed method revolves around the concept of attention consistency under spatial transforms, aiming to extract information about multiple classes from each sampl... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful review. The strengths you highlighted align perfectly with the core contributions of our paper, and they indeed encapsulate what we take great pride in. Your review is undoubtedly one of the most exhilarating ones we have received in over a year. Thank you v... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their time and effort in reviewing our paper, as well as for their insightful feedback!
We are encouraged that Reviewers 31BV, Ux1i, and ss3S all find our main idea of mining extra knowledge about minor classes from training samples of both major and minor cla... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents an approach to tackle the imbalance problem in facial expression recognition. The approach makes use of information from the majority class to help improve performance of the minority classes. The proposed approach is evaluated on two public FER datasets. Different backbone networks are eval... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful review, which has assisted us in enhancing the quality of our paper.
**Weaknesses:**
**1.** Thanks for your valuable suggestion. We have conducted experiments on AffectNet with both 7 and 8 expression classes. Due to the time consumption, we use ResNet-18 ... | null | null | null | null | null | null |
CLIP4HOI: Towards Adapting CLIP for Practical Zero-Shot HOI Detection | Accept (poster) | Summary: This paper proposes CLIP4HOI, a two-stage framework for zero-shot HOI detection, where the generalizable knowledge of CLIP is leveraged for interaction classification. To facilitate better transferability and avoid data-sensitive knowledge distillation, CLIP is tuned to a fine-grained HOI classifier. Extensive... | Rebuttal 1:
Rebuttal: We are grateful for the insightful comments and clarify the concerns as follows.
***
Q1: The novelty of this work is marginal. Some key components are reused in existing HOI detection methods.
A1: This work targets to: 1) Alleviate the problem existing in existing top-performing one-stage zero-... | Summary: This paper proposes a two-stage zero-shot HOI detection paradigm which uses information information from large-scale vision and language models like CLIP. The proposed approach, called CLIP4HOI, first extracts all feasible human-object pairs and generates pairwise proposals. In the second stage, CLIP4HOI uses ... | Rebuttal 1:
Rebuttal: We are grateful for the insightful comments and clarify the concerns as follows.
***
Q1: The authors claim that prior models over-fit to the join positional distribution of seen human-object pairs. However, I didn't see any convincing evidence to show if this actually happens and if it happens, ... | Summary: This paper introduces a new framework to leverage CLIP knowledge for zero-shot HOI detection. The paper proposes an HO interactor for pairwise HOI proposal generation to avoid the overfitting issue associated with the joint localization of humans and objects. Instead of using distillation, this paper directly ... | Rebuttal 1:
Rebuttal: We are grateful for the insightful comments and clarify the concerns as follows.
***
Q1: The HOI proposal method in this paper (HO interactor) requires traversing all feasible HO combinations for pairwise HOI proposal generation, which may lead to increased computational complexity. The author s... | Summary: The paper addresses the problem of Zero-shot Human Object Interaction (HOI) detection, which aims to detect bounding boxes of both seen and unseen interactions.
To achieve this, the paper leverages semantic knowledge from pretrained CLIP models to recognize novel combinations of object-verb as well as unseen v... | Rebuttal 1:
Rebuttal: We are grateful for the insightful comments and clarify the concerns as follows.
***
Q1: Justification for the generalization ability of the proposed method.
A1: We would like to kindly emphasize the advanced performance of our method that demonstrates the generalization ability. In Table 1, we... | Rebuttal 1:
Rebuttal: The following is a common question raised by Reviewer baYx and Rad1.
***
Q1: The paper omits some important baselines, such as HOICLIP. Although HOICLIP is a CVPR2023 paper, it was uploaded to Arxiv in Mar. 2023. The authors should provide a detailed discussion and comparison with it.
A1: Thank... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection | Accept (poster) | Summary: This paper proposed an unsupervised algorithm for detecting abnormal nodes in a graph. Based on the one-class homophily assumption and the overwhelming presence of normal nodes in a graph, this paper uses the truncated affinity maximization to enable stronger local affinity for normal nodes than abnormal ones.... | Rebuttal 1:
Rebuttal: Thank you very much for the overall positive rating and constructive comments. We are grateful for the positive comments on our paper clarity, research motivation, and empirical justification. Please see our response to your comments one-by-one below.
> **Weaknesses #1**: To add some supervised g... | Summary: This paper studies the problem of graph anomaly detection and the authors proposed a novel method based on an identified property named one-class homophily. It is observed that normal nodes have strong connections with each other while abnormal nodes have weaker connections. Existing GAD methods overlook this ... | Rebuttal 1:
Rebuttal: Thank you very much for the overall positive rating and constructive comments. We are grateful for the positive comments on our studied problem, technical contribution and empirical justification. Please see our detailed response below.
> **Weaknesses/Questions #1**: Lack of theoretical analysis ... | Summary: In this paper, the authors introduced a novel unsupervised anomaly scoring measure (local node affinity) for GAD, and further proposed a Truncated Affinity Maximization (TAM) for GAD. TAM learns tailored node representations for our anomaly measure by maximizing the local affinity of nodes to their neighbors, ... | Rebuttal 1:
Rebuttal: Thank you very much for the overall positive rating and constructive comments. We are grateful for the positive comments on our research motivation, readability, our technical design and empirical justification. Please see our response to your comments one-by-one below.
> **Weaknesses #1**: In th... | Summary: Graph anomaly detection aims to identify abnormal nodes in a given graph. The manuscript argues that the existing graph anomaly detection datasets have one-class homophily where the homophily of normal nodes is much stronger than abnormal nodes. To utilize the one-class homophily phenomenon in graph anomaly de... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments and questions. We are grateful for the positive comments on our design, empirical justification and ablation study. Please see our detailed one-by-one responses below.
> **Weaknesses/Limitations #1**: Does one-class homophily always hold?
Please ... | Rebuttal 1:
Rebuttal: Dear All Reviewers,
Thank you very much for the time and effort on reviewing our paper, and for the constructive and positive comments. Our rebuttal consists of two part: **Global Response** where we address shared concerns from two or more reviewers and **Individual Response** where we provide a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits | Accept (poster) | Summary: This paper addresses Off-Policy Evaluation (OPE) in contextual bandits, a significant problem in fields such as healthcare and personalized recommendation systems. OPE involves evaluating the performance of new policies using only existing data generated by a current policy, which can pose a challenge due to h... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their useful comments and acknowledging the practical relevance of our proposed method.
We clarify some of the misunderstandings below.
> Eq.(3) may be unstable in cases where the target and behavior policies greatly differ, and there are many actions whe... | Summary: This paper proposes a marginal OPE estimator that directly corrects the distribution shift wrt rewards instead of correcting the distribution shift wrt actions. The proposed estimator generalizes the idea of marginal IPS (MIPS) and achieves the minimum variance among them when the marginal importance weight is... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time reviewing our paper and for appreciating the clarity of our paper. Below we clarify some of the questions raised:
> As author(s) discuss in the limitation, one potential concern lies in the accuracy of the marginal importance weight. In my understanding, the p... | Summary: This paper introduced a new off-policy evaluation (OPE) method called Marginal Ratio (MR) to contextual bandits problems. Conventional methods such as Inverse Probability Weighting (IPW) and Doubly Robust (DR) rely on estimating policy ratios, which could incur large variances when there is low overlap between... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our idea and that our ``simulation results show great potential'' of our method. Below we clarify some of the questions raised by the reviewer:
> [...] I think the assumption of knowing the ratio $w(y)$ is much stronger than knowing the impo... | Summary: The paper looks at the problem of off-policy evaluation (OPE) in contextual bandits.
The problem consists of estimating the expected reward obtained from a policy different from the one that collected the data.
The authors first analyze classic OPE estimators used for this problem, such as Inverse Probability... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the novel point of view presented in our paper and our careful experimental analysis. Below we address some of the questions raised:
> Since the problem with IPW and DR is high variance, a really simple way to reduce the variance for such estimators is the ... | Rebuttal 1:
Rebuttal: Firstly, we would like to thank the reviewers for taking the time to review our paper, appreciating the quality of our work and providing many insightful comments regarding it. To address some of the concerns raised, we have conducted additional experiments and included the results in the attached... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of off-policy evaluation (OPE) using a variant of the inverse propensity score (IPS) estimator on logged bandit data. The authors specifically aim to reduce the variance of IPS by focusing on the shift in the marginal distribution of rewards instead of the policies themselves, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments on our contributions and soundness of our approach. Below we respond to the questions raised:
> In terms of computational efficiency, how does the efficiency of the MR estimator compare to IPS?
In the setting of off-policy evaluation, computing... | null | null | null | null | null | null |
Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy | Accept (poster) | Summary: The authors present a method to jointly learn important features as well as a $k$-nearest neighbour graph for data. They extensively motivate and evaluate their method.
Strengths: - Great method, very novel and sensible.
- Excellent and thorough evaluation.
- Method is well-justified by theoretical explanat... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's positive comments on our work. We provide our response below:
### Response to the point in Weaknesses
**P1: "I would recommend a few sentences about the intuition behind the method."**
**A1:** Thank you. The main focus of our work is to select features in ne... | Summary: This paper proposes a deep FS method that simultaneously conducts feature selection and differentiable k-NN graph learning based on the Dirichlet Energy. The Dirichlet Energy identifies important features by measuring their smoothness on the graph structure, and facilitates the learning of a new graph that ref... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the feedback and suggestions. Below we answer all questions and provide some additional experimental results.
**Q1: Concern about the limited novelty of the proposed method**
**A1:**
Thank you for the suggestion about highlighting the novelty in Sect... | Summary: This article proposes an unsupervised feature selection method by minimizing the Dirichlet energy, and the energy function is on the other hand based on the k-NN graph computed from the selected features. In this sense, the features and the k-NN graph are jointly learned. To avoid discrete operations, the auth... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and positive comments. Below we provide our response:
### Response to the points in Weaknesses
**P1: Suggestion about a more computationally efficient method for differentiable sorting in Weakness 1**
**A1:** Thank you for this constructive c... | Summary: In this paper, the authors propose an unsupervised feature selection method using Dirichlet energy. The proposed method learns the KNN graph and the feature selection jointly to reduce the influence on the feature selection quality by the noisy and irrelevant features.
The feature selection component minimiz... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive comments. We provide our response below.
### Response to points in Questions
**Q1: "How can the method be generalized to handle noncontinuous categorical features?"**
**A1:** Thanks. Recall that Dirichlet Energy is inherently reliant on the ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the positive reviews and constructive comments that help us to emphasize the contributions of our approach. We are encouraged to hear that the reviewers found the approach **interesting** (Reviewers EQBi, f5Nx), **novel** (Reviewers EQBi, MNAm), and **well-motivated... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
XYZ Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing | Reject | Summary: This work introduces XYZ Data Efficiency, a framework that combines curriculum learning and data routing techniques to improve data efficiency in training recent large models. In detail, authors implemented an efficient difficulty metric calculation method for large datasets by utilizing map-reduce, on top of ... | Rebuttal 1:
Rebuttal: Thank you for your comments and below are our replies.
<Comment 1> "Firstly, I would argue that random-LTD has almost nothing to do with data efficiency. It looks to me that random-LTD is actually closer to some regularization, particularly dropout [1]. Just because one can achieve the same perfo... | Summary: In this paper, the author proposes XYZ data efficiency framework to improve the data/training efficiency in the foundation model training. The proposed framework mainly consists of two techniques, i.e., (1) the efficient data sampling via general curriculum learning library and (2) efficient data routing via r... | Rebuttal 1:
Rebuttal: Thank you for your comments and below are our replies.
<Comment 1> "Missing the full term of “CL” in the introduction section (line 44). The first explanation shows in line 64."
<Reply 1> Thank you for catching this and we will make sure to fix this and double check all terminologies in the fina... | Summary: This paper draws inspiration from the observation of training costs increasing quadratically with data size, leading to a focus on enhancing data efficiency. To address this issue, the paper presents a framework that optimizes data utilization, improves training efficiency, and enhances model quality. The fram... | Rebuttal 1:
Rebuttal: Thank you for your comments and below are our replies.
<Comment 1> "While data efficiency is recognized as crucial for various tasks, the paper could provide a more comprehensive study and presentation of how the proposed method enhances models across different data sizes. A more thorough investi... | Summary: This paper proposes XYZ Data Efficiency, a framework that makes better use of data, increases training efficiency, and improves model quality. The proposed framework features efficient data sampling, efficient data routing, and an easy-to-use practical framework that is integrated into an existing library.
S... | Rebuttal 1:
Rebuttal: Thank you for your comments and below are our replies.
<Comment 1> "My main concern about this paper is the evaluation observations. Specifically, it seems that under lower-budget training settings (e.g., training with less data and training time), the improvement over the baseline actually shrin... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces XYZ, a data sampling and routing framework designed to enhance the efficiency of training large transformer models. XYZ incorporates a user-defined curriculum learning metric for data sampling and leverages token dropping to reduce computational overhead. The authors propose random layer-... | Rebuttal 1:
Rebuttal: Thank you for your comments and below are our replies.
<Comment 1> "One notable weakness of the proposed framework is its relatively limited performance compared to the baseline when operating at a smaller data scale, as indicated in Figure 6. Further investigation and clarity on the factors cont... | null | null | null | null | null | null |
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima | Accept (spotlight) | Summary: This paper analyzes the convergence properties of Sharpness-Aware Minimization (SAM) with **constant** perturbation size $\rho$ and gradient normalization applied to the updates; this has not been done in prior works. Both deterministic and stochastic settings are considered. The authors show the following:
1... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for dedicating their valuable time to evaluate our paper. Below are our responses to the questions raised.
> **Weakness 1. Non-convergence seems obvious.**
As the reviewer correctly points out, if deterministic SAM finds a fixed point $\tilde{x}$, it must hold that $\n... | Summary: SAM is a very practical algorithm for improving generalization in deep learning, however, even the convergence properties of SAM are not well understood. The paper studies the convergence/non-convergence of SAM under various standard setups in optimization, including smooth/nonsmooth, convex/strongly convex/no... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their detailed evaluation of our paper. We are truly grateful for your interest in our lower bound results. The responses addressing the questions raised are outlined below.
> **Question 1. Can we prove similar lower bounds for $n$-SAM?**
Indeed, we have conducted ... | Summary: This paper studies the convergence properties of sharpness-aware minimization in a specific setting with the use of gradient normalization and arbitrary constant perturbation. The paper established convergence rates for both deterministic and stochastic SAM with various assumptions on the convexity of the obje... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for devoting their time to evaluate our paper. Here are our responses to the weaknesses raised.
> **Weakness 1. Comparison with [1].**
Our proofs might share similarities with those in [1]. However, our analyses overcome unique difficulties and hence different fro... | Summary: This paper examines convergence properties of sharpness-aware minimization, which is an empirically-popular algorithm for training deep neural networks, yet whose theoretical properties are poorly understood. This paper makes several contributions to the analysis of sharpness-aware minimization, which are nove... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for dedicating their time in evaluating our paper. Your interest in our lower bound examples is deeply appreciated. Below, we provide our responses addressing the raised questions.
> **Weakness 1. The results in the main text are a bit dense.**
Thank you for provi... | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely thank all the reviewers for their careful evaluation of our paper and their valuable questions and comments. Your reviews have been tremendously helpful in improving our manuscript. We are glad that many reviewers recognized the comprehensiveness of our upper and lowe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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