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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RDumb: A simple approach that questions our progress in continual test-time adaptation | Accept (poster) | Summary: Test-time adaptation techniques seek to adapt a model to new unlabelled samples without access to the original training data. However, TTA approaches are typically not evaluated for long runtimes. This work proposes a new benchmark Continuously Changing Corruptions (CCC), which is a stream of corrupted ImageNe... | Rebuttal 1:
Rebuttal: Thank you for your review. We’re happy you found our paper to be easy to follow, and the experiments to be extensive. We want to address your questions:
> I would encourage the authors to add error bars to their tables, so that it is clear that the differences are statistically significant
Good ... | Summary: This paper proposes a new benchmark for continual test-time adaptation (CTTA): CCC (Continually Changing Corruptions).
Experiments for existing state-of-the-art approaches using their proposed benchmark demonstrate that even a non-adapting model performs better than existing approaches for this benchmark.
Furt... | Rebuttal 1:
Rebuttal: Thank you for your review. We’re glad you found the dataset novel and challenging, and our experimental results meaningful! We addressed all mentioned weaknesses below and replied to your questions.
> In CoTTA, there is a setting of gradually changing the corruptions [...]. So the idea of gradual... | Summary: The paper newly introduces a new benchmark dataset for Continual Test-Time Adaptation (CTTA) named Continually Changing
Corruptions (CCC), and suggests a simple technique - repeatedly initialize the learned model weights during CTTA. CCC is composed of interpolated corruption data and its data scale varies at ... | Rebuttal 1:
Rebuttal: Thanks for your review! We agree with your assessment that our results are quite surprising. We hope to clarify your concerns and address the remaining weaknesses below:
> “Even though this remedy outperforms baselines”
This sounds like a possible misunderstanding. While RDumb indeed outperforms... | Summary: The paper proposes a new benchmark (dubbed CCC) for test-time adaptation, which generalizes previous corrupted imagenet benchmarks. Specifically, the CCC gradually draws a sequence of corruptions (e.g. gaussian noise or motion blur), and gradually interpolates between two consecutive corruptions, creating a st... | Rebuttal 1:
Rebuttal: Thanks for your review. We’re happy you found our dataset interesting, and our paper easy to read. We want to address the weaknesses and questions below.
> For a fair comparison with COTTA, how would such method perform if the probability of resetting the weights was determined by a similar cross... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback. We are glad to hear that reviewers found our paper easy to follow, our proposed dataset interesting, and our method to be simple and effective.
We commented on all reported weaknesses and addressed the reviewers' questions in the individual ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model | Accept (poster) | Summary: The paper combines diffusion modeling and conformal prediction to predict state-action trajectories with uncertainty quantification. The proposed approach then performs uncertainty-aware model-based planning with strong results on several established offline RL benchmarks.
Strengths: The approach has fairly s... | Rebuttal 1:
Rebuttal: >Q1: Comparisons to other uncertainty quantification methods Zhan et al [1], Yu et al. [2], Kidambi et al [3].
A1:Thanks for pointing this out. We will make the writing of Sections 3.3 and 3.4 more concise with clearer expression.
Zhan et al. [1], Yu et al. [2], and Kidambi et al. [3] have made ... | Summary: In this paper the authors study how to measure uncertainty in a planner with a generative diffusion model, and how to reduce uncertainty during prediction. Results are presented on several MDPs.
Strengths: + coupling planners to diffusion models is an interesting idea worth exploring.
+ measuring and reducing... | Rebuttal 1:
Rebuttal: >Q1: It is not clear if the task is planning or imitation learning.
Thanks for pointing this out. We aim to address planning with uncertainty-aware diffusion models. We involve generating a sequence of actions to achieve a desired goal or optimize an objective. We focus on decision-making and det... | Summary: This work addresses the challenge of uncertainty estimation for planning. The authors propose the use of diffusion models for learning dynamics, which have demonstrated effectiveness in overcoming challenges such as multi-modal action distributions. To quantify the uncertainty of these dynamics models, they em... | Rebuttal 1:
Rebuttal: >Q1: More details regarding the computational aspects of training and inference.
A1: Thanks for the suggestion! Yes, the framework with conformal prediction is a little slower than the framework without conformal prediction. Regarding the computational aspects of training and inference, as the su... | Summary: The work proposes uncertainty quantification for learned dynamics model and imitation learning. The authors incorporates an uncertainty statistic as part of the loss to train their dynamics model that uses a diffusion model architecture. They show that doing so brings performance improvement empirically on com... | Rebuttal 1:
Rebuttal: >Q1: English
A1:Thanks for pointing this out. We have fixed these grammatical errors, awkward sentence structures, and confusing statements throughout the paper. We will update them in the new version of the paper.
**Is model uncertainty used for planning during test time?**
No. Currently, PlanC... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their valuable feedback on our work. Thank you for the many positive comments: (i) acknowledging the novel use of conformal prediction in diffusion models (all reviewers), (ii) noting the reasonably well-written and clear exposition (gfdz, wAeC, BQNc), (iii) re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper describes a method for learning a dynamics model that uses conformal prediction for explicit representation of the uncertainty. The dynamics model is used for sequential decision making such as planning in a maze or learning control in one of the D4RL problems.
Strengths: * The primary strength of ... | Rebuttal 1:
Rebuttal: >Q1: What did this paper accomplish?
A1: Thanks for the comments! Instead of developing a method for sequential decision-making that achieves the highest performance (reward) on D4RL, we are the first to augment the `diffusion` RL algorithm with uncertainty awareness. The progress in the D4RL ben... | null | null | null | null | null | null |
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards | Accept (poster) | Summary: In this paper, the authors study the problem of generalized linear bandits with heavy tailed rewards. They propose two algorithms based on truncation and mean of medians. The algorithms both achieve near optimal regret bound of $\tilde{O}(dT^{\frac{1}{1+\epsilon}})$. These regret bounds improve upon previous r... | Rebuttal 1:
Rebuttal: Thank you sincerely for your time and effort in reviewing our work. We have carefully considered your concerns and our responses are provided as follows.
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**Weaknesses: According to the introduction of previous methods for generalized linear bandits and bandits with heavy-tailed rewards, the... | Summary: This paper considers Generalized Linear Bandit (GLB) with heavy-tailed rewards, i.e., the rewards $y_t=\mu(\langle x_t,\theta^\ast\rangle)+\eta_t$ only allows a bounded $(1+\epsilon)$-order moment. By utilizing the truncation and the mean of medians technique (both for handling heavy-tailed r.v.s), the authors... | Rebuttal 1:
Rebuttal: Thanks sincerely for your time and effort in reviewing our work. We have carefully considered your concerns, and are very happy to respond more questions during the rolling discussion.
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**Q1:The CRTM algorithm looks pretty like equipping the OL$^2$M algorithm by Zhang et al. (2016) with the... | Summary: This paper studies the problem of generalized linear bandits with heavy-tail rewards. Due to the heavy-tailedness, methods and algorithms for linear bandits with sub-gaussian rewards cannot be directly applied. To handle such issues, existing works have developed certain strategies, two of which are the trunca... | Rebuttal 1:
Rebuttal: Thank you sincerely for your time and effort in reviewing our work. We have carefully considered your concerns, and are very happy to respond more questions during the rolling discussion.
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**Questions: In the theoretical proof, what are the major differences from previous works TOFU/BTC? Fo... | Summary: This paper proposes two novel algorithms which improve computational complexity and regret bounds over previous algorithms for generalized linear bandits with heavy-tailed rewards by combining the truncation strategy (or means of medians strategy) with the online Newton step.
Strengths: This work improves the... | Rebuttal 1:
Rebuttal: Thank you sincerely for your time and effort in reviewing our work. We have carefully considered your concerns and our responses are provided as follows.
----
**Weeknesses: Among the input values for the algorithm, $S, \epsilon$ and $v$ is not known in practice.**
Huang et al. [2022] proposed an... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing | Accept (poster) | Summary: This work presents an efficient adapter design for transfer learning of large retrained models. The key idea is to enable adapter to be shared across layers and imposing low-rank constraint, so that the overall trainable parameters can be reduced. Given the re-composed linear adapter, existing reparameterizati... | Rebuttal 1:
Rebuttal: We greatly appreciate your dedicated efforts in reviewing our work. While we value your contribution, we would like to address a few factual errors that have come to our attention. Your understanding and collaboration in rectifying these inaccuracies would be immensely valuable to us.
**1. Regard... | Summary: This paper introduces a parameter-efficient transfer learning method named Adapter Re-Composing (ARC), which mainly focuses on investigating the reusability of adapted parameters. The authors propose to apply a shared adapter to all the layers (blocks) of the pre-trained model, and they use different Re-Scalin... | Rebuttal 1:
Rebuttal: We value the positive feedback you have shared and address the raised concerns as follows:
**1. Regarding the diversity of re-scaling coefficients.**
**Re:** We appreciate the thoughtful insights you've provided. To comprehensively address this matter, we conducted an extensive analysis of the re... | Summary: The paper explores ARC, which is a novel parameter-efficient fine-tuning method which uses a similar architecture as adapters but introduces inter- and intra- layer weight sharing. Some down- and up- projection weights are shared but every adapter position uses an independent set of per-channel scaling factor ... | Rebuttal 1:
Rebuttal: We appreciate the constructive comments. We address the concerns as follows:
**1. Regarding the motivation and justification of shared adaptation matrices in weakness 1**.
**Re:** We appreciate your feedback. In response, we have incorporated the suggested analysis to provide a robust rationale f... | Summary: This paper proposes to further reduce the parameters of the adapter by introducing a weight-sharing scheme between different layers. To accommodate the variations across different layers, re-scaling coefficients are learned to re-compose the layer-adaptive adaptation matrices. Experiments are conducted on 24 d... | Rebuttal 1:
Rebuttal: Thank you for your diligent review of our paper and your acknowledgment of the strengths within our work. We have taken your feedback seriously and have formulated a detailed response to the specific issues you raised, which we present as follows:
**1. Regarding incorporating advanced data augmen... | Rebuttal 1:
Rebuttal: **This concludes the the Joint Response to all reviewers.**
We appreciate the diligent efforts undertaken by both the reviewers and the ACs in thoroughly reviewing our manuscript. It is noteworthy that a consensus has been reached among four out of the five reviewers regarding the novelty and eff... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a novel parameter-efficient fine-tuning method called Adapter Re-Composing (ARC). ARC effectively reuses parameters across different layers, resulting in remarkable improvements in performance across 24 image classification datasets while utilizing fewer learnable parameters.
The experime... | Rebuttal 1:
Rebuttal: Thank you for recognition of our work strength. We extend our sincere appreciation for the invaluable guidance you have provided. We respond to your concerns as follows.
**1. Regarding the performance of ViT-Huge is lower than ViT-Large.**
**Re:** We wish to highlight that upon a thorough compari... | null | null | null | null | null | null |
Correlation Aware Sparsified Mean Estimation Using Random Projection | Accept (poster) | Summary: The authors propose a compression algorithm that optimizes the accuracy in a setting where each client can send $k+O(1)\ll d$ values to the server. The algorithm leverages random projections and proposes a way to make the resulting estimate unbiased, which is valuable for DME.
Strengths: + DME is an important... | Rebuttal 1:
Rebuttal: W2 \& W3 \& Q1: See common response $\textbf{Quantization vs. Sparsification}$.
W1 \& W4 \& Q2: See common response $\textbf{Computation Time}$. Also, we included additional results on comparing the encoding and decoding wall-clock time of different estimators (see the pdf attachment in common r... | Summary: This paper studies the distributed mean estimation (DME) problem. In particular, the paper proposes a new DME technique called Rand-Proj-Spatial. In Rand-Proj-Spatial, each client uses SRHT for dimensionality reduction and sends the transformed lower-dimensional vector to the server. The server then recovers ... | Rebuttal 1:
Rebuttal:
W1: The worst case MSE of prior works on sparsification techniques, e.g. [1] and [2],
are also on the order of $O(d/n)$.
Note we did not claim that our approach improves the asymptotic accuracy bounds. Just like [1], our method focuses on utilizing practically available side information to impro... | Summary: This work considers the problem of distributed mean estimation, wherein each node in a set of distributed nodes contains a vector, and the goal of the parameter server is to estimate the mean of those vectors. Unlike some other works, no distributional assumption is assumed over the vectors, and the error metr... | Rebuttal 1:
Rebuttal: W1: We indeed had a lot of discussions on the title before the submission. It is hard to give a precise yet succinct title. Other possible title candidates are "Projection Based Correlation Aware Distributed Vector Mean Estimation", "Unbiased Sparsification Induced Distributed Vector Mean Estimati... | Summary: In distributed learning, computing the mean of the vectors sent by the clients is an important subtask. Motivated by this, the distributed mean estimation problem is studied in this paper. The two main techniques commonly used for these problems are Quantization and sparsification. Rand-K was one prominent spa... | Rebuttal 1:
Rebuttal: W1: We gave the reason why we want our algorithm to be $\textit{non-adaptive}$ to client vectors in Section 1 line 82 - 89.
The motivation of studying correlation information of clients is well explained and experimentally demonstrated in the prior work [1]. For example, in distributed optimizati... | Rebuttal 1:
Rebuttal: 1. $\textbf{Quantization vs. Sparsification.}$ There are two major techniques to reduce the communication cost of distributed vector mean estimation (DME): vector quantization and sparsification. The two techniques are orthogonal to each other. Vector quantization reduces the number of bits to rep... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper discusses the problem of communication-efficient distributed vector mean estimation and proposes a new estimator called Rand-Proj-Spatial that improves upon existing techniques. The paper highlights the challenges of distributed optimization and Federated Learning, where communication cost can be a ... | Rebuttal 1:
Rebuttal: W1: See common response $\textbf{Quantization vs. Sparsification}$.
W2: See common response $\textbf{Computation Time}$.
Q1: $\textit{Two key novelties:}$ 1) the design of a general encoding-decoding algorithm that generalizes sparsification techniques and enables one to better use correlati... | null | null | null | null | null | null |
Score-based Data Assimilation | Accept (poster) | Summary: This paper proposes an approach to solve data-assimilation tasks using techniques from score-based generative modeling. Given observations and trajectories from the data distribution, a posterior distribution over an entire state-trajectory is to be computed. The score function of the posterior is estimated us... | Rebuttal 1:
Rebuttal: Thank you for your enthusiastic review and for acknowledging the code provided in the supplementary material. We hope that the following answers will satisfy you.
**Weaknesses & Limitations**
> From my understanding, the method has yet to be tested on (settings on the scale of) actual real-world... | Summary: This paper presents a technique to tackle data assimilation problems (inverse problems involving partially observed time series with a dynamical model acting as a prior over the reconstructed states) using a score based model to estimate the prior distribution $p(x)$ from data. The method used, at its core, le... | Rebuttal 1:
Rebuttal: Thank you for your in-depth review and feedback. Most of your concerns are sensible and will be addressed in the manuscript.
**Weaknesses & Limitations**
> In particular, using the blanket to compute equations (11) and (12) could be a bit more detailed, and the link between the result of appendi... | Summary: The authors propose score-based data assimilation framework that relies
on score based generative modeling for trajectory inference/state estimation
of a dynamical model described by an SDE.
To make the procedure efficient, the authors employ three novelties, partly
adopted from existing literature.
They trai... | Rebuttal 1:
Rebuttal: Thank you for your review and reading the manuscript in details. We hope that the following answers will satisfy you.
**Weaknesses & Limitations**
> [...] However, the novelty and the contribution in the existing literature is relatively low compared to existing approaches.
We will address the ... | Summary: Under a data assimilation setting —where the hidden state of a dynamical system is only accessed through noisy observations— the authors consider the problem of inferring the latent state as well as learning a faithful generative model. In order to accomplish this, they take a score-based diffusion model appr... | Rebuttal 1:
Rebuttal: Thank you for your review and the legitimate concerns you have raised. We hope that the following answers will satisfy you.
**Weaknesses & Limitations**
> [...] the biggest weakness would be that the true underlying dynamics are assumed to be known (or at the very least, can be easily sampled fr... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the quality and pertinence of their reviews. All reviewers declare that the methods are sound, interesting and well presented.
The main concerns of reviewers DaY5 and bwmG regard the assumption of a known physical model and the novelty of the contributions... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry | Accept (poster) | Summary: The paper presents an analysis of the latent structure of diffusion models using differential geometry. The authors propose a method to define a geometry in the latent space by pulling back the Euclidean metric from the U-Net bottleneck space *H* via the network encoder. This approach enables the identificatio... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our strengths:
- uncovering the effect of text prompt and dataset complexity on the latent space.
- confirming coarse-to-fine behavior of diffusion models (DMs).
- enhancing credibility through experimental validation.
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> [W1] The paper lacks comparisons with othe... | Summary: In this submission, the authors probe the latent space, xt ∈ X, of diffusion models (DMs) from a geometric perspective, utilizing the pullback metric to identify local latent basis in X and corresponding local tangent basis in H. To confirm their findings, they edit images via latent space traversal. The autho... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the strengths of our paper: the distinctive idea for editing in diffusion models (DMs), and enhancing comprehension of the latent space dynamics, e.g., the evolution of geometric structure over time and the influence of various text conditions.
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> [W1] ... the meth... | Summary: This paper studies the geometry of latent spaces of diffusion models (Dms) using the pullback metric. In the analyses, they mainly examine change of the frequency representations in latent spaces over time and the change of the structure based on text conditioning.
After the rebuttal:
I checked all reviewer ... | Rebuttal 1:
Rebuttal: > [W1] Some of the statements and claims are not clear as pointed in the Questions.
> [Q1] It is stated that "we employ a metric on the Grassmannian manifold.” Why and how did you define the space by the Grassmannian manifold?
[Q1] These subspaces (as a vector space) of $\mathcal{T_{\mathbf{x}}}... | Summary: The paper proposes a study on the latent space of diffusion models and on how to manipulate it. It takes advantage of an observation made by previous work [22] on the flatness and semantic structure of the U-Net model used in DDIM and uses pullback metric from the latent space of the U-Net to the space of dif... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the strengths of our paper: first to study the behavior of the space of diffusion models (DMs), e.g., the coarse-to-fine behavior, the divergence of tangent space across different samples, and the meaningful image editing with pullback metric.
---
> [W1] It is not rea... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable advice. Here, we compile reviews we want to share with all the reviewers. Please see our responses addressing the specific concerns below:
---
### 1. Improving Fig. 1 for clarity
**Reviewers *6jtZ* and *iEgM* suggested modifying Fig. 1 since it has too d... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Batchnorm Allows Unsupervised Radial Attacks | Accept (poster) | Summary: This paper shows that for batch normalized deep image recognition architectures, intermediate latents that are produced after a batch normalization step by themselves suffice to produce adversarial examples using an intermediate loss solely utilizing angular deviations, without relying on any label. The succes... | Rebuttal 1:
Rebuttal: Thank you for the review, our comments in response are as follows to the weaknesses :
Weakness 1: The proposed method is most suitable for the cases where a feature extractor (known to us) has been used to train a model (unknown to us). Generally in these cases the first K layers of the new fine... | Summary: In this paper, the authors proposed a label-free attack utilizing a portion of all layers, which does not require to have gradient access to the full model, and the generated adversarial methods generalize to the case where the model was fine-tuned afterwards. These results have relevance at the intersection o... | Rebuttal 1:
Rebuttal: Thanks for your in-depth review. We address your concerns below :
Regarding weakness 1 : This is right, sometimes the service provider does not provide details. However, we often do know enough about the model’s initial layers as they are often based on some available public model. For example, R... | Summary: The authors present and evaluate an algorithm to construct adversarial examples without labels by minimizing the cosine similarity between intermediate layers. The authors show that the attack only works with BatchNorm.
Strengths: - The paper presents strong evidence that the attack is successful on multiple ... | Rebuttal 1:
Rebuttal: Thank you for the review. We would first like to note that you state "The authors show that the attack only works with BatchNorm." Empirically, the attack is successful on other architectures - we carry out experiments on Layernorm architectures (ViT).
Regarding weaknesses :
Weakness 1 : Moved ... | Summary: This paper proposes an adversarial attack (angular attack) method that does not require any label information and works by only accessing the network's first part (up to a specific layer). The attack is based on the assumption that the BN layer converges and forms a hyperspherical latent space, where an angula... | Rebuttal 1:
Rebuttal: Hi, thank you for the review. We address the points raised as follows :
Weakness 1 : Table 9 denotes the absolute correlation. It is meant to demonstrate that this value falls monotonically.
Yes, Table 4 and 47 are the same, we apologize for this confusion. We have broken out the results of each... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you for your reviews, and have directly responded to all of you without any common rebuttal. Please let us know if your concerns are appropriately addressed fully. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Continuous-Time Functional Diffusion Processes | Accept (poster) | Summary: In this work the authors propose an extension to the diffusion models framework for function spaces.
In particular, given a Hilbert space $H$, they introduce a noising process based on a Wiener process on $H$. Depending on some assumptions on the type of noise, they then show the existence of the time-reversal... | Rebuttal 1:
Rebuttal: *Perhaps the main weakness is the limited experimental setups and lack of ablation studies. As such it is hard to really understand the practical benefits of this specific approach.
In particular, one advantage of working with functional data is to be able to handle data at discretised at arbitrar... | Summary: This work proposes *functional diffusion processes (FDPs)*, which generalizes the SDE-based continuous-time framework for diffusion models for data living in Hilbert spaces. The work builds on the theory of infinite-dimensional SDEs and their time reversals, as well as an application of the infinite-dimensiona... | Rebuttal 1:
Rebuttal: *The experiments (section 6) are the weakest part of the paper.
The proposed methodology obtains significantly worse FID scores than standard (Euclidean) diffusion models. However, note that this is achieved with far fewer parameters.*
Indeed, even if FID scores obtained with FDPs are not SOTA, t... | Summary: The paper introduces a novel continuous-time diffusion-based generative model on function space. Unlike previous works in this area, which primarily focused on discrete-time formulations, the authors concentrate on stochastic differential equations (SDE) in their approach.
The paper begins by defining the for... | Rebuttal 1:
Rebuttal: *Weaknesses:
Most of the content in the paper is good. However, some improvements are needed in the experiment.
In particular, the characteristic of the function-spaced model is its resolution invariance. However, the analysis of this aspect is missing in the paper's results. I believe that some a... | Summary: The authors propose a model approach called Functional Diffusion Processes (FDPs) that generalizes score-based diffusion model to infinite-dimensional function space. The authors derive the reverse time dynamics and sampling theorems to find a subset of functions on a countable set of samples without losing in... | Rebuttal 1:
Rebuttal: *The theoretical parts of the paper appears to be well structured. However, the paper itself does not appear to be self contained and has many references to the appendices to understand. For example, the paper makes many references to the assumptions being made, but they are never mentioned in the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thorough analysis of our work and for their very positive feedback on our paper.
We here provide some general comments which are common to all reviewers, and clarify the minor technical points directly in the messages to individual reviewers.
*Implicit N... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors introduce a framework for infinite dimensional diffusion models in Hilbert spaces, including deriving a reverse process and novel ELBO loss objective. The authors introduce a novel score model network architecture.
Strengths: - This is a timely paper with a lot of interest in the community. There ... | Rebuttal 1:
Rebuttal: *Weaknesses:
The implicitly defined INR encoder, g, detailed in equation 16, considers gradient descent in an inner layer. Will this not be quite slow and hence generation / sampling would also be quite slow?*
Please refer to the common remark for all reviewers, above.
*Empirical performance / F... | null | null | null | null | null | null |
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees | Accept (poster) | Summary: This paper studies the problem of graph learning from stationary signals. The authors propose an algorithmic framework, LogSpecT, which addresses the shortcomings of an existing body of work that relies on graph learning using spectral templates.
Strengths: 1. The problem setting is graph learning for stati... | Rebuttal 1:
Rebuttal: Thank you for your comments. We now provide responses to the questions and concerns you have raised.
**Q1:** There may be a minor concern that the technical contributions of the paper are narrowly focused on overcoming the shortcomings of graph learning with spectral templates.
**Answer:** I... | Summary: In this paper, the authors considered the problem of learning graphs from stationary signals. In order to overcome the infeasibility issue of an existing method called rSpecT, the authors proposed a novel formulation by introducing the log barrier term to learn graphs without isolated nodes. The feasibility ca... | Rebuttal 1:
Rebuttal: Thank you so much for your positive comments and advice. We hope the following clarification can answer your questions.
**Q1:** The descriptions of the experiments conducted on real networks require further improvement. Specifically, please provide more details regarding the number of observation... | Summary: This paper addresses the problem of learning a graph from stationary graph signals. To this end, the authors introduce two new optimisation problems called LogSpecT and rLogSpecT. They prove some recovery guarantees on the optimal value of (r)LogSpecT and give a convergence rate when the signals are sub-Gauss... | Rebuttal 1:
Rebuttal: Thanks for your comments and advice. We hope that the clarification may help to clear your concerns.
**Q1:** - There are no error bars. They should be added to really show the results of each method and to be able to compare them. - A table with all the measurements reported could be a good thing... | Summary: In this paper, the authors improve upon an existing method, rSpecT, to impute graphs from stationary signals. The latter frames the recovery of the (weighted) adjacency matrix as a convex optimization problem with constraints —- namely the commutativity of the covariance matrix and the adjacency matrix, as wel... | Rebuttal 1:
Rebuttal: Thank you for your comments and advice. We hope the clarification can clear your concerns.
**Q1:** The authors claim that their method is more computationally efficient with the use of ADMM. Do they have plots showing the running time? ADMM, although fast at each iteration, can be slow to converg... | Rebuttal 1:
Rebuttal: We thank all the reviewers for your insightful comments and helpful advice. In the global response, we explain the additional experiments and results shown in the attached pdf. We hope our clarifications can help to clear up reviewers' concerns.
**1. Stability of LogSpecT and comparison with Kal... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This is an emergency review, regrettably, the paper is outside of my expertise.
Review:
This is a theoretical work concerned with graph learning from signals. The work identifies an issue with the feasibility of a previous well known model in the field, SpecT, then introduces a novel model, LogSpecT, for whic... | Rebuttal 1:
Rebuttal: Thanks for your comments. Here are some illustrations based on your questions/suggestions.
**Q1:** There isn't any mention of the word "neural"
**Answer:** Graph learning from signals studied in this paper has much relevance in the machine learning and neural science community.
For example, it h... | null | null | null | null | null | null |
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery | Accept (poster) | Summary: The paper presents an approach to optimize hard text prompts for generative models through efficient gradient-based optimization. The method automatically generates hard text-based prompts for both text-to-image and text-to-text applications, allowing users to easily generate, discover, and mix and match image... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Below, we address specific points you raised:
> You mentioned that longer prompts do not necessarily produce better results and can lead to overfitting. Could you elaborate on the mechanisms behind this phenomenon and suggest potential strategies to mitigate this issu... | Summary: The paper presents an easy-to-use approach to automatically obtain hard prompts for images. The work introduces PEZ, a gradient-based approach to obtain hard prompts for images. The experiments comparing a popular baseline show improved CLIP score. Finally, the qualitative results show that the method can dist... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address each of your points below:
> 1. The contribution of the work is limited.
The changes between **PEZ** and $FluentPrompt$ may appear subtle, but they are important for performance, and the comparison can be found in the appendix. **PEZ's** main difference is... | Summary: This paper works on hard prompt optimization with gradient methods, especially a discrete text prompt is discovered using CLIP, and optimized to prompt stable diffusion.
Strengths: 1. Without hand-crafted design of hard prompt, the proposed solution directly discover and optimize prompt with gradient descent,... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address each of your points below:
#### Weaknesses
> The proposed solution works on discrete text prompt optimization without constraints on meaningfulness of the text, making it hard to directly understand to the effectiveness of prompt optimization.
While we do... | Summary: This work proposes a new paradigm which optimizes discrete prompts simply by gradient projection to bridge the gap between relatively “easy-to-optimize” soft prompts and their hard counterparts. Their methodology is straightforward and effective in downstream text-to-image task requirements. And more specifica... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address each of your points below:
>1. I think adding more baselines in your main table would definitely be a plus! Currently, you mainly compare this to a heuristic-based yet popular method called "CLIP interrogator". Can you compare your approach with more divers... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and thoughtful feedback. We have attached a PDF containing additional figures, showing explicit failure cases of the approach as requested by one reviewer.
We would like to emphasize the significance of this work. Our proposed, general-purpose, prompt opt... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Federated Learning via Meta-Variational Dropout | Accept (poster) | Summary: The submission proposed a novel Bayesian meta-learning approach metaVD for federated learning. metaVD learns to predict client-dependent dropout rates via a hypernetwork, helping address the model personalization and limited non-i.i.d. data problems. At the same time, metaVD compressed the model parameter, all... | Rebuttal 1:
Rebuttal: **We thank the reviewer for insightful comments.**
Here are answers to your questions:
**1. Bayesian PFL work is not discussed.**
When we started our research last year, we were aware of only a few papers [17,15] dealing with the personalization of models using Bayesian methods. Meanwhile, we r... | Summary: This paper proposes a new federated learning (FL) framework to address the various issues of FL. Specifically, this framework (a) leverages Bayesian FL to address the issue of non i.i.d. data among clients, (b) instantiates its BFL model with a variational dropout posterior to efficiently handle large amount o... | Rebuttal 1:
Rebuttal: **Thank you for the helpful review!**
In this work, we extended the hypernetwork to approximate the dropout variable of NN's weight to enable the full utilization of the weight uncertainty in the aggregation, regularization, and personalization of FL. Our efficient composition is also implemented... | Summary: This paper proposes a novel Bayesian personalized federated learning approach using meta-variational dropout. The proposed approach employs a shared hypernetwork to predict the client-dependent dropout rates for each model parameter, enabling effective model personalization and adaptation in the limited non-i.... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the positive and encouraging feedback!**
As the review suggested, the recommended recent (Bayesian) PFL works will be thoroughly compared in the Background section.
We will update the difference between the recent PFL approaches [1,2,3,4] and ours.
We will also add t... | Summary: The paper introduces a novel approach called meta-variational dropout (MetaVD) for addressing challenges in federated learning (FL). Traditional FL faces issues such as model overfitting and divergence of local models due to non-i.i.d. data across clients. MetaVD leverages Bayesian meta-learning to predict cli... | Rebuttal 1:
Rebuttal: Thank you for the encouraging feedback on our work!
We appreciate introducing the important PFL works (FedSLR, FedALA). We will cite them in the paper.
**PFL Baseline**
We summarize the baselines and related works **presented** in the submitted paper
|**Method**|category|status|
|-----------|--... | Rebuttal 1:
Rebuttal: # General comment to all reviewers
We sincerely thank the reviewer for our work's positive and encouraging feedback.
Here, we summarize some common answers to the reviewers.
## Bayesian PFL
Since we hear the reviewers' suggestion of discussing some recent Bayesian PFL works, we will add a thou... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Graph of Circuits with GNN for Exploring the Optimal Design Space | Accept (poster) | Summary: Automatic circuit design and optimization is an active field of research. Many different algorithms have been proposed with different design objectives, e.g., layout optimization, size optimization, and topology generation. Prior works adopted GNNs to represent circuit topologies, but the authors suggest a dif... | Rebuttal 1:
Rebuttal: **W1, L1**: Thanks for your comments.
**Accuracy of the surrogate model**:
The model under consideration exhibits somewhat modest performance in metrics such as GAIN (0.6) and UGB (0.6). However, a contrasting trend emerges when evaluating metrics like GM, PM, Noise, Power, Frequency, and Delay... | Summary: - This paper propose a GNN based framework, dubbed as Graph of Circuits Explorer (GCX), to optimize circuit design by predicting the performance parameters of the nodes in circuits, e.g., Gain, Bandwidth, Noise, etc.
- This paper 1) utilizes a semi-supervised learning framework is employed for the graph-based ... | Rebuttal 1:
Rebuttal: **W1**: Thanks for the feedback. We had performed following set of experiments highlighting the robustness of our proposed algorithms to some of the commonly encountered problems with vanilla GNN architectures:
(1)**Over-smoothing in GNNs**: Over-smoothing is an inherent problem with deeper GNNs ... | Summary: This paper proposes a graph based semi supervised framework that is capable of driving analog design
Strengths: * The paper is generally well written and organized.
* The figures are nicely drawn to make understanding necessary concept easier.
* The proposed mehtod is well illustrated, the idea of using semi-... | Rebuttal 1:
Rebuttal: **Q1**: This is a very important question and thanks for pointing it out.
**Algorithmic viewpoint**: Optimality of the design parameters depends on how well the objective function is optimized. For the test cases we considered, FOM was chosen as the objective function (details are discussed in su... | Summary: This article proposes a graph-based circuit design framework to help address the issue of label scarcity in circuit design. The paper is well-written, virtually free of errors or inconsistencies. Information is presented accurately and timely, creating a smooth narrative flow between the main paper and supplem... | Rebuttal 1:
Rebuttal: **W1, Q1, L1**: Thanks for raising the concern. We experimented by creating deeper GNNs and the results are as follows:
| GCX(.) | Gain ($p$=50%) | Gain ($p$=70%) | UGB ($p$=50%) | UGB ($p$=70%) | GM ($p$=50%) | GM ($p$=70%) | PM ($p$=50%) | PM ($p$=70%) | Noise ($p$=50%) | Noise ($p$=70%) | ... | Rebuttal 1:
Rebuttal: **Q1)** **Reviewer-5fbs**: Transient Analysis Simulation results
Pdf: /pdf/793cd3be4e4ac128448190e6509a1ac73e210ca8.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The design automation of analog circuits poses significant challenges in terms of the large design space, complex interdependencies between circuit specifications, and resource-intensive simulations. To address these challenges, this paper presents an innovative framework called the Graph of Circuits Explorer.... | Rebuttal 1:
Rebuttal: **W1**: Thanks for the feedback. We tried experimenting with Graph Transformer architecture across different performance metrics with appropriate hyper-parameter tuning. The loss function shows a swift decline however the obtained $R^2$ scores are very poor.
With our current time constraints in ... | null | null | null | null | null | null |
Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels | Accept (poster) | Summary: The paper adresses the issue of coarse to fine learning, _i.e._ coarse labels are present during training but the metric are computed on fine-grained labels. The paper introduces a method Poincaré embeddings with hierarchical cosine margins (PE-HCM). The paper suggests embedding the image representation in the... | Rebuttal 1:
Rebuttal: ## Thank you for the positive comments. Below please find our responses to some specific comments.
### Comment_1:
> *Additional related works*
Thanks for your kind reminder. These works do share some connections with our method. We did not notice them because these works have different task-o... | Summary: This paper proposes a method developed in the hyperbolic space to embed visual embeddings to deal with the task of fine-grained learning from coarse labels. By discovering the advantages of hyperbolic space, the authors design the hierarchical cosine margin manner and an adaptive hierarchical cosine distance, ... | Rebuttal 1:
Rebuttal: ## Thank you for the positive comments. Below please find our point-to-point responses.
### Comment_1:
> *Explain the motivation of Figure 1 in more details*
Thanks for your suggestion. Figure 1 primarily illustrates the properties of the Poincaré disk space and the impact of cosine distance c... | Summary: The paper presents a novel method, PE-HCM, for fine-grained learning from coarse labels. The authors propose the use of the hyperbolic space for sample embedding and introduce a hierarchical cosine margins manner to enhance discriminative ability. The method combines supervised learning with coarse-grained lab... | Rebuttal 1:
Rebuttal: ## Thank you for the positive comments. Below please find our point-to-point responses.
### Comment_1:
> *Visualization and interpretability*
Thank you for your suggestion. We have provided the visualization by t-SNE on CIFAR-100 in the global rebuttal file. As shown, it can be observed that c... | Summary: This paper addresses the challenge of learning fine-grained embeddings from coarse labels, where detailed distinctions required for fine-grained tasks are often lacking. The authors propose a novel method that embeds visual embeddings into a hyperbolic space and enhances their discriminative ability using a hi... | Rebuttal 1:
Rebuttal: ## Thank you for the positive comments. Below please find our responses to some specific comments.
### Comment_1:
> *Limited analysis of failure cases*
Thanks for this suggestion. We fully concur that a deep dive into the scenarios where our model underperforms can provide valuable insights a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments. We provide point-to-point responses to each reviewer, as well as a supplmentary PDF for some visualization results.
Pdf: /pdf/5ee7862cbc57d4c6968ec57cd5a690899734a3aa.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models | Accept (poster) | Summary: This paper investigates the task of data pre-selection by learning a better representation from the joint feature space of both vision and text in an unsupervised manner. The paper focuses on training text prompts to extract joint features with enhanced representation, specifically with the BLIP-2 parameters k... | Rebuttal 1:
Rebuttal: Dear reviewer X1Uz,
We would like to thank you for your valuable comments. Below is our response to your questions:
Q1: “BLIP-2 is pre-trained with prompt and image together. This explains why using only image features yields poor performance. Consequently, the evidence presented does not convin... | Summary: The paper addresses data pre-selection (akin to active learning) problem using the highly successful vision-language models (VLMs) of late. In relation to existing approaches, the proposed approach has a few advantages, e.g., no need to have a small initial set of labeled data, no need to have multiple rounds ... | Rebuttal 1:
Rebuttal: Dear reviewer uJMt,
We would like to thank you for your valuable comments. Below is our response to your questions:
Q1: “One important ablation that could be useful is running the approach without the learnable prompts/contexts. What I mean is updating only and but not employing in Algorithm 1. ... | Summary: This paper presents an unsupervised approach for data preselection, which aims to select instances for labeling from an unlabeled dataset in a single pass. The authors leverage the text features in multimodal models, specifically BLIP2, to enhance the representation for data preselection. They argue that a wel... | Rebuttal 1:
Rebuttal: Dear reviewer soCe,
We would like to thank you for your valuable comments. Below is our response to your questions:
Q1: “Could you compare your proposed method when built on top of CLIP as well? Including a comparison with CLIP as a baseline would provide valuable insights into the effectiveness... | Summary: The paper studies the problem of data pre-selection, which aims to select instances for labeling from an unlabeled dataset to enhance performance for downstream tasks with a limited annotation budget. The authors suggest that combining visual and textual features in a joint space can result in a better represe... | Rebuttal 1:
Rebuttal: Dear reviewer ZwSf,
We would like to thank you for your valuable comments. Below is our response to your questions:
Q1: “Is it fair to use the prompt for CLIP model to assess the zero-shot performance of the BLIP-2 model? It's supposed to train the prompt for BLIP-2 from scratch.” [Question cont... | Rebuttal 1:
Rebuttal: We extend our gratitude to all the reviewers for their meticulous comments and constructive suggestions. We are heartened by the reviewers' keen interest in our work and their recognition of its novelty, both in terms of the task and methodology.
We wish to emphasize the significance of our appro... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a novel method to perform data preselection for the task of image classification. Data preselection refers to the task of finding the images for annotating labels and then used for training. The paper builds around the powerful visual-language model BLIP, and proposes learnable prompts as i... | Rebuttal 1:
Rebuttal: Dear reviewer ucTW,
We would like to thank you for your valuable comments. Below is our response to your questions:
Q1: “The decision to annotate 200 images per benchmark (LINE 265 - 273) seems arbitrary. Why this number? It would be great if the number can be varied and then plot the model perf... | null | null | null | null | null | null |
Regret Minimization via Saddle Point Optimization | Accept (poster) | Summary: This paper focuses on regret minimization in sequential decision-making under uncertainty. It introduces the average-constrained decision-estimation coefficient, a saddle-point objective that characterizes the worst-case regret, enabling optimization of the information trade-off directly by the algorithm. More... | Rebuttal 1:
Rebuttal: Thank you for the review. We provide clarifications on the contributions and impact below.
**Importance and impact of the new algorithm:**
- Anytime algorithms are of great importance in practice as the horizon is often not known in advance. A theoretically sound anytime version of the E2D algori... | Summary: The authors consider a framework to solve the bandit problem by means of the minimax problem, which has been rapidly developed in recent years.
Among them, the paper focuses on the decision-estimation coefficient (DEC).
The DEC was developed in a series of studies by Foster+ and is known to characterize the up... | Rebuttal 1:
Rebuttal: Thank you for the review and questions. We address the points raised below:
**High-dimensional bandits:**
- We believe that the average-constrained DEC results do allow us to directly obtain results for high-dimensional linear bandits as shown in Remark 2. We will happily improve the clarity of ... | Summary: This paper studies the estimation-to-decisions framework for sequential decision-making problems with structured observations. They propose the ANYTIME-E2D algorithm, improving precious approaches with a novel bound for linear bandits. Numerical simulations are presented to show the performance of their algori... | Rebuttal 1:
Rebuttal: Thank you for the review and valuable comments. We address the points raised below:
**Significance and originality compared to prior work:**
The average-constrained DEC objective and the corresponding anytime analysis has several advantages compared to the approach proposed by Foster et al:
- Ou... | Summary: This paper focuses on regret minimization in sequential decision-making through min-max optimization. The authors introduce an anytime variant of the estimation-to-decisions algorithm that utilizes the average-constrained decision-estimation coefficient. The proposed algorithm is shown to effectively balance e... | Rebuttal 1:
Rebuttal: Thank you for the review and valuable comments. We address the points raised below:
**Contributions compared to the existing literature:**
We discuss the relation to the existing E2D literature in detail in Section 3.1. While our results build on the framework by Foster et al (2021; 2023), we ma... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their time and valuable inputs.
We respond to each review individually below.
We further point out that our submission contains an experimental evaluation of the proposed approach on two simple test cases in Appendix E. While the focus of the paper is on d... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Test-Time Distribution Normalization for Contrastively Learned Visual-language Models | Accept (poster) | Summary: This paper reveals a mismatch between the pre-training objective of contrastively trained vision-language models and their downstream usage. The authors propose Distribution Normalization (DN) to solve this problem. The results on a wide variety of downstream tasks show the effectiveness of the proposed metho... | Rebuttal 1:
Rebuttal: ### 1, Add TPT baseline
Thanks for providing the related work, and we will include the comparisons in a revised version. Here are the results of TPT in our setting and a comparison of our results. (top1/top5). We found that our CLIP+TTA+DN* achieves a comparable top-1 accuracy and a higher top-5 ... | Summary: This paper proposes distribution normalization (DN) for contrastively trained vision-language models. The idea is motivated by an analysis of the InfoNCE loss. The authors identify that the common practice of taking dot product for zero-shot inference is only a zero-order approximation of the InfoNCE loss. The... | Rebuttal 1:
Rebuttal: ### 1, Two more baselines missing
Thanks for providing the related work, and we will include the comparisons in a revised version. Here are the results of TPT in our setting and a comparison to our results. (top1/top5). We found that our CLIP+TTA+DN* achieves a comparable top-1 accuracy and a high... | Summary: CLIP is trained using InfoNCE loss where positive and negative pair alignment is done during training. However, at inference, we simply take the dot product with text embeddings which is not the optimal/similar to the way pretraining was done. Authors propose to rectify this inference and training objective mi... | Rebuttal 1:
Rebuttal:
### 1, proposed approach does not work well on MSCOCO retrieval image to text.
In the setting of mscoco image to text, while DN does not perform well with CLIP-B32, it has shown effectiveness with other CLIP variants such as CLIP-B16 and CLIP-L14 as follows. A method's performance may depend on t... | Summary: This paper addresses the problem of retrieval and classification accuracy using vision-language representation. The authors claimed that there is a mismatch between training objectives and inference-time operation. Specifically, InfoNCE used negative information but test time only use positive similarity scor... | Rebuttal 1:
Rebuttal: ### 1, The claim that 0.1 is small is not convincing
In our paper, we claim that the value of the referred expression is smaller than 0.1 (or even much smaller than 0.1 according to the reviewer) and therefore second and higher order terms are negligible. The result of this simplification is to ma... | Rebuttal 1:
Rebuttal: We appreciate the reviewers’ agreement on the efficiency and wide applicability of our proposed DN, and reviewer XtRE for recognizing the effectiveness of DN. There were several issues pointed out by multiple reviewers which we will address here in the general response.
### Performance Gain:
Fi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focus on the misalignment of training and testing of CLIP model. Specifically, CLIP is trained with an InfoNCE loss containing both positive and negative samples, while tested lack negative samples information. They reveal that the common downstream practice of taking a dot product is only a zeroth-... | Rebuttal 1:
Rebuttal: ### 1, The necessity of performing this task at test time is questionable.
We are not sure about what “this task” refers to, and would be happy to give more explanations if the reviewer can please clarify this point. However, just to clarify as best as we could understand the comment, DN is condu... | null | null | null | null | null | null |
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision | Accept (poster) | Summary: The paper proposes the Interactive Multi-Fidelity Learning (IMFL) framework to develop small domain-specific Large Language Models (LLMs) under limited annotation budgets. Specifically, IMFL balances low-fidelity LLM annotations and high-fidelity human annotations to maximize model performance. Experiments on ... | Rebuttal 1:
Rebuttal: Dear reviewer ogFx,
Thank you very much for taking the time to engage with our paper thoroughly, and constructive comments. We’re happy to hear that you thought our work is practical and effective, addressing the ground challenges of deploying LLMs in domain-specific tasks. Please find our respon... | Summary: This paper studies the problem of how to fine-tune a relatively small, domain-specific language model under the constraints of limited annotation resources. The authors propose an Interactive Multi-Fidelity Learning (IMFL) approach. This approach aims to optimize the performance of the fine-tuned language mode... | Rebuttal 1:
Rebuttal: Dear reviewer YvhV,
Thank you very much for your detailed and thoughtful comments. We are glad to hear that you found the paper interesting, highly meaningful, and innovative, and our experiments are sound and strong to verify the effectiveness. Below we address the constructive comments and feed... | Summary: This article is devoted to a new algorithm for development of small domain-specific LMs under limited annotation budgets. one of the main ideas of the algorithm is to use as data annotators a combination of a Human and LLM (Large language models). The authors also proposed two innovative developments in their a... | Rebuttal 1:
Rebuttal: Dear reviewer EwLY,
Thank you for your detailed review and suggestions to improve the paper. We are glad to hear that you found that our idea is new, and effective and our designs are innovative and useful in broader tasks. Below we address the concerns and questions raised in the review.
> **... | Summary: This paper introduces an algorithm to fine-tune LMs for domain specific tasks under a certain budget constraint. They use a mix of human and a high-fidelity LM for annotations. They fix the annotation budget for each and introduce an algorithm to sample from the unannotated pool and distribute it between human... | Rebuttal 1:
Rebuttal: Dear reviewer Ltph,
Thank you very much for the constructive comments and feedback. We are happy to hear that the reviewer found our method effective, and our results are well explored and discussed. Below, we have tried to address all of your feedback and questions. Please take a look and let u... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you all for taking the time to review our paper and we sincerely appreciate all the feedback. In particular, we feel encouraged to see that the reviewers find that
- **The topic of our paper is important, interesting, and practical**: “This paper is interesting and highly ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Delegated Classification | Accept (spotlight) | Summary: This work provides a framework for incentive-aware delegation of machine learning tasks. It considers a principal-agent game, where the principal can spend a limited budget on the outsourcing the training of a machine learning model, with the hope of getting the most accurate model, and the agent provides a ma... | Rebuttal 1:
Rebuttal: Thank you for the encouraging review and positive feedback! We address your questions below:
> In Section 2 Lines 110-113, the authors discuss that $h_n$ is a stochastic quantity. It would be helpful to add a sentence here that $h_n$ is distributed according to a distribution that depends on $n$ ... | Summary: The paper introduces an interesting problem, and provides interesting theoretical results based on a connection to classical result on statistics. The problem setup follows the standard principal-agent problem with moral hazard, and the principal commits to a contract to incentivize the agent behaving favor of... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and insightful questions!
> What happens if the principal does not have a budget constraint?
In the non-budgeted setting, and when MLRP holds, min-pay optimal contracts take on a rather extreme form: a single non-zero payment that can be arbitrarily large an... | Summary: This paper presents a novel theoretical principal-agent framework for examining the incentive-aware delegation of machine learning tasks. In this context, a principal can design a monetary contract to stimulate an agent to exert private efforts towards training a classifier. In the proposed framework, the agen... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and insightful feedback!
> My main concern regarding the paper lies within the technique results. It is noted that many of the characterizations about the optimal contract align closely with, or can be derived from, recent research on the algorithmic principal-ag... | Summary: The paper studies the problem of a decision maker (the principal) delegating the task of training a machine learning model to an agent. Both parties are strategic, and the principal must commit to a contract to encourage the agent to invest effort (e.g., labeling training samples). The authors consider the p... | Rebuttal 1:
Rebuttal: Thank you for the feedback and the great suggestions! We address your questions below:
> Figure 1: at this point it's not totally clear to me what role $m$ plays in the model. In particular, how should $m$ be chosen (either by nature or by the principal), and how does the choice affect the perfor... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features | Accept (poster) | Summary: This work proposes a defense method against backdoor attacks. The proposed method involves inserting a trainable transformation layer inside a backdoor model while keeping other model parameters fixed that is supposed to purify the poisonous features while allowing benign features to pass without modification.... | Rebuttal 1:
Rebuttal: Thank you for your dedicated time reading our paper and providing us with your meticulous review. Your insightful questions and concerns are greatly appreciated. Please inform us if these responses effectively address all your inquiries or if there are additional questions you'd like to raise.
**... | Summary: This paper proposes propose a backdoor defense method by inserting a learnable neural polarizer into the backdoored model as
an intermediate layer, in order to purify the poisoned sample via filtering trigger information while maintaining benign information. To more effectively remove backdoor, this paper leve... | Rebuttal 1:
Rebuttal: Thank you for your valuable time in reading our work and positive review on our techniques and experimental results. We appreciate your insightful questions and comments.
**Q1. Neural Polarizer (NP) needs to reverse the backdoor trigger and the target label, which is similar to Neural Cleanse. Th... | Summary: The paper proposes a lightweight and effective backdoor defense by inserting a trainable neural layer block. Without modifying original backdoor model, the proposed method can remove backdoor behaviors by filtering poisoned features via the trainable neural block. The authors conduct sufficient experiments to ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for carefully reading our paper. We are encouraged by the positive comments of simple but effective method and sufficient experiments.
**Q1. More results using more network architectures, e.g., vgg. Does this proposed method work for a network without batch normali... | Summary: This paper proposes a novel defense method to filter trigger information from poisoned samples. It inserts a learnable intermediate layer (called neural polarizer) into the backdoored model, and proposes bi-level optimization solution to approximate perturbation and target label. Experimental results demonstra... | Rebuttal 1:
Rebuttal: Firstly, we would like to show our sincere appreciation to the reviewer for the valuable time and constructive comments on our submission.
**Q1. The variants NPD-TP (assume the target label is known) and NPD-TU (assume having full access to benign samples) relax the limitation, and is kind of unf... | Rebuttal 1:
Rebuttal: # Common Response
We sincerely thank all reviewers for their time and constructive comments.
**Q1. Analysis of the mechanism and novelty of our neural polarizer defense (NPD) method.**
**R1：** We aim to present **a systematic analysis** about NPD's mechanism and novelty covering **three critical... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Auditing for Human Expertise | Accept (spotlight) | Summary: While humans and machines oftentimes make differing decisions, it's unclear whether humans make these decisions based on extra factors or information unavailable to machines. To understand this situation, the paper proposes a statistical test to determine if expert predictions are independent of the labels, wh... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Our responses are provided below.
**In response to**
> "What are other choices for the function F, and would they be equally valid?"
As noted in Section 3, our test is valid for any choice of $F(\cdot)$. However, the *power* of the test will certainly depend on the c... | Summary: This paper proposes a statistical framework for measuring whether, in the context of algorithmic predictions, human experts incorporate valuable information in their decision making that is unknowable to the algorithm. The authors formalize this question as a simple hypothesis test: “are human expert predictio... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Our responses are provided below.
**In response to**
> "could your test (or a small modification) detect if human experts are using additional information in a negative way?''
1. Yes, this algorithm could absolutely be used in such a setting, though note that we req... | Summary: This paper proposes a method for determining whether a human expert is usefully using outside information that a model does not incorporate in order to make decisions. The goal is to test whether complementarity, humans working with models, is possible for a given task. The paper sets forth an algorithm, Exper... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Our responses are provided below.
**In response to**
> "The primary weakness of the paper…is that the problem setup focuses on variables, or features, rather than also considering the functional form for the prediction itself."
**We reproduced our original case study... | Summary: This paper proposes a hypothesis-testing approach to identify whether a set of predictions made by a human expert uses additional information that is conditionally independent from the input covariates. The paper provides theoretical guarantees for the test in a general setting and asymptotically. The proposed... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Our responses are provided below.
**In response to**
> "Can the test help improve decision outcomes? Typically, the primary goal in human-AI decision-making is to achieve complementarity (e.g., as discussed in [1]), particularly by leveraging the complementary skills ... | Rebuttal 1:
Rebuttal: We thank all four of the reviewers for their thoughtful and constructive feedback. We address feedback provided by more than one reviewer in this comment, with additional responses to individual reviewer concerns provided in-line below each review.
# High dimensional feature spaces
> **7Ctt:** "T... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams | Accept (poster) | Summary: The paper introduces a novel learning approach designed for robot teams to acquire a preferred policy for collaborative task completion using human expert-generated data. Additionally, the method enables the robots to gain an understanding of the theory of mind of each individual agent within the team.
S... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and positive feedback. Please find below a point-by-point response to your comments and questions:
### **Weaknesses**:
● **Complexity of Tasks**: First, we believe the complexity of our tested domains, particularly in hard scenarios of the PCP ... | Summary: The paper introduces a model, called MixTURE, for learning multi-agent collaborative policies from human demonstrations, addressing the challenges of coordinating heterogeneous agents in complex tasks. MixTURE leverages a mutual information maximization-based differentiable communication module to reason about... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and positive feedback. Please find below a point-by-point response to your comments and questions:
### **Weaknesses**:
● **Discussion on MixTURE’s Applicability to Continuous Domains**: Generalization to continuous state and action spaces will ... | Summary: This paper proposes a Mixed-Initiative Multi-Agent Apprenticeship Learning (MixTURE) framework to teach a team of agents using demonstrations provided by individual human experts. It learns both a cooperative policy for each agent and the inter-agent communication policy for each agent. The proposed MixTURE, i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and positive feedback. Please find below a point-by-point response to your comments and questions:
### **Weaknesses**:
● **Justifying the Learned Communication**: Please note that MixTURE operates under a CTDE paradigm: the differentiable commu... | Summary: This paper proposes a learning framework for multi-agent coordination using communication from human expert demonstrations: Mixed-Initiative Multi-Agent Apprenticeship Learning (MixTURE). A human expert teaches a team of robots to collaborate on a task by demonstrating actions for every robot, while the robot ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and positive feedback. Please find below a point-by-point response to your comments and questions:
### **Weaknesses**:
● **Assumption Regarding Human Expert’s Access to the Joint Observation of All Robots**: We concur that a human's decision-ma... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes a Multi-Agent Learning from Demonstration approach for learning multi-agent policies for collaborative tasks. The approach learns from human demonstrations of the joint policy, but does not require demonstrations of inter-agent communication, which is learned during training via online inter... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and positive feedback. Please find below a point-by-point response to your comments and questions:
### **Weaknesses**:
● **Sensitivity to Different Moving Objective Parts**: The initial two components of the objective – the GAIL loss and the PP... | null | null | null | null | null | null |
Training Neural Networks is NP-Hard in Fixed Dimension | Accept (poster) | Summary: The authors analyze the fpt of training NN. Their main result is that finding a global minimum is hard even for fixed dimension.
Strengths: I like the direction of studying the FPT of training NN. The reductions are new to the best of my knowledge and could find further applications.
Weaknesses: The autho... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
Indeed our paper is not concerned about generalization.
The goal is to understand the complexity of training in fixed dimension (which was an open question).
Note that hardness for few dimensions implies hardness for arbitrary dimensions (as we explained) and ... | Summary: The authors show that learning a 2-layered neural network with ReLU activation function, in an underparameterized regime, is NP-hard when the training error is 0. Specifically, they highlight that such learning cannot be accomplished in time complexity dependent on $k^{f(d)}$, where $k$ is the number of neuron... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
We will fix the issue with the variable $\ell$.
Yes, "full-dimensional" means $d$-dimensional. We will make this precise.
Yes, a breakpoint lies on only one hyperplane. We will fix this. | Summary: This paper mainly studies the complexity of training two-layer ReLU networks when they are not over-parameterized. Specifically, the authors prove that training a two-layer ReLU network to zero or arbitrary loss value is NP-hard when the input dimension $d\geq 2$. This result answers an open question (Question... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
Our goal was to settle the complexity of the training problem in the general case (without further assumptions on the input), which turned out to be hard. We agree with you that this is a worst-case result, which does not necessarily reflect practical conditio... | Summary: This paper focuses on two layer neural networks with ReLU or linear threshold activations. The authors show that considering the input dimension (dimension of the data as a constant) equal with 2, there is no polynomial algorithm with respect to the number of data (n) and hidden nodes (k, k<n) that decides whe... | Rebuttal 1:
Rebuttal: Thank you very much for your review.
We agree that the algorithm for the convex case is unlikely to be useful in practice.
From a theoretical side, this result is interesting since it gives a partial positive answer to the open Question 2.
It is thus a step towards resolving this question and sho... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Video-Mined Task Graphs for Keystep Recognition in Instructional Videos | Accept (poster) | Summary: This paper aims to recognize the keysteps in instructional videos using automatically mined task graphs. These task graph is automatically discovered from a set of narrated instructional videos and contains all keysteps in a given vocabularly, i.e. it is not limited to a single task. This allows dependences be... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments and insightful feedback.
**Q1. Lack of clarity in supervision used vs. prior work**
This seems to be a misunderstanding. All the baselines use the same level of supervision. Specifically, task graph construction does not use ground-truth keys... | Summary: In this work, the authors propose to address keystep recognition in instructional videos. To achieve this goal, they attempt to build a task graph from videos, which show how keysteps are related to each other. Based on this graph, one can further update the preliminary keystep assignment, when the initial pre... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging comments and insightful feedback.
**Q1. The task graph is pre-computed offline or built online? I assume, it should be built offline, according to the unannotated dataset of narrated instructional videos. Moreover, when working on another dataset, the ... | Summary: This paper addresses the problem of key-step recognition and localization in instructional videos by learning and leveraging a probabilistic task graph. The proposed method first localizes key-steps mined from text sources (such as wikihow) in videos by measuring the similarity between visual-narration feature... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments. Our responses show where multiple requests from the reviewer were addressed in the submitted paper. Also please note the concurrent work policy of NeurIPS re: [85].
**Q1. Comparison with [85]**
Paprika [85] is a contemporaneous work per the conference guideline... | Summary: The paper considers the task of "keystep" recognition. Keystep is one of the N sub-tasks that are performed sequentially to achieve a goal / task. Keysteps can have causal dependencies. Prior work has the following limitations:
(1) only considers each keystep in isolation, without considering the overall task... | Rebuttal 1:
Rebuttal: We thank the reviewer for encouraging comments and insightful feedback.
**Q1. Do we expect that a single threshold value across an entire dataset is a reasonable choice? Alternately, is there a way to have continuous bayesian approach incorporating the evidence and the prior for all samples?**
T... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments.
**Three reviewers lean towards acceptance**. We believe our responses below clarify the items raised in the reviews, which include questions about metrics and a concurrent paper [85] (*vrnW*) and a possible misunderstanding about supervisi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on the procedural activity understanding task. Considering that the feature-keystep matching in current methods is independent and fails to encapsulate the rich variety, the authors propose a video-mined task graph as a prior to update the preliminary keystep assignments.
Strengths: 1. Usin... | Rebuttal 1:
Rebuttal: We thank the reviewer for encouraging comments and feedback.
**Q1. What is the cost for building the video-mined graph? Will this graph leads to great overhead?**
The overhead is minimal since the only extra computation is counting transitions given similarity scores followed by averaging. Ther... | null | null | null | null | null | null |
Temporally Disentangled Representation Learning under Unknown Nonstationarity | Accept (poster) | Summary: This paper addresses the problem of identifying latents representations from sequential data which is stationary within contexts, and non-stationary between contexts. Existing work has either conditioned on observed auxiliary variables that indicate which context one is in (e.g. time contrastive learning and t... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewer for the informative feedback. Please see our point-to-point response below.
> Weaknesses: ... But I'm not sure that there are any practitioners who would be able to judge whether those assumptions hold in their data ...
Firstly we would like to mention t... | Summary: This paper gives identifiability results in a new setting, along with an estimation method and experiments validating the estimation method.
There are existing identifiability results in the nonstationary setting if you assume that the auxiliary variables are observed. There are other existing identifiability... | Rebuttal 1:
Rebuttal: We appreciate that you read our paper very carefully and your informative feedback, which has helped improve our paper. Below please see our response to your concerns.
>Q1: In the Cartpole experiments, why do you only provide Accuracy and MSE A metrics for your method and not for baseline methods... | Summary: This paper presents identifiability results that handle nostationary time-delayed causally-related processes without auxiliary variables. In addition, the authors propose NTDC, which is a neural network that is based on their identifiability results. The method is evaluated on a few tasks in comparison to a fe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review and valuable feedback. We respond to your concerns point-by-point below.
> Q1.1: One of the main weaknesses of the paper is its presentation. In particular, a lot of jargon is used without properly introducing the terms. For instance, domain indices... | Summary: The study focuses on unsupervised representation learning for sequential data with time-delayed causal influences. Identifiability results for disentangling causally-related latent variables have been established in stationary settings using temporal structure. However, existing work only partially addresses t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful review and valuable feedback. We respond to your concerns point-by-point below.
> Q1: Could you provide more motivational examples of the model structure in Figure 1(d) other than the mouse movement example?
Here are some examples with explanation:
1. For ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Nonparametric Identifiability of Causal Representations from Unknown Interventions | Accept (poster) | Summary: The paper discusses the task of identifying causal variables from high-dimensional observations under non-parametric mixing functions and causal mechanisms. This is done under the assumption of single-node, perfect interventions being available for all causal variables, as well as distinct paired perfect inter... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our work. We address your questions and comments below.
___
> Restrictiveness of the assumption of access to all single-node perfect interventions.
While we agree that this is a strong assumption, it has previously been shown to be necessary even for more rest... | Summary: The paper studies the problem of inferring causal relationships between $n$ latent variables through observations under a mixing function. Given data $X$ from multiple environments (each of which corresponds to an unknown perfect atomic intervention), where $X$ is the observation of the latents under a fixed m... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our work. We will fix the typo and add an explanation for the CRL abbreviation. We answer your main question below.
> There are a lot of assumptions (which is okay, if they are well-justified and discussed). Can you explain or discuss why each of them is necessar... | Summary: This paper proposed to identify the latent causal representations and their underlying causal structure, which is a very challenging and interesting problem. The \sim_{CRL} is introduced to describe the equivalent class up to elementwise operations and permutation, which is sufficiently meaningful for practica... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our work. We address your two main questions and other comments below.
### Main questions
> “Intuitively, it appears that with paired interventional data, we can identify the latent representation with a unique permutation. For example, if we intervene to place ... | Summary: This paper gives an identifiability result in a setting that is relevant to causal representation learning, where we wish to infer latent causal variables and their causal graph from high-dimensional observations. They work in a setting that is more general than prior work that relies on, for example, weak sup... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our work. We address your questions and comments below.
> “The main text of the paper does not have an experiment section.” “There is no estimation method or experiment results. Other identifiability papers often contribute an estimation method [...], perform dis... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback and time in reviewing our work. All reviewers recommend acceptance, rating the soundness and contribution of our work as good and its presentation as good or excellent.
We are pleased to read that our paper is “well-motivated and interesting” (`E44d`), ”... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models | Accept (poster) | Summary: There are powerful ODE solvers to speed up the sampling process of diffusion models. Despite being quick, ODE solvers do not usually reach the optimal quality achieved by SDE solvers which are however slow. To tackle this problem, the paper proposes SEEDS, which is based on Exponential Integrator in the stocha... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful lecture of our work. Below a discussion to the raised questions, which will be included in the paper.
> Would you please include the connection between the proposed method SEEDS-1/2/3 and the stochastic Runge-Kutta method properly combined with Exponential ... | Summary: This paper proposes an off-the-shelf (i.e., no further training) few-step sampler for diffusion probabilistic models (DPMs). By isolating linear terms in the exact solution of diffusion SDEs and using a change-of-variable method, the proposed sampler, SEEDS, simplifies the integrals and can approximate the sol... | Rebuttal 1:
Rebuttal: We thank and agree with reviewer daVP on the poor choice of words for presenting the well-known the variation-of-parameters formula and we remove all mentions of novelty in this point giving the impression of overselling our work. Instead, we highlighted in the general rebuttal response what we fi... | Summary: The paper proposed an efficient method for solving diffusion SDEs with some convergence guarantee. Experiments are done to verify their claims.
Strengths: The experiments show SEEDS-3 achieves optimal results with minimal NFE on many data sets.
Weaknesses: 1. Authors claim they have found a novel representa... | Rebuttal 1:
Rebuttal: We thank reviewer MHs8 for their comments. We have clarified in a general rebuttal response what building principles in SEEDS are our own contributions. In particular:
- Building principle of eq.5 will not be presented as novel in the revised version
- The truncated Itô-Taylor expansion, the SETD ... | Summary: This paper proposes a sampling method that achieves 3 to 5 times faster computation by exploiting the semi-linearity in the time-reversal stochastic differential equation (SDE). It analytically computes the linear part and reaches optimal quality sampling. This sampling method demonstrates comparable results t... | Rebuttal 1:
Rebuttal: We thank reviewer CfgK for their effort reading our work. We have clarified in a general rebuttal response what building principles in SEEDS are our own contributions. In particular,
- eq.5 will not be presented as novel in the revised version
- The change-of-variables used in the EDM case (Prop. ... | Rebuttal 1:
Rebuttal: ## General Rebuttal Response
As reviewers 1-4 pointed out, and with whom we fully agree, the widely known variation-of-parameters formula is not a contribution of ours and we will modify this in our manuscript. In our effort to clarify the building principles of SEEDS, we unintentionally stressed ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims to accelerate the SDE solvers through exponential integrators. The authors compute the linear part of the SDE analytically, employ a more accurate interaction of the stochastic part. Experimentally, the proposed method improves over the previous ODE solvers.
Strengths: - The paper proposes to ... | Rebuttal 1:
Rebuttal: We thank reviewer nfDR for their effort reading our work.
> Is this work a straightforward application of prior works ([1], [2]) to SDEs?
We have clarified in a general rebuttal response what building principles in SEEDS are our own contributions. In particular:
- Building principle of eq.5 wil... | null | null | null | null | null | null |
Localized Symbolic Knowledge Distillation for Visual Commonsense Models | Accept (poster) | Summary: This paper proposes a framework that can generate global, local, and dynamic descriptions. ChatGPT can generate question-and-answer pairs containing specific regions or descriptive phrases with the proposed tricks. A critic model is trained to filter the generated data. Finally, the localized corpus is used to... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that the **workload is impressive** and **achieves promising zero-shot performance for three localized visual reasoning tasks.** We now address the reviewer's comments in the following section.
**1. The proposed framework is more like engineering work. The ... | Summary: This paper aims to build a instruction following model for **localized** visual commonsense reasoning. The work differs from existing works in that it is able to reason about localized image regions with boxes, without using complicated referring expressions. The paper first collects data by distilling LLMs, i... | Rebuttal 1:
Rebuttal: We thank the reviewers for acknowledging that “the dataset method collection is well designed” and “effectiveness of critic filtering is well-analyzed”. We address the concerns raised by the reviewers.
**Bounding Box Size Distributions and Performance Analysis**
Figure 2 in the rebuttal shows th... | Summary: In this paper, authors argue that for multimodal LLMs, the previous input formulation is too rigid: either needs to specify the region models should focus on, or brings a verbose object description to refer to the region. A more natural referring expression strategy can help models better understand where the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for positive feedback that we explore an interesting problem of localized commonsense reasoning that **previous works under-explore**, and acknowledge our novelty (**the first work about automatically acquiring visual commonsense**). The reviewer is **glad to see the distille... | Summary: The paper introduces Localized Visual Commonsense models that enhance vision-language (VL) models by allowing users to specify specific regions within images. The authors train their model by collecting commonsense knowledge from a large language model (LLM) using global literal image descriptions and automati... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and valid concerns regarding how to guarantee the dataset quality, which we address below.
**1. How does this Dataset reflect knowledge? How do the authors define what are knowledge-related questions?**
In our work, we investigate extracting commonsense kno... | Rebuttal 1:
Rebuttal: We thank the reviewers for positive comments acknowledging that our work supporting region-level reasoning is "important and interesting tasks for many applications" (reviewer Cm2R), and "represents a novel solution to a more general multi-modal model" (reviewer Ta54). Reviewer UzVU notes that "th... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work presents a localized visual common sense model that can support region-level inputs for knowledge reasoning. Specifically, the proposed methods prompts large language models to collect commonsense knowledge from global image descriptions and local descriptions. A critic classifier trained over a smal... | Rebuttal 1:
Rebuttal: We thank the reviewer for agreeing that support for **region-level references and reasoning is important for many applications**, and our experiments and ablations are comprehensive that include human evaluations of generative models. We address the comments raised by the reviewer below.
**1. A n... | null | null | null | null | null | null |
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training | Accept (poster) | Summary: The paper proposes a pruning-based defense method against backdoor attacks on FL. Specifically, the malicious clients are limited to updating model parameters within an isolated subspace, which reduces the attack surface (or "poison-coupling") for malicious clients as well as the computation/communication cost... | Rebuttal 1:
Rebuttal: We thank the reviewer for very detailed comments.
# C1 (Problematic dropbox link)
We have stopped sharing the problematic Dropbox link. Apology for this un-intensional error.
The original submission used both the anonymous github URL (abstract) and the dropbox URL (page 4). Our intention of sha... | Summary: The paper proposes a backdoor defense. The method is based on robustly estimating a sparse parameter subspace which is used to restrict updates. Each client will vote for a set of parameters it considers "important" to update, and non-important neurons will be frozen for that iteration. The paper shows that th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very encouraging review and helpful comments. We have provided some security analysis in the supplementary material (see Appendix A.4). A formal verification of the security guarantee is an important research result by itself. It is on our future research agenda. Espe... | Summary: Federated learning (FL) is a promising approach for privacy-preserving ML applications. However, it is also vulnerable to backdoor attacks. Although some pruning-based methods have been proposed to defend against backdoor attacks, the authors note that it is difficult to prune malicious channels (via Lipschitz... | Rebuttal 1:
Rebuttal: We thank this reviewer for the constructive, encouraging, and helpful comments. We below respond to the three weaknesses:
# W1 (Strict assmumption on Lockdown protocal)
*The answer is NO.* Lockdown defense does not assume that malicious clients have to adhere to the lockdown training protocol. Co... | Summary: The paper addresses the vulnerability of federated learning (FL) to backdoor attacks and the limitations of existing defense solutions in resource-constrained scenarios. It introduces "Lockdown", an isolated subspace training method to counter the poison-coupling effect present in traditional pruning-based def... | Rebuttal 1:
Rebuttal: Thanks for the review comments. Below we try to address the reviewer's concern.
# About the poison-coupling effect (Q1)
**(Poison coupling effect)** We define the poison-coupling effect based on the empirical observation that the parameters used for poisoning by a small percentage of compromised ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Causal Effect Regularization: Automated Detection and Removal of Spurious Correlations | Accept (poster) | Summary: In this paper, the authors proposed a causal effect regularization technique - CausalReg, which can effectively identify and remove the spurious but unknown attributes. CausalReg is robust to no-identifiable issues, finite sample error, and noise. The experimental results demonstrate the superior performance o... | Rebuttal 1:
Rebuttal: > **W1: Precision and Recall experiment is missing: Some experiments should be done to reflect the precision and recall of the discovery of spurious and causal attributes.** (part of the remark left out for brevity)
A: We thank the reviewer for suggesting this experiment. However, classifying an ... | Summary: The authors propose to reduce the effect of spurious attributes on the classification of, say, images, by first estimating the true causal effects and then regularizing the effect of spurious attributes to be closer to the estimated effects. They show, in a theoretical scenario, that this approach is sound and... | Rebuttal 1:
Rebuttal: > **W1: The proof sketches do not currently give any intuition about the shape of the proofs, they simply restate what has to be proved.**
A: We thank the reviewer for pointing this out. We will update the paper to include a more technical proof sketch.
> **W2: It is unclear what the benefit ... | Summary: The paper proposes a new method to detect and remove spurious attributes. First, the paper gives the sufficient conditions that are needed for estimating the causal effects with theoretical proof. To detect the spurious attribute, the proposed method estimates the causal effect based on a deep learning-based e... | Rebuttal 1:
Rebuttal: > **Q1: Add discussion or comparison with other related works**
A: We thank the reviewer for pointing this out. Given limited time and computation constraints, we have added results on three new baselines (JTT[34], EIIL[31], and IRM [35]) on two datasets – Syn-Text-Unobs-Conf and MNIST34 dataset... | Summary: The authors study the problem of learning under the presence of spurious correlations, given multiple attributes, some of which may be spurious. They first propose three causal graphs to represent the data generating process, and show that two them allow for identification of the causal effect of the attribute... | Rebuttal 1:
Rebuttal: > **W1: The authors demonstrate theoretical guarantees for two causal graphs shown (DGP-1 and DGP-2).** (part of the remark left out for brevity)
A1: We politely disagree with the reviewer's comment. In summary, our method does not assume the knowledge of underlying DGP and thus is invariant to a... | Rebuttal 1:
Rebuttal:
> **Our method does not assume knowledge of the underlying DGP. We describe three commonly occurring DGPs only to illustrate identification properties** (Reviewer nk8u, quUc):
**Identifiability of causal effect**: We have listed different DGPs (data-generating processes) common in the real worl... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Strategic Behavior in Two-sided Matching Markets with Prediction-enhanced Preference-formation | Accept (poster) | Summary: This paper studies the matching market with returning agents and
proposes an important strategic behavior that returning agents can
attack future predictions by distort short-term interactions. The
authors formulate the systems as (repeated) three phases and study a
simplified setting to derive informative con... | Rebuttal 1:
Rebuttal: We thank Reviewer UhQP for their feedback and question. We are grateful both for their appreciation of the novelty and importance of the topic and for their concern about the simplicity of the model.
Regarding the question, we apologize for the lack of clarity surrounding the link between adversa... | Summary: This paper is looking at problems that arise in two sided matching markets, where preferences of the agents are being informed by various prediction mechanisms. In particular, the paper makes an argument that the existence of a predictive model used by agents to inform their preference has interesting strateg... | Rebuttal 1:
Rebuttal: We thank Reviewer EhdA for their supportive feedback and insightful questions! We are especially grateful for their engagement with both the paper and supplementary material.
To address the first question, as mentioned by the reviewer, DA poses additional challenges as we would need to consider m... | Summary: This paper introduces a fresh perspective on attacks called *adversarial interaction attacks*, which can occur in markets that involve both a returning and a non-returning side. In such markets, agents' preferences are shaped by prediction mechanisms. While previous research has examined strategic behavior sep... | Rebuttal 1:
Rebuttal: We thank Reviewer Zyns for their feedback on and engagement with our manuscript! We appreciate the positive feedback on the significance and novelty of our work and the expressed concern regarding the simplicity of our model.
Regarding the latter, we tried to follow the model-simplicity principle... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the organizers and reviewers for the opportunity to further discuss our work in this response phase and author-reviewer discussion! We are grateful for the extra work involved and will try to address the questions through direct responses. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint | Accept (poster) | Summary: This work studies the problem of temporal singal reconstruction for corrupted data. Under a perfect perceptual-quality constraint, the previously regarded optimal model, i.e, the Kalman filter is shown to confronted with a fundamental dilemma. A recursive formula for perceptual filters is proposed and be empir... | Rebuttal 1:
Rebuttal:
*Is the method can be generalized beyond MSE?*
Please note that in Thm. 4.1 we have a general form for linear perfect-perceptual quality filters (Eq.14). While the form (14) is optimal for MSE, it can be used as a representation for linear filters in general (but see restriction below).
The cons... | Summary: This article addresses the problem of optimal causal filtering under a perfect perceptual-quality constraint. The authors introduce the concept of an unutilized information process and present a recursive formula for perceptual filters. The study demonstrates the effects of perfect perceptual-quality estimatio... | Rebuttal 1:
Rebuttal: Thank you for your positive response.
Our focus in this work was on analytic closed form results. To achieve this we applied our algorithms and analysis to the Gauss-Markov setting, where we also focus our empirical efforts. Future work, extending our results to more general domains, will be abl... | Summary: This paper aims to study the problem of optimal causal filtering under a perceptual-quality constraint. The main contribution of this paper is to provide a mathematical framework and a closed-form solution to the aforementioned problem under some mild conditions. The experimental results on a video reconstruct... | Rebuttal 1:
Rebuttal: Thank you for your comments.
We did our best to make our work readable. We will make an effort to clarify equation derivations and improve readability in the final version.
As for Eq.16, it is a direct consequence of the MMSE orthogonality property, see e.g. [8]. We will clarify this in the text... | Summary: This submission introduces the Perceptual Kalman filter as a tractable solution to the online state estimation problem under perfect perpetual-quality constraints. This means the authors review the problem of state estimation under perceptual constrains (i.e. the joint distribution of any sub-sequence of state... | Rebuttal 1:
Rebuttal: Thank you for your comments.
Nonlinear filtering is in general analytically intractable even in the standard setting of classic state estimation without constraints. Given the absence of current theoretical analysis of perceptual constraints in filtering, it is natural to focus on the linear sett... | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments.
Following some of your remarks, we conducted an additional experiment to demonstrate different MSE gaps between temporally consistent and inconsistent filters (based on harmonic oscillators with different dynamics). Detailed description and results appea... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes | Accept (poster) | Summary: The authors propose a new model called temporal conditioning spiking latent variable models (TeCoSLVM) that uses spiking neurons to simulate neural response to visual stimuli. They claim that this approach helps to preserve information in spike trains and adapt to temporal dependencies. In experiments with ret... | Rebuttal 1:
Rebuttal: **Dear reviewer,**
**Thanks very much for your detailed review and positive comments, which have greatly encouraged us! Our response is as follows.**
> Decoder:
>
> 1. I couldn't find information on the actual form of the decoder in the main paper. In particular, how is $\psi_{dec}$ computed
>... | Summary: The authors model retinal ganglion cell responses to natural stimuli using a spiking latent variable model. They employ the (variational) Information Bottleneck (IB) method to compress the visual representation, similar to last year’s NeurIPS paper by Rahamni et al. However, this work differs in using binary r... | Rebuttal 1:
Rebuttal: **Dear reviewer,**
**Thank you very much for your thorough review and positive comments, which have greatly encouraged us! Our response is as follows.**
> Stimuli are converted to spikes, then to real valued signals (LVM), then back to spikes (ganglion cells). This does not align with visual pr... | Summary: This paper proposes a spiking latent variable model of neural response to natural visual stimuli. The model is trained to directly predict spike trains instead of trial-averaged firing rates, and designed to work, at test time, on sequences that are longer than sequences seen during training.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: **Dear reviewer, many thanks for your detailed review and comments! Our response is as follows.**
> A main claim of the paper is that the model generalizes to longer time scales [...]
**Re:** We would like to point out that our model only uses single-step stimulus input for prediction and lever... | Summary: This paper proposes a model, which is composed of spiking neural networks and a conventional recurrent network, to reproduce neuronal responses, i.e., retinal ganglion cells. The author claims that the novelty of this model is incorporating the spiking network, which makes the model directly outputs spikes and... | Rebuttal 1:
Rebuttal: **Dear reviewer, thank you very much for your detailed review and positive comments, which have greatly encouraged us. Our response is as follows.**
> I understand the variational information bottleneck framework, and the text of this part is clearly written. However, it is unclear the details of... | Rebuttal 1:
Rebuttal: We sincerely thank all *Reviewers* for their thorough reviews and *Chairs* for their efforts. We have responded to each point in your comments separately. *Due to word count limitations, we had to shorten some of your reviews when quoting them.* Please find our replies in your corresponding panels... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding | Accept (spotlight) | Summary: The authors propose an HAI system called IntegrAI that should help people make better decisions about when to defer to AI predictions, make predictions themselves, or combine their decisions.
As part of their system, the authors perform semantic clustering using LLMs to find areas of disagreement between AI a... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed review, we appreciate your time and all the important comments.
**Error Measurements Typo**: Before we delve into each point in your review, we want to clarify a typo in our paper. We report standard deviations instead of standard error for all results when ... | Summary: This paper discusses some research problems a very interesting scenario where human and AI need to collaborate to achieve a certain goal. The authors propose to learn rules grounded in data regions and described in natural language, which illustrates how the human should collaborate with the AI agent.
The pap... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed review, we appreciate your time and all the important comments. Please read below for our response.
## Weaknesses:
1. We will improve the presentation and clarify of the paper in our next iteration. In particular, we will clarify the methods in section 4 bett... | Summary: This paper presents an approach to allow effective human-AI teaming. The approach describes a process of semantic discovery of regions in an embedding space following by generating natural language descriptions of the regions and then learning refinement of the regions using counterexamples. Once regions and r... | Rebuttal 1:
Rebuttal: Thank you so much for your review, we appreciate your time to review our paper and the important considerations you raise.
Weaknesses:
We agree that we can add more experiments on more datasets. We do plan to add more ablation experiments and on more datasets in the appendix (on ImageNet16H, CIF... | Summary: The authors attempt to improve human-AI collaboration through a system that (1,2) identifies similar regions in a dataset (both in terms of the task similarity and in terms of human behavior—usage of AI advice—on the task) and optimizes for how humans should ideally use the AI in the given region, (3) generate... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed review, we appreciate your time and all the important comments.
## Weaknesses:
1. It is true that we test our onboarding method on one user study task in the main body of the paper. However, the purpose of the user study is to test the behavior of various hu... | Rebuttal 1:
Rebuttal: Thank you for the reviews of our paper and the insightful suggestions. Please find under each review our rebuttal.
We wanted to iterate the main claims and contributions of our paper:
- We propose a novel region discovery algorithm (sec 4.1) and evaluate it on four different datasets and show ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Path to Simpler Models Starts With Noise | Accept (poster) | Summary: The authors propose a possible explanation for the often large Rashomon ratios in tabular datasets (criminal justice, healthcare, etc.). The explanation involves both the dataset generation process and choices made by the practitioners who train the model. The main thesis is that label noise leads to the adopt... | Rebuttal 1:
Rebuttal: Thank you for the review, we really appreciate it. Please see below our response to your questions.
**Weakness point 1 (example of a “simple model”)**: Yes, we will add an example to Introduction. Consider a hypothesis space of linear models with real-valued coefficients in m dimensions. Then a s... | Summary: How does noise influence the set of models that have similar performance? This paper presents a study that decomposes the problem in three stages, first showing how noise in labels harms generalisation, second, lower generalisation capability leading to more restrictive model choices, and lastly showing that t... | Rebuttal 1:
Rebuttal: Thank you for appreciating the goals of our paper, which makes initial steps towards understanding broad trends that we believe have been under-studied.
**Regarding weakness point 1**: Given that we can’t prove things in all generality (though of course, we would like to), we did our best to pro... | Summary: The Rashomon set is the collection of all models that perform almost equally well in a given dataset. The Rashomon ratio is the fraction of models that are members of a given hypothesis class and simultaneously are in the Rashomon set. The paper studies the relationship between noise in the data generation pro... | Rebuttal 1:
Rebuttal: Thank you for the review.
**Weakness 1 (Theorem 4 and 9 bounds):** Theorem 4: Indeed the size of the true Rashomon set is not computable as we pointed out. We also pointed out that it doesn’t really matter - the bound still tells us what’s going on whenever the Rashomon set is large. The paper (S... | Summary: The authors explore how the Rashomon Ratio (the fraction of all models that are in the Rashomon set) changes in the presence of label noise. They show that increased label noise causes the expected variance of the ERM’s performance to increase. Then, they hypothesize that this increased variance leads practiti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. We address the questions point-by-point below.
**Weakness point 1.1 (uniform label noise vs real-world patterns of noise in Theorem 2):** Apologies for the confusion. In the text, we were not trying to assert that Theorem 2 holds more generally, but that vari... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the reviews. Below we provide a generalization of Theorem 2 to non-uniform label noise. In the response file, we also provide additional figures and analysis.
**Theorem** (Generalized Theorem 2 to non-uniform label noise). Consider 0-1 loss $l$, infinite true data ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
$k$-Means Clustering with Distance-Based Privacy | Accept (poster) | Summary: The present paper focuses on the application of a distance-based privacy notion called rho-dist-DP in the context of clustering. This privacy notion aims to protect an individual data point that moves at most a specified distance (rho) in the underlying metric space. The authors demonstrate that by leveraging ... | Rebuttal 1:
Rebuttal: We thank the reviewers for the valuable comments.
Q: “The primary inquiry addressed in this paper revolves around whether a better performance can be achieved by relaxing DP into a distance-based notion. Regrettably, the answer to this question appears to be rather straightforward: yes. The reaso... | Summary: This paper proposes a definition called "distance-based privacy" which is relaxation of differential privacy. Their definition differs from standard differential privacy in its notion of neighboring instances; whereas standard DP allows an arbitrary replacement of a single item from the space, their definition... | Rebuttal 1:
Rebuttal: We thank the reviewers for the valuable comments.
Q: The relaxed definition is a strictly local definition [...] However, by the way it's defined, this means for cities B and C, both within a distance …
A: We would like to recap the definition and the properties of (eps, delta, rho)-dist-DP to a... | Summary: In this paper, the author proposed efficient (ε, δ, ρ)-dist-DP algorithms for k-means and k-median problems to protect the privacy of exact locations as well as achieving good performance.
Strengths: The author proposes new efficient (ε, δ, ρ)-dist-DP algorithms of k-means and k-median problems, which success... | Rebuttal 1:
Rebuttal: We thank the reviewers for the valuable comments. We will apply the suggestions on the structure of the paper to improve readability.
Q: What is PDF in line 141? The definition of abbreviation should be added before using it.
A: PDF means probability density function - we will update the paper... | Summary: The paper studies the problems of solving k-means and k-median under a restricted location
privacy model, in which privacy is protected when a point moves by at most distance \rho.
It gives algorithms for these clustering problems with additive errors which are a function
of \rho, instead of the diameter \Lamb... | Rebuttal 1:
Rebuttal: We thank the reviewers for the valuable comments.
Q: It is not clear how \rho would be chosen for this model to be used. The authors do not consider this point either in the theoretical part, or through the experiments.
A: As we discussed in Section 7, rho is a free privacy parameter like epsi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Guiding Large Language Models via Directional Stimulus Prompting | Accept (poster) | Summary: This paper proposes a Directional Stimulus Prompting method to provide fine-grained guidance for the output of large language models (LLMs). This method introduces a small trainable policy model (e.g. FLAN-T5) to generate hints for each query to guide LLMs towards desired outputs. This policy model can be trai... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for recognizing the strengths in our manuscript and for the detailed feedback. We value these suggestions and hope our response can address the concerns accordingly.
**Response to W1: The technical novelty of the proposed method is limited. The training of ... | Summary: This paper introduces a novel approach to effectively guide black-box LLMs towards generating desired outputs. The proposed approach involves utilizing a relatively small policy model to generate "directional stimulus," which serves as specific information to assist LLMs in performing tasks such as summarizati... | Rebuttal 1:
Rebuttal: We deeply appreciate your recognition of our work's strengths and the constructive feedback and suggestions. We hope our response can address your concerns accordingly.
**Response to W1: robustness to different prompt formats.**
1. **Robustness of DSP**: Through the experiment detailed in the "R... | Summary: In this paper, a prompting framework called Directional Stimulus Prompting (DSP) is proposed which provides a more fine-grained guidance and control over LLMs by adding directional stimulus into the prompt. These directional stimulus or hints are generated by a small tunable model which is fine-tuned using sup... | Rebuttal 1:
Rebuttal: We genuinely appreciate your acknowledgment of our work's strengths and your valuable feedback.
Regarding the concern about the exclusive presentation of results for the flan-t5-large model, we would like to underscore that our proposed DSP is a general framework and it is not tailored or confine... | Summary: This paper introduces a interesting module named 'Directional Stimulus Prompting' (DSP). This innovative module functions by generating cues or hints to aid black-box Large Language Models (LLMs) in response generation. For instance, in a summarization task, providing keywords can guide the LLM towards generat... | Rebuttal 1:
Rebuttal: We greatly appreciate your feedback and insightful suggestions. Below is our response to address these points.
**Response to W1: exclusive use of automatic metrics in the final analysis**
To address the concern about the exclusive use of automatic metrics in the final analysis, we incorporated G... | Rebuttal 1:
Rebuttal: ### Additional experiment
We greatly appreciate all the reviewers' insightful suggestions and feedback. We conducted an additional experiment in which we use DSP to provide query-specific trigger prompts for chain-of-thought reasoning using two widely-used datasets MultiArith [1] and AQuA [2]. Our... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces Directional Stimulus Prompting (DSP), a new prompting framework that introduces directional stimulus into the prompt, which could provide black-box LLMs with fine-grained and query-specific guidance toward the desired outputs.
The experiments on summarization and dialogue response genera... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for acknowledging the strengths of our paper and offering valuable feedback. We aim to address your concerns in the following response.
Regarding the concerns about the scope of our experiments, we expanded our experiments with an additional reasoning task on two r... | null | null | null | null | null | null |
UP-NeRF: Unconstrained Pose Prior-Free Neural Radiance Field | Accept (poster) | Summary: This paper solves an interesting problem — joint pose and NeRF optimization on in-the-wild image collections. Unlike prior works such as BARF, this work aims at handling unconstrained images with varying illumination and transient occluders. To tackle this problem, this work incorporates learnable camera param... | Rebuttal 1:
Rebuttal: **We appreciate your constructive feedback on our work and we will reflect them. Below are our responses to the reviewer's questions.**
---
**Comment 1**: The proposed method has limited technical novelty.\
**Answer**: As the reviewer admitted, Unposed-NeRF in the outdoor scene is a challenging p... | Summary: This paper tackles NeRF training from in-the-wild Internet photos without pre-computed camera poses. The main idea is to leverage (self-supervised) image features and a carefully designed optimization strategy, together with ideas from existing work, including modeling transient regions and using mono-depth su... | Rebuttal 1:
Rebuttal: **We appreciate the detailed comments and suggestions. We address all the questions below. We hope that our answers resolve the reviewer's concerns and lead to support.**
---
**Answer of "W1 - Complicated pipeline with hyperparameters"**
Although our method has several hyperparameters, the metho... | Summary: The paper produces a novel approach for optimisation of pose in NeRF scenarios. The core novelty is the addition of a candidate head that improves network stability when the images poses are not yet converged, along with some other tweaks like a transiency inference head or a feature field.
Edit: I have read... | Rebuttal 1:
Rebuttal: **Thank you for the detailed review and comments. We appreciate your support for our work. The questions will be addressed below.**
---
**Comment 1-1**: The definition of “unconstrained images” should be introduced earlier on. \
**Answer**: We appreciate for the good point. We will add the defini... | Summary: The paper proposes a novel method for optimizing NeRF without a pose-prior and on in-the-wild image collections containing transient occluders and varying lightings. The main contributions are four fold. Firstly, the authors propose a candidate head for NeRFs that uses image-level representations via a learned... | Rebuttal 1:
Rebuttal: **Comment 1**: Mismatch between Equation 4 and Figure 2. \
**Answer**: In lines 158-159, we mentioned the loss $\mathcal{L}_{\text{rgb}}$ is replaced with the loss $\mathcal{L}_{\text{feat}}$, but readers can be confused because feature-surrogate bundle adjustment (Sec 3.2) was described after ca... | Rebuttal 1:
Rebuttal: Multiple reviewers asked about our Candidate Head. We address the common comment below.
Q. **Additional explanation of Candidate Head**
During the initial phases of joint training for NeRF and camera pose estimation, NeRF struggles to accurately capture intricate scene details. This limitation ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a joint camera pose and NeRF optimisation method that can handle transient scenes, including moving objects and various light conditions, by integrating NeRF-W, BARF, NoPe-NeRF, and DINO-based feature-metric loss in a sophisticated way.
The method is evaluated on four scenes in the Phototo... | Rebuttal 1:
Rebuttal: **Thank you for the constructive comments, including new experimental settings. We address the reviewer's questions below.**
---
**Comment 1**: Experiments in more scenes.
**Answer**:
We provide more experimental results. As requested, we set an error criterion of 20 degrees, which was used by ... | null | null | null | null | null | null |
Bayesian Optimisation of Functions on Graphs | Accept (poster) | Summary: This article proposes a Bayesian optimization approach to solve node-level tasks with graph-structured data. The presented framework, combined with three kernels that capture the covariance functions on graph-structured data, models each node by its ego-graph of a learnable size. The authors argue that this ap... | Rebuttal 1:
Rebuttal:
We appreciate the reviewer’s comments and are glad they have positively remarked on our paper’s novelty, soundness and clarity. We believe that we have addressed the reviewer’s concerns below, and in light of this, we hope that the reviewer will consider increasing their rating.
> Significance: ... | Summary: The authors consider the Bayesian optimization for functions defined on a graph (e.g., finding a node in a graph to min/max some function on that graph). The authors propose a local modeling approach for such problem on generic, large-scale and potentially unknown graphs. The authors demonstrate the advantages... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. It seems that the primary concern is 1) how our method deal with unknown graphs and 2) some details of BayesOpt. We address both below, and we hope the reviewer will consider increasing their rating in light of our response.
> The experiments see... | Summary: This paper solves an optimization problem defined on a graph. Since the problem defined on a graph requires the need to search for a solution in a combinatorial manner, it is a challenging problem. To tackle such a problem, the authors investigate diverse kernels on a graph and a local search strategy on a g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. It seems to us that the biggest concern comes from the discussions of the experimental results and the relative strengths of different methods in different situations. We agree with the reviewer that such a discussion would be helpful for users,... | Summary: The paper presents a Bayesian optimization algorithm for functions defined on the nodes of (potentially unknown) graphs. The algorithm combines local modelling via trust regions to account for the potentially unknown nature of graphs with random restart to avoid becoming stuck in local minima. Novel kernels ... | Rebuttal 1:
Rebuttal:
We thank the reviewer for their positive and insightful impact and are glad the reviewer found our discussions novel, compelling and sound. Please see below for our response to the concerns the reviewer raised.
> Minor point: figure 2 - equation (??).
We thank the reviewer for pointing this out... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback. We are glad that they acknowledged the novelty (all reviewers), clarity of writing (4pXS, nGA9, GnE3), soundness (nGA9, sVur, GnE3, 4pXS), and extensiveness (GnE3, tMg5) and strength (sVur, nGA9) of experiments. We address common concerns below.
## Neces... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper extends the use of Bayesian optimization based methods for optimization of the functions over the nodes of graph this algorithm is dubbed as BayesOptG. Paper mainly focuses on following three aspects:
1. Kernel design - Authors introduce suitable kernels, diffusion kernel, polynomial kernel and sum ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and positive feedback. We are glad the reviewer commented positively on our method's strength and experimental results. Please see below for our response to the reviewer’s concerns.
> little to no intuition is provided why the given choices of kernel fun... | null | null | null | null | null | null |
Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets | Accept (poster) | Summary: The central theme of this paper is the Strong Lottery Ticket Hypothesis (SLTH), which conjectures that « randomly initialised networks contain sparse subnetworks […] that perform well without any training » (lines 26-30). Pruning large networks in order to obtain those « strong lottery tickets » would be a way... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time and valuable comments. We hope to clarify some issues brought up by the reviewer.
Weaknesses (major)
1. While our proof is based on a well-known probabilistic technique (the second moment method), our analysis is entirely novel, except for the Young’... | Summary: This paper tries to discuss the Strong Lottery Ticket Hypothesis (SLTH) in the context of structured pruning in convolutional neural networks (CNNs). The SLTH suggests that randomly-initialized neural networks possess subnetworks that can perform well without any training. While unstructured pruning has been e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and the comments.
Weaknesses
1. We can change the section’s title but we remark that in Section 3 we state our contribution more formally and, to do so, we need to introduce some preliminaries.
2. We thank the reviewer for the reference. We will add it to the ... | Summary: The paper investigates a structured version of strong lottery ticket hypothesis (SLTH) for CNNs, which is important for its computational efficiency against the standard unstructured counterpart. For this purpose, they prove a variant of subset-sum lemma for their situation. Using their subset-sum lemma and sp... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for their time and comments. We address all main concerns.
Weaknesses
1. We apologise, but we are not sure we understood the issue brought by the reviewer. The polynomial overparameterization comes from recent results on the multidimensional random subset sum probl... | Summary: The authors show that structured Strong Lottery tickets are contained within convolutional neural networks.
In order to construct this proof, the authors propose the Multidimensional Random Subset Sum lemma which approximates a target vector by using a set of random vectors which are normally scaled normally d... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for all the observations pointed out in the review. We try to address the weaknesses, the question and the limitation.
Weaknesses
1. We believe it is possible, but so far we could not check all the necessary adaptations. Such improvement requires further investigat... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments. We are pleased that reviewers appreciated our contribution. Only one of the evaluations leaned towards the negative, and it seemed to be due to misunderstandings that we hope to have solved.
Some reviews brought similar questions, to which we re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper extends analytical work on the strong lottery ticket hypothesis from unstructured to structured pruning, and shows the applicability to Convolutional Neural Networks.
Strengths: I'm not an expert in this domain, but both the contribution looks strong to me.
Clarity:
The introduction is extensive a... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work and the valuable comments. We try to address all concerns.
Weaknesses
1. Please refer to point 3 of the general answer.
2. We will expand the text in the main body of our document to further discuss the significance of our results by recovering some... | null | null | null | null | null | null |
Language Is Not All You Need: Aligning Perception with Language Models | Accept (poster) | Summary: This paper propose a pretrained multimodal large language model, which can achieve impressive zero-shot performance on many downstream tasks.
The experimental results verify the transfer knowledge from language to multimodal or from multimodal to language.
However, compared to the very similar model FROMAG... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions on our paper.
**About compared to LLM**
> They only compared the language model trained on their own corpus. In fact, the multimodal version of KOSMOS-1 sees more text data and image description, leading to the comparison a little unfair. On t... | Summary: This work introduces KOSMOS-1, a multimodal large language model trained on large-scale text corpora, image-caption pairs, and interleaved image-text data. KOSMOS-1 can perform classic captioning/VQA tasks in a zero-shot or few-shot in-context prompting fashion. It can also perform OCR from visual document, an... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions on our paper.
**Improving Raven IQ test**
> The small-scale Raven IQ test with 50 samples is undoubtedly challenging and interesting, however, it is hard to say KOSMOS-1 is better than random chance because it is only marginally better by 5.3... | Summary: This paper presents a vision/language model trained on text and interleaved image/text data. It uses a ViT to encode the image into tokens and a transformer predict output tokens from the previous ones. It is trained on web-scale data and then fine-tuned on NLP instruction tuning data.
Strengths: - Despite a ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and suggestions on our paper.
**About related work**
> Related work is poorly discussed. There is no related work section, and as far I can see there is not much discussion about similar works such as other V/L foundation models or models like BLIP that adapt ... | Summary: This paper proposed KOSMOS-1, a Multimodal Large Language Model (MLLM) that can take image embeddings as additional input to auto-regressive LLM. Trained on both text-only and image-text web-crawled data, KOSMOS-1 can get strong performance across a variety of tasks including language understanding, perception... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments.
**Question 1**
> Comparison with recent MLLM works either in related work or experiments was missing. For example, MiniGPT4, LLaVA, etc.
We will add the comparison with recent MLLM works. The table below presents the comparison with MiniGPT4 and... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman | Accept (poster) | Summary: The paper builds an extension to the $k$-FWL isomorphism test algorithms called $(k,t)$-FWL+. The proposed extended family subsumes the existing $k$-FWL and adds finer variants to the expressivity landscape of isomorphism testing algorithms. The $(k,t)$-FWL+ algorithm is a combination of two modified schemes t... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of our contribution and constructive comments! We reply to all your concerns below.
### Weaknesses:
>1. Further discussion on $(k, t)$-FWL in the main paper.
**Answer:**
Thanks for the helpful comment! We mentioned the exponentially growing on the time complex... | Summary: The Weisfeiler-Leman algorithm has been established as the formal tool of choice for measuring the expressivity of GNNs. The paper tackles the problem of designing "higher-order" GNNs, which has been previously considered by several papers.
The authors propose and combine two new ideas to supplement k-WL: (1... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments! We reply all concerns below.
### Weaknesses:
>1. Further clarification on the novelties.
**Answer:**
We agree that the Graph Isomorphism problem can be solved by brute-force enumerating all possible mappings in quadratic space. However, our $(k, t)$-FWL does n... | Summary: The authors propose generalizations of k-WL and k-FWL to (k, t)-FWL, and k-FWL+, which when combined, is notated as (k, t)-FWL+. They argue that (k, t)-FWL+ allow a more "flexible and fine-grained" space for exploring the graph expressiveness hierarchy, which is helpful in designing new GNN architectures. As a... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive comments! We fix typo in revision and reply to all other concerns below.
### Weaknesses:
>1. Lack of clarity.
**Answer:**
We apologize for the confusion regarding the intuitions and motivations as we want to ensure the correctness and completeness of... | Summary: The paper deals with supervised learning on graphs. Specifically, it proposes a more fine-grained version of the (folklore) k-WL (k-FWL) hierarchy. In turn, these variants are neuralized via standard techniques.
Specifically, the authors propose the $(k,t)$-FWL, which extends the $k$-FWL by the parameter $t$... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments! We will add corresponding references in the revision and reply to all other concerns below.
### Weaknesses:
>1. Further clarification of the definitions.
**Answer:**
We are sorry for the confusion. Briefly speaking, $Q^F_{\mathbf{w}}(\mathbf{v... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive and insightful comments. Here we respond to some general concerns mentioned by the reviewers. **All tables mentioned in the response are provided in the PDF**.
### 1. Further clarify the motivations and contributions.
**Motivations:** We try to t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors start by considering the k-dim Weisfeiler-Lehman test (WL). To solve the well-known problems of k-WL/FWL (high space complexity $O(n^k)$ and rigid design space), the authors propose two extensions of the k-FWL, named (k, t)-FWL and k-FWL+.
The first extension is based on the simple observation that... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments! We revise all references and add contents for Appendix in our revision and reply to all other concerns below.
### Weaknesses:
>1. The novelties in the proofs.
**Answer:**
Thanks for your mention. For all proofs, we mainly follow the ... | null | null | null | null | null | null |
Textually Pretrained Speech Language Models | Accept (poster) | Summary: The authors explain a method to improve the performance of a speech language model by reusing the weights of a language model trained on text.
Strengths: Using a well-trained text-based language model as an initial model seems like a good idea.
If the authors' claims can be generalized, it can be used as a g... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that using text LMs as initialization for SpeechLMs is a good idea, as our empirical results demonstrate.
**Regarding theoretical justification:** We agree that providing theoretical justification of why TWIST outperforms Cold-Init models is interesting. Ho... | Summary: This work proposes TWIST, Textually Warm-Initialized Speech Transformer-based LMs, a technique to initialize SpeechLMs with pretrained textual LMs. Different textual LMs, tokenizers, models and dataset sizes are evaluated using TWIST. The authors find that a warm start with a textual LM helps compared to a ran... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the extensive evaluation we conducted across different textual LMs, tokenizers, models and dataset sizes. We also appreciate the reviewer for noting that our work is one of the first ones to work on large-scale speech language modeling and the introduction o... | Summary: This paper proposes a simple initialization method for speech LMs named, TWIST (Textually Warm-Initialized Speech Transformer Language Models). Instead of cold-initializing a speech LM, twist initializes it with LLM weights (minus the token embeddings, which are replaced with speech vocabulary). The authors sh... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s acknowledgement of the new benchmark contributions, the benefit of using TWIST in terms of convergence speed and quality while being simple and easy to use.
**Regarding reporting only one model size for Bloom/Pythia (weakness 1, question 2):** To have a fair compariso... | Summary: This paper studies the effect of textual LM on SpeechLMs. They propose TWIST, which initializes the SpeechLM model with a pre-trained textual LM, and then finetune with the speech datasets.
This paper provides a complementary exploration of generative spoken language modeling, including front-end processing of... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer believes our work is “highly valuable and can provide direction for following research.” We also thank the reviewer for acknowledging the contributions of our paper including the rigorous empirical results, the insights into the design considerations of SpeechLMs an... | Rebuttal 1:
Rebuttal: We thank all reviewers for their detailed responses and valuable feedback. We are happy the reviewers acknowledge the use of our method (TWIST) benefits the training and quality of speech language models (SpeechLMs). Reviewer 6BNW mentioned that **TWIST is effective, simple and easy to use**, whil... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent | Accept (spotlight) | Summary: This paper investigates the approximate heavy-tailed behavior of stochastic gradient descent (SGD) in local minima in practical settings and its correlation with overall performance. It shows that the stationary distribution of offline SGD exhibits approximate power-law distribution, and the approximation erro... | Rebuttal 1:
Rebuttal: Thank you for taking the time to invest in our paper and for the encouraging feedback. In the following, we try to address your remaining concerns. However, before proceeding, we would also like to thank you for pointing out the interesting and relevant paper. We apologize for not including it pre... | Summary: This manuscript considers the problem of multi-pass stochastic gradient descent with a finite batch-size and strongly convex objective. The key result is to show that when the stationary distribution of the parameters is heavy-tailed in the infinite data (one-pass) limit, then the stationary distribution of th... | Rebuttal 1:
Rebuttal: We are grateful for your in-depth review of the paper and the feedback you shared. In the following, we aim to respond to your concerns.
> First, the setting is rather restricted (strongly convex goals)
We agree that considering strongly convex objective functions may seem restricted. However, t... | Summary: In this paper, the authors study the heavy-tail distribution for the parameters in offline stochastic gradient descent algorithm (SGD). Theoretical results are provided for a quadratic loss and a strongly convex problem, while numerical results cover more realistic cases such as fully connected NN or CNN. In t... | Rebuttal 1:
Rebuttal: We appreciate the encouraging feedback and valuable comments you provided. Moving forward, we will address your primary concern:
> It appears to me that the fig.1 and fig.3 are not as convincing as the rest of the paper. The axes are unlabeled in these figures.
We have taken your feedback into a... | Summary: This paper considers offline SGD and proves an approximate power-law tail behavior of the stochastic gradient for strongly convex objectives, confirming the heavy-tail heuristic encountered in practices. Explicit tail estimations are obtained, and as a intermediate result, nonasymptotic Wasserstein convergence... | Rebuttal 1:
Rebuttal: We are grateful for your encouraging feedback and for taking the time to understand the paper clearly and go through the mathematical proofs. If any questions arise regarding our work, we remain at your disposal. | Rebuttal 1:
Rebuttal: We want to extend our gratitude to the reviewers for their insightful feedback. In the following, we outline the modifications implemented in response to their input.
**In response to the clarity of the presented figures**:
* Every plot has been carefully labeled and experimental paragraphs nomen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Inner-Outer Aware Reconstruction Model for Monocular 3D Scene Reconstruction | Accept (poster) | Summary: The authors propose a method for 3D reconstruction given images and the respective camera poses. Existing methods predict a TSDF volume and convert to 3D mesh using marching cubes. However, the authors proposed method as a coarse to fine strategy which uses a classifier to classify a voxel into surface, inner-... | Rebuttal 1:
Rebuttal: **Q1: The impact of classifying the difference between inner surface voxels and outer surface voxels is not entirely clear.**
**Re:** We showed the impact of classifying the difference between inner surface voxels and outer surface voxels with qualitative experiment results in the ablation study ... | Summary: The paper presents a multi-resolution method for volumetric 3D reconstruction from an input video of a static scene captured by a moving camera. Camera poses must be externally provided. The key observation is that non-surface voxels inside and outside the surfaces have different properties which have been ign... | Rebuttal 1:
Rebuttal: **Q1: The difference between using separate or shared TSDF and occupancy branches is not clear.**
**Re:** To clarify the difference, we explain how existing methods and IOAR predict occupancy and TSDF. Conventionally, a shared 3D CNN is used to refine the 3D feature volume. Then the TSDF and occu... | Summary: This paper modifies previous coarse-to-fine frameworks to classify voxels into outer-surface, inner-surface, and surface voxels. In addition, the TSDF branch is added to further improve the performance. Extensive experiments show the good performance of the proposed method.
Strengths: 1. The motivation is int... | Rebuttal 1:
Rebuttal: **Q1: The novelty of this paper is somehow limited since their model is built based on the coarse-to-fine framework like previous methods (e.g., 3D-Former and VoRTX).**
**Re:** Using the basic coarse-to-fine framework does not mean the novelty of IOAR is limited. As we have introduced in the abs... | Summary: This paper proposes an inner-outer aware reconstruction (IOAR) model for monocular 3D scene reconstruction. Different from existing methods, IOAR can classify the inner-surface voxels and outer-surface voxels, which could lead to better occupancy prediction and TSDF prediction. More specifically it proposes a ... | Rebuttal 1:
Rebuttal: **Q1: only ScanNet is used for testing. would be nice to test on more.**
**Re:** In the paper, we have tested our model on three different datasets: ScanNet (Tables 1 and 2), ICL-NUIM (Table 3), and TUM-RGBD (Table 3). Following previous methods [11, 12], we evaluate the performance of IOAR on IC... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a novel method for monocular 3D scene reconstruction, termed Inner-Outer Aware Reconstruction (IOAR). In contrast to prior works, IOAR incorporates a unique classification process for outer surfaces, inner surfaces, and surface voxels. Additionally, the method distinctly separates the occ... | Rebuttal 1:
Rebuttal: **Q1: While the contributions are impactful, they are rather direct and simplistic, which could potentially limit the depth and breadth of the paper.**
**Re:** The insight behind IOAR is intuitive, but we believe it can inspire future works on 3D reconstruction. The starting point of IOAR is the... | null | null | null | null | null | null |
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning | Accept (poster) | Summary: The paper proposed locally regularized Context Optimization for OOD detection inspired by CoOP. Their primary claim is that the CLIP feature contains many ID-irrelevant nuances, such as backgrounds. Hence pushing the ID and these embedding from each other will lead to a better separation between ID and OOD sam... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to them appropriately as follows. We will add suggested experiments and explanations in the updated manuscript.
> Q1. The novelty of LoCoOp
A1. While CoOp learns to bring the global image features and GT text feature closer together... | Summary: This paper focused on the problem of vision-language prompt learning for few-shot OOD detection, i.e., using CLIP model to detect OOD images from unseen classes using only a few labeled in-distribution (ID) images. Previous zero-shot methods may encounter a domain gap with ID downstream data, while fully fine-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to them as follows. We will add suggested experiments and explanations in the updated manuscript.
> Q1. The ID accuracy with zero-shot and fully-supervised methods
A1. The results of the ID accuracy and OOD performance on the ImageNet v... | Summary: In this paper, the authors propose a CLIP-based few-shot OOD detector named Local regularized Context Optimization (LoCoOp). The method LoCoOp uses learnable prompts and local tokens' class scores to optimize the OOD detection performance. During the few-shot training phase, the authors extract the ID-irreleva... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to them as follows. We will add suggested experiments and explanations in the updated manuscript.
> Q1. Visualization results
A1. Thanks for pointing this out. We attached the visualization results. From this result, we can see the OOD ... | Summary: The task of this article is to use the CLIP model for image classification, and the target scene is few-shot data and out-of-distribution (OoD). The author confirms the final OoD region by querying the ID-independent region in the image, and further optimizes the model through this region. Remarkably, the auth... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to them as follows. We will add suggested experiments and explanations in the updated manuscript.
> Q1. The accuracy of locating regions not related to IDs.
A1. Thanks for the question. That is correct, and the accuracy of the segm... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for giving excellent and positive comments and recognizing our contributions: “Few-shot out-of-distribution detection is interesting and practical” (**Lcii, uDwi, RaSR**), “LoCoOp is well motivated and interesting” (**Lcii, uDwi, wEUW, RaSR, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a vision-language prompt learning method named local regularized context optimization (LoCoOp) for few-shot out-of-distribution detection. The proposed LoCoOp method performs OOD regularization that uses the portions of CLIP local features as OOD features during training. The experimental ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and respond to them as follows. We will add suggested experiments and explanations in the updated manuscript.
> Q1. The effectiveness of LoCoOp in a fully supervised setting.
A1. As the reviewer pointed out, our LoCoOp can be applied to ... | null | null | null | null | null | null |
Multi-Agent First Order Constrained Optimization in Policy Space | Accept (poster) | Summary: The paper introduces a fresh approach to tackle the problem of safe Multi-Agent Reinforcement Learning (MARL) within a fully-cooperative multi-agent environment where all agents share a common reward function. The authors propose a novel algorithm known as Multi-Agent First Order Constrained Optimization in Po... | Rebuttal 1:
Rebuttal: *Q1*: It would be beneficial if the authors could provide more details on how they plan to address computational complexity in practice. Are there any strategies or methods that could be used to reduce the computational complexity?
*A1*: We introduce the implementation of these two hyperparameter... | Summary: This paper proposes a first-order method to solve the safety-constrained multi-agent policy optimization problem. The method is evaluated on two benchmarks and shows better performance over the baselines.
Strengths: - The studied problem is important and might interest the community.
- The writing is easy to... | Rebuttal 1:
Rebuttal: *Q1*: My main concern is about the correctness of the derivation in the proposed method. The proof of Theorem 1 says both Equation (7), the objective and Equation (8), the cost constraint, are linear w.r.t. $\pi^{i_h}$, which looks strange to me. The equations involve advantage estimation which de... | Summary: The paper builds over previous work (1) to construct a safe policy optimization algorithm based on first-order methods. It shows empirical improvement over some baselines.
Strengths:
# quality
The quality of this work is fairly satisfactory, as all the needed theory is introduced and a practical counterpar... | Rebuttal 1:
Rebuttal: *Q1*: The work seems not to be much original since it consists of a fair extension of a previously cited paper.
*A1*: We believe our work is not merely an extension of a previously cited paper, which we think you refer to MACPO. As is discussed in the paper, although MACPO offers an impressive so... | Summary: The paper proposes a new method called first-order constraint optimization in multi-agent policy space (MAFOCOPS), which solves constraint optimization problems in a non-parametric policy space and then projects the updated policy back into the parametric policy space to achieve feasible strategies that meet s... | Rebuttal 1:
Rebuttal: *Q1*:The performance of the method is sensitive to certain hyperparameters, such as the Lagrange multipliers and the safety bound. While the paper claims that the method is relatively insensitive to variations in these hyperparameter values, it would be helpful to provide a more detailed analysis ... | Rebuttal 1:
Rebuttal: Thank all reviewers for your time and valuable suggestions. We hope our rebuttal could address your concerns. We would appreciate it if you could re-evaluate our submission and we are looking forward to discussions if you have any other concerns. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets | Accept (poster) | Summary: This manuscript is motivated from training a universal object detector on the mixture of unlimited counts of datasets. Concretely, the previous works cannot scale the MoE due to budget, and the conventional MoE using balanced route suffers from knowledge sharing issues. To tackle the aforementioned issues, the... | Rebuttal 1:
Rebuttal: We thank the reviewer for investing their time in reviewing our work and suggesting improvements.
Q. “The contributions of …”
We want to thank the reviewer for their feedback and wish to re-highlight our contributions.
- DAMEX does not require test-time dataset labels as DAMEX learns to route... | Summary: The paper aims to develop a universal object detector that is applicable to a wide range of object detection datasets. For this aim, the authors proposes Dataset-aware Mixture of Experts (DAMEX). DAMEX is an extension of the vanilla Mixture of Experts layer to the multi-dataset scenario, in which each dataset ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in reviewing our work.
Q. “I think this …”
We note the review feedback and their suggestion for GLIP. We answer the question under common answers and report the results in Table A1 (rebuttal pdf).
Q. “The authors use UODB…”
We understand reviewer's concer... | Summary: This work, motivated by the goal of developing a “universal detector,” seeks to understand how best to train a model across a large set of existing curated, labeled datasets that might differ in collection strategy, labeling standards, categories of interest, etc. They posit that the best strategy is to train ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in understanding our work and providing feedback to improve the manuscript.
Q. “The claims of performance…”
Duly noted. As mentioned in common concerns, we will change our writing to better convey the performance gains to the readers.
We understand reviewer... | Summary: This paper introduces a DAMEX layer based on the idea of assigning samples conforming to the charactersitiscs of a dataset to the corresponding expert. The underlying thought is to build dataset-relevant modules and ensemble them all together. Previous approaches leverage Mixture of Experts to scale their mode... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback.
Q. “There is no introduced novelty…”
We believe that performance gain and novelty are inter-related.
Sparsely activated networks are composed of components (experts), each of which learn to handle a subset of the complete set of training case... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback on DAMEX. Through their comments, we have gained a valuable insight of the paper from a reader’s perspective, and we are thankful for their suggestions on improving our manuscript.
1. **Comparison against Vision-Language Foundation models (R1, R4)**
Our... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper tackles the problem of mixture-of-datasets training for object detection. The authors propose a mixture-of-expert-based (MoE) model that utilizes dataset-specific features to tackle mixing of heterogeneous datasets. The backbone is based on DINO transformer and one expert is assigned to one dataset.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and investing their time in understanding our work.
Q. “The design of assigning…”.
We would like to thank the reviewer for this question that prompted us to run a new experiment and improve our manuscript further. DAMEX allows the user to incorporate hum... | null | null | null | null | null | null |
When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality | Accept (poster) | Summary: This paper presents theoretical results on estimates of the integral of f^q based, and when regression adjusted control variates can lead to better Monte Carlo estimators.
Strengths: The results look to be novel and improve existing ones in this area. Furthermore, this is an area that is important in practice... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the feedback on our work. Below are our responses to the questions raised in the review:
1. Connection between application and our theoretical work
We already discussed related application in subsection 1.1 "Regression-Adjusted Control Variate (RACV)" of t... | Summary: This paper studies whether we can learn a control variate to reduce variance in Monte Carlo sampling.
Strengths: This paper studies whether we can learn a control variate to reduce variance in Monte Carlo sampling.
Weaknesses: To someone who's not familiar with this area of research, the paper lacks introd... | null | Summary: The authors study the theoretical properties of Monte Carlo estimators of the moments of a Sobolev function with nonparametric regression-adjusted control variates. In particular, they show that when a certain smoothness assumption is satisfied, then the regression-adjusted rule achieves the minimax optimal ra... | Rebuttal 1:
Rebuttal: We are really grateful for the reviewer's valuable feedback on our work. For questions raised in the review, we list our answers below:
1. A coherent story connecting all the results
We already built a “story” connecting the results presented in the paper as the reviewer suggested. The story is... | Summary: In this papers the authors consider estimating the $q^{\rm th}$-moment of a function $f$ (i.e. $\int f^q(x)dx$) by observing samples of $x_i,f(x_i)$ when $x_i$-essentially follows a uniform distribution over (a compact) domain of integration. The paper first develops an information theoretic lower bound (using... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful feedback on our work. Below are our responses to the questions raised in the review:
1. Relation between our work and classical non-parametric theory
Regarding a missing discussion on the classical non-parametric theory of estimating functionals... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Double Gumbel Q-Learning | Accept (spotlight) | Summary: The authors propose a novel off-policy algorithm DoubleGum based on their new noise model which is claimed to be more stable for training and reaches higher performance for various discrete and continuous environments.
Strengths: The paper is well-written and well organized. The authors proposed a novel nois... | Rebuttal 1:
Rebuttal: Many thanks for your review!
We are happy to read that you found our algorithm novel and that our paper was well-written and organized.
> TD3, DDPG MoG-DDPG in figure3, then it is just DDPG (best w/wo Twin Networks) in figure 5
Figures 3 and 5 show two different ways of evaluating DoubleGum. T... | Summary: The paper provides empirical evidence that it is inaccurate to assume the temporal difference error follows a homoscedastic normal distribution. The paper proposes to use Gumbel distribution to make a replacement. The optimal action value is considered as the learned value estimation plus a noise sampled from ... | Rebuttal 1:
Rebuttal: Many thanks for your review!
We are delighted to read your positive feedback on our experimental methodology and reproducibility, which we care deeply about.
> The whole idea based on one assumption, estimating the temporal difference error with a Gumbel distribution is better than Normal distri... | Summary: The paper proposes a combination of Gumbel noise with Q-learning. The idea is based on the high level observation that regular L-2 loss based Q-learning can be understood as maximum likelihood estimation with Gaussian noise. The paper derives the practical algorithm and shows some improvements over baselines.
... | Rebuttal 1:
Rebuttal: Many thanks for your review!
We are very happy that you found both theoretical and empirical novelty within our paper, and that our resulting DoubleGum algorithm was practical and easy to implement.
> The definition of the equality in distribution look a bit confusing to me in Eqn 9
We have rea... | Summary: Typically, TD learning assumes that the TD error follows a normal distribution with a fixed variance (induced by the Bellman squared loss). The authors argue that this assumption is too coarse in practice since the maximization of noisy Q-values across actions (when backing up the value in TD learning) is usua... | Rebuttal 1:
Rebuttal: Many thanks for your review!
We are very happy you highlighted our strong theoretical analysis of TD-errors, our justification for our resultant algorithm (DoubleGum), and that you found the writing clear and very easy to follow.
> [not convinced that] the proposed method is able to improve upon... | Rebuttal 1:
Rebuttal: Many thanks to all reviewers for their comments and feedback!
We are delighted that all five reviewers mentioned the strong experimental analysis of our DoubleGum algorithm and that four reviewers highlighted the novelty of our theoretical analysis (dXQf, WFvy, uPm8, Mh9L). In addition, we are a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Instead of modeling the TD error with a homoscedastic normal distribution, this paper tries to utilize two heteroscedastic Gumbel distributions for more complex and accurate error modeling. Based on this assumption, the authors presents a modified Q-learning algorithm, DoubleGum for solving discrete and contin... | Rebuttal 1:
Rebuttal: Many thanks for your review!
We are very happy that you found our modeling of the TD-error novel.
> Experimental results don't show outstanding performance improvements across multiple benchmarks
We have uploaded Figures 14 and 15 in our attached 1-page `.pdf`, which show aggregate learning curv... | null | null | null | null | null | null |
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks | Accept (poster) | Summary: The paper examines the causal and moral judgments made by large language models (LLMs) and their alignment with human intuitions. To do this, the researchers created a dataset of stories from 24 cognitive science papers, annotating each story with factors that influence people's judgments, such as norm violati... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions and comments. We will make sure that we incorporate all the feedback.
> Evaluating with more models: The paper primarily focuses on GPT-type models and its variants. Could the authors elaborate on why they chose to focus on these models? Would the inclusion... | Summary:
This paper investigates to what extent LLMs can align with human intuitions when making causal and moral judgments. To do this, they collected a dataset of stories from 24 cognitive science papers and created a causal and moral judgment challenge set. They evaluate different LLMs about their alignment with hu... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions and comments. We will make sure that we incorporate all the feedback.
> The paper wants to analyze the alignment between humans and models, however it lacks some description of how they conducted the human study.
Thank you for bringing this up. We have pro... | Summary: This model presents a new challenge set of hard edge cases intended to test models understanding of the nuances of the directness of causation and moral culpability, by collecting them from a set of cognitive science papers. This has the clever effect of not only getting challenging stories, but those which wo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions and comments. We will make sure that we incorporate all the feedback.
> quibble: A seemingly left-over note on line 232: " This is very very interesing, make the flow better."
Thank you for pointing this out – we have removed this in our paper.
> I'd be v... | Summary: This paper focused on large language models' causal and moral intuitions and investigated the alignment between LLMs and humans' causal and moral judgments. For this purpose, the authors collected story datasets from the field of cognitive science and manually annotated each story with human judgments and unde... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback and for appreciating that a carefully curated set of examples and insights from philosophy and cognitive science can help provide a deeper understanding of the implicit biases and tendencies in LLMs. We will make sure to incorporate all the suggestions you hav... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The motivation of this paper is that people constantly make lots of causal and moral judgments to reason about why did what things and why. This paper contributes a dataset of stories compiled from cog sci papers, with detailed annotation of the factors that contributes to the human judgment. Then, the paper l... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful review and the positive feedback that our intention to build a challenge set to evaluate and understand causal and moral reasoning of LLMs and their alignment with humans. We will make sure to incorporate all the suggestions.
> The size of ... | null | null | null | null | null | null |
Generating Images with Multimodal Language Models | Accept (poster) | Summary: The paper introduces a new method called GILL that effectively integrates frozen LLMs with pre-trained image encoder and decoder models to create coherent image and text outputs. The authors show GILL's superior performance over baseline generation models in tasks involving longer and more complex text and its... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. We are glad that the reviewer found our proposed approach innovative and inspiring, and recognized that it is efficient. We are pleased that the reviewer found the paper well-written, and appreciated GILL’s improved capabilities over baseline mode... | Summary: The paper proposes a novel method to stitch together LLMs and text conditional image diffusion models, to process interleaved image-text and output interleaved image-text. The paper evaluates their methodological contributions on several tasks where outputting images is required.
Strengths: S1. The ability to... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. We are glad that the reviewer recognized the creativity and generality of our approach in combining pretrained LLMs and visual models, and appreciated the impressive qualitative results, strong ablations, and accessible finetuning cost. We address... | Summary: The authors train adapters to map embeddings of pre-trained image encoders and decoders to pre-trained LLMs. This allows them to input interleaved images with text into a pre-trained LLM and also make the LLM generate [IMG] tokens as required, which can be fed into a decoder to generate images or can be used t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and valuable comments. We are glad that the reviewer appreciated the promising results from our paper, the possibility for interesting follow-up work, and recognized that it is a clean idea which introduces fundamental capabilities (image generation) to m... | Summary: This paper proposes to use LLM to do image generation. Their approach consists of two stages of training.
In the first stage, they try to learn a linear layer to make VIT visual feature space is compatible with LLM space.
In the second stage, they learn r new tokens representing image. They hided states of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We are pleased that the reviewer found the writing clear, and that they liked the idea and motivation of the paper. We address specific queries below, and will incorporate all feedback.
## 1. Whether GILL Can ‘Generate Images’
We consider GILL to... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments. We are glad that all reviewers appreciated the creativity and novelty of our method, and found it innovative and inspiring (reviewer Gk8n), appreciated its creative exploration (reviewer QYwk), ideas and motivation (reviewer bnn7), and recognized... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Identification of Nonlinear Latent Hierarchical Models | Accept (poster) | Summary: The paper introduces a class of latent DAG models which allow for hierarchical structure between latent variables. The class of models allows for more general structure than previously considered classes (e.g., tree-structured latent models). The model also allows for general nonlinear relationships between va... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. We respond to your concerns as follows and will include your suggestions as indicated.
**Q1: Related work on interventional-based causal identification.**
Thank you for the suggestion! We agree that there are two possible ways to learn causal representat... | Summary: The goal of this paper is to identify the hierarchical graph structure and latent variables for general nonlinear latent hierarchical causal models. The paper reduce the problem to identification of the so-called basis model and proves the connection between latent hierarchical model and basis model.
Strengt... | Rebuttal 1:
Rebuttal: Thank you so much for your careful assessment and valuable feedback! Below, we respond to your concerns raised in Weaknesses and Questions.
**Q1: Strengths of the assumptions.**
Thank you for the feedback! We admit that our approach relies on a number of assumptions; this is because our approach... | Summary: This paper addresses the problem of identifying latent variables and causal structures from observational data in the context of nonlinear latent hierarchical causal models. Such models are common in real-world applications involving biological, medical, and unstructured data such as images and languages. The ... | Rebuttal 1:
Rebuttal:
Thank you for your encouraging comments and valuable suggestions! We will include your feedback and the discussions in our revision. Below are our responses to individual questions.
**Q1: Computation complexity and wall-clock times.**
Thank you for the wonderful suggestion – this will help futu... | Summary: This paper presents an identification strategy that allows the unique reconstruction of a latent hierarchical model, including both the graphical structure and (up to invertible transformations) the values of the latent variables. Assumptions include faithfulness, some structural assumptions (weak compared to ... | Rebuttal 1:
Rebuttal: We deeply appreciate your thorough reading and valuable insights! Below, we provide individual responses to your comments.
**Q1: Differentiability is not explicit.**
Thank you for pointing this out. This condition should definitely be given, and we have made this assumption explicit in our revis... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback and dedicated time! We are encouraged that they find our theoretical contribution significant/substantive (wEjX, gbV3, mbLW, jcJm) and well-supported by experimental results (wEjX, mbLW). We address the individual comments in separate responses an... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Active Negative Loss Functions for Learning with Noisy Labels | Accept (poster) | Summary: In this work, the authors propose a new class of theoretically robust passive loss functions named Normalized Negative Loss Functions (NNLFs). By replacing the MAE in APL with the proposed NNLFs, this paper improves APL and proposes a new framework called Active Negative Loss (ANL).
Strengths: 1. The motivati... | Rebuttal 1:
Rebuttal: We appreciate the suggestion from the reviewer and will incorporate it into the updated version of our paper.
1. Typos and mathematical notations.
We thank the reviewer for pointing out the typos and the unclear mathematical notations. We will check our paper carefully and correct the relevan... | Summary: This paper proposes a robust loss function for learning from label noise. The authors first find that negative losses proposed in APL are scaled versions of MAE, which is not training-friendly. To solve this problem, the authors propose the Active Negative Loss (ANL) framework which improves the APL loss by re... | Rebuttal 1:
Rebuttal: We appreciate the suggestion from the reviewer and will incorporate it into the updated version of our paper.
1. The three components of NNLF.
Our goal is to create a new loss function to replace the MAE in APL. This means that: 1. The loss function must conform to the definition of passive l... | Summary: This paper proposes robust loss function to improve training of DNNs using noisy labels. Previously proposed work of Active Passive Loss functions is improved.
Strengths: Paper is well written and contains theoretical foundation of the proposed work. There are a lot of derivations which give a broad understan... | Rebuttal 1:
Rebuttal: We appreciate the suggestion from the reviewer and will incorporate it into the updated version of our paper.
1. Active and passive loss functions.
We agree that there is no clear distinction between active and passive loss functions in [1]. Since our work follows [1], we directly use these d... | Summary: This paper introduces a novel type of loss function called Active Negative Loss (ANL), which builds upon the Active Passive loss function (APL) framework. The authors identify a limitation in APL, where the passive loss function, being a scaled version of Mean Absolute Error (MAE), can lead to slower convergen... | Rebuttal 1:
Rebuttal: We appreciate the suggestion from the reviewer and will incorporate it into the updated version of our paper.
1. A simple combination of the NL and APL.
First of all, although our ANL may seem like a simple combination of existing techniques, we have a strong motivation for doing so: finding ... | Rebuttal 1:
Rebuttal: Following the suggestions of the reviewers and in order to further validate the effectiveness of our method, we conducted a set of experiments on CIFAR-10N [1], CIFAR-100N [1], Animal-10N [2], and Clothing-1M [3]. For some experiments, we can only compare a few methods due to time constraint. The ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Model-enhanced Vector Index | Accept (poster) | Summary: In this study, the authors present a new approach called Model-enhanced Vector Index (MEVI) that combines ideas from both autoregressive sequence-to-sequence models for indexing and dense retrieval models (twin-tower architectures), and includes Residual Quantization. This approach offers significant advantage... | Rebuttal 1:
Rebuttal: Trank you for your thoughful comments and suggestions.
We address the points you mentioned in the weaknesses part.
Major comments
1. We add another dataset Natural Questions (NQ). We take AR2 (https://arxiv.org/abs/2110.03611) as the dense retriever and HNSW as the ANN algorithm. The results a... | Summary: This work introduces residual quantisation codebook into k-mean clustering to generate semantic IDs. These semantic IDs are used to provide initial cluster-level ranking and prune the corpus into document subset with thousand documents. The interpolation between the dual-encoder similarity and a derived cluste... | Rebuttal 1:
Rebuttal: We appreciate the time you took to review our paper.
First, we address the five points you mentioned in the weaknesses part.
1. From the taxonomy perspective, our proposed MEVI is a generation-enhanced dense retrieval method, not a traditional cluster-based retrieval method. The clustering proce... | Summary: This paper proposes to improve dense neural IR models by adding a RQ structure (hierarchical clustering based on residuals at each step) before the ANN search. The structure is used to construct a semantic identifier string for each document. The authors thus use a generative approach to retrieval combined wit... | Rebuttal 1:
Rebuttal: Thank you for your detailed review.
First, we address the three points you mentioned in the weaknesses part.
1. On the MSMARCO Passage dataset, we add the experiment results with another state-of-the-art dense retrieval model AR2 (https://arxiv.org/abs/2110.03611) in the following table. We also... | Summary: This paper present a new method for ensembling generative retrieval and embedding-based dense retrieval. Prior generative retrieval work that solely relies on a generation model to generate the retrieval document, but it is difficult to scale to large corpus. In contrast, this paper uses the generation model t... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
First, we address the five points you mentioned in the weaknesses part.
1. On the MSMARCO Passage dataset, we add another state-of-the-art dense retrieval model AR2 (https://arxiv.org/abs/2110.03611) in addition to T5-ANCE. AR2 performs better than RocketQA-... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for taking time in reading our paper and providing valuable comments. We briefly address common questions here.
1. We add another dataset, Natural Questions (full document set version from DPR https://arxiv.org/abs/2004.04906), for comparison. We take AR2 (https://arxiv... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This research paper introduces a deep text retrieval model that possesses the capability to effectively manage a large corpus comprising millions of documents. The model achieves remarkable recall performance while maintaining relatively low latency. Moreover, in addition to surpassing the performance of exist... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper.
First, we address the points you mentioned in the weaknesses part.
1. We conduct experiments on another popular dataset, Natural Questions (NQ). We take AR2 (https://arxiv.org/abs/2110.03611) as the dense retriver and HNSW as the ANN algoritm. A... | null | null | null | null | null | null |
Generative modeling for RNA splicing code predictions and design | Reject | Summary: This paper presents a tissue specific transformer based splicing prediction model, TrASPr along with a Bayesian Optimization algorithm, BOS, capable of designing RNA with desired properties. The authors start by demonstrating the performance of TrASPr on RNA splicing data from both mouse and human tissues. Nex... | Rebuttal 1:
Rebuttal: Reviewer Kz6p
**Weaknesses**:
==========
[-] **Reproducibility code**; the authors claim for reproducibility however no code was provided. Providing the code could improve the understanding and evaluation of the presented framework.
**Reply**:
Agreed! Code will be made available after publicat... | Summary: The authors develop a new framework to predict alternative splicing of RNA. They then deploy it with adaptations and Bayesian Optimization to design new sequences.
Strengths: I find the validation using the RBP KD experiment interesting. It is great that the knowledge of the biological system can be used to i... | Rebuttal 1:
Rebuttal: Reviewer XeDB
Weaknesses:
===========
(1) Table 1 results of rAUPRC and AUROC are confusing. Can the “feature” and “Model” terms be a bit better defined?
Reply:
Agreed. The “feature Model” terms should be removed there. They simply refer to the fact that the AE+MLP model is based on predefined/... | Summary: The authors propose a new machine learning framework called TrASPr, which is a transformer-based architecture with pretrained RNA language models that is tailored for the prediction and design task of RNA alternative splicing. The authors demonstrate that TrASPr accurately predicts tissue-specific `percent spl... | Rebuttal 1:
Rebuttal: Reviewer LDfP
Weaknesses:
=========
(1) Some additional work and its relationship to this research should be discussed, such as "RNA Alternative Splicing Prediction with Discrete Compositional Energy Network," which also focuses on the prediction of PSI scores in a tissue-specific setting.
Rep... | Summary: The paper tackles two tasks in alternative splicing of pre-mRNA, where multiple unique mRNAs are produced by including different segments. First, the authors proposed a transformer-based splicing prediction model, TrASPr. A 6-layer transformer model is pre-trained with 1.5M pre-mRNA sequences centered in splic... | Rebuttal 1:
Rebuttal: Reviewer aUkC
Weaknesses:
Major comments:
**Originality**: While the methods are novel for their first use for RNA alternative splicing, they still seem like mostly direct applications of widely known machine learning methods. For example, pre-training and fine-tuning of language models seem tr... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their valuable comments. In particular, we appreciate the comments on the importance of the problem we address, the improvement in RNA splicing prediction, the experiment design to support our claim, as well as the positive feedback regarding the flow o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose two approaches to deal with the problems of alternative splicing (AS). A transformer architecture-based tissue-specific splicing code model, TrASPr, and a Bayesian Optimization algorithm were performed on the latent variable space of VAE to address the design of RNA sequences with specific... | Rebuttal 1:
Rebuttal: Regarding MAJIQ version - we used 2.2
Regarding Weaknesses and Limitations listed by reviewer wcPZ:
We are unsure how to interpret the “biological soundness' ' and ‘lack of reliability’ criticism. We have worked hard to show not only do the model predictions outperform current SOTA but correspo... | null | null | null | null | null | null |
Decompose Novel into Known: Part Concept Learning For 3D Novel Class Discovery | Accept (poster) | Summary: - This paper proposes a novel part-based algorithm for 3D novel class discovery (NCD). Authors propose Decompose Novel Into Known parts (DNIK) that leverages knowledge about parts of known objects to discover novel classes.
- Authors identify that the main problem with learning 3D features for object discovery... | Rebuttal 1:
Rebuttal: Thank you for your detailed and helpful feedback! We sincerely appreciate your positive feedback that our work "has impressive improvements" and "is very easy to follow". Your suggestions provide valuable guidance for us to improve this study. We address your thoughts point by point below.
>Q1: G... | Summary: In this work, they address 3D novel class discovery (NCD) that discovers novel classes from an unlabeled dataset by leveraging the knowledge of disjoint known classes. The key challenge of 3D NCD is that learned features by known class recognition are heavily biased and hinder generalization to novel classes. ... | Rebuttal 1:
Rebuttal: Thank you for your attentive comments! We are glad you thought “the paper is well written and motivation is pretty good”. We address your feedback point by point below.
>Q1: This paper does not consider hierarchical part representation.
A1: We thank the reviewer for the suggestive comment. The f... | Summary: This paper tackles the problem of novel category discovery in the 3D shape recognition domain, a framework leveraging the 3D parts and the part-wise relation is proposed which the motivation is learning the parts from the known classes could help the model capture more transferrable features or concepts for th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful observations on the generalized category discovery (GCD) problem. Your careful analysis has given us many new perspectives to consider! We sincerely appreciate you thought "the idea \[...\] is interesting" and "like the organization of our paper". We address your thou... | Summary: This work presents a framework, called Decompose Novel Into Known parts (DNIK), that addresses the challenge of 3D Novel Class Discovery (NCD) – identifying new classes from an unlabeled dataset using the knowledge of known classes. Current methods, heavily biased towards known classes, struggle to generalize ... | Rebuttal 1:
Rebuttal: Thank you for your attentive comments! We are glad you thought “the studied direction is important” and “the components \[...\] are sound and reasonable.” We address your feedback point by point below.
>Q1: Previous literature exploited "harder" task, such as segmentation. Can the framework be ex... | Rebuttal 1:
Rebuttal: # **General Response**
Dear reviewers and AC,
We sincerely appreciate your valuable time and efforts spent reviewing our manuscript. We are grateful that reviewers found "the proposed method outperforms all baselines consistently and significantly on all metrics" (Reviewer zZT9), "the studied di... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problem of 3D NCD (novel class discovery). The objective is to discover novel classes by leveraging information learned from the known classes. This paper proposes a novel framework, DNIK, for 3D novel class discovery (3D NCD) by leveraging part concepts and part-wise relations learned... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and useful suggestions! We are glad you think the proposed method "can bridge the gaps between known and novel shapes effectively" and "outperforms baselines consistently". We address your thoughts point by point below.
> Q1: The unseen class number is assumed ... | null | null | null | null | null | null |
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks | Accept (poster) | Summary: The paper proposes a method to learn low-dimensional continuous-time representations of network nodes, based on the collection of interaction events among them. More precisely, the events are in the form of $(i,j,t)$, where $(i,j)$ is the pair of nodes involved in the interaction event, and $t$ is the occurren... | Rebuttal 1:
Rebuttal:
> The introduction is way too high-level. The authors should be more specific about the problem setting in this paper, for example, why we care about dynamic models, continuous-time event data, low-dimensional representation of nodes etc.
See global response.
> The related work is not specific.... | Summary: The paper presents a framework called Intensity Profile Projection (IPP) for continuous-time representation learning in dynamic networks. The authors aim to address the challenge of capturing temporal dynamics and evolving relationships in dynamic networks with both high statistical precision and interpretabil... | Rebuttal 1:
Rebuttal:
> Lack of comparison with state-of-the-art methods: Although the paper claims improved performance over existing methods, it does not provide a comprehensive comparison with some existing continuous models such as GraphODEs[1,2,3,4] which combines neuralODE with GNNs to model network evolution ov... | Summary: To represent the continuous dynamic network, authors provide the framework based on the intensity profile. First, the intensity between nodes is estimated, which produces the intensity profile. Low dimension reduction via SVD is applied on the intensity, and then each node embedding is obtained by the low dime... | Rebuttal 1:
Rebuttal: > The proposed method is not novel enough. SVD decomposition is a very common technique for the reduction of dimensions,
This is unreasonable. Dismissing our algorithm as "not novel" because it contains an SVD is like dismissing an optimisation algorithm because it uses SGD. The novelty of the al... | Summary: The authors propose an approach for learning time-varying node embeddings from continuous-time dynamic network data, which consist of a set of instantaneous timestamped relational events between nodes (e.g., messages from one social media user to another). Their proposed approach learns a projection that minim... | Rebuttal 1:
Rebuttal: > There's a large body of related literature on probabilistic generative models for continuous-time networks using point process models such as Hawkes processes that should be discussed. Many of these models are based on stochastic block models or latent space models and are thus also learning nod... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and expertise. We summarise some of the positive comments made by reviewers: "a unique and innovative approach to continuous-time representation learning for dynamic networks", "Simple but powerful method", "among the first, if not the first, in the literature... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Expressive Sign Equivariant Networks for Spectral Geometric Learning | Accept (spotlight) | Summary: The authors proposed a novel sign-equivariant model that is equivariant to the sign flip of eigenvectors for spectral graph learning. They demonstrated that the proposed architecture could capture the information when sign-invariant models would fail. The authors then made theoretical analyses of the sign-equi... | Rebuttal 1:
Rebuttal: > “Proposition 1 lacks some detail (See Question 1 & 2).”
> “In Proposition 1, the first result claims that if $f$ is sign invariant and the eigenvalues are distinct, then the node-wise representations are the same for automorphic nodes. Consider the complete graph of 3 nodes, the adjacency matri... | Summary: The paper proposes a sign-equivariant design for processing spectral features in geometric deep learning.
This is particularly useful to process the eigenvectors of graph Laplacians and generate graph positional encodings, especially for link prediction tasks.
The equivariant, rather than invariant, approach g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments on the choice of group representations in intermediate layers. Here, we respond to the comments; we will add discussion about this to the main paper, which we think will be interesting to people in the equivariant machine learning community.
> “... | Summary: This paper contributes construction and analysis of sign equivariant neural network architectures for processing eigenvectors while respecting their symmetries. While a similar approach has been proposed by a prior work (Lim et al., 2023), this work is motivated by the fact that sign invariance, pursued in pri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We are especially glad that the reviewer liked section 2.1 on multi-node representations and link prediction, as we originally had trouble writing it, but spent time iterating on it.
> “W1. The description of how sign equivariant network is applied in App... | Summary: This paper focuses on addressing the sign ambiguity problem of eigenvectors. It argues that previous sign-invariant models are insufficient for some applications, e.g., link prediction and multi-node tasks. To solve this problem, the authors propose a sign-equivariant neural network with provable expressivenes... | Rebuttal 1:
Rebuttal: We thank the reviewer for their work in improving our paper! We are glad that the reviewer appreciates our theoretical characterization of sign equivariant functions and improvements over sign invariant networks. Here we address the comments:
> “The major weakness of this paper is that it lacks e... | Rebuttal 1:
Rebuttal: We would like to sincerely thank all of the reviewers for the work they put into their reviews. The reviews are thoughtful, and the reviewers are each clearly experts in at least some of the several subject areas related to the submission (geometric deep learning, orthogonally equivariant models, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes to build sign equivariant neural networks with applications of the method in O(n) equivariant modeling and graph representation learning. They show that corresponding equivariant MLPs are inexpressive, by contrast to the proposed method which is universal (non-permutation equivariant versio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their in-depth comments and suggestions for our paper! Here we address the comments:
> “(W1) I think the main result of this paper could be obtained from the results of [Villar 2021]. ... Sign equivariance is O(1) equivariance, so it is a particular case of O(n) equivari... | null | null | null | null | null | null |
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction | Accept (poster) | Summary: Inductive Conformal Prediction (ICP) provides a coverage guarantee of constructed prediction sets, while does not provide any guarantee on the efficiency of prediction sets. The efficiency depends on a conformity score function, and direct approaches optimize the score function to minimize the efficiency of pr... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback. We address each of your questions below:
- *Efficiency bound (Theorem 2):* As you noted, we don’t directly optimize the generalization bound in Theorem 2 in our algorithm. This is due to the fact that the optimizable part of the second term... | Summary: This work first derives coverage and efficiency generalization bound using PAC-Bayes for inductive conformal prediction. Furthermore, a practical algorithm, basing bayesian learning and conformal training, is proposed to learn efficient nonconformity score functions. In experiment section, the proposed algorit... | Rebuttal 1:
Rebuttal: Thank you for your insightful and helpful comments. We address your criticisms and questions below:
- *Data-splitting:* As you point out, in the experiments we split the data, using part of it to tune the prior, and the rest to simultaneously tune the posterior and also achieve test-time coverage ... | Summary: The efficiency of set-valued predictor is crucial and less explored, this paper use the framework of PAC-Bayes to obtain the generalization bounds on both the coverage and the efficiency of set-valued predictors. The authors also propose a novel algorithm to optimize the efficiency without a separate hold-out ... | Rebuttal 1:
Rebuttal: Thanks for your detailed feedback. We address your questions below:
- *Constraint Satisfaction:* The constrained optimization solver we use in our experiment ensures that solutions satisfy Corollary 2.1 by construction. In particular, we solve the constrained optimization problem with an augmented... | Summary: Accurate uncertainty estimation is crucial in building robust and trustworthy machine learning systems. This paper utilizes PAC-Bayes theory and inductive conformal prediction (ICP) to develop a practical algorithm that fine-tunes parameters and score functions using calibration data, while ensuring inference ... | Rebuttal 1:
Rebuttal: Thanks for your review and insightful criticisms and comments. We address specific comments below:
- *Section 5.2:* We acknowledge that section 5.2 is a very succinct summary of the optimization approach. We will update our manuscript to go into more details of the algorithm, and add a section in ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and valuable feedback on our manuscript. We are glad the reviewers all found our paper clear, sound, and well-structured. Furthermore, we are glad to hear that the reviewers appreciated the novelty of our approach in applying PAC-Bayes generaliza... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the setting of inductive conformal prediction (ICP). In this setting, given a learned point-predictor, a calibration set and a scoring function is used to obtain a set-valued predictor that contains the correct label with high probability. A drawback of previous approaches is that, for ICP... | Rebuttal 1:
Rebuttal: Thank you for your very thorough review and helpful comments! Below are answers to your specific questions:
- *Theorem 1:* This is a great observation. Indeed, in the Maurer-Langford-Seeger bound, the two arguments to the binary KL are the empirical risk and the generalization risk: for a fixed h... | null | null | null | null | null | null |
SpikeBERT: A Language Spikformer Trained with Two-Stage Knowledge Distillation from BERT | Reject | Summary: This paper proposes SpikeBERT, a spiking-based BERT model for text classification. It employs LIF spiking neurons an surrogate gradients for backpropagation. The training method consists of a two-stage distillation process (pre-training + task-specific). The experiments conducted on different benchmarks for En... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
#### **Q1: Why choosing LIF neurons instead of other neuron models? Why these choices for the architectural parameters?**
R1: Thank you for your reminding.
Firstly, LIF neuron is one of the most widely used spiking neurons, and other neurons can be seen as... | Summary: This paper presents SpikeBERT, an improved version of the Spikformer spiking transformer model for language tasks. SpikeBERT utilizes a two-stage knowledge distillation method that combines pre-training with BERT and fine-tuning with task-specific data. Experimental results show that SpikeBERT outperforms stat... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
#### **Q1: Although SpikeBERT significantly reduces energy consumption during inference, the two-stage knowledge distillation process may introduce additional costs. It would be helpful if the authors could provide insights on this matter.**
R1: Yes, the ... | Summary: The paper presents SpikeBERT, an implementation of BERT-based models on a Spiking Neural Network (SNN) architecture, motivated by theoretical energy efficiency benefits.
The paper presents the transformer architecture and a two-stage distillation approach which first distills a general purpose BERT model into... | Rebuttal 1:
Rebuttal: #### **Q1: The claims of improved energy efficiency.**
R1: We apologize for any confusion this may have caused.
Our claim of improved energy efficiency is that the energy consumption is mainly reduced **at the inference time**. Once spiking neural networks (SNNs) are well software-trained, they... | Summary: This work develops SpikeBERT, which extends Spikformer to perform language processing tasks, and proposes a two-stage knowledge distillation method for better training it. Experiments validate the improved accuracy of SpikeBERT over previous SNNs and the improved efficiency over vanilla BERT.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
#### **Q1: The novelty and technical contributions of this work are limited.**
R1: (1) Model Architecture: As shown in Figure 1, in addition to introducing the word embedding layer and the layer normal module, we also make the shape of the attention map y... | Rebuttal 1:
Rebuttal: We thank the reviewers for your insightful comments, which helped us to significantly improve the manuscript. The following major changes have been made in the revised manuscript:
(1) We have added the performance of SpikeBERT and baseline models on GLUE benchmark. However, the performance of bas... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors have proposed SpikeBERT which is an energy efficient Spiking Neural Networks(SNN) for natural language representation. The architectural design for SpikeBERT is inspired from Spikformer which is an SNN for computer vision, with the following major changes:
- Spiking Patch Splitting for images is r... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
#### **Q1: The novelty (4 aspects) of the paper is limited.**
R1: (1) As shown in Figure 1, in addition to introducing the word embedding layer and the layer normal module, we also make the shape of the attention map yielded by Spiking Self Attention (SSA) t... | null | null | null | null | null | null |
Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space | Accept (poster) | Summary: This work studies the support vector machines for data with latent hierarchical relationships, which are represented in hyperbolic spaces. Similar to the linear SVM in Euclidean spaces, the SVM in hyperbolic spaces have been seen in the literature but they are challenging due to some issues such as non-convexi... | Rebuttal 1:
Rebuttal: # Response to Reviewer 38iz
We thank the reviewer for the valuable comments and questions. Please find our responses below:
> 1. computational speed of the proposed algorithm
The complexity of our approach depends on the Riemannian gradient-based optimization technique that we chose to use. In ... | Summary: This paper presented a novel large margin classifier, dubbed HoroSVM, whose decision boundaries are horospheres in hyperbolic space, and proved it’s a convex optimization problem.
This paper presented several experiments depicting the competitive performance of the classifier in comparison to SOTA.
Strengths... | Rebuttal 1:
Rebuttal: # Response to Reviewer 1ZfQ
We thank the reviewer for the valuable comments and questions. Please find our responses below:
> 1. random perturbations over real-world dataset
The noisy label experiment was conducted using synthetic data, which
allows us to have full control over the level of noi... | Summary: The paper proposes a large margin classifier in hyperbolic space, Poincare ball models. To this end, horospherical decision boundaries (which are based on the Buseman function and are different level sets of the Busmann function at the ideal point) are used for the large-margin classifier. They also show that ... | Rebuttal 1:
Rebuttal: # Response to Reviewer yt6v
We thank the reviewer for the valuable comments and questions. Please find our responses below:
> 1. comparison with [33]
The reason for the absence of comparison with Weber et al. [33] in our experiments are:
(1). Weber et al. [33, Sec 6] focus on theoretically ... | Summary: Based on recent successes with hyperbolic embeddings of data with a (latent) hierarchical structure, this paper proposes a new type of SVM in hyperbolic space. Their SVM, named HoroSVM, uses horospheres as decision boundaries and the authors derive a way to compute the distance of a point to such a horosphere.... | Rebuttal 1:
Rebuttal: # Response to Reviewer cEx8
We thank the reviewer for the valuable comments and questions. Please find our responses below:
> 1. geometric motivation for horospheres
Our motivation behind developing a classifier based on horosphere decision boundaries was to create a large-margin classifier in ... | Rebuttal 1:
Rebuttal: # General response to all reviewers
We would like to thank all reviewers for their valuable comments. We are particularly encouraged that they consider the proposed method innovative (1ZfQ), beneficial for future research (cEx8), that the theoretical result is valid (cEx8, yt6v, 1ZfQ, 38iz), the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits | Accept (poster) | Summary: This paper considers the classical regret minimization problem in the stochastic multi-armed bandit setting with arms having distributions with support bounded from above by a known constant B, and sometimes also lower bounded by b. Typically, all the known asymptotically optimal algorithms for regret-minimiza... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and your precise questions, as well as your helpful suggestions.
We first answer to the points detailed in the weakness section of your review.
* (1) In our bandit algorithm, a sub-linear upper bound on the portfolio regret avoids over-exploration while a sub-l... | Summary: In this paper they provide fast optimal algorithms for (non-parametric) stochastic bandits.
Specifically, they consider the MED family of algorithms that require the computation of $\mathcal{K}_{inf}$.
Based on this family of algorithms they construct new variants that require the computation of $\mathcal{K}_{... | Rebuttal 1:
Rebuttal: Thank you for your encouraging evaluation of our work and for your helpful questions and suggestions.
* **Portfolio regret assumption** We agree that this crucial assumption should be mentioned sooner in the paper, we will follow your suggestion and introduce it already in Section 2.
* **Questi... | Summary: This paper consideres classic multi armed bandit problem. The focus is on developing asymptotically optimal algorithms with efficient computational and memory complexity. The results for the proposed algorithms are reported in Table 2. FMED and FIMED compute KL divergence for the armed that is pulled while usi... | Rebuttal 1:
Rebuttal: Thank you for your comment, in the following we hope to address your concern on the technical contribution of our paper. Though the results from Honda \& Takemura (2010) and the literature on portfolio selection are the starting point of our work, we believe that we solved several significant tech... | Summary: This paper studies non-parametric stochastic bandits. In particular, the author proposes algorithms named Fast (Indexed) Minimum Empirical Divergence (FMED, FIMED), and Online (Indexed) Minimum Empirical Divergence (OMED, OIMED). These algorithms are designed based on Minimum Empirical Divergence (MED). Regret... | Rebuttal 1:
Rebuttal: Thank you very much for your questions, that we hope to address in the following.
* **Second-order terms** In the revision we will detail the scaling of the $o_{\epsilon}(.)$ term in Theorem 1, and include the following short discussion. In our proof, all its components are explicit, so the scali... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments on the paper, and appreciate their overall positive feedback.
Following the suggestions of all reviewers, our revision will mainly serve the purpose of providing additional intuitions to some technical parts of the paper. We also thank the revie... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This papers studies asymptotically optimal algorithms for regret minimization in the multi-armed bandit setting with bounded reward distributions. The main contribution is an efficient approximation of the KL constraint of the lower bound, leading to faster algorithms with (asymptotic) regret guarantees. The p... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and for proposing several ways to further improve the clarity of our paper. In the following we answer to the points raised in your review, in order. Please feel free to ask for any further clarification during the discussion phase.
* **Remarks in the Strengths ... | null | null | null | null | null | null |
Brain encoding models based on multimodal transformers can transfer across language and vision | Accept (poster) | Summary: There is a fastly growing literature analyzing how deep learning models' representations have predictive power over fMRI brain measurements. These papers typically train a regressor model (typically a linear regressor) to predict the fMRI measurements from the neural models' representations. Current encoding m... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and making these suggestions. We have addressed your concerns in order below:
>Since the narrative stories used in the story experiment are completely different from short movie clips…
Thank you for raising this point. We will describe the concepts contained in ... | Summary: This paper aims to fit fMRI data using transformer models. Of note, the paper aims to extrapolate from models that fit fMRI responses to visual stimuli to be able to account for fMRI responses to what may presumably be language stimuli. The results throughout the paper are extremely weak but are presented as i... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and making these suggestions. We have addressed your concerns in order below:
>It would be great to start by demonstrating that the fMRI data actually relates to the visual and story stimuli…
We analyzed a publicly released dataset that has been used in previous... | Summary: This work seeks to identify multi-modal processing in the brain. The proposed technique can be summarized as follows: a language encoding model is trained to predict neural story responses from story representations, and a vision language encoding model is used to predict movie responses from movie representat... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and making these suggestions. We have addressed your concerns in order below:
>The experiment described in section 5.1 and figure 2 is difficult to interpret on its own…
We respectfully disagree with this assessment. Take the suggested example where the BridgeTo... | Summary: In this paper, the authors used a pre-trained multimodal transformer, the BridgeTower model, to obtain feature representations of language and visual stimuli in the fMRI experiments. They trained encoding models on these representations and brain responses, and demonstrated that models trained on one modality ... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and making these suggestions. We have addressed your concerns in order below:
>The authors used a publicly available dataset containing fMRI responses from only five subjects, which is quite limited. Some of the discoveries, including brain regions for representa... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to carefully read our paper, and for providing detailed feedback. We are excited to see that many reviewers agree that our approach is novel and yields interesting insights, and we sincerely appreciate the concerns and suggestions raised by all reviewers.... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies whether we can use multi-modal transformers to predict brain activities. It specifically uses neural nets’ encoding for languages to predict brains’ activities for seeing vidieos, and neural nets’ encoding for images to predict brains’ activities. Their finding is that these prediction model... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and making these suggestions. We have addressed your concerns in order below:
>I feel the paper has a very high bar on the audience, i.e., requiring one to have sufficient knowledge on transformers, MRI, and statistical analysis (multiple hypothesis test). I don’... | null | null | null | null | null | null |
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning | Accept (poster) | Summary: The paper proposes a class-distribution-aware method to deal with the semi-supervised multi-label learning (SSMLL) problem. The main motivation is that the conventional pseudo-labeling methods cannot be applied to SSMLL scenarios, since instance is assigned with more than one ground-truth labels. To solve this... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We are glad that you considered our work “well-structured, easy to follow”. We are glad to answer all your questions.
**Q1:** It seems that only one threshold can determine the positive and negative pseudo-labels for each class. It is suggested to explain th... | Summary: This work studies pseudo-labeling for multi-label semi-supervised learning. Differing from the traditional instance-aware pseudo-labeling methods, they propose to assign pseudo-labels to unlabeled data in a class-aware manner to capture the true class distribution of the unlabeled data. This work proposes CAT ... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments. We are glad that you considered our work “well-structured, solid experiments, helpful to the community”. We are glad to answer all your questions.
**Q1:** The key idea relies on the observation that the class proportions of positive and negative labels in la... | Summary: This paper proposes a Class-Aware Pseudo-labeling (CAP) method to solve semi-supervised multi-label learning (SSMLL) problem by controlling the assignment of positive and negative pseudo-labels for each class through a class-distribution-aware thresholding (CAT) strategy.
Strengths: 1. This paper proposes a ... | Rebuttal 1:
Rebuttal: Thanks for your great efforts for reviewing our paper. We are glad that you considered our work “easy to read, well-organized, solid”. We are glad to answer all your questions.
**Q1:** Lack of some references. This paper is devoted to solving the SSMLL problem. Therefore, the authors should revie... | Summary: This papers introduces a novel method called Class-Aware Pseudo-Labeling (CAP) to address the challenges of semi-supervised multi-label learning (SSMLL). Traditional pseudo-labeling methods struggle with instances associated with multiple labels and an unknown label count, often leading to the introduction of ... | Rebuttal 1:
Rebuttal: Thanks for your appreciation of our paper. We are glad that you considered our work “novel, a significant innovation, an important contribution”. We are glad to answer all your questions.
**Q1:** The paper does not provide a clear explanation for the choice of using an exponential transformation ... | Rebuttal 1:
Rebuttal: **About the limitation of the paper** (to reviewers xWdd, tMT4)
In general, the performance of pseudo-labeling depends mainly on two factors, i.e., the quality of the model predictions and the correctness of the estimated class distribution. Our work focuses on the latter. It is a promising futur... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Robust Representation Learning via Asymmetric Negative Contrasting and Reverse Attention | Reject | Summary: This paper empirically shows previous supervised adversarial training methods have two shortcomings: (1) The features of the natural examples and those from other classes are not distinguishable and (2) the features of the natural and adversarial examples are not aligned. To mitigate these two issues, the auth... | Rebuttal 1:
Rebuttal: We are grateful for your approval of the strengths and your constructive suggestions. The answers to your questions are as follows:
**W1) More models needed and comparison experiments with RobustBench.**
**A1)** We have followed your advice to make a comparison with the current state-of-the-art ... | Summary: This paper focuses on robust feature learning by combining two approaches: Adversarial Contrastive Learning and Robust Feature Selection. Specifically, it defines two characteristics for features: exclusion and alignment. The authors aim to enforce exclusion through Asymmetric Negative Contrast (ANC), which id... | Rebuttal 1:
Rebuttal: We are grateful for your constructive suggestions. The answers to your questions are as follows:
**Q1) Why does AT omit learning robust features? Is it possible to theoretically demonstrate why AT is unable to learn robust features? AT has good performance in adversarial robustness with enough da... | Summary: In this paper, the authors address a notions of exclusion and alignment in representaion learning for robust adversarial training (AT). They propose a generic framework for AT that includes asymmetric negative contrast and reverse attention in order to obtain robust representation. In addition, they propose to... | Rebuttal 1:
Rebuttal: We are grateful for your approval of the strengths and your constructive suggestions. The answers to your questions are as follows:
**Clarity and W1) Some parts of the paper need a clear explanation to easily show our ideas.**
**A1)** Thanks for your advice about clarity, we will add a dotted bo... | Summary: This paper presents two characteristics of robust features, exclusion and alignment, and proposes a novel adversarial training method with asymmetric negative contrast and reverse attention. For exclusion, it introduces asymmetric negative contrast loss and generates adversarial negative examples by targeted a... | Rebuttal 1:
Rebuttal: We are grateful for your approval of the strengths and your constructive suggestions. The answers to your questions are as follows:
**W1) The unclear computing details for Reverse Attention (RA).**
**A1)** Subject to page limitation, we focus on the principle and function of our method in the pa... | Rebuttal 1:
Rebuttal: Dear **ALL** reviwers,
We are very grateful for your time and constructive suggestions. Here, we first summarize the **strengths** acknowledged by multiple reviewers.
We are encouraged by the approval of Reviewer PqGt, Reviewer BjXk, Reviewer K7R1 and Reviewer 2AVw for our **inspirable motivatio... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work aims to improve the adversarial training (AT) techniques from the perspective of learning robust representation representations. Specifically, the authors highlight two characteristics of having robust features. Exclusion: the similarity of features of samples of one class should be very less from th... | Rebuttal 1:
Rebuttal: We are grateful for your approval of the strengths and your constructive suggestions. The answers to your questions are as follows:
**W1) The wrong predicted class leads to the wrong weighted feature and degraded performance.**
**A1)** The wrong predicted class always causes misclassification,... | null | null | null | null | null | null |
The s-value: evaluating stability with respect to distributional shifts | Accept (poster) | Summary: The paper presents a metric (s-value) that quantifies the uncertainty of statistical estimators in terms of their distributional instability. In addition, the techniques proposed can quantify the effect of directional shifts and the authors also discuss how the s-value can be used to improve estimation accurac... | Rebuttal 1:
Rebuttal: Thank you for the helpful suggestions and interest in our work. We hope that the additional evidence and our explanations will address your concerns.
> The interpretation of the s-value in (1) for scalar parameters is somehow clear since it measures the smallest divergence needed to change the si... | Summary: This work defines a novel statistical measure of the “stability” of a parameter in a distribution with respect to changes in that distribution. From a high level, it is defined as the minimum KL distance to a perturbation that flips the sign of the parameter. A method is given to calculate the s-value for mean... | Rebuttal 1:
Rebuttal: Thank you for your time in evaluating our manuscript. We hope that we can address your concerns with this rebuttal.
> One weakness is that the calculation of the s-value uses a theorem that is only applicable to mean value parameters.
Please note that we have many results that cover more general... | Summary: This paper proposes a measure to quantify the instability of a statistical parameter under distribution shifts, which calculates the minimal KL divergence to flip the sign of the estimated parameter. The authors demonstrate its usage in helping to collect target samples in transfer learning. The idea is clear ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We are excited to hear that you “love this paper”!
> The experiments seem a litter inadequate. I view this paper as technical work, but there are almost no baselines to compare in experiments. There are some recent papers sharing similar ideas (efficiently ... | Summary: For a given data distribution P0 and family of distributions around it, mathcalP, the authors propose a measure of stability in estimating
a parameter theta(P) when there are distribution shifts within mathcalP. The idea is that you have data P0, but might really be interested
in estimating theta for some P' ... | Rebuttal 1:
Rebuttal: We appreciate your careful and thorough review. We are glad that you find it “great that the authors are bringing together the two areas” [distribution shift in ML for predictive tasks and estimation problems in statistics]!
Thank you for your thoughtful comments on writing, we will address them ... | Rebuttal 1:
Rebuttal: # Rebuttal Summary
We thank you for your constructive feedback. We appreciate that you find the approach “novel and interesting” (qNys), that you “appreciate the quality of this paper” (oQ9C), that the “theoretical analysis is solid” (msmy), that it is “a promising initial step towards novel meas... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work introduces the s-value, a novel metric to measure the stability of statistical parameters. It is defined as the exponential of minus the largest KL-divergence for which the statistical parameter is 0. The smaller the s-value the more stable the parameter is. When the statistical parameter is the mean... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. Also, we are glad to hear that you “appreciate the quality of this paper”.
> I found the experiments hard to interpret, e.g. in Figure 4 and 5, what is beta? Maybe you could improve the legend for those figures to make them easier to interpret. In Figure 4, w... | Summary: This paper proposes a novel metric, called the s-value, for evaluating the stability of statistical parameters with respect to distributional shifts. This metric is based on a variational problem involving the KL divergence between the target distribution and the shifted distribution, which can be solved via a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful remarks.
> Can you prove the consistency of $\hat s_E (\mu, P_n) $ under a weaker assumption on $\hat f_n(E)$?
Yes, such a result can be obtained under $L_p$ convergence and a boundedness assumption, with small modifications to the current proof. We are happy to rel... | Summary: This paper proposes method to quantify instability of a statistical parameter with respect to pertubrations around the KL divergence ball. This has implications to detect where statistical conclusions no longer hold when there is a distribution shift. The authors show this metric across both overall and direct... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback and comments.
> What is the reason for a partial transfer if a full transfer works better than or just as well?
Cost! Full transfer relies on having data on all covariates. Some of the data might be very expensive to collect. Partial data is often available ... | null | null |
Learning to Tokenize for Generative Retrieval | Accept (poster) | Summary: As one of the mainstream paradigms for document retrieval, generative approaches have enjoyed a steady growth of interest thanks to the recent thriving of large language models. Document tokenization is a crucial step in generative retrieval, which is rule-based in most existing methods, usually generalizing p... | Rebuttal 1:
Rebuttal: Thanks for your time and valuable comments!
**Presentation of method section**
- Thanks for your suggestions, we will add more explanations to the final paper to improve presentation.
- Firstly, the reconstruction loss optimizes both the encoder-decoder and the codebook. Concretely, $z_T$ is t... | Summary: Current document retrieval systems map documents and queries to doc ids. These docids are assigned randomly, by clustering by using text/attribute information. The authors propose a learned document tokenization scheme where the the semantics of the documents are encoded into learned docids. These docids are u... | Rebuttal 1:
Rebuttal: Thank you for your insightful review! We will address each of your points in turn.
**Compared with Rajput et al.**
- Thanks for your insightful comment. First, we believe it is beneficial to joint the modeling of tokenization and retrieval tasks on text retrieval tasks. From the perspective of ... | Summary: GENRET, the proposed model learns to tokenize documents into short discrete representations (i.e., docids) via a discrete auto-encoding approach. Authors develop a progressive training scheme to capture the autoregressive nature of docids and diverse clustering techniques to stabilize the training process.
St... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We appreciate your feedback and will address each of your points in turn.
**Line 173-174**
- Thanks for your comments and we will add more explanation and clarification in this part in our final paper. Specifically, we employ straight-through gradient e... | Summary: This paper introduces GenRet, an auto-regressive retriever that focuses on finding the right clusters (or document ID or document tokenization) approach. Compared to previous generative retriever approaches, GenRet uses three different losses, progressive training, and clustering techniques. Overall, the paper... | Rebuttal 1:
Rebuttal: Thanks for your time and insightful comment. We would like to address your questions in turn.
**Compare to dual-encoder**
- Thanks for your insightful comment. We further compared the models with different values of M and K. The results are in the following table. We find that the performance of... | Rebuttal 1:
Rebuttal: To all.
We appreciate all the reviewers for the constructive comments. We have included four figures in our newly updated PDF:
- Figure 1 presents a case study of document content matched with the relevant docid generated by GenRet. It suggests that documents with similar docids share closely re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper works on an emerging research direction, generative retrieval, where retrieval is considered as a generating the document ids. This paper proposed to learn a seq2seq model to generate docids from the document. The challenge lies in how to propagate the retrieval loss (from another seq2seq model: qu... | Rebuttal 1:
Rebuttal: Thanks for your time and insightful comment. We would like to address your questions in turn.
**Two reasons for different numbers for ANCE/docT5query**
- First, please note that we are focusing on the document retrieval task in our paper, whereas the results you referenced are from the passage r... | null | null | null | null | null | null |
Simple and Asymmetric Graph Contrastive Learning without Augmentations | Accept (poster) | Summary: This paper proposes an asymmetric contrastive learning framework for the homophilic and heterophilic graphs, which does not rely on graph augmentations and homophily assumptions. The theoretical analysis and empirical results further support the effectiveness of the proposed method.
Strengths: 1. This paper i... | Rebuttal 1:
Rebuttal: Dear reviewer gZUY, we thank you for your valuable suggestions and positive feedback. **We are happy to hear that you found our paper to be well-written and strong in both empirical and theoretical aspects.** The following is our point-to-point response to your comments:
**(C1). This paper is so... | Summary: This work first points out that existing GCL can fail to generalize to heterophilic graphs, then develops a new framework called GraphACL based on an encoder capturing one-hop neighbourhood context and two-hop monophyly. Experiments validate the effectiveness of the proposed method.
Strengths: The paper is we... | Rebuttal 1:
Rebuttal: Dear reviewer cxpZ, **thank you for the great summarization of our contributions on both theoretical and empirical analysis, and we appreciate your very positive and encouraging comments.** Please see our responses below:
**(C1). There are no significant weaknesses in this paper. The applicati... | Summary: This paper presents GraphACL, which aims to tackle limitations of other graph contrastive learning works which have implicit or explicit homophily assumptions, and suffer in learning effective representations for heterophilic tasks. The approach is designed to leverage the principle of monophily, and the auth... | Rebuttal 1:
Rebuttal: Dear reviewer ycWA, **we appreciate your perception that our model is implementation-wise simple, strongly effective, and intuitive. We thank your insightful comments and give our responses below:**
**(C1). Lines 69-80 could greatly benefit from a toy example**
**(R1).** Thanks for your great su... | Summary: This paper propose a simple and effective contrastive learning framework named GraphACL for both homophilic and heterophilic graphs. In particular, GraphACL can capture both one-hop local neighborhood context and two-hop monophily similarties in one single objective. The authors theoretically analyze the learn... | Rebuttal 1:
Rebuttal: Dear reviewer 1K5Q, **we appreciate your great summarization and recognition of our contributions and your positive comments on our work: "well written," "early investigation," and "sufficient experiments and theories."** Please find our responses to your comments below:
**(C1). For the first c... | Rebuttal 1:
Rebuttal: **We sincerely thank all the reviewers for their insightful comments and helpful suggestions. Overall, the reviewers praised our work's originality, soundness, and clarity. We deeply appreciate the numerous positive comments on our work, such as describing it as "simple, effective, and intuitive,"... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents GraphACL, a contrastive learning method for graph representation learning. It aims to address the limitations of current methods that only consider the homophily property of graphs or rely heavily on graph augmentation methods. The authors propose an asymmetric predictor approach where the o... | Rebuttal 1:
Rebuttal: Dear reviewer fMCY, **we appreciate your positive feedback on our paper's soundness, novel insights, and contribution. Please find our detailed responses below:**
**(C1). The explanation of how GraphACL works and how it captures both the one-hop neighborhood context and the two-hop monophily si... | null | null | null | null | null | null |
Provably Safe Reinforcement Learning with Step-wise Violation Constraints | Accept (poster) | Summary: This paper studies safe RL with step-wise violation constraints, different from the popular CMDP with
an additive expectation cost constraint. The step-wise violation constraint is more suitable for safety-critical
systems. The authors propose an algorithm that provides violation and regret bound. They then fu... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper! We really appreciate your positive comments about our work. Please find our responses to your comments below. We will be happy to answer any further questions you may have.
**1. Discuss with [3].**
[3] considers the same step-wise constra... | Summary: This paper formulates and studies a strict step-wise violation constraint reinforcement learning problem, where a non-negative state-dependent cost is cumulated at each step. They show lower bounds for regret and safety violations. A model-based algorithm that matches the lower bound is provided, while another... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper! Please find our responses to your comments below. We will be happy to answer any further questions you may have.
**1. Can the cost be (state, action)-dependent?**
The reason why we choose the state-dependent cost is to follow the setting... | Summary: The authors propose a new formulation for the safe RL problem Safe-RL-SW, whose violation constraints are step-wise, different from the existing work. A model-based general algorithmic framework SUCBVI is proposed and the theoretical guarantees on the upper bound and lower bound of its regret are provided. The... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper. We will be happy to answer any further questions you may have.
**1. Comparison with existing works**
We provide a brief summary of existing papers with instantaneous constraints and list the assumptions.
**(1). Gaussian Process (GP) Str... | Summary: This paper considers online RL problem for an MDP with stage-wise constraints. The stage-wise constraints basically specify the set of unsafe states which must be avoided at all times. While there is a lot of recent work on CMDPs, this formulation, which is actually more relevant is less studied. The authors p... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper! Please find our responses to your comments below. We will be happy to answer any further questions you may have.
**1. Unfortunately, the results are weaker than what has been achieved recently for CMDP problems: some of the formulations o... | Rebuttal 1:
Rebuttal: Thanks for the responses of all the reviewers! The experiment curve with variance is attached in the PDF. We only plot the first few
episodes to show the variance of each algorithm more clearly.
Pdf: /pdf/ec30f75fc76df3e99d709cd8a33344032b258d99.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies an episodic constrained reinforcement learning problem with step-wise constraints on states. The authors first extend the classical UCB-VI to step-wise constraints and prove the sub-linear optimality gap and step-wise constraint violation. A lower bound is also provided to show optimal depend... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper! We will be happy to answer any further questions you may have.
**1. Comparison with existing works and applications**
We provide a brief summary of existing papers with instantaneous constraints and list their assumptions.
**(1). Gaussian... | null | null | null | null | null | null |
Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention | Accept (poster) | Summary: This paper extends the FlashAttention to support the structured sparse attention, enabling attention to leverage sparsity to achieve further acceleration on the basis of FlashAttention. In this way, the prior QK-sparse attention and Hash-sparse attention methods can be further accelerated during training and e... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your feedback! We hope the following answers your questions and may bring you to raise your score.
> The application of this method will be limited, only suitable for two calculation modes, QK-sparse attention and Hash-sparse attention.
... | Summary: This paper extends Triton implementation of FlashAttention to support two forms of sparse attention: key/query dropping and hashing-based attention. Source code for the kernels are made available.
Strengths: The paper gives clear descriptions of the released kernels and provides comprehensive validation exper... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our work. We hope those clarifications are addressing your concerns and may justify raising your score.
> The K/Q dropping sparse attention seems not useful in training large language models. It seems very rare that we would mask certain tokens completely an... | Summary: This paper proposes an improved version of FlashAttention to support irregular block sparsity due to queries/keys-dropping or hashing. The proposed method modifies the mechanism used in FlashAttention to arbitrary indexing of queries/keys, which can be viewed as combining both FlashAttention with either QK-dro... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
> Can you provide more details in the SCFA kernel design?
Our kernel design relies on the FlashAttention algorithm as a starting point. On top of this, we (i) reshape input tensors to bring an interesting structure of the attention matrix, and (ii) exploit this struc... | Summary: The work proposes a new method to speed up and improve the causal self-attention of transformer-based language models for long sequences. The method uses a kernel called SCFA that can handle any sparsity pattern and causal mask in the attention matrix. The method also introduces two dynamic schemes to sparsify... | Rebuttal 1:
Rebuttal: We thank you for your review and your appreciation of our work!
> When you run the comparsion, proposed solution running on 2 or 3 A100, while Reformer alwasy run on single GPU. Is it correct? if yes, why?
Given our limited resources, we decided to use our multi-GPUs infrastructure to experiment... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to review our work. We improved and optimized our Hash-sparse kernel and new results are shown in the figures provided with this rebuttal. Compared to our previous version, still without sacrificing perplexity, we further increase the training speed of a ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a method for fast causal attention by combining dynamic sparse attention (query & key dropping or from hashing) with FlashAttention, called SCFA (Sparse Causal FlashAttention). The paper shows that with the right preprocessing, attention with query & key dropping (QK-sparse) can also be done... | Rebuttal 1:
Rebuttal: Thank you for the valuable time spent reviewing our paper!
> The motivation for QK-sparse attention could have been explained better. I'm personally not as familiar with this method. Why would one want to drop query \& key?}
There is a large body of work [1,2,3,4,5] that suggests it is possible ... | null | null | null | null | null | null |
Attacks on Online Learners: a Teacher-Student Analysis | Accept (poster) | Summary: This paper sheds light on the vulnerabilities of online learners to adversarial attacks and provides insights into the manipulation of learning dynamics. The findings highlight the need for robust defenses against such attacks and contribute to the growing body of research on data poisoning attacks in machine ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the constructive criticism and the relevant questions. Please see below for our response.
**Weaknesses**
This paper primarily focuses on linear regression models and does not extensively explore the impact of adversarial attacks on other types of machine learning models... | Summary: Data poisoning attacks have been extensively studied in the offline setting, while poisoning attacks in the online setting have received little attention. In the online setting, the attacker is forced to craft attacks exploiting the data stream, taking into account the state of the model and the possible futur... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the constructive criticism and the relevant questions. Please see below for our response.
**Weaknesses**
Attacker only modifies the labels instead of the entire inputs.
* The case of online poisoning of the labels has not been addressed before, despite its relevance in... | Summary: This paper analyzes the robustness of online learners when the labels of the received data are manipulated. In particular, it analyzes a student-teacher online learning problem where the attacker poisons the labels provided by the teacher before feeding the student the labeled batch. The setup is analyzed both... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the constructive criticism and the relevant questions. Please see below for our response.
**Weaknesses**
Examples of real world scenarios where attackers have access to manipulate the labels before feeding the labeled batches to the learner.
* **There are several pract... | Summary: The authors conduct theoretical analysis and empirical evaluation of poisoning attacks in the online learning setting, where the attacker can intervene in the labels of sequentially provided data. As a result of the theoretical analysis, the authors show that the strength of the attack becomes discontinuously ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the constructive feedback. Please see below for our response.
**Weaknesses**
There is little discussion of threat models and attacker models when viewed as a security issue. There is no disagreement that poisoning in an online setting is a serious threat, but the paper ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive criticism. We are thrilled to see that the reviewers broadly agree this is an important, underexplored problem, and that our theoretical and empirical analysis offers some novel, non-trivial insights. We certainly agree that the scenario ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper provide an analysis of steady state of linear learners trained via SGD under online setting, where the found a phase transition in terms of attack strength. Some experiments are also conducted to demonstrate the insight from the theory.
Strengths: The formulation of attacking learning model under on... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive criticism and for the enthusiastic description of the strengths of our paper.
**Weaknesses**
Overall my feeling is both empirically and theoretically, the paper is not strong. Theory side, the main concern I have on the work is that the problem might be... | null | null | null | null | null | null |
Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow | Accept (poster) | Summary: This article proposes a robust detection algorithm to address the issue of detection errors caused by asynchronous information transmission in multi-agent collaborative perception tasks. For late fusion, a robust prediction algorithm is designed using a BEV flow map generation algorithm, which provides a metho... | Rebuttal 1:
Rebuttal: ## W1: Enhancing Clarity of Formulas, Methods, and Experiments
Thank you for your feedback. We will revise the corresponding expressions in the final version for better understanding. We have provided data tables in the appendix corresponding to the main text, which can be referred to more accura... | Summary: The paper proposes CoBEVFlow, a new system for collaborative 3D perception that can handle temporal asynchrony among multiple agents. CoBEVFlow compensates for motion to align asynchronous collaboration messages and has two advantages: it can handle irregular time stamps without discretization and only transpo... | Rebuttal 1:
Rebuttal: ## W1: Training Process Complexity and Practical Application
The training process does require three stages, but it is not time-consuming and does not limit practical application scenarios for two reasons.
1. First, each of the three modules is lightweight. The training of the individual detectio... | Summary: This work points out that there is a time delay among agents and the delay period is not fixed. Thus, when these agents share their environment perception information in BEV, there will be an uneven spatial mismatch. To address the aforementioned problem, this work constructs a benchmark and proposes a strateg... | Rebuttal 1:
Rebuttal: ## W1: Communication Delay in Practical Applications
In practical applications, it is straightforward to know the communication delay because different vehicles can easily acquire a unified world timestamp.
1. A unified world timestamp is easily obtainable for agents. Several existing technologie... | Summary: The paper introduces CoBEVFlow, a new asynchrony-robust collaborative 3D perception system designed to enhance multi-agent perception abilities. The method compensates motions to align asynchronous collaboration messages from different agents, aiding in dealing with real-world issues such as communication dela... | Rebuttal 1:
Rebuttal: ## W1: Limited Evaluation and Applicability
1. Besides vehicle-to-vehicle collaboration communication, we also report the result of experiments on the vehicle-to-infrastructure dataset DAIR-V2X in the main text. This dataset includes information from both the vehicle side and the roadside units, e... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback!
In this work, we propose CoBEVFlow, an asynchrony-robust collaborative 3D perception system based on bird’s eye view flow, to address the issues caused by temporal asynchrony among agents.
In the main text, we conducted experiments on two datasets: IRV2V a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Segment Everything Everywhere All at Once | Accept (poster) | Summary: The paper proposes SEEM, a method that unifies several segmentation tasks. The network takes text and/or different kinds of visual prompts as inputs. The model then outputs the corresponding segmentation masks for the referred objects. In particular, SEEM trains a unified prompt embedding space. A set of learn... | Rebuttal 1:
Rebuttal: **Q1: Lack motivation and details (e.g. self-attention mask, output embeddings).**
Sorry for the confusion, we explain the motivation of self-attention mask in details here.
* Inputs: Text, user input interactive scribbles, and none.
* SEEM Decoder Inputs: (1) Queries. (2) Prompts. (3) Image Fea... | Summary: This paper introduces SEEM, an innovative and interactive model for segmentation. SEEM stands out with its versatility in handling various prompts, such as points, boxes, scribbles, masks, and texts. The model's design is elegantly simple yet highly effective. Additionally, SEEM incorporates memory prompts to ... | Rebuttal 1:
Rebuttal: **Q1: Ablation study on prompt type.**
Thanks for the valuable suggestion, ablation the prompt type is intuitive to study the effectiveness of each counter part, here are the results:
| | Panoptic | Grounding | Interactive | | COCO | | | Ref-COCOg | | | V... | Summary: [Task] In this work, authors introduc SEEM, a promptable and interactive model designed for comprehensive image segmentation. SEEM aims to segment all objects in an image simultaneously, addressing various segmentation tasks. The key contribution of SEEM is its novel decoding mechanism, which allows for divers... | Rebuttal 1:
Rebuttal: **Q1: Performance on open-vocabulary segmentation is lower in comparison with X-Decoder, and performance on video instance segmentation performance on DAVIS is lower than UNINEXT.**
We agree with the reviewer that our open-vocabulary performance on ADE20k is *potentially* lower than X-Decoder an... | Summary: This paper proposes a universal segmentation model, SEEM, for all segmentation tasks. A visual sampler module unifies different kinds of human inputs and they are encoded into a joint visual-semantic space together with image and text so that the SEEM model can learn semantic labels for masks. The proposed SEE... | Rebuttal 1:
Rebuttal: **Q1: Insufficient Analysis for ablation study.**
Thanks for the suggestion. We have analyzed the ablation study in Section 4.2 of the main paper. To make it more comprehensive, we further summarize and analyze the main findings in Table 4 below:
*Removing LVIS mask annotations (Row 2 vs. Row 6)... | Rebuttal 1:
Rebuttal: First of all, **we thank all reviewers for their valuable comments and suggestions!**
We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. We are glad to find that reviewers generally recognized our contributions:
**Model.** A strong generalization ability that is able... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection | Accept (poster) | Summary: This paper presents a reconstruction-based method for multivariate time series anomaly detection. It is a memory-guided Transformer which contains the gated memory module. Because of the training instability in updating memory items incrementally when the items are
initialized randomly, the authors propose a t... | Rebuttal 1:
Rebuttal: We are thankful for your meticulous review and feedback on our paper. Your perceptive observations have steered us towards enhancing and refining our work.
$\textbf{Weaknesses}$
1. We appreciate your keen attention to detail and will proceed to correct the grammar error on line 88, the typo on ... | Summary: The authors propose a memory-guided transformer with a reconstruction paradigm for multivariate time series anomaly detection. The time-series encoder, memory parameters of normal patterns, and projection heads are updated during training time in a two-step fashion. On test time, input queries are projected on... | Rebuttal 1:
Rebuttal: We're grateful for your detailed evaluation and insight on our research paper. We will make sure to incorporate the parts that you suggested for clarity and reflect their feedback on paper.
$\textbf{Suggestion}$
As you suggested, we will emphasize in the main manuscript that our proposed MEMTO is... | Summary: The paper used memory network to capture frequently present normal pattern in date set. While training, the memory are built in latent space in the autoencoder framework. Along with reconstruction error, the distance between a representation with the closest memory unit is used to calculate anomaly score. If ... | Rebuttal 1:
Rebuttal: We are grateful for your careful reading of our paper, and your feedback. Your insightful feedback has guided us in expanding and improving our paper.
$\textbf{Weaknesses}$
$\textbf{Literature surveys missed graph based anomaly detection techniques for multivariate time series}$
We will also in... | Summary: The authors of this paper focused on tackling the problem of over-generalization in reconstruction-based deep models and made a contribution to the field of multivariate time series anomaly detection. The main challenge they encountered was dealing with complex dependencies and inter-variable correlations with... | Rebuttal 1:
Rebuttal: Thanks for your dedicated review of our work. Your critical feedback have helped us to extend and refine the paper. We provide a detailed response to your comments.
$\textbf{Weakness}$
- $\textbf{Lack of theoretical proof}$: We agree that we lack theoretical proof, and we have mentioned this in ... | Rebuttal 1:
Rebuttal: We are very grateful to all the reviewers for your careful reading of our paper and helpful feedback. We will make sure to incorporate the parts that you suggested for clarity and reflect your feedback on the revised paper. We have compiled the results of additional experiments related to the revi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Can Language Models Solve Graph Problems in Natural Language? | Accept (spotlight) | Summary: The applications of Large Language Models (LLMs) have been extended beyond natural language, covering more complex tasks that might have an implicit graph structure, such as planning for robots. This work introduces a benchmark named Natural Language Graph (NLGraph) to examine the explicit graph reasoning of a... | Rebuttal 1:
Rebuttal: Thank you for your time and positive feedback!
> The evaluations are only conducted on text-davinci-003, therefore, some conclusions may be limited to this model.
While we evaluate text-davinci-003 on the whole dataset, we also provide the results of code-davinci-002 on several tasks in the NLGr... | Summary: The paper curates a benchmark dataset called NLGraph that contains 8 types of graph reasoning problems. Given that various previous works have used LLMs to solve real-world tasks that implicitly require some form of simple graph reasoning, this dataset aims to isolate and analyze the ability of LLMs to graph r... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our novel contributions as well as raising valuable questions.
> The performance of the model depends heavily on how the prompts are constructed. While Figure 1 shows the instructions used in the prompts clearly, to ensure reproducibility, the complete prompts for more... | Summary: This paper investigates whether large language models (LLMs) are able to solve graph algorithm problems in natural language. A benchmark NLGraph contains 29,370 problems, covering 8 graph reasoning tasks with varying complexity from simple tasks such as connectivity, cycle, and shortest path to more complex pr... | Rebuttal 1:
Rebuttal: Thank you for your helpful and constructive feedback!
> Using a programming style of prompting techniques (e.g. PAL[1], PoT[2]) for solving these graph reasoning tasks is more intuitive. It could potentially address the problem of generating too many tokens of code-davinci-002.
Yes, this is a v... | Summary: This work tried to answer the question, "Are LLMs capable of mapping textual descriptions of graphs and structures to grounded conceptual spaces and solving graph algorithm problems explicitly with natural language?" The answer to this question has profound implications for large language model applications wi... | Rebuttal 1:
Rebuttal: Thank you for your time and positive feedback!
> If we can include more LLM fine-tuning results, it will be of great help.
It would be valuable and interesting to see LLM fine-tuning results, but it is too expensive to fine-tune LLMs such as GPT-3. As we will make the NLGraph benchmark publicly... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewers for providing us with valuable feedback. These constructive comments have been instrumental in improving the quality of our work. We are glad that our efforts have been well-received by the reviewers, and we are confident that their i... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper evaluates and studies the performance of state-of-the-art LLMs on graph reasoning-based tasks. For this, the authors construct a testbed of graph and structured reasoning tasks, comprising of 29K problems on 8 graph reasoning tasks such as topological sorting and max flow. Each task has three subset... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and helpful suggestions!
> Lack of Systematic Comparisons between LLMs
We believe it is interesting to see how more recent LMs (e.g., GPT-4) perform on the NLGraph benchmark. However, due to the budget we had and as we will make the NLGraph benchmark publicly ... | null | null | null | null | null | null |
Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings | Accept (poster) | Summary: This focus of this paper on a new type of kernel for reservoir computing (using nonlinear vector autoregressive delay embeddings) that is faster and more accurate than related kernels.
The three main contributions
* Introduction of a new kernel for time-series modeling
* State of the art results on uni- and ... | Rebuttal 1:
Rebuttal: We warmly thank reviewer $\color{orange}{\textbf{XEWY}}$ for the thorough review and positive feedback.
We really appreciate the favorable comments on clarity of the writing and elegancy of the underlying idea, as well as positive remarks on how we bring together different research areas.
As for t... | Summary: This paper presents a kernel that can be applied to time series - both
univariate and multivariate - that draws inspiration from reservoir
computing. Practically, it constructs an expanded time series by
sampling lags and then computing polynomial combinations of the
original and lagged data through time. Ex... | Rebuttal 1:
Rebuttal: We warmly thank reviewer $\color{magenta}{\textbf{Nbgs}}$ for the extensive review and constructive feedback.
Among all, we really appreciate the kind words on the clarity of our paper and figures, as well as showing interest in how we connect our work to different research areas.
As for the raise... | Summary: The authors introduce a new time series kernel based on Nonlinear Vector AutoRegressive (NVAR) processes, following recent literature on its equivalence to reservoir dynamics. The kernel operates on time delay embeddings and enables the computation of similarity between time series with different lengths. The ... | Rebuttal 1:
Rebuttal: We warmly thank reviewer $\color{green}{\textbf{8sG2}}$ for the extensive review and positive feedback.
We particularly appreciate the positive remarks on clarity of the presentation and soundness of the work, which we are happy to see reflected in the scores.
In addition, we value your comment on... | Summary: This work proposes a feature extraction based on a non-linear vector autoregressive model (called NVAR kernel in the paper). The NVAR method constructs a deterministic feature matrix with the original input time series, lagged versions of the time series (parametrized by the lag and spacing parameters) and the... | Rebuttal 1:
Rebuttal: We deeply thank reviewer $\color{blue}{\textbf{hHQP}}$ for the extensive review and positive feedback.
We really appreciate the positive remarks on the general quality and clarity of our paper.
Similarly, we value the highlighting of a form of novelty and significance.
As for the raised concerns a... | Rebuttal 1:
Rebuttal: We would like to remark here our thanks to all reviewers for their extensive reviews.
We are happy to hear that reviewers acknowledged novelty ($\color{red}{\textbf{RJLc}}$, $\color{blue}{\textbf{hHQP}}$) and relevancy ($\color{blue}{\textbf{hHQP}}$, $\color{green}{\textbf{8sG2}}$) of our work, as... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In their paper, the authors propose a new method for deriving a kernel from time series data. They combine ideas from reservoir computing with nonlinear vector autoregressive models, based on recent theoretical work exploring their similarities.
The primary idea of the paper is to construct a kernel by modelin... | Rebuttal 1:
Rebuttal: We warmly thank reviewer $\color{red}{\textbf{RJLc}}$ for the thorough review and positive feedback.
We really appreciate the kind words on the clarity of our paper, as well as the novelty and innovative utilization of recent developments from a related field.
Lastly, we are also grateful for spot... | null | null | null | null | null | null |
Cross-modal Active Complementary Learning with Self-refining Correspondence | Accept (poster) | Summary: This paper tackles a new challenge in image-text matching, namely, noisy correspondence, which refers to the mismatched image-text pairs that can mislead the model to learn incorrect cross-modal associations during training, resulting in a suboptimal cross-modal model that computes inaccurate similarities for ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and insightful suggestions. We will address your concerns and questions one by one as follows.
**Q1. here is a typo in lines 48-49: CSRL should be CRCL.**
Thank you for your detailed review. We will correct all typos in the next version.
**Q2. This paper verifi... | Summary: This paper tackles a latent challenge in image-text matching, which is the presence of noisy correspondences between images and texts. The paper introduces a general framework that combines a robust loss function and a correspondence correction technique to enhance the existing models’ ability to cope with noi... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and insightful suggestions. Attached is our point-by-point response.
**Q1. Missing related works [1,2,3].**
We appreciate your valuable feedback and the related works you mentioned. We will include a discussion and a comparison of these works in the next versio... | Summary: This paper presents a novel framework (CRCL) for cross-modal correspondence learning that can handle noisy image-text pairs. The key idea of CRCL is to use a complementary active loss (ACL) that balances between discriminative learning and robust learning. ACL leverages the rectified correspondence labels to a... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and insightful suggestions. We will address your concerns and questions one by one as follows.
**Q1. In Eq.3, there is a typo: $i_j$ should be $I_j$.**
Thank you for your careful review. We will correct it in the next version.
**Q2. In Eq.11, some symbols are ... | Summary: This manuscript focuses on image-text matching under the noisy correspondence setting. To achieve a noise robust multi-modal representation, the authors propose two components, including a Active Complementary Loss (ACL) and a Self-Refining Correspondence Correction (SRCC). In ACL, a complementary contrastive ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and constructive suggestions. We will address your concerns and questions one by one as follows.
**Q1. Lacks necessary explanation in figures. Actually, the pure text claim to the proposed method could be harder to grab.**
Thank you for your constructive comment... | Rebuttal 1:
Rebuttal: This is a global response. We add the illustration of our method in the attached pdf file.
Pdf: /pdf/14578ecc24bc5e6edc52cae27261189cd2f403ea.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on the problem of noise correspondence in image-text matching tasks. To address this issue, this paper proposes a generalized cross-modal robust complementary learning framework, which not only reduces the risk of erroneous supervision from the active complementary loss but also obtains stab... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We have carefully looked into all comments. Attached is our point-by-point response.
**Q1. Regarding the concern of novelty .**
Thanks for your comment but we disagree with your opinion. Although some methods are proposed to address noisy correspondence (NC), t... | null | null | null | null | null | null |
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning | Accept (spotlight) | Summary: For class-imbalanced learning, many studies modified the loss function to emphasize the learning on minority classes by reweighting or logit adjustment. These studies showed high epirical classification performance, but existing generalization analysis of such studies is not unified. In this paper, the authors... | Rebuttal 1:
Rebuttal: > **Q1**: Code is not submitted. Thus I can not reproduce the experiment results in the paper.
**A1**: Thanks for your constructive concern! According to the policy
> If you were asked by the reviewers to provide code, please send an anonymized link to the AC in a separate comment (make sure th... | Summary: In this paper, the authors study the problem of class imbalance. To be specific, they identify that there is a gap between the generalization theory of re-weighting & logit adjustment techniques and the practice. To be specific, they identify that the existing generalization bounds fail to account the imbalanc... | Rebuttal 1:
Rebuttal: Thanks very much for your nice suggestions, and we would like to make the following response.
> **Q1**: Typos and grammatical errors.
**A1**: Thanks for your careful reading! We will correct these typos and grammatical errors in the future version.
---
> **Q2**: The paper uses many many acron... | Summary: This paper proposes a sharpened generalization bound of imbalance learning by directly bounding the balanced empirical risk. The authors achieve this by generalizing the Lipschitz Continuity to the Local Lipschitz Continuity with a group of constants, which, in VS Loss, is parameterized by a re-weighting term,... | Rebuttal 1:
Rebuttal: Thanks very much for your nice suggestions, and we would like to make the following response.
> **Q**: The authors only perform their experiment on the ResNet family for all the baselines and their proposed method. I hope the authors can provide the experiment result of their proposed method on ... | Summary: This paper provides a unified generalization analysis of the loss-modification approaches for imbalanced learning. It analyzes the gap between balanced accuracy and empirical loss (the loss may involve re-weighting and logit-adjustment approaches). It further provides empirical analysis that matches the theore... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, and the response is as follows.
> **Q1**: The meaning of bounding $\mathcal{R}_{bal}^L$.
**A1**: In the `non-asymptotic` level (finite samples), bounding $\mathcal{R}\_{bal}^L$ can put more emphasis on minority classes, which is consistent with your unders... | Rebuttal 1:
Rebuttal: Dear reviewers,
First, we would like to express our sincere gratitude for your valuable comments. Following the valuable suggestions, we have carefully polished and improved the corresponding details. Now we present a brief summary of the response.
- **We clarify some important concepts** such a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision | Accept (poster) | Summary: This paper proposed a method DSGAS to automcatiicaly design the architectures in an unsupervised manner. It discovers the optimal architectures by learning the latent graph factor.
Strengths: It is interesting and novel to automate the design of GNNs in an unsupervised manner, which holds significant potenti... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the comments and suggestions. We have carefully reviewed each point raised and make responses to the reviewer point by point as follows.
> The necessity of designing disentanglement is not clear. It is true that GNNs may preferred different graph factors. What ... | Summary: This paper addresses the problem of unsupervised graph neural architecture search, which has received limited attention in existing literature. The authors propose a novel approach called Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) to discover optimal architectures capturing latent gr... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments. We respond to the reviewer’s comments point by point as follows.
> While the paper presents a novel approach, further details regarding the implementation of the proposed DSGAS model in the main paper would be beneficial for the readers t... | Summary: This paper gives a pioneer solution for graph neural architecture search with limited labels. The key idea is to train a super-network containing disentangled factor-wise architectures by a self-supervised learning specially designed for graph neural architecture search. In this way, the paper addresses the ke... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable suggestions, which are helpful for the improvement of the paper. We respond to the reviewer’s comments point by point as follows.
> Although it may fall outside the scope of this paper, i'm curious about whether the model can be applied into areas ... | Summary: This paper mainly focuses on the problem of graph neural architecture search without labels. The authors find that the key problem is to discover the latent graph factors that drive the formation of graph data as well as the underlying relations between the factors and the optimal neural architectures. To this... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed comments and suggestions. We respond to the reviewer’s comments point by point as follows.
> Can the authors explain the details of the intuition for the proposed contrastive search?
Thank you for your suggestion. We provide the details of the int... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors study the problem of unsupervised graph neural architecture search, which remains unexplored in the literature. The authors propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures capturing vario... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed comments and insightful questions. We make responses to the reviewer’s comments as follows.
> What is the relationship between supervised and unsupervised nas paradigms in terms of the optimization problem, e.g., line 84 in the main paper?
Thank y... | null | null | null | null | null | null |
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space | Accept (poster) | Summary: The authors proposed a hierarchical spatial transformer model for many irregular point samples in continuous spatial domain. Compared with existing methods, the proposed method can model implicit spatial dependency across irregular samples and in multiple scales in continuous space. The proposed model uses a q... | Rebuttal 1:
Rebuttal: Question: how are the thresholds of uncertain/certain predictions determined in UQ metric?
Response: Thank you for your positive feedback. About how to choose thresholds of uncertainty in the UQ metric, we discussed them in the supplementary materials in section 7.1. Here we briefly describe how ... | Summary: This paper proposes a hierarchical transformer to model a large number of irregular point samples in continuous space. This is achieved by a quad-tree hierarchy which could learn the multi-scale spatial representation. So the long-range interactions are recorded.
The experiment is performed in three real-worl... | Rebuttal 1:
Rebuttal: Dear reviewer:
**W1 & Q1: how the quadtree technique could solve uniformly distributed cases.**
Thank you for the comment. In fact, our HST model does not require samples to be sparsely distributed for efficiency gain. For uniformly distributed point samples, the quadtree will be a balanced (com... | Summary: This paper proposes a quad-tree partition of irregularly distributed sample locations. This leads to an algorithm with O(NlogN) complexity.
Strengths: Quad-tree idea is nice, even though not original for irregular grids or pixels.
Weaknesses: The targeted problem in the paper is regression. But the model is ... | Rebuttal 1:
Rebuttal: Dear reviewer:
**W1 & Q1:the encoder-decoder module for regression and the vocab for the decoder.**
Response: The confusion may come from the naming. Although the encoder-decoder architecture was originally used in machine translation (discrete vocab), common encoder-decoder architectures, e.g.,... | Summary: This paper proposes a hierarchical spatial transformer model for a large number of point samples in continuous space. The model is important for geoscience applications, such as water quality monitoring and air quality monitoring, and operator learning for numerical models. The novel idea includes continuous p... | Rebuttal 1:
Rebuttal: Dear reviewer:
Thank you for your positive feedback and careful reading. We have done thorough proofreading and corrected the typos in the paper. For the second question about how the proposed model can be used in numerical simulations, we use multiphase flow as an example to explain. In this cas... | Rebuttal 1:
Rebuttal: This PDF contains a figure illustration of evenly distributed input points. It illustrates how our model can reduce computational costs in this case by selecting a subset of keys in attention calculation.
Pdf: /pdf/7d679834ead8c8bde2a4c6d339eba3c8a9cf7fe4.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers | Accept (poster) | Summary: The paper generalizes ProxConnect with forward-backward quantizers and introduces ProxConnect++ that includes some binarization techniques as special cases. With the derived ProxConnect++, the paper proposes BNN++ to illustrate the effectiveness of ProxConnect++. Experiments show the advantages of BNN++ on ima... | Rebuttal 1:
Rebuttal: We would first like to thank Reviewer fayS for the great review and questions. Below we address your concerns:
**Design new proximal quantizers:**
(1) Prior to our work, existing implementations mostly designed the backward quantizer in an ad hoc fashion (based on graphic approximations of the s... | Summary: Thanks to authors for submitting their work to NeuRips 2023. After nicelly flowing introduction the lines 47-58 lay goals of the paper and its contributions out in the context of recent advances, cf. Dockhorn et al. [13], PC (ProxConnect). In particular, the paper generalizes PC to forward-backward binarizatio... | Rebuttal 1:
Rebuttal: We would first like to sincerely thank Reviewer5jjv for appreciating our contribution and providing valuable suggestions. Below we address your concerns:
**(1) Novelty on theory:**
We agree that PC++ is an extension of PC, and we have followed the reviewer's suggestion to label the convergence r... | Summary: This paper proposes a new framework for training neural networks with binary weights, which generalizes ProxConnect and takes it as a special case. In the new framework, forward and backward quantizers are defined. A consistency result of the two quanziters is derived (Theorem 1). Extensive experiments are con... | Rebuttal 1:
Rebuttal: We first thank Reviewer WcKF for the positive review and great questions, which we address blew:
**Novelty on theory:**
- We agree with Reviewer WcKF (and also Reviewer 5jjv) that PC++ is an extension of PC, and we are more than happy to credit the theoretical convergence property of PC++ to Doc... | Summary: This paper studied binary neural networks which extend the existing theory of ProxConnect(PC) to ProxConnect++ and explored the fully binarized scenario, where the dot-product accumulators are also quantized to 8-bit integers. The authors also proposed BNN++ with non-linear forward and backward approximation t... | Rebuttal 1:
Rebuttal:
**Additional References**:
We would first like to thank Reviewer YJmg for providing the additional references, especially Bi-Real net, R-BNN, IR-Net, and ReActNet, which are closely related to our work and expanding our PC++ family. We agree that these are important papers that deserve proper d... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers again for their extremely informative reviews that helped us improve the paper. Here we want to provide additional figures and tables (in the new one-page PDF file) that we will add to our final draft:
(1) **Figure 1:** According to Reviewer YJmg's suggestions... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs | Accept (poster) | Summary: This paper studies the problem of policy searching for constrained MDPs. The authors devise two Lagrangian-based named regularized policy gradient primal-dual (RPG-PD) method and optimistic policy gradient primal-dual (OPG-PD) method, respectively. Their methods are single-time-scale and thus insensitive to hy... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing our paper, and the valuable feedback. Please find our specific remarks as follows.
---
## Weaknesses
> - *The convergence of the proposed methods relies on the strong duality property of CMDPs, which may not be true for general paramet... | Summary: This paper studied the policy gradient primal-dual approach for constraint MDP with last iterate convergence guarantee. A regularized policy gradient primal-dual method is first proposed where regularization on the policy and dual variables are introduced to add curvature to the minimax problem and with approp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing our paper, and the valuable feedback. Please find our specific remarks as follows.
---
## Questions
> *The first inequality in Page 29 between Line 1045 - Line 1046: could you justify the usage of Lemma 27, since \pi^*_\tau may not be t... | Summary: This work shows the first non-asymptotic and policy last-iterate convergence for single-time-scale algorithms in the CMDP literature. In particular, it provides nearly dimension-free sublinear last-iterate policy convergence, sublinear last-iterate policy convergence with function approximation, and problem-de... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing our paper, and the valuable feedback. Please find our specific remarks as follows.
---
## Weaknesses
>1. *... a more formal definition of the "single-time-scale" and "two-time-scale" ... help readers better understand the introduction.... | Summary: Two single-timescale algorithms (RPG-PD and OPG-PD) are proposed, and their finite-time convergence rates have been derived. The iteration complexity guarantees for RPG-PD are (nearly) dimension-free but are sublinear. Guarantees for OPG-PD are linear but depend on problem-dependent quantities.
Strengths: 1... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing our paper, and the valuable feedback. Please find our specific remarks as follows.
---
## Weaknesses
>1. *Minor typos: At several places, the references are to the results in the appendix, e.g., Theorem 18 below the statement of Thm. 4.... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning | Accept (poster) | Summary: The core innovation of the method lies in decoupling the single-channel view-level representation, which is common in deep multi-view learning methods, into a shared representation and a view-proprietary representation with a cross-channel contrastive loss. The authors have conducted sufficient experiments to ... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition and we respond to your comments below:
W1: In Eq.(1), the necessary explanation about the coefficient 2 in the numerator is lacking. The authors should also provide more explanation about the motivation of such design.
A1: Thanks for your comments, we hav... | Summary: This paper proposes a general framework for missing multi-view and missing multi-label classification. The main innovation is to decouple each view feature into two different shared and private features. The paper also applies random masks to the raw features to make the network learn from limited informatio... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition and we respond to your comments below:
W1: The one-HL metric does not seem to be of much significance because it does not effectively evaluate the performance of different methods according to the author's experimental results.
A1: Yes, the metric "1-HL" ... | Summary: This paper proposes a new framework for incomplete multi-view weak multi-label classification (iMvWMLC), which is a challenging task that involves missing views and labels in the data. The framework consists of four main components: a two-channel decoupling mechanism (called MTD) that extracts shared and view-... | Rebuttal 1:
Rebuttal: Your comments are greatly appreciated and we respond to these concerns as follows:
W1: The similarity matrix calculated based on weak tags does not take into account the interference caused by unknown tags. The paper should address the potential interference caused by unknown tags in the calculat... | Summary: This paper studies incomplete multi-view weak multi-label learning problem, which is important. The authors propose a masked two-channel decoupling framework based on deep neural networks. They develop cross-channel contrastive loss, a label- guided graph regularization loss, and random fragment masking strate... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing the manuscript! We respond to the comments below:
W1. This work just combines some existing widely-used techniques. Thus the work is a bit incremental, does not provide new insights to me.
A1. Indeed, we admit that the techniques or ideas used in our metho... | Rebuttal 1:
Rebuttal: Thanks to all reviewers, and this is our new supplementary material.
Pdf: /pdf/73ca79a00c8a697b85fdb95836518025bda75d9b.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Efficient and Accurate Winograd Convolution via Full Quantization | Accept (poster) | Summary: This paper proposed to fully quantize the Winograd convolution post-training under the observation of disruption of consistency between different transformation procedures, the new proposed Factorized Scale Quantization is suitable in the Winograd domain. The experiments demonstrate significant improvements co... | Rebuttal 1:
Rebuttal: **Q1:** The experiments only demonstrate the quantization bit, there is no computation cost or inference time comparison which are very important to this work, it is difficult to know how much the proposed improve the efficiency other than accuracy.
**A1:** Thank you for your suggestion. Because... | Summary: This paper proposes PTQ-Aware Winograd (PAW), Factorized-scale quantization and a iterative optimization algorithm to solve the problem of quantization on Winograd domain. These methods not only fully quantize the whole Winograd Convolution, but also surpass the existing Winograd quantization methods in terms ... | Rebuttal 1:
Rebuttal: **Q1:** In the explanation in Section 4.1:
Is the motivation of this section: In the original Winograd method, A, B, and G satisfy some strict mathematical relationship with each other, but the perturbation brought by quantization destroys the strict relationship between them, and the small pertu... | Summary: The paper proposes a PTQ-Aware Winograd (PAW) method to improve the performance of deep learning inference with quantized parameters and Winograd Convolution. In particular, all steps of the Winograd operation are combined and optimized with a unified objective to reduce the domino effect of quantization in di... | Rebuttal 1:
Rebuttal: **Q1:** The overhead of proposed quantization algorithm needs to be compared with the QAT approach, since both methods perform training during the quantization process.
**A1:** Thank you for your suggestion. QAT methods require much more GPU resources and training data than PTQ. **When applying ... | Summary: This paper proposes a post-training quantization algorithm for Winograd convolution, which overcomes the inconsistency in domain transformation by adjusting the transformation matrices together (PTQ-Aware) via a unified optimization procedure, and achieves full quantization by a new factorized scale quantizati... | Rebuttal 1:
Rebuttal: **Q1**: Could you briefly compare your proposed algorithm with some QAT methods that also achieve full-quantization (e.g. the two papers I listed in the Weakness section) and summarize the advantage of your algorithm?
**A1**: Thank you for your suggestion. This response includes three parts: **th... | Rebuttal 1:
Rebuttal: Thank you reviewers for your helpful feedback and constructive advice. Based on the reviewers' questions, comments, and recommendations, we have made many revisions that may significantly improve the quality of the paper.
**Here, we explain the common concern of reviewers on the computation cost ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a Post-training-quantization-aware Winograd (PAW) method to optimize all transformation procedures required by the Winograd algorithm to achieve post-training quantization of pre-trained ResNet models. A useful factorized scale quantization (FSQ) method is also proposed to balance the diffe... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our article and providing helpful feedback. We have scrutinized the manuscript and made corresponding modifications, e.g., correcting some issues in tables and references. We have also polished the paper per your recommendations and explained some specific ter... | null | null | null | null | null | null |
Topological RANSAC for instance verification and retrieval without fine-tuning | Accept (poster) | Summary: This work introduced a new approach of geometrical verification for image retrieval and matching, which is not based on pixel-perfect robust estimation, but on something called "topological common sense". The authors discuss the limitations of the commonly used Spatial Verification strategies, and draw inspir... | Rebuttal 1:
Rebuttal: Dear Reviewer JDZk,
Thank you for acknowledging the significance of our method and recognizing its merit in terms of its robustness and technical accuracy. We wish to address the concerns you've raised:
Explainability: We'd like to emphasize that we aren't suggesting our method, TP, offers super... | Summary: To address the limitations of SPatial verification (SP), the authors introduce the topological consistency to the RANSAC process for image retrieval without fine-tuning. With the socalled homeomorphism regions, the proposed method can achieve better results than some typical methods on four datasets.
Strength... | Rebuttal 1:
Rebuttal: Dear reviewer pXos,
Thank you for your meticulous feedback. We understand and acknowledge your concerns and have accordingly undertaken additional experiments and provided clearer explanations.
# Main Contribution and Novelty
Our contribution lies in our innovative adaptation of RANSAC. We repl... | Summary: This paper reexamines the classical instance recognition problem in computer vision, a problem that holds great significance in applications such as image retrieval. In recent years, data-driven approaches that relying on fine-tuning pre-trained deep models have drawn much attention. However, these methods not... | Rebuttal 1:
Rebuttal: Dear Reviewer WpBp,
Thank you for your positive remarks regarding our work. We truly appreciate your support for the direction of classical RANSAC, especially at a time when it isn't the prevailing trend. We concur with your observation that our findings underscore the potential of RANSAC-based m... | Summary: This paper proposes an approach in the area of image retrieval, more specifically landmark retrieval. Authors propose a new method for spatial verification that replaces standard and commonly used spatial model in RANSAC-based approaches, with topological one. Experiments show SOTA performance using handcrafte... | Rebuttal 1:
Rebuttal: Dear Reviewer X5kj,
Thank you for your thorough feedback and for recognizing the strength of our contribution. We value your insights and would like to address each of your questions:
Time and Memory Cost: We have provided details on time costs in lines 246-248 of our paper. Our TP, implemented ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
First and foremost, we'd like to thank all the reviewers for the time and effort dedicated to reviewing our work. Your feedback has been instrumental in highlighting areas of improvement, and we've conducted additional experiments in response to your insightful comments.
Addition... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Long Sequence Hopfield Memory | Accept (poster) | Summary: This paper proposes to introduce nonlinear interaction terms in the Amari-Hopfield type recurrent networks for learning long binary sequences. Analytical results on the network capacity and a new learning algorithm are provided.
Strengths: 1. The analytic calculation of the network capacity on random sequence... | Rebuttal 1:
Rebuttal: Thank you for the clear criticism and insightful comments, especially the suggestion to use the Moving MNIST dataset and include simulations demonstrating robust sequence retrieval, which we have included the global rebuttal. We have also sketched an outline for the capacity of biased patterns.
*... | Summary: This paper combines two modifications to the basic Hopfield network to create a network capable of creating and recalling long sequential memories (i.e. sequences of states of the network). The sequence capacity of the proposed network is bounded analytically, and supported by numerical simulations. This model... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions, especially those on improving the rigor of the technical analysis. Since the original submissions, we have proven rigorous bounds for the Polynomial DenseNet and are working on rigorous bounds for the Exponential DenseNet, which has proven to be substant... | Summary: This is well presented paper one the storage of sequence memories in Hopfield-like networks. It builds on the recent modern Hopfield networks, but now with asymmetric weights connecting adjacent memories within a sequence. Theoretical capacity limits are calculated and then compared to simulations. Adaptations... | Rebuttal 1:
Rebuttal: Thank you for your compliments and insightful suggestions. Indeed, this is an extension of existing literature of modern Hopfield networks but we believe that it introduces a general solution for error-correction and the robust retrieval of long sequences. We were primarily focused in this work on... | Summary: This paper focuses on computational memory that stores sequence data. Existing work that considers Hopfield-like neural networks suffer from limited sequence capacity due to the crosstalk issue. To this end, this paper introduces a nonlinear interaction term inspired by Dense Associative Memories, enhancing pa... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. We have gone through the weaknesses and addressed them point by point. We are happy to go into more detail for any of these responses.
> Besides Hopfield-like associative memory (AM), there is another class of AM namely predictive coding net... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and suggestions. We found that there were some common themes across the reviewers' comments: sequence retrieval for correlated patterns, robust recall of patterns under noise, and a comparison with other sequence recall methods. We individually respond to each... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
An Efficient Doubly-Robust Test for the Kernel Treatment Effect | Accept (poster) | Summary: This paper proposes Augmented Inverse Propensity Weighted cross Kernel Treatment Test (AIPW-xKTE), which is a doubly robust test with provably valid type-I error based on kernel mean embeddings to test for distributional treatment effect. The paper has one result, Theorem 4.1, showing the asymptotic normality ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
- The bottleneck of this procedure would probably be the estimation of kernel conditional mean embeddings, which has n3 complexity? Perhaps it would be worth looking at speeding this up, through approximate kernel ridge regression methods.
We agree that lo... | Summary: The paper proposes a test of the null hypothesis that a binary treatment has no effect on the the potential outcome distribution. The test combines ideas of kernel mean embedding, double robustness, and cross U-statistics.
Strengths: Originality
-The connection between kernel embeddings of effect distributio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We would like to address the following weaknesses and questions raised by the reviewer.
- 1
We have replaced the word "efficient" by "computationally efficient" in lines 7 and 307 to clarify that we are referring to computational efficiency.
- 2
We have r... | Summary: The paper focuses on studying Augmented Inverse Probability Weighting (IPW) for distributions instead of means. The outline of the paper is as follows:
1. The authors provide motivation for the problem.
1. They review several tools used to solve the problem, including Maximum Mean Discrepancy, Conditional Mea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We would like to address the following weaknesses and questions raised by the reviewer.
- The main limitation of this paper is that I struggle to think of a practical scenario in which I would have an interest in testing differences in the distribution of t... | Summary: The paper introduces a test for the treatment effect which also takes distributional changes into account. The test strongly builds upon the recent works ( Kim and Ramdas, 2023) and ( Muandet et al. (2021)). The main novelty arises from extending the test in Kim and Ramdas, 2023 to the setting of treatment eff... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We would like to address the following weaknesses and questions raised by the reviewer.
- Figure 2 is misleading to some degree. BART and Causal Forests are estimating the mean of the treatment effect and therefore necessarily fail in scenarios III and IV.
... | Rebuttal 1:
Rebuttal: We upload a PDF containing the changes in Figure 1, Figure 2 and Table 1, now with error bars / standard errors, suggested by one of the reviewers. We have included error bars in the remaining figures of the paper, not shown in here for space constraints.
Pdf: /pdf/ebebf945a9d1b8803c3ab71b9b56b99... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a statistical test to determine whether the distributions of the two counterfactuals are the same. This goes beyond the well-known average treatment effect, which only tries to understand whether the means of the distributions are the same. The first work in this direction was by (Muandet ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. We would like to address the following weaknesses and questions raised by the reviewer.
- The main weakness is that the contribution is not highly novel, in that the test proposed is basically a combination of the KTE test from (Muandet et al., 2021) and th... | null | null | null | null | null | null |
Tools for Verifying Neural Models' Training Data | Accept (poster) | Summary: This paper proposes a protocol for verifying that a model trainer submits data and learned weights, and a verifier checks whether the weights are correctly learned from the submitted data.
Such a protocol is useful for trustworthy AI. The paper defines the problem of Proof-of-Trainind-Data (PoTD). It is inspir... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Below, we respond specific points made in your review:
**W1:** *The paper says that some attacks can be treated by existing Proof-of-Learning methods. It seems trivial since PoTD poses stronger requirements, as written in the paper. The paper should discuss the proble... | Summary: The authors propose Proof of Training Data (PoTD), a variant of Proof-of-Learning (PoL) protocols that focuses on training set attacks, rather than the training algorithm itself. A valid PoTD protocol should be able to, at least in theory, spot when a machine learning model has been trained on a different trai... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Below, we respond specific points made in your review:
**W1:** *The PoTD protocol proposed by the authors have considerable overlap with the existing PoL proposals. I am inclined to see it as a variant of these existing efforts, rather than a novel, independent idea.*... | Summary: The paper presents a novel protocol called Proof-of-Training-Data, which a third party auditor can verify the data used to train a model. Here, the auditor will require training data, training code, and intermediate checkpoints. Experiments on two language models have demonstrated that known attacks from the P... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Below, we respond specific points made in your review:
**W1:** *It is still not immediately clear to me why we need this brand-new protocol (Proof-of-Training-Data).*
**Response:** We were uncertain as to whether you were unclear about “the difference between the def... | Summary: This paper describes techniques and tools that can be used for verifying the "provenance" of large neural models, to evaluate their risks. These techniques and tools are part of "protocols" used by a model trainer to convince a "verifier" that the training data was used to produce the model parameters. The aut... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Below, we respond specific points made in your review:
**W1:** *I find that the use of "proofs" in the title and throughout the paper is misleading as the authors do not present techniques that amount to an actual proof.*
**Response:** The use of the word “Proof” in ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their useful feedback, and are glad that many of them enjoyed the paper. We have written detailed responses to each reviewer’s comments, and thank the reviewers for their recommendations.
**For Reviewer pydU** we attach the figure referred to in our response.
Pdf: /pdf/... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection | Accept (poster) | Summary: This paper contributes a new parameter and neuron pruning methods for OOD detection. Built upon the energy-based score, this paper defines the sensitivity of a parameter (neuron) wrt the energy-score by using gradient.
Strengths: The arguments are clear and easily understood. The method is well motivated by r... | Rebuttal 1:
Rebuttal: **Comment** We thank Reviewer qR61 (R4) for the helpful suggestions.
> **W:** The major weakness is the theoretical explanation between the sensitivity and OOD performance, but this is clearly pointed out in the paper. The usage of the sensitivity is based on intuition, while this is a good tec... | Summary: This paper proposes a parameter and neuron pruning strategy to enhance out-of-distribution detection. The approach involves removing near-zero- and high-sensitivity parameters, which are measured by the average gradient corresponding to all training in-distribution samples. Empirical results demonstrate the su... | Rebuttal 1:
Rebuttal: **Comment** We thank Reviewer tCft (R3) for the insightful questions and suggestions, which really helped us improve our paper. Here, we respond to the questions and suggestions point by point.
> **W1:** While the proposed principle shows promising results, providing theoretical explanations for... | Summary: This submission proposes a post-hoc method for detecting out-of-distribution samples, by pruning the final classification layer, using a sensitivity metric based on the gradients of the energy scores. The proposed method is mainly validated on residual networks and visual transformers based on the ImageNet dat... | Rebuttal 1:
Rebuttal: **Comment** We thank Reviewer 8qxy (R2) for the careful reviews and insightful suggestions, which really helped us improve our paper. Due to space constraints, part of the responses can be seen in our global response.
***
> **W1:** Typos and grammar issues.
**A:** We have corrected the typos i... | Summary: This paper proposes to adopt weight and neuron pruning for OOD detection. The proposed method is able to be combined with training-based approaches, demonstrating SOTA performance.
Strengths: 1. The motivation of the method is reasonable and sound.
2. The results of OPNP+ReAct is strong.
Weaknesses: 1. It... | Rebuttal 1:
Rebuttal: **Comment**
We thank Reviewer Dntp (R1) for the feedback and suggestions. Here, we respond to the concerns point by point.
> **W1:** It is not new for the ML community that sparsity can help improve OOD detection. Numerous related works have been proposed to adopt sparsity/pruning to improve OOD... | Rebuttal 1:
Rebuttal: ## Comment
We sincerely thank all the reviewers for their careful reviews and constructive suggestions, which helped us improve our submission. Here, we provide the response to several common concerns and suggestions raised by reviewers.
## Insight Justification
We provide three remarks to explai... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning | Accept (poster) | Summary: This paper introduces GIMLET, a unified graph-text model for instruction-based molecule task pretraining. It leverages natural language instructions and decoupled graph encoding techniques to improve the interaction of graphs and texts. Through experiments and evaluations, the paper demonstrates the effectiven... | Rebuttal 1:
Rebuttal: ### Response to question 1 in weaknesses:
Thank you for your reminder regarding reproducibility. We will open-source both our pretraining dataset and code to ensure that reproducibility is achievable. We also introduce some important hyperparameters here:
| Hyperparameters | Value |
| ----... | Summary: The paper proposes a new unified language model for graph and text data for instruction-based molecule zero0shot learning. The paper tries to address the problem of a supervised fine-tuning approach where labeled data by instruction tuning. The paper first treats both graph nodes and instruction tokens as inpu... | Rebuttal 1:
Rebuttal: ### Response to question 1 in weaknesses:
**Graph-to-text tasks related work**
Thanks for the reminding of related work. We will add this and other related work in the next version of the paper.
The difference between the mentioned work and ours lies in the task objectives. The mentioned work ... | Summary: This paper proposes a unified language model for both graph and text data with two main tech contributions: (1) a unified graph-text transformer encoder with a distance-based joint position embedding to encode graphs, and (2) textual instructions to enhance transferability among tasks. Zero-shot tests on class... | Rebuttal 1:
Rebuttal: ### Response to question 1 in Weaknesses and question 1 in Questions:
**Why use the language model (T5)?**
Thank you for your question regarding the task setting. This study focuses on instruction-based zero-shot learning for molecule property prediction. Our approach aims to predict properties ... | Summary: This paper presents GIMLET, a unified graph-text model for instruction-based molecule zero-shot learning. The proposed model uses natural language instructions to tackle molecule-related tasks in a zero-shot setting. GIMLET overcomes existing limitations and significantly outperforms molecule-text models. The ... | Rebuttal 1:
Rebuttal: Thanks for your questions. We would like to respond to question 3 first, which pertains to the fundamental aspect of the task our paper is working on.
### Response to question 3:
Sorry for the confusion. In this work, we propose to investigate the feasibility of employing instructions to accompl... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper addresses the challenge of molecule property prediction, especially the label insufficiency caused by costly lab experiments.
It uses natural language instructions to handle molecule-related tasks in a zero-shot setting. The authors propose GIMLET, a model that unifies language processing for both ... | Rebuttal 1:
Rebuttal: ### Response to question 1 in Questions and question 2 in Weaknesses:
Thank you for your insightful question. We conduct experiments to illustrate the impact of varying pretraining scale and model size on GIMLET. To manipulate the pretraining scale, we explore different task numbers (tasks select... | null | null | null | null | null | null |
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model | Accept (poster) | Summary: For the Vision-Language Model task, which usually requires a small number of samples for fine-tuning, this work proposes a Meta-Adapter method. The Meta-Adapter method is based on the gated multi-head attention mechanism, and can be generalized to unseen categories without additional fine-tuning after a small ... | Rebuttal 1:
Rebuttal: **Q1: The explanation of the formulas could be more clear.**
**A1:**
Many thanks. Accordingly, we will provide a more comprehensive explanation of the formulas in Section 3.2, including additional descriptions and analyses of the symbols and their functions.
**Q2: Why is it called Meta-Adapter... | Summary: This paper proposes a meta-adapter structure for CLIP like vision-language backbone. Specifically, a cross-attention with a gate mechanism are used to construct the meta-adapter. It aims to improve the few-show learning ability for current CLIP backbone. Compared with other baselines such as CLIP-adapter and T... | Rebuttal 1:
Rebuttal: **Q1: Limited technical contribution of the meta-adapter.**
**A1:**
The main contribution of this paper is to introduce meta-learning into clip adapters for the first time, to achieve online few-shot learning for visual-language models. We mainly pursue building a new framework for clip adapter... | Summary: The main goal of the paper is to explore an approach that is light-weight to allow a CLIP-pretained model to perform well in few-shot settings. The proposed approach (called Meta-Adapter) essentially learns an additional multi-head attention network with an additional gating function. The approach is simple an... | Rebuttal 1:
Rebuttal: **Q1: More ablation studies of the Meta-Adapter.**
**A1:**
Thanks for this insightful suggestion. Accordingly, we further conduct more ablation studies to demonstrate the advantages of our design. As shown in Table 7, the results demonstrate that multi-head attention contributes most significan... | Summary: This paper proposes Meta-Adapter which can refine the CLIP features guided by the few-shot samples in an online manner. The major challenge of adapting CLIP with few-shot samples is over-fitting. Compared with offline approaches CoOp or online approaches TIP-Adapter, Meta-Adapter alleviates the over-fitting pr... | Rebuttal 1:
Rebuttal: **Q1: Limited comparisons with existing approaches.**
**A1:**
Thanks for the suggestions. As shown in Table 7, we provide the ablation studies between our meta-adapter and other offline methods. For a fair comparison, similar to CoCoOp, the experiments adopt a base-to-novel generalization setti... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes an online adaptation method for CLIP (no fine-tuning of few-shot samples of unseen categories are required, unlike CoOp, CoCoOp, and CLIP-Adapter), called Meta-Adapter. The main claim seems to be that Meta-Adapter is more robust than the most related approach Tip-Adapter, which relies heavi... | Rebuttal 1:
Rebuttal: **Q1: Experiments on more image classification datasets.**
**A1:**
Thanks for the valuable suggestion. As shown in Table 7, we conduct the experiments in the other 3 datasets as the reviewer mentioned. Similar to the reported results in the main paper, our method also achieves consistent gains o... | null | null | null | null | null | null |
Block-local learning with probabilistic latent representations | Reject | Summary: The present work proposes a block-wise learning strategy, whereby the architecture is split into several blocks, with each block receiving an error signal stemming from a local (block-wise) loss. As this technique makes use of a parametrized twin network to compute these error signals, it also bypasses the so-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the vary detailed and valuable feedback. We made multiple changes to make the paper more accessible for a broad audience. We also clarified that the proposed method in fact combat the locking problem by adding pseudo code and additional explanations. We will provide addit... | Summary: The authors present a block-local learning rule as an alternative to end-to-end gradient backpropagation to train neural networks. They present a probabilistic view of neural network representations and assuming an exponential family of distributions, derive a learning rule that can be understood as forward an... | Rebuttal 1:
Rebuttal:
We thank the reviewer for ecognising the novelty of the proposed twin-network architecture, and pointing out several ways to improve the paper. We have made a number of changes to make the paper more accessible as suggested, which we will detail below.
We also would like to thank all the reviewe... | Summary: This paper introduces a novel framework for block-local training of deep networks. It proposes a twin network design that propagates information backwards from targets to the input to provide auxiliary local losses. This design allows forward and backward propagation to occur in parallel preventing the problem... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and recognising the novelty of the twin-network architecture, and the strength of the empirical results. We have added an analysis of the speedup achievable with our model and results related to block-size.
**Responses to specific questions:**
1) H... | Summary: In this work, the authors address the problem of weight transport and weight locking issue in backprop by introducing a new bio-plausible algorithm known as block-learning to train NNs. The model uses different forward and backward weights, creating a twin network-like scheme to learn efficient signals via loc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and pointers to previous literature. We disagree that our algorithm is limited in novelty — in fact there are several novel and key differences between our work and the works referred by the reviewer as described below. Since the primary goal of our ... | Rebuttal 1:
Rebuttal: **General response to the reviewers:**
We would like to thank all the reviewers for their constructive comments and questions. Please note that we have uploaded an updated version of our main text as well as supplement. We have indicated all major changes using blue color text. We have also respo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a novel approach to the estimation of deep neural network parameters using block-localized backpropagation in conjunction with belief propagation. This approach is much more parallelizable, thus should help for distributed training, enabling horizontal scaling across devices.
The proposed ... | Rebuttal 1:
Rebuttal: The authors would like to thank the reviewer for the valuable comments and recognising the novelty and significance of the approach. We have improved clarity of the paper and added more details about the algorithm and hyper-parameters.
**Further responses inline:**
1) How big should the twin net... | null | null | null | null | null | null |
SatLM: Satisfiability-Aided Language Models Using Declarative Prompting | Accept (poster) | Summary: This paper aims at improving reasoning with large language models (LLMs) by prompting them in a way that they parse the problem into a language that is understandable by a SAT solver, and then employ an off-the-shelf SAT solver to solve the problem. Empirical results on multiple datasets show the benefit of th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and feedback.
**Q1: It is not clear how the approaches such as [1, 2] that use LLMs as a tool within a reasoning algorithm and how the decomposition-based approaches such as [3, 4] can be fit into the parse-plan-execute framework.**
A: At a high level, all ... | Summary: This paper propose SATLM, which aims to solve the problem of planning error when using CoT and ProgramLM. Specifically, SATLM use a LLM to translate natural language problems into formal the language that is accepted by a solver and let the solver to do planning as well as calculation. The results on several r... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and feedback.
**Q1: Can more complicated tasks such as those in MathQA and MATH be expressed in first-order logic? If expressible, how intuitive would it be to translate more complicated tasks to first-order logic formulas?**
A: Our work can handle most of ... | Summary: This paper presents a framework to augment LLMs with symbolic solvers to compensate known flaws in LLMs' reasoning (e.g., planning and arithmetic). The main novelty of this paper is that LLMs is prompted to generate declarative specifications (rather programs) so that off-the-shelf automatic theorem provers li... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and feedback.
**Q1: How does SatLM differentiate itself enough from the previous Faithful CoT paper as a framework?**
A: SatLM uses SAT specifications that can encode a wide range of reasoning problems spanning arithmetic reasoning, logical reasoning, and s... | Summary: This work looks at using an LLM to generate a declarative task specification from a natural language specification for reasoning tasks and leverage an automated SAT solver to solve the problem. They showcase that SATLM performs better than using Chain of thought or ProgramLM (which converts natural language in... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and feedback.
> The main contribution of this work seems to be about using LLMs as a semantic parser.
We disagree a bit with this interpretation. Our main point is that, for these types of reasoning problems, it makes more sense to decompose the problem int... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper describes a new approach to solving NL reasoning tasks using large language models (LLMs). Specifically, the key idea is to combine LLMs with SAT solving, where LLMs are only used in the first parsing step. The authors of the paper call this approach satisfiability-aided language modeling (SATLM). T... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and feedback.
**Q1: While SatLM can capture more types of errors (specifically, UNSAT and AMBIG) than PROGLM, there is no discussion on how this kind of error can be handled. How easy is it to debug such errors? How can a user generate a fix or solution if ... | null | null | null | null | null | null |
Predict-then-Calibrate: A New Perspective of Robust Contextual LP | Accept (poster) | Summary: The authors study a risk-averse variant of contextual linear optimization, using VaR as the risk measure. The authors develop two heuristic approaches. In both of these approaches, the problem of minimizing the VaR is approximated by a robust optimization problem. The authors propose two different ways of spec... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and the raised questions. We believe the clarification of these questions will make the positioning of our work clearer.
Individual/conditional coverage guarantee:
We thank the reviewer for noting the point, and we have also mentioned in our paper ... | Summary: The author(s) propose a novel method of robust contextual LP, which extends the conventional contextual LP problem by allowing some uncertainties from the prediction model. It is very well-written, and states very clearly what is the problem setting, and in which direction this work advances. The model propert... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the comments and feedback.
Typo in Proposition 1:
The inequality (8) should be “>=” instead of “<=”. We thank the reviewer for noting this mistake. We agree with the intuitions mentioned by the reviewer and now the inequality becomes aligned with these intuitions.
... | Summary: The authors study a risk-sensitive contextual LP setting. They seek to predict the objective function of the LP from a context vector using a generic machine-learning algorithm, and then use this prediction to achieve a low (good) objective value in the LP. Their insight is this can be done cleverly with calib... | Rebuttal 1:
Rebuttal: We thank the reviewer for bringing up the finance application. We are not quite experts in the finance domain, so we didn’t mention it in the first place. However, in the next version of our paper, we will include more discussions about it. Generally, this financial application leads to a natural ... | Summary: This paper considers the contextual linear optimization problem, where one is given a vector of covariates $z$ that can be used to predict a cost vector $c$, and one wishes to solve the following LP:
$ \max \ E[ c \mid z]^T x$
$ \text{subject to}: A x = b, x \geq 0 $
This is the risk-neutral version of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the comments, and in particular, for the detailed suggestion improving our paper.
Motivation for the robust formulation:
We thank the reviewer for raising the point. Our problem setup lies at the intersection between robust optimization and contextual optimization, ... | Rebuttal 1:
Rebuttal: We thank the reviewers for spending the time reading our paper, and for all the helpful comments. The raised questions inspire us to think about important aspects that we haven't come across when we write the paper. We look forward to further discussions in the coming week.
We'd like to take the... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers a risk-sensitive version of the contextual LP problem by replacing the original risk-neutral expected cost objective with VaR. The authors propose a new paradiam termed "predict-then-calibrate" that first learns a prediction model, and then uses calibration to quantify the uncertainty of t... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the comments and feedback, and we hope our response to the raised questions further clarifies the positioning of the predict-then-calibrate framework, and in what way it is connected with the existing results and potential future works.
The choice of model $\hat{h}:... | null | null | null | null | null | null |
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization | Accept (poster) | Summary: This paper explores two pathologies associated with KSD thinning: the inability to distinguish mixing weights and concentration on low-probability regions of the target. The authors provide formalisations of when these pathologies can occur (Theorem 2.3 and Theorem 2.4), supported by empirical evidence. To add... | Rebuttal 1:
Rebuttal: Thank you again for the detailed review, and the questions raised. We anwser to the main points below. See also the global rebuttal for additional insights.
**Theorem 3.3.** Regarding Theorem 3.3, “concentrated at $x_0$” means that all particles are located at $x_0$. We will improve clarity of th... | Summary: In this article the author(s) studied the Stein thining method, an algorithm for post-processing outputs of MCMC based on the kernelized Stein discrepancy (KSD). This article first theoretically analyzed two pathologies of KSD, and then proposed methods to mitigate the two issues by regularizing the KSD object... | Rebuttal 1:
Rebuttal: Thank you again for the detailed review, and the many suggestions provided. We hope that we tackle the main points in the global answer above. We answer to the remaining specific points below.
**Assumption 2.2.** Notice that Assumption 2.2 is satisfied when the two modes of $q$ have a similar KSD... | Summary: This paper proposes a regularized version of Stein thinning for post-processing the output of Markov Chain Monte Carlo algorithms. The regularization addresses two common challenges of Stein thinning: insensitivity to mode proportions and samples concentrating at stationary points. The paper analyzes the above... | Rebuttal 1:
Rebuttal: Thank you again for the review, and the questions raised. Notice that we tackle the main points in the global answer above.
**MMD variations.** The variations of the MMD with respect to dimension d in Figure 5 indeed depend on the considered examples, with different observed behaviors for the Gau... | Summary: The goal of Stein thinning is to post-process the outputs of Markov chain Monte Carlo (MCMC) methods by minimizing the kernelized Stein discrepancy (KSD) between the produced chain and the target distribution. It is useful and has become fast a quite popular method in Bayesian inference because it automaticall... | Rebuttal 1:
Rebuttal: Thank you again for the review and the positive comments. We hope that we adress the identified weaknesses in the global answer above.
---
Rebuttal Comment 1.1:
Title: Response to rebutal
Comment: I read the authors' rebutal and other reviewers' comment. Other reviewers seem to share some of my ... | Rebuttal 1:
Rebuttal: We greatly thank the reviewers for their positive comments about our article and relevant suggestions. We explain below how we will improve the article clarity following the reviewer guidelines. We tackle the main points below, and also provide specific answers to each reviewer.
**Computational c... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model | Accept (poster) | Summary: This paper extends the analysis of exact recovery of the Stochastic Block model to node attributed graphs (CSBM). This opens up a new dimension as the desired clustering must not only be optimal in the sense of the SBM but also respect the node attributes (which are assumed to be distributed based on the block... | Rebuttal 1:
Rebuttal: Dear reviewer,
thank you for the time spent reviewing our paper. Please find below answers and comments to the weaknesses and questions you have raised. We hope this will help clarify and strengthen our contribution.
Weaknesses:
* This is not the case. Please note that Figs. 2 and 3 of the pap... | Summary: This paper studies clustering of node-attributed Stochastic Block Model (SBM). The authors provide information-theoretic threshold for exact recovery under generic distributions for both edge weights and node attributes. In addition, the authors propose a clustering algorithm based on iterative likelihood maxi... | Rebuttal 1:
Rebuttal: Dear reviewer,
thank you for the time spent reviewing our paper. Please find below answers and comments to the weaknesses and questions you have raised. We hope this will help clarify and strengthen our contribution.
Weaknesses:
* Indeed, the distributions used to compute $\psi^*$ and $\phi^*$... | Summary: This paper studies community recovery in sparse, weighted networks, which is an important setting that is more general than the commonly-studied undirected, unweighted networks. The authors' first main contribution is to establish the information-theoretic conditions for exact community recovery in this settin... | Rebuttal 1:
Rebuttal: Dear reviewer,
thank you for the time spent reviewing our paper. Please find below answers and comments to the weaknesses and questions you have raised. We hope this will help clarify and strengthen our contribution.
Weaknesses:
* Indeed, Alg. 1 is similar to [19] and the main (and only) diffe... | Summary: This paper studies community detection in node-attributed stochastic block models. Although these models have been studied before, this work has two main contributions: (i) the edge weights in the model are now not necessarily binary, but can also be weighted. The node attributes don’t have to be Gaussian, and... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for the time spent reviewing our paper. In order to address the weaknesses pointed out in your review, we have performed novel experiments using synthetic data sets with a larger number of clusters ($k=3$ and $k=4$). Results using more clusters are qualitatively similar t... | Rebuttal 1:
Rebuttal: First and foremost, we would like to thank all four reviewers for their time spend reviewing our paper and their valuable comments.
Some of the questions raised by the reviewers can be addressed with further experiments, as the following:
* how does Algorithm 1 perform when the network has more ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Loss Dynamics of Temporal Difference Reinforcement Learning | Accept (poster) | Summary: In summary, they study how learning dynamics and plateaus depend on feature structure, learning rate, discount factor, and reward function in the case of batch TD(0) for policy evaluation.
This paper applies concepts from statistical physics to study typical case learning curves for TD learning in linear FAs ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the supportive review and good questions. We attempt to address the questions below.
### Response to Questions
1. About the discount factor analysis: The scenario we study is slightly different than the one studied by van Seijen et al. (2019). In our simualtions, we... | Summary: This paper looks to introduce a new theory for the learning dynamics in the online batch policy evaluation setting for temporal difference learning. The paper introduces the Gaussian Equivalence Conjecture, which postulates that the learning curves in TD can be modeled by Gaussian features (with per-time-step ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support and useful questions and suggestions.
### Response to Weaknesses
We thank the reviewer for their comment. These concerns were shared by others and we have added new theoretical results and simulations to show the generality of our approach.
Specifci... | Summary: This work utilizes concepts from statistical physics to analyze the learning dynamics of TD(0) (policy evaluation) under the assumption of Gaussian equivalence, online batch update and linear function approximation. Using the theory, it demonstrates how learning rate annealing and reward shaping can improve le... | Rebuttal 1:
Rebuttal:
We thank the reviewer for their good questions and for pointing us to the LSTD literature. Below we address the weaknesses and questions.
### Response to Weaknesses
We thank the reviewer for their comment and have added both derivations and simulations to show the generality of our framework. ... | Summary: This paper provides a theoretical model that predicts the dynamics of TD learning. The theory assumes that the distribution of feature vectors is, in some sense, equivalent to a Gaussian distribution and predicts the value estimate at each iteration. The theory reveals a rich set of phenomena such as plateaus ... | Rebuttal 1:
Rebuttal:
We thank the reviewer for their careful reading of our paper and their detailed questions. We tried our best to clarify and address each of the weaknesses and questions raised below.
**Response to Weaknesses**
1. We will add an acknowledgement that our paper relies on an assumption (Gaussi... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and suggestions for additional theoretical justifications and experiments. Based on the reviews, we have added a more in depth analysis of non-Gaussian features at arbitrary dimension and can show that the learning curves close under fourth moments of the ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces the concept of statistical physics to analyze reinforcement learning models. The authors propose a theory of learning dynamics for RL, with an emphasis on the role of linear function approximation. They investigate how strategies such as learning rate annealing and reward shaping can posi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and useful questions. We respond to the weaknesses and questions below.
**Responses to Weaknesses**
The reviewer is correct that our theory is limited to linear function approximation. This allows us to make strong predictions, but does not capture intere... | null | null | null | null | null | null |
Bayesian target optimisation for high-precision holographic optogenetics | Accept (spotlight) | Summary: This paper proposes a new method for limiting off-target optogenetic stimulation based on Gaussian Process modeling. In holographic photostimulation, the goal is to excite specific neurons via targeted laser light, but widespread expression of opsins may result in additional neurons not in the desired populati... | Rebuttal 1:
Rebuttal: Thanks again for your helpful feedback.
**Figs 4-5 should clearly be labeled as _simulation experiments_ based on real data**
Thanks for noting this ambiguity. We will update the figure captions and lines 254-256 accordingly.
**Missing references**
We thank you for pointing out that we had not... | Summary: The problem the authors tackle in their manuscript is the problem of target selection in holographic optogenetics. Briefly, of the many neurons in a field of view, many experiments require the selective stimulation of a small subset of cells, while minimizing off-target stimulation that may muddy the interpret... | Rebuttal 1:
Rebuttal: Thank you again for spending the time to review our submission and for providing your comments!
**Sensitivity to sample motion**
Thanks for raising this relevant point. It is true that sample motion could affect the optimality of the computationally identified stimuli. In practice, we would ther... | Summary: The authors present a method for reducing off-target stimulation of neurons during photostimulation by modifying the laser power and target locations. They use a Bayesian optimization approach to determine neuron responses (ORFs) to stimulations at different targets and laser powers and then choose the optimal... | Rebuttal 1:
Rebuttal: Thanks very much for your insightful comments and feedback! Unfortunately we had to cut much of our response to meet the character limit -- we would have liked to address every point and with more detail.
**Is it reasonable to think that 4 ORFs are enough?**
We do not have a sense of the variabi... | Summary: The authors present a set of methods to efficiently characterize the activation field under optogenetics and optimize the optogenetic stimulation patterns to target certain cells while avoiding the others.
Strengths: The paper tackles an important problem for reproducing the neural responses with high resolu... | Rebuttal 1:
Rebuttal: Thanks again for spending the time to review our submission and for providing your comments.
**The experimental validation of the method is limited. It is unclear how real-data was used in analysis (Sec 4.2) and if it represents what would happen in a real experiment.**
Thanks for noting that th... | Rebuttal 1:
Rebuttal: Thank you all for your excellent feedback and positive evaluation of our submission! We presented Bayesian target optimisation, a computational approach to overcoming off-target stimulation in two-photon optogenetics experiments. We are delighted that every reviewer clearly understood the motivati... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sparse Modular Activation for Efficient Sequence Modeling | Accept (poster) | Summary: The paper introduces Sailboat which builds upon MEGA. MEGA uses a combination of an Exponential Moving Average (EMA) block (which can be interpreted as a specific parameterization of the kernel from SSM models) and Gated Attention Units (GAU). MEGA explores both full attention and chunked attention. Sailboat i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive review of our work and the constructive feedback on our manuscript. In the following, we address the remaining concerns to hopefully motivate a clear acceptance score.
**Technical clarity:** Please refer to our answers to Q1, Q2 and Minors. We ... | Summary: This paper introduces a framework for representation learning, which involves multiple modules that can be applied dynamically on different inputs. The authors implement this framework using a combination of linear state space models (SSMs) and a gated attention unit (GAU), also combining ideas from adaptive c... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive review of our work and the constructive feedback on our manuscript. In the following, we address the remaining concerns to hopefully motivate a clear acceptance score.
**Presentation clarity:** Please refer to our Answers to the Q1, Q2, Q3, Q4.... | Summary: This method introduces a novel architecture for long text modeling, which builds upon traditional linear state space models (SSMs). Since SSMs have shown inferior performance, combining SSMs with self-attention has become a popular approach. In this paper, several efficiency-related questions are considered in... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive feedback on our manuscript. In the following, we address the remaining concerns to hopefully encourage a positive evaluation.
**Presentation clarity of the method section:** Please refer to our answers to Question 2 and 3. We will provide ... | Summary: The authors employ a hybrid model combining linear state space models and attention modules, while incorporating sparse attention within the attention modules to reduce memory usage and improve speed. The proposed method, Sailboat, outperforms previous approaches in terms of quality, speed, and memory efficien... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive review of our work and the constructive feedback on our manuscript. In the following, we address the remaining concerns to hopefully motivate a clear acceptance score.
**Comparison with Flash Attention(FA):** FA and SMA focus on improving mode... | Rebuttal 1:
Rebuttal: # Global Response
## Updated Results
For the Sailboat-mem model, we find that down-scaling the attention matrix $QK^T$ with the window size $w$ instead of the compressed sequence length $r$ can lead to substantially better results (as shown in Equation (5) near the Line 521 of **Appendix A.2**)... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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