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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Canonical normalizing flows for manifold learning | Accept (poster) | Summary: The authors address the interesting question of how to disentangle the relevant manifold directions properly in the latent space of manifold learning normalizing flows (MLF). After a comprehensive theoretical background, the main motivation is presented in form of a toy example of a simple noisy line embedded ... | Rebuttal 1:
Rebuttal: W1: We appreciate the reviewer for their insightful comments. We would like to emphasize that calculating L1 for the off-diagonal elements is not a trivial conceptual step while it may seem like a simple implementation detail, and this distinction mitigates many of the drawbacks of previous method... | Summary: The paper studies the current manifold learning methods. It compares canonical manifold learning flow (CMF) with other manifold learning methods and demonstrates that CMF can learn the orthogonal features existing in data. From synthetic data on Moebius, the paper shows the benefits of using CMF. Also, the pa... | Rebuttal 1:
Rebuttal: W1: We appreciate the reviewer for highlighting this matter. A comparative table can be found in Appendix 6 of the supplementary material. To summarize, there exists no substantial computational cost distinction between the RNF and CMF methods, as both encounter the calculation bottleneck of the J... | Summary: The authors introduce a new method for regularising manifold learning flows. Essentially, it attempts to reduce the entanglement between the dimensions of the learned manifold by encouraging non-diagonal elements of the metric tensor to be small. Leveraging the already necessary computation of $J^\top J$, wher... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewer for their precise summary.
W1: We thank the reviewer for the insightful remarks. Indeed, we have developed the method in the context of manifold learning flows in order to solve an existing pathology as well as dis-entagling the latent space. While being sp... | Summary: This paper studies the problem of learning a latent representation for data supported on a low-dimensional manifold. It proposes to promote orthogonality of the tangent vectors arising from a learned chart, on top of existing rectangular flow loss. Experiments are provided to demonstrate the effectiveness of t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's suggestions. While we acknowledge that different presentation approaches could have been explored, we believed that, given space constraints, the current explanation best encapsulated the work. We are encouraged by the fact that the presentation is also accepted by th... | Rebuttal 1:
Rebuttal: We express our gratitude to both the reviewer and the chair for their valuable time and insights. We have diligently addressed each of the reviewer's comments individually. Furthermore, we have expanded our testing to encompass additional tabular datasets and incorporated CelebA, 64x64 FID test sc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication | Accept (poster) | Summary: The authors study the benefits of using randomized communication topologies for decentralized optimization of non-convex functions. Critically, the communication protocols studied are not based on picking/sampling a communication graph that remains fixed throughout learning. Instead the authors study the case ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and provide a detailed rebuttal below.
___
> Q: Which quantity is bounded in (3)? The first term in EL Oracle in Theorem 1 and (3) are identical but the comments mention an advantage in the first term. Clarifying this would help.
Re: The quantity bounded i... | Summary: This paper proposes a decentralized learning algorithm based on random communication, i.e., each node sends its model to a random set (with a fixed size) of other nodes at each round. This paper theoretically shows the superiority of random communication in terms of transient iterations over other decentralize... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and provide a detailed rebuttal below.
___
> Q: Only applicable to networks with high connectivity. other application scenarios need to be motivated.
Re: Thank you for your insightful comment.
You correctly identified that the implementation of our decen... | Summary: This paper considers Epidemic Learning, a framework for distributed optimization where each node in a network pushes gradient-descent updates to a uniform random subset of $s$ nodes in the network. Theoretical bounds on the rate of convergence are derived as well as the number of ``transient iterations," showi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and provide a detailed rebuttal below. Given that the reviewer has such a good intuition about the problem and our solution, we are a bit surprised by the low confidence score of the review.
___
> Q: summary of where the convergence benefits arise ...
Re:... | Summary: This paper proposes a decentralized learning algorithm in which each node
updates its model from a set of s random nodes in a system with n > s
nodes. The authors provide a theoretical analysis of the convergence speed
and the number of transient iterations, i.e., the number of rounds required
to reach linear ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and provide a detailed rebuttal below.
___
> Q: Epidemic learning is not a good and informative title.
Re: We can change the title to add some words about the novel element of our approach. For example, we consider changing the title to: "Epidemic Learnin... | Rebuttal 1:
Rebuttal: Firstly, we would like to thank all reviewers for the thorough and insightful comments on our submission. We appreciate the detailed feedback and the points raised, which have offered some valuable new insights.
Below we address two comments that were raised by multiple reviewers.
## Transient I... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores decentralized learning algorithms with the aim of faster model convergence while comparable accuracy compared with conventional DL methods. The new proposed algorithm - epidemic learning (EL) - leverages a dynamically changing, randomized communication topology to train a machine learning m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and provide a detailed rebuttal below.
___
> Q: A major concern is that the proposed EL algorithm is not significantly different with semi-dynamic and time-varying and randomized topologies (they are introduced in the related work section). Especially, gos... | null | null | null | null | null | null |
Expressivity-Preserving GNN Simulation | Accept (poster) | Summary: The paper deals with supervised machine learning with graphs, specifically with expressive GNNs, and how to implement them efficiently. Specifically, it investigates the expressive power of graph transformations to transform an input graph such that an ordinary 1-WL-equivalent message-passing GNN can simulate,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback.
### Concerning the Weaknesses
>Section 3 just contains more or less obvious results known within the graph learning community. (This is also clearly acknowledged by the authors in the introduction and the appendix.). However, with this in mind, it is unclea... | Summary: The paper investigates the idea of simulating (replacing) non-standard message passing networks (MPNs) using simple standard MPNs by first applying a graph transformation (new nodes and edges). The paper provides a formal construction of graphs and their generalizations in the form of relational structures tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and appreciate that the reviewer acknowledges that our paper is precise, clear, has convincing experiments, and provides a novel analysis of graph neural networks.
> At the same time, I must admit, that the formal approach does require quite some... | Summary: The authors introduce methods for simulating certain graph neural networks (GNNs) using standard message-passing algorithms composed with graph transformations. To do so, the authors introduce a class of nonstandard message-passing algorithms they call "augmented message passing" (AMP) algorithms, demonstrate ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very encouraging and positive feedback!
On the reviewers remark:
> I think this work is very nicely done, and only recommend some further exposition on the potential utility of the authors' work.
We will provide further information on the utility of this work in th... | Summary: The paper formally introduces the notions of MPNN simulating GNN with graph transformation. With the definitions, the authors further investigate which class of GNN can be simulated by MPNN.
Strengths: The work presents the first systematic theoretical investigation toward understanding which GNN can be simul... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and appreciate that the reviewer acknowledges the `first systematic theoretical investigation toward understanding which GNN can be simulated by MPNN'. Concerning the weakness and question:
> (Weakness) The simulation is based on expressiveness equ... | Rebuttal 1:
Rebuttal: Dear reviewers, we thank you very much for your detailed comments and respond to your reviews individually below. We appreciate that you acknowledge the novelty, originality, the systematic theoretical investigation, and the well-conducted experiments.
Dear all, to further extend the generality... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work presents a simulation theory/method for efficiently approximating non-standard GNN functions via standard MPNNs plus graph transformers. It starts from the cases that can be strongly simulated and extends to weak simulation for a comprehensive conclusion. A simulation algorithm is also proposed and v... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and appreciate that the reviewer acknowledges our rigorous investigation of a practically motivated novel problem.
The reviewer mentions that the "structure of paragraphs" reduces readability. Could you give us more details so we can fix it? Do you me... | Summary: The paper proposes a novel approach to simulate state-of-the-art graph neural networks (GNNs) using standard message passing. The authors introduce graph transformations that preserve the expressivity of GNNs and allow for better code optimization and competitive predictive performance on various molecular ben... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and appreciate that the reviewer acknowledges our paper novel approach, thorough evaluation, and highly original paper which is `well-written and easy to follow'. The reviewer raises only some lack of clarification as weakness which we address below.
... | null | null | null | null |
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning | Accept (poster) | Summary: This paper presents a novel approach to multicalibration by leveraging game dynamics and no-regret learning algorithms. The central idea is that multicalibration can be modeled as a multi-objective learning problem where an adversary and a learner play against each other, guided by no-regret dynamics.
The aut... | Rebuttal 1:
Rebuttal: Thank you for your review. Below we respond to your questions.
**On providing Pseudocode**\
We already provide detailed step-by-step pseudocode for every algorithm in the Appendix: see Algorithms 2, 3, 4, 5, 6, 7. We also have fully released our source code for the experiments (see supplemental m... | Summary: The paper provides a two-player dynamics framework that seeks to unify many strands of recent work on multicalibration and multiobjective optimization. With three possible setups considered: No regret against No regret, Best response against No regret, and Best response against Best response, efficient algorit... | Rebuttal 1:
Rebuttal: We thank you for your positive review. We appreciate your feedback on including a discussion about which dynamics are used where and lead to which improvements—we will make some revisions to further improve the flow. We also appreciate your suggestion to include additional comments on our sqrt-con... | Summary: This work exploits connections to game dynamics to propose a unifying algorithmic framework to address the multicalibration problem which has been recently used for tackling fairness concerns in machine learning. More precisely, based on the classic game dynamics approach used in learning problems, it is shown... | Rebuttal 1:
Rebuttal: Thank you for your review. Below, we address your comments and questions.
**On game dynamics being a common approach for multi-objective learning.**\
While it appears intuitive that game dynamics must have a role to play in multicalibration, given that no-regret learning plays a role in calibrate... | Summary: The authors proposed a unified framework for multicalibration learning by exploiting its connection to the game dynamics in multi-objective learning. Strong theoretical guarantees were given and its extension to address group fairness was discussed.
Strengths: 1. The analysis of the game dynamics in multi-obj... | Rebuttal 1:
Rebuttal: We thank you for your positive review. Below, we address your comments and questions.
**On experiment comparisons to baselines existing algorithms.**\
Our experiments already include comparisons to existing and baseline multicalibration algorithms (see lines 1189-1191 of the appendix for further ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides a unifying framework for the algorithm design and performance analysis of multicalibrated predictors.
In this paper, the multicalibraion problems is placed in the setting of multi-objective learning.
Under this interpretation, approaches based on game dynamics is proposed and analyzed.
It i... | Rebuttal 1:
Rebuttal: Thank you for your review. Below we address your comments and questions.
**Is the game dynamics approach surprising?**\
We agree that writing min-max optimization problems as a zero-sum game is a common first step in many analyses. But it is the next steps that matter, and for multicalibration it... | null | null | null | null | null | null |
Emergent Correspondence from Image Diffusion | Accept (poster) | Summary: The authors find that intra and cross-category correspondences are implicitly learnt by diffusion models trained self-supervised on large datasets. The paper proposes an approach to extract this knowledge as features from pre-trained Unet-based diffusion models. In particular, to compute the features of a part... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback! Please see our response below:
**Run-time of DIFT**.
Please refer to point 2 of global rebuttal above.
Briefly speaking, DIFT is actually fast because it doesn’t need to run diffusion inversion thus only one network inference is involved (see the second la... | Summary: The paper proposes to use off-the-shelf generative networks based on denoising diffusion models to find local correspondences. The paper is extremely simple: instead of generating samples purely from random noise or doing some kind of image-based conditioning, the method just adds random noise to the input ima... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback! Please see our response below:
**Implementation details and evaluations on HPatches**.
- Image size: all images are resized to 768x768 then fed into the network to extract feature maps.
- Number of points: following CAPS, we use SuperPoint to extract keypoin... | Summary: The paper addresses a classical computer vision problem, ie, points correspondence. The authors show that the feature maps of the decoder of a diffusion model U-Net enable robust feature matching with a simple nearest neighbor search. Semantic or geometric correspondence can be achieved, by selecting the appro... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback! Please find our response as below:
**Clustering on U-Net features**.
Please refer to point 1 of the global rebuttal above and Fig.1 of the attached pdf, where we we visualize the first three PCA components of DIFT$\_{sd}$ on the segmented instance pairs and... | Summary: This paper introduces DIFT, a method to yield emergent correspondence from image diffusion models without training or additional fine-tuning.
The method is simple - given an image (or an image pair), DIFT adds noise to the image to simulate the forward diffusion process, and pass it to the U-Net of a pretraine... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback! Please find our response as below:
**Missing evaluation on PF-PASCAL**:
Here’re the comparison on PF-PASCAL:
| Method | PCK@$\alpha_{img}$=0.1 |
| ----- | ----- |
| PWarpC [Truong et al. CVPR 2022] (see review by rXbs) | 87.6 |
| DIFT$\_{sd}$ | 84.6|
| O... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and valuable feedbacks! We are encouraged that reviewers find that our paper is well-written and easy to follow (u3f5, 4nEG, zLC3, rXbs), that our approach achieves good performance with a simple technique (all 5 reviewers!) along with cool and novel ideas... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors proposed a method for semantic correspondence using pretrained diffusion model as a feature extractor of the images. Without explicitly training on the additional data/annotations, a simple feature matching based on winner-take-all strategy with cosine distance metric surpasses the previous works o... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback! Please find our response as below:
**Limited novelty**.
We would like to point out that the "backbone" we are proposing to use comes from a diffusion model which is trained with a generative modeling objective that has prima facie little to do with learning... | null | null | null | null | null | null |
CaMP: Causal Multi-policy Planning for Interactive Navigation in Multi-room Scenes | Accept (poster) | Summary: This paper introduces the multi-room interactive navigation problem and proposes a novel model that is motivated by counterfactual reasoning. In particular, the paper posits that obstacle objects serve as a confounding factor when understanding the relationship between actions taken and the outcomes observed /... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the appreciation and suggestions for our work. We address the concerns in the following lines.
Q1. Paper writing clarity. The reviewer questions about "the difference between do(A) and A", "what the counterfactual situations here are (e.g., more examples like L131-133)"... | Summary: Broadly, the paper tackles the Interactive Navigation task: navigating to a goal and interacting with obstacles as necessary, e.g. pushing a chair out of the way.
They use the ProcTHOR simulator with 12k multi-room scenes and generate navigation episodes that are suitably cluttered with obstacles. Their embo... | Rebuttal 1:
Rebuttal: We appreciate the detailed questions and reply to them in the following lines.
Q1. Action space and positioning of objects.
A1. Our CaMP agent is trained using exactly the same action space as other baselines. In particular, the agent makes only 90-degree turns when taking *RotateRight* and *Rot... | Summary: This paper introduces a causally-inspired hierarchical policy framework for the interactive navigation task in the AI2THOR environment. The framework consists of a master policy, intent policy, and three sub-control policies. The intent policy embeds intuitive intents from the sub-control policies into the mas... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive criticism. We address the concerns in detail in the following lines.
Q1. Model design explanation. The reviewer wonders “the connection between confounding bias resulting from unmeasurable obstacles and the counterfactual policy design”, “how the weighte... | Summary: This paper tackles the problem of interactive visual navigation; i.e., an agent navigating in an enviornment where it is allowed to affect the configuration of the environment (e.g., by moving objects around or picking them up), to improve navigation performance. The key idea is to learn a hierarchical policy ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive concerns and address them in detail in the following lines.
Q1. Evaluation metrics and experiment design.
The reviewer is concerned about the evaluation of InterNav that “in the interactive navigation scenario, typical path length metrics like SPL are n... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TOA: Task-oriented Active VQA | Accept (poster) | Summary: This paper proposes task-oriented active VQA (TOA), which uses LLM as an implicit knowledge source and answers the question through a sequential hypothesis-verification process. This method can more accurately attend to the essential information in the images and reduce the introduction of irrelevant informati... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript, for providing such valuable feedback and your appreciation of our novelty regarding the knowledge-based VQA and LLM, the better interpretability of our approach, and our experimental results! Here are our response for each of your questions. ... | Summary: This paper addresses knowledge-based visual question answering by leveraging a large language model (LLM) as a knowledge source. To mitigate the limited capability of vision models, the paper suggests letting the LLM predict hypothesis and actively gather visual evidence. Experimental results show that the pro... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our writing, motivation, and experimental results! We have considered all your suggestions and have made the necessary revisions or clarifications to improve the quality of our manuscript as follows. We have attached a one-page pdf in our global response to demon... | Summary: Early methods for Knowledge-based visual question answering (VQA) explicitly retrieve knowledge from external knowledge bases, often introducing noisy information. Current large language models like GPT-3 as implicit knowledge sources cannot effectively understand image inputs. Thus, extracting the image infor... | Rebuttal 1:
Rebuttal: We are very grateful for your insightful comments, especially your appreciation of the novelty of our proposed approach and our experimental results. For the concern you expressed in the Weakness section, we have addressed it in our **Global Response-2**. We paste the response here for your conven... | Summary: This paper tries to solve the knowledge-based visual question answering (VQA) by proposing a new approach that utilizes LLMs for calling visual modules in a task-oriented manner. The method employs a reasoning-hypothesis-verification process in multiple rounds to progressively find the answer. Evaluations are ... | Rebuttal 1:
Rebuttal: We appreciate your valuable and constructive comments. We have made the necessary revisions or clarifications to improve the quality of our manuscript as follows. We have attached a one-page pdf in our global response to demonstrate our prompting instruction and in-context learning examples. Here ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for your insightful and constructive reviews! In this global response, we want to address the common questions inquired by different reviewers, and also demonstrate the necessity of making hypotheses in our proposed method.
**[Global Response-1: Demonstration of ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Covariance-adaptive best arm identification | Accept (poster) | Summary: This work focuses on best arm identification, that is, determining the arm with highest average return within a small number of samples (sample complexity). The twist is that authors propose a framework where sets of arms (of arbitrary size) can be queried at the same time, and the set of corresponding rewards... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
In the context of developing lower bounds, the conventional approach involves creating two distributions of the arms that are identical except for one arm, which is made optimal in one of the distributions. Subsequently, a lower bound is est... | Summary: Authors investigate the stochastic Best Arm Identification (BAI) problem when the arms have an unknown correlation structure.
In contrast, the majority of BAI literature focuses on independent arms with unknown variances.
Authors propose algorithms for BAI in two settings (bounded random variables and gaussian... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
* **On Question 1:** about the formulation of our results. We apologise for not being clear. Please note that such presentation of the guarantees is standard in the literature of best arm identification with fixed confidence, see for instance: Karni... | Summary: This paper considers the problem of best arm identification with covariance in the fixed confidence setting, where arms can be dependent and rewards can be sampled simultaneously. The authors design algorithms that adapt to the unknown covariance of arms and prove that substantial improvement can be achieved o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
* **On Question 1 (part 1):** Comparison of Theorems 4.1 and 5.1: Theorem 4.1 deals with bounded variables. Here, the sum of bounded variables may exhibit a sub-exponential tail, which leads to the additional $1/(\mu_i-\mu_j)$ term in the complexity... | Summary: This paper focuses on the question of identifying $\epsilon$-optimal arms given a confidence input $\delta$, or in other words, under the PAC model. Instead of pulling only one arm and observing the rewards, the authors leverage the underlying structure of arm distributions by allowing multiple queries per rou... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
* **About the reviewer's summary and Question 1:** Please note that our objective in this paper is not identifying $\epsilon$-optimal arms (also known as $(\epsilon,\delta)$-PAC setting) but identifying the (exact) best arm with probability at least... | Rebuttal 1:
Rebuttal: We thank the reviewers for the valuable feedback. We address below some points raised by the reviewers:
* **Link with bandits literature with dependent arms:**
+ **Bandits on graphs:** Previous studies on graph-based bandit problems with side observations in the stochastic setting (such as ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This study addresses the problem of best arm identification (BAI) in the context of dependent arms. Unlike conventional settings, efficiency can be enhanced by exploiting the inherent correlation structure. This setting holds broad applications, including in clinical trials. Specifically, the authors concentra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
* **On Question 1**: The notation $\epsilon \mathcal{N}(1,1)$ stands for $\epsilon X$, where $X \sim \mathcal{N}(1,1)$. We can write $\mathcal{N}(\epsilon,\epsilon^2)$ if it appears clearer, though we wanted to emphasize $\epsilon$ as a scaling fac... | null | null | null | null | null | null |
Model-Based Control with Sparse Neural Dynamics | Accept (poster) | Summary: This paper proposes a method for pruning neural networks with ReLu activation functions during training. When employed for learning the dynamics of a control system, this often leads to networks with few activation functions performing similarly as large networks. This allows to apply mixed integer programming... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our paper, and for your constructive suggestions that helped improve our work.
> I think a complexity (e.g., in O notation) should be provided to give the reader an impression how severe the computation times grows.
We agree with the reviewer that a more precise... | Summary: This paper proposes a new framework for model-based control. The approach focuses on learning a sparse deep neural network and using a mixed-integer program solver for closed-loop planning. Experimental results are presented on several tasks including object and rope manipulation tasks. The results show that t... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper, and for your insightful feedback that helped improve our work.
> Please comment further on the results in Figure 5; there is a trend but why is this a significant advance?
In both Object Sorting tasks, our method using MIP and a sparsified model of 24 and 36 Re... | Summary: This paper proposes a framework for model-based planning with forward dynamics represented as sparse neural networks. The paper examines different ways of inducing sparsity in MLP and GNN based forward models, and performs real robot manipulation experiments investigating the tradeoffs with sparsity and perfor... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper, and for your thoughtful comments and suggestions that have contributed to the refinement of our work.
> there aren't comparisons to prior model-based RL approaches, e.g. PETS, Dreamer, MBPO etc
We conducted additional experiments employing two model-based RL met... | Summary: This paper focused on the combination of predictive control and model learning. An autogressive dynamic model based on a ReLU neural network is first learned over the observation space. The authors then aimed to sparsify it after introducing the indicator mapping function. To make the optimization feasible, th... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our paper, and for your insightful suggestions that have improved our work.
> It would be more convincing if the authors can test on commonly used reinforcement learning benchmarks
We conducted further experiments on two additional environments, Reacher-v4 and Ca... | Rebuttal 1:
Rebuttal: We thank the reviewers for dedicating their time and effort in reviewing our paper, and we deeply appreciate their thoughtful comments and insightful feedback. We appreciate the reviewers agreeing that our approach is novel, well formulated, tackles an interesting problem, and that our paper is we... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Offline Primal-Dual Reinforcement Learning for Linear MDPs | Reject | Summary: This paper studied offline RL in linear MDP setting, where the transition and reward have low-rank structures and the feature $\phi$ is known. The authors formulated the problem in a primal-dual way and proposed a gradient-based algorithm. They provided convergence guarantees, which only requires coverage over... | Rebuttal 1:
Rebuttal: We appreciate your feedback on our work. In response, we kindly highlight that the major technical novelty of our work is a reparametrization trick with which we adapt the conventional LP formulation to a novel framework for offline learning via primal-dual optimization. This allows us to derive l... | Summary: This paper proposed an primal-dual framework for offline reinforcement learning in linear MDP Contrary to the more common case of finite horizon, they considered the case of infinite horizon with discounted reward. They reduced the problem of offline reinforcement learning to a problem about solving the saddle... | Rebuttal 1:
Rebuttal: We thank you for the very careful reading of our paper.
Indeed, the works you reference provide many different algorithm variations and sample complexity bounds. Our comparison could definitely be improved a bit. We plan to fix this in the final draft of our paper, by adding to the appendix a mor... | Summary: This paper studies offline reinforcement learning (RL) with linear function approximation and partial data coverage. The authors propose a primal-dual optimization method based on the linear programming (LP) formulation of RL. They prove a $O(\epsilon^{-4})$ sample complexity in both discounted setting and ave... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and for aknowledging many of the strengths of our work.
We agree. Two of the main weaknesses of our work are that it is limited to the linear MDP setting, while many related works consider a more general function approximation setting; and the absen... | Summary: This paper studies offline reinforcement learning with linear function approximation. They propose a primal-dual algorithm, formulating linear RL into a minimax problem and solving it with gradient descent-ascent. Sample complexity analysis is provided for infinite-horizon discounted and average-reward MDPs, w... | Rebuttal 1:
Rebuttal:
> 1. The newly defined coverage ratio $C_{\phi,c}$ is a little strange when $c\neq \frac{1}{2}$. For example, when we choose $c=1$ and thus we don't need the knowledge of $\Lambda$, the coverage ratio $C_{\phi,1}=\sum_{x,a} (\frac{\mu^*(x,a)}{\mu_B(x,a)})^2$. Then when $\mu^*=\mu_B$, $C_{\phi,1}$... | Rebuttal 1:
Rebuttal: We thank all reviewers for the work invested into evaluating our paper, and the thoughtful feedback they shared with us. We are also glad about the many strengths of our work which have been highlighted, signifying that the amount of effort we put in this paper has not gone unnoticed.
No work com... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the problem of offline reinforcement learning (RL) for linear Markov Decision Processes (MDPs) under the infinite-horizon discounted and average-reward settings. The authors propose a primal-dual optimization method based on the linear programming formulation of RL, which allows for effici... | Rebuttal 1:
Rebuttal: > 1. I am confused about the requirement of $\Lambda$ to be invertible (line 140) as this seems to be very closely related to the uniform coverage condition where we assume that the smallest eigenvalue of $\Lambda$ is lower bounded from zero. I am wondering what is the key difference between them.... | Summary: The authors investigate offline RL in linear MDPs and introduce a novel LP-based method. They assert that their proposed approach achieves the lowest sample complexity of $O(1/\epsilon^4)$ among computationally efficient algorithms. In comparison, existing computationally efficient algorithms can achieve $O(1/... | Rebuttal 1:
Rebuttal: Thank you for the time spent reviewing our paper, and the feedback you provided!
> * I am uncertain about whether it is appropriate to claim that existing offline RL algorithms in linear MDPs achieve $O(1/\epsilon^5)$. It appears that [38] may have better sample complexity. In Table 1 of the manu... | null | null | null | null |
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization | Accept (poster) | Summary: The manuscript proposes a novel federated learning method to alleviate the negative impact of the "client drift" problem and enhance consistency in the FL paradigm. The manuscript also analyzes the intrinsic impact of local consistency on optimization error, test error, and generalization error. Several experi... | Rebuttal 1:
Rebuttal: Thank you very much for your review and comments on our work. We'll answer your questions one by one, including some misunderstandings and some essential academic questions. We are also very honored to share some of our understandings with you.
## About the question of "The hyperparameters were ... | Summary: This paper proposes to initialize the local state by moving away from the current global state toward the reverse direction of the latest local state. They demonstrate theoretically and empirically that this revision can help consistency for better performance. The method is also a practical plug-in that could... | Rebuttal 1:
Rebuttal: Thank you very much for your review and affirmation of our work. We'll answer your questions one by one in the following, including some misunderstandings and some essential academic questions worth exploring. We are also very honored to share some of our understandings with you.
## About the qu... | Summary: This paper aims to solve the “client-drift” problem in Federated Learning, which is caused by the NonIID data. Specifically, this paper proposes initializing the local model of each client with its personalized model to alleviate the problem. Further, the paper theoretically analyzes the impact of inconsistenc... | Rebuttal 1:
Rebuttal: Thank you very much for your review and affirmation of our work. We'll answer your questions one by one in the following, including some misunderstandings and some essential academic questions worth exploring. We are also very honored to share some of our understandings with you.
## About the qu... | Summary: This paper proposes an efficient stage-wise initialization for the federated learning paradigm, named FedInit, which could be extended as a plug-in to several existing methods. It provides the theoretical analysis on both the convergence and generalization to illustrate how consistency term affects the FL. Exp... | Rebuttal 1:
Rebuttal: Thank you very much for your review and affirmation of our work. We'll answer your questions one by one in the following, including some misunderstandings and some essential academic questions worth exploring. We are also very honored to share some of our understandings with you.
## About the que... | Rebuttal 1:
Rebuttal: **We are very grateful to all the reviewers for their valuable comments.**
We make individual responses to each reviewer to address the concerns they raised. Here we submit the one-page .pdf file which contains some experiment curves mentioned by review sALG and a figure to illustrate the princip... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Deep Momentum Multi-Marginal Schrödinger Bridge | Accept (poster) | Summary: The paper tackles the multi-marginal Schrodinger bridge space in phase space, by proposing a computationally tractable solver (DMSB). It leverages alternating Bregman projections to adapt the iterative proportional fitting algorithm in order to deal with multiple convex constraint sets.
The DMSB framework con... | Rebuttal 1:
Rebuttal: # To Reviewer EhNn
We express our gratitude to the reviewer for their valuable feedback. The summary provided is accurate, and the questions raised are both intriguing and perceptive.
Kindly find below our itemized responses, organized in order to address each of the reviewer's concerns.
#### **1... | Summary: The paper aims at solving efficiently in high dimensions the multi marginal momentum Schrödinger Bridge, that is Schrödinger Bridge in phase space with multiple marginal constraints. They also tackle the issue of marginals constraints where only the positions are enforced. They reach this objective by proposin... | Rebuttal 1:
Rebuttal: # To Reviewer 3Whf
We deeply thank the reviewer for all the comments. The summary is accurate and the questions are interesting and insightful.
Please kindly see our itemized replies below in order to address the reviewer's concern.
#### **1. How does the log-likelihood minimization is tied to t... | Summary: In this paper, the authors present an algorithm, DMSB (Deep Multi-Marginal Momentum Schrödinger Bridge), to approximate solutions to an extension of the Schrödinger Bridge (SB) problem into phase space (mmmSB), where (i) marginal constraints on the position are given across time and (ii) stochasticity is only ... | Rebuttal 1:
Rebuttal: # To Reviewer A2vV
We extend our sincere gratitude to the reviewer for the valuable comments. Please kindly find below our itemized responses, presented in an effort to address each of the reviewer's concerns.
#### **1. This paper lacks a theoretical result**
As discussed in the conclusion sectio... | Summary: The paper addresses the topic of multi-marginal trajectory inference in high dimensions using Schrödinger Bridge (SB). In particularly, the authors focus on the so-called momentum SB in phase space where the resulting trajectories in position space are smooth interpolations between the intermediate marginals. ... | Rebuttal 1:
Rebuttal: # To Reviewer 5VcR
We deeply thank the reviewer for all the comments. The summary is accurate and the questions are interesting and helpful.
Please kindly see our itemized replies below for addressing the reviewer's concerns.
#### **1. a more intuitive explanation or interpretation of propositi... | Rebuttal 1:
Rebuttal: # To All Reviewers
We thank all reviewers for their valuable comments. We are excited that the reviews identified the novelty of our contribution, appreciated our experimental validations and acknowledging the significancy of our work.
The common criticisms rather came from insufficient complexi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors extend the diffusion Schrodinger bridge methodology to mult-marginal setting whereby each marginal is ordered sequentially, in addition, introducing momentum into the diffusion Schrodinger bridge framework. The method shows excellent performance on trajectory inference tasks.
Strengths: - The pro... | Rebuttal 1:
Rebuttal: # To Reviewer 17Zf
We would like to express our sincere gratitude for your valuable feedback and comments. We truly appreciate the time and effort you invested in assessing our submission.
Please kindly see our itemized replies below in order to address the reviewer's concerns.
#### **1. The comp... | null | null | null | null | null | null |
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model | Accept (poster) | Summary: The paper proposes DiffTraj: a trajectory generation model based on probabilistic diffusion. The model is trained using real spatio-temporal trajectory data, and aims to generate new trajectories with similar characteristics. This is motivated by the purpose of preserving privacy information that may be presen... | Rebuttal 1:
Rebuttal: We highly appreciate your high-quality review and valuable suggestions. Due to space limitations, we merged some of the weaknesses and questions you mentioned. We also added a new **one-page PDF** of the results. Please kindly check it out. We clarify your concerns below:
> [W1]
Thank you for yo... | Summary: The paper provides an innovative diffusion probabilistic-based model to simulate realistic GPS trajectories. The main contributions are as follows: 1) The paper introduces a diffusion-based probabilistic model that captures spatio-temporal dependencies in GPS trajectories. This model allows for personalized tr... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and perspectives. We respond to each of the points as follows:
> [W1]
Thank you for pointing out the experimental setup. We understand the importance of these details for the reproducibility of our work.
To address this, we have provided an in... | Summary: The paper introduces a good approach for generating realistic GPS trajectories based on a diffusion probabilistic model. The paper addresses the challenge of generating personalized trajectories that capture both temporal and spatial dependencies while ensuring privacy preservation.
The paper proposes the Dif... | Rebuttal 1:
Rebuttal: We are delighted that the reviewer found our motivations and ideas interesting and original. Thank you for your positive opinions and insightful comments.
> [W1]
Thank you for highlighting the importance of discussing our diffusion probabilistic model's assumptions.
1. Noise assumption: we assu... | Summary: This paper adapts DDPM for trajectory generation within smart cities. The major contributions include the combination of different factors and the integration of some existing modules. The experiments over two real-world datasets can demonstrate the efficacy of the proposed model.
Strengths: 1. The paper is w... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the importance of our work and for the well-written paper. We also appreciate the detailed comments posed by the reviewer. Please find below the point-to-point responses to the reviewer's comments.
> [W1]
Thank you for your feedback on our paper's technical ... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments and perspectives of the reviewers, and the attached figures and tables of results are included as a supplement **PDF** to the rebuttal.
Pdf: /pdf/8d431a968a28b3b581e197c642b4e7eb04ee6906.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension | Accept (poster) | Summary: This paper generalize existing inconsistency results to non-interpolating models and more kernels to show that benign overfitting with moderate derivatives is impossible in fixed dimension.
Moreover, this paper proves that interpolation with spiky-smooth kernels can be consistent and such kernels can be induce... | Rebuttal 1:
Rebuttal:
We thank you for carefully reading our paper and for providing detailed feedback.
**Question 1: Can Theorem 5 be extended to $H^s(\Omega)$, $\Omega\subset \mathbb{R}^d$?**
There is a technical obstacle to generalizing Theorem 5 to open bounded sets $\Omega \subseteq \mathbb{R}^d$, which could p... | Summary: This work studies the generalization behavior of overfitting methods in terms of the smoothness of the estimator, showing that only non-smooth estimators can interpolate benignly. They give a discussion of this result in the context of NTKs and their corresponding infinite-width architectures.
Strengths: Orig... | Rebuttal 1:
Rebuttal: We thank you for carefully reading our paper and for providing detailed feedback.
**Remark 1: Benign overfitting lacks motivation.**
The considerable interest in benign overfitting is mainly motivated by the great successes of overfitted NNs. In this sense, overfitting is motivated by empirical ... | Summary: This paper extends previous results on the inconsistency of ridgeless kernel regression in fixed dimension by showing that non-interpolating estimators whose norm grows comparably to the minimum-norm interpolator are also inconsistent. On the other hand, it is shown that so-called spiky-smooth kernels whose de... | Rebuttal 1:
Rebuttal: We thank you for carefully reading our paper and for providing detailed feedback. We agree that all of your suggestions are important clarifications and will include them in the updated version. They will certainly improve the updated version of the paper. Concretely, we will:
- add a discussion ... | Summary: In this paper, the authors studied the problem of benign overfitting for kernels and wide neural networks (in kernel regime) in fixed dimension. The authors showed that benign overfitting is possible if and only if the learner model has large derivatives. This implies that benign overfitting is not possible fo... | Rebuttal 1:
Rebuttal: We thank you for carefully reading our paper and for providing detailed feedback.
**Remark: The results only cover the kernel regime.**
We would like to point out that this paper is the first to establish benign overfitting with a neural model in the challenging regime of low dimension, and the ... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their detailed feedback. The following remarks have been raised multiple times and we will include a discussion in the revised version of our paper:
**Question 1: Can benign overfitting with spiky-smooth kernels achieve optimal rates?**
Since minimum norm inter... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explains that benign overfitting when the dimensionality of data is fixed is possible if one looks for estimators differently from conventional minimal norm. If one allows the estimator to be spiky, benign overfitting can still be possible. These results are extended via NTK to two-layer infinite-wi... | Rebuttal 1:
Rebuttal: We thank you for carefully reading our paper and for providing detailed feedback.
**Q1: Is regularization better than benign overfitting?**
We would like to emphasize that we do not promote overfitting. Instead this paper is the first to show that benign overfitting of kernels and neural network... | null | null | null | null | null | null |
OBJECT 3DIT: Language-guided 3D-aware Image Editing | Accept (poster) | Summary: The paper studies the problem of object-centric image editing. The authors first curate a dataset based on Objaverse by selecting high-quality textured samples, and then simulate+render them on a plane. The objects can be manipulated in 3D and rendered correspondingly, which generates the groundtruth for train... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our submission and for providing valuable feedback. We will now respond to the highlighted questions and concerns.
**Realism of dataset**
As per reviewer suggestions, we have improved the realism of our dataset in 3 ways: (i) In line with common ... | Summary: This paper constructs a dataset containing 400K examples, which is used for the task of language-guided 3D-aware image editing. This paper also proposes a model, named 3DIT, to solve this task. The model is based on 2D diffusion model, which first goes through the pre-training of text-to-image generation and Z... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our submission and for providing valuable feedback. We will now respond to the highlighted questions and concerns.
**Incorrect shadow behavior in GIF, dataset realism and diversity**
The original dataset at the time of submission used a single di... | Summary: The paper formulates a task of 3D aware editing using the language guidance. The task aims to insert, remove, translate or rotate objects in a scene (2D images) by maintaining the details like shadows, 3D consistency of the object, changes in the object sizes due to perspective projections etc. The model is ba... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our submission and for providing valuable feedback. We will now respond to the highlighted questions and concerns.
**Method clarifications**
Our approach extends zero123 with a CLIP text encoder (the same as the one used in the original StableDif... | Summary: The authors propose a large dataset of 3D aware image edits along with editing instructions built on the objaverse dataset. They also introduce a model finetuned on Zero-1-to-3 for 3D aware editing tasks which include object insertion, removal, translation and rotation. Comparisons are provided against state ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our submission and for providing valuable feedback. We will now respond to the highlighted questions and concerns.
**Novelty**
There are 3 main novel contributions in our work:
1. We propose the task of language-guided 3D-aware image editing.
2. ... | Rebuttal 1:
Rebuttal: # Common statement
We are encouraged by all the positive comments and thank all of the reviewers for their valuable feedback. Reviewers found our model to be “a novel approach to language guided 3D-aware image editing” (Reviewer 1T99), “the manipulations possible with the method can preserve the ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The 3DIT model is a language-guided 3D-aware image editing tool that allows for effective object editing while considering scale, viewpoint, lighting, and object occlusions. The model builds upon previous work in scene rearrangement and image generation, and incorporates a diffusion process to render object tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our submission and for providing valuable feedback. We will now respond to the highlighted questions and concerns.
**Lack of ablation**
We have extensively evaluated our model's capability using multiple metrics across tasks, single/multitask mode... | null | null | null | null | null | null |
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning | Accept (poster) | Summary: The paper presents an method to perform point cloud registration using event camera data.
The method first learns a feature representation (E2PT) from a point cloud of events. This representation is used as input to standard registration networks.
The experimental section shows better accuracy compared to othe... | Rebuttal 1:
Rebuttal: Thanks for the thorough review and valuable suggestions. Due to space constraints, detailed explanations of the methodology are contained in Sec. A.1.1 of the appendix, including the technical details about the LA, STA, and FP modules. Our approach builds upon the 3D point-based architecture (Poin... | Summary: This paper proposes a learning-based event-to-point cloud registration method, which encodes event spatio-temporal data into a grid-shaped feature tensor, and propose a framework to construct E2P datasets using existing SLAM datasets. Experiments are conducted on MVSEC-E2P and VECtor-E2P datasets, and state-of... | Rebuttal 1:
Rebuttal: Thanks for the positive comments about our writing and experiments. In the following, we address the reviewer's concerns and back up our responses with additional experiments. We hope that with major concerns like efficiency analysis and training details resolved, the reviewer will consider improv... | Summary: This paper proposed a Event-Points-to-Tensor (EP2T) network, which treats event data as spatio-temporal point clouds, to process event signals without losing the spatiotemporal information of event signals (especially temporal information, compared with other voxel grid-based methods). In terms of experiments,... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the value of this work and providing an in-depth review. We provide the responses to the questions/concerns below.
**The motivation, advantage, and limitation of EP2T+tensorization-based approaches** First of all, there is no existing event to point cloud r... | Summary: In this paper, the authors proposed the first learning-based work that can handle event-to-point cloud registration (E2P). More specifically, a novel Event-Points-to-Tensor (EP2T) network is proposed to encode the data from the event camera into features tensors in the form of a 2D grid. The temporal patch agg... | Rebuttal 1:
Rebuttal:
Thanks for your positive comments about the novelty, writing, and experiments of this work. Please see the following responses to your concerns.
**Time and memory efficiency** We have uploaded a new PDF containing an analysis of the time and memory efficiency of E2PNet. As shown in Tab. 2 of th... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive comments about the novelty (R1, R2, R4), significance (R1, R2, R4), writing quality (R1, R2, R3), and experiments (R1, R2, R3, R4) of this work.
A common question/concern was the efficiency of the proposed method. We have conducted several experiments and... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation | Accept (poster) | Summary: This paper proposes a neural network approach to learning the Whittle index policy. In addition, the paper gives a finite-time analysis for the algorithm, and shows that the algorithm indeed learns the Whittle index values for restless bandits.
Strengths: The paper’s sections are well-written and the techni... | Rebuttal 1:
Rebuttal: Thank you very much for your review and constructive comments, as well as giving the positive rating of our work. Here we would like to address the reviewer's concerns and hope that can help raise the rating of our paper. The detailed responses are as follows:
**Weakness #1:** While the paper is ... | Summary: This paper presents a neural Q learning method to compute the Whittle indices in restless multi-armed bandit problems. The paper provides an algorithm using two-timescale stochastic approximation (2TSA) to update the parameters in the neural networks and the Whittle indices jointly with different learning rate... | Rebuttal 1:
Rebuttal: Thank you very much for your review and constructive comments. Here we would like to address the reviewer's concerns and hope that can help raise the rating of our paper. The detailed responses are as follows:
**Weakness \#1:** ... project step...
**Our Response:** Thank you for this insightful ... | Summary: This paper investigates the finite-time analysis of the Whittle index-based Q-learning policy for the RMAB problem under neural function approximation. The authors formulate the algorithm as a nonlinear two-time-scale stochastic approximation problem and present a convergence rate of $K^{2/3}$.
Strengths: 1. ... | Rebuttal 1:
Rebuttal: Thank you very much for your review and constructive comments. Here we would like to address the reviewer's concerns and hope that can help raise the rating of our paper. The detailed responses are as follows:
**Weakness #1:** It is unclear whether the approximated Q-functions converge or not.
*... | Summary: This paper studies Whittle index-based Q-learning with neural network function approximations restless multi-armed bandits (RMAB) problem, which is a model-free low-complexity reinforcement learning (RL) heuristics for RMABs. Since state-action space of RMABs is exponentially growing with the number of arms, c... | Rebuttal 1:
Rebuttal: Thank you very much for your review and constructive comments, as well as giving the positive rating of our work. Here we would like to address the reviewer's concerns and hope that can help raise the rating of our paper. The detailed responses are as follows:
**Weakness #1:** ...provide a table ... | Rebuttal 1:
Rebuttal: The attached Pdf contains a table for **Reviewer KAmk** and **Reviewer rZoR**, and a figure for **Reviewer rZoR.** The detailed response to the corresponding comments are provided below in our rebuttal to each reviewer.
Pdf: /pdf/7eb7d09e27fac333bdbca0278671abfdd80d4e51.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides a finite-time analysis of Neural-Q-Whittle. The authors propose Neural-Q-Whittle, a novel Whittle index-based Q-learning algorithm with neural network function approximation for RMAB. Their analysis leverages a Lyapunov drift approach to capture the evolution of two coupled parameters, and ... | Rebuttal 1:
Rebuttal: Thank you very much for your review and constructive comments, as well as giving the positive rating of our work. Here we would like to address the reviewer's concerns and hope that can help raise the rating of our paper. The detailed responses are as follows:
**Weakness #1:** Compared with exist... | null | null | null | null | null | null |
Visual Programming for Step-by-Step Text-to-Image Generation and Evaluation | Accept (poster) | Summary: This paper makes two contributions. VPGen is a T2I generation framework that first generates object/count, then layout, and finally image. VPEval is a T2I evaluation pipeline that provides a more comprehensive analysis correlated to human. Authors demonstrate that the step-by-step VPGen approach generates imag... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback and for pointing out our strengths in providing useful image generation and evaluation frameworks. We hope to address your questions and concerns below.
**W1. Standard image quality metric (FID).** Please see the general response.
**W2-1. Prompts without an exac... | Summary: This paper proposes a visual-program-based evaluation method VPEval to evaluate text-to-image models. Their method relies on LLM which can call different expert models in different tasks like object detection, OCR, spatial understanding, etc. to evaluate the consistency between the text and the generated image... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback and for pointing out our strengths of the effectiveness of VPGen and VPEval, as well as clear writing. We hope to address your questions and concerns below.
**W1. Introduction of Visual Programming.** We’d like to bring your attention to Sec 1, where we explain h... | Summary: This paper extends previous work in the vision and language space that use visual programs as an intermediate step, to the problem of text to image generation and subsequently, its evaluation. It proposes VPGen, a neuro-symbolic method that is composed of specific modules that count objects, generates layouts ... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback and for pointing out our strengths of providing a strong interpretable T2I model, providing a much-needed interpretable T2I evaluation method, and having a well-written and detailed paper. We hope to address your questions and concerns below.
**W1. Additional err... | Summary: This paper proposes a text-to-image (T2I) generation approach along with a new evaluation framework. This can be summarized in Figure 1. First, VPGen (Sec. 3) breaks T2I down into 3 steps, with an LM to generate a “program” of objects and layouts that is then fed into the final generation module. Second, VPEva... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback and for pointing out our strengths - a well-written paper and solid experiments. We hope to address your concerns below.
**W1-1. Challenging/Complex prompts.** Please see the general response.
**W1-2. Prompts without an exact number of counts.** Thanks for the s... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and for recognizing our strengths:
- addressing an important/foundational problem in text-to-image generation (aEEd)
- developing strong/interpretable/useful text-to-image generation and evaluation frameworks (aEEd, 1fZw, GZ2e, PtVL, bJid)
- provi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper delves into the intersection of large language models (LLMs) and their applications in vision-and-language tasks, specifically focusing on text-to-image (T2I) generation. The authors identify a gap in the existing literature: no thorough analysis has been conducted on the synergy of LLMs and various... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback and for pointing out our strengths in addressing important issues in the image generation community regarding spatial control and having an adaptable/interpretable evaluation framework.
**W1. Image quality metric (FID).** Please see the general response.
**W2. E... | null | null | null | null | null | null |
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations | Accept (poster) | Summary: The proposer proposed a contrastive learning method for graphs. The goal is to learn representations for nodes in an unsupervised way, by maximizing mutual informations between the local representation and global representation of a graph, training a single encoder to learn node representations. The method pro... | Rebuttal 1:
Rebuttal: First and foremost, we would like to express our gratitude for the time and effort you dedicated to reviewing our paper, providing us with constructive feedback. We're glad to learn that you found our paper well-written, with a concise and useful preliminary section. We highly appreciate your posi... | Summary: This work proposes a model and contrastive learrning method for acquiring a comprehensive specturm of graph representation by employing filters of various levels. They are adaptively aggregated with learnable weights for downstream supervision tasks. As a result, this approach performs well on both homophilic ... | Rebuttal 1:
Rebuttal: We thank you for dedicating time to thoroughly review our paper and for providing comprehensive feedback on our work. We appreciate your recognition of the strengths of our research. It is encouraging to note that you found value in our experiments across data with multiple characteristics, demons... | Summary: The paper proposes a contrastive learning model for learning node embeddings on a graph, with two technical innovations: First, the authors propose a new augmentation scheme during contrastive learning. Secondly, the authors re-map high dimensional embeddings into lower dimensional space using random Fourier f... | Rebuttal 1:
Rebuttal: We thank you for taking the time to review our paper. We truly appreciate your insightful feedback. We are glad to know that you found the presentation of our paper mostly clear, with readable figures and clearly presented results.
# Comment Response
1. **Explaining Augmentation:** We acknowled... | Summary: The paper proposes a few approaches to improve the contrastive learning framework of the unsupervised graph representation learning (UGRL) problem. Building on top of the prior works in supervised GRL and UGRL areas (e.g., filter bank construction, etc.), the authors argued that 1) filter-based augmentations (... | Rebuttal 1:
Rebuttal: Firstly, we would like to extend our sincere gratitude for your comprehensive and constructive feedback on our paper. We appreciate your recognition of the strengths in our work. Specifically, we are pleased that you found value in our novel approach of leveraging different filter banks as additio... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable feedback. Reviewer qpib's recognition of our novel approach leveraging filter banks is appreciated. Reviewer Ck44's positive remarks on clear presentation and readable figures are noted. Reviewer wrwF's acknowledgment of our approach's versatilit... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks | Accept (spotlight) | Summary: Conformal Prediction (CP) outputs a prediction set that contains the true label with a certain likelihood given assumptions on exchangeability. It is a well-known and popular uncertainty quantification (UQ) technique. The authors propose a technique that unites GNNs and CP called conformalized GNN (CF-GNN). T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback and for acknowledging that our paper tackles an interesting problem, that it is well-written, and that the evaluation is extensive. The reviewer raises great questions and we respond to them below:
> Why focus only on transductive settings? Extens... | Summary: The authors propose Conformalized Graph Neural Networks (CF-GNNs), which extends conformal prediction to graphs for uncertainty quantification. The framework allows a GNN to produce confidence intervals for its predictions, based on an uncertainty estimation on a withhold calibration set. Under permutation inv... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful feedback and for acknowledging that our paper is original and makes solid theoretical and empirical contributions. The reviewer has raised great questions and we respond to them below:
> Ways to make baseline UQ methods empirically reach $1-\alpha$ coverage?
... | Summary: This paper presents a new approach, known as conformalized Graph Neural Networks (CF-GNN), designed to bring reliable uncertainty estimates to graph-structured data prediction models. The study's primary contribution is the innovative adaptation of conformal prediction (CP) to Graph Neural Networks (GNNs). The... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful feedback and for noting that our paper is interesting, timely, and easy to follow. The reviewer raises great questions and we respond to them below:
> Clarification on the construction of the correction dataset
We thank the reviewer for raising this issue, wh... | Summary: This paper proposes a conformal prediction method tailored for graph-structured data. The proposed correction method is topology-aware and based on an empirical observation that inefficiencies correlate highly with network edges. The method updates node predictions based on its neighbors, and it is trainable a... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful feedback and for noting that our paper tackles a well-motivated problem, that our method is rigorous and novel, and that our experiments are in-depth. Below, we address the excellent questions raised, with numbers corresponding to those in the review (e.g., [W1... | Rebuttal 1:
Rebuttal: > Summary of main points
We thank the reviewers for their valuable feedback and constructive suggestions for improvement. Overall, all five reviewers considered our work well-written and well-motivated, and all appreciated the theoretical rigor and strong empirical performance of our proposed met... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the problem of providing faithful and "efficient" uncertainty estimates for GNNs. Here faithful means the unknown groundtruth is contained in the prediction set with a probability higher than a threshold; efficient means the prediction set should be as small as possible. Specifically, the pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback, and for recognizing that our approach is simple yet effective. We appreciate the thoughtful questions posed and address them in detail below:
> [1] Train a separate model for every $\alpha$?
The reviewer recognizes a vital part of our approac... | null | null | null | null | null | null |
A generative model of the hippocampal formation trained with theta driven local learning rules | Accept (poster) | Summary: The paper is an application of the learning scheme derived in Bredenberg et al. 2021 to representation learning and path integration in the MEC and HPC. By adapting the aforementioned scheme to continuous time, and by relating the proposed oscillatory 'gating' signal to theta oscillations that have been observ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review, we are glad that you find our contribution “very important” and “clearly written”. We respond to your key points below and including an additional simulation to answer one of your questions. Please kindly inform us if there's anything else required to boost your... | Summary: In this work, the authors give a continuous version of the 'impression learning' [1] and use the theta oscillation to modulate wake-sleep phase.
[1] Bredenberg, Colin, et al. "Impression learning: Online representation learning with synaptic plasticity." Advances in Neural Information Processing Systems 34 (2... | Rebuttal 1:
Rebuttal: Thank you this review. We’d like to clarify our contributions in case they were misinterpreted. As the manuscript states; “The primary contribution of this paper is to introduce a biologically plausible model of sequence learning in the hippocampus which unifies its capacities as a generative mode... | Summary: This work presents a neat model that incorporates aspects of hippocampal function under one umbrella:
* first, the input from the environment (z) goes into the sensory layer (p) and activates the internal state (g) in a certain way, the model captures this an "inference" or "wake" stage of the training
* next... | Rebuttal 1:
Rebuttal: Thank you for this incredibly thorough review. Your insightful comments have led to meaningful improvements. We respond point-by-point below but these first paragraphs are reserved to further your philosophical discussion about the goals of computational modeling.
### General response about the ... | Summary: The Hippocampus is postulated as a generative model to learn latent state representations and generate sensory predictions to solve spatial and nonspatial tasks. Theta-band oscillations are used to gate information flow into the generative model to modulate learning. A ring attractor develops within the genera... | Rebuttal 1:
Rebuttal: Thank you for this detailed review which has led to a number of changes to the paper. We have addressed your comments and questions below but please respond with any additional questions which we’re happy to answer. Apologies if our answers seem at all "curt", we are heavily constrained by the 60... | Rebuttal 1:
Rebuttal: We thank all reviewers for their detailed and thoughtful comments and are glad they found it to be a "well written" and "logically presented" paper about a model which "could prove to be very important". We respond to each review individually but, for the benefit of all, here summarize three major... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization | Accept (poster) | Summary: This work tries to develop a PAC-Bayesian spectrally-normalized robust generalization bound.
Strengths: This work tries to understand robustness from theoretical perspective.
Weaknesses: 1. unclear definitions:
second line in Eq. (4)
$\mathbf{x}(\mathbf{w})=\arg \inf_{\left\|\mathbf{x}-\mathbf{x}^{\prime... | Rebuttal 1:
Rebuttal: We thanks Reviewer Ccn3 for the comments and questions.
___
**Q1, Q3 and Q4.** Unclear definition, what is $W_i-W_i$. Miss citation.
A: Thanks for pointing out the typo and missing citation. Some prime’ are missed due to the full/half width issue. We fixed the typo in the updated version. In $W_i... | Summary: This paper improves previous PAC-Bayesian bounds on robust generalization. The previous bound in Farnia et al. (2018) has a term that is not bounded, and this work provides a bound to that term using the Lipschitzness of feed-forward ReLU networks. The basic idea is that coordinate-wise Lipschitzness preserves... | Rebuttal 1:
Rebuttal: We thanks Reviewer FsEL for the comments and questions.
**Comment 1.** My only concern is that the significance of this work might not be obvious to a person who is not very familiar with this field.
A. Thanks for the suggestions. We understand that the significance of this work might not be ob... | Summary: In this work, authors use PAC-Bayesian bound to characterize the generalization gap of adversarial robustness. Their work is mostly based on the bound derived from (Neyshabur et al., 2017b) so the resulting bound is valid for a deterministic model.
Strengths: The major contribution from this work is the ne... | Rebuttal 1:
Rebuttal: We thanks Reviewer AB19 for the comments and questions.
**Comment.** I think the theoretical contribution is incremental.
A. Thanks for the comment, we will first answer this comment in the beginning. The technical novelty of our research goes beyond mere improvement. The **technical novelty is... | Summary: This paper provides a tighter bounds for robust generalization compared to previous results and as tight as standard generalization.
Strengths: 1. The paper studies an important topic on adversarial robustness and provide a tighter bound with detailed theoretical analysis.
2. The paper is well-written and the... | Rebuttal 1:
Rebuttal: We thanks Reviewer nNum for the comments and questions.
___
**Q1:** As the paper is an improvement on Farnia et al. (2018), thus the technical novelty is less significant.
**A:** Thanks for the question. the technical novelty of our research goes beyond mere improvement. The **technical novelty**... | Rebuttal 1:
Rebuttal: We thanks all the Reviewers for the comments and questions. We will first answer three common questions.
**Common Question 1:** Significance of the result. (Reviewer nNum & AB19)
**A:** Our finding is not just an incremental improvement on Farnia et al. (2018); it holds **significant importance*... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Understanding the detrimental class-level effects of data augmentation | Accept (poster) | Summary: This paper studies Data augmentation (DA). Although DA improves overall accuracy, recent studies have pointed out that it can adversely affect individual class accuracy by up to 20% on ImageNet. This happens due to a lack of understanding of how DA impacts class-level learning dynamics. This research offers a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their clear understanding of our submission and for noting the importance of our study! Please see our general response in which we detail our findings on new datasets, architectures and data augmentations (notably including the suggested ViT model). We observe that our i... | Summary: The authors explore the role of random resize crop in ImageNet performance. First, they improve on analysis in prior work and show that class-level performance degradation has been over-stated, and that when multi-label annotations are used one of the labels is often still predicted. Next, by inspection, the a... | Rebuttal 1:
Rebuttal: Thank you for your insightful review! We appreciate that you find our work interesting, significant and clearly written.
**Model, dataset, data augmentation**: We agree that confirming the applicability of our insights and methods to other models and datasets is crucial. To that end, we have prov... | Summary: The paper presents a meta study on the effects of data augmentation over classes. In particular, authors works on ResNet50 architectures trained on ImageNet, and show how for some classes, strong data augmentation drastically decreases the per-class accuracy. The paper focuses on random resized crop augmentati... | Rebuttal 1:
Rebuttal: Thank you for your feedback! Please also see our separate general post, which contains new experiments inspired by your comments and clarification on the setup. Inspired by your feedback we have significantly increased the scope of our paper, and included results for new architectures, datasets an... | Summary: The authors study the effect of data augmentations on the classwise performance under data imbalance. They focus on the rezised cropping operation and distinguish between 4 different failure cases by using multi-label annotations. They also show that it is possible to recover some of them but using an informe... | Rebuttal 1:
Rebuttal: Thank you for your review and constructive feedback! We respond to your questions below, and we will remain available for any further discussion and clarifications throughout the discussion period.
### Average performance improvement
The reviewer raises an interesting point regarding the average... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback! We are happy that the reviewers found our paper is “systematic and well constructed” (nQVM), “bears significant practical implications” (wB8d) and “sheds light on a process which is often used as a black box” (EjrR). Data augmentation (DA) is essential in... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Complexity of Derivative-Free Policy Optimization for Structured $\mathcal{H}_\infty$ Control | Accept (poster) | Summary: This paper considers solving the $H_\infty$ control problem using zero-th-order policy optimization. The main results are sample complexity bounds for both the exact Oracle setting and the model-free setting. Numerical simulations are conducted to demonstrate the effectiveness of the algorithm.
Strengths: Thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking time and effort to review our manuscript. We sincerely appreciate all your valuable comments and suggestions. Please see our responses below.
**Related Work: The related work section could be more structured. What are the major technical challenges (and the id... | Summary: This paper focuses on the structured $H_\infty$ control problem. They provide sample complexity bounds for policy optimization in $H_\infty$ control problem.
The results are provided for two separate scenarios namely:
- Exact Oracle Setting (exact $J(K)$ for any $K$ is available, for the given closed loop s... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking time and effort to review our manuscript. We sincerely appreciate all your valuable comments. Please see our responses below.
**More discussion on the oracle is warranted.**
We appreciate the valuable suggestion from the reviewer. Our paper considers two ... | Summary: This paper studies the static output feedback $\mathcal{H}_{\infty}$ control problem. It proposes a derivative-free policy optimization algorithm via randomized smoothing and further provides sample complexity analysis for the cases with exact and inexact zeroth-order oracles. To validate the performance of th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in evaluating our manuscript. We value your insightful comments and suggestions. Below are our responses.
**Would the authors highlight any novel techniques they apply in the analysis or explain what makes the problem different from other nonsmooth non... | null | null | Rebuttal 1:
Rebuttal: We deeply appreciate the insightful feedback provided by the reviewers. In response to the comments from Reviewer vRAL, we have attached a PDF file containing the updated plots from the main paper. Each comment from the reviewers has been addressed below. We hope our explanations have resolved the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Star-Shaped Denoising Diffusion Probabilistic Models | Accept (poster) | Summary: This introduces the so-called Star-Shaped DDPM (SS-DDPM). Instead of using Gaussian forward diffusion step in a Markovian manner, the diffusion process is directly conditioned on the data, in order to construct the "true" reverse process (posterior), resulting in the star-shaped diffusion process. This opens t... | Rebuttal 1:
Rebuttal: Thank you for the kind comments!
We will release the source code with the camera ready version of the paper.
Regarding your question:
In general, we follow the same intuition as when designing the forward process in DDPM. We start with low noise ($x_1$, $x_2$, ... should be reasonably close to $... | Summary: The paper introduces a new probabilistic structure for denoising models that does away with a Markov forward process. The authors derive the reverse process in terms of a sufficient statistic that allows for efficient reverse sampling. The form of the model is derived for a variety of noising distributions and... | Rebuttal 1:
Rebuttal: Thank you for the comments! We would like to address your questions below.
**Weakness 1:**
We used Figure 5 to illustrate this point in the main text, however we should probably make this point more clear in the text. The close connection between $G_t$ in SS-DDPM and $x_t$ in DDPM is crucial to u... | Summary: This paper proposes a star-shaped diffusion probabilistic model, which is non-Markovian, and more like an autoregressive way of predicting intermediate states xt. Specifically, the authors define a forward process q(xt|x0) that each intermediate state xt is directly related to the initial state x0. The reverse... | Rebuttal 1:
Rebuttal: Thank you for the comments! We would like to address your concerns and questions below.
**Weakness 1 (1):**
Eq. 13 is a standard definition of the exponential family of distributions with a standard notation. $\mathcal{T}(x_t)$ is the sufficient statistic, $\eta(x_0)$ is the natural parameter, $h... | Summary: This paper proposes a non-Markovian diffusion model named the star-shaped diffusion model that generates a sequence of noised image from the original image in the forward process.
This paper studies the theoretical foundation of such new type of model showing that if the forward process is based on a subset o... | Rebuttal 1:
Rebuttal: Thank you for the comments! We would like to address your questions below.
**Weakness 1:**
At this stage our goal was to demonstrate that the model can be successfully applied with a variety of noising distributions rather than perfecting the model for each individual task or finding the best tas... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents the Star-Shaped DDPM (SS-DDPM), a general recipe for designing a diffusion model with a noising process lying in a general subset of the exponential family. With a Gaussian noising process, SS-DDPM recovers the DDPM. Diverse experiments on synthetic and practical image and text datasets demo... | Rebuttal 1:
Rebuttal: Thank you for the comments! We would like to address your questions below.
**Weakness (1):**
We have tried to summarize the most crucial technical details in Section 3. Due to space limitations, we had to put more details in the appendix. Unfortunately, the NeurIPS template makes it more difficul... | Summary: This paper proposed a star-shaped denoising diffusion probabilistic model (DDPM), which extends DDPM to non-Gaussian noises. As a result, the backward/generative process requires conditioning on tails. The authors then propose an efficient tail conditioning strategy which works when the forward process follows... | Rebuttal 1:
Rebuttal: Thank you for the comments! We would like to address your questions below.
**W1 and Q2:**
DDPM is a special case of SS-DDPM, so it is safe to expect SS-DDPM to perform at least as good as DDPM. At this stage our goal was to demonstrate that the model can be successfully applied with a variety of ... | null | null | null | null |
LayerNAS: Neural Architecture Search in Polynomial Complexity | Reject | Summary: The paper introduces LayerNAS, which is a method for neural architecture search (NAS). The idea is to reduce the computational cost of NAS, which is exponential in the number of layers. As such, the paper introduces a layerwise search option with the idea being that the current layer can be directly determined... | Rebuttal 1:
Rebuttal: 1. **"What is the difference in the obtained architectures depending on the cost set as threshold?"**
It is difficult to interpret the detailed architectural differences between LayerNAS and other models. However, in general, I found that LayerNAS models have more base filters and fewer expanded ... | Summary: The paper tries to overcome a drawback of Neural Architecture Search (NAS), an enormous search space that hard to traverse whole space to design a well-optimized network.
From an assumption that a previous layer in a network doesn’t affect the subsequent layers, the paper converts multi-objective NAS to a comb... | Rebuttal 1:
Rebuttal: **"Is it sure ... aren’t suboptimal solutions?"**
LayerNAS traverses on a finite complete search space, which significantly aids in avoiding suboptimal results.
Assume we are searching for the optimal model $s_1..s_n$, and we store all possible model candidates on each layer. During the search ... | Summary: The authors propose LayerNAS with polynomial complexity. Namely, this work transforms the multi-objective NAS problem into a Combinatorial Optimization problem with proper assumptions.
LayerNAS is benchmarked against recent NAS arts on ImageNet classification task, as well as on dedicated NATS-Bench in terms ... | Rebuttal 1:
Rebuttal: 1. **"Format flaws"**
Thanks for the comment. We have revised the abstract into a concise single paragraph.
2a. **"Table 2 is not informative enough"**
We have endeavored to provide more information in Table2.
| Model | Top1 Acc. | Params | MAdds | Training Epochs | Data Augmentation |
|--|--... | Summary: This paper propose a novel approach of breaking down NAS problem into a Combinatorial Optimization problem.
Strengths: The paper is well-written and easy to follow. The authors provide clear explanations and examples throughout the paper.
Breaking down the search problem into a Combinatorial Optimization p... | Rebuttal 1:
Rebuttal: We are honored to have our work recognized by you. Thank you very much! | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diverse Shape Completion via Style Modulated Generative Adversarial Networks | Accept (poster) | Summary: This paper proposes a new conditional generative network that can produce diverse completions of a partially observed point cloud. The stochasticity is introduced via style modulation. A style code is learned to explicitly carry shape category information leading to better completions. Moreover, diversity pena... | Rebuttal 1:
Rebuttal: We thank reviewer Ahkf for their valuable and constructive comments.
**No visual samples of diverse synthesis are shown**
Qualitative comparisons to other methods were included in Figures 1, 4 and 5 in our main paper. In each of these figures we show 3 completions produced for the same partial ... | Summary: The paper proposes to reconstruct partial point cloud inputs using a multi-modal process, where the generator can output multiple plausible shapes. The key idea is to have a separate network (StyleEncoder) that extracts style from an input, in addition to a separate network (PartialEncoder) that extracts struc... | Rebuttal 1:
Rebuttal: We thank reviewer R39m for their valuable and constructive comments.
**Paper is difficult to follow. Helps to state key contributions.**
We'd like to clarify any confusions you've had. Meanwhile, note that 2 other reviewers found the paper well-written. First, we want to note that our work is... | Summary: The paper proposes a diverse shape completion method by extracting style codes from complete shapes and learning a distribution over them. Moreover, diversity penalties and discriminators at multiple scales are introduced as well to prevent conditional modal collapse to generate various object shapes. To verif... | Rebuttal 1:
Rebuttal: We thank reviewer ohCS for their valuable and constructive comments.
**Diverse information for a single class seems to be missing.**
Our method is trained per category similar to other multimodal shape completion works such as cGAN [8], IMLE [9], and PVD [10]. Thus, the "style codes" we learn c... | Summary: The goal of multimodal shape completion is to generate many different plausible completions of an incomplete shape. Based on the conditional GAN, this paper introduces two key concepts to improve the diversity and accuracy of multimodal completion. One is to use style codes instead of random noise, which means... | Rebuttal 1:
Rebuttal: We thank reviewer iNun for their valuable and constructive comments.
**Ablation study on $L_{comp}$ and $L_{div}$**
In our global response we have provided a new table where we train our method with each of $L_{comp}$, $L_{div}$, and $L_{part}$ set to 0. Please see our global response for furth... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to read and review our work. We have tried our best to answer and address any questions and clarifications in each individual reviewer response. In the rest of our global response, we discuss a common ablation that was requested across several reviewers a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes a novel GAN for diverse shape completion from partial point clouds. To enable diverse completions, a style-based generator is introduced that leverages style codes from a learned distribution of complete shapes for style modulation. Further, a multi-scale discriminator and a diversity penalt... | Rebuttal 1:
Rebuttal: We thank reviewer A3pQ for their valuable and constructive comments.
**Diversity penalty ablation**
In our global response we have provided a new table where we train our method without diversity penalty $L_{div}$. Please see our global response for further comments on this ablation. We'll adop... | null | null | null | null | null | null |
PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance | Accept (poster) | Summary: The current restoration approaches based on diffusion prior rely on prior knowledge of the degradation process, and thus fail to seamlessly adapt to different scenarios. Motivated by this, the paper proposed a " partial guidance" approach to directly modeling the distribution properties of high-quality images... | Rebuttal 1:
Rebuttal: **Identity-preserving evaluation on blind face restoration.** We provide a quantitative evaluation regarding identity-preserving issues in the table below. Considering the importance of identity-preserving in blind face restoration, we introduced reference-based restoration in Sec. 4.5 of the manu... | Summary: This paper proposes to use some simple properties to guide the reverse diffusion process. The proposed approach makes no assumptions about the degradation process. This paper also shows many different face restoration visual results to demonstrate the superiority of the proposed method.
Strengths: 1. The pro... | Rebuttal 1:
Rebuttal: **Technical differences between our method and classifier guidance.** To model the desired properties of high-quality images, we devise an instantiation named partial guidance by adapting classifier guidance in image restoration (IR) tasks. While the rough idea is inherited from the classifier gui... | Summary: This paper proposes partial guidance, an approach exploiting pre-trained diffusion models for face restoration. Instead of making assumption about the specific degradation process, partial guidance models properties of high-quality images such as structure and color statistics to implement classifier-guidance... | Rebuttal 1:
Rebuttal: **Quantitative evaluation and user study.** We believe that PSNR and SSIM fail to reflect the true image quality. For example, in Tab.1 of CodeFormer, input images achieve the third highest PSNR scores and the highest SSIM scores across all methods. Thus, we exclude them from full-reference metric... | Summary: This paper proposes a novel solution for blind face restoration. Instead of modeling the degradation process, the authors propose to model the desired properties of high-quality images as classifiers. Similar to guided diffusion, the authors guide the diffusion generation process with specific classifiers to a... | Rebuttal 1:
Rebuttal: **Quantitative evaluation and user study.** Please refer to the global response in "Author Rebuttal" for detailed elaboration.
**Setting of hyperparameters.** As mentioned in A.2 of the supplementary material, while it is principally flexible to tune the hyperparameters case by case, we provided ... | Rebuttal 1:
Rebuttal: We are encouraged that the reviewers find our work novel and interesting [Reviewer GVHx, ggEw]; practical and versatile in multiple image restoration tasks [Reviewer GVHx, FgM3, hvsJ, ggEw]; presenting impressive and outstanding visual results [Reviewer GVHx, FgM3, ggEw]; well-written and easy to ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions | Accept (poster) | Summary: This paper introduces a new concept of conditional convergence of an algorithm (i.e. convergence of the distribution restricted to e.g. a local mode), and presents extension of recent results using e.g. Poincare Inequality to bound this new measure.
Strengths: I think this paper tackles an important problem: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the importance of our problem. After reading the review, we find that there could be several misunderstandings. We begin with clarifying these misunderstandings and then address other concerns.
**On "convergence of LMC".** In this paper, when we talk about ... | Summary: The paper studies MCMC algorithms like the Langevin dynamics and Gibbs sampler on non-log-concave distributions. Many natural distributions are non-log-concave and multimodal, for example, mixtures of Gaussians and the posterior distribution of Gaussian mixtures. While classical results show that MCMC algorith... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and positive feedbacks. Your concerns are correspondingly addressed as follows.
**Q1**: Typos & mistakes.
**A1**: We thank the reviewer for pointing out the typos. We will update our draft with the following changes:
a. Lemma 1: In line 416, it should be $\m... | Summary: This work studies the convergence of MCMC algorithms for sampling from non-log-concave distributions. This is much less well-understood than the log-concave setting. The authors introduce the notion of conditional mixing, this occurs when the markov chain is close to the true (conditional) distribution when co... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and positive feedback. Your concerns are correspondingly addressed as follows.
**Q1**: Lack of high-level technical overview.
**A1**: We thank the reviewer for the suggestion. We will expand our related work to provide more overview to the problem. Due to limit... | Summary: This paper, following the framework of Balasubramanian et al., shows that for target distributions that are non-log-concave, isoperimetric inequalities on subsets of the state space will yield fast mixing for the conditional distributions of MCMC on that space. This adds formal justification for the observed p... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our paper, and for the comments and suggestions. Here are our main responses:
**Q1**: I would recommend a more detailed survey of the literature on discrete space MCMC with relevant comments be included in the Related Work.
**A1**: Thank you for... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
RFold: RNA Secondary Structure Prediction with Decoupled Optimization | Reject | Summary: In this paper, the authors propose a way to decouple the optimization process of RNA secondary structure prediction. Specifically, they decompose the constraint satisfaction problem into row-wise and column-wise optimization. Instead of hand-crafted features, attention maps are used to learn the pair-wise inte... | Rebuttal 1:
Rebuttal: Dear Reviewer FGw2,
Thank you for your constructive and insightful comments! We appreciate the time and effort you've put into this review and would like to sincerely address your concerns below:
***
**Q1** The proposed method cannot achieve the best recall on ArchiveII and bpRNA-TS0 datasets. ... | Summary: The paper introduces RFold for RNA secondary structure prediction (a prediction of LxL binary matrix). It proposes to add a row-column-wise softmax at output of the model, before computing the L2 loss with respect to the ground truth. The experimental results show higher precision and recall compared to prior ... | Rebuttal 1:
Rebuttal: Dear Reviewer G61p,
Thank you for your thoughtful comments. We hope to address your concerns through the following responses.
***
**Q1** Novelty may be limited.
**A1** Thank you for your efforts in the review process. Please allow us to elaborate on our work.
In general, we can divide deep-l... | Summary: This work presents an efficient and accurate approach for end-to-end RNA secondary structure prediction.
The optimization problem formulation and its solution are well defined.
The results are strong and supported by visualizations and ablation studies.
Strengths: The key strengths of this work are:
1. Infere... | Rebuttal 1:
Rebuttal: Dear Reviewer RVaK,
Thank you for your thoughtful and inspiring comment!
***
**Q1** The key similarities and differences between Rfold and Ufold?
**A1** Thank you for your meticulous review and insightful question! We can divide general deep-learning-based RNA secondary structure prediction me... | Summary: The paper proposes RFold, a simple and effective RNA secondary structure prediction algorithm. It adopts attention maps to learn informative representations for RNA rather than hand-crafted features. Then, based on a decoupled optimization process, RFold simplifies and guarantees satisfying the hard constraint... | Rebuttal 1:
Rebuttal: Dear Reviewer RVaK,
Thanks for your professional and constructive comments! We respond to the questions as follows:
***
**Q1** The data split might overestimate the prediction performance.
**A1** We apologize for the confusion. We did not perform the data splitting ourselves. Rather, all the ... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their insightful and constructive feedback on our manuscript. We are encouraged by their recognition of our work as being **interesting and promising** (Reviewer RVaK, ZyLM, FGw2). Furthermore, the fact that they regard our methodology as **novel in the domain*... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance | Accept (poster) | Summary: This paper has two contributions:
(1) MoDo algorithm which is a variant of MGDA with a double sampling to obtain an unbiased stochastic estimate of the gradient problem.
(2) A solid theoretically analysis on the error of multi-objective optimization.
Strengths: This paper has a very detailed analysis on the ... | Rebuttal 1:
Rebuttal: Thanks for acknowledging the strengths of our work. Our point-to-point response to your comments and suggestions follows next.
>**W1.** In the experiments, authors only compare MoDo with MGDA, but there are many other algorithms, like CAGrad, GradNorm, Uncertainty Weight. This baseline is not eno... | Summary: This work considers the multi-objective learning problem. The classic idea of dynamic weighting in MOL is to take gradients from each objective and to weight them using a fixed procedure to avoid conflicts between different objectives. Empirically, however, there often seems to be performance degradation when ... | Rebuttal 1:
Rebuttal: Thanks for recognizing our work as a strong one! We will respond to the weaknesses and questions point by point as follows.
> **W1.** Empirical benefit of MoDo.
We have more results in the submitted **Appendix D.2** to demonstrate its better performance on other datasets. Also see **General Res... | Summary: This paper studies three-way trade-off in multi-objective learning: 1) optimization error caused by sampling and stochastic training; 2) generalization error that measures the difference between source and target sets; 3) conflict-avoidance direction error that is the bias between the calculated direction and ... | Rebuttal 1:
Rebuttal: Thanks for acknowledging the strengths of our work. Our point-to-point response to your comments follows next.
> **W1 & Q1-3 & Limitation.** MoDo has limitation on sample efficiency. Is it possible to reduce improve?
- Theoretically, MoDo is not necessarily worse on total sample complexity; see ... | Summary: This paper studies the multi-objective optimization, and in particular, focus on the generalization and stability analysis. By decomposing the Pareto stationarity error into the generalization and optimization error, the authors then analyze and upper-bound these two errors respectively. The distance to the co... | Rebuttal 1:
Rebuttal: Thanks for appreciating our problem setup and analysis! We will respond to the weaknesses and questions point by point as follows.
> **W1. & W2.** Derivations of (4a) and (4b).
This is a **standard derivation for the MGDA algorithm**. Similar derivations are provided in [25, Section 3.1], [47, S... | Rebuttal 1:
Rebuttal: ## General Response
We appreciate the reviewers' constructive comments. All reviewers agree the paper has made solid theoretical contributions, and it has "a bigger picture" concerning three types of errors -- optimization, generalization, and CA distance unique in MOL in a holistic framework. It... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Generalized Information-theoretic Multi-view Clustering | Accept (poster) | Summary: This paper proposes a new framework for unsupervised multi-view learning based on information bottleneck theory. The paper defines three desiderata for multi-view representation learning in terms of mutual information, namely, comprehensiveness, concentrate, and cross-diversity. The paper further introduces a ... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work and constructive comments.
**Q1:** The authors should clarify how their definition of comprehensive, concentrative, and cross-diverse multi-view representation differs from the one used by Completer [22], which also maximizes the mutual information betwe... | Summary: This paper reformulates the multi-view clustering problem from an information-theoretic perspective and propose a general theoretical framework. The authors extend the information bottleneck theory to unsupervised multi-view learning and achieve representation learning and clustering by leveraging deep neural ... | Rebuttal 1:
Rebuttal: Thanks for your compliment and constructive suggestion.
**Q1:** The datasets used in the experimental part are a bit less and small, and there are many challenging and large datasets in the field of multi-view clustering, the authors should add more experiments to enhance the sufficiency.
**A1:*... | Summary: This paper presents an innovative information-theoretic framework for multi-view clustering, which overcomes the limitations of existing methods that rely on strict semantic consistency assumptions. By leveraging deep neural networks, the proposed method achieves more stable and superior clustering performance... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful and detailed feedback.
**A1:** Under the information bottleneck principle, [1] introduced the unsupervised information bottleneck objective Eq.(2), which is essentially the same as $\beta$VAE [2]. By summarizing the deep clustering methods DEC [3] and VaDE [4... | Summary: In this paper, the authors introduce representation learning with the unsupervised information bottleneck to multi-view clustering. Based on the framework of information bottleneck, the authors theoretically summarize 3 key properties (comprehensiveness, concentrate, and cross-diversity) required by multi-view... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful and constructive feedback.
**Q1:** In Line112-113, "minimize and $I(\textbf{Z}^{(1)}; \textbf{X}^{(2)})$ and $I(\textbf{Z}^{(1)}; \textbf{X}^{(2)})$" seems wrong.
**A1:** I am sorry for the serious typos, here should be to minimize $I(\textbf{Z}^{(1)}; \text... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mechanism Design for Collaborative Normal Mean Estimation | Accept (spotlight) | Summary: - The authors study normal mean estimation in a collaborative setting. N agents each aim to obtain a good estimate for the unknown mean while incurring as little cost for data acquisition as possible.
- The authors show that a naive data aggregation mechanism leads to freeriding. Then, they propose a novel me... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions.
*Recommended vs non-recommended strategies:* We have shown that the recommended strategies are a Nash equilibrium, which means that when all other agents are following the recommended strategies, then the best response for an agent is to also follow th... | Summary: The paper considers a collaborative mean estimation
setting where a set of agents can all collect
i.i.d samples from an underlying Gaussian distribution,
and their goal is to share data with each other in order
to estimate the mean of the distribution.
Each agent has a fixed cost for collecting each sample
an... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions.
*Definition of IC and IR:* We agree with you on this. There are two common notions of IC that are used in the literature, dominant strategy (DSIC) and Bayes-Nash (BNIC). We don't have a DSIC and while we have a Nash equilibrium, we are clearly non-Baye... | Summary: The author consider the problem of designing a data-sharing mechanism that encourages a group of $m$ agents to share their iid collected samples truthfully and further uses the shared data to refine their estimations of the normal mean. To ensure truthful reporting, the mechanism introduces additional noise in... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions.
*Specific form of penalty:* Yes, you are correct. We do believe that these results can be extended to other penalty forms and supervised learning problems, but we may need to relax from an exact to an approximate Nash equilibrium. This is because for o... | Summary: The paper designs a mechanism that collects data from n agents to estimate the mean of a Gaussian distribution. The agents incur costs to collect data, they can misreport data, and they strategically choose the level of effort and the data to report. They propose a mechanism that corrupts the returned datasets... | Rebuttal 1:
Rebuttal: Thank you for mentioning the paper by (Cai et al., 2015). This is a nice and relevant paper that we were not aware of, but will be sure to include it in the revision. While Cai et al., 2015 study a general supervised learning problem, when applied to mean estimation, their setting is more restrict... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies collaborative normal mean estimation, where strategic agents collect i.i.d samples from a normal distribution at a cost. This paper designs a "truthful" mechanism such that the strategic players will try to collect data instead of doing some "random" thing that harms the system and benefits ... | Rebuttal 1:
Rebuttal: Thank you for the comments and ideas.
*Multi-round mechanisms and privacy:* We are actually looking at multi-round mechanisms now :) While we can build on our current paper, there is still more work needed to solve this problem. As for privacy, we believe the rigorous way to study this would be ... | Summary: The authors study a collaborative normal mean estimation problem, where m strategic agents are trying to estimate the mean \mu of an unknown normal distribution with given variance. The agents can acquire samples drawn from the distribution at a cost of c per sample. In addition, each agent can share their sam... | Rebuttal 1:
Rebuttal: Thank you for the comments and suggestions. Yes, we agree it would be interesting to establish the lower bound for the 2-approximation, and leave it to future work.
*Definiton of IC:* We agree with you on this. There are two common notions of IC that are used in the literature, dominant strategy... | null | null | null | null |
Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift | Accept (poster) | Summary: This manuscript presents convergence rates for kernel methods under covariate shift. Results fit quite a general framework, including common classification and regression losses. Two approaches are analyzed: (i) a usual M-estimator and (ii) an importance-sampling-like M-estimator. It is shown theoretically and... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive comments and valuable suggestions! Our point-by-point responses to your comments are given below.
**Major remark 1**
Thanks a lot for your concern with the novelty of our proofs. We admit that our proofs use many classical empirical process techniques, s... | Summary: The paper provides a unified analysis of convergence properties for different kernel-based estimators under covariate shift. The analysis covers different loss functions and is focused on standard and importance weighted empirical risk estimators. The former are specified in Eq. (1) and the latter in Eq. (3). ... | Rebuttal 1:
Rebuttal: We greatly appreciate your dedicated time reviewing our paper and valuable insights! Our point-by-point responses to your comments are given below.
**Weakness**
Thanks a lot for your valuable suggestions. We want to point out that due to the space limit, only a small fraction of numerical exper... | Summary: The authors study the covariate shift setting of nonparametric (kernel) methods
(Regularized Empirical Risk Minimization with optional importance weighing) with
an analysis which includes a wide array of losses and and two conditions on the
importance function. They establish sharp convergence which corroborat... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and helpful comments on our work! Our point-by-point responses to your comments are given below.
**Weakness**
Thanks a lot for your comments. We admit that the true importance ratio is unknown in practical applications, which needs to be estimated from the u... | Summary: This paper studies the generalization guarantees of non-parameteric methods in RKHS under covariate shift.
Compared to previous work (Ma et al. AOS2023), the authors extend their results from the squared loss to general Lipchitz loss functions.
The derived results show that
- under the uniformly bounded case ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our paper and the valuable feedback you provided. We have carefully considered your comments and have made significant efforts to address each of your concerns.
**weakness 1**
Thanks a lot for your precious suggestion. Detailed discussions on Assumption 2 fo... | Rebuttal 1:
Rebuttal: Thank you sincerely for your insightful comments and for dedicating your valuable time and effort toward the thorough evaluation of our paper. We have carefully considered all questions, concerns, and comments raised by the reviewers. The insights and suggestions from reviewers have greatly contr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face | Accept (poster) | Summary: The paper studies an interesting and important question, i.e., how to automate LLMs to call existing models for solving specific tasks. The authors propose a novel framework that contains the following steps: (1) task planning, (2) model selection, (3) task execution, and (4) response generation. The experimen... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
***Q1: Whether open-source LLMs can be leveraged for the framework?***
Yes. The open-source LLMs are also suitable for our framework. In our experiments (please see Table 3, 4, 5), we also deploy open-source LLMs for ev... | Summary: This paper presents a pipeline to manipulate many autonomous agents (mainly open-sourced models in Hugging Face) . Tother with these models could solve NLP, CV, audio and Video tasks, the resulted HuugingGPT could could complicated multi-modal tasks that might be decomposed a sequence of atomic tasks or a ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
***Q1: The method is not scientific from a traditional point of view.***
Thanks for your question. The success of our framework benefits from the advent of powerful LLMs (i.e., ChatGPT or GPT-4). Therefore, we adopt a s... | Summary: The authors propose HuggingGPT , a collaborative system for solving AI tasks, which is composed of a large language model (LLM) and numerous expert models from ML communities. They provide methods for each of the four stages involved in HuggingGPT's workflow: task planning, model selection, task execution, and... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive comments. We will continue to refine our paper and devote more effort to the subsequent works to facilitate the community to better understand and explore this new research direction. | Summary: This paper considers large language models (LLMs) like ChatGPT as a controller and presents a new framework called HuggingGPT, which connects various AI models in the existing ML community (i.e., HuggingFace). Specifically, HuggingGPT consists of four steps including task planning, model selection, task execut... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
***Q1: More details about demonstration examples.***
The number of demonstration examples is set as 3 in our default settings. Here, we select demonstration examples that contain more tasks and complex task dependencies... | Rebuttal 1:
Rebuttal: # To All Reviewers
We sincerely thank each reviewer for providing constructive comments for our paper, which are very helpful to improve our paper. Below are our responses to some general issues:
***Q1: Model Selection***
Thanks for the comments of each reviewer. Here, we will provide more det... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a framework that uses LLM as controller over modularized and specialized task models to plan and execute a complex task. The approach is to prompt LLM to decompose a given task command into a execution DAG, and for each step, parse model specifications (as metadata expressed in HuggingFace ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below are our responses to your concerns:
***Q1: The biggest selling point of this paper is planning but the planning strategy in this paper is actually rather simple.***
Thanks for your question. Below are our answers:
- First, we want to highlight that whil... | null | null | null | null | null | null |
What Can We Learn from Unlearnable Datasets? | Accept (poster) | Summary: This paper comprehensively evaluated existing unlearnable examples and showed a surprising result. Unlearnable examples aim to prevent the model from learning useful features from the data. However, results show that several methods that networks actually can learn useful features. This is revealed by applying... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review, for mentioning that our findings are “very interesting and new in this field,” and for writing that our paper provides “valuable insights for future works.”
> Class-wise noise is known to be easily detected…Yu et al. [34] show averaging across clas... | Summary: This paper suggests that DNNs can learn useful features from unlearnable datasets and provides a counterexample, demonstrating that linear separability of perturbations is not a necessary condition. They propose the Orthogonal Projection method to recover unlearnable datasets.
Strengths: 1. A new method calle... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough feedback and for recognizing that our work “suggests the risk of using class-wise perturbations.” That was one of our goals in this work.
> About weakness 1: 1.1 In Figure 2, the performance of DFR on different unlearnable datasets is not consistent. At th... | Summary: This paper conducts an analysis of the properties of unlearnable dataset methods to evaluate their potential for future viability and security assurances. It is demonstrated that neural networks possess the ability to learn generalizable features from unlearnable datasets, while also suggesting that image priv... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review, for mentioning that our work “gives us a different view to unlearnable examples,” and for recognizing that our results “challenge some widely held beliefs about unlearnable datasets.” Those were our goals in this work.
> The author uses the DFR meth... | Summary: This paper studies the problem of the actual learnability of unlearnable datasets. Specifically, the authors have demonstrated that unlearnable datasets that are generated by existing methods can actually be used to learn generalizable features. In addition, the authors show that it is not necessary to make po... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We appreciate your mentioning that our “experiments are extensive'' and that the paper "is very well written.”
> previous work has intentionally relied on ideas beyond linear separability for generating unlearnable datasets [a]
Thank you for le... | Rebuttal 1:
Rebuttal: We'd like to thank everyone again for their reviews. A few reviewers mentioned wanting to see average images of a class to compare them to the learned weights from the first step of Orthogonal Projection. In the attached PDF, we include an additional figure which performs this visualization for tw... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection | Accept (poster) | Summary: The paper considers the application of deep learning (DL) for network intrusion detection systems (NIDS). The paper correctly points out that, in real contexts, NIDS must be continously updated with new data-points to mitigate the impact of "concept drift". A way to do so is by employing "continual learning" (... | Rebuttal 1:
Rebuttal: We sincerely request the reviewer ZBnA read the **responses to weakness section** mentioned in the pdf (we could not specify here due to space limitations) before reading our responses to questionnaires.
**RESPONSES TO QUESTIONARIES**
**R1**: The proposed mode does not require huge system infr... | Summary: The authors propose techniques for improving how to select samples for
replacement in the memory for continual learning and how to estimate
virtual SGD in MIR to reduce computation.
For replacement in memory, CBRS does not keep track of class counts,
and replacement might occur on non-majority samples in the ... | Rebuttal 1:
Rebuttal: **Q1: Alg 1: "select a class that is the largest, having higher running statistics value and non-zero samples in the buffer. Otherwise, select a class with next higher running statistic value that has m_c >γ(c)." It seems the next highest class is selected when the largest class has no samples in ... | Summary: This paper improves upon existing memory replay-based continual learning methods for anomaly detection. First the authors extend class balancing reservoir sampling (CBRS) and develop ECBRS, using global information in order to keep more accurate information about class imbalance. Second, the authors proposed a... | Rebuttal 1:
Rebuttal: **Q1) Do the authors have an idea on why PAPA achieves better performance as compared to MIR in Table 3? Since PAPA performs an approximation, I would have expected the AUC scores to be slightly lower than those for MIR**.
R1). We sincerely thank the reviewer for bringing up this important observ... | null | null | Rebuttal 1:
Rebuttal: This pdf contains a table1 representing the results corresponding to the question (Q4) raised by the reviewer **KabC**. Specifically, table results are obtained on CIFAR-100 and CLEAR-10 datasets. Another table (table2) contained the results on newer datasets like CTU-13, modified CICIDS-2017 an... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency | Accept (spotlight) | Summary: This paper introduces a novel method to train an interpretable surrogate of pre-trained time series classification models. TimeX produces a latent embedding of time series observation and outputs classification probabilities that are both consistent with a reference model. At the same time, it identifies time... | Rebuttal 1:
Rebuttal: Thank you for providing valuable comments and critiques of our work. We have worked hard to improve the communication of our method, and we kindly ask you to raise your score. Please reach out to us with any questions.
### W1a: State-of-the-art models
Thank you for pointing out this misleading sta... | Summary: This paper proposed TimeX that creates an interpretable surrogate model for pretrained models. To ensure faithfulness to the reference model, this paper introduces a self-supervised objective, model behavior consistency, a novel formulation that ensures the preservation of relationships in the latent space ind... | Rebuttal 1:
Rebuttal: Thank you for the useful feedback about our work. We appreciate the reviewer’s acknowledgement of the novelty of our work, as well as the utility and diversity of our extensive evaluations. We have responded to your concerns in the below comments, running 1 additional experiment. We encourage you ... | Summary: This paper presents an in-hoc interpretability mechanism to explain time series prediction. In particular, the authors train an interpretable surrogate model by learning H^E and G^E in the embedding space.
The objective function optimizes model behavior consistency by considering the distance in the training ... | Rebuttal 1:
Rebuttal: We are very grateful for your constructive feedback about our work. We appreciate that you recognized the core contributions of our work, as well as the novelty of multiple components of TimeX, such as discrete masking, landmark explanations, and model behavior consistency learning. We respond to ... | Summary: TimeX proposes a explanation module that is trained along-side the model to provide more consistent explainiations. This is done by making the internal embeddings of explaination modules consistent with that of full model such as distance consistency and label consistency.
Strengths: 1. The methodology is wel... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and criticism about our work. We greatly appreciate your claims that our work is “well motivated and presented” and that our explainer provides “significant performance improvements” over baseline explainers. We address concerns about novelty and computational ... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for thoughtful and insightful feedback! We are pleased that reviewers are excited about the novel contributions of our work. Reviewers remark that TimeX **“provide[s] interpretable explanations that are both faithful to the model’s predictions and informative about t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a novel time series interpretability model called TIMEX. The challenge of interpreting time series models arises from the need to identify both the specific time series signals influencing predictions and their alignment with interpretable temporal patterns. TIMEX addresses the issue of mo... | Rebuttal 1:
Rebuttal: Thank you for your extensive comments, critiques, and praises for our work. We hope our response and experiments further convince you of TimeX’s novelty and effectiveness, and we kindly ask you to consider raising your score. Please reach out if there are additional questions.
### W1 and W2: Irreg... | null | null | null | null | null | null |
Convergence of Adam Under Relaxed Assumptions | Accept (spotlight) | Summary: The paper removes the Lipschit gradient assumption for the adaptive SGD (ASGD), making the ASGD broader to wide applications. Under the weak assumption, the authors still proved the optimal convergence rate. Moreover, they propose a variance-reduced version with an accelerated complexity. The results are inte... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback! Below we will try to address the concerns and questions.
- First, we thank the reviewer for the suggestion about numerical demonstrations for VRAdam. We will consider empirically comparing the performances of Adam and the variance-reduced version o... | Summary: The paper proposes a new proof strategy for the convergence of Adam in the
non-convex setting. The new analysis relaxes the typical assumptions
in the following ways: 1) it assumes relaxed smoothness, where the norm of the Hessian
grows sub-quadratically with the gradient norm; 2) it does not require
bounded g... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback! Below we will try to address the concerns and questions in the comments.
**For the weaknesses:**
- Regarding the bounded noise assumption, we want to first clarify that, it is not hard to generalize it to sub-Gaussian noise, as we discussed aroun... | Summary: This paper studies the convergence of Adam over non-convex objectives. To begin with, this paper proposes a new non-uniform smoothness condition called $(\rho, L_0,L_1)$ smoothness condition, which generalizes $(L_0,L_1)$ smoothness condition proposed in [Zhang et al. 2019]. The authors then prove the high-pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We will try to address the concerns and questions of the reviewer below.
**For the weaknesses:**
- First, regarding the dependence on $\lambda$, although it is worse than that in the papers mentioned by the reviewer, a non-zero $\lambda$ allows u... | Summary: This paper studies the convergence of the Adam algorithm. Under a more general local smoothness assumption, the convergence of Adam to stationary points is proved without assuming boundedness of the loss gradient. Here the key technique is to show that the loss gradient along the trajectory is indeed bounded, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback! Below we will try to address the concerns and questions of the reviewer.
Although our analysis of Adam relaxes the assumptions made in previous papers, it does not provide a better theoretical understanding of the benefit of momentum or the advanta... | Rebuttal 1:
Rebuttal: In the global rebuttal, we prove some preliminary experimental results to help address the concerns of some reviewers regarding the hyper-parameters of Adam, including $\beta,\beta_{\text{sq}}, \lambda$. We train a small MLP on the Cifar10 dataset with Adam. The default parameters are $\eta=0.001,... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides convergence results for Adaptive Moment Estimate (Adam) and its variance-reduced variant under generalized smooth and bouned-noise assumptions.
Strengths: This paper mainly studies the convergence of Adaptive Moment Estimate (Adam) under a generalized smooth assumption. The authors drop th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. Below we will have some discussions about the weaknesses in the comments.
- First, it is true that our hyper-parameters depend on problem-dependent parameters like $\rho,L_0,L_\rho,\sigma$, and the theory does not provide many insights regarding ho... | null | null | null | null | null | null |
Advancing Bayesian Optimization via Learning Correlated Latent Space | Accept (poster) | Summary: Recent advances in Bayesian optimization have shown that it is possible to exploit latent spaces of variational auto-encoders or generative models to perform the optimization of any function defined over a structured space. However, since the optimization takes place in the latent space. There is an inherent g... | Rebuttal 1:
Rebuttal: We highly value the feedback provided by the reviewer. We offer responses to address the issues below.
**Q1. Is there a justification for the value of the frequency to halve the search space?**
We adopt every setting for the Trust region from TURBO [16], which is an established technique. For ex... | Summary: The paper addresses the problem of Bayesian optimization with the help of a low-dimensional latent space (here learned using a VAE augmented with ad-hoc loss terms).
The paper contributes:
- An analysis/recap of the state-of-the-art highlighting issues raised in other works, particularly the need for smoothne... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback. We present comprehensive responses below.
**Q1. Unsure why $\mathcal{L}\_z$ is needed**
The $\mathcal{L}\_{\mathbf{z}}$ serves a specific purpose in our approach. If we were to rely solely on the Lipschitz loss and exclude $\mathcal{L}\_{\mathbf{z}... | Summary: This paper proposes several heuristic regularization constraints for learning a Bayesian optimization latent space. It argues that the learned latent space needs to be aligned to the black-box function values, and this is achieved via keeping the Lipschitz constant small and the mean latent distance (of traini... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback. Below, we present comprehensive responses addressing the questions raised by the reviewer.
**Q1. Performance comparison w.r.t. wall-clock time.**
We provide the results on Guacamol dataset with respective tasks with respect to wall-clock time as re... | Summary: This paper proposes a latent space Bayesian Optimization approach based on the intuition that distances in the latent space should be correlated with differences in objective value. CoBO iteratively updates a variational autoencoder (VAE) to align distances in latent space with differences in objective functio... | Rebuttal 1:
Rebuttal: We appreciate the thorough and detailed feedback. We will address the issues below.
**Q1. More evaluations on DRD3.**
We conducted the experiment with DRD3 for a duration of up to 5 days, reaching a maximum of 2500 oracle calls within the available resources. We also provide the optimization cur... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thorough and thoughtful feedback. We will address all issues raised by the reviewers below. Especially, we will provide answers to common questions that we have received from multiple reviewers in this General section.
**Q1. Regarding hyperparameter search and sel... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Closing the gap between the upper bound and lower bound of Adam's iteration complexity | Accept (poster) | Summary: This paper presents a convergence analysis of Adam under only the smoothness and bounded variance conditions.
Strengths: - The strength of the paper is to present a convergence analysis of Adam under only the smoothness and bounded variance conditions, in contrast to the existing analyses (Section 3). We are... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and time. After reading the review, we realize that there could be misunderstandings over our contribution, which we try to clarify as follows.
**On the novelty and contribution.** From the theoretical perspective, characterizing the upper bound and lower bo... | Summary: This paper analyzes the iteration complexity of Adam. It Is first pointed out that upper bounds in prior work do not match existing lower bound; the reason is that the lower bound is proved under smoothness and bounded noise variance, while prior upper bounds make more assumptions. This paper then proves a gen... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive feedback. Your concern is dually addressed as follows.
**Q**: Can SGD match the lower bound? Alternatively, can we show that Adam converges faster than SGD, which is usually observed in practice?
**A**: Thanks for asking. SGD can also meet the... | Summary: Adam is one of the most popular stochastic optimization algorithms especially in deep learning, the existing convergence theories do not achieve a tight upper bound that meets the lower bound. Moreover, many of them require additional assumptions, such as bounded gradient. This paper shows that Adam can achiev... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive comment and positive feedback. The raised typos have been dually corrected, and other concerns are dually addressed below.
**Q1**: Relation to [1] is not clear.
**A1**: Thanks for asking. We did not include a discussion on this because the... | Summary: This paper gives a new analysis of the Adam algorithm intended to close the gap between the upper bound of Adam's iteration complexity and the existing lower bound for first-order nonconvex optimization. The authors show that existing analysis of Adam either uses the bounded gradient assumption, achieves a sub... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for your constructive comment and positive feedback. The raised typos have been corrected and below we dually respond to your comments.
**Q1**: Proof in the deterministic case will be helpful.
**A1**: Thanks for the helpful suggestion. We will take your advice... | Rebuttal 1:
Rebuttal: We thank ACs, SACs, PCs, and reviewers for the efforts and time spent in handling our paper. According to the suggestions of Reviewer oCro and Reviewer b4JJ, we include several experiments to support our theoretical claims. The plots can be found in the attached pdf file. Specifically:
1. We run ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Credal Marginal MAP | Accept (poster) | Summary: The paper studies algorithms for the marginal MAP (MMAP) problem in Credal networks that generalize Bayesian networks. The paper gives an overview of the problem, existing algorithms for Bayesian networks and generalizes the algorithms to credal networks. Overall, two exact and multiple heuristic approaches ar... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions.
Regarding the relationship between the CN and BN algorithms, in principle, if the probability intervals in a CN are tight, namely they collapse to point probabilities, then the CMMAP task we defined for CNs collapses to the MMAP task for BNs. In this c... | Summary: This paper is about a generalisation of marginal MAP (MMAP) for Bayesian networks (BNs). The authors allow the BN parameters to vary in (credal ) sets. The goal is, therefore, to find the configuration with the maximum upper (wrt the credal sets) probability. The authors first consider exact inference. A numbe... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions.
We are already exploring several ideas around interval dominance as well as alternative criteria such as maximality and/or E-admissibility. Therefore, we are very thankful for your suggestions and we plan to address these issues in our future work.
Ex... | Summary: This work presents novel algorithms for performing exact and approximate marginal MAP inference in credal networks with discrete-valued factors, and evaluates the computational and inferential effectiveness of these algorithms on a number of benchmarks.
Strengths: Firstly, I enjoyed reading the paper and thin... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions.
We will revise the presentation and will try to expand the discussion of the algorithms in order to address the concerns identified during the review. Certainly the content is very technical but we will do our best to make it more didactic.
We apprec... | Summary: The paper proposes inference algorithms for credal networks for the marginal MAP inference task. The idea is to use variable elimination methods for this task. An exact inference algorithm is proposed as well as approximations using mini-bucket partitioning. Further, stochastic local search procedures combined... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions.
One way to address the tradeoff accuracy vs complexity in the case of algorithm CMBE for example is to also bound the size of the sets of potentials that are propagated during elimination (or approximate somehow these sets of potentials). This way we m... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Siamese Masked Autoencoders | Accept (oral) | Summary: In this paper, the authors propose a simple extension to Masked Autoencoders (MAE) to be able to pre-train on videos: SiamMAE. Two frames are sampled, independently encoded, and then asymmetrically masked. A transformer decoder is used to predict the missing patches in the masked image. The authors show that b... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We address the reviewer concerns below:
>*Novelty over MAE*
We agree that our method is a simple modification of MAE albeit one which has not been explored in the past. We hope that the simplicity, efficacy and extensive empirical analysis of our met... | Summary: The paper proposes to use Siamese Masked Encoders for establishing correspondence for video input data. Uses the concept of predictive learning based on Masked Auto Encoder. Paper proposes to use asymmetric masking for present and future frames. Achieves best results in self-supervised setting for video label ... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We address the reviewer concerns below:
>*Frame sampling with overlap analysis*
To perform overlap analysis, we sampled video frames from the Kinetics-400 validation set with the specified frame gap and calculated two image similarity metrics: mean sq... | Summary: * This paper propose Siamese Masked Autoencoders for learning visual correspondence from videos called SiamMAE.
* SiamMAE randomly sample a pair of video frames and randomly mask 95% of patches of the future frame, and the pair of video frames are passed into visual encoder(VIT), and cross attention decoder ... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We address the reviewer concerns below:
>*Experiments on video and image recognition*
We address this in our global response and re-iterate here for convenience.
We agree with the general sentiment of the comment, emphasizing the evaluation of a self... | Summary: This paper focuses on the self-supervised learning for video representations. The proposed SiamMAE operates on pairs of randomly sampled video frames and asymmetrically masks them, and then predicts the missing patches for visual representation learning. SiamMAE achieves significant performance and outperforms... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We address the reviewer concerns below:
>*Results on video classification tasks*
We address this in our global response and re-iterate here for convenience.
We agree with the general sentiment of the comment, emphasizing the evaluation of a self-supe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thoughtful and constructive feedback. We are glad that **all** the reviewers found our method simple, clever and intuitive, our analysis and ablations to be thorough and convincing, and results impressive. The main aim of this rebuttal is to improve this ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the problem of using self-supervised learning from video to obtain a representation that is well-suited to the task of estimating correspondence between a pair of images. They propose a significant modification of the MAE training procedure which is adapted for estimating correspondence: o... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. We address the reviewer concerns below:
>*Emphasis on predicting the future*
We agree with the reviewer that reversing the temporal order should not significantly alter the results. We conducted two additional ablation studies: one where we always pre... | null | null | null | null | null | null |
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective | Accept (poster) | Summary: This paper explains the phenomenon of robust overfitting in adversarial training from a minimax game perspective. The author considers AT as a minimax game between the model trainer and the attacker, pointing out the imbalance between them leads to the network memorizing non-robust features, causing robust ove... | Rebuttal 1:
Rebuttal: We thank Reviewer kwEx for your time and efforts in reviewing this work. Below, we address your main concerns of this work.
---
**Q1.** What exactly are the false non-robust mapping and the falsely memorized non-robust features? Can the authors use the intuitive and precise statement to explain ... | Summary: This paper empirically shows that robust overfitting is caused by the over-memorization of the non-robust features after learning rate decay. To mitigate the issue of robust overfitting, the authors propose to use a stronger training attack, a smaller learning rate decay rate, and a bootstrapped adversarial tr... | Rebuttal 1:
Rebuttal: We thank Reviewer EtSp for appreciating the comprehensiveness and solidness of the verification of our understanding. Below, we address your main concerns.
---
**Q1.** It would be better for the authors to provide some analyses from a theoretical perspective (possibly using game theory which cou... | Summary: This paper studies the robust overfitting phenomenon in adversarial training. Meanwhile, this paper focuses on a specific problem “the robust overfitting occurs when we use learning rate decay techniques.” This paper proposes a game perspective to explain the robust overfitting. It claims that the robust overf... | Rebuttal 1:
Rebuttal: We thank Reviewer 55GS for your careful reading and appreciation on the novelty and solidness of our work. Below, we address your main concerns of this work.
---
**Q1.** For Figure 2 (b), I guess the red line denotes w/ LR decay and the blue line denotes w/o LR decay.
**A1.** Thank you for point... | Summary: This paper investigates the phenomenon of robust overfitting in adversarial training and explains it from a minimax game perspective. The authors analyze how the decay of the learning rate disrupts the balance between the model trainer and the attacker, leading to robust overfitting. They propose a method call... | Rebuttal 1:
Rebuttal: We thank Reviewer Znmp for your careful reading and for appreciating the proposed understanding and method for robust overfitting. Below, we address your main concerns of this work.
---
**Q1.** Diffference between the defined robust/non-robust features and the previous one in Ilyas et al.
**A1.... | Rebuttal 1:
Rebuttal: The Rebuttal PDF can be seen in the attached file, which contains
- Figure A: an intuitive illustration for the proposed understanding of robust overfitting;
- Figure B: a plot of the training process comparing ReBAT with vanilla AT;
- Table A, B, C: additional comparison experiments between diff... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Error Discovery By Clustering Influence Embeddings | Accept (poster) | Summary: This work presents a method for discovering subsets of the test set of a multi-class classification task on which a trained model incorrectly classifies a large portion due to the same root cause. The method uses a factorized low-rank approximation of a bilinear influence function, parameterized using the Hess... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your concerns below.
**Weaknesses**
- **Methodological Contribution**: We agree that the actual computation of the influence embeddings being clustered in the main algorithm is builds on the work of Schioppa et al. [2022]. However, we make 3 key co... | Summary: The paper presents a method to discover groups of test examples on which the model performs badly, and the misclassification of the examples is caused by the same reason (defined as coherence).
This problem is known as slice discovery. The method leverages influence functions to compute the influence explanati... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your concerns below.
**Weaknesses**
- **Reproducibility**: Regarding reproducibility, we plan to release all our code, datasets, and additional artifacts to replicate our analyses.
**Questions**
- **Choosing number of clusters K for InfEmbed exper... | Summary: This paper proposes a heuristic clustering-based method for identifying errorneous groups of test examples. I'm erring on the side of caution here and go with a weak reject, but I have limited familiarity with the subfield.
AC note: score increased 4 -> 6 after rebuttal.
Strengths: * Error analysis tools are... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your concerns below.
**Weaknesses**
- **Methodological Contribution**: We agree that the actual computation of the influence embeddings being clustered in the main algorithm is a straightforward application of Schioppa et al. [2022]. However, we be... | Summary: In this paper, the authors propose InfEmbed on the slice discovery problem. The method is derived from the influence function and surrogate embedding representations are proposed for reducing complexity. Overall, the paper is well written and the derivation of the method is reasonable. Some pros and cons are d... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your concerns below.
**Weaknesses**
- **Trying other clustering algorithms**: We actually did try 3 other clustering algorithms (DBSCAN, spectral clustering, gaussian mixture model) on the Spotcheck benchmark, and found that no matter what clusteri... | Rebuttal 1:
Rebuttal: **General Response**\
We thank all the reviewers for their generous feedback, and for noting that the work addresses an important problem (**Reviewers 2vT2, uzoE, yzdi, MYaZ**), is novel (**Reviewers 2vT2, 6oL7**), provides a thorough empirical evaluation (**Reviewers 2vT2, 6oL7, yzdi**), and wel... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a method (InfEmbed) for discovering coherent slices of data such that the model fails on samples within a slice due to similar reasons. InfEmbed uses k-means to cluster a representation proposed in the work called influence embeddings, where samples with similar influence embeddings have si... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your concerns below.
**Weaknesses**
- **Coherence scores and Experiments**: See discussion in the first bullet point of the next section.
- **How does InfEmbed promote label homogeneity?**: While the analysis in Section 3.5 only considered gradient... | null | null | null | null | null | null |
Operation-Level Early Stopping for Robustifying Differentiable NAS | Accept (poster) | Summary: This paper studies the robustness issue of DARTS from the perspective of overfitting. It uses gradient matching scores to measure the overfitting issues, and proposes an early-stop strategy to address the problem of saturated skip connections in normal DARTS. The proposed approach has been evaluated on a numbe... | Rebuttal 1:
Rebuttal: Thank you for the helpful and insightful review, which is very helpful for us to further improve this paper. Next, we will answer your questions one by one, and we hope this will improve your acceptance of the paper.
**W1.**
Thank you for pointing this out. We apologize for not providing a detail... | Summary: The authors are adressing the issue of converging to a degenerated solution (many skip connections) using DARTS. The authors connect this behavior to an overfitting to the train data. To remedy this issue, they suggest to apply early stopping based on the correlation of gradients during the architecture parame... | Rebuttal 1:
Rebuttal: Thanks a lot for your considerate feedback. We sincerely appreciate your engagement in the review. Next, we will address your concerns one by one, and we hope this will improve your view of the paper.
**W1.**
Thanks a lot for your careful and insightful observation. In fact, in Figures 3(a) and 3... | Summary: This paper demonstrates the fundamental reason for the domination of skip connections in DARTS from the new perspective of overfitting of operations in the supernet, using preliminary experiments, and proposed the operation-level early stopping method to mitigate this phenomenon by using the GM score metric d... | Rebuttal 1:
Rebuttal: Thank you for the helpful and insightful review. Next, we will answer your questions one by one, and we hope this will improve your acceptance of the paper.
**W1.**
Thanks for the valuable suggestion. As shown in Figure 3 in our attached PDF, we compare the Kendall rank correlation coefficients f... | Summary: The paper focuses on the robustness issues in differentiable NAS, specifically the domination of skip connections. It first analyzes the issue from a novel perspective, proposing that the domination of skip connections arises due to the overfitting of operations in the supernet during training. Then, the paper... | Rebuttal 1:
Rebuttal: Thank you for the helpful and insightful review. We are glad to receive your positive response and acknowledgment of our work. Next, we will answer your questions one by one, and we hope this will improve your acceptance of our paper.
**W1.**
Thanks for the valuable advice. Gradient matching aim... | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
We would like to thank all the reviewers for their great efforts, insightful comments, and valuable suggestions, which are very helpful for us to further improve this paper. To the best of our efforts, we’ve diligently tried to address all the specific comments, including t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary:
This paper propose a method namely operation-level early stopping to address the skip-connection domination issue in domain of differentiable architecture search (DARTS). Though this problem is heavily explored in the past, the authors believe that the key reason of skip-connection domination is because of th... | Rebuttal 1:
Rebuttal: Thank you for the insightful review, which is very helpful for us to further improve this paper. Next, we will answer your questions one by one, and we hope this will help to address your concerns and improve your view of the paper.
**W1.**
Thank you for pointing this out. We apologize for not pr... | null | null | null | null | null | null |
SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities | Accept (poster) | Summary: This paper presents a new dimensionality reduction algorithm, named SNEkhorn. By uncovering the novel links between Entropic affinities (EAs) and Optimal Transport (OT), the authors derive EAs with symmetric doubly stochastic normalization and the fixed row-wise entropy, which is the key to SNEkhorn. Besides, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the manuscript, her/his assessment and relevant remarks.
> How does the computational complexity for the proposed method compare to t-SNE? Does the proposed method spend the same amount of time as t-SNE while achieve better results?
This is a very... | Summary: This is a very interesting paper about an application of the Sinkhorn algorithm to symmetrize the matrix of entropic affinities in methods of DR like SNE, t-SNE, etc.
Strong theoretical contribution.
Some experiments to illustrate.
Strengths: The paper is very interesting for its vision, state of the art acro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for his/her careful reading of the manuscript and insightful comments.
> The quantitative assessment might be partly questionable: the trustworthiness is not the best DR QA indicator; what was the neighbourhood size, by the way?
This is an interesting point. W... | Summary: The paper presents a novel approach to dealing with entropic affinities (EAs) used in machine learning for dimensionality reduction tasks, specifically in the popular t-SNE algorithm. It addresses the limitations of current symmetrization methods applied to EAs, which can compromise the entropy and stochastici... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the appreciation of our work, insightful comments and questions.
### Answers to weaknesses:
> Clarity of Presentation: While the abstract provides a high-level overview, some concepts, such as entropic affinities, optimal transport, and dual ascent, might... | Summary: Existing DR approaches employ EAs after heuristic symmetrisation (symmetric-SNE). This leads to less faithful embeddings with low silhouette scores. This paper avoids such heuristic symmetrisation by enforcing symmetrisation in a related, new OT based formulation.
Towards this goal, firstly, the EA problem is... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for her/his insightful comments and questions. Our answers to the questions raised can be found below.
> It seems a primary reason for having Q_Z^{ds} in SNEkhorn is to enable fast objective-gradient computation via sinkhorn. Is this true?
It is true that it e... | Rebuttal 1:
Rebuttal: We first would like to thank all the reviewers for their remarks and questions.
You may find attached a pdf with some new results to answer the various points raised. The new results are as follows.
## New results
### Figure 1 : Robustness to noise
In Figure 1, we focus on reviewer s51e's ques... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Private Everlasting Prediction | Accept (oral) | Summary: This work proposes the notion of private everlasting prediction. Given a training dataset, the predictor responds to a sequence of queries and privacy has to be preserved for both the training data and all queries. The authors explore the PAC learnability problem under this model and show that the sample compl... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Below we address the points you make:
**> In the algorithm GenericBBL, $\tau>1.1\times 10^{10}$, seems too large. Can the constant be made any smaller?**
We have not optimized constants as our contribution focuses on asymptotic complexity. The paper intr... | Summary: The paper discusses private everlasting prediction, which extends private prediction to answer an unlimited sequence of prediction queries. The goal is to present a generic private everlasting predictor with low training sample complexity. The paper introduces definitions for everlasting prediction and everlas... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Below we address the points you make:
**> Limited applicability: The paper focuses on the theoretical aspects of private everlasting prediction and does not provide concrete practical applications or empirical evaluations**
We agree that the construction o... | Summary: This paper provides an intriguing path to evading known lower bounds for differentially private PAC learning. Whereas nonprivately the sample complexity of learning is proportional to the VC dimension, the private sample complexity for (pure) DP is characterized by the representation dimension, which can be mu... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Below we address the points you make:
**> There are two obvious limitations of the main result that yield very interesting open problems… computational efficiency and sample complexity**
We believe that - even with these questions unresolved - the concept ... | Summary: This work studies differentially private prediction. It has two major contributions:
a) Prediction corresponds to being given an initial labeled training set, and then subsequently making predictions on other data points based on it. This paper shows that differentially private prediction can be performed on... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. Below we address the points you make:
**> the notion of privacy defined... is not as strong as would be nice since the label for a data point needs to depend on the data point**
We don’t see the fact that the adversary does not get to see the label of one ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Importance of Exploration for Generalization in Reinforcement Learning | Accept (poster) | Summary: The paper motivates the importance of exploration for generalization in contextual MDP with a tabular example, where the context is either the starting state or an uncontrollable random deviations in the transition model. The authors introduce an exploration method for distributional DQN, called EDE, which est... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and insightful questions! We were glad you found our insight interesting and well made and our literature review extensive.
> Connection between two parts (Q2)
As we explain at the beginning of Sec 4, the first part is meant to show that good exploration can he... | Summary: This paper proposes an exploration method for value-based RL for contextual MDPs (CMDP) motivated by the idea that good generalization in RL requires attention to RL specific problems such as exploration. The method uses an ensemble of quantized Q-functions to estimate the epistemic uncertainty about the value... | Rebuttal 1:
Rebuttal: Thank you for the generous review and strong support of our paper! We were excited to hear you found our paper to be an "important contribution to the RL community" and the empirical results to be "exceptionally thorough and well presented".
>exploration-exploitation tradeoff
>
Indeed this is a ... | Summary: This paper introduces a method called EDE (Ensemble Distributional Exploration) that promotes the exploration of states with high epistemic uncertainty through an ensemble of Q-value distributions. The authors evaluate EDE and compare it to several baselines on Procgen and Crafter.
Strengths: **Originality**
... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your valuable feedback! We were glad to hear you think our paper "opens up new possibilities" and is "particularly relevant in today's context".
> computational cost
>
We agree that this is an important topic. Estimating uncertainty in an... | Summary: The paper proposes that effective exploration is important for generalization and shares a value-based method which gets good generalization performance on procgen and outperforms Rainbow on crafter.
Strengths: originality: The idea to leverage improved exploration to improve generalization is novel/seldomly ... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our paper and the detailed questions! We were glad to hear you found the idea novel and the evaluation of very high quality. We hope our answers below will address your remaining concerns.
> Figure 1 could be improved to show some abstraction
Figure 1 i... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to provide valuable feedback on our work. Overall, the reviewers found our paper to be clear and easy to follow, the idea of using exploration to improve generalization to be novel and well-motivated, and the experimental results to be thorough and st... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper focuses on the importance of exploration in the generalizability in contextual MDPs. The proposed method is built on QR-DQN where the epistemic uncertainty can be separated from aleatoric uncertainty via ensemble. The epistemic uncertainty is then used in a UCB manner to promote exploration. The resu... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review and support for the paper! We were glad to hear that you found our paper "well-organized and easy to follow" and our proposed method "novel, well-motivated, and empirically strong". We hope our answers below will address your remaining questions.
>The motivati... | null | null | null | null | null | null |
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels | Accept (poster) | Summary: The paper studies the problem of noisy label learning. The paper adopts an optimal transport approach to generate pseudo labels for noisy samples. Particularly, the paper builds on the existing method and adds additional regularization terms to enforce the consistency between sample classes and learned represe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and constructive suggestions on our paper. We address your detailed comments below:
> **Q1**. the proposed method is a regularization of the existing OT pseudo-labeling (PL) method. The novelty is thus limited.
**A1**. Firstly, we would like to jus... | Summary: This paper introduces a novel formulation of Optimal Transport (OT), named Curriculum and Structure-Aware Optimal Transport, for generating pseudo labels by considering both inter- and intra-distribution structures of samples. Moreover, to efficiently estimate the distribution's structure, the authors adopt a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback on our paper. Here we address your detailed comments below:
> **Q1(1)**. The comparison between classical OT (row(a)) and Structure-aware OT (row(b)) in Table 3 suggests that the performance improvement brought by the proposed two regularization... | Summary: This paper introduces CSOT, an approach to address the challenge of noisy labels in machine learning models. CSOT incorporates optimal transport formulation to assign reliable labels during training, considering the structure of the sample distribution. The authors also propose an efficient computational metho... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments which helped us improve the quality of our work. In the following, we have provided a point-by-point response to the comments.
> **Q1**. Lack of a comparison with a baseline algorithm called UNICON.
**A1**. Thanks for your constructive suggesti... | Summary: This paper proposes a novel optimal transport formulation, called Curriculum and Structure-aware Optimal Transport (CSOT) for learning with noisy labels. CSOT considers both the inter- and intra-distribution structure of the samples to construct a robust denoising and relabeling allocator. It’s worthing noting... | Rebuttal 1:
Rebuttal: We thank the reviewer for our paper's positive feedback and constructive suggestions. Here are our responses to the reviewer's comments.
> **Q1**. Researchers or practitioners interested in using CSOT may need to invest additional effort in adapting or developing specialized solvers.
**A1**. Tha... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of our paper and help with improving our manuscript. We sincerely appreciate that you find our work:
* proposes novel objective which is a solid idea (Reviewer 7s92)
* proposes a convincing, reliable, interesting and plausible method (Reviewer Zh5c)... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new noisy label learning approach based on Optimal Transport (OT) and Pseudo-Labeling (PL). Specifically, the authors extent OT-based PL with the consideration of the intrinsic coherence structure of sample distribution. Consequently, this paper proposes a novel optimal transport formulat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments which helped us improve the quality of our work. In the following, we have provided a point-by-point response to the comments.
> **Q1**. OT-based Pseudo-Labeling (PL) is not proposed for the first time and curriculum learning to address the iss... | null | null | null | null | null | null |
Learning Causal Models under Independent Changes | Accept (poster) | Summary: The author propose a score-based method for causal discovery from multi-environment data. Identifiability of the causal model and the environment partition were shown for the proposed score function. The proposed algorithm was evaluated on synthetic and multiple real data sets.
Strengths: 1. The toy example ... | Rebuttal 1:
Rebuttal: We thank you for your time and comments, please find our responses below.
**Score function**
> The score function (4) is written in terms of the true conditional probability, while the empirical score function is not defined. In the algorithm, it was not mentioned how to estimate the score funct... | Summary: The authors present a novel approach for causal discovery that goes beyond partially directed graphs by utilizing Gaussian Process (GP) models. The proposed method aims to identify
the correct causal model under certain conditions. The key idea is to leverage algorithmic independence to achieve a concise and l... | Rebuttal 1:
Rebuttal: We thank you for your comments, which we address in the following.
**Limitations**
> It seems that the critical part of the paper is Section 3.5 where the authors actually operationalize theoretical results in the previous sections. But I felt that the authors somehow hide some issues? E.g., find... | Summary: This paper addresses the problem of causal discovery with heterogenous data coming from multiple contexts where contexts are characterized by soft/hard interventions. Previous work differs in assumptions on how non-iid data is produced, the primary assumption being the Sparse Mechanism Shift assumption which a... | Rebuttal 1:
Rebuttal: We thank you for your detailed feedback.
**Independent Mechanism Shift Assumption**
> The core result of identifiability beyond the MEC depends on the independent mechanism shift assumption. I wasn't convinced about why this assumption makes sense.
The assumption is easiest to understand in the... | Summary: The authors study the problem of causal discovery from data under different conditions/contexts. The approach uses an algorithmic model of causation, where the idea is that causal mechanisms provide short (or simple) descriptions of the observed data. Under this principle, the authors propose a score function ... | Rebuttal 1:
Rebuttal: Thank you for your time and detailed comments, which we address in the following.
**Identifiability given a single context**
> The DAG from a single context is identifiable as it is a nonlinear additive noise model.
While the DAG is indeed identifiable for *many* generic functions, the results o... | Rebuttal 1:
Rebuttal: **Summary Response to All Reviewers**
We thank all reviewers for their detailed comments and summarize our response to the main concerns below.
- **Identifiability from a single, few, or many contexts:** We clarify under which conditions the causal DAG is identifiable from a single context to mo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting | Accept (spotlight) | Summary: To capture semantic information and repetitive patterns concurrently, the authors propose WIT framework. By utilizing bi-granular information transmission and HVGSU, the framework can model the inherent repetitive pattern as well as correlation of time series. The author also use a generic RAN to reduce time c... | Rebuttal 1:
Rebuttal: We are sincerely grateful to Reviewer 2QPt for their constructive feedback and recognition of our work.
> Q1: The authors may include more experiments such as robustness check to further evaluate the performance of the framework. Can authors include more experiments to further evaluate the perfor... | Summary: This paper focuses on the long-range time series forecasting problem. An interesting model, Water-wave Information Transmission and Recurrent Acceleration Network is proposed, which captures both short- and long-term recurrent patterns via bi-granular information transmission. The proposed model also captures ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable comments and recognition provided by Reviewer Ku6W regarding our work.
> Q1: Some previous RNN-based models, such as ConvLSTM / PredRNN / PredRNN++, have yet to be compared.
Thank you for your suggestion. We have supplemented the experimental results for thes... | Summary: The paper studies a Water-wave Information Transmission and Recurrent Acceleration Network (WITRAN) framework to model dependencies in a long historical time series. Inspired by Timesnet, the WITRAN introduces a water-wave information transmission strategy to model temporal information. This is implemented by ... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to Reviewer r3j3 for their comprehensive review, which included thought-provoking questions and valuable insights.
> Q1: Although using the water-wave structure is new to me, I am still curious about why this design is needed in long-range time series forecasting.... | Summary: This paper studies the problem of long-range time series forecasting problem and proposes a WITRAN model. The paper analyzes and compares previous forecasting methods from the perspective of information transmission process and design a water-wave information transmission mechanism, which simultaneously captur... | Rebuttal 1:
Rebuttal: We extend our sincere appreciation to Reviewer 1moF for their valuable insights and for recognizing the significance of our research.
> Q1: It seems that the proposed model is suitable for the time series that naturally contains bi-granular periodicity, such as traffic flow with daily and weekly ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading, and detailed and considerate feedback.
# 1 The Supplementary Baseline Experimental Results
Due to space limitations, we will report the results of the baseline experiments we conducted in this section. We have included several classic RNN-based m... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion | Accept (poster) | Summary: The paper introduces a pretraining method for molecule joint auto-encoding (MoleculeJAE) for 2D molecular topology and 3D molecular geometry. Their approach adopts SE(3) symmetry and is trained by fitting the joint distribution of the trajectories from the forward process of the diffusion model.
The authors t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Certain aspects of your concerns related to motivation and experiments have also been covered in the general response. Please also review the comprehensive response provided there for further clarifications.
**Weaknesses**
A：Thank you f... | Summary: The paper proposes MoleculeJAE, an auto-encoder for both 2D and 3D molecule diffusion trajectories. The model learns the trajectories jointly in a self-supervised manner. Empirically, MoleculeJAE achieves competitive results on property and force prediction benchmarks.
Strengths: The joint diffusion of 2D and... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. Certain aspects of your concerns related to motivation and experiments have also been covered in the general response. Please also review the comprehensive response provided there for further clarifications.
**Weaknesses: Motivation of M... | Summary: This paper proposes a new representation learning method for molecules using 2D and 3D structures. The joint distribution between original molecules and augmented molecules is decomposed into reconstructive and contrastive tasks. The proposed model, MolecularJAE, simultaneously tackles both tasks with the help... | Rebuttal 1:
Rebuttal: We express our sincere gratitude for your meticulous examination and insightful feedback. Now we want to address your concerns and clarify misunderstandings in detail.
**Weaknesses**
1. **Incorporating Physical Rules**: We appreciate your suggestion to integrate such knowledge into our diffusio... | Summary: The authors propose an auto-encoding method for learning molecular embeddings from both 3D and 2D information jointly. The method is loosely related to diffusion methods in that embeddings of data augmentation trajectories are learned via a score-based reconstruction loss and contrastive loss. The embeddings g... | Rebuttal 1:
Rebuttal: We express our sincere gratitude for your meticulous examination and insightful feedback.
**Weaknesses: Organization of Section 2 and Section 3.2 and Equation Missing $e_m$** : To rectify this, we will enhance the clarity of Section 2 by trimming the general introduction of the diffusion mechani... | Rebuttal 1:
Rebuttal: ## General Response
We thank all the reviewers for their time, and valuable feedback for improvements. All relevant works and typos mentioned by reviewers will be discussed in the revised version. Now, we clarify and address some common issues that have been raised by the reviewers.
**Motivation... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Color Equivariant Convolutional Networks | Accept (poster) | Summary: This paper questions the importance of color variations for neural classifiers and proposes to use color-equivariant architectures in the case of unbalanced datasets.
To demonstrate the validity of the presented approach, the authors conduct experiments both on synthetic controlled datasets and on common objec... | Rebuttal 1:
Rebuttal: We thank reviewer w9HG for the helpful remarks and interest in our work. Please find a detailed response to your questions and suggestions below:
---
**Weaknesses:**
> 1. While the idea of extending equivariance from geometric to photometric transformations is definitely interesting, the submit... | Summary: The authors introduce a color equivariant convolutional neural network. To achieve this the authors represent the image in HSV format, and achieve hue equivariance using methods for rotational equivariance. This is possible since hue can be represented by an angle. The authors show that the proposed approach o... | Rebuttal 1:
Rebuttal: Great thanks for your helpful comments and appreciation for our work. Please find detailed answers to your questions and remarks below:
---
**Questions:**
> 1. regarding equation 3. I believe the correlation should be between the feature maps and $C^{l+1}$ filters [7]. I think it could be clari... | Summary: Paper proposes color-equivariant CNN layers by imposing equivariance to H_n (a discrete subgroup of SO(3)) in the RGB space which is imposes hue equivariance. Implementation follows the framework of Group-equivariant CNNs. Experiments show marginal improvements over standard CNNs for in-distribution test data ... | Rebuttal 1:
Rebuttal: Many thanks for the helpful comments, please find our detailed response to your questions and remarks below:
---
**Weaknesses:**
> 1. Definition of color equivariance considered in the paper seems to be restricted as it only considers the hue dimension. [...]
Yes! While hue is arguably the most ... | Summary: This paper proposes color equivariant convolutional networks (CE-CNNs), a novel convolutional neural network architecture that achieves equivariance to hue changes. 
They introduce color equivariant convolutions that apply a discrete set of hue rotations to the convolution filters during the forward pass... | Rebuttal 1:
Rebuttal: We highly appreciate your helpful comments and the recognition of our efforts. Please find a point-by-point response to your remarks below.
---
**Weaknesses:**
> 1. The method is demonstrated on image classification, but it's unclear how well it would generalize to other tasks like detection or ... | Rebuttal 1:
Rebuttal: Thank you for the highly detailed and constructive feedback! In this rebuttal we address all points raised by the review team, leading to multiple improvements, including: evaluations against relevant baseline methods, clarifications, and insights. Below, we answer individual questions per reviewe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network | Accept (poster) | Summary: The authors propose a novel feature selection algorithm, aiming to detect interactions between features at instance-level, contrary to the usual feature selection algorithms that selects the same features for every sample. Initially, the authors propose a highly memory-demanding approach, requiring an $m \time... | Rebuttal 1:
Rebuttal: Hi Reviewer eecX:
Thanks for reviewing our paper and offering helpful comments. Below are responses to your questions.
### **W1: Originality**
Please kindly allow me to highlight our papers' originality here, as our writings may not be optimal and can confuse the reviewer. Our initial intuition ... | Summary: This work proposes a hybrid-grained feature interaction selection approach for deep sparse networks, which targets both feature field and feature value. The proposed approach uses a decomposed space that is calculated on the fly to explore the expansive space of feature interactions. The work also introduces a... | Rebuttal 1:
Rebuttal: Hi Reviewer Bzgv:
Thanks for reviewing our paper and offering detailed comments. Below are responses to your questions.
### **W1: Unclear expression of our novelty**
Thanks for recognizing the novelty of our method. This comment does remind us of the importance of constantly highlighting our con... | Summary: This paper tackles the problem of modeling fine-grained feature interactions in high-dimensional sparse features.
A hybrid-grained feature interaction selection method is proposed, which operates on both field and value for deep sparse networks.
To handle the increase in computation, a decomposed form of the s... | Rebuttal 1:
Rebuttal: Hi Reviewer 57Bi:
Thanks for your effort in reviewing our paper and appreciating our effort. Below are responses to your questions.
### **W1: generalization**
We deeply agree with the reviewer about the importance of generalization.
To investigate this aspect, we include ablation studies regard... | Summary: This paper introduces a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks and decomposes the selection space using tensor factorization and calculating the corresponding parameters on the fly.
Strengths: Extending the selection g... | Rebuttal 1:
Rebuttal: Hi Reviewer 21ZP:
Thanks for your effort in reviewing our paper and offering constructive suggestions.
### **W1: small datasets**
We thank the reviewer's helpful suggestion in extending datasets with relatively different statistics. Our response to your concern is split into the following two p... | Rebuttal 1:
Rebuttal: Hi Reviewers and PCs:
We want to thank all your effort in helping us improve this paper. Below are some of the common concerns. Kindly notice that we try to use points to answer reviewers' questions, as some of the weaknesses and questions are repetitive or similar.
### **CW1: result significanc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis | Accept (poster) | Summary: To be effective, a basis must be tailored to the specific set of time series data and exhibit distinct correlation with each time series within the set. As far as we know, the state-of-the-art methods are limited in their ability to satisfy both of these requirements simultaneously. To address this issue, the ... | Rebuttal 1:
Rebuttal: *Q1 - comparison with self-supervised methods*
First, We would like to clarify that our model should not be classified as a self-supervised model in a strict sense. This is because our model utilizes a supervised loss function for predictions, while the alignment loss function and smoothing loss ... | Summary: This paper addresses the problem of finding effective bases for time series forecasting models. Current methods are limited in their ability to satisfy the requirements of being tailored to specific time series data and exhibiting distinct correlation with each time series. To tackle this challenge, the author... | Rebuttal 1:
Rebuttal: *Q1 - motivation for baisis*
As mentioned in Lines 20-22 on Page 1, bases are defined as **sequences that capture the underlying temporal patterns for a set of time series and serve as the key factors driving changes in the data over time**. They may encompass trends, seasonalities, and other vit... | Summary: This paper studies basis learning for time series forecasting for which the past and future basis representations are aligned. Contrastive learning is used to build the time series basis and similarity between the past values and basis is used for time series prediction. The experiments on several time series ... | Rebuttal 1:
Rebuttal: *Q1 - past & future consistency*
Thank you for bringing this to our attention. **We have shown the consistency of representations for the past and future sequences in our model in Figure R1 in the PDF file attached to the general response**, where the attention mechanisms exhibit significant simi... | Summary: This paper proposed BasisFormer which is an end-to-end time series forecasting model that leverages learnable and interpretable bases. BasisFormer treats the historical and future sections of the time series as two distinct views and using contrastive learning. By making use of Coef module and Forecast module,... | Rebuttal 1:
Rebuttal: *Q1 - Visualization*
Thanks for pointing this out! We have incorporated the visualization of the attention map of the BCAB module on the traffic dataset, as depicted in Figure R1 in the PDF file attached to the global response. This visualization demonstrates that different time sequences have di... | Rebuttal 1:
Rebuttal: **General Response to All Reviewers**
We sincerely thank all the reviewers for their valuable suggestions. We are delighted by the unanimous recognition of our work and appreciate the reviewers' positive feedback on the carefully designed network architecture and the use of contrastive learning i... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors of the paper propose a time series forecasting architecture with a self-supervised method for basis learning, called BasisFormer. Their assumption is that the selection of basis for a time series is consistent across both historical and future sections of the time series. They introduce a Coef modu... | Rebuttal 1:
Rebuttal: *Q1 – Comparison with time series discretization*
We appreciate your suggestion to include a comparison with methods involving the discretization of time-series, such as Moskovitch and Shahar (2015). Unfortunately, we were unable to find the corresponding code and data for comparison. Therefore, ... | null | null | null | null | null | null |
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction | Accept (poster) | Summary: The authors tackle a very relevant problem in computed tomography: metal artifact reduction. They recognize some severe gaps in conventional methods (both model-based and deep-learning based) and propose a theoretically sound approach to solve these gaps using implicit neural representations in a somewhat simi... | Rebuttal 1:
Rebuttal: We value the time and effort the reviewer dedicated to our work. It's genuinely uplifting to know that you consider our work "*reading this paper was a pleasure*". Below, we provide point-to-point responses to address your concerns.
---
**Q1. You assume that you can discretize the energy levels.... | Summary: This paper introduces Polyner, an extension of implicit neural representation to a nonlinear inverse problem with a forward model that simulates the polychromatic nonlinear CT acquisition process. This design allows Polyner to reduce metal artifacts without external training data and exhibits better generaliza... | Rebuttal 1:
Rebuttal: Thanks for your efforts and valuable comments. Below, we provide point-to-point responses to address your concerns.
---
**Q1. Clarify whether the supervised ACDNet and DICDNet models were fine-tuned on the IID setting of the DeepLesion dataset**
**A1:** The two supervised methods (ACDNet and DI... | Summary: The paper with title: Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction presents an Implicit neural representation-based method for CT metal artifacts reduction, outperforming existing supervised and unsupervised approaches.
Strengths: 1. This paper presents a novel INR-based m... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We are pleased to receive your positive feedback. Below, we provide point-to-point responses to address your concerns.
---
**Q1. Regarding the results for the real-data - I noticed that for real-data, the gap or improvements of Polyner is not as ... | Summary: The paper proposes a new implicit neural representation (INR)-based polychromatic x-ray CT reconstruction technique called Polyner. In normal CT reconstruction, the tissues of the body do not vary substantially in their attenuation coefficients, so the overall forward operator can be simply modeled as linear. ... | Rebuttal 1:
Rebuttal: Thank the reviewer for the valuable comments. We are encouraged by your recognition of our work. Below, we provide point-to-point responses to address your concerns.
---
**Q1. Most ablations consider loss functions and forward models - did the authors consider modifications to the INR architectu... | Rebuttal 1:
Rebuttal: ### **Global Responses**
We sincerely thank the reviewers for their insightful comments and suggestions!
We are encouraged by the reviewers' recognition of the novelty (ibgY, KPnC, QLvh), strong motivation (ibgY, oo65, QLvh), technical (oo65, QLvh) and theoretical (QLvh) soundness of our researc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Personalized Dictionary Learning for Heterogeneous Datasets | Accept (poster) | Summary: This paper tackles the problem of personalized dictionary learning (PerDL) for heterogeneous datasets that share some commonality. The authors propose a federated meta-algorithm called PerMA that can provably recover both global and local dictionaries from heterogeneous datasets. They show the applications of ... | Rebuttal 1:
Rebuttal: The authors truly appreciate the reviewer's insightful comments.
> The paper does not compare PerMA with other existing methods for dictionary learning or federated learning, such as personalized PCA (PerPCA), which would be helpful to evaluate its performance and advantages.
We kindly refer the... | Summary: This work tackles the problem of heterogeneity in federated learning through dictionary learning. The authors name this problem _Personalized Dictionary Learning_ (PerDL), which seeks to learn (linear) representations for the heterogeneous datasets from clients, which are supposed to share common characteristi... | Rebuttal 1:
Rebuttal: We really appreciate the reviewer's helpful comments and detailed suggestions.
> W1/S1
We thank the reviewer for this comment and apologize for not mentioning federated learning in the abstract. Rest assured, we will address this issue by adding federated learning motivation to our abstract.
>... | Summary: This paper studies the problem of personalized federated learning with each client conducting dictionary learning on heterogeneous tasks. This paper splits the learned dictionary into a global dictionary and local dictionaries. It provides the conditions that two types of dictionaries can be provably identifie... | Rebuttal 1:
Rebuttal: We are grateful for your insightful comments and suggestions.
> The experiments are somewhat weak.
We kindly refer the reviewer to our general response, where we have included numerical comparisons between our method and other existing methods in our global response. Our method indeed exhibits ... | Summary: This paper proposed a challenging problem named Personalized Dictionary Learning (PerDL), which learned a shared global dictionary and individual local dictionary for heterogeneous datasets.
In order to investigate the feasibility of the problem, several definitions and assumptions are provided to make the t... | Rebuttal 1:
Rebuttal: > Can it be compared with other methods, such as personalized PCA?/Is it possible to compare with other baselines in Dictionary learning or Federated learning?
Thank you for this helpful suggestion. We kindly refer the reviewer to our general response, where we have included numerical comparisons... | Rebuttal 1:
Rebuttal: We are thankful to the reviewers for carefully reading and commenting on the strengths and weaknesses of our paper. A recurring comment among the reviewers was on the limitation of our experiments. We have thoroughly addressed this comment by conducting more experiments on our method and comparing... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TIES-Merging: Resolving Interference When Merging Models | Accept (poster) | Summary: This paper identified two sources of performance degradation when merging fine-tuning models, (i) redundant parameter (ii) sing conflict and proposed TIES-MERGING to improve them.
Strengths: 1. The motivation to merge fine-gunning and performance is compelling.
2. Experiments were conducted with both NLP and... | Rebuttal 1:
Rebuttal:
**Weakness 1, 2 and Question 1:** Theoretical support for the proposed method.
**Answer:** Our work is supported by several past works that shed light on different components of our method. We discuss each of them below and will add a similar discussion to the updated paper.
**Why Merging Work... | Summary: The paper presents a novel method, TIES-MERGING, to merge models in the weight space for multitask learning. It observes an interference problem when linearly interpolating weights, and proposes a simple yet effective two-step solution: parameter trimming for small changes during fine-tuning and sign conflict ... | Rebuttal 1:
Rebuttal:
**Weakenss 1:** The contributions, though valuable are incremental.
**Answer:** We note that the improvements of TIES-Merging over Task Arithmetic are precisely due to the role of trimming values and electing signs when merging since there are no other differences between the methods. Moreover, ... | Summary: This paper delves into the challenge of integrating multiple task-specific fine-tuned models into a singular, multitask model, without necessitating additional training. The authors identified that current methodologies overlook the interference that occurs between parameters of different models. This interfer... | Rebuttal 1:
Rebuttal:
**Weakness 1:** Theoretical Justification
**Answer:** Our work is supported by several past works that shed light on different components of our method. We discuss each of them below and will add a similar discussion to the updated paper.
**Why Merging Works:** As mentioned in L91-102, model m... | Summary: Merge mutiple **fine-tuned** neural networks, each from an unique task (dataset), into one neural network by weight averaging. Denate the weights of pretrained network as $\theta_0$, fine-tuned network as $\theta_{\tau}$, the weight updating direction $\tau = \theta_{\tau} - \theta_0$
The paper introduces tw... | Rebuttal 1:
Rebuttal:
**Weakness 1 (Part 1):** Method still legs behind MultiTask training.
**Answer:** While we agree that the gap between TIES merging and multitask training is an important limitation, we note that multitask learning requires simultaneous training on all the training data at once. In contrast, when... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and for providing constructive comments for enhancing the paper. We appreciate that reviewers recognized:
- That our paper fills a crucial gap in the current literature (ioFQ).
- Our notable experimental contribution (reviewer ioFQ) via robust, comprehens... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes to resolve the interference of model merging, a solution to combine multiple task-specific models into a single multitask model. It demonstrates two major sources of interference, including redundant parameter values and sign conflict and proposes solutions to resolve the interference.
St... | Rebuttal 1:
Rebuttal: **Weakness 1:** Necessity of Section 7.3 and pruning away the Top-k% parameters can cause a significant performance drop is well-known in the literature.
**Answer:** As mentioned in L166-168, we would like to clarify that we are pruning the task vectors (i.e. the difference between the fine-tuned... | null | null | null | null | null | null |
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case | Accept (poster) | Summary: In the vein of the seminal work by Sakaue and Oki for L-convex functions, this paper proposes a new method to accelerate M-convex function minimization using past predictions, a technique known as warm-start.
The contributions are as follows:
1) Present a framework to accelerate M-convex function minimizatio... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments. We are glad that the reviewer found our experimental results promising. Below, we respond to each comment.
> **Weakness 1.** This paper is very theoretically dense and tries to tackle a lot of problems at once, the contributions are not clear. I th... | Summary: This paper applies the learning-augmented algorithms framework to a class of discrete optimization problems called M-convex. A function defined on an integer grid is M-convex if for every x, y it holds that f(x)+f(y) >= f(x-ei+ej)+f(y+ei-ej) for some base vectors ei, ej. The paper complements the line of resea... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed and constructive comments. We are pleased that the reviewer recognizes the technical strengths of the algorithms in Sections 4.1 and 4.2 and the significance of our result for Box, a first demonstration of the potential to surpass the lower-bound result using ... | Summary: The goal of the work is to minimize M-convex functions with prediction. The work mainly focuses on a subclass of M-convex functions that use Laminar, Nested or Box constraints.
Strengths: Minimizing M-convex functions is an important class of discrete optimization problems that has wide variety of application... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful reading and constructive feedback. We are pleased that the reviewer understood our main contribution and appreciated the importance of our results. Below are our responses to the comments.
> **Question 1.** In section 4.2, it is clear that finding the node for... | Summary: Extending the warm-starting techniques in L-convex function minimization by Sakaue and Oki (2022), the authors study the problem of acclerating M-convex function minimization with predictions. The idea is to start from a (possibly infeasible) predicted solution, project the rounded solution to the feasible reg... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for providing valuable comments. Below we respond to each comment.
> **Weaknesses 1.** The framework proposed in this paper for M-convex optimization is somewhat similar to the one in Sakaue and Oki (2022) for L-convex optimization. So novelty in the general frame... | Rebuttal 1:
Rebuttal: # Global response on experiments and technical novelty
We sincerely thank all reviewers for providing valuable feedback. Given the mixed reviews, we deem it necessary to begin by addressing key comments. Below, we address comments on experiments and technical novelty.
## Experiments
First, we re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies some classes of $M$-convex minimization problems, to which the recent framework of "warm-starts with predictions" is applied. The paper provides provable time complexity bounds on the standard greedy algorithm for $M$-convex minimization where the bounds are dependent on the $\ell_1$-distanc... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful comments. We are delighted that the reviewer has found our improvements using predictions upon existing methods promising. We respond to each comment below.
### On weaknesses
> The technical contributions of this paper are limited. The greedy algorithm and... | Summary: The paper discusses the growing interest in accelerating optimization algorithms using machine-learned predictions. It highlights the work of Sakaue and Oki, who introduced a general framework for employing predictions to warm-start the L-convex function minimization method, demonstrating its effectiveness for... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback. We present our response to each comment below.
> **Weakness 1.** The main theoretical results appear to be straightforward. The computational time complexity relies on the distance between the initialization and the optimal solution, assumin... | null | null | null | null |
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach | Accept (spotlight) | Summary: Drawing inspiration from physics principles, the paper proposes the use of conservative Hamiltonian neural flows to construct GNNs that are robust against adversarial attacks. The adversarial robustness of different neural flow GNNs is empirically evaluated on several benchmark datasets, considering a variety ... | Rebuttal 1:
Rebuttal: # Improve Paper Writing: Weakness 1 and 2
We greatly value your feedback. Our paper delves into the relationship between the stability of graph ODE models and adversarial robustness. This connection is elaborated upon in segments such as Remark 2, lines 116-125, 165-180, and 280-285, where we cor... | Summary: This paper proposes a robust GNN model by leveraging the notion of Hamiltonian Energy Conservation. Specifically, authors first analyze the stabilities and limitations of several neural ODE-based GNNs, which motivate the proposed model HANG that is inspired by Hamiltonian classical mechanics. Experimental resu... | Rebuttal 1:
Rebuttal: |Dataset|Attack|HANG|HANG-GUARD|HANG-quad|HANG-quad-GUARD|
|-|-|-|-|-|-|
|Cora|clean|87.13±0.86|86.54±0.57|79.68±0.62|81.23±0.70|
||PGD|78.37±1.84|86.23±0.55|79.05±0.42|80.91±0.67|
||TDGIA|79.76±0.99|85.56±0.34|79.54±0.65|81.11±0.76|
||MetaGIA|77.48±1.02|86.0±0.60|78.28±0.56|80.10±0.53|
|Citeseer|... | Summary: This paper explores the robustness of Graph Neural Networks (GNNs) against adversarial attacks. Drawing inspiration from principles in physics, the authors propose a novel model called Hamiltonian Neural Flows for constructing GNN models. The effectiveness of the proposed method is evaluated on various benchma... | Rebuttal 1:
Rebuttal: # Clarify and New Attacks:
Thank you for your valuable feedback and suggestions. We truly value the time and effort you've dedicated to reviewing our paper.
To provide some clarity, our work offers a fresh perspective on GNNs' adversarial robustness by probing the stability of graph ODE-based G... | Summary: Since neural ordinary differential equation networks can show inherent robustness, in this work the authors try to perform an extensive study on different graph neural flows along with their stability on different stability notions like BIBO stability, Lyapunov stability, Structural stability and Conservative ... | Rebuttal 1:
Rebuttal: # White Box Attack: Weakness 1
We are grateful for your attention to the robustness evaluation. We **do include the results of the white-box attack in Table S3 of our supplementary material.** The results clearly demonstrate that both HANG and HANG-quad exhibit superior robustness compared to the... | Rebuttal 1:
Rebuttal: In response to the reviewers' feedback, we have undertaken several substantial efforts to enhance the quality and comprehensibility of our paper. Here is a summary of the key actions we have taken:
1. **New Experiments**: We have executed a series of new experiments to bolster our findings and co... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
QuIP: 2-Bit Quantization of Large Language Models With Guarantees | Accept (spotlight) | Summary: This work proposes a unified framework for weight quantization with error feedback together with preprocessing and postprocessing transformation that makes the model more quantization-friendly. Authors derive theoretical bounds on the quantization error and investigate the failure cases of OPTQ quantization. T... | Rebuttal 1:
Rebuttal: **Evaluating on more models.**
Thanks for the feedback; we agree it is important to evaluate our quantization method on additional models. At the suggestion of the reviewers, we conducted additional experiments on LLaMa-2-70b, and share preliminary results below. We will include a fully comprehens... | Summary: The work presented in this paper introduces quantization with incoherence processing, which enables better quantization with fewer bits per parameter. The authors provide a theoretical analysis for adaptive rounding methods and present experimental results demonstrating performance of 2-bit quantization..To do... | Rebuttal 1:
Rebuttal: **What’s the best use of bits.**
We conducted a thorough analysis of the experimental data submitted in our paper. The reviewer is accurate in their observation that 2 bit quantization with QuIP is not worthwhile in terms of total memory budget. However, QuIP is the first method we are aware of to... | Summary: - The authors coin a family of adaptive rounding methods for layer-wise quantization, establish that the state-of-the-art method GPTQ is optimal within this family, and prove quality guarantees.
- The paper further introduces incoherence preprocessing, together with a Kronecker-factor based inference scheme, w... | Rebuttal 1:
Rebuttal: **Evaluating on more models.**
Thanks for the feedback; we agree it is important to evaluate our quantization method on additional models. At the suggestion of the reviewers, we conducted additional experiments on LLaMa-2-70b, and share preliminary results below. We will include a fully comprehens... | Summary: This paper introduces QuIP, an algorithm for weight quantization in large language models, with theoretical guarantees. The experimental results demonstrate that QuIP achieves nearly lossless performance when using 3-bit quantization for models larger than 3B, and it shows good performance with 2-bit quantizat... | Rebuttal 1:
Rebuttal: **Evaluating on more models.**
Thanks for the feedback; we agree it is important to evaluate our quantization method on additional models. At the suggestion of the reviewers, we conducted additional experiments on LLaMa-2-70b, and share preliminary results below. We will include a fully comprehens... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful feedback.
In summary, reviewers noted how the proposed method pushed the boundary of LLM quantization down to 2 bits, provided a novel theoretical understanding of adaptive layerwise rounding algorithms, and conducted extensive experiments. Previous quant... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FaceComposer: A Unified Model for Versatile Facial Content Creation | Accept (poster) | Summary: The advancement of generative models has significant progress in automatic facial content creation. However, current models pose challenges due to their high customization and inefficiencies. To address this, a unified model called FaceComposer is proposed.
- The model leverages images, videos, multi-modal fa... | Rebuttal 1:
Rebuttal: Thank you for your comments!
$\textbf{1. Confusing interface.}$
We apologize for the confusion caused by the user interface. Here, we will provide a detailed explanation of the demo page in Figure A6. The five rectangular boxes in the first row represent five condition inputs: Mask, PNCC, Sketch... | Summary: The paper presents an all-in-one pipeline that can perform face generation, editing and animation and can be driven by multiple signals such as audio, text and sketches. The proposed model is based on a Latent Diffusion Model (LDM) and works by decomposing images faces into several representations capturing id... | Rebuttal 1:
Rebuttal: Thank you for your comments!
$\textbf{1. Novelty and contributions.}$
We would like to clarify our primary contribution, which is the unified generative framework with various means of controllability for versatile facial content creation. Our design enjoys some merits in both training and infer... | Summary: This paper presents a facial content generation framework named FaceComposer, which is based on a Latent Diffusion Model (LDM). The primary aim of this framework is to facilitate text-conditioned face synthesis/editing and animation. The conditions employed in this model encompass a variety of aspects, includi... | Rebuttal 1:
Rebuttal: Thank you for your comments!
$\textbf{1. Comparative methods.}$
Thanks. Table R1 displays the comparison results between FaceComposer and StyleTalk. It can be observed FaceComposer has slightly better performance than StyleTalk, stemming from two aspects: 1) FaceComposer employ FLAME to represen... | Summary: The paper proposes a unified framework for facial generative models that allows text/spatial/audio condition facial editing tasks. The results are reasonable and comparable to prior works. Based on the stable diffusion prior, this model can generalize well to different style domains.
Strengths: - The paper pr... | Rebuttal 1:
Rebuttal: Thank you for your comments!
$\textbf{1. Pros and cons of different conditions.}$
It is agreed that the analysis of the pros and cons of jointly training with different conditions is important for our unified framework design, we list them below and will add them in the final version.
$\textbf... | Rebuttal 1:
Rebuttal: To all reviewers:
We thank all reviewers for their efforts in reviewing our paper and appreciate their valuable comments. We will address their individual concerns in the rebuttals per review. Here, we list some concerns in common. If not specified, Table(Figure) \*/A\*/R\* represent the correspo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Calibrating “Cheap Signals” in Peer Review without a Prior | Accept (poster) | Summary: The paper tackles a problem in peer review where reviewers may provide noisy/biased ratings for papers. The paper investigate a "one-shot" scoring process inspired by the "Surprisingly Popular" method, that can rank papers by their true quality without any prior knowledge, even if different papers have differe... | Rebuttal 1:
Rebuttal: ## Reviewer VrqZ
Thank you for your insightful comments and suggestions.
### W1: The presentation of the paper could be improved
In the final version, we will relocate the symbol clarifications (Table 1, currently in the appendix on page 17) back to section 2 for ease of reference. Besides, we ... | Summary: This paper proposes a method to calculate peer-reviews scores for papers in the presence of systematically biased noise, such that the score of a paper with a higher expected score in a noise-free regime is higher than the score of a paper with a lower expected score in a noise-free regime with high probabilit... | Rebuttal 1:
Rebuttal: ## Reviewer tv7v
Thank you for your insightful comments and suggestions.
### W1: Suggestion for limiting the main body of the paper to the binary decision case:
Thank you for your suggestion. We will adopt it in the final version. This allows us to focus the main body on the binary signal case,... | Summary: The paper considers the problem of comparing two papers based on noisy ratings, where the noise can be arbitrarily biased for different papers. The paper elicits from each reviewer both a rating and a distribution of predicted ratings from other reviewers’ (based on a Bayesian update of the common prior, known... | Rebuttal 1:
Rebuttal: ## Reviewer 6t8A
Thank you for your insightful comments and suggestions.
### W1: Assumption of common noisy prior is very strong
We interpret the scenario where different reviewers have different priors as if they share a common prior but possess distinct private information. The "common prior ... | Summary: This paper aims to detect and correct bias in Peer Review. They propose a one-shot noise calibration process without any prior information. Experiments are conducted on the binary case to show the effectiveness of the proposed method.
Strengths: 1. The studied problem is important.
2. Theoretical guarantee is... | Rebuttal 1:
Rebuttal: ## Reviewer UucL
Thank you for your insightful comments and suggestions.
### W1: Rationality of the proposed surprisal scores
Our understanding of your question suggests that you are seeking a more intuitive explanation of Figure 1, especially concerning why our method yield identical scores in... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful suggestions and comments on our manuscript. We are pleased that the reviewers recognize the novelty (Reviewer SV22, tv7v, VrqZ), clarity in writing and organization (Reviewer SV22, UucL, tv7v), as well as the sound theoretical guarantees and synthetic ex... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper tries to solve famous problem of removing bias and noise during peer-review process. The authors address the problem in one-shot setting (without historical data) and propose a novel approach that allows to remove influence of bias and noise (under some assumptions on bias and noise) on the ranking o... | Rebuttal 1:
Rebuttal: ## Reviewer SV22
Thank you for your insightful comments and suggestions.
### Q1: Whether the assumptions are realistic
**Positively correlated**
In the assumptions mentioned in lines 59-60, "positively correlated" indicates that the correct answer has a positive correlation with the signal, as... | null | null | null | null | null | null |
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation | Accept (poster) | Summary: The paper tackles the problem of uncertainty estimation specifically for multi-modal data, i.e., inputs consisting of different sources. Specifically, it improves the popular Neural Process (NP) in three aspects: dynamic context update, multi-modal Bayesian aggregation, and a novel attention mechanism based on... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We would like to address the following statements.
> “While the paper presents three innovations and claims that they are all tailored to multi-modal data, only the Bayesian aggregation is inherently related to multi-modal inputs. The dynamic ... | Summary: This work proposes a confidence calibration algorithm for multimodal classification problems. The algorithm includes three key components: 1) dynamic context memory, 2) multimodal Bayesian aggregation, and 3) adaptive radial basis function. The algorithm is evaluated on multiple benchmarks using classification... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions. We would like to address the following questions.
> "Line 71, from my understanding, the context set C and target set T contains both training and test datasets. But $N_C +N_T=N_{\\text{train}}$. Why is the summation of $N_C$ and $N_T$ only eq... | Summary: This paper proposes a multimodal neural processes (neural network generaliation of Gaussian processes) model.
The overall approach has several novel elements:
* A way to maintain a dynamic context set throughout training (e.g a support set for few-shot learning, these context sets are needed for neural proces... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We would like to address the reviewer’s suggestions.
> “It is pretty unclear what are the actual multiple modalities in the datasets that are used for experiments”
We acknowledge that we have missed the details of the input modality in Appendi... | Summary: This paper proposes a new method for multimodal uncertainty estimation by extending neural processes. The authors summarize three challenges to do that and give solutions correspondingly. Experimental results show that the proposed method is more robust and outperforms existing baselines.
---
Thanks for the ... | Rebuttal 1:
Rebuttal: Thank you for your time and comments. We would like to address the following points.
> “However, existing works discussed the multi-view data [16,21]. The reason why extending the neural processes for multimodal data (instead of extending/improving [16,21]) remains unclear.”
As we stated our mot... | Rebuttal 1:
Rebuttal: **Continued Rebuttal for Reviewer hoCV**
> “It would be good to consolidate a single table (for one dataset) to directly show the effectiveness of each component in terms of accuracy, uncertainty, and robustness.”
Thank you for the suggestion. We would like to provide the first and the last data... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper extends one of the promising uncertainty estimation method: Neural Process from unimodal to multimodal. This is motivated by the fact that: current techniques are predominantly designed for unimodal data, and directly applying them to multimodal information is ineffective. However, this extension po... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and comments. We would like to address the following statements.
> "It lacks adequate contextual knowledge, such as an explanation for why the context memory is required, why it is used to store training samples rather than using a context featu... | null | null | null | null | null | null |
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners | Accept (poster) | Summary: The paper investigates the potential of using synthetic images generated by text-to-image models to train self-supervised image embedding models. Methodologically, the authors propose StableRep, a multi-positive contrastive learning method that treats multiple images generated from the same text prompt as posi... | Rebuttal 1:
Rebuttal: Thank you very much for considering our work as interesting and novel! The concern you raised is a great question, and we will try to answer it from two aspects:
1. While the dataset distillation hypothesis is unavoidable, we have a different interpretation from the "distribution" perspective. S... | Summary: In this paper, the authors investigate the potential of learning visual representations using synthetic data generated by text-to-image models. The authors choose Stable Diffusion for exploration and extensive experiments demonstrate that self-supervised models trained on synthetic data can perform better or a... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback. Below we try to address your concerns:
> Thus, a more cost-effective and interesting setting should be to generate a synthetic ImageNet dataset using category labels as text prompts (e.g., a photo of [category])...I am curious about whether the self-supe... | Summary: The authors presents a novel method for learning visual representations using synthetic data. The authors leverage text-to-image generative models (Stable Diffusion) to synthesize images from textual prompts, which are then used to train a self-supervised visual representation model. The synthetic data generat... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work as novel and acknowledging that we study a very important problem.
> Limited motivation behind why synthetic images: It is unclear to me as a reader why Synthetic Images are being used in Section 2.2 as training data for pre-training. It seems that there is no ... | Summary: This paper investigates how synthetic data generated with the text-to-image diffusion model Stable Diffusion can be leveraged for representation learning.
To this end, the paper analyzes established representation learning approaches such as SimCLR and CLIP, but trained on the synthetically generated data. Fur... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our method is nice and that our experiments are encouraging, and we appreciate the constructive feedback. We address your concerns or questions one by one as below.
> How does the choice of generative model influence the results for representation learning?
This is a... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful comments and feedback! We are glad to see the reviewers found:
1. the paper is well-written and easy to follow (by reviewer HH2w, AnD7, DyTC)
2. this paper approaches an interesting timely research question / very important problem (by AnD7, DyTC, FSj... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies pre-training by using generated image from diffusion models. It presents StableRep that generate different images with the same caption by using stable diffusion models. The model is hence pre-trained by using the generated samples and contrastive loss. Extensive experiments demonstrate the ... | Rebuttal 1:
Rebuttal: Thank you for the review. In the following we address your concern in detail.
> the comparison between the proposed method and the reported results in [24] is missing
This is a reasonable comparison, which we have included in the question 1 of "global rebuttal" shared to all reviewers (please f... | null | null | null | null | null | null |
Fitting trees to $\ell_1$-hyperbolic distances | Accept (poster) | Summary: The paper introduces a new algorithm, HCCRootedTreeFit, for fitting tree metrics to a given distance matrix. The algorithm is designed to minimize the ℓ1 distortion of the fit. The authors provide a detailed explanation of the algorithm, its theoretical properties, and an extensive experimental evaluation. The... | Rebuttal 1:
Rebuttal: * Thank all the reviewers for their comments and fruitful feedback. Below each review, we have posted a rebuttal that directly addresses concerns and clarifies any misunderstandings. If you wish to obtain further clarification, please reply in the relevant thread, and we will get back to you as so... | Summary: The authors consider the tree fitting problem for a given distance. The authors cast the tree fitting problem as finding the relation between the hyperbolicity vector and the error of tree embedding. The authors propose an algorithmic approach with provably tight $\ell_1$ error. The authors also illustrate the... | Rebuttal 1:
Rebuttal: * Thank all the reviewers for their comments and fruitful feedback. Below each review, we have posted a rebuttal that directly addresses concerns and clarifies any misunderstandings. If you wish to obtain further clarification, please reply in the relevant thread, and we will get back to you as so... | Summary: The paper formulate the $l_p$ tree fitting problem introduces a new algorithm, HCCROOTEDTREEFIT, for building trees in hyperbolic space by investigating the relationship between hyperbolicity (ultrametricity) vectors and the error of tree (ultrametric) embedding, which outperforms previous methods both theoret... | Rebuttal 1:
Rebuttal: * Thank all the reviewers for their comments and fruitful feedback. Below each review, we have posted a rebuttal that directly addresses concerns and clarifies any misunderstandings. If you wish to obtain further clarification, please reply in the relevant thread, and we will get back to you as so... | Summary: This paper introduces a novel tree fitting algorithm named HCCRootedTreeFit. First, the authors motivate the need for a better tree fitting algorithm by stating that current methods "assume almost nothing about the underlying discrete point set, when, in fact, many real application data sets are close to hiera... | Rebuttal 1:
Rebuttal: * Thank all the reviewers for their comments and fruitful feedback. Below each review, we have posted a rebuttal that directly addresses concerns and clarifies any misunderstandings. If you wish to obtain further clarification, please reply in the relevant thread, and we will get back to you as so... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation | Accept (poster) | Summary: This paper proposes a conditional 3D generation method by pre-aligning features of different modalities when training a 3D AE. After that, a latent diffusion model is applied to generate latent vectors for 3D decoder conditioned on text/image. The results quantitatively and qualitatively shows that the propose... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our paper's originality and importance to the community. Thanks for the appreciation for our visual results demonstrations and thoughtful comments. We will attempt to address your concerns in four aspects in the following.
**Q1: Qualitative results of the di... | Summary: This work is about 3D shape generative model focused on image-conditioned and text-conditioned generation. The authors aligned the latent space of a shape autoencoder to CLIP's image encoder and text encoder. Then generative diffusion models are trained on the aligned latent space. This enables shape generatio... | Rebuttal 1:
Rebuttal: Thanks to reviewer XFVg for the positive feedback and insightful comments. Moreover, we are encouraged that the reviewer appreciates our results and enjoys reading the manuscript. We reply to the concerns in six aspects below. Furthermore, we will update Table 1 and related discussion in future re... | Summary: This paper proposed a conditional generation model which aims to solve the alignment issue in image-to-shape or text-to-shape generation. The key idea is to learn a aligned representation among 3D shapes, images, and texts. To achieve that, the author proposes SITA-VAE with contrastive loss to force the shape'... | Rebuttal 1:
Rebuttal: Thanks to reviewer rVoM for the thoughtful comments. We appreciate the reviewer's approval of our align-before-generation approach. Furthermore, we are encouraged that the reviewer recognize our visual demonstrations. We dedicated replies to the reviewers' comments and questions in five aspects be... | Summary: This paper proposed a VAE-based text-to-3D shape generation method. The authors designed an alignment-before-generation approach to narrow the gap between 3D shapes and the 2D or text condition. They first train a Shape-Image-Text-Aligned Variational Auto-Encoder to align the representations between the 3D sha... | Rebuttal 1:
Rebuttal: Thanks to reviewer fo3T for the positive feedback and thoughtful comments, and we are encouraged that the reviewer recognizes our effort on visual demonstrations and enjoys reading the manuscript. In the following, we reply to the individual concerns in three aspects.
**Q1: What if the condition ... | Rebuttal 1:
Rebuttal: # To All Reviewers:
We express our gratitude and appreciation to all the reviewers contributing to the review process. The reviewers, fo3T, rVoM, XFVg, and 73Gb, have commended the paper for:
1. well-written presentation.
2. Good visual demos.
3. Solid technical foundation.
We are also thankful... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Text Alignment Is An Efficient Unified Model for Massive NLP Tasks | Accept (poster) | Summary: While next word prediction produces task-general models that can do a wide variety of tasks when prompted, it is not an efficient formulation in that it requires very large models. On the other hand, fine-tuned models achieve higher performance at smaller sizes but are specific to a few tasks. This paper propo... | Rebuttal 1:
Rebuttal: Thank you for your comments. Your recognition of our ALIGN framework, experiments and paper writing is truly encouraging. We also appreciate your list of suggested experiments.
**Performance on unseen tasks**
Despite we group some training and evaluation datasets into the same type, they can hav... | Summary: This work proposed a text alignment model for a wide range of tasks that aims to measure the degree of alignment between their information. To be more specific, 5.9M examples from 28 datasets are used to fine-tune RoBERTa model. Experimental results show that the text alignment-enhanced model delivers comparab... | Rebuttal 1:
Rebuttal: **Previous Pre-finetuning Works**
We’d like to clarify that our text alignment method is **not** pre-fine-tuning. Instead, we develop a unified alignment model that is directly applied to a diverse set of tasks **without any additional finetuning**. As we pointed out in the related works section,... | Summary: This paper leverages the fact that a lot of popular comparison based NLP tasks like entailment, paraphrase detection, semantic similarity judgement, multiple choice passage based QA etc. amount to learning a specific similarity function between two sets of sequences that is a proxy for “information alignment” ... | Rebuttal 1:
Rebuttal: **Comparison with task-specific models**
The goal of our work is to design a model that can perform well on a range of tasks without further task-specific fine-tuning. As a result, we compare with LLM that can be used in a similar way (do not require fine-tuning) and include the task-specific fin... | Summary: This paper proposes a way to cast a variety of classification tasks into a single text alignment task. The authors found out that using the text alignment task could generate better results on certain downstream tasks, compared to Flan-T5 and GPT-3.5.
Strengths: The paper presents a novel approach by framing ... | Rebuttal 1:
Rebuttal: **More baseline - multitask learning**
We add comparison with multitask learning as suggested, where the model just trains with exactly the same set of tasks without casting into alignment format. Please see the General Response for results. The results show our alignment model has better perform... | Rebuttal 1:
Rebuttal: We thank all reviewers for your thoughtful and positive comments. We're encouraged by your appreciation that our text alignment framework is well-motivated (6RaC), novel (ayzf), and interesting (Bk1X); the resulting model has strong performance (ayzf, 6RaC, zhhb), enables interesting use cases (6R... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression | Accept (poster) | Summary: The authors study random feature (RF) regression in the high-dimensional setting.
They focus on comparing the variance of the Bayesian posterior distributions (a measure of uncertaintiy in the Bayesian setting) to the variance of the maximum-a-posteriori estimator (a measure of uncertainty in the frequentist ... | Rebuttal 1:
Rebuttal: ## Weaknesses
* We thank the reviewer's suggestions for manuscript improvements. We respond to specific items below.
* We have provided access to an anonymized code used to produce the Figures to the AC.
## Questions
* At the "interpolation threshold" of $N=n$, two main features are notable.
... | Summary: The authors compare in this paper the asymptotic posterior variance of the predictive risk in the random feature model associated with a Gaussian prior on the weights with the asymptotic \textit{frequentist risk} which had been derived by Mei and Montanari. The asymptotic here is in terms of the dimension d... | Rebuttal 1:
Rebuttal: ## Weaknesses
- As pointed out, the risk $\mid f_d - \widehat{f}\mid^2$ appearing in Mei and Montanari's work is viewed as stochastic in the input. Even for a specific instantiation of the training data $(\boldsymbol{y,X,\Theta})$, however, the quantity depends on the "truth"
$f_d$ and is unknown... | Summary: This paper considers the random feature model. Two objects are studied: (1) The posterior predictive distribution (in particular, the variance), and (2) The MAP estimator. This paper gives asymptotic formulas for these two quantities under the proportional regime. Comparison between these two quantities are ma... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. We agree that the Gaussian prior structure is restrictive. Incorporating more diverse priors (log-concave, scale mixture of Gaussian, etc.) is an interesting technical challenge. Extension to log-concave priors seems to be the natural immediate next step.
2. We agree that our ma... | Summary: The main focus of the paper is on the comparison between the posterior predictive distribution and the frequentist risk associated with the maximum a posteriori estimator for random features ridge regression model. The target function is assumed to be a sum of a linear model, a non-linear function given by a G... | Rebuttal 1:
Rebuttal: ## Weaknesses
- We agree that the assumptions can be quite restrictive. We address the input distribution below.
Besides the input distribution, we also assume a Gaussian prior structure on the weights and a form of analytic expansion of both the learned function class and the activation functio... | Rebuttal 1:
Rebuttal: We thank all reviewers for the careful and constructive feedback. We list below a general re-emphasis of the main point of our work and propose changes at the camera-ready stage that directly address the reviewers' concerns.
## General Rebuttal
- Broadly speaking this paper examines what is simi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs | Accept (poster) | Summary: The authors analyze the dynamics and implicit bias of gradient flow with the square loss when learning a single ReLU neuron using a one-hidden-layer ReLU network. They assume that the training data are correlated with the teacher neuron (the angles are smaller than $\pi/4$), and that gradient flow starts from ... | Rebuttal 1:
Rebuttal: > In Assumption 1:
> - Item (iv): why is it a measure-zero event?
> - Items (iv) and (v): I think that the assumptions should specify properties of the training data and the training algorithm. Then, the properties of the trajectory should be shown using these assumptions. Can you specify items (... | Summary: The paper studies the problem of learning a single ReLU using a 2-layer ReLU network using gradient flow on both layers. The main assumption is that the data is correlated with the target neuron, while other milder assumptions are also used (e.g. specific initialization and spectral assumption on the data matr... | Rebuttal 1:
Rebuttal: > [...] where in the first phase the learned weights either align with the target neuron or deactivate, [...]
Please see the first item in our response to reviewer PDpC.
> The assumption that the data is correlated with the target neuron is pretty strong. The motivation for taking an angle of at... | Summary: This paper studies how a two-layer ReLU network can fit a single neuron. The authors consider the case where all
training points are correlated with the teacher neuron and show that gradient flow from small initialization can
converge to a zero-loss solution.
They divide the training into two stages. In the... | Rebuttal 1:
Rebuttal: > In the first stage, the neurons are small and will align with the teacher neuron or deactivate, [...]
Just to clarify that the alignment in the first phase is with the vector $\mathbf{\gamma}\_{[n]} = \frac{1}{n} \sum_{i = 1}^n y\_i \mathbf{x}\_i$, whose direction is in general different from t... | Summary: Convergence and implicit bias of non-linear networks is an important open question in deep learning theory. The paper studies these questions in the case of regression with ReLU networks with a single teacher neuron. It proves that at a vanishing initialization scale the student neurons align with the teacher... | Rebuttal 1:
Rebuttal: > b) The technical analysis follows the same strategy as Boursier et. al. Some aspects are easier as there is only one saddle to escape.
We think that the presence of the negative labels and the consequent second saddle in Boursier et al. did not introduce major difficulties in that work, i.e. th... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their positive and encouraging reviews, and for comments and questions, which will help us improve the submission.
We attach a PDF with plots from a few additional experiments, and refer to it in our responses to some of the reviewers.
In what follows we elaborat... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks | Accept (poster) | Summary: The authors consider the task of learning curve extrapolation, i.e., the aim is to predict the performance of a given model wrt. e.g., accuracy/log-likelihood over time, given current observations.
Their proposal primarily relies on Prior-data fitted networks (Müller et al., 2022) trained on samples from a pri... | Rebuttal 1:
Rebuttal: ### Regarding the weaknesses you raised:
**Its backbone is a prior-data fitted network. However, that model itself is only briefly introduced in a single paragraph with a figure that is barely understandable without reading the original paper. The paper requires a proper discussion of this approa... | Summary: The authors in this submission applied the prior-data fitted neural networks (PFNs) in the learning curve extrapolation task, for which the main goal is to predicit the performance of a machine learning model in later epochs, based on the information from earlier epochs. The target is modeled as a linear combi... | Rebuttal 1:
Rebuttal: ### Regarding the weaknesses you raised:
**In my opinion the selection for the 3 parametric basis curves seems a bit ad-hoc. It's not convincing whether they are sufficient to fit different learning curves. Furthermore, selection of hyperparameters in priors there also lacks details.**
Thanks fo... | Summary: The authors propose applying prior-data fitted NNs to learning curve extrapolation.
The authors demonstrate that this approach outperforms MCMC inference and is substantially faster.
Strengths: The paper has a novel idea of applying approximate inference via meta-learning learning curves.
The method appear... | Rebuttal 1:
Rebuttal: ### Regarding the weaknesses you raised
**The paper contains only two experiments. One is based on synthetic data.**
We evaluated LC-PFN in three different experimental setups (~ Section 4.1, 4.2, and 4.3). In Section 4.1, we evaluate LC-PFN (and MCMC) on samples of the prior. From a learning cu... | Summary: The authors used prior fitted networks to perform learning curve extrapolation. Crucially, they demonstrate that their method vastly outperforms approximate Bayesian inference via MCMC (both in terms of inference time and predictive log likelihood). Moreover, they demonstrate that the proposed approach outperf... | Rebuttal 1:
Rebuttal: ### Regarding the weaknesses you raised:
**The biggest weakness to me is that I don't think the authors spent time on the potential difficulty of using MCMC for this problem. Specifically, the prior, and the corresponding posterior, is constrained on some non-standard subset that prevents standar... | Rebuttal 1:
Rebuttal: In this global response, we would like to thank all reviewers for their constructive feedback. We are glad that our work was generally well-received and we address specific concerns / questions raised by the reviewers in our individual responses. Multiple reviewers expressed some concerns about ou... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Physics-Informed Bayesian Optimization of Variational Quantum Circuits | Accept (poster) | Summary: The paper introduces a new approach for BO of VQEs. BO can be a good match for this problem since it models the noise (measurement and circuit level) of quantum circuits. The main idea of the paper is to use a kernel that is adapted to the form of the VQE objective assumed when variational parameters are assoc... | Rebuttal 1:
Rebuttal: We thank the referee for highlighting the strengths of our work. We hereby address the concerns raised in the review:
## **Weaknesses**
> 1. Specific ansatz: ...
The set of single-qubit rotation gates and entangling CNOT gates are indeed universal, and allow for synthesizing all unitary operatio... | Summary: In this work, the authors integrate a quantum kernel method with the EMICoRe architecture to further improve the NFT framework of Bayesian Optimization. The simulation results show the advantages of the proposed approach.
Strengths: (1) The method of leveraging the quantum method for Bayesian Optimization is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. However, it seems as if the referee misunderstood the paper, as our work cannot be characterized as quantum kernel learning. In essence, quantum kernel leverages a quantum circuit to calculate a certain kernel, i.e., the calculation of the kernel is non-clas... | Summary: The authors propose a method for Bayesian Optimization for Variational Quantum Eigensolvers, which they call NFT with EMICoRe. This method uses a novel VQE kernel, which constrains the function space of the Gaussian Process underlying the BO to include only valid VQE objective functions (using the representat... | Rebuttal 1:
Rebuttal: We thank the referee for their insightful comments and positive feedback on our work. We take this opportunity to address and respond to the comments below:
## **Weaknesses**
> The experimentation could be expanded. Particularly, it would be interesting to see how the model performs on an actual ... | Summary: I have reviewed the rebuttal, and I intend to maintain my decision. I appreciate the authors made the efforts to clarify most of my concerns. However, I believe it is essential to address the issue of hardware noise in the present study, a point that previous research seems to have overlooked. I strongly encou... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and appreciation of our manuscript.
## **Weaknesses**
> The numerical results presented in the paper indicate that the proposed method does not exhibit a clear advantage when the number of observed points is limited, as compared to the baselines.
I... | Rebuttal 1:
Rebuttal: We thank the four reviewers for their valuable feedback. To streamline our reply, we place the referenced tables at the bottom.
- Some reviewers suggested additional experiments for other parameter choices of the target Hamiltonians. We stress that the Ising Hamiltonian, which is studied along w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Explanation Shift: How Did Distribution Shift Impact the Model? | Reject | Summary: This paper introduces a new concept called "explanation shift" for detecting shifts in data distributions with the changes of the attribution distributions on machine learning models. The authors argue that current methods for detecting shifts have limitations in identifying changes in model behavior. Explanat... | Rebuttal 1:
Rebuttal: > Some key empirical studies are missing. In Section 5.3, the authors evaluate their methods on some real datasets to detect novel group distribution shift and geopolitical and temporal shift. However, the authors did not perform the same experiments by using baseline methods. Thus it is unclear w... | Summary: This paper uses explanation shift as a way to detect different types of distribution shift between the training set and unseen (test) data sets. The method is based on measuring the changes between the explanation provided by an explanation approach such as Shapley values, for the two data sets for a trained m... | Rebuttal 1:
Rebuttal:
> Section 4.1 provides examples where the proposed method works but simple distribution shift evaluation fails. But this does not provide any guarantee whether in general the proposed model is better or not. The same is true for Section 4.3. Section 4.2. provides a disposition but as mentioned by... | Summary: Detecting shifts in data distribution between training and deployment is critical for ensuring models function as intended and operate in their domain of applicability. However, detecting such shifts is challenging. In this paper, the authors propose an approach based on techniques from the explainability lite... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for helpful comments.
>L115-119 - Related work in explainability. Lundberg et al. is not the only work relating explainability and distributional shift. For example, Crabbe et al (2020) use example-based explanations to detect out-of-distribution samples, while... | Summary: The submission proposes an approach to improve model monitoring by rather evaluating changes in explanations instead of input features. The authors provide synthetic examples to justify their method and compare it empirically to existing strategies on tabular datasets.
Strengths: - The paper addresses an im... | Rebuttal 1:
Rebuttal:
We would like to thank the reviewer for helpful comments.
> Why are only observational Shapley values considered? Have there also been conducted experiments based on the interventional Shapley Values? The interventional approach is more applicable in general and has been proven to result in Shap... | Rebuttal 1:
Rebuttal: Experiments comparing Interventional vs Observational SHAP value calculations. To be added to the appendix.
Pdf: /pdf/2dd5fa064c6a1c752086d20db2125f77c2737253.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Symbolic Policy Learning with Differentiable Symbolic Expression | Accept (poster) | Summary: # I have reviewed this draft once before.
This paper proposes a meta-RL method to generate explainable symbolic policies. ESPL contains a symbolic network in the search space and a path selector to find the compact symbolic policy.
Strengths: I think there exists some novelty in ESPL because it contains sy... | Rebuttal 1:
Rebuttal: Thanks a lot for your advice on further improving this paper. We would like to discuss them one by one. Any further discussion will be appreciated.
>**Q1.** I think the suggestions from the previous conference are not incorporated, and the draft is not improved much.
Thank you again for the time... | Summary: This paper proposes to apply differentiable symbolic regression for policy learning. It shows promising results in multiple RL environments (including good average performance and learned interpretable symbolic policies).
Strengths: This paper shows promising results of differentiable symbolic regression in ... | Rebuttal 1:
Rebuttal: Thanks for your detailed review. We are glad to discuss your concerns one by one. Any further discussion will be appreciated.
>**Q1.** Nevertheless, the model is less novel as similar techniques have been explored in domains such as differentiable interpreters (e.g., Terpret [1] or DiffForth [2]) ... | Summary: The paper "Efficient Symbolic Polmicy Learning via Gradient Descent" proposes a new neural symbolic architecture for agents learned via reinforcement learning. Authors propose an architecture with an alternance of symbolic and linear layers. To finally obtain simple expressions, a probabilistic mask is learned... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments. We would like to clarify the concerns as follows:
>**Q1.** I cannot understand, if the architecture is new ...
A1. (1) This paper aims to propose an efficient and effective method for learning symbolic policies. Symbolic policies are designed for sequential de... | Summary: The paper proposes ESPL, a method for learning symbolic policies in environments with low-dimensional state spaces. ESPL uses a densely connected neural network structure (like DenseNet), where the activations in each layer are replaced with a hand-picked set of functions, such as multiplication, division, log... | Rebuttal 1:
Rebuttal: We appreciate your positive review, insightful feedback and constructive comments that help improve the quality of the paper! We are glad to answer your questions and would appreciate any further response.
> **Q1.** The discovered symbolic policies in Table 1 seem to be somewhat more complex than... | Rebuttal 1:
Rebuttal: > **Theoretical analysis of cartpole.**
The dynamic of cartpole system can be defined as:
$\\ddot{x}=\\frac{8fa+2m \\sin \\theta(4L\\dot{\\theta}^2-3g\\cos \\theta)}{8M-3m \\cos2\\theta+5m}$
$\\ddot{\\theta}= \\frac{g \\sin \\theta- ( \\cos \\theta(fa+Lm \\dot{ \\theta}^2 \\sin \\theta))/(m+M... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask? | Accept (poster) | Summary: The authors perform a large-scale empirical analysis of models from the NAS-Bench-Macro benchmark which motivates to the development of (1) the node-path balancing principle and (2) Node-Path Balancing Pruner (NPB) -- a data-agnostic pruning-at-initialization (PAI) scheme. At a high level, the node-path balanc... | Rebuttal 1:
Rebuttal: We would like to thank you for the time to review our work. We would like to address all weaknesses pointed out by you point-by-point below:
> Q1. Looseness/inexactness of the node-path balancing principle
>
A1: In Neural Architecture Search (NAS) context, it's important to consider that other as... | Summary: This paper examines Pruning at Initialization (PaI) methods using two novel metrics: the number of effective paths and the number of effective nodes. The authors find that layer reshuffling negatively impacts the performance of sparse neural networks obtained through PaI methods in the extreme sparsity regime.... | Rebuttal 1:
Rebuttal: We would like to thank you for the time to review our work. We would like to address all weaknesses pointed out by you point-by-point below:
> Q1. While many previous PaI methods prioritize the weight magnitude as the importance metric, the main motivation behind NPB focuses on the topology of the... | Summary: This paper posits that the performance of (neural network) pruning at Initialization methods depends on a balance between effective nodes and paths. With this framework, authors explain why randomly shuffled subnetworks are sometimes more effective than subnetworks found by pruning at initialization methods. F... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing that our proposed method is technically sound and yielding commendable empirical results. We would like to address all the weaknesses pointed out by you point-by-point below:
> Q1. The NAS experiments are interesting, but it wasn't clear to me to co... | Summary: Given the numerous pruning-at-initialization (PaI) methods, the performance of them are still far from satisfactory compared to the post-training pruning methods. In this work, the authors provide a novel perspective to understand the relationship between the performance and the architecture of the subnetworks... | Rebuttal 1:
Rebuttal: We would like to thank you for the time to review our work and are glad that you find our work is well-written with sufficient experiments. We would like to address all the weaknesses pointed out by you point-by-point below:
> Q1. Wouldn't directly cutting down the width of the network a perfect ... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive feedbacks. We have performed the additional experiments as requested by the reviewers and have provided the results in this pdf file.
We hope that our responses address your concern. If you have any additional questions, uncertainties, or areas you wou... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a Pruning at Initialization (PaI) method that considers the balance between the number of effective nodes and effective paths. This design principle is based on the observations on the NAS benchmark as well as layer-wise reshuffling. The pruning problem is nicely formulated as a multi-objec... | Rebuttal 1:
Rebuttal: Thank you for your time to review our work. We would like to address all the weaknesses pointed out by you point-by-point below:
> The sweet spot of the proposed framework seems to be the extreme sparsity regime (> 99%)
>
A1: We respectfully disagree with the reviewer. Our experiments have been co... | null | null | null | null | null | null |
Adversarial Resilience in Sequential Prediction via Abstention | Accept (poster) | Summary: The authors study online learning under clean-label attacks. Since it is online learning, such attacks can be seen both as poisoning as well as evasion (adversarial examples). In this direction the authors propose the use of abstention when the classifier is not confident for a prediction and along these lines... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. We will promptly fix typographical errors in a revision. Here we address the major concerns/questions from the reviewer.
**Proper versus realizable**: Realizability and properness are somewhat orthogonal desiderata in learning theory. Realizability is a requi... | Summary: This paper proposes a sequential prediction setting in which an adversary injects adversarial examples with clean labels, and the algorithm is allowed to abstain from predicting. This setting lies between the stochastic and the fully adversarial settings, which are known to be characterized by the VC and Littl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. Here we address the questions/weaknesses pointed out by the reviewer.
**Looseness of the bounds**: For the known distribution setting, this is only loose by a factor of $d$ compared to the fully stochastic setting with no adversarial/OOD data. Sin... | Summary: The paper presents a new protocol for beyond-worst-case sequential prediction, incorporating the option of abstention. It introduces two main algorithms: the first achieves an error rate of $O(d^2 \log T)$ for classes with VC dimension $d$, while the second realizes an error of $O(\sqrt{T})$ for a specific ins... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. Here we address the major concerns raised by the reviewer.
**Novelty of the model**: The inclusion of abstention itself is not sufficient to get any guarantees in this setting. We need the correct notion of regret to accompany this. For instance... | Summary: This paper proposes a pipeline and algorithms for machine learning prediction with abstention. The authors first propose the optimization framework to learn a model which allows abstention. They then consider different distributions and propose different algorithms for the learning process. Their theoretical a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. Here we address the major concerns raised by the reviewer.
**Significance of the model in the real-world**: The key benefit of our model and algorithms is the fact that they produce 'certain' predictions. In particular, the model is correct whe... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On Evaluating Adversarial Robustness of Large Vision-Language Models | Accept (poster) | Summary: This paper aims to assess the adversarial robustness of vision components in large vision-language models, which is an increasingly significant issue due to the prevalence of such models. The experiments conducted in this study are extensive, encompassing evaluations on UniDiffuser, BLIP, BILP-2, Img2Prompt, M... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions, we have uploaded a rebuttal PDF.
***Q1: It would be nice to see how baseline defense, such as adversarial training on CLIP [1], would help mitigate the adversarial vulnerability of the proposed attack***
Thank you for the insightful comments.... | Summary: The paper evaluates the pixel-space adversarial robustness of large vision-language models (VLMs), where the targeted attack has only black-box access to the large VLM systems. The paper introduces two adversarial strategies: transfer-based and query-based. The transfer-based strategy performs white-box attack... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions, we have uploaded a rebuttal PDF.
***Q1: There is a high chance that the victim system shares mutual information with these white-box components. It will be beneficial if the authors elaborate more on the source of transferability***
Indeed, e... | Summary: This paper focuses on black-box targeted adversarial attacks on multimodal vision/language models via transfer. They observe particular vulnerability to transfer attacks because an adversarial image can be constructed in a fully-differentiable manner w.r.t. a model like CLIP and then transferred over to the bl... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions, we have uploaded a rebuttal PDF.
***Q1: (Minor) I find the figs to be generally cluttered***
Thank you for the comments. In the revision, we will rearrange our figures to make the content more clear.
***Q2: I would have liked to see experime... | Summary: The authors propose to generate adversarial attacks on different vision-language models (VLM) like BLIP, MiniGPT4, and UniDiffuser. The proposed method is simple and straightforward. To perturb the image, the authors propose to maximise the similarity (inner dot product) between the image-image / text-text / i... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and suggestions, we have uploaded a rebuttal PDF.
***Q1: It would be good if the authors can also share the results on weaker attacks like PGD-10 and FGSM.***
In **Table A** of the rebuttal PDF, we follow the Reviewer’s suggestion to report additional results... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and we have responded to each reviewer individually. We have also uploaded a rebuttal PDF that includes:
- **Table A**: CLIP score ($\\uparrow$) with fewer PGD steps against different VLMs;
- **Table B**: FlagEmbedding score ($\\uparrow$) as... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Practical Equivariances via Relational Conditional Neural Processes | Accept (poster) | Summary: This paper is an extension of the conditional neural process. The primary contribution is to augment the conditional neural process with the relational structure. The developed model was examined in synthetic regression, Bayesian optimization, Lotka-Volterra simulation, and others.
Strengths: 1. This paper is... | Rebuttal 1:
Rebuttal: Thank you for your time and effort spent reading our paper. We would like to clarify what we perceive as misunderstandings of the nature and contribution of our paper. Our core contribution is in providing a simple, effective way to build exact equivariances (with a focus on translational equivari... | Summary: This work introduces a new member of the neural process model family that is designed for biasing the model towards representing equivariances in the data. It does this by including relational information among the context set, and between the predicted and context inputs in the encoder for a new input. Also, ... | Rebuttal 1:
Rebuttal: Thank you for your very positive comments and useful remarks about our work. We address your remarks and questions below.
### Weaknesses
> *The full RCNP variant has tractability issues, although it is shown that the simpler RCNP using the diagonal elements of the relational matrix performs sati... | Summary: The paper presents a novel approach for incorporating equivariance into conditional neural processes (CNPs) which can scale to high dimensions. Modelling equivariance is essential to improve the performance of CNPs. Unlike previous approaches that use convolution and become impractical with increased input dim... | Rebuttal 1:
Rebuttal: Thank you for your very positive and insightful comments, we are glad to see that you find our paper particularly well-suited for NeurIPS. In the following we address your questions and points raised.
### Weaknesses
> *Given the extensive use of RGNP in the experiments and its significant role i... | Summary: The authors propose a class of neural processes that can be constructed to enforce invariance to particular properties like translation and rotation.
Strengths: - As far as I know, the proposed architecture and technique for enforcing invariances in Section 3 is novel.
- Improvements are shown over standard n... | Rebuttal 1:
Rebuttal: Thank you for your positive review and comments, and we are glad to see that you found our paper interesting.
Regarding your concern about enforcing particular invariances, we would like to comment that it is indeed true that our method only applies to equivariances that can be enforced via a com... | Rebuttal 1:
Rebuttal: We thank the anonymous reviewers for their comments and suggestions for improving our paper. We are glad to see that the majority of reviewers found the paper interesting and of impact. We provide clarifications and detailed answers to perceived weaknesses and raised questions in our individual re... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
For SALE: State-Action Representation Learning for Deep Reinforcement Learning | Accept (poster) | Summary: This paper proposes a new RL algorithm called TD7, which is based on TD3, and adopts additional techniques including (1) learning state-action representations, (2) LAP prioritized replay, (3) a behavior cloning term in the learning objective, and (4) checkpoints, where (3) and (4) are applied exclusively to th... | Rebuttal 1:
Rebuttal: Thank you for the review and the feedback.
**Combining SALE with other algorithms:** Thank you for bringing up this concern. In the table below we have included results with SALE applied to SAC. Learning curves are included in the PDF of the general response (Figure 2). No modifications or hype... | Summary: The paper introduces an approach dubbed SALE for learning embeddings that model the interaction between state and action in low-level state environments. The authors extensively study the design space of these embeddings and integrate SALE into the TD3 algorithm along with 3 other components to form a new algo... | Rebuttal 1:
Rebuttal: **Is part of this review missing?**
We just wanted to double check that this review is complete. It currently reads as if some of the weaknesses were mistakenly deleted. If some other weaknesses did indeed get removed, let us know so we can improve the paper accordingly. Regardless, thank you for... | Summary: This work introduces a novel state-action representation learning framework SALE and two other techniques (e.g. checkpointing, a new type of Q value clipping) that substantially improve the data efficiency and final performance of TD3 in online and offline RL.
Strengths: 1. The work studies joint state-action... | Rebuttal 1:
Rebuttal: Thank you for the review and helpful comments.
**Reorganization and writing:** Thank you for bringing this up and providing concrete suggestions. We will add transition statements and expand the introduction to clearly establish the challenges associated with our section on stability. We will als... | Summary: This paper proposes TD7, an improved version of the popular TD3 algorithm with 4 additional techniques: state-action learned embeddings (SALE, the major one among the four), using policy checkpoints for stable evaluation, an existing prioritized experience replay method called LAP, an existing offline RL algor... | Rebuttal 1:
Rebuttal: Thank you for your comments and considerate questions.
**Intuitive explanations:** Space was obviously a bit of a concern with the paper (and fortunately the camera-ready allows for an additional page). We would be happy to expand on some of the intuition/reasoning behind our improvements with th... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their comments, suggestions for improvement, and interest in the paper.
Overall, there were two main comments that were repeated among the reviewers which were:
**Does TD7 work on other benchmarks?** To answer this, we ran TD7 on 15 new environments f... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a couple of ideas to improve the empirical performance of the TD3 algorithm on continuous-action RL problems.
The core contribution is to show that learning state and action embeddings that are designed to predict themselves in successive timestep can help achieve more reward. This is pr... | Rebuttal 1:
Rebuttal: Thank you for the very detailed review, and we appreciate the highlighted positives in our work! We address your key points below.
**Contribution:** We will aim to tighten up the writing in the introduction and provide a stronger and clearer message for the paper. We believe our paper adds to th... | null | null | null | null | null | null |
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles | Accept (poster) | Summary: The authors provide a theoretical analysis of the case of ensemble learning with linear ridge regression for the case where heterogeneous feature subsampling is used. The authors make a number simplifying assumptions about the distribution of the data (Gaussian distribution and noise, linear function), and usi... | Rebuttal 1:
Rebuttal: *Weaknesses:
The work makes many simplifying assumptions that are unlikely to match most real-world learning problems (e.g. Gaussian data, linear teacher function), and analyzes may not be in practice tractable beyond the simpler cases such as analyzed in Section 2.3. with globally correlated data... | Summary: The authors provide theoretical results on generalization for ridge regression for the case of an ensemble of regression models trained on feature subsets, with noise, and correlation.
They relate this to the previously observed double-descent phenomena and characterize how the fraction of subsampled features... | Rebuttal 1:
Rebuttal: Thank you for writing a thorough review of our submission. Please see the response to all reviewers for a discussion of the meaning of “readout noise”, a proposed table clarifying the meaning of the parameters referenced in proposition 2 and figures 2, 3, and 4, discussion of a comparison between... | Summary: This paper introduces a theoretical investigation into ensembling methods applied to linear ridge regression with feature subsampling. It builds upon previous research in this area by extending the analysis to include scenarios with varying readout dimensionality.
By employing the replica method, the authors ... | Rebuttal 1:
Rebuttal: Thank you for pointing out typos, we will correct them. We thank the reader for their comments and suggestions. The weaknesses identified in this review revolve mainly around the fact that we have derived our results using the replica method from statistical physics. We do in the remarks made di... | Summary: This article characterizes the asymptotic performance curve of a heterogeneous feature ensembling framework for ridge linear regression, in the limit of comparably large numbers of data samples and variables. For having different error peaks in a double-decent performance curve as a function of the data sample... | Rebuttal 1:
Rebuttal: Thank you for writing a helpful review. Your questions about cross-validated regularization and real-world experiments are addressed in the global rebuttal and attached PDF. | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews. Please find below a description of updates to the paper and responses to comments which were raised in multiple reviews.
-We have changed the definition of the “readout noise” so that it is present both during training and evaluation of the model. This lea... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides an asymptotic analysis of ensembles of ridge regressors using varying numbers of subsampled features. The authors consider a Gaussian data model with feature noise and readout noise in addition to label noise. Using the replica method from statistical physics, they obtain precise asymptotic... | Rebuttal 1:
Rebuttal: Thank you for writing a thorough review of our submission. Please see the global response as well as the below.
We had two interpretations of the feature noise in mind when creating the problem setup. The first is as an inherent noise due to stochasticity in a physical neural network, such as an... | null | null | null | null | null | null |
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy | Accept (poster) | Summary: This paper studies data pruning in noisy label setting. Built on re-labeling models, it proposes Prune4ReL that finds a subset to maximize the re-labeling accuracy. In particular, it introduces neighborhood confidence as the criteria for selection, as well as a greedy algorithm to select the subset. Evaluation... | Rebuttal 1:
Rebuttal: `Q1. The proposed data pruning method does require model training as many sample selection methods for robust learning do; I think these methods should also be considered as baselines and compared, even though they are not specifically designed for data pruning.`
Thank you very much for helping ... | Summary: This paper studies the task of data pruning, specifically in the setting of label noise. The authors propose a method to perform data pruning by maximizes the total neighborhood confidence of the training examples (which is equivalent to maximizing the relabeling accuracy).
The authors theoretically analyze ... | Rebuttal 1:
Rebuttal: `Q1. Their empirical results show mixed results in the comparison against existing methods. In Table 1, the results are slightly better than existing baselines on a small number of tasks but predominantly match the performance of existing methods`
Thank you very much for your careful review. We a... | Summary: The paper proposes a novel data pruning algorithm, Prune4ReL, that maximizes the neighborhood confidence of the entire training examples, which is proportional to the likelihood of correct re-labeling. The paper demonstrates the effectiveness of Prune4ReL on four noisy datasets, where it outperforms baselines ... | Rebuttal 1:
Rebuttal: `The writing of this article is very clear and easy to follow up. Moreover, the methodology in this paper is also reasonable with necessary theoretical analysis. I enjoy this work very much!`
> We are very glad to hear that you enjoy reading our paper.
`My only concern about this work is the expe... | Summary: The paper proposes Prune4ReL, which prunes a noisy training dataset such that the performance of a Re-labeling trained downstream model is maximized.
Unlike previous work, the paper targets pruning a *noisy* dataset and explicitly considers the learning algorithm of the downstream model. The proposed method i... | Rebuttal 1:
Rebuttal:
We sincerely appreciate the reviewers' constructive comments and positive feedback on our manuscript.
`Q1. Uniform sampling is usually the second-best baseline. It performs especially well in CIFAR-10N Random/Worst and Clothing-1M. Can the authors elaborate more on this?`
To improve the perform... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewers' positive feedback and valuable comments. Most reviewers agreed that (1) **the problem setting and methodology are reasonable and novel**, (2) **the theoretical analysis of the methodology is sound**, and (3) **the evaluation was performed extensively**. Because... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fair Graph Distillation | Accept (poster) | Summary: This paper proposes fair graph distillation (FGD), as an advanced graph distillation approach to generate fair distilled graphs. FGD focuses on the group fairness issue in graph distillation methods and aims to generate fair distilled graphs with respect to sensitive attributes for nodes. This paper proposes a... | Rebuttal 1:
Rebuttal: # 1. More baselines in graph distillation are needed.
The vanilla baseline we utilized in our work is derived from the graph data condensation method introduced in [2] tailored for graph data using gradient matching, which is the same as [1]. Thus, [1] can not be directly adopted in graph data due... | Summary: This paper aim to address the issue of fairness in graph data distillation, a process that condenses large real graphs into smaller distilled versions for more manageable computation with GNNs. They proposed FGD by introducing a new bias metric called coherence and using a bi-level optimization algorithm, whic... | Rebuttal 1:
Rebuttal: # 1. Are there any fairness studies in dataset distillation in other fields?
In the existing literature, fairness studies related to dataset distillation in the fields of CV or NLP are not commonly found. The only fair distillation work we can found is [1]n which studies the fairness problem on th... | Summary: This paper discovered the fairness problem in the distilled GNN methods and then introduce a fair graph distillation process to generate fair distilled graph data. To propose the algorithm, they also introduce a new bias metric, coherence, and propose a bi-level optimization framework, FGD, for fair graph dist... | Rebuttal 1:
Rebuttal: # 1. What is the full name of FGD？ The full needs to be provided when this word first appears.
The acronym FGD stands for Fair Graph Distillation. We will include the full name in Line 54.
# 2. Why the sensitive attribute S is a diagonal matrix, not a vector with the dimension of number of node? ... | Summary: This paper focuses on the task of graph distillation (GD) from a fairness perspective. The authors found that current GD method amplifies bias in GNN training compared to training on original graphs. Since the distilled graphs do not contain node attributes, it's intractable to directly apply previous debiasin... | Rebuttal 1:
Rebuttal: # 1. The authors' design of the coherence loss in section 3.4 is confusing
The confusion stems from a misunderstanding of the coherence loss definition in section 3.4. Here, the function $\mathbf{\pi}^s\left(\boldsymbol{Z}^{\prime}\right)$ represents the probability for **all samples** belonging t... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial? | Accept (poster) | Summary: The development of online media referral platforms has provided a source of income for media content creators, and the incentive strategies of the platforms may influence the creators' creative trends. The incentive model that tends to reward may also invariably encourage creators to over-serve the majority us... | Rebuttal 1:
Rebuttal: **Q1: Discussion of limitations** One limitation of our work pertains to the focus of our model, which mainly addresses the challenges encountered by those "strong platforms" which dominate content distribution (e.g., Instagram reels & TikTok). On these platforms, creators’ impressions and rewards... | Summary: The authors study strategic content creation in recommendation systems, focusing on the induced game's social welfare. The authors assume that the provider's rewards are entirely determined by the platform's payments, not clicks/engagements. This separation between the ranked results and the creators' incentiv... | Rebuttal 1:
Rebuttal: **Negative result being restrictive** We admit that theoretically demonstrating the limitation of monotonicity in general is challenging, and we identify it as an intriguing future work. In the meantime, our experiment result empirically suggests a constant fraction of welfare loss beyond the clas... | Summary: This paper studies the incentives in recommendation systems. Specifically, it studies how to design the platform's reward mechanism to steer the creators' competition towards a desirable welfare outcome. Firstly, it shows a class of mechanisms called "Merit-based Monotone Mechanisms" lead to a constant fractio... | Rebuttal 1:
Rebuttal: **The applied value of our model** Nowadays more and more content recommendation platforms realize that designing proper incentives for creators is crucial for optimizing social welfare and maximizing their total revenue (such as YouTube and Facebook). However, most of these platforms simply emplo... | Summary: This paper considers the game played by content creators in recommendation systems, which they call the content creator competition game. This game is centrally defined by a rewarding function M, decided by the platform, which rewards content creators based on how users engage with their content. The paper foc... | Rebuttal 1:
Rebuttal: **Clarification of monotonicity** The reviewer’s understanding of monotonicity is correct. In many competing environments, a unilateral improvement of one player’s utility could decrease other players’ utilities but does not necessarily decrease the total utility across all players. For example, w... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the overall positive and informative feedback. In the following, we respond to the questions one by one. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Compact Neural Volumetric Video Representations with Dynamic Codebooks | Accept (poster) | Summary: A method for compressing Volumetric Videos is presented in this work. It is based on NeRF with a factored multi feature plane representation. The features of the model are compressed in two stages. In the first, a codebook for features is constructed based on the average contribution a feature has to the total... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful suggestions. We address the major concerns below:
**Q1: Writing could be a bit clearer.**
**A1:** Thanks for your suggestion. We will improve our writing and include more detailed descriptions of our methods in the revised paper.
**Q2: Only evaluated on ... | Summary: The paper addresses the challenge of representing high-fidelity volumetric videos with low storage cost. The authors proposed a novel neural representation called the dynamic codebook, which aims to reduce the spatial and temporal redundancy of feature grids inherent to scenes due to self-similarity. It achiev... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful suggestions. We address the major concerns below:
**Q1: It would be helpful to provide insights into the time required for training the model and how it compares to other models.**
**A1:** In Section 4, we mentioned the details of training time: "We train... | Summary: The proposed method applies the codebook technique to explicit feature plane representation and a dynamic codebook is further proposed for dynamic scenes. Experimental results demonstrate the good performance of the proposed methods.
Strengths: 1. The proposed method significantly reduces the model size while... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful suggestions. We address the major concerns below:
**Q1: Introducing codebook to feature plane representation is reasonable but not of sufficient novelty.**
**A1:** We would like to emphasize that we have two core contributions, which make our method disti... | Summary: The authors of this paper propose a dynamic codebook, which optimizes away codes of low importance to rendering the scene, clusters 70% of the least importance codes and optimizes the remaining codes for every time fragment. The process the authors introduce are intuitive and each step furthers compression or... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful suggestions. We address the major concerns below:
**Q1: The dynamic codebook construction increases the total end to end time and rendering time.**
**A1:** Firstly, the dynamic codebook has a slight influence on the rendering time as the indexing process ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a novel approach for representing volumetric video using a dynamic codebook that incorporates the temporal correlation of features. This addresses the drawback of existing feature grid-based methods, which overlook this correlation.
Strengths: 1. The proposed method uses a multidimensional... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful suggestions. We address the major concerns below:
**Q1: Limited novelty and no apparent improvements compared to existing works.**
**A1:** We would like to emphasize that we have two core contributions, which make our method distinct from previous works:
... | null | null | null | null | null | null |
Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs | Accept (poster) | Summary: The authors identify two major issues of text-to-motion generation as overemphasis on action names and the coarseness of sentence-level representations.
To this end, hierarchical semantic graphs are adopted to factorize coarse sentences into fine-grained action concepts, thus refining the generated motion from... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments, and for noting that our method is "interesting". We address the questions as below.
**Q1**: Is it possible to generate with action level or specific level only?
**A1**: As suggested, we train networks to generate only the actio... | Summary: This work decomposes the motion description into three levels including motion, action and specifics, and proposes hierarchical semantic graphs to achieve fine-grained control of motion generation.
Experiments with the proposed method on HumanML3D and KIT datasets demonstrate better motion generation and more... | Rebuttal 1:
Rebuttal: Thanks for providing constructive feedback, and for noting that "the ability to continuously refine the generated motion is meaningful and helpful to the community." We address the questions below.
**Q1**: Experimentally demonstrating the "overemphasis" of the Transformer and proving that... | Summary: This paper proposes a coarse-to-fine diffusion model coupled with a hierarchical semantic graphs to address the text-to-motion generation problem. To preserve the fine-grained control signals from captions, three-level textual features are extracted through GAT. Then, three diffusion models are adopted to reco... | Rebuttal 1:
Rebuttal: Thanks for taking the time and effort when reading our paper and providing constructive comments. We address the questions below.
 
**Q1**: Please present more examples to prove that the imbalance problem exists in the other methods and how your method addresses it.
**A1**: We will prove t... | Summary: This paper presents a novel motion generation pipeline that utilizes a 3-level hierarchical semantic graph. The entire reverse process of the motion diffusion model is divided into three stages: overall motion, local actions, and action specifics. The semantic graph is extracted through semantic role parsing a... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments, and for noting that our method is "novel", "intriguing" and "well-explained". We address the questions as below.
**Q1**: Why is the video length of MotionDiffuse shorter than other methods in the demo video?
**A1**: This is b... | Rebuttal 1:
Rebuttal: # Global Response
We sincerely thank all PCs, SACs, ACs, and Reviewers for their time and efforts when handling our paper.
All reviewers appreciate the contributions of our method:
* Both Reviewers DRsM and Xx1r point out that "**the motivation is clear**, and **the proposed solutions a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Robust Model Reasoning and Fitting via Dual Sparsity Pursuit | Accept (spotlight) | Summary: This paper is about model fitting. The authors consider a scenario in which it is unknown whether correspondences between points in 2 images stem from 3D points that are (1) generally distributed, (2) lie on a plane, or (3) lie on a plane and the motion between the 2 images is not projective but affine. The la... | Rebuttal 1:
Rebuttal: # Q: About the embedding of homography matrix and its geometric relationship.
**R:** We would like to thank the reviewer’s positive feedback.
It seems that the reviewer was concerning about proposition 1. As for proposition 1, we agree that the homography constraint derives two independent equa... | Summary: The paper studies the geometric model fitting problem with unknown model type and heavy outliers. It proposes a unified optimization model with dual sparsity constraints that combines the outlier rejection, true model reasoning and parameter selection. Moreover, a fast numerical algorithm is proposed to solve ... | Rebuttal 1:
Rebuttal: # Q1: About problem (13), (14), and the rank-term?
**R1:** For better understanding, we should introduce problem (13) first, which is actually modeled for finding all $r$ independent sparse bases, by supposing $r$ is known. But for our unknown model fitting task, $r$ is unknown in advance, thus ... | Summary: This paper considers the robust model fitting problem in the presence of outliers, which is a fundamental problem in low-level CV. The aim is to simultaneously achieve outlier rejection, model selection, and model parameter estimation in a unified formulation. Toward this end, the authors propose to cast the j... | Rebuttal 1:
Rebuttal: # Q1: About using geometric distance.
**R1:** We highly agree that minimizing geometric error (GE) could obtain better performance in accuracy, since the error entry with geometric distance is more stable. But the non-convex nature indeed makes it hard to optimize. Although there exist effective m... | Summary: Considering that existing model estimation methods highly rely on the correct definition of model types, this paper introduces a unified optimization modeling DSP to simultaneously reason out the model type and estimate model parameters from contaminated data. For such purpose, the authors proposed Sparse Subs... | Rebuttal 1:
Rebuttal: # Q1: How to ensure the input data properly normalized?
**R1:** The related distribution of the input data is their intrinsic nature, we cannot change it. But to ensure the estimation easier, we first normalized the input points of each image into zero mean and one standard error, then we scaled ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the task of robust model reasoning and fitting in an unified optimization framework that can estimate the geometric model accurately without knowing the predefined model in advance while being robust to outliers as well as highly efficient. The authors propose a novel sparse subspace recov... | Rebuttal 1:
Rebuttal: # Q1: About the optimality if using the Acceleration Strategy (AS).
**R1:** The optimality has not changed if using AS. We can see from Fig. 2 that, at convergence stage, solution or loss value are identical for using AS or not. This is because our used AS is not an approximation for original prob... | null | null | null | null | null | null |
The Tunnel Effect: Building Data Representations in Deep Neural Networks | Accept (poster) | Summary: The paper offers an empirical study of deep neural networks. The focus is on the role of intermediate layers in building a representation that is linearly separable and can eventually solve the task. The work highlights the fact that this linearly separable, low-rank representation emerges at a depth that is a... | Rebuttal 1:
Rebuttal: Thank you for your thorough review.
**The fact that a linearly separable representation emerges well before the final layer is not completely novel.**
We do agree that this observation has been made in multiple works, including those we cite. We consider the primary value of our work to be *con... | Summary: The tunnel effect is described for deep overparameterized networks, whereby early layers form a linearly separable representation while later layers form a "tunnel" which passes this representation to the output without substantial change, other than compression (reducing its rank, a.k.a. discarding informatio... | Rebuttal 1:
Rebuttal: Thank you for the effort you put into reviewing our work. We find your feedback valuable and helpful in improving the quality of the paper!
**The procedure for computing the numerical rank should be given fully.**
We added the following clarification in the text:
> The threshold $\sigma$ is set ... | Summary: This paper proposes the Tunnel Hypothesis: Training of deep networks splits layers into two distinct phases: (1) extractor phase and (2) tunnel phase. Extractor phase learns the linearly separable features whereas the tunnel phase compresses the representations. The authors provide evidence towards degrading e... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their valuable feedback.
We acknowledge some presentation issues, which we discuss in the general answer. Please let us know if you find it satisfactory. We discuss the other issues below.
**Increasing depth does not help because representations are learned up... | Summary: This paper shows an effect of deep neural networks when trained for classification tasks — the initial layers create linearly separable features, and the later layers collapse the features for the final prediction. This phenomenon is explored with extensive experiments.
Strengths: - The paper explored a very... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
**Considering similar findings in other papers, enhance the related work section with comparisons and clarify contributions.**
We have added the references [1] and [2] to the related works section:
>Several recent works~\cite{ansuini2019intrinsic,li2022... | Rebuttal 1:
Rebuttal: ## General Response
Dear reviewers,
Many thanks for providing valuable feedback in your reviews, both positive and negative. We are delighted to note that all the reviewers prized the scale of our experimentation and found our results very interesting (xLcw, dmw5), with xLcw reporting that they a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Stability of Random Forests and Coverage of Random-Forest Prediction Intervals | Accept (poster) | Summary: This work studies the random forests stablity for regression problem, and the authors presents theoretical analysis on the upper and lower boudns for the coverage probability of prediction intervals constructed from the out-of-bag error of random forests. The theoretical guarantee is based on a light-tail assu... | Rebuttal 1:
Rebuttal: We thank the reviewer for agreeing that "It is an interesting problem on the theoretical understanding of random forests." We will seriously take the reviewer's comments into consideration and make revisions accordingly. Below are point-by-point responses to the reviewer's "Weaknesses" comments.
1... | Summary: Random forests are one of the most used Machine Learning methods. Its standard variant (for regression) takes the following form. GIven a random sample $D=(X_i,Y_i)_{i\leq n}$ of covariate/response pairs, one takes $B$ bootsrapped samples from $D$ and trains a tree regressor on each boostrapped sample (using o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our work. In particular, we are happy that the reviewer considers that "the result is significant" and "original," obtaining "a lightweight method to compute (nearly-)valid prediction intervals from random forests, which are often used in practice... | Summary: In this paper the authors considers the issue of stability of the often used in practice Random Forest algorithm and provide theoretical bounds on the $\varepsilon$-stability upto an order of $O_{\mathbb{P}}(|Y|^2_{(n)}/n)$ (i.e. the largest in magnitude observation) when fitting the method with $n$-iid sample... | Rebuttal 1:
Rebuttal: We thank the reviewer for considering that "this paper works with a more practical version of random forests." We also believe the most important point of our results is that they apply to the practical version of random forests, and are thus strongly relevant to applied machine learning. We theor... | Summary: The paper presents new and strong set of results on stability of (greedy version of) random forests. Theoretical (resp. numerical) evidence is provided to support stability for light-tail (and heavy-tail) assumptions on marginal distribution of squared response. New finite sample upper and lower bounds are pro... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewer for the positive comments of our work, and the suggestion of a “Strong Accept.” The reviewer states that “the paper can be regarded as a demonstrative work that justifies the merit of random forests for both point and interval prediction.” This statement contains e... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their time and comments.
We are encouraged that three of them give positive evaluations to our work and suggest to accept our manuscript with ratings 6, 7, and 8, respectively. All three reviewers agree that our theoretical work is relevant for applied machine ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning DAGs from Data with Few Root Causes | Accept (poster) | Summary: The paper studies a new causal discovery method, in which it assumes that the DAG data is produced by few data-generating events whose effect percolates through the DAG. They propose a simple but effective method to learn the true DAG based on the few roots assumption. The proposed method outperforms baselines... | Rebuttal 1:
Rebuttal: # Response to reviewer Aevs
## Weaknesses
**Optimization robustness to noise.** The optimization problem doesn't contain noise explicitly. It is the convex $L^1$ relaxation of the noise-free version of the optimization problem. Doing this relaxation allows some robustness to (low magnitude) noi... | Summary: This paper considers a new setting of linear DAG learning problem. Based on a linear transform of linear SEM, authors propose to study a new setting where there are few "root causes", with potential measurement noise in the data. Identifiability is proved and the true DAG is shown to be the global minimizer of... | Rebuttal 1:
Rebuttal: # Response to reviewer LucQ
## Weaknesses
**Motivation for root causes.** Please see our general reply for a better motivation and also our success in a causal discovery competition with real-world data.
**Assumptions on the root causes.** Yes, within the scope of sparse root causes we only con... | Summary: This paper considers learning of linear SEMs (weight matrix) under a data generation process that differs from the common formulation. It is assumed that each sample is generated from only few number of non-zero noise variables in which the set of noise variables is stochastic. The main theoretical result is i... | Rebuttal 1:
Rebuttal: # Response to reviewer STdi
## Weaknesses
**Motivation for root causes.** Please see our general reply for a better motivation and also our success in a causal discovery competition with real-world data.
**Root causes support.** Note that we assume varying support of the root causes in the data... | Summary: This paper presents a new formulation of linear SEM by specifying a structure on the noise variables, which seems to impose zero-inflated distrbutions to achieve the "few roots" modelling goal. Identifiablity is given and guarantee for $L^0$ minimization estimator is provided for a special noise-free case. The... | Rebuttal 1:
Rebuttal: # Response to reviewer NZKD
## Strengths
**Improvement against other methods.** Our algorithm offers significant improvements over the baselines in most scenarios of the simulation experiment (Table 1 and Fig. 2) and especially when the number of nodes in the graph is scaled up (Table 3).
## We... | Rebuttal 1:
Rebuttal: # General Comments
We thank all the reviewers for their kind reviews and their effort and interest in understanding and commenting on our work. We will incorporate these comments in an improved revision. Here, we would like to address two main points that arose from the comments.
**Motivation for... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement | Accept (poster) | Summary: This work first analyzes real-world datasets for multivariate time series forecasting and finds out two problems that are not well handled by previous works: 1) unexpected noise; 2) heterogeneity between variables. A GNN-based model, named CrossGNN, is proposed to fill the gap. CrossGNN consists of three compo... | Rebuttal 1:
Rebuttal: Dear Reviewer LSa5,
Thanks for your providing a positive feedback to our manuscript and encourage us to make further improvements. Now, we have addressed your concerns by supplementing both experimental studies and concise technical descriptions.
**W1: Fixed Graph Structure for Different Inputs... | Summary: CrossGNN is a linear complexity GNN model designed for MTS forecasting, addressing two obstacles: self-attention mechanisms assigning high scores to outlier points and real-world data homogeneity and heterogeneity. By combining Adaptive Multi-Scale Identifier (AMSI), Cross-Scale GNN, and Cross-variable GNN, Cr... | Rebuttal 1:
Rebuttal: Dear Reviewer MKp9,
Thank you for your insightful advice for polishing our manuscript. We have conducted sufficient experiments and analysis to dispel your concerns. The details can be found below.
**W1: Noisy illustration issue in Figure 1 (b) & Whether it works when outliers removed.**
(1) T... | Summary: Overall Comment：
This article addresses two issues in multi-variate time-series modeling: i) How to address signal noise in multivariate time series, and ii) How to address interactions between multiple variables to extract information. The article proposes two GNN models to solve these problems, including the... | Rebuttal 1:
Rebuttal: Dear reviewer zRqD,
Thank you for your valuable insights for polishing our manuscript. We have conducted additional experiments and analysis to address your concerns.
**W1&Q1 Which module is more important.**
Thank you for your question in our experimental analysis. Our ablation experiments in... | Summary: This paper aims to deal with the temporal fluctuations and heterogeneity between variables, caused by unexpected noise, for better multivariate time-series forecasting. Specifically, the authors propose a linear complexity CrossGNN model, including Cross-Scale GNN which captures relationships inter- and intra-... | Rebuttal 1:
Rebuttal: Dear Reviewer F3ix,
Thanks for your valuable comments for improving our manuscript. Firstly, we have incorporated a comparison with the SoTA baselines. Subsequently, we have seriously addressed and clarified the concerns you raised as below.
**W1: Lack of SoTA baselines.**
Based on your suggest... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank you for your valuable time and constructive comments on our manuscript and we have made sufficient improvements of our work according to your comments. Here we list the major improvements below.
- **Enhanced Clarifications**: We have enhanced our manuscript... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Proximity-Informed Calibration for Deep Neural Networks | Accept (spotlight) | Summary: This paper quantifies and proposes a mitigation for a phenomenon in DNN training, where more 'unusual' examples (here defined as having a higher average distance to its K=10 nearest neighbors) are generally more miscalibrated across a range of models and tasks. The authors propose a new proximity-aware calibra... | Rebuttal 1:
Rebuttal: We appreciate reviewer 4HP9's constructive feedback. We are glad the reviewer enjoyed the logic flow of this paper, finds the problem important and the solution simple, logical and effective.
**Q1: I felt that the 'atypical' (low proximity) examples could have been better characterized. In partic... | Summary: This paper studies the prevalence of proximity bias in calibration, i.e. the rate of miscalibration on samples that are far away from their nearest neighbors in the data ("low proximity"). The authors empirically show that this type of miscalibration is present across many models, and propose a new post-traini... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer 4gc8 for the constructive feedback and we are glad that the reviewer finds the investigation and methodology novel, the claim technically sound, the experiment extensive, and the proximity bias issue significant. Here we answer all the questions and hope they can addres... | Summary: The article focuses on the problem of uncertainty quantification in classification.
Calibration provides some guarantees on the estimated class probabilities on average. However, subgroups can still be miscalibrated. The article first aims to characterize these subgroup miscalibrations through proximity levels... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer 1D75 for the constructive comments and we are glad that the reviewer finds the problem interesting, the solution a good direction, and the study complete. Here we answer all the questions and hope they can address the concerns.
**Q1: In equation (5) on the confidence s... | Summary: This work addresses the problem of proximity bias and confidence calibration by performing a comprehensive empirical study of various pretrained ImageNet models. The empirical findings provide insights on persistence of proximity bias even after performing calibration using existing post-hoc calibration algori... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer Wiui for the constructive suggestions on the writing of the experiment part. We are also glad that the reviewer finds the phenomenon study comprehensive, the experiment evaluation thorough, the observation interesting, and the issue we identify to be important. Here we ... | Rebuttal 1:
Rebuttal: **The following questions primarily pertain to the details of how to compute the bias index, and therefore, we have grouped them together for convenience and clarity.**
To provide further clarity, let's revisit the process of how we calculate the bias index:
1. To do the hypothesis testing, we f... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing | Accept (poster) | Summary: Ray tracing faces the difficulties of being applied practical real-time applications due to high levels of noise when sample counts are low. Sample counts are often limited to as low as 4 when considering real-time applications (~30ms latency). As such, the paper proposes an end-to-end training of a RL-based s... | Rebuttal 1:
Rebuttal:
Dear Reviewer YEyS, thank you for your time and interesting remarks.
> What is the exact novelty in comparison to previous works? The proposed framework seems to be combination of RL-based adaptive sampling [22], the use of sampling importance network [23], spatiotemporal reservoir (spatiotempor... | Summary: This paper proposes two techniques to improve the performance of Monte-Carlo patch tracing on real-time image rendering: 1) keep all previously sampled values to improve spatial-temporal information reuse; 2) use reinforcement learning to optimize the sampling importance network, avoiding the explicit numerica... | Rebuttal 1:
Rebuttal:
Dear Reviewer 1MpK, thank you for your time and interesting remarks.
> Line 71 describes some methods that improve the spatiotemporal reuse. But there are no further discussion about their difference to the proposed method.
The mentioned methods are ReSTIR and derivative work of ReSTIR. Those m... | Summary: This paper tackles the issue of Monte-Carlo path tracing which is an important field for computer graphics and rendering. The paper first analyzes the current state of the art and identifies mainly two flaws which are addressed thereafter: First, the authors introduce a spatio temporal latent space serving as ... | Rebuttal 1:
Rebuttal: Dear Reviewer WTeK, thank you for your time and interesting remarks.
> For Table 1, there is no information regarding the dataset and resolution of the images/videos used to assess the presented values. This is confusing, because the PSNR values are shown for 4 spp, resulting in an inference time... | Summary: This paper proposes to use reinforcement learning (RL) to improve adaptive sampling effectiveness in Monte Carlo ray tracing. Another key contribution they claim is the use of a latent space representation to encode temporal information, which improves the reuse of spatiotemporal pixel information across fram... | Rebuttal 1:
Rebuttal: Dear Reviewer CLYz, thank you for your time and interesting remarks.
> Additional images for qualitative comparison could be helpful for visualizing some of the key concepts discussed in the paper. For example, visualizing qualitative differences between various sample counts, or dedicated visual... | Rebuttal 1:
Rebuttal: Based on the feedback of Reviewers WTeK and YEyS, and if permitted, we will add the following section to the paper or at least to the appendix. Please look at individual rebuttals for all other comments and answers.
```
Limitations: The current method does not consider the application of after-ef... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Algorithms for the Inhomogeneous Spiked Wigner Model | Accept (poster) | Summary: This paper considers approximate message passing algorithms for reconstructing a rank-1 signal when corrupted by a symmetric matrix of noise with a block-variance structure; it is assumed the signal _x^*_ has iid coordinates generated from a prior distribution. One then forms the matrix $Y = x^* (x^*)^T/\sqrt... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their insightful comments and valuable suggestions. We will incorporate all the suggestions into the final version, regardless of acceptance.
• Weakness 0: The main motivation of this paper was to approach the analysis of an inhomogeneous spiked Wigner... | Summary: This paper studies the (symmetric) rank-1 matrix estimation problem with inhomogeneous noise. Here inhomogeneous noise refers to a symmetric noise matrix that is block-wise constant where the number of blocks is a constant relative to the dimension.
This paper proposes an approximate message passing (AMP) alg... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their insightful comments and valuable suggestions. We will incorporate all the suggestions into the final version, regardless of acceptance.
Weakness 2: From the information theoretical analysis for these models, no recovery of the hidden truth is pos... | Summary: The paper provides an analysis of an AMP algorithm for the spiked Wigner model with inhomogeneous noise. The paper builds on the matrix AMP framework to derive the state evolution equations for the considered AMP recursion for the studied model. The paper further shows that if the denoising functions are the B... | Rebuttal 1:
Rebuttal:
We express our gratitude to the reviewer for their insightful comments and valuable suggestions. We will incorporate all the suggestions into the final version, regardless of acceptance.
Weakness, Page 1: Indeed the matrix $\tilde{\Delta}$ and the partition function $g$ are assumed to be known. ... | Summary: This paper considers the spiked Wigner problem with inhomogeneous noise, i.e. the inverse problem of estimating a rank-matrix through an inhomogeneous noise channel. This problem naturally arises in many applications and a universality result makes the problem considered quite general with regards to the noise... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their insightful comments and valuable suggestions. We will incorporate all the suggestions into the final version, regardless of acceptance.
• Weaknesses: We acknowledge that we did not delve into potential stability issues or convergence problems tha... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Hierarchical Multi-Agent Skill Discovery | Accept (poster) | Summary: This paper introduces a framework that concurrently learns the individual skills for each agent and the team skill for the entire team, amalgamating these skills to perform multi-agent tasks. The discovery of skills is grounded in a probabilistic graphical model and employs variational inference tools for scal... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We hope we can address your concerns below.
**Q1**: Weaknesses (a) in the Official Review.
**A1**: Thank you for pointing out our potential limitations. For (1), as mentioned in lines 252-258, HMASD performs only one timestep of centralized execut... | Summary: The paper proposes a two level hierarchical model for cooperative multi-agent RL. The key idea is to use variational inference based skill discovery over joint and individual policies. Intuitively, the objective can described as follows: i) find individual options, that are diverse (in terms of state visitatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We hope we can address your concerns below.
**Q1**: My main concern is that most of the improvement comes from implicit exploration bonus that arises from skill discovery objective, rather than from decomposition of the main task into subs tasks. I... | Summary: This paper proposed HMASD, a two-level hierarchical algorithm for discovering both team and individual skills in MARL. The high-level policy based on the transformer structure generates team skills and individual skills in an autoregressive manner, and the low-level policies output primitive actions according ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We hope we can address your concerns below.
**Q1**: Some important baselines are missing in the experiment section, such as HSD and CMAE.
**A1**: HSD is an old method proposed in 2019. It performs poorly even on the dense reward SMAC as shown in... | Summary: This paper focuses on applying unsupervised skill learning to multi-agent reinforcement learning. For this purpose, the authors proposed a two-level hierarchical algorithm for discovering both team and individual skills in MARL, where individual skills refers to the abilities of individual agents and team skil... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We hope we can address your concerns below.
**Q1**: Have the authors encountered instability problems when training as there are so many components combined with the MARL algorithm?
**A1**: Yes, in the early version of HMASD, we found that HMASD... | Rebuttal 1:
Rebuttal: We have uploaded a one-page PDF containing a new figure that visualizes the learned individual skills on the SMAC scenario 3m.
Pdf: /pdf/29e24de865cbac59eaad54cbfd675aa65cdd1cbd.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents Hierarchical Multi-Agent Skill Discovery (HMASD) that can discover both team and individual skills in MARL. The authors formulate multi-agent skill discovery as an inference problem in probabilistic graphical models. The model consists of a skill coordinator that reasons about team and indi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We hope we can address your concerns below.
**Q1**: In figure 6, there is no orange line and does that mean MAPPO fails to learn anything?
**A1**: Yes, the orange line is covered by the blue and green lines. Both MAT and MAPPO fail to learn anythi... | null | null | null | null | null | null |
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks | Accept (poster) | Summary: The authors propose a form of knowledge distillation for a retriever-reader architecture. It uses rationales to guide the neural reranker to retrieve more relevant passages for reasoning, instead of passing the query to the retriever and retrieving the most similar passages. The paper includes an interesting s... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. We initially address all your concerns and questions below:
---
> W1. It is not clear what type of retriever the baseline methods that include knowledge augmentation.
Thank you for pointing it out and we will include more detail... | Summary: The paper focuses on distilling the chain-of-thought reasoning capability from large LMs to small LMs in knowledge-intensive tasks. Since small LMs do not encode sufficient knowledge required for reasoning, the paper proposes to augment small models with a knowledge retriever that obtains relevant documents fo... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. We initially address all your concerns and questions below:
---
> W1. One concern: The consequence of using multiple rationales to train the small LM, since this would misguide the model to learn that answer prediction does not r... | Summary: This paper proposes a retrieval-augmented knowledge distillation approach for QA tasks. This approach, KARD, extends reasoning distillation, which uses an LLM such as GPT-3.5 as a teacher model and distills a student model by learning from question and rationale pairs (generative loss). KARD has a retriever th... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive and helpful comments. We initially address all your concerns and questions below:
---
> W1. Gains become marginal with large model sizes.
As stated in the main paper lines 108-110, memorization of training data is essential for achieving good perform... | Summary: In this paper, we propose the KARD model for small model Q&A through knowledge distillation + KB retrieval. The authors show experimentally that the model can outperform other models of 3B using only 250M parameters.
Strengths: 1. a model of LLM knowledge distillation + KB retrieval is proposed.
2. KARD outpe... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments. We faithfully addressed all your concerns and questions below:
---
> W1. Comparison against other knowledge-augmented LMs.
Thank you for your suggestion. Please note that our main argument is that **knowledge augmentation is important when ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your considerable efforts in reviewing our paper and providing insightful reviews to our work. We appreciate that reviewers find our proposed idea well-motivated and sound.
We have responded to the individual comments from the reviewers below, and believe that we h... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper deals with the challenge of utilizing small LMs in knowledge intensive tasks. As recent LLMs have shown promising capabilities in tasks that require reasoning, however, deployment of such model can remain limited due to cost or data limitations. Thus, the authors turn to face the challenge of reasoni... | Rebuttal 1:
Rebuttal: We appreciate your time and effort in providing constructive feedback, and we address your concern and questions below.
---
> W1 & W6. Limited evaluation which significantly hinders the reliability of work
We perform additional experiments on another dataset OpenbookQA [1] and with another dec... | null | null | null | null | null | null |
Robust Bayesian Satisficing | Accept (poster) | Summary: The paper proposes robust Bayesian satisficing, a new setting of BO that is similar to distributionally robust BO. Robust Bayesian satisficing aims to achieve a 'good enough' expected value given by some threshold $\tau$ and relaxed by the distribution distance between a reference and a true distribution. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and valuable insights.
### Weaknesses
#### Technical Concerns
- **(...how $\tau$ can be selected in a real-world problem...)** One real-world experiment concerning safe dose allocation for diabetes patients is presented in the supplementary doc... | Summary: This paper studies a contextual Bayesian optimization problem when the true and reference distributions of the context can be different due to distribution shifts. The authors propose an algorithm called robust Bayesian satisficing algorithm (RoBOS) based on the idea of robust saisificing (RS). Through theoret... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and valuable insights.
### Weaknesses
- **(...justification for robust satisficing regret)**
Robust satisficing regret evaluates how our algorithm fares against the robust satisficing action. In particular, the true robust satisficing action $x... | Summary: This paper studies robust satisficing in contextual Bayesian optimization under distribution shift in the distributions of the context.
They show that under some assumptions their algorithm achieves sublinear lenient regret and under some relaxed assumptions they achieve sublinear robust satisficing regret.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and valuable insights.
### Weaknesses
**(...refining the writing style and ensuring a more polished presentation...)**
We have undertaken a thorough revision to address the concerns raised. Specifically, we've refined the writing for better clar... | Summary: I think the main contributions of this paper are as follows:
- Proposes a new decision-making framework called robust Bayesian satisficing (RBS) which combines robust satisficing with Bayesian optimization. RBS aims to achieve a satisfactory solution under distributional shifts by observing a predefined satis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments and valuable insights.
### Weaknesses
- **(Some assumptions...)**
Indeed to achieve sublinear lenient regret, some assumptions have to be made on the distribution shift. Nothing much can be said in an adversarial environment where the reference ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their careful reading of our paper and constructive comments. Here we address the common questions raised by the reviewers.
- **(Real-world experiments and how to select $\tau$)** One real-world experiment concerning safe dose allocation for diabetes patients i... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Better Dynamic Graph Learning: New Architecture and Unified Library | Accept (poster) | Summary: This paper studies the problem of continuous-time dynamic graph learning. The authors proposed a Transformer-based architecture DyGFormer to learn the dynamic edge representation, which mainly consists of a neighbor co-occurrence encoding scheme to count the co-occurrence of nodes, a patching technique to spli... | Rebuttal 1:
Rebuttal: Thanks for your constructive reviews. In response to your comments, we have clarified the relationships between the learned representations of links and nodes. We have also specified the baselines whose observations are different from previous reports. We would be pleased to explain more if furthe... | Summary: In this paper, the authors considered the dynamic graph representation learning (a.k.a. dynamic network embedding) problem and proposed a novel transformer-based architecture - DyGFormer, with several original designs (e.g., a neighbor co-occurrence encoding scheme, a patching technique, etc.) Moreover, the au... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments. We have clarified some presentations and provided detailed settings. We have also discussed the mentioned references and tested methods on larger datasets. We hope our answers can sufficiently address your concerns, and if that is the case, we kindly ask you to co... | Summary: This paper proposes a new dynamic graph learning architecture and implemented it as part of a new unified graph library with extensive experiment results to verify the effectiveness of the proposed algorithms.
The author proposes a new Transformer-based architecture DyGFormer for dynamic graph learning. The ... | Rebuttal 1:
Rebuttal: Thanks for the helpful feedback. We have explained the motivation for our neighbor co-occurrence encoding. We have also clarified some presentations and our opinions on high-order interactions for dynamic graph learning. We are glad to answer more if there are any further issues.
**W1: The paper ... | Summary: This paper propose DyGFormer, a new Transformer-based architecture for dynamic graph learning, whose novelty mainly includes a neighbor co-occurrence encoding scheme and a patching technique. Moreover, it introduce DyGLib, a unified library to promote reproducible, scalable, and credible dynamic graph learning... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Following the reviews, we have emphasized the novelty of DyGFormer in the designs of the neighbor co-occurrence encoding scheme and patching technique. We have also added introductions and empirical comparisons with the PINT baseline. We hope our answers have suf... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their valuable feedback and helpful comments on our work. We are delighted by the reviewers’ acknowledgments that the proposed DyGFormer is novel with original designs (Reviewers Ea5H, eUNf, ErNY, and zX7A), the presented DyGLib is of high quality and a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a transformer-based architecture (DyGFormer) for dynamic graph learning, based on a node co-occurrence encoding scheme and patching. Further, they present DyGLib a library for uniform evaluation of dynamic graph learning techniques. Extensive experimental evaluations over diverse datasets s... | Rebuttal 1:
Rebuttal: Thanks for the helpful reviews. We have analyzed how the properties of datasets affect DyGFormer and explained the reasons for its varying performance under various negative sampling strategies. We have also discussed the possibility of integrating DyGLib and TGL. We hope our answers have well add... | null | null | null | null | null | null |
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding | Accept (poster) | Summary: The paper proposes State2Explanation, a framework for training RL agents in such a way that both the human and agent benefit, the Protégé Effect as the authors state. The basic idea is to learn a joint embedding space with "temporal" concepts that actively helps the agent train better by shaping their rewards.... | Rebuttal 1:
Rebuttal: Dear Reviewer GSRV, we thank you for your valuable feedback. Below we provide responses to weaknesses and questions:
**W: single idea missing..**
We disagree with this comment. To our knowledge, our framework S2E is the first unified framework that considers how concept-based explanations can pr... | Summary: Inspired by the Protege effect, learning and developing explanations should provide a dual benefit, both to the readers of the explanations, and to the developers of the explanations. Based on this idea, the paper proposes State2Explanation, an algorithm to learn joint embeddings between state-action pairs and... | Rebuttal 1:
Rebuttal: Dear Reviewer g9Rv, we thank you for your valuable feedback. Below we provide responses to weaknesses and questions:
**W1: “Concepts for domains are dependent on expert knowledge for state-action pairs, making it unclear how easy it would be to generalize beyond well-studied games. This is especi... | Summary: The paper proposes a framework to incorporate explanation concepts to sequential decision tasks. The framework can be applied both to the training of the agent by improving RL and to provide explanations to end-users during deployment. The framework is tested using two simple games, Connect 4 and Lunar Lander,... | Rebuttal 1:
Rebuttal: Dear Reviewer ppHL, we thank you for your valuable feedback. Below we provide responses to weaknesses and questions:
**W1:** Thank you for pointing out--we will increase our image sizes.
**W2 & W3:**
We believe that our S2E framework will be applicable to more complex scenarios; however, two c... | Summary: The authors propose a unified framework called State2Explanation (S2E) that combines learning a joint embedding model between state-action pairs and concept-based explanations. The authors draw inspiration from the Protégé Effect, which suggests that explaining knowledge reinforces self-learning. They propose ... | Rebuttal 1:
Rebuttal: Dear Reviewer smxt, we thank you for your valuable feedback. Below we provide responses to weaknesses and questions:
**W: “..the proposed method should be tested on more complex tasks to test its effectiveness. The authors should also consider how concepts can be better selected beyond expert-def... | Rebuttal 1:
Rebuttal: We thank all our reviewers for their detailed comments. Firstly, we are encouraged that reviewers saw the importance of our framework S2E in providing a dual benefit to the end-user as well as RL agent. Reviewer ppHL found our work provided “sound theoretical and empirical analysis”, and reviewer ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning | Accept (poster) | Summary: This paper proposes an imputation-free framework with two novel approaches to improve spectral clustering on incomplete data. Firstly, the authors introduce a new kernel correction method that enhances the quality of the kernel matrix estimated on incomplete data with a theoretical guarantee, benefiting classi... | Rebuttal 1:
Rebuttal: **Response to Reviewer hJi1**
Thank you very much for your positive feedback on the originality of our proposed method and its significance. We are delighted to hear that you found *our work satisfying in terms of quality and clarity*. Your comments are greatly appreciated and will help us furthe... | Summary: This paper studies the spectral clustering problem when there is missing data. The paper proposes a new algorithm for correcting the computed kernel matrix by projecting the matrix to the nearest symmetric PSD matrix, and using this corrected kernel for clustering. The paper also combines the new kernel correc... | Rebuttal 1:
Rebuttal: **Response to Reviewer GE1q**
Thanks for your positive feedback on the novelty of our proposed method and its potential applications for future research directions. Your comments are greatly appreciated and will help us further improve our work, especially for convincing empirical evaluation.
--... | Summary: The authors introduce an imputation-free framework for correcting a kernel obtained from incomplete data. They propose the corrected kernel to be a PSD matrix with bounded elements that is closest to the initial kernel (calculated from incomplete data) in Frobenius norm. They show that the corrected kernel is ... | Rebuttal 1:
Rebuttal: **Response to Reviewer 7MjD**
Thanks for your valuable comments on our work. We greatly appreciate the time and effort you have dedicated to thoroughly evaluating our paper and providing detailed feedback. We will modify it accordingly.
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**Comment 1**: It is not clear how p-norm based penalt... | Summary: The paper proposes a new kernel correction method to address the issue of incomplete data. Existing approaches aim to recover the distance matrix of complete data, starting from that of the incomplete data. In contrast, the proposed method (Section 3.2) formulates the problem as finding a positive semi-definit... | Rebuttal 1:
Rebuttal: **Response to Reviewer 7HKb**
Thanks very much for your detailed feedback on the contributions of our work. We are delighted to hear that you found *the proposed approach is technically sound* and *the experiments show clear improvement over existing approaches*. Your comments are greatly appreci... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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