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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Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant | Reject | Summary: This paper proposes a second-order version of Nesterov's accelerated gradient (NAG) descent and Adagrad for logistic regression by incorporating an approximation to the Hessian. The authors call this the "quadratic gradient". Specifically, a diagonal approximation to the Hessian for logistic regression is prop... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1: The enhanced NAG method descri... | Summary: This paper proposed a new approach to improve the gradient used by first-order optimization methods in logistic regression by utilizing a constant bound to the Hessian matrix. The authors demonstrate how to use their method under a fully Homomorphic-Encryption scenario. They test their method on many real-worl... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1 : The cited paper [7] also use... | Summary: This paper proposes the quadratic gradient for privacy-preserving logistic regression. Such gradient is used together with Nesterov’s accelerated gradient (NAG) and Adagrad on Homomorphic Encryption techniques.
Strengths: This paper tackles the privacy-preserving (in the sense of encryption) logistic regressi... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1 : Overall, it is hard to under... | Summary: The paper proposes a new gradient method that can be efficiently used under homomorphic encryption. The proposed method replaces the gradient $g$ by an approximation of $H^{-1}g$, where $H$ is the Hessian. This approximation is done using a specific diagonal matrix, that speeds up convergence while being possi... | Rebuttal 1:
Rebuttal: $\textbf{Response}$
We would like to thank the reviewers for their input. Their comments have been thoroughly considered, and altering the manuscript in accordance with these comments will significantly improve the quality of our paper in the next submission.
**C1 : Are there settings where the... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Path following algorithms for $\ell_2$-regularized $M$-estimation with approximation guarantee | Accept (poster) | Summary: A novel grid point selection scheme and stopping criterion for any general path-following algorithms are proposed in this paper. However, the presentation of this paper is poor, and the contributions are not clear.
Strengths: A novel grid point selection scheme and stopping criterion for any general path-foll... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions.
We summarize our responses below.
**Weaknesses and Questions**
1. Thanks for the suggestion. Following your suggestion,
we introduced the definition of regularized $M$-estimation
earlier in the Introduction section in our revision.
2. Than... | Summary: This paper proposes a new path following algorithm for 2-regularized M-estimation with an approximation guarantee. The algorithm includes a grid point selection scheme and an adaptive stopping criterion to optimize the trade-off between model fit and complexity in machine learning algorithms. The paper also pr... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions.
We summarize our responses below:
**Weaknesses and Questions**
1. Thanks for the suggestion. We have moved all the *runtime versus global approximation error* plot for the simulation studies (Figure S1 and Figure S3 of current supplementary fi... | Summary: This paper considers this problem of the ($\ell_2$-regularized) $M$-estimation problem of computing solutions to the regularized objective $(e^t – 1) L(\Theta) + (1/2) \| \Theta \|_2^2$ for all $t$, denoted $\theta(t)$. The paper proposes a method for approximating theta(t) for carefully chosen values of t, su... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions.
We summarize our responses below:
**Weaknesses**
* Thanks for the suggestion, we have added the proof strategy for Theorem 1. Basically, the proof of Theorem 1 starts with relating the local approximation error to the suboptimality of $\theta_... | Summary: The submission 9422 proposed a novel selection scheme for grid search and stopping criterion for a more general path-following algorithms, while not trying to compute/derive the whole solution spectrum. The analysis of the authors indicates that under certain assumptions, their proposed approximate path-tracki... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions.
We summarize our response as follows.
**Weaknesses**
* Thanks for your suggestions on numerical simulations. Following your suggestion, we have added a real-world data example in our numerical studies section (please see plots in Section 1 of o... | Rebuttal 1:
Rebuttal: Thanks for all of your valuable comments and suggestions.
Following your suggestions,
we have made some changes to our submission, which we feel greatly improved the presentation.
Main changes include:
* We moved the formal definition of $M$-estimation earlier in the Introduction section, and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Taylor TD-learning | Accept (poster) | Summary: This paper proposes an algorithm for improving model-based actor-critic methods in continuous state and action spaces. When using Dyna-style updates, the algorithm can compute an expected update over a small noise distribution using a linearization to reduce the variance of the performed update. This method is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments!
> For example, I would avoid using the term "Monte-Carlo" estimates when discussing sampling of TD updates since it can lead to some confusion due to "Monte-Carlo" often being used to refer to MC estimates of the return in contrast to the bootstrap ... | Summary: This paper,
1. Proposes Taylor TD, a model-based RL algorithm that uses a Taylor series expansion to analytically estimate the expected TD update over a distribution of nearby state-action pairs. This reduces variance compared to standard MC TD updates.
2. Provides theoretical analysis showing the variance of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments!
> In Figure 2, Taylor TD3 seems to provide only very small performance improvements
While we agree with the reviewer's assessment, it is important to note that there is no single method that performs competitively with TaTD3.
Particularly, MBPO is ... | Summary: The author presents a variance reduction trick in TD learning by taking the analytical expectation of the gradient using first order taylor expansion. Standard RL, replaces the expectation over the gradient of critic (Q-value) with a sampled value from the replay buffer or online learning samples. The paper pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments!
> Add references for the other variance reduction work ... Risk-sensitive RL and robust RL
Thanks for these suggestions! We will add your reference [1], and we did a literature review on risk-sensitive RL approaches (e.g., [2],[3],[4],[5]), which... | Summary: The authors are proposing a method for reducing the variance of TD-learning updates for model-based RL applied to problems with continual state-action spaces. The method relies on Taylor expansion of noise terms in action and initial state distribution, reducing the contribution of those to the variance of TD-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments!
> the authors should be clearer in the main body of the paper about the additional computational demands of the proposed method.
We raised this point in the Limitations section of the original manuscript, and provided a reference to the Appendix fo... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models | Accept (poster) | Summary: The paper provides theoretical results characterizing the generalization capabilities of methods based on Wasserstein distributionally robust approaches. In particular, the results presented extend the conditions under which the performance guarantees are not affected by the curse of dimensionality and are app... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and suggestions. Here is a detailed response to all remarks and questions.
- *"The paper contribution with respect to the state of the art needs to be better described. In particular, the extension to non-linear models of the scaling 1/sqrt(n)."* Tha... | Summary: This paper presents generalization bound for Wasserstein DRO and entropic regularized Wasserstein DRO (or called Sinkhorn DRO in Wang et al.) formulations. Those generalization bounds do not suffer from the curse of dimensionality. The theoreical analysis is also supported by two examples in Section 3.4.
Str... | Rebuttal 1:
Rebuttal: We heartwarmingly thank the reviewer for the numerous suggestions, comments and references. It is a pleasure for us to read that "The theoretical analysis is interesting from two aspects [...]" and that "it is great that the authors present statistical analysis for entropic regularized Wasserstei... | Summary: This work proves generalization guarantees for Wasserstein DRO models that only require the radius of order $O(n^{-1/2})$ under mild assumptions for general classes of models. This provides concentration results that do not suffer from the curse of dimensionality.
Strengths: The theoretical contribution is th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their reading, comments and suggestions.
- *"adding some discussion or examples for which the assumptions and thus the results in this paper do not hold can be beneficial;"* We agree that such examples were missing in the submission. In the main rebuttal, we provide more... | Summary: This paper provides generalization guarantees of Wasserstein DRO for a general class of functions, in which the radius scales as $1/\sqrt{n}$ and does not suffer from the curse of dimensionality. Moreover, these guarantees hold for any distribution in the neigbourhood of the true distribution, so that they sti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their reading, comments and questions. We very much appreciate the comment "the results in this paper are novel and make a non-trivial contribution to the DRO community." Here are point-by-point answers to your questions.
- *"It would be better if more complicated and po... | Rebuttal 1:
Rebuttal: We thank the reviewers for suggesting adding non-linear examples to the paper. We discuss three examples (kernel models, neural networks and family of invertible mappings) that we will add to the revision of the paper.
We also discuss a numerical illustration of our main theorems that we will also... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
$SE(3)$ Equivariant Convolution and Transformer in Ray Space | Accept (spotlight) | Summary: This paper presents a series of methods on SE(3) equivariant convolution and transformer, which can operate on ray space. The experimental results show that the proposed method can help establish SE(3) reconstruction of signed distance functions and neural radiance fields. I'm not an expert in equivariant netw... | Rebuttal 1:
Rebuttal: **1. “The entire method description section is obscure to me.”**
As mentioned in the response to Reviewer VaKN, we will change the order of presentation in the method section. We will first define the two problems of generalized rendering and reconstruction from multiple views and then elaborate ... | Summary: The aim of this paper is to build equivariance constraints into neural rendering and 3D reconstruction networks. As far as I can tell from the paper, the representation used is a mapping from a ray space to a vector space. The paper describes equivariant convolution and transformer layers that operate over the... | Rebuttal 1:
Rebuttal: **W1:** We agree with the reviewer, and we will present the ray space definition and the context of IBRnet in the introduction. The mathematical definition and background of rays were initially placed in the appendix, a decision we acknowledge as not ideal.
**W2:** We will rearrange and rewrite t... | Summary: This paper introduces a method for leveraging geometric priors in 3D reconstruction and novel view rendering when the input views are insufficient. The authors propose learning priors from 2D images using a 2D canonical frame and a 3D canonical frame. They achieve coordinate frame equivariance by introducing a... | Rebuttal 1:
Rebuttal: **W1:** Indeed, lines 131-138 introduce material that is beyond the standard toolbox even for a mathematically avid NeurIPS reader. Nevertheless, this material is essential for establishing the definition of the generalized convolution as depicted in Equation 3 of the paper. We agree with the rev... | Summary: This paper proposes to study the geometric priors for 3D reconstruction and neural rendering with a novel perspective of multi-view equivariance. A theoretically-sound definition of the ray neighborhoods with SE(3) is obtained with the theoretical deduction of characterizing ray space with group theory. Then, ... | Rebuttal 1:
Rebuttal: **1. “The core of this paper is studying the geometric relationship between rays to define the ray neighborhoods, and then, convolution and attention can be induced in the ray space. If I understand correctly, what's the relationship between the point correspondences and the neighbors in the ray s... | Rebuttal 1:
Rebuttal: We extend our gratitude for the invaluable feedback and suggestions provided by the reviewers. We are pleased to note that all reviewers appreciated the contribution (2 excellent, 3 good) and the soundness (1 excellent, 4 good). All reviewers listed multiple strengths of the approach and none of t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a novel approach for reconstruction from multiple views using equivariant shape priors. The paper proposes the $SE(3)$-equivariant generalized convolution as the fundamental operation on a light field whose values may be radiance or features. Input ray features and producing output ray featu... | Rebuttal 1:
Rebuttal: **1. “The figures in this paper could be redesigned; some of them (Figure 4, 5) are so small that it is difficult to read the text and symbols they contain. In addition, the effect shown in Figure 9 is not intuitive.”**
We will improve the design of Figures 4 and 5, and make them larger while sav... | null | null | null | null | null | null |
InsActor: Instruction-driven Physics-based Characters | Accept (poster) | Summary: This work proposes InsActor, a framework for instruction-driven character animation. Given human instruction and/or way points, InsActor first leverages a diffusion model to generate state sequences of the character. Next, it uses a skill embedding model to convert the state sequence into physically plausible ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We would like to address your concern as follows:
> Q1: The work appears to have limited novelty, as it is somewhat a straightforward combination of character motion synthesis with diffusion methods such as [13] and a low-level trajectory optimiz... | Summary: This paper tackles the problem of generating text-conditioned character animation that is physics-based. It proposes a two-stage approach that first generates a kinematic motion conditioned on text using diffusion, and then tracks this motion with a physics-based motion VAE in the learned latent space. Experim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. Due to the character limit, we would like to address your major concerns here. For more detailed questions (e.g. tracking details), we are glad to discuss them during the discussion phase.
> Q1: To better motivate the need for physics in text-to-m... | Summary: This work proposes InsActor, a physics-based character control framework that enables controlling agents to follow high-level instructions. It employs a high-level diffusion motion planner and a low-level character controller to achieve mapping between text-based instructions and physics-based motion control. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for helpful comments. We would like to address your concern as follows:
> Q1: The main weakness I find in this work is its qualitative results. The simulated character is jittery, floaty, and appears to have foot sliding, which is unexpected for a physics-based method.
Our ... | Summary: The paper proposes an approach for generating physically plausible human motion from open text prompts. The approach combines high-level trajectory generation with guided diffusion and a low-level skill model encoded in a latent space to correct for physical plausibility during execution in simulation with PD ... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive comments. We would like to address your concern as follows:
> Q1: While the resulting motion is fairly good, it isn’t yet achieving the level of fidelity to make it generally useful in generative contexts (e.g. game or animation characters)
This is a great ... | Rebuttal 1:
Rebuttal: We thank reviewers for the encouragement and insightful feedback. We are glad that the reviewers found
* the problem “interesting”(R-ndRp) and “important” (R-s1C4); and
* the result “good” (R-Mekg) and “promising”(R-ndRp) and InsActor has the potential to be used in many tasks (R-K1zb); and
* the... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents an approach for generating animations using a language-conditioned neural network in particular a diffusion model. The underlying goal is to generate physics based human character animation in an easy and intuitive fashion. The approach leverages a diffusion model to generate high-level stat... | Rebuttal 1:
Rebuttal: We thank the reviewer for comprehensive comments. We would like to address your concern as follows:
> Q1: A major question that the paper is not addressing is the generation of actions on objects and surroundings. Potentially that could be included into InsActor through the waypoint system (maybe... | null | null | null | null | null | null |
A new perspective on building efficient and expressive 3D equivariant graph neural networks | Accept (poster) | Summary: This paper presents the expressive power of equivariant graph neural networks in the context of 3D local isomorphism from a 2D local isomorphism perspective. Similar to the subgraph isomorphism of GNNs, the paper proposes three types of isomorphism in 3D space: Tree Isometric, Triangular Isometric, Subgraph Is... | Rebuttal 1:
Rebuttal: ## Response to Reviewer b3yr
- **W1**: I have to say that the notation in this paper is confusing, which imposes a burden on me. For instance, h is defined as a node feature on line 81, and f is a tensor-valued function on line 70. However, f is then used as a bijection on line 112. Does this mea... | Summary: This paper introduces local structure encoding and frame transition encoding for more expressive representation learning in 3D graph neural networks. The local structure encoding is inspired by observations in the proposed local hierarchy of 3D graph isomorphisms; specifically the observation that subgraph iso... | Rebuttal 1:
Rebuttal: ## Response to Reviewer gxFS
Thanks for your meticulous examination and insightfull feedback.
**Q1**: SpookyNet is not cited or discussed although it shows very strong performance in the analysis. This method appears to have local and global features, it would be interesting to see where it land... | Summary: Authors analize the expressive power of 3D equivariant GNNs and introduce a new expressive equivariant GNN architecture based on local node and edge wise frames.
Strengths: The proposed way to achieve equivariance is well grounded and conceptually very interesting and novel.
The construction is also based on ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 7xD9
Thanks for your meticulous examination and insightfull feedback.
**Response to the Weakness**:
We appreciate your discerning observation and insightful feedback regarding the theoretical aspect of the local hierarchy of 3D Graph Isomorphism in Section 3. Indeed, t... | Summary: This paper investigates expressive message passing architectures for processing 3D geometric graphs with permutation and Euclidean symmetries. The authors first provide an investigation of a local hierarchy of isomorphism separability. Then, the authors show cases where powerful local invariant models may fall... | Rebuttal 1:
Rebuttal: ## Response to Reviewer Wt9q
> typos (W2, W4, W11, W12, W13 (Alg.1 and 2))
Thanks for your meticulous examination and useful feedback. We will fix them in the revised version.
> Other questions
**Due to 6k characters' limitation, we only provide short answers. We are happy to explain more if y... | Rebuttal 1:
Rebuttal: ## General Response
We thank all the reviewers for their valuable comments and appreciate that all the reviewers find our study novel and interesting for 3D Equivariant GNNs. As mentioned by the reviewers,
- the problem we are focusing on is challenging and of significance to the community (Revi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Hardware Resilience Properties of Text-Guided Image Classifiers | Accept (poster) | Summary: The paper studies the hardware resiliency of image classifiers, that is misclassification rates under random-bit flipping. They show that initializing the last classification layer using CLIP embeddings can greatly improve hardware resiliency.
To obtain CLIP embeddings, for every class, GPT3 produces C differ... | Rebuttal 1:
Rebuttal: **q1:Positioning of the paper**
**a1:** We would like to clarify that we don't exactly *finetune* CLIP models. We augment a standard, randomly initialized image model, with an additional projection head initialized with rich textual features. The rest of the model is randomly initialized as with ... | Summary: This paper presents a software-based approach to enhance classifier resilience to hardware errors. It leverages GPT-3 to enhance text descriptions for target classes, followed by utilizing the CLIP text encoder to generate text embeddings. These embeddings initialize the classifier head, enabling it to learn r... | Rebuttal 1:
Rebuttal: **q1: Top2Diff Clarifications**
**a1:** We thank the reviewer for highlighting this point. To clarify, we use the same exact procedure for hardware resiliency evaluation as the referenced paper [39]. It is true that we do not have a “ground truth” at runtime, but [39] claims that a strong indicat... | Summary: This paper provides a method to enhance the reliability of image classification models against hardware errors. For this purpose, the authors propose to combine textual and visual information to improve the reliability of neural networks by up to $14\times$, in comparison with traditional error detection and c... | Rebuttal 1:
Rebuttal: **q1: Does the proposed method require textual information during inference?**
**a1:** No, the model only uses the textual information during *training*, to make it more resilient. Please note that in this paper we focus on a *closed-set* classification setting. We only use the text information t... | Summary: This paper studies how CLIP can be used to mitigate the effect of hardware failures on image classification models. The authors do this by incorporating embeddings from the clip text encoder of class based inputs (queried through GPT) to initialize the classification layer. The authors evaluate this technique ... | Rebuttal 1:
Rebuttal: **q1: How robust are CLIP models to hardware failure?**
**a1:** We perform error injection on the CLIP model in the zero-shot setting below and compare it against the baseline (a standard model trained on ImageNet in a supervised manner). We use the resnet50 backbone version of CLIP.
|Backbone|A... | Rebuttal 1:
Rebuttal: We thank the reviewers for all their feedback and comments. We respond to common themes across all reviewers in this global rebuttal, and then answer further specific questions per reviewer individually.
**Q1. Vision model evaluation for hardware resilience, and the use of CLIP-text features**
O... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints | Accept (poster) | Summary: The authors propose a pseudo-semantic loss for deep generative models with logical constraints. The authors consider autoregressive generative distributions (modeled by RNNs, Transformers, etc.), which are more expressive, and go beyond the standard approach of enforcing the constraints on fully-factorized dis... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for engaging with our work and their valuable feedback.
We are happy to see them acknowledge the importance of the problem tackled and the value of both the proposed approximation and experimental evaluation.
[“ It's not clear how one relates A, B, and C to t... | Summary: The paper presents a new way of computing a loss function measuring the degree of satisfaction of logical constraints for autoregressive models. This is an important task, as autoregressive models are now more and more used and calculating the degree of satisfaction of even simple constraints made of a single ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments.
We are delighted they found the paper easy to follow, the problem to be timely, and the approach to be interesting.
["The authors claim that they are the first to learn with constraints under auto-regressive generative models. Wh... | Summary: The method proposes as new neuro-symbolic loss for autoregressive models.
The proposed method approximates the ture expectation of the constraint satisfaction using a pseudo likelihood term computed only in a neighbourhood of the node.
Strengths: I think the paper proposes an interesting extension of neuros... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable feedback and for engaging with our work. We are particularly happy with their appreciation for the language detoxification experiment, which we believe has great potential for real-life impact.
["My first concern/doubt is about the exploitation... | Summary: This paper proposes adding a pseudo-semantic loss into the training of autoregressive models, so that the model can learn logical constraints. Specifically, this approach includes a data augmentation by perturbation (e.g., by Hamming distance), and then adding a penalty loss for generation steps that are not f... | Rebuttal 1:
Rebuttal: Thank you for your response.
[The writing of this paper is of low quality. For example, the meaning of Figure 1 is vague. As a front-page figure, it is even harder to understand than the algorithm pseudo-code.]
The figure is just meant to convey our approach in broad strokes. An autoregressive m... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their valuable feedback towards improving our paper.
We are happy to see the reviewers' excitement regarding the posed problem, the proposed solution as well as the empirical evaluation, with an emphasis on the LLM detoxification experiment.
There hav... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design | Accept (poster) | Summary: This paper introduces a novel approach called the hierarchical training paradigm (HTP) to address the antibody co-design problem. It leverages both geometric neural networks and large-scale protein language models and proposes four levels of training stages to efficiently exploit the evolutionary information e... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for your valuable feedback and constructive comments on our paper. We appreciate your recognition of the strengths of our work and acknowledge the weaknesses pointed out. We have carefully reviewed your feedback and would like to address each concern:
1. Cod... | Summary: In this study, the author points out that the efficacy of existing co-design methods is predominantly limited by the small number of antibody structures. They propose a hierarchical training paradigm (HTP), a novel unified prototype to exploit multiple biological data resources and aim at fully releasing the p... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our study. We appreciate your positive feedback on the motivation of our work and the clarity of the writing, as well as your constructive comments that will help us improve the quality and accuracy of the paper. We have carefully considered each of the points... | Summary: This paper proposes a hierarchical training paradigm for antibody sequence-structure codesign. It incorporates different sources of data, including general protein sequences, antibody sequences, general protein-protein complexes, and antibody-antigen complexes. The motivation is that there are a lot more data ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and comments on our paper. We appreciate your acknowledgment of the potential importance of incorporating multiple sources of biological data for model pre-training. We have carefully considered your concerns and would like to address them as follows:
* Data L... | Summary: Antibody sequence-structure co-design and fix-backbone design is a very appealing task for both industry and academia especially in the context of drug design. The paper introduces a hierarchical training paradigm (HTP) as a potential solution of this problem. Moreover, the offered approach deal with a major i... | Rebuttal 1:
Rebuttal:
Thank you for your thoughtful and constructive review of our paper. We greatly appreciate your feedback and are pleased to know that you find the work strong in several aspects.
Firstly, we are glad to hear that you acknowledge the originality and importance of the problem addressed in our paper... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to all four reviewers for your insightful and constructive feedback on our proposed hierarchical training paradigm (HTP) for antibody sequence-structure co-design and fix-backbone design. We are encouraged by your positive reception of our work and your recognition ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adversarial Examples Are Not Real Features | Accept (poster) | Summary: This paper builds upon the work of [12] "Adversarial examples are not bugs, they are features" written by Ilyas et al. The authors of [12] introduced the concept of robust and non-robust features. According to [12], non-robust features alone can be useful for the classification task. The authors of the current... | Rebuttal 1:
Rebuttal: #### Title: Response to reviewer NVbH
Dear Reviewer NVbH,
Thanks for your careful reading and we are glad to hear that you appreciate the novelty and insights of this work. Below, we address your main concerns.
---
**Q1.** Some suggestions regarding the presentation.
**A1.** Thanks for your ... | Summary: This work analyzes the robustness of deep networks under various tasks, analyzes how adversarial attacks between models trained on different tasks transfers between them, and does so through a newly proposed framework.
Strengths: This paper delves into the robustness of models trained under different tasks (S... | Rebuttal 1:
Rebuttal: #### Title: Response to reviewer nwrB
We apperciate your careful reading and constructive comments on the presentation details. We will revise the paper carefully and take into consideration your suggestions in the revision.
Below, we address your concerns on the paper content.
---
**Q1.** Wh... | Summary: This work challenges the theory on "robust" and "non-robust" features from Ilyas et al. With an extended and more generalized formulation, the authors show that "non-robust" features are indeed very task-specific, and that even supposedly "robust" features are mostly task-specific and hardly provide robustness... | Rebuttal 1:
Rebuttal: #### Title: Response to Reviewer GnEu
Dear Reviewer GnEu,
Appreciate for your careful reading and acknowledging our contributions on the definitions and evaluation of robust features. Below, we address your main concerns, especially those concerning the task-reliance of robust features.
---
**... | Summary: In this study, the authors dispute the argument from "Adversarial examples are not bugs, they are features," where it was suggested that adversarial examples exist due to non-robust yet useful image features. They used self-supervised learning algorithms on both robust and non-robust datasets to test this theo... | Rebuttal 1:
Rebuttal: #### Title: Response to Reviewer WcXm
Dear Reviewer WcXm,
We appreciate for your careful reading of our work as well as recognizing our finding that standard training on robust dataset does not yield true robustness. Below, we address your main concerns.
---
**Q1.** The author should verify whe... | Rebuttal 1:
Rebuttal: The supplementary material for rebuttal.
Pdf: /pdf/22ed6540caab3544be6eb7a41e382f0f2303468c.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Imagine That! Abstract-to-Intricate Text-to-Image Synthesis with Scene Graph Hallucination Diffusion | Accept (poster) | Summary: In this paper, text-to-image synthesis under the abstract-to-intricate setting is studied. Firstly, the input prompt is hallucinated and expanded into feasible specific scene structures by the proposed SGH mechanism. Then, text-to-image synthesis is implemented through a diffusion-based synthesizer by graduall... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and valuable comments. Your suggestions will surely help consolidate our paper. Following we present the point-to-point response to address your concerns. And if you feel our responses effectively relieve your concerns, please kindly reconsider your evaluation.... | Summary: This paper studies a new setup of generating intricate images from abstract prompts. To overcome the issues of vision distraction and wrong binding of using text as the condition to generate images, the author proposes a two stage pipeline by first generating scene graph from abstract text inputs, and then con... | Rebuttal 1:
Rebuttal: We sincerely thank you that you acknowledge the strengths of our work. Your support is the source of the power to push us forward and further enhance the paper. Following, we show the response to your questions one by one.
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**Q1: One claim of the paper is that SG guidance helps image generati... | Summary: The paper proposes a novel approach to text-to-image (T2I) synthesis, specifically focusing on generating intricate visual content from simple abstract text prompts, aka., abstract-to-intricate (A2I) setting. The proposed mechanism, named scene-graph hallucination (SGH), expands the initial scene graph (SG) of... | Rebuttal 1:
Rebuttal: We appreciate it so much that you went through our work so deep. And we are very much excited to receive your such strong support, which will definitely push us moving forward. Following we will answer your questions one by one.
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**Q1: In Figure 1, the issues of vision distraction and wrong b... | Summary: The paper proposes a new setting (or sub-domain) of text-to-image generation (T2I), namely abstract-to-intricate T2I. To tackle the new setting, the authors propose a method (Salad) to enrich the scene graphs parsed from the text prompts based on the discrete diffusion models. The enriched scene graphs are use... | Rebuttal 1:
Rebuttal: We sincerely thank you for going through our paper carefully, and providing valuable constructive feedback, which will surely benefit our work. Following we will address your concerns. And we sincerely hope you can raise your evaluation when you feel that we relieve your concerns.
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**Q1: Task ... | Rebuttal 1:
Rebuttal: # General Response to All Reviewers
Dear reviewers,
Thanks for all of your time to write valuable and constructive comments. Your feedback will definitely assist us in enhancing the quality of our paper, and thus we are committed to incorporating your suggestion in our revision process. Meanwhil... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims at the abstractive setting for text-to-image generation (T2I). They propose scene-graph hallucination (SGH) to perform imagination over the scene graph of the input prompt and make up the missing information. Then SGH can perform better T2I with the completed scene graph. Experiments on the COC... | Rebuttal 1:
Rebuttal: We would like to thank you for taking the time to provide valuable feedback! especially for the recognition of our work, such as 'well-written and easy to follow', ‘goal of abstract-to-intricate T2I is important’, and 'effective', as your support means a lot to us. Following, present the point-to-... | null | null | null | null | null | null |
Rubik's Cube: High-Order Channel Interactions with a Hierarchical Receptive Field | Accept (poster) | Summary: This paper proposes the Rubik’s cube convolution operator to model the high-order channel-wise interactions. The Rubik’s cube convolution applies the spatial-shifting mechanism across channel-wise groups, which is the zero-FLOP and zero-parameter. Moreover, only the point-wise convolution and dot-product are a... | Rebuttal 1:
Rebuttal: **1. Efficiency of the proposed RubikConv.**
We report the model size, FLOPs (an image with 400\*600\*3 pixels), and average running time on the LOL test set (including 15 400\*600\*3 images) in Table 5. The running time is measured on a workstation with an NVIDIA GTX 3090 GPU. We only replace tw... | Summary: This paper proposes Rubik’s cube convolution operator, which is very simple and efficient, especially requiring zero FLOPS / parameters. This novel component improves the several low-level vision networks, enabling the high-order channel interaction and enlarging receptive field of the standard convolutions. S... | Rebuttal 1:
Rebuttal: **1. About the improvement differences and the variants of RubikConv.**
Thanks for the suggestion. Relationship modeling in the channel dimension has been proven effective in the literature for tasks like low-light image enhancement and image de-blurring. Previous works, such as the bright channe... | Summary: This paper proposes the Rubik’s Cube, which can replace the standard convolution layer in the traditional paradigm. With shift operation and high-order channel Interaction, the Rubik’s Cube can generate a hierarchical receptive field and activate the potential of channel interactions. Experiment results show t... | Rebuttal 1:
Rebuttal: **1. Results on Gaofen2.**
Thanks for your reminder. It is a typo and we will revise it. The performance of MutNet on GaoFen2 is shown in Table 1.
Table 1: Quantitative comparisons of guided image super-resolution
| Model | Config | PSNR | SSIM | SAM | EGAS | | | | |
|----... | null | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Near Optimal Reconstruction of Spherical Harmonic Expansions | Accept (poster) | Summary: ### Result ###
The paper studies the problem of recovering a function from a finite number of noisy observations, for the class of "square-integrable functions on the unit sphere" (denoted by $L^2(\mathbb{S}^{d-1})$ when the sphere in question is of degree $d$).
The paper shows, by developing an algorithm, t... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's detailed and insightful comments.
> `Would the authors be able to situate their results in the context of machine learning? For instance, in lines 20-21, the potential applications mentioned seem to be leaning towards physics/astronomy; it would be quite inte... | Summary: A technique is proposed to recover spherical harmonic expansions for functions defined on a d-dimensional sphere from a set of function evaluations. Spherical harmonic expansions are recovered by solving an optimizaiton problem via a kernal approach, which is accompanied by theoretical guarantees. Numerical ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our paper. We greatly appreciate your insights and suggestions.
> `It does not seem that NeurIPS is the appropriate venue for this work. While the field of deep learning on the sphere is an active area of research (e.g. Cohen et al.), this contribution woul... | Summary: The paper studies the approximation of a function $f \in L_2(\mathbb{S}^{d-1})$ from its evaluations, via a degree-q spherical harmonic expansion. To this end, an efficient kernel regression based algorithm is proposed which recovers such a degree-q expansion of f, from the evaluations of f on $\mathbb{S}^{d-1... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our paper. Below we answer your concerns and questions.
> `It’s a bit unclear to me whether the results hold only in the noiseless case, or is the method actually robust to noise. For instance, if we the function evaluations are corrupted with iid centred G... | Summary: To develop kernel regression based algorithm to recover degree-q expansion of f \in L^2(S^{}d-1| - by only evaluating on uniformly sampled points on S^{d-1}.
Strengths: The ideas used in this paper is deeply technical and ideas are complicated. It first re-formulates the problem as least squares regression a... | Rebuttal 1:
Rebuttal: > `Motivate the problem - how it could be applied in real life?`
Many thanks for your valuable feedback on our paper.
We understand the importance of motivating the problem and highlighting its real-life applications. In response to your comment, we would like to elaborate on the practical signi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Consider the $d$-dimensional unit sphere $\mathbb{S}^{d-1}$, and any function $f:\mathbb{S}^{d-1}\rightarrow\mathbb{R}$ defined on the sphere with bounded L2 norm $\|\|f\|\|_{\mathbb{S}^{d-1}}$. This function $f$ can be evaluated at any point $\vec w \in \mathbb{S}^{d-1}$ on the sphere, but it is expensive to ... | Rebuttal 1:
Rebuttal: Many thanks for your valuable feedback on our paper. Below we answer your questions.
>`Discuss why this isn't trivial that we need \beta_{d,q} samples to learn \beta_{d,q} parameters in a polynomial.`
You're absolutely correct in pointing out that there are $\beta_{d,q}$ degrees of freedom, th... | null | null | null | null | null | null |
Game Solving with Online Fine-Tuning | Accept (poster) | Summary: The paper aims to address the game solving problem: providing a game theoretic value to all states in a game. To address the task the paper extends AlphaGo to the game solving case using an approach that distributes game playing scenarios to solvers on parts of the full game tree. Building off prior distribute... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and constructive comments. We address your concerns and questions below. All numbered citations in our rebuttal refer to the sources in the paper’s bibliography.
> results are only in terms of solving a single game
Please refer to “Generalizability to other ... | Summary: This paper proposes a parallel setup for solving games, which includes online fine-tuning of trained proof cost networks to improve their estimations of the proof cost of nodes specifically for subsets of the state space that the prover is currently focusing on. Experiments show significant reductions in the n... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and constructive comments. We address your concerns and questions below.
> The main contribution (online fine-tuning) seems relatively limited in novelty.
Please refer to “Novelty and contribution” in the rebuttal for all reviewers (at the top) for more info... | Summary: The paper addresses the problem of computing provably optimal solutions to game instances. A naive game solving algorithm must therefore address all the possible actions an opponent may pick before emitting a judgement about the game instance value. A leading paradigm when developing this category of algorith... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and constructive comments. We address your concerns and questions below. All numbered citations in our rebuttal refer to the sources in the paper’s bibliography.
> Purely experimental work applied to a specific subset of go instances.
Please refer to “Genera... | Summary: The paper proposes an AlphaZero-based MCTS search procedure modified for game solving (vs the original game playing objective). The algorithm consists of a carefully-engineered distributed MCTS procedure leveraging GPU-based learned proof-cost-network estimates to grow the search tree in promising directions. ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and constructive comments. We address your concerns and questions below. All numbered citations in our rebuttal refer to the sources in the paper’s bibliography.
> Could you please describe the parts of the proposed algorithm or technical ideas that are novel... | Rebuttal 1:
Rebuttal: Dear all reviewers,
We appreciate your time and effort in reviewing our paper. We would like to address the common concerns raised by reviewers. The numbered citations refer to the sources in the paper’s bibliography.
* Novelty and contribution
First, to the best of our knowledge, we are the f... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Parts of Speech–Grounded Subspaces in Vision-Language Models | Accept (poster) | Summary: The paper presents an innovative solution to address the problem of polysemy within CLIP's embedding space. The authors propose a novel approach that involves decomposing CLIP embeddings into distinct subspaces, with each subspace representing a specific part of speech. This decomposition technique enables the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the praise of the paper! We address all comments and questions raised in the two sections below:
- `it is important to note that there are instances of polysemy that cannot be disambiguated solely based on part of speech. […] it remains unclear whether the proposed meth... | Summary: The paper gives a closed-form solution to project CLIP representation of image/text into a subspace with disentangled modes. The proposed method is demonstrated qualitatively in text-to-image generation, and quantitatively by zero-shot classification.
Strengths: - The subspace projection proposed by the paper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their praise of the method as simple, fast, and effective. We address the two weaknesses raised below (where [LX] refers to line number X of the submitted paper):
- `However, the paper (claims to have wide applications in generation tasks) fails to compare to any curren... | Summary: The following work proposes a geometry-aware approach to identifying subspace projections within the CLIP embedding space. The projections allow one to limit the CLIP embeddings to the subspaces corresponding to individual parts of speech (noun, adj... ). This allows for more fine-grained controllability when ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their praise of the paper and thorough review. We address the 3 stated weaknesses below:
- `it would be nice to see some sort of experiment that demonstrates a significant loss in downstream task performance or even a simple plot based analysis as in the supplementary i... | Summary: This work proposes to learn subspaces for disentangling the visual representation in the CLIP space, based on the parts of speech of the prompt. A closed form solution is presented where the norm corresponding to the embedding of word of interest has a maximum norm while the norm of the rest is minimized. Qual... | Rebuttal 1:
Rebuttal: We thank the reviewer for their assessment of the paper as “clearly motivated” and “very well written”. We address below the weaknesses raised and answer the two questions asked:
- `Effect of the prompts: From the quantitative results the role of the subspaces is not clear. For example, by remov... | Rebuttal 1:
Rebuttal: We thank all four reviewers for their thorough comments and positive assessment of the paper:
- `Reviewer-3FWH` states that the work is “clearly motivated” and the method “easy to implement”.
- `Reviewer-QWgZ` praises the “principled approach to handling the non-euclidean nature of the CLIP embe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow | Accept (poster) | Summary: The paper proposed Lagrangian motion magnification using the pre-trained optical flow network. Magnification loss induces that the optical flow of the magnified frame matches with the optical flow of a given frame by $\alpha$ times; color loss regularizes the color consistency between a given and magnified fra... | Rebuttal 1:
Rebuttal: Thank you for your constructive and comprehensive feedback. We would like to address your points below:
**The Term “Self-Supervised”**
We term our method “self-supervised” because it does not need ground truth magnified motions, as opposed to previous work DeepMag. It is common practice in the M... | Summary: This paper proposes a self-supervised model to solve the Lagrangian motion magnification problem without needing ground-truth labels. The network takes as input the two input frames and a magnification factor that ranges from 1 to 16, and outputs a generated frame that has magnified motion from the first frame... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful review. We would like to address your questions as follows.
**Importance of Motion Magnification**
Motion magnification has applications in fields ranging from medical imaging to structural engineering to micro-expression analysis. Motion magnification ... | Summary: The paper introduces an optical-flow-based lagrangian motion magnification method, learned through self-supervised learning. The architecture is very simple -- just a U-Net that inputs two temporally consecutive frames and outputs a motion-magnified image. To train the U-Net, the method uses an off-the-shelf o... | Rebuttal 1:
Rebuttal: We thank you for your positive and comprehensive assessment of our paper. Below, we address your questions and concerns:
**Occlusions and Disocclusions**
This is an excellent observation. As noted our model produces qualitatively good results even in the presence of occlusions and disocclusions.... | Summary: This paper uses the classical method of Lagrangian to self-supervise the task of motion magnification. Thanks to the proposed self-supervision-based technique, the proposed method can also be adapted during the test time. As shown in Figure.1, the proposed method is simple, where the optical flow vectors of vi... | Rebuttal 1:
Rebuttal: Thank you for your positive and well thought-out comments. We would like to address the questions that you have:
**Pre-Trained Optical Flow**
We agree with the assessment that our model’s performance is closely tied to the performance of the underlying flow model, and will add a discussion of th... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful, thorough, and constructive reviews which have helped to improve our manuscript greatly. Please find individual responses to your questions and comments below. We look forward to the discussion period.
Pdf: /pdf/864f8da40d647972b05dc27467996f994b9bfb... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper focuses on learning a pair-wise motion magnification model in a self-supervised manner. The authors employ recent optical flow models to estimate the flow fields between the original and the motion magnified image pairs. The UNet concatenates a sinusoidal encoded magnification factor with the origin... | Rebuttal 1:
Rebuttal: Thank you so much for your thoughtful and positive review. We appreciate that you believe our method to be simple yet effective, and are glad that you found our paper well-organized and well-written. Below, we address some of your questions and suggestions.
**Analysis of UNet**
We agree with you... | null | null | null | null | null | null |
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems | Accept (spotlight) | Summary: This paper introduces a novel approach known as Feature Multiplexing, which allows multiple features to share a single representation space in machine learning systems. This is significant for web-scale systems which handle hundreds of features with vocabularies of up to billions of tokens, where the standard ... | Rebuttal 1:
Rebuttal: Thank you for your review. We address the weaknesses and questions below.
**W1 and Q1:**
- **Regarding training time:** Embedding table size rarely affects the model training time in models of practical interest. As long as there is enough memory to support the embedding tables (i.e., enough CPU ... | Summary: The authors present a method for multiplexing embeddings of various features in the recommender and similar applications, i.e., sharing the feature embeddings in order to save space and improve performance, especially at lower memory budgets. They provide a detailed overview of the relevant prior work, and giv... | Rebuttal 1:
Rebuttal: Thank you for your review. We address the questions and weaknesses below. In particular, we think there is a misunderstanding about our experiment results (which we would like to clarify).
**Regarding datasets:** This is a good point - we agree that it would be valuable to have more detailed expl... | Summary: This paper proposes that in web-scale machine learning systems, features from different fileds can share the embedding matrix without significantly affecting the model's performance. The insight lies in that different feature fields are processed by different model parameters. Therefore, compared to inter-feat... | Rebuttal 1:
Rebuttal: Thank you for your review. We address the questions raised in review below.
**Regarding experiment results:** There seems to be a misunderstanding about collisionless embeddings. This method is not usually a feasible baseline, and it is included in our benchmark evaluation only as a headroom refe... | Summary: The paper introduces a novel "Feature Multiplexing" framework which uses a shared representation space (embedding table) for multiple sparse features. This approach aims to find a balance between model size and accuracy for industrial level recommender system. Besides, the authors provide a theoretical analysi... | Rebuttal 1:
Rebuttal: Thank you for your review. We address the weaknesses below.
**Regarding significance of results:** It is completely fair to be skeptical about the significance of a +0.1% improvement. However, seemingly small improvements can translate to huge numbers in large online systems. For example, Anil et... | Rebuttal 1:
Rebuttal: We'd like to thank all of the reviewers for their efforts. We have included a rebuttal PDF that contains:
- Vocabulary distributions for Critoe, Avazu, and Movielens. This plot helps to explain the differences in behavior for embedding algorithms on Criteo vs Avazu and Movielens.
- A revised versi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift | Accept (poster) | Summary: This paper proposes an augmentation technique to handle covariate shift during graph classification by creating new "environmental factors" and simultaneously preserving "stable" factors. Since unseen environmental factors occur during covariate shift, the authors propose using an adversarial augmentor to find... | Rebuttal 1:
Rebuttal: Thanks for your valuable time and comments! We provide detailed responses as below, and some necessary results are in our **one-page PDF** in the Global Response above. We sincerely hope that addressing your concerns will help change your rating of our work!
### Q1. Limited Novelty & Motivation ar... | Summary: The paper studies on the covariate shift problem in graph classification tasks, as opposed to the more commonly studied correlation shift. The authors propose a novel data augmentation strategy, Adversarial Invariant Augmentation (AIA), guided by two principles, environmental feature discrepancy and stable fea... | Rebuttal 1:
Rebuttal: We gratefully thank you for the positive feedback and constructive comments! To address your concerns, we provide point-to-point responses, and some necessary results are in our **one-page PDF** in the Global Response above.
### Q1. Adversarial training is inefficient.
In Appendix F (from Supple... | Summary: The paper proposes a data augmentation technique on graph datasets. The model consists of two main components: adversarial augmenter and stable feature generator. The adversarial augmenter tries to generate new samples by generating dropping masks for the nodes and edges adversarially, within some augmentation... | Rebuttal 1:
Rebuttal: Thank you for your time and comments! We have responded to your concerns as follows. We sincerely hope that addressing these concerns will help change your rating of our work!
### Q1. Covariate shift is a well-studied problem.
- **We're talking about Graph Covariate Shift.** We agree with you t... | Summary: This paper proposes to enable GNNs handle covariate shifts through the use of synthetic augmentations. An inherent challenge in construction task-specific augmentations is to appropriately handle style (or environment features) and content (or stable features). While other existing works have also emphasized t... | Rebuttal 1:
Rebuttal: Thank you for rating our paper as "well written and easy to follow", "clearly", "sufficiently novel", and "well presented"! According to your concerns, we provide the following responses.
### Q1: Concerns about adversarial augmenter, stable feature generator, masks and regularizer.
- **Differen... | Rebuttal 1:
Rebuttal: # Global Response and One-page PDF from Authors
We appreciate all the reviewers' efforts for reviewing this submission. We are delighted that our paper was noted for being "*well written and easy to follow*", "*clear",* "*sufficiently novel*" by **Reviewer reTY**; "*interesting*", "*new*" by **R... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network | Accept (poster) | Summary: The paper presents a method to reconstruct high resolution face images from feature vectors extracted from a face recognition (FR) system. The reconstructed face images can be used to attack FR systems to gain access under whitebox and blackbox scenarios
The paper also introduce five different scenarios that o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in reviewing our paper and for the comments.
Below, we tried to address the concerns raised by the reviewer:
> The novelty in the proposed framework is limited. Most of the components are based on the existing works, e.g. StyleGAN3 and WGAN.
We acknowledge t... | Summary: The paper studies the template inversion attack on FR systems. The paper involves both white- and black-box attacks. Moreover, five different attacks are considered.
Strengths: This paper poses a potential threat to FR systems in that attackers can reconstruct victim's facial images with the features stored ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. We are happy that the reviewer found our paper easy to follow. We appreciate the reviewer's comments on the strengths of our work. Below, we tried to address the concerns raised by the reviewer:
> My main concern is about the limited application ... | Summary: In this paper, the authors introduce a new method for reconstructing high-resolution realistic face images from facial templates within a face recognition (FR) system. They employ a pre-trained StyleGAN3 network and train a mapping from facial templates to the intermediate latent space of StyleGAN using a GAN-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. We are happy that the reviewer found our paper well-organized and easy to read. We appreciate the reviewer's comments on the strengths of our work.
Below, we tried to address the concerns raised by the reviewer:
> The investigation on face reco... | Summary: The paper proposes a high-resolution face reconstruction method for template inversion attacks. They make use of a GAN-based framework, StyleGAN3, by learning a mapping from facial templates to its intermediate latent space. They evaluate their method on 5 different attacks in whitebox and blackbox scenarios.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in reviewing our paper and for their valuable comments. We are happy that the reviewer found our paper clear and easy to understand. Below, we tried to address the concerns raised by the reviewer:
> The novelty of the proposed method mainly lies in the introdu... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and valuable comments.
We tried to address point-by-point the concerns raised by the reviewers in individual responses.
For simplicity, we use the following numbers to refer to each reviewer in our responses:
- R1: Reviewer S2tQ
- R2: Reviewer T7AV
- R3: R... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this work, the authors propose a new method to execute the template inversion attack against face recognition systems. By simply having access to the feature vectors (latent representations) of the original faces, the adversary can reconstruct a face like the original, therefore showing that face recognitio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Below, we tried to address the concerns raised by the reviewer:
### Reply to Weaknesses
**Reply to Weakness 1:** We acknowledge the reviewer's comment and apologize for the typos as well as grammatical errors. We will fix these errors and will ca... | null | null | null | null | null | null |
Block Broyden's Methods for Solving Nonlinear Equations | Accept (poster) | Summary: In this work the authors introduce block variants of both good and bad Broyden's methods, which exhibit explicit local superlinear convergence rates. The block good Broyden's method, in particular, demonstrates a faster convergence rate compared to existing Broyden's methods that are not dependent on the condi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and helpful comments.
> In the experiment section Figure 1 and Figure 2 are a little bit confusing as for example Figure 1(a) and Figure 1(d) represents the experiments for the same N so they can be grouped under the same subfigure.
**Response:**
We thank t... | Summary: This paper extends the Broyden family quasi-Newton method into block setting and shows explicit local linear convergence rate under mild conditions. More specifically, the authors studied both the “good” and “bad” Broyden algorithms, namely the update on the Hessian/Jacobian and the inverse Hessian/Jacobian re... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and helpful comments.
> Perhaps the biggest question I have toward the comparison of “good” and “bad” Broyden is that for the good Broyden method, still it computes the inverse of ${\bf B}_t^{-1}$, which means that the dependency on the condition number is i... | Summary: This paper studies block Broyden's methods for solving nonlinear equation systems and presents explicit local superlinear convergence rates. For the block good Broyden's method, the convergence rate is independent of the condition number of the Jacobian matrix at the solution, which that of the block bad Broyd... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and helpful comments.
> In Algotithm 1 and Algotithm 2, the Jacobian matrix is explicitly needed even when $k=1$, while that is not the case for classic Broyden's methods.
**Response:**
Our algorithms **do not** require the full information of the Jacobian ... | Summary: The paper proposes variants of block Broyden's methods for solving nonlinear equations. Explicit convergence rates are established, and the numerical experiment validates the theoretical analysis.
Strengths: The paper provides explicit convergence rates of the proposed block Broyden's methods. The theoretical... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and helpful comments.
> The novelty of the proposed block Broyden's method is limited. The proposed methods are very similar to the ones given in [1] (Section 7 and Table 8.1 in [1]). The distinction should be clearly explained in the main paper.
**Respon... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed and helpful comments. We response to the common issues raised by the reviewers here.
### **1. Additional Experiments**
We have compared the performance of the JFNK (Jacobian-Free Newton Krylov) method with ours on H-equation in Figure 1. We observe th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation | Accept (poster) | Summary: The authors introduce a novel method named Equivariant Flow-Matching (EquiFM) for generating 3D molecules, aiming to enhance both categorical features (atom types) and continuous features (atom coordinates). The authors highlight the current limitations of diffusion models, particularly their instability and s... | Rebuttal 1:
Rebuttal: Thank the reviewer for the constructive and insightful comments. We address all your concerns about the computational complexity, the evaluation, and the details of the methods in the following paragraph, and any further discussions are welcome.
### Q1. Computational Complexity and Applicability... | Summary: This paper addresses the molecular generation problem.
The authors propose a conditional flow-matching-based method that employs different generative paths for coordinates and node-wise features.
In addition, EGNN is used for the SE(3) invariant vector field, and an ICP-like algorithm is used for the equivari... | Rebuttal 1:
Rebuttal: Thank you very much for the detailed and insightful comments! The responses to your concerns are listed below:
### Q1. The ablation study is not easy to understand, such as (E)OT+VP_{xxx} in Table 3
As mentioned by the reviewer, in Table 3, the term "$EOT+VP_{linear}$" does refer to the method wh... | Summary: The contribution is a new 3D generative model for molecules. The model is trained using a novel flow-matching objective. The flow-matching objective for coordinates of atoms is novel in that coordinates of 'source' atoms are permuted and rotated to align with 'target' atoms. There is also some exploration of h... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and appreciate the effort in reviewing our work. We will address your concerns on the ablation studies, the evaluations, and the method details in the following paragraph, and any further comments are welcome!
### Q1. Presentations and typos in Line... | Summary: The paper applies the Flow Matching framework for 3D molecule generation achieving state of the art performance on common benchmarks. The authors introduce several innovations to the FM framework to adjust it for the equivariant data setting with different data modalities within a single sample (e.g., 3D coord... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and the recognization of our work. And we address your concerns in the following:
### Q1. How would OT maps scales to larger sets and how computationally prohibitive is it?
I first want to clarify that OT maps are not needed during inference. And... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to the reviewers and the area chairs for the efforts and time spent reviewing our work and the crucial, insightful, and constructive comments. Here we would like to highlight the main concerns of the reviewers and the corresponding responses here:... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Creating a Public Repository for Joining Private Data | Accept (poster) | Summary: The paper considers a scenario where a data owner wishes to publish a private view of their data set, so that others can evaluate join (aggregations) against this data, and related questions. Formally, the problem is to support the following problem: the data owner has a high-dimensional vector which is spars... | Rebuttal 1:
Rebuttal: > Could the optimization results be interpreted in terms of defining a subspace embedding? I think the result follows if we can argue that the SumOverJoin bound holds for all vectors within a subspace.
This sounds interesting. Could you say more? Do you mean a subspace in the vector space of func... | Summary: The authors study the problem of computing a function of a join of two datasets on a user key so that the two datasets are owner by independent parties that never share their data directly. Instead the sender party gives out a DP sketch of the data that is later used by any receiver to compute an approximate a... | Rebuttal 1:
Rebuttal: Thanks for the comments and questions, based on which we did two new experiments, shown in Figure 2 of the attached pdf:
## What's the largest $k$ for which we get meaningful results? (Weakness 1, Question 1)
To investigate the effect of large $k$, we used the EMNIST `bymerge` dataset consisting... | Summary: The paper considers the problem of publishing a privacy-preserving version of a dataset consisting of an identifier and a sensitive attribute. Additionally, it should be possible to join this published dataset by the identifiers with another dataset and compute aggregate statistics on the sensitive attributes ... | Rebuttal 1:
Rebuttal: Thank you for these insightful comments.
## How much noise to add / how to choose ε
The reviewer points out we did not clearly describe how much noise should be added. Many of the points in the review relate to this topic; we'll address them in this section.
### Specifying noise and accuracy in... | Summary: Edit: Change from Weak Accept to Accept under the expectation that the reviewer discussions are incorporated into the manuscript.
The manuscript presumes that two independent parties hold different data related to persons in a repository. As the data is vertically partitioned across the repositories, cross-re... | Rebuttal 1:
Rebuttal: Thank you for your insights. We have done two new experiments based on them, shown in Figure 1 in the attached pdf and described below.
## Many-to-many relationships (W1, Q1)
The reviewer points out our work is limited to datasets with unique keys. In practice, we imagine repeated keys will be c... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments and suggestions. The attached figure includes experiments responding to questions from reviewers eoei and dAeb.
Pdf: /pdf/2bfe364b6d3a3fc048fb04ba044761430ccdf802.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games | Accept (poster) | Summary: The authors propose a doubly smoothed best-response dynamics for two-player zero-sum Markov games, with matrix games as the degenerate case. Upper bounds of Nash Gap with bias and regularized Nash Gap without bias are presented.
Strengths: The technical sections are concrete and solid. The idea of simultaneou... | Rebuttal 1:
Rebuttal: We thank the reviewer for the nice comments about our learning dynamics and the presentation of our technical sections. We next provide a point-by-point response to the reviewer's comments.
**Comment:** The "first form of smoothing" in the policy space ...
**Response:** To our knowledge, fictit... | Summary: This paper studied a best-response type learning dynamics in two-player zero-sum stochastic games called doubly smoothed best-response dynamics. The dynamics uses smoothed value function updates with softmax smoothed best-response, and combines minimax value iteration. Ths dynamics is payoff-based, convergent,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the nice comments about our presentation. We next provide a point-by-point response to the reviewer's comments.
**Comment:** The claim that the proposed dynamics is convergent and rational seems not rigorous since the smoothing bias persists over time and no convergence... | Summary: Authors propose algorithms for learning the Nash equilibrium in two-player games and two-player stochastic games. The algorithm for games is effectively a single-time scale algorithm (Doubly smoothed best response dynamics). While , the algorithm for stochastic games (Double smoothed best response dynamics wit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comments about our work. We next provide a point-by-point response to the reviewer's comments.
**Comment:** If the game has a non-interior Nash, do you still get convergence? Lemma D.1 seems to say that all strategies are played with a probability lower b... | Summary: The authors focus on the problem of finite-sample convergence analysis of independent best-response-type learning dynamics in two-player zero-sum stochastic games. The dynamics are payoff based and stimulate the Shapley operator (that is known to be a contractive). Under the assumption that for any pair of pol... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comments about our work.
**Question:** Can you please explain if your results carry over for other settings like Markov potential Games?
**Response:** For potential games, it was shown in Swenson et al. (2018) that continuous best-response dynamics prova... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort in reviewing this work. We here provide the response to the common comments raised by the reviewers. The point-by-point response to each reviewer's individual comments is provided under the corresponding review.
**Common Comments:**
*Major commen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition | Accept (poster) | Summary: I thank the authors for interesting paper and the effort in expanding the field of face recognition.
The paper proposes a new loss function USS (and its variants) for face recognition. The motivation is to jointly learn a threshold that can be used during the verification process. The authors show the efficac... | Rebuttal 1:
Rebuttal: **Q1. The authors do not show whether learning the $t$ is really necessary.**
Thanks for the suggestion.
We have compared the performance of $L_{\text{uss}}$ and $L_{\text{naive}}$ in **Table A**, it is evident that $L_{\text{uss}}$, by adding the $t$, achieves a remarkable enhancement over $L_{... | Summary: The paper proposes a unified threshold integrated sample-to-sample loss for face recognition, which addresses the limitations of existing methods in exploring the cross-sample relationship and setting a unified threshold. The proposed loss function achieves exceptional performance on multiple benchmark dataset... | Rebuttal 1:
Rebuttal: **Q1. It is unnecessary to use complex symbolic formulas to express simple concepts, such as in lines 105 to 124.**
Thanks for the comments. The reviewer appears to be well-versed in face recognition research and reckons that these formulas present a simple concept. However, it is essential for u... | Summary: This paper proposes UniTSFace, a new approach to face recognition that uses a unified threshold integrated sample-to-sample loss (USS loss). The USS loss features a unified threshold for distinguishing positive from negative pairs and can be enhanced with an auxiliary margin. The authors show that the USS loss... | Rebuttal 1:
Rebuttal: **Q1.The paper mentions that the unified threshold is limited to a sample-to-sample loss. Have you considered extending the unified threshold to sample-to-class loss, and if so, what are the potential benefits and challenges of doing so?**
We thank the reviewer for pointing this out. We are actua... | Summary: This paper presents an interesting idea by focusing on the unified threshold of distinguishing positive from negative pairs in face recognition. Although face recognition has undergo a big step towards real application driven by deep learning, this submission has some new proposal for sample-to-sample based lo... | Rebuttal 1:
Rebuttal: **Q1. From Table 1, it shows that BCE loss is much worse than USS loss. This seems strange and I concern if the experiment is wrong for this loss. The reasons should be discussed.**
Firstly, we believe that the results are correct and we want to emphasize that the results are not cherry-picked. F... | Rebuttal 1:
Rebuttal: We thank all reviewers for appreciating the state-of-the-art performance of our UniTSFace; especially the recommendations from reviewers qAqH and nR9r that “It is interesting to focus on the threshold for distinguishing positive from negative pairs, which is paid less attention,” and “the advantag... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Distributionally Robust Linear Quadratic Control | Accept (spotlight) | Summary: The paper **Distributionally Robust Linear Quadratic Control** considers the case of finite-time linear quadratic optimal control with process and measurement noises and known time-varying dynamics. The novelty lies in the fact that the distributions of the initial state and noises are unknown but lie in an am... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments.
W1) Thank you for suggesting an investigation into the expected cost under different distributions within the ambiguity set. We note that due to the construction of our distributionally robust control problem, the expected cost of our optimal polic... | Summary: The paper proposes a robust method for controlling LQ systems, where process and observation noise distribution laws are unknown, but noise samples are assumed to be independent, zero mean and have their distributions lying close to a a nominal Gaussian distribution in Wasserstein-2 space.
A pair of relaxed s... | Rebuttal 1:
Rebuttal: * **Extension of Theorem 3.5:** We sincerely appreciate your insightful comment, which has led us to a significant advancement in our work. Specifically, we managed to extend the applicability of our findings to cases where the nominal distribution is an elliptical distribution with finite first- ... | Summary: The paper proposes a distributionally robust version of the output feedback linear quadratic control problem. The goal is to control a system with known partially observed linear dynamics in the face of stochastic disturbances. The stochastic disturbances (both measurement and state disturbances) are drawn fro... | Rebuttal 1:
Rebuttal: * **Extension of Theorem 3.5**: We sincerely appreciate your insightful comment, which has led us to a significant advancement in our work. Specifically, we managed to extend the applicability of our findings to cases where the nominal distribution is an elliptical distribution with finite first-... | Summary: - The paper considers a standard LQ setup with uncertainty in the distributions of system noise, observation noise and initial state.
- Described as Zero-Sum Game between the controller and nature, they show the optimal decisions for both players. Specifically they show that the worst-case distribution is Gaus... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comment, which has led us to a significant advancement in our work. Specifically, we managed to extend the applicability of our findings to cases where the nominal distribution is an elliptical distribution with finite first- and second-order moments. This e... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewers for their thoughtful comments and questions.
In the reviews, a common concern was raised about our main result in Theorem 3.5. This theorem establishes the optimality of a linear control policy and identifies the worst-case distribution as a Gaussian dist... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations | Accept (poster) | Summary: The paper studies online adaptive policy selection for nonlinear time-varying discrete-time dynamical systems. The algorithm named GAPS is proposed and is shown to achieve optimal regret, which closes the regret gap between online convex optimization (OCO) and online policy selection. En route, a general proof... | Rebuttal 1:
Rebuttal: Thanks for your comments and please find the response to your comments below.
> The algorithm needs $\Omega(\log T)$ memory instead of a constant complexity to $T$.
To the best of our knowledge, there is no algorithm that can achieve sublinear regret with $O(1)$ memory length in our setting. It ... | Summary: This paper proposes an algorithm, GAPS, for online adaptive policy selection in time-varying systems. The algorithm is shown to achieve optimal $O(\sqrt{T})$ regret based on the contractive perturbation property of the online policy-induced dynamics. Numerical results are provided to verify the performance of ... | Rebuttal 1:
Rebuttal: Thanks for your comments and please find the response to your concerns below.
> The results require a slow change of the policy parameter sequences and an $\varepsilon$-time-varying contractive perturbation.
We require the policy parameter sequences to change slowly for two reasons: 1. Bound the... | Summary: This paper studies online adaptive policy selection for nonlinear time-varying discrete-time dynamical systems. At time step $t \in\mathcal{T}$, the policy picks a control action $u_t$, and the next state and the incurred cost are given by $x_{t+1}=g_t\left(x_t, u_t\right), c_t:=f_t\left(x_t, u_t\right)$, wher... | Rebuttal 1:
Rebuttal: Thanks for your comments. Please see our global response about the generality and complexity of our assumptions. Detailed responses are below.
> The implication of the $R_C > R_S + C\|x_0\|$ in Assumption 2.2 is not clear.
The goal of Assumption 2.2 is to guarantee that the critical contractive ... | Summary: The paper studies online adaptive policy selection for nonlinear systems. The algorithm proposed by the authors, GAPS, is a gradient-based algorithm that achieves the first optimal regret bound in the convex case, and the first local regret bound in the case when convexity does not hold. The authors provided n... | Rebuttal 1:
Rebuttal: Thanks for your comments and please find the response to your comments below.
> It would be interesting to see the comparison against benchmarks in the online control literature including GPC.
The Gradient Perturbation Control (GPC) in Hazan and Singh, [2022] can be viewed as a special case of o... | Rebuttal 1:
Rebuttal: Several reviewers were concerned that our assumptions are restrictive. We argue that they are perhaps more complicated, but *less* restrictive than the assumptions in the most closely related work (e.g., [1, 3, 7]). In particular, we relax the common linear-time-varying dynamics assumption, and ou... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new algorithm, Gradient-based Adaptive Policy Selection (GAPS), for online adaptive policy selection with time-varying dynamics and costs. For analysis, it proposes a general analytical framework for online policy selection. Under this framework, by restricting the problem and policy clas... | Rebuttal 1:
Rebuttal: Thanks for your comments and please find the response to your concerns below.
> It would be nice to have the result of online gradient descent (OGD) oracle in the numerical experiments.
Thank you for the suggestion. For the rebuttal, we performed an experiment comparing GAPS to the OGD oracle as... | Summary: The paper studies online adaptive policy selection in systems with time-varying costs and dynamics.
This paper proposes an algorithm that obtains optimal regret bound in the convex case and a local regret bound in the non-convex setting with four assumptions: (1) the dynamics are contractive starting from a ... | Rebuttal 1:
Rebuttal: Thanks for your comments and please find the response to your concerns below.
Our assumptions generalize the assumptions in the most closely related previous work on online control – please see the global rebuttal for details.
The intricate nature of the contractiveness and stability ball assump... | Summary: This paper presents a novel algorithm, Gradient-based Adaptive Policy Selection (GAPS), for online policy selection in time-varying systems. The authors introduce a general analytical framework for online policy selection via online optimization. The paper also provides theoretical guarantees for the performan... | Rebuttal 1:
Rebuttal: Thanks for your comments.
Our major assumption (Assumption 2.2) is about the joint properties of both the dynamical system and the policy class when composed together in a closed loop. Thus, it is not particularly restrictive on the dynamical system when one has the freedom to choose/design the c... | null | null |
Knowledge Distillation for High Dimensional Search Index | Accept (poster) | Summary: The paper proposes a new method called KDindex to learn lightweight indexes by distilling knowledge from high-quality ANNS. The method outperforms existing learnable quantization-based indexes and state-of-the-art ANNS methods by learning to keep the ranking order, adding the reconstruction loss to minimize th... | Rebuttal 1:
Rebuttal: 1. [Other related works]
> **Comments:** This paper misses some related work, such as Poeem, JPQ, and MoPQ.
Thank you for your valuable advice. The mentioned related works, including Poeem, JPQ and MoPQ, focus more on the joint learning of both retrieval embedding models and the quantiz... | Summary: This paper proposes to train an quantization-based approximate nearest neighbor search index using query and its approximate kNN items which are obtained using a teacher kNN search model. Authors propose loss functions to incorporate the ranking of kNN items as well as additional constraints to prevent trivial... | Rebuttal 1:
Rebuttal: 1. [test-time inference process]
> **Comment**: a clear description of test-time inference process would be helpful.
For comparsion among quantization methods in Table 1, Asymmetric distance computation (ADC) is used.
To compare with more ANNS methods in Table 2, an inverted fil... | Summary: This work addresses the problem of learning a lightweight index for high-dimensional similarity search problems. Lightweight index is desirable in many applications which can't afford a high precision heavyweight index due to higher storage cost or computational constraints. Unlike past work on lightweight ind... | Rebuttal 1:
Rebuttal: 1. [computational cost of distillation]
> **Comments**: As the knowledge distillation process involves retrieving the top-K results for each query using the teacher search model and this is repeated in each iteration, the computational cost of distillation is high.
Thanks for your thoughtful a... | Summary: The paper proposes a method to compress indexes for ANNS by using knowledge distillation. They propose to use a graph-based index teacher model and use the top-k nearest results obtained from the teacher indexes to act as the supervision signals to optimize the compressed function. The student model is optimi... | Rebuttal 1:
Rebuttal: 1. [Training and Indexing time for the KDindex]
> **Comments**: The paper doesn’t mention the training time for the KDindex. In addition to the storage, the indexing time for the index is also important.
Thank you for your suggestion, and we conduct an analysis on the complexity of tra... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for all the time and effort you spent in writing reviews for KDindex. We polish the description and conduct more experiments folloing reviews. The figures (datasets distribution and time-recall curves) and table (time complexity) are attached in PDF.
Authors.
Pdf: /pdf/... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization | Accept (spotlight) | Summary: This work proposes optimization algorithms that achieve optimal convergence rate and reproducibility for the settings of convex optimization and convex-concave minimax settings. This work settled some of the open questions from the previous work, and extend the results to the minimax setting.
Strengths: - Cle... | Rebuttal 1:
Rebuttal: We thank the expert reviewer for recognizing our contribution and for the very positive feedback.
1. **Details about Inexact-EG.**
> Inexact-EG seems like a new method developed in this work. Is it a direct extension of Devolder et al. to the minimax setting?
Extragradient (EG) is proposed by K... | Summary: The authors proposed and studied optimization algorithms in both the convex and the convex-concave minimax setting, where both the criteria of convergence and reproducibility are measured. The authors provided upper bounds on these criteria for the proposed algorithms that matches nearly all of the lower bound... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the contribution of this work. | Summary: The authors investigate the reproducibility problem of algorithms that solve convex and convex-concave minimax optimization problems. They propose new methods with better reproducibility guarantees while maintaining the theoretical state-of-the-art convergence rates.
Strengths: *I want to acknowledge that I ... | Rebuttal 1:
Rebuttal: We thank the reviewer for positive feedback and valuable suggestions. We will mention the limitations that our algorithm requires knowledge of $\epsilon$ and $D$, whereas previous methods do not. We will also add and correct the citations following the reviewer's suggestion. Thanks for the pointer... | Summary: The paper considers the problem of ensuring reproducibility in convex optimization. It builds on a recent framework for understanding reproducibility initiated by Ahn et al. The paper considers both minimization and minimax optimization (the latter being a new setting investigated in this work). The key result... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and insightful suggestions.
1. **Writing of the paper.**
> The paper does not motivate reproducibility adequately on its own. Since the paper has a lot of different results, some more intuition behind the specific bounds and technical ideas obtaine... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies the problem of reproducibility in convex optimization. The notion of reproducibility, borrowed from prior work, measures the "stability" of the output of a procedure under noisy initialization or gradient computation. For the smooth convex setting, they design an algorithm based on running ac... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable suggestions.
1. **Definition of reproducibility.**
> Does this definition indeed reflect reproducibility in practice? There are other sources of instability not accounted for in the analysis. Is disregarding potential numerical instability arising in other ... | Summary: In this paper, the authors introduce a novel algorithmic framework that can achieve near optimal convergence while preserving optimal reproducibility, for minimizing smooth convex objectives and minimax optimization of convex-concave objectives. Here, reproducibility under inexact initialization oracles, inexa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the questions. Here are some clarifications.
1. **The convexity assumption on the objectives.**
> The paper only covers convex objectives. What kind of modifications to the proposed algorithmic framework or the assumptions on the objectives will allow the results to be ... | null | null | null | null |
On the Generalization Properties of Diffusion Models | Accept (poster) | Summary: This paper studies theoretical bounds on score-matching diffusion models' generalization ability, in terms of KL divergence between the true distribution and the learned distribution generated by the model. It is assumed that the score model is parametrized as a time-dependent (time refers to $t$ in the diffus... | Rebuttal 1:
Rebuttal: # Response to Reviewer uaj6
Thank you for your comprehensive review and valuable feedback to help improve the paper. We detail our response below, and please kindly let us know whether our response addresses your concerns.
---
**Weakness 1**: There are two points regarding the setup concern:
... | Summary: This paper provides some generalization bounds for Diffusion Models when seen as score-based models. These bounds are similar to previous literature for GANs and consider two scenarios. In the first one, the target distribution has compact support, and in the second case, it corresponds to a one-dimensional 2-... | Rebuttal 1:
Rebuttal: # Response to Reviewer WQ2A
Thank you for your comprehensive review and your valuable feedback to help us improve the paper. We detail our response below, and please kindly let us know whether our response addresses your concerns.
---
> **Q1**: Some of the motivations of the paper can seem obsc... | Summary: The paper aims to characterize the generalization gap of score-based diffusion models. The setting is as follows. The input is $n$ samples from some unknown distribution $p_0$, and the goal is to approximate $p_0$ by a distribution $p_{0,\hat{\theta}_n}$
where the parameter $\hat{\theta}_n$ is a minimizer of... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4Zu3
Thank you for your detailed review and feedback to help us improve the paper. We detail our response below, and please kindly let us know whether our response addresses your concerns.
---
> **Weakness 1**: Convexity concern.
**A1**: The convexity arises from the fo... | Summary:
This paper proves generalization bounds for diffusion models. With specific stopping, they show that the generalization error goes to zero, with a specific upper bound on the rate that scales polynomially with the sample size and the model capacity. Theorem 1 is the main convergence result, while in Theorem ... | Rebuttal 1:
Rebuttal: # Response to Reviewer ab6x
Thank you for your comprehensive review and your valuable feedback to help us improve the paper. We detail our response below, and please kindly let us know whether our response addresses your concerns.
---
> **Q1**: While it's generally well-written, it could've bee... | Rebuttal 1:
Rebuttal: # Summary of new results (as required)
We sincerely appreciate all reviewers for their insightful and constructive feedback. Besides answering questions in detail to address all of the reviewers’ comments, we want to summarize and highlight new key results as follows, and all of the results are i... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Modulated Transformation in GANs | Accept (poster) | Summary: The paper applies ideas from Spatial Transformer Networks in the context of generative models, specifically GANs, introduces a new module that gives the GAN generator a more natural way to generate content at spatially varying locations. The resulting models are better able to generate content with smooth geom... | Rebuttal 1:
Rebuttal: **Q1. "What exactly are the new capabilities of these models?"**
Compared to existing GANs that perform convolution at *fixed* locations (*i.e.*, shared by all samples), we offer the generator an addition degree of freedom though performing convolution at *variable* locations. For this purpose, w... | Summary: The paper introduces learnable modulated convolutions to the literature on GANs. Instead of using standard convolutions with fixed 3x3 kernels, the paper proposes to learn the kernel spatial offsets to allow flexibility in the receptive field of generators. The proposed module can be added to any GAN based on ... | Rebuttal 1:
Rebuttal: **Q1. About the technical novelty introduced in Sec.3, and "It seems that the method naively applies DCN into existing GANs without any modifications of novel analysis. It is therefore not clear where the technical challenge the paper solves".**
Disagree. Unlike DCN, which is originally evaluated... | Summary: In this paper, the authors equip the generator in generative adversarial networks (GANs) with a plug-and-play module, termed as modulated transformation module (MTM). This module predicts spatial offsets under the control of latent codes, based on which the convolution operation can be applied at variable loca... | Rebuttal 1:
Rebuttal: **Q1. About "the proposed Spatial Temporal Latent Code Modulation is somewhat similar to deformable convolution".**
About the rationale of performing convolution at variable locations, our philosophy is indeed similar to the family of spatial transformation networks, to which deformable convoluti... | Summary: The paper proposes a modulated transformation for GANs. Specifically, they propose learning the offset of each convolutional layer by learning additional convolution layers to predict offsets where the inputs are the latent code and the current intermediate layer features. The proposed method improves the expr... | Rebuttal 1:
Rebuttal: **Q1. About the motivation of mitigating the limitation of AdaIN.**
This should be a misunderstanding. AdaIN has become a standard operation in GANs, which helps model the cross-instance variation. This work inherits the AdaIN design and does *not* intend to improve this operation. Instead, our p... | Rebuttal 1:
Rebuttal: Thank all reviewers for their valuable comments and suggestions. We additionally included some geometry of synthesized samples from various viewing points and qualitative comparisons in **the newly uploaded one-page PDF**.
Pdf: /pdf/a8a586b04a9eb575a2bcd1fcb6c968d18eeee0af.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards robust and generalizable representations of extracellular data using contrastive learning | Accept (poster) | Summary: The authors propose a contrastive learning method for obtaining representations of extracellular recordings which could be used for spike sorting and cell type classification. Transformer based encoder is used to generate low-dim representations of random views of spike waveforms which are then compared using ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their strong assessment of our work and for their detailed comments and questions. The review is very useful and led us to some changes that strengthened the paper significantly. In the following response, we address each point raised by the reviewer.
- The reviewer corre... | Summary: Rebuttal Update: I thank the authors for answering my questions and for the changes and additional experiments that they conducted. I have raised my score accordingly.
This paper proposes a contrastive framework to do spike sorting and cell type identification.
Strengths: The paper is well written. It is eas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very detailed review and for their useful feedback. We hope to address their concerns point-by-point in the following response.
- The reviewer pointed out that the manuscript lacks extensive benchmarking especially with the large number of spike sorting methods curr... | Summary: This paper presents a novel method for self-supervised learning of useful representations for data from extracellular, multielectrode recordings in electrophysiology. The method uses a transformer architecture with causal spatiotemporal attention masks, and contrastive learning based on a set of desirable and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their strong assessment of our work and for their useful questions/feedback. We hope to address their concerns and questions in the following response.
- The reviewer correctly pointed out that CEED is untested for spike sorting of more diverse data with waveforms that ha... | Summary: The paper proposes an approach based on transformer and contrastive learning for tackling problems from extra cellular recordings, including spike sorting and cell type classification. Experiments show the validity over standard PCA and traditional approaches in the field.
Note after rebuttal: I have apprecia... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that the paper is well-written, interesting, and that the results are promising. Since the reviewer has highlighted these strengths, we hope that by addressing their concerns, we can improve the rating.
- The reviewer indicated that the experimental setup is... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the detailed and useful reviews. Using this feedback, we have made improvements to CEED and run a number of new experiments which we detail below. We also provide some discussion of shared reviewer concerns below. Please see the attached PDF for the referenced tables... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
VideoComposer: Compositional Video Synthesis with Motion Controllability | Accept (poster) | Summary: This work aims to allows users to flexibly compose a video with textual conditions, spatial conditions, and temporal conditions.
It introduces a novel framework namely VideoComposer based on the paradigm of compositional generation.
To be specific, it introduces the motion vector from compressed videos as an e... | Rebuttal 1:
Rebuttal:
**Q1: What is the difference between the roles of the Style of CLIP and Single Image of STC-encoder? They both seem to provide content to videos.**
Thank you for highlighting this point.
- **Style condition**. The style condition mainly encapsulates the holistic characteristics of the input im... | Summary: This work proposes a new method called VideoComposer for conditional video generation, especially for video-to-video translation. VideoComposer is constructed upon the Video Latent Diffusion Model and introduces an STC-encoder to integrate multiple spatial and temporal conditions such as RGB images, sketches, ... | Rebuttal 1:
Rebuttal:
**Q1: Integrating the Condition Fusion into U-Net through cross-attention.**
The suggestion to use cross-attention for Condition Fusion in the U-Net is appreciated. While our current choice might not potentially be the optimal one, improving the micro-design of conditioning is beyond the scope o... | Summary: VideoComposer is a tool designed to enhance video synthesis by incorporating textual, spatial, and temporal conditions. It uses motion vectors from compressed videos to guide temporal dynamics and employs a Spatio-Temporal Condition encoder to effectively integrate spatial and temporal relations of inputs. Thi... | Rebuttal 1:
Rebuttal:
**Q1: An ablation study could be conducted on VideoComposer, where each component is removed in turn to evaluate its impact on overall performance. This would help evaluate the value of training under multiple conditions versus a single condition.**
Thanks for your suggestion. We want to clarify... | Summary: The paper presents a method for compositional video synthesis. It introduces motion vectors from compressed videos as a control signal for temporal dynamics. The motion vector can be combined by other conditions such as sketch, and depth map. Both qualitative and quantitative results show that the proposed met... | Rebuttal 1:
Rebuttal: **Q1: Discuss the GAN-based methods like MoCoGAN in the Related Work.**
We greatly appreciate your intention of improving the Related Work by comparing with GANs, such as MoCoGAN.
- Different motivations. MoCoGAN is an unconditional method that aims to improve the quality of video generation, whi... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their positive comments and constructive feedback on our paper. We are encouraged that VideoComposer is recognized for its merits, including
- clarity in presentation [Reviewer akz1, EXcw]
- superior performance both quantitatively and qualitatively over prior meth... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language. | Accept (poster) | Summary: In transformers architecture a word sequence is first represented by a sequence of word embeddings. This sequence of vector is then iteratively transformed by the successive transformer layers of the model. This paper proposes to characterize the trajectories taken by word embedding sequences. Features are int... | Rebuttal 1:
Rebuttal: Weaknesses:
While the starting idea is nice, the submission needs to be improved. Many important points remain unclear. Here is a list in reading order (more or less).
Concerning the dataset: UD is a multilingual dataset, what is the language ? Why selecting only very short sentences ?
We focused... | Summary: This paper hypothesizes that the deep, casually-masked transformer models learn to predict by linearizing representational trajectories. This hypothesis is rooted in observations from the neuroscience literature. The hypothesis is tested through experiments that probe:
(1) the degree to which representatio... | Rebuttal 1:
Rebuttal: Strengths:
We agree with the reviewer on this point, and to our knowledge, no prior studies in neuroscience have investigated geometrical properties of language networks related to straightening and connecting it behavior of these models.
Weaknesses:
We thank you the reviewer for bringing thi... | Summary: This work investigates whether large language models learn to "straighten" the word-by-word representation of sentence as it passes through the model layers. The word-by-word curvature of the sequence embeddings is defined as the angle between two consecutive word embeddings (i.e. arccos of the cosine similari... | Rebuttal 1:
Rebuttal: W1.
we agree with the reviewer that the causation is a harder question to answer. And will emphasize in the updated version of the paper that we are showing the evidence at the correlation in this work. We intend to extend our work in causation direction by training models and biasing their repre... | Summary: This work examines the an hypothesis regarding neural trajectory straightening as a mechanism, by which neural language models achieve next word prediction. Specifically, this hypothesis connects between the objective of next word prediction and extrapolation to the embedding of the next word in neural represe... | Rebuttal 1:
Rebuttal: Discussion of prior work:
Thanks for the reviewer for bringing this issue to our attention, we will include a more through discussion of prior work in our revised manuscript. There are a number of prior work that utilized geometric approaches to understand the internal representation of language ... | Rebuttal 1:
Rebuttal:
Pdf: /pdf/f7360df41b06309afb15fe6352db7e478bf0301d.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides evidence that autoregressive language models - specifically the GPT family straighten the internal trajectory of word sequences, making them more linear, in order to better predict next words. They show that trained models decrease sequence curvature across layers, larger models straighten ... | Rebuttal 1:
Rebuttal: Weaknesses:
The paper did not perform any ablation studies to see what is causing the trajectory straightening. Removing different components of the transformer like the feed-forward layer, and seeing the effects on straightening may lead to some more insights. It is not clear if straightening dep... | null | null | null | null | null | null |
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge | Accept (poster) | Summary: Causal effect estimation from data often requires assumptions about the causal relationships, either through explicit causal graph structures or implicit conditional independence statements. When confounding exists, the front-door adjustment becomes important for estimating the causal effect of treatment on th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and time. Below, we respond to their comments:
1. **Figure 5**:
We thank the reviewer for pointing out this typo. We will correct this in the revised version.
2. **It might be necessary to explicitly state that $y$ is not a child node of $t$**:
... | Summary: The authors proposed a method for estimating causal effects without requiring the knowledge of fully-specified causal graph, focusing on the case where unobserved confounding between treatment and outcome exists. This approach using a front-door-like adjustment formula has a novel contribution in that it can e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and time. Below, we respond to their comments:
1. **Using inverse variance weighting + double machine learning**:
We thank the reviewer for pointing out the possibility of variance and bias reduction using inverse variance weighting and double m... | Summary: The paper investigates the problem of estimating the average treatment effect of variable "t" on variable "y" within the Pearlian framework. The paper proposes an algorithm that enables causal effect estimation using a front-door-like criterion while relying on only a limited knowledge about the underlying gra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and time. Below, we respond to their comments:
1. **How and why does the exhaustive search run fast for the random class of graphs generated in Section 4.1?**:
We thank the reviewer for this question. We do not claim that the exhaustive search ru... | Summary: This paper proposes a method for estimating causal effects between the treatment variable and the outcome variable using front-door adjustment beyond the Markov equivalence class. This method is applicable when there are unobserved confounders between the treatment and outcome variables and does not require kn... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and time. Below, we respond to their comments:
1. **Assumption 3 requires knowledge of all direct descendant nodes**:
We agree with the reviewer that requiring the knowledge of all the children of the treatment variable is crucial for our method... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Customizable Image Synthesis with Multiple Subjects | Accept (poster) | Summary: This paper aims to generate controllable images with multiple subjects as constraints. A residual token embedding is learned to shift the raw subject to the customized subject. A layout prior is further provided as the spatial guidance for subject arrangement. The experimental results demonstrate the effective... | Rebuttal 1:
Rebuttal: ### **Author Response to Reviewer oYd1**
We greatly appreciate all of your valuable suggestions, which play a pivotal role in enhancing the quality of our paper. Bellow we address all your concerns.
#### **Q1: About generated results with more than 6 subjects.**
**A1:** Thanks. As shown in Fig. 3... | Summary: This paper proposes a method to achieve customizable image generation with multiple subjects. To achieve the subject generation, it is done by learning a residual prediction for the subject tokens. To make multiple subject tokens combinable, it proposes a text-embedding preservation loss to make the embedding ... | Rebuttal 1:
Rebuttal: ### **Author Response to Reviewer msmN**
We greatly appreciate all of your valuable suggestions, which play a pivotal role in enhancing the quality of our paper. Bellow we address all your concerns.
#### **Q1: More details about the layout guidance method.**
**A1:** During inference, the layout pr... | Summary: This paper introduces a novel method for multi-subject, subject-driven text-to-image generation. The authors develop an efficient system that embeds single subject information effectively and seamlessly combines these separate subject embeddings to generate the final multi-subject image. The key concept is to ... | Rebuttal 1:
Rebuttal: ### **Author Response to Reviewer Nfe2**
We greatly appreciate all of your valuable suggestions, which play a pivotal role in enhancing the quality of our paper. Bellow we address all your concerns.
#### **Q1: Ability to maintain subject detail.**
**A1:** Thank you for the questions raised by the ... | Summary: This paper studies how to efficiently represent a particular subject as well as how to appropriately compose different subjects. The author finds that the text embedding regarding the subject token can serve as a simple yet effective representation. To capture features of a specific subject, the author propose... | Rebuttal 1:
Rebuttal: ### **Author Response to Reviewer QhyH**
We sincerely appreciate the affirmation from the reviewer for our work. It serves as a strong motivation for us! Bellow we address your concerns separately.
#### **Q1: About difference between residual token embedding and Textual Inversion.**
**A1:** Textua... | Rebuttal 1:
Rebuttal: ## Author Response to All:
Dear reviewers,
We thank all reviewers for their time and efforts in reviewing our paper. These constructive reviews can bring multiple improvements for our manuscript. We are encouraged that the reviewers appreciate our method, including
* innovatively designs [Review... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a new method for generating new images of any combination containing given objects. It combines individually learned single-subject residuals for multi-subject generation without retraining. The authors proposed to use text-based embedding to represent individual objects. Once a residual to... | Rebuttal 1:
Rebuttal: ### **Author Response to Reviewer X4Jp**
Thank you for your positive comments and valuable feedback on our work! We are excited and encouraged by your support! Bellow we address your concern separately.
#### **Q1: About the main contributions.**
**A1:** Thanks. As you have pointed out, using diff... | null | null | null | null | null | null |
Scattering Vision Transformer: Spectral Mixing Matters | Accept (poster) | Summary: Assistant
The article is about the Scattering Vision Transformer, or SVT, which is a new adaptation of transformers for computer vision tasks. Its unique feature is the use of a spectrally scattering network, which captures fine-grained information about an image and addresses the issue of information loss cau... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments, which we believe are insightful and shall help in improving the quality of the final submission.
1. A background section on DT-CWT [46] shall be added to the final version of the paper to help readers understand it, before explaining SVT method det... | Summary: This paper proposed to use DTCWT in the transformer model in early stage, with the motivation of saving computation cost without information loss. The proposed scattering module has a low-pass and a high-pass branch. Tensor multiplication & Einstein multiplication are applied to the LF & HF branch respectively... | Rebuttal 1:
Rebuttal:
1. **Comparing SVT with ViT with initial CNN layers:** We have conducted an experiment where the initial layers of a ViT are convolutional networks and later layers are attention layers to compare the performance of SVT. The results are captured in Table 4 in the attached PDF, where we compare SV... | Summary: This paper proposes to use Dual-time Complex Wavelet Transforms to decompose images into high and low frequency components in vision transformers. With this technique, it claims to address the problem of attention complexity without the loss of information as in Fourier or DWT based Transformers. This claim is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments, which shall result in significant improvements to the quality of the final camera-ready version of the paper
1. One of the claims of the paper is the ability of DTCWT [46] to separate the low-frequency and high-frequency components of the image. ... | Summary: The paper proposes SVT, a novel vision transformer model that addresses the challenges of attention complexity and capturing fine-grained information in images. SVT utilizes a spectral scattering network and the Dual-time Complex Wavelet Transforms (DTCWT) to decompose image features into low-frequency and hig... | Rebuttal 1:
Rebuttal: We thank the reviewer for his comments.
1. **Language:** Thanks for pointing out this – we shall undertake a thorough revision of the paper to address all language issues.
2. SVT is a generic recipe for componentizing the transformer architecture and efficiently implementing transformers with le... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive suggestions. We intend to incorporate the feedback to obtain an improved revision of our paper – we sincerely believe that the comments shall improve the quality of the paper significantly.
We provide clarifications for the points raised by the re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces the Scattering Vision Transformer (SVT), which utilizes a spectrally scattering network to capture fine-grained information in images and addresses the invertibility issue. SVT incorporates a novel spectral mixing technique using Einstein multiplication for efficient channel and token mixi... | Rebuttal 1:
Rebuttal: We thank the reviewer for his insightful comments on the paper and to guide us on the possible research directions.
1. Regarding the invertibility VS redundancy trade-off, we conduct an experiment to show that invertibility helps in comprehending the image and not just contributing to the perform... | null | null | null | null | null | null |
Multi-modal Queried Object Detection in the Wild | Accept (poster) | Summary: Based on recent vision-language fundamental models such as GLIP and Grounding DINO, the authors propose an improved multimodal query pipeline. A Gated Class-scalable Perceiver is used to apply cross-attention for both the language and vision query inputs. A masking strategy for text tokens is further proposed ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Below, please find our responses to each of the concerns or questions raised in the review:
> **Training efficiency on time and data**
We acknowledge that our model is built upon pretrained GLIP/GroundingDINO models, which indirectly utilizes their pretrain... | Summary: The paper propose MQ-Det, a novel module that integrates both language and visual queries efficiently for object detection tasks. This module enhances each category token with vision queries, providing rich detailed visual context to the text-based models. The proposed method is experimentally tested on LVIS a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Please find our responses below:
> **Comparison in the zero-shot scenario**
We acknowledge that our setting is different from the zero-shot setting in previous language-queried detectors, since we use additional visual exemplars along with texts as category... | Summary: The paper introduces MQ-Det, a novel approach for open-vocabulary object detection that combines textual descriptions and visual exemplars as category queries. MQ-Det aims to address the limitations of existing text-queried object detectors by incorporating visual information and providing various granularity ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. Please find our responses below:
> **Training data**
Thank the reviewer for your valuable reminder. Our model is indeed built upon pretrained GLIP/GroundingDINO models, which indirectly utilizes their pretraining datasets. We will provide the f... | Summary: In this paper, the authors proposed MQ-Det which leverage both textual and visual information for object detection in the wild. The proposed plug-and-play GCP module is very compatible with existing mainstream architectures. The authors conducted extensive experiments on multiple benchmark datasets and showed ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Please find below our responses to the questions raised in the review:
> **More explanation on redundancy with joint input in the last row of Tab. 3 (b)**
The learning difficulty derives from the simple concatenated input $cat(\hat v_{i}, t)$, which equall... | Rebuttal 1:
Rebuttal: Dear reviewers, area chairs, senior area chairs, and program chairs,
We sincerely thank for the valuable comments. It is pleasure that this work has been fully recognized by Reviewer RdSN (Accept) and Reviewer JLFL (Weak Accept), including "**a novel approach**", "**a simple yet effective archit... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Faster Differentially Private Convex Optimization via Second-Order Methods | Accept (poster) | Summary: This paper studies DP convex optimization by using second-order methods. The authors present a private version of the cubic regularized Newton method and prove it faster than the first-order methods under strongly convex case. They also provide efficient second-order method for solving the DP logistic regressi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments on our submission. We respond to the weaknesses raised.
> **W1**: The algorithm for solving the general strongly convex functions seems straight forward:
We respectfully disagree. The most natural way to privatize the non-private Newton’s method is to ad... | Summary: This work investigates the use of second-order methods in differentially private convex optimization for machine learning. The authors propose a private variant of the regularized cubic Newton method and demonstrate its quadratic convergence and optimal excess loss for strongly convex loss functions. They also... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We respond to the weaknesses and questions below.
> **W1**: Laplace noise and pure DP:
We can potentially use laplace noise to design algorithms with stronger pure-DP guarantee. However, it has been observed empirically and theoretically that the ... | Summary: The paper introduces the use of second-order methods in differentially private optimization. In particular, two algorithms are proposed. One is based on cubic regularized Newton and works for strongly-convex functions. The other one is designed specifically for the logistic regression problem. Numerical experi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We respond to the weaknesses raised below.
>**W1**: Justification of using second-order methods and comparison with first order methods
Our goal in this paper is to understand whether second-order methods are compatible with DP. For the theory, ... | Summary: This paper considers the problem of differentially-private convex optimization and discusses how second-order information can be utilized to accelerate the optimization process while also achieving the optimal excess error, with DP involved. The main contributions of this paper are the two proposed algorithms,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We respond to the weaknesses and questions below.
> **W1**: The authors could clearly state their contributions and remark on the significance:
The existing DP optimization literature is almost exclusively restricted to first-order (and zero-order... | Rebuttal 1:
Rebuttal: We are extremely grateful to the reviewers & AC for their time & comments. Their comments have been very constructive. We respond to each review individually, but we respond to some common points here:
**On the slow convergence of DP-SGD:** Several reviewers questioned the claim that DP-(S)GD con... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces an algorithm that utilizes second-order information to enhance the speed of private convex optimization, concurrently ensuring optimization excess error is minimal.
The outcomes of the experiments indicate that employing second-order information can expedite Differential Privacy (DP) opti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We respond to the weaknesses and questions below.
> **W1** potential impact
DP optimization is an important area with numerous practical applications. Our results are a substantial deviation from the existing DP optimization literature, which is a... | Summary: This paper focuses on the problem of private convex optimisation using second order information where the main motivation is the acceleration of private convex optimisation problem as DP-SGD shows slow convergence. This is done in two parts. Firstly, they introduce the privatised version of the cubic method of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We respond to the main points below.
> **W1:** The scope of the paper is limited to logistic regression.
For simplicity and clarity, many of our results focus on logistic regression, but our techniques are more widely applicable. Logistic regress... | null | null | null | null |
Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tailed Noise | Accept (spotlight) | Summary: The author(s) proposed an improved convergence analysis of clipped gradient descent with heavey tail noise. By the improved analysis, the author(s) can either improved the logarithmic dependency of $T$ or relax the known time horizon assumption.
Strengths: - The author(s) proposed a new analysis framework for... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We will add a table in the revision of the paper.
In Line 190: The term $\left\langle \nabla f(x_{t}),\theta_{t}\right\rangle$ does not appear in Eq (5) because we have decompose it into $\left\langle \nabla f(x_{t}),\theta_{t}^{u}\right\rangle +\left\lan... | Summary: The paper addresses the problem of deriving high-probability complexity bounds for the convex and non-convex optimization problems on closed convex sets under the assumption that the noise in the stochastic gradient has bounded $p$th moment for some $1<p\leq 2$. The key feature of the results is that they have... | Rebuttal 1:
Rebuttal: We thank we reviewer for pointing out the significance of our contributions and for the detailed review.
**Regarding the deterministic term**: The power of T in the term is $O\left(T^{\frac{1-2p}{3p-2}}\right)$. We can see that as $p\to1$ the rate becomes precisely $O\left(T^{-1}\right)$, which ... | Summary: This paper considers the high probability guarantees for clipped stochastic gradient descent with heavy-tailed noise, in the setting of Zhang et al. (2020) where the unbiased gradient noise has finite p-th moments, where $p\in(1,2]$. The bound obtained in the paper is time-optimal and in the non-convex case, m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments.
**Regarding the assumption** $E [ ||\widehat{\nabla}f(x)-\nabla f(x)||\_*^p | x ]\le\sigma^{p}$:
The reviewer is correct that in the small mini-batch setting, the variance may not be bounded. The assumption to the bounded $p$-th moments is a step towards... | Summary: This paper provides theoretical guarantees for clipped gradient methods in the presence of heavy-tailed gradient noise distributions, where the noise has a bounded $p$th moment for some $1 < p \leq 2$. The convergence guarantees are established with high probability. The authors' approach distinguishes itself ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and feedbacks.
As pointed out by reviewer 2mhk, we believe that our paper makes “an important contribution to the stochastic optimization literature”. This includes:
1. Achieving the optimal rates in $T$ in all considered cases, closing the upperbound and l... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors consider clipped stochastic gradient methods: clipped-SMD in convex case (in non-convex case the y consider clipped-SGD), clipped-ASMD in convex case. They provided improved high-probability convergence analysis, which has some similar steps with analysis from (Gorbunov et.al 2020, S... | Rebuttal 1:
Rebuttal: We thank the reviewer for the compliments and feedback.
**Regarding the weakness**:
We use the general definition to address smoothness and non-smoothness in a unified way. The proofs for the non-smooth case follows very similarly to the smooth case. We give a sketch below in response to the r... | null | null | null | null | null | null |
Resilient Constrained Learning | Accept (poster) | Summary: Reasonable requirement specification in constrained learning has long been hindered by the presence of compromises and limited prior knowledge about the data. As a treatment, this paper proposes resilient constrained learning that adapts the requirements while simultaneously solving the learning task. Specific... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. In what follows, we address the points raised by the reviewer.
We respectfully disagree with the claim that the experimentation conducted is *(Weaknesses 1) simplistic*. Both the federated and invariant learning are benchmark setups used in recen... | Summary: This paper proposes an approach to solve the constrained learning problem (where the considered function is convex), named resilient constrained learning. The main idea is to relax the learning constraints according to how much they affect the considered task. It can be viewed as a generalization of the standa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. In what follows, we address the main points raised by the reviewer.
*some important parts are put in the appendix, like the algorithm 2* The algorithm referenced below theorem 1 should be Algorithm 1, which is indeed included in the main text. Al... | Summary: This paper introduces the concept of resilient constrained learning, which aims to find a compromise between reducing the objective loss and staying close to the original problem. This paper presents conditions for achieving a resilient equilibrium and provides equivalent formulations of resilient relaxation. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. In what follows, we address the points raised by the reviewer.
*... the inequality in eq (1) should be...* Thanks for the observation, it was a typo.
*...the paper does not comprehensively compare existing approaches to constrained learning,...*... | Summary: This paper proposes a novel resilient learning approach for constrained learning problem. In the presented approach, constraints are interpreted as nominal specification that can be relaxed to find a better compromise between objective and requirements. The first main contribution of this paper is to relax con... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. In what follows, we address the main points raised by the author.
*(Weakness 1) Some basic concepts should be explained more clearly, such as the definition of nominal specification.* Thanks, we will add the clarification that nominal specification means... | Rebuttal 1:
Rebuttal: # Response to all Reviewers
We sincerely thank all reviewers for their efforts in reviewing our paper. We are glad to see that all reviewers have expressed that this is a novel and well-motivated method with high potential impact in practical applications. We are also thankful for the reviewers i... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale | Accept (poster) | Summary: This work presents a text-conditional speech synthesis model with an optimal transport path and conditional flow matching. The model is trained with a large-scale cross-lingual dataset for zero-shot style transfer and content editing.
Strengths: The work adopts a conditional normalizing flow with an optimal t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address common questions in Author Rebuttal above and other questions here. We will incorporate all feedback in the final version.
**1. The concept to train the model by infilling speech given audio and text has been already presented in man... | Summary: The paper proposes voicebox, a text-guided generative model for speech at scale. By abstracting many speech tasks into speech infilling tasks, voicebox is able to conduct zero-shot text to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation in monolingual or crossl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address common questions in Author Rebuttal above and other questions here. We will incorporate all feedback in the final version.
**1. As Table 5 shows, VB is able to generate speech with different styles. Can this feature be used in Table ... | Summary: This paper proposed Voicebox, a non-autoregressive flow-matching model designed to infill speech by leveraging given audio context and text. Notably, Voicebox capitalizes on a substantial amount of data, consisting of 50,000 hours of speech, which contributes to its impressive performance across various speech... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address common questions in Author Rebuttal above and other questions here. We will incorporate all feedback in the final version.
**1. The demos provided in the supplementary materials are not as satisfying as described in the paper. For ex... | Summary: This paper proposes VoiceBox, a speech infilling model based on a flow-matching generative model. VoiceBox is trained to fill in masked speech based on unmasked speech and given text, and it can perform various tasks depending on how the mask is applied to the speech during inference. VoiceBox allows for speec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address common questions in Author Rebuttal above and other questions here. We will incorporate all feedback in the final version.
**1. In order to calculate SIM-r as mentioned in the paper, were both the reference speech prompt and all gene... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. We are glad that they found Voicebox is highly versatile and can perform many different tasks (**FPC6, K4NY, pReF, Peh7**) by abstracting them into speech infilling (**Peh7**). We are encouraged that they found Voicebox is well-evaluated (**K4N... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present Voicebox, the text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, sty... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address common questions in Author Rebuttal above and other questions here. We will incorporate all feedback in the final version.
**1. Do you need tags for different task inferences?**
**No.** Voicebox performs different tasks by preparing... | null | null | null | null | null | null |
Query-based Temporal Fusion with Explicit Motion for 3D Object Detection | Accept (poster) | Summary: This paper proposes a small component that can be added on-top of single-frame 3D object detectors and perform (late) temporal fusion. This comes at a tiny computational cost and provides some performance improvements.
Strengths: S1) The background, as described in this paper in the introduction and related w... | Rebuttal 1:
Rebuttal: Thank you for your patient and detailed review. We try to address your comments below.
**Missing references:** Sorry for missing these references. We will cite them into our submitted paper.
**I would have expected an experiment with BEV-based fusion in which the background was removed:** Thanks... | Summary: The paper introduced a Query-based Temporal Fusion Network. It faciliate the object queries in previous frame to enhance the
current object queries by aproposed Motion-guided Temporal Modeling module, which utilizes both spatial and motion information to construct a cost matrix for efficient temporal fusion. T... | Rebuttal 1:
Rebuttal: Thank you for your patient and detailed review. We try to address your comments below.
**Difference from CenterFormer:** Thanks. In fact, our method is different from CenterFormer. CenterFormer conducts the temporal fusion between the current queries and historical BEV features, which is a sparse... | Summary: This paper proposes a new strategy of fusing temporal information for camera-lidar based 3D object detectors. The main method is to design a plug-and-play module, which uses predicted velocity and vehicle ego information to compute the correspondence matrix among queries explicitly. This module can be integra... | Rebuttal 1:
Rebuttal: Thank you for your patient and detailed review. We try to address your comments below.
**Analyze the attention and MTM:** Good suggestion. As shown in table, We replace the MTM with attention operation and find that there is no noticeable performance gain. The main reason is that objects with sim... | Summary: In this paper, the authors propose a simple and effective Query-based Temporal Fusion Network (QTNet). The main idea is to exploit the object queries in previous frames to enhance the current object queries by the proposed Motion-guided Temporal Modeling (MTM) module, Experimental results show the proposed Q... | Rebuttal 1:
Rebuttal: Thank you for your patient and detailed review. We try to address your comments below.
**Difference of using motion information with MPPNet and MSF:** Thanks. The main idea of our QTNet is different from MPPNet and MSF. MPPNet utilizes the velocity prediction for motion compensation in tracking, ... | Rebuttal 1:
Rebuttal: We upload a pdf, which contains the visualization about orientation, results on the nuScenes leaderboard, and the illustration about our progressive temporal fusion mechanism.
Pdf: /pdf/9bd27c627ae0accf5bfeb19d70fc25a29d64f02c.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces an approach that leverages object queries as a form of temporal memory. By establishing an attention map between queries from current and previous frames, the method effectively captures and encodes explicit motion information using velocity prediction and pose transformation. The propose... | Rebuttal 1:
Rebuttal: Thank you for your patient and detailed review. We try to address your comments below.
### W1: In line 126, the memory bank stores $Q_{t-1}$ , however in line 154, you actually use previous fused queries ${Q^{'}}_{t-1}$.
Sorry for the misunderstanding. In fact, we only store historical queries $[Q... | null | null | null | null | null | null |
Emergent Communication for Rules Reasoning | Accept (poster) | Summary: This work investigates the emergent communication framework for reasoning rules. That is, unlike prior studies that focus on communication about perceived low-level contexts, this paper proposes a cognition-oriented environment to encourage agents to reason and communicate about high level-rules. To this end, ... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments. We would like to clarify the concerns as follows:
1. > Clarify this work's position: study the origin of the human languages or develop intelligent communicating artificial agents.
Our work's position closer to the later, i.e., developing intelligent c... | Summary: This paper takes the ever-popular Lewis signalling game for emergent
communication and studies experiments on rule-focused communication as opposed
to the perception-focused communication of prior work. In particular, it uses
a modified version of Raven's progressive matrices to formulate a signalling
game di... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments. We would like to clarify the concerns as follows:
1. > Comparison with previous work using symbolic dataset.
We compare with previous emergent language work using symbolic datasets (cited in our paper) from multiple angles: research goals, task orientation... | Summary: This paper proposed a new environment along with the training framework for emergent communication of abstract rules. They designed a context generation pipeline rule-RAVEN to avoid overfitting and a two-stage curriculum training method for more stable convergence. They evaluated the emerged language from the ... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments. We would like to clarify the concerns as follows:
1. > Experiments/explanations about 1a and 1b.
(1a) The communication training process benefits from both the structural and functional requirements.
From the listener's perspective, structural... | Summary: This paper introduces a novel setting for abstract reasoning (i.e., RAVEN problems) by proposing a speaker-listener framework for communicating higher-level abstract rules. The authors propose an unbiased dataset (rule-RAVEN) to overcome overfitting in the original RAVEN-family datasets (I-RAVEN), and propose ... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments. We would like to clarify the concerns as follows:
1. > More comparisons to existing formulations [1].
Similar to many previous works, the agent communicative formulation (i.e., the definition of 'abstract concepts') in [1] identifies the **inner-contex... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes an emergent communication game over abstract visual concepts, inspired by Raven's progressive matrices tests. The basic idea is to evaluate neural speakers and listeners on a communication game, where the speaker sees a collection of images encoding some abstract rule (e.g. "number of objec... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful comments. We would like to clarify the concerns as follows:
1. > Downstream transfer to visual reasoning tasks, and set more realistic environments requiring communication of rules over visual (image) inputs.
We demonstrate the emerged language's transfer perfor... | null | null | null | null | null | null |
Composable Coresets for Determinant Maximization: Greedy is Almost Optimal | Accept (poster) | Summary: The paper investigates the composable coresets for the determinant maximization problem that aims to pick $k$ vectors with the maximum volume. The authors prove that the widely-used greedy algorithm also provides composable coresets with an approximation factor of $O(k)^{3k}$, followed by showing a local optim... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback. Below, we have addressed your comments and questions.
> The paper is a follow-up work of [IMGR20]. The improved analysis for the greedy algorithm is interesting but not the first result of a composable coreset.
This is correct. However, our algorithm clos... | Summary: This work studies the determinant maximization problem, in which the input is a set of $n$ vectors in $d$ dimensions, and the goal is to select a subset of $k\leq d$ of these vectors which maximizes the determinant of the Gram matrix of the $k$ vectors, which is also the volume of the parallelepiped spanned by... | Rebuttal 1:
Rebuttal: We thank you for reviewing our paper and your positive feedback. In what follows we address your concerns and questions.
----------
> While the result is very important, the techniques might be viewed as incremental over the analysis of local search given in prior work
[IMGR20] discusses and a... | Summary: The paper gives a tighter analysis for the greedy algorithm for constructing composable coreset for the determinant maximization problem. The greedy algorithm performs well in practice; however, the previous analysis was giving an approximation factor much farther from the lower bound. The authors in the paper... | Rebuttal 1:
Rebuttal: We thank you for reviewing our paper and your positive feedback. We argue that while this is primarily a theoretical contribution, it is valuable for the following reasons:
- While it was already known that greedy does well in practice, our locality theorem, along with our experiments showing a lo... | Summary: The paper considers the problem of picking k among n vectors that maximize the volume of the parallelepiped spanned by the selected vectors, which is equal to the squared determinant of the corresponding Gram matrix. In particular, the authors focus on the composable coreset setting of the problem where given ... | Rebuttal 1:
Rebuttal: To Reviewer rYtN,
We thank you for your positive feedback. Below, we have addressed your comments and questions.
-----
> The Greedy algorithm description in the preliminaries Section 1.1.1 does not match the pseudocode in Algorithm 1 with no mention of the relation between them. It is good to... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their positive and constructive feedback.
Bellow, we give a proof that the locality property can recover and in fact marginally improve the k! guarantee for the offline version of the greedy algorithm [CMI09]. We will include it in the final version. While the focus... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Use perturbations when learning from explanations | Accept (poster) | Summary: This paper proposes a novel approach to Machine Learning from Explanations (MLX), the setting in which data points for learning are paired with human-annotated “explanations”, usually in the form of image masks marking regions that are known to be irrelevant with respect to the downstream prediction task. Prev... | Rebuttal 1:
Rebuttal: We thank the reviewer for their passionate assessment of our work. We are very glad that the reviewer is appreciative of the novelty of our contribution, intuition from our analysis and completeness of experiments. We enjoyed addressing their concerns on the future relevance of our contributions. ... | Summary: This paper reinterprets Machine learning from explanations (MLX) as a robustness problem, where human explanations define a lower-dimensional manifold for perturbations. The paper points out that the previous regularization-based MLX approaches require strong model smoothing in order to be globally effective a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review. We tried our best to address their concerns on contribution and generalizability of our method; we are more than happy to answer any further concerns.
> The contribution of the proposed method is restricted to the combination of two established ro... | Summary: This paper proposes a new approach to machine learning from explanations (MLX). This new approach is still based on human-provided explanations of (ir)relevant features for each input in training, but recasts the MLX problem itself essentially into a robustness problem. The authors achieve SOTA performance whe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time. We are glad that the reviewer found our method intuitive and our experiments convincing.
> Obtaining human-specified masks is at best a lot of effort …
We agree that manually specifying explanation masks can be impractical. However, the proced... | Summary: The paper is in the domain of MLX (machine learning from explanations). In this approach, human annotated data for each input example is available denoting features that are _relevant_ and which are _irrelevant_. It is desired that the model doesn't learn from irrelevant features.
In this paper, the authors u... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments.
> The prior works (such as Sagawa et al., 2019; Piratla et al., 2021) have several more evaluation datasets, which the approach is not evaluated on. … Please address the choice of limiting the evaluation to the 3 datasets used. The generality and ... | Rebuttal 1:
Rebuttal: We thank the reviewers for detailed assessment and queries. We are glad that all the reviewers found our paper well presented and well motivated. We are also happy for an overall positive assessment of our work.
Reviewer Hnkb, WA6d, haST raised some concerns regarding overall generality of our w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos | Accept (poster) | Summary: This paper presents an algorithm for the realization of novel view synthesis in dynamic scenarios, leveraging multi-view video data. In order to tackle this task, the paper introduces two key components: a keyframe dynamic field and an interframe velocity field. These fields serve the purpose of accurately rep... | Rebuttal 1:
Rebuttal: **Q1: ... D-NeRF ... Nerfies ... explored this concept.**
**A:** We agree with the reviewer that both prior works are pioneering in this field. Nevertheless, we observe that only learning the deformation actually does not truly understand the physical motions. That means they are good at interpo... | Summary: This paper proposes a new representation of dynamic scenes, using keyframe NeRF + Velocity Field.
This representation disentangles appearance & geometry from velocity, which allows many exciting applications like future frame extrapolation, unsupervised semantic 3D scene decomposition, and dynamic motion trans... | Rebuttal 1:
Rebuttal: **Q1: Lacking limitation discussions. The current paper clearly has several limitations and they are expected to be discussed in detail in the paper. (1) As briefly mentioned in the broad impact, this paper doesn't have real-world data to validate whether it could work on real scenes.**
**A:** As... | Summary: This paper proposes a model for dynamic 3D scenes from multi-view videos. Different with previous method, this paper proposes a physical velocity field instead of a deformation field. The method uses a dynamic radiance field to model key frames, and use a velocity field to warp the key frame to intermedia fram... | Rebuttal 1:
Rebuttal: **Q1: The method shows inconsistent performance on interpolation.**
**A:** Comprehensive and consistent results for the interpolation of our method are provided in the Appendix. We are not clear about the request of this comment, but we are always open to discussion with the reviewer.
**Q2: ...... | Summary: The paper aims to model dynamic 3D scenes from multiple-videos. The method simultaneously learns for geometry, appearance, and physical velocity of the 3D scenes from video frames. The show different application like future frame extraploation, unsupervised semantic 3D scene decomposition and dynamic motion tr... | Rebuttal 1:
Rebuttal: **Q1: There has been many datasets proposed to tackle the problem of dynamic scene understanding, What is the advantage or difference with respect to these datasets has not been well studies.**
**A:** The primary goal of our method is to model dynamic 3D scenes for future frame extrapolation, by ... | Rebuttal 1:
Rebuttal: Firstly, we would like to thank all five reviewers for their valuable comments. We genuinely appreciate the time and effort invested in reviewing our paper. Based on the comments provided, we have made significant improvements to our paper:
- Additional experiments on public real-world datasets, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents the framework to simultaneously learn the geometry, appearance, and velocity from only video frames. The paper's contribution lies in three directions: 1) Keyframe dynamic radiance fields to learn the time-dependent volume density and appearance. 2) Interframe velocity field to learn the tim... | Rebuttal 1:
Rebuttal: **Q1: All the evaluation results are presented on a synthetic dataset where the motion information is constrained ideally. It is unclear from the experiments how well the results translate to real-world scenarios.**
**Q2: All the results presented in this work are evaluated only on the authors' d... | null | null | null | null | null | null |
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection | Accept (poster) | Summary: The authors explore and analyze the existing vehicle-infrastructure cooperative 3D object detection framework, and propose to adopt flow-based rather than still images to extract temporal features from multiple LiDAR input frames. The experimental results on DAIR-V2X dataset is better compared to the existing ... | Rebuttal 1:
Rebuttal: Dear Reviewer Wmh8,
Thank you for providing valuable feedback on our work. We will address each of the limitations you have pointed out in your comments.
**W1. We have conducted experiments on more datasets like OPV2V[1],** which also focuses on cooperative 3D detection. Our forthcoming versi... | Summary: This work proposes a flow-based feature fusion framework called Feature Flow Net (FFNet) for vehicle-infrastructure cooperative 3D object detection (VIC3D).
FFNet can generate aligned features for data fusion to transmit information for fusion and solve uncertain temporal asynchrony and transmission costs.
The... | Rebuttal 1:
Rebuttal: Dear Reviewer enaU,
Thank you for your valuable feedback on our work. We have carefully considered your suggestions and would like to respond to each of your main comments regarding our weaknesses and questions.
**W1.** We sincerely acknowledge your suggestion, and in our upcoming version, we ... | Summary: This paper introduces FFNet, a flow-based feature fusion framework that incorporates a feature flow prediction module to predict future features and addresses asynchrony issues. Instead of transmitting feature maps extracted from static images, FFNet transmits feature flow by leveraging the temporal coherence ... | Rebuttal 1:
Rebuttal: Dear Reviewer NKqy,
Thank you for providing valuable feedback on our work. We will address each of the limitations you have pointed out in your comments.
**W1.** Regarding the potential limitations of our work concerning application scenarios, **we have extended our experiments to encompass the... | Summary: In this paper, a cooperative detection framework, named Feature Flow Net (FFNet), is presented to address temporal asynchrony and limited communication condition challenges in vehicle-infrastructure cooperative.
Specifically, FFNet transmits feature flow to generate aligned features for data fusion, providin... | Rebuttal 1:
Rebuttal: **Resolving Anomalies in Evaluation Results using the mAP@3D Metric**
Dear Reviewer CL6E:
Thank you for your insightful question and for bringing to our attention the anomalous results in the mAP@3D metric.
**Phenomenon.** We acknowledge the existence of abnormal performance in mAP@3D. As evid... | Rebuttal 1:
Rebuttal: **Conducting FFNet on More Datasets and Driving Contexts**
In response to some reviewers' concerns regarding the sufficiency of our experiments (specifically, Reviewer Wmh8's concern about limited dataset usage and Reviewer NKqy's concern about limited application scenarios), we have taken their ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision | Accept (spotlight) | Summary: The authors present a new method for training diffusion models based on partial observations of underlying signals. Based on a dataset containing groups of multiple partial observations of the same signal, as well as a differentiable model of the forward operator, this method can train a diffusion model for sy... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. The reviewer notes that our “results seem strong, both visually and quantitatively”, we “outperform the tested baselines”, and that our paper “addresses an important problem”. We now address the remaining concerns:
### Proposition
We **intentional... | Summary: This work proposes a framework that allows learning a conditional diffusion model for a signal without direct observations. The proposed method integrates differentiable forward models with conditional diffusion models. It is evaluated for the task of learning image-conditioned 3D scenes from 2D images with po... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful read and insightful comments. The reviewer notes that our method “addresses an important problem” and shows “promising results”, our architecture is “well-designed and intuitive”, and our results are “comprehensive and solid’. We now address remaining conce... | Summary: This paper incorporates a differentiable forward model (e.g., rendering function) into a denoising probabilistic process in order to sample from distributions of underlying signals consistent with partial observations. For example, in inverse graphics the proposed approach would enable sampling from the distri... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. The reviewer notes that our paper tackles a “key challenge” in training 3D diffusion models, and that our general formulation is a “great way to broaden interest in advances in diffusion models”. We now address the concerns:
### HoloDiffusi... | Summary: This paper focuses on denoising diffusion models, a type of generative model used to capture complex signal distributions. However, current approaches can only model distributions when training samples are available, which is not always the case in real-world tasks. In fields like inverse graphics, where the g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. The reviewer nicely summarizes our paper and notes that the “writing is super clear”, we solve “an essential problem in the present era”, and that our experiments “demonstrate the effectiveness...”. We now address the points raised in the review.
### Rel... | Rebuttal 1:
Rebuttal: We thank all reviewers for their efforts. The reviewers note that we solve “an essential problem in the present era” (5usH), we tackle a “key challenge” in training 3D diffusion models (bLEB), our architecture is “well-designed and intuitive” (LcVC), and “results seem strong, both visually and qu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data | Accept (poster) | Summary: This paper proposes a novel approach called the Neural Relation Graph framework for identifying label noise and outlier data in large-scale datasets with real-world distributions. The approach utilizes a relational structure of data in the feature-embedded space to detect label errors and outlier data, and int... | Rebuttal 1:
Rebuttal: We thank you for your valuable efforts and time in providing insightful feedback. We would like to address the questions below.
**Limitations of the proposed approach (with different domains)**
- Thank you for your feedback. In Table 1-b from Sec 4.1.2, we demonstrate that our method consistent... | Summary: The authors identified the issue of how existing label errors in the training data and the OOD in the test set can affect the model training and evaluation and further proposed a novel approach utilizing the learned feature embeddings and label information to compute the relations between data instances. The r... | Rebuttal 1:
Rebuttal: We thank you for your valuable efforts and time in providing insightful feedback on our work. We would like to address questions from the reviewer below.
**Difference between the Coil (label error) and the Envelope (outlier)**
- In the case where a data point is associated with a different grou... | Summary: This paper proposes a new method using graph structure for detecting label errors and outlier data. Briefly speaking, the algorithm utilizes data feature embeddings to generate relation graph and using the new defined data relation function, its algorithm can capture mislabeled and outlier data from a global p... | Rebuttal 1:
Rebuttal: We thank you for your valuable efforts and time in providing insightful feedback on our work. We would like to address questions from the reviewer below.
**Discussion about feature spaces**
- Thank you for the pointer. We would like to mention that specific information regarding the feature spa... | Summary: The paper under review outlines a novel method utilizing graph structure to identify label errors and outlier data. It proposes an algorithm that makes use of data feature embeddings to produce relation graphs. By incorporating a newly defined data relation function, the algorithm can globally capture mislabel... | Rebuttal 1:
Rebuttal: We thank you for your valuable efforts and time in providing insightful feedback. We would like to address questions below.
**Advantage over the semi-/self-supervised techniques**
- Thank you for the pointer. We would like to clarify that the goal of our paper is to identify the problematic dat... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your valuable effort and time in providing helpful feedback on our work. We sincerely appreciate your encouraging comments, including:
- The paper introduces a novel method, provides a fresh perspective into the problem, and is highly original. (Reviewer nM5G, 6KY3,... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Spectral Evolution and Invariance in Linear-width Neural Networks | Accept (poster) | Summary: This paper studies gradient descent training in single-hidden-layer neural networks in the linear-width regime (i.e., that in which the input dimension, hidden layer width, and number of training datapoints together tend proportionally to infinity). Its primary result is that the bulk spectra of the conjugate ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and additional references on random feature models and deep Bayesian networks under LWR. We will add these references and provide additional comments and comparisons. In the following, we address the comments and questions.
1. The goal of this pap... | Summary: This paper examines the spectrum of the conjugate kernel and neural tangent kernel of real networks before and after training. Based on analyzing the spectrum in some experiments, several observations about how the spectum evolves during training are made. In particular, there seems to be different phases depe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and thoughtful feedback. In the following, we address the technical comments and questions.
1. Our experiments are robust to dimensions as long as the width $h$, sample size $n$, and feature dimension $d$ are large. Anecdotally, when these dimensi... | Summary: The paper carries out an empirical study of the spectral properties of finite-width neural network kernels after training, and compares them to their infinite-width counterparts to assess if there is an improvement. They study these changes with respect to various hyperparameters such as learning rate and opti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and thoughtful feedback. In the following, we address the technical comments and questions.
1. **Q: Real datasets such as MNIST?** We will present experiments of the MNIST dataset and show that the spikes in the kernel matrix are related to the d... | Summary: This paper analyzes the spectral properties of feedforward neural networks under a student-teacher setting. A linear-width regime is considered, where the sample size and network width grow comparably with the input feature dimension. The paper shows that the spectra of weight and kernel matrices are invariant... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and thoughtful feedback. In the following, we address some of the technical comments and questions.
1. **Q: Why feature dimension goes to infinity?** The LWR, where the input feature dimension $d$ goes to infinity proportionally to the sample siz... | Rebuttal 1:
Rebuttal: **Overall Summary:**
We would like to thank all reviewers for their evaluation of our work and their helpful comments. The key contribution of our work is to describe how the spectral evolution of both weight and kernel matrices changes during different training procedures. We focus on a simple t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Considerable attention in deep learning theory has been given to the analysis of neural networks in the kernel regime, which neural networks approach as they approach infinite width. While this analysis has led to insights, there is an important gap between the theorized generalization performance of neural ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and thoughtful feedback. Per the reviewer's suggestion, we will provide more detailed captions for the figures to help readers. We will also clearly state our primary findings and conclusions in the introduction. We have summarized this work's main... | null | null | null | null | null | null |
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL | Accept (poster) | Summary: This paper proposes contrastive introspection (ConSpec), an algorithm for learning a set of prototypes for critical states via the contrastive loss. ConSpec works by delivering intrinsic rewards when the current states match one of the prototypes. This paper also conducted experiments in various environments.
... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their feedback.
> *some minor issues are: (1) the title in the pdf (Contrastive Introspection:. ..) seems to mismatch with the one appear in openreview (ConSpec: …). (2) The font of citations appears to be confusing. E.g., from line 19-20 in the introduction, the manuscr... | Summary: The paper noticed that in real-world MDP, success is often contingent upon a small set of steps. While Bellman equation can theoretically do credit assignment over long-horizon, reward is hard to propagate under Bellman-based methods in practice. The authors therefore propose a novel algorithm that uses contra... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their questions and feedback.
**Success/failure:** As discussed in section A.3, we used only 2 simple definitions of success across tasks, and in each case, tie “success” to reward in a natural way. In the first definition, success is based on whether a reward is achiev... | Summary: Proposes an auxiliary reward module to be used in RL algorithms, that learns features (‘prototypes’) of critical states in successful trajectories. For new observations, the method then uses cosine similarity to the learned features as a reward bonus. The method is evaluated on a unity-based env, grid-worlds, ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their feedback.
**Are prototypes required?:**
The reviewer raises an interesting question: could a system learn critical states implicitly, without resorting to the use of prototypes? As an example, they note that in theory one could use Decision Transformers (DT) and le... | Summary: This paper introduces ConSpec, a reinforcement learning (RL) algorithm designed to identify critical steps and improve performance in continuous control tasks. ConSpec utilizes contrastive learning to learn prototypes of critical steps and employs a contrastive loss to differentiate successful and failed exper... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the feedback.
**Contrastive instability with no stop gradient (SG):** To our knowledge, in self-supervised learning the SG is used (e.g. BYOL Grill et al 2020, or SimSiam Chen et al 2021) to prevent representational collapse (rather than learning instability), and only n... | Rebuttal 1:
Rebuttal: **Message to all reviewers:**
Thank you for the comments, questions, and suggestions. In this global response we will address those issues highlighted by multiple reviewers. We will respond to individual reviewer comments in the individual responses.
First, we want to summarize and clarify the ce... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Coop: Memory is not a Commodity | Accept (spotlight) | Summary: The authors propose to consider memory fragmentation when using tensor rematerialization on dynamic computation graphs. With three new memory management methods, sliding window algorithm, cheap tensor partitioning, and recomputable in-place, the authors enhance the checkpointing method with lower computation o... | Rebuttal 1:
Rebuttal: We thank the reviewer’s appreciation for our work, and we address the questions below. We use W and L to denote major weakness and limitation correspondingly.
> **W1:** It is better to introduce the hardware specifications about cost density. The authors can also mention the arithmetic intensity.... | Summary: Tensor materialization trades the memory with recomputation. Prior tensor materialization methods do not consider the memory fragmentation problem of the memory system used in deep learning frameworks, which makes them evict unnecesary tensors. The authors of this paper proposed a memory-system-aware remateria... | Rebuttal 1:
Rebuttal: We thank the reviewer’s appreciation for our work. Please see below our responses to your comments. We use Q, W, L to denote question, weakness, and limitation correspondingly.
> **W1&L1:** The underlying memory allocator must be able to merge the freed chunks if they are contiguous.
The reviewe... | Summary: This paper proposes an optimization framework called Coop to solve the severe memory fragmentation, which is overlooked by prior tensor rematerialization works. Coop designs a sliding window algorithm to determine evicted tensor, guaranteeing the freed memory is contiguous and available for a new tensor. Furt... | Rebuttal 1:
Rebuttal: We thank the reviewer’s appreciation for our work. Please see below our responses to your comments. We use Q, W, L to denote question, weakness, and limitation correspondingly.
> **W1:** It would be better to give an algorithm to describe the framework comprehensively.
We greatly appreciate y... | Summary: This paper considers the rematerialization problem for DNN training and studies it from the perspective of memory fragmentation.
Strengths: Please see the "Questions" section.
Weaknesses: Please see the "Questions" section.
Technical Quality: 2 fair
Clarity: 2 fair
Questions for Authors: - I think this pa... | Rebuttal 1:
Rebuttal: We thank the reviewer’s appreciation for our work. Please see below our responses to your comments.
> **Q2:** Comparison between Coop and Checkmate
We agree that a comparison with Checkmate could further demonstrate Coop's contributions. Checkmate's publicly available code includes two git branc... | Rebuttal 1:
Rebuttal: We thank the reviewers' appreciation and valuable advice for our work. Some new figures and tables are in the attached pdf file.
Pdf: /pdf/b9ce83047ddc794d0eab3308d79c200efa121cd6.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Structure of universal formulas | Accept (poster) | Summary: This paper studies function approximation in the spirit of the Kolmogorov-Arnold representation theorem.
A definition of expressivity classes is made which distinguishes pointwise and uniform approximation.
Function families with the form of neural networks are proposed and approximation results proven.
Stren... | Rebuttal 1:
Rebuttal: *How could this line of work bear on or influence approximation theory which (like ref. [4]) is relevant for practical computation?*
Thank you for this reasonable question.
1. You mention Kolmogorov-Arnold, but the big difference between KA and our results is that the KA-type models include very... | Summary: The paper advances the basic understanding of the expressive power of fixed-complexity function families on [0, 1]. It defines three classes of function families: $\mathcal{G}_0$, whose members are infinite VC-dimension families, $\mathcal{G}_1$, whose members can approximately fit any finite number of points,... | Rebuttal 1:
Rebuttal: Thank you for the careful reading and positive evaluation of our work! We agree with all your fixes, thanks!
---
Rebuttal Comment 1.1:
Title: I have read the authors' response
Comment: Thank you for the response. I leave this comment to note that I have read it. | Summary: The authors isolate the study universal hypothesis subclasses of $C([0,1])$ for the compact-open and point-open topologies respectively, and they compare universality here to infinite VC-dimension. The results are interesting, though perhaps not that surprising nor novel, but are still publishable (in my opin... | Rebuttal 1:
Rebuttal: Thank you for the comprehensive reading of our paper including the proofs, and a positive evaluation of our work. We also appreciate your many comments, though we respectfully disagree with some of them.
**Requested changes.**
0. We don't actually see what is imprecise in the examples you give, ... | Summary: The authors study "universal formulas" -- specifically, families of parametric
mappings $(f_{\omega})\_{\omega \in \Omega} \subset C([0, 1])$ where $\Omega$ is
a domain in a finite-dimensional euclidean space, such that $(f_{\omega})$ is
actually dense in $C([0, 1])$ with the sup-norm -- motivated in part by
a... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of our paper and giving a very detailed and careful summary of our work.
Indeed, the GeLU function unfortunately does not belong to our polynomial-exponential-algebraic class.
All your minor fixes are spot-on (except for the last one: we did actually mean "exist... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the careful reading of our work and useful feedback, both positive and critical.
As we mention in the response to Reviewer 2cHj, our work is partly motivated by an experimental observation that neural networks with the activation function $\sin$ apparently can easi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Creating Multi-Level Skill Hierarchies in Reinforcement Learning | Accept (poster) | Summary: The paper proposes a method to discover skill hierarchy by applying hierarchical graph clustering methods to expose the structure. The proposed method introduces Louvain method for skill hierarchy revealing and produces skill hierarchy with multiple-grained of action abstractions. The proposed method is tested... | Rebuttal 1:
Rebuttal: Thank you for the time and attention you have given to our paper.
LA: Louvain Algorithm
**Q1: How about the learning efficiency of the proposed algorithm? Does the size of the graph limit the learning efficiency of the proposed method compared to the original reinforcement learning algorithm?**
... | Summary: The paper proposes a graph-partitioning-based method to automatically learn multi-level hierarchies of skills at varying timescales in reinforcement learning. The method assumes existence of a complete state transition graph and applies Louvian algorithm to create a hierarchy of state clusters. By merging stat... | Rebuttal 1:
Rebuttal: Thank you for the time and attention you have given to our paper.
**Q1: Can you explain “we use each of the h partitions to define a single layer of skills, resulting in a hierarchy of h levels”?**
We will give an example using Figure 2 in the paper. In Rooms, the algorithm produces 4 partitions... | Summary: Given a graph of connectivity of the state space of an environment, this paper proposes to use a hierarchical clustering technique, the Louvain algorithm, to provide reinforcement learning with a multi-level skill representation. This allows policies to take actions at variable time scales, and experimental r... | Rebuttal 1:
Rebuttal: Thank you for the time and attention you have given to our paper.
**Although the description of the Louvain algorithm and most of the rest of the paper is quite thorough and well presented, the method for training a policy using the hierarchical clustering output is left comparatively sparse. Es... | Summary: This paper addresses the problem of discovering a hierarchy of skills based on the state-transition graph. The proposed approach builds on an existing method called the Louvain algorithm, which creates a hierarchy of state clusters; i.e., the lowest level will place nearby states in the same (small) cluster, t... | Rebuttal 1:
Rebuttal: Thank you for the time and attention you have given to our paper.
**Q1: Why did the authors select Q-learning as the baseline for primitive actions instead of a more recent, and better-performing, algorithm?**
The main point of the baseline algorithm is to allow a comparison of the agent's perf... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling | Accept (spotlight) | Summary: This paper presents SimMTM, a simple pre-training framework for masked time-series modeling. The core idea is to train the model to reconstruct the time series by aggregating information from multiple masked series rather than one single masked series. While the temporal pattern is destroyed in a single masked... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 5Qee for providing a meaningful review and insightful suggestions.
#### **About the Weaknesses**
**Q1**: The confusing of $Eq. (4)$.
Thank you for your suggestion. Your understanding is correct. We will provide a clearer description and modify $Eq. (4)$ as follows:
... | Summary: Pre-training models have been using self-supervised learning in various fields (NLP, Vision). However, masked modeling, a representative method of self-supervised learning, was difficult to apply to time-series tasks. Randomly masked data is difficult to show good performance because semantic information of ti... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer L1Yx for providing an insightful review.
#### **About the Weaknesses**
**Q1**: A part that does not match the notation in the formula and Fig. 2.
Thank you for your careful reading. To facilitate summation operations and distinguish different sample rep... | Summary: The authors propose SimMTM, a novel representation learning method for time-series data based on random masking. They experimentally verify the effectiveness of the proposed method from both in-domain and cross-domain perspectives. Comprehensive comparative experiments with existing methods are conducted, demo... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer ZNHw for the detailed and insightful suggestions.
#### **About Presentation Quality**
(1) We will rephrase $Eq. (3)$ as $\mathbf{R}=\text{Sim}(\mathcal{S})\in\mathbb{R}^{D\times D},D=N(M+1),$
(2) $\mathbf{R}_{\text{u},\text{v}}$ compute the cosine distance via transp... | Summary: This paper proposes a simple pre-training framework for masked time-series modeling. Instead of reconstructing origin data directly, which is unsuitable for time series, this paper recovers masked time points by the weighted aggregation of multiple neighbors outside the manifold. The reviewer appreciates the n... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer xbHA for providing an insightful review.
#### **About the Weaknesses**
**Q1**: The relationship with manifold learning needs more description. What is the progress in this aspect?
Masked modeling is a mainstream paradigm in self-supervised pre-training.... | Rebuttal 1:
Rebuttal: ## Summary of Revisions and Global Response
We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for improving our paper further.
Since the standard masked modeling seriously ruins vital temporal variations of time series, this paper pres... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Scaling Laws for Hyperparameter Optimization | Accept (poster) | Summary: The authors present a hyperparameter tuning scheme based on optimizing surrogate powerlaws. They claim substantial improvements over baselines on hyperparameter tuning benchmarks.
Strengths: The writing is generally clear, and the method is well described. The authors report substantial improvements over base... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful review. We provide the following clarifications on the questions raised by the reviewer:
- **Regarding “It is very hard to assess Hypothesis 1 from Figure 1. This figure seems like it ought to be a final summary figure after some more illustr... | Summary: AFTER REBUTTAL: I acknowledge reading the rebuttal. After the reviewer discussion, I will keep my score the same.
----
Gray-box hyperparameter optimization involves optimizing neural network hyperparameters by evaluating performance at low budgets and terminating configurations if they seem unpromising. This... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful review. We provide the following clarifications on the questions raised by the reviewer:
- **Regarding “The function $f\left(\lambda\right)$ gets overloaded with $f\left(\lambda, b\right)$ or as $f\left(b\right)$ in various parts.”:**
We... | Summary: The paper proposes a novel surrogate for multi-fidelity HPO that uses an ensemble of power-law models to estimate the future validation loss of hyper-parameter configurations at intermediate stages of training. The principal novelty of the work lies in exploiting the observation (made in other work) that learn... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful review. We provide the following clarifications on the questions raised by the reviewer:
- **Regarding “Significant work is done by line 319. We follow the common practice of conducting HPO with small transformers and then deploying the disco... | Summary: This paper proposes to combine power law patterns in learning curves, which have been recently popularized by scaling laws, to improve efficiency and performance of Bayesian optimization (BO) based hyperparameter optimization (HPO). Authors provide detailed discussions on how to model the surrogate function an... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful review. We provide the following clarifications on the questions raised by the reviewer.
- **Regarding “the abstract of this paper is too simple, and I wasn't able to grasp the general direction or the concept of the paper by reading them.”:**... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for the thorough reviews and for helping us improve the quality of our work. Below, we summarize the main clarifications:
- **Time performance and ecological insights (Reviewer 7buU):** Hypothesis 2 of Section 6 provides a comparison between all methods fo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes deep power laws ensembles for hyperparameter optimization. More precisely, it is used as a surrogate for Bayesian Optimization (BO) to estimate the performance of a hyperparameter configuration leveraging ensembles of deep power law functions. Furthermore, it is combined with multi-fidelity ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful and detailed review of our work. We provide the following clarifications on the questions raised by the reviewer.
- **Regarding the remarks from the section Weaknesses**:
- **In terms of related work:**
We will cite both... | null | null | null | null | null | null |
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration | Accept (poster) | Summary: The paper introduces hierarchical semi-implicit variational inference (HISIVI) as an extension of semi-implicit variational inference (SIVI). HISIVI incorporates interpolating distributions between prior and target data to train conditional layers progressively, resulting in accelerated sampling in diffusion m... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! Here are our response to your concerns.
Q1: The novelty of HSIVI compared to related work is not clearly demonstrated. It is recommended to include more related works and comparison: cascaded diffusion models [1], diffusion schrodinger bridge [2], flow match... | Summary: Semi-implicit variational inference increases the expressiveness of variational posteriors but introduces intractabilities to their inference. This work adds to the semi-implicit variational inference (SIVI) work of Yu and Zhang (https://openreview.net/forum?id=sd90a2ytrt) which uses an alternative objective (... | Rebuttal 1:
Rebuttal: Thank you for your detailed and valuable feedback. We will address your concerns in the following aspects:
### Weaknesses
#### Originality
Answer: First, HSIVI not only extends SIVI by allowing multiple conditional layers which greatly enhances the flexibility but also introduces a sequence of aux... | Summary: Authors propose a hierarchical semi-implicit variational inference framework by extending the existing SIVI. Authors showed that the proposed HSIVI, given pre-trained score networks, can be used to accelerate the sampling process of diffusion models with the score matching objective. The numerical results show... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedbacks and suggestions! Here are our responses to them.
Q1: Extending SIVI to a hierarchical model, by itself, is not very novel. However, the application to diffusion acceleration is interesting.
A1: Thank you for acknowledging the novelty of the application to dif... | Summary: The authors extend semi-implicit variational inference to have multiple layers of latent variables, vastly increasing the. expressiveness of the model. A nice formulation of the asymptotic lower bound plus a training scheme is introduced as well.
Strengths: This paper is an extension that on the surface seems... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable feedback! Below are our answers to your comments:
Q1: My only qualm is ... But for speeding up sampling of diffusion models, it seems like training the joint distribution leads to impressive results. This opens up the question of whether the sequence... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. First, we'd like to address some common issues raised by the reviewers.
### Novelty/Originality
As a semi-implicit variational inference method, HSIVI further enhances the expressiveness of semi-implicit variational posteriors by allowing ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a hierarchical semi-implicit variational inference that stacks multiple semi-implicit layers to construct a flexible generative distribution. The paper introduces a sequence of distributions that interpolate between the target and a base distribution. Each pair of intermediate inference an... | Rebuttal 1:
Rebuttal: Thank you for your careful review and helpful feedback! We address your concerns as follows.
Q1: The validity of the training procedure needs more concrete explanations or theoretical results.
A1: The training procedure is similar to SIVI-SM (https://openreview.net/forum?id=sd90a2ytrt), with the... | Summary: This paper presents a new method called Hierarchical Semi-Implicit Variational Inference (HSIVI) that enhances the expressiveness of Semi-Implicit Variational Inference (SIVI) on complex target distributions. HSIVI works by applying SIVI to a hierarchy of latent variables, and using the variational distributio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We address your specific questions and comments below.
Q1: The main weakness of this work is that the method seem fairly complicated to implement in a practical setting. The method is presented in a general way, with an algorithm box. Ho... | null | null | null | null |
Reducing Shape-Radiance Ambiguity in Radiance Fields with a Closed-Form Color Estimation Method | Accept (poster) | Summary: This paper use closed-form spherical harmonics (SH) color-field to regularize the training of NeRF-based models. The SH coefficient of each 3D point has a closed-form solution given a set of observed viewing angles. This work further takes the transmittance from the density field to weigh each observed angle. ... | Rebuttal 1:
Rebuttal: # Weaknesses
__W1__: Thank you very much to point out that. We will carefully examine and fix these incorrect terms.
__W2__: Thanks for your comments. The goal of this work is to study the shape-radiance ambiguity problem of the NeRF model. The black background is a more challenging setting, wit... | Summary: While NeRFs achieve photorealistic results, they suffer from the shape and radiance ambiguity that is inherent in joint reconstruction. To alleviate this issue, the authors aim to separate the estimations using a closed-form radiance estimation. They leverage Spherical Harmonics to represent the radiance given... | Rebuttal 1:
Rebuttal: # Weaknesses
__W1__: Thanks a lot for your useful suggestion. We will carefully prepare an overview figure that clarifies which part of the experiments is about evaluation and which is about regularization. We will also add links to the symbols, and how the method slots in existing NeRFs.
__W2__... | Summary: While nerfs have shown impressive properties in the ability to reconstruct the geometry of three dimensional scenes, there are still some cases where the performances are not as good as expected. This paper focuses on one of these problems, the untangling of the geometry from the color. For that, the authors p... | Rebuttal 1:
Rebuttal: # Weaknesses
__W1__: Thanks for your comments. The NeRF can be categorized into implicit and explicit models. We focus on the explicit ones in this work. They model the density and color as volumes. The color volume does not receive features from the density one, and so the they can be estimated i... | Summary: The paper proposes a novel regularization technique for optimizing radiance fields to achieve more accurate geometry alongside realistic novel views. The regularizer is based on a closed-form estimation of spherical harmonic coefficients for view-dependent color, as a function of the training pixel colors and ... | Rebuttal 1:
Rebuttal: # Presentation weaknesses
Thank you very much for your detailed comments and suggestions about the presentation. We will try our best to fix these in the final version of the paper.
# Evaluation weaknesses
__W1__: Thanks for your comments. The goal of this work is to study the shape-radiance a... | Rebuttal 1:
Rebuttal: Dear all,
We would like to deeply appreciate comments from all reviewers, which are invaluable to improve our paper. We have carefully considered all comments and reponsed in a point-by-point way.
In order to address all concerns and questions raised by reviewers, and make our paper better, we s... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning to Configure Separators in Branch-and-Cut | Accept (poster) | Summary: The authors use machine learning to decide how and when to toggle the on/off switches for different cutting plane families provided by SCIP, the fastest open-source MIP solver. Their learning algorithm outperforms the default setting of SCIP on various benchmark sets.
Strengths: The techniques used to deal wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the actionable feedback in terms of interpreting the results, using other MILP solvers, and providing pointers to relevant theoretical works. We have made great efforts to address the reviewer’s concerns in our response below and have included additional experiments in th... | Summary: The paper proposes a pipeline for a learning-enhanced cut separation management in modern MIP solvers (specifically, in the academic solver SCIP). To pick a promising subset of the large number of possible settings, the authors derive a data-driven and theoretically motivated method to focus on specific config... | Rebuttal 1:
Rebuttal: We are delighted and truly appreciate the reviewer‘s positive feedback on our work. We made a great effort to integrate empirical methodology with theoretical justification, aiming at bridging an important gap in the existing learning for MILP literature that has been predominantly empirical. We p... | Summary: This paper studies learning to manage separators to improve MILP solvers. Specifically, it learns a policy to determine which separators to use to generate cutting planes. The task is well formulated and experiments on many datasets demonstrate the effectiveness of the proposed model.
Strengths: 1. The paper ... | Rebuttal 1:
Rebuttal: We appreciate the insightful suggestions from the reviewer and provide our response below. We hope the reviewer may also take a look at the general comments for our additional experiments, covering (1) interpretations of effective separators for each MILP class from our learning results (2) applyi... | Summary: This paper uses machine learning to decide which families of cutting planes should be applied in each of a finite number of rounds when a discrete mathematical optimization problem is solved by an MILP solver. The authors conduct experiments on the SCIP solver, in which they show that their selection of separa... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable input on paper presentation and other insightful questions. We provide our responses next, and present our new experiments in the general comments, covering (1) analysis of effective separators for each MILP class from our learning results, and applying our metho... | Rebuttal 1:
Rebuttal: # General Responses to All Reviewers
We thank each of the reviewers for their detailed and constructive comments. We provide the following additional interpretations and new experimental results in response to the reviewers’ suggestions.
$\ $
### [GR1] Interpretation analysis: The learned model r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning From Biased Soft Labels | Accept (poster) | Summary: The authors analyse the learnability properties of (biased) soft labels, e.g., originating from a teacher model in a student-teacher setup, with respect to classifier consistency and ERM learnability. To this end, two indicator measures of the quality of the proxy labels are suggested, which are used in a theo... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We are truly grateful for your comprehensive review and the positive recognition of our work's relevance, technical soundness, and valid theoretical results. Your feedback provides us with valuable insights that will undoubtedly enhance the quality of our paper. We would like to ad... | Summary: This paper studies the effectiveness of biased soft labels in knowledge distillation and weakly-supervised learning. The paper introduces two indicators to measure the effectiveness of soft labels, and proposes moderate conditions to ensure that biased soft label learning is classifier-consistent and ERM learn... | Rebuttal 1:
Rebuttal: Dear Reviewer,
**Q1. Experiments on Tiny-ImageNet.**
- A: We have added experiments on Tiny-Imagenet, which can be found in the PDF attached to the rebuttal.
**Q2. Why not compare the proposed method with other state-of-the-art weakly-supervised methods.**
- A: Our primary contribution is the ... | Summary: The authors propose a clever theoretical framework for studying learnability under supervision with imperfect soft labels.
Strengths: - Learning from imperfect soft labels is an important and interesting area that is prevalent not only in knowledge distillation but also in cognitive science, human-AI interact... | Rebuttal 1:
Rebuttal: Dear Reviewer,
**Q1. Clairty and defination.**
- A: We apologize for any confusion caused by the term "large-biased soft labels" mentioned in the paper. Here, we provide a rigorous definition for "large-biased soft labels" to clarify its meaning.
> Definition 1. (Bias of soft labels) Give a d... | Summary: This paper aims to study the effectiveness of biased soft labels for knowledge distillation. They propose two indicators to measure the effectiveness of the biased soft labels: unreliability degree and ambiguity degree. They provide a theoretical guarantee that the biased soft labels are effective in training... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive feedback. We appreciate the time and effort you took to review our paper, and we value the insights you've provided.
First and foremost, we would like to express our sincere gratitude for your positive remarks on the originality, theoretical depth, ... | Rebuttal 1:
Rebuttal: We provided three supplementary experiments in the PDF:
* In Figure 1, we show efectiveness of the proposed indicators on Tiny-Imagenet. As $\eta_k(f)$ and $\gamma_k(f)$ decrease, Acc (i.e., accuracy of the student) increases. The experiments on Tiny-Imagenet demonstrate a trend similar to that o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores the concept of learning from weak, or "soft," labels that may be biased or diverge from the ground truth labels in a dataset. In contrast to existing theories that focus on the importance of close alignment between soft and ground truth labels, the authors probe the efficacy of learning fro... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive feedback. We appreciate the time and effort you took to review our paper, and we value the insights you've provided.
Your acknowledgment of our paper's strengths, especially the theoretical analysis, the introduction of new indicators, and the exten... | null | null | null | null | null | null |
Combating Representation Learning Disparity with Geometric Harmonization | Accept (spotlight) | Summary: The authors tackle the problem of contrastive learning in the context of imbalanced datasets where some classes have many more samples than other classes. Since no label information can be used, importance sampling is not an option and without any interventions, contrastive learning tends to overrepresent majo... | Rebuttal 1:
Rebuttal: While the reviewer raised so many questions, we sincerely appreciate the reviewer's time and effort on our submission. Probably due to the difference in small research directions, the reviewer has some misunderstanding in the setting, background and evaluation of this topic. We do hope that the fo... | Summary: The paper investigates the SSL in a long-tailed distribution setting, where traditional contrastive learning may not work well.
The authors attribute that issue to the "sample-level" learning of SSL and propose Geometric Harmonization (GH) to regularize the "category-level" during training. GH can be a plug-an... | Rebuttal 1:
Rebuttal: We gratefully thank you for your time and efforts devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **Q1:** Empirical studies on BYOL, MoCo-v3 and maybe a more recent branch of SSL, namely mask autoencoder (MAE).
**A1:** Thanks ... | Summary: - The paper proposes to tackle the task of self-supervised learning on datasets sampled from long-tailed distributions.
- Typical SSL approaches tend to perform a lot worse for the minority classes, thus hurting performance.
- The proposed approaches termed Geometric Harmonization encourages category-level un... | Rebuttal 1:
Rebuttal: We gratefully thank you for your time and efforts devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **Q1:** Clarity: Given that geometric harmonization is the key contribution of this work, I think the authors should give the rea... | Summary: This paper concentrates on the long-tailed distribution problem in SSL representation learning. To overcome the challenge in vanilla SSL methods, where head classes with the huge sample number dominate the feature regime, the authors propose the Geometric Harmonization (GH) method to encourage category-level u... | Rebuttal 1:
Rebuttal: We gratefully thank you for your time and efforts devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **Q1:** About the surrogate label allocation. What is the advantage to choose the discriminative clustering [1], what if we choos... | Rebuttal 1:
Rebuttal: We gratefully thank all the reviewers for their devoted efforts and constructive suggestions on this paper. We are glad that the reviewers have some positive impressions of our work, including:
- Explorations on an **important and useful** problem (M9kH, pT3P, G4d9).
- **The motivation is clear*... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studied self-supervised representation learning with implicit class-imbalance data, in contrast to most prior works that assume class-balance. To do this, this paper proposed to augment the existing contrastive learning method with a novel geometric harmonization (GH). Intuitively, GH pulls samples ... | Rebuttal 1:
Rebuttal:
We gratefully thank you for your time and efforts devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **Q1:** The geometric harmonization relies heavily on the assumption that samples from the same class intrinsically lie in neigh... | Summary: This paper addresses the class-imbalance problem under the SSL setting. It shows that the Vanilla SSL methods pursuing the sample-level uniformity easily lead to representation learning disparity, where head classes with the huge sample number dominate the feature regime but tail classes with the small sample ... | Rebuttal 1:
Rebuttal: We gratefully thank you for your time and efforts devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **Q1:** Cross-dataset transferring experiments.
**A1:** We appreciate the reviewer’s constructive advice and conduct more compre... | null | null | null | null |
How do Minimum-Norm Shallow Denoisers Look in Function Space? | Accept (poster) | Summary: The paper studies the shape and properties of one layer networks on a denoising problem. Specifically, the authors compute a closed-form solution for a NN trained offline with regularization and show that its ability to generalize is better than the eMMSE estimator -- which acts as a piece-wise constant functi... | Rebuttal 1:
Rebuttal: __Answers to the questions:__
__C:__ What happens in practice when the noise level increases?
__R:__ Please see Figure 1 (in the pdf file attached to the 'global' rebuttal) for the MSE of NN denoiser and eMMSE vs $\sigma$ (the noise level). As can be seen from Figure 1, the NN denoiser performs... | Summary: This paper studies how two-layer ReLU denoising networks look like when minimizing common losses such as the empirical minimum mean square error, an empirical alternative that draws finitely many noisy samples, as well as representation costs that find the data interpolating function with minimal norm of the w... | Rebuttal 1:
Rebuttal: __Response to the weaknesses points:__
__C:__ Do larger models inherit some of the shown properties?
__R:__ There are several properties that we hypothesize to hold for larger models (but it goes beyond the scope of this paper). (1) We generalized Proposition 2 to the case of obtuse Simplex (See... | Summary: The elementary properties of NN solutions for the denoising problem have been explored, with a focus on offline training of a one hidden-layer ReLU network. When the noisy clusters of the data samples are well-separated, there exist multiple networks with zero loss, even in the case of under-parameterization, ... | Rebuttal 1:
Rebuttal: __Response to the weaknesses points:__
__Weakness 1.1:__
__C:__ Is the proposed model perform well in real image-denoising applications?
__R:__ Practically successful image-denoising architectures are deep and not fully connected. On such architectures, it is very challenging to obtain any the... | Summary: This paper looks at the denoising problem and compares the neural network denoiser to what the paper calls the eMMSE denoiser. The eMMSE denoiser is the optimal denoiser (in function space) given finite data and known noise distribution. In contrast, the Neural Network denoiser has access to a finite number of... | Rebuttal 1:
Rebuttal: __Response to the weaknesses points:__
__C: (1)__ the paper switches between functional representations and the parametric representation
__R:__ We are not sure what is the source of the confusion. For parametric representation we only use $h_{\theta}$, as defined in Equation 6 in Section 2. For... | Rebuttal 1:
Rebuttal: We thank the reviewers for the helpful feedback and remarks, and for your interest in the paper. We have addressed all of them, and we will revise the paper, as detailed below. The rebuttal is in comment-response (C/R) format.
Also, after the initial submission we have strengthened some of our ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data | Accept (poster) | Summary: This paper proposes a scRNA-seq pretraining method, overcoming the scalability and resolution weaknesses of previous works. Experiments on various downstream tasks proves the efficiency and effectiveness of the proposed method.
Strengths: The paper is well structured and easy to follow in general. The challen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. Here we showed additional analysis and discussions to further strengthen our work. If our response does not fully address your concerns, please post additional questions and we will be happy to have further discussions.
**A1**: Introducing padd... | Summary: In this study, the authors propose an asymmetric encoder-decoder transformer for scRNA-seq data, called xTrimoGene, for large scale dataset pre-training. Quantitative comparison with various SOTA approaches shows the advantage in efficiency and scalability. In addition, a series of downstream analysis further ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. Here we showed additional analysis and discussions to further strengthen our work, with a focus on the advantage over CellTypist. If our response does not fully address your concerns, please post additional questions and we will be happy to have... | Summary: The manuscript proposes an adaptation of the key contributions of [1] to the scRNA-seq data modality, which is dubbed xTrimoGene. Additionally, the authors introduce a new embedding encoder, their "auto-discretization strategy". Several ablation experiments motivate specific choices in the model. Finally, xTri... | Rebuttal 1:
Rebuttal: **WA1**: We are grateful for all your comments and will revise our manuscript to make more discussion and emphasize the connection. In our scenario, this asymmetric encoder-decoder architecture is not only efficient but also designed for handling the high sparsity in scRNA-seq data. Specifically, ... | Summary: Here the authors propose xTrimoGene, a scalable transformer-based model for learning representations of scRNA-seq data. The authors demonstrate that their proposed method is more computationally efficient than alternatives for training transformers on scRNA-seq data, and they also validate their model on cell ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We have shown additional discussions and experiments to further strengthen our work. The point-by-point responses to the comments are as follows. If our response does not fully address your concerns, please post additional questions and we will ... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to all the esteemed reviewers for dedicating their time and expertise to meticulously evaluate our work. Their valuable feedback has significantly contributed to enhancing the quality and depth of our research. We have conscientiously delved into every facet of the ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Finding Safe Zones of Markov Decision Processes Policies | Accept (poster) | Summary:
The work introduces the SafeZone problem for safe reinforcement learning (RL). Instead of learning for the optimal policy under the constrained Markov Decision Process (MDP) as many traditional methods deal with a safe RL problem, the work attempts to search for a SafeZone, a subset of state space that is pol... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review!
Our focus is theoretical, the experiments are a minor part of the paper and were only made for demonstration.
We don't offer an alternative solution to constrained MDP; instead, we approach a different problem and give a different solution. As we ... | Summary: This paper introduces the SafeZone problem: Given an MDP and a policy, find the smallest set of states such that the probability of leaving this set of states (the escape probability) lies below a given threshold using trajectory samples. The authors provide various examples of applications of the SafeZone pro... | Rebuttal 1:
Rebuttal: Thank you for the helpful and detailed review. In what follows we address your comments:
**Proof of Theorem A.2 is given in Appendix D**
We apologize for the confusion regarding the proof of Theorem A.2 and we will mention that it appears in Appendix D and that there are more figures in App E in... | Summary: The paper introduces a new problem that involves finding a SafeZone: given the possibility to interact with an MDP using a fixed policy, the learner as to find a subset of states F of minimal size s.t. the probability that a trajectory visits a state outside F is at most a given parameter \rho. They show that ... | Rebuttal 1:
Rebuttal: Thank you for the helpful and detailed review. We will address your comments below.
**MDP with a single policy**
Our setting does allow for any number of policies (multiple agents) by using a single mixed policy as follows: each agent $i\in\{1,\ldots,n\}$ has some policy $\pi^i$. The mixed poli... | Summary: The paper proposes an innovative definition called the SAFEZONE and uses it to describe the escape probability of the sampled trajectory. This paper analyzes several naïve algorithms and proposes an algorithm to overcome the weaknesses in naïve algorithms, especially when considering the size of safety states.... | Rebuttal 1:
Rebuttal: We appreciate your evaluation of our paper. We will address your valuable suggestions and enhance the clarity and motivation of our paper. We are grateful for your positive comments on our paper's concepts and algorithms.
The motivation of our paper, as discussed in the introduction and discussio... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their valuable and informative reviews! We are glad you appreciate the new ֿSafeZone definition and our theoretical contribution. If we misunderstood any of the questions, we would be happy to clarify any further information during the discussion period. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Optimal Caching and Model Selection for Large Model Inference | Accept (poster) | Summary: The paper presents an innovative approach to mitigating the challenges posed by large language models (LLMs), namely high resource consumption and latency, by employing a cache to store previous queries and a model selector to choose the most efficient model for query processing. The authors propose an optimal... | Rebuttal 1:
Rebuttal:
Thank you for your valuable comments and suggestions. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> Model Selection Scope: The experiments seem to consider a limited number of model options. The approach may encounter difficulties when scaling up to situations... | Summary: * this paper proposes a theoretically optimal algorithm for efficient large-scale deployment of LLMs
* particularly; they combine caching and model selection to reduce the inference cost
* they provide theoretical guarantees for both caching w and w/o model selection
Strengths: * large scale models are deploy... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. Please find our responses to your comment below.
## Comment 1
**Reviewer:**
> How can we assess whether caching is relevant in a particular setup? for example, when using publicly available datasets like OpenAssistant; what if there are no ... | Summary: This paper presents an LLM inference framework design that aims to reduce inference cost by caching and model selection. This work first provides an optimal formulation for jointly optimizing both caching and model selection in both offline and online settings. Then, evaluations on simulations and two tasks sh... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> That authors try to solve should not be simplified to “cost saving” only, but it should be formulated as “a cost-accuracy trade-off”. I really want the authors t... | Summary: The paper addresses the computation costs of large language models. Specifically, the paper proposes a framework where previous queries are cached and retrieved and where given a query to process, an appropriate model is selected to answer based on the query. The framework flow is the following: given a query ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We have corrected all the typos and added all the suggested details in the revision. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> The experiments can benefit from further analysis, e.g.:
- Performance/ac... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the valuable comments and suggestions, which have helped us greatly improve the paper. Here we briefly summarize our additional experiments done for the rebuttal period.
In our original experiments in the main text, we include 2 experiment tables with $100... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper investigates the combination of caching and model selection strategies to reduce the inference costs of large models (LLMs in particular). In the result, the authors propose a theoretically grounded algorithm that demonstrates promising results in practice.
Strengths: 1. The problem of the cost-eff... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We have corrected all the typos and added all the suggested details in the revision. Please find our responses to each comment below.
## Comment 1
**Reviewer:**
> In my opinion, Section 5.2 lacks some important details and clarifications. ... | null | null | null | null | null | null |
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions | Accept (poster) | Summary: The manuscript introduces a synthetic combinations estimator, which computes causal effects for unseen treatment combinations under some reasonable assumptions on the data generating process. Building on theory from potential outcomes and synthetic interventions, the authors show how to exploit information sha... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, and address their concerns as follows.
>My main critique of this manuscript is one I am sure the authors will have anticipated – there are no empirical results in the main text! I strongly encourage the authors to move Fig. 2 to the main text an... | Summary: The paper studies the problem of estimating potential outcomes in the presence of a combinatorial number of intervention choices. Under some assumptions, they propose a two-phased algorithm "Synthetic Combinations": first exploit structure across combinations of interventions (via "horizontal regression") and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, and address their concerns as follows.
> I am skeptical about the low-rank assumption on the matrix of Fourier coefficients $\mathcal{A}$. While it is true low-rank assumptions are common in prior matrix completion settings, they usually dire... | Summary: The goal paper studies the problem of recovering $N\times 2^p$ unit-specific outcomes for N heterogeneous units and any combination of p possible interventions from small number of experiments and observations.
Prior to this work, the problem has been studied under assumptions of latent similarity or regulari... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, and address their concerns as follows.
>It is not completely clear how realistic is the scenario when latent similarity and intervention regularity holds simultaneously. I can imagine that in some datasets one or the other may hold, while both a... | Summary: __Disclaimer__: This is my first time reading & reviewing a paper from the field of Combinatorial Interventions. My expertise is in NLP.
This work studies latent structure across units and combinations of interventions, assuming similar outcomes across units and regular interaction. An estimation procedure, S... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback! We note that we perform additional experiments in the global response on a real-world dataset on recommendation systems for combinations of movies that highlight the benefit of our approach as compared to other methods (e.g., Lasso and matrix comp... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive feedback! A primary concern amongst reviewers was a lack of empirical evaluation
of Synthetic Combinations. Here, we present a real-world data experiment on recommendation systems for sets of movie
ratings to address these concerns, and highlight the empir... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Object-Centric Slot Diffusion | Accept (spotlight) | Summary: This paper proposes Latent Slot Diffusion (LSD), an object-centric learning framework combining the Slot Attention module and a Latent Diffusion Model (LDM) based slot decoder. The model is trained in an auto-encoding manner, where the loss is the slot-conditioned denoising loss in the LDM-based decoder. Exten... | Rebuttal 1:
Rebuttal: ### We genuinely appreciate your positive recommendations and insightful feedback!
> I appreciate the authors' efforts in having SLATE+ with the same VAE for a fair comparison. Have you tried training a VAE from scratch, and comparing this LSD variant with SLATE (which also trains dVAE from scrat... | Summary: Previous work has shown that transformer-based image generative models can be trained for object-centric learning which can handle complex scenes. That is, given unstructured observation, transformer-based models learn to find latent compositional structures to bind relevant features.
This work makes an attem... | Rebuttal 1:
Rebuttal: ### We sincerely appreciate for your constructive recommendation and valuable insights!
> If I am not mistaken, the object-centric encoder is trained independently rather than jointly with the latent slot diffusion decoder. I would suggest the authors clarify this. Thus, control experiments might... | Summary: The paper studies the diffusion models into object-centric learning. The authors introduce the concept of latent slot diffusion (LSD) that can replace slot decoders conditioned on object slots. The model can work in an unsupervised compositional mode without requiring annotations such as text. Their experiment... | Rebuttal 1:
Rebuttal: ### We would like to express our appreciation to your insightful feedback!
> Object segmentation using LDS is yet to be perfected, leading to suboptimal downstream applications.
Thank you for your observation. While the object segmentation in LSD may have room for improvement, it's crucial to und... | Summary: This work introduces a methodology for using diffusion-based models to obtain object-centric representations.
This is done using slot representations, obtained from the Slot Attention module applied to the original image, as a conditioning variable for a latent diffusion model. The diffusion model and the Slo... | Rebuttal 1:
Rebuttal: ### We sincerely thank you for your positive recommendation and thoughtful comments!
> ... lack of error bars
We agree that incorporating error bars can better evaluate model’s robustness, and we intend to provide the results here. However, due to constraints in computing resources, we only manag... | Rebuttal 1:
Rebuttal: ## **General Response**
We thank all reviewers for their insightful and positive feedback! We are encouraged that they find our work **novel** (uRVS, ZjQw), **timely** (ZjQw), and **both intuitive and nontrivial** (QNuS). They also highlighted its **practical implications** (uRVS) and **potential... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Slot-guided Volumetric Object Radiance Fields | Accept (poster) | Summary: This paper presents slot-guided volumetric object radiance fields (sVORFa), a learning method for 3D object-centric representation. They proposed three key techniques, scene decomposition based on global pooling and slot-based representations, hyper-network to generate per-object radiance fields, and scene com... | Rebuttal 1:
Rebuttal: Thank you for your careful review and helpful suggestions!
$\textbf{Q1:}$ Need ablation studies on main components.
$ \textbf{A1:}$ We conduct some experiments to compare our proposed components with previous methods. $\textbf{Firstly}$, we show the effectiveness of the transformer-based module.... | Summary: This paper proposes a method, sVORF, for unsupervised object-centric 3D representation learning. Given a single image as input, it decomposes the scenes into individual 3D objects and 3D backgrounds which thus support object segmentation, novel view synthesis (named scene generation in the paper), and scene ed... | Rebuttal 1:
Rebuttal: Thank you for your careful review and helpful suggestions!
$\textbf{Q1:}$ Demonstrate the effectiveness of the proposed method on more complex scenarios.
$\textbf{A1:}$ To validate our method on more challenging scenarios, we conduct a preliminary experiment on the MSN dataset. The MSN dataset c... | Summary: This paper presents a method called sVORF for learning 3D object centric representations. A transformer decompose a single input image into slots which are then each mapped to volumentric radiance fields by a hypernetwork. These object-radiance fields are then composed into a 3D scene. The effectiveness of sVO... | Rebuttal 1:
Rebuttal: Thank you for your careful review and helpful suggestions!
$\textbf{Q1:}$ Information about the length of training. How does that compare to uORF, COLF, OSRT?
$\textbf{A1:}$ For the CLEVR-567 and Room-Chair datasets, we train sVORF for approximately 7 hours using 8 Nvidia RTX 2080 Ti GPUs with b... | Summary: This paper introduces sVORF, a compositional method for representing scenes as a collection of slots that are parametrized as per-object radiance fields. The proposed model can be used to perform novel-view synthesis, as well as semantic segmentation in 3D. Moreover, the proposed compositional representation, ... | Rebuttal 1:
Rebuttal: Thank you for your careful review and helpful suggestions!
$\textbf{Q1:}$ Experiments on more challenging scenarios.
$\textbf{A1:}$ To validate our method on more challenging scenarios, we conduct a preliminary experiment on the MSN dataset. The MSN dataset comprises $\textbf{11,733 distinct sha... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful comments, according to the feedback and suggestions, we mainly added the following experiments:
- [uYMS, JyjT, 3Ksj, thZt] We conduct experiments on more challenging MultiShapenet (MSN) dataset. The results demonstrate our model’s ability to decouple more ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes sVORF to tackle the challenging problem of ‘unsupervised’ scene decomposition by learning a series of slot-guided neural radiance fields. Inferring object-level NeRF from a hyper-network instead of costly GRU modules, the overall pipeline can be efficiently trained with on a collection of m... | Rebuttal 1:
Rebuttal: Thank you for your careful review and helpful suggestions!
$\bf{Q1}$: Limited unique contribution compared to previous work like uORF.
$\bf{A1}$:Unlike the previous work like uORF, our method has two unique contributions. $\textbf{Firstly}$, we use a transformer-based module instead of the GRU m... | null | null | null | null | null | null |
Semi-Implicit Denoising Diffusion Models (SIDDMs) | Accept (poster) | Summary: This paper proposes the Semi-Implicit Denoising Diffusion Model (SIDDM) to enable fast sampling while maintaining high-generation quality. Specifically, SIDDM applies an implicit model to match the marginal distributions of the reverse diffusion process. Meanwhile, SIDDM models explicit conditional distributio... | Rebuttal 1:
Rebuttal: > Larger step (e.g., 16) performs worse than smaller step sizes.
In the baseline model DDGANs [11], they also observe that increasing the number of diffusion steps degrades the generative quality. The authors of DDGANs hypothesize that increasing the number of diffusion steps needs more capacity... | Summary: This method propose a way to achieve fast sampling during inference without compromising sample diversity and quality of diffusion model, and shows their effectiveness on both conditional and unconditional generation, qualitatively and quantitatively. A theoretical framework is also proposed which looks reason... | Rebuttal 1:
Rebuttal: > Conditional setting
Thanks for the agreement on our work and the suggestions. It is our mistake that we did not highlight our conditional experiments. In fact, our experiments and the comparisons are conditional generative models on the Imagenet1000 and we have some additional preliminary resu... | Summary: In this paper, the authors propose to use an implicit model to match the marginal distributions of noisy data and the explicit conditional distribution of the forward diffusion. Specifically, the adversarial loss is applied to the marginal distributions obtained by forward and backward process, and KL loss is ... | Rebuttal 1:
Rebuttal: Hi, thanks for the questions, we would love to make the details more readable. Also, due to rendering issue, we place it in the rebuttal pdf.
> Line 129-137 is really difficult to follow
We agree with the reviewer that those line are not written well. Here is our new rewrite and we hope it clari... | Summary: The paper presents a method called Semi-Implicit Denoising Diffusion Model (SIDDM) aimed to accelerate the sampling process, enhance scalability to large datasets, and improve model performance. It improves upon the DDGAN model by reformulating the denoising distribution of diffusion models with explicit and i... | Rebuttal 1:
Rebuttal: 1. FID-sampling speed tradeoff on ImageNet.
We have shown the results of FID sample steps in Table 7 of the rebuttal pdf.
2. 'SIDDMs w/o AFD (ours)' has a large gap on the performance compared with DDGANs.
This is a good question; In Equation 7, our GAN objective models distribution between margi... | Rebuttal 1:
Rebuttal: We want to thank all reviewers evaluating the paper and will fully address all the reviewers' concerns. We put additional Tables and Figures in the rebuttal pdf file, please find the corresponding Tables and Figures for our reponses below. If our answers are satisfactory, we would be thankful if y... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper addresses the issue of making diffusion models faster (faster sampling) while still yielding high quality, diverse samples, from a large phenomenological space (scaling to large datasets). This is achieved by a novel semi-implicit denoising diffusion model (SIDDM). The idea extends the approach follo... | Rebuttal 1:
Rebuttal: > Explain the behavior on the MoG dataset using a larger suite of metrics
We pick the Unbiased FID mentioned in Borji, CVIU 2021 and MMD metric for our evaluation of the MOG generative quantitation, and the results are shown in Table 6 MMD is a kernel-based nonparametric metric to measure the dif... | null | null | null | null | null | null |
On Private and Robust Bandits | Accept (poster) | Summary: In this paper, the authors study private and robust multi-armed bandits (MABs), where the heavy-tailed rewards are contaminated. They first present its minimax lower bound, characterizing the information-theoretic limit of regret with respect to privacy budget, contamination level, and heavy-tailedness. Then, ... | Rebuttal 1:
Rebuttal: Thanks for your positive evaluation of our paper!
Your observation is really sharp and points to a subtle part of Huber model.
In fact, your observation is exactly the reason that we choose an upper bound $\alpha_1$ on the actual contamination level $\alpha$ and state the upper bound results in ... | Summary: The paper studies the MAB problem where the agent receives rewards that are both heavy tailed and contaminated according to Huber's process (provides true rewards w.p. $1-\alpha$ and provides samples from an arbitrary, unknown distribution w.p. $\alpha$). For the case of heavy-tailed rewards, both settings wit... | Rebuttal 1:
Rebuttal: Thanks for the suggestions on the presentation. We will address them as follows.
- **Description of the problem setting** We have stated the MAB protocol in the first paragraph of the introduction section. As suggested by the reviewer, we will re-emphasize it in the preliminary part (Section 3.1... | Summary: This work studies the specific setting of heavy-tailed bandits with Huber contamination and differential privacy constraints. They first give a regret lower bound, tightly characterizing the minimax rate in terms of all the parameters involved in this setting. Then, they provide matching (up to log terms) uppe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation of our paper! We would like to provide the following clarifications to the reviewer's questions.
**Minimax vs. problem-dependent bound** Thanks for your sharp comments. Let us approach your question in the following steps.
- Problem-dependent u... | Summary: The paper studies the private and robust multi-armed bandits, where the rewards are heavy-tailed or contaminated. It proposes a meta-algorithm which is based on private and robust mean esitmation sub-routine incorporating reward truncation and Laplace mechanism.
Strengths: The paper establish the minimax reg... | Rebuttal 1:
Rebuttal: Thanks for your time and comments. We are glad you find our analyses are relatively complete. We will recap your comments and present our detailed response. We hope our answers will resolve your concern.
**Unkown and infinity horizon T**
- We first clarify when $T$ is unknown and infinity, the ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning | Reject | Summary: This paper presents a new approach to automatic curriculum learning designed specifically for multi-agent coordination problems.
Strengths: - The main strength of the paper, in my opinion, is the well-formulated approach to the curriculum learning problem. To the best of my knowledge of the related literature... | Rebuttal 1:
Rebuttal: Dear Reviewer iWBE,
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-----
**Q:** Make a reasonable argument against the lack of other baselines in the work
**A:** Thanks for pointing this out. We compared different t... | Summary: This paper presents Skilled Population Curriculum (SPC) which is a method for learning a curriculum to help a team of agents complete a complex task. SPC models the problem of choosing tasks for agents as a contextual bandit problem, and builds on top of the Exp3 algorithm to solve this bandit problem. SPC als... | Rebuttal 1:
Rebuttal: Dear Reviewer k74L,
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-----
**Q1:** computational resources
**A:** IPPO is directly trained in GRF 5v5, while SPC or other curriculum learning baselines are trained in tra... | Summary: The paper introduces a new automatic curriculum learning framework, Skilled Population Curriculum (SPC), for multi-agent reinforcement learning. The algorithm includes three major components: (1) a contextual bandit conditioned by student-policies representation for automatic curriculum learning; (2) An attent... | Rebuttal 1:
Rebuttal: Dear Reviewer oZVt,
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-----
**W1:** It seems that the manuscript introduces various components.
**A:** Thanks for your valuable comments on the current presentation. Ple... | Summary: This work introduces the Skilled Population Curriculum (SPC), an automated curriculum learning algorithm designed for Curriculum-enhanced Dec-POMDP. The goal of SPC is to enhance the student's performance on target tasks via a sequence of training tasks provided by the teacher. The SPC functions as a nested-HR... | Rebuttal 1:
Rebuttal: Dear Reviewer p3Bh,
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-----
**W1:** Complexity and Generalizability.
**A:** While we acknowledge that our study introduces an intricate pipeline, this approach has been ad... | Rebuttal 1:
Rebuttal: We thank you for your endorsement of the motivation and the experimental studies of our work We appreciate your positive comments on the quality and experimental studies of our work. If you have any further questions or comments, we will be happy to discuss or fix them further. We are looking forw... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the multi-agent RL problem with sparse reward and a varying number of agents. The authors propose a novel automatic curriculum learning strategy to solve complex cooperation tasks in this setting. Their curriculum strategy involves a teacher component and a student component. The teacher com... | Rebuttal 1:
Rebuttal: Dera Reviewer reFd,
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-----
**W1:** I am unsure about the broader applicability of the contextual representation of the student policy using an online clustering algorithm... | null | null | null | null | null | null |
Best Arm Identification with Fixed Budget: A Large Deviation Perspective | Accept (spotlight) | Summary: The authors study the problem of best arm identification with fixed budget in the $K$-arm bandit problem. Using tools from the large deviation literature, they propose a new methodology to analyse algorithms for this problem. They apply this method to a state of the art algorithm and obtain sharper asymptotica... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and positive feedback.
1. About
> Could you please discuss more the theoretical guarantees of the two algorithms? Is there settings where one would perform better than the other, and vice-versa?
In general, we cannot say that one of our two algorithms, CR-A o... | Summary: This paper studies the problem of best arm identification (BAI) with fixed budget (FB), with bandit feedback on K arms. The asymptotically optimal sample complexity for this problem has been of significant interest recently and has proved surprisingly difficult to understand compared to the fixed confidence pr... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and positive feedback.
1. About
> The result seems a little incremental as they most concern specific algorithms; the paper does not seem like it will help much toward an eventual solution to the general FB-BAI problem. But getting better non-sharp bounds is s... | Summary: This paper considers the problem of best arm identification with fixed budget, where the learner pulls an arm in each round for $T$ rounds, then outputs a candidate for the arm with the largest mean. The objective is to minimize the probability of mis-identification. Characterizing the instance specific comple... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and positive feedback.
1. About
> The presented bounds are not easy to read (especially in Section 4). It would be nicer to provide a more detailed discussion about the comparisons with bounds in the literature.
Thanks for this remark. We will add a more deta... | Summary: This paper contributes an important tool geared towards closing the performance gap for best arm identification problems under the fixed budget setting, a well-known open problem in the area. In particular, the result is a large deviation bound on the sample means of the arm rewards as a function of any large ... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and positive feedback.
1. About
> I don't think there are any major weaknesses except stating that the considered problem is the "last fundamental open problem in MABs" is quite presumptuous.
We agree and we will rephrase.
2. About
> Hasn't it been shown, a... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Differentiable sorting for censored time-to-event data. | Accept (poster) | Summary: This paper concerns a novel way to solve risk stratification for time-to-event data in the presence of right-censoring. Particularly, the authors solved the proportional hazard model via the differentiable sorting algorithm. Experiments were done on simulated and real-world benchmark datasets.
Strengths: - Th... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our manuscript. We've addressed each point you raised and provided further clarifications where necessary:
**Weakness 1 (Theoretical and practical advantages of the method)**
*Theoretical Advantages*:
Diffsurv integrates differentiable sorting networks, the... | Summary: The authors propose a deep learning-based survival model that utilizes a novel differentiable sorting objective capable of handling censored time-to-event data. In contrast to previous pairwise sorting objectives, the proposed listwise sorting objective achieves the transitive property inherent in survival dat... | Rebuttal 1:
Rebuttal: We appreciate your feedback and hold your insights in high regard. Our goal is to respond to every concern you have mentioned:
---
**Weakness 1 (Writing)**
Thank you for the feedback on typographical and grammatical errors. We've addressed these and ensured greater consistency in notation and e... | Summary: This paper investigates the problem of survival analysis under the proportional hazards (PH) hypothesis with deep learning models.
The main issue with previous approaches (Katzman et al. 2018 and Kvamme et al. 2019 as main representatives) is that the Cox loss is only computed in small mini-batches, potential... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough evaluation and positive feedback on our manuscript. Your insights into our work's potential impact and originality are highly encouraging. We acknowledge your concerns, especially regarding the clarity of notations and the handling of the top-k task. In this r... | Summary: The paper aims to address the survival problem as a ranking problem. The author asserts that by taking into account the transitivity property of ranking, sorting networks can be employed to tackle the current ranking problem effectively. Furthermore, the paper presents a solution to handle numerous potential p... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We value your insights and aim to address each concern you've raised. Here are our responses and clarifications:
---
**Weakness 1 and Limitation 2 (Predicting the time of event vs ranking and calibration scores)**
While estimating absolute timings for event occurren... | Rebuttal 1:
Rebuttal: We sincerely appreciate the thorough and constructive feedback provided by all four reviewers.
As suggested by multiple reviewers, we have further investigated the runtime (both theoretically and empirically) and calibration of our proposed survival ranking method.
---
**Theoretical and empiric... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization | Accept (poster) | Summary: This paper studies the global convergence of local SGD for One-hidden-layer Convolutional Neural Networks. The authors present solid theoretical understanding. They show that without overparameterization and injecting noise, local SGD have global convergence by new proof techniques and new understanding.
Str... | Rebuttal 1:
Rebuttal: Thanks for taking your precious time to review our paper and please see our responses to your questions.
**1. My main question pertains to Assumption 1 about the teacher target. It appears that this assumption suggests that stronger conditions on $a^{*}$ are required for larger values of k. I am ... | Summary: This paper provides a rigorous theoretical justification on how the local SGD (without the injection of noise) can find the global minima for CNN without relying on the NTK-type analysis (i.e., overparametrization) under federated learning framework.
Strengths: A. The contributions of the paper are clearly w... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work and please see our responses to your questions.
**1. Why is the f (Z, w, a) CNN? It looks to me like a shallow fully connected neural network. What is the exact structure of $w$?**
We apologize for the confusion. Please note that we follow the assumption and t... | Summary: The paper asserts that local SGD achieves convergence to the global minimum in the presence of Gaussian input. The experimental setup involves a two-layer student model, where the ground truth is generated by a two-layer teacher model. The authors highlight their main contribution as a proof that does not nece... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper.
**1. Could the authors clarify the purpose and significance of the intermediate step $\check{v}\_{t+1}$.**
The purpose of the intermediate step $\check{v}_\{t+1}$ is to establish an exact recursion to characterize how the global angle $\phi\_{t+1}$ changes in t... | Summary: This paper considers a federated learning setting for a one-hidden-layer convolutional neural network without overparameterization. It is proven that vanilla local SGD (where in each iteration each node updates the model by the local gradient and then all nodes synchronize the model parameters) can converge t... | Rebuttal 1:
Rebuttal: Thanks for taking your precious time to review our paper! Next, we provide detailed responses to your comments and questions.
**1. The online learning assumption, i.e., each node samples data i.i.d. from the same distribution in each iteration, is crucial to the analysis of this paper, but it dev... | Rebuttal 1:
Rebuttal: Dear Reviewers:
We appreciate your constructive reviews and would like to thank you for your time in helping us to improve our paper. We have responded to the reviews and questions individually and have added several new simulations in the PDF file. Here we provide a general response to address t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Provably Robust Estimators for Inverse Problems via Jittering | Accept (poster) | Summary: This work considers introducing Jittering to the inverse problems for the benefits of robustness to the l2-worst-case. In this work, some analytical results are provided in proving the robust estimation with some assumptions, which I think are reasonable. Then, empirical experiments verify the effectiveness on... | Rebuttal 1:
Rebuttal: Thanks for the feedback and noting that the motivation is quite nice, the assumptions are reasonable, and the results show promising performance.
Comments on the weaknesses:
- **Relation of theory and experiment:** The theory gives insights into worst-case robustness and justifies using jitterin... | Summary: This work studies the design of estimators for the solutions of inverse problems that are adversarially robust, in the sense that they minimize the maximum MSE after being contaminated with an additive and L2 bounded perturbation. The authors show that, for linear estimators and for signals lying on linear sub... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and are very pleased to hear the reviewer enjoyed reading our paper, that the results are novel and interesting, and that the idea is neat and elegant.
- **Question 1, concerning robust risk vs an approximation of the robust risk**: Thanks for the su... | Summary: The authors study the effectiveness of jittering in the setting of inverse problems, specifically considering denoising, compressive sensing, and deconvolution problems. Jittering is a well-known regularization technique for classification problems. The authors prove in the linear setting that the robust risk ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and for noting that we make a strong contribution for denoising, and that even though 'they study a linear model they were able to provide insights in the non-linear model (U-net) that was studied empirically'.
Comments on the weaknesses and answers... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful feedback. In this post, we address one of reviewer *koTN*'s questions.
**Metrics quantifying the smoothing effect**: During the rebuttal, we measured the total variation (TV) norm of reconstructed images for the deconvolution task over a large test dataset... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback | Accept (poster) | Summary: The authors propose combining a diffusion model with a discriminative model for test-time adaptation: They propose adapt a pretrained classifier using a diffusion model with its likelihood loss. They show results on several OOD robustness benchmarks, commonly used in TTA.
Strengths: I like the idea of combini... | Rebuttal 1:
Rebuttal: **Q6.1: Backbone architecture unclear for baselines.**
Please see Q1.1 and Q1.2 in the global comment.
**Q6.2: Add RN50 backbone to baselines.**
We have included ResNet50 in our online TTA results in Q1.2. Here, we report TTA results using ResNet50 under single-example settings.
||ImageNet|Im... | Summary: The paper discusses a method that uses generative models as test-time adapters for discriminative models. The authors propose a technique to adapt pre-trained classifiers and CLIP models to individual unlabeled images. They achieve this by modifying the text conditioning of a text-conditional pretrained image ... | Rebuttal 1:
Rebuttal: **Q5.1: Marginal Improvements in Open-Set Datasets**
You are correct that the improvements on open-set problems are not as high on the closed-set problems. We think this is mainly because we use Stable Diffusion as our diffusion model. Stable Diffusion conditions the image generation on the text... | Summary: This paper proposes a test-time adaptation (TTA) method that adjusts pretrained image classifiers at test time by leveraging the power of pretrained, text-conditioned generative diffusion models. Given an unseen image, the proposed method Diffusion-TTA minimizes the diffusion loss with respect to the weights o... | Rebuttal 1:
Rebuttal: **Q4.1: Improvements over TTT-MAE unclear**
If we consider the **after-TTA absolute scores** our method significantly outperforms TTT-MAE on all except ImageNet-V2 dataset. On ImageNet, ImageNet-A, ImageNet-R, ImageNet-C, and ObjectNet, our method gets (+1.1, +4.5, +10.5, +13.9, +4.8) boosts ... | Summary: Authors aim to improve test-time adaptation of CLIP and ImageNet trained models on different distribution data. They leverage text conditioned image diffusion model to adapt image classifier’s parameters by maximizing image diffusion likelihood. In particular, image classifier’s output probabilities are used a... | Rebuttal 1:
Rebuttal: **Q3.1: Straight forward extension of Diffusion Classifier**
We believe this is not the case for the following reasons: Diffusion Classifier searches over **discrete** class labels while Diffusion-TTA optimizes the parameter of the diffusion model and the classifier thru gradient descents. Grad... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and for spending the time to read our paper in detail.
Below we address the common concern of all the reviewers:
**Q1.1: Use same backbones when comparing against TENT and CoTTA baselines.**
We re-evaluate COTTA and TENT using the same backbone as ours.... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposed Diffusion-TTA, a test-time adaptation method that, given a test image, updates the classifier’s weights to maximize the image likelihood (i.e., the denoising score matching loss) using the pre-trained diffusion models. Specifically, the classifier uses the clean input image to predict a dist... | Rebuttal 1:
Rebuttal: **Q2.1: Synthetic SD Data and SD features baseline use ResNet-50 classifier and not CLIP.**
You are correct, Synthetic SD Data, SD features, and Diffusion Classifier are not very relevant or fair baselines for Table 1 of the paper as they don’t use a pre-trained classifier. However, we thought sh... | null | null | null | null | null | null |
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization | Accept (poster) | Summary: This paper studies the bilevel optimization problem, an in particular, focuses on developing effective learning rate schemes for bilevel optimization. In specific, the authors propose two adaptive step-sizes methods named stochastic line search (SLS) and stochastic Polyak step size (SPS) as variants of methods... | Rebuttal 1:
Rebuttal: **Reply to weakness 1:** We would appreciate if the reviewer could elaborate on specific issues that might be unclear to them. This would enable us to address these concerns and enhance the overall clarity of our work.
**Reply to weakness 2:** Let us clarify the roles of these hyperparameters. St... | Summary: This paper presents an adaptive step size algorithm for bi-level optimization, which improves the shortcomings of the existing BO method that requires careful adjustment of the upper and lower learning rate, and gives a proof of convergence. Experiments also verify the robustness of the method to learning rate... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our work.
weakness 1: Theorem 1 only states that f is convex rather than strongly convex, but the full text lacks explanation for the case of multiple solutions at the lower level
**Reply to weakness 1:** Let us please clarify that Theorem 1 is for the ... | Summary: This work studies adaptive step-size methods for both single-level optimization and bilevel optimization. The authors propose two novel variants of stochastic line search (SLS) and stochastic Polyak step size (SPS), and they unify these variants into a general envelop strategy. Importantly, these variants are ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback!
We appreciate your careful reading of our paper and thank you for pointing out a few typos, which will be fixed in the revision.
Q1: Can the authors explain in more details why it is possible to set the number of inner steps to be 1 in Line 213?
**Reply t... | Summary: The paper introduces the use of stochastic adaptive-step size methods, namely stochastic Polyak step size (SPS) and stochastic line search (SLS), for bi-level optimization. This approach addresses the challenge of tuning both the lower and upper-level learning rates.
Strengths: 1. SLS and SPS can be seen as s... | Rebuttal 1:
Rebuttal: Thank you for your overall positive assessment of our work.
weakness: The paper compares the proposed algorithms to vanilla SGD or Adam versions, but it does not provide a comprehensive comparison with other existing algorithms for bi-level optimization. This may limit the understanding of the re... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you for taking the time to carefully read and review our work. We are confident that integrating your suggestions will further improve our paper; thus we plan on doing so in the revision.
In the specific replies below, we comment on specific issues brought up by different... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Rethinking Conditional Diffusion Sampling with Progressive Guidance | Accept (poster) | Summary: This paper proposes a generalized classifier guidance method for diffusion models with progressive guidance along both the class and temporal dimensions, to handle the adversarial effect and diversity suppression problems of the vanilla classifier guidance. In experiments, the proposed method shows advantages ... | Rebuttal 1:
Rebuttal:
### Question 1: Low improvement on CIFAR-10
We have analyzed this problem and discovered that the problem lies in the semantic labels of CIFAR10 itself. Compared to ImageNet, the labels in CIFAR-10 has less relevant information than each other. Most of the shared information between classes is ba... | Summary: In this paper, the authors proposed a new classifier guidance technique for diffusion models named Progressive Guidance (PROG). PROG is an extended version of the classifier guidance method and incorporates the relevant classes' information (beyond the target class alone) to determine the guidance direction, p... | Rebuttal 1:
Rebuttal: ### Question 1: The novelty of the work
Since the reviewer does not mention the reason why our work is not novel, we summarize our three main novelties below:
[**Nov1**] : Detects and justifies **diversity suppression** problem due to classification gradient. As far as we know, this problem has *... | Summary: To tackle the generative issue of low diversity and artifacts of classier guidance for conditional diffusion generation, this paper proposes an entropy view for calculating the conditional score gradient. The proposed method proposes two modifications for classifier guidance, 1) exchanging one-hot class labels... | Rebuttal 1:
Rebuttal:
### Question 1: Extend the proposed method into Text-to-image guidance.
GLIDE[11] is an easy extension of classifier guidance for text-to-image guidance. The sampling equation for GLIDE is shown below:
$$x_{t-1} = \mu _t + \sigma _t * \mathbf{z} + s \sigma_t^2 \nabla _{x_t} (f(x_t) . g(c))\quad... | Summary: This paper proposes to inject the graident of other clasess to improve the diversity for conditional sampling of diffusion model.
Strengths: 1. The general idea is simple, the gradient of the classifier tends to use the most discriminative feature and thus hurts the performance, so we use the gradient of othe... | Rebuttal 1:
Rebuttal:
### Question 1: Elaborating on details
1. For equation 4, the number of timesteps is 1000. However, similar to [10], we adapt the respace to 250 timesteps (skip four iterations each time). All other hyperparameters are detailed in Table 10 (Appendix)
2. "*Do you just use classifier guidance aft... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to the esteemed reviewers for their insightful and constructive feedback, which has significantly contributed to the enhancement of our manuscript. In this joint reply, we address recurring inquiries raised by multiple reviewers and references for other rebuttals, t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work points out that diffusion models with classifier guidance only focus on the given category but ignore the other relevant category information. Thus, this work proposes the Progressive Guidance (PG) method to address two problems, i.e., lack of diversity and the adversarial effect (samples have high s... | Rebuttal 1:
Rebuttal: ### Question 1: Clarify the performance of classifier guidance in some cases.
Sometimes, the proposed classifier guidance performs poorly than classifier-free guidance, such as FID/sFID and Robustness score on ImageNet256x256. However, the two methods can be considered comparable on this case due... | Summary: In this work authors propose to address challenges of classifier guidance Diffusion models and propose progressive-guidance where during sampling/reverse diffusion process. Initial iterations of reverse process receives classifier gradient from multiple relevant classes instead of just target class so that mor... | Rebuttal 1:
Rebuttal: ## Weakness discussion
### Weakness 1: Noise-aware classifier performance at different noise-level and the sensitivity associated with $\\gamma$.
In the ablation study in section 6.3, we have already discussed the sensitivity of $\\gamma$ value that has affected the generated image quality. Tabl... | null | null | null | null |
On Representation of Natural Image Patches | Reject | Summary: - This study proposes a new framework to combine neural coding concepts of information transmission and probability density modeling.
- This framework is based on an even code principle where the output response density strives to be even, given some arbitrary input density.
- The authors show that this coding... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have spent in evaluating our paper.
* Regarding the concern on a false dichotomy
In this paper, we aim to explore what an optimal early-stage information processing system would look like from first principles with as few assumptions as possible. ... | Summary: This paper presents a method for the representation of elementary natural images, based on the observation that classical studies in computational neuroscience focus mainly on methods to improve code efficiency, but that this could be complemented by a study of probability density modeling between neighboring ... | Rebuttal 1:
Rebuttal: Thank you for your valuable review. We will address your concerns point by point.
* Regarding the limitation to elementary signals
Our aim is to explore what an optimal early-stage information processing system would look like, from first principles with as few assumptions as possible. We assu... | Summary: This paper explores the relationship between the information theory approach and the probabilistic generative model approach in the context of understanding neural coding. The author suggests that maximizing the information-carrying capacity of output channels and modeling the input probability distribution ca... | Rebuttal 1:
Rebuttal: Thank you for your review and insightful feedback. We appreciate your time and effort in providing us with valuable comments. We will address your concerns and questions in detail.
* Regarding the learning of edge detectors and orientation-selective neurons in many previous studies
The aim of th... | Summary: The authors studies simple of neural encoding. The question is whether two
distinct goals, accurate transmission of information and learning the
distribution of environmental stimuli can be achieved simultaneously. The
authors argue that yes, it can, using the key assumption of a uniformly
partitioned input sp... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We will address your concerns and questions point by point.
* Regarding maximizing the entropy of the output distribution
In Appendix A, we have proven that for our model, maximizing mutual information (rate of transmission), defined as $H_Q - H(y|x)$, i... | Rebuttal 1:
Rebuttal: Attached are the two figures referenced in the rebuttal.
Pdf: /pdf/ef70b16448c0cf952a313d09e2a15c6f8c813be2.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimistic Rates for Multi-Task Representation Learning | Accept (poster) | Summary: This work studies multitask representation learning for some (general) function hypothesis classes. The estimators correspond to the composition of a "complex" function $h$ (corresponding to the shared representation) and a specific task regressor $f_i$ from a simple hypothesis class. This work aims at boundin... | Rebuttal 1:
Rebuttal: Thank you for the feedback and appreciating our contributions. Regarding the typos pointed out and suggestions, we appreciate the careful reading and have made corrections in the revision.
---
## About the setting with the optimal predictor
We thank the reviewer for the suggestion. Indeed, fro... | Summary: This paper shows novel statistical rates for generalizing to a target task via multi-task representation learning (MTRL) that attain the optimistic 1/nt + 1/m rate, where n is the number of samples per source task, t is the number of source tasks, and m is the number of samples per target tasks, when the optim... | Rebuttal 1:
Rebuttal: Thank you for the feedback and appreciating our contributions.
Regarding [XT21], thank you for pointing us to this work -- we will add a discussion as suggested, in the related work section.
---
Rebuttal Comment 1.1:
Comment: Thank you to the authors for their response. I am maintaining my scor... | Summary: This paper aims to examine the optimistic rates for multi-task representation learning. The authors illustrate that the rate may be faster than the standard rate, depending on the complexity of the learning tasks. The analysis comprises multiple theoretical contributions.
Strengths: 1. The theoretical analysi... | Rebuttal 1:
Rebuttal: Thank you for your questions, suggestions, and appreciating our contributions.
---
## Regarding the assumptions on the loss function
Our main result, Theorem 1, actually only uses Assumption 1.1., boundedness and non-negativity of the loss $\ell$. We will change the writing to indicate this.
Fo... | Summary: The authors consider the transfer learning and the multi-task learning setting in a Representation Learning context: multiple source tasks are used to learn a good common representation to facilitate the learning process of a target task (transfer learning) or of the same source tasks (multi-task learning). Un... | Rebuttal 1:
Rebuttal: Thank you for the feedback and appreciating our work.
---
## Our theoretical contribution and technical difficulties
We see our work as foundational, extending our understanding of multi-task representation learning. For a more complete discussion recall our Section 1.1 - Our techniques, dedicate... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Partial Matrix Completion | Accept (poster) | Summary: This paper introduces the learning problem of simultaneously estimating an unknown ground truth matrix based on the observed entries and providing a set of weights/confidence scores for each unknown entry with two properties: (1) the RMSE, weighted by the confidence scores, satisfies generalization bounds anal... | Rebuttal 1:
Rebuttal: Thank you very much for your very detailed review of our work!
Weaknesses:
1. Lemma 3 in Appendix A1: Thank you for pointing out the mistake. We will change this. In fact, the convexity of the constraint set follows directly from the (non-trivial) fact that max-norm is a well-defined norm.
2.... | Summary: Typical matrix completion methods aim to recover the whole matrix, based on strong conditions on the matrix itself as well as the sampling distribution. In contrast, this paper proposes a method to complete a subset of the entries with high confidence, which can bypass the need of these conditions. More specif... | Rebuttal 1:
Rebuttal: Thank you for taking the time and efforts in reviewing our work! Here are the resonses to your concerns:
Weaknesses:
1. The authors define the "Partial Matrix Completion Problem" (Line 223) as finding a specific $C$; but the theoretical guarantee does not seem to align directly.
**Response**: O... | Summary: This paper considers a twist on the standard matrix completion problem where one is required to only
complete a subset of entries (not the whole matrix) that includes the entries shown. This allows them to consider
substantially more observation patterns unlike the standard missing-at-random response. In this ... | Rebuttal 1:
Rebuttal: Thank you for taking the time and efforts in reading and reviewing our work! Here are our responses to your concerns:
Weaknesses:
1. The proposed algorithms provide noisy completion even when the observations are noiseless (i.e. no obvious notion of completion to the inherent noise level). This ... | Summary: The authors discuss the Partial Matrix completion and present an algorithm for completing a large subset of the entries with high confidence. They also aim to find the number of entries that can be completed (coverage) with a small error where the samples are from an unknown sampling distribution.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to read and review our paper. We note that despite the score you gave us, your comments are generally positive, and you recognize the contribution and soundness of our work. Out of the four weaknesses you pointed out, three seem to be stylistic issues, and w... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology | Reject | Summary: This paper proposes a new technique to extract latent factors from psychological questionnaires. The method improves on previous methods both in terms of its ability to handle more flexible inputs (i.e., missing values and confounding variables) and in the interpretability of its outputs (i.e., the scale of it... | Rebuttal 1:
Rebuttal: ### Weaknesses
> many of the figures are incredibly small. Could some of the figures be moved to an appendix?
We thank the reviewer for pointing this out, and apologize for the incredibly small figure ticks and text due to page limit. To address this, we:
- removed both $x$- and $y$-axis ticks'... | Summary: This paper proposes a matrix factorization formulation in order to extract latent features from questionnaires for psychopathology.
The proposed formulation is very similar to the well-known Non-Negative Matrix Factorization (NMF), with an l1-penalty on the dictionary and activation matrices. The biggest maj... | Rebuttal 1:
Rebuttal: ### Weaknesses
> The manuscript is hard to follow at times ... I understand that maybe the authors' clinical collaborators might verbally confirm that these findings are sensible, but it would have really helped if the authors could do a user study to more quantitatively argue that their proposed ... | Summary: The paper proposes a non-negative matrix factorization with a customized regularization term to identify interpretable latent factors from psychopathological questionnaires. The input data is represented in a matrix and a non-negative matrix factorization algorithm is applied to the input matrix. The factor ma... | Rebuttal 1:
Rebuttal: ### Weaknesses
> The technical contribution is somewhat limited.
> More recent methods for questionnaire data analysis should be compared.
We ask the reviewer to please refer to the common response section for a detailed answer.
---
Rebuttal Comment 1.1:
Title: Thank you for the response
Comm... | Summary: This paper presents an algorithm for factorizing matrix with multiple constraints desired in the study of psychiatric disorders using clinical questionnaires. These constrains include those that had been studied previously, such as sparseness in both the factor and loading matrices and non-negative values in b... | Rebuttal 1:
Rebuttal: ### Weaknesses
> the practical significance of this approach is limited given the existence of large amount of existing works in matrix factorization research.
We ask the reviewer to please refer to the common response section for a detailed answer.
> There is no clear statistical significan... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for taking the time to provide thoughtful feedback on our paper. We were pleased to see that, in general, reviewers agree that the paper is methodologically sound, clearly presented, and has an appropriate experimental evaluation.
Some issues have also been ra... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Content-based Unrestricted Adversarial Attack | Accept (poster) | Summary: This paper proposes a novel unrestricted attack framework called Content-based Unrestricted Adversarial Attack (ACA). The author argues that current unrestricted attacks have limitations in terms of maintaining human visual imperceptibility, generating natural adversarial examples, and achieving high attack pe... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. What excites us is not the high rating you have given us, but the questions you have proposed. This assures us that you have a deep knowledge of the field and have recognized the significance of our work to the community. **Therefore, we hope you can champion ou... | Summary: The paper introduces a novel attack framework called Content-based Unrestricted Adversarial Attack, which aims to generate diverse and natural adversarial examples with high transferability. The authors argue that existing methods, such as lp norm-based attacks, have limitations in terms of perceptual similari... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in our work. We supplement the experiments with the attack and defense, and release the code. Hope you can further support our work.
**[Q1: Comparison with state-of-the-art attacks.]** Thanks for your valuable suggestions, we supplement the attack experiments of... | Summary: In this paper, authors propose an unrestricted untargeted attack based on optimising the latent space of stable diffusion model. The generated adversarial samples are empirically shown to be more transferable than the existing semantic attacks. Moreover, authors validate the effectiveness of adversarial sample... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and we address your concerns as follows.
**[Q1: Technical novelty of latent space optimization.]** Techniques for latent space optimization using Generative Adversarial Networks (GAN) are common, but latent optimization using diffusion models has not been widely ... | Summary: This paper proposes the Adversarial Content Attack based on the diffusion model. The proposed attack method first maps the image onto a low-dimensional manifold of natural images and then moves images along the adversarial gradient on the manifold to generate photorealistic adversarial examples. The authors co... | Rebuttal 1:
Rebuttal: We thank you for valuable advice. We will follow your advice to complement the discussion of related work and the explanation of image quality. We would appreciate it very much if you can champion us.
**[Q1: Related works about model debugging.]** Thanks for your help in improving the quality of ... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' valuable feedback and the efforts of the program chair and area chair. So, we are committed to addressing the issues you raised and improving our manuscript accordingly. Next, we provide point-to-point responses to each reviewer and address all details.
**[Q... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders | Accept (poster) | Summary: The paper proposes a structure adaptation algorithm specifically tailored to the well-known Variational Autoencoders (VAEs). The main motivation lies in the fact that the structure of a generative model plays a significant part in the overall performance, something that is very under-explored in related VAE li... | Rebuttal 1:
Rebuttal: **Novelty of our framework comparing with other beta-Bernoulli based methods**: Our work diverges fundamentally from [1,2,3] by treating the expansion of VAE network structures as a stochastic process within a comprehensive framework, in contrast to their exclusive focus on the regularization of V... | Summary: The paper proposes a method called AdaVAE that adapt the sizes (depth and width) of the inference and the generative networks in VAEs.
It extends the idea of beta-Bernoulli process VAE to use a beta process over the expected number of activated neurons over depth and Bernoulli processes per layer for the actua... | Rebuttal 1:
Rebuttal: We'd like to thank Reviewer p4pj for your constructive comment. They are valuable for our future research.
**Relationship to [11,36, 54]**: Our experiment results in Figure 3 shows the methods as [11,36] only imposing an IBP prior to regularize the dimensionality of latent variables is not suffi... | Summary: This paper introduces a Bayesian structural adaptation framework that automatically adapts VAE network structures (both encoder and decoder) to the current data. By modeling the number of hidden layers as a beta process and performing layer-wise dropout regularization with the conjugate Bernoulli process, the... | Rebuttal 1:
Rebuttal: We'd like to thank Reviewer jhJ9 for your constructive comments. They are valuable for our future research.
**Application to expressive VAE variants**: BIVA is an extension of LVAE on which we conducted rigorous tests. We have additional results on BIVA/LVAE on the MNIST dataset as follows:
| | ... | Summary: The paper proposes a novel VAE structural adaptation strategy called AdaVAE based on Bayesian model selection to enhance model performance. It introduces a scalable estimator that facilitates joint inference on both encoding/decoding network structures and latent variables. The paper conducts a comprehensive a... | Rebuttal 1:
Rebuttal: We'd like to thank Reviewer 9dFM for your time and your constructive comments. They are valuable for our future research.
We agree with Reviewer 9dFM that extending the evaluation to include more real-life, challenging datasets will be valuable. It is part of our future work. In addition, we cond... | Rebuttal 1:
Rebuttal: Attached here is the reconstructed samples of the cifar-10 dataset (related to the additional experimental results.)
Pdf: /pdf/96803a768fef34c27910e16b2bb1a2424af70912.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
One Fits All: Power General Time Series Analysis by Pretrained LM | Accept (spotlight) | Summary: In this paper, the authors suggest leveraging pre-trained large language models, specifically utilizing a frozen Transformer backbone, for comprehensive time series analysis. The unique method involves fine-tuning only the input embeddings of time series data and the output layers. The authors substantiate the... | Rebuttal 1:
Rebuttal: We wish to express our sincere gratitude to Reviewer qj2Q for the positive assessment of our work. Your endorsement is both affirming and motivating, and it strengthens our resolve to continue our research in this field. We concur with Reviewer qj2Q's observation that the presentation of our paper... | Summary: This paper shows that fine-tuning language models along with some task specific layers, e.g. input and position embedding and lay norms, can achieve comparable and sate-of-the-art performance on various time series tasks, including forecasting, imputation, anomaly detection and classification tasks.
Strength... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer dRZ for the favorable evaluation of our methodology's simplicity and effectiveness, as well as its state-of-the-art performance across diverse time series tasks. We deeply appreciate your detailed and perceptive feedback. Rest assured, we are committed to addressing and... | Summary: The authors discuss a method by which a deep transformer model pre-trained on an NLP or CV task can be adapted to a wide variety of applications involving classification or prediction of scalar time series data. This includes an embedding scheme to represent the time series input in the same embedding space as... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer kdgy for the positive evaluation of our work's intriguing and challenging contributions. We're deeply heartened by the sentiment that "The idea of simply using a pre-trained language model with minimal adaptation is surprising and worth discussion in the scientific comm... | Summary:
The paper presents a unified framework for time-series tasks using pre-trained language models. The author make a united model through a fine-tuning approach that focuses on fine-tuning specific parts of the pre-trained language model, rather than the entire set of parameters. As a result of this adaptation, ... | Rebuttal 1:
Rebuttal: We deeply appreciate Reviewer GLop's positive acknowledgment of our methodology's efficacy and numerical performance. We are especially grateful for the detailed and insightful feedback provided. Rest assured, we are dedicated to addressing your concerns and enhancing our work.
***Q1 for Reviwer ... | Rebuttal 1:
Rebuttal: We thank the Reviewers for the insightful comments and detailed feedback. We were delighted that reviewers find our paper has the following advantages:
**Innovative Findings**: Using pretrained transformers for time series.(qAFk, kdgy, qj2Q)
**Clear Writing:** Easily comprehensible content.(qA... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes to use pretrained Transformer for time series analysis.
They freeze the attention and FFN layers but fine-tune the position embeddings and add-norm modules to adapt the model to a given task.
This results demonstrate that this method leads to strong performances in time series analysis tas... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer qAFk for the favorable evaluation of our work's theoretical and empirical contributions. It's particularly encouraging to note your belief that the findings merit presentation to the ML community. Such endorsements validate and inspire our continued dedication to this r... | null | null | null | null | null | null |
Triangulation Residual Loss for Data-efficient 3D Pose Estimation | Accept (poster) | Summary: This paper focuses on the task of multi-view 3D human/animal pose estimation, and proposes Triangulation Residual Loss, termed as TR Loss, to constraint multi-view geometry consistency in a self-supervised manner. TR Loss is used to minimize the distances between the predicted 3D point to the view rays, and si... | Rebuttal 1:
Rebuttal: > Q1: In Fig. 1, there are three groups of methods discussed; however, the quantitative comparisons with those of the second group (cf. Fig. 1b), especially in terms of accuracy and efficiency, are not presented in the experiments.
R1: We chose GeneralTriang[10] as a comparison method here. As t... | Summary: The authors present an approach for 3D pose estimation that leverages multi-view stereo cues. A weak 2D keypoint detector is refined using an unsupervised triangulation consistency from posed cameras (TR loss) which iteratively minimizes triangulation discrepencies. The method is experimentally evaluated on an... | Rebuttal 1:
Rebuttal: The authors thank the reviewer for constructive and helpful feedback. To address your questions and concerns:
> Q1: L16 claims a "plug-and-play module ... However, the 2D keypoint detectors are not changed in experiments.
R1: To demonstrate the "plug-and-play" ability, we complemented the experi... | Summary: This paper proposes to perform 3D pose estimation from multi-view RGB images. The key contribution is to develop a new loss function to enable effective training with only 2D pose supervision. This loss function is intended to iteratively optimize the geometric consistency between multi-view rays of each keypo... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' careful review and constructive comments. To address your questions and concerns:
> Q1: Reproducibility. The results obtained in this paper is very attractive. The code may not be released. The 5-lines implementation details at Sec. 4.1 clearly are not enough for repr... | Summary: The paper proposes an triangular residual loss to optimize for the optimization of the 3D locations of the pose estimation. the iterative optimization framework address the problem of the erroneous predictions of the keypoints using learning based methods. The results are shown on multile pose estimation datas... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful and constructive review. To address your questions and concerns:
> Q1: Compared to the previous methods using a single loss over the 3D triangulation, in the current framework using iterative formulation causes additional compute and time to optimize. Analyzin... | Rebuttal 1:
Rebuttal: We thank the reviews for their insightful and valuable comments.
In summary, reviewers are positive about the novelty, formulation, impact, performance, and potential of our method as mentioned:
"address an important topic for the greated (3d) vision community, ... the formulation is elegant. " ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper addresses the problem of predicting human or animal joint positions in 3d from a set of posed images (with calibrated cameras). In order to do so the authors propose to train a neural network that leverages the multi-view constraints between images and predicts the 3d positions in and end-to-end fash... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful and constructive review. To address your questions and concerns:
> Q1: Eq. (9) suggests to ..., References to related work are also missing, ...
R1: Thanks to the reviewer for recommending "robust differentiable SVD". We will add the reference and discuss t... | null | null | null | null | null | null |
Labeling Neural Representations with Inverse Recognition | Accept (poster) | Summary: The paper proposes a new explainability method, named INVERT, that matches the learned representations with human concepts. Specifically, it tries to provide explanations to individual neurons by matching human concepts that the neuron predominantly detects. Compared to previous approaches, INVERT is computati... | Rebuttal 1:
Rebuttal: We thank the reviewer LJJZ for the time spend reviewing our work and we are thankful for the detailed comments. In following, we will answer to the described shortcomings of our work and answer the raised questions.
*The Lack of evidence that this method generalizes to other configurations.*
**A... | Summary: This paper introduces INVERSE, a scalable approach called Inverse Recognition, that links learned representations to human-interpretable concepts by leveraging the ability to differentiate between concepts The applicability of INVERSE is demonstrated in diverse scenarios, including identifying representations ... | Rebuttal 1:
Rebuttal: We extend our gratitude to Reviewer bbMD for their insightful and constructive feedback. We greatly appreciate the time and effort invested in reviewing our work. We are heartened by the positive acknowledgment of the significance of our contribution, as well as the recognition of the quality of o... | Summary: The paper introduces a method to understand what semantic concepts different neurons of a deep network are looking at. They do so by comparing the similarity between different concepts (taken from a pre-defined concept bank) and neuron representations using the AUROC metric, and assign the concept with the hig... | Rebuttal 1:
Rebuttal: Dear Reviewer UsfE,
We extend our sincere gratitude for the dedicated time and the insightful comments you have shared with us. Your thorough review has proven invaluable in refining our work, and we deeply appreciate your efforts.
In response to the questions and concerns you raised, we would li... | Summary: This paper addresses the limitations of existing global explanation methods for Deep Neural Networks (DNNs) and proposes a new approach called Inverse Recognition (INVERT). Unlike previous methods, INVERT does not rely on segmentation masks, provides scalable interpretability, and enables statistical significa... | Rebuttal 1:
Rebuttal: Dear Reviewer 6wCX,
We extend our sincere appreciation for the dedicated time and thorough evaluation of our manuscript. We deeply value the recognition of the simplicity of our approach and the validation of our experiments' practical applicability.
We will proceed to address the questions that... | Rebuttal 1:
Rebuttal: We extend our deepest gratitude for your invaluable dedication and expertise in assessing our work. The thoughtful feedback of all the reviewers has been instrumental in refining our research and elevating its quality. The positive reception of our work has been immensely gratifying, and with this... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer | Accept (poster) | Summary: This paper investigates a federated long-tailed learning (Fed-LT) task in which each client holds a locally heterogeneous dataset; if the datasets can be globally aggregated, they jointly exhibit a long-tailed distribution. The authors propose the Fed-GraB to coordinate the global long-tailed distribution and ... | Rebuttal 1:
Rebuttal: > **1. What's the correlation between uj in Eq.3 and beta_j in Eq.4? And what's the correlation between beta_j and (beta_j^pos, beta_j^neg)?Where is the φ in line 196 be applied?**
>
We apologize for any confusion caused due to the missing of certain details in the paper. We will reiterate the d... | Summary: This paper presents an approach, Fed-GraB, for addressing the challenges of Federated Long-Tailed Learning, an issue characterized by data heterogeneity and privacy concerns. The authors tested their method on several benchmark datasets, which significantly outperforms state-of-the-art baselines. The paper is ... | Rebuttal 1:
Rebuttal: > **1. How the proposed Fed-GraB model *uniquely and effectively* addresses these challenges?**
>
Fed-GraB comprises two components, Self-adjusting Gradient Balancer (SGB) and Direct Prior Analyzer (DPA), each addressing distinct challenges.
Challenge1: In the context of Federated long-tail lea... | Summary: In this paper, a self-adjusting and closed-loop gradient re-balancing framework Fed-GraB was proposed and long-tailed learning tasks processing performance was improved.
Strengths: This paper presents a methodology named Fed-GraB, which incorporates a Self-adjusting Gradient Balancer (SGB) module. This module... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful feedback. For weakness 1 and 2, we will ensure the equation contains proper ending punctuation and markers in the revised manuscript.
> **1. The definition of IFG is not clear. Please give a formula that says how to define IFG. And how to divide the dataset t... | Summary: This paper considers the globally long-tailed distribution of data and its impact on federated learning (FL). To address this issue, the authors propose the Fed-GraB algorithm that re-balances gradients. The problem is timely and the proposed algorithm is novel. Overall, the paper is well-written, and simulati... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing thorough and insightful comments on our paper.
> **1. Is the proposed algorithm applicable to non-cross entropy loss or cross-entropy with other regularizers as well as non-classification tasks?**
>
We thank the reviewer for this comment. Our proposed algorit... | Rebuttal 1:
Rebuttal: # For Reviewer 3
> **5.The proposed model needs to be corroborated with theoretical analyses or at least insights, especially regarding the function and operation of the DPA modules.**
>
Regarding SGB, we provided the expected target $z_j(t)$ under ideal equilibrium conditions in Equation 2 of ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Pengi: An Audio Language Model for Audio Tasks | Accept (poster) | Summary: Pengi is a novel audio language model that leverages transfer learning to frame all audio tasks as text-generation tasks. It takes an audio recording and text as input, and generates free-form text as output. The input audio is represented as a sequence of continuous embeddings by an audio encoder, and the tex... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing our contribution! We address every question and hope that our response resolves your concerns. Any follow-up questions are welcome.
***
**Questions 1. Please add more baseline - a cascade model for each task.**\
For Table 3, we chose CLAP because it... | Summary: This paper explores Large Language Models (LLMs) in the context of audio processing. The core idea is to do audio-injected instruction tuning for pre-trained LLM. The method is simple: collecting a lot of audio-text paired data and use it to fine-tune a pre-trained CLAP audio encoder together with a frozen LLM... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing our contribution and novelty! We take every comment seriously and address every concern point by point. We hope that our response resolves your concerns. Any follow-up questions are welcome.
***
**Questions 1. Table 3: In Pengi's framework, CLAP serv... | Summary: This work is inspired by the visual language models (VLM) in the literature and presents an audio language model (ALM) for various audio tasks, including both open-ended and close-ended tasks. It also presents a comprehensive evaluation of the proposed ALM on a range of both open-ended and close-ended tasks, a... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing our contribution and novelty! We hope that our response resolves your concerns. Any follow-up questions are welcome.
***
**Questions 1. I feel this work is somewhat limited in that it only uses a relatively small language model (GPT-2 line, 124M para... | Summary: This paper proposes a new audio-language learning model by treating existing audio tasks as text-generation tasks. The model architecture allows both open-ended and close-ended tasks. By evaluating on 22 downstream tasks, this paper shows competitive performance on many of them.
Strengths: The idea of treatin... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contribution and providing constructive feedback! We address every question and hope that our response resolves your concerns. Any follow-up questions are welcome.
***
We present a novel and unified model that can handle both open-ended and close-ended au... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for recognizing our contribution and providing constructive feedback, especially for acknowledging that **this paper presents a novel audio language model** (Reviewer vWxh, 4yQP), **Performance on audio captioning is solid.** (Reviewer sUJx), **One of the first... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present an approach to combine multiple non-ASR audio tasks into a single model. Motivated by audio language models and VLM, the authors propose using a frozen LLM to create an audio LM that can be used for open-ended (purely generative) and close-ended (classification) tasks. The main idea is to u... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contribution and providing detailed comments. We hope that our response resolves your concerns and welcome any follow-up questions.
***
**Q1. The authors tokenize and map ... re-use the same tokenizer as the LLM**\
We conduct an experiment and discuss the ... | null | null | null | null | null | null |
NeRF Revisited: Fixing Quadrature Instability in Volume Rendering | Accept (poster) | Summary: This paper describes a modification to the way NeRF performs approximate integration for volume rendering. Instead of assuming piecewise constant opacity, they propose to use piecewise linear opacity. They argue that this resolves what they dub the "quadrature instability" in NeRF. They re-derive the quadra... | Rebuttal 1:
Rebuttal: Thank you for finding our formulation to be a novel modification. We address each of the concerns below.
## Q1: DIVeR citation
Thank you for bringing up the paper. We will cite DIVeR in our revision.
## Q2 Comparison with DIVeR
We plug our method into DIVeR by using their voxel-based representat... | Summary: Neural rendering methods like nerf relies on the integration of contributions (both color and density) along rays to predict views. Since analytical computation of the integral is not possible (the color and the density being both prediction from neural networks, usually MLPs), an approximation is estimated. T... | Rebuttal 1:
Rebuttal: Thank you for finding our work simple, very clear and well-written with elegant ideas that are mathematically driven. We appreciated that you highlighted that our approach is a plug-and-play to the popular nerf-based methods and that our experimental results are convincing with non-negligible impr... | Summary: This paper presents a fix for the "quadrature instability" arising from sampling of points for numerical integration used by NeRF2020 and its successors.The sampling inconsistence may not be big on classical rendering techniques, but can be significant when using neural architectures. Opacity and color values ... | Rebuttal 1:
Rebuttal: Thank you for finding our formulation clean that results in a method that is simple but effective. We appreciate that you find our work to be of interest to researchers working in the area of radiance fields and for highlighting that our improvement is significant in particularly difficult situati... | Summary: The paper addresses a fundamental limitation of existing NeRF formulation, namely piecewise constant integration, which results in sensitivity to point sampling, summarized as quadrature instability in this paper. To address this, the paper proposes a new formulation based on piecewise linear quadrature for de... | Rebuttal 1:
Rebuttal: Thank you for finding our paper clear and theoretically sound that addresses a fundamental problem of the existing NeRF formulation leading to quantitatively and qualitatively improvements in NeRF reconstruction. Below we address the questions that were raised.
## Q1 Small improvements from overa... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and for finding our approach novel (HvcE), simple (WoBC, am3W) and effective (am3W) that introduces a clean (am3W), elegant (WoBC) and theoretically sound (tVLL) formulation that is clear and well-written (WoBC). As summarized by the reviewers, we tackle a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
How Does Adaptive Optimization Impact Local Neural Network Geometry? | Accept (poster) | Summary: This paper aims to understand why Adam works better than SGD for language model training from the perspective of local geometry of the training loss around the algorithm iterates.
Strengths: The problem that this paper considers is indeed a very important question, and this work sets out to answer the questio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and answer their questions below.
1. **Fonts of plots**. Thanks for your advice. We will increase the fonts and adjust the legends and axes of the plots.
2. **Low-rank structure.** Figure 8 is to depict a low-rank trend of the spectrum instead of a mathem... | Summary: This paper aims to study the connections between an optimization algorithm and the geometric properties observed during the training of a neural network with that algorithm. The authors argue that when analyzing these connections, it is important to consider the local iterates. To address this, they propose th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and answer their questions below.
1. **Contribution of our statistic to fast optimization.** We actually discuss and demonstrate the contribution of small $R^{\text{OPT}}_{\text{med}}$ to fast optimization. Please see the first paragraph on Page 7 and Appe... | Summary: This study makes the comparison between the adaptive optimization method (Adam) and the non-adaptive one (stochastic gradient method with momentum) through a lens of ``uniformity'' of diagonal components of the Hessian denoted by $R_{\rm med}^{\rm OPT}(t)$. The comparison is conducted experimentally for variou... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and answer their questions below.
1. **About our metric $R^{\text{OPT}}_{\text{med}}(t)$.** First, we did consider another type of variant, which is a singular value-based metric. More discussions and experiments can be found in the paragraph starting from... | Summary: This paper aims to study the interaction between optimizers and the local loss landscape. The authors introduce the notion of $R_{\mathrm{med}}^{\mathrm{OPT}}$ to characterize the uniformity of the Hessian diagonal. Through extensive experiments, they show that, compared with SGD, Adam biases the trajectory t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and answer their questions below.
1. **Diagonal approximation.** We actually add empirical evidence on the diagonal approximation in Appendix F. In Appendix F.1, we conduct experiments on a language transformer model and demonstrate that the trend of loss ... | Rebuttal 1:
Rebuttal: The attached figure is to address a question raised by Reviewer pmE6.
Pdf: /pdf/7a0d3903708a9579158f97d71e860e2a3eff125a.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper provides a new explanation of why adaptive gradient methods perform better than SGD with momentum though extensive experiments and theoretical analysis. The key insight is that adaptive gradient methods especially Adam bias toward solutions with uniform Hessian diagonal values, and this property may ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and answer their questions below.
1. **Comparison in the opposite setting.** In Appendix A.8, we have empirical results on image tasks, which provide the comparison in the opposite setting. As is shown in A.8, on image tasks, Adam does not converge faster ... | null | null | null | null | null | null |
Generalizing Nonlinear ICA Beyond Structural Sparsity | Accept (oral) | Summary: This paper utilizes the structural sparsity assumption on the support of the Jacobian matrix of the mixing function to extend the identifiability of nonlinear ICA in more settings including under-completeness, partial sparsity and source dependence, flexible grouping structures. It is a technically solid paper... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed reading and insightful questions. All of these constructive suggestions have further improved the quality of the updated manuscript. Please find our point-by-point response below.
**Q1:** Implementation availability.
**A1:** Thanks for finding our work inter... | Summary: The article serves as an extension to the work of Zheng et al. 2022, which posited the identifiability of nonlinear ICA based on specific structural sparsity assumptions related to the mapping of sources and mixtures. This current article expands on that by addressing the undercomplete case—where the number of... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the time dedicated and constructive feedback, which has greatly improved the quality of the updated manuscript. Please find the responses to all your comments below.
**Q1:** More discussion on the assumption (i) in Thm. 3.1.
**A1:** Thanks so much for the suggestio... | Summary: The paper extends identifiability theory of nonlinear ICA (NICA), and deep latent variable models in general, by utilizing structural sparsity. In particular, previous works have shown that NICA can be identified if there is some observed auxiliary data or latent dependencies that essentially capture the induc... | Rebuttal 1:
Rebuttal: Thanks so much for your time and insightful comments. We are glad that you find our results important and the paper of good quality. Please find our point-by-point response below.
**Q1:** Limited coverage of some works.
**A1:** Thanks for the suggestions. We have now detailed the discussion in t... | Summary: This paper extends a recent result from Zheng et al 2022, which introduces an assumption they call “structural sparsity” to induce identifiability in nonlinear ICA without relying on a common (but arguably unrealistic) assumption that the observed variables are conditionally independent given observed auxiliar... | Rebuttal 1:
Rebuttal: We are very grateful for your detailed reading and insightful suggestions. Your comments on the quality of our paper and the strength of our contributions mean a lot to us. Please kindly find our point-by-point response below.
**Q1:** There are many other interesting real-world tasks that would b... | Rebuttal 1:
Rebuttal: We extend our sincere thanks to each of the reviewers for their thoughtful insights and the time devoted to reviewing our manuscript. We are encouraged that all of the reviewers have found our paper of good quality in various ways. With appreciation, we have provided detailed, point-by-point respo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary:
The paper introduces more flexible ways to perform nonlinear independent component analysis (nonlinear ICA). Nonlinear ICA involves identifying the sources s from the observed x when both s and x are related by x = f(s) and f is a nonlinear function. Previous work has developed a method for this problem und... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer’s time on carefully reading our manuscript and providing insightful suggestions. These have undoubtedly further improved the manuscript. Please kindly find our detailed, point-by-point response below.
**Q1:** Suggestions on expanding empirical comparisons.
**A1:**... | null | null | null | null | null | null |
Theoretical and Practical Perspectives on what Influence Functions Do | Accept (spotlight) | Summary: This paper reexamines the assumptions used in the deduction of influence function (IF) methods in order to explain the failure of IF in predicting leave-some-out-retrain performance. The authors find that all five assumptions used in the previous deduction will be violated to different degrees in practice and ... | Rebuttal 1:
Rebuttal: **Weaknesses bullet point #1**: For (1) we can point the reader to two relevant references. First, Schioppa et al. who compare ABIF with exact Hessian on retrieving mislabeled examples (Fig. 2). Second, Fisher et al. (``Influence Diagnostics under self-concordance’’; Fig. 3 and the discussion in... | Summary: Influence functions allow one to measure the effect of up-weighting a training sample on the test loss (or functions of the model parameters) of a test example. This paper studies several of the assumptions needed for hessian-based influence functions to produce valid leave-out-one estimates of the effect on a... | Rebuttal 1:
Rebuttal: **Comparison to Bae et. al**: Thank you for the suggestion, we will expand the discussion of Bae et al. Some comparison points:
* Bae: Only Hessian-based influence functions; Us: we discuss Hessian and Gradient-based influence functions covering e.g., also TracIn.
* Bae: convexity assumption is ... | Summary: This paper studies the limitations of Influence Functions (IF) based on assumptions about convexity, numerical stability, training trajectory and parameter divergence. The authors discuss each of those assumptions in detail and propose ideas to address the shortcoming of those limitations. The authors describe... | Rebuttal 1:
Rebuttal: **Comparison to SimInfluence**: A key difference is that, while Guu et al. (Simfluence) discuss the limitation of additivity in TracIn from a modeling perspective, we point at the limitation from its theoretical derivation. Our *first step* is showing that the original derivation is incomplete: s... | Summary: This paper verifies assumptions of Influence Functions that show discrepancies in theory and empirical results, convexity, numeric stability, training trajectory, and parameter divergence. Although the other assumptions are addressable, parameter divergence is not. Hence, the paper proposed a solution to it an... | Rebuttal 1:
Rebuttal: **Reply to Weaknesses bullet point 2 & Questions**
*Regarding the fading effect for BERT results*: Multiple runs (n=9) were used to build confidence intervals; the goal is to show that eventually 0 falls inside the confidence interval. For the methods Hessian, TracIn, TracIn(3) we observe that 0 ... | Rebuttal 1:
Rebuttal: We thank the reviewers for all the comments and suggestions and the positive feedback! Concerning readability (e.g., moving some proofs from the Appendix to the main body, explaining Arnoldi, explaining $C^k$-differentiability, Theorem 1 and Eq. (7-8) in more detail and improving the plot present... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SOL: Sampling-based Optimal Linear bounding of arbitrary scalar functions | Accept (poster) | Summary: This paper proposed a method to upper and lower bound a scalar function within a convex set using linear functions. The proposed method works by solving a sequence of discrete linear bounding problems with an increasing number of sample points.
Strengths: 1. The proposed method works for neural networks with ... | Rebuttal 1:
Rebuttal: Thank you for your review and the feedback.
[proposed method does not seem to improve the "fraction of properties certified" ...] We would like to point out that this can be easily explained by how LinSyn was tuned.
While their method does not provide any tuning parameters for the user, it has a ... | Summary: This paper describes an approach for finding a linear upper bound of scalar functions that are Lipschitz. To find a bound that approximately optimizes discrepancy with the target function, it samples points to construct LP instances whose solutions bound the corresponding "discrete" bounding problem, and conti... | Rebuttal 1:
Rebuttal: Thank you for your review and the feedback.
[exposition of the algorithm] Thank you for the suggestion. We will try to rearrange some of the paragraphs to make the narrative more coherent.
[information on the missing details discussed in (3)]
Here are the details. We will make them more clearly ... | Summary: [Context]
Neural network verification algorithms are based on bounding the activations and outputs of neural networks.
This is achieved by propagating linear bounds through the network.
For each non-linear operation in the network, it is required to provide linear bounds of the operation: given the range of i... | Rebuttal 1:
Rebuttal: Thank you for your review and the detailed feedback.
[Missing description] We should definitely describe the 1d algorithm in more details. The idea of the bisection is as follows
1) guess the value $g(x_c) = y$ which we want to optimize;
2) check its feasibility by finding $a = \min_{x_i < x_c} \... | Summary: This paper focuses on finding tight linear bounds for the activation functions in neural networks, which are used for certifying the robustness of a neural network.
It aims to provide optimality guarantees for the tightness of the bounds of a scalar function represented by the neural net.
Two settings are con... | Rebuttal 1:
Rebuttal: Thank you for such a detailed feedback. We will definitely address clumsiness in formulations and unintentional ambiguities.
[average time complexity of SOL] Unfortunately, we cannot provide a rigorous analysis of the average time complexity, since the dynamics of the estimated linear bound
betwe... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a new method for finding tight (in the l1 error sense) linear bounds for a general non-linear activation functions. Under their definition of tightness, the authors propose a method for finding optimal bounds for any convex function. For non-convex functions with Lipschitz continuity, the... | Rebuttal 1:
Rebuttal: Thank you for your review and for the interesting question.
[significance of optimizing $L_1$ error] Right now we don't see any straightforward way to generalize our approaches to alternative tightness measures.
Moving from $L_1$ measure to, for example, $L_2$ poses several immediate problems:
1)... | null | null | null | null | null | null |
Inverse Preference Learning: Preference-based RL without a Reward Function | Accept (poster) | Summary: The paper proposes a way to learn from preferences in the offline setting without learning a reward model. The main insight is that reward and Q functions are interchangeable and the policy learning using preference can be formulated directly as a function of Q which represents the reward function implicitly. ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and effort in reviewing our work and have made a number of changes as a result.
**Theoretical Underpinnings**
The reviewer raised a number of questions regarding the theoretical underpinnings of IPL, namely its optimality and regularization. We... | Summary: This paper addresses the problem of learning a policy given offline pairwise preference data. The proposed algorithm, inverse preference learning, directly learns a value function without explicitly learning the reward function first. This setup simplifies the common two-step approach of first inferring the re... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their in-depth review. We hope to have addressed all the reviewers' concerns, and would invite them to additionally examine the new experimental and theoretical results produced in response to other reviewers.
**Clarification of Results in Table 2**
In our... | Summary: This paper proposes a new algorithm, inverse preference learning, to learn from (offline) preferences between behavior segments. In particular, they show that human preferences can be modeled using only the Q-function, therefore eliminating the need to learn a separate reward function. The authors show this ap... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments.
**Weakness 1: Reward modeling cost**
While reward models in RLHF in language models can be smaller, as in InstructGPT in control-based domains, the current trend is the opposite: reward models are getting bigger. Kim et al. [1] and Early e... | Summary: RLHF pipelines typically consist of (1) training a reward model over human preference data and (2) using this trained reward model with a well-known RL method. This two stage training is computationally expensive. The authors of this paper develop an algorithm "Inverse Preference Learning" to directly learn th... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! The reviewer largely had theoretical questions about our work. We believe the answers to these questions will help all readers, and will correspondingly update the paper to include all the information below.
**1. Cascading Errors**
The reviewer asked if we co... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you all for your detailed feedback. We have responded to all reviewers individually with more content but wanted to make a global list of the major changes that we have made to the manuscript and additional experiments included in our allowed one page upload.
**Theoretica... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective | Accept (poster) | Summary: Edit: Updating score from 6 t o7 based on the discussions.
Extracting useful representations from unlabelled data for label efficient training of medical image segmentation task is a widely studied problem. This work approaches learning useful representations using contrastive learning (CL) within a semi-supe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our contribution to the self-supervised field, appreciating the strong performance improvement on multiple datasets, and providing constructive suggestions for the presentation of our work! We’ve made a substantial revision to the paper, which addr... | Summary: The authors present ARCO, a novel semi-supervised contrastive learning framework that employs a stratified group sampling strategy (i.e. **SG** and **SAG**) to compute gradient estimators with reduced variance, thereby enhancing representation learning in dense contrastive learning. By improving dense contrast... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our contribution and providing suggestions for the presentation of our work! In particular, we agree that discussing the potential application and more detailed classification are necessary to position this work properly. If you have further concer... | Summary: This paper proposes two new sampling strategies, SG and SAG, for contrastive learning in semi-supervised frameworks. Compared with randomly sampling pixels for contrastive learning, pixels are grouped into several subsets, and then pixels are sampled from each subset. The proposed method is proven to reduce th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our contribution to the medical image analysis field, appreciating the good performance improvement on our multi-class medical segmentation task, and providing constructive suggestions for the presentation of our work! We’ve made a substantial revi... | Summary: The authors propose a sampling strategy to improve contrastive-learning-based medical image segmentation performance with limited labeled data and training stability. The sampling strategy is used in conjunction with a previously-published contrastive semi-supervised training strategy. The main contributions i... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our contribution, appreciating the strong performance improvement on our medical image segmentation tasks, and providing constructive suggestions for the presentation of our work! We’ve made a substantial revision to the paper, which addresses all ... | Rebuttal 1:
Rebuttal: In response to the query from Reviewer-J4c5 (Question 3), we appreciate the emphasis on the depth of our analysis. Given the constraints of this rebuttal format, we wish to assure our commitment to augmenting the ablation section for a more comprehensive understanding.
To fully understand the c... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Where Did I Come From? Origin Attribution of AI-Generated Images | Accept (poster) | Summary: This paper addresses the problem of distinguishing between images generated by a generative AI model or acquired by a camera (real images). The motivation comes from the concerns of AI community about the potential misuse and intellectual property (IP) infringement associated with image generation models.
Th... | Rebuttal 1:
Rebuttal: Thank you very much for your precious time, thoughtful
comments, and recognition of the significance of our work.
We hope the following results and clarifications can
adequately address your concerns.
**Q1**: Missing description of computational costs.
**A1**: Thanks for your valuable comment. T... | Summary: This paper develops an origin attribution method to determine whether a specific image is generated by a particular model. The key idea is based on reverse-engineering generation models, and the decision is made by thresholding the reconstruction loss. The proposed method is evaluated on eight different image ... | Rebuttal 1:
Rebuttal: Thank you for your time and insightful comments. We have run
all the suggested experiments. We hope the following new
clarifications and results can address your concerns. We are
willing to perform more experiments if you have further
suggestions.
**Q1**: The authors state that "this paper is the... | Summary: The method proposes a model-agnostic attribution method. Given a synthesized image as input, the goal is to attribute the correct model that generates the image. Different from prior work, the method is not restricted to a fixed set of models. The main idea of the work is to use reconstruction error -- the mod... | Rebuttal 1:
Rebuttal: Thank you very much for your time and insightful comments.
We hope the following new clarifications and results can
address your concerns.
**Q1**: Using reconstruction error is not new for membership
inference. Although membership inference focuses on
whether an image is used for training the mod... | Summary: This paper proposes a new problem: given an image $x$ and an generate model $M$, predict whether this image $x$ belongs to $M$ or not. To this end, this paper proposes to do hypothesis testing based on the reconstruction error after conducting latent optimization to reconstruct the given image $x$. They hypoth... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments. We hope the
following new clarifications and results can address your
concerns. We are happy to provide further responses and
perform more experiments if you have further suggestions.
**Q1**: The first is that, this framework will only work if
the re... | Rebuttal 1:
Rebuttal:
We sincerely thank all reviewers for their thoughtful
comments and precious time. We provide our responses below
to address concerns. Please let us know if there is anything
still not clear. We are willing to answer more questions and
perform more experiments if the reviewers have further
concern... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Linker-Tuning: Optimizing Continuous Prompts for Heterodimeric Protein Prediction | Reject | Summary: The paper proposes a strategy of predicting heterodimers with ESMFold.
Chains of heterodimers are linked by glycine linkers and inputed to esm. The output representations are then added with a learnable embedding layer, and then folded with the folding module in ESMFold. The finetuning is only done for the l... | null | Summary: In this paper, the author applies prompt tuning in the heterodimeric protein prediction task. Instead of using the poly-Glycine linker, this method automatically finds the best linker in the continuous space. The author compares this method with several existing methods including the current start-of-art algor... | null | Summary: Inspired by the prompt tuning technique used in the field of NLP, the authors leverage the prompt tuning to adapt the single-chain pre-trained ESMFold for heterodimer protein structure prediction. To be specific, a learnable soft prompt is placed between protein chains. With such links, the pre-trained ESMFold... | null | Summary: This work predicts the structure of heterodimeric protein chains by optimizing poly-G linkers that connect two chains of a heterodimer.
Strengths: - This research, compared to other existing deep learning methods, finds an alternative to protein complex prediction methods, which is to make use of poly-G link... | Rebuttal 1:
Rebuttal: Thank you for your suggestions. We would like to clear up some misunderstandings and answer your questions.
1. About ProteinMPNN [1], it is a protein design model that takes structure as input and predicts amino acid sequence. It is not a structure prediction model. So we do not compare our model ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Prioritizing Samples in Reinforcement Learning with Reducible Loss | Accept (poster) | Summary: This paper proposes an experience replay priority scheme based on the notion of reducible loss (ReLo), estimated as the difference in Q-loss between using the online Q-network and the target Q-network, and show in experiments in DM Control Suite, Mujoco tasks, and Atari environments that it improves upon prior... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive criticism and recommendations for making this paper better. We discuss the points below.
### Sticky Actions
Yes we used sticky actions in Atari and MinAtar, utilizing the default values of 0.25 and 0.1 respectively.
### Decoupling Target Network and Hel... | Summary: The authors propose a novel form of prioritized experience replay based on the “learn-ability” of the sample, or how much the training loss on a sample can be reduced.
They point out that previous methods that prioritize data with high TD error are not optimal if the high TD error is irreducible error, which... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable insights and suggestions to improve the clarity of the paper. First, we would like to apologize for the lack of explanation and coherence in the paper. We have fixed all the points raised in the review. We will address them sequentially.
### Notation Clari... | Summary: This work introduces a novel experience replay sampling technique (ReLo-based sampling) that prioritizes sampling experience that has the greatest potential for reducing the agent’s loss. Empirically, ReLo-based sampling yield better performance than uniform random sampling and prioritized experience replay (P... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and suggestions to improve the paper. We strongly believe that these suggestions would make the paper better. We address all the concerns and questions below.
### How ReLo can help even without stochasticity in rewards or dynamics
Yes, this can be... | Summary: The author proposes a simple modification on prioritized experience replay, preventing unlearnable samples from being prioritized. Prioritized experience replay (PER) prioritizes samples with high TD errors, but some samples that have irreducible TD errors due to stochasticity may also be prioritized. This wil... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful suggestions and comments to help improve this paper. We have incorporated the recommendations into the paper and discuss them below.
### Motivation of ReLo in RL and target networks
In Mindermann et al. (2022) the hold out model is trained on a validation data... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We greatly appreciate your valuable feedback and comments and we will incorporate the suggestions into the revised version of the paper. We would like to clarify a few points that multiple reviewers mentioned before addressing each comment individu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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