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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
Segment Anything in 3D with NeRFs	Accept (poster)	Summary: This paper introduces a simple and efficient method for general 3D segmentation based on SAM (a powerful 2D foundation model) and NeRF-style representation. Instead of building a 3D foundation model from scratch, SA3D uses two steps to lifts 2D SAM segmentation results to 3D in a more concise and efficient way...	Rebuttal 1: Rebuttal: We sincerely thank you for your valuable comments. We answer your questions as follows and hope the response could clear your concerns. ### Weaknesses > W1: ... SA3D can only segment one object at a time. A1: SAM achieves “segment everything” by densely prompting on the image. These prompts...	Summary: This paper proposes to lift 2D segmentations from foundation models such as SAM to 3D by iterating between SAM and NeRF, without re-training or re-defining either. Given a trained NeRF model, the model first renders a view, which is also processed by SAM given a user click. With the segmentation by SAM, the mo...	Rebuttal 1: Rebuttal: We sincerely thank you for your valuable feedback and hope our following clarifications and responses could clear your concerns. ### Weaknesses > W1: Demonstrate the use of this framework to lift another foundation model’s output into 3D. A1: Thanks for the suggestion. We try to lift SEEM [1...	Summary: This paper proposes a method for segmenting a pre-trained NeRF by utilizing SAM. Given a pre-trained NeRF, it first asks users to provide prompts (e.g., some points) for a reference view. It then utilizes the SAM to generate a 2D segmentation for the reference view and utilizes the 2D mask to optimize a 3D seg...	Rebuttal 1: Rebuttal: Thanks for your instructive comments. ### Weaknesses > W1: ... minutes for a single segmentation ... A1: The time cost reported in our paper is an upper bound. As shown in Table 4 (global response text), SA3D achieves satisfactory segmentation with a few sampled views, which only requires < ...	Summary: The authors propose a combination of the newly introduced Segment-Anything Model (SAM) with Nerf, yielding the Segment Anything in 3D (SA3D) system. SA3D cam take a 3D scene reconstructed by Nerf and based on a user prompt (e.g. a few keypoints) can carve out distinct 3D objects from the scene. This is shown ...	Rebuttal 1: Rebuttal: We sincerely thank you for your efforts on the review work and your detailed comments. We answer your questions as follows and hope the response could clear your concerns. ### Weaknesses > W1: Overstatement: ... "Our research ... as long as ..." suggests more ... A1: Thanks for the suggestion...	Rebuttal 1: Rebuttal: ## Common Response for Time Cost Analysis We thank all reviewers for the insightful comments. Since several reviewers (Q1 of Reviewer b3JM, W1 & W6 of Reviewer JhYG, W2 of Reviewer Xgwo) express concerns about the time overhead of our method, we discuss this issue here. We provide per-scene ti...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Gaussian Membership Inference Privacy	Accept (poster)	Summary: The proposed f-MIP method incorporates a practical membership inference attack threat model, offering easily interpretable privacy guarantees. This approach improves utility, especially when the attacker's capabilities are realistically constrained. Through a theoretical analysis of likelihood ratio-based memb...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and for referring to our approach as interesting and promising. We will answer the specific questions below. > The paper lacks a theoretical exploration of the relationship between f-MIP and f-DP [...] in Figure 1, it remains unclear whether they actually s...	Summary: The authors introduce a new privacy notion called f-membership inference privacy (f-MIP), which relaxes strict Differential Privacy (DP) assumptions thereby promising better model utility. The paper proposes a theoretical analysis of membership inference attacks on DP-SGD based on trade-off curves (similar to ...	Rebuttal 1: Rebuttal: We thank the Reviewer for their thoughtful review and were pleased to hear that the Reviewer found our paper to be clearly structured and to be a solid contribution. We will answer the individual points raised below. > It would be nice to see an investigation on the validity of the initial as...	Summary: the submission extends the work on privacy guarantees specifically for protecting data membership inference attacks, and the extension is on using the Gaussian distribution to characterize the trade-off function. The proposed Gaussian Membership Inference Privacy has the same parametrization as the Gaussian Di...	Rebuttal 1: Rebuttal: We are very grateful for the positive comments, which highlight the relevance of the studied Membership Inference threat model, our ability to analytically capture SGD steps with f-MIP, and which highlight the finding that we can obtain MIP through averaging of gradients only. We will address the ...	Summary: Authors came up with a privacy definition that is more relaxed than DP. It is called Gaussian Membership Inference Privacy (GMIP) which consists of a hypothesis testing which is supposed to decide whether a single instance is present in the training data. DP implies GMIP. The proposed privacy framework is appl...	Rebuttal 1: Rebuttal: We thank the Reviewer for their thoughtful review and the questions raised. We will clarify the individual points below. > I believe that standard notions from statistical hypothesis testing are reinvented, and the results seems not so surprising taken into account results already published in te...	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive feedback, which allows for direct improvements of our work. Inspired by the reviewer comments, we plan to make the following changes and intend to use the additional page provided in the camera-ready version for the following additions: * We add a ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a notion of Gaussian membership inference privacy (GMIP) to capture the information leakage of a training algorithm about a data point $x$ when all the remaining training datasets are randomly drawn from a data distribution. The new GMIP definition has two main benefits compared to the prior...	Rebuttal 1: Rebuttal: We thank the Reviewer for their thoughtful review and the questions raised. We clarify the individual points below. > [W1] The main weakness is that the proved GMIP bound is based on several approximation arguments and relies on assumptions about the gradient distribution, the model dimension, an...	null	null	null	null	null	null
Permutation Decision Trees using Structural Impurity	Reject	Summary: The authors proposed the permutation decision trees method, which uses Effort-To-Compress as the impurity measure to model the order dependencies of data instances, and extended the proposed permutation decision tree to a variant of random forest. They also did some experiments to compare the performance of th...	Rebuttal 1: Rebuttal: Addressing Weakness 1: The applicability of the proposed method is not limited to just timeseries data. In our paper, we are interested in the use case where order in which data instances are presented plays a crucial role, we are not interested in the dependency on a single data instance. In...	Summary: In traditional decision tree algorithms such as CART and C4.5, impurity measures are used to split internal nodes. The paper proposes a decision tree induction method by using effort-to-compress (ETC) measure, which can capture order dependencies in the data. With ETC’s ability to capture order dependencies, p...	Rebuttal 1: Rebuttal: Addressing Weakness 1: In the revised manuscript, we have thoughtfully included a dedicated section titled "Model vs. Domain Interpretability, Temporal Generalizability, and Causal Decision Learning." In this section, we have explored the interpretability aspects of our proposed model, highlig...	Summary: The paper "Permutation Decision Trees using Structural Impurity" introduces a novel split criterion for the training of decision trees that also takes the order of labels inside the training data into account. This way, to obtain a forest, one only needs to shuffle the data before training individual trees. Mo...	Rebuttal 1: Rebuttal: Addressing Weakness 1: In response to the reviewer's feedback, we have made significant improvements to the manuscript. We have now included a dedicated section that presents a thorough performance comparison between the permutation decision tree and the classical decision tree, using various...	Summary: The paper proposes a novel in Decision Tree literature splitting criteria based on Effort To Compress (ETC) gain. Use of this criteria is justified by a desire to work with data that doesn't conform to i.i.d. assumption about the generating distribution. There is an experiment on a synthetic data that shows th...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for the valuable feedback. Please find the point by point response to each of the weakness pointed out. Addressing Weakness No 1: In our paper, we are interested in the use case where order in which data instances are presented plays a crucial role, we are not intere...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for the valuable feedback. We have modified the manuscript and addressed the comments raised by the reviewers. Following are the changes made: 1. A concrete example that illustrates the practical use case of the proposed Permutation Decision Tree. Please find the link f...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model	Accept (spotlight)	Summary: The paper proposes PbGMR-GMUS to encode the graphs of physics systems into low-dimensional features, which are further reconstructed into the desire graph representations. An attention-based model is integrated into flow-based generative models to predict the future dynamics in the latent space. The proposed m...	Rebuttal 1: Rebuttal: We appreciate your important support. And we seriously address your concerns accordingly; they are extremely helpful to the submission. > Q1: Miss citation at L62 for GMR-GMUS. Response: Thanks for pointing it out. We already added reference in the revised version. > Q2: On top righ...	Summary: This paper introduces a novel mesh-based machine-learning approach for modeling stochastic fluid dynamics. Similar to [11], the state transitions are modeled in a compact latent space (referred to as the mesh-reduced space) rather than the high-dimensional mesh space. Compared with [11], this paper makes sever...	Rebuttal 1: Rebuttal: First and foremost, we express our heartfelt gratitude for your warm acknowledgment of the novelty of the learning-based probabilistic model, meticulously simulating fluid systems across intricate mesh topologies. Here are our responses to your questions. > Q1: The proposed model appears to b...	Summary: The authors present a unified framework for solving deterministic and stochastic physical dynamical systems on high-dimensional mesh space. Instead of updating values at each discretized mesh, the paper introduces an approach that evolves states in a low dimensional latent space through encoding states and phy...	Rebuttal 1: Rebuttal: The reviewer is positive about the application and performance to wide range of PDE-simulation problems of the proposed framework. This is an excellent support to our work. And reviewer encourages us to further experiment to highlight our novelty over previous models. Here are our responses to you...	Summary: The authors present a new approach for modeling fluid dynamics. The approach involves using GNNs to derive global latent space representations and construct a transformer-based conditional generative models for the dynamics. The resulting model is able to generate stochastic predictions from given initial cond...	Rebuttal 1: Rebuttal: Thanks for the reviewer's supportive evaluation of our work. We are trying our best to address your concerns with the following answers. > Q1:: A number of minor typos in texts and figures (see section below). Response: Thanks for your careful reading. We already fixed them in the revise...	Rebuttal 1: Rebuttal: Dear reviewers, We would like to express our gratitude for your constructive feedback on our manuscript. The insights provided have been invaluable in refining our work. We have uploaded one-page supplementary experimental results in response to your comments. Here we want to highlight several c...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Constructing Non-isotropic Gaussian Diffusion Model Using Isotropic Gaussian Diffusion Model for Image Editing	Accept (poster)	Summary: Gaussian diffusion is a common concept used in image processing, physics, and machine learning. It refers to a process where values (like the pixels in an image) are "smoothed out" or diffused according to a Gaussian function (also known as a normal distribution or bell curve), which provides a natural and mat...	Rebuttal 1: Rebuttal: Q1: The link of $a$ and $b$ to the quality of the generated images. We present the visual results when the hyper-parameters $a$ and $b$ vary respectively in Figure 6 of the main paper. Keeping $b$ constant, when $a$ is small, all element values of the weighting matrix obtained after Sigmoid ...	Summary: This paper presents a novel Non-isotropic Gaussian Diffusion Model (NGDM) for image-to-image translation and image editing tasks. The central idea of NGDM is to add independent Gaussian noises with different variances to different pixels, thus achieving controllable image translation and editing based on the a...	Rebuttal 1: Rebuttal: Q1: Comparison with more methods and experiments on more datasets. For Cat $\rightarrow$ Dog translation task, we add SDDM [Sun S, et al., ICML2023] for comparison. SDDM decomposes the score function into an image “denoising” part and a content “refinement” part for translation. Differently,...	Summary: The proposed model use differentiated reverse sampling strategy for image editing and translation. Strengths: The proposed model use differentiated reverse sampling strategy for image editing and translation. Weaknesses: 1. Please include the user study results on evaluating the natural image editing for qua...	Rebuttal 1: Rebuttal: Q1: Please include the user study results on evaluating the natural image editing for qualitative evaluation. We conduct user study by inviting 40 participants and providing each of them with 30 randomly selected source images where the corresponding generated results of different methods are...	Summary: The authors proposed a Non-isotropic Gaussian Diffusion framework, which they used for Image-to-Image translation and editing, which apparently are basically “soft inpainting” tasks. The authors crafted a non-uniform version of regular gaussian diffusion but ultimately used some knowledge from it to drive a re...	Rebuttal 1: Rebuttal: Q1: Necessity of theoretical description and Eqs. (6)&(8) We clarify the necessity of theoretical description and Eqs. (6,8) as follows. Firstly, our motivation is to achieve controllable image editing by adding noise with different variances to different pixels of the image. This motivates ...	Rebuttal 1: Rebuttal: Dear ACs and reviewers, Thanks for the insightful comments and suggestions on our paper. We have carefully responded to the comments of each reviewer. Meanwhile, we have uploaded a PDF file to show visual results as support material. We will revise our paper accordingly in the final version if ac...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a non-isotropic gaussian diffusion model, in contrast to current popular isotropic gaussian diffusion models. The paper also proposes new forward and reverse diffusion processes in accordance to the non-isotropic gaussian corruption framework that is proposed. The motivation behind using non...	Rebuttal 1: Rebuttal: Q1: On the performance improvement over baselines and the way to select visualization samples We tackle controllable image editing that modifies the image regions related to the editing task while leaving the other regions unchanged. As shown in Figure 2 in the paper, our method can better pr...	Summary: The authors proposed a Non-isotropic Gaussian Diffusion Model for the task of image-image translation and image editing. The NGDM is achieved by adding different noise variances to different image pixels so as to control the regions to edit. Experimental results have demonstrated the state-of-the-art quality o...	Rebuttal 1: Rebuttal: Q1: The reason for implementing NGDM by controlling each pixel’s denoising steps. It is empirically known that the diffusion model can generate more diverse novel content if adding noise with a larger variance to the image while preserving the image information if adding smaller variance nois...	null	null	null	null
A General Theory of Correct, Incorrect, and Extrinsic Equivariance	Accept (poster)	Summary: The authors analyze how equivariant models behave under mismatches between the symmetries of the model and data distribution. They advance three notions, correct, extrinsic, and incorrect pointwise equivariance. They then propose bounds on the error for equivariant neural networks that are sensitive to the d...	Rebuttal 1: Rebuttal: The Authors thank the reviewer for their insightful review. Please see our response below. > equivariance is a property of maps rather than spaces... This is a good point, we definitely agree with the reviewer that ‘equivariance’ is a property of maps rather than spaces. In the revision, we will...	Summary: This paper provides a general theory of correct, incorrect, and extrinsic equivariance of functions, mainly extending the framework of Wang et al., 2023 to more general case of pointwise equivariance of functions defined for pairs of group element and input data. The theory mainly concerns deriving lower bound...	Rebuttal 1: Rebuttal: The authors thank the reviewer for their helpful comments. Please see our response below. > W1. A weakness of this work is that, while certain cases are presented where extrinsic equivariances can be harmful, it offers little principled understanding or general theory of in which specific cases e...	Summary: An obvious limitation of equivariant networks is the assumption that the symmetry they hard-code matches the symmetry of the underlying ground truth function exactly. What happens when the symmetry is only partially present in the domain affecting a symmetry mismatch between the ground-truth function and the n...	Rebuttal 1: Rebuttal: The authors thank the review for their careful review. We are glad that the reviewer acknowledges that we have addressed the problems from the earlier iteration. If you have any other questions regarding our paper, please don’t hesitate to let us know and we are more than happy to discuss them.	Summary: The paper analyzes error bounds for models constrained to satisfy symmetries that only partially agree with the ground truth functions. The main suggestion is to generalize previous definitions, to the point level, allowing to derive a lower bound on the model error with respect to the volume of the portion of...	Rebuttal 1: Rebuttal: The authors thank the reviewer for their thoughtful review. Please see our response below. > I feel the text should elaborate more on the assumptions taken in the analysis of error bound. For example, it is assumed that the model assigns labels by taking a majority vote in an orbit. How reasonabl...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper presents an extension of a lower bound on error in finite labeled classification introduced in "The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry" and investigates lower bounds on error for G-equivariant and G-invariant regression, offering valuable insights into the ...	Rebuttal 1: Rebuttal: The authors appreciate the reviewer’s insightful comments and questions. Please see our response below. > The paper's focus on pointwise definitions rather than emphasizing their implications may obscure the novelty of the results, potentially hindering a clear understanding of the significance o...	Summary: This work addresses the situation in which data and model equivariance does not match exactly. It extends previous work from Wang et al., by proposing a pointwise version of their definition of correct, incorrect and extrinsic equivariance. The usefulness of these new concepts are demonstrated in three new per...	Rebuttal 1: Rebuttal: The authors thank the reviewer for their careful review. Please see our response below. > I think there is a mistake in the xlabel of fig 7b. We apologize for any confusion regarding the `-` in `incorrect - correct` and the other x label in Fig 7. The `-` here is not meant to denote subtraction...	null	null	null	null
ViSt3D: Video Stylization with 3D CNN	Accept (poster)	Summary: This paper proposes a 3D CNN based video stylization method which explicitly disentangles motion and appearance and adopts multi-phrase training. Experiments show that the method achieves high quality results. Strengths: The proposed 3D CNN based framework and multi-phrase training is novel and effective, the...	Rebuttal 1: Rebuttal: Thank you for the valuable comments, we answer your questions and concerns as follows. ### Why only three methods compared Video stylization is relatively less popular cf. image stylization. We reported the recent methods which are leading in this task to the best of our knowledge. We have furth...	Summary: Image stylization is more popular than video stylization, research on video stylization is few due to its challenging. This manuscript first applied 3DCNN to video stylization task, it first explicitly disentangles motion and appearance, and then stylizes the appearance part, and then adds back the motion to d...	Rebuttal 1: Rebuttal: Thank you for the valuable comments, we answer your questions and concerns as follows. ### Disentangle motion and appearance by 4 appearance subnets The disentanglement of motion and appearance, by the 4 appearance subnets, happens by the phase 2 training. In Phase two we keep the 3D CNN encoder...	Summary: The paper studies the task of video stylization. The paper aims to stylize the video using 3D CNN and AdaIn3D. To perform the stylization, the authors propose to disentangle motion and appearance first, and then stylize the appearance part using AdaIn 3D. The results show state-of-the-art results compared to t...	Rebuttal 1: Rebuttal: Thank you for the valuable comments, we answer your questions and concerns as follows. ### Unclear why four appearance subnets needed, affect on performance if the number of appearance subnets is reduced The different appearance subnets work at different scales. The C3D architecture can be seen ...	Summary: This paper proposes ViSt3D, which utilizes 3D CNN (C3D) as the encoder backbone for video style transfer. However, the motion and appearance information in C3D is intrinsically entangled. To address this problem, ViSt3D aims to separate these two features with appearance subnets and AdaIN3d. Results on sports1...	Rebuttal 1: Rebuttal: Thank you for the valuable comments, we answer your questions and concerns as follows. ### 3D CNN vs 2D CNN, solved problem, motivation Stylization is not a deterministic problem, in the sense that there is no single correct stylization for a (input content, target style) pair. It is akin to a ...	Rebuttal 1: Rebuttal: We thank the reviewers and ACs for their valuable time and constructive comments on the paper. The reviewers have raised many valid points and concerns which we have answered to the best of our abilities and hope that the reviewers will find them satisfactory. We would like to reiterate that vid...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning	Accept (poster)	Summary: The paper presents a refined analysis of TS in Bayesian regret reinforcement learning. Regret bounds are derived for tabular, linear, and finite mixture MDPs. The paper uses an information theoretical approach: the information ratio, representing the exploration-exploitation trade-off, is analyzed and bounded ...	Rebuttal 1: Rebuttal: Thank you very much for your questions and comments improving our paper. Please read the Author Rebuttal beforehand. W: 1. See Q.6 and L.1 for experiments and empirical validations. 2. See L.4 and replies to reviewers: W.2 of 4ohk and W.1 of bSE3. Q: 1. (a) Here are more refs: [1111.1797] fo...	Summary: The paper shows a refined analysis of Thompson Sampling in RL. The analysis leverages the notion of Kolmogorov dimension, and results in an improved rate of the regret. The authors further presented the bounds in terms of several specific settings, which match the state-of-the-art results. Strengths: The wr...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments, which we will include accordingly in our final revision as detailed below. We also invite the reviewer to read the author rebuttal beforehand. Below are our replies: Weaknesses: 1. Thank you for the comment. Given other reviewer's remarks, we have re...	Summary: The paper presents uniform Bayesian regret bounds for Thompson Sampling by utilizing a uniform bound of information ratio and specific bounds of the Kolmogorov dimension in different settings. Strengths: 1. The paper presents a uniform Bayesian regret bound for Thompson Sampling which yields results in a va...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments improving the paper, which we will include in our final revision. We also invite the reviewer to read the author rebuttal beforehand. Below are our replies: Weaknesses: 1. Thank you for pointing this out. The phrase "we first define" was...	Summary: The authors propose the novel Bayesian regret analysis for posterior sampling for reinforcement learning algorithm. The proposed regret bounds are applicable in a large variety of different RL settings, such as tabular, linear and finite mixture MDPs. Strengths: - The novel analysis for posterior sampling alg...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments improving the paper, and invite them to read the author rebuttal beforehand. Below are our replies: Strengths: Thank you for the strength highlights. We also like to point out that the mentioned results are corollaries of our main contribution on gene...	Rebuttal 1: Rebuttal: We thank very much all reviewers for their questions and comments, which we will include in our revision. Please note that we had to address each reviewer's response within the character limit. We kindly invite all reviewers to ask any further questions they have in the discussion period. Please ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper considers the problem setting of Bayesian reinforcement learning, in which both the transition function and the reward function are sampled from a known prior distribution. The authors study Thompson sampling in this setting and prove a Bayesian regret bound of order O(\lambda \sqrt{ d T}) where \la...	Rebuttal 1: Rebuttal: First, we would like to thank the reviewer for their thorough and careful summary of our paper and our results. We greatly appreciate the time and the comments made to improve the manuscript. We invite the reviewer to read the Author Rebuttal beforehand. Weaknesses: $\bullet$ Thank you for r...	null	null	null	null	null	null
Any-to-Any Generation via Composable Diffusion	Accept (poster)	Summary: They present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its in...	Rebuttal 1: Rebuttal: Thank you for the review! Please find our response below: > 1. "Missing discussion of limitation and societal impact." The discussion of limitation and societal impact can be found in Section D of the appendix. We attach those paragraphs below: Deepfakes and Misinformation: As part of a comm...	Summary: This paper presents a method that can generate any combination of output modalities, including language, audio, image, or video, from any combination of input modalities. The idea here is to align four modalities in a shared feature space first, and then learn to generate one or more modalities based on the sh...	Rebuttal 1: Rebuttal: Thank you for the insightful review. We are committed to addressing the concerns raised to enhance the quality of this paper. > **1. "The method part is not clear. The relation among image diffusion model, video diffusion model, vision encoder and vision unet is confusing. Since 4 diffusion model...	Summary: The paper introduces Composable Diffusion (CoDi), an innovative generative model capable of producing any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities simultaneo...	Rebuttal 1: Rebuttal: Thanks, we agree that specific comparisons can provide more insights and clarify the unique contributions of CoDi. Here's how we position our work with different areas and community in extension to the the discussion in the related work section: >**1. "Comparison with previous diffusion models."...	Summary: The paper presents a new generative model called Composable Diffusion (CoDi). This model is capable of generating any combination of output modalities from any combination of input modalities, including language, image, video, or audio. Unlike other models that are limited to a subset of modalities like text o...	Rebuttal 1: Rebuttal: Thank you for the review. > 1. "Evaluation Metrics: Incorporating user studies or other qualitative evaluations could provide a more comprehensive understanding of the model's performance." We perform a small scale user study due to time constraints. We will perform more comprehensive studie...	Rebuttal 1: Rebuttal: We are glad all reviewers appreciated our work and found it well-motivated (7M7p, dG2H, mZPw, hGLR), well-written (7M7p, dG2H, mZPw), and original in introducing Composable Diffusion as a novel model (7M7p, dG2H, mZPw, hGLR). The recognition of CoDi's capability to generate any combination of outp...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Learning to Compress Prompts with Gist Tokens	Accept (poster)	Summary: Due to the computationally inefficient nature of long prompts for language models (LMs) today, this work proposes one framework to compress prompts into a smaller set of "gist" virtual tokens. Different from existing work that distills the context for a single NLP task, this work focuses on a distribution of t...	Rebuttal 1: Rebuttal: Thank you to reviewer nmKf for their detailed and thoughtful review! We appreciate you find the paper “elegant and interesting”, with “really promising” compression ratios and “solid and interesting” technical contributions. The main concern of reviewer nmKf seems to be confusion about the gist c...	Summary: Prompting is the current way of using LLMs, but it occupies the context spaces. Instead of training the LLMs (e.g. fine-tuning), the paper presents a way to compress the prompt into gist tokens, which can be efficiently cached and reused. The method shows 26x compression rate, 40% FLOP reduction and wall clock...	Rebuttal 1: Rebuttal: Thank you to reviewer tZ74 for the detailed and thoughtful review! We appreciate that you find our method well motivated, straight-forward, and intuitive. The main concern of reviewer tZ74 is that the efficiency gains reported in the paper are “not significant”, regarding (1) the wall clock time r...	Summary: The authors tackle the problem of wasted compute and waste context window space from repeatedly encoding a prompt. The authors propose gisting, in which gist tokens are inserted after the prompt, and the attention mask is modified such that tokens after the gist tokens cannot attend to tokens before the gist ...	Rebuttal 1: Rebuttal: Thank you to reviewer W7yi for the detailed and careful review! ## On low inter-annotator agreement > The overall experimental results would be more convincing if human agreement was high. **On the contrary, when comparing two models of equal quality, we do not expect high inter-annotator agree...	Summary: This paper presents "gisting", which learns language models to compress instruction prompts into smaller sets of compressed context -- that includes special '<GIST>' tokens and the activation stacks above these tokens. Compressing instruction prompts allows saving context windows and saving compute for encodin...	Rebuttal 1: Rebuttal: Thank you to reviewer 3Sa1 for their detailed and helpful review! We are glad you find the paper “novel and useful”, with “very solid and well-designed experiments and evaluations”, as well as “very easy to follow”. Here are some responses to your comments and questions: > However, if I understan...	Rebuttal 1: Rebuttal: Thank you to reviewers 3Sa1, W7yi, tZ74, and nmKf for their uniformly detailed and constructive reviews, and to the area chair for overseeing this process! We are glad that a majority of the reviewers are currently positive on the paper, and that reviewers found our ideas “novel and useful” (3Sa1...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection	Accept (poster)	Summary: To solve the challenges with limited high-quality labels and non-IID client data in federated learning, the authors present a pioneering SSFOD framework, designed for scenarios where labeled data reside only at the server while clients possess unlabeled data. Meanwhile, they propose the FedSTO, which consists ...	Rebuttal 1: Rebuttal: ### W1. No explanations for personalized pseudo-labeling for unlabeled clients - Thank you for your question. We apologize if the presentation of the “Personalized Pseudo Labeling for Unlabeled Clients” was not clear in the main body of the paper due to space constraints. We have provided a compre...	Summary: This paper introduces a novel framework called Semi-Supervised Federated Object Detection (SSFOD) to tackle the problem of object detection in a federated learning setting. In this framework, the server possesses labeled data, while the clients hold unlabeled data from different distributions. The proposed app...	Rebuttal 1: Rebuttal: ### W1. Communication efficiency and scalability aspect + # of clients and size of unlabeled data increases - We thank the reviewer for this thoughtful comment. Communication efficiency and scalability are indeed crucial considerations for any FL implementation. In our work, we are mindful of thes...	Summary: This work focuses on a practical application of federated learning, federated semi-supervised learning for object detection. It assumes that the server has labeled data and the clients only have unlabeled data. The proposed method is two-fold: selective training and orthogonal enhancement. Strengths: - This p...	Rebuttal 1: Rebuttal: Thank you for your comments. We address each below. ### W1. Novelty - We would like to respectfully disagree with the assertion that the novelty of our work is limited. When trying to naively apply existing techniques to the SSFOD setting, we observed notably poor performance, which led to our nov...	Summary: This paper explores Semi-Supervised Federated Object Detection (SSFOD), a pioneering framework for distributed data sources with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving. The authors present a two-stage strategy, FedSTO, encompassing Selective Tr...	Rebuttal 1: Rebuttal: Thank you for your review. We address your comments below. ### W1. Additional references (such as `Federated learning with label distribution skew via logits calibration') - Thank you for pointing out the omission in our references list and for suggesting the inclusion of "Federated learning with...	Rebuttal 1: Rebuttal: We extend our gratitude to all the reviewers for providing comprehensive and thoughtful feedback on our manuscript. We appreciate your valuable insight into the strengths and areas for improvement of our work. ### Summary of Strengths cited by Reviewers - Novelty in Approach and Framework: We...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper presents a Semi-Supervised Federated Object Detection (SSFOD) framework featuring a two-stage training strategy, FedSTO, designed to address the challenges of heterogeneous unlabeled data in federated learning. The proposed framework employs selective training and orthogonality regularization with p...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback in helping refine our work. ### W1. Results of fully labeled FL & W8. mAP@0.75 - As you suggested, we added fully-supervised IID and non-IID FL in Table A of our Global response's PDF as well as comprehensive evaluations using mAP@0.75. These results demonstrat...	null	null	null	null	null	null
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier	Accept (poster)	Summary: This paper presents a classifier based approach to provide a measure of miscalibration in Bayesian computation, including for methods such as Approximate Bayesian Computation (ABC) and Simulation-Based Inference (SBI) methods like neural posterior estimation. The method enables the test statistic to be learned...	Rebuttal 1: Rebuttal: We are grateful for your careful reading and constructive reviews. Please find below a detailed point-by-point response to all of your comments. Further development of the experiments would be quite useful/more complex but controlled examples, for instance the SLCP problem? Thank you for you...	Summary: In this paper the authors focus on the challenge of comparing two conditional distributions p, q from their samples. In particular, this is useful as a check for bayesian computations. To achieve this, the authors propose the use of a probabilistic classification approach where they create a new dataset combin...	Rebuttal 1: Rebuttal: We are grateful for your careful reading of our manuscript and your constructive review comments. We have now addressed your concerns to improve and clarify the manuscript. Please find below a detailed point-by-point response to all of your comments and questions. **The main weakness of the pa...	Summary: In this paper, the authors consider conducting frequentists tests for simulation-based inference. Specifically, the authors want to test if an inference engine q(theta \| y) produces samples from the true posterior p(theta \| y). The key idea is to use classifiers to determine similarities between samples genera...	Rebuttal 1: Rebuttal: We are grateful for your careful reading and insightful comments. We have now addressed your concerns to improve and clarify the manuscript. Please find below a detailed point-by-point response to all of your comments and questions. We wish our explanations to help bring clarification. **From t...	Summary: This work generalizes the well known Simulation-Based Calibration method to a setting where the tests on the posterior approximation consistency are based on classifiers. The paper has a nice balance between theoretical discussion (Section 3) and more practical issues (Section 4) making it easy for readers to ...	Rebuttal 1: Rebuttal: We are grateful for your careful reading and constructive comments. We have now addressed your concerns to improve and clarify the manuscript. Please find below a detailed point-by-point response to all of your comments and questions. Figure 2 index Sorry for this confusion. We will reindex...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their careful review and insightful comments. In addition to the point-by-point response we make to each reviewer individually, below we will give three shared responses, including additional experiments (in the submitted .pdf file). ### 1. Additional...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Characterizing the Optimal $0-1$ Loss for Multi-class Classification with a Test-time Attacker	Accept (spotlight)	Summary: The paper generalizes lower bounds on the adversarially robust error on a finite dataset from binary to multi-class classification. Strengths: 1. The paper is well presented and easy to read, which is no mean feat for the amount of theory that is introduced and developed. 2. The formalization and assumptions ...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback. We are encouraged that you find our presentation clear and experiments interesting. We address your questions below: 1) This is a good observation. The technical issue with equation (1) is that for a particular $h$, the function $(x,y) \mapsto \sup_{\tild...	Summary: Deep learning techniques achieve state-of-the art performance on various classification tasks, but alarmingly, they are highly susceptible to adversarial perturbations. It is currently unknown whether there even exist classifiers that achieve low adversarial training risk on standard datasets. This paper aims ...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed engagement with our work and aim to address their concerns below, particularly those regarding the correctness of the main Lemmas in the paper. We are confident that Lemma 1 is correct. We show below that your suggested counterexample vector is achieved by...	Summary: This work proposes to theoretically evaluate the robustness of a multi-class classifier by setting the lower and upper bounds of the optimal loss, i,e, the lowest loss achievable for a given hypothesis family. The lower bound is established by extending the conflict graph-based framework previously applied to ...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive appraisal of our paper and comments to improve it further. We address their comments below: Further details to evaluate the contribution: Our contribution lies in both theoretical and experimental aspects of characterizing the optimal robust 0-1 loss f...	Summary: This paper aims to analyze the optimal 0/1 loss under the most strongest test time attack. The study commences by formulating the problem of obtaining the optimal classifier (based on 0/1 loss) as a linear program on a graph. Subsequently, the authors address the high computational complexity of calculating th...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful feedback and positive appraisal of our paper. We are glad they found it clear and easy to follow. We address their questions and concerns below: Comparison to bounds for verified classifiers: Thank you for the interesting prompt. We checked the avail...	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful and constructive engagement with the paper. As reviewers ourselves, we greatly appreciate the reviewers’ efforts at providing thorough and insightful commentary on the paper. We have addressed all the reviewer’s concerns in the respective rebuttals, incl...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DOSE: Diffusion Dropout with Adaptive Prior for Speech Enhancement	Accept (poster)	Summary: This paper describes a new method for providing noisy-signal conditioning information (y) to the diffusion steps of a diffusion-based speech enhancement algorithm. Three specific innovations are proposed: (1) improve dependence of x_0 on y by dropping out x_t, at random with Bernoulli probability p. (2) In o...	Rebuttal 1: Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions. *** >Q1: The dropout described in Eq. (13) is then not referenced again for the rest of the paper. I think that's because Eq. (13) affects the $T$-step training process, while equations (14)-(18) are ab...	Summary: This paper presents a solution to the problem of condition-collapse in denoising diffusion models for speech enhancement by introducing the adaptive prior and sample dropout techniques. The paper is well-written and provides valuable insights into the functioning of the denoising diffusion probabilistic model ...	Rebuttal 1: Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions. *** >Q1: One main weakness in my opinion is the understanding of Proposition 2. I do not understand how a diffusion model has high probability of recovering ground-truth if the inequality 23 from ap...	Summary: This paper focuses on a new approach in the field of speech enhancement called DOSE, which effectively addresses the problem of conditional collapse by incorporating conditional information into a diffusion enhancement model.DOSE employs two effective conditional enhancement techniques that can significantly i...	Rebuttal 1: Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions. *** >Q1: I think adaptive prior is manipulating the sampling trajectory. What is the relationship with condition optimizer? The unchanged noisy observation $y$ has been provided as the condition. The...	Summary: This paper proposes a novel model-agnostic approach called DOSE for speech enhancement (SE) using denoising diffusion probabilistic models (DDPMs). In this paper, the authors focus on addressing the challenge of incorporating condition information into DDPMs with two efficient condition-augmentation techniques...	Rebuttal 1: Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions. *** >Q1: To replicate the experiments, more training details and configuration should be provided. We reported our configurations in Sec 5, line 278-283. We added more experimental details incl...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for providing high-quality reviews and insightful feedback. --- We are encouraged that reviewers think our paper "provides valuable insights into the functioning of the denoising diffusion probabilistic model for speech enhancement'' (R4), "an interesting an...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors propose a model-agnostic method called DOSE that employs two efficient condition-augmentation techniques to incorporate condition information into DDPMs for SE. Experiments demonstrate that the approach yields substantial improvements in high-quality and stable speech generation. Strengths: 1. In-...	Rebuttal 1: Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions. *** > Q1: It seems that the authors adopt the adaptive prior. Does it only use in the inference process? What is the difference from the adaptive prior in PriorGrad? Yes, the adaptive prior is ...	null	null	null	null	null	null
ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns	Accept (poster)	Summary: This paper proposes to drape multi-layered garments on SMPL-based human bodies with different poses and shapes. The method is inspired by the commonly adopted sewing pattern and learns to map garments from 2D panels, i.e. front and back panels, to 3D surfaces. The author first deforms the single-layered garmen...	Rebuttal 1: Rebuttal: Thank you for your valuable reviews. Below are our responses. 1. The experiments of layering. We conducted an experiment to further evaluate our layering model by measuring the intersection ratio between garment layers and between garments and the body. We generated 673 unseen bodies with unse...	Summary: The authors address the task of draping individual multi-layer garments on human body models. In this context, they introduce a respective garment representation suitable for this task. Garments are represented as a set of individual 2D panels whose shape is defined based on a signed distance function (in more...	Rebuttal 1: Rebuttal: We thank you for your valuable reviews. Below are our responses to your comments. 1. Table 1 only shows results for shirts. What are the results for the other categories like? The results for trousers and skirts can be found in Table 1 and Table 2 of the supplementary material respectively, wh...	Summary: This paper introduces ISP, a novel system that can, for the first time, drape a 3D human body with multi-layer garments, without the need of physics-based simulation. Several technical contributions addresses the key aspects of garment draping. To enable learning of garment sewing patterns, the authors cleverl...	Rebuttal 1: Rebuttal: Thank you for your appreciation of our work. We address your questions and comments as follows: 1. A combination with photometric losses can result in more accurate wrinkle estimation. True, incorporating such a loss could significantly enhance our current method. However, formulating an effec...	Summary: The article introduces a 3D clothing generation and driving method inspired by the traditional clothing production process, which has made certain contributions to the reconstruction of clothing, especially multi-layer clothing. This method can achieve the reconstruction of multiple pieces of clothing, the edi...	Rebuttal 1: Rebuttal: We thank you for your acknowledgement of our contribution in cloth modeling and multi-layer clothing. Below are our responses to your comments and questions. 1. Fewer types may lead to limitations in the generalization ability. We could train on more types if we had the data for them, which wo...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their valuable suggestions and constructive comments. We have carefully considered all of the suggestions and concerns raised by each reviewer and responded to each of them below. We will implement these suggestions in our revised paper. The attached PDF ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Solving Inverse Physics Problems with Score Matching	Accept (poster)	Summary: The authors leverage the framework of score matching, which has become popular for training diffusion-based models for generative tasks, to reverse physical processes defined by forward stochastic differential equations (SDEs). Given a system state at time t=T, they propose to solve for the initial conditions ...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review and helpful suggestions. - We restructured our paper based on the feedback we received. In particular, we moved the related work section forward so that it follows the introduction and moved parts of the experimental details to the appendix to impro...	Summary: The paper proposes using score matching to learn the backward process of a given forward SDE, notably coming from a physics application. The paper states that this can be used to simulate backward the distribution of the initial condition given the end state, by starting from the end state and drawing trajecto...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review and helpful suggestions. - SMDP-ODE and $p(\mathbf{x}_0\|\mathbf{x}_t)$: We updated section 2.3 to more clearly distinguish the different design choices for the ODE and SDE sampler. Since the inference of the ODE method is similar to the trajecto...	Summary: The authors propose diffusion-based inverse problem solvers involving the temporal evolution of physics systems. The method utilize a combination of score function and an inverse physics simulator, which corresponds to reverse of drift term in diffusion models, to moves the system’s state backward in time. The...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review and helpful suggestions. - Conventional methods as baseline: We evaluated an additional baseline which represents a more traditional method for solving inverse problems for the buoyancy-driven flow with obstacles experiment. This baseline is bas...	Summary: This paper proposes a diffusion-based unrolled strategy for learning solve ordinary differential equations (stochastic or not). After introducing the problem at stake and proposing two training strategies for solving it (namely a 1-step loss approach and a multi-step approach), the authors draw a theoretical p...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review and helpful suggestions. - Structure of sections and extended literature review: In line with feedback from other reviewers, we have moved the related work section to appear after the introduction. We also removed some of the experimental detail...	Rebuttal 1: Rebuttal: We thank all reviewers for their helpful suggestions and comments. Based on the feedback, there are several updates that are of interest to all reviewers: - Additional baselines and physics informed diffusion approaches: We have included additional baselines for the buoyancy-driven flow prob...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work describes a new method for solving inverse problems for time-evolved physical systems. It does this by combining two components: a time-independent reverse physics simulator (based on a priori domain knowledge or learned) and a learned time-dependent correction term. The posterior for the initial sta...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review and helpful suggestions. - Previous work for inverse problem tasks: We have updated the related work section of the paper to include additional references to prior work on inverse problems, particularly the heat equation. The buoyancy-driven flo...	Summary: The paper proposes an approach to sample from the posterior distribution based on score-based diffusion models with a particular focus on inverse problems in physics. The proposed approach, to my understanding, has two novel contributions: - they use reverse-physics simulations and augment them with score es...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable review and helpful suggestions. - Refinement of model outputs: We agree that the reverse-physics simulator and the score network can be unified in a single joint model. Nonetheless, a clear distinction between the simulator and score network makes sens...	null	null	null	null
Learning Functional Transduction	Accept (spotlight)	Summary: This paper proposes a new deep learning approach for the problem of meta-learning. Inspired by the theory of reproducing kernel Banach space, the proposed method jointly trains a deep transformation as a representation, and a parametrized kernel function $K(vi, \cdot)$ of the problem instances, at the meta-tra...	Rebuttal 1: Rebuttal: Thank you very much for your time and interest in reviewing our work. We would like to bring some answers and clarifications in relation to your comments, and hope they might decide you to increase your score. It seems that your concern is about not properly evaluating our approach against other m...	Summary: This paper proposes a method for transductive learning based on reproducing kernel banach spaces (RKBS). The resulting model is capable of learning in-context in the sense that given a new instance of a learning problem or a new dataset $\mathcal{D}$, it can infer the resulting functional relationship at inf...	Rebuttal 1: Rebuttal: Thank you for your review and the interesting suggestions that you shared. We are happy about your positive assessment and we reply specifically to your comments below. > Regarding adding "an experiment involving in-context learning of language patterns" - This is an intriguing suggestion. We ...	Summary: SUMMARY AFTER REBUTTAL: as described below, most of my comments were addressed and the authors made a significant job in including new results. I have increase my score during the rebuttal phase and I strongly vote for acceptance. --- Neural operators are neural networks that are trained to approximate a...	Rebuttal 1: Rebuttal: Thank you for your time and for this thoughtful review. We are happy that you seem enthusiastic about our approach and we try to answer specifically to your remarks and questions below: > Regarding “the initial discussion on the difference between transduction and inference”. - Indeed, we defi...	null	null	Rebuttal 1: Rebuttal: We would like to thank again all three reviewers for their interest and precious feedback. We are happy that this work has been positively regarded by reviewers Sczv and f2BS while reviewer w6wT seemed less confident. We answered directly to specific remarks and questions in each review thread (se...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Sharpness-Aware Minimization Leads to Low-Rank Features	Accept (poster)	Summary: The submission provides a study of sharpness-aware minimization (SAM) on the numerical rank of features. The conclusions are that (1) SAM reduces feature rank throughout the training, with more rank reduction for larger neighborhood size rho, (2) intermediate values of rho result in representations that are mo...	Rebuttal 1: Rebuttal: We thank you for the detailed feedback. --- > *W1: In different experiments representations are extracted from different points of the network.* We observed the following behavior for the penultimate layer (consistent across CIFAR-10, CIFAR-100, and Tiny ImageNet): - at the very beginning ...	Summary: This submission studies a new property of deep networks trained with sharpness-aware minimization (SAM), namely feature rank reduction. The existence of this property is supported by experimental analysis on image classification, and on contrastive language-image tasks, as well as theoretical analysis on a two...	Rebuttal 1: Rebuttal: We thank you for the extremely detailed feedback! --- > the rank differences between SGD and SAM are less substantial when we consider the optimal values of $\rho$ We totally agree: the low-rank effect can be much stronger if we are allowed to sacrifice some accuracy compared to the best SAM ...	Summary: The paper proposes a new optimization method called Sharpness-Aware Minimization (SAM) that aims to improve generalization performance in deep learning models. The authors demonstrate that SAM can effectively reduce the generalization gap and improve the accuracy of various models on different datasets. The pa...	Rebuttal 1: Rebuttal: We thank you for the positive comments. > *Sensitivity to batch size: … it is crucial to investigate the relationship between batch size and performance in the proposed method. However, the authors only show the results for batch sizes of 128 and 256 in this paper and the appendix. … For example...	Summary: This paper investigates the effect of Sharpness-Aware Minimization (SAM) on low-rank features learned by neural networks. The authors present empirical evidence of low-rank features for different models trained with SAM on four classification tasks, as well as for contrastive text-image training. They also pro...	Rebuttal 1: Rebuttal: We thank you for the feedback. > The paper is interesting, but only the observational results are presented instead of the methodological contributions based on the observation. We believe a new paper does not necessarily have to present a new methodological contribution. For example, ["Unders...	Rebuttal 1: Rebuttal: We thank the reviewers for the detailed feedback and positive evaluation such as - “The implications of SAM-trained low-rank features are discussed in detail, including more efficient retrieval and feature quantization” (Reviewer jzeR) - “The paper also provides a mechanistic understanding o...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network	Accept (poster)	Summary: In this paper, the authors propose to improve the existing deep graph matching paradigm via positional reconstruction. The authors claim that existing deep GM works mainly focus on the visual feature to compute the affinities while neglecting the location and position of the key points in the graphs. To this e...	Rebuttal 1: Rebuttal: We deeply appreciate your thorough evaluation and insightful comments on our paper. Your feedback has significantly contributed to refining the clarity and impact of our work. We are committed to addressing your queries and suggestions: Claim of Novelty Regarding Spatial Information: We sincerel...	Summary: This paper introduces a positional reconstruction encoder-decoder (PR-EnDec) to model intrinsic graph spatial structure for image key-points matching. By using graph to represent the image structure, the proposed model can utilize the high-order spatial information by reconstructing the locational structure o...	Rebuttal 1: Rebuttal: We extend our sincere appreciation for your diligent review and insightful feedback on our paper. Your thoughtful comments have greatly contributed to the enhancement of our work. We are committed to addressing your concerns and queries: Experimental Comparison and Dataset Choice: We thank you f...	Summary: The paper introduces an improved method for graph matching in semantic keypoint matching - the Positional Reconstruction Encoder-Decoder Network (PR-EnDec) and an end-to-end graph matching network PREGM. PR-EnDec efficiently learns node spatial embedding and reconstructs the locational structure of graphs from...	Rebuttal 1: Rebuttal: We extend our gratitude for your meticulous evaluation and constructive feedback on our paper. Your insights have significantly contributed to the refinement of our work. We are committed to addressing your concerns and queries: Lack of Visualizations: We sincerely appreciate your suggestion reg...	Summary: This paper presents a new method to improve graph matching by supplementing visual features with positional encodings. Specifically, an encoder-decoder model is pre-trained to reconstruct a graph’s relative spatial relation based on node coordinates only. The encoder is additionally trained under a contrastive...	Rebuttal 1: Rebuttal: We sincerely appreciate your thorough review and valuable feedback on our paper. Your insightful comments have greatly contributed to the refinement and clarity of our work. We are pleased to address your specific concerns and queries: Detailed Configurations of Multi-Head Self-Attention Layers:...	Rebuttal 1: Rebuttal: The attachment contains the experimental results for IMC-PT. Pdf: /pdf/e0312598bd89c334be67095e2681a98cf4090f4f.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors introduce a positional reconstruction encoder-decoder (PR-EnDec) to model intrinsic graph spatial structure, and present an end-to-end graph matching network PREGM based on PR12 EnDec. The PR-EnDec consists of a positional encoder that learns effective node spatial embedding with the affine transfo...	Rebuttal 1: Rebuttal: We sincerely appreciate your time and effort in reviewing our paper on graph matching. Your feedback is invaluable to us as it provides insights that contribute to the overall quality of our work. To address your question about the purpose of graph matching, we are glad to provide a brief explana...	null	null	null	null	null	null
Going beyond persistent homology using persistent homology	Accept (oral)	Summary: - The paper presents a comprehensive analysis of two types of color filtrations on graphs, focusing on their expressiveness. - The paper introduces a novel topological summary RePHINE that combines both node and edge persistence diagrams. The proposed summary is proven to be more expressive than either node ...	Rebuttal 1: Rebuttal: Thanks for your constructive feedback. In the following, we address all your questions. W1: "One weakness of the paper lies in the experimental evaluation section, which could benefit from a more comprehensive comparison with existing methods, such as the method proposed in [28]." We note t...	Summary: In this paper, the authors discuss the limitations of message-passing graph neural networks (MP-GNNs) in terms of the Weisfeiler-Leman test for isomorphism. They explore the use of persistent homology (PH) to augment graph models with topological features but highlight the challenge of identifying the class of...	Rebuttal 1: Rebuttal: Thanks for your feedback. We reply to your comments/questions below. **"There is room for further exploration and validation in real-world experiments. The current evaluation primarily focuses on controlled simulated datasets, limiting our understanding of RePHINE's performance in practical scena...	Summary: The authors introduce RePHINE, which calculates 0-dimensional persistent homology (PH) with respect to the filtration on edge colors, augmented with so-called missing holes and vertex color information. They establish the necessary and sufficient conditions for distinguishing graphs. RePHINE is shown to be mor...	Rebuttal 1: Rebuttal: Thank you for your detailed and thoughtful review. You have raised very pertinent points. Below, we address your questions/comments. W1: "The related work seems not be detailed enough and is hard to identify." Thanks for your comment. In the Introduction, we decided to group references toget...	Summary: The discriminative power of the persistent homology (in certain homological degrees) of vertex- and edge-filtered graphs is characterized in terms of combinatorial structure of the graphs. It is shown that there exist pairs that can be distinguished by the persistent homology of vertex-filtrations but not by t...	Rebuttal 1: Rebuttal: We are grateful for your insightful comments and suggestions to improve the paper. Below we address your concerns. **W1/Q1: "the empirical claims about the performance of the RePHINE would be better substantiated with experimental evaluation on more datasets." / "How does your main strategy (RePH...	Rebuttal 1: Rebuttal: We are grateful to all the reviewers for their time and insightful comments, as well as to the (senior) area, program, and general chairs for their service to the community. We are glad to note the positive response of all the reviewers, and specifically, their acknowledgments that our work is **...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper provides a theoretical analysis of the expressive power of Persistent Homology (PH) features in distinguishing different colored graphs. The paper characterizes the family of graphs that is separable by a 0-dimensional PH using either node filtering or edge filtering and identifies the failure cases....	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful feedback. We address all your questions below. W1: "the paper does not position itself with respect to recent methods for graph classification" Thanks for the opportunity to position our work appropriately. Persistent homology methods that we conside...	null	null	null	null	null	null
Noether Embedding: Efficient Learning of Temporal Regularities	Accept (poster)	Summary: The paper presents a method for detecting, and embedding, temporal regularities from time-stamped event data, where events have a discrete type, and temporal regularities correspond to one base type preceding another head type by a characteristic relative time. A temporal regularity (TR) is defined in the foll...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for spending valuable time reviewing our manuscript and providing insightful comments. We have improved our paper accordingly but discovered some misunderstandings concerning the content and contributions of the work. Our responses are provided below. **The fo...	Summary: This paper defines the tasks of temporal regularity (TR) detection and query and their evaluation metrics, and proposes Noether Embedding (NE) that enables encoding TRs from limited event samples and rapid retrieval of TRs in constant time complexity. NE possesses time-translation symmetries of temporal regula...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for spending valuable time reviewing our manuscript and providing insightful comments. We have improved our paper accordingly, and our responses are as below. Q1: ‘So how does NE differ?’ & ‘why couldn't we detect time regularities by counting?’ A1: T...	Summary: This paper defined the complementary problems of TR detection and TR query, formulated their evaluation metrics, and adopted classic datasets for evaluations. Towards the TR problem, this paper proposed Noether Embedding (NE), which for the first time, enabled both the data-efficient formation and rapid retrie...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for spending valuable time reviewing our manuscript and providing insightful comments. We have improved our paper accordingly but also discovered some misunderstandings concerning the content and contributions of the work. Our responses are as below. **From ‘W...	Summary: This paper introduced a new task, $\textit{temporal regularity mining}$, and proposed a Noether Embedding to rapidly retrieval TR. Strengths: - The argued temporal regularities sound interesting. - Good writing. Weaknesses: - The proposed temporal regularity mining is not a new task with a new paradigm, wh...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for spending valuable time reviewing our manuscript. However, there do exist many factual errors, which are justified below. From ‘Weaknesses’ Q1: ‘The proposed temporal regularity mining is not a new task with a new paradigm’ A1: To our best kno...	Rebuttal 1: Rebuttal: Three main justifications are provided below. ## 1. Novelty of the problem Q: ‘The authors overclaimed the first contribution…the problems were not new’ A: To our best knowledge, the problem is new. Our main problem is how to enable event embeddings with an efficient TR learning capabil...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces Noether Embedding (NE), a new model for efficient learning of temporal regularities with event embeddings. Experiments conducted on three datasets show the superior performance of this work compared to classic embeddings in detecting valid TRs and querying TR intervals. Strengths: 1. Th...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for spending valuable time reviewing our manuscript and providing insightful comments. We have improved our paper accordingly and our responses are as below. From ‘Weaknesses’ Q1: ‘In Table 2 of the Appendix, the recall rate of NE is lower than that o...	Summary: In this paper, the authors introduce the concept of temporal regularities (TR), which indicates temporal associations invariant to time shifts between events. The authors claim that existing models are lack of the TR learning capability. Based on this idea, the authors define two tasks, TR detection and TR que...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for spending valuable time reviewing our manuscript and providing insightful comments. We have improved our paper accordingly and our responses are as below. From ‘Weaknesses’ Q1: ‘The event embedding implementation part is not very clear. Details are...	null	null	null	null
General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence	Accept (poster)	Summary: This paper studies the Tsallis regularized MDPs, and proposes a practical algorithm for Tsallis KL divergence based on Munchausen RL. The experiments show that the resulting algorithm MVI($q$) performs notably better than its counterpart MVI. Strengths: 1. The paper is well-written with sufficient introductio...	Rebuttal 1: Rebuttal: Addressing the reviewer’s comments on weakness: 1. We thank the reviewer for pointing out the typo $\exp_q Q_2$ in Eq. (8). It should be corrected as $\exp_q (\frac{Q_2}{1 + (1-q)Q_1})$. As the reviewer pointed out, permutation is changeable so there exists many way to expand the term $\exp_q (\s...	Summary: This paper introduces a principle way of generalizing KL-divergence regularized RL into Tsallis KL regularized RL. There have been studies that replaced Shannon entropy with Tsallis entropy to obtain sparsemax policies, but they had limited success. On the other hand, this paper extends Munchausen value iterat...	Rebuttal 1: Rebuttal: Entropy: Tsallis entropy truncates action support but Shannon entropy does not truncate. For softmax policies induced by Shannon entropy or KL divergence to ignore some actions, the temperature would have to be set to infinity, which is impossible in practice. On the other hand, the entropic i...	Summary: The paper introduces the idea of Tsallis KL divergence for regularizing RL algorithms. They first introduce the Tsallis entropy based regularization formally whilst intuiting the how the q exponential and q logarithm, as defined by Tsallis 1998, have a truncation effect on the divergence. They also formalise t...	Rebuttal 1: Rebuttal: Averaging: Averaging is a feature of other KL regularization as well. The primary difference is the form of averaging. KL regularization induces a uniform average of the history, as can be seen from line 84. On the other hand, Tsallis KL inherits this uniform average plus an additional cross p...	Summary: Preface: this paper was assigned to me as an emergency review paper, so I had less time to do an in-depth review. The paper tried to extend Munchausen Reinforcement learning by replacing the commonly used KL divergence with a more generalised form, i.e., Tsallis KL, and empirically demonstrated its benefits u...	Rebuttal 1: Rebuttal: We appreciate you getting in an emergency review, and understand you did not have as much time. Your comments are nonetheless appreciated. We would like to address the overall goal and contribution of the paper. It is not yet certain if Tsallis KL regularization will prove to be an effective cho...	Rebuttal 1: Rebuttal: Included figures for rebuttal: - Figure 1: MVI(q) on Acrobot-v1 across different $q$ - Figure 2: MVI(q) on CartPole-v1 Pdf: /pdf/665249bf686802b415bd277a20f563b81eecb603.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Beyond Normal: On the Evaluation of Mutual Information Estimators	Accept (poster)	Summary: The authors propose a method for creating expressive distributions via injective mappings that maintain their original MI. They state this in Theorem 2.1 and prove it in the Appendix. In addition to this, the authors benchmark a variety of estimators for MI on a set of tasks, including high-dimensional, long-t...	Rebuttal 1: Rebuttal: Thank you for your review. > The main theoretical result, Theorem 2.1, appears to have already been demonstrated in the appendix of the following paper: Kraskov et al. (2004) [...] How is Theorem 2.1 different from that of the result in the appendix of Kraskov et al. (2004)? We believe that The...	Summary: This paper identifies a clear problem with many mutual information estimation benchmarks: most of them focus on simple (normal) distributions. The authors present a new set of forty (!) tasks that contain ground-truth informations, which can be constructed by noting that only injectivity is needed for an infor...	Rebuttal 1: Rebuttal: Thank you for the positive assessment and encouraging words. > Out of curiosity: could modern invariant neural network architectures be used to obtain MI estimates invariant to diffeomorphisms? Thank you for your insightful question! Sadly, we cannot achieve full invariance to diffeomorphisms fo...	Summary: A test benchmark for the evaluation of mutual information estimators is established and many different estimators compared. The test cases contain student-t and normal distributionas and their injective transformations. Difficult cases are discussed and evaluated in more detail. Strengths: The code is reprodu...	Rebuttal 1: Rebuttal: Thank you for the detailed comments. > The choice of the distribution used is not sufficiently argued. In particular, it is known that no MI estimator can evaluate MI correctly on arbitrary distributions. (...) Indeed, one version of a no-free-lunch theorem for MI estimation follows from the fac...	Summary: This paper focuses on the topic of mutual information and shows how to construct a diverse family of distributions with known ground-truth mutual information. It's worth noting that obtaining a closed-form solution for mutual information is highly dependent on the specific assumptions and functional forms used...	Rebuttal 1: Rebuttal: Thank you for your thorough review. Regarding the questions and limitations: > Not all joint distributions can be represented in the form of $P_{f(X)g(Y)}$, limiting the applicability of the benchmark to a specific set of distributions. Extending the family of distributions with known mutual info...	Rebuttal 1: Rebuttal: We would like to thank the Reviewers for their insightful comments and appreciate that they find that our work "provides valuable insights into the performance, strengths, and limitations of different estimators" (AkdF), that our "results on heavy tails are particularly interesting" and that t...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes	Accept (poster)	Summary: This paper proposes a unified prototypical contrastive learning framework, named Cross-modality Aligned Prototypes (CAPro), to learn visual representations with correct semantics. CAPro exploits web data across modalities to formulate semantically-correct textual and visual prototypes. The authors propose text...	Rebuttal 1: Rebuttal: A4.1 Thank you. A4.2 First, the Eqs. (5)(6) are exactly the same as Eqs. (1)(4) in MoPro [28] because we adopt the same prototypical and instance-wise contrastive learning. Second, the control flow in Eq. (7) is inspired by Eq. (5) in MoPro [28], but we differ in that the labels of the top-$K$ m...	Summary: This paper dives into the study of webly-supervised learning and aims to utilize the neglected alt-text of web images to enhance the learning process. To this end, the authors propose the approach called Cross-modality Aligned Prototypes (CAPro). CAPro is adopted with two modules, namely, text matching&enhacem...	Rebuttal 1: Rebuttal: A3.1 Thank you. A3.2 We would like to explain the differences between our work and previous studies in noisy correspondence learning. First, the reasons behind these two problems are different. The semantic noise is caused by the polysemy retrieval keywords which are used to crawl web images. Fo...	Summary: To handle the label noise problems especially the semantic noise in webly supervised learning, the authors propose a unified prototypical contrastive learning framework named as Cross-modality Aligned Prototypes (CAPro). It exploits web data across modalities to formulate semantically-correct textual and visua...	Rebuttal 1: Rebuttal: More explanations will be added to the manuscript. A2.1 Thank you for the comments. A2.2 First, we would like to further explain the function of each module in Fig. 2: Siamese image encoders: extract features $\mathbf{v}_i$, $\mathbf{v}_i'$ from inputs $\mathbf{x}_i$ and their augmented counter...	Summary: The authors propose a prototypically-aligned contrastive learning framework for vision and language in order to enable better web-scraping of fine-grained, rare concepts that are easily confused or mapped to other more common concepts when either vision or language is considered in isolation (what they term “s...	Rebuttal 1: Rebuttal: A1.1 We improve Fig. 1 (c) with non-polysemy performance (see PDF). A1.2 CAPro is indeed a complicated, systematic solution. It is specifically designed to address web noise instead of piling up tricks. It follows a similar paradigm of prototypical learning as MoPro [28] and PCL [27]. Different t...	Rebuttal 1: Rebuttal: Dear reviewers, area chairs, and senior area chairs, We sincerely thank that all four reviewers are positive towards our paper, and provide detailed, constructive comments. According to these comments and suggestions, we will modify our manuscript by: 1) improving all figures to make them easy ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models	Accept (oral)	Summary: This work proposed a new framework that augments the diffusion-based synthesis with physical dynamical simulation in order to generatively co-design task-driven soft robots in morphology and control. The extensive experiments in simulation to verify the effectiveness of DiffuseBot. Strengths: 1. The paper is ...	Rebuttal 1: Rebuttal: We thank reviewer vV19 for bringing up several concerns. We provide additional experimental results (see the one-page pdf in the global response) and discussion as the below. Limited novelty. We aim to design robots with physical function and simple pattern generation will not get us there. ...	Summary: This paper presents DiffuseBot, a framework that uses physics-augmented diffusion models to generate soft robot designs and control strategies for various tasks. The authors propose to optimize the embeddings conditioned by the diffusion model to improve the physical utility of the generated robots, and to ref...	Rebuttal 1: Rebuttal: We appreciate the reviewer syw2 recognizing our work as well-written, novel, and with extensive results. We address the remaining questions as the below. Limitations or failure scenarios. Please check the paragraph about limitations in the global response. Hyper-parameter choices. Hype...	Summary: This paper introduces DiffuseBot, a physics-augmented diffusion model designed for generating and optimizing the morphologies and control mechanisms of soft robots. DiffuseBot aims to bridge the gap between virtually generated content and physical utility in the domain of soft robotics. Firstly, it combines th...	Rebuttal 1: Rebuttal: We thank reviewer zr6A for acknowledging that our approach is interesting and promising. We address remaining questions as the below. Clearer exposition in diffusion as co-design. Gradient-based optimization is shown to achieve more efficient and effective design search in soft robot co-desi...	Summary: The paper introduces DiffuseBot, a system that aims to simplify and automate the design of soft robots in simulation and real-world systems. DiffuseBot uses diffusion-based algorithms to co-design soft robot morphology and control for specific tasks, combining the diversity of evolutionary algorithms with the ...	Rebuttal 1: Rebuttal: We appreciate reviewer 7Vvn for recognizing our paper as a robust, comprehensive and innovative work supported by extensive experiments and a proof-of-concept physical robot to demonstrate the potential of future research. We address the remaining suggestions as the below. **Simulation and physic...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful and constructive feedback. We are encouraged to hear the reviewers acknowledge, - that the proposed approach is robust, innovative, and extends beyond theoretical construct to practical, real-world applications (reviewer 7Vvn), interesting and promising ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks called DiffuseBot. DiffuseBot bridges the gap between virtually generated content and physical utility by (i) augmenting the diffusion process with a physical dynamica...	Rebuttal 1: Rebuttal: We thank reviewer 5Fqn for positive comments on the soundness and the contribution of our work. We address the remaining questions as below. Challenges of physical robot fabrication. Thanks for recognizing the contribution of our work in spite of these non-trivial challenges of real-world tr...	null	null	null	null	null	null
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions	Accept (poster)	Summary: In this paper, the authors compare the causal frameworks of the Rubin causal model (RCM) and the structural causal model (SCM) from a logical perspective. This is a pretty theoretical work in which RCMs are connected to SCMs using the notion of representability which describes whether an RCM can be represent...	Rebuttal 1: Rebuttal: We thank the reviewer for their meticulous comments. We appreciate the call for more examples and clarifications and have added several new examples, with discussion, to the appendix. Please see also our "global" response to all reviewers. Concerning the minor criticism and comments: - Line 53:...	Summary: The paper offers a mathematically rigorous comparison of the potential outcomes and structural causal models frameworks for causality. Their comparison goes further than previous work, in part by invoking the idea of an abstraction, and in part by invoking recent axiomiatizations for probabilistic SCMs. As a ...	Rebuttal 1: Rebuttal: Thank you for the detailed review! Responses to questions: 1. Yes, fixing a set of allowed interventions over an SCM yields an RCM with these three properties. However an RCM with these properties does not always come from an SCM (see Ex. 3). We have clarified this on line 181, changing "possibl...	Summary: This paper compares the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Specifically, the authors show that RCMs, when encoded with the composition and reversibility properties, represent the same space of counterfactual distributions as SCMs. Moreover, they show tha...	Rebuttal 1: Rebuttal: Thank you for the constructive comments. In response to your very helpful questions: 1. We have chosen to focus on effective RCMs because this assumption is almost always made in practice. Effectiveness is so desirable that (as we have noted) one would go so far as to introduce additional varia...	Summary: The paper presents a logical framework to represent both the Rubin potential outcomes (PO) approach and the structural causal model (SCM) approach to causality. It shows that under mild assumption (composition and reversibility) every PO model is representable by an SCM. The paper proceeds to show how the unde...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful comments! We very much appreciate the point about SWIGs and alternative frameworks for reasoning about counterfactuals. In fact, in an earlier draft we had included some remarks about SWIGs in particular, including how they fit into our logical framework...	Rebuttal 1: Rebuttal: We are sincerely grateful to the reviewers for their truly helpful and constructive feedback, and also for their encouraging remarks about the work and its significance. We feel that addressing their constructive suggestions has improved the paper and made it more effective. In the individual res...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Computing a human-like reaction time metric from stable recurrent vision models	Accept (spotlight)	Summary: This paper introduces a new measure of visual artificial network computation 'time', $\xi_{cRNN}$, as the time-averaged uncertainty of a convolutional RNN trained with an evidential deep learning loss (Sensoy et al.). The authors proceed to analyse the dynamics of this network as it solves a range of classific...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback and useful comments. We offer clarifications, additional numerical analyses, and visualizations to address concerns raised in this review. We hope the reviewer sees value and effort in our responses and is willing to adjust their score. > **Fixed ...	Summary: The authors in this work propose a combination of model output uncertainty predictions along with stable recurrent vision architectures in order to derive a proxy for models' reaction time to process input static images. The authors use the previously published horizontal GRU architecture combined with stable ...	Rebuttal 1: Rebuttal: We thank the reviewer for their extremely positive feedback and very helpful comments. We have provided clarifications and additional numerical analyses below to address concerns raised in this review. > The figures are intuitive and appreciate the video presentations We really appreciate th...	Summary: The authors of the paper present a novel concept of crafting a metric for analyzing the temporal alignment between Recurrent Neural Networks (RNNs) and human behavior. The innovative approach, rooted in the estimate of task uncertainty via the application of the Derelict distribution, is both interested and pe...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback and very helpful comments. We have provided clarifications and additional numerical analyses below to address concerns raised in this review. > **Moving beyond Euclidean distance between cue and fixation dots to other factors such as object topolo...	Summary: This study introduces a metric for evaluating the alignment between model and human behavior wrt task complexity reflected in reaction times. The metric is easy to compute and shows qualitative correspondence with human RTs in different tasks. Strengths: The paper is well written and easy to understand. The ...	Rebuttal 1: Rebuttal: We thank the reviewer for their overall positive feedback and insightful comments. >Does instantaneous uncertainty have to go to zero? What happens in the case of ambiguous stimuli? We thank the reviewer for this observation and their suggestion to test the model on ambiguous stimuli. We t...	Rebuttal 1: Rebuttal: We thank the reviewers for their time in reading our manuscript and for their extensive feedback. In this general response, we address some common themes across the reviews. We provide detailed answers to specific reviewers' comments in subsequent responses. To go with this rebuttal, we also provi...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Hierarchical VAEs provide a normative account of motion processing in the primate brain	Accept (poster)	Summary: - This empirical study develops a new framework for neural representation of motion in cortex. - The authors propose a novel stimulus synthesis method for model training, parametrically generating optic flow fields w/ low-dim latent structure, rather than using pixel-space (i.e. image-computable) inputs. - The...	Rebuttal 1: Rebuttal: First, we want to contextualize all “weaknesses” about the brain alignment score (Fig. 8) in the greater scope of the manuscript. Fig. 8 is one of the 5 metrics we use to evaluate the learned representations (the others being untangling, disentanglement, completeness, and neural prediction). All 5...	Summary: The paper introduces a synthetic data framework called Retinal Optic Flow Learning (ROFL) and uses that framework to test the performance of unsupervised models on two learning tasks: reconstructing grand truth variables and predicting the response of MT neurons. By imposing a latent hierarchical structure, th...	Rebuttal 1: Rebuttal: > As far as I understood…comment them for. We struggled with how to answer this review. It seems that comments alternate between not understanding or knowing the relevant literature, and asserting (confidently) that the work was not novel and/or incorrect. Furthermore, in several cases, we alread...	Summary: The authors present a framework to evaluate motion detection in different DNNs. First, they intoduce a new concept to create flowfields for optical sitmuli, which include local and global motion and additionally fixation points. They use the parametrized stimuli to train a new hierarchical VAE (cNVAE) and comp...	Rebuttal 1: Rebuttal: > Evaluation... We like these suggestions and find some particularly exciting. Although, we consider several of them interesting future directions, given the manuscript already covers a lot of ground (see general response for a summary). In short, the present work is meant as an empirical report ...	Summary: The paper investigates the alignment of representations in deep generative models with activity in mammalian nervous systems. They provide a novel dataset on motion perception (Retinal Optic Flow Learning or "ROFL") against which to test computational models of Helmholtzian analysis-by-synthesis. The dataset g...	Rebuttal 1: Rebuttal: > As the authors admit in the Discussion section, they trained their compressed Nouveau VAE (cNVAE) on optical flow data rather than on video/pixel data. We appreciate this point, and it relates fundamentally to key choices in our approach. The raw visual image (photons on the retina) is processe...	Rebuttal 1: Rebuttal: Here we address the most common concerns and highlight additional analyses inspired by them. We realize our work did not come across clearly to all reviewers and we offer a brief summary of our main points first: We were motivated by the idea that representations of the natural sensory world invo...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition	Accept (oral)	Summary: The paper aims at a fairer face recognition model. First, the authors conduct large-scale experiments to show that architectures and hyperparameters matter for fairness (Section 3). Concretely, a wide range of models in different architectures and hyperparameters are evaluated in terms of performance (metric:...	Rebuttal 1: Rebuttal: Thank you for your time and thoughtful feedback on our manuscript. We appreciate that you find our approach well-motivated, our angle of architectures and hyperparameters novel, our experiments extensive, our method reproducible and the paper well-written and easy to follow. We address each of you...	Summary: This paper propose a brand new framework (NAS+HPO) to mitigate biases in FR. The discussion is extensive and interesting. But experiments on authoritative face recognition dataset are required, e.g., Ms1m, Glint360 and webface260m. Strengths: a. The presentation is easy to follow. b. The discussion is extensi...	Rebuttal 1: Rebuttal: We’d like to first thank you for your time and thoughtful feedback on our manuscript. We appreciate that you find our presentation easy to follow, our discussion extensive and interesting. We have conducted new analysis and answer your question below: New Results *The Effect of Pretraining...	Summary: The paper focuses on Bias Mitigation for face identification, i.e., ensuring that face identification works “well” for different identities: gender, race, etc. Unlike prior work, which focuses on model backbone agnostic methods to mitigate bias, this work explores the relevance of the inductive bias encoded in...	Rebuttal 1: Rebuttal: Thank you for your time and thoughtful feedback on our manuscript. We appreciate that you see the novelty in our work being the first to systematically conduct a large-scale analysis on the problem of fairness face recognition with different architectures and hyperparameters. We address each of yo...	Summary: This paper presents a new perspective on bias mitigation in machine learning models, challenging the conventional belief that one should first find the highest-performing model and then apply a bias mitigation strategy. The authors propose that finding a fairer architecture offers significant gains compared to...	Rebuttal 1: Rebuttal: We first thank you for your time and thoughtful feedback on our manuscript. We are glad that you find our approach novel and interesting. We address each of your questions below: W1: Rank Disparity Definition Thank you for raising this point. Precisely as per the definition of rank disparity,...	Rebuttal 1: Rebuttal: We first thank all the reviewers for their insightful feedback and suggestions. Our work shows that bias in face recognition systems is actually inherent to their architectures and hyperparameters, and we can design fairer systems by searching for fair architectures, in fact significantly surpassi...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors offer a fresh view on mitigating fairness bias in ML by leveraging neural architecture (NAS) search and hyperparameter optimization (HPO). The authors demonstrate their idea on the exemplary problem of face identification, where fairness biases have tangible consequences on society. They utilize a ...	Rebuttal 1: Rebuttal: We thank you for your time and thoughtful feedback on our manuscript. We appreciate that you see our view on mitigating fairness bias in ML as fresh and interesting, our solution systematically devised, and our experiments straightforward to follow. Further, we are glad that you find our results i...	null	null	null	null	null	null
Unexpected Improvements to Expected Improvement for Bayesian Optimization	Accept (spotlight)	Summary: This paper addresses a major weakness of Bayesian expected improvement acquisition functions, which are ubiquitously used for black-box optimization tasks such as computational hyperparameter tuning, materials science, and biomedical research. It is very common to use gradient-based optimizers to find local ma...	Rebuttal 1: Rebuttal: Thank you for your detailed review and encouragement. __Regarding the scaling of the incumbent__ Thank you for pointing us to the heuristic of scaling the incumbent by a factor. We have encountered and experimented with this heuristic in the past, and while it can be employed to try to avoid nu...	Summary: The paper proposes LogEI, family of acquisition functions with improved numerical stability over EI that makes it more suitable for gradient-based acquisition function optimization, all while retaining similar optima as EI. Pathologies of EI are visualized and analyzed, and the approximation error between qEI ...	Rebuttal 1: Rebuttal: Thank you for your detailed feedback about areas that deserve additional discussion. We seek to clarify the points raised in the following. __Equivalence of optima of analytical LogEI and EI__ In the CR, we will clarify this statement through a brief Lemma. If the maximum of EI is greater than...	Summary: This paper identifies a numerical pathology with the expected improvement (EI) family of acquisition functions: the vanishing gradients of the acquisition function leads to failure in acquisition function optimization. A set of modified EI acquisition functions that fix the numerical pathologies have been prop...	Rebuttal 1: Rebuttal: Thank you for your encouraging your review. > It is great to have an error bound of the qLogEI. However, for acquisition functions, preserving the relative order of values is more important than absolute difference. I wonder to what extent qLogEI preserves the relative order of values compared t...	Summary: In this paper, the authors identify both through examples and theoretical analysis several numerical pathologies inherent to the computation and optimization of the Expected Improvement (EI), a popular acquisition function at the heart of Bayesian Optimization (BO) algorithms. They subsequently propose a nume...	Rebuttal 1: Rebuttal: We thank you for your positive review.	Rebuttal 1: Rebuttal: We thank the reviewers for their detailed and predominantly positive reviews. We are attaching a one-page pdf with additional figures to help answer questions that arose during the review process, and are responding to each reviewer's questions in detail below and in the comments. __Generality of...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Boosting with Tempered Exponential Measures	Accept (poster)	Summary: This work proposes a generalization of the popular ADABOOST algorithm based on the use of the t-logorithm/exponential. Their method is derived by replacing the standard relative entropy with the _tempered_ relative entropy (introduced in eq. 2), and solving a constrained optimization problem (eq. 3). This resu...	Rebuttal 1: Rebuttal: We thank the reviewer for having evaluated our paper despite its heavy notational content. > The major limitation is that performance is highly sensitive to the choice of t, [XQ4B:A] We respectfully disagree with the argument: our theory says that regardless of the value of $t \in [0,1]$, the r...	Summary: The paper introduces a generalization of the classic adaboost algorithm to apply to a family of certain exponential losses, called TEMs (tempered exponential measures). They demonstrate the validity of their approach both theoretically and empirically. Strengths: This technique allows their method to o...	Rebuttal 1: Rebuttal: We thank the reviewer for having evaluated our paper, despite its heavy technical nature. (weaknesses) > My main issue here is the notational choices and somewhat unclear technical presentation. [J18J:A] We apologize for the inconvenience, also probably due to several typos – fortunately spotte...	Summary: The paper proposes a variant of AdaBoost algorithm based on a generalized exponential function parametrized by the temperature. The generalized exp function also induces a new criterion for splitting nodes of decision trees. The paper shows a training error bound of the AdaBoost variant. The experimental resul...	Rebuttal 1: Rebuttal: > A crucial weakness [...] generalization ability [...] margin maximization properties. [4pmA:A] we conjecture in L187 that similar rates of convergence (as the one we provide for the 0/1 loss) hold for margins as well; we also do not discuss generalization. We would like to point out also that ...	Summary: This paper introduces a generalization of Ada Boost called “t-Ada Boost”. Boosting algorithms aggregate multiple weak classifiers into a strong classifier. Ada Boost is a well-established boosting algorithm. t-Ada Boost generalizes Ada Boost by introducing an additional parameter $t$, which is a “tempering” pa...	Rebuttal 1: Rebuttal: (questions) > Originally, I went looking for the full plots from the experiments, Excellent suggestion ! We will oblige. > Please include the code, We commit to sharing all codes, inclusive of the plotting code > If not, then I think it is important to execute multiple runs and give a stand...	Rebuttal 1: Rebuttal: We would like to thank all six reviewers for their work and appreciate the global positive tone of reviews given the notation-heavy nature of our paper. To ease the cross-search among the pieces of our rebuttal, we have put tokens of the form [Reviewer-Id:Letter] in rebuttals, making it easily...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a generalization of the ADABOOST algorithm using tempered exponential measures. To do this, they begin by introducing $log_t$ and $exp_t$ and the generalization of entropy. A first theorem is proposed to explain how to find the solution for the minimization of this entropy and an adaptation ...	Rebuttal 1: Rebuttal: First, we thank the reader for having evaluated our paper despite its heavy notational nature. (weaknesses) > For me, a big weakness of the paper is the introduction of numerous notations that I didn't always find interesting [KBAC:A] We would like to emphasize that it has been asked that we c...	Summary: The paper presents an estension of ADABoost algorithm for binary classification, called t-ADABoost, by modifying the weight optimisation under simplex constraint formulation of the original algorithm. The generalised formulation involves optimisation of modified Bregman divergence between new and old weights u...	Rebuttal 1: Rebuttal: First, we would like to thank the reviewer for reading our paper and noticing the typos mentioned. (weaknesses section) > Part of section 5 and Algorithm 1 are confusing [6L9a:A] We sincerely apologize for the confusion, resulting from our choice of organization, and unwanted typos. Correct fo...	null	null	null	null
Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture	Accept (poster)	Summary: In this study the authors propose the incorporation of mechanistic biologically inspired filtering and normalization components in deep convolutional networks (DCNs) with the goal of increased alignment of model responses to V1 neural responses and tuning properties. The authors add center-surround receptive f...	Rebuttal 1: Rebuttal: We thank the reviewer for their insights, all of which have helped us improve this work. We ran additional experiments to answer questions about the drop in classification accuracy and reveal insights about the contribution of each component to explaining neural activity. In the latter experiment,...	Summary: The paper considers deep networks as a model of the visual stream, specifically V1. The authors systematically study the impact of various biological additions to deep networks on alignment of deep nets' representations with V1 recordings. Strengths: The paper considers several features of the early visual st...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful feedback and questions. Our responses to the concerns raised are provided point-by-point below. >The results in Tab. 3 suggest that V1-like features significantly hurt ImageNet performance -- the best V1 model is 16% less accurate than the best ImageNet ...	Summary: The authors incorporated four well-known architectural components of V1 into an earlier layer of the CNN, resulting in a reduction in task performance but an improved alignment with V1 neurons' behaviors. Their study demonstrated that cortical magnification led to the most significant enhancement in alignment,...	Rebuttal 1: Rebuttal: We thank the reviewer for their insights. We have broken down the raised concerns and provide our responses below. >The introduction of brain architectural components was expected to enhance the alignment of the model with V1 data in the Brain-Score test, so the results are not particularly surpr...	Summary: This paper incorporates a wide range of biologically inspired components into the initial stages of a ResNet model to see if these result in improved alignment with properties of V1 neurons. Specifically, the authors incorporate architectural components for Center-Surround, Local Receptive Fields, Divisive No...	Rebuttal 1: Rebuttal: We thank the reviewer for their insights. Our responses to each raised concern are provided in the points below and we will add these details to the camera-ready paper. >Re: The paper would benefit from an explicit discussion about novelty We appreciate this feedback and have run additional expe...	Rebuttal 1: Rebuttal: We thank the reviewers for their insightful and productive feedback. We were grateful to read that the reviewers agreed that the biologically-constrained models proposed in this work significantly outperform previous SOTA on explaining neural activity observed in macaque V1 (Y5Aa, yZMG), were syst...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Topological Obstructions and How to Avoid Them	Accept (poster)	Summary: The authors investigate two types of topological obstructions that pose challenges for models aimed at learning a particularly structure in the embedding space. Specifically, the authors identify figure eight local minima and mismatches in winding numbers are two defects that make learning the right latent str...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your kind review and constructive comments. We appreciate your thorough review and detailed questions! We are also delighted that you recognize the topic of our work as an important one. We believe we can address most of your concerns. We will respond to your question...	Summary: This paper theoretically and empirically characterize obstructions to training Homemorphic encoders with geometric latent spaces, such as local optima due to singularities or incorrect degree or winding numbers. Strengths: Originality: The paper is original in its approach to addressing topological obstructi...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your kind review. We are delighted that you recognized our theoretical and empirical contributions as significant and novel! We will respond to your questions and comments below: Motivation for learning a homeomorphic representation: Thank you for your comment....	Summary: This paper explores the challenges of encoding data into geometric spaces and proposes a solution using Group-Flow Variational Autoencoders (GF-VAEs). The authors discuss how incorporating geometric inductive biases can improve interpretability and generalization but also present obstacles due to topological c...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your kind review. We are delighted that you recognized our contributions as significant! We will respond to your questions and comments below: Assumptions in Propositions 3.1 and 3.2: The reviewer raises a valid point about the idealistic nature of our assumptio...	Summary: The paper addressed several topological obstructions that cannot be easily solved during the optimization of VAE. To solve this problem, the paper proposed to train a special NF to escape the defect, which the authors called GroupFlow. The paper then evaluated the proposed method on synthetic image datasets wi...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your kind review. We are delighted that you found our theory and proposed approach clear and interesting! We will respond to your questions and comments below: Applicability to other Lie groups: Thank you for your question! Yes, our theory does apply to other co...	Rebuttal 1: Rebuttal: We sincerely appreciate the valuable feedback provided by all the reviewers. Your constructive comments and efforts in evaluating our paper are highly appreciated. We are pleased to see that the general consensus is that our theoretical analysis of topological obstructions, along with the proposed...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild	Accept (poster)	Summary: The authors present Decorate3D, a technique for text-driven texturing of a 3D mesh given a NeRF representation of a given scene. To this end, the authors introduce a two-stage texturing scheme. First, the NeRF is decomposed into a 3D mesh and view-dependent texture map. Second, given the reconstructed diffuse ...	Rebuttal 1: Rebuttal: Thanks for your detailed comment. We kindly remind you to check our supplymentary material that provides some video results. We think the video results are helpful to dispel your concerns. *** * Q1: Concern on results of TEXTure. Could the authors please clarify and verify how the results fo...	Summary: This paper proposes decorate3D, a method for re-texturing real-world 3D objects using text-conditioned image diffusion models. The proposed method can be split into a 3D reconstruction phase and a re-texturing phase. In the 3D reconstruction phase, a 3D mesh is reconstructed from set of multiview images via Ne...	Rebuttal 1: Rebuttal: * Q1: Novelty: Most components utilized in this method are either ..., such as NeuS for mesh reconstruction, disentangling view-dependency via differentiable rendering of two MLPs, using depth condition for text-to-3D, and applying super-resolution diffusion models on UV textures ... As such, ...	Summary: This paper proposes a method to edit the textures for neural fields (NeRFs) using score distillation sampling and also export a mesh model with texture that can be used in traditional graphics pipelines (i.e. game engines, VFX). More specifically, the main contributions that I see from this work is the "Few-vi...	Rebuttal 1: Rebuttal: Thanks for your insightful suggestions that are helpful in improving our paper. We will carefully check and refine the descriptions that may lead to ambiguity. As suggested by the reviewer, we demonstrate more ablations of each component of the proposed pipeline and show the results in the one-p...	Summary: This paper introduces Decorate3D, which enables text-guided 3D model editing by extracting and editing a learned UV texture. Specifically, Given multi-view images, Decorate3D first generates 3D mesh and UV textures based on NeuS. Then, it optimizes neural textures by the guidance of 3D structure (depth) and st...	Rebuttal 1: Rebuttal: Thanks for your valuable comment and positive feedback. We have demonstrated extra ablations, including visual results and quantitative ablation study results. Please refer to the one-page response pdf. *** * Q1: The proposed method uses few-view resample training to obtain a UV texture that ...	Rebuttal 1: Rebuttal: Thanks to all the reviewers for their constructive suggestions. Extra visual results and quantitative evaluations are included in our submitted one-page pdf document, as suggested by reviewers: * (1) Figure A shows more ablation study results on the proposed components of Decorate3D including ini...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation	Accept (poster)	Summary: This paper leveraged large language model (LLM) based and mutation-based strategies to generate high-quality test cases for the popular dataset HumanEval. The extended dataset HumanEval+ provides a better code generation benchmark for assessing the performance of LLMs such as ChatGPT and GPT4. Experimental res...	Rebuttal 1: Rebuttal: > Q1:How is the quality of the seed inputs generated by ChatGPT and will the quality of seed inputs affect your approach significantly? The quality of seed inputs and the effect it has on mutation-based test generation and fuzzing has been well-studied in prior work [5, 6]. Similarly, the quality...	Summary: In this paper, the authors introduce EvalPlus, an evaluation framework crafted to assess the code generation of LLMs. Combining LLM and mutation-based techniques, EvalPlus diversifies the generated test inputs, thereby broadening the evaluation spectrum for LLM-produced code. Through comprehensive experimentat...	Rebuttal 1: Rebuttal: > Q1:how do you envision expanding this work to more complex environments? (strategies, modifications and challenges to ensure EvalPlus is effective in assessing code generated for real-world projects?) To begin with, we want to re-emphasise that our main contribution is to show that the existi...	Summary: The authors propose an evaluation framework for validating the correctness of large language model-generated code. In particular, the framework first utilizes ChatGPT to generate multiple seed inputs, which are then expanded into a large set of inputs through type-aware mutation. In addition, to ensure evaluat...	Rebuttal 1: Rebuttal: > Q1:I wonder if GPT-4 can generate more interesting seed inputs than ChatGPT. Please note that the EvalPlus input generation component does not rely only on ChatGPT but is general and can be implemented using any other foundational models like GPT-4 or LaMDA. We have also thought about using GPT...	Summary: This paper introduces a rigorous evaluation framework EvalPlus for program synthesis driven by automated test generation. For automated test generation, this work proposes to combine both LLM-generated tests and mutation-based input generation to largely augment the text inputs for an existing code benchmark o...	Rebuttal 1: Rebuttal: > Q1:Can you explain more on the difference between EvalPlus and AlphaCode/CodeT? Great question. First, we want to clarify that our main contribution is not the input generation technique but rather our generated dataset (HumanEval+) and accompanying rigorous study on recent popular LLMs. In sho...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful comments and suggestions to improve the paper! We address the main questions (labeled as Q) and concerns (labeled as C) in the response to individual reviewers below. Furthermore, we will also revise the paper accordingly to address all other minor s...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper describes an enhanced test dataset and test-driven evaluation of code generation by LLMs. The paper compares different LLMs and a stricter metrics using the enhanced evaluation dataset and shows that these LLMs are about 15% less correct than is reported based on earlier test datasets. Strengths: ...	Rebuttal 1: Rebuttal: > Q1:Have you tried any A/B experiment or other human evaluation ... in a real application? In this work, we focus on improving the evaluation of functional correctness that can be deterministically, objectively and automatically measured through testing and verification techniques. Contrastivel...	null	null	null	null	null	null
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models	Accept (poster)	Summary: Energy discrepancy is presented as a new loss for the training of EBM. ED interpolates between the losses of score matching and maximum-likelihood estimation. Efficacy of ED on a latent variable energy-based model is demonstrated to tackle manifold hypothesis as an important challenge in the adoption of likel...	Rebuttal 1: Rebuttal: Thank you very much for the positive and helpful comments. Here is our answer regarding the w-stabilisation: > How important is the w-stabilization term for the numerical experiments? If w is small(close to 0), are numerical experiment results still hold? > ANSWER: We found that w-stabilisa...	Summary: This paper has proposed a new loss funciton, i.e., Energy Discrepancy to train energy-based models without computaiton of MCMC. The proposed loss function could be directly derived from the energy function without relying on MCMC samples. Strengths: The proposed energy discrepancy could be directly computed f...	Rebuttal 1: Rebuttal: Thanks for your insightful comments and helpful suggestions! Our answers are listed below. > Model in [1] utlizes MCMC-based maximum likelihood algorithm to train a normalizing flow model for image generation. Could the proposed energy discrepancy used in this model? > ANSWER: Thank you for...	Summary: This paper proposes a new loss function for training energy-based models, called energy discrepancy (ED). ED does not rely on score functions and MCMC samples. Instead, it is defined as the difference between the energy function of data and some conditional samples. They prove that optimizing this objective fu...	Rebuttal 1: Rebuttal: Thank you very much for your constructive feedback! Here are our responses to the mentioned weaknesses and questions: Weaknesses: > The authors do not train EBMs directly on the data space. Instead, they train a VAE with a EBM prior. Since CD or SM can lead to competitive EBMs, I am wonderin...	Summary: The authors propose a new loss function for training energy-based models, which they dub "energy discrepancy." The aim is to provide a viable alternative to contrastive divergence and score matching based methods that suffer from near-sightedness -- these approaches lack global information and can have diffic...	Rebuttal 1: Rebuttal: Thanks a lot for your valuable comments. Your suggestions are very helpful in further improving the work, and we are refining the manuscript accordingly. > My main concern is that the w-stabilisation procedure seems to be doing a lot of heavy lifting and that is tailored to the Gaussian case. Giv...	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive and extensive comments that help us to improve this work. We would first like to summarise the paper according to the reviewers: - Our work proposes a new practical, easy to implement, and fast training technique for energy-based models that does not...	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this paper, the authors consider the problem of training energy-based models. Due to the limitations of two current approaches known as approximate maximum likelihood methods (which might lead to malformed estimators of the energy function) and score-based methods (which fail to resolve global features in t...	Rebuttal 1: Rebuttal: Thank you very much for your constructive comments. Here is our response to your questions: > There are some places that the authors introduce results without necessary intuitions. For instance, in line 96, they directly suggest using Gaussian kernels without explanation. > ANSWER: Thank yo...	null	null	null	null	null	null
Large Language Models are Visual Reasoning Coordinators	Accept (poster)	Summary: 1. The author proposes to utilize a language model as the coordinator between different outputs from different VLMs, leveraging their strengths for visual reasoning. 2. The proposed method achieves SOTA results on multiple visual reasoning benchmarks. 3. The analysis shows how zero-shot / fine-tuned language ...	Rebuttal 1: Rebuttal: We kindly request the reviewer to check our detailed responses and revisions in the following. Your time, effort, and affirmation of our research are truly valued. Q: Different dataset requires different visual / reasoning capabilities and knowledge…. We appreciate the reviewer's insightful ...	Summary: The paper proposes an ensemble based approach to solve visual reasoning problems. The paper proposes to use an instruction fine-tuned large language model to integrate answers to visual reasoning problems provided by vision language models. The paper presents two variants of the aggregation model -- using fine...	Rebuttal 1: Rebuttal: We appreciate the reviewer for your feedback on our paper, and we will provide our responses to these concerns in the following. We have also made targeted modifications to the paper, and these issues will help improve our paper. Our work aims to demonstrate that by using LLM, multiple VLMs can ...	Summary: The paper introduces a new paradigm called Cola, which aims to coordinate multiple vision-language models (VLMs) for visual reasoning tasks. While several VLMs have demonstrated strong commonsense reasoning abilities in different domains, effectively combining their capabilities remains a challenge. Traditiona...	Rebuttal 1: Rebuttal: We would like to extend our gratitude to reviewer’s insightful critique of our paper. These thoughtful comments have guided revisions that will undoubtedly enhance the quality of our work. We hope that by highlighting the potential of language models (LMs) to coordinate multiple vision-language mo...	Summary: The paper introduces a novel approach to ensembling multiple vision-language models (VLMs) for solving visual reasoning tasks. More specifically, the authors propose to use a language model (LM) to coordinate answers from various VLMs, which outperforms traditional ensemble approaches. Multiple experiments dem...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer's insightful critique and thoughtful suggestions for improving our work. In response, we have substantially revised the paper to better demonstrate the effectiveness of language models for coordinating multiple vision-language models. We humbly ask the reviewe...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewers' time and effort in providing thoughtful feedback on our work. We are pleased that the reviewers recognize the novelty of our Cola framework for coordinating multiple VLMs for visual reasoning. We also appreciate the suggestions to strengthen the paper through a...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Uncertainty-Aware Instance Reweighting for Off-Policy Learning	Accept (poster)	Summary: This paper delves into the issue of off-policy learning, the objective of which is to devise a new action selection policy based solely on the logged feedback derived from a logging policy. The paper pays particular attention to scenarios in which the logging policy remains unidentified and its estimation prov...	Rebuttal 1: Rebuttal: # Reply to Reviewer 19wU We thank the reviewer for pointing out the related work on calibration, and for posing valuable questions that have assisted in clarifying crucial arguments. > [Q1] "Difference between UIPS and the line work of distributionally robust off-policy learning (OPL)." In S...	Summary: The paper considers a scenario in off-policy evaluation where we don't have access to the action probabilities of the logging policy, which we need to compute the propensities in IPS. Prior work would estimate these probabilities from data, but would ignore the uncertainties associated with these estimates. In...	Rebuttal 1: Rebuttal: # Response to Reviewer YnY8 We thank the reviewer for the positive comments on our work and valuable suggestions. We have clarified several important arguments as outlined below. And we will diligently address other suggestions by further polishing our paper, making the figure captions more info...	Summary: This paper proposes an Uncertainty-aware Inverse Propensity Score estimator (UIPS) for off-policy learning, taking into account the uncertainty in the estimated logging policy. The authors demonstrate that the commonly used method of estimating the logging policy can lead to biased estimators, particularly for...	Rebuttal 1: Rebuttal: # Response to Reviewer QiPS We thank the reviewer for valuable suggestions provided, which help clarify important arguments and enhance the overall quality of the paper. > [Q1] "Why are the probabilities not recorded in the data?" The absence of ground-truth logging probabilities and taking th...	Summary: This paper proposes UIPS, a method that models the uncertainty of the estimated logging policy to improve off-policy learning. It assigns weights to each observation instance instead of simply dropping those with high uncertainty. The paper deduces the optimal form of weights from minimizing the upper bound of...	Rebuttal 1: Rebuttal: # Reply to Reviewer ha2F We appreciate the reviewer's positive feedback, insightful questions, and suggestions for improving the paper. We will incorporate the suggested revisions, including providing a detailed derivation step for the 'log trick' and offering further explanations on the metrics...	Rebuttal 1: Rebuttal: # General Response We thank all reviewers for their insightful comments and suggestions, which will significantly help us strengthen our paper. In the following, we will first respond to the common suggestions from all reviewers, and then respond to each reviewer individually. > [CQ1] "Explain ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Alternating Updates for Efficient Transformers	Accept (spotlight)	Summary: This work proposes an efficient way to increase the width of transformer models, i.e. Alternating Updates (AltUp). AltUp can increase the width of one existing model with little computation overhead. Authors evaluated their approach on T5 model and see some improvements on well-established benchmarks including...	Rebuttal 1: Rebuttal: Thank you for your careful consideration of our paper and constructive feedback. ### Effectiveness of AltUp We would like to point out that AltUp achieves significant speedups in wall-clock time (not only theoretical FLOPS) compared to dense baselines as shown in our evaluations in Sec. 5. In...	Summary: The paper proposes a novel technique named “AltUp” to expand Transformer’s feature dimension while preserving the computation cost. The key idea of AltUp is to divide wide hidden features into multiple blocks, where only one block is processed by Transformer sub-layers, while the other blocks are computed thro...	Rebuttal 1: Rebuttal: Thank you for your supportive review and insightful suggestions. Please find below our specific responses. 1. Thank you for suggesting experiments on other architectures. We conducted a preliminary study on a lightweight BERT model which has 12 layers, 256 model dimensions, and 4 attention heads....	Summary: The study introduces Alternating Updates, a novel method to increase the capacity of transformer models without significantly raising latency. AltUp broadens the token representation, operating on a subblock of the widened representation at each layer, and employs a predict-and-correct mechanism for updating ...	Rebuttal 1: Rebuttal: We are grateful for the reviewer’s careful consideration of our paper and their helpful feedback. Please see our specific comments below. ### Comparison to other efficient transformer variants As we mention in our response to Reviewer RDoa, the favorable properties of our method and its operatio...	Summary: This paper proposes AltUp, a new method for reducing the inference cost of Transformers by not computing blocks of the FFN layers. The authors find real-world speedups at inference time without sacrificing accuracy on benchmark tasks. Strengths: The results are strong - it is impressive to get real-world spee...	Rebuttal 1: Rebuttal: Thank you for your supportive review and helpful feedback. Please see our specific comments below. ### Comparison to Deja Vu Thank you for your helpful reference to Deja Vu [1]. As we state in our coverage of prior work, virtually all prior approaches in conditional computation, such as MoE, ap...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Online Convex Optimization with Unbounded Memory	Accept (poster)	Summary: This paper focuses on online convex optimization with memory, a topic with increasing attention recently. Traditional framework assumes that the current environment is only affected by the decisions of a limited past, while this work considers that the current environment is affected by \emph{all} previous dec...	Rebuttal 1: Rebuttal: Thank you for your review. We are glad you found the motivation meaningful, and the liked the simplicity and clarity of our proofs. ## Novelty of the extension from OCO with finite memory to OCO with unbounded memory and technical contributions. * Our formulation of OCO with unbounded memory b...	Summary: This paper introduces a problem called online convex optimization (OCO) with unbound memory, which generalizes an existing problem called OCO with memory. To address this problem, the authors propose an algorithm called FTRL and analyze its regret. Moreover, the authors demonstrate that this new problem and th...	Rebuttal 1: Rebuttal: Thank you for your review. We are glad that you liked the generality of our framework and the simplification of the results for online linear control. ## Novelty of the extension from OCO with finite memory to OCO with unbounded memory. * Our OCO with unbounded memory framework and upper bound...	Summary: The paper considers an online learning problem between a learner and adversary. The learner chooses action $x_t$ each round, and the state $h_t$ evolves according to the dynamics $h_t = Ah_{t-1} + Bx_t$. The oblivious adversary commits to a loss function $f_t$ each round. The learner suffers cumulative loss $\...	Rebuttal 1: Rebuttal: Thank you for your review. We are glad that you liked the definition of effective memory capacity, and tight upper and lower bounds on regret. ## "While the problem proposed in this paper is seemingly new, it does seem to shed much new algorithmic ideas and insights." * From context (i.e., the...	Summary: This paper studies a generalization of online convex optimization (OCO) with memory. The setting allows the current stage cost to depend on all past decisions via a discrete-time linear dynamical system. The authors proposed a follow-the-regularized-leader algorithm that can achieve a sublinear static regret a...	Rebuttal 1: Rebuttal: Thank you for your review. We are glad that you liked the definition of effective memory capacity, and tight upper and lower bounds on regret. ## Application to Online Linear Control with Adversarial Disturbances. * You are correct that our formulation of OCO with unbounded memory bears a stron...	Rebuttal 1: Rebuttal: We have responded to each reviewer individually. This global rebuttal only includes plots for the experiments requested by Reviewer xqDp. Pdf: /pdf/bf6696b3bed33d47f1c0a9899ea0ffa42e11bd7d.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Energy Guided Diffusion for Generating Neurally Exciting Images	Accept (poster)	Summary: In this work, the authors first employed attention readout to train a model for predicting neural responses ($y$) from image ($x$), aiming to address the issue of attention effects in the V4 area. Subsequently, they applied the image ($x$) from text ($y$) method proposed by Prafulla [1] to generate images ($x$...	Rebuttal 1: Rebuttal: We appreciate your review. We respond to your concerns below. In case you have any more questions we would be happy to discuss them. ### RE 1: Color MEIs The diffusion model generates color images, so in principle, it can generate color MEIs. We attach some examples (Fig. B). Since the encoding m...	Summary: The authors tackle the problem of synthesizing most exciting inputs for neurons in the higher visual cortex (V4 in their case) of macaque, with data collected using electrophysiology. The paper makes two claimed contributions: 1. The authors propose a new encoding architecture which uses a data-driven CNN co...	Rebuttal 1: Rebuttal: We appreciate your helpful review. Please find our responses to your questions below. If you have any further questions we are happy to discuss. ### RE: Prior work and scope We will include and discuss the additional prior work, and make sure to make it even clearer that we do not claim to have i...	Summary: Further characterizing the complex coding properties of V4 neurons might require (1) better encoding models of neuronal activity as well as (2) better methods to generate informative most exciting inputs (MEI). The paper tackles (1) by proposing a new readout mechanism for a convolutional data-driven core base...	Rebuttal 1: Rebuttal: We would like to thank you for your in-depth review. Please find our responses to your questions below. ### RE Ablation study See general response. ### RE: Apparent decrease in driving neural response We apologize for the confusion in Fig. 5b. The values there are shown as normalized to the max...	Summary: This paper proposes using the prior implicit in a diffusion model as a regularization term when generating images that maximally excite neurons. The authors refer to this as “Energy Guided Diffusion” and compare this to a standard gradient assent procedure with a smoothness prior. The authors also introduce a...	Rebuttal 1: Rebuttal: We would like to thank you for your in-depth review and we are glad you enjoyed the paper. Please find the responses to your questions below: ### RE A: Identifying what contributes to the improved performance We performed an ablation study showing that the Attention readout is critical for imp...	Rebuttal 1: Rebuttal: We appreciate the thorough and constructive reviews and are glad to see that the reviewers found our work to be ingenious (GN1s), novel (YaDz, GN1s, H4Az, BnXF), well-written (H4Az) and clear (GN1s, H4Az). We are happy to see that Reviewer YaDz *...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Adjustable Robust Reinforcement Learning for Online 3D Bin Packing	Accept (poster)	Summary: This work proposed a novel adjustable and robust Reinforcement Learning framework for Online 3D Bin Packing task. The proposed method can achieve balance between performance and the worst-case environment. Strengths: The writing is clear, and the experimental results demonstrate that the proposed method works...	Rebuttal 1: Rebuttal: We highly appreciate the valuable feedback provided by the reviewer. We have carefully considered these concerns, and we would like to address them in the following responses. Q1: Continuous setting results Due to space limit in the main text, for the experimental results under the continuous se...	Summary: For solving the online 3D Bin Packing Problem (BPP), the authors employ an iterative procedure to search for relevant hybrid dynamics and refine their corresponding strategies. By optimizing the weighted sum of returns, AR2L algorithm which improves the robustness of the packing policy achieves a balance betwe...	Rebuttal 1: Rebuttal: We would like to express our gratitude to the reviewer for providing valuable feedback. We appreciate the reviewer's insights, and we would like to address the concerns raised. Q1: Illustration of schematic figure Thank you for your valuable suggestions. We agree that it can make the comparis...	Summary: This paper investigates the online three-dimensional bin packing problem (3D-BPP) and extends the PCT algorithm by proposing an adjustable robust reinforcement learning (AR2L) framework that balances the performance of policies in average and worst-case scenarios. The paper designs a permutation-based adversar...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for recognizing our contribution in developing the permutation-based attack method and the adjustable robust reinforcement learning algorithm. We are grateful for the reviewer's valuable feedback, and we would like to address their concerns as follows. Q1: Th...	Summary: This work addresses the problem of 3D bin packing problem (3D-BPP). Specifically, it develops a permutation-based attacker and subsequently proposes an adjustable robust reinforcement learning (AR2L) framework. This allows an algorithm to consider both the average and worst-case performance with the attacker, ...	Rebuttal 1: Rebuttal: We would like to express our sincere appreciation to the reviewer for acknowledging our contribution in developing the permutation-based attack method and the adjustable robust reinforcement learning algorithm. We are grateful for the feedback provided by the reviewer, and below, we address these ...	Rebuttal 1: Rebuttal: Dear Reviewers: We appreciate your valuable comments and have made clarifications to all of your questions and concerns in our response. Below are some shared concerns among reviewers. Q1: Practicability and generalizability of AR2L To validate the practicality of AR2L in real-world scenarios, ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper is in the category of work that uses reinforcement learning for combinatorial optimization problems. More specifically, it focuses on the online version of the 3d-bin-packing problem (3D-BPP), and proposes a robust RL solution to address the uncertainty that arises from the permutation of an adversar...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We appreciate your valuable feedback. However, we would like to clarify that there may be some misunderstandings regarding our settings. We would like to take this opportunity to address your concerns. Some shared concerns about AR2L's empirical p...	null	null	null	null	null	null
Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations	Accept (poster)	Summary: The paper studies diffusion models, which comprise a class of generative models based on stochastic differential equations. In diffusion models, data is first transformed into Gaussian noise via a stochastic differential equation (usually an Ornstein-Uhlenbeck process), and then a backward process, which turns...	Rebuttal 1: Rebuttal: Thank you for your appreciation and valuable feedback of our paper. ### Q1: More understanding about the upper bound. This suggestion is insightful. We are also interested in exploring the structure of this upper bound. But we are afraid that a simple and heuristic interpretation of this form...	Summary: In this work the authors try to understand the impact of noise in the reverse process of diffusion models in the presence of an approximate score network. In particular, they look at how the Kullback-Leibler divergence between the true data distribution and the denoised distribution evolves w.r.t. the diffusio...	Rebuttal 1: Rebuttal: Thank you for taking the time to provide detailed and valuable feedback on our paper. The summary from the reviewer regarding the take-home message is accurate. For a detailed error distribution illustration in score-matching loss over time, see the supplementary PDF. We'll enhance presentation in...	Summary: The authors focus on reverse diffusion process in the presence of non-negligible error in the score function, and estimate KL divergence between the data distribution and the distribution generated by reverse process. They analyze how the KL divergence varies with the diffusion coefficient and demonstrate that...	Rebuttal 1: Rebuttal: Thank you for taking the time to provide valuable feedback on our paper. ### Q1: Need to clarify low-dimensional data distribution. We acknowledge that the previous description lacks informative detail, and we aim to provide further elucidation as follows: Given that many realistic datasets exh...	Summary: The paper provides a theoretical analysis of the estimation error of SDE and ODE methods along with some numerical experiments. Strengths: By perturbing the score function, the authors study how the estimation error changes in ODE and SDE methods. Weaknesses: It is commonly known that the sample generation ...	Rebuttal 1: Rebuttal: Thank you for taking the time to provide feedback on our paper. It's noteworthy that our findings align with certain factual aspects you raised. In the ensuing discussion, we will predominantly focus on two pivotal matters: - the significance of refining score function estimation, with the inten...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for comments and suggestions. We are glad by the positive reception of the proposed question, and we hold respect and appreciation for the critical and diverse perspectives provided. ### About practicality and motivation: A concern or question is about the dis...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper explores the difference between ODE-based probability flow and SDE-based diffusion models when score training errors are present. Specifically, they investigate how setting the generative diffusion coefficient h impacts sample quality. Strengths: - The question the authors are trying to answer seems...	Rebuttal 1: Rebuttal: Thank you for taking the time to provide feedback on our paper. We value your insights regarding the presentation of equations and variables. We are dedicated to enhancing the comprehensibility of the revised manuscript by incorporating more detailed explanations and motivations. It’s important to...	null	null	null	null	null	null
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding	Accept (poster)	Summary: The authors address the problem of expressiveness in graph neural networks. Positional encodings using spectral approaches have suffered from sign and basis invariance. The authors propose Laplacian canonization which finds unique representations, and they analyze what properties the canonization should preser...	Rebuttal 1: Rebuttal: We thank Reviewer R8b6 for appreciating the simplicity and significance of our paper. We address your concerns as follows. --- Q1. SignNet model with k=all in their paper (Table 1) achieve better performance than MAP in the current paper (Table 3). Authors should explain decision to not incl...	Summary: This paper introduces a new approach called Laplacian Canonization (LC) for ensuring the sign and basis invariance of spectral embeddings. This is done by determining the canonical direction of eigenvectors in the pre-processing stage. They propose to perform the Laplacian Canonization via Maximal Axis Project...	Rebuttal 1: Rebuttal: We thank Reviewer for appreciating our paper. We address your questions in the following points. --- Q1. The authors claim that the method can canonize more than 90% of all eigenvectors, however, this has only been tested on molecular graphs. Are there any guarantees for other graphs? *A1...	Summary: The recently emerging graph transformers uses the spectral embedding. The spectral embedding has two empirically known problems; I) sign invariance ii) basis invariance. The existing remedy for these problem comes at the cost. This paper addresses this problem by the method this paper proposes called Laplac...	Rebuttal 1: Rebuttal: We thank Reviewer NB7V for appreciating the simplicity and theoretical guarantees of our approach. We address your main concerns below, especially those on the meaning of studying sign and basis invariance. --- Q1. We do not know why the sign and basis invariance hinder the performance o...	Summary: The authors propose Laplacian canonization, a way to select canonical Laplacian embeddings that resolve the sign and basis ambiguities often present in graph embeddings. The proposed method is a preprocessing step that is relatively fast. The authors perform experiments to evaluate the performance of Laplacian...	Rebuttal 1: Rebuttal: We thank Reviewer zwuu for appreciating the originality and effectiveness of the proposed canonization method. We address your concerns as follows. --- Q1. One potential weakness is the theoretical portion of the paper, whose results are rather marginal and unsurprising. However, I see this ...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper explores the Laplacian canonization approach to address the sign and basis ambiguities of eigenvectors. Previous sign- and basis-invariant methods suffer from high complexity and the proposed canonization method is light-weighted and can be used for any graph neural networks. Since the Laplacian can...	Rebuttal 1: Rebuttal: We thank Reviewer pkjp for appreciating our method and theoretical results. We address your concerns on parameter sizes. --- Q1. To ensure fairness, the authors should ensure that different methods have similar model parameters. A1. In our experiments, we allow a flexible choice of mode...	null	null	null	null	null	null
Exponentially Convergent Algorithms for Supervised Matrix Factorization	Accept (poster)	Summary: This paper proposed a novel supervised dictionary model, with two variations, one feature-based and one filter-based. The problem setting is on classification tasks with both high- and low-dimensional feautures, where the high dimensional features are learnd through dictionary learning, and then intergrated in...	Rebuttal 1: Rebuttal: Thank you very much for your positive feedback on our submission. The reviewer has provided the following comments: **`Q1. It would be beneficial to conduct benchmarking against deep neural networks in order to gain insights into gaps, if any, in performance and to understand potential trade-off...	Summary: The work focuses on a generic supervised dictionary learning formulation, which is convex in each of the variable-blocks but not overall. The idea is to stack up the matrix-variables and obtain an equivalent low-rank optimisation problem with a convex objective. And, then this is solved using a projected grad...	Rebuttal 1: Rebuttal: We greatly appreciate the reviewer’s overall positive comments. > Though theoretical guarantees are proven, from simulations it is not clear how much is the improvement of pgd vs baseline (BCD) in terms of time. For e.g. in figure 2, it would have been very helpful if BCD is also included. **Re...	Summary: This paper explores the optimization of supervised dictionary learning, focusing on a non-convex objective function with a matrix factorization structure. The authors propose a reformulation of the problem as a minimization task with a low-rank constraint. To solve this reformulated problem, they employ a proj...	Rebuttal 1: Rebuttal: `Clarification question A` Response: We would like to express our gratitude for the thoughtful comments provided by the reviewer. To begin, we wish to emphasize that our theoretical analysis comprises three parts: > 1. (Thm. C.2) Establishing exponential convergence results for the genera...	Summary: This paper proposes a variant of supervised dictionary learning (SDL), provides some theoretical guarantee on finding the global minimizer of the problem with arbitrary initialization, and showcase its application in pancreatic cancer. Strengths: Theoretical analysis on dictionary learning (DL) has a rich lit...	Rebuttal 1: Rebuttal: We appreciate your evaluation of our work, which greatly contributes to its enhancement. We value your expertise in the field and understand your concern about the term DL being closely linked to sparse representation. While we acknowledge the prevalent association of DL with recovering overcomple...	Rebuttal 1: Rebuttal: We submit an optional 1-page PDF to show the revised Figures 2 and 3 with captions. In Figure 2, we present a comprehensive comparison between our LPGD algorithm and BCD. In Figure 3, we include additional experimental details, focusing on breast cancer classification, which successfully identifie...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics	Accept (poster)	Summary: The authors propose Implicit Transfer Operator (ITO) learning, which aims to learn a surrogate of a molecular dynamics (MD) simulation process. Since standard MD simulations integrate Newton's equations of motion numerically, small integration time-steps are necessary, making simulations costly when processes ...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our manuscript and providing insightful comments and questions. We believe your input will help us shape a much improved camera ready version. Please find commentary and replies to your concerns and questions below. _Comments on weaknesses:_ 1 and 2. We bel...	Summary: The paper develops a conditional diffusion model to sample unnormalized densities - e.g., the Boltzmann distribution of molecules to replace molecular dynamics simulations. Given a molecule structure, noise is added to it and the diffusion model denoises this distribution to the distribution generated by MD. T...	Rebuttal 1: Rebuttal: We thank the reviewer for taking time to read our manuscript thoroughly, and providing their insightful comments. We in particular appreciate the comments about the presentation, which will help us prepare a much improved camera ready version. We are addressing their highlighted weaknesses and que...	Summary: This paper presents implicit transfer operator (ITO) learning for the simulation in molecular dynamics. Their approach adopts the SE3 equivariant MPNN architecture (ChiroPaiNN) to parameterize the transition kernels in the denoising diffusion probabilistic model. The method displays a decent performance on sev...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough review of our manuscript and the comments, in particular regarding the presentation of the maths and comparison to previous work. We believe the input will help us prepare a much improved version for the camera ready version. _Comments on weaknesses:_ 1....	Summary: The proposed approach aims to learn molecular dynamics via an implicit transfer operator framework that can perform modeling at multiple time scales. The framework is using diffusion model with SE3 equivariant architecture. The approach has shown capability in stable and self-consistent modeling at multiple ti...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their favorable and supportive evaluation and helpful comments and questions on our paper. We reply to the questions below and welcome a discussion about any outstanding doubts. _Questions:_ 1. The number of time-scales chosen during training, can be under...	Rebuttal 1: Rebuttal: Global response. see attached pdf. Pdf: /pdf/58b23013f0958cdfff6349cdacbe3b91b34d4a89.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: In this work, the authors proposed a framework that combines SE(3)-equivariant MPNN and conditional DDPM, called Implicit Transfer Operator (ITO) learning, as a method to efficiently sample observales from MD simulation trajectories. Such a framework is validated on Muller-Brown potential data generated by the...	Rebuttal 1: Rebuttal: Thank you so much for taking the time to carefully read our manuscript and providing constructive input and criticism as to how we can improve it. Below, you will find a point-by-point reply to your concerns and questions. We are looking forward to continue the discussion, please do not hesitate t...	null	null	null	null	null	null
Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis	Accept (poster)	Summary: When conventionally trained, neural networks do not demonstrate structural properties like input separable functions or reusability of sub-modules. The authors investigated this phenomenon and proposed iterative pruning to enhance structural properties of neural networks. They demonstrate the effectiveness of ...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive comments. We provide responses to the identified weaknesses and questions raised next: **W1: The scope is a bit limited. The paper only discusses two properties, input separable functions and reuse of sub-modules. The examples are a bit too simple.Thi...	Summary: This paper conducts an investigation of hierarchical modularity in neural networks by studying boolean networks. The paper studies simple hierarchically modular boolean functions and learns them using MLPs. It then propose metrics to discover this modularity by examining 1) input separability and 2) reusabilit...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive comments about this work. **Weakness: Currently, the experiments on MNIST to verify the method to discover modularity do serve as a proof of concept but more extensive experiments would be needed to confirm the effectiveness of the proposed approach to i...	Summary: The paper proposes a methodology for uncovering hierarchical modularity in neural networks (NNs). It combines iterative pruning and network analysis to reveal the underlying hierarchy of sub-functions in tasks. The paper demonstrates the effectiveness of the method on both Boolean functions and vision tasks us...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive comments and appreciate your valuable feedback. Below we provide response to the weaknesses and questions: **W1: One potential weakness of the paper is the limited discussion and analysis of the results regarding the failures or limitations of the pro...	null	null	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their comments, time and effort. We have carefully thought about and responded to individual reviews, focusing on the weaknesses pointed out and the questions asked. If additional details, explanations, or clarifications are needed, we will be happy to prov...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Towards Efficient Image Compression Without Autoregressive Models	Accept (poster)	Summary: This paper aims at providing a efficient and effective entropy model to achieve better trade-off between performance and complexity for learned image compression. They introduce a correlation loss to force the latent to be spatially decorrelated so that it can fit the independent probability model better. Str...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and suggestions. > As shown in the RD curves, the BD-rate gains are obvious in the low bit-rate regime, however, there are less or even no gains in the high bit-rate regime. I think these results require further analysis, such as why it works better in the...	Summary: This paper proposed a correlation loss to decrease the correlation among spatially-neighbored elements in the latent space. By only modifying the loss function, this method acts as a plug-in method for the existing neural compression methods with no complexity increasing. Experiments show improvement in the co...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and suggestions. > For SwinT and Cheng with checkerboard, the correlation loss seems to only work for lower bitrates, the author may provide some explanation Please refer to our general response for a detailed explanation. > Besides, The current tested b...	Summary: This paper presents a new loss to improve neural image compression models. The authors identify a potential issue with typical neural image compression models where the hyperprior, which predicts entropy parameters over a quantized latent representation, assumes conditional independence between latents, but th...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and suggestions. > Ideally, the correlation loss presented in the paper would be applied to a SOTA compression model leading to a new SOTA. For instance, the paper cites (He 2021), which introduced the checkerboard decomposition for entropy modeling, but ...	Summary: This paper focus on efficient learned image compression. Different from existing method, which generally aims to parallelize the autoregressive operations, this paper propose to speed up the framework by removing the whole autoregressive model by introducing the correlation loss, aiming to decorrelate the late...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments and suggestions. > The caption of Figure 4 states that the correlation loss can provide more flexible parameterized distribution models with significant spatial redundancy reduction. However, from my perspective, the plots show in Figure 4 cannot showcase ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful comments and suggestions. # Additional Experiment Results During the rebuttal period, we have performed additional experiments as shown in Figure 2 of the attached PDF, which will be the updated version of the Figure 1 of the main manuscript. The u...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation	Accept (poster)	Summary: This paper proposes Demand-driven Navigation (DDN), which leverages the user’s demand as the task instruction and prompts the agent to find an object which matches the specified demand. This paper also proposes a method of first acquiring textual attribute features of objects by extracting common sense knowled...	Rebuttal 1: Rebuttal: REPLY: Thank you! We have clarified some questions, address your concerns, and hope to hear back from you if you have further questions! Q1: L40, the second condition is a bit questionable. A1: Thank you very much for your insightful suggestion. We understand the concern about the second con...	Summary: This paper presents a new visual navigation setting, where the goal is not specified by objects or images but described by a sentence. The sentence encodes the essential information to search for specific objects during navigation. Different from VLN, the task is able to analyze the demand within each sentence...	Rebuttal 1: Rebuttal: REPLY: Thank you! We have clarified some questions, address your concerns, and hope to hear back from you if you have further questions! Q1: The introduction part is overly lengthy. A1: Thank you for your valuable feedback. We understand your concern, and we agree that the introduction may h...	Summary: This paper introduces a new task called Demand-Driven Navigation (DDN) that, unlike previous Visual Object Navigation (VON) tasks that evaluate the ability of an agent to find a specific object in an unknown environment, considers fulfilling the demand of a human. This new task is motivated by the lack of real...	Rebuttal 1: Rebuttal: REPLY: Thank you! We have clarified some questions, address your concerns, and hope to hear back from you if you have further questions! Q1: Additional ablation. A1: Thank you very much for your advice. We show ablation experiments for pre-training of attribute modules in the main paper. Now...	Summary: This paper proposes a Demand-driven Navigation (DDN) problem to leverages the user’s demand as the task instruction and prompts the agent to find an object which matches the specified demand. Then the authors proposed a method by learning demand-conditioned object attribute features from LLMs and align them to...	Rebuttal 1: Rebuttal: REPLY: Thank you! We have clarified some questions, address your concerns, and hope to hear back from you if you have further questions! Q1: One important baseline is missing. The agent could explore the environment using a heuristic algorithm like FBE. At each time step, the agent detects al...	Rebuttal 1: Rebuttal: ## Common Response ## We thank all reviewers for appreciating our DDN task, method and experiments. "The idea of demand-driven navigation is interesting and novel." (JLnR) "The proposed attribute module is interesting and helpful for extracting attributes of objects." (nwVS) "Authors propose dive...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes the novel task of demand-driven navigation, where a robot must navigate towards a goal object that satisfies the human user's demand (e.g., for the demand "I am thirsty", the robot has to find water/juice/tea, etc.) Citing limitations of navigation methods used for other object-goal navigat...	Rebuttal 1: Rebuttal: REPLY: Thank you! We have clarified some questions, address your concerns, and hope to hear back from you if you have further questions! Q1: how often does an object demanded not be present in the scene, and therefore, a functionally equivalent object was needed to satisfy the demand? A1: T...	null	null	null	null	null	null
Momentum Provably Improves Error Feedback!	Accept (poster)	Summary: This paper introduces a modification to the EF21-SGD algorithm by incorporating momentum, resulting in a new algorithm named EF21-SGDM. The innovative analysis accompanying this new method successfully addresses the challenges associated with EF21-SGD, reducing the sample complexity from $\Omega(\sigma^2/\epsi...	Rebuttal 1: Rebuttal: > Although the paper's sample complexity in each iteration number is independent of $\varepsilon$ , it still depends on the variance term, $\sigma$. This dependence should be explicitly stated to ensure a comprehensive understanding of the algorithm. The sample complexity of each iteration of ou...	Summary: This paper proves that momentum helps EF21-SGD. It makes several non-trivial contributions. First, it theoretically shows that EF21-SGD cannot converge when batch-size is small. Second, it proposes a simple remedy to this issue, i.e., incorporating momentum to EF21-SGD. Third, it proves that EF21-SGDM can conv...	Rebuttal 1: Rebuttal: > Question 1. In table 1, the authors claim that NEOLITHIC uses a large mini-batch, which may not be correct. While NEOLITHIC uses R times larger batch-size than EF21-SGD per iteration, it runs R times fewer iterations than EF21-SGD (see the NEOLITHIC algorithm in Huang et. al., 2022). On averag...	Summary: The authors present a new method called EF21-SGDM by combining EF21 and Polyak's momentum SGD. The theoretical contribution is that , it improves the communication and sample complexities of previous error feedback algorithms under standard smoothness and bounded variance assumptions. They also propose a doubl...	Rebuttal 1: Rebuttal: > 1. Compared with theoretical contribution, the algorithm itself is straightforward, i.e., combining two existing methods EF21 and SGDM. In our opinion, the simplicity of the algorithms should be viewed as a strength rather than weakness of our work. Multiple ways to combine EF21 and SGDM....	Summary: The authors propose a new version of EF-SGD which uses momentum. The authors show that under standard assumptions the proposed method has a better convergence rate. The authors claim that in several cases it is hard to perform large batch sampling like when performing RL training. To overcome this problem they...	Rebuttal 1: Rebuttal: > 1. EF21 SGD although theoretically motivated is not a practical due to only 1 way compression and often high overhead, authors should comment on the real world implication of EF21-SGD and EF21-SGDM. In our work, we specifically focus on the uplink communication (from clients to the server) sin...	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback and the overall positive evaluation of our work. We are glad that the reviewers appreciate that our studied problem is “well motivated”, the paper is “well-written and easy to follow”, the analysis is “novel, comprehensive, solid”, the assumpt...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Reliable learning in challenging environments	Accept (poster)	Summary: The paper develops learning methods that provide theoretical guarantees for point-wise predictions in challenging scenarios at test-time. In particular, the methods presented address situations affected by adversarial attacks and distribution shifts. The paper provides a theoretical contribution to a relevant ...	Rebuttal 1: Rebuttal: Weaknesses > 1. The main limitation I can see in the paper is the reliance on the realizable case. I guess that reliance cannot be easily avoided but it may be worth to further discuss and emphasize that issue. For instance, the guarantees in the paper are contingent to the fact that we are i...	Summary: The authors explore advesarial test-time attacks and distribution shift. They propose a learning algorithm with performance guarantees. Strengths: The paper is well written with many intuitive illustrations. The authors use refusal as a means to guarantee reliability. As noted by the authors, the trivial cl...	Rebuttal 1: Rebuttal: Weaknesses > The authors' analysis is limited to the realizable case; that the true target function is a member of the hypothesis class. While this simplifies analysis, it limits the applicability of the authors' results since in most real-world applications we do not know whether this assum...	Summary: This paper presents robustly-reliable learners with optimal guarantees for environments where the training and test data are not drawn from the same distribution, e.g., natural distribution shift and adversarial attacks during test time. The main idea is that for a given point, the robustly-reliable learner ei...	Rebuttal 1: Rebuttal: > A computationally efficient method is presented only for the case of linear separators. It is not clear how easily the presented tools can be used to obtain practical algorithms for more general cases (e.g., neural networks) in practice. An evaluation on a simple synthetic scenario to demonstrat...	Summary: This paper studies the problem of classification under found different kinds of adversarial loss functions: 1) ST which I think is by far the most popular adversarial loss. This is same as the expected sup loss of [Madry et al. 2018](https://arxiv.org/abs/1706.06083). 2) TL which is equivalent to the the "exac...	Rebuttal 1: Rebuttal: Weakness > 1. Writing: We will use the extra page in the camera-ready version to bring to the main body some of the results that now appear in the Appendix. Concerning illustrations, we already have 4 illustrations in the main body and in fact, the other reviewers appreciated the clarity an...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Exact Verification of ReLU Neural Control Barrier Functions	Accept (poster)	Summary: This paper studies the exact conditions under which a learned CBF $b$ with ReLU activations yields the positive invariance property. Even though the set of inputs for which the CBF is non-differentiable has measure zero, the paper shows by example that safety can be still violated. This is because the slope of...	Rebuttal 1: Rebuttal: We conducted an experimental study to compare NCBFs with different activation functions, including ReLU, sigmoid, and tanh. The results are shown in the pdf attachment to the general rebuttal, and are discussed in detail in the rebuttal to Reviewer wvvT above. In particular, we find that our propo...	Summary: Neural control barrier functions offer wider expressive power but do not satisfy the continuously differentiable assumption with ReLU activation functions. In this paper, the authors propose a method to verify that a neural ReLU CBF is a valid CBF and is hence capable of rendering a set forward-invariant. This...	Rebuttal 1: Rebuttal: We thank the reviewer for identifying some minor mistakes in the manuscript. The missing reference refers to Eq. (27) from the supplementary material and will be corrected. The definition of complete collection will be modified to $\cdots \cap \overline{\mathcal{X}}(\mathbf{S}^{\prime})$. Distance...	Summary: The authors consider the problem of synthesising control barrier functions parametrised as ReLU neural networks for non-linear deterministic dynamical systems. The authors first extend standard approaches for synthesising control barrier functions to the case where the barrier function is non-differentiable in...	Rebuttal 1: Rebuttal: In order to evaluate our approach on more complex neural networks, we conducted additional experiments as shown in Tables 2 and 3 of the pdf attachment to the general rebuttal. We verified an NCBF for the two-dimensional Darboux example with two hidden layers of 512 neurons each. The verification ...	Summary: In this paper, the authors present a new strategy for verifying neural control barrier functions (NCBFs) to ensure safe control of nonlinear systems. Specifically, the authors address the challenge posed by NCBFs when ReLU activation functions are employed. This renders existing verification strategies inappli...	Rebuttal 1: Rebuttal: In order to compare with NCBFs that have differentiable activation functions, we conducted the following additional simulation study. We considered three test systems, namely, the Darboux, obstacle avoidance, and spacecraft rendezvous test cases. For the Darboux and obstacle avoidance systems, we...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for providing detailed comments that have helped to improve the quality of our manuscript. We have provided rebuttals to the comments of each reviewer. We have also attached a pdf file containing figures and tables from additional simulations that were requeste...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a technique to verify the safety of NCBF-based control policies. Usually, the verification depends on the CBF to be continuously differentiable. This is not the case for NNs, however using NCBFs is beneficial as it allows to encode more complex safety constraints. Their approach identifies t...	Rebuttal 1: Rebuttal: Regarding the benchmarks considered in this work, we have evaluated our approach on additional test systems and compared with dReal and Z3. Our results are summarized in Tables 2 and 3 of the pdf attachment to the general rebuttal. Our approach verified a three-dimensional system (obstacle avoidan...	null	null	null	null	null	null
Learning Unseen Modality Interaction	Accept (poster)	Summary: This work studies the problem of learning interactions of unseen modality combinations. Specifically, all training data is modality-incomplete, and the model must learn to perform inference on modality-complete data. The paper claims to be the first to study inference under such settings, and proposed two nove...	Rebuttal 1: Rebuttal: *We thank the reviewer for their time and effort. We are glad that the review appreciated the new setting of unseen modality interaction, the new method proposed for this and found the experiments and evaluations comprehensive.   Figure 1.* We will make Figure 1 clearer by present...	Summary: The paper is about multimodal learning and deals in particular with the mismatch between modality combinations at training and inference time. The authors propose to project the multimodal features in a shared space, apply an alignment, and enforce the discriminative ability of the method with a dual branch p...	Rebuttal 1: Rebuttal: *We thank the reviewer for their time and effort and are glad the reviewer found our paper well-written.   Benefit of supersetting training modalities.* We show the benefit in Table 2 where we compare our model and multimodal baselines, which use all available modalities, to unimod...	Summary: This paper introduces a method that can enhance the performance of multimodal models in scenarios involving unseen modality interactions. Strengths: 1, The issue of "unseen modality interaction" explored in this paper is quite novel. 2, This paper maps the features of different modalities into the same space...	Rebuttal 1: Rebuttal: ***We thank the reviewer for their time and effort. We are glad to hear the reviewer found our proposed problem of “unseen modality interaction” novel and that the reviewer appreciates that number of parameters required for fusion does not significantly increase as the number of modalities increa...	Summary: This paper tackles the issue of unseen modality interaction, which challenges the conventional assumption of modality completion during training. The approach taken in this study involves formulating a training setting that accounts for modality incompleteness. Subsequently, the proposed method focuses on proj...	Rebuttal 1: Rebuttal: *We thank the reviewer for their time and effort and are glad that the reviewer found our paper addresses a practical problem and appreciate the experiments conducted on various modalities.   Alignment Loss.* We apologize for the unclear text. The learnable tokens are not modality-...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Monte Carlo Neural PDE Solver	Reject	Summary: The authors propose an unsupervised neural technique for solving PDEs based on the classical correspondence between (parabolic) partial differential equations (PDE) and stochastic differential equations (SDE) as given by the Feynman-Kac formula. Specifically, they propose minimizing the error between the neura...	Rebuttal 1: Rebuttal: Thank you for your time and feedback! Based on your comments, we provide some replies to address your concerns as follows: The trajectories of NSE. Thanks for your suggestion! We add figures in the PDF of the Global Response B part to show the variation of trajectories and errors for eac...	Summary: The authors present MCNP, a new unsupervised training loss for surrogate simulation networks. This loss is based on the link between stochastic processes and PDEs, sampling one-step Brownian motion to estimate the PDE solution. The learned network takes an initial state and the target simulation time to comput...	Rebuttal 1: Rebuttal: Thanks for your valuable feedback! According to your constructive comments, we make some replies as follows: W1 & W3. The experiments. To address your concerns, we added an experiment to simulate Kolmogorov flow (see Global Response. B). Furthermore, we chose PDEs with analytical solutio...	Summary: The authors propose Monte Carlo Neural PDE Solver (MCNP Solver) which leverages the Feynman-Kac formula to train neural PDE solvers in an unsupervised manner. I'm willing to revise my score based on the rebuttal from the authors to the questions that I raised below. Strengths: * That paper addresses and inte...	Rebuttal 1: Rebuttal: Thank you for your time and valuable feedback! Based on your constructive comments, we provide some replies to address the weaknesses and questions: Weakness & Q1. The PDE types in the experiments of this paper Thank you for your question! Besides the 1D diffusion equation and 2D NSE, we als...	Summary: Designing neural PDE sovler using deep neural networks is a challenging task for which several solutions have been proposed in the literature using for instance networks that encode the initial conditions or physics informed neural networks. The authors propose to use Monte Carlo methods to train neural PDE s...	Rebuttal 1: Rebuttal: Thanks for your time and valuable feedback! According to your constructive comments, we make some replies to the weaknesses and questions: W1. The assumption in Theorem. The assumptions are reasonable for most cases and common in the PDE literature: - The solution can be expressed via Fouri...	Rebuttal 1: Rebuttal: ## Global Response A. Errata of the main Theorem We found a typo in the main theorem, and we fix it as follows: - The convection-diffusion equation in Eq. 13 should be: $$ \frac{\partial u}{\partial t} = \kappa \Delta u + \beta \frac{\partial u}{\partial x}, $$ where $\beta \frac{\part...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a new physics informed neural network based solver that utilizes the connection between PDEs and SPDEs. This is achieved through the Feynman-Kac formula and applies to a large class of PDEs. It comes with a bound on the error at each step in the rollout. The results are compared with multipl...	Rebuttal 1: Rebuttal: Thank you for your time and valuable feedback! According to your constructive comments, we make some replies to the weaknesses and questions: W1: The quality of the writing could be improved. Thank you for the valuable comments. We will take your suggestions on polishing the final version. ...	null	null	null	null	null	null
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes	Accept (poster)	Summary: In this paper, the authors extend the theory of proximal policy optimization-based methods in the linear mixture MDPs and propose an optimistic variant PPO algorithm (OPPO+) for stochastic linear MDPs and adversarial linear MDPs with full information. The proposed algorithm adopts a multi-batched updating rul...	Rebuttal 1: Rebuttal: Thanks for your review and positive feedback. We will try to address your concerns in the following. Q1: What does it mean for the proposed algorithm to be "optimistic"? What I mean is that for example, in value-based algorithms, optimism refers to the estimated values of the algorithm being ...	Summary: The paper proposes a theoretical analysis of the PPO algorithm. Some novel techniques such as batch-wise update are proposed so the algorithm can work on the adversarial setting of linear MDPs. A regret bound is given, which is better then or comparable to previous results. Strengths: The paper is well writ...	Rebuttal 1: Rebuttal: Thanks for your review and positive feedback. We will try to address your concerns in the following. Q1: Comparison with OPPO for linear mixture MDPs. A1: We agree that linear mixture MDPs and linear MDPs are two different types of MDPs with linear function approximation. We have discuss...	Summary: This paper studies the theoretical performance of an optimistic variant of PPO in episodic adversarial linear MDPs with full-information feedback (i.e., without assuming the reward functions are linear in the feature map), and establishes a regret bound of O(d^3/4 H^2 K^3/4) that matches the optimal regret bou...	Rebuttal 1: Rebuttal: Thanks for your review and positive feedback. We will try to address your concerns in the following. Q1: One of the claimed novelties is the multi-batched updating mechanism, which coincides with the similar idea of "policy switch" in literature. However, there is no discussion of the existin...	Summary: This work resolves the known issue for generalizing the policy-based algorithm proposed in [Cai el al.] for linear mixture MDPs to linear MDPs, by multi-batch updating and a bew covering number argument. The proposed model-free policy optimization algorithm advances the theoretical study of PPO in adversarial ...	Rebuttal 1: Rebuttal: Thanks for your review and positive feedback. We will try to address your concerns in the following. Q1: In Remark 3.3, the authors claim that they achieve the state-of-the-art regret bound for adversarial linear MDPs with full-information feedback. does it mean their result matches or impro...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Incentivized Communication for Federated Bandits	Accept (poster)	Summary: The authors study a new problem in federated bandits that involves incentivizing clients to share data. They propose a solution called Inc-FedUCB, which offers incentives in a linear contextual bandit setting. They demonstrate that Inc-FedUCB can achieve near-optimal regret levels with guarantees on communicat...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive suggestions to clarify problem formulation and data valuation design, as well as for helping us improve the overall organization of the paper. [Q1]: Line 126: Did the authors assume that all the clients share the same $\theta_\star$? If true, the as...	Summary: This paper introduces a novel federated learning protocol, so that 1) the setting is online instead of the more common offline setting, and 2) during each iteration, each client only chooses to participate (sharing information with the central server) if the client is gaining a sufficient amount of utility via...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive suggestions to clarify data valuation design and improve the organization of notations. [Q1]: We may need a more organized guide to notations in such a paper with a large number of variables. In particular, I didn’t find the definition of $g$ in $V_{...	Summary: The paper studied the problem of incentivizing data sharing in federated learning under the linear contextual federated bandit model with self-interested clients. While most previous works in federated bandit assume that all clients are willing participants in model sharing, this assumption is often unrealisti...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive suggestions to clarify the important problem formulations, improve the presentation of results, and strengthen the discussion on the broader societal impact on fairness. [Q1]: The paper made an important assumption that the server knows the vector of...	Summary: This paper introduces an incentivized communication problem for federated bandits. They study the contextual linear bandit setting and propose the first incentivized communication protocol, namely, INC-FEDUCB, that achieves near-optimal regret with provable communication and incentive cost guarantees. Strengt...	Rebuttal 1: Rebuttal: We thank the reviewer for the appreciation of our work, and the constructive suggestions to enhance the presentation of our theoretical results. [Q1]: The paper lacks intuitive explanations for the technical results. It might be great to add some high-level idea of the proofs for the theorems...	Rebuttal 1: Rebuttal: # General response to the reviewers: We sincerely thank all the reviewers for their thoughtful comments and constructive suggestions, which will significantly help us strengthen our paper. It is encouraging that all reviewers appreciate the novelty and importance of the studied problem and our pr...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints	Accept (poster)	Summary: This paper discusses parametric estimation under a communication setup. This setup adds variation to classic parametric estimation and focuses on the setup $\theta \to X^n \to Y^n$ where the goal is to estimate $\theta$ given $Y^n$, which is generated in an interactive, sequential manner with $Y_i$ possibly de...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed reading and acknowledging the novelty and significance of the paper. Below we address technical questions raised in the review. #### ***It may be helpful to highlight the difference in technical contributions (for example, proof techniques) between this work...	Summary: This work investigates the distributed parameter estimation problem under local information constraints such as communication constraints, local privacy constraints, and restricted measurements. The authors focus on information-theoretic lower bounds for the minimax error rates of these problems and present "p...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments. We will improve our paper based on the suggestions in future revisions.	Summary: This paper studies parameter estimation problem in a setting with the following two important assumptions: 1. Raw samples X_i's are generated from a certain parametric distribution, however, they are not directly observed. Instead, Y_i's which are generated from channels subject to certain constraints are obse...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed reading and suggestions on improving the presentation of the paper. Below we address technical concerns raised by the reviewer. #### ***Is it possible to explain why in the main bounds (Theorem 1 & 2), TV distance was used? Is this the only reasonable choi...	Summary: This paper studies distributed parameter estimation under local information constraints. In this problem, independent samples $X_1,..., X_n$ are generated from an unknown distribution $p_\theta$ from a parametric family $P_\Theta$ of distributions. The samples are not accessible directly, rather available is o...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed reading and acknowledging the generality of our technique. Below we address the questions raised by the reviewer. #### *The significance of the generalization compared to [4]* We stress that the results and techniques of [4] are specific to discrete dist...	Rebuttal 1: Rebuttal: We thank the reviewers for their careful reading of our submission, and are delighted to see their very positive assessments of our work. We will take into account their comments and suggestions in the final version of our paper, and respond individually to their questions below. We first address ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The work considers the problem of proving sample complexity lower bounds for parameter/density estimation in a distributed setting with information constraints (local privacy, communication constraints etc). What distinguishes this paper from prior work is that it focuses on the scenario where there is interac...	Rebuttal 1: Rebuttal: We thank the reviewer for the very positive assessment of our work. We refer the reviewer to the global response for our discussion on generalizing the technique beyond distributions with independent marginals. --- Rebuttal Comment 1.1: Comment: Thank you very much! My score remains unchanged.	null	null	null	null	null	null
Convolutional State Space Models for Long-Range Spatiotemporal Modeling	Accept (poster)	Summary: This paper presents ConvS5, a convolutional state-space model that aims to model long-range spatiotemporal dependencies in video data. They extend the prior S5 model to operate in a convolutional state-space, and retain the long-range benefits of state-space models by using a point-wise convolutional kernel fo...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback. We are glad the reviewer appreciated the comprehensive experiments as well as strong results. --Re. technical novelty: We respectfully disagree that the proposed approach is a straightforward extension of S5. Please see the Technical Contrib...	Summary: This paper proposes a new state space model for spatiotemporal modeling by introducing inductive bias of spatial locality. The core idea is to extend SSMs to ConvSSMs (just like extending FC-LSTM to ConvLSTM), which has a inherent convolutional structure. The new model also establishes an equivalence between t...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback. We are glad the reviewer appreciated the simplicity and effectiveness of the proposed method. --Re. convSSM variants: We present the general ideas of ConvSSMs (tensor-valued states, linear transitions and continuous-time parameterization) in Se...	Summary: The paper builds upon ConvRNNs and proposes the use of SSM (State Space Models) as a replacement for RNNs. This allows for efficient computation using parallel scan. The proposed method is evaluated on the Long Horizon Moving-MNIST Generation and Long-range 3D Environment Benchmarks datasets, where it achieves...	Rebuttal 1: Rebuttal: We thank the reviewer for taking the time to review our paper and provide feedback. --Re. motivation: Thank you for this feedback. We were a little surprised since several other reviewers found the design well-motivated and clear (e.g. see Reviewer Kz49). Here we will walk through the motivat...	Summary: This paper proposes convolutional state space models (ConvSSMs), that combine ConvLSTM with the long sequence modeling approaches like S4/S5. In particular, authors propose ConvS5, that allow parallelizing the stateful autoregression of convRNNs across the sequential direction. Naively applying parallel scan s...	Rebuttal 1: Rebuttal: We appreciate the time the reviewer took to review our paper and thank the reviewer for their positive feedback. We are glad the reviewer found the design of our method well-motivated and principled and also recognized the broad benchmarking of our paper and strong results of our approach. -- **R...	Rebuttal 1: Rebuttal: # General Response We thank the reviewers for reviewing our submission and providing constructive feedback. We provide a general response here and respond to each reviewer individually. We presented the ConvS5 spatiotemporal sequence model which has parallelizable training, fast autoregressive inf...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces a method for long-term sequential modeling. The authors extend prior RNN-based work for sequential modeling, i.e. S5[20], by substituting the linear operations with convolutions. The authors show the superiority of their method on the future prediction task with transformers. A favorab...	Rebuttal 1: Rebuttal: We thank the reviewer for the time they took to review our paper and for their feedback. We are glad the reviewer found the proposed method easy to follow. -- Re. flops: Please see the flops comparison in the computational cost section of the General Response above. Note the appendix of the o...	null	null	null	null	null	null
Langevin Quasi-Monte Carlo	Accept (poster)	Summary: This paper analyzes the effect of using quasi-random numbers in place of the usual IID Gaussians for the driving noise of a Langevin algorithm. Assuming that the loss is strong convex and a the quasi-random numbers are completely uniformly distributed, a bound on the Monte Carlo estimation error is derived. Th...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We address your questions as follows: - Extension to non-convex losses or external random variables In the "global response", we provided a comprehensive discussion about the strong convexity and smoothness assumptions and discussed to what extent these a...	Summary: For suitably smooth functions defined on a bounded support, it is well known that quasi Monte Carlo (QMC) can achieve faster rates of convergence than standard Monte Carlo when the goal is to integrate the function. This paper studies whether techniques from QMC can be beneficial for Langevin Monte Carlo (LMC)...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We address your questions as follows: - Strong assumptions/bounded variation: In the "global response", we have provided a comprehensive discussion on the smoothness, strong convexity, and bounded Hardy-Krause variation assumptions. In particular, to add...	Summary: In this paper, the authors proposed the Langevin quasi Monte Carlo algorithm (LQMC), that is, to use a completely uniformly distributed series (CUD quasi random number) in the Langevin Monte Carlo instead of iid pseudo random numbers. The quasi-random number is generated by the LFSR method. For a smooth and st...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We address your questions as follows: - Numerical experiments are performed with synthesized data: We understand the importance of real data experiments and have taken your suggestion into account. We have conducted new experiments using realistic data an...	Summary: The paper analyses a variant of Langevin Monte Carlo which, instead of using standard Gaussian random variables (using standard random number generation), instead uses correlated random variables following the quasi-MCMC literature. The paper demonstrates that this provably improves the efficiency of certain s...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We appreciate your recognition regarding the differences in our analysis compared to the standard literature on Langevin Monte Carlo (LMC). Traditional results in LMC primarily focus on studying convergence through metrics like the KL divergence between the ...	Rebuttal 1: Rebuttal: # Global response We thank the reviewers for their insightful and constructive feedback. The positive evaluations of our work as "novel, impactful, significant, promising, and important" are encouraging, and we appreciate the recognition of the clarity and cleanliness of the paper. In this global ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Tanh Works Better with Asymmetry	Accept (poster)	Summary: This paper proves the hypothesis that asymmetric saturation benefits network performance by swapping the position of Batch Normalization and Tanh activation functions. The Swap model generates high sparsity and asymmetric saturation which enables Tanh to behave like a one-sided activation function. Experimenta...	Rebuttal 1: Rebuttal: Thank you for your positive feedback and valuable suggestions. Weakness 1. Our main contribution lies beyond the combination of BN and Tanh. We have delved into understanding the significance of asymmetry in the activation functions, using Tanh as a base. This analysis can offer insights for ...	Summary: This paper investigates the performance of different activation function orders in deep learning models with batch normalization. The authors focus on the conventional order, where batch normalization is placed before the activation function, and the swapped order, where batch normalization is placed after the...	Rebuttal 1: Rebuttal: Thank you for your positive feedback and valuable suggestions. Weakness: Limited scope - Possible reasons for the Convention ReLU performance As the original author of BN[1] suggested, Convention seems to be fundamentally better than Swap. The good performance of Convention ReLU derives fro...	Summary: This paper investigates neural network classifiers with bounded activation functions. The authors first observe that swapping the batch norm and activation order improves performance with bounded activation functions. They then observe that asymmetric saturation and sparsity occurs in the swap model compared t...	Rebuttal 1: Rebuttal: Thank you for your positive feedback and valuable suggestions. Weakness 2 The table below presents the accuracy of VGG16 models trained on CIFAR-100. The accuracy of both leaky ReLU and ELU is the best hyperparameter, and the accuracies are averaged over three seeds. \| \| Convent...	Summary: The paper investigate the order of Batch Normalization and activation functions, and founded bounded activation functions like Tanh works better in the swapped order unlike bounded one like ReLU. To explain this, the authors analyze the asymmetric saturation levels at both the layer and channel levels and find...	Rebuttal 1: Rebuttal: Thank you for your positive feedback and valuable suggestions. Weakness 1. Question 1. The robustness of the noise obtained through the sparsity results in improved performance. Sparse representation indicates that relatively few units represent the data sample. Thus, even if the perturb...	Rebuttal 1: Rebuttal: We thank the reviewers for their positive feedback and valuable suggestions. Pdf: /pdf/e3cbcc2d105648f17375fadcc64c8192f2a6d4af.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Robust Knowledge Transfer in Tiered Reinforcement Learning	Accept (poster)	Summary: The paper presents an extension of "Tiered-RL", a multi-fidelity RL framework where a "low-fidelity" environment is executed in parallel with the "high-fidelity" environment, with the purpose of training faster while keeping near-optimal regret. The paper is a theoretical exploration without empirical evaluati...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. Below we address the concerns raised by the reviewer. ### Experiment Thanks for the suggestion. We conduct some empirical evaluation of our algorithm on toy examples, which well validates our theory. Please refer to our global response for more d...	Summary: The authors look at the tiered reinforcement learning setting, which is a parallel transfer learning framework where the goal is to transfer knowledge from a low-tier source task to a high-tier target task in order to reduce the exploration risk of the high-tier task. Additionally, these tasks are solved in pa...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable feedback. Below we address the concerns raised by the reviewer. ### Experiments that compared performance with other tiered RL algorithms We highlight that the general setting of tiered RL without prior knowledge of task similarity and/or with multiple sour...	Summary: The authors propose a robust parallel knowledge transfer reinforcement learning algorithm for single or multiple source tasks without knowledge on model similarity using the previously defined Tiered Reinforcement Learning framework. The paper remove the limitation on prior knowledge about the task similarity ...	Rebuttal 1: Rebuttal: We thank the reviewer for valuable feedback. Below we address the concerns raised by the reviewer. ### About the appendix We are afraid the reviewer might have overlooked the Supplementary Material part of the submission, where we include the full paper with appendix in a zip file. If spa...	Summary: This paper studies the tiered reinforcement learning setting, a transfer learning setting where the goal is to transfer information from a low tier task to a higher tier one while learning both to solve both tasks in parallel. Contrary to prior work the author do not assume that both tasks share the same rewar...	Rebuttal 1: Rebuttal: We thank the reviewer for the very positive and valuable feedback. Below we address the questions raised by the reviewer. ### For the experiments We conduct some empirical evaluation on toy examples, which well verifies our theory. Please refer to our global response for more details. ### More ...	Rebuttal 1: Rebuttal: We thank reviewers for their hard work and valuable feedbacks. ## General Remarks on Experiments Several reviewers (Reviewer 5dMC, Reviewer 9F2D, Reviewer BLAr, Reviewer MBdo) pointed out the lack of empirical evaluations as a main weakness. In our humble opinion, as a theory paper, our results p...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper extends the work of Huang et al (2022) on Tiered Reinforcement Learning (where the objective is to transfer knowledge from the low tier (risk-tolerant) source task to a high-tier (risk-averse) target task while solving the two two tasks in parallel) by relaxing the assumptions of identical reward and...	Rebuttal 1: Rebuttal: We thank the reviewer for valuable feedback. We will fix the typos as mentioned. Below we address the main questions raised by the reviewer. ### “this paper results from a minor relaxation of…” We humbly disagree to credit our contributions as “minor relaxation” of the assumptions. First of al...	null	null	null	null	null	null
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds	Accept (poster)	Summary: In this manuscript, the authors develop a special module that can adaptively learn a suitable filtration function for persistent homology (PH) and its downstream tasks. In particular, their learning module is specially designed, so that resulting persistent homology is isometry invariant. Their model has been ...	Rebuttal 1: Rebuttal: We appreciate the detailed comments and suggestions for improving our paper. We will reflect all the comments and suggestions in our final version. In the following, we respond to specific concerns and questions raised by the reviewer. > 1. One of the key point for the submission is to "propose a...	Summary: The paper develops a neural network to learn weights of given points in addition to other internal parameters to classify 3D clouds of unlabeled points on several public datasets. Strengths: The authors should be highly praised for a rigorous approach to an important problem of point cloud classification by...	Rebuttal 1: Rebuttal: We are grateful for the detailed comments and suggestions for improving our paper. First, let us explain the problem setting and our motivation for this study to resolve your misunderstanding. In this paper, we deal with the classification of labeled point clouds. In this setting, *point clouds...	Summary: A neural network that learns the filtration for persistent homology on given point cloud data is introduced, theoretically justified, and evaluated experimentally on 2 data sets. Strengths: (S1) If this is indeed the first work that considers learning filtrations on point clouds, I find the idea very relevant...	Rebuttal 1: Rebuttal: We appreciate the detailed comments and suggestions for improving our paper. We will reflect all the comments and suggestions in our final version. In the following, we respond to specific concerns and questions raised by the reviewer. > (W1) The need for learned … Thank you for your important s...	Summary: This paper investigates the extraction of global topological features using the framework of persistent homology. The authors have proposed a neural network architecture to learn the filtration weights for each point in an end-to-end and data-driven manner, which is later supported by an approximability theore...	Rebuttal 1: Rebuttal: We are grateful for the detailed comments and suggestions for improving our paper. We will reflect all the comments and suggestions in our final version. In the following, we respond to specific concerns and questions raised by the reviewer. > Weakness 1. It looks like there are two contributions...	Rebuttal 1: Rebuttal: We appreciate the detailed comments and suggestions for improving our paper. We will reflect all the comments and suggestions in our final version. In the following, we show you some information that we would like to share with all of the reviewers. 1. A reviewer pointed out that Figure 1 is con...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work proposes a novel framework to obtain adaptive topological features for point clouds based on persistent homology by introducing an isometry-invariant network architecture for a weight function and proposes a way to learn a weighted filtration. The work theoretically proves that any continuous weight ...	Rebuttal 1: Rebuttal: We appreciate the valuable feedback for improving our paper. We will reflect all the comments and suggestions in our final version. In the following, we respond to specific concerns and questions raised by the reviewer. > It is not clear why different weight functions affect the qualities of the ...	null	null	null	null	null	null
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion	Accept (poster)	Summary: The paper looks at the problem of learning a generative model for sampling 3D environments from a description based on a graph and natural language. The graph does correspond to objects that participate in the scene, and textual descriptions attached to nodes and edges further specify the scene. Textual descri...	Rebuttal 1: Rebuttal: ## 1. Overview paragraph on optimization Thanks for the useful advice! We now extend our overview section (L111-116) by explicitly cross-referencing figures and the subsections. Further, we rephrase the sentence in L143 as: *“We guide the distribution of embedding space through employing the KL lo...	Summary: Summary: This paper presents a generative model to generate 3D scenes from scene graphs. Their model is fully generative without the need of any shape database or embeddings. The 3D scenes generation model is pipelined into finding the scene layout and the construction the shapes of the nodes using a diffusion...	Rebuttal 1: Rebuttal: ## 1. The baseline based on the text-to-shape model It is a method we modeled for experimentation. The motivation is introduced in L36-40, the main paper. We have also further explained the implementation of this baseline in Supp. Mat. Section 7 in detail. This baseline consists of a layout genera...	Summary: This paper addresses the task of controllable scene synthesis of indoor rooms, conditioned on a semantic scene graph that captures spatial, style and support relationships between objects in a scene. In particular, they introduce CommonScenes, a generative model capable of converting scene graphs into 3D scene...	Rebuttal 1: Rebuttal: ## 1. Training details We perform one-stage training. As mentioned in the L129, the BCG is created and encoded on-the-fly by the contextual encoder $E_c$. We agree that the overview needs to indicate one-stage training clearly. As for LDM, we train a single LDM for all object types conditioned on ...	Summary: Gist: The paper presents a framework, called CommonScenes, for generating 3D indoor scenes given scene graphs as inputs. CommonScenes is a dual-branch framework where one branch generates the scene layout using a VAE and the second one generates what the authors call "compatible" 3D shapes using latent diffusi...	Rebuttal 1: Rebuttal: ## 1. Text-to-shape baseline We have explained this baseline, named Layout+txt2shape", more in detail in Supp. Mat. Section 7. This baseline solely considers the shape generation from text input, which we establish through the text-to-shape SDFusion [8]. ## 2. Complexity, contribution, and motiva...	Rebuttal 1: Rebuttal: # Thank you for your insightful comments! We would like to thank all reviewers for their insightful and valuable comments. In summary, they highlighted the significance of the work (“scene generation is an important problem” (xttF), “structured input modality gives rise to many applications” (...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Single-Stage Visual Query Localization in Egocentric Videos	Accept (poster)	Summary: The paper proposes VQLoC, an end-to-end trainable framework for Visual Query Localization (VQL) on long-form egocentric videos. Compared with Ego4D's multi-stage approaches, VQLoC proposes a single-stage process that efficiently localizes visually specified objects. It establishes both query-to-frame and frame...	Rebuttal 1: Rebuttal: We appreciate your detailed and helpful suggestions, and we are glad to see the highly positive comments. We will address your questions below. 1. [Local window size ablation] We experiment with smaller window sizes 3 and 1, where 1 means there is no temporal reasoning within the model. As s...	Summary: The author proposes a new single-stage end-to-end method for visual query localization. The proposed method builds a holistic understanding of query-video relationship, then spatial-temporal localization is performed. The proposed achieves major performance and speed gain over previous methods on major benchma...	Rebuttal 1: Rebuttal: We appreciate your helpful feedback, and we will address your concerns as follows. 1. [Benchmark] Thank you for your valuable suggestion. In this paper, we focus on the special properties of the visual query localization task on egocentric videos (L33-43), e.g. drastic head motion, large ...	Summary: The paper introduces a new approach to address the visual query localization problem in egocentric videos. The major contribution is that the proposed method, a single-stage model, simplifies the previous multi-stage frameworks and eliminates the need for off-the-shelf object detectors, tracking, and similar c...	Rebuttal 1: Rebuttal: We highly appreciate your insightful comments and your suggestions on the paper details. We will address your questions as follows. 1. [Speed comparison] We apologize for the confusion. The speed improvement was not in comparison to STARK but the prior state-of-the-art VQL methods (SiamRCNN /...	Summary: This paper tackles the problem of query localization of visually specified objects in egocentric human-activity videos. The main technical contribution for the solution is to design a single-stage transformer-based architecture to model the query-to-frame correspondence matching and frame-to-frame corresponden...	Rebuttal 1: Rebuttal: We appreciate your valuable comments. We will address your concerns as follows. 1. [Novelty and related work] We respectfully disagree with the comment. Our method is not a simple adaptation of the existing methods. Its novelty is appreciated by other reviewers, and we reiterate our contrib...	Rebuttal 1: Rebuttal: We appreciate the insightful feedback and detailed suggestions from the reviewers. It’s great to see the highly positive comments, including “positively impacts the egocentric community” (R-59Vz), “The paper introduces a novel and technically sound method” (R-YaTy), “is simple and performs well” (...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes VQLoC, an end-to-end method for egocentric visual query localization based on a holistic understanding of the query-video relationship. The key component is a spatio-temporal transformer, which can effectively model the relationships between the query and frames. The presented approach can p...	Rebuttal 1: Rebuttal: Thanks for the positive comments, and your acknowledgment of our potential impact to the community! We will address your questions as follows. 1. [Weakness Q1] Our study intentionally focuses on VQ2D due to its unique challenges, e.g. the ‘needle-in-the-haystack’ problem, and the large variat...	null	null	null	null	null	null
Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data	Accept (spotlight)	Summary: The paper proposed a new method for partial information decomposition (PID) on multivariate Gaussian distributions. The issue of bias was discussed, and a correction method was provided. The method was tested on synthetic canonical examples and real data. Strengths: 1. The introduction clearly lays out the pr...	Rebuttal 1: Rebuttal: Weaknesses > 1. The paper does not provide enough real data to show effectiveness in real applications. We thank the reviewer for raising this point. We have tried to ameliorate this concern by demonstrating how the method works with a larger number of neurons (i.e., PCA components) in the s...	Summary: Partial Information Decompositions (PIDs) play an essential role in neuroscience research. One constraint of the broader usability of PIDs is the computational difficulty of computing PIDs for high-dimensional neural recordings. To address this concern, the authors propose a method to compute and estimate a PI...	Rebuttal 1: Rebuttal: Weaknesses: > No analysis is done to show the stability of delta-PID over increasing dimensionality. According to the paper, there are two differences between delta-PID and ~-PID. The first one is the "additivity" property, which is clearly shown by Examples 8-9 in Section 4. The second one i...	Summary: The authors propose a new, efficient method for computing Partial Information Decompositions (PIDs) on multivariate Gaussians. They build their approach around the $\sim$-PID approach, as this allows them to preserve an additivity property (allowing PIDs to be computed on independent systems separately and the...	Rebuttal 1: Rebuttal: Weaknesses > Given one of the major motivating factors given in the introduction is the need for efficient estimators of PID that can accommodate higher dimensional neural data, I was disappointed that in the end the authors chose to apply their method to a PCA-reduced version of the Allen In...	Summary: This article proposes an upper bound on mutual information (more precisely: on the "unique information" / "union information" that appear in "Partial Information Decomposition") that is easier to compute. This is done by replacing an infimum over all distributions matching given marginals by an infimum over on...	Rebuttal 1: Rebuttal: Weaknesses: > The main issue is that the definition of the upper bound actually supposes that the distribution MXY that is studied is Gaussian, at least marginally … This crucial point is not discussed in the paper. It is true that our definition applies only to Gaussian P_MXY (in fact, it m...	Rebuttal 1: Rebuttal: We sincerely thank all of the reviewers for taking the time and effort to provide thoughtful and constructive reviews of our work. We use this space to address a few comments that were common across reviewers, and to explain the additional analyses we perform in response to these comments. We als...	NeurIPS_2023_submissions_huggingface	2,023	Summary: First, I would like to thank the authors for the work they put into contributing to the field. In their paper, the authors provide a new method to estimating a well-known measure, the Partial Information Decomposition (PID). Within the topic of PIDs, their method computes a specific version of PID, the ~-PID....	Rebuttal 1: Rebuttal: Weaknesses: > The paper is an improvement of an already existing method which makes it important and publishable yet disqualifies it for a very high grade. We thank the reviewer for raising this concern, and providing us with an opportunity to explain why we believe our advance is significan...	null	null	null	null	null	null
Optical Transformers	Reject	Summary: The authors analyze the performance, efficiency, and robustness of free-space optical dot-product engines for Transformer accelerations. Measurement results on an SLM-based optical system are demonstrated on some layers in a GPT-like model. System performance/efficiency are estimated and compared to digital co...	Rebuttal 1: Rebuttal: Re: Novelty and Contributions to ML We are not aware of any previous study on how ONNs would behave in the regime of LLMs, which are at least 100 − 1000× larger than any model simulated for ONN hardware so far. Due to the unavoidable noise in analog physical computing, the fact that ONNs work...	Summary: This paper propose a photonic hardware accelerator to process the inferences of large language models, i.e., transformers, using optical multiply-accumulate (MAC) operations. Optical MACs are suitable for computations with large operands, thereby leading to asymptotic energy advantages over the digital hardwar...	Rebuttal 1: Rebuttal: Hello and thank you for your feedback. The overheads related to data access (RAM, DAC/ADC) are indeed very important in considering whether an ONN platform may have any energy advantage. All energy values reported in this work did take those into account. We acknowledge that explicitly mentioning ...	Summary: This paper explores the feasibility and benefits of employing optical computing techniques for machine learning and specifically focusing on large language models (LLMs). The paper builds upon earlier work on optical neural networks, primarily [61] (Wang et al., Nature, 2022), which experimentally demonstrate...	Rebuttal 1: Rebuttal: Re: "How far are current optical systems from the 10GHz operating frequency", "...benefits of optical be undermined if more modest rates were assumed? If the system were to be run at lower frequency the energy estimates would not change significantly for smaller models, but can for the hypoth...	Summary: The authors perform experimental analysis with a spatial light modulator to optically perform the computations of the linear components of the Transformer architecture. These measurements allow them to create a noise-model that is then used to simulate a GPT-2 like model and measure performance (validation per...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's helpful comments. We provide here further explanation on the reviewer's questions and concerns in a point-by-point manner. Re: "All the discussion ... in the lens of energy consumption" Latency and speed are important factors in determining the viability of...	Rebuttal 1: Rebuttal: We wish to thank the reviewers for their time and dedication in providing valuable feedback for this work. We hope that the additional explanations we provided here serve to clarify the scope of this work and address some of the common concerns of the reviewers about our energy calculation assumpt...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Budgeting Counterfactual for Offline RL	Accept (poster)	Summary: This paper proposes a novel offline RL algorithm BCOL that builds on the idea of limiting the numbers of counterfactual decisions. Instead of enforcing policy or value regularization, BCOL follows the decisions of the behavioral policy in the majority of the states, and only makes counterfactual decisions for ...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We hope you will consider increasing your score after reading our responses. Please let us know if there are more questions > “why BCOL can learn where to extrapolate" > “not enough intuitive or theoretical support”. Indeed, it is the very central question...	Summary: This paper proposes a TD approach to induce counterfactual decisioning in offline RL agents. Basically, the approach suggests using a count-based budget that gives scope for making decisions that are not exhibited by the behaviour policy. The paper implements this approach in various standard benchmarks and sh...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We hope you will consider increasing your score after reading our responses. Please let us know if there are more questions. > “Tuning of the B” We agree with the reviewer on the algorithm’s behavior with high and low B. Such a behavior (spanning over the ...	Summary: This paper gives a total new solution to offline RL, instead of introduing pessimism by behavior constraint or value regularization, budgeting the number of counterfactual decisions, which naturally reduces overestimation. This paper also gives a good formulation of the problem and provides a nice solution to ...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and positive feedback. Please see our responses to your comments and let us know if there are more questions. > This method introduces two hyperparameters to tune That is a fair point. Our method does have two hyperparameters, but this does not diminish...	Summary: This paper designs an new algorithm for offline RL. It also conduct experiment to validate its algorithms. Strengths: The algorithm proposed is new. The intuition behind is presented clearly. Weaknesses: 1. The performance of the proposed algorithm does not demonstrate substantial superiority compared to the...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We hope you will consider increasing your score after reading our responses. Please let us know if there are more questions. > The performance of the proposed algorithm does not demonstrate substantial superiority compared to the Contrastive Q-Learning (CQL) ...	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback that we've used to greatly improve the paper. We have responded to the concerns of the reviewers as individual comments below. We are glad that the reviewers found that our method is simple and straightforward (FoC4), novel (4X4f, wQce, bnLz), and is a so...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals	Accept (poster)	Summary: This paper focuses on devising a new inductive bias for cutting-edge graph application and present a general framework through the lens of variational analysis. To this end, the authors first introduce a new selective mechanism that can be easily integrated into existing GNNs and effectively address the trade-...	Rebuttal 1: Rebuttal: We appreciate this reviewer’s insightful comments. We provide answers to the four major questions below. 1. $\bf Q$: Lack of detailed knowledge of the EL equation for GNN researchers. $\bf A$: We apologize for this oversight. We will add more details and relevant work in the Supplementary....	Summary: This work studies a novel design of GNN leveraging the connection with graph diffusion. Specifically, this work considers the discrete GNN model in view of continuous graph diffusion functional formulated as the Euler-Lagrange equation. This work proposes a new design of GNN with selective inductive bias which...	Rebuttal 1: Rebuttal: We appreciate the valuable comments from this reviewer. We will incorporate all suggestions into the final version. We will add a “Related Work” section in the Supplementary which includes the relevant GNN work in solving over-smoothing issues (suggested by Reviewer HWVs) and neuroimaging backgr...	Summary: The authors connects graph neural networks with discretizations of PDEs and connects their limiting behavior with E-L equations. By changing regularization / smoothing term from second order to first order, the authors designed a new E-L equation and its corresponding GNN. The author then present numerical exp...	Rebuttal 1: Rebuttal: We are glad that the reviewer is enthusiastic about this work. We will outline the steps of our algorithm in a table (likely in the Supplementary material). Also, we appreciate the comment regarding the layout of Fig. 5. We will split it into two figures in the final version. We agree with this ...	Summary: The authors propose a framework based on the Euler-Lagrange equation to derive specialized GNNs by discretizing continuous diffusion functionals. By deriving a new GNN layer from the Total Variation functional, they manage to control the oversmoothing problem in existing GNN architectures and improve node clas...	Rebuttal 1: Rebuttal: We appreciate the constructive comments from this reviewer. We answer the general question first and then clarify each specific comment. General questions: We appreciate the insightful question regarding the homophily assumption. In our GNN-PDE-COV framework, similar to current GNN models, we ...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful feedback. We are thrilled by the reviewers’ comments which they consider our paper as being original (Reviewer CRUA), creative (Reviewer HWVs), novel yet simple (Reviewer VF1D, sJUp, HWVs), clear (Reviewer sJUp, HWVs), great (Reviewer CRUA), very well ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors develop a framework for linking discrete (message passing) GNNs to continuous graph diffusion functional networks using Euler-Lagrange equations of heat kernels. Via this framework, they analyze the causes of oversmoothing in current GNNs. By noting that the main cause of oversmoothing is the mini...	Rebuttal 1: Rebuttal: We are sincerely thankful for all constructive comments provided by this reviewer. We are pleased that the reviewer recognizes the merits of our new GNN technique, as the major concerns are centered on more extensive comparisons on diverse datasets and models. We have prepared all necessary result...	null	null	null	null	null	null
Blurred-Dilated Method for Adversarial Attacks	Accept (poster)	Summary: The authors propose the Blurred-Dilated method (BD), which utilizes BlurPools and dilated convolutions on the source model when an adversarial attack is applied, to increase the transferability of the transfer-based attack. The method replaces the MaxPool layer with MaxBlurPool, Conv with ConvBlurPool and Aver...	Rebuttal 1: Rebuttal: Q1. If we consider replacing max pooling with BlurPool, can we interpret it as replacing a non-linear function with a linear function? Thus, does the Linearity Hypothesis in LinBP apply to BD as well? Thank you for providing a new perspective to explain the effectiveness of our method. However, ...	Summary: In this work, the author generates adversarial examples to attack other models by using BlurPools and dilated convolutions on the source model. The results show that increasing the model with BlurPools and dilated convolutions can generate more transferable adversarial examples. Strengths: The work is well-wr...	Rebuttal 1: Rebuttal: Q1. The novelty of the proposed method. Please see Q2 of Reviewer hwet. Q2. "In my opinion, adding a blur layer inside the network and methods based on input augmentation (such as padding and resizing) are not fundamentally different." (1) Our method can help to preserve more low-level featu...	Summary: This paper proposes a new Blurred-Dilated method for generating transfer attacks. The authors focus on generating transfer attacks as a more realistic attack model by looking at how the substitute model's architecture can be changed to increase the transferability of adversarial attacks. By introducing blurred...	Rebuttal 1: Rebuttal: Q1. Why are dilated convolutions only applied at the later layers? (1) We introduce dilated convolutions to reduce downsampling in the model, since we want to preserve more low-level features of an image during forward propagation (Please see the answer to Q1 of Reviewer hwet for the motivation ...	Summary: The paper proposes a novel transfer-based black box adversarial attack called Blurred-Dilated method. They authors consider the model modification approach and propose to reduce downsampling operations on the source model for the attack. They conduct extensive experimenting and compare with the previously prop...	Rebuttal 1: Rebuttal: Q1. Can the BD method be applied to other popular vision architectures such as Vision Transformer? We can first add CNN blocks to Vision Transformers as in (Dosovitsky et al. 2021). We can then apply our BD method to the CNN blocks in the hybrid model to improve the transferability of adversari...	Rebuttal 1: Rebuttal: Dear Reviewers, We sincerely appreciate all of your precious time and constructive comments. We are greatly encouraged by the positive comments on our work. We will carefully revise our manuscript by adding the suggested experimental comparisons, presenting more detailed explanations, and fixing ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper investigates a modification to vision networks that causes adversarial examples generated by attacking them to be more transferable. In particular, the paper suggests using dilated convolutions + blurred downsampling, which the authors motivate as retaining a maximum amount of original feature inform...	Rebuttal 1: Rebuttal: Q1. The motivation of our approach. (1) We observed that: 1) The adversarial samples generated by ResNet usually have better transferability than those generated by other models. We think the reason may be that the skip connections of ResNet connect the lower-layer feature maps to the higher-laye...	null	null	null	null	null	null
Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance	Accept (spotlight)	Summary: This paper considers an important problem in learning on point clouds -- the equivariance under the SE(3) group. Namely, the authors address the requirement of inter-part equivariance, essential for handling real-world scenarios, where an object can consist of multiple moving parts, or a scene can contain mul...	Rebuttal 1: Rebuttal: Thanks for your feedback and for appreciating our work! Here’re our responses to your questions and comments and we hope that they could address your concerns as well: W1. Time complexity of the proposed method is missing. In our experiments, we set k=20 iterations for evaluation. But in pr...	Summary: The authors introduce a method for object part segmentation of point clouds that is equivariant/invariant to SE(3) part transformations. The core of their method is a neural network with points and segmentation as input and segmentation as output. The network is assumed to be contractive and is then used to pe...	Rebuttal 1: Rebuttal: Thanks for your feedback and for appreciating our work! Here’re our responses to your questions and comments and we hope that they could address your concerns as well: **I suspect that equation 1 wants to express linear blending of part transforms. However, I don't think it is correct: Could the ...	Summary: The paper proposes an equivariant network for part-based (or multi-object) point cloud segmentation. The approach is equivariant to separate SE(3) transformations of each part/object. This is ensured by introducing a Banach fixed-point network. The network takes the point-could and the current segmentation as ...	Rebuttal 1: Rebuttal: Thanks for your feedback and for appreciating our work! Here’re our responses to your questions and comments and we hope that they could address your concerns as well: 1. Generalization to larger datasets and more complex scenes. For larger scenes, we believe a major difficulty would be the ...	Summary: The paper propose a Banach, an approach for part-based point-cloud segmentation. In particular, the authors propose an approach to enforce equivariance of the part-segmentation, by construction. They propose a fixed-point framework with one-step training and iterative inference. They propose a part-aware segm...	Rebuttal 1: Rebuttal: Thanks for your feedback and for appreciating our work! Here’re our responses to your questions and comments and we hope that they could address your concerns as well: Inference and training time. In our experiments, we set k=20 iterations for evaluation. But in practice, we plot the IoU w.r...	Rebuttal 1: Rebuttal: We thank all reviewers for their constructive feedbacks, and are glad that they find our work presenting novel and compelling ideas as well as convincing results. Here we provide a brief summary of our response, including additional theoretical explanations and experimental evaluations. Detailed ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a neural network architecture that is equivariant to transformations in SE(3) for each object part independently. The network is part of a fixed-point framework where the network is trained with a single step but during testing an iterative approach is used that converges to the desired segm...	Rebuttal 1: Rebuttal: Thanks for your feedback and for appreciating our work! Here’re our responses to your questions and comments and we hope that they could address your concerns as well: Equivariant convolutions. The VNN baseline we compare to is also using a graph convolution network backbone. These types of ...	null	null	null	null	null	null
Learning and Collusion in Multi-unit Auctions	Accept (poster)	Summary: This paper studies a setting where a single seller runs repeated multi-unit auctions. In each multi-unit auction, there are $K$ identical units of good for sale. Each buyer individually has a valuation for the good with decreasing marginal returns and submits a bid vector. The seller will allocate the units ac...	Rebuttal 1: Rebuttal: Thank you for your review. - Regarding Section 1.2 and Coarse Correlated Equilibria (CCE) and K+1-th pricing rule: Let us first note that in essence, our contribution introduces a learning algorithm for bidding which can enhance comprehension of CCEs within uniform price auctions. This can be do...	Summary: This paper studies the setting in the multi-unit auction where there are $K$ items to allocate and buyer are not necessarily unit-demand and have quasi-linear valuations with decreasing marginal returns. The bidder set separate bid for each item, and each item goes to its highest bidder, where the price can be...	Rebuttal 1: Rebuttal: Thank you for your review. - Expanding to policy regret: Achieving a deeper understanding of policy regret involves transforming our current setup into a contextual bandit framework with an exponential range of contexts. This challenge presents an exciting avenue for future research. - Regarding...	Summary: The paper considers a repeated autcion setting where the players submit their bids for an item of which $K$ units are avaialble. An auctioneer computes a price $p$ and allocates the j-th unit to the owner of the j-th highest bid, charging the price p$ for each unit. (I did not understand this part completely,...	Rebuttal 1: Rebuttal: Thank you for your review. - Regarding the model: The model can be located on page 2 immediately after the introduction. We placed the model early within the paper to facilitate a comprehensive understanding of our contributions within the framework of our proposed model. - Regarding the allocat...	Summary: This work systematically considers computational, learning-theoretic, and game-theoretic aspects of multi-unit auctions with uniform pricing. In such auctions, $K$ identical goods are sold to agents with quasilinear utility with a uniform pricing scheme set to either the $K$th or $(K+1)$th highest overall bid ...	Rebuttal 1: Rebuttal: Thank you for your review. - About lower and upper bounds: Despite our attempts to enhance these bounds, the ones presented in the paper remain the best results we could attain. Thus we leave this as a future work. Thank you for your understanding. - Regarding $K_t$: When the number of units $K_...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper studies the problem of learning to bid in a multi-item auction. There are k identical item to be sold. Each bidder has decreasing value v_1, v_2, ..., v_k with value v_j for j'th item allocated to the bidder. The auction allocates to the k highest bidder and charge k'th or (k+1)'th price. It's worth...	Rebuttal 1: Rebuttal: Thank you for your review. - Regarding equilibria, the possibility of learning algorithms promoting collusion is a significant worry. Thus, using an auction like the K+1-st, which features equilibria with zero prices, presents issues. These equilibria are not just resistant to coalition deviation...	Summary: In this paper, the author proposes efficient algorithms that players can use for bidding, in both the offline and online settings. Furthermore, the paper shows regret lower bounds and then analyzes the quality of the equilibria in two main variants of the auction. It focuses on studying uniform price auctions ...	Rebuttal 1: Rebuttal: Thank you for your review. - The uniform price auctions play a pivotal role in determining the allocation of CO2 emission licenses in the EU Emissions Trading System (EU ETS). See also the paper “Reducing Inefficiency in Carbon Auctions with Imperfect Competition, ITCS 2020”, which explains the a...	null	null	null	null
On Computing Pairwise Statistics with Local Differential Privacy	Accept (poster)	Summary: In this paper, the authors analyzed the problem of privately computing the quadratic form in the model of differential privacy, and provide non-interactive local DP algorithm with MSE upper/lower bounds with gap log(k). The paper further develops results for an interactive algorithm for the same problem and pr...	Rebuttal 1: Rebuttal: Thank you for your review & questions. Please find the answers to your questions below. 1. This is indeed a great question and is why we also pose it as an open question in our paper (lines 344-345). We conjecture this might be necessary since even the seemingly simpler heavy-hitter problem also ...	Summary: This paper studies the computing of pairwise statistics with local differential privacy by considering the quadratic form computation. In order to obtain the lower boudn and uppoer bounds, it proposes the inter-reductions between quadratic forms and linear queries. Strengths: Studying pair statistics with lo...	Rebuttal 1: Rebuttal: ## High-level Response: We completely agree with the reviewer that the reductions in Section 1.2 are simple, _but only in hindsight!_ On the other hand, we think of this as a significant contribution of our work for the following reason. While previous works [BBGK20 (AISTATS’20), CM22 (VLDB’23),...	Summary: This paper studies the problem of computing Quadratic Forms $h_x^TWh_x$ under Local Differential Privacy, where $h_x$ is the normalized histogram representation of a vector $x\in [k]^n$. In particular, reductions to and from the problem of computing linear queries are established, through which algorithms and ...	Rebuttal 1: Rebuttal: Thank you for your review & questions. Please find the answers to your questions below. ## $\\epsilon$ value in Theorem 5: We remark that the lower bound requirement $\\epsilon \\geq 1/\\sqrt{n}$ for non-trivial utility is present in essentially _all known_ local DP results and is supported by T...	Summary: The paper considers the computation of quadratic forms of histograms under local differential privacy (LDP). Previously, special cases of this problem have been studied, but this paper presents a general theory analogous to the existing theory for linear queries. The problem is studied both in the standard LDP...	Rebuttal 1: Rebuttal: Thank you for your review & questions. Please find the answers to your questions below. - While we omitted the constants (following the precedence of not reporting exact constants in most previous work), it is not hard to compute them in Corollary 7. We will consider adding them in the revision....	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering	Accept (poster)	Summary: This paper addresses the challenging task of answering complex queries on incomplete knowledge graphs, where missing knowledge introduces additional complexity. Previous approaches either employed end-to-end architectures with opaque reasoning processes or relied on simple neural link predictors, sacrificing i...	Rebuttal 1: Rebuttal: Thank you for your review. Regarding the following, - The authors haven't discussed the limitation of this work in the paper. We tried our best to run all possible ablations and analyses for this paper, and some additional experiments to run came up during this rebuttal phase. We will add results...	Summary: The paper proposes a score adaptation model called CQDA for efficient complex query answering on incomplete knowledge graphs. The authors address the problems in existing methods that are either hard to interpret or require intensive training. CQDA is a parameter-efficient model that recalibrates neural link p...	Rebuttal 1: Rebuttal: Thank you for your review! Regarding your concerns – - In Figure 1, the paper shows that the score scale may be different for different subqueries, [..] However, the paper also mentions that they follow previous work to add normalization as below [..] In CQD, for the t-norm and t-conorms to be ap...	Summary: The paper proposes a novel approach called CQD^A for answering complex queries on incomplete knowledge graphs. The authors address the challenge of answering complex logical queries in the presence of missing knowledge by re-calibrating neural link prediction scores. They introduce an adaptation component that...	Rebuttal 1: Rebuttal: We thank you for your comments and valuable feedback. We would like to address the following points: - It would be beneficial to provide some concrete examples or explanations to illustrate this interpretability. We would like to refer you to our global response, where we clarify why CQD$^\mathca...	Summary: This paper proposes an adaptation model on top of neural link predictors to learn score re-calibration to suit complex query answering task.Shows empirical evaluation on standard benchmark datasets to show its value. One of the benefits is a simple calibration model with lesser training data on complex questio...	Rebuttal 1: Rebuttal: We thank you for your comments and valuable feedback. We would like to address the following points: - Contribution seems incremental. Our analysis of CQD points to a fundamental limitation of CQD that is not necessarily trivial to solve if we want to maintain its favourable properties, such as d...	Rebuttal 1: Rebuttal: We thank the reviewers for their time and valuable feedback. We appreciate that reviewers acknowledge CQD$^\mathcal{A}$ proposes a novel (jfF5, 5Xze) and technically sound method (jjs4) for data-efficient complex query answering.We have incorporated the provided feedback into our work, including a...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes CQD$^\mathcal{A}$ an adaptive variant of the complex query decomposition (CQD) approach. The authors identify that the neural link predictors used in CQD produce uncalibrated scores that can be in very different ranges of each other. The authors show that CQD$^\mathcal{A}$ can adaptively lea...	Rebuttal 1: Rebuttal: We thank you for your comments and valuable feedback. Regarding your questions, - Negations are modelled by either a standard or strictly cosine functions. In theory this could be added to vanilla CQD as well. Even though the link prediction scores are not explicitly calibrated in the original fo...	null	null	null	null	null	null
How a Student becomes a Teacher: learning and forgetting through Spectral methods	Accept (poster)	Summary: The authors propose a novel technique that allows identifying an invariant subnetwork in a student model that mirrors the characteristics of the teacher in terms of computing neurons, path distribution, and topological attributes. Strengths: - The manuscript is clearly structured, and the subject of research...	Rebuttal 1: Rebuttal: We would like to express our gratitude to the referee for their positive review and insightful suggestions. In relation to the 'Figures' and 'Spelling/Wording' sections, we are prepared to implement the suggested corrections should the paper progress to the camera-ready stage. Regarding the Gener...	Summary: This paper analyses the performance of a spectral parameterisation/regularisation scheme for neural networks. After first introducing the spectral approach, the authors describe student-teacher experiments where they attempt to distil a fixed teacher network’s behaviour into a student network. The authors show...	Rebuttal 1: Rebuttal: We thank the referee for the insightful feedback and in the following we will address all of their raised points. TWO MAJOR QUESTIONS (number of active nodes and path analysis): In the presented results, we propose that the data originates from a 10-dimensional space and is subsequently proj...	Summary: The authors consider a knowledge distillation setting involving a teacher and a student neural network. The authors exploit a few interesting tricks -especially the use of a spectral parameterization of the network, and special regularizers- to show that it is possible to enforce learning a submodule within a ...	Rebuttal 1: Rebuttal: We express our gratitude to the referee for their feedback. We acknowledge the need for more comprehensive results. In response, we've conducted four additional more complex scenarios, with the outcomes presented in Figure 1 of the accompanying PDF file. The experimental framework remains consiste...	Summary: This work focuses on the teacher-student paradigm in theoretical machine learning and shows that, for a unique optimization scheme that involves directly optimizing on the eigenvalues/eigenvectors of the data, a stable subnetwork in the student can be identified that can mirror the complexity of the teacher ne...	Rebuttal 1: Rebuttal: We appreciate the referee's feedback and recognize that the work may not fall directly within their domain of expertise. Regarding the implementation with other data, we agree with the referee and therefore have extended our analysis to four more complex and realistic datasets. Delving deeper, our...	Rebuttal 1: Rebuttal: We express our gratitude to the chairs and referees for their valuable effort in providing constructive feedback on our manuscript. We have carefully considered each of the comments made and addressed them individually and in-depth in the replies to the referees. We have taken note that all the re...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper tried to understand and extend a new parametrization, spectral parametrization, for fully connected networks. They empirically show that in the teacher student setup, even when the student network is highly over parametrized, the student network under that parametrization will converge to a somehow ...	Rebuttal 1: Rebuttal: We would like to thank the referee for pointing us to the insightful reference concerning implicit regularization resulting from the commuting nature of our parametrization. Unfortunately, the other mentioned reference came out after the submission of our work to the conference. However, we conten...	null	null	null	null	null	null
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows	Accept (poster)	Summary: The authors investigate the temporal integration window of several transformer language models (but focus on gpt2) by evaluating the effect of word swaps on the activations of individual units as a function of their distance from the swapped word. They then characterize these mean integration curves of the uni...	Rebuttal 1: Rebuttal: Thank you for your supportive review. We have addressed your comment about needing to better motivate the chosen functional forms in our general response. We show that the exponential-power law function provides substantially better prediction accuracy (measured using three different metrics) th...	Summary: Transformer models have the potential to acquire essentially arbitrary patterns of attention during training. But what patterns do they acquire in practice? This is the question taken up in the present paper. The data for the paper are 40 word sequences from the classic Brown corpus. The language models examin...	Rebuttal 1: Rebuttal: Thank you for your constructive critique. We have addressed your critique about the need for more extensive quantitative comparisons in our general response since other reviewers raised similar points. We tested a much wider range of different parametric forms motivated in part by the papers cit...	Summary: This paper studies the relationship between the length of context and its influence on language model outputs across layers and units. They propose a model-agnostic procedure that swaps words at a given distance and measuring the change in the activations. Doing this for a range of distances and many different...	Rebuttal 1: Rebuttal: Thank you for the supportive review. We have addressed your question about the impact of our work on relevant fields in our general response because other reviewers raised similar questions. Below we have clarified the word swap procedure and the method used to select the swapped word: Word s...	Summary: The authors provide an experimental understanding of LLMs that how LLMs have inherent Integration windows to take account of the global meaning of the given sentence, by developing a novel method called “word-swap procedure,” which is model agnostic. The authors investigate the behavior of the Integration wind...	Rebuttal 1: Rebuttal: To address your comments, we will test additional, larger models including LongT5. LongT5 is an encoder-decoder architecture that can accept very long sequences and has been trained on multiple tasks, addressing each of the issues that you raised in your review. The T5 model has also shown strong ...	Rebuttal 1: Rebuttal: We were pleased the reviewers overall felt that our work addressed a timely question, that the methods were well-motivated and described, and that the results were interesting and robust across multiple experiments. We thank the reviewers for their constructive critiques. Below, we address comment...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper describes a novel method for measuring and characterizing integration behavior in large language models. This method is used first to look just at temporal integration windows—i.e. how do inputs at different lags affect model activations at a specific time—in GPT-2, revealing a transition from expon...	Rebuttal 1: Rebuttal: Thank you for the supportive review. We chose to address your questions about the impact of this work and the generalization of our paradigm to supra-sentential scales in our general response since similar questions were raised by other reviewers. We now plot integration windows on a log-log s...	null	null	null	null	null	null
HeadSculpt: Crafting 3D Head Avatars with Text	Accept (poster)	Summary: This work proposes a text-to-avatar creation pipeline, building upon dreamfusion and magic3d. To alleviate the geometric ambiguity, the authors replace the vanilla stable diffusion with a landmark-conditioned diffusion model finetuned with controlnet. They also use textual inversion to obtain specific token fo...	Rebuttal 1: Rebuttal: # 5. Response to reviewer `#Gcqm` We thank reviewer `#Gcqm` for agreeing with our motivation and acknowledging the results as "impressive and compelling". Below we respond to the doubts put forward by the reviewer - **we regularly refer to the general response above and the provided one-page pdf ...	Summary: Existing text-driven 3D generative models could have many problems, such as geometric artifacts (e.g., Janus problem), and visual inconsistency. This work focuses on 3D head avatar generation, which utilizes FLAME model to incorporate human geometric priors into generation, hence resolving those problems in ...	Rebuttal 1: Rebuttal: # 4. Response to reviewer `#thuE` We are grateful the reviewer `#thuE` took the time to thoroughly review our work and found that our method "effectively addresses the limitations encountered in current text-guided 3D generation approaches". We provide our responses regarding the raised concerns a...	Summary: The paper proposes a novel method for performing text-to-3D generation for specifically human (or humanoid) heads. These generated heads can be further edited and refined using more fine-grained detailed text prompts while still preserving the identity of the generated asset. This is accomplished with two main...	Rebuttal 1: Rebuttal: # 3. Response to reviewer `#ztub` We really appreciate the feedback provided by reviewer `#ztub`. Thanks for finding our presentation clear and our contributions novel and regarding our method as a fundamental basis that future work could "build upon". Below we address the proposed concerns about...	Summary: Authors proposed a solution for generating human heads based on text description. The technology is based on popularised concepts of control signal for pre-trained diffusion model and static 3D model to guarantee consistent head. Moreover, authors presents the concept of the “back” of the head with a textual i...	Rebuttal 1: Rebuttal: # 2. Response to reviewer `#ooKH` We thank reviewer `#ooKH` for recognizing that our solution is "interesting" and our qualitative and quantitative evaluation "shows superiority". We address the proposed questions as follows - **we regularly refer to the general response above and the provided one...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewers' thoughtful feedback recognizing the presentation, novelty, and performance of our method. The reviewers suggested additional experiments and illustrations to highlight strengths, clarify limitations, and illustrate future directions. We are pleased to have condu...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Cascading Contextual Assortment Bandits	Accept (poster)	Summary: This paper studies the contextual cascading assortment bandit problem, and proposes low regret algorithms for this problem. Strengths: 1. This paper studies a novel problem by combining ideas from assortment bandits and cascading bandits. 2. The paper introduces some new algorithmic ideas. Weaknesses: It s...	Rebuttal 1: Rebuttal: Thank you very much for your time to review our paper and for your valuable feedback. We truly hope that we can resolve any doubts and misunderstandings of our result if there are any. Here are our responses to each comment and question. Structural Properties of Optimal Cascading Assortment? ...	Summary: This paper studies a new contextual combinatorial multi-armed bandit model, which generalizes the contextual cascading bandits and assortment bandits. For the offline problem when item parameters are known, the authors propose a 0.5-approximate solution. For the online problem where parameters are not known a...	Rebuttal 1: Rebuttal: Thank you very much for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question: Typo in Algorithm UCB-CCA+ * Yes, it is a typo. Thank you very much for catching it. It should be corrected to $\theta_t$. **Combinatorial Optimization...	Summary: This paper studies the Cascading Contextual MNL bandits problem. Two effective algorithms UCB-CCA and UCB-CCA+ are proposed. Compared to existing cascading bandits and MNL bandits, the regrets of the two algorithms have some better dependence on the length of cascades and \kappa. Numerical simulations demonstr...	Rebuttal 1: Rebuttal: Thank you very much for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question: improving dependence on $d$? - We believe that you are referring to $\tilde{\mathcal{O}}(\sqrt{dT})$ regret in Theorem 4 of [19]. Then, please note that...	Summary: This paper studies a new combinatorial bandit problem that generalizes the existing cascading and assortment bandits. The authors first propose a UCB-based algorithm, UCB-CCA, that achieves a tighter regret bound than existing bounds for cascading contextual bandits by eliminating the dependence on cascade len...	Rebuttal 1: Rebuttal: Thank you very much for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question: Comparison with [A] * Thank you for introducing [A]. We are more than happy to compare our work with [A]. * First of all, we would like to point out th...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude for your overall positive feedback and recognizing the significance of our contributions. We introduce a novel combinatorial bandit model, the cascading contextual assortment bandit, which generalizes two of the prominent existing combinatorial band...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces the cascading contextual assortment bandit problem and provides a UCB type algorithm. This problem is motivated by online content recommendation systems. They develop a UCB algorithm that is applicable to this problem setting, and prove that their algorithm improves upon existing regret b...	Rebuttal 1: Rebuttal: Thank you very much for your time to review our paper and for your valuable feedback. Here are our responses to each comment and question: Numerical Evaluations - First of all, since our problem setting and proposed model, the cascading assortment bandit, are novel, there are not existing me...	null	null	null	null	null	null
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations	Accept (spotlight)	Summary: This work proposes a method for adapting stopping tests of randomized experiments in heterogeneous populations. Specifically, the authors motivate the problem, namely why heterogeneous treatment effects lead to late stopping of randomized experiments, for instance when a minority group is harmed. They then pro...	Rebuttal 1: Rebuttal: Thank you for your detailed review: we appreciate your positive feedback and constructive suggestions. We address each of your comments in further detail below. **R6: _Overall, the work makes various idealised assumptions in both the theoretical results, and in the (synthetic) experiments consid...	Summary: The paper focuses on stopping tests for harm in clinical trials or A/B testing where heterogeneous treatment effect is involved. The proposed method contains two phases: First, the population harmed by the trials is identified via conditional treatment effect estimation. Second, weighted versions of widely-use...	Rebuttal 1: Rebuttal: Thank you for your detailed review: we appreciate your positive feedback and constructive suggestions. We address each of your comments in further detail below. **R5: _In lines 191 to 193, $(x_i,y_i)$ is excluded from training set when estimating $\tau(x_i)$ Does it mean that the CATE estimation...	Summary: The authors propose an approach to early stop clinical trials in order to prevent subgroup level harms. Their approach involves first estimation of sequential estimation of a an individualized treatment effect using machine learning methods followed by reweighting the test statistic at each iteration with the ...	Rebuttal 1: Rebuttal: Thank you for your detailed review: we appreciate your positive feedback and constructive suggestions. We discuss each of your comments in detail below, and incorporate our responses in the revised paper. **R4: _The authors propose to use "bootstrap" to compute this quantity. This seems practica...	Summary: The authors propose CLASH, a method for early stopping in RCTs and A/B tests on heterogeneous populations. They some theoretical results that show that CLASH works, and provide simulations and one real experiment. Strengths: - Clear writing - Good motivation - Excellent exposition of theoretical results for r...	Rebuttal 1: Rebuttal: Thank you for your detailed review: we greatly appreciate your insightful feedback. We address each of your constructive suggestions below, which we believe have further strengthened the paper. **R3: _The biggest issue with this paper has to do with the experiments. I'll happily raise my score ...	Rebuttal 1: Rebuttal: We thank the reviewers for their positive comments and constructive feedback. In this global response, we focus on two themes raised by multiple reviewers: (1) additional experiments, and (2) the decision to stop only on the harmed group. We separately provide responses to individual reviewers. #...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a method to decide when to stop a clinical trial or an A/B study in cases where the treatment/intervention only causes harm to a minority group of participants, in which traditional methods can fail to detect the need for stopping an experiment. The paper includes a thorough theoretical ana...	Rebuttal 1: Rebuttal: Many thanks for your detailed review and positive feedback: we’re glad you enjoyed reading our paper and appreciate your encouragement. We address each of your constructive suggestions below, which we believe have further strengthened the paper. **R2: _The simulations could include a wider range...	Summary: A two-stage approach CLASH was proposed to determine the early stopping time of a randomized experiment when the treatment is harmful to a subset of the population. The indicators of the harmed groups are estimated by causal machine learning methods in stage 1, and the early stopping time is determined using t...	Rebuttal 1: Rebuttal: Thank you for your detailed review and positive feedback: we’re glad you found our work useful and impactful. We appreciate your constructive comments, which we address below and in our revised paper. **R1: _The simulation settings seem relatively simple and difficult to interpret: only several...	null	null	null	null
PoET: A generative model of protein families as sequences-of-sequences	Accept (poster)	Summary: This paper proposed an autoregressive generation pre-trained model of protein families. The models are trained over the sequences-of-sequences being organized by a set of specific protein sequences. It utilized a shared in-sequence position encoder to capture conditioning among sequences in an order independen...	Rebuttal 1: Rebuttal: We thank the reviewer for their consideration of our paper, and for their questions and comments. We note that there seems to be some misunderstanding regarding certain aspects of our method and experiments, which we hope to address below in addition to the reviewer’s questions. We will also clari...	Summary: This paper proposes an autoregressive generative model (protein evolutionary transformer, PoET) of whole protein families. Current generative protein language models are either difficult to direct to produce a protein from a specific family of interest or must be trained on a large multiple sequence alignment ...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive assessment of our work and for their constructive comments and questions. These are addressed below. - As the title stated, the core of this paper is a generative model of protein families, but they only do a downstream task, ie, fitness prediction. In thi...	Summary: The authors of the paper present Protein Evolutionary Transformer (PoET), an autoregressive transformer-based model that is able to generate sets of related protein sequences, enabled by the proposed novel Transformer layer. This sequences-of-sequences generating method benefits from transfer learning, is able...	Rebuttal 1: Rebuttal: We thank the reviewer for their overwhelmingly positive response and comments. In answer to the reviewer’s questions: - As mentioned in "Weaknesses": Limitations section missing. We sought to discuss limitations throughout the paper as appropriate, but we will revise the paper to include an expl...	Summary: Current generative protein language models focus on generating individual protein sequences and are not specifically trained to generate sequences for an entire protein family. The paper introduces a new autoregressive generative model that tackles this limitation by generating Multiple Sequence Alignments (MS...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive comments and excitement about the potential impact and performance of our method. In particular, we appreciate that the reviewer feels that our “...paper makes a big contribution to the protein design community considering the amount of work in the paper.” ...	Rebuttal 1: Rebuttal: Dear Reviewers, We thank all of you again for your positive comments and constructive suggestions and questions. Here, we present additional analyses addressing some common themes in the reviews. This discussion is intended to be viewed with the figures in the attached review supplement PDF. We w...	NeurIPS_2023_submissions_huggingface	2,023	Summary: - This paper introduces PoET, a novel autoregressive transformer architecture that learns a distribution over protein families. - More specifically, PoET takes as input a concatenated set of homologous sequences, for example retrieved with a MSA. The model architecture consists of a stack of several TieredTra...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive comments and suggestions. In the manuscript, we strove to provide a comprehensive analysis of the prompt engineering aspects, which we hope will be relevant for using PoET and also for future work in retrieval-/homologue-augmented protein ML models. We’re g...	null	null	null	null	null	null
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents	Accept (poster)	Summary: This paper proposes a method named MolRL-MGPT for drug molecular generation. Concretely, GPT-based agents are used to iteratively generate candidate compounds, and a special reward signal is adpoted to encourage agents to explore in diverse directions. The experiments on GuacaMol benchmark show the superiority...	Rebuttal 1: Rebuttal: Thanks for your valuable review comments! We address your main concerns below: *Q1 (in weaknesses)*: It is not appropriate to call the proposed method a “RL-based” method though the score function can be treated as the “reward”. RL follows Markov decision process and aims to maximize the accu...	Summary: This paper creates a multi-agent reinforcement learning approach to promote diversity in the search space of small molecules during molecular optimization. Because of the complicated nature of early stage drug discovery research, the diversity is useful during early work in the identification and validation o...	Rebuttal 1: Rebuttal: Thanks for your approval and valuable review comments! We address your main concerns below: *Q1 (in weaknesses)*: A minor weakness of this method is that the performance of the code is somewhat slow, which is somewhat understandable given the slow individual query of the GPT models and the ch...	Summary: This paper proposes a novel multi-agent reinforcement learning algorithm with agents parameterized with a pre-trained GPT architecture for de novo drug design. The authors propose a modified objective function with an intrinsic reward inspired bonus to encourage diversity between agents and also propose to us...	Rebuttal 1: Rebuttal: Thanks for your valuable review comments! We address your main concerns below, and we sincerely hope that you will reconsider and upgrade your rating: *Q1 (diversity comparison, in experiments 1, 2)* A1: To further demonstrate the advantages in diversity of our approach, we have added se...	null	null	Rebuttal 1: Rebuttal: We would like to express our sincere appreciation to all the reviewers for your valuable feedback on our paper, and we have responded to all your questions (in the corresponding rebuttal sections). We also add some supplementary experimental results in the pdf, including more baselines on the ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Task-aware Distributed Source Coding under Dynamic Bandwidth	Accept (poster)	Summary: This paper proposed a task-aware distributed source coding framework called NDPCA (Neural Distributed Principal Component Analysis). This framework aimed to solve the problem of efficient compression of correlated data in multi-sensor networks. In section 2 and 3, the authors provided a formulation of the prob...	Rebuttal 1: Rebuttal: Thank you for the comments. Weakness: 1. The key focus of our study is to harmonically combine linear DPCA modules and neural networks, leveraging their different purposes. The linear DPCA module is designated to measure the importance of sources with singular values, and the neural networks...	Summary: This paper studies compression in a distributed computing setting, named neural distributed principal component analysis(NDPCA). The proposed NDPCA can adapt to available bandwidth and flexibly allocates bandwidth to multiple sources according to their contribution to the final task. Experiments demonstrate th...	Rebuttal 1: Rebuttal: Thank you for the feedback. Yes, our NDPCA can be interpreted as a resource allocation algorithm, but the key focus of our study is to harmonically combine linear DPCA modules and neural networks, leveraging their different purposes. The linear DPCA module measures the importance of sources u...	Summary: This work targets compressing the correlated data to be communicated in a multi-sensor network. The multi-sensor network pipeline is defined as the following steps: (1) each edge sensor compresses the data and transmits it to a central node, and (2) the central node decompresses the data and passes it to a mac...	Rebuttal 1: Rebuttal: Weakness: 1. In our study, we consider a scenario where satellites send data to a mission control center on Earth, using independent encoders due to their distance apart, with the control center having a joint decoder. Similarly, as the IoT era nears, factories will use sensors in distant location...	Summary: This paper proposes a solution for multi-view machine learning with distributed computing and limited bandwidth. Different views of data are encoded and then compressed to be transmitted to the decoder for learning tasks. It assigns higher bandwidth for data with better quality to make a tradeoff between diffe...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful feedback. Weakness: Regarding the use of CIFAR10, we acknowledge its age but included it as a toy example for quick iteration and sanity checks due to its manageable size. Thus, we can try different methods to improve uncorrelatedness and linear compre...	Rebuttal 1: Rebuttal: This is the attached file to show the latest results from our additional experiments with 4 data sources. Pdf: /pdf/284df6158c9603435b28d55dfa962e9747d80c8d.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper presents the neural distributed principal component analysis (NDPCA) method that compresses features from multiple sensor sources with a given total bandwidth limit. NDPCA carries the following novelties. First, it is task-aware. The algorithm trains the compression networks by minimizing the final ...	Rebuttal 1: Rebuttal: We express our gratitude to the reviewer for their valuable feedback, and we will address each point in detail. Weakness: 1-1. Our study was inspired by the scenario presented in the introduction, involving multiple distant satellites transmitting data to an Earth-based mission control center. E...	Summary: This paper proposes a distributed task-specific compression method called NDPCA, composed of both a neural network autoencoder and a linear PCA reconstruction. Given multiple sources of data, NDPCA first compresses the information separately using different independent neural network encoders. Next, it applies...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our paper. Weakness: Both our DPCA and NDPCA frameworks have been designed to accommodate multiple sources effectively. In particular, our DPCA linear formulation demonstrates its effectiveness with multiple sources, as outlined in lines 137-139 of the orig...	null	null	null	null
Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives	Accept (poster)	Summary: This paper considers the problem of instructing goal-conditioned RL agents to follow specifications expressed in Linear Temporal Logic (LTL) formulae. The proposed method works as follows. First, construct a Buchi automaton from the LTL specification, which is then converted to a directed graph representation....	Rebuttal 1: Rebuttal: We appreciate your insightful feedback and constructive comments! We present our response to each of your concerns and questions below. R1. Comparison with the baselines: "The baselines should be alternative methods for computing a high-level plan, with the same assumption that the low-level ...	Summary: The paper proposes a new technique for multi-task RL when the tasks are specified using a high-level language (LTL in this case). The approach involves identifying a set of skills corresponding to a set of reachability and safety objectives and training policies for them. While training these policies (which a...	Rebuttal 1: Rebuttal: We appreciate your insightful feedback and constructive comments! We present our response to each of your concerns and questions below. R1. Single path problem: "The LTL task is eventually reduced to following a single path in the automaton graph. But one might have to use different high-leve...	Summary: This paper presents a method to transfer learned or planned goal-directed skills in domains to novel tasks represented by linear temporal logic within the same domain. The key idea of this paper is to train goal conditioned policies to achieve (and avoid) Boolean goals, and to compose them temporally to achiev...	Rebuttal 1: Rebuttal: We appreciate your insightful feedback and constructive comments! R1. Positioning in context of prior work We appreciate your suggestion to discuss the related work [1,2,3]. In particular, we will credit [1] for inspiring our approach to managing the logical composition of value functions. ...	Summary: This paper considers the problem of learning to solve a linear temporal logic (LTL) tasks in a Markov Decision Process (MDP). Given a fixed Markov Decision Process, this is done by: 1. Pre-training a goal-conditioned policy to solve a uniform sampling of reach-avoid tasks, where goals correspond to atomic ...	Rebuttal 1: Rebuttal: We appreciate your insightful feedback and constructive comments! We present our response to each of your concerns and questions below. **R1. The proposed approach is ultimately heuristic and is susceptible to being "catastrophically myopic." In particular, the greedy sequence of shortest path pr...	Rebuttal 1: Rebuttal: We greatly appreciate the valuable feedback and suggestions provided by the reviewers! We will begin by addressing the primary concern raised by the majority of the reviewers in the global rebuttal. We will address the concerns of each reviewer in the individual review responses. ### **How do we ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment	Accept (poster)	Summary: The paper introduces CaST, a novel framework designed to address challenges in Spatio-Temporal Graph (STG) forecasting. CaST tackles issues related to temporal out-of-distribution and dynamic spatial causation by leveraging a causal lens and employing techniques such as back-door adjustment and front-door adju...	Rebuttal 1: Rebuttal: We are grateful for your thorough evaluation of our paper and the feedback you provided. Thank you for recognizing the novelty in the design of our framework and the comprehensiveness of our analysis. [Weaknesses] wrt complex design decisions. Thank you for your feedback. In fact, our ma...	Summary: The paper presents a novel framework, CaST, designed to address the challenges of temporal Out-Of-Distribution (OOD) issues and modeling the underlying dynamic spatial causation in Spatio-Temporal Graph (STG) forecasting. The authors construct a Structural Causal Model (SCM) to uncover the causal mechanisms of...	Rebuttal 1: Rebuttal: Thank you for your thorough review and constructive feedback on our paper. We appreciate the time you've taken to provide valuable insights. Please find our responses below. [Weaknesses] wrt the novelty of temporal disentanglement. Thank you for your feedback. We acknowledge that the tem...	Summary: The paper introduces a model for out-of-distribution prediction in spatio-temporal data. The confounding effects are decoupled in spatial and temporal contexts, and treated with frontdoor and backdoor adjustments, respectively. Empirical evidence shows improved performance. Strengths: - The tackled problem is...	Rebuttal 1: Rebuttal: Thank you for your detailed review of our submission. We appreciate the insights and constructive feedback you have provided. We are grateful for your acknowledgment of the relevance, novelty, and performance of our work. Below, we address your comments in a point-by-point manner. *[Weaknesses]...	Summary: This paper studies the problem of Spatio-Temporal Graph forecasting under the lens of causal treatments. The authors proposed a framework consisting of two major components to mitigate the commonly seen limitations for spatial-temporal GNNs, i.e., 1) the backdoor environment disentanglement block, which models...	Rebuttal 1: Rebuttal: We would like to sincerely thank you for the time and effort put into reviewing our submission. Your feedback is invaluable and helps enhance the quality of our paper. We appreciate your acknowledgment of the originality, quality, and clarity of our work. Below, we respond to your comments point-b...	Rebuttal 1: Rebuttal: Dear Reviewers, We would like to express our sincere gratitude to all the reviewers for their thorough evaluation and constructive feedback on our manuscript. Your insights have been invaluable in enhancing the quality and clarity of our work. We have made several revisions accordingly to address...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent	Accept (poster)	Summary: This paper focuses on the theoretical understanding and algorithmic contributions of Gaussian-Stein Variational Gradient Descent (Gaussian-SVGD) for Gaussian Variational Inference (GVI). The paper discusses the dynamics of Gaussian-SVGD and provides convergence rates in both mean-field and finite-particle sett...	Rebuttal 1: Rebuttal: Thanks for the detailed review and incisive questions. 1.About why we should use Gaussian-SVGD; "If the target is non-Gaussian, why would you use Gaussian-SVGD over standard SVGD?": We interpret the question more as when we should use Gaussian-SVGD rather than why. It is important to...	Summary: This paper delves into an examination of Gaussian-SVGD through the analysis of mean-field PDE and discrete particle systems. It offers evidence for finite particle convergence and supports these findings with empirical validations. Strengths: Through clear modeling, the article provides a comprehensive analy...	Rebuttal 1: Rebuttal: 1.Linear kernels are rarely used to implement SVGD. It would be more meaningful if the significance of this linear system could be demonstrated. Signficance of GVI First, we should emphasize again that Gaussian-SVGD is NOT SVGD with a linear kernel when the target is not Gaussian. We shou...	Summary: This paper studies the Stein variational gradient descent and its variants with a bilinear kernel on the space of Gaussian measures. The authors prove the rate of convergence of the dynamics, proposed finite particle algorithms and proved a uniform-in-time propagation of chaos, and finally prove the convergen...	Rebuttal 1: Rebuttal: Thanks for the detailed review and incisive questions. 1.Intuitive explanation of the convergence rate: The intuition could be obtained from the looking at corresponding ODE arising in the analysis. The mean-field dynamics of SVGD with bilinear kernel (for $K_1, K_2$ or $K_3$) takes form of...	Summary: This work performs a theoretical investigation of Gaussian SVGD, a special case of SVGD restricted to the submanifold of Gaussian densities by means of a bilinear kernel. The authors characterize the mean-field dynamics of Gaussian SVGD, with a particular focus on Gaussian targets and obtain finite-particle gu...	Rebuttal 1: Rebuttal: 1.*Could you obtain discrete-time finite-particle rates* Yes. We have the following quantitative result: Theorem: For a centered Gaussian target, suppose the SVGD particle system with $K_{1}$ or $K_{2}$ is initialized by $\bigl(\boldsymbol{x}\_{i}^{(0)}\bigr)\_{i=1}^{N}$ such that $\...	Rebuttal 1: Rebuttal: General Clarifications ================= 1.Quantitative results for finite particle, discrete-time setting We have an updated version of Theorem 3.9 (discrete-time, finite-particle) with the following quantitative convergence rates: Theorem: For a centered Gaussian target, suppose...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work studies the behavior of Gaussian-SVGD (SVGD projected to the space of Gaussian distributions, using bilinear kernels) and its variants. The authors studied the behavior of the mean-field PDEs and established for Gaussian targets finite-particle convergence results which are significantly better than...	Rebuttal 1: Rebuttal: Thanks for the detailed review and incisive questions. 1. Restrictions to Gaussian families and affine kernels: - For a large class of Bayesian inference problems, thanks to the Bernstein von Mises theorem, the posterior (target) distribution is approximately Gaussian in the limit of large...	null	null	null	null	null	null
Tight Bounds for Machine Unlearning via Differential Privacy	Reject	Summary: This paper studies the machine unlearning problem from the perspective of differential privacy. Specifically, the authors propose to use differentially private models directly so that unlearning update is not necessary (or unlearning is an identity map), and the motivation is to make the unlearning procedure i...	Rebuttal 1: Rebuttal: We appreciate the reviewer’s reading of our paper, and thank them for their comments; however, we believe that their assessment of our work hinges on a few misunderstandings, that we hope to clarify below: * Regarding the situation where the number of deletion requests exceeds the deletion capacit...	Summary: This paper studies connections between machine unlearning and differential privacy (DP). In machine unlearning, the goal is to remove up to, say, $m$ of the examples from a dataset of size $n$, in such a way that the produced model is close (in some form of statistical or computational distance) to the model t...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments, and are grateful for their pointers to the literature. We agree that our literature review was lacking, and apologize for that: we will make sure to update it (using these pointers, and others) in the final version, to provide a clearer and more accurate p...	Summary: This paper addresses the concept of "machine unlearning" within the framework of differential privacy. The authors provide tight bounds on the maximum number of data points that can be successfully unlearned without significantly impacting the model's accuracy. They also establish the analog of key properties ...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and comments, and address both of their questions together. Our paper does focus on convex and strongly convex losses, as is common in a significant part of the learning and optimization literature; we note that while this assumption on the loss is not always s...	Summary: The paper studies approximate unlearning with procedures which do not store any side information (and satisfy differential privacy) in convex learning problems and establishes tight upper and lower bounds. Strengths: 1. Machine unlearning has recently gained much interest owing to privacy regulations. The pap...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their time and valuable feedback; we address below their main question, regarding quantitative improvement upon previous work; and will incorporate these into the final version of our paper. Our improvements regarding the deletion capacity of unlearning algorit...	Rebuttal 1: Rebuttal: We thank the reviewers for their careful reading of our submission, and are grateful for their detailed comments and suggestions. We will address their specific comments in the final version of our work, and respond individually to their questions and concerns below.	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work derives tight bounds for the deletion capacity of machine unlearning algorithms that are differentially private. These bounds are stated in terms of a deletion capacity formulated as a function of data points that can be removed before the estimation risk (an accuracy measure) becomes too large. The ...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their time, comments and positive assessment of our work; we address below their questions, starting with the main one (“Limitation”). Indeed, the reviewer is correct, in that our paper analyzes unlearning algorithms which do not do anything (at deletion reques...	null	null	null	null	null	null
Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction	Accept (poster)	Summary: This paper presents a novel approach called Probability Tree State Abstraction (PTSA) to improve the efficiency of Monte Carlo Tree Search (MCTS) algorithms, which have shown remarkable performance in challenging tasks. The computational complexity of MCTS algorithms is influenced by the size of the search spa...	Rebuttal 1: Rebuttal: Thanks for your uplifting review and valuable feedback. We appreciate your comments and would like to address them below. Questions: 1. We apologize for not providing a clear explanation of the pruning/delete/add actions in the paper. We have provided more detailed explanations of the actio...	Summary: This paper proposed a novel search algorithm, PTSA, to improve the search efficiency of MCTS. Empirical results show that PTSA can be integrated with Sampled MuZero and Gumbel MuZero and can reduce the original branching factor by 10% up to 45%. Strengths: The proposed PTSA algorithm can reduce the branching ...	Rebuttal 1: Rebuttal: Thank you for the detailed review and the explicitly listed concerns. We hope that our answers will help to resolve the concerns. Weaknesses: 1. We appreciate your feedback and acknowledge your concern regarding the limited number of Atari games used in our evaluation. We have conducted more...	Summary: The paper proposes a novel tree state abstraction function (PTSA) for use during MCTS. The primary contributions of the paper are algorithmic and empirical. The key idea involves aggregating paths in the tree if their Q-values (as probabilities) along the path closely match an existing path with the same paren...	Rebuttal 1: Rebuttal: Thanks for your valuable feedback. We address each comment and concern below. Weaknesses: 1. We appreciate your feedback and will provide a more careful description of the implementation details in the final version. We have provided more detailed explanations of the pruning/delete operation...	Summary: To accelerate MCTS, the paper proposed a novel probability tree state abstraction (PTSA) algorithm to improve the search efficiency of MCTS. They define states that are similar by using path transitivity and claim that such a method can have fewer mistakes. According to the results of Atari and Gomoku, the me...	Rebuttal 1: Rebuttal: Thanks for your helpful comments. We hope that our answers will help to resolve your concerns. Weaknesses: 1. For the first problem, during the early stage of training, inaccurate estimation of the V values may prevent state abstraction methods from correctly aggregating states, which is a c...	Rebuttal 1: Rebuttal: ## General Response ## We thank all reviewers for their valuable feedback. We have carefully considered your suggestions and conducted additional experiments (shown in the uploaded PDF) to address your concerns, as outlined below: 1. We have conducted more Atari experiments (Assault, Seaquest, B...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper suggests a method of abstracting the state space explored by a Monte Carlo Tree Search (MCTS) algorithm, in order to reduce the complexity of the search. We can create an abstraction of the state space for MCTS by considering an abstraction over entire paths in the tree - two paths of equal length, ...	Rebuttal 1: Rebuttal: Thank you for your uplifting review and valuable feedback. We appreciate your comments and would like to address them below. Weaknesses: Thanks for your feedback on the paper's readability. We will include more clear and coherent explanations of our method to improve the readability. 1. $Q(...	null	null	null	null	null	null
Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization	Accept (poster)	Summary: This paper provides generalization analysis of federated zeroth order optimization. Strengths: The paper is well-written and addresses a relevant problem. The theoretical results appear correct, although I haven't thoroughly checked them. Weaknesses: Since I'm not super familiar with this direction, I just h...	Rebuttal 1: Rebuttal: Q1: Line 105: what is the intuitive reason for the requirement $b_2\geq d$? A1: Thanks for your constructive comments. The requirement $b_2\geq d$ is adopted by many previous work. For example, as mentioned in the second paragraph of Introduction in [1], “deterministic zeroth-order approa...	Summary: The analysis of Federated Zeroth-Order Optimization is limited now. This work considers the zeroth-order optimization in federated learning and establishes the generalization error bound of FedZO under the Lipschitz continuity and smoothness conditions. Strengths: 1. The analysis of Federated Zeroth-Order Op...	Rebuttal 1: Rebuttal: Q1: ... it is necessary to use experiments to verify the necessity of the zero-order algorithm. A1: Thanks for your constructive comments. As your mentioned, there are some cases where gradient information is expensive to obtain and even unavailable [1], such as federated hyperparameter t...	Summary: This paper presents a detailed analysis of Federated Zeroth-Order Optimization (FedZO) by developing the analysis technique of on-average model stability. The authors establish generalization error bounds for FedZO and refine them using heavy-tailed gradient noise and second-order Taylor expansion. They extend...	Rebuttal 1: Rebuttal: Q1: ... compare with other asynchronous optimization algorithms. A1: Thanks. Following your constructive comments, we have added comparisons with existing error bounds of asynchronous optimization algorithms [1] and [1][2] in Tables 1 and 2, respectively. Limited by the length of Rebuttal...	Summary: This paper studies the theoretical analysis for federated zeroth-order optimization (FedZO). The main contributions of this paper include 1) deriving the generalization bound of synchronous FedZO with different assumptions (bound gradient, heavy tail gradient noise). The main technical is to establish the rela...	Rebuttal 1: Rebuttal: Q1: Perhaps, it will be beneficial if the authors could provide some experimental study to validate the theories. A1: Thanks for your constructive comments. Considering the gap in the generalization analysis of federated zeroth-order optimization algorithm, the major contribution of this ...	Rebuttal 1: Rebuttal: Thanks for the comments of all reviewers. Considering the limitation of character count, we provide two figures and three tables in "global response". Figure 1 denotes the structure of the asynchronous FedZO algorithm. Figure 2 denotes some sub-Weibull survival curves with varying tail paramete...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper fills the gap of theoretical guarantee for the Federated zeroth-order optimization (FedZO) algorithm. It provides the initial generalization error bound for FedZO and presents refined generalization and optimization bounds. The structure of the paper is logical and the accompanying theoretical proof...	Rebuttal 1: Rebuttal: Q1: ... not explain what PL stands for ... A1: Thanks for your constructive comments. As we all know, under the non-convex condition, local optimal model isn’t equivalent to global optimal model. Assumption 3 simply requires that the gradient grows faster than a quadratic function as we m...	Summary: This paper studies the generalization and optimization analysis of the Federated zeroth-order optimization (FedZO) algorithm. It develops tailored techniques for the federated setting to establish generalization bounds. One bound relies on Lipschitz continuity, and the second removes this dependency. The optim...	Rebuttal 1: Rebuttal: Q1: ... introduced a new algorithm and conducted a comparative analysis against FedZO. A1: Thanks for your constructive comments. Our major contribution is exploring the theoretical generalization upper bounds of federated zeroth-order optimization algorithm which seem to be a gap, especi...	Summary: This paper provides theoretical guarantees for both synchronous and asynchronous FedZO algorithms. It first establishes a generalization error bound of FedZO under conventional assumptions. Then, the bounds are further improved by using the second-order Taylor expansion and heavy-tailed gradient noise. The the...	Rebuttal 1: Rebuttal: Q1: ... overlooked Assumptions 3 and 4 of [1] ... A1: Thanks for your constructive comments. In the original FedZO [1], the reason for making Assumptions 3 and 4 is to connect the gradient of local empirical loss and the gradient of global population risk. The gradient of population risk ...	null	null
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games	Accept (poster)	Summary: This paper considers the problem of online learning in time-varying games under different setups. Specifically, the authors consider the case where all the players apply optimistic gradient descent (OGD) algorithm with a certain choice of learning rate. The main results that in the two-player bilinear game set...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their constructive feedback. Below, we stress the key differences between our results and the ones in [60]. Starting from Section 3.1, we indeed build on a number of ingredients from [60], as we carefully acknowledge throughout the paper; this includes the dyna...	Summary: In this work the authors consider no-regret learning in multiagent games where the underlying game varies across different rounds. They study several classes of games and various learning algorithms that the agents can use. Naturally, the results they obtained are parametrized by variation measures of the unde...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. “Some parts of the paper might be a bit hard to follow for non-experts, especially in Section 1.1.” We will make sure to introduce further background in the revised version of the introduction. *“What are the technical challenges to general...	Summary: This paper studies learning dynamics in games that change over time. This is a similar setting to [60], but while [60] focuses on regret guarantees, this paper focuses on iterate convergence to NE. The main result states that for bilinear zero-sum games, running optimistic OGD guarantees that, $$ \sum_{t=1}^T...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. “The main text lacks proofs/proof sketches. So it is impossible to understand the main ideas and techniques, even at a high level, without diving into the full technical proofs in the appendix” We will make sure to provide high-level proof s...	Summary: The paper studies optimistic gradient descent for time-varying games. Authors prove convergence bounds for zero sum games involving the first order variation of approximate nash equilibrium, which can be significantly tigher than variation bounds involving exact nash equilibria and second order bounds in payo...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for their feedback. We will make sure to provide further background in order to make the paper more self-contained in the revised version. We also thank the reviewer for spotting a typo; we will fix it in the revised version. --- Rebuttal Comment 1.1: Comment: Th...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Lovász Principle for Unsupervised Graph Representation Learning	Accept (poster)	Summary: The paper introduces the "Lovasz Principle", an unsupervised or semi-supervised graph representation learning approach inspired by the Lovasz number. The motivation for using the Lovasz Principle is well established, and the authors extensively discuss related work and similar approaches. The experimental setu...	Rebuttal 1: Rebuttal: Weakness 1: Handle vector comparison between "similar" and "different" graphs Response: Given two graph sets $\mathcal{S}_ 1$ and $\mathcal{S}_ 2$, we denote $c_ i$ as the handle vector for $G_ i$, $i=1,2$. We define the following cross-set difference: $\ell_ {c}(\mathcal{S}_ 1, \mathca...	Summary: This paper presents a technique for unsupervised graph-level (and potentially node-level) representation learning on graphs. The main idea behind the proposed method is the concept of the Lovász number, a graph invariant that is related to several graph properties and is computed by solving a min-max optimis...	Rebuttal 1: Rebuttal: We highly appreciate your comprehensive review, insightful comments, and positive assessment. Weakness: Lack of theoretical justification & ambiguity regarding what the model is learning * We assumed that the training data ($N$ graphs) are from the same distribution. Thus, the graphs sh...	Summary: This paper centers on graph-level representation learning, aimed at converting graphs into vectors useful for downstream tasks like graph classification. The authors propose a unique learning principle named the Lovász principle, inspired by the Lovász number in graph theory. The Lovász number, a real number s...	Rebuttal 1: Rebuttal: Question 1: Applications for graph prompt learning Response: We appreciate your suggestion and will cite papers [1,2] in the revised paper. Graph prompt learning involves transferring learning in graphs. Challenges arise when refining a pre-trained Graph Neural Network (GNN) for specific ...	Summary: The authors present a new method for graph representation in supervised, semi-supervised and transfer learning based on the Lovasz theta function [Lov79]. They also incorporate local information using Lovasz subgraph numbers inspired by the work on Lovasz theta kernal [Joh+14]. They also present empirical eval...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for recognizing our contribution. Our responses are as follows. On SLN * The definition of "subgraph Lovasz number" is in [Joh+14, Definition 2]. Given a graph $G = (V, E)$, let $G[S]$ be a subgraph of $G$ induced by a vertex subset $S \in V$. The "subgrap...	Rebuttal 1: Rebuttal: * We thank the area chair and all reviewers for processing our submission. We have provided detailed responses to every reviewer. In the revision, we will fix minor issues pointed out by the reviewers and improve the presentation of this work. We will also add a comprehensive description of the da...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This submission presents a new approach to graph-level representation learning inspired by the Lovász number in graph theory. Specifically, the study proposes using the Lovász principle as a novel framework for unsupervised/semi-supervised graph representation learning. It offers a method to utilize handle vec...	Rebuttal 1: Rebuttal: Concern about the novelty Response: We have to clarify that our method is very different from [1] (Lovasz Convolutional Networks (LCN)). LCN was motivated by the observation that removing certain vertices from a graph doesn't affect the graph's global properties such as the Lovasz num...	null	null	null	null	null	null
The Adversarial Consistency of Surrogate Risks for Binary Classification	Accept (poster)	Summary: This paper proves the necessary and sufficient condition for a loss function to be adversarially consistent. In the previous literature, either adversarial consistency for restricted hypothesis spaces or negative results for adversarial consistency has been known. This paper follows this research line to provi...	Rebuttal 1: Rebuttal: - Thank you for catching those typos. We have corrected them in our paper. - Regarding proposition 2: Yes you are correct that the counter example $\mathbb P_0=\mathbb P_1$ is particularly malicious. We are currently writing a follow-up paper that identifies all the counterexamples to consis...	Summary: The paper provides a sufficient and necessary condition for surrogate loss to be adversarial consistency, and provide $\rho$-margin loss that satisfies the proposed condition so that it can replace 0-1 loss. Strengths: Understanding the consistency of loss in the adversarial setting is a rather ongoing topic ...	Rebuttal 1: Rebuttal: - Somewhat surprisingly, there are surrogate losses very close to the $0$-$1$ loss that are not adversarially consistent. Let $\psi(\alpha)=1/(1+\exp(\alpha))$) be the sigmoid loss and define $\phi_K(\alpha)=\psi(K\alpha)$. Notice that for large $K$, $\phi_K$ is also a close approximation to the ...	Summary: The paper analyzes the consistency of surrogate losses in the case where there is an adversary that perturbs the sample data. Unlike in empirical risk minimization, where many convex surrogate losses have been proven to be consistent, the authors show that no convex surrogate losses are adversarially consisten...	Rebuttal 1: Rebuttal: - We apologize we forgot to define $\tau$ towards the end of the introduction. In the shifted sigmoid loss, the constant $\tau$ is any positive number. The remaining notation in this paragraph seems to be properly defined, but in our revision we will try to clarify this part of the paper furth...	Summary: this paper tackles the problem of consistency in adversarial classification. Consistent losses are losses whose minimization lead to the minimization of the 0/1 loss. Although consistent losses are known for a long time in standard classification, they were not known in the adversarial setting. This paper show...	Rebuttal 1: Rebuttal: Weaknesses: - We will look for ways to clarify Section 4 by adding additional context for the main Propositions. Regarding Proposition 2, the statement is of course crucial for our main theorem, and this example is both simple and illuminating. - Your comments on lines 139, 211: we ha...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Two-Stage Predict+Optimize for MILPs with Unknown Parameters in Constraints	Accept (poster)	Summary: Predict+Optimize is an emerging paradigm that lies in the intersection of classical optimization (particularly mixed integer programming) and machine learning. Specifically, it considers the setting where a parameterized optimization problem: $$ x^{\star}(\theta) = \operatorname*{argmin}_{x} f(x;\theta) \text{...	Rebuttal 1: Rebuttal: We appreciate your positive review of our paper, concrete suggestions and also your questions. We respond to your points below to address your remaining reservations about our work. Hopefully you are more convinced by our contributions. Please let us know if you have any additional questions. ---...	Summary: The paper presents a novel '2-stage' framework for Predict+Optimize with uncertain parameters in the constraints. In the first "stage" a soft commitment is made based on the predictions, and in the second, the commitment is updated based on updated information in such a way that the objective value plus a pena...	Rebuttal 1: Rebuttal: Thank you for your positive review of our work. We also appreciate the points you raised in the weaknesses section. Please see our response below. The paper is also further strengthened now by our additional experiments in the overall response. We hope to further convince you of the merits of our ...	Summary: The authors propose a framework for learning latent variables in optimization problems that appear both in the constraints and objective. In this setting, the user is given features and asked to provide a solution to an optimization problem where the objective and constraints of the optimization problem are pa...	Rebuttal 1: Rebuttal: Thank you for pushing us on running more experiments and comparing with more baseline methods, which strengthens the paper. We believe we have adequately addressed your concerns through the additional experiments presented in the overall response, along with the following discussion. We are happy ...	Summary: The authors develop a two-stage predict-and-optimize approach. There is a recent paper of Hu et al. [9] that extends the predict-and-optimize framework to having unknown parameters in the constraints. The authors argue that the Hu et al. approach, which requires defining both a "correction function" and a "pen...	Rebuttal 1: Rebuttal: Thank you for your comments and detailed review of our work. Below, we address the points you raise in "weaknesses". Significance: We believe there might be a misunderstanding of the main message of our work. Our point is not "everything is better if we do it the way of Mandi and Guns". In fa...	Rebuttal 1: Rebuttal: Thank you for your constructive and in-depth feedback for improving the paper. We are encouraged by the reviewers recognizing that our paper 1) tackles an important problem (reviewers FnYQ, Jj2a) in Predict+Optimize, 2) presents a simple/sensible, elegant and flexible solution (reviewers Pw5Y, Jj2...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Collaborative Score Distillation for Consistent Visual Editing	Accept (poster)	Summary: The paper introduces an approach to achieve consistent visual editing by leveraging a pre-trained pix2pix diffusion model. The authors propose a generalization of the SDS loss (originating from DreamFusion) to a CSD loss, which utilizes Stein variational gradient descent. This new loss function enables the joi...	Rebuttal 1: Rebuttal: Dear reviewer yGH2, We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows. --- [W1] Lack of detailed explanation for reproducibility As for reproducibility, we comprehensively included the details required to implement ou...	Summary: This paper presents a novel method called Collaborative Score Distillation (CSD) for consistent visual synthesis and manipulation. The proposed CSD-Edit utilizes pre-trained text-to-image models and can be competent for panorama image editing, video editing and 3D scene editing tasks, and generate inter-sample...	Rebuttal 1: Rebuttal: Dear reviewer j3eS, We sincerely appreciate your thoughtful comments, efforts, and time to improve our manuscript. We respond to each of your questions and concerns one-by-one in what follows. Please let us know if you have any comments/concerns that we have not addressed up to your satisfaction...	Summary: The paper presents a novel method, Collaborative Score Distillation (CSD), for diffusion models. The authors propose a new approach to score distillation that leverages the inter-sample relationships to generate more consistent and coherent images. The paper also introduces CSD-Edit, an extension of CSD, which...	Rebuttal 1: Rebuttal: Dear reviewer qUzm, We sincerely appreciate your efforts and comments to improve the manuscript. We respond to your comment in what follows. --- [W1] Some of the results which are not entirely satisfactory For your information, we further clarify our experimental results and commit to in...	Summary: This paper presents a novel method for achieving consistent visual synthesis using a diffusion model. Specifically, the authors extend Score Distillation Sampling to accommodate more complex visual modalities, represented as multiple images. They introduce a principled method to jointly optimize these multiple...	Rebuttal 1: Rebuttal: Dear reviewer v8pz, We sincerely appreciate your thoughtful comments, efforts, and time to improve our manuscript. We respond to each of your questions and concerns one-by-one in what follows. Please let us know if you have any comments/concerns that we have not addressed up to your satisfaction...	Rebuttal 1: Rebuttal: Dear reviewers and AC, We sincerely appreciate your valuable time and effort spent reviewing our manuscript. As reviewers highlighted, we believe our paper presents a principled and novel method (v8pz, qUzm) that performs effective visual editing of versatile modalities (v8pz, qUzm, j3es, yGH2),...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Transformer-based Planning for Symbolic Regression	Accept (poster)	Summary: The paper introduces TPSR, a Transformer-based Planning strategy for Symbolic Regression. TPSR incorporates Monte Carlo Tree Search into the transformer decoding process, enabling the integration of non-differentiable feedback such as accuracy and complexity. Experimental results show that TPSR outperforms exi...	Rebuttal 1: Rebuttal: Thank you for the valuable feedback on our work. We appreciate your positive comments on the clarity and potential of our work in symbolic regression. --- > * My main concern is about the novelty of the approach. A very similar idea has been recently investigated in [1] where the authors also co...	Summary: Authors propose a transformer-based planning (using MCTS) strategy to solve symbol regression task. Different from traditional decoding method, the new method is able to integrate non-differentiable feedback into the transformer-based process of equation generation. Experiments demonstrate the significent perf...	Rebuttal 1: Rebuttal: Thank you for the insightful comments and questions on our paper. We appreciate your positive remarks regarding the importance of this study. --- > * Are there experiments to show changes of performances, if we change the selection set of mathematical operators and symbols? > In symbolic regre...	Summary: This paper proposes to incorporate Monte Carlo Tree Search (MCTS) on top of pretrained transformer-based SR models to guide equation sequence generation. This is to address the challenges where existing methods purely rely on the pretrained transformer’s output and without accounting for external performance ...	Rebuttal 1: Rebuttal: We appreciate your recognition of our motivation and contribution. The typo that you raised has been corrected and the response to your comments is provided below. --- > * In Equ. (1), how to select $\beta(s)$? It would be better to show its effect on the performance in the ablation study as wel...	Summary: This submission proposes a neural network-based approach to symbolic regression (SR), namely generating equations as sequences. It leverages the power of pretrained SR transformer models and the MCTS algorithm to tradeoff the fitting accuracy and equation complexity. Experimental results on the SRBench and the...	Rebuttal 1: Rebuttal: Thank you for your valuable review of our submission. We appreciate your positive feedback and would like to address your concerns. Terminology: Single-Instance SR. We acknowledge that the distinction might not be clearly conveyed by the term "single-instance SR" itself. The intention behind ...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for dedicating their time and expertise to review our manuscript. Please refer to the attached PDF where we have included the Tables and Figures referenced in the subsequent responses. We hope our clarifications address your concerns and look forward to further...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposed to combine pretrained Symbolic Regression models with MCTS procedure to improve SR performance without finetuning the pretrained models. Experiments are conducted to demonstrate the improved performance the proposal. Strengths: 1. Proposed a new MCTS based decoding procedure to improve pre...	Rebuttal 1: Rebuttal: Thank you for the comments and thoughtful questions. Please find our answers below. Summary of Pretrained SR Details. We adopted the pretrained SR model backbone from [18], using its embedding module to embed data points, and leveraging Transformers encoder and decoder modules for representat...	null	null	null	null	null	null
Convex-Concave Zero-Sum Markov Stackelberg Games	Accept (poster)	Summary: This paper develops policy gradient methods using stochastic gradient estimates from the trajectories of play for computing in polynomial time Stackelberg equilibria in convex-concave games, most notably including a certain class of reach-avoid problems. The authors also demonstrate through experiments the ben...	Rebuttal 1: Rebuttal: Thank you for your review, and for taking the time to point out several minor issues! Regarding the weaknesses. Although our results are an extension of the convergence guarantees provided by Goktas et al. [1] to a setting with a stochastic first-order oracle, we view our main contribution ...	Summary: This paper considers a convex-concave min-max Stackelberg game and proposes algorithm which converges to the Stackelberg equilibrium. The paper also proposes a policy-gradient based mechanism which also converges to the Stackelberg equilibrium for the Markov game. Strengths: Stackelberg game is an important ...	Rebuttal 1: Rebuttal: Thank you for your review! 1) Our model is not captured or covered by [A1], because in [A1], the action space of the followers does not depend on the leader’s action. Due to space limitations, we were unable to include a discussion of bi-level optimization more broadly; however, we can use the a...	Summary: The authors propose a policy gradient method to solve the zero-sum stochastic Stackelberg game from noisy gradient estimates computed from observed trajectories of play. When the games are convex-concave, the authors prove that the proposed algorithms converge to Stackelberg equilibrium in polynomial time. St...	Rebuttal 1: Rebuttal: Thank you for your review! Regarding the weaknesses. We did include a proof of reach-avoid games being an instance of convex-concave zero-sum stochastic Stackelberg games in Appendix C (see Theorem C.1. and the ensuing proof). However, it seems like we failed to add a forward reference to th...	Summary: The paper considers the setting of convex-concave zero-sum stochastic Stackelberg games. In these games, there are two players, a leader and a follower. The leader's strategies constrain the feasible strategy set of the follower. First, the leader commits to a certain strategy, and then, the follower best-resp...	Rebuttal 1: Rebuttal: Thank you for your review! Regarding the weaknesses. Beyond convex-concave domains, convergence to a Stackelberg equilibrium unfortunately becomes NP-hard [1]. As global convergence guarantees to (recursive) Stackelberg equilibrium have not been obtained for more general zero-sum (stochastic...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their time! Summary of our contributions: We present polynomial-time first-order methods to compute Stackelberg equilibrium in convex-concave min-max Stackelberg games, assuming access to only a first-order gradient oracle (Theorem 3.1). We then in...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Graph Contrastive Learning with Stable and Scalable Spectral Encoding	Accept (poster)	Summary: The paper proposes a method for Graph Contrastive Learning (GCL) by contrasting the spatial and spectral views ($Sp^2GCL$). The spatial view is obtained using a message passing GNN. For the spectral view the authors propose an equivariant model called EigenMLP. EigenMLP precomputes the k smallest eigenvalues a...	Rebuttal 1: Rebuttal: Thanks for your positive comments! > Q1: How to obtain multiple positive views? In the absence of multiple views the learning may be limited to fixed representations and may not scale well for larger models. A1: Constructing multiple positive views is important for contrastive learning. To a...	Summary: The authors present a novel approach called Sp2GCL that combines spatial and spectral views of graphs using EigenMLP, an informative and stable spectral encoder. The proposed method shows promising results in learning effective graph representations and outperforms other spectral-based methods in terms of both...	Rebuttal 1: Rebuttal: Thanks for your helpful suggestions! > Q1: The contribution of the article is considered limited as the traditional Graph Neural Network (like GCN), which can be understood as spectral filtering in the spectral domain, is similar to the EnigenMLP proposed in this work. A1: EigenMLP is fundam...	Summary: In this paper, the authors propose eigenMLP,an informative, stable, and scalable spectral encoder, which is invariant to the rotation and reflection transformations on eigenvectors and robust against perturbations. Based on eigenMLP, spatial-spectral contrastive framework is proposed to capture the consistenc...	Rebuttal 1: Rebuttal: Thanks for your positive comments! > Q1: I think the eigenMLP is the main contribution but I don't know why the authors try to highlight that the spatial-spectral contrastive framework is a contribution. A1: We agree that the main contribution of our paper is the design of EigenMLP, which is...	Summary: This paper proposes a graph contrastive learning model with spatial and spectral augmentations, with a novel spectral encoder EigenMLP that could address the stability issue from eigen-decomposition. To exploit the strength of spatial and spectral domains, SP2GCL deploys two augmentation views for the contrast...	Rebuttal 1: Rebuttal: Thanks for your valuable suggestions! > Q1: As the authors emphasize the stability of their method, more theoretical and empirical analysis are expected to validate it. A1: Here are the theoretical and empirical analyses: 1. Theoretical analysis: In Section 5.1, we theoretically analyze ...	Rebuttal 1: Rebuttal: We extend our gratitude to all the reviewers for their valuable feedback and insightful suggestions. We have diligently addressed the majority of the questions and suggestions raised during the official review process, and have provided comprehensive responses to individual reviewers in the corres...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Model Sparsity Can Simplify Machine Unlearning	Accept (spotlight)	Summary: This paper considers the benefit of leveraging sparsity to improve standard unlearning techniques. They empirically verify that across a wide range of datasets and architectures. the sparsity benefits unlearning. Strengths: 1. The recap of this paper on the unlearning literature and metrics is very thorough ...	Rebuttal 1: Rebuttal: We greatly appreciate your insightful comments that precisely recognize the strengths of our work. We are particularly excited about citing the referenced paper that establishes a connection between model pruning and generalization. Below, we offer our detailed responses to your comments, categori...	Summary: The authors propose to consider network sparsification as a way to improve machine unlearning (MU), the task of unlearning evidence of a given set of training samples from a neural network. In particular, they consider LTH based pruning schemes as well as a regularized training loss to complement standard MU a...	Rebuttal 1: Rebuttal: Thank you very much for the very insightful comment. Below, we provide our point-to-point responses to the comments, denoted by [W] for weaknesses and [Q] for questions. W1: The overall idea of combining sparsification with MU is not necessarily innovative. Response to W1: We are sorry t...	Summary: This paper studies the machine unlearning problem from the perspective of model sparsification. Specifically, the paper proposes two types of model sparsification methods: data-independent (e.g., OMP) and data-dependent (e.g., sparsity-aware unlearning). An extensive set of simulations are shown in the paper t...	Rebuttal 1: Rebuttal: We appreciate Reviewer hHiz for providing a detailed summary of our strengths. Below, we present our detailed responses to the comments, indicating [W] for weaknesses and [Q] for questions. W1: How to decide the level of model sparsity? And how to decide the trade-off coefficient $\ga...	Summary: The paper proposes that model sparsity leads to models that are easier to "unlearn from". The authors discuss in depth the technical measures that are and that should be used to evaluate various methods of unlearning, and suggest and demonstrate that sparsity is an effective tool in boosting these measures acr...	Rebuttal 1: Rebuttal: We thank Reviewer MsfJ for acknowledging the contributions, soundness, and presentation quality of our paper. And greatly appreciate Reviewer MsfJ for proposing these insightful questions. Below, we provide our responses to the comments, denoted by [W] for weaknesses and [Q] for questions....	Rebuttal 1: Rebuttal: Dear Reviewers, ACs, and PCs: We are glad to receive valuable and constructive comments from all the reviewers. We have made a substantial effort to clarify reviewers' doubts and enrich our experiments in the rebuttal phase. In our responses, Tab. Rxx or Fig. Rxx refers to the new Reb...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Described Object Detection: Liberating Object Detection with Flexible Expressions	Accept (poster)	Summary: The paper presents a new multi-modal computer vision task, called Described Object Detection, which is a superset of existing OVD and REC tasks. In particular, the DOD task seeks to create models which can detect multiple instances of something in images, from textual descriptions, which could include describi...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedbacks. *Due to the page limit, please refer to the [general response (author rebuttal)](https://openreview.net/forum?id=0hwq2vOHT4&noteId=EtVOyLQxeQ)* and the PDF file there for the description and diagram of annotation process of D3 dataset, and the fi...	Summary: In this paper, the authors propose the definition, dataset, evaluation metrics and benchmark results of a new task - Described Object Detection (DOD). DOD is designed to detect objects aligned with full, presence or absence langauge descriptions. The proposed evaluation metrics have three groups based on full,...	Rebuttal 1: Rebuttal: Thanks for the comments. *Due to the page limit, please refer to the general response (author rebuttal)* for the motivation of DOD task.** ## 1. Is DOD a superset of OVD or not? We want to clarify that DOD is a superset of OVD: The D3 dataset is designed solely for evaluation and does not incl...	Summary: This paper introduces a new Described Object Detection (DOD) task, which extends the existing Open-Vocabulary Object Detection (OVD) and Referring Expression Comprehension (REC) tasks into a more general paradigm. For this new task, the authors build a Description Detection Dataset (D3), and find the troublema...	Rebuttal 1: Rebuttal: ### 1. Analysis of existing methods on DOD may be not fair because this work does not introduce new training dataset. Our approach focuses on evaluating DOD using OVD/REC-trained models and providing insights for transitioning to DOD, emphasizing differences in training tasks and formats. Curren...	Summary: Brief Summary: The paper presents a new task Described Object Detection which extends open vocabulary object detection (OVD) to use phrases. This, in turn, extends referring expression (REC) to include objects not seen in the training data. To this end, the authors introduce a new dataset called D3 building on...	Rebuttal 1: Rebuttal: We thank the review for the positive feedback on the contribution and design on the dataset. as well as the visual and experimental analysis and findings. *Due to the page limit, please refer to the [general response (author rebuttal)](https://openreview.net/forum?id=0hwq2vOHT4&noteId=EtVOyLQxeQ...	Rebuttal 1: Rebuttal: We thank the reviewers (R1: Q5jG, R2: SdJt, R3: EwWL, R4: YHXi, R5: wkHv) for their positive feedbacks, such as the contribution of the dataset (R2, R5), the significance of the target problem (R3, R5), the design of the dataset is throughly considered (R2), the absence description characteristics...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a task called described object detection which involves detecting objects through free form text queries, encompassing referring expression comprehension as well as open vocabulary detection. Strengths: ## Clarity The paper is written quite clearly. ## Originality, Quality and Significance...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. *Due to the page limit, please refer to the [general response (author rebuttal)](https://openreview.net/forum?id=0hwq2vOHT4&noteId=EtVOyLQxeQ)* for the description of annotation process of D3 dataset.** ### 1. Annotation using CLIP is insufficient. DOD do...	null	null	null	null	null	null
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition	Accept (poster)	Summary: The paper studies imbalanced node classification problem and propose a novel perspective to understand graph imbalance via bias-variance decomposition.By leveraging graph data augmentation, the paper develops a regularization technique to approximate the model's variance. The effectiveness of the method is ev...	Rebuttal 1: Rebuttal: We appreciate your comments that the theoretical and experimental contribution of RVGNN is strong for tackling imbalance problems. We address all your concerns below: * Q1**:The related work section could be more detailed. The method in Sec. 4 resembles existing techniques. Discussing differen...	Summary: This paper is majoring on the imbalance problem in graph node classification. The authors confirmed the relationship between model variance and the degree of dataset imbalance by adopting the Bias-Variance Decomposition. Furthermore, they diverted a regularization term for approximating the variance of the mod...	Rebuttal 1: Rebuttal: We sincerely thank you for the comments. We appreciate your comments that the theoretical and experimental contribution of RVGNN is strong for tackling imbalance problems. We address all your concerns below: * Q1**: This paper contains many typos and abuse of the notation that may make the rea...	Summary: This paper focuses on semi-supervised imbalanced node classification tasks. Specifically, the authors first establish a theoretical result that connects the imbalance ratio with the model variance and then propose a new regularization term related to the variance based on the graph augmentation technique. Expe...	Rebuttal 1: Rebuttal: We sincerely thank you for the comments. We appreciate your comments that the theoretical and experimental superiority is strong for our work. We address all your concerns below: * Q1**: The theoretical results rely on strong assumptions, which may be difficult to satisfy in real-world tasks. ...	Summary: This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. It also provides a novel theoretical perspective for addressing the problem of imbalanced node classification in GNNs. Strengths: 1 The article is well written an...	Rebuttal 1: Rebuttal: We sincerely thank you for the comments. We appreciate your comments that the theoretical and experimental superiority is strong for our model. We address all your concerns below: * Q1**: The use of L to represent the set of labeled nodes is still relatively rare in the definition of graphs, a...	Rebuttal 1: Rebuttal: We thank all the reviewers for their constructive and insightful feedback. - We have thoroughly revised the paper, making sure to correct all the typos and standardize the notation throughout. - We have uploaded our revised Figure 2(Pipeline of RVGNN) here, in this figure, additional elem...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a theory that relates data imbalance to model variance and designs a method to mitigate the bias of class imbalance. For the theory, this paper finds that the variance of each class is proportional to the invert of the number of samples in that class. For the method, this paper uses a reg...	Rebuttal 1: Rebuttal: We sincerely thank you for the comments. We appreciate your comments that our novelty that first connects the model variance and class imbalance problem on the graph. We address all your concerns below: * Q1**: $L_{IR}$ objective is more closely related to variance than contrastive learning. ...	null	null	null	null	null	null
Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing	Accept (poster)	Summary: This paper studies the problem of gauge equivariant convolutional and attentional architectures on meshes and proposes to introduce non-linear activations to enhance the model. The experiments on three models shows the performance of the proposed method. Strengths: S1. The studied problem is important. S2. ...	Rebuttal 1: Rebuttal: We thank you for the detailed feedback and hope to have addressed all of your concerns. > The most important point is the novelty. We respectfully disagree that our work only differs from [16] by adding a non-linear activation. Our work explores non-linear message passing with gauge-equivariance...	Summary: The authors describe a message-passing mesh-based gauge equivariant architecture. The architecture is described as the natural follow-up from previous equivariant architecture. In short, an edge network aggregates information between source, target nodes, and edge features. Then, these "messages" are aggregate...	Rebuttal 1: Rebuttal: Thank you for the detailed comments and positive feedback. > The main weakness of the method is the requirement of a reasonable topology ... the method is relatively slow (or slower) compared to baselines (eg GemCNN). We respectfully argue that meshes can approximate any Riemannian manifold, whic...	Summary: The authors propose a gauge equivariant method for simulating PDEs on the surface of meshes. Different from the convolutional and attentional prior works, the authors use non-linear message passing with gauge equivariant layers. They compare to the prior works in the FAUST shape classification and the simulati...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive and thoughtful review. Please see our response below. >The authors should include a reference to [1], which simulates fluid dynamics with gauge equivariant methods. Thank you for pointing out this paper. It is indeed relevant and we will include it in the fi...	Summary: This paper aims at solving complex partial differential equations on surfaces. Given the fact that most existing work neither incorporate surface geometry nor consider local gauge symmetries of the manifolds, this paper proposes a novel gauge equivariant network, known as Hermes, that can achieve higher perfor...	Rebuttal 1: Rebuttal: We thank the reviewer for the helpful references and hope to have addressed all of your questions. >It is not clear what is the relationship between Hermes and previous methods that use graph networks to perform mesh-based simulation, such as [1]. Could authors elaborate on the differences with t...	Rebuttal 1: Rebuttal: ## Summary of Response We thank the reviewers for the detailed feedback and constructive suggestions and hope that we have addressed all concerns. The reviewers all appreciate the importance of the problem and the clear benefit of combining nonlinear message passing with a gauge-equivariant metho...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work proposes a novel graph neural network architecture for gauge-equivariant learning on meshes. Compared to prior work using convolutional or attentional methods, this paper shows a nonlinear message passing method for their gauge-equivariant graph neural network. A comparison with two baselines, GemCNN...	Rebuttal 1: Rebuttal: We thank the reviewer for the supportive and insightful feedback. > In Line 143 the authors noted the addition of a residual connection to the HermesBlocks ... tested in an ablation study? We performed an ablation study and results are shown in Table 5 of the uploaded page. The impact on model pe...	null	null	null	null	null	null
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent	Accept (poster)	Summary: This paper provides an analysis of the convergence rate of finite-sample Stein Variational Gradient Descent (SVGD) for sub-Gaussian targets with Lipschitz scores. In contrast to previous works such as Liu 2017, Duncan et al. 2019 and Korba et al. 2020, the presented results offer convergence guarantees that ho...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback. We are pleased that the reviewer found our proof strategy reasonable, our contribution crucial, and our exposition comprehensive. We respond to the detailed comments below. ### “the Algorithm 2 outputs …”, e.g. L166 Thank you for pointing that out...	Summary: In this work, the authors present a novel analysis of finite-particle Stein Variational Gradient Descent (SVGD) and derive a unified convergence bound for this algorithm. The convergence bound provides an explicit measure of how close the finite-particle SVGD algorithm gets to its target. To establish this con...	Rebuttal 1: Rebuttal: We thank the reviewer for acknowledging our contributions and for the feedback. We respond to the comment about suitable venues below. ### Optimization journal vs. NeurIPS We firmly believe that NeurIPS is an ideal venue for this work, as the original SVGD algorithm and analysis were published a...	Summary: The authors provide the first convergence guarantee for finite particle Stein Variational Gradient Descent (SVGD). Although I am not an expert on this topic, I believe this problem remained open for a long time, and it should be the first of many finite particle results to come. While $(\log\log n)^{-1/2}$ i...	Rebuttal 1: Rebuttal: Thank you for the positive feedback. We are glad that you found our contribution significant, our work well presented, and our proof concise and clean. We provide responses to the detailed comments below. ### Main conceptual challenge in establishing a finite particle guarantee, and how this work...	Summary: This work studies the non-asymptotic convergence rate of Stein variational gradient descent (SVGD), an algorithm for approximating a target probability distribution with a collection of particles. This work presents a finite-particle convergence rate for SVGD, which provides a measure of how quickly the algori...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback. We are pleased that the reviewer found our technical tools sound, our discussion of assumptions thorough, and also shared our vision for future refinements. Below is our detailed response: ### Assumed familiarity of optimization/probability theory ...	Rebuttal 1: Rebuttal: # Response to questions shared by Reviewer g2wP, igw1, and awG8: ### Source of $n$ dependence, potential avenues for rate improvement, and challenges involved. The unified error bound of Theorem 3 reveals that the dependence on n arises from the tradeoff between the KSD discretization error bound...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null

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