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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
Modeling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network	Accept (poster)	Summary: In this paper, the author tries to build up a visual system like human eyes. In this study, the authors propose a two-stage model that combines trainable motion energy sensing with a recurrent self-attention network to capture the computations in V1-MT, the core structure for motion perception in the biologica...	Rebuttal 1: Rebuttal: We genuinely appreciate the time and effort of the reviewer. We respond to the concerns pointed out by the reviewer as follows. ---- Do not provide much visual evidence to prove their system: We entirely agree that for a topic as intricate as motion perception, dynamic visual demonstration...	Summary: This paper proposes an image-computable model of human motion perception, bridging the gap between biological computation and CV models. The proposed model contains a two-stage approach that combines trainable motion energy sensing with a recurrent self-attention network for adaptive motion integration and sep...	Rebuttal 1: Rebuttal: Thank you for the reviewer's comprehensive comments and for recognizing the value of our work. --- The gap between our two-stage model and SOTA models in the CV field regarding optical flow prediction performance: We recognize that our model's optical flow prediction performance may not ...	Summary: I have read the authors' rebuttal and will maintain my already high rating. This paper proposes a new model of the dorsal pathway (V1->MT) using a two-stage architecture. The first stage uses spatiotemporal filters tuned by supervised learning, while the second stage uses a dynamic connection between motion d...	Rebuttal 1: Rebuttal: Thanks for the reviewer's appreciative comments regarding our work, and I'd like to address the reviewer's concerns as follows. --- On the neural implementation of dynamic graph construction: We admit that the specific neural implementation of the attention mechanism is still unclear. Thi...	Summary: This paper proposes a novel model of motion analysis. It takes inspiration from biological motion processing to try and solve the aperture problem, leveraging a combination of biologically inspired constrained structure with learned parameters. This produces strong partial correlations of motion components in ...	Rebuttal 1: Rebuttal: Thanks for the constructive feedback from the reviewer. ----- Missing reference: We will include the recommended reference: Tsotsos et al., "Attending to visual motion," CVIU 2005, in the introduction and discussion part. Clarification of Figure 1: We understand that Figure 1 seem...	Rebuttal 1: Rebuttal: We provided more comparison results in the attached PDF. Pdf: /pdf/4b618ff20a505b27e2c446bd7b6593600198cc9c.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a new V1-MT model using a normalized Gabor model of V1, followed by a recurrent self-attention stage. It uses dense optic flow as a supervised objective. The authors perform extensive in silico neurophysiology to show the model units qualitatively look like V1 and MT. They also show that th...	Rebuttal 1: Rebuttal: Many thanks for thoroughly reading our research and comprehensive comments. Firstly, we appreciate the reviewer's nitpicks and will make the suggested modifications. --- General Response: - We should clearly state the purpose of our study. From a scientific perspective, we aim to explain a w...	null	null	null	null	null	null
D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts	Accept (poster)	Summary: D2CSG presents a neural architecture for inferring CSG programs that reconstruct complex 3D shapes. The architecture is composed of two branches, a cover branch and a residual branch, which are differenced from one another to form the complete shape. The work is largely an extension of, and a strong improvemen...	Rebuttal 1: Rebuttal: it would be useful to show that the insights found useful in improving CAPRINet to D2CSG could be extended to other architectures / systems. A: The dual complementary idea can be applied to other primitive-based methods for better concavity reconstruction. For example, ExtrudeNet only use...	Summary: The paper presents an unsupervised network learning method for reconstructing CSG trees from CAD models. The network is an enhancement of CAPRI-NET, and features the fixed operations (from bottom to top) of intersection -> union -> difference on primitives modeled by quadratic surfaces, where the difference is...	Rebuttal 1: Rebuttal: It is not very clear how the dropout pruning is applied in the training process. A: Dropouts are not applied in the last step but during the training process; please refer to lines 221-222 in the paper. After dropouts, the network parameters will be tuned. We iteratively perform this proc...	Summary: The paper proposes a reconstruction approach to building constructive solid geometry (CSG) from other 3D modalities like meshes and point clouds. The key contribution of the paper is a dual representation that considers both the shape and its complement that are built with Boolean intersection and union operat...	Rebuttal 1: Rebuttal: One weakness of choosing an overfitting approach as opposed to learning from datasets is challenges related to robustness to noise and outliers. A: Yes, unintended geometry could be produced for noisy point clouds or low-resolution meshes. This is a general issue for overfitting-based me...	Summary: This paper proposed a method for unsupervised learning of CSG trees from mesh or point cloud. An auto-decoder approach is used, i.e., each training shape is represented by a learned latent code. Compared to previous approach CAPRI-Net, the proposed approach used complementary primitives and dual branch design ...	Rebuttal 1: Rebuttal: It will be easier to follow the paper if there are some visualizations explaining the insights of complementary primitives and dual branch. A: We showed additional visualization results for the ablation study, see Figure 2 in the PDF file attached to the global response. **Q1: How is the...	Rebuttal 1: Rebuttal: We thank all the reviewers for their insightful comments and encouraging remarks. We are glad to see reviewer recognitions that our approach is “novel,” “effective,” "compelling,” and “significantly” outperforms existing methods. Since the reviewer questions were all technical in nature, seeking m...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors propose a novel, unsupervised method to reconstruct the CSG tree given a 3D shape. The authors prove that all CSG trees can be formulated with a boolean difference operation as the last step. Therefore, to generate the final reconstructed shape, the proposed method uses two branches, cover and resi...	Rebuttal 1: Rebuttal: The generated CSG trees might not look natural to designers. A: We generally agree with the reviewer and there is definitely more that is left to do to get there. One intrinsic difficulty is that there is no (single) ground truth for the CSG assembly of a given CAD shape. Each 3D shap...	null	null	null	null	null	null
Non-Stationary Bandits with Auto-Regressive Temporal Dependency	Accept (poster)	Summary: The authors propose a bandit algorithm in the restless setting, when the rewards have an auto-regressive structure. Strengths: 1. Contributes to the non-stationary case, in contrast to the vast majority of results in the stationary setting. Weaknesses: 1. Knowledge of (single parameter) alpha, and the probl...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback! We’ve addressed your questions below and will integrate the discussions into our revised paper. Regarding learning the AR parameter, * In Sec. 8, we introduced a MLE-based approach that learns the AR parameter. We provided theoretical guarantees for our...	Summary: This work uses AR(1) model to model the non-stationary multi-armed bandit (MAB) problem. This paper considers a new performance metric, dynamic steady-state regret, and establishes lower bound on regret. Furthermore, This paper proposes the AR2 algorithm and provides a relatively tight regret upper bound. Str...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback! We’d like to address each of your questions below. We will also integrate the following discussions and clarifications into our revised paper. (1) Stationarity in time series vs. bandits. We appreciate your comments regarding the AR-1 model's stationa...	Summary: This paper studies the problem of non-stationary bandit learning in bandits where rewards have auto-regressive temporal dependency. More specifically, this paper considers bandits where the evolution of the expected mean reward of each arm undergoes an independent AR-1 process truncated to a bounded interval. ...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback! We’d like to address each of your questions below. (1) Regarding the alternation mechanism, * We'd like to first clarify that we do not always explore and exploit at the same intensity. At the exploration step, we only pull a triggered arm during odd roun...	Summary: This paper studies a non-stationary bandits problem where the reward rate of each arm evolves according to the AR(1) model, i.e., shrinks by a known multiplicative factor and adds an indep noise. They proposed an algo that strikes a balance between (i) exploration v. exploitation and (ii) remembering v. forget...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback! We’d like to address your questions below. We will incorporate the following discussions and fix the typo in our revised paper. (1) Dependency of function $g$, upper and lower bounds on $\sigma$. * Our function $g(k, \alpha, \sigma)$ represents the pro...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude to all reviewers for your valuable and insightful feedback! We have carefully addressed each reviewer’s comments and questions below. For Figures 5, 6 and Table 3 crafted during the rebuttal period, please kindly refer to the attached PDF in this glob...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Optimistic Active Exploration of Dynamical Systems	Accept (poster)	Summary: This paper addresses the problem of active exploration of a (Markovian) dynamical system with continuous states and actions. In the absence of any cost function, the proposed objective is the maximization of the one-step information gain on the dynamics model. The paper presents an algorithm, called OpAx, to m...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and for sharing other relevant work. Based on their summary, we believe that the reviewer misunderstood our objective. In particular, our objective is not the one step but $T$-step information gain on the model. This is a crucial difference since it allow...	Summary: The paper presents an active exploration method (OPAX) for dynamics model learning. The method seeks to maximize information gain, while being optimistic about unknown dynamics with respect to the achievable information gain. Theoretical results establish a connection between information gain and model complex...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback and acknowledge that the reviewer correctly highlighted the strengths of our work. Below we have addressed the reviewer’s concerns. ## Weaknesses: W1 Outperforming baselines: We are happy that the reviewer recognized that the main contributions of the pape...	Summary: The paper presents some insights into active exploration for model-based reinforcement learning. For certain kinds of environments, the authors show a convergence guarantee for model uncertainty. They augment their analysis with an empirical study of an agent using their approach OpAX. Strengths: The authors ...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback and for pointing out our typos. We have addressed them in the paper. ### Weaknesses W1 Empirical evaluation of theoretical results: We designed the pendulum experiment (line 242) on GPs, i.e., RKHS setting, precisely to evaluate our theoretical findings e...	Summary: This paper studies provable exploration in model-based reinforcement learning and proposes an algorithm with optimistic active exploration based on information gain. Theoretical results and experimental results are provided to support their method. Strengths: 1. The paper presents a practical algorithmic impl...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. Below we address the reviewer’s concerns. ### Weaknesses W1 Novelty of our work: We refer the reviewer to the author rebuttal section. If the reviewer is still concerned with the novelty of our work, we’d be happy to receive more detailed feedback on th...	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback. It seems that some key contributions to our work have missed the attention of our reviewers. We have made our contributions more clear in the revised paper and included additional references. We highlight our contributions below for clarification. 1. We ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a task-agnostic active exploration algorithm for non-linear dynamic systems as long as it can be well calibrated. By combining the optimistic exploration principle and some standard baysian techinques, they give a general convergence bound as well as the more specfic bound in gaussian proce...	Rebuttal 1: Rebuttal: Thank you for your feedback, our response follows. ## Weaknesses W1 Computational complexity: Solving an optimization problem for general nonlinear systems is challenging, and out of scope for this work. For our problem formulation, standard trajectory optimizers such as iLQR[1], iCEM[2] or po...	Summary: This paper proposes and studies a rather intuitive algorithm for active learning in nonlinear dynamical systems in the episodic setting. They establish consistency (in terms of mutual information) and provide supporting numerical experiments. Strengths: * The proposed algorithm is intuitive and it is satisfy...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback and for providing additional references. As the reviewer highlighted, the shared references do not consider the challenging active learning/unsupervised learning setting and focus more on the supervised learning problem. We have included the references in t...	null	null	null	null
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts	Accept (poster)	Summary: This paper studies reasoning shortcuts (RS) in neuro-symbolic learning. RS refers to that the neural network does not learn a generalizable concept. This work provides a systematic characterization of RS, which not only gives a formal definition of RS, but also identifies key conditions of its occurrence. Seve...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments about our work, in particular for finding it well-motivated and well-written. Below, we address the reviewer’s concerns. Evaluated approaches are not exhaustive We study reasoning shortcuts (RSs) in NeSy problems where knowledge is explicit a...	Summary: This paper proposes mitigation and evaluation strategies for "reasoning shortcuts" in neuro-symbolic reasoning models, roughly corresponding to when the concepts (i.e., latent variables) extracted do not match the "true" ground-truth factors. The paper then examines reasoning shortcuts on several proposed, sim...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments about our work, in particular for finding it excellent, novel, and well-presented. Below, we address the reviewer’s concerns. Disentanglement vs. RSs Thank you for bringing up this interesting connection. There is a strong link between RSs and...	Summary: _Background_: The authors are interested in studying the properties of sequential, two-stage neuro-symbolic pipelines. Stage 1 is a neural network that reduces a high dimensional input (eg: an MNIST image) to a low-dimensional relaxation of a symbolic space (eg: 10 dimensional onehot vector) and Stage 2 is a p...	Rebuttal 1: Rebuttal: We thank the reviewer for their interest in our paper and for finding it clear and significant. Below we address the points raised by the review. Typos and clarifications: Thank you for pointing these out, we will update Tables 1 and 2 accordingly. As indicated in the main text, our architec...	Summary: The paper presents a in depth analysis of reasoning shortcuts and the impact they have of the learning process of neuro-symbolic learners. On the ground of the performed analysis, they propose 4 different mitigation strategies to alleviate the problem: 1. Knowledge-based mitigation 2. Data-based mitigation...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive comments about our work, in particular for finding it important, needed, and well-presented. Below, we address their concerns. Is MTL more practical than further constraining the prior knowledge? We agree that, compared to constraining the knowledge onl...	Rebuttal 1: Rebuttal: We are grateful to all reviewers for taking the time to evaluate our paper and appreciating the motivation (p1H9), the theoretical analysis (Qpe9), and the significance of our work (yCW5, szNE, Qpe9), as well as the quality of the presentation (yCW5, szNE, Qpe9, **p...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Emergent Communication in Interactive Sketch Question Answering	Accept (poster)	Summary: The work focuses on multi-turn sketch-based emergent communication. Authors propose a novel two-round interactive task, named Interactive Sketch Question Answering (ISQA). They suggest an architecture and an implementation, based mainly on existing components (MCAN, Fast-RCNN) while incorporating several novel...	Rebuttal 1: Rebuttal: Thank you for your time and valuable suggestions. Here are our detailed responses. --- W1: Human survey We agree with your opinion and added a human survey. The human evaluation results are consistent with the CLIP distance. See general response 3. --- **W2: CLIP-based loss provides addi...	Summary: This paper proposed a new multi-round visual communication task with an interactive system for emergent communication. During the game, the sender needs to sketch on the canvas to communicate a target image, while the receiver needs to answer a question regarding the target image and give feedback on the sende...	Rebuttal 1: Rebuttal: Thank you for your time and valuable suggestions. Here are our detailed responses. --- W1: Which agents contribute more. Our setting considers a collaboration between the sender and the receiver. Through the proposed feedback mechanism, both agents have equal rights to affect the interacti...	Summary: In this paper, the authors proposed a new task about emergent communication by tackling visual question answering as an iterative sketch question answering process. The authors proposed a three-factor evaluation metric, including question answering performance, drawing complexity and human-interpretability. A ...	Rebuttal 1: Rebuttal: # Response to Reviewer APpT Thank you for your time and valuable suggestions. Here are our detailed responses. --- W1: a) Uniqueness of the proposed task? Comparison to classification. Comparison with [14]. b) Pattern with one/two round communication. c) Communication gets better when the...	Summary: The authors present a new problem setup for sketch-based emergent communication, distinguishing itself from existing work primarily through communication taking place iteratively over multiple rounds. The authors also argue that the reliance on downstream tasks for evaluations allows for communication protoco...	Rebuttal 1: Rebuttal: W1: First, our work falls into the topic of emergent communication. Emergent communication aims to facilitate human-like communication between intelligent agents, as delineated in [1,2,3,4]. In this work, we propose a novel question answering setting to promote multi-round, bilateral, reciproca...	Rebuttal 1: Rebuttal: General Response 1: Uniqueness of the proposed ISQA task for emergent communication: We propose a novel task setting, whose goal is to promote multi-round, bilateral, interactive communication between a pair of a sender and a receiver. To achieve this goal, our proposal has three unique chara...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Parallel-mentoring for Offline Model-based Optimization	Accept (poster)	Summary: This paper introduces a novel offline model-based optimization (MBO) framework that incorporates a fine-tuning strategy for ensemble proxy models. Instead of directly using three independent proxy models, the paper suggests a "voting" strategy. In this scheme, based on the majority order of two (near) inputs, ...	Rebuttal 1: Rebuttal: ## Weaknesses > My primary concern revolves around hyperparameter selection. Please refer to the global response "On Additional Hyperparameters". > The proposed fine-tuning strategy lacks the original regression objective; the model might produce inaccurate values at training points after fine...	Summary: The aim of the paper is to tackle the out-of-distribution problem in offline model-based optimization methods. To this end, the authors drew inspiration from recent studies that proposed better ensemble proxies and weak ranking supervision signals. They proposed a new approach called parallel-mentoring, which ...	Rebuttal 1: Rebuttal: ## General Reply We greatly appreciate your detailed feedback and insights. We're committed to addressing each point raised thoroughly. Thank you. ## Weaknesses > The research question is poorly defined, and the statement of the importance of the research is lacking. The out-of-distribution pro...	Summary: This paper introduces an innovative study that revolutionizes offline model-based optimization (offline MBO) by introducing a novel ensemble method. The proposed approach addresses a critical challenge in offline MBO, namely the handling of potentially inaccurate pseudo-labels generated by proxy models, partic...	Rebuttal 1: Rebuttal: ## General Reply We value your constructive feedback and will incorporate it diligently in our revisions. Thank you. ## Weakness > The analysis lacks consideration for ensemble techniques We thank you for highlighting the importance of ensemble. In our manuscript, we've employed and discussed two...	Summary: The paper studies offline model-based optimization, which maximizes a black-box objective with a dataset of designs and scores. Most approaches rely on training a dataset proxy to approximate the black-box objective and perform SGD on the objective. This paper proposes using three proxies and voting-based supe...	Rebuttal 1: Rebuttal: ## General Reply We sincerely appreciate the effort and time you've invested in providing us with your insightful and constructive feedback. Your comments are invaluable to us as they offer opportunities for refining, rectifying, and enhancing the content of this paper. We are committed to meticu...	Rebuttal 1: Rebuttal: Dear Reviewers, Thank you for your thorough examination of our paper and for sharing insightful feedback. We recognize your concerns and, in this rebuttal, address two main common points raised. ## On Additional Hyperparameters > review from usfZ: The algorithm appears to be overly complex, int...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Offline RL with Discrete Proxy Representations for Generalizability in POMDPs	Accept (poster)	Summary: This paper proposes ORDER, an offline RL solution where the agent have access to states in the offline data, but can only witness observations where some groups of dimensions are masked when deployed due to occlusion or perturbation in real-life scenarios. To address this problem, a 3-step solution is proposed...	Rebuttal 1: Rebuttal: Dear Reviewer: Thank you for your thoughtful questions and feedback on our paper. Below, we provide detailed responses to each of your questions: # Q1. The embedding is generated assuming that (groups of) dimensions are independent to each other? While our model considers individual state facto...	Summary: The work looks to tackle a subset of the partially observable offline reinforcement learning problem setting, where a dataset of offline experience (of full states and masked states) is given during training, and an agent is tested on masked state features during test time. The authors propose the ORDER traini...	Rebuttal 1: Rebuttal: Dear Reviewer: Thank you for your thoughtful questions and feedback on our paper. Below, we provide detailed responses to each of your questions: # Clarifying Our Problem Setting and Its Real-World Relevance Real-world Motivations: Our specific assumptions, though tailored, are rooted in re...	Summary: This work study the masked partial observability in reinforcement learning and propose a novel method ORDER to address this challenge. ORDER leverages the alignment of discrete state representations and significantly improves robustness and generalization performance across diverse masked partial observability...	Rebuttal 1: Rebuttal: Dear Reviewer: Thank you for your thoughtful questions and feedback on our paper. Below, we provide detailed responses to each of your questions: # Single Mask Token Justification Thanks for your feedback. Your query regarding the use of a single mask token is apt. The primary intention behind ...	Summary: In this paper, the authors present a three stage method for offline RL in POMDPs. It is assumed that the agent has access to full observations (state) during training, but only masked versions of this state (partial observations) during inference or deployment. In the first stage of training, a mapping is lear...	Rebuttal 1: Rebuttal: Dear Reviewer: Thank you for your thoughtful questions and feedback on our paper. Below, we provide detailed responses to each of your questions: # Response to Typographical Comments: 1. Line 164: You're right, and we apologize for the oversight. It should indeed be "i" instead of "ii". ...	Rebuttal 1: Rebuttal: Response to Reviewers: Dear Reviewers, Thank you for your comprehensive feedback on our manuscript. We have addressed each comment individually in the subsequent sections. Additionally, an attached PDF provides further clarifications with relevant tables. Your insights are crucial for refin...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data	Accept (poster)	Summary: This paper contributes a new model evaluation framework called 3S-Testing, which uses (conditional) deep generative models to create synthetic test sets. To provide uncertainty estimations, 3S uses deep generative ensemble method. It is empirically confirmed that the better performance on small subgroups (with...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for your time and constructive feedback. We wish to clarify each point in turn. ## 1. Reasons for using generative models. The paper only uses a deep generative ensemble (DGE) [24] to model the uncertainty over the generative model parameters and estimate generative erro...	Summary: The authors propose to use synthetic data to evaluate models, especially under distribution shifts or in areas of the input space with low coverage. The authors use CTGAN to empirically validate their idea, and apply it to tabular data. The paper is a resubmission from ICLR 2023 ( https://openreview.net/forum...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for your time and feedback. We would like to address each of your comments in turn. ## 1. Large datasets and other limitations We agree that the paper would benefit from the inclusion of specific failure cases, including very large datasets for which 3S may not be neces...	Summary: This paper proposes to use synthetic test data to improve the estimation of model performance for tabular datasets when insufficient test data is available. Their approach of generating synthetic test data conditioned on subgroups improves performance estimation for underrepresented subgroups and can accuratel...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for your time and feedback. We would like to address each of your comments in turn. ## 1. Extreme underrepresentation failure case. We agree that the paper would benefit from the inclusion of specific failure cases, including very small subgroups. We discuss this failur...	Summary: In this paper, the authors propose the utilization of synthetic data for evaluating models and introduce an automated suite of synthetic data generators called 3S. 3S offers two key advantages: it enables reliable and detailed evaluation, and it measures model sensitivity to distributional shifts. The paper ex...	Rebuttal 1: Rebuttal: Dear reviewer, Many thanks for your time and positive feedback. We agree that the paper would benefit from an extended limitation section and specific failure cases. We discuss these in the general response under “Limitations and Failure Cases”, as we think this discussion will be of interest t...	Rebuttal 1: Rebuttal: Dear reviewers, Some reviewers suggested a longer limitations discussion, others were interested in specific failure cases. We address these points here. In the camera-ready paper we will extend the discussion to reflect these. ## Limitations We summarise 3S’s main limitations (where applicabl...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a model evaluation framework, generating the synthetic test to mitigate the challenges of model evaluation with limited real test sets, such as unbalanced subgroups and distribution shifts. Strengths: The idea of using synthetic data to improve the testing and evaluation of machine learnin...	Rebuttal 1: Rebuttal: Dear reviewer, Thank you for your time and feedback. We would like to address each of your comments in turn. ## 1. Counterintuitive [line 155] We do not want to give the impression that the equation in line 155 holds in general—though we hope the experiments are convincingly consistent and argu...	null	null	null	null	null	null
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction	Accept (spotlight)	Summary: The authors propose to use the diffusion on both the protein sequence and the protein structure for pretraining. Additionally, they also take the correlation between different conformers of the same protein into consideration and maximize the mutual information between their trajectories via mutual denoising. ...	Rebuttal 1: Rebuttal: Thanks for your insightful comments and golden suggestions! We respond to your concerns as below: >Q1: The random side-chain perturbation does not reflect the actual "physics underlying the conformational change Please see the global response for details. >**Q2: Discarding the mutual inform...	Summary: The authors propose a novel pre-training method for proteins by jointly modeling sequences and structures with a diffusion model (DiffPreT). The encoder, which is the noise prediction network, learns representations for the sequence and the structure, respectively. This representation can then be used for down...	Rebuttal 1: Rebuttal: Thanks for your careful reading! While some questions may be due to misunderstanding, we find that your suggestions are very helpful for us to improve the quality and clarity of our paper! We respond to your concerns below: >**Q1: What noise level is used to get the representation for downstream ...	Summary: In this work, the authors perform a thorough investigation of different pre-training strategies on joint sequence-structure diffusion models for representation learning, rather than generative modeling. They evaluate on an EC prediction task and four tasks from the Atom3D benchmark. They find that joint sequen...	Rebuttal 1: Rebuttal: Thanks for your insightful comments and great suggestions! We respond to your concerns below: >Q1: Lack of evaluation against other downstream predictors and backbone models for downstream prediction. **We would like to argue that the focus of the paper is the development and evaluation of ...	Summary: The paper proposes to use joint protein sequence and structure diffusion as a pretraining task, which they call DiffPreT. In order to account for the fact that proteins can exist as ensembles of conformers, the paper further proposes to generate pairs of conformers, use the diffusion forward process to corrupt...	Rebuttal 1: Rebuttal: Thanks for your appreciation of our work! We respond to your questions and concerns below: >Q1: The evaluation on more types of downstream tasks would make this paper more significant. Thanks for the suggestion. We believe this additional experiment in the global response showcases the poten...	Rebuttal 1: Rebuttal: We extend our gratitude to all reviewers for valuable feedback. We’ve made significant improvements based on your suggestions. Here is a brief summary of important points: >New benchmark results on protein engineering task (Reviewer MBi3) During the rebuttal period, we followed your suggest...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Latent Graph Inference with Limited Supervision	Accept (poster)	Summary: The authors figure out that the graph sparsification operation results in the supervision starvation problem in latent graph inference (LGI). They propose to identify k-hop starved nodes and diminish the starved nodes by incorporating a regularization adjacency matrix into the initial one. They further reduce ...	Rebuttal 1: Rebuttal: We really appreciate that the Reviewer identifies our contributions and provides constructive comments. We address your concerns as below. W 1: Parameter sensitivity. Thank you for this valuable comment. In Sec. 4.2, we discussed how $\tau$ and $\alpha$ affect performance. In our method...	Summary: The paper points common LGI methods suffer from the issue of `supervision starvation`. It also observes this issue is actually caused by the graph sparsification operation. To address this problem, the paper proposes to restore the corrupted affinities and replenish the missed supervision. It presents the `CUR...	Rebuttal 1: Rebuttal: We really appreciate that the Reviewer recognizes our contributions and originality, and gives us useful suggestions. We give our response below. W 1: Why CUR Decomposition is more efficient. Thank you for this important comment. As stated in Lines 48-53, Sec. 1, when we say “more efficie...	Summary: The paper proposes a method for latent graph inference (aka graph structure learning) based on the idea of mitigating the supervision starvation problem present when jointly learning the underlying graph structure and node representations. The paper claims that the supervision starvation problem is caused by t...	Rebuttal 1: Rebuttal: We really appreciate the Reviewer’s valuable comments. We address your concerns as below. W 1: Best validation performance. We thank the Reviewer for this important suggestion, and we agree that the setting you mentioned is more reasonable. *To show the results of the models correspondin...	Summary: This paper proposes a new method for the latent graph inference problem. The motivation of the new method is the existence of supervision starvation nodes caused by graph sparsification operation. To reduce the number of supervision starvation nodes, the authors propose a CUR matrix decomposition based method ...	Rebuttal 1: Rebuttal: We really appreciate the Reviewer’s approval and constructive comments. We address your concerns as below. W 1: Thank you for the constructive comments. We respond to this question in the following aspects: Reason. The reason why some nodes may receive no supervision is not because ...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper introduces a model-agnostic enhancement for current LGI (latent graph inference) methods, aiming to address the assumed issue of unlearned features in a substantial number of nodes and edges, which are believed to negatively impact LGL's generalization performance. It proposes to learn a weighted res...	Rebuttal 1: Rebuttal: We thank the Reviewer for providing detailed comments. We address your concerns as below. W 1: Graph size. We would like to clarify that there is no direct relationship between improvement and graph size. We kindly remind the Reviewer that our method aims to eliminate starved nodes so a...	null	null	null	null	null	null
Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods	Accept (poster)	Summary: This paper addressed the uncertainty quantification problem in regression models, specifically focusing on individual calibration to characterize prediction model quantiles. To overcome these limitations, they proposed simple nonparametric calibration methods that are both computationally efficient and statist...	Rebuttal 1: Rebuttal: We thank the reviewer for the sincere comments and for pointing out our missing literature. In the past week, we spent quite much time working on these papers and related literature. We’d like to summarize our findings below. We hope this better clarifies our positioning, and we look forward to ha...	Summary: This paper studies uncertainty quantification for the regression problem. In particular, it considers the estimation of conditional quantiles (of the residuals) via the kernel method. The convergence rate of the proposed estimator is established, along with a matching lower bound. The proposed method is eva...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our work and raising inspiring questions. Direct estimation: We include more experiments using that directly estimate the conditional quantiles by optimizing pinball loss as the attachment. It does show the advantage of the two-step procedure. We also prov...	Summary: The paper considers the uncertainty quantification problem for regression models. First, they proposed an algorithm for simple nonparametric quantile estimator. Then, they further proposed the nonparametric regression calibration algorithm. They also provide theoretical analysis of the proposed algorithms and ...	Rebuttal 1: Rebuttal: We thank the reviewer for the questions and comments. The choice of the kernel function: This simple nonparametric method performs rather robustly with respect to the choice of the kernel function. Essentially, all standard choices of kernels specify a localized weighting regime for error quan...	Summary: This paper proposes a new method for quantile regression and for calibrating prediction intervals in regression. The paper first proposes a simple quantile regression method and shows that this method estimates the true quantile curve at the minimax-optimal rate. For calibrating prediction intervals, the basic...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback and the raised questions. We believe clarifying these questions improves the positioning of our work. Decomposition/two-step procedure: As noted by the reviewer, our paper adopted a two-step approach which first predicts the mean and then calibrates the qua...	Rebuttal 1: Rebuttal: We thank the reviewers for taking time to read our papers and for all the helpful feedback. We look forward to more follow-up discussions in the coming week. We provide an individual response to each reviewer, and we'd like to use the extra space below to discuss the assumptions of our paper in ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Maximum State Entropy Exploration using Predecessor and Successor Representations	Accept (poster)	Summary: This paper addresses the problem of maximum state entropy exploration in environments without rewards. Especially, it proposes a method to learn an history-based policy maximizing the entropy over the states sampled in a single trajectory. The method, called $\eta\psi$-Learning, combines predecessor representa...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and valuable feedback. We aim to address the concerns in the following: > MEPOL as baseline We thank the reviewer for pointing this out and have added MEPOL[1] as baseline (Figure 1 of rebuttal PDF) and observe that $\eta\psi$-Learning outperforms MEPOL on con...	Summary: This paper shows a combination of "succesor" and "predecessor" representations can be used to develop an efficient maximum entropy exploration policy. Strengths: - Overall, the paper is clearly written and makes a useful contribution to the exploration literature. - As far as I know, the ideas are novel. - ...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and valuable feedback. We aim to address the concerns in the following: > I think the authors are a bit loose with their arguments about human cognition. We thank the reviewer for bringing this up and agree with the fact that it is not clear how cognition dep...	Summary: This paper proposes a new exploration method under maximum entropy RL settings. At each time step, the agent selects the action that maximises the expected entropy of the finite-length trajectory. The trajectory entropy is decomposed into two terms, based on variants of the predecessor representation and succe...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and valuable feedback. We aim to address the concerns in the following: > The predecessor representation part (for the computation of the entropy of past trajectory) seems unnecessary and does not contribute to action selection, could the authors elaborate on t...	Summary: This paper proposes a novel exploration algorithm in RL by combining the successor representation with the predecessor representation and maximising episode-level entropy of state visitation. The proposed approach demonstrates improvement over the MaxEnt baseline on simple Gridworld and continuous control envi...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and valuable feedback. We aim to address the concerns in the following: > In the experiments, the proposed approach is only compared with the MaxEnt baseline. We thank the reviewer for pointing this out. We did an experiment with the sparse MountainCar enviro...	Rebuttal 1: Rebuttal: Firstly, we thank the reviewers for their time and constructive feedback. We hope to address the concerns during the rebuttal and would be happy to answer more questions. In this work, we developed an algorithm to learn exploratory policies at convergence that can explore the state space efficie...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions	Accept (poster)	Summary: This paper proposes new convergence results for single-call stochastic extragradient methods under weaker conditions. More specifically, the authors consider quasi-strongly monotone and weak Minty VI problems, both under an unconstrained finite-sum (or arbitrary sampling) setting. The authors propose the expec...	Rebuttal 1: Rebuttal: We thank the reviewer for a detailed review and positive evaluation. Below, we address questions and concerns raised by the reviewer. \[My main question is around the practicality of the proposed step size rules...\] We appreciate the reviewer's concern regarding the adaptive stepsizes. Howev...	Summary: This paper studies the single-call stochastic extragradient methods for solving two classes of structured variational inequality (VI) problems, i.e., (i) quasi-strongly monotone problems and (ii) weak Minty variational problems. These two classes generalize the assumptions of strong monotonicity and comonotoni...	Rebuttal 1: Rebuttal: We thank the reviewer for a detailed review and positive evaluation. Below, we address questions and concerns raised by the reviewer. \[...the convergence analysis is more likely for infinite-sum problems, especially when the authors also consider mini-batch in this paper.\] We consider only ...	Summary: This work studies single-call stochastic extra-gradient method for quasi strongly monotone and weak Minty Variational Inequality (VI). They relax the commonly-used bounded noise variance assumption and used the expected residual condition. Strengths: The paper is well-written and the problem is relevant. Wea...	Rebuttal 1: Rebuttal: We thank the reviewer for a detailed review. Below, we address questions and concerns raised by the reviewer. \[...the condition itself is not a contribution\] Here, we want to highlight that the ER condition in minimization literature involves the functional value, i.e. the right-hand side i...	Summary: The paper explores single-call stochastic extragradient methods like stochastic past extragradient (SPEG) and stochastic optimistic gradient (SOG), which are increasingly popular and efficient algorithms for tackling large-scale min-max optimization and variational inequalities problems (VIP) commonly found in...	Rebuttal 1: Rebuttal: We thank the reviewer for a detailed review and positive evaluation. Below, we address questions and concerns raised by the reviewer. \[... question the novelty of this research\] We politely disagree with this remark. Our work on the single-call method uses a different proof technique compa...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable feedback and time. In particular, we appreciate that the reviewers acknowledged the following strengths of our work: - Reviewer rrNt acknowledges the problem (considered in our work) is relevant and needs to be addressed. - All the reviewers identify rela...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Federated Compositional Deep AUC Maximization	Accept (poster)	Summary: This work aims to address the challenges of imbalanced data in FL. To this end, the authors propose to optimize AUC score. Some experiments are conducted to verify the effectiveness of the proposed method. Strengths: The paper is easy to follow. The notations are well-defined. The studied problem is promising...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's comments and suggestions. We address the reviewer’s comments below. First, our method is significantly different from the heterogeneous federated learning approaches. Specifically, most existing heterogeneous federated learning approaches consider a setting wher...	Summary: This paper firstly studies the federated compositional AUC maximization problem, which includes both the local and global imbalanced distributions, and proposes the momentum-based algorithm LocalSCGDAM to solve this problem. The SOTA convergence rates are established and various experiments are used to evaluat...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's comments and suggestions. We address the reviewer’s comments below. Answers for weakness. 1. First, when the data distribution is imbalanced, directly minimizing the cross-entropy loss function cannot learn a good classifier since it may ignore the minority...	Summary: The paper proposes a new federated learning algorithm to address the class imbalance problem. Instead of using cross-entropy loss functions, the proposed algorithm directly optimizes the AUC score by solving a federated stochastic compositional minimux optimization problem. Specifically, the paper proposes to ...	Rebuttal 1: Rebuttal: We are grateful for the reviewer's comments and suggestions. We address the reviewer’s comments below: Answer for weakness. 1. Firstly, our method is significantly different from the heterogeneous federated learning methods. Specifically, our paper aims to address a more challenging issue t...	null	null	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection	Accept (poster)	Summary: The manuscript presents an innovative model called PPi (Pretraining-based model for Patient-independent seizure detection) for patient-independent seizure detection utilizing SEEG data. SEEG provides detailed and three-dimensional brainwave information which is advantageous for seizure detection. However, chal...	Rebuttal 1: Rebuttal: We thank the reviewer for all the insightful comments. Responses to specific comments are listed below. * W1: Add supporting references to strengthen the argument for SSL tasks. Thanks for the suggestions. [1], [3], [4] are some works which also apply SSL to extract time-domain features. ...	Summary: This paper proposes a model called PPi that pre-trains on SEEG data using two self-supervised tasks, followed by a channel background subtraction step as well as a brain region enhancement task for patient-independent seizure detection. Experiments on two public datasets and an internal dataset suggest that PP...	Rebuttal 1: Rebuttal: We thank the reviewer for all the insightful comments. Responses to specific comments are listed below. * Q1: Replace some notations with plain language in the manuscript. Thank you for this good suggestion. The plain language version for Definition 1 is in line118-119: "Our goal is to ut...	Summary: Seizure detection using stereoencephalographic data is crucial for epilepsy diagnosis. Owing to the large variety of seizure patterns and pathology, automated seizure detection is quite challenging, and manual annotation of data remains necessary. This study proposes a model to detect seizures from SEEG data i...	Rebuttal 1: Rebuttal: We thank the reviewer for all the insightful comments. Responses to specific comments are listed below. * Q1&Q2: Preprocessing steps for the datasets. For the public datasets, we first remove the power line noise and then dowm sample the data to 500Hz. For the clinical dataset, we remove ...	Summary: This article deals with domain shifts in seizure detection with stereoelectroencephalography (SEEG), an emerging acquisition method in this field. To tackle this problem, the authors propose two different self-supervised tasks to learn meaningful features from the SEEG and propose also two preprocessing techni...	Rebuttal 1: Rebuttal: Thank you for your constructive and detailed comments. Responses to specific comments are listed below. * Q1: Calculation method of performance improvement value in the manuscript. We apologize that the calculation of the improvement may not be clearly stated in the manuscript. Actually, ...	Rebuttal 1: Rebuttal: ### Global response We thank the reviewers for their close read of this manuscript and their insightful comments. In response to reviewers' comments, we additionally performed 2 sets of experiments in the ablation study and 7 sets of experiments in the case study. Several important suggestions ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a patient-independent seizure detection framework called PPi for stereoelectroencephalography (SEEG) data. It utilizes self-supervised learning for taking into account discriminability of brain areas and contextual coherence of SEEG signals to preserve the patterns of different channels. Th...	Rebuttal 1: Rebuttal: We thank the reviewer for all the insightful comments. Responses to specific comments are listed below. * W1: Clarification for the unclearness of novelty raised by presentation. Thanks for pointing this out. The novelty of the proposed method are listed below: * Our method contains tw...	Summary: This paper presents a pretraining-based model for patient-independent seizure detection (PPi) on SEEG data in the clinical scenario. The proposed method adopts a self-supervised pretraining strategy to extract information from SEEG signals while preserving the unique characteristics of each channel, and applie...	Rebuttal 1: Rebuttal: We thank the reviewer for all the insightful comments. Responses to specific comments are listed below. * Q1: Highlight the effectiveness of PSD-based features in the proposed method. Thank you for the good suggestion. There are some related works which support the effectiveness of PSD-ba...	null	null	null	null
Safety Verification of Decision-Tree Policies in Continuous Time	Accept (spotlight)	Summary: This paper introduces a novel verification algorithm that enables the verification of decision tree policies for continuous-time systems (also applicable to discrete-time systems). This approach ensures a sound and compact representation of reachable sets using Taylor models, which can be efficiently propagate...	Rebuttal 1: Rebuttal: Thank you for the excellent review. > To enhance the clarity of the paper, it would be beneficial to treat the verification procedure as background information in a dedicated section, allowing the paper to primarily focus on its contribution to the analysis of decision tree policy reachability. ...	Summary: The paper presents an approach for verifying safety (reach-avoid) properties of controlled systems where the state space of the system is continuous, its dynamics are continuous-time, and the policy/controller is described by a decision tree that chooses actions from a continuous action space. The proposed re...	Rebuttal 1: Rebuttal: Thank you for the excellent review. > exploiting the special structure of DTCS for verification is one of the key insights of the presented work but a clear explanation of this special structure and how it is exploited is only cursorily explained. Thank you for the suggestion. We will improve th...	Summary: The paper puts forward a verification method for decision tree-based systems in continuous time. The method implements a reachability algorithm that computes over-approximations of the set of reachable states for a sequence of time intervals until a time horizon is reached. The approximations at each step are ...	Rebuttal 1: Rebuttal: Thank you for the excellent review. > The paper does not include a discussion on the scalability of the proposed method. Our experiments already include a relatively large decision tree (depth 10, 177 nodes) for the drone quadrotor problem to show that large trees are also admissible, and as we ...	Summary: This paper presents a method to solve the reach-avoid problem for dynamical systems controlled by a decision tree in continuous time. The authors assert that this is the first paper to solve the problem in the continuous time setting. In the paper, the authors first provide a good overview of decision trees, t...	Rebuttal 1: Rebuttal: Thank you for the excellent review. > The novelty of the approach is not adequately explained. I believe that it is novel, but the authors need to explicitly state which parts of the algorithm are specifically designed for use with DTCS and which are just adapted from the typical reachability alg...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Doubly Constrained Fair Clustering	Accept (poster)	Summary: The paper investigates the fair $k$-center problem and considers the combination of two fairness notions: Group Fairness (GF) and Diversity in Center Selection (DS). The authors show that a constant approximation algorithm for one constraint (GF or DS only) can be extended to a constant approximation algorithm...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. > What about $k$ median and means? We would like to note that DS for the k-median and k-means was only solved very recently in the paper “Approximation Algorithms for Fair Range Clustering” which appeared in ICML 2023 (a couple of months ago). Therefore, ...	Summary: This paper studies how to combine several notions of fairness together in one clustering. Fairness is a popular notion in the context of clustering, however most of previous works had focused on a single notion of fairness at once. This paper studies two specific notions of fairness which are called group fair...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. We will fix the typos in lines (138, 211). We will also be explicit about the violation in GF. Furthermore, we believe we presented an elegant solution to the problem and that our modular approach of applying GF or DS algorithms to finally solve both GF+DS c...	Summary: The paper investigates the relationship and intersection between two constraints for fairness clustering: Group Fair, in which populations should be fairly represented in each cluster, and Diversity Selection, in which centers should be fairly selected. It is shown that a solution for one of the constraints ca...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. > Do you believe the same results would be replicated for other datasets? We have run the dataset over a reasonable number of datasets as followed in previous fair clustering papers (see e.g, Kleindessner et al 2019, Esmaeili et al 2021). Furthermore, we ...	Summary: This paper considers two common notions of fairness in clustering: (I) Group Fairness (GF) and, (II) Diversity in Data Selection (DS). The authors show how to boost an approximate algorithm that satisfies only GF/DF to an approximate algorithm that satisfies them both (with constant violations and constant tim...	Rebuttal 1: Rebuttal: We thank the reviewer for making these points. > The algorithms heavily depend on existing approximate algorithms for clustering with GF or DF. So the result should mostly be regarded as an enhancement of the existing algorithms. There are a collection of technical details which distinguish ou...	Rebuttal 1: Rebuttal: We thank the reviewers for their careful reading and constructive criticism. ## Experiments and Datasets: We note that we have tested our algorithms on two datasets from the UCI repository: Adult shown in Section 7 and Census1990 shown in Appendix F. Further, please see the message below with t...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
A Robust and Opponent-Aware League Training Method for StarCraft II	Accept (spotlight)	Summary: The paper aims to reduce the computational complexity of league training (the AlphaStar system) for Starcraft II by introducing heuristics. Exploiter agents are used in AlphaStar to target weaknesses in the primary policy (main agent) being trained. The paper proposes to condition the exploiter agents on strat...	Rebuttal 1: Rebuttal: Thanks very much for your review. We respond to all your questions below, and we are happy to provide further details if there is anything still unclear. Weaknesses: It is not clear how the long-run scaling looks? Is there evidence that the AlphaStar model trained equivalently would do worse?...	Summary: This paper describes an AlphaStar-like approach for training a top-human-level StarCraft 2 agent, with three core additions/changes to the approach that was used to train AlphaStar: 1. Training exploiters in the league conditioned on certain goals: exploitative exploiters which are conditioned on the $z$ stat...	Rebuttal 1: Rebuttal: Thanks very much for your review. We respond to your questions below. Q1: Can you provide any clarification in particular on the point I described above about Table 1? A1: Yes. The agents of AlphaStar were trained for 44 days. With the scale of our computational resources, an agent can ...	Summary: This paper introduces ROA-Star, an improvement to the AlphaStar training framework for StarCraft II. ROA-Star addresses two identified issues in AlphaStar: diminishing efficiency of exploiters as the training progresses and the Main Agent's inability to adapt to opponent strategies in real time. As a solution ...	Rebuttal 1: Rebuttal: Thanks very much for your review. We respond to your questions below. Q1 (weaknesses): This paper could be further strengthened by refining the writing. A1: Thanks very much for your suggestion. We will edit the paper according to your advice. Q2 (Questions): Do the authors have ...	Summary: The authors present a modification to the AlphaStar training algorithm that increases robustness and allows for faster responses to opponent behaviour by explicitly encoding the opponent's strategy in the model's representation along with adding scouting as an ancillary objective. Strengths: The method exceed...	Rebuttal 1: Rebuttal: Thanks very much for your review. We respond to your questions in the following. Q1: Did the authors attempt to examine the opponent model latent space? A1: Thank you for your suggestion. To show the agent's 'awareness' of the opponent's strategy, we add an experiment to visualize the l...	Rebuttal 1: Rebuttal: We have submitted a PDF that includes three figures. In response to reviewer sqN1's question, we demonstrate the agent's 'awareness' of the opponent's strategy in Figure 1 (in the new pdf). Additionally, based on the suggestions from reviewer eEEJ, we have refined the original Figure 4 and Figure ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
On the Power of SVD in the Stochastic Block Model	Accept (poster)	Summary: This paper studies the power of vanilla-SVD algorithm, algorithm without any pre-processing or post-trimming steps, in the symmetric stochastic block model and proves it recovers all clusters in the balanced case, which answers an open question in [Vu18]. Strengths: The main contribution of this work lies in ...	Rebuttal 1: Rebuttal: We thank this reviewer for the suggestions on improving our presentation. We will improve our writing accordingly. Regarding the question about $k=\omega(\log n)$: Yes, our proof works perfectly in this case. As long as the parameters $n,p,q,k$ satisfy the requirement in Theorem 1, our alg...	Summary: In order to understand the behavior of spectral steps in clustering problems, this paper studies the power of vanilla-SVD algorithm in the SBM. This work shows that vanilla-SVD algorithm recovers all clusters correctly in the symmetric setting. Strengths: 1. To theoretically understand the power of practical...	Rebuttal 1: Rebuttal: Thanks for pointing out the typo and your suggestion on the citation format; we shall certainly improve our paper accordingly. Regarding experiments (Weakness #3 and Question #1, #2): We study the same algorithm (SVD) as in [AFWZ20], [EBW18] [PPV+19] and we give better analysis. Since it i...	Summary: This paper investigates the effectiveness of the vanilla-SVD algorithm in the stochastic block model (SBM) and demonstrates that it can accurately recover all clusters in the symmetric setting. The authors address an open question raised by Van Vu in the symmetric setting. Strengths: 1. The vanilla algorithm...	Rebuttal 1: Rebuttal: We thank this reviewer for useful comments and questions. Regarding weakness: We did not propose a new scheme. Instead, we provided a theoretical analysis for a very popular algorithm — SVD (singular value decomposition). Given this algorithm has already been widely used in practice. We did ...	Summary: The manuscript mention that the paper contributes by providing a theoretical understanding of the power of vanilla spectral algorithms in clustering problems, specifically in the stochastic block model (SBM). It also presents a novel analysis of matrix perturbation with random noise. These contributions sugg...	Rebuttal 1: Rebuttal: We really appreciate this review’s feedback and questions. We answer them below. Regarding the weakness: Since this is a theoretical paper, some formulas are necessary. However, we will try our best to make this paper accessible to most readers. In fact, results and proofs in random matrix t...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper provides rigorous, theory-based evidence that vanilla spectral algorithms (i.e., methods that run SVD on the adjacency matrix without any further processing) succeed in finding many communities in symmetric stochastic block models. In contrast to Davis-Kahan approaches, the authors adopt an analysis...	Rebuttal 1: Rebuttal: We thank this reviewer for their appreciation of our work, useful suggestions and relevant questions. Regarding weakness: We believe the extra $\log^6 n$ factor can be improved by future works. This term stems from the concentration inequality we used as a black box. A refined analysis of th...	null	null	null	null	null	null
Replicable Reinforcement Learning	Accept (poster)	Summary: This paper discusses the development of algorithm frameworks for replicability, which is a response to the replicability crisis in social, behavioral, and data sciences. The paper introduces provably replicable algorithms for machine learning and statistics, including replication results for control problems,...	Rebuttal 1: Rebuttal: Dear Reviewer vrU3, Thank you for your feedback. We are glad that you find our work convincing and valuable. In response to the comments in the Weaknesses and Questions sections: “Although the method has a strong mathematical and theoretical analysis, it would be better to have more sufficient ...	Summary: This paper studies an important topic of RL replicability. Under some assumptions, this work gives definitions of rho-replicable and proposes two algorithms: Rep-PVI and Rep-RMAX, and shows their reproducibility properties through proof. Strengths: Overall, the development of the paper is smooth, the topic i...	Rebuttal 1: Rebuttal: Dear Reviewer u4VH, Thank you for your feedback! We are glad that you find the topic of replicable RL important and novel. In response to the comments in the Questions section: “I may understand it wrongly, but with those assumptions, I wonder if the basic Q-learning algorithm also enjoys the ...	Summary: This paper studies replicable reinforcement learning. And they show that stochastic sample-based value iteration can be done replicably and explore the space of an MDP to find an optimal policy. Furthermore, they give some theoretical results. The effectiveness of the replicable algorithm is validated by simpl...	Rebuttal 1: Rebuttal: Dear Reviewer fsAq, Thank you for your feedback. We appreciate that you think that our notion of replicability provides a good foundation for studying reproducibility in RL. In response to the comments in the Weaknesses and Questions sections: “at each iteration, many episodes interacting with ...	Summary: This paper proposes a new reinforcement learning algorithm based on the replicability crisis and gives proof for the proposed method, providing a new perspective in this field. Strengths: 1. The idea of replicable reinforcement learning is brand new and may provide a new perspective for reinforcement learning...	Rebuttal 1: Rebuttal: Dear Reviewer BN9y, Thank you for your feedback, we appreciate that you see the direction of replicable reinforcement learning as a novel perspective. In response to the comments in the Weaknesses and Questions sections: “The experiments are insufficient. Though the proposed replicable reinfo...	Rebuttal 1: Rebuttal: Dear Reviewers, Thank you for your valuable feedback! We are grateful for the questions, suggestions, and opportunity to clarify the intent of our work. We would first like to provide a set of common answers to the points raised by reviewers here. We will provide the responses to individual re...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Understanding Deep Gradient Leakage via Inversion Influence Functions	Accept (poster)	Summary: Key Contribution: This work proposes a new method for analysis of privacy risk in the deep leakage from gradients attack, which does not rely on assumptions of the model architecture or attack optimization method. Approach: The work defines an “Inverse Influence Function” (I2F) which is able to determ...	Rebuttal 1: Rebuttal: Thanks a lot for the positive views of the quality and significance of our work! We are glad to address your concerns. W1: (Originality) Fan et al. (2020) can generalize to other architectures, like CNNs. A1: Thanks for the question. - Fan et al. (2020) mentioned that their method can b...	Summary: This paper proposes inverse influence function (IIF), the indicator of how reconstructed input (from gradient inversion attack) changes with respect to a gradient change. This function can be simply formulated using Jacobian and gradient change. The correlation between the proposed measure and reconstruction q...	Rebuttal 1: Rebuttal: We really appreciate the valuable comments from the reviewer. We are glad to address the concerns. W1: No experiments on large DNNs and large datasets A1: Thanks for the suggestion. In Fig.5 of the attached PDF, we evaluate our metric on the large model (ResNet152) and large dataset (Ima...	Summary: The authors propose Inversion Influence Function ($I^2F$), a closed-form lower-bound approximation that estimates the recovery $L_2$-norm caused by gradient perturbation in gradient inversion attacks. Detailed mathematical proof and experiments are provided, with comparisons of privacy vulnerability with regar...	Rebuttal 1: Rebuttal: We really appreciate the affirmation of our contribution. W1: In Lemma B.3, factor 2 is missing A1: Thanks for pointing this out. We will revise it accordingly. W2: The explanation of Fig.7 and why comparing the convergence of power iteration and the attack. A2: - **What do dif...	Summary: This paper proposes to leverage influence function as a tool for understanding and analyzing the privacy risk in gradient leakage by connecting the private gradients with the recovered images. Inversion Influence Function (I^2F) is introduced as an efficient approximation of deep leakage attacks. Theoretical j...	Rebuttal 1: Rebuttal: Thanks for the positive comments on our contribution and novelty toward understanding the gradient inversion attack! W1: For complex NN and non-convex loss func, the inversion mapping $G_r$ may be not bijective A2: Thanks for your comments. Indeed $G_r$ may be not bijective but it is not...	Rebuttal 1: Rebuttal: Thanks to all the reviewers for their patient reading and valuable comments. We are trying our best to address the concerns of all the reviewers. Here we attach a PDF for more empirical results. Pdf: /pdf/e755cb980c131598ed782d12db4b5dbc1ab35ad0.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper aims to understand how private information leaks from gradients in model training. To this end, the authors propose to use influence analysis to analyze how gradient perturbations can affect the quality of samples reconstructed from private gradients. Specifically, they first prove that under some a...	Rebuttal 1: Rebuttal: We really appreciate the affirmation from the reviewer of our contributions and innovations. We are glad to address the concerns as follows: W1: Only MNIST and CIFAR10 are in experiments. MNIST is a little simple. Also, the models are too small A1: Thanks for the suggestion. In Fig.5 o...	null	null	null	null	null	null
A Bounded Ability Estimation for Computerized Adaptive Testing	Accept (poster)	Summary: This is a very interesting paper that proposes a coreset-based algorithm to evaluate students' ability to answer questions from a question bank. The main innovations are defining a true student's probability and proposing a coreset algorithms with pseudo labels. Strengths: originality\ I didn't have dire...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback on our CAT paper. We appreciate the time and effort you have invested in reviewing our work. For your concerns regarding the fairness of CAT itself, first of all, let me introduce the background of CAT: as stated in Section 1, CAT is a personalized question...	Summary: This paper proposes a method for computerized adaptive testing by selecting questions that have similar gradients in their expected responses to other questions in the question bank. Results show that this method works well on real-world datasets. Strengths: The proposed method seems sound. Experimental resu...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments and questions regarding our manuscript. Please feel free to share any further insights or suggestions you might have. > Q1: The proposed method is largely unsurprising and I do not find it to be significantly different than existing active learning metho...	Summary: This paper tries to answer a question in computerized adaptive testing: how to select a question suitable for student without knowing the ground-truth of his/her true ability. To this end, the authors find the theoretical approximation of the true ability and provide theoretical and experimental analysis to su...	Rebuttal 1: Rebuttal: We greatly appreciate your thoughtful insights and suggestions regarding the generality of the expected gradient difference approximation proposed in our paper. Your perspective on the broader applicability of this approach is indeed intriguing and aligns well with our intentions. > Q1: The e...	Summary: This paper investigates the problem of effective question selection in Computerized Adaptive Testing (CAT), with the goal of designing a procedure that minimizes test length while maximizing estimation accuracy of student ability. While student ability doesn’t have a known ground-truth, they use the student a...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude for your high appreciation of the contribution and novelty presented in our paper. Your positive feedback means a lot to us. We also appreciate your valuable suggestions regarding the refinement of certain technical aspects in the paper to enhance cla...	Rebuttal 1: Rebuttal: We appreciate all the reviewers' thorough assessment and valuable feedbacks. Their thoughtful evaluation has provided valuable insights that have significantly contributed to the improvement of the manuscript. The reviewers' positive comments encompassing different dimensions are truly encouraging...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors advocate to propose a method to better estimate students ability by using as few questions as possible. They redefine Computer Adaptive Testing(CAT) as a adaptive subset selection of question to estimate students ability and propose a gradient based selection method to select items that minimizes t...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We understand that there might be room for improvement in terms of clarity. Regarding the questions you raised, we have carefully considered each point and have made following responses: > Q1: How is ground truth "ability" $\theta_0$ and $\Theta$ define...	null	null	null	null	null	null
Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization	Accept (poster)	Summary: As existing approaches for uncertainty estimation have been limited by the guidance for calibrating the prediction risk and model confidence, the paper proposes a novel fine-grained reward maximization (FGRM) framework, which addresses uncertainty estimation by reinforcement learning based model tuning with an...	Rebuttal 1: Rebuttal: Thank you for your positive feedback regarding the novelty of our proposed fine-grained parameter update scheme and the great empirical performance of our method. Our responses to your comments are as follows. > * While enjoying superior empirical performance, it would be better to provide some ...	Summary: This paper introduces a novel Fine-Grained Reward Maximization (FGRM) framework to improve uncertainty estimation in deep segmentation models for safety-critical applications. The approach uses a reinforcement learning-based model tuning paradigm to optimize and calibrate the model. The FGRM framework is the f...	Rebuttal 1: Rebuttal: We appreciate the reviewer's valuable comments, providing us with opportunities for improvement and clarification. We would like to address each of your comments in detail as follows. > * Comparison with other methods: In the related works and experiments, lack of discussion and comparison with t...	Summary: This paper proposes a novel method for uncertainty estimation. A segmentation network is first pre-trained by considering a generative model where the segmentation of an input $x$ is drawn from a Dirichlet distribution, which enables MLE. The main contribution of the paper is then the reinforcement learning ...	Rebuttal 1: Rebuttal: We sincerely appreciate your insightful comments and positive feedback regarding the recognition of “many novelties” in our paper, clear presentation of our methodology and strength of experimental evaluation. We would like to provide the necessary clarifications and improvements in response to yo...	Summary: This paper empirically studied uncertainty estimation in safety-critical scene segmentation. The authors employed reinforcement learning (RL) methodologies, including fine-grained reward maximization (FGRM) framework and fisher information matrix for parameter updates. Additionally, to calibrate prediction ris...	Rebuttal 1: Rebuttal: Thank you for your positive comments on the significance and importance of the task tackled in our work, the novelty of using reinforcement learning (RL) in uncertainty estimation, and our strong experimental results. Our detailed responses to your comments are as follows. > * Applying the propos...	Rebuttal 1: Rebuttal: We appreciate the reviewers for taking their time to review and provide constructive feedback. We are glad to see that most reviewers recognized the novelty of our method, the strength of our experimental evaluations, and the good presentation of our paper. We have made every effort to address a...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces a new uncertainty estimation method for medical imaging segmentation tasks. The method relies on a pre-trained segmentation network that uses evidential learning to produce the parameters of a Dirichlet distribution over the class probabilities, which can be translated to aleatoric and ep...	Rebuttal 1: Rebuttal: We sincerely thank you for your positive comments on our thorough ablation study, clear benefits over baselines, and novel methodology. We would like to address each of your questions below. > * Why is this considered a reinforcement learning algorithm? Reply: Thank you for your comment. Our me...	null	null	null	null	null	null
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models	Accept (poster)	Summary: This paper presents a novel method to connect vision-language instruction tuning with large language models. To implement this, the authors introduce the adapters rather than heavy bottlenecks between vision-language input tokens and LLM. A routing mechanism is designed to adaptively choose the right direction...	Rebuttal 1: Rebuttal: We highly appreciate your careful review for this paper. Your beneficial feedback and valuable suggestions indeed improve our paper a lot. Below, we response to your key concerns point by point. Comment#1： Why do we need the routing between different kinds of inputs? Can we just use dete...	Summary: This paper presents a cost-efficient method to fine-tune LLMs thus enabling their multimodal reasoning capabilities. The main technical contribution includes using Mixture-of-Modality Adapation, which adopts lightweight adapters to bridge the gap between modality gaps. In the meanwhile, MMA also allows automat...	Rebuttal 1: Rebuttal: We sincerely appreciate your time and efforts spent in this paper. Below, we response to your concerns point by point. Comment#1： Evaluation is not sufficiently convincing. Results on image captioning and VQA should be reported. Response: Thanks for this constructive comment. Fol...	Summary: > Update: I bumped up my rating to 6 after rebuttal This paper proposes, LaVIN, an efficient and effective vision-language instruction tuning scheme to adapt LLMs. Specifically, the authors utilize parameter-efficient modules to adapt the LLaMA LM – they insert several adapters to the image encoder and mixtur...	Rebuttal 1: Rebuttal: We highly appreciate your time and effort in reviewing this paper, and also thanks for positive rating and beneficial feedback. Below, we response to your key concerns point by point. Comment#1： I think the proposed method resembles LLaMA-adapter a lot, maybe the authors should better no...	Summary: This paper proposes Mixture-of-Modality Adaptation (MMA), which adopts lightweight adapters to bridge the gap between LLMs and VL tasks. The adapter utilizes a router to automatic shift between single-modal and multi-modal instructions. When applying MMA to LLaMA and training on both single-modal and multi-mod...	Rebuttal 1: Rebuttal: We sincerely appreciate your careful review and constructive suggestions for this paper. Below, we response to your key concerns point by point. Comment#1： The adapter idea has been extensively explored in previous efficient VL training, and using adapter to efficiently bridge vision and L...	Rebuttal 1: Rebuttal: Dear Reviewers: We thank all reviewers for their valuable and encouraging comments on the novelty and technical contributions of our paper, such as "it surpasses some existing models that have larger size", "achieve competitive performance given small number of training parameters ", *"LaVI...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper presents a novel method to do efficient vision language fine-tuning. Through a mixture of modality adaptation mechanism, the model can close the gap between different modalities. Additionally, the paper proposes a routing algorithm to switch between multiple tasks. The training cost of the proposed ...	Rebuttal 1: Rebuttal: We highly appreciate your time and effort in reviewing this paper, and also thanks for your constructive comments on our work. Below, we response to your concerns point by point. Comment#1： The model is only tested on one dataset, which ight not be convincing enough regarding the effectiv...	null	null	null	null	null	null
Rewiring Neurons in Non-Stationary Environments	Accept (spotlight)	Summary: The paper presents a bio-inspired rewiring technique to improve deep reinforcement learning (DRL), especially in continual learning in non-stationary environment. The rewiring is implemented using permutation matrix P for all the hidden layers in MLP. There are several benefits. First, by using a set of differ...	Rebuttal 1: Rebuttal: Thank you for the very constructive comments. Our responses are provided below. [W1] Justification for multi-mode strategy * In Figure 2a of the global response, we compare the exploration efficacy of our multi-mode strategy against pink noise [1]. While the single-mode baseline with pink noise ...	Summary: The authors propose a means to efficiently expand the capacity of a neural network, namely connection permutations. The approach interleaves permutations matrices between layers of a neural network, such that input-output relationships can be adapted during learning in addition to learning the weight matrices....	Rebuttal 1: Rebuttal: Thank you for the very constructive comments. We would clarify as follows. [W1] Memory overhead * Our method requires caching one previous weight $\boldsymbol{W}^{t-1}$ and all previous permutations. Among them, the weight accounts for most of the overhead, while the permutations prove to be hig...	Summary: The authors propose a new architectural method for continual reinforcement learning. The method relies on training not just the weights of the network, but also the arrangement of the neurons in each layer (implemented as permutation vectors based on a learned score vector for each layer of neurons). The met...	Rebuttal 1: Rebuttal: Thank you for providing valuable feedback. Our responses to your questions are below. [Q1] NumPy notation in Eq. 3 * Thanks for pointing out the NumPy notation in $\boldsymbol{I}[\boldsymbol{z}_l,:]$. It rearranges the rows of the identity matrix $\boldsymbol{I}$ according to the indices $\bolds...	Summary: The paper proposes a new method for continual reinforcement learning. The idea is to leverage a neuron rewiring mechanism implemented as additional permutations of neurons between the NN weights. In the proposed algorithm, several such permutation sets are maintained, which correspond to different policies tha...	Rebuttal 1: Rebuttal: We deeply appreciate your valuable suggestions, and we would like to address your main concerns as follows: [W1] Misleading statements in Section 3.4 * We apologize for any confusion around Section 3.4. Specifically, in line 195, our aim was to present a new interpretation of catastrophic forget...	Rebuttal 1: Rebuttal: We thank all reviewers for the insightful comments, which are important for improving our work. Alongside our individual responses, we have meticulously prepared a PDF file containing figures that effectively address numerous frequently raised concerns. Below is a concise summary of these figures....	NeurIPS_2023_submissions_huggingface	2,023	Summary: This work studies the continual reinforcement learning setting. This paper proposes a method to permute the neurons in the network, and these permutations allow the exploration of a large part of the weight space. The proposed method caches weights from the prior tasks, which helps to mitigate forgetting. An a...	Rebuttal 1: Rebuttal: Thank you for the very helpful comments. Here are our responses to the concerns raised. [W1] Evaluation could be more rigorous * Having reading the paper by Patterson et al. [1], we wholeheartedly concur that our work stands to gain from a more rigorous evaluation. To this end, during the rebutt...	null	null	null	null	null	null
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network	Accept (poster)	Summary: In a recent line of research, Generative Flow Networks (GNFs) are used for structure learning by drawing DAGs (that represent BN structures) from an implicit DAG distribution that fits the data (Deleu et al 2022). The contribution of the present paper is extending this line of research by drawing joint DAG and...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments; we believe that checking the soundness of theoretical results when available is an integral part of the reviewing process, and we appreciate it that you took the time to do so. **However, we want to draw the attention of all reviewers and the...	Summary: The authors present a method for learning a posterior distribution over Bayesian networks using an extended version of GFlowNets that can jointly construct the underlying DAG and associated parameters in a two-stage process. They use an objective based on a generalization of detailed balance to train their flo...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed comments. Due to limited space, we focus our responses to the Questions, and defer the responses to other points raised in the review in a separate comment below. > *Fig. D.3c: is this the negative LL (lower=better) or just LL? Depending on t...	Summary: The authors utilize a generative flow network to jointly model the structure of the DAQ and its parameters. Strengths: The authors utilize GFlowNets to jointly model the structure of the DAQ and its parameters. The experiments on simulated data and real-world data are promising. Weaknesses: I am not an exper...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for taking the time to read our submission, despite it not being in their area of expertise. The limitations of our work, along with broader impacts, are discussed in Appendix B.	Summary: This paper presents a new method for Bayesian structure learning from observational data, based on the framework of GFlowNets. GFlowNets have been used for Bayesian structure learning, but previous approaches view the distributions over the parameters and structures of a model modular and may learn them with...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed review and comments on our submission. > But the benefit of doing so is less convincing in the paper. We have not seen theoretical or analytical study on why jointly learning them with one GFlowNet is useful. We invite you to read the gene...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for taking the time to review our submission and for their valuable comments. While we are addressing specific questions in individual rebuttal comments, we would like to add a few points here which will be of interest for all reviewers and Area Chairs. If...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The present paper extends upon prior literature in making use of GFlowNets for structure learning. The extension is concerned with finding an adequate approach to learning a complete Bayesian network, that is, the graph's structure and its parameters. Theory to support the consistency of the representation and...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their review and their enthusiasm regarding our submission. > 'Just' learning the parameters brings a completion to the structure learning approach for GFlowNets but IMHO poses a more marginal/incrimental improvement > We invite you to read the general ...	Summary: This paper uses GFlowNets to learn a generative model that samples from the posterior distribution of both the graphical structure and corresponding parameterisation of Bayesian networks given some observation. The paper demonstrates that sub-trajectory balance conditions suffice to ensure that the GFlowNet in...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their review and their suggestions to improve the presentation of our method. > I also find the paper hard to read and understand technically for a reader non-familiar with GFlowNets. > We are sorry to hear that the presentation was not sufficiently cle...	null	null	null	null
Achieving Cross Modal Generalization with Multimodal Unified Representation	Accept (poster)	Summary: This paper is focused on a novel and promising setting, which is the zero-shot generalization ability in other modalities that lacks annotations. It is meaningful in real-world scenarios even though it still requires paired multimodal pretraining. In addition, I agree with the authors that the ability of the n...	Rebuttal 1: Rebuttal: Dear Reviewer w8Lb: Thank you very much for taking time to read our paper and giving such insightful comments. Please see the following for your point-by-point response. --- Weakness 1. The organization of the introduction We greatly appreciate your useful comments to make our paper bette...	Summary: This paper proposes to learn a unified discrete representation from paired multimodal data during pre-training. During the downstream task, it can achieve zero-shot generalization ability in other modalities when only one modal is labeled. Specifically, it develops a Dual Cross-modal Information Disentangling ...	Rebuttal 1: Rebuttal: Dear Reviewer bPma: Thank you very much for your acknowledge of our paper. We are glad to answer your questions point by point. Weakness 1. The compared methods are out-of-date. The authors should provide more latest works for comparison Multi-modal unified representation is a challenging ...	Summary: The paper proposes a model to learn a unified discrete representation from paired multimodal data during pre-training. Then in downstream tasks, the model can achieve zero-shot generalization ability in other modalities when only one modality is labeled. The two key contributions are the Dual Cross-modal Infor...	Rebuttal 1: Rebuttal: Dear Reviewer y1JE: Thank you so much for taking time to read our paper and providing valuable comments. We are glad to respond your questions point-by-point. Weakness. The comparison with original commitment loss Thank you for pointing out our missing ablations, we conduct a series of ex...	Summary: In this paper the authors propose a novel task called Cross Modal Generalization (CMG), where they aim to use unlabelled internet scale paired multimodal data during pre training and then use it for zero shot generalization to other modalities in downstream tasks. The authors claim that disentagling modality s...	Rebuttal 1: Rebuttal: Dear Reviewer WtGp: Thank you very much for your insightful comments and your acknowledgement of our proposed Dual Cross-modal Information Disentangling module. Let us illustrate your questions point by point. --- Weakness 1. For comparison with MAE-based and CLIP-based method We much app...	Rebuttal 1: Rebuttal: Dear reviewers, We much appreciate for your acknowledgement of our work and helpful, insightful comments. Following the reviewers' suggestions, we have made a major revision of the paper and conducted a series of new experiments to address the reviewers' concerns. We have also updated two figures...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper first introduces a new task called Cross Modal Generalization, which aims to learn a unified discrete representation from paired multi-modal data during pre-training, and realize zero-shot generalization in other modalities in downstreams tasks. This paper proposes Dual Cross-modal Information Disen...	Rebuttal 1: Rebuttal: Dear Reviewer jARS: We appreciate your positive feedback and providing very valuable suggestions. Let us respond to your questions point by point. --- For Weaknesses: This paper only perform modality transfer on one pair of modalities, A & V. Thanks for asking! Yes, most of our pretraining...	Summary: The paper proposes a new pretraining task called Cross Modal Generalization (CMG) for learning unified multimodal representations. The goal is to map different modalities (e.g. audio, visual, text) to a shared discrete latent space during pretraining, such that the model can generalize to unseen modalities in ...	Rebuttal 1: Rebuttal: Dear Reviewer kKgn: Thank you very much for your acknowledgement of our methods and giving us positive feedback! We will respond to your question as follows: In the formula (1), are the ${\Phi}^{a}$ and ${\Phi}^{b}$ the same in the implementation? Sorry about the misunderstanding. For dif...	null	null	null	null
Trans-Dimensional Generative Modeling via Jump Diffusion Models	Accept (spotlight)	Summary: This paper proposes a new diffusion model based on jump diffusion processes. Compared with previous discrete and continuous formulations, the model introduces the usage of a transition kernel, which models the jump process in a semantically meaningful manner. The method absorbs standard constructions like diff...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their review and positive feedback. We are very pleased to hear that our approach is considered to be elegant and theoretically sound with wide applicability. We address the specific comments from the review here. > *For the molecule task, the metrics are b...	Summary: This paper focuses on varying dimensional datasets and proposes a novel generative model to solve the varying dimensional problems. The proposed model is theoretically valid and has an interesting and novel contribution to extending the traditional score-based generative model by generating both state values a...	Rebuttal 1: Rebuttal: We thank the reviewer for their review and we appreciate the praise for our method’s novelty and thorough experiments. We address your questions below. > *I am confused about how to properly define $K^\text{del}(i \| n)$ in $\overrightarrow{K}_t(\mathbf{Y} \| \mathbf{X})$ Any clarification about th...	Summary: This paper addresses the problem of modelling data of various dimensions. This is achieved by generalising diffusion models as jump diffusion processes, allowing the content and dimension of data to be jointly modelled. The forward process gradually corrupts the data with gaussian noise while also gradually de...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their review and positive comments on the paper. We are especially happy to hear that our proposed method is considered to be a compelling solution to an important problem. You have raised important points regarding the new dimension distribution and the tim...	Summary: This paper proposes jump diffusion, which is a novel diffusion model to handle data with varying dimensions. The proposed method is derived from a special forward process that contains a jump part that changes the dimension of the generated samples. The corresponding backward process and the learning objective...	Rebuttal 1: Rebuttal: We thank the reviewer for their engagement with our proposed methodology and thoughtful questions. We are grateful that our work is considered to be a solid framework to tackle a very important problem. We answer questions and respond to comments below. > *It would be very nice if I can hear comm...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their analysis of our paper and very helpful reviews. We were pleased that reviewers considered our work to be a novel and theoretically sound method for tackling the important problem of modeling data with varying dimensionality. We address comments m...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks	Accept (poster)	Summary: The paper proposes a new method for generating top-K adversarial perturbations -- modifying the input such that the classifier predicts the specified K classes, in order. The work addresses this with a two-stage approach, where the first stage computes an adversarial perturbation to the representation, subject...	Rebuttal 1: Rebuttal: Thank you for your time and efforts reviewing our submission. We address your concerns as follows. Comment 1: Alternatives to the Ordered Top-K Attack which are easier to optimize. > Response: As discussed in our global response, ordered Top-$K$ adversarial attacks exploit the principle ...	Summary: It identifies that while sufficient to capture top-K attack constraints, hand-crafted surrogate losses are not necessary and often introduce inconsistency and artifacts in optimization. It eliminates the need of introducing surrogate losses. Instead, it keeps the top-K attack constraints in the vanilla form an...	Rebuttal 1: Rebuttal: Thank you for taking your time reviewing our paper. We address your concerns as follows. Comment 1: \"The presentation can be improved.\" > Response: We agree and will carefully revise and proofread the paper. > Regarding noted issues in our matrix in Eqn. 8, it indeed has too many no...	Summary: This work proposes a novel approach, QuadAttack to learning ordered top-K adversarial attacks with a low cost. The method is based on a quadratic programming formulation that optimizes the attack objective. Notably, this work extends to a larger K(Top-K). For example, the K is improved from 10 to 15 compared t...	Rebuttal 1: Rebuttal: Thank you for your efforts reviewing our submission. We address your concerns as follows. Comment 1: \"QuadAttack is not always better than baseline methods. Some deep analysis is lacked.\" > Response: Thank you for your detailed review. We appreciate the concern raised regarding the c...	Summary: This paper introduces QuadAttackK, a new approach to compute ordered top-K adversarial attacks. The main contribution of this paper is to formulate and efficiently solve the top-K adversarial attack problem via quadratic programming (QP). The experiment results on ImageNet models show that the proposed method ...	Rebuttal 1: Rebuttal: Thank you for your time reviewing our paper. In the following, we address your comments point by point. Comment 1: \"The problem of computing ordered top-k adversarial attacks lacks some motivation.\" > Response: Please refer to the \"Elaborated Motivations of Learning Ordered Top-$K$ Ad...	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their constructive feedbacks which help us to greatly improve our submission. We first address some common concerns. Drastically Improved Results. Please refer to Table 1 in our global response PDF for the improved results. In optimization, perturbati...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Scalable Membership Inference Attacks via Quantile Regression	Accept (poster)	Summary: This paper introduces a new class of membership inference attacks based on performing quantile regression on the distribution of confidence scores induced by the model under attack on points that are not used in training. The approach is computationally efficient and does not require knowledge of the model's a...	Rebuttal 1: Rebuttal: Thanks for your careful reading and useful feedback --- below we address your specific questions. We're happy to further discuss any of these points if there are any remaining questions or confusions! > *I appreciate it if the authors could also compare the proposed method to the model-based atta...	Summary: This work presents a novel membership inference attack that offers computational efficiency compared to state-of-the-art (SOTA) approaches. While the paper addresses a topical issue and provides an alternative attack method, there are several weaknesses that need to be addressed for a more comprehensive and co...	Rebuttal 1: Rebuttal: Thanks for your careful reading and useful feedback --- below we address your specific questions. We're happy to further discuss any of these points if there are any remaining questions or confusions! > *The theoretical results presented in the paper focus on controlling the false positive rate ...	Summary: The paper proposes a new membership inference attack based on training an attack model with the pinball loss. This avoids the use of shadow models, a common technique for membership inference, while often outperforming attacks which do use shadow models. They evaluate their attacks on a variety of image and ta...	Rebuttal 1: Rebuttal: Thanks for your careful reading and useful feedback --- below we address your specific questions. We're happy to further discuss any of these points if there are any remaining questions or confusions! > *Some magic happens in the experiment section in the paragraph starting at line 290 (page 7). ...	Summary: The main focus of this paper is about the membership inference attack problem, i.e., determining whether a particular example was used in training or not. Most existing such attacks estimate the distribution of some test statistics, which are usually computationally expensive. In contrast to existing approach...	Rebuttal 1: Rebuttal: Thanks for your careful reading and useful feedback --- below we address your specific questions. We're happy to further discuss any of these points if there are any remaining questions or confusions! > The parameter $\alpha$ is important yet hard to adjust manually. $\alpha$ is the desired fa...	Rebuttal 1: Rebuttal: We thank the reviewers for their careful reading and useful feedback. In the attached pdf we include an additional experiment over the CINIC10 dataset to address some of the reviewers' specific concerns. We're happy to further discuss any of these points if there are any remaining questions or con...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors propose a novel class of membership inference attacks based on quantile regression applied to confidence score distributions. The proposed 'black-box' algorithm does not require knowledge of the model's architecture and performs competitively with state-of-the-art shadow model attacks while being c...	Rebuttal 1: Rebuttal: Thanks for your careful reading and useful feedback — below we address your specific questions. We’re happy to further discuss any of these points if there are any remaining questions or confusions! > *In my opinion, the main contribution of this paper, which involves using the quantile regressio...	Summary: This paper studies the question of membership inference attack (MIA), which can be formalized as a hypothesis testing (HT) problem. The main contribution of this paper is introducing a new class of MIA. The authors claim that the proposed method is competitive with SOTA MIA methods while being more computatio...	Rebuttal 1: Rebuttal: Thanks for your careful reading and useful feedback — below we address your specific questions. We’re happy to further discuss any of these points if there are any remaining questions or confusion! > *Theorems 1 and 2 are established at the population level. The approximation error of is not disc...	null	null	null	null
Tailoring Self-Attention for Graph via Rooted Subtrees	Accept (poster)	Summary: The paper introduces Subtree Attention (STA), a new graph attention mechanism that overcomes limitations of existing mechanisms in graph learning. STA combines fully-attentional structure with rooted subtrees, approximating masked global attention under extreme settings. By computing attention weights among mu...	Rebuttal 1: Rebuttal: Thank you for the thoughtful feedback on our manuscript. We provide the following detailed responses to your major concerns. > Q1. "The performance improvement of NAGphormer is limited, as demonstrated in Table 1. For instance, when evaluating photos, NAGphormer achieved a score of 95.49±0.11, wh...	Summary: This paper introduces a novel multi-hop graph attention mechanism called SubTree Attention (STA) to address the limitations of both local and global attention in Graph Neural Networks (GNNs). STA allows the root node to attend directly to further neighbors in the subtree, enabling it to gather information from...	Rebuttal 1: Rebuttal: Many thanks for the reviewer's thoughtful feedback. We provide the following detailed responses to your major concerns. > Q1. "Lack of baselines (BernNet,..) The authors ignore some important baselines of propagation-based GNNs, including BernNet and successive work." A1. Indeed, BernNet is a co...	Summary: The present manuscript proposes a novel graph attention layer, that lies in the middle of the local aggregation scheme of message passing and the global (non-structured) nature of a full-attention. The newly proposed Subtree Attention constructs for each node the similarity pairs of key and query matrices from...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer's constructive feedback and positive remarks on our work. We provide the following detailed responses to your major concerns. In order to organize our response logically, we jointly address the first weakness and the question raised by the reviewer. >Q1. "The...	Summary: In this paper, the authors propose a novel Graph Transformer called STA-GNN (SubTree-attention-GNN) to address the over-smoothing and over-squashing in the message-passing scheme. Different from the previous Graph Transformer, STA-GNN's attention mechanism is called SubTree Attention (STA), which computes a no...	Rebuttal 1: Rebuttal: Thank you for the detailed comments and valuable questions. We provide details to clarify your major concerns. > Q1 & Q2. (Concerns related to time and space complexity) A1 & A2. Firstly, we would like to highlight one point: while we indeed introduce an efficient algorithm, the primary motivat...	Rebuttal 1: Rebuttal: We extend our sincere gratitude to the reviewers for their invaluable feedback on our work. We are delighted to see comments such as "The Subtree attention is a novel and interesting idea" (Reviewer smZ8), "Good theoretical analysis is provided." (Reviewer K8yB), and "**can be very useful...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities	Accept (poster)	Summary: The authors combine several recent approaches for object-centric learning. In particular, they use the overall framework which is a combination of SAVi with a slot mixer decoder from [13]. The main objective is optical flow prediction, like in SAVi, but they combine it with DINO feature reconstruction from Din...	Rebuttal 1: Rebuttal: Thank you for the constructive suggestions to improve our paper. > The proposed approach is merely a combination of several recent techniques for object centric learning. […] The novelty of the proposed framework is minimal All in all, we find this statement not a fair characterization of our co...	Summary: The paper aims to learn an effective object-centric feature representation for video segmentation. They build their method upon the slot attention-based framework and self-supervised ViT encoders (DINO), and propose a temporal feature similarity loss for object-centric learning. Their proposed method yields th...	Rebuttal 1: Rebuttal: Dear reviewer, thank you for your detailed review and interesting suggestions to improve our paper. Below we address your questions. We hope that our additional experiments will lead you to consider upgrading your rating. > One of my concerns is the use of the pre-trained ViT encoder. […] It is s...	Summary: This paper proposes VideoSAUR for unsupervised video object segmentation / grouping. The key idea proposed in this paper is to use a temporal feature similarity loss, in combination with a feature reconstruction loss. The grouping is implemented with recurrent Slot Attention. Experiments are conducted on vario...	Rebuttal 1: Rebuttal: We thank the reviewer for their very positive feedback! We are glad you like the paper for its “strong experimental results” and that you find the paper “clear, consistent and easy to follow”. We answer your questions below. > How does the proposed approach works on more challenging datasets? > ...	Summary: The paper considers the problem of unsupervised video-based object-centric learning. It incorporates a temporal feature similarity loss that encodes temporal correlations and introduces a motion bias for object discovery. This loss helps to achieve state-of-the-art performance on the synthetic MOVi dataset. Th...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive feedback! We are glad you find the paper to be “very well-written” and “pleasant to read”. We hope that the reviewer would find additional experiments requested from other reviewers interesting. Their description could be found in the general response.	Rebuttal 1: Rebuttal: We thank the reviewers for their feedback and appreciate that they found our paper "well-written", "pleasant to read", and "well-organized". In addition, the reviewers recognized that our "comprehensive ablation study” is “extensively done” and that it brings insights into the method’s performance...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
A unified framework for information-theoretic generalization bounds	Accept (poster)	Summary: The paper studies the generalization error of the statistical learning algorithms from the information-theoretic point of view. In particular, by leveraging a “decorrelation lemma”, the authors show how various previously established upper bounds on the generalization error, including the common information-th...	Rebuttal 1: Rebuttal: Regarding the decorrelation lemma: both our decorrelation lemma and the Donsker-Varadhan lemma use convex conjugate pairs to bound a product or an expectation of a product. The main difference is that, when using the decorrelation lemma, we can work with some functional of the density ratio $d\mu/...	Summary: This paper describes some steps of the standard formula to obtain generalization error bounds: (i) decoupling of the joint distribution + (ii) chaining. Then, they use this formula contributing mainly on the first front by deriving new decoupling results based on Orlicz $\psi\_p$-norms that can be cast into th...	Rebuttal 1: Rebuttal: It is not the intent of our work to cover all existing generalization error bounds or even to replace existing approaches (many of which, as this review correctly points out, make use of various decoupling lemmas). Most of the additional references listed in the review are indeed very relevant, an...	Summary: The paper presents a unified perspective of information-theoretic generalization bounds through decorrelation and coupling/chaining. Various existing generalization bounds in the literature are recovered or generalized via this perspective. The Fernique-Talagrand upper bound on the expected supremum of subgaus...	Rebuttal 1: Rebuttal: For Theorem 3: if the output of the algorithm does depend on the data, we have to choose suitable couplings and prior, such that the sum of the two terms on the right-hand side of (12) is minimized. We doubt that there exists a single procedure for finding such optimal choices in general cases (or...	Summary: This paper proposes a unified framework for deriving information-theoretic generalization bounds for learning algorithms. The main technical result relies on a probabilistic decorrelation lemma based on a change of measure and Young’s inequality in $L_{\psi_p}$ Orlicz spaces. Combining it with other techniques...	Rebuttal 1: Rebuttal: Since our decorrelation lemma can be used as an alternative to Donsker-Varadhan, it should be possible to obtain the ICIMI bounds using our approach since a key lemma used to prove these bounds also makes use of Donsker-Varadhan. Moreover, it should be possible to obtain the results of [8] and [12...	Rebuttal 1: Rebuttal: We would like to thank all the reviewers for their detailed and careful reviews. In fact, we wish all of NeurIPS reviews adhered to such a high standard! While we address the points raised by each reviewer in individual rebuttals, we would like to clarify a few points that were common to all the r...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Riemannian Projection-free Online Learning	Accept (poster)	Summary: The authors present online algorithms for geodesically convex losses on Riemannian manifolds. Their algorithms do not call the expensive operation of projection onto a feasible set. Instead of projection, they rely on two oracles to provide a direction of descent: a separation oracle and a linear oracle. Both ...	Rebuttal 1: Rebuttal: Thank you for the time and effort you put into understanding our work. Your supportive and constructive review means a lot to us. Below, we have provided answers to your specific questions and addressed your concerns. > I am not sure why $\tilde{\mathcal{K}} = (1-\delta) \mathcal{K}$ is considered...	Summary: The paper considers Riemannian online optimization problems over sets of constraints and tries to tackle them by avoiding projections onto the constraint set. This is an already established line of research in Euclidean optimization and the results follow the structure of Garber and Kretzu (2022). The results ...	Rebuttal 1: Rebuttal: We appreciate your detailed and constructive feedback. Your specific questions and concerns have been addressed as follows. > I see some slight discrepancy in the selection of parameters and worst-case regret bounds between this paper and Garber and Kretzu (2022) (I will refer to that as GK from n...	Summary: The paper focuses on constrained Riemannian online optimization on the Hadamard manifold. Existing Riemannian online optimization methods often require projections, which present computational complexity challenges in high-dimensional settings. To address this issue, the authors have developed a projection-fre...	Rebuttal 1: Rebuttal: Thank you for your thoughtful and helpful feedback. We will revise typographical errors accordingly. We have addressed your specific questions and concerns below. > I'd appreciate if the authors could expound on their primary insights in this paper. Specifically, it would be beneficial to understa...	Summary: The authors study online learning on homogeneous Hadamard manifolds with geodesically-convex, Lipschitz losses. They focus on projection-free methods and in particular they develop algorithms that use either a separaton oracle or a linear optimization oracle. They study adaptive regret algorithms for the full ...	Rebuttal 1: Rebuttal: Your insightful comments and constructive criticism are highly valued. We've responded to your specific questions and concerns in the following sections. >The paper contains some mistakes, and I am not sure if one could fix at least one of them while keeping the stated results. Thank you for brin...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors propose a generalization for online learning on the Riemannian manifolds via separation and linear optimization oracles. The core idea here is to use the oracles to construct an infeasible projection, which may not be the nearest point in the constrain/decision set $\mathcal{K}$, but are in $\mathc...	Rebuttal 1: Rebuttal: Thank you for your constructive comments and suggestions. We appreciate the time and effort spent in reviewing our work, and we will revise the draft accordingly. Below, we address your specific questions and concerns: > For example, the Lemma 19 in the draft points to Wang et al. 2023 Lemma 45, ...	null	null	null	null	null	null
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks	Accept (poster)	Summary: The paper investigates the relationship between graph topology and feature evolution in Graph Neural Networks (GNNs). The paper starts by discussing the phenomenon of Neural Collapse (NC) in instance-wise deep classifiers, where within-class variability decreases and class means align to specific symmetric str...	Rebuttal 1: Rebuttal: General comment: We would like to thank Reviewer GzMi for the helpful feedback. Q: Analyzing only intra-class (within-class) variability without discussing inter-class variability is meaningless. A: We have included a thorough analysis of the increase in between-class variability...	Summary: This paper investigates the feature evolution in Graph Neural Networks (GNNs) via the lens of Neural Collapse. They conduct an empirical study that reveals a decrease in within-class variability in the deepest features of GNNs, but not to the extent observed in instance-wise classification settings. By proposi...	Rebuttal 1: Rebuttal: General comment: We are grateful to Reviewer pASb for an encouraging review and for raising interesting questions. Q: Regarding theoretical results and a discussion on NC2. A: We agree with the reviewer's point that NC2 might also hold to some extent for GNNs. We show these metri...	Summary: This work tries to investigate the feature evolution in GNNs in inductive setting. In particular, it focuses on the Neural Collapse problem using the SSBM data model to ensure the existence of this phenomenon. Moreover, it proposes to verify that the extent of NC in GNNs is not as severe as in instance-wise cl...	Rebuttal 1: Rebuttal: General note: We would like to thank Reviewer t9NA for the constructive feedback. The 2 minor issues will be fixed in the revision. The importance of neural collapse (NC). In standard DNN settings (e.g., image classification on MNIST), the classifiers tend to exhibit NC once they perf...	Summary: EDIT: I am changing my score based on the revision. I think this is a very interesting paper. In particular, it helps us understand why GNNs work well, but not super-duper well. CNNs get full NC, but GNNs don't This work discusses neural collapse of GNNs. Much of the work on neural collapse focuses on unconst...	Rebuttal 1: Rebuttal: General note: We sincerely thank Reviewer 4uoY for the detailed feedback. Due to character limits, please find the responses to all the key questions below. All the minor issues will be fixed in the revision. **Q: I don't understand the inequality of the form $P(A\|B) < P(A)$ given in 444....	Rebuttal 1: Rebuttal: Our response to the general comments on motivation, theoretical modeling, practical significance, over-smoothing, and a fix to Theorem 3.2. Modifying Theorem 3.2 to ignore the non-rigorous diagonal block case As pointed out by one of the reviewers, we have updated Theorem 3.2 to leverage...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Is RLHF More Difficult than Standard RL? A Theoretical Perspective	Accept (poster)	Summary: The authors consider the RLHF setting. First, in the case where there (a) exists a ground-truth utility function and (b) feedback is positive with higher probability when there is a larger difference in rewards, they derive an algorithm to iteratively winnow down a ball of reward functions. When such structure...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive evaluation and detailed feedback. We would address the reviewer’s concerns as follows. Q1. Formatting issues. A1. Thank you for pointing out these issues. We will correct them in the final version since we cannot update the paper during the rebut...	Summary: Refined writing: The objective of this paper is to establish a theoretical foundation for reinforcement learning based on human feedback preferences. The authors conduct an analysis on two aspects: (1) utility-based preferences in tabular MDPs, linear MDPs, and MDPs with low Bellman-Ruler dimension, and (2) ge...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments, and we will address them below. Q1. The first part of analyses primarily focus on the utility-based preferences, which is applicable to simple linear MDPs or MDPs with lower Bellman-Ruler dimensions. It is challenging to extend the conclusion that "hu...	Summary: The authors study the problem of learning in preference-based RL and investigate the question of whether preference based RL is any harder that reward based RL. They show that for preferences that are based on an underlying reward function, preference based RL is no harder than reward based RL for most of the ...	Rebuttal 1: Rebuttal: We thank the reviewer for their positive evaluation and detailed feedback. We would address the reviewer’s questions as follows. Q1. “The paper assumes that humans can provide preferential feedback at trajectory level” A1. We would like to clarify that in the utility-based setting, our ...	Summary: The authors attempt to show the conditions under which RLHF is theoretically identical to standard RL where a reward function is specified as a part of the environment. The algorithm P2R Interface is given as way to learn from preference feedback such that all requirements are met for RLHF to be identical to s...	Rebuttal 1: Rebuttal: Q1. The paper is difficult for me to follow and understand. It is very full of jargon for which no explanation is provided. A1. We kindly ask the reviewer to specify the sections of the paper they found ambiguous. We are eager to provide clarifications where needed. Q2. It is not we...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Meta-in-context learning in large language models	Accept (poster)	Summary: The paper studies an ability of LLMs, called meta-in-context learning, which showcases that LLMs can recursively improve their in-context learning with demonstrations. The authors illustrate this capability with a regression task and a two-armed bandit task. The analysis demonstrates that LLMs are not only abl...	Rebuttal 1: Rebuttal: Dear Reviewer 6CRk, We thank the reviewer for their helpful comments and we have made a response to each of their comments along with suggested changes to the paper: > 1. The paper highlights the meta-in-context learning capability of LLMs. The capability allows LLMs to be recursively improved vi...	Summary: The authors demonstrate that the in-context learning abilities of large language models can be recursively improved via in-context learning itself. The paper misses the method section. I don't know the details and cannot tell the difference from previous work. I didn't get the novel part of the method. It...	Rebuttal 1: Rebuttal: Dear Reviewer NP3F, We thank the reviewer for their helpful comments and we have made a response for each of their comments along with suggested changes to the paper: > Strengths: The problem explored is critical. The experimental analyses are interesing. We thank the reviewer for exposing the ...	Summary: The authors undertake a study of "meta in-context learning" as a capability of Large Language Models, specifically focused on GPT-3 (with some initial experiments on GPT-4). The authors define meta in-context learning following a task-trial structure, in which the agent observes multiple tasks each consisting ...	Rebuttal 1: Rebuttal: Dear Reviewer 3waD, We thank the reviewer for finding the results thoroughly investigated and clearly conveyed. We also appreciate the comment on originality and belief in its potential impact for applying LLMs to multi-task setups rather than fine-tuning. We also thank the reviewer for their hel...	Summary: The paper explores a phenomenon referred to as “meta in-context learning” in large language models, where the in-context learning abilities of these language models can be recursively enhanced through in-context learning itself. To demonstrate this, the researchers examine two idealized domains: a one-dimensio...	Rebuttal 1: Rebuttal: Dear Reviewer xRJY, First, we thank the reviewer for finding the paper interesting with a clear message conveyed from the experiments. We also thank the reviewer for their helpful comments and we have made a response for each of their comments along with suggested changes to the paper: > I found...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their valuable and thoughtful feedback. * Reviewer ShPz found “the paper an enjoyable read” and stated that “the [our] research is relatively well motivated, the analysis are carefully detailed, and the method is clearly explained.” * Reviewer xRJY said th...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper demonstrates that large language models (LLMs) are capable of meta-in-context learning: updating their in-context-learning abilities when prompted with examples of several tasks The authors empirically show this capability on several learning paradigms, including supervised learning (1D linear regre...	Rebuttal 1: Rebuttal: Dear Reviewer ShPz, We appreciate that the reviewer found our paper well-motivated, clear enough to be replicated easily and the idea elegant and possibly impactful. We also thank the reviewer for their helpful comments and we have made a response to each of their comments along with suggested ch...	null	null	null	null	null	null
The noise level in linear regression with dependent data	Accept (poster)	Summary: The focal point of this paper is very precise, namely least-squares linear regression under data which need not be independent. Regardless of linearity, when the model is properly specified (realizability, i.e., the expected squared error minimizer is included in the model), martingale-based arguments are well...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and effort spent on evaluating our manuscript. * We have clarified the distinction between local and global complexities by adding the following sentence to Section 3.1: "In other words, they compete against the worst distribution at a given level of mixing, ...	Summary: This paper deals with linear regression with dependent ($\beta$-mixing) data. It provides an upper bound of the OLS error in terms of the sample size and the effective dimension of the covariate matrix. Strengths: This paper studies the linear regression with dependent data. The main idea is to decompose the...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and effort reviewing our submission. By our estimation, the reviewer's main concern is a lack of novelty, in the sense that similar estimates on the random walk component of our analysis (i.e. the "numerator" in the estimation error) have appeared previousl...	Summary: The paper explores the impact of noise level in linear regression for dependent data by blocking technique, which can accommodate a broad type of dependent structures. Theoretical justification of the non-asymptotic guarantee and excess risk bound are provided, imposing any realizable assumptions on the nois...	Rebuttal 1: Rebuttal: We do not agree with the assessment provided by reviewer 8guK and believe it should be disregarded. There is no real criticism of our work in this review other than sweeping and unsubstantiated (and sometimes contradictory) claims. The main points of contention appear to be 1) related work, 2) cla...	Summary: This paper studies the risk bounds of OLS for linear regression with dependent data. In particular, the label noise is allowed to be non-martingale. It shows that, after a burn-in phase, OLS with dependent data archives a bound of the same order as if the data is iid, provided that the failure probability is m...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and effort spent on reviewing our manuscript. We provide brief answers to their questions 1,2 and 4 below (with 3 being more of a statement). * Answers to Questions. 1. (hypercontractivity/moment equivalence). For sake of argument, let us assume that the pr...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper gives finite-sample bounds on the excess risk of ordinary least squares regression in the non-realizable case with dependent ($\beta$-mixing) data. The result asymptotically matches the predictions of the central limit theorem. The dependence on the mixing behaviour of the process is relegated to ter...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and effort spent on evaluating our manuscript. * While we agree that there is trade-off between generality, obtuseness and being concise, we prefer to leave the statement of Theorem 3.1 as is. Our motivation for this is that we already have an informal theor...	null	null	null	null	null	null
A Unified, Scalable Framework for Neural Population Decoding	Accept (poster)	Summary: Neural population decoding refers to inferring the behavioral output of organisms from a recording of a population of neuronal recordings. This paper introduces a transformer architecture to improve the accuracy of decoding. The efficiency of the system is increased by representing input spike trains with even...	Rebuttal 1: Rebuttal: Thank you for your feedback and questions! We are very excited to hear that you found the paper to be “novel” and also appreciate our use of “multiple experiments done in different labs”. In what follows, we will provide point-by-point replies to your questions. > 1. “I oppose alluding to 'found...	Summary: Deep learning and transformer models have shown great promise in identifying structure from large datasets. With recent advancements in neural recording methods, it is now possible to generate rich and heterogeneous recordings from large populations of neurons across multiple brain regions and experimental con...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review of the paper! We are very excited to hear that you found the paper to be “presented clearly and is technically sound” and also agree that “extracting shared variability across multiple datasets is crucial for neuroscience”. In what follows, we will provide poi...	Summary: This paper introduces a novel method for developing models that can predict the activity of neural populations by learning from data recorded across different sessions, tasks, and animals. The method is uses a custom tokenization procedure for spikes and a deep neural network architecture based on PerceiverIO....	Rebuttal 1: Rebuttal: Thank you for your thoughtful review of the paper! We are very excited to hear that you found the proposed technique to be “technically solid and elegant” and also agree that it “addresses a real need in neuroscience labs”. In what follows, we will provide point-by-point replies to your questions...	Summary: The authors describe a novel method for the task of neural decoding: using the time series of activity recorded from a population of neurons to predict the activity of scientifically relevant target variables. The describe their approach, called POYO, based upon the tokenization of spike data, the application ...	Rebuttal 1: Rebuttal: Thank you for your comments and questions, and for finding that our work "has the potential to be impactful"! In what follows, you will find our point-by-point response to your main concerns, and results from new experiments that we ran to address your feedback. Please let us know if there’s anyt...	Rebuttal 1: Rebuttal: We would like to thank all of the reviewers for their great feedback and suggestions! The reviewers agreed on the impact of the work and acknowledged the innovations behind the work for multi-session neuroscience. Some highlights and praise from the reviewers: - Method and Approach: “the prop...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
CEIL: Generalized Contextual Imitation Learning	Accept (poster)	Summary: This paper aims to develop a simple and scalable IL method, which is applicable to a wide range of IL settings (e.g., offline/online, LfD/LfO, etc.). To achieve this, the imitation policy are decoupled into a contextual policy and a latent variable. Experiments on a wide range of tasks illustrate effectivenes...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and thoughtful feedback. We will address your concerns one by one. Q1: results of online HalfCheetah. A1: Thank you for the suggestion. We have provided online HalfCheetah results, Figure 3, and more comparison results in Atari and Adroit domain, F...	Summary: The objective of this work is to create an imitation learning (IL) method that functions effectively across a variety of common IL settings, which include online, offline, Learning from Demonstrations (LFD), Learning from Observation (LFO), and cross-domain. To achieve this, a dual-level expert matching goal i...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and thoughtful feedback. We will address your concerns one by one. Q1: the experimental evidence. A1: We have carried out new experiments in Atari and Adroit domains (see results, Figure 1, in the PDF file in the "global" response). We can see tha...	Summary: This work proposed ContExtual Imitation Learning (CEIL), a general method that can be applied to multiple settings, including learning from observations (LfO), offline IL, cross-domain IL, and one-shot IL. CEIL incorporates the hindsight information-matching principle within a bi-level expert matching objectiv...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and thoughtful feedback. We will address your concerns one by one. Q1: the relationship between the pre-defined return $f_R(\tau)$ in Equation 2 and $f_\phi(\tau)$ in Equation 3. A1: Compared to the pre-defined return $f_R(\tau)$, $f_\phi(\tau)$ c...	Summary: This paper presents a method that aims to address Imitation Learning (IL) tasks by simultaneously updating the embedding function of a contextual variable, an optimal contextual variable, and a policy conditioned on that variable. The proposed method learns the conditional policy by minimizing the trajectory s...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and thoughtful feedback. We will address your concerns one by one. Q1: guidance on the process of selecting the hyperparameters in the Appendix. A1: For the size of the embedding dictionary, we selected it from a range of [512, 1024, 2048, 4096]. W...	Rebuttal 1: Rebuttal: Dear reviewers, Thank you for all of your constructive suggestions, which have helped us improve the quality of our paper. This general response provides a summary of the experimental requirements you suggested. Below, we list the content of each chart presented in the submitted PDF. + Figure...	NeurIPS_2023_submissions_huggingface	2,023	Summary: One recent idea in reinforcement learning is to learn a sequential model that can predict state-action transitions and rewards, and then obtain a strong policy by inferring actions conditioned on high reward. This has the major benefit that even low-reward trajectories provide useful information for learning. ...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and thoughtful feedback. We will address your concerns one by one. Q1: whether the strong performance will generalize to very different settings (e.g. Atari). A1: As suggested by the reviewer, we have carried out new experiments on Atari and Adroi...	null	null	null	null	null	null
Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds	Accept (poster)	Summary: This paper takes the popular idea of SAM from Euclidean space to the Riemannian space, and proposed a new optimization framework known as Riemannian SAM, which is a generalization of an existing technique. At the same time, the authors provided a convergence analysis of this newly proposed framework. Strength...	Rebuttal 1: Rebuttal: Thanks for your constructive feedbacks. [On Motivation on Riemannian SAM formulation] We believe that considering sharpness-aware minimization in Riemannian optimization could help the optimization. The "flat minima hypothesis" in Euclidean space was put forth, suggesting that neural network...	Summary: The authors propose an extension of the sharpness-aware minimization (SAM) technique on Riemannian manifolds, for which specific computations are feasible as the retraction map and the vector transport. The proposed optimization method considers the nonlinear geometry that is implied due to the manifold when p...	Rebuttal 1: Rebuttal: Thanks for your constructive feedbacks. [On Q1 and Q2] As the reviewer pointed out, in most practical cases, the manifolds under consideration are often embedded in Euclidean space or are subsets of $\mathbb{R}^d$ with an appropriate Riemannian metric. For instance, in the case of one of an ...	Summary: This paper introduce the new objective function SAM (Shapeness-awared minimization) on optimization problems on Riemannian manifolds. The motivation is that, with the success of SAM on the Euclidean space, the new Riemmanian metric, which is typically different from the flat Euclidean metric, can introduce mor...	Rebuttal 1: Rebuttal: Thanks for your constructive feedbacks. [On Technical Limitations] We appreciate your valuable feedbacks, but we do not agree with the reviewer’s opinion. Due to space constraint of rebuttals, we answer this concern in **1. Riemannian SAM is a non-trivial extension of Euclidean SAM with nove...	Summary: This paper extends the Sharpness-Aware Minimization (SAM) algorithm to Riemannian manifolds. They show that the new model subsumes Fisher SAM as a special case, and it leads to a new algorithm (called Lorentz SAM when specified to the Lorentz manifold). The main proposed algorithm is given in Algorithm 1, whi...	Rebuttal 1: Rebuttal: Thanks for your constructive feedbacks. [On Contributions] Thank you for your important feedbacks, but we believe that our main example, hyperbolic representation learning, is not a simple “one example” but has a sufficiently general use cases, and that proposing an optimization technique th...	Rebuttal 1: Rebuttal: Due to the space constraints on each rebuttal, we answer some important questions in general response here. 1. Riemannian SAM is a non-trivial extension of Euclidean SAM with novel theoretical insights * [On technical side] We would like to emphasize that our Riemannian SAM does NOT mer...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
The Simplicity Bias in Multi-Task RNNs: Shared Attractors, Reuse of Dynamics, and Geometric Representation	Accept (poster)	Summary: This paper studies how recurrent neural networks form shared attractors and reuse dynamics in the multitask setting. A simplicity bias is revealed, i.e., RNNs will not create new attractors unless necessary. The authors further investigate how task similarities (symmetry, gradients) can be translated to repres...	Rebuttal 1: Rebuttal: Our work stems from neuroscience questions, but we thank the reviewer for causing us to speculate on possible engineering applications. Q1: How do you expect your conclusions to scale to more complicated datasets (e.g., contains 1000 tasks)? The concept of convergence to shared dynamical objects...	Summary: While the relationship between task requirements and neural dynamics in Recurrent Neural Networks (RNNs) has been studied for individual tasks, the dynamics of multiple tasks working together remain largely unexplored. The study introduces a systematic framework to examine multiple tasks in RNNs, minimizing in...	Rebuttal 1: Rebuttal: We deeply appreciate the reviewer's thorough examination of our work and the invaluable feedback provided. We acknowledge the areas of improvement, especially in references and technical accuracies, and commit to rectifying them diligently. Computational cost: The networks highlighted in this sub...	Summary: This paper uses a multi-task RNN setup and tries to make sense of what computations get shared, and how they get shared. Strengths: A novel task set-up is used with gated, orthogonal, and parallel settings. The overall problem trying to be tackled is interesting. Weaknesses: 1) This paper is very confusingly...	Rebuttal 1: Rebuttal: As written in the general response, we sincerely apologize for the lack of clarity. We hope the new figures 3 and 4 convey our first steps towards resolving this issue, and we will make this a high priority for the final manuscript. Relation to Yang & Driscoll. We indeed drew inspiration from the...	Summary: The paper proposes "simplicity bias" in RNNs when learning multiple tasks simultaneously. In particular, the paper focuses on investigating the formation of attractors in the dynamic system of RNN when tasks with variant difficulties are handled jointly. The RNN develop attractors sequentially, and simple attr...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive evaluation of our work. Specific comments have been addressed in the general response to all reviewers, encompassing areas such as study limitations, GRU and LSTM discussions, and clarity aspects (e.g., Figure 2). We will refine the abstract to enhance its pr...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive comments. Responses to common themes are here, and detailed individual responses are below. Clarity: We sincerely apologize for the lack of clarity noted. We have already started clarifying the figures (examples in the 1-page PDF), and will continue do...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation	Reject	Summary: The paper addresses the problem of continual test time adaptation by proposing to add two branches of low-rank and high-rank adapters. The paper claims that the low-rank adapter learns the domain-agnostic knowledge, whereas the high-rank adapter captures the domain-specific knowledge. The paper also proposes a...	Rebuttal 1: Rebuttal: - Q1 'More intuitions of the low-rank adapter and high-rank adapter': Thank you for the constructive advice, please refer to the global rebuttal Q1, including the justifications of H-divergence verification[18], Class Activation Mapping (CAM) visualization, and long-term CTTA experiment. - Q2 'CNN...	Summary: This paper proposes to utilize domain-specific and domain-agnostic knowledge to tackle the error accumulation and catastrophic forgetting problem and boost the performance of continually test-time adaptation task. The proposed visual domain adaptor (ViDA) aims to adapt current domain distribution and maintain ...	Rebuttal 1: Rebuttal: - Q1 'Extra parameter': We appreciate your insightful feedback. In the final version, we will provide clearer explanations of parameter usage. Regarding Lines 64 and 195, we elaborate on the fact that ViDAs can be re-parameterized and projected into the original model due to their linear relations...	Summary: This paper aims to address continual test-time adaptation (CTTA) with parameter-efficient fine-tuning techniques, i.e., adapter. The authors find that the low-rank adapter i.e., standard bottleneck structure, can extract domain-invariant knowledge. On the other hand, a high-rank adapter can extract more domain...	Rebuttal 1: Rebuttal: - Q1 'Different domain representation of ViDAs': Thank you for the comprehensive comments. The experiment analysis of all adapters with the same structure is shown in Reviewer#vo5W Q3 while more justifications are illustrated in the global rebuttal Q1. - Q2 'Why teacher model': Motivated by the fa...	Summary: This paper proposes a continual test-time adaptation method by designing a visual domain adapter (ViDA) to handle both domain-specific and domain-agnostic knowledge. To adapt to different distribution shifts, a homeostatic knowledge allotment strategy is proposed to adaptively merge knowledge from each ViDA wi...	Rebuttal 1: Rebuttal: - Q1: 'Different domain representation of low-rank ViDA and high-rank ViDA': Thank you for the constructive advice, please refer to the global rebuttal Q1, including the justifications of H-divergence verification[18], Class Activation Mapping (CAM) visualization, and long-term CTTA experiment. - ...	Rebuttal 1: Rebuttal: To ALL: - Q1. 'Different domain representations of low-rank ViDA and high-rank ViDA'. - Thank you for the comprehensive comments, and we will add the in-depth analyses and justifications in the final version, including different domain representations of low-rank and high-rank ViDAs. In ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Stable and low-precision training for large-scale vision-language models	Accept (poster)	Summary: The paper proposes methods to improve the training of large, vision-language models. On the one had, the authors propose a new linear layer for int8 quantized trainings, dubbed SwitchBack. On the other hand, a new optimizer is presented, StableAdamW, which results from combining AdamW with the update clipping ...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful comments and thorough review. - Weakness 1: We agree that the paper would benefit from a discussion on whether we can expect the results to generalize to downstream applications. Our analysis in Section D of the supplementary material suggests that the app...	Summary: The paper is a study of different ingredients necessary to train an int8-quantized CLIP model. They mainly address two aspects: 1. quantization: they built on top of techniques previously applied to LLM inference (LLM.uint8) and expand it to the CLIP setting to optimize both inference and training; the 13-2...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful review. We hope the following addresses your interesting questions and comments. - Weakness 1: We believe there are a number of reasons that the speed-up is only 13-25%. For one, 16-bit baselines have had years of hardware support and optimization, while th...	Summary: This paper introduces an efficient and stable INT8 training method for models similar to CLIP. The proposed method offers a training latency improvement of 10-20%, which could account for a significant portion of training costs for larger models. The authors leverage LLM.int8() kernels for training, taking int...	Rebuttal 1: Rebuttal: Thank you very much for your insightful comments. We hope the following addresses any concerns. - Weakness 1: We absolutely agree that resorting back to higher precision is a weakness and a more clever technique could alleviate this. We thank you for highlighting this as we believe it’s one of th...	Summary: This paper proposed methods for accelerating and stabilizing CLIP training. To accelerate training, the authors proposed the SwitchBack method, which quantizes the precision to int8 for the first two matrix multiplies but switches back to higher precision for the weight gradient. The proposed method speeds up ...	Rebuttal 1: Rebuttal: Thank you very much for your insightful review and careful attention to details. We hope the following answers your questions. - Question1: Regarding the typo in equation 1. Thanks very much for this catch, this is indeed a typo. - Question 2: We find that the patch embedding layer is the source ...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors introduce new methods for accelerating and stabilizing training for large language-vision models. For acceleration, SwitchBack is proposed, which use high-precision for backwardd pass to compute the gradients for the weights. For stability, the introduce an AdamW-Adafactor hybrid (StableAdamW). S...	Rebuttal 1: Rebuttal: Thank you very much for your review, we hope this rebuttal addresses your concerns. Novelty We thank you for highlighting the gradient bifurcation method, and we will update the paper to include this important reference. As prior work has observed (e.g., [1]), quantization becomes more diff...	null	null	null	null	null	null
HASSOD: Hierarchical Adaptive Self-Supervised Object Detection	Accept (poster)	Summary: The paper proposes a self-supervised object detection method (in fact, it can also do instance segmentation). This is based on an idea of bottom-up merge by strong feature representation like from DINO feature. The image patch grouping is hierarchical because of setting different stopping thresholds. This make...	Rebuttal 1: Rebuttal: We appreciate the detailed feedback you provided for our submission. We are encouraged by your acknowledgement that our method is “new”, figures are “good to understand”, and “experiment results look good”. We provide the following clarifications in response to your concerns: 1. Clarification on ...	Summary: The authors propose a self-supervised object detection approach by employing a self-supervised pre-trained model (i.e., DINO) to hierarchically and adaptively group regions into object masks using multiple pre-defined thresholds based on cosine similarity in the feature space. The authors then adapt the Mean T...	Rebuttal 1: Rebuttal: We appreciate the detailed feedback you provided for our submission. We are encouraged by your acknowledgement that our “experiments are extensive”, “results are promising”, and “paper is well-written and easy to follow”. We provide the following clarifications in response to your concerns: 1. Ge...	Summary: This goal of this paper is to improve the performance of self-supervised object detection by enhancing the detection pseudolabels. In addition to semantic masks provided as detection labels to an object detector, the authors utilize a rule-based approach to automatically generate a hierarchical structure of ob...	Rebuttal 1: Rebuttal: We appreciate the detailed feedback you provided for our submission. We are encouraged by your acknowledgement that our method tackles “an important problem of hierarchical structure of objects”, is “completely self-supervised”, and shows “promising quantitative results”. We provide the following ...	Summary: This paper proposes a hierarchical Adaptive Self-Supervised Object Detection (HASSOD), an approach that learns to detect objects and understand their compositions without human supervision. HASSOD employs a hierarchical adaptive clustering strategy to group regions into object masks based on self-supervised ...	Rebuttal 1: Rebuttal: We appreciate the detailed feedback you provided for our submission. We are encouraged by your acknowledgement of our “good results”, “quantitative comparisons”, and overall writing. We provide the following clarifications in response to your concerns: 1. Lack of Analytics and Ablation Study Rega...	Rebuttal 1: Rebuttal: In this response, we provide clarification on the common questions and concerns raised by the reviewers. 1. Clarification on Learning Procedure (Reviewers s64Y, wiMX) Overall, HASSOD adopts a two-stage discover-and-learn approach, as illustrated in Figure R2 in the rebuttal PDF. This two-stage...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Sparsity-Preserving Differentially Private Training of Large Embedding Models	Accept (poster)	Summary: This paper targets the learning of embedding models with DP-SGD. The main idea is to utilize the sparsity of gradients to the embeddings. If a mask indicating the sparsity of gradients is given, one can simply adapt DP-SGD by adding noises only to the masked dimensions. They propose two variants, DP-FEST and ...	Rebuttal 1: Rebuttal: We genuinely appreciate the valuable feedback provided by the reviewer and have addressed them in a point-by-point manner below. We are more than willing to engage in further discussions with the reviewers should any follow-up questions arise. ### Q1. A better title > There seems to be a mism...	Summary: This work aims at improving the performance of DP-SGD on models with a large embedding layer. The main idea is zeroing out the insignificant coordinates such that the amount of added Gaussian noise is reduced. The proposed methods achieve substantial gradient size reduction with marginal performance loss compa...	Rebuttal 1: Rebuttal: We genuinely appreciate the valuable feedback provided by the reviewer and have addressed them in a point-by-point manner below. We are more than willing to engage in further discussions with the reviewers should any follow-up questions arise. ### Q1. Missing placement of contribution? > The ...	Summary: This paper considers an interesting and less-studied aspect of DP-SGD: Applying naively to embedding models can destroy gradient sparsity, reducing training efficiency. To address this issue, the paper proposes two algorithms DP-FEST and DP-AdaFEST that apply DP while maintaining gradient sparsity during the ...	Rebuttal 1: Rebuttal: We genuinely appreciate the valuable feedback provided by the reviewer and have addressed them below. We are more than willing to engage in further discussions with the reviewers should any follow-up questions arise. ### Q1. Lack of rigorous privacy analysis > The paper lacks any rigorous ana...	Summary: This paper focuses on the concept of gradient sparsity in large embedding models during training, with a particular focus on privacy-preserving methods. The commonly used DP-SGD approach adds noise to all embedding gradients, even those that may not appear in the current batch, in order to ensure privacy durin...	Rebuttal 1: Rebuttal: We genuinely appreciate the valuable feedback provided by the reviewer and have addressed them in a point-by-point manner below. We are more than willing to engage in further discussions with the reviewers should any follow-up questions arise. ### Q1. Lack of discussions of language models > ...	Rebuttal 1: Rebuttal: We express our gratitude to the AC and all reviewers for their time and valuable feedback. We appreciate the acknowledgment that "the question addressed in this work is of great importance" and that "the proposed method is very intuitive/effective." Below, we provide a summary of the comments and...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
What’s Left? Concept Grounding with Logic-Enhanced Foundation Models	Accept (poster)	Summary: This paper introduces Neuro-FOL, a method combining a large language model for generating FOL programs, an FOL executor and trainable concept grounding modules to improve performance on a number of visual/3D QA style tasks. In the method, a LLM interpretor is used to generate a FOL program that is not specif...	Rebuttal 1: Rebuttal: Q: Sufficient data for training grounding modules. A: Thank you for bringing up this point! We agree that Neuro-FOL may fail with insufficient data, which is a limitation for most learning systems trained solely on one given dataset. We provide three main perspectives here. 1. Neuro-FOL is n...	Summary: The paper presents an approach for solving visual reasoning tasks across multiple domains such as 2D image QA and robotic object manipulation. The approach (Neuro-FOL) uses an LLM to generate a first-order logic (FOL) program that is executed with a combination of learned and predefined modules. One aim is to ...	Rebuttal 1: Rebuttal: Q: Domain-independent reasoning & joint training. A: We realize that there may be confusion in our descriptions about "domain-specific" vs. "domain-independent" and "reasoning" vs. "grounding." Here, we clarify that Neuro-FOL is a framework of "domain-independent reasoning" (composed of an LL...	Summary: This paper proposes an approach for general-purpose language grounding across a variety of domains and tasks. The approach first uses an LLM to generate a domain-general program, which can be implemented across different domains using domain-specific functions, represented as neural networks (and domain-genera...	Rebuttal 1: Rebuttal: Q: Language and perception. A: Thank you for bringing up these examples! There are indeed nuances that require interactions between language and vision. Similar to what you suggested, there are two candidate approaches: 1) interpreting language semantics in a context manner, by considering, e...	Summary: The manuscript proposes a framework that leverages an LLM to map queries to executable first-order-logic programs, with domain-specific grounding functions. The manuscript includes experiments on multiple tasks and domains. Strengths: The paper is mostly well-written. The paper considers an interesting topic...	Rebuttal 1: Rebuttal: Q: Alternative reference frame for “left”. A: We thank you for bringing up this point! In domains where we need reference frames to disambiguate concepts like “left” (e.g., in 3D), the grounding of such concepts will explicitly consider the viewpoint of the agent — usually specified in the la...	Rebuttal 1: Rebuttal: We thank all the reviewers for their constructive feedback! We have added additional data efficiency experiments on CLEVR, baseline results from VisProg & ViperGPT for comparison to Neuro-FOL, statistics and analyses of Neuro-FOL performance from the HumanMotionQA and ReferIt3D tasks, and code exa...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Belief Projection-Based Reinforcement Learning for Environments with Delayed Feedback	Accept (poster)	Summary: This work proposes a solution to the delayed feedback problem, in this case a fixed timestep delay. There are approaches to solving this problem in the literature, often associated with cumulative error or un-traceability given longer delays. One common approach — using augmented states by concatenating observ...	Rebuttal 1: Rebuttal: Dear Reviewer ozYa, We want to sincerely thank you for the valuable effort you put into providing constructive feedback. We have carefully considered each of your comments, and provide additional details as follows: Novelty In addition to its applicability in continuous domains, BPQL has se...	Summary: The authors propose a projection approach for more compactly representing the value function of a delayed system in reinforcement learning. A complete algorithm based on SAC with neural network approximations is presented and benchmarked on four Mujoco environments where it outperforms competing approaches. S...	Rebuttal 1: Rebuttal: Dear Reviewer iPGm, Thank you very much for your positive review and for acknowledging our contributions. We have carefully read your comments, and we would like to provide additional details as follows: Additional experiments on a realistic control task >“The experiments are only on four ra...	Summary: This paper aims to tackle the delayed feedback RL problem. In such a problem setting, usually there is some fixed/constant delay in the environment, so that the observed rewards are delayed by $d$ timesteps. Traditionally, augmented state spaces have been used to solve such an issue, however, for large delays ...	Rebuttal 1: Rebuttal: Dear HRKg, We sincerely appreciate your positive review and your interest in the problem space our paper aims to address. Based on your suggestions, we have conducted additional tests and provide the details as follows: Different types of delay & Ablation >“I think it would be interesting to...	Summary: This paper addresses the issue of delayed feedback in reinforcement learning, where the observations and rewards received by the agent are those generated by the environment multiple (`d`) time steps ago, most commonly caused by latency in hardware. This problem is typically solved by augmenting the policy and...	Rebuttal 1: Title: Response to emergency reviewer 3qfv Comment: Dear 3qfv, We want to sincerely thank you for the effort you put into providing valuable comments, and provide additional details as follows: Impact of additional input of actions >"Clearly augmented SAC is suffering but, unless there is something pa...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a new belief projection-based method for approximating delayed states in reinforcement learning. The method is derived from basic principles with properties of the projection demonstrated. The method is then transformed into a practical, model-free approach, that is extensively evaluated and...	Rebuttal 1: Title: Response to emergency reviewer MGmU Comment: Dear reviewer MGmU, We sincerely appreciate the reviewer for providing valuable feedback and showing interests in our manuscript. We have carefully considered each of your comments, and provide additional details as follows: Motivation >"The belief p...	null	null	null	null	null	null
Unsupervised Semantic Correspondence Using Stable Diffusion	Accept (poster)	Summary: The authors propose an approach to establish semantic correspondences, ie correspondences between different instances of the same object, using a pre-trained stable diffusion models, and without any task-specific finetuning. In particular, they leverage the fact that intermediate attention layers of unet diffu...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive feedback and for recognizing that our approach provides new insights for the estimation of semantic correspondences with stable diffusion. We are glad that the reviewer finds our results convincing. We address the reviewer's concerns below: ### **W-1: Mi...	Summary: This paper proposes a model that finds semantic correspondence between a pair of images, with a pretrained diffusion model. By optimizing the prompt embeddings, such correspondence can be read out via attention maps in the UNet. Empirical study shows the good performance of this proposed model over exising bas...	Rebuttal 1: Rebuttal: We thank the reviewer for constructive feedback. We appreciate that the reviewer finds our approach effective and our qualitative results across classes impressive. We address the reviewer's concerns below: ### W-1: More information about optimization setup and implementation We provide deta...	Summary: This paper focuses unsupervised semantic correspondence and proposes to leverage the semantic knowledge within fashionable text-to-image diffusion model to accomplish this task. The method optimizes a learnable text prompt to maximize source image attention value of query location. Then, the optimized text pro...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive feedback and for recognizing that our approach is effective and our results on unsupervised semantic correspondences are promising. We address the reviewer's comments below: ### **W-1: Missing motivation of using text-to-image (t2i) diffusion models and ...	Summary: This paper explores unsupervised semantic correspondence tasks with stable diffusion model. Specifically, the authors proposed to first optimize the prompt embeddings of stable diffusion model, to maximize attention on the region of interest, then the optimized prompts are used for semantic correspondence. Exp...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and for recognizing that our approach achieves state-of-the-art results. We address the reviewer's concerns below: ### *W-1: Importance of prompt-optimization for the correspondence task* Optimization of the prompts is critical. Without it, one...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback. We are glad that the reviewers acknowledge that our method is novel and provide new insights, with solid experimental results, and clear exposition. We provide detailed responses in the individual rebuttals and provide the figures and the tab...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes an approach for unsupervised semantic correspondence by employing a pre-trained Stable Diffusion generator. The basic idea is that since text-to-image diffusion models are capable of generating realistic images, they must understand the semantics of the objects they generate and thus be capa...	Rebuttal 1: Rebuttal: We thank the reviewer for the constructive feedback and for recognizing that our proposed approach is novel, effective and our experimental results are solid. We address the reviewer's comments below: ### ***Discussion on the extension of the approach to tasks such as semantic segmentation, class...	null	null	null	null	null	null
Dynamically Masked Discriminator for GANs	Accept (poster)	Summary: This paper presents a novel method for training Generative Adversarial Networks (GANs) based on online continual learning, addressing the persistent challenge of GAN training instability. The method considers the time-varying distribution of generated samples and prompts the discriminator to learn new informa...	Rebuttal 1: Rebuttal: Thanks for the reviewer's positive feedback and insightful comments. We are delighted to learn that the reviewer agrees that " novel method", "excellent pilot study", "well explained", "nice flow chart", "promising image results", and "multiple datasets". **Q1. Asking for more explanations on the...	Summary: The paper introduces a new method to enhance GAN training. Taking a continual learning perspective, the authors contend that the discriminator faces challenges in modeling the real/fake distribution due to the dynamic nature of the generated samples' distribution over time. Consequently, this slows down the le...	Rebuttal 1: Rebuttal: We appreciate the reviewer for the positive comments and constructive suggestions. The reviewer agrees that "a new method", "novel way to mask feature maps", "well-written and easy to understand", "the results are encouraging", and "surpasses several existing GAN methods". **Q1: Experimental s...	Summary: This paper proposes a new training method of generating adversarial networks from the perspective of online continuous learning. In order to address the challenges posed to the discriminator by the dynamic changes in the generated data distribution during training, the authors propose to detect whether the dis...	Rebuttal 1: Rebuttal: We appreciate the reviewers for the careful reviews and constructive suggestions. We are encouraged that the reviewer agrees that our paper is "well-organized and well-written", "new training method ", "provides a comprehensive review", "Pilot Study section is helpful for readers to understand th...	Summary: In this paper, the authors propose a novel perspective for generative adversarial training via continual learning, which achieves better results on the generative quality for local details and better quantitative metrics. To realize continual learning, the authors propose two models, 1) the discriminator slow-...	Rebuttal 1: Rebuttal: We appreciate the reviewer's valuable comments. We are encouraged that the reviewer agrees that our work is “interesting and promising”, “novel and exciting”, “simple and straightforward yet effective to be trained”, “probably would be general”, “well-explained”, and “well-written”. **Q1.The con...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes Dynamically Masked Discriminator (DMD) which automatically adjusts the discriminator by dynamically masking the features when learning slows down, forcing the discriminator to learn new knowledge in the generated data. It consists of two modules: (1) discriminator retardation detection and (...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their insightful feedback and constructive comments. We are encouraged by the reviewer's positive comments, such as “well-motivated”, “clearly organized”, “easily integrated into any existing discriminator”, “significance and broader impact of the work”, "ex...	null	null	null	null	null	null
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis	Accept (poster)	Summary: The paper introduces a new task called "real-world audio-visual scene synthesis", which involves synthesizing new videos with spatial audios along arbitrary novel camera trajectories in a scene. The proposed NeRF-based approach integrates a prior knowledge of audio into audio generation and associates it with ...	Rebuttal 1: Rebuttal: We thank the reviewer for your insightful comments. Below are our responses to specific questions. ### Weakness W1: Novelty of AV-NeRF. In addition to AV-Mapper, our paper introduces two essential components -- the innovative Audio-NeRF architecture and a novel coordinate transformation mecha...	Summary: The paper introduces a novel task called real-world audio-visual scene synthesis to generate novel views and spatial audio for any camera trajectories. The system contains a visual NeRF to synthesize novel view images, an audio NeRF to generate acoustic masks, and an audio-visual mapper to enable the visual in...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for your constructive comments and greatly appreciate your strong positive acknowledgment of our work. Below, we address each specific question. ### Weakness W1: Single sound source. In our paper, we initially focused on a simplified scenario with a single soun...	Summary: Inspired by the task of novel view synthesis, this paper additionally considers the audio modality and thus proposes an interesting new task: real-world audio-visual scene synthesis. Specifically, the task is to synthesize new videos with corresponding spatial audio along arbitrary novel camera trajectories by...	Rebuttal 1: Rebuttal: We thank the reviewer for your thoughtful review and valuable feedback. We address specific questions below. ### Weakness W1: Rationality of AV-Mapper. Great question! We leverage RGB and depth images as implicit indicators of environmental material properties and geometry. While these im...	Summary: This paper describes an intriguing task that focuses on the synthesis of new perspectives of real-world audiovisual scenes. The task is to synthesize a new video with spatial audio along an arbitrary novel camera track in an audiovisual scene given a video recording of that audiovisual scene. A data acquisitio...	Rebuttal 1: Rebuttal: We thank the reviewer for your helpful suggestions and encouraging comments. We address specific comments below. ### Weakness W1: NeRF acceleration. We concur with the reviewer's observation that one limitation of the vanilla NeRF is its slow rendering speed. In our study, we actually employ ...	Rebuttal 1: Rebuttal: We would like to express our gratitude to all the reviewers for their valuable comments and feedback. We address the specific questions of each reviewer individually. Additionally, we attach a PDF file containing some figures in response to certain reviewers. To reviewer 4psZ: we show some ad...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes an audio-visual (AV-) NeRF model to synthesize binaural audio masks at novel poses by using audio-visual samples from a video walkthrough of a 3D scene. The synthesized audio masks can be convolved with any arbitrary anechoid audio signal to retrieve the corresponding binaural audio at the n...	Rebuttal 1: Rebuttal: We thank the reviewer for your constructive comments and encouraging remarks. We address specific comments below. ### Weakness W1: Compare with ViGAS. Because ViGAS is a concurrent work to our paper, we did not compare AV-NeRF with ViGAS in our submitted paper. However, in response to the rev...	null	null	null	null	null	null
Beyond MLE: Convex Learning for Text Generation	Accept (poster)	Summary: Based on the ideas of convex optimization, the paper proposes a new loss function for the training of text generation models. The experimental results show that it has certain advantages over MLE, whether in autoregressive models or non-autoregressive models. Strengths: 1. The paper has done a relatively deta...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We provide discussions and explanations about your concerns as follows. > Some key experimental hyperparameters were not provided, such as the value of T, which I could not find in the main text or the appendix; The notation is a bit confusing (for exampl...	Summary: Authors have proposed the novel way set of loss functions to learn neural sequence model in both AR and NAR factorization. The core idea is to sharpen the model distribution so that is comes closer to the one-hot target distributions which are typically used for closed-form generation task such as NMT. They ha...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We provide discussions and explanations about your concerns as follows. > The resulting BLEU scores for AR setting are too close and not convincing. I think it is required to perform multiple random seed training or other way of estimating the translation...	Summary: The objective of the authors in this paper is to develop a machine translation model that doesn't need to learn the complete output distribution given the input, but instead focuses on highly probable outputs based on p_data. To achieve this, the authors suggest replacing the logarithm in Maximum Likelihood Es...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We provide discussions and explanations about your concerns as follows. > Equation (5) may not hold true for non-autoregressive models as these models employ latent variables that interconnect the output tokens. Thank you for your comment regarding Equat...	Summary: This paper proposed a new learning objective for language modeling, which is extended from Maximum likelihood estimation (MLE). More specifically, the author replaced the log function in log-likelihood with an arbitrary increasing function f. Then the author analyzed the properties of the loss under scenarios ...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We provide discussions and explanations about your concerns as follows. > About Baselines: The method should have been evaluated on more baselines. For AR model, the author only compared the proposed method with the vanilla transformer. For NAR, only two ...	Rebuttal 1: Rebuttal: We thank reviewers for their valuable feedback and insightful comments on our paper. In response to the concerns and recommendations, we have conducted additional experiments and provided supplementary results in the attached response PDF. In Table 1, we present the results of tuning the hyper-par...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper discusses the potential limitations of likelihood/KL-guided training for natural language generators from a measure theoretical perspective. It analyses the discrepancy between maximizing the likelihood (thus recall-friendly) and producing high-quality samples (thus precision-oriented) in autoregres...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We provide discussions and explanations about your concerns as follows. > Include additional experiments on diverse text generation with VAEs. You can try interpolation and I'm very curious if the proposed approach is eventually capable of mitigating the ...	null	null	null	null	null	null
Survival Instinct in Offline Reinforcement Learning	Accept (spotlight)	Summary: The authors make an interesting and important observation about offline Reinforcement Learning. They note that an agent can learn from offline data even when the reward signal is not the same one as that used to train the online agent. Moreover it can be vastly different, or even with no reward signal at all, ...	Rebuttal 1: Rebuttal: We are happy that the reviewer overall feels positive about our paper. We want to thank the reviewer for reviewing our paper and pointing out typos in detail. We will fix these typos in the final version. Adversarial Reward Regarding the question on adversarial reward, we agree with the re...	Summary: This work displays a new observation that offline reinforcement learning (RL) algorithms can develop efficient policies, even when trained with incorrect reward labels. The authors attribute this resilience to the pessimistic nature of offline RL algorithms and the inherent biases in data collection processes....	Rebuttal 1: Rebuttal: Quantifying Positive Bias We would like to clarify that positive bias is a general concept of when approximate optimality to the CMDP implies approximate optimality w.r.t the true reward, while "longer trajectories in the data have a smaller optimality gap" refers to a special form of posit...	Summary: This paper reports a very interesting new phenomenon that would be interesting to the offline RL community. It demonstrates that even when trajectories have the wrong reward labels, offline RL can learn good policies. The paper's experiments attempt to dissect the reasons for why this surprising phenomenon em...	Rebuttal 1: Rebuttal: When positive bias arises & clarification on "long timescale trajectories" We thank reviewer Sf8f for providing feedback to our manuscript. The robustness to reward is attributable to an interplay between survival instinct in ofﬂine RL algorithms (due to their pessimistic nature) and positiv...	Summary: In this paper, the authors show that offline RL algorithms have an implicit survival instinct that often allows them to learn good policies with incorrect rewards. The authors argue that this is due to the data being positively biased and the pessimism that constrains offline RL algorithms to the data. This ar...	Rebuttal 1: Rebuttal: Cause of Phenomenon in Fig 3 We would like to first clarify that length bias means the length of an in-support trajectory positively correlates with its return (line 220), not that the dataset has many long trajectories. In fact, a dataset would have no length bias if it has lots of long ba...	Rebuttal 1: Rebuttal: We thank all reviewers for providing feedback to our manuscript. It is encouraging to see that reviewers generally find our empirical findings and theoretical analysis relevant to the offline RL community. We are excited to see that the reviewers propose a few interesting future research direction...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning	Accept (poster)	Summary: This work aims to adapt large pre-trained vision-language model to few-shot tasks. To this end, the authors propose to decouple the category-related and category-independent information to alleviate overfitting when adapting large model to few samples. They claim to maintain the visual encoder's ability to ext...	Rebuttal 1: Rebuttal: Q1 : Motivation is not very convincing. A1: Sorry for the confusion. Our primary objective focuses on leveraging the pre-trained CLIP model for downstream few-shot tasks. Specifically, when we have a dataset with limited samples, our aim is to enable CLIP to quickly adapt to it, and we also h...	Summary: This paper aims to enhance the performance of pre-trained CLIP for few-shot learning tasks while maintaining their generalizability and mitigating the risk of overfitting. To achieve this goal, the key contribution of this paper is the spurious information extractor that captures category-independent informati...	Rebuttal 1: Rebuttal: Q1: Is using features from CLIP kind of conflicting to few-shot settings? A1: The consideration of CLIP features within the setting of few-shot learning remains a valid approach. Despite CLIP's extensive pretraining on vast datasets, it is important to acknowledge the potential for significant **...	Summary: This paper introduces a novel approach to tackle few-shot learning utilizing the powerful pretrained CLIP model. The primary objective is to construct multiple CLIP text prompts with different context and construct a term to regularize the learning process. This helps prevent overfitting to irrelevant correlat...	Rebuttal 1: Rebuttal: Q1: The validation of the basic assumption. A1: It is important to point out that our basic assumption is that in few-shot learning, due to insufficient sample size to fine-tune the model, the model is prone to overfitting into the causal and spurious information it currently sees. That is, ...	Summary: This paper studies the problem of fine-tuning a pre-trained CLIP to downstream classification tasks with few-shot samples. The authors propose to fine-tune the category-dependent feature while retain the category-independent feature in order to improve the robustness of the fine-tuned model. The proposed metho...	Rebuttal 1: Rebuttal: Q1: Why retaining spurious features can help model robustness? A1: Sorry for the confusion. In fact, our objective is not to preserve spurious features of an image, but to preserve the ability of CLIP to distinguish spurious features. Illustrated in Figure 1a, CLIP demonstrates the capability...	Rebuttal 1: Rebuttal: ## Additional discussion in related work. In the context of few-shot learning, full fine-tuning of the pre-trained model often results in overfitting due to the limited sample size, which consequently diminish the model's generalization ability. Therefore, it is common practice in few-shot learni...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a new few-shot learning method leveraging the pre-trained multi-model backbone CLIP. The method aims to eliminate the spurious information within the text embedding, and consequently regularize the image features to deliver better few-shot learning results. To do so, the authors propose to ...	Rebuttal 1: Rebuttal: Q1: Lack of clear visualizations to show how causal information is disentangled from spurious information. Better class centroids do not indicate the effect of disentanglement. Similarity maps on both learned causal and spurious representations are encouraged to be shown. A1: Figure 1 of the pape...	Summary: This paper presents a fine-tuning method for pre-trained models in few-shot learning via CLIP's text and visual feature alignment capability. Specifically, the authors use text information to assist in decoupling spurious information from causal information while keeping the spurious information unchanged duri...	Rebuttal 1: Rebuttal: Q1: The presentation and illustrated figures could be improved for better clarity. A1: Thanks for your feedback. In response to your suggestion, we have revisited and updated the visual representation of Figure 1. In the modified figure, the data flow and the loss term during the fine-tuning can ...	null	null	null	null
Data Market Design through Deep Learning	Accept (poster)	Summary: This paper studies the market design problem, specifically for data markets. In particular, different from existing analytic approaches, the proposed approach is based on (deep) learning to recover/discover market designs. They adopt and extend an existing RochetNet architecture to both single- and multi-buyer...	Rebuttal 1: Rebuttal: Justification for using Deep Learning: Please note that the goal here is to learn a differentiable _function_ that represents an entire mechanism, i.e., a complete mapping from all possible inputs to all possible outputs. The goal is not to simply find a pointwise output for a given input beca...	Summary: This paper introduces a deep learning application to the data market designs that find optimal signaling schemes to maximize the revenue of data sellers. The proposed method is designed to handle truthfulness and obedience (i.e., buyers following recommendations). The overall approach follows the prior framewo...	Rebuttal 1: Rebuttal: Thanks for the feedback regarding the clarity. We will make the necessary edits and add interpretations to our lemmas and equations to make them more readable. The response to the questions including concerns regarding the scale are addressed below --- **1. Matching Utilities under binary states...	Summary: The authors are concerned with a problem of "data market design". In such a setting, a mechanism designer with access to an unknown world state interacts with buyers who have private types, and need to take actions whose payoffs vary depending on the world state. These buyers purchase (in the single buyer case...	Rebuttal 1: Rebuttal: Thanks for the review and the feedback. We've discussed concerns regarding the weaknesses - especially regarding Methodological Contributions and Scaling up in the global comment [here](https://openreview.net/forum?id=sgCrNMOuXp&noteId=OyHh7ioeDB). --- Rebuttal Comment 1.1: Title: Acknow...	Summary: This paper introduces a deep learning framework for the automated design of data markets, a novel and timely application in the field of economics. The authors address the data market design problem, which involves designing a set of signaling schemes to maximize expected revenue. The paper extends previous wo...	Rebuttal 1: Rebuttal: Novelty We've addressed this in the global comment [here](https://openreview.net/forum?id=sgCrNMOuXp&noteId=OyHh7ioeDB). --- Comparison with other baselines We are unaware of other computational baselines, and we already compare them with the theoretically optimal designs from [1, 2]...	Rebuttal 1: Rebuttal: We thank all the reviewers for their helpful feedback. Three main comments are regarding non-convexity of our formulation, novelty and scaling up. We address these concerns here. The remaining questions and concerns are addressed in the individual responses. --- **Non-Convex Formulations and Alt...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The authors present a novel approach to the problem of data market design, which seeks to find a set of signaling schemes, each revealing some of the information known to a seller and having a corresponding price, where the goal is to maximize expected revenue. Then, the authors introduce the application of a ...	Rebuttal 1: Rebuttal: Interpretability We can interpret the designs learned by RegretNet in a few ways. - We can plot the probability of recommending the correct action for different agent types. We adopt this approach and visualize these as heatmaps in the main paper (Figure 2) and Appendix (Figures 8, 9, and 1...	null	null	null	null	null	null
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond	Accept (poster)	Summary: This paper proposes a new notion of data diversity that expands all the prior data diversity conditions in offline reinforcement learning literature. Based on this notion, the paper develops concrete value sampling-based, regularized optimization-based and posterior sampling-based algorithms with corresponding...	Rebuttal 1: Rebuttal: We thank you for your positive feedback. We elaborate further on the data diversity notion below. --- Question: About (the advantage of) data diversity Our response: Our notion of data diversity provides a tighter characterization of distribution shift than the previous notions of ...	Summary: This paper investigates the problem of sample-efficient learning from historical datasets in the context of offline reinforcement learning and explores the role of data diversity and posterior sampling in improving the efficiency of RL algorithms. The authors propose a new notion of data diversity and study th...	Rebuttal 1: Rebuttal: We thank you for your positive feedback. We address all of your concerns below. --- #### Concern 1: Compare with [1][2] Relevant refs: > [1] Ming Yin, Mengdi Wang, Yu-Xiang Wang. “Near-optimal offline reinforcement learning with linear representation: Leveraging variance informa...	Summary: This work aims to point out what enables the sample efficiency of offline reinforcement learning. The authors first define a new notion of data diversity based on the inducing the Bellman error under one policy with the Bellman error under a different policy. The authors then propose a unified view, Generic Of...	Rebuttal 1: Rebuttal: We thank you for your positive feedback. We address all of your concerns and questions below. --- #### Question 1: Practical issues of posterior sampling Our response: We emphasize that the focus of our paper is on the statistical aspects of offline RL and not on the comput...	Summary: This paper introduces a novel data coverage measure called "data diversity," which is more stringent than existing data coverage measures like single-policy concentrability. The authors claim that actor-critic algorithms based on VS, RO, and posterior sampling can achieve state-of-the-art sample complexity. Th...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback. We address all of your concerns below. --- #### Concern 1: "This policy ($\hat{\pi}$) is not a Markov policy ... " Our response: We understand our notation in line 7 of Algorithm 1 might have caused some confusion. To clarify, $\hat{...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Retrieval-Augmented Multiple Instance Learning	Accept (poster)	Summary: This work proposes RAM-MIL: an approach that uses feature alignment in MIL with an optimal transport based method to retrieve nearest bag representations between the train set and target set. This approach shows consistent improvements in ID and OOD settings. Ablation studies demonstrate the relative effective...	Rebuttal 1: Rebuttal: Thanks for the comments. Here are the responses to weaknesses (W), Questions (Q) and limitations (L). Response to W1: We report the computation time of RAM-MIL using L2, Hausdorff, approximate-OT and OT for 1 pair of whole slide images as follows. The approximate-OT provides a close performa...	Summary: The paper proposes a new Multiple instance learning method called RAM-MIL. The proposed method is a two-stage approach which mainly involves the following steps: 1. Train an existing MIL model on the D0 to extract feature representations of each instance 2. Retrieve nearest neighbor from retrieval set to form ...	Rebuttal 1: Rebuttal: Thanks for the comments. Here are the responses to weaknesses (W), Questions (Q) and limitations (L). Response to W1: Thanks for the comments. As retrieval (also dubbed as external memory) has achieved great successes in applications like large language models [1][2], we believe it is worth ...	Summary: The authors propose a novel MIL framework which integrates Wasserstein Distance and Optimal Transport to match instance representations across bags. This method can be further leveraged to match representations across bags from different domains, thereby enabling Cross Domain Adaption. Strengths: The autho...	Rebuttal 1: Rebuttal: Thanks for the comments. Here are the responses to weaknesses (W) and Questions (Q). Response to W1: Thanks for mentioning the potential issue. Please note that we follow the double-blind reviewing rules and the submission does not contain any identifying information. For Figure 1, some slid...	Summary: This work presents a method for multiple instance learning-based method for slide retrieval based on optimal transport, called RAM-MIL. The idea is as follows: 1) first train a standard ABMIL model for supervised classification, 2) pre-extract the features from ABMIL to get slide-level features, 3) compute the...	Rebuttal 1: Rebuttal: Thanks for the comments. Here are the responses to weaknesses (W) and Questions (Q). Response to W1: Thanks for mentioning the related works. We will add them into the related works sections. For the claim of contribution, we meant to say, “the out-of-domain performance of Attention-based M...	Rebuttal 1: Rebuttal: We thank all reviewers for the valuable feedbacks and constructive comments. We have prepared the response and revised the manuscript accordingly to address your concerns. The major concerns and the corresponding answers/explanations/clarifications are as follows: 1. Related works (5E1m, WXnS, 7v...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper introduces the Retrieval-AugMented MIL (RAM-MIL) framework, which addresses the challenge of performance deterioration in MIL models when tested on an out-of-domain test set. The proposed framework achieves state-of-the-art performance in both in-domain and out-of-domain scenarios. The RAM-MIL frame...	Rebuttal 1: Rebuttal: Thanks for the comments. Here are the responses to weaknesses (W). Response to W1 and W2.1: Thanks for mentioning the related works. We will add discussions about [1, 2, 3, 4] into the related works and [2] will be added to comparisons in the evaluation. Note that [1, 2, 3, 4] are NOT ...	null	null	null	null	null	null
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset	Accept (poster)	Summary: This paper proposes an automatic pipeline to create captions for videos, where multiple modalities are involved in the generation process of the captions. Authors initially train separate audio and video-based captioners. Then, those audiovisual-driven captions, alongside subtitles are restructured into a sing...	Rebuttal 1: Rebuttal: weakness 1 Considering that the motivation of our work is to build a more general comprehensive omni-modality foundation model, it unavoidably involves multiple modalities (text, audio, vision, subtitle), multiple features (single/fusion modality features, global/local features), multiple l...	Summary: This paper introduces a large-scale dataset called VAST-27M, which contains audios, videos, subtitles, and generated captions. The authors employ an LLM to combine all forms of textual information to create omni-modality captions. They train an omni-modality video-text foundational model on this dataset. Exten...	Rebuttal 1: Rebuttal: weakness 1 + Our work needs to generate 27M omni-modality captions, and locally deployed open-resource large language modal (such as Vicuna) can fulfill parallel generation which is efficient. However this is not feasible for ChatGPT due to its API is relatively inefficient and requies addi...	Summary: The paper proposed to build a new multi-modal foundation model that includes vision, audio, subtitle and text modalities. The designed architecture of the model is very similar to previous works, such as BLIP. The paper also made another contribution, a new multi-modal video dataset VAST-27M. VAST-27M reuse vi...	Rebuttal 1: Rebuttal: weakness 1 + Subtitle is usually achieved by ASR or OCR techniques and can be a supplementary modality for video understanding, while captions are usually objective descriptions about objects existed or events happened in images or videos. Even though they are both represented as text, they ...	Summary: This paper proposes a large-scale omni-modality video caption dataset based on an existing video-text dataset, namely VAST-27M. Specifically, the authors utilized the VL models to generate captions and LLM to generate omni-captions for videos. Then a vision-audio-subtitle-text model is trained for a range of v...	Rebuttal 1: Rebuttal: weakness 1 About the technical contributions of VAST model have been explained in our global rebuttal [Common Question 1]. weakness 2 (1) Using origin captions in OM-VCM, OM-VCG and OM-VCC can keep consistence with BERT whose parameters are inherit for text encoder initializatio...	Rebuttal 1: Rebuttal: We sincerely thanks all reviewers for your recognition of our work VAST (dataset and foundation model), and very insightful reviews. Your reviews and suggestions are very important for us to improve VAST model and paper. We will add necessary detail descriptions and model comparisons in the next v...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Unleashing the Full Potential of Product Quantization for Large-Scale Image Retrieval	Accept (poster)	Summary: This paper presents a deep product quantization (PQ) method for approximate nearest neighbor (ANN) search. The motivation is to reduce performance degradation when short codes are applied to large-scale datasets with a large number of categories. The proposed method learns discriminative PQ subvectors by CosFa...	Rebuttal 1: Rebuttal: Q1. Retrieval performance. We evaluate the unseen retrieval performance on the Glint360k dataset: Glint360k is exclusively used for training, while Mageface and Facecrub are employed for testing. Q2. Evaluation protocol. In fact, the protocol we described is widely recognized and imple...	Summary: In this paper, a framework for large-scale image retrieval is proposed, which is based on deep hashing and product quantization (PQ). The key contribution of this framework is the alleviation of the performance crash problem that arises when using very short PQ codes to save space and computation. The performa...	Rebuttal 1: Rebuttal: Q1: More results. Different network structures. Thank you. Because of the limited time, we have supplemented the results of two outstanding methods, OrthoHash and GreedyHash, which have shown relatively good performance on iResnet50 in our paper. Specifically, we evaluated their performan...	Summary: This paper presents a novel deep hashing framework based on product quantization. It is different from conventional PQ in learning a set of predefined PQ codes of the classes via a softmax-based differentiable PQ branch. The proposed method is validated to be effective on large scale datasets, including Glint3...	Rebuttal 1: Rebuttal: Q1: Further detail. Thank you for your suggestion, now we provide further detail, and they will all be added to the supplementary material later. PQ code duplication removal. We provide further explanations here. For the sake of clarity, if we need to modify $n$ items of PQ codes to ac...	Summary: This paper discusses a new deep hashing algorithm based on product quantization, which effectively addresses the issues of high computational cost and low accuracy. The algorithm successfully learns predefined PQ codes for different classes, achieving concise, efficient, and distinguishable codes. It has been ...	Rebuttal 1: Rebuttal: Q1:The increase of complexity and training time. In fact, the number of parameters in the PQ branch is very small. Let $M$ denote the number of segments in PQ, $D$ denote the dimension of the embedded features, and $K$ denote the number of cluster centers. The number of parameters in the PQ b...	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable comments. Reviewers kMZn, WmwN, and AU32 have mentioned that the method proposed in this paper is novel(new) and have affirmed its potential applicability. Reviewers hk7b also acknowledged the contributions of our method. The PDF file contains Figure 1, F...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing	Accept (poster)	Summary: This paper has thoroughly analyzed the activation outlier problem during quantizing of the transformer model, and further indicates the limitation of existing works. Based on the comprehensive studies, the author proposes two components, namely clipped softmax and gated attention, for regularizing the activati...	Rebuttal 1: Rebuttal: ## Weakness See general response to all reviewers. ## Question At the moment, we do not have an explanation of why this is the case but will further investigate this issue. ## Limitation 1 See general response to all reviewers. ## Limitation 2 We agree that this will greatly improve the reprodu...	Summary: The paper proposes two architectural modifications that mitigate the activation outlier problem that makes transformers challenging to quantize. Particularly, the authors conduct a comprehensive analysis of the underlying causes of outlier issues in various pre-trained Transformer models across different tasks...	Rebuttal 1: Rebuttal: ## Weakness 1 Indeed, in most of real-world applications the vanilla PTQ might not be good enough. At the same time, our proposed methodology is complementary and can be combined with most of more advanced PTQ and weight compression techniques ([1],[2] as well as [3-6] and many more). Our main m...	Summary: In this paper, the authors show that strong outliers are related to very specific behavior of attention heads that try to learn a “no-op” or just a partial update of the residual. To reduce outliers, the authors propose two simple (independent) modifications to the attention mechanism. This enables them to qua...	Rebuttal 1: Rebuttal: ## Weakness 1 Please note, that in all result tables we report next to the floating-point and quantized perplexity/accuracy also two metrics that quantify the magnitude and the frequency of the outliers: 1) Maximum infinity norm - measures the magnitude of the outliers (by definition). 2) Kurtosis...	Summary: This paper analyzes the activation outlier problem. It is shown that the outliers are related to the behavior of transformer networks trying to learn not to update residuals (no-op). To achieve the exact zeros needed in the attention matrix for a no-update, the input to the softmax is pushed to be larger and l...	Rebuttal 1: Rebuttal: ## Weakness 1 See general response to all reviewers. ## Question 1 At the moment, we do not have an explanation of why this is the case but will further investigate this issue. ## Question 2 Thanks for the suggestion. We investigated this and included the visualization, please see Figure 1 in th...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful and positive feedback! We are encouraged they found our work is well-organized and easy to follow (Akrm), has comprehensive experiments (sWjn, Akrm), solid and insightful analysis (sWjn, Vxgc ,Akrm), and presents a simple yet effective methodology, whic...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DreamSparse: Escaping from Plato’s Cave with 2D Diffusion Model Given Sparse Views	Accept (poster)	Summary: This paper introduces a framework of 3D reconstruction from sparse using 2D image priors from pretrained diffusion models. It proposes a 3D geometry module which extracts 3D features from 2D images, and then incorporates these features into the diffusion process at novel views, enabling 3D awareness and view c...	Rebuttal 1: Rebuttal: Thanks for your time and effort sharing critical feedback regarding our work. We have addressed your points and questions below. >Section 4.3.2 naming the task "Scene Level Novel View Synthesis" sounds a bit overclaimed to me. Though I agree that compared to pure object settings with foreground m...	Summary: This work proposes an approach for finetuning a pretrained 2D diffusion model for novel view synthesis given a few or only a single input image at test time. The two key contributions are a 3D geometry module and a spatial guidance module. The geometry module fuses features from multiple input views. The spati...	Rebuttal 1: Rebuttal: Thank you for your time and effort sharing critical feedback regarding our work. We have addressed your points and questions below. > These methods are also missing from Table 1 and it is not clear why. Thanks for pointing it out. We would like to add discussions with recent existing works in Ta...	Summary: The paper proposes a method for novel view synthesis using diffusion models. Given a sparse set of views, the method extracts per-pixel features for each of the views and reshapes these features to have a depth dimension by splitting the feature channels. Each feature sequence of per-pixel features, along with...	Rebuttal 1: Rebuttal: Thank you for your time and effort sharing critical feedback regarding our work. We have addressed your points and questions below. >While the method is encouraged to produce 3D consistent results due to the color estimated reconstruction loss, there is no actual 3D constraint in the architecture...	Summary: The paper presents a method for novel view synthesis given sparse image observations of a scene. A diffusion model is used to generate the novel views. A 3D structured conditioning input is first computed, and a pretrained 2D diffusion model is fine-tuned to take this input and compute the novel view rendering...	Rebuttal 1: Rebuttal: Thank you for your time and effort sharing critical feedback regarding our work. We provide the following response to address your concerns about results limitation and novelty . and thus respectfully hope you can consider the response to the final decision. > The paper lacks technical novelty. ...	Rebuttal 1: Rebuttal: Thanks for the time and effort sharing critical feedback of every reviewer regarding our work. o address the questions and points raised by the reviewers, we have provided additional experimental results in our response PDF. 1) In the hydrant-scene dataset, we've plotted a figure (Figure 1 in the...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper aims to leverage large-scale 2D diffusion models as priors to improve the task of novel view synthesis in the setting of sparse input views. The paper proposes an approach consisting of two stages: a first 3D-aware stage in which features from context views are aggregated in an encoded feature volum...	Rebuttal 1: Rebuttal: Thank you for your time and effort sharing critical feedback regarding our work. We have addressed your points and questions about performance comparison and training costs below. > Comparison with other methods We agree with you and would like to open a discussion on concurrently proposed 3D no...	null	null	null	null	null	null
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing	Accept (poster)	Summary: The paper proposes DiffPack, a torsional diffusion model that accurately predicts the conformation of protein side-chains given their backbones. DiffPack learns the joint distribution of side-chain torsional angles by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous per...	Rebuttal 1: Rebuttal: Thanks for your suggestions! Here is our response to your concern. ___ Q1: Is there a metric available for assessing the overall conformational plausibility of the generated structures? This is an interesting question! Assessing the overall conformational plausibility of generated structures,...	Summary: The paper proposes DiffPack a torsional diffusion model to learn side chain placements. In particular, the authors presents a few modification to vanilla torsional diffusion models that improve the empirical results obtaining strong empirical performance. Strengths: The authors propose a number of modificatio...	Rebuttal 1: Rebuttal: Thank you for your insightful questions. We'd like to first clarify that our work's main focus is on formulating a new approach to the sidechain packing task, rather than proposing general improvements to the diffusion process. Below are our specific responses to your queries. ___ **Q1: Justificat...	Summary: This paper focuses on the task of sidechain packing in proteins, where one wishes to predict the positions of the side-chain atoms given the positions of the backbone atoms. To this end, the main contribution of the paper is DiffPack, an extension of recently proposed torsional diffusion models to the side-cha...	Rebuttal 1: Rebuttal: Thanks for your suggestions! Here is our response to your concern. ___ Q1: Potential Concern in Incremental Technical Contribution Thanks for your review! Formulation of diffusion models on Riemannian manifold(e.g. torsion space) was first proposed by Valentin De Bortoli[1] and further applie...	Summary: The protein side-chain packing problem consists of predicting the positions of atoms in amino-acid side chains given the backbone structure and residue identities. The paper proposes to do this using a diffusion model that accounts for physical constraints and models side chain structures as joint distribution...	Rebuttal 1: Rebuttal: Thanks for your time and review! We think that the majority of your detailed concerns about our method are covered in the Appendix. We'll revise the order as you've recommended to prevent any confusion. Here's our response to your concerns. ___ Q1: Clarity about GearNet Score Prediction For a...	Rebuttal 1: Rebuttal: We would like to extend our sincere gratitude for your time and thorough review of our paper. Your insightful suggestions and comments have provided us with valuable perspectives, enabling us to enhance the quality of our work. As there is some common issues raised, we choose to respond to them in...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Why Differentially Private Local SGD -- An Analysis of Synchronized-Only Biased Iterate	Reject	Summary: This paper studies DP-LSGD and compares its performance with DP-SGD. The paper first provide the convergence result of FedAvg under the bounded variance assumption (Theorem 3.1 and 3.2) and provide the convergence analysis of DP-LSGD-GC under the bounded gradient assumption 4.1 and similarity assumption 4.2. f...	Rebuttal 1: Rebuttal: Thanks for your comments which also inspire us to properly describe the strength of DP-LSGD. 1). On Assumption 4.1: We apologize for the confusion. Actually we do not assume a bounded clipping error; instead we only assume its second moment is bounded. We will stress this in a revision. The deta...	Summary: This paper proposed a unified analysis of the convergence of (DP)-Local SGD, which covers (DP) parallel SGD as a special case with K=1, for both convex and non-convex optimization. Under this unified analysis, one can identify error effects due to non-iid objectives, clipping, and DP noises and the convergence...	Rebuttal 1: Rebuttal: Thanks for your comments. 1). Stochastic local gradient and the convergence rate of federated learning: Thanks for this very sharp question. We first answer the question about the generalization of our results with local stochastic gradients. We totally agree, in a complete picture of DP-LSGD, t...	Summary: This paper propose a differentially-private local stochastic gradient descent both for centralized and distributed settings. The authors argue that the proposed method has less number of clipping and in turn produce less clipping bias compared to its counterpart DP-SGD which do not involve local steps. They al...	Rebuttal 1: Rebuttal: Thanks for your comments. 1). Regarding Assumptions 4.1 and 4.2: With regards to Assumption 4.1, we apologize for the confusion caused. Actually, we did not assume the incremental norm is globally bounded but only a bounded second moment. It is worth noting that Assumption 4.1 can be seen as bei...	Summary: The paper focuses on Differentially-Private Local SGD (DP-LSGD), and studies its advantages over the foundational technique of DP-SGD. In particular, the authors show why DP-LSGD provides higher clipping efficiency and less clipping bias compared to DP-SGD. The authors start by showing a convergence analysis o...	Rebuttal 1: Rebuttal: Thanks for your comments. 1). First, we apologize for the confusion caused about the main contributions and technical novelty of our paper. As we have partially explained in the global response, the key theoretical contributions are mainly twofold. On one hand, technically, to our knowledge, thi...	Rebuttal 1: Rebuttal: We would like to express our gratitude to all reviewers for their insightful and helpful comments. In this global response, we address the common concerns raised by the reviewers. We begin by describing what we think the three key contributions and technical innovations of this paper are. The fi...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This submission studies the Differentially-Private Local Stochastic Gradient Descent (DP-LSGD), and shows that DP-LSGD with multiple local iterations can produce more concentrated local updates and less clipping bias compared to DP-SGD, assuming that the stochastic gradient is of bounded variance. The main...	Rebuttal 1: Rebuttal: Thanks for your positive assessment for our work. As you mentioned, we believe a more efficient implementation of DP-LSGD would be a promising direction for further work on DP optimization/learning, especially from a system engineering perspective. We present the first step, though at the cost of ...	Summary: The authors provide a unified analysis of the clipping bias and the utility loss in privacy-preserving gradient methods for centralized and distributed setups. The conclusion shows that LSGD behaves as an efficient variance reduction of local update, where multiple local GDs with a small learning rate cancel o...	Rebuttal 1: Rebuttal: Thanks for your comments. We apologize for the confusion caused about our experimental setup. Our experiments focus on the application of DP-SGD and DP-LSGD in the centralized setup, which is essentially equivalent to a federated learning model of $n$ nodes (users), each holding a single distinct ...	null	null	null	null
DISCOVER: Making Vision Networks Interpretable via Competition and Dissection	Accept (poster)	Summary: This paper proposes a Dissection of Competitive Vision Networks (DISCOVER) to build interpretable neural networks. It aims to understand the neuron functionality for image inference. Moreover, this paper introduces the Jensen-Shannon divergence between the probability of concept presence and the probability of...	Rebuttal 1: Rebuttal: We thank the reviewer for kind comments concerning our approach and for pointing out potentially related work to ours. We believe however that the reviewer is a bit harsh concerning the novelty of the proposed framework. We support this claim by highlighting the pivotal differences between our wor...	Summary: This paper proposes to use stochastic local winner-takes-all (LWTA) layers in vision networks combined with CLIP-Dissect to improve the ability of a neuron to capture a concept. A stochastic LWTA layer groups pre-activation neurons into $B$ blocks of $U$ features each and within each block, chooses one feature...	Rebuttal 1: Rebuttal: We thank the reviewer for his insightful analysis and positive comments for our work. We will correct and update the phrasing on the noted typos/comments for the camera-ready. > It’s not fully clear whether the approach is being presented as an ante-hoc method or a post-hoc method. This constit...	Summary: This paper proposes a novel method for interpreting vision networks by combining an architecture that incorporates stochastic local-winner-takes-all activations with the CLIP-DISSECT framework. The author makes use of the probabilistic interpretation of LWTA as a categorical distribution to derive a similarity...	Rebuttal 1: Rebuttal: We thank the reviewer for his kind comments on the usefulness of our approach. > Clarity could be improved: The figures are not very helpful. Especially fig 1 could be improved by simplifying it (e.g. without showing individual neurons and weights). In my opinion Fig 2 is not necessary and could ...	Summary: The paper proposes DISCOVER, a novel framework for creating interpretable vision networks. It utilizes stochastic Winner-Takes-All layers and multimodal vision-text models to uncover the specialization of each neuron. The framework achieves high activation sparsity, allowing for direct examination of concept r...	Rebuttal 1: Rebuttal: We thank the reviewer for his constructive comments on improving the presentation of the strengths of our work. > Similarity of WTA to Linear Coding (LLC). The LWTA approach does indeed bear some similarity with Locality-constrained linear coding relating to the grouping of features and selecti...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The work proposes a interpreatable network that uses concepts from competition networks specifically there are for a specific output multple copies of the neuron and the neurons compete to provide the winning activation for that output. The method proposes MLP and convolutional versions of their network. The ...	Rebuttal 1: Rebuttal: We thank the reviewer for his insightful analysis and crucial comments and questions. > 1. The method is essentially a stochastic version of maxpooling where the deterministic version is exactly maxpooling. Is there an ablation to show how the stochastic version is more appropriate than a determi...	null	null	null	null	null	null
Ordering-based Conditions for Global Convergence of Policy Gradient Methods	Accept (oral)	Summary: The paper theoretically proves that, for finite-arm bandits with linear function approximation, the global convergence of policy gradient (PG) methods is not dependent on approximation error, but rather, on the ordering properties of the reward representation. The global convergence is achievable for both stan...	Rebuttal 1: Rebuttal: We appreciate that the reviewer recognized the contribution of the work. We answer the questions as follows. >discussion of how to generalize the results to MDPs Generalizing the results to MDPs is an important and challenging next step, as we mentioned in the conclusions. Since other review...	Summary: This work studies the global convergence of standard softmax policy gradient and natural policy gradient for finite-arm bandits with linear function approximation (i.e., considering a log-linear policy). It is shown that the approximation error is not crucial for characterizing global convergence since the lat...	Rebuttal 1: Rebuttal: Thank you for carefully reading and checking the results. The main concerns are addressed as follows. >Comment on Khodadadian et al. 2022 Thank you for pointing this out. We will add a remark to mention the similarities and differences between Theorem 2 and Khodadadian et al. 2022. >**1. (a...	Summary: This paper challenges (arguably) the current best known policy gradient (PG) convergence analysis, which is the conventional approximation error based analysis originally proposed by the seminal work of Agarwal et al. (2021). To this end, the authors consider the finite-arm bandits with log-linear policy and s...	Rebuttal 1: Rebuttal: We appreciate that the reviewer understood and recognized the contribution of the work. We answer the questions as follows. >figures ... y-axis We mentioned that $\pi_{\theta}^\top r$ is used as the vertical axis value in Line 148, and will add this to the figure. >**Line 52-53 ... approxim...	Summary: The paper studies softmax policy gradient and natural policy gradient methods for multi-arm bandits problems using linear function approximation. The authors provide examples to illustrate the global convergence of these methods when the standard function approximation error is not zero. To better characterize...	Rebuttal 1: Rebuttal: The review seems to focus on issues arising from misunderstanding or miscommunication. We hope the following can help clarify matters. >global convergence is abused ... First, global convergence simply means $\pi_{\theta_t}^\top r \to r(a^*)$, i.e., policy's reward approaching that of th...	Rebuttal 1: Rebuttal: We thank the reviewers for their careful reading, valuable comments, and recognition of the contributions. This first, common feedback answers a question raised by multiple reviewers. >Generalization to MDPs (Reviewers m2vF, P4aQ, BpdK) Extending the results of this work to MDPs is an import...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper considered the problem of global convergence condition of policy gradient (PG) methods with linear function approximation motivated by three observations: i) global convergence under linear function approximation can be achieved without policy or reward realizability; 2) approximation error is not a...	Rebuttal 1: Rebuttal: We appreciate that the reviewer understands and recognizes the contributions of this work. We address the main concerns as follows. >Is there a systematic way to check the existence of such a $w$ given $r$ and $X$: Yes, checking the existence of $w$ is known as linear feasibility in the...	null	null	null	null	null	null
Weakly-Supervised Audio-Visual Segmentation	Accept (poster)	Summary: This paper presents a new framework for weakly supervised audio-visual segmentation which does not need pixel level annotations. This is achieved via the proposed multi-scale multiple-instance contrastive learning approach which can capture audio-visual alignment in multiple scales. Comparison with existing me...	Rebuttal 1: Rebuttal: Dear Reviewer XL4i, Thank you for appreciating our approach. We address your comments below. > Details on multiple sources. We use two or three sound sources from 2,120 frames for evaluation. For pseudo-labels for multi-source scenarios, we use the background activation maps as pseudo-labels ...	Summary: This work introduces a new setting for audio-visual segmentation, Weakly-Supervised Audio-Visual Segmentation (WS-AVS). The authors address the challenge of the costly and not always available pixel-level masks by employing weakly-supervised audio-visual segmentation. This framework uses multi-scale multiple-i...	Rebuttal 1: Rebuttal: Dear Reviewer DJ6f, Thank you for appreciating our approach. We address your comments below. > Clarification on contributions/novelty. Our paper includes two main technical contributions: 1) We are the first to investigate a new weakly-supervised multi-modal problem that predicts sounding ob...	Summary: This paper proposes a new contrastive learning approach with pseudo-mask generation/refinement process for weakly supervised video segmentation with audio guidance. The authors show that their method outperforms several other state-of-the-art methods for the visual segmentation task when training and testing o...	Rebuttal 1: Rebuttal: Dear Reviewer yiGS, Thank you for the detailed review. We will address your concerns below. > Large-scale experiments. This is a great suggestion! We extended our method to experiments on Flickr-SoundNet and VGG Sound Source, and reported the comparison results of CIoU with existing approache...	Summary: The authors propose a weakly-supervised audio-visual segmentation framework called WS-AVS that predicts sounding source masks from audio and images without pixel-level ground truth masks. It leverages multi-scale contrastive learning in audio-visual fusion to capture multi-scale alignment, addressing modality...	Rebuttal 1: Rebuttal: Dear Reviewer CQaV, Thank you for the detailed review. We will address your concerns below. > Clarification on novel aspects. Our paper includes two main novel aspects: 1) We are the first to investigate a new weakly-supervised multi-modal problem that predicts sounding object masks from both...	Rebuttal 1: Rebuttal: Dear all reviewers: We extend our heartfelt gratitude to each of you for generously dedicating your valuable time and expertise to reviewing our work. We acknowledge and deeply appreciate the insightful comments and critiques provided by all the reviewers. In response to your invaluable feedback...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
D-Separation for Causal Self-Explanation	Accept (poster)	Summary: In this paper, the authors focus on self-explaining rationalization and they propose a Minimum Conditional Dependence (MCD) criterion to select causal rationales from a causal perspective. Strengths: 1. Rationalization is a worthwhile direction to explore in interpretable research. 2. The paper is well writte...	Rebuttal 1: Rebuttal: Thank you very much for your valuable comments and suggestions. A1 (Novelty): Regarding the comparison with [1,2,3], we agree that the causality in rationalization is not a new topic. However, our research differs significantly from [1,2,3]. The limitaions of [1,2] ([3] is similar to [1]) h...	Summary: This paper studies selective rationalization. Many methods use the maximum mutual information (MMI) criterion to find the most indicative rationale to explain a target label. As it has been shown in the past, this criterion is, by design, sensitive to spurious correlation. This paper proposes a novel criterion...	Rebuttal 1: Rebuttal: We greatly appreciate your detailed review and constructive feedback on our paper! A1 (regarding [1,2]): Our key differences with [1,2] are discussed in L66-85 of Sec.1 and App.C, where we delve into their limitations and theoretical flaws. [1] employs IRM and [2] uses backdoor adjustment to ...	Summary: The maximum mutual information criterion is commonly used for rationalization, but it uncovers associations rather than causal relationships. The authors propose to identify non-causal features that are independent of the labels given the causal features and the ‘minimum conditional dependence’ criterion, whi...	Rebuttal 1: Rebuttal: We sincerely thank you for dedicating your time and expertise to review our paper. Your insightful comments and suggestions are highly valued and appreciated. A1 (related work): Thank you for pointing out this issue. Due to page limitations, we foucsed mainly on papers closely tied to our wor...	Summary: This work focuses on the problem of rationalization that aims to extract explanatory sentences that serve as rationales along with training a predictor for the downstream task. Prior work on rationalization has typically employed the maximum mutual information (MMI) criterion. However, this criterion does not ...	Rebuttal 1: Rebuttal: Thank you deeply for taking the time to thoroughly review our paper. We are truly grateful for the insights and recommendations you've provided. Q1: It would be useful for the reader to see some examples of generated rationales via MCD, and also highlight failure cases when MCD fails to retri...	Rebuttal 1: Rebuttal: We are deeply grateful to every reviewer for their in-depth analysis and constructive feedback on our manuscript. Here are the figures and tables of some of the new experimental results, attached to rebuttal.pdf. Pdf: /pdf/0bbde21d2c879c5200edfce7993604988dd25321.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets	Accept (spotlight)	Summary: This paper suggests a new graph combinatorial optimization method by leveraging a generative flow network (GFlowNet) and other novel technics to improve it. Maximum independent sets (MIS) and their variants are very important problems as they can be applied to several high-impact tasks such as network communic...	Rebuttal 1: Rebuttal: > Please make an explicit description of each technique and give a reference idea of where you are inspired by and what is the major difference, e.g., sub-trajectory balance vs. transition-based learning (assume that reader is also not familiar with the sub-trajectory balance). We do not use the ...	Summary: This paper presents a new approach for learning to solve graph combinatorial optimization by GFlowNets. The GFlowNets (or broadly, the generative models) own the advantage of discovering multiple (near)-optimal solutions, which is better than standard RL or SL. The authors modify GFlowNets to work with larger-...	Rebuttal 1: Rebuttal: > The authors mentioned that "there are attempts to fix these issues (Kwon et al; Ahn et al.)" but these are not compared in experiments. Please note that we have compared with Ahn et al, which is the “PPO” in Table 1. The Kwon et al. work is designed for routing problems (e.g., TSP) which are d...	Summary: This paper leverages generative flow networks (GFlowNets) to obtain diverse solution candidates for combinatorial optimization problems on graphs without expert supervision. GFlowNets were recently introduced as a way to sample structured objects $\mathbf{x}$ with a likelihood $P(\mathbf{x}) \propto R(\mathbf...	Rebuttal 1: Rebuttal: > To me, it appears that the main novel idea in the paper is the application of GFlowNets to learning combinatorial problems (which is already a worthy one). The idea of training from subsampled transitions instead of a full trajectory is also new. … However, I would be very surprised if the MDPs ...	Summary: The authors propose an approach for using GFlowNets for solving some NP-Hard combinatorial optimization problems on graphs. The GFlowNet approach is designed to better perform credit assignment for fitting a constructive policy that adds nodes on the graph to a currently considered subset. The authors propose ...	Rebuttal 1: Rebuttal: > directly apply GFlowNet to CO with minor modifications See the general response. > formulate architecture to encode certain properties… modify GFlowNet to make use of alternative information We appreciate the reviewer's insightful suggestions about interesting future directions. Indeed, we us...	Rebuttal 1: Rebuttal: ## General Response We sincerely thank all the reviewers for your insightful comments and suggestions. > About the contribution and novelty of our work While our approach builds upon the general GFlowNet framework, we have made significant designs and innovations to ensure its scalability and ...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning	Accept (poster)	Summary: Bisimulation methods tend to fail when applied to offline RL. The authors performed a bisimulation error analysis and concluded that missing transitions, inevitable when training with a fixed offline dataset, lead to inaccurate metric predictions. Therefore, training policies based on the representation learne...	Rebuttal 1: Rebuttal: Thank you for your detailed review and feedback on our paper. We have included new experimental results and some general discussion, please refer to our generic response and uploaded file to address these points. - **Question: It is doubtful that learning a bisimulation operator in the offline se...	Summary: The paper investigates why bisimulation-based methods, which are known to work well in the online setting, fail in the offline case. It hypothesizes that missing transitions and reward scaling issues are a source of this failure, and proposes a way to mitigate these issues. It includes experiments on various b...	Rebuttal 1: Rebuttal: Thank you for your detailed review and feedback on our paper. We have included new experimental results and some general discussion, please refer to our generic response and uploaded file to address these points. - **Question: It appears that the approach is limited to a single and known behavior...	Summary: The paper discusses the pitfall of bisimulation-based methods in offline RL. It identifies missing transitions in the finite dataset as a significant problem for bisimulation-based methods, leading to ineffective estimation. The paper also highlights the importance of reward scaling in bounded cosine distance ...	Rebuttal 1: Rebuttal: Thank you for your detailed review and feedback on our paper. We have included new experimental results and some general discussion, please refer to our generic response and uploaded file to address these points. - **Question: While expectile regression is applicable to both online [1] and offlin...	Summary: This paper focuses on understanding and ameliorating the relatively poor performance of bisimulation-based representations in offline RL. The authors identify two main issues: overfitting to incomplete data and reward scaling. When the offline dataset is missing states, the representation that’s learned may no...	Rebuttal 1: Rebuttal: Thank you for your detailed review and feedback on our paper. We have included new experimental results and some general discussion, please refer to our generic response and uploaded file to address these points. - **Question: In the D4RL results, SimSR + RS + EBS only clearly outperforms the oth...	Rebuttal 1: Rebuttal: # General response ## 1.The severity of the proposed problem ### How do bisimulation-based objectives perform in other (online or goal-conditioned) settings? - Various methods, such as DBC[48], MICo[6], SimSR[46], and PSE[A], have consistently demonstrated positive results in online settings, r...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper addresses the problem of bisimulation-based representations in offline reinforcement learning tasks, which are less effective than alternative methods. To address this issue, the paper proposes a tailored reward scaling strategy and an auxiliary loss function based on bisimulation metrics to learn r...	Rebuttal 1: Rebuttal: Thank you for your review and favorable assessment of our work! We appreciate the time and effort you have put into evaluating our work. Based on your feedback, we included additional rebuttal experiments, on benchmark D4RL tasks and on visual image-based offline tasks, to demonstrate the signific...	Summary: Summary ------- This submission\'s goal is to understand why bisimulation methods suffers in the offline RL setting. The authors go on to propose two methods that help learn a better bisimulation metric, that result in representations that more faithfully capture state abstractions from offline datasets. The ...	Rebuttal 1: Rebuttal: Thank you for your detailed review and feedback on our paper. We have included new experimental results and some general discussion, please refer to our generic response and uploaded file to address these points. ## The severity of the proposed problem Our study focuses on offline state repres...	null	null	null	null
Cocktail: Mixing Multi-Modality Control for Text-Conditional Image Generation	Accept (poster)	Summary: This paper proposes an approach to enhance the controllability of generative image models through the fusion of multiple modality signals. The proposed model is a strict generalization of ControlNet which is able to incorporate geometric constraints into the generation, including pose, edges, and segmentation ...	Rebuttal 1: Rebuttal: We are deeply grateful for your comprehensive review and favorable assessment of our paper. Your commendation regarding the soundness, presentation, and contributions of our research not only encourages us but also validates the effort we have invested in this work. We will try to include more sa...	Summary: This paper studies text-conditional diffusion models and proposes a pipeline for image generation with multi-modal control signals. The pipeline contains three new ingredients. Firstly, the paper introduces gControlNet, a generalized version of ControlNet, that can take different conditions (such as sketch, se...	Rebuttal 1: Rebuttal: Thank you for your detailed reading and valuable insights concerning the gControlNet and its components. Allow me to clarify your concerns: #### Regarding the downsampling network $\mathcal{M}$: $\mathcal{M}(C^k)$ is a simple convolutional network consisting of eight convolutional layers and ...	Summary: This study introduces ChatIR, a chat-based image retrieval system that engages in a conversation with the user to clarify their search intent and retrieve the desired image from a large corpus. The system leverages Large Language Models to generate follow-up questions to an initial image description and achiev...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful comments and suggestions regarding our work. In the revised version, we will restructure the manuscript to improve clarity. Moreover, I would like to address your concerns about the performance of our model across different experimental data and the supporte...	Summary: The paper presents Cocktail, a novel pipeline for multi-modal and spatially-refined control in text-conditional diffusion models. The authors address the challenge of ambiguous descriptions in linguistic representations by incorporating additional control signals. They propose three main components: gControlNe...	Rebuttal 1: Rebuttal: We appreciate your insightful comments and questions. Your feedback helps us to address important aspects of our work that deserve more detailed consideration. Here's our response to your observations. #### Ethical Considerations: We acknowledge your concern about the potential misuse of our ...	Rebuttal 1: Rebuttal: We have organized the comments from the reviewers, and below are several issues that have been highlighted by multiple reviewers. Clarification of Benefits and Analysis: In the principal section of this submission, metrics such as CLIP, FID, and aesthetic scoring were employed to gauge our m...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes Cocktail, a pipeline to mix various modalities into one embedding. The model is based on a variant of ControlNet and infuses control signals from disparate modalities into the pre-trained diffusion model. It is also equipped with a sampling approach named spatial guidance sampling that cons...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and observations regarding our model. #### Clarify the specific benefits of our proposed method. The metrics introduced in the main text focus on the quality of image generation. After training, the quality of the images generated by our model outperform...	null	null	null	null	null	null
A Measure-Theoretic Axiomatisation of Causality	Accept (oral)	Summary: This paper builds a rigorous foundation for causal reasoning. It does so by defining causal space, an extension of the concept of probability space by adding stochastic kernels. This richer structure allows the authors to define notions like interventions and causal effects, and generalize the standard framewo...	Rebuttal 1: Rebuttal: We thank the reviewer very much for the time and effort spent in reviewing our submission, and for the positive evaluation. Your comments make it clear that you understood and agreed with our motivation behind this project, for which we are humbled and extremely grateful. We are also very grateful...	Summary: This paper presents a completely novel mathematical description of causal systems, which generalizes the most well-known causal formalisms such as SCMs and potential outcomes. It does so by taking the probability distributions as the foundation, and use this to construct a measure-theoretic approach to causal ...	Rebuttal 1: Rebuttal: We thank the reviewer very much for the time and effort spent in reviewing our submission, and for the positive evaluation. Thank you also for suggestions for improvements; we will reflect them in future versions of this work. We will do our best below to address your concerns in the Questions sec...	Summary: The paper develops new foundations for causality based on the measure-theoretic foundations of probability pioneered by Kolmogorov. Interventions are formalized as transition kernels and it's shown that the new approach is more general than existing ones in a number of respects. Strengths: The paper is clearl...	Rebuttal 1: Rebuttal: We thank the reviewer for the time and effort spent in reviewing our submission, and for the overall positive evaluation. We also thank you for raising interesting points and suggestions for improvements, which we will take into account in future versions of this work. Below, we will try our best ...	Summary: This paper proposes a measure-theoretical axiomatization of causalality with the notion of causal space and with a collection of causal kernels encoding the causal information. Strengths: The paper offers an axiomatization of causality which is based on the measure-theoretical foundation of probabaility theo...	Rebuttal 1: Rebuttal: Thank you for reviewing our submission. We regret that you have a rather negative view of the paper, and we hope that our clarifications below, as well as the other reviews, can give you a more positive view. Definition 2.2 This is the main definition of the paper, containing the two axioms...	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their time and effort in reviewing our paper, and for making many valuable suggestions that are sure to improve the draft. We are very grateful for your time, and do not take it for granted. We are also very grateful for the mostly positive reviews, and kin...	NeurIPS_2023_submissions_huggingface	2,023	Summary: (Preamble warning: given the amount and time I had to review Neurips papers, and given other duties, I focused solely on the main content of the paper, not consulting the appendices that were actually longer than the paper.) The paper proposes and discusses a new framework, based on measure-theoretic probabi...	Rebuttal 1: Rebuttal: Thank you for your time and effort in reviewing our submission; we are very grateful for your overall positive evaluation of the paper and your valuable suggestions. We will do our best below to address your concerns and queries. Weaknesses Thank you for this point. We are aware that other ...	null	null	null	null	null	null
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series	Accept (poster)	Summary: This paper investigates the contrastive learning for medical time series and develops multiple contrastive objectives at different levels (patient, trial, observation, sample). The downstream applications is the classification on EEG, ECG, EMG, EOG signals. The proposed method has obtained the best or competit...	Rebuttal 1: Rebuttal: We are happy that you are interested in our paper and feel our paper is well-written and easy to understand. Thank you very much for your three valuable concerns! Here we respond in detail to each of your concerns. If you do not feel we have sufficiently justified a higher score, please let us kno...	Summary: This paper proposes a multi-granularity contrastive learning method (named COMET) on medical time-series data. The methods build postive and negative pairs from patient-level, trail-level, sample-level, and observation-level. The proposed method is evaluated on two EEG, and one ECG dataset. Strengths: 1. The ...	Rebuttal 1: Rebuttal: We are grateful for your thoughtful feedback and happy that you feel our paper is well-organized and the figure is informative. The following response to the questions about our method's weaknesses. * Q1**: The proposed method is not new since many multi-granular contrastive learning models ex...	Summary: Medical time-series data, unlike domains such as CV and NLP, lack data labeling but contained more layers of information corresponding to observation, trial, and individual physiologies. Unlike previous methods that overlooked the multi-granularity, the authors proposed COMET to trained the combined contrastiv...	Rebuttal 1: Rebuttal: Thank you for your very thoughtful feedback. We highly appreciate you carefully reading our figure, method, appendix, and code! We are happy you feel our paper follows a clear logic and is easy to follow!! Again, thank you for your comments to help us improve our paper. Here are the response to yo...	Summary: This paper proposes a multi-granularity framework leveraging data consistencies at different levels inherent in medical time series data. The model learns with contrastive loss designed at every data granularity, i.e., observation, sample, trial, and patient levels. The method is evaluated with three binary cl...	Rebuttal 1: Rebuttal: We are happy you feel our paper is easy to follow, interesting, and effective. We briefly respond to your concerns point-by-point. If you do not feel we have sufficiently justified a higher score, please let us know where we can further improve our work. Thank you again! * Q1** The contrastiv...	Rebuttal 1: Rebuttal: We appreciate the valuable feedback provided by all the reviewers. We highly appreciate the reviewers who believe our work is solid, effective, easy to follow, and well-written, with a logical presentation of the methodological contribution of the proposed method. In response to the reviewers' ins...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Projection-Free Online Convex Optimization via Efficient Newton Iterations	Accept (poster)	Summary: Building upon recent progress in projection-free OCO, this paper presents a new IPM-like algorithm attaining optimal regret with improved efficiency, which calculates Hessians only $O(\sqrt{T})$ number of times. The algorithm assumes access to a self-concordant function by gradient oracles and Hessian oracles....	Rebuttal 1: Rebuttal: Thank you for your positive review. “There is an author's comment in line 160, which I suspect violates the double-blind rule...” Unfortunately, this was a typo that we only spotted shortly after the submission deadline. **“I think the paper can benefit from a detailed comparison with the ...	Summary: This paper introduces new projection-free algorithms for online convex optimization, which utilize Newton iterates with a self-concordant barrier for the target set. The authors establish a state-of-the-art regret bound for this algorithm. Strengths: Strengths And Weaknesses: The paper presents a new approach...	Rebuttal 1: Rebuttal: Thank you for your review. “It would be beneficial to have a comparison of the gradient complexity between this method and other related works.” In previous works, the gradient complexity refers to the number of gradient computations of the losses. Our algorithm only requires a single gradie...	Summary: This paper proposes new projection-free algorithms for online convex optimization over a convex domaim $\mathcal{K}$. Specifically, this paper proposes efficient Newton iterations to obtain projection-free online convex optimization. Strengths: This paper proposes an efficient online Newton method which is c...	Rebuttal 1: Rebuttal: Thank you for your review. “This paper seems only using existing method to the online Newton.” Our algorithm does apply novel techniques to achieve the desired regret guarantee in the general OCO setting. In fact, applying existing techniques such as those in [Mhammedi and Gatmiry 2023] fails...	Summary: This paper proposes a "projection-free" algorithm for online convex optimization. The proposed algorithm adopts a self-concordant barrier of the constraint set as the regularizer, automatically ensuring the feasibility of the actions. The proposed algorithm only requires computing the inverse of an approximate...	Rebuttal 1: Rebuttal: Thank you for you positive review and helpful suggestions. "I wonder how reasonable the error tolerances in the definitions of the approximate gradient and Hessian are…" To get a sense of this, let’s look at the case of a polytope in $\mathbb{R}^d$ with $m$ constraints. Here, we want to use t...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Error Bounds for Learning with Vector-Valued Random Features	Accept (spotlight)	Summary: This paper presents error analysis for learning with vector-valued random features, where the output takes vector values. Specifically, the paper considers random feature ridge regression and derive a general bound for the population risk functional, based on which the paper gives several results such as conve...	Rebuttal 1: Rebuttal: We start by thanking the reviewer for your appreciation of the merits of our paper and your welcome suggestions to improve it. We will correct the identified typos in the revision. Below, we address the concerns raised by the reviewer and thank the reviewer in advance for their patience in readin...	Summary: This paper presents a theoretical error analysis of infinite-dimensional input-output ridge regression with vector-valued random features. This one is the first analysis adapted to infinite-dimensional outputs and even improves existing results for finite-dimensional outputs. Several by-products come with the ...	Rebuttal 1: Rebuttal: We start by thanking the reviewer for your appreciation of the merits of our paper and your welcome suggestions to improve it. We will fix the use of RF-RR acronym in a revised version of the paper, thank you for catching that. Additionally, based on suggestions from the other reviewers, we have i...	Summary: This paper investigates the theoretical aspects of learning vector-valued operators with random features. Specifically, it studies the convergence of the random feature estimate $\Phi(u;\hat{\alpha})$ to the true underlying function $\mathcal{G}$. Its main results are: Theorem 3.4: Error bound for fixed $\lam...	Rebuttal 1: Rebuttal: We start by thanking the reviewer for reading our paper and for their comments. We have fixed the typos in the revised version of our paper. Regarding the reviewer's criticism of the structure and readability of the paper, we agree that splitting into proofs in appendix and theorem/lemma statement...	Summary: The paper proposes a statistical analysis of the risk associated to learning with vector-valued random features (vv-RF) in the context of ridge regression. The analysis shows that $\sqrt{N}$ random features are enough to attain a $\mathcal{O}(\frac{1}{\sqrt{N}})$ squared error (matching minimax rates) and str...	Rebuttal 1: Rebuttal: We start by thanking the reviewer for your appreciation of the merits of our paper and your welcome suggestions to improve it. Below, we address the concerns raised by the reviewer and thank the reviewer in advance for their patience in reading our detailed reply. 1. We believe the reviewer's comm...	Rebuttal 1: Rebuttal: At the outset, we would like to thank all five reviewers for their thorough and patient reading of our article. Their fair criticism and constructive suggestions will enable us to improve the quality of our article. If accepted, a revised camera-ready version of the article, with changes as outlin...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a learning theory for ridge regression in random feature models in the setting when the input-output map $\mathcal{G}: \mathcal{X} \mapsto \mathcal{Y}$ is vector-valued (potentially infinite-dimensional). The model under consideration is a random feature model $\phi: \mathcal{X} \times \Thet...	Rebuttal 1: Rebuttal: We start by thanking the reviewer for your appreciation of the merits of our paper and your welcome suggestions to improve it. Below, we address the concerns raised by the reviewer and thank the reviewer in advance for their patience in reading our detailed reply. 1. The construction of "good" ra...	null	null	null	null	null	null
On skip connections and normalisation layers in deep optimisation	Accept (poster)	Summary: This paper studies optimisation in deep architectures, and presents a framework for theoretically capturing the role of architectural components like skip connections and normalisation layers/weight normalisation. This framework considers the curvature (smootness) and regularity properties of multi-layer netwo...	Rebuttal 1: Rebuttal: We thank the reviewer for their attentive and detailed critique. I'm not sure the extent to which the theoretical bounds… You’re correct that the theoretical bounds are not practical. Thank you though for recognising that this is not a unique problem with our work, and is common to virtually al...	Summary: Paper deals with convergence of deep learning making use of neural Tangent Kernels (NTK) theory and its recent advances. It is a theoretical work, claiming to provide ... "l: 7-11 Abstract: ... the only proof of which we are aware that a class of deep neural networks can be trained using gradient descent to gl...	Rebuttal 1: Rebuttal: We thank the reviewer for their polite critique. We must first clear up what appear to be some misapprehensions in the reviewer’s interpretation of our work that are apparent from the Summary and Strengths section. 1. The reviewer seems to be under the impression that we are applying NTK theory ...	Summary: The paper provides a novel proof strategy showing the guaranteed convergence to a global optima if a particular class of deep neural networks with non-linear activation functions provided there are skip connections. Such results have been known for linear networks so it is nice that it also applies to non-lin...	Rebuttal 1: Rebuttal: We thank the reviewer for their careful reading of our work and for their kind words. Although technically elegant… The reviewer is correct on the point that our proof technique only works in the strictly overparameterised regime. However, the reviewer is incorrect that most people do not work ...	Summary: This paper proposes a formal theoretical framework to analyze gradient and convergence properties of multilayer deep networks. The authors make the following contributions: (1) they provide the first proof that certain deep networks can be trained to global optima at infinity using gradient descent; (2) the au...	Rebuttal 1: Rebuttal: We thank the reviewer for their attentive reading of our submission. Some of the assumptions… We agree that the assumptions are strong, although they are not exceptionally strong in comparison to other theoretical works. It is also not entirely true that models usually have an increasing number...	Rebuttal 1: Rebuttal: We refer the reviewers to the attached PDF for plots of our ImageNet experiment run at different learning rates, as requested by reviewer LNGS. Note that every trial we did of the modified network with the largest learning rate diverged. This is not surprising, given that it has been observed prev...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
ELDEN: Exploration via Local Dependencies	Accept (poster)	Summary: This work proposes `ELDEN, Exploration via Local DepENdencies`, a framework with an intrinsic reward that encourages the discovery of new interactions between entities such as the agent or objects that have some influence on each other. The method uses partial derivative of the learned dynamics to model the lo...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed reading of our paper and for the very constructive suggestions! We hope our responses adequately address the following concerns regarding the evaluation of our work. >The results as they stand are not very convincing. That being said, these are hard problems...	Summary: The paper proposes an intrinsic exploration reward that explicitly takes dependencies between different entities in the environment into account. Such an intrinsic reward helps sparse-reward RL in a few skill-based domains. Strengths: - I find the idea of modeling dependency graphs between entities interestin...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following concerns regarding the evaluation of our work. > The experiment domains use high-level primitives which results in relatively few steps in order to acco...	Summary: This paper proposes a novel intrinsic reward called ELDEN for reinforcement learning. ELDEN encourages the discovery of new local dependencies between entities. Experiments on some robotic tasks are carried out to valid the idea. Strengths: 1. The idea is novel and interesting. 2. The usage of dynamics model...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following concerns regarding the significance and evaluation of our work. > Weakness 1: compare ELDEN (and RL in general) with LLMs Thank you for making this poi...	Summary: This paper proposes ELDEN, a method for intrinsic reward based on local dynamics dependencies between factored state variables. It learns an ensemble of factored dynamics models, and uses the magnitude of the partial derivatives to detect local dependencies between state variables, and uses the ensemble varian...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed reading of our paper and constructive suggestions! We hope our responses adequately address the following questions about our work. > A common weakness, not specific to ELDEN, is of course the assumption of the factored state space. Hopefully future work can...	Rebuttal 1: Rebuttal: We thank all reviewers for the detailed reading of our paper and constructive suggestions! In the global response, we would like to describe the setup of additional experiments and the results can be found in the attached pdf. - Comparison with RND: Using the challenging crafter (Minecraft 2D) dom...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Cross-links Matter for Link Prediction: Rethinking the Debiased GNN from a Data Perspective	Accept (poster)	Summary: This paper addresses the bias between internal links and cross-links by augmenting cross-links and combining two models consisting of the original and debiased models. Specifically, the authors show that the number of cross-links is fewer than internal links in three real-world datasets. Thus, with Jaccard coe...	Rebuttal 1: Rebuttal: ### [About the concerns on heterophilous graphs (W1)] 1. Additional statistics on multiple heterophilous graphs are provided. To validate the data bias on heterophilous graphs, we conduct community detection with Louvain algorithm on six datasets that have low homogeneity ratios (Hom. Ratio)....	Summary: This work finds that current GNN methods have severe data bias because GNNs like to connect new links inside the local neighbors and ignore the distant ones. To address this problem, the authors investigate the bias across different communities and propose a general framework. In this framework, the authors de...	Rebuttal 1: Rebuttal: We are sincerely grateful to the reviewer for the careful evaluation and insightful comments. We would like to address the concerns raised in the feedback in the following responses. ------ ### [About the impact of community detection algorithms (W1)] By comparing the experimental resulst base...	Summary: The authors aim to explore the issue of bias in the link prediction task for GNNs. Specifically, they develop methods to mitigate the bias resulting from graph topology - on internal links versus cross-community links. Their work relies on debiasing node embeddings and a fusion component that retains aspects o...	Rebuttal 1: Rebuttal: Sincere thanks for the reviewer's thorough evaluation and constructive comments. With respect to the reviewer's insightful feedback, we have organized our rebuttal as follows: ------ ### [About the datasets (W1)] We would like to address the concerns to the datasets utilized in our paper from t...	Summary: The paper introduces a twin-structure framework for mitigating bias in link prediction methods based on Graph Neural Networks. Current link prediction approaches often prioritize performance without considering biases on sensitive attributes of nodes, leading to social risks and information cocoons. The propos...	Rebuttal 1: Rebuttal: Thanks for your constructive and meticulous comments! We have carefully examined the mentioned issues and have prepared the following rebuttal to address these concerns. ------ ### [About subgraph-based GNNs (W1, Q1)] 1. The reasons for not using subgraph-based GNNs. Subgraph-based GNNs are...	Rebuttal 1: Rebuttal: ## Response for All Reviewers We sincerely appreciate the dedication and effort put forth by all the reviewers. We hope that our responses have effectively addressed your concerns and contributed to a deeper understanding of our research. **Due to space limitations, we have included most of the e...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper addresses the issue of bias in GNN link prediction and proposes a twin-structure framework to mitigate the bias and improve performance. The framework includes an embedding fusion module and a debias module, which work together to reduce the bias between cross-links and internal-links without hurtin...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for these insightful and enlightening comments. Specifically, we aims to address the concerns of the reviewer with the following responses. ------ ### [About the presentation (W1)] According to the official guidelines of NeurIPS 2023, accepted papers are allo...	null	null	null	null	null	null
VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning	Accept (poster)	Summary: This paper utilizes probabilistic inference to address the problem of offline safe RL by introducing non-parametric variational distributions. Pessimistic estimation of Q-values are used to avoid extrapolation errors caused by OOD actions. Extensive comparative numerical experiments are carried with respect ...	Rebuttal 1: Rebuttal: Dear review wQ6W: Thank you for the insightful comments which help us improve the paper. We'll answer your questions one by one below. We are also very honored to share our understanding with you. + __Q1: "The paper is not well-organized written. $\cdots$ rather than deriving literally step b...	Summary: This paper introduces an interesting algorithm for offline safe RL, called Variational Optimization with Conservative Estimation (VOCE). The primary challenge tackled by the authors is the influence of safety constraints and out-of-distribution (OOD) actions, which often hamper the optimization of high-reward ...	Rebuttal 1: Rebuttal: Dear review kEdv: Thank you for your all valuable suggestions and meticulous comments. We will incorporate your suggestions in the revision. Below we respond to your key concerns point by point. Please let me know if there are any further questions. + __Q1: "It seems that many claims and argu...	Summary: This paper applies CQL to offline safe RL tasks. It introduces a non-parametric policy to search for the actions that satisfy the constraints. Lagrangian multipliers are introduced for constraining the KL divergence w.r.t. the current policy and the additional constraints. CQL-style additional terms are introd...	Rebuttal 1: Rebuttal: Dear review GTEu: Thank you for your suggestions and constructive comments. We will incorporate your suggestions in the revision. Below we respond to your key concerns point by point. Please let me know if there are any further questions. + __Q1:" The novelty of the method could be more exte...	Summary: The paper presents a variational approach to offline safe reinforcement learning (RL), where the class of variational distributions is the set of policies which satisfy the cost constraints. It is shown how to obtain the closed-form solution for the optimal variational distribution, and how to extract a parame...	Rebuttal 1: Rebuttal: Dear review cmps: Thank you for your all valuable suggestions and meticulous comments. We will incorporate your suggestions in the revision. Below we respond to your key concerns point by point. Please let me know if there are any further questions. + __Q1: "There is no discussion of the runtim...	Rebuttal 1: Rebuttal: Dear reviewers: We thank all reviewers for your time and suggestions, and we expect to have a further discussion. We have responded to your questions in detail accordingly. If you have further questions or concerns, we still reply before the end of the author-reviewer discussion. Thank you very...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Q-DM: An Efficient Low-bit Quantized Diffusion Model	Accept (poster)	Summary: This paper presents a quantization-aware training framework for diffusion models. The authors identified activation distribution oscillation and quantization error accumulation as the main causes of the performance drop. To close the performance gap, the authors developed a timestep-aware quantization (TaQ) me...	Rebuttal 1: Rebuttal: Q1: What does "PTQ4DM" exactly refer to in Tab. 2? Tab.2 cites [1] while referring PTQ4DM to [2] in Line 243. As far as I am concerned, PTQ4DM refers to a recently published work [2], presenting a method for calibration data collection in diffusion quantization, then what does Line 242 mean by...	Summary: The paper proposed a quantization-aware training scheme for diffusion models, based on the well-known method, LSQ. In the paper, they identified the bottleneck come from a large distribution oscillation on activations and accumulated quantization error caused by the denoising process. Then, they suggest meth...	Rebuttal 1: Rebuttal: Due to the character number constraint of rebuttal, we abridge the question in the rebuttal part. All experiments below are conducted on 50-step DDIM sampler with 32×32 generating resolution on CIFAR-10 dataset. Q1: Regarding TaQ and NeM. A1: Our method is proposed based on the observe...	Summary: In this paper, a novel method called Q-DM is introduced, which enables the creation of low-bit quantized diffusion models. The authors first give extensive analysis about two primary challenges faced by low-bit quantized DMs: significant distribution oscillation on activations and accumulated quantization erro...	Rebuttal 1: Rebuttal: Q1: Is the model quantized during all the training and sampling process? Since these may lead to different inference speed. And are the quantization-related parameter all the same across different timestep? A1: Yes, the model is quantized during both the training (to quantized value in fl...	Summary: The paper proposes two method to mitigate the accuracy degradation caused by quantization of diffusion models: one is time-step aware quantization (different calibration data and range for each time-step of diffusion), the other is using a full-precision network for training time distillation. Experiments on i...	Rebuttal 1: Rebuttal: Q1: The benefits of per-time-step quantization has been known since earlier works like PTQ4DM [1] and Q-Diffusion [2]. It is desirable to know the proposed method and prior works. Note that QAT and PTQ do not make much difference here, as the underlying motivation of per-time-step quantization...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper identifies two challenges in low-bit diffusion models (DMs): activation distribution oscillation and quantization error accumulation. To tackle these challenges, the paper introduces two novel techniques: Timestep-aware Quantization (TaQ) and Noise-estimating Mimicking (NeM). Experimental results dem...	Rebuttal 1: Rebuttal: Q1: The main contribution of this paper involves the utilization of statistical mean and variance to address distribution oscillation. Essentially, this approach is akin to applying a shift and scale operation following quantized operations, which is a common technique employed in low-bit quan...	null	null	null	null	null	null
SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting	Accept (poster)	Summary: The paper "SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting" propose to model uni-variate time-series with two interleaved networks, that model 'fine-grained' and 'coarse-grained' time-steps. This has the advantage of reducing signal paths, reducing inference error acc...	Rebuttal 1: Rebuttal: Thank you very much for your helpful and thoughtful review, and your positive comments regarding the paper's core idea, clarity of presentation, and thorough evaluation. ### Regarding concept of "confidence" in Figure 4 > it's a bit unclear what is meant with 'more confident' - I think it is pro...	Summary: The authors propose new ways execute the recurrence in auto-regressive forecasting models. The approach is practical and shouldn't be too complicated to implement for a forecasting practitioner. Experiments show performance uplifts on common real and toy datasets. Strengths: The approach is interesting and ...	Rebuttal 1: Rebuttal: Thank you so much for your detailed and constructive review, and your kind words regarding the advantages of the approach and the paper's organization, clarity, and overview of related work. ### Regarding parallelization of training > One important advantage that is mentioned but not emphasized...	Summary: The author proposed SutraNets, a novel method for neural probabilistic forecasting of long-sequence time series. It addresses challenges faced by previous autoregressive approaches, such as error accumulation and modeling long-distance dependencies. SutraNets treat long predictions as multivariate predictions ...	Rebuttal 1: Rebuttal: Thank you for your very useful comments, and for your kind words regarding the paper's key idea, organization, and clarity. ### Regarding selection of high and low frequencies > What is the deviation point of high and low frequency? What is the connection between traditional signal analysis? Th...	Summary: This manuscript proposes SutraNets for long-range probabilistic forecasting on time series data and pixel sequences. SutraNets is a type of recurrent neural network that transforms a long series into a collection of shorter sub-series. Sub-series forecasts are generated autoregressively, sequentially conditio...	Rebuttal 1: Rebuttal: Thank you very much for your helpful and insightful review. Your pointers to related work will enable us to substantially improve the paper. It is also gratifying to know that Figure 2 was valuable and contributed to the overall good presentation. ### Regarding prior probabilistic forecasting mod...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper proposes a novel method for probabilistic forecasting of long-sequence time series. It uses an autoregressive generative model to factorize the likelihood of long sequences into products of conditional probabilities. The proposed model SutraNets treat long and univariate prediction as multivariate ...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful feedback, and your support for the paper's core idea. You raise an important point regarding Transformers: we did not spend enough time describing how SutraNets could be applied to Transformers. While we did mention in line 45 that SutraNets could be appli...	null	null	null	null	null	null
Doubly Robust Augmented Transfer for Meta-Reinforcement Learning	Accept (poster)	Summary: This study introduces the DRaT (Doubly Robust Augmented Transfer) algorithm, an advanced extension of hindsight-based transfer methods, which tackles not only reward mismatches but also discrepancies in transition dynamics. The authors provide a theoretical analysis to establish the optimality of their interva...	Rebuttal 1: Rebuttal: Comment 1 - Computational Overhead Analysis and Wall-Clock Time Comparison: Thanks for this valuable suggestion. Due to the space limit, please refer to our General Response to the Common Concern of "Providing Time Complexity Analysis of DRaT", for the detail discussion on the computational o...	Summary: The paper identifies that some existing approaches ignore varying dynamics in meta-RL and proposes a new method for addressing this. In particular, the paper focuses on minimizing the mean-squared error of value functions and showed that doubly robust estimators suffer from a high-variance problem. Consequent...	Rebuttal 1: Rebuttal: Comment 1 - Concern on Figures 2 and 3: Please note that though compared to Fig. 2, the current baselines (e.g., PEARL \[4\] and HFR \[15\]) in Fig. 3 may have a smaller performance gap with our DRaT, they still suffer from a larger standard deviation of performance (as indicated by the large...	Summary: This paper focuses on the meta-reinforcement learning setting with sparse reward. Previous work with hindsight-based sample transfer approaches requires the assumption that tasks differ only in reward functions. This paper proposes a doubly robust augmented transfer (DRaT) approach that allows both dynamics mi...	Rebuttal 1: Rebuttal: Comment 1- Providing Detailed Experiment Settings: Thanks for this valuable suggestion. We will provide in the final version more details about the meta-RL setting in experiments, as follows. The randomization of dynamics on all the environments in our experiments are implemented by generati...	Summary: The paper introduces Doubly Robust Augmented Transfer (DRaT), a novel approach for dealing with sparse-reward scenarios in meta-reinforcement learning (Meta-RL). DRaT transfers informative trajectories from various tasks to a target task, effectively handling dynamics mismatches and different reward functions....	Rebuttal 1: Rebuttal: Comment 1 - Providing Time Complexity Analysis of DRaT: Thanks for this valuable suggestion. Due to the space limit, please refer to our General Response to the Common Concern of "Providing Time Complexity Analysis of DRaT" for the detail discussion on the time complexity analysis of DRaT, wal...	Rebuttal 1: Rebuttal: General Response: We would like to thank all the reviewers for their helpful comments. Here, we will respond to the common concern on the time complexity of our proposed DRaT. For orther concerns, please see below our responses to each reviewer’s individual comments, where the newly added figu...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
RH-BrainFS: Regional Heterogeneous Multimodal Brain Networks Fusion Strategy	Accept (poster)	Summary: The submission is not in my area, and it's difficult to give reasonable comments about this submission. My research interests focus on medical image reconstruction. please find another appropriate reviewer to review this paper. Strengths: N/A Weaknesses: N/A Technical Quality: 2 fair Clarity: 2 fair Quest...	null	Summary: This paper proposes a novel approach called the Regional Heterogeneous Multimodal Brain Networks Fusion Strategy (RH-BrainFS) to address the issue of regional heterogeneity between structural connectivity (SC) and functional connectivity (FC) in brain networks fusion. The proposed approach includes a brain sub...	Rebuttal 1: Rebuttal: Answers to weaknesses： 1. For the first weakness, in fact, we have followed this work (Zhu et al.) , but this work does not disclose their code, and we cannot compare them. 2. For the second weakness, the initial motivation for fusion bottleneck was that we believed that there was regional heterog...	Summary: The paper identifies a gap in the literature of multimodal brain networks fusion in which current methods are said to only use "simple patterns" to fuse modalities, ie, concatenation, weighted summation, and self-attention. To tackle this issue, the paper proposes RH-BrainFS, a new model fusing structural conn...	Rebuttal 1: Rebuttal: First of all, thank you for your recognition of our work, but for the disclosure of data, it does need to be followed up by our discussion with multiple parties (including but not limited to the hospital side). Answers to weaknesses： 1. Maybe we did ill-define this issue, and the lack of clarity ...	Summary: The author proposes a novel regional heterogeneous multimodal brain networks fusion strategy to alleviate the issue of regional heterogeneity of multimodal brain networks. This strategy uses a graph convolutional network for the extraction of initial features of nodes (brain region from AAL atlas) and a transf...	Rebuttal 1: Rebuttal: Answers to weaknesses： 1. For the first weakness, we can understand your concern, and we have thought about it in the same way, but we have not come up with a good way to reflect the regional heterogeneity of brain networks at this time. Your suggestion of exploratory experiments based on brain r...	Rebuttal 1: Rebuttal: In response to some reviewers and ethics reviewer's questions about the negative social impact, we would like to explain the following. First, regarding the dataset collection process, the Human Connectome Project (HCP) dataset, as a publicly available dataset that has been used in numerous previ...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The article discusses the use of multimodal fusion as a research technique in neuroscience to extract complementary information from multiple modalities. Since previous research has neglected the regional heterogeneity between structural connectivity (SC) and functional connectivity (FC) and used inefficient w...	Rebuttal 1: Rebuttal: Answers to weaknesses： 1. For the first weakness, "forbidden interactions" means that we remove direct interactions between SC and FC (just as in the standard Transformer model, a direct calculation of attention scores for different modal markers is a direct interaction). The initial motivation fo...	null	null	null	null	null	null
AND: Adversarial Neural Degradation for Learning Blind Image Super-Resolution	Accept (poster)	Summary: This paper proposes a method of AND to learn neural degradation for the task of blind image super-resolution. The core idea is learning to degrade HR images by neural networks, trying to synthesize real-world degradations. Based on the synthesized data, a restoration model can be well trained. The proposed AND...	Rebuttal 1: Rebuttal: Q1: The idea of learning degradation to better learn blind image super-resolution is not new, and has been studied in existing works, like "To learn image super-resolution, use a GAN to learn how to do image degradation first" (ECCV2018). A1: You seem to misunderstand the main contributio...	Summary: This paper proposes an adversarial approach for blind image super-resolution. Instead of using combinations of synthetic degradations (e.g., Gaussian blur, JPEG compression), this paper proposes to use a degradation network to construct LR patches from HR patches during training. The degradation network is opt...	Rebuttal 1: Rebuttal: Q1: Why do the degradation network and restoration network correspond to degradation and restoration respectively? Is it possible that the degradation network attempts to enhance the HR input and the restoration network degrades back? A1: As demonstrated in Algorithm 1 of our supplementar...	Summary: This work proposed a novel blind SR method via adversarial neural degradation. Utilizing the proposed adversarial neural degradation model can generate various nonlinear degradations effects and no supervisions are required. This makes the proposed method can deal with various real SR datasets. Experiments als...	Rebuttal 1: Rebuttal: Q1: In table 1, the authors are expected to provide the SR results with full supervisions by sota sr methods. The proposed method is not required to outperform these methods since they are full-supervised but the proposed method is zero-shot. This can help the readers know the gap between the ...	Summary: This paper proposed a new image degradation system for blind image super-resolution tasks. The proposed method uses a neural network system to learn and capture the image degradation operations, combined with a image restoration network, the proposed method achieved satisfactory performance. Strengths: The p...	Rebuttal 1: Rebuttal: Q1: The paper is focusing on the degradation system and only adapted an existing SR model to be trained with the proposed system. It is not a weakness per see, but a new GAN system designed with the degradation system could have better performance. A1: Thank you for your advice. This work...	null	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper presented a blind super-resolution algorithm, in which a neural network is used to represent image degradation, followed by the incorporation of adversarial learning mechanisms to study "hard cases". The technique to represent image degradation involves initializing a neural network, ensuring the ini...	Rebuttal 1: Rebuttal: Thank you for bringing the ICML 2023 paper to our attention, and we will discuss the contribution of the ICML paper in the revised version of our paper. As several of your questions are rooted in this paper, please let us first elaborate on the connection between the ICML paper and our research. ...	null	null	null	null	null	null
Soft-Unification in Deep Probabilistic Logic	Accept (poster)	Summary: This work proposes a neural symbolic framework, DeepSoftLog, which extends DeepProbLog with a soft equivalent operation. The authors also develops four properties that such a soft equivalency operation should hold. The experimental results demonstrate that DeepSoftLog has better performance than the state-of...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for taking the time to read and review our paper. We will address the reviewer's comments, ordered by topic. > In the experiment section, all images are encoded with one-hot embedding, which is quite problematic. The authors want to stress that the images are embe...	Summary: This paper studies the notion of soft-unification, first employed by the Neural Theorem Prover to learn logic rules in an end-to-end differentiable manner. They outline several properties that need to hold for soft-unification to be semantically meaningful and efficiently trainable; properties which previous...	Rebuttal 1: Rebuttal: We want to sincerely thank the reviewer for taking the time to read and review our paper. We are happy to hear that the reviewer found the paper very well written. > I'm not really sure what the point of evaluating on MNIST-addition is. The authors seems to be using it to argue for the scalabilit...	Summary: This work proposes DeepSoftLog, a neuro-symbolic framework that generalizes ProbLog by combining soft-unification with probabilistic semantics. It first defines some properties of the soft-unification that it believes to be required for meaningful semantics and efficient training. Further, it examines some of ...	Rebuttal 1: Rebuttal: We first sincerely thank the reviewer for taking the time to read and review our paper. > I don't understand the example in the introduction: what is the meaning of Listing 1, what are the ~newstate1 & ~newstate4, and why DeepSoftLog would learn to set ~newstate1 = ~state2 & ~newstate4 = ~state1?...	null	null	Rebuttal 1: Rebuttal: As was requested by some reviewers, we have expanded section 5.3 with additional experiments and a neural baseline. We have attached the new section 5.3 as a pdf. Pdf: /pdf/eb05abd1bcb2fab730e4e28f205cfdddb1ee6cc8.pdf	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner	Accept (poster)	Summary: The paper proposes a new approach to pre-training language models called Prompt-based Continued Pre-training (PCP). PCP combines the idea of instruction tuning with conventional continued pre-training. The authors argue that PCP can improve the performance of prompt-based fine-tuning on a variety of natural la...	Rebuttal 1: Rebuttal: We are grateful for the reviewer (bM1j)’s thoughtful and thorough evaluation of our paper. We are genuinely appreciative of the positive feedback concerning simplicity and effectiveness of our approach, solid experimental evidence, and the quality of our presentation. We would like to respond to t...	Summary: This work re-examines a well-known technique in the NLP literature, called continued pre-training, that can be utilized to enhance the performance of language models on downstream tasks (in this case, for classification and regression tasks). The authors have revealed that the conventional approach to continu...	Rebuttal 1: Rebuttal: We express our gratitude to the reviewer (fyTk) for the insightful and comprehensive assessment. It is heartening to receive positive feedback on the simplicity and effectiveness of our approach, as well as the quality of our analysis. Furthermore, we appreciate the acknowledgement of our contribu...	Summary: This paper explores the problem of continued pre-training on task-related text. The authors discovered that conventional continued pre-training methods may not be very effective and can even have a negative impact on fine-tuning performance. To address this, they introduce prompt-based continued pre-training. ...	Rebuttal 1: Rebuttal: We appreciate the effort and time by the reviewer (pe8v). We are heartened by their positive appraisal of our work and their recognition that no major weaknesses have been identified in the paper. We would like to respond to their invaluable feedback as follows: ***Is it possible to extend this a...	Summary: This paper makes a contribution by studying how to adapt pre-trained models to downstream tasks. The authors identify the limitations of TAPTs and show when they do not work well. They then propose PCP, a better algorithm that can adapt a pre-trained model to a target task. PCP is shown to be more effective th...	Rebuttal 1: Rebuttal: We are grateful for the reviewer (TYSB)’s thoughtful and thorough evaluation of our paper. We sincerely appreciate the positive feedback regarding our intriguing analysis and contribution to identify the limitations of the previous approach. We would like to address the reviewer's valuable feedbac...	Rebuttal 1: Rebuttal: We appreicate all the reviewers for dedicating their time and effort to evaluate our work. We are thrilled to receive positive feedback on the novelty of our approach (CwVD,pe8v), the simplicity and effectiveness of our approach (CwVD,fyTk,bM1j), solid experimental evidence (CwVD,pe8v...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The proposed method (CPC) is built on top of pseudo-labeling and continued pre-training via masked language modeling. This method provides an alternative way to use pseudo-labeled data before fine-tuning the model on downstream tasks. It improves the TAPT method and other semi-supervised methods for text class...	Rebuttal 1: Rebuttal: We appreciate the effort and time by the reviewer (CwVD). We are thrilled to receive positive feedback on the novelty and simplicity of our method, along with the extensive experiments that underscores the effectiveness of our method. We would like to address the reviewer's valuable feedback as fo...	null	null	null	null	null	null
On Certified Generalization in Structured Prediction	Accept (poster)	Summary: This paper proposes a PAC-Bayesian risk bound for the task of structured prediction. Under the assumption that the data is generated by Knothe-Rosenblatt rearrangement, this method distills random output variables into a Wasserstein dependency matrix, which paves the way for improved generalization bounds of ...	Rebuttal 1: Rebuttal: # Thank you for your careful reading of our manuscript and insightful review It has been shown by [Bogachev2005] that any atom-free data distribution can be represented as the unique KR rearrangement of an atom-free reference distribution (such as the normal distribution or uniform distribution)....	Summary: This paper establishes a new PAC-Bayesian risk bound for the structured prediction problem. Technically, it assumes the data are generated by the Knothe-Rosenblatt rearrangement of a factorizing reference measure, and then obtains generalization bounds with the Wasserstein dependency matrix. Strengths: 1. Dif...	Rebuttal 1: Rebuttal: # Thank you for your careful reading of our manuscript and insightful review (1) We agree that, in their most abstract form, the results of [1*] do not require an MRF assumption. However, in our view, the key issue which reduces the relevance of this theory to practitioners is computability. Thi...	Summary: This work derives a novel PAC-Baeysian risk bound for structured prediction based on generative models, a triangular and monotone transport and Wasserstein dependency matrices. Strengths: This is technical a paper with rigorous theoretical analysis. The flow is easy to follow. The authors have made very detai...	Rebuttal 1: Rebuttal: # Thank you for your careful reading of our manuscript and insightful review Bounds which ignore the size of the structured object can not make any useful statement on generalization from a single example. For instance, the typical setting of node classification with graph neural networks conside...	Summary: This paper develops a PAC-Bayesian bound on the risk of structured predictors which decreases with the number and size of examples. The work builds on concentration of measure results (e.g., [33]) and continues the line of work in [36] by removing the assumption that data are generated by a Markov Random Field...	Rebuttal 1: Rebuttal: # Thank you for your careful reading of our manuscript and insightful review # W1 & Q2 The recent work of [Baptista] discusses a parametric class of monotone triangular maps which can serve as approximations of KR rearrangement. A possible direction of future work is to specialize this constructi...	Rebuttal 1: Rebuttal: # We thank all reviewers for their insightful and constructive reviews. We respond in detail to each reviewer in the corresponding sections. Below, we summarize our responses to points which were raised by at least two reviewers. # Connections/implications to practice, KR-based generative mode...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper studies PAC-Bayesian risk bound for structured prediction using concentration of measure. Specifically, authors characterize stability and dependency with Wasserstein dependency matrix under the data assumption that data are given by Knothe-Rosenblatt (KR) rearrangement of a factorizing reference me...	Rebuttal 1: Rebuttal: # Thank you for your careful reading of our manuscript and insightful review 1. We do not claim to make a contribution to the mathematical literature on measure concentration with respect to functions of dependent random variables. Rather, we contribute to machine learning by applying the concept...	null	null	null	null	null	null
Focused Transformer: Contrastive Training for Context Scaling	Accept (poster)	Summary: This paper proposes an improved training approach for memory-augmented Transformers on language modeling tasks. In particular, the authors identify the distraction issue in memory-augmented models, whereby the attention mechanism tends to focus on irrelevant contexts in the regime of long sequences. To address...	Rebuttal 1: Rebuttal: We thank the reviewer for a thoughtful review. Regarding the computational cost. We thank you for raising this important concern, which we have added to the limitation section. We also note that two factors mitigate this issue to some extent. As you noted, the increased cost occurs only in th...	Summary: This paper proposes the Focused Transformer (FoT), which modifies one layer of the Transformer model's Attention layer to Memory Attention, thus enabling the model to learn almost infinite length context without being limited by the constraints of local attention. Specifically, the paper also proposes a cross-...	Rebuttal 1: Rebuttal: We thank the reviewer for the encouraging review. The description of the method outlines the general idea, and we admit that it might be hard to infer details from it. We think presenting the details in the text would be quite cumbersome; thus, we plan to include the shortened versions of the cod...	Summary: This work presents a modified method to train Transformers with memory. The memory is based on k-Nearest Neighbors (with exact match). The work is based on the Memorizing Transformer paper from Y. Wu et al. 2022. The model assumes a transformer model with a memory attention (an attention layer that has inputs ...	Rebuttal 1: Rebuttal: We thank the Reviewer for their thoughtful feedback. We acknowledge some deficiencies in the presentation. We focus on the long-context capabilities; the appropriate clarification is described in the general answer. In more detail, we aim for a single-stage method that can incorporate a large numb...	Summary: The paper proposes a key challenge (distraction issue) when extending the attention layer to external (key, value) pairs, either from previous context of the same document or from other documents. This paper proposes the Focused Transformer (FoT), a model that can utilize a long context by retrieving kNN (key,...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive feedback. We admit deficiencies in clarity raised by the reviewer. We note that we focus on the long-context capabilities; see also the general response. In our experiments, we tested FoT in both single-doc and multi-doc scenarios to assess its pote...	Rebuttal 1: Rebuttal: We would like to thank you for all your valuable feedback, both positive and negative, which we believe will help us to improve the quality of our work. We are delighted to note that the reviewers (Sux9, v42b, GWpw) prized the simplicity of our method and noted the potential impact (Sux9) of ext...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Deconvolving Complex Neuronal Networks into Interpretable Task-Specific Connectomes	Reject	Summary: This paper addresses the challenge of identifying elementary functional neuronal networks and their combinations in the context of complex tasks, using task-specific functional MRI (fMRI) data. The central problem it tackles is the deconvolution of task-specific aggregate neuronal networks into elementary netw...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed review and helpful comments. Weakness: 1.Reproducibility: The study could be enhanced by applying the proposed method to other datasets or by resampling the existing dataset. This would help to assess the generalizability of the method and the robustness o...	Summary: This paper presents a decomposition method for task-functional connectivity. It proposes canonical task connectomes which derives sub-structure of functional brain connectivity which group connectomes which identify elementary components of the overall connection. The authors use supervised non-negative matrix...	Rebuttal 1: Rebuttal: We thank the reviewer for detailed review and helpful comments. Weaknesses: The review cites weakness in motivation, clarity in presentation, use of only one study, and need for additional baselines. We have corrected all issues relating to presentation and motivation. Furthermore, we have added...	Summary: The paper presents a new approach to identify task-specific building blocks of neuronal activity from fMRI data by using supervised matrix factorisation. The identified patterns generalise from the train to a test set and match expectations on the brain activity for the different tasks from the neuroscience li...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed review and helpful comments. Weaknesses: The review identifies weaknesses in the study of computational efficiency and comparison with existing methods. To address these concerns, we have added experiments on a new larger dataset from the Cambridge Center f...	Summary: This contribution presents a novel method to find a functional basis for a database of task fMRI acquired from different subjects. The functional basis, dubbed canonical task connectomes, is shard across large cohorts; can be composed into task-specific networks; and is predictive of task efficacy. The author...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed review and helpful comments. Weaknesses: The review identifies two weaknesses, lack of generalizability across studies and stability of functional basis across cohorts. We have added a new dataset from the Cambridge Center for Aging and Neurosciences (CamCA...	Rebuttal 1: Rebuttal: We thank the reviewers for their positive and constructive feedback. Now we address some common questions raised by the reviewers in the following two aspects: 1. Motivation and contribution of this work Discriminating tasks serve as a downstream objective following the identification of physiolog...	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper presents a novel framework for fMRI analysis that aims to deconvolve complex neuronal networks into task-specific elementary networks called "canonical task connectomes." The proposed method utilizes supervised matrix factorization to identify these task-specific networks and demonstrates their inte...	Rebuttal 1: Rebuttal: We thank the reviewer for detailed review and helpful comments. Weaknesses: 1.Comparison to state of the art methods: We have also added comprehensive experiments and comparisons to ICA, which is the most commonly used method in this domain. Please see the results in author rebuttal PDF. Our s...	null	null	null	null	null	null
GALOPA: Graph Transport Learning with Optimal Plan Alignment	Accept (poster)	Summary: The paper proposes a novel paradigm for self-supervised graph learning based on optimal plan alignment named GALOPA, which leverages optimal transport theory to align the optimal transport plans for graphs and node representations, resulting in an improvement in the quality of graph representations. Unlike exi...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for describing our work as interesting and demonstrating superior performance and good design for the experiment. We respond to the reviewers’ questions below. > Q1. The time complexity analysis may be better placed in the text than in the appendix. Thanks fo...	Summary: In this submission, the authors proposed a new self-supervised method for graph representation learning. Unlike existing contrastive learning methods, the authors consider 1) the consistency between the optimal transport plan defined on the graph pairs and that defined on their node embeddings and 2) the cons...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for describing our work as interesting and for recognizing the valuable insights of our work for graph self-supervised learning. We respond to the reviewer’s concerns below and in the global response above. > **Q1. The sensitivity analysis of $\sigma$ and $...	Summary: This paper proposes a new paradigm for self-supervised graph learning GALOPA based on optimal transport. It seeks to align optimal transport plans from graph space to node representation space instead of distance alignment in graph contrastive learning. The extensive experiments show that the optimal transpor...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for describing our work as interesting and significant. We respond to the reviewers’ questions below. > Q1. The connection and difference between the two parts of Loss is not clear. Thanks to the reviewer's suggestion, we describe the relationship between thes...	Summary: In this paper, the authors study a new method "GALOPA" for self-supervised learning on graph. For the two input views of graphs, they first compute the optimal transport plans w.r.t. fused GW distance between input graphs G1 and G2; for the corresponding outputs Z1=f(G1), Z2=f(Z2) of GNN f(), they compute th...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for constructive feedback and for describing our work as good reference for self-supervised learning and demonstrating interesting findings in the experiments. We respond to the reviewer’s concerns below and in the global response above. > **Q1. Large scale...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their insightful feedback. Here we respond to common/main concerns raised by reviewers. > GQ1. The algorithms to compute the FGW (Eq. (6)) between graphs and the OT distance (Eq. (7)) between node embeddings. (zpGL, bARL) We describe the optimization...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance	Accept (poster)	Summary: In the nascent area of 'federated analytics', the canonical problem is frequency estimation: each client holds a label (or a collection of labels), and the aim is to recover the frequency distribution of these labels, or at least information on the most frequent labels (heavy hitters). Prior work has demonstr...	Rebuttal 1: Rebuttal: Thank you for your comments and suggestions. In the following, we will address your questions. --- Q1. “The novel contribution is not extremely high. The first set of results are shown by plugging parameters into theorems from prior work, and some manipulation of probabilities. The second se...	Summary: The paper considers the problem of federated frequency estimation under the Secure Summation constraint. Motivated by the tail-bound analysis of CountSketch, the authors propose a two-round communication approach that can significantly reduce the sketch size and improve the overall communication size. Later, t...	Rebuttal 1: Rebuttal: We appreciate your positive evaluation! Please find our answers to your specific questions below. --- Q1. “What is the difference in communication latency between the proposed algorithm vs. baselines?” A1. In our experiment, the client/server communication is simulated so the latency is...	Summary: This paper tackles the problem of Federated Frequency Estimation (FFE) by leveraging the Federated CountSketch algorithm (Algorithm 2 in this paper). Compared to previous FFE methods the authors argue that if the tail of the underlying frequencies is light or small (\emph{i.e., } the smaller frequencies have c...	Rebuttal 1: Rebuttal: Thank you for supporting our paper! We will address your comments as follows. --- Q1. “For the two-phase method in section 2, the authors suggest that frequencies follow the Zipf law; thus, they estimate the polynomial coefficients by a kind of pilot study on a small number of clients. It is...	Summary: This paper explores several variants of the count sketch method for federated frequency estimation (FFE). - With only one communication, they provide a refined instance-dependent analysis for CountSketch and find that the sketch size depends on unknown problem-dependent quantities. They then propose a two-phas...	Rebuttal 1: Rebuttal: Thank you for your comments and suggestions. We address your concerns as follows. --- Q1. “From Figure 1 (a)(d), … It seems that the minimax optimal one is better than the instance optimal one in the achieved accuracy, which is quite counterintuitive. Is there anything I missed? Could the au...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
EgoEnv: Human-centric environment representations from egocentric video	Accept (oral)	Summary: This work aims to learn human centric environment representations from first person camera views. Their novel approach utilizes a transformer-based approach that encodes the local environment state at each time-step in an egocentric video is defined as a set of objects, along with their approximate distances, ...	Rebuttal 1: Rebuttal: > W1. This work does not consider the influence of motion blur if the person wearing the camera makes sudden movements, making it challenging to apply this work to real environments. > Q1. The authors should elaborate on how their models would deal with motion blur and sudden movements of the came...	Summary: In this paper, the authors address the limitation of current video understanding methods that only analyze short video clips in isolation, without considering the broader context of the camera-wearer's environment. They propose an approach that establishes a connection between egocentric videos and the surroun...	Rebuttal 1: Rebuttal: > W1/Q1. In my opinion, the core of this method is a pertaining process that refines a feature into a better one by implicitly seeing its surroundings. In this sense, in all the experiments, EgoEnv features have knowledge about the surroundings however other features do not. Thus, one important ex...	Summary: The paper proposes a novel framework to learn environment-aware video representations from egocentric videos. The framework can be trained on synthetic data and incorporated into various existing approaches for real-world downstream tasks including RoomPred and NLQ. Experiments demonstrate that models equipped...	Rebuttal 1: Rebuttal: > W1 The method hugely relies on the surrounding background objects in the 3D scene. When a person navigates in a scene with clean backgrounds, it might be challenging for the method to encode expressive environment features. When dealing with easy real-world instances in the RoomPred task, the me...	Summary: In the paper, a model is introduced to extract vector embeddings of environments using images from a first-person perspective. The model is trained in a simulated environment where a virtual agent moves around and collects images to learn about its surroundings. The model's performance was tested in two differ...	Rebuttal 1: Rebuttal: > W1. Although the paper is well-written and structured, many important experiments and discussions were excluded and can only be found in the 18-page supplemental material. One example is the full ablation procedure which is solely available in the supplemental material. We tried to prioritize t...	Rebuttal 1: Rebuttal: Thanks to all the reviewers for their effort and constructive feedback. All five reviewers recommend accepting the paper, with two recommending strong accept (5, 6, 7, 8, 8). We address common concerns shared by reviewers below. Limitations of the proposed approach We will emphasize the limi...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The work presents an approach to learn spatial environment representations for egocentric videos. Such representations encode the camera-wearer’s (seen and unseen) local surroundings/environment. Previous approaches mostly focus on learning representations over a longer temporal space, however, understanding o...	Rebuttal 1: Rebuttal: > W1 It would be great to discuss about the accuracy of the pose embedding learning network on the simulated network since the overall model is dependent on it. Thanks for the suggestion. As mentioned in L157, the pose embedding network is trained to predict relative pose discretized into 12 angl...	null	null	null	null	null	null
Matrix Compression via Randomized Low Rank and Low Precision Factorization	Accept (poster)	Summary: This work studies the problem of computing a low rank approximation when the low rank factors are under a bit budget constraint, that is, we must output factors L and R with bounded bits such that LR approximates a given input matrix A in the Frobenius norm. The authors show that by incorporating sketching int...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for reading our paper and writing the review. We hold in high regard the voluntary nature of the review process, and in what follows, we engage with your concerns. > Structured sketching We agree that SRHT sketch also have an equalizing effect, and can indeed lead to fa...	Summary: This paper introduces a novel low-rank matrix factorization algorithm that is using sketching matrix idea and quantization, such as they do: 1. Use Gaussian RV to generate sketch of the matrix and compute the approximate basis 2. Use Quantization with Q - to get Q(AS) 3. Use Q(AS) and Q' to get Q'(W) 4. Retu...	Rebuttal 1: Rebuttal: Dear Reviewer, We are grateful for the valuable time you spent in reading our paper and writing the review. We value the fact that the review process is a voluntary endeavor, and in the sections below, we tackle each of your concerns to address and clarify your questions. > Table 1 & 2 is a bit ...	Summary: The authors investigate combining low-rank matrix factorization and (uniform scalar) quantization. Through theoretical analysis and experiments they demonstrate that this can yield much higher accuracy than directly quantizing the input matrix. One natural choice is to compute the SVD of the matrix and quantiz...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for the time you invested in reading our paper and writing the review. We appreciate that reviewing is a voluntary effort and below, we address each of your concerns to resolve your queries. > $1 + \epsilon'$ relative Frobenius error approximation LPLR requires $m=O(k/\e...	Summary: The paper studies compression of low-rank matrices by simultaneous low-rank factorization and quantization. It proposes a method that first quantizes the randomized rangefinder as the first low-rank factor and then quantize the minimizer of reconstruction error with respect to the remaining factor as the secon...	Rebuttal 1: Rebuttal: Dear Reviewer, We are grateful for the time you spent in reading our paper and writing the review. We address your concerns below: > Difference between LPLR and LPLR-SVD in experiments LPLR refers to our main algorithm in Alg. $1$, in which the left low-rank factor is $Q(AS)$, where $S$ is the ...	Rebuttal 1: Rebuttal: Dear Reviewers, We greatly appreciate the time you invested in reviewing our paper and sharing your concerns. As part of the global response, we have included deliberations regarding the scenarios in which LPLR demonstrates its practical utility and potential advantages over established baseline...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposed a memory efficient approach to approximate a matrix $A$ by: low-rank approximation $A=LR$ and quantization. The LPLR algorithm first applies a quantized random projection (RP) as the $L$, and then solve a minimization problem for the right loew-rank factor $R$, which is also quantized afterw...	Rebuttal 1: Rebuttal: Dear Reviewer, We are grateful for the time you spent in reading our paper and writing the review. We address your concerns below. > $Q(AS)$ for classification The works of Li & Li (2019), although related, are different from matrix compression addressed in our work. They study nearest neighbo...	Summary: The paper studies the low-rank factorization of the matrix in the low-precision setting and proposes a new algorithm which is a combination of randomized low-rank approximation method and quantization. The paper formally analyzes the guarantee of the proposed algorithms and also give experiments on real world...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for the time you invested in reading our paper and writing the review. We appreciate that reviewing is a voluntary effort and below, we address each of your concerns to resolve your queries. > Choice of $m$ Indeed, the sketch size $m$ is an important design parameter....	Summary: The paper introduces a low rank, quantized/low precision matrix factorization which decomposes an n x d matrix A in the form A= LR, where L (of size n x m) and R (of size m x d) are low rank factors. L and R are computed using a random projection matrix S in the form L = Q(AS) and R = Q'(W^) where W^ is the ...	Rebuttal 1: Rebuttal: Dear Reviewer, We are grateful for the valuable time you spent in reading our paper and writing the review. The voluntary nature of the review process is truly valued. In what follows, we address your questions: > Similar Frobenius norm errors for different rank choice For naive quantization, t...	null	null
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors	Accept (spotlight)	Summary: This paper presents a novel fMRI-to-image approach (MindEye) that can achieve excellent reconstructions of natural scenes. The main idea is to retrieve and reconstruct viewed images from fMRI with brain activity information. The model consists of two parallel submodules. The retrieval submodule uses contrastiv...	Rebuttal 1: Rebuttal: > Q1: Most of the modules are existing models. The novelty of the paper requires further elaboration. A1: MindEye relies on models trained on billions of image/text samples. NSD provides <30,000 fMRI samples per participant. We argue that part of the novelty of our approach is to use models like...	Summary: The authors present MindEye, an innovative fMRI-to-image approach that utilizes contrastive learning and a diffusion prior for retrieval and reconstruction tasks. They conduct thorough comparisons, establishing MindEye's superiority over existing methods in terms of both qualitative and quantitative evaluation...	Rebuttal 1: Rebuttal: Thank you for your comments and feedback! > Q1: Lack of Methodological Originality: The study relies heavily on external state-of-the-art models, which diminishes the originality of the methodology. The authors predominantly employ simple MLPs and a pre-trained diffusion prior, which limits the n...	Summary: This paper introduces a method for fMRI-to-image conversion that facilitates realistic image reconstruction from brain recordings. The proposed method comprises two stages: semantic (high-level) and perceptual (low-level) pipelines. In the semantic stage, fMRI voxels are projected into the image space using a ...	Rebuttal 1: Rebuttal: > Q1: I think the main weakness of the method is the use of pre-trained models ... the overall novelty of the paper might not be very high. A1: MindEye relies on external state-of-the-art models that were trained on billions of image and text data samples. Critically, none of these existing model...	Summary: The paper improves the fMRI reconstruction method using contrastive learning strategy and diffusion prior model. The concept is relative simple but the details of the proposed method, which is the key to make difference, is well-implemented. First, the BiMixCo implements a contrastive loss between the fMRI vox...	Rebuttal 1: Rebuttal: > Q1: The performance gap between the reconstruction and retrieval is not clearly explained. The performance gaps between MindEye and other baselines are vast while those for reconstructions are not as much. It seems like the reconstruction results for the baselines are copied from the original pa...	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their thorough comments, thoughts, and suggestions on our manuscript. We have done our best to answer all questions and concerns. We summarize some of the core revisions and clarifications below. * New Appendix A.9 (see attached pdf to this response) table sho...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
Break It Down: Evidence for Structural Compositionality in Neural Networks	Accept (spotlight)	Summary: This paper studies the structural compositional problem is a novel perspective. It first defines structural compositionality as the extent to which neural networks decompose a complex task into a series of subroutines and implement them modularly. Then, the paper designs several clever experiments to show that...	Rebuttal 1: Rebuttal: Thank you for your excellent feedback and questions about our work! We will edit the figures for clarity in the final version of the paper. We agree that a figure describing how subnetworks are distributed throughout a model would be a valuable addition to the paper, and are working on creating su...	Summary: The paper investigates the concept of structural compositionality in neural networks - subnetworks that implement specific subroutines, such as computing the syntactic number of specific words in a sentence. Strengths: 1. Conceptual modularity in neural networks has been an idea that has been studied and str...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments on our work! The related works that you’ve referenced are extremely interesting, and will strengthen our discussion - thanks for pointing them out! We will add a new section to our discussion that describes the relationship between structural compositionality...	Summary: This research paper explores the concept of compositionality in neural networks, a contentious topic in the field of AI. Compositionality, which is a defining feature of human cognition, allows for abstract and flexible processing of language and visuals. The debate lies in whether neural networks need explici...	Rebuttal 1: Rebuttal: Thank you for your excellent feedback and questions about our work! We agree that the current work is a purely empirical study, and so we attempted to demonstrate the effect on various architectures and domains to help convince the reader that structural compositionality is a property of a wide cl...	Summary: The authors investigated to what extent the standard neural networks of the present day trained on the tasks solvable by composing subroutines result in modular structures reflecting the tasks' compositional nature (called structural compositionality in this study). To answer this question, they took the follo...	Rebuttal 1: Rebuttal: Thank you for your excellent feedback on our work! We have updated the README in the anonymous repository. We agree that it is valuable to add an extended discussion of Csordas et al. 2021 in this paper, and we will include a full paragraph in our related work section to elaborate on the relations...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
DäRF: Boosting Radiance Fields from Sparse Input Views with Monocular Depth Adaptation	Accept (poster)	Summary: The paper proposes a method to better use monocular depth in a few-shot NeRF setup. There are mainly two technical contributions to me: 1. Applying mono depth constraint to unseen view; 2. Un-distorting (scale and shift) monocular depth in a per-patch manner, rather than per-image. --- After rebuttal: I h...	Rebuttal 1: Rebuttal: > Q1. Specific definition of few-shot setting and its visualization > We agree with your statement that the precise definition of few-shot cannot be defined naively as \|S<20\|, described in our L118-L120. As you have said, its definition should be based on view-angle and general coverage of t...	Summary: The paper tackles the problem of sparse view NeRF reconstruction by leveraging on monocular depth estimation networks as a prior. The main difference between DaRF and existing work is it also computes for a depth loss on unseen views, in contrast to prior works that only constrain depth on the training views. ...	Rebuttal 1: Rebuttal: > Q1. Contribution of DaRF / comparison with SCADE > Thank you for your comment. While SCADE [1] does have a similar setting to ours (leveraging MDE for few-shot NeRF reconstruction), we would like to emphasize that there are critical differences that position our work orthogonal to SCAD...	Summary: This paper addresses the problem of few-shot NeRF reconstruction. The authors propose using monocular depth estimation (MDE) networks to provide geometry prior to NeRF at both seen and unseen viewpoints. They propose overcoming the ambiguity problems associated with monocular depths by MDE adaption. Experiment...	Rebuttal 1: Rebuttal: > Q1. Details of MDE adaptation > Thank you for pointing this out. Equation 6 has a notation mistake on our part, as we do use monocular depths predicted from ground truth input image, $D^_{i}$, and not the one from depth rendered from NeRF, $\bar{D}^_{i}$. Our accurate methodology, in...	Summary: The paper presents a new few-shot neural radience field approach based on joint monocular depth adaption. The main idea of the proposed approach is to utilize the monocular depth estimator to improve the geometry prior of NeRF representation. The motivation is reasonable. Also, it presents attractive performan...	Rebuttal 1: Rebuttal: > Q1. Comparison with SCADE > Thank you for your comment. First of all, we would like to emphasize that our paper is not based on SCADE [1] nor use it as its baseline but instead suggests a method orthogonal to it. There is a fundamental difference between our work and SCADE’s approach t...	Rebuttal 1: Rebuttal: # General Response We would like to first thank all the reviewers for their helpful suggestions and constructive reviews. We are greatly encouraged by their assessment of our work as well-motivated (VhkU), with novel and interesting (oMo1, VhkU) findings of effectively **resolving MDE’s a...	NeurIPS_2023_submissions_huggingface	2,023	Summary: The paper proposes a sparse-view NeRF framework that jointly trains a NeRF with a monocular depth estimator. By adapting the MDE network to the target scene, the predicted depth will provide better geometry prior for the NeRF model. Strengths: The joint training of MDE and NeRF improves the model's ability co...	Rebuttal 1: Rebuttal: > Q1. Benefits of MDE compared to MVS networks > Thank you for pointing this out. It is true that MVS networks can be used for better NeRF training, as shown in previous works [1, 2]. However, their approach of leveraging image features combined from multiple viewpoints serves as a critical...	null	null	null	null	null	null
PointGPT: Auto-regressively Generative Pre-training from Point Clouds	Accept (poster)	Summary: This paper investigates self-supervised point cloud learning by introducing the GPT concept (e.g., point order) to the masked modeling framework. Specifically, the clustered point patches are arranged into ordered sequences based on spatial proximity. Then, the masked patches can be predicted without leaking t...	Rebuttal 1: Rebuttal: @Q1 - The effectiveness of PointGPT's designs. (1) Relative direction prompt is able to enhance the generalization ability at a negligible cost. Ablation experiments are conducted in a high-capacity model training scenario, where superior pre-training generalization is required. We r...	Summary: This paper proposes PointGPT, a new self-supervised learning strategy for 3D representation learning. PointGPT follows the success of autoregressive pre-training paradigm in NLP and adapts it into 3D point clouds. With larger pre-training dataset and a post-pre-training stage, PointGPT achieves SOTA performanc...	Rebuttal 1: Rebuttal: @Q1 - Learnable parameters. Thanks for your suggestions! We will provide the number of learnable parameters (Params) in the revised article. For a fair comparison, we do not consider methods that utilize cross-modal information and teacher models, and mainly present the number of model param...	Summary: This paper propose an auto-regressively generative pre-training paradigm for point cloud feature encoding. By incorporating GPT, the disorder and low information density properties of point clouds are addressed. Besides GPT, a dual masking strategy is proposed to improve the pre-training performance. The propo...	Rebuttal 1: Rebuttal: @Q1 - Overall object shape leakage. (1) The overall object shape leakage is attributed to the positional encoding leakage of masked regions. Point cloud data is constituted by the spatial positions of individual points. However, previous methods rely on introducing positional encoding in...	Summary: This paper proposed a point clouds pretraining method named PointGPT, which extends the generative pretraining approach of NLP to point clouds. With point patch partitioning and sorting, point embeddings are feed into a transformer decoder for autoregressive prediction. Besides, a dual masking strategy is prop...	Rebuttal 1: Rebuttal: @Q1 - Morton code sorting. (1) Morton code sorting effectively preserves the adjacency relationships between points, allowing points that are close in three-dimensional space to maintain adjacency after sorting. However, the left-to-right and top-to-bottom (`L2R&T2B`) sorting method stru...	Rebuttal 1: Rebuttal: We thank all reviewers for their thoughtful feedback! We are pleased to find that reviewers 5U9K and 4dwU appreciate PointGPT as interesting and excellent work. Moreover, reviewers 4dwU and LJmd consider our motivation to be reasonable and well-explained, effectively mitigating information leakage...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences	Accept (poster)	Summary: The authors consider the class of noisy multi-layered sigmoid recurrent neural networks, noisy meaning that noise is added to the output of each neuron in the network. They prove that the sample complexity of noisy is significantly better than non-noisy one with one clean upper bound and one clean lower bound....	Rebuttal 1: Rebuttal: We thank the reviewer for their suggestions and comments. We discuss the questions and concerns mentioned by the reviewer in the following. Response to weaknesses. We agree with the reviewer that practical result can back up the results of the paper and we hope to have this as a future work. ...	Summary: This work studies the sample complexity of PAC learning noisy recurrent neural networks with respect to the ramp loss. Noisy recurrent networks are defined as multi-layered feed forward networks with sigmoid activation where independent mean-zero Gaussian noise is added to the activation. The main result is t...	Rebuttal 1: Rebuttal: We thank the reviewer for their assessment and feedback. We address the concerns and questions raised by the reviewer in the following. Response to Weaknesses. Using noise in training neural networks is a natural heuristic to avoid overfitting and has been used in various scenarios (e.g., in ...	Summary: This paper studies learning recurrent neural networks with sigmoid activations, where a small amount of noise of magnitude sigma is added to each layer. This paper shows that there is a log(T/sigma) scaling of the sample complexity with the number of recurrent compositions. This is surprising because, withou...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their careful assessment of the paper and we appreciate their helpful comments. The questions and concerns mentioned by the reviewer are discussed in the following. Response to Weaknesses. Yes, the key technical contribution is to prove a general result...	Summary: In this paper, the authors consider a class of noisy recurrent neural networks under the ramp loss setting, and prove that the noisy class can be learned with $O(w \log (T/\sigma))$, where $w$ is the width, $T$ is the length of the sequence, and $\sigma$ is noise variance. The derived results demonstrate the...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their comments and feedback. In the following, we address the concerns and questions mentioned in the review. Response to Weaknesses 1. As we mentioned in Line 139, the only features of the ramp loss that we use to derive the upper bound are its Lipschi...	null	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image	Accept (poster)	Summary: This paper proposes image constraint neural radiance field, a representation that takes a reference image into account. Such a representation helps the task of 3D reconstruction from single image with the guidance from pretrained diffusion models. Experiments demonstrates the proposed method can help boost the...	Rebuttal 1: Rebuttal: We thank Reviewer mCGc for the positive review and constructive feedback. We clarify the concerns raised by the reviewer here. > 1. For viewpoints other than the reference view, the shapes and images are blurry. Compared to the reference view, the renderings do appear less crisp in novel viewpoi...	Summary: This paper proposes a novel parametrization for NeRF designed to facilitate the task of single image (+ foreground mask) to 3D model generation. The authors modify the volumetric rendering equation of the NeRF volume to include explicit constraints given by the single available view. In particular, by construc...	Rebuttal 1: Rebuttal: We thank Reviewer 1JJW for the appreciation of our work, positive review and the constructive discussion. We discuss some of the questions raised by the reviewer here. ### Minor Weaknesses > 1. Scale Ambiguity: ... an inherent ambiguity of any methods based on a single view but I wonder if a ...	Summary: This work proposes an approach for 3D generation from a single input image. Given an input image, a monocular depth estimate and an estimated instance mask, an image constrained radiance field is optimized following the score distillation approach from DreamFusion. They key idea is to enforce a hard constraint...	Rebuttal 1: Rebuttal: We thank Reviewer ZVcc for the constructive feedback and suggestions. We address the reviewer's comments here with additional experiments and discussion. > 1. My main concern is that the central claim of the paper, that integrating the image as a hard constraint over using a reconstruction loss ...	Summary: This paper introduces Image Constrained Radiance Fields (ConRad), a novel 3D representation that constrains initial radiance fields to a reference view image without requiring training. ConRad is adept at accurately modeling the input image in one reference view and is effectively integrated with Dreamfusion-s...	Rebuttal 1: Rebuttal: We thank Reviewer GS6t for their appreciation of our work, positive review and valuable suggestions to improve our paper. > 1. It would be beneficial for the authors to include information on the training time required for ConRad to convert a single image to 3D, and compare this with the trainin...	Rebuttal 1: Rebuttal: We thank all the reviewers for their time and feedback. We address individual comments to each reviewer separately. Please find supporting material attached here as a PDF. Pdf: /pdf/4a64abd6ad2918e5f7ed89790b2e5bd7853847e0.pdf	NeurIPS_2023_submissions_huggingface	2,023	Summary: This paper presents ConRad, a new method for reconstructing 3D objects from a single RGB image. At the core of ConRad is a neural radiance field that by design satisfies the reference view constraint. That is, the representation will always render the input RGB image at the reference view. This hard constraint...	Rebuttal 1: Rebuttal: We thank Reviewer yDN8 for the appreciation of our work, overall positive review and constructive feedback. We clarify the questions raised by the reviewer here. > 1. Line 174, ... I think it makes more sense to set $\eta$ to 0.9. This is an error in Equation (3). The equation should be $$ 1 - ...	null	null	null	null	null	null
AdaPlanner: Adaptive Planning from Feedback with Language Models	Accept (poster)	Summary: LLMs have shown success as autonomous agents that make and execute plans in sequential decision problems. Existing methods either make open-loop plans, limiting adaptability to the environment, or closed-loop plans. Existing closed-loop methods, apart from DEPS, keep the plan static but simply modify immediate...	Rebuttal 1: Rebuttal: [Weakness 1] We appreciate the reviewer's comments. Due to the space limitation, we postponed the detailed introduction of the baseline to the Appendix 8.2. We evaluated AdaPlanner against a selection of representative baselines, both training-based and LLM-based. - In ALFWorld, we compared ...	Summary: Briefly summarize the paper and its contributions. This is not the place to critique the paper; the authors should generally agree with a well-written summary. The paper proposes AdaPlanner, an LLM-based adaptive planner for text-based sequential decision-making tasks. The planner is adaptive in the sense tha...	Rebuttal 1: Rebuttal: Due to the 6000 characters limit, we only address the important questions. For detailed clarification, we will provide them during the discussion period. [Weakness 1] Due to the page limit, we primarily discussed the main components of AdaPlanner: closed-loop structure, code-styled prompting,...	Summary: The paper presents AdaPlanner, a closed-loop planning method that uses a large language model (LLM) to solve tasks in text-based environments. AdaPlanner operates by decomposing a complex task into manageable sub-goals and predicting environmental feedback for each. During execution, it refines its actions bas...	Rebuttal 1: Rebuttal: [Weakness 1] Thank you for your valuable feedback. In the skill acquisition stage, AdaPlanner harnesses adaptive closed-loop planning to solve unseen tasks using limited or no demonstrations. The successful solutions found in this trial-and-error process are called the candidate discovered sk...	Summary: This paper looks at explicit closed-loop systems with LLMs for adaptive planning utilizing environmental feedback. They showcase better planning performance on ALFWorld and MiniWOB++ environments over existing state-of-the-art works like ReAct and Reflexion. Strengths: The paper is well written and the experi...	Rebuttal 1: Rebuttal: [Weakness 1] Thank you for your insightful comments. We assume “conflicting causal link” is referring to the existence of the non-revocable actions and states.” In MiniWoB++, a significant number of tasks indeed present multiple conflicting causal links. For example, a mistaken click in the t...	Rebuttal 1: Rebuttal: References [1] M. Shridhar, X. Yuan, M.-A. Cote, Y. Bisk, A. Trischler, and M. Hausknecht. ALFWorld: Aligning text and embodied environments for interactive learning. In International Conference on Learning Representations, 2021. [2] G. Kim, P. Baldi, and S. McAleer. Language models can solv...	NeurIPS_2023_submissions_huggingface	2,023	null	null	null	null	null	null	null	null

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