title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Unchosen Experts Can Contribute Too: Unleashing MoE Models’ Power by Self-Contrast | Accept (poster) | Summary: This paper proposes a training-free strategy that utilizes unchosen experts in a self-contrast manner during inference. It can be seen as a decoding method utilizing divergent information from different routing strategies. This method introduces slightly more latency overhead and improves the performance of va... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We appreciate that you highlight the motivation, idea and the clarity of our work. We provide detailed responses to address your specific concerns as outlined below.
> W1: This inference method employs two models, one with top-2 routing and the other with ran... | Summary: This paper proposes a novel approach called Self-Contrast Mixture-of-Experts (SCMoE) to improve the utilization and performance of Mixture-of-Experts (MoE) models. The key contributions are:
1. Exploratory studies showing that increasing the number of activated experts in MoE models does not always improve ou... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the novelty, simplicity and effectiveness of our work. This is a great honor for us. We aim to address your concerns below.
> W1: The idea of SCMoE is somewhat similar to the cited paper "Contrastive decoding: Open-ended text generation as optimization (Li et. al... | Summary: This paper introduces SCMoE, a decoding time algorithm which can be applied off the shelf to boost MoE models' performance by contrasting the chosen experts in strong and weak activations. Experiment results show that this algorithm has an empirical advantage over baselines methods in coding, commonsense knowl... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We appreciate your praise of the Originality, Quality, Clarity and Significance of our work, which is a great encouragement for us. We would like to address your concerns below:
> W1: Commonsense reasoning in StrategyQA, which is a multi-hop reasoning dataset... | Summary: The paper explores to leverage the contrastive information existing between different routing strategies of the MoE model to facilitate a better token decoding during inference in a training-free fashion. The paper is built upon two interesting observations:
(1) increasing the number of activated experts does... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and willingness to take further discussions with us. We appreciate your praise of the insights (S1, S2) and the detailed experimental setup (S3, S4) in our work. We provide point-to-point response to address your concerns as follows:
> W1: One question- the au... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Inferring stochastic low-rank recurrent neural networks from neural data | Accept (poster) | Summary: The authors propose fitting stochastic low-rank RNNs to neural recordings using variational sequential Monte Carlo methods. Such techniques permit modeling of noisy sequences (i.e., trial-to-trial variability), identification of a low-dimensional latent dynamical system, generative sampling of neural trajector... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing that our approach "appears original in its combination of existing ideas” and that we provided “thoughtful” evaluations and demonstrations of our method. We clarified some raised issues below
>Section 2.2 could benefit from being made more accessible to reader... | Summary: This paper proposes a low-dimensional, nonlinear dynamics model of neural data based on low-rank RNNs. In particular, the model dynamics are a discretization of the dynamics of a low-rank RNN in the space spanned by the column factors of the low-rank RNN matrix. The model is fit using variational SMC. The appr... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that the paper is generally clear, and we hope to address their concerns, in particular with respect to when the inputs are time-varying.
>It appears that the formulation of the model in the low-dimensional space loses some generality relative to the origina... | Summary: The authors describe an elegant method to infer a low-dimensional description of stochastic neural dynamics using variational Sequential Monte Carlo and Low Rank RNNs. They apply this method to simulated data and to three experimental datasets (EEG, hippocampus, and motor cortex). They also describe an elegant... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on our manuscript and their appreciation for modeling RNNs with stochastic dynamics! We have provided some additional analyses based on your suggestions, which we believe strengthens the original manuscript.
>mostly I would be interested in SMC me... | Summary: This work focused on inferring stochastic low-rank structure from neural data. It developed a low-rank RNNs as state space models, and using sequential monte Carlo (SMC) to learn the model's parameters. The proposed model is efficient in finding all fixed points in a polynomial cost instead of exponential cos... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's acknowledgement of the importance of the question we focus on, and the efficiency of our proposed methods for fitting low-rank models and finding their fixed points. We here hope to clarify some one of the raised concerns.
>Low-rank RNN has been demonstrated on many a... | Rebuttal 1:
Rebuttal: **General response**
We thank the reviewers for their extensive comments and insightful feedback on our manuscript. Our paper introduced a method for obtaining low-dimensional descriptions of stochastic neural dynamics, tackling a “well-established (drM9)” and “important question in neuroscience”... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Interaction-aware 3D Gaussian Splatting for One-shot Hand Avatars | Accept (poster) | Summary: This paper proposes a method to create a 3D Gaussian Splatting (GS)-based avatar for interacting hands from single-image inputs. Its main contribution is to propose a two-stage GS framework (1) to leverage cross-identity priors via learning-based features and also to (2) well preserve per-identity information ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer’s confirmation of sufficient technical novelty. To address the concerns of the reviewer, we conduct several ablation studies and provide extra visual examples in the RF. Our responses are listed as follows:
**W1 Effects of off-the-shelf MANO regressor and mask det... | Summary: The paper proposes an approach to achieve one-shot interacting hand avatar reconstruction via 3D Gaussian Splatting. The authors design a two-stage framework: the first stage learns learning-based features and optimization-based identity maps, and the second stage performs one-shot reconstruction by optimizing... | Rebuttal 1:
Rebuttal: We sincerely thank you for your suggestive comments. We have performed multiple experiments and presented additional real-world results to further validate the proposed method. Our point-by-point responses are listed as follows:
**W1 Shadow modeling**: Thank you for your suggestive advice. Our c... | Summary: This paper proposes a novel two-stage interaction-aware GS framework to create animatable avatars for interacting hands from single-image inputs. The proposed method disentangle the 3D presentation of hands into optimization-based identity maps and learning-based latent geometric features and neural texture ma... | Rebuttal 1:
Rebuttal: We are grateful for your acknowledgment of the technical contributions and novelty of our method. We have considered your as well as other reviewers' suggestions carefully and tried to solve them as follows:
**W1&W2 Presentation**: Thank you for pointing this out. We will enhance our presentatio... | Summary: - The author extends the concept of one-shot hand avatar creation from the single hand in the previous OHTA paper to two hands.
- The author proposes a novel two-stageGS framework for the reconstruction and rendering of avatars. This framework utilizes 2D identity maps to represent identity information and ass... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer’s valuable comments and suggestions. We have conducted extensive ablation studies and provide more in-the-wild results to further verify the proposed method. Our responses are listed as follows:
**W1 Contribution and novelty**: We feel it is necessary to clarify t... | Rebuttal 1:
Rebuttal: First, we thank ACs for organizing such a wonderful reviewing process and reviewers for their constructive comments that help to improve our paper greatly. We appreciate the confirmations from the reviewers on the proposed method, including Reviewer #NUjU "successfully applied the Gaussian splatti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fully Unconstrained Online Learning | Accept (poster) | Summary: The paper presents the first $\tilde{O}(G \lVert w_\star \rVert \sqrt{T} + \lVert w_\star \rVert^2 + G^2)$ guarantee for online convex optimization without assuming known-in-advanced bounds of either the gradients or comparator norms. This result matches the best possible rate given known bounds in the main re... | Rebuttal 1:
Rebuttal: Thanks for your review!
Q1: Yes, in a certain sense. The way to to obtain the improved PoA using [1] is simply to only do the clipping suggested by [1] and not the artificial constraints - instead just rely upon the coarse bound on $\|w_\star\|$. We could do the same thing by just replacing our r... | Summary: This paper studies online convex optimization without prior knowledge of the Lipschitz constant of the losses or constraints on the magnitude (in $\ell_2$-norm) of the competitor. They provide a regret bound that almost matches the optimal regret with knowledge of these quantities (with some additive terms ind... | Rebuttal 1:
Rebuttal: Thank you so much for your very detailed review and you careful comments on the presentation. We will work hard to incorporate your comments into the final version. In the interest of brevity, below we respond to just a few of your comments:
* For the "optimal" value of $\gamma$ our bound is alwa... | Summary: The authors propose a new algorithm for parameter-free online convex optimization without knowing the Lipschitzness of losses. Here, parameter-free online learning refers to a framework that achieves the optimal regret upper bound without knowing the magnitude of an (optimal) comparator.
The new parameter-fre... | Rebuttal 1:
Rebuttal: Thanks for your work reviewing our paper!
W1 (Regarding the density of the presentation): We chose a presentation intended to communicate all of the ideas, but it might become difficult to follow and we will work to make things clearer in the revision. For the epigraph-based learning, the idea is... | Summary: The authors consider the task of unbounded online convex optimization
without prior knowledge of the magnitude of the comparator nor the largest gradient.
In this context, they propose a parameter-free method whose regret against any arbitrary comparator
$w_*$ matches the optimal bound of $\mathcal{O}(G||w_{... | Rebuttal 1:
Rebuttal: Thanks for your detailed review! Below we answer your questions.
Q1: It is may not be strictly "necessary" to replace the regularization with a constraint - we suspect that in fact a suitable variation on e.g. FTRL based algorithms would also achieve the same goals. However, the constraint approa... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper considers the fully unconstrained online convex optimization. At each round, the learner needs to choose a vector w_t and then observes the loss vector g_t and suffers the loss of the inner product of w_t and g_t. The goal is to design a learning algorithm to minimize the regret with respect to a co... | Rebuttal 1:
Rebuttal: Thanks for your work reviewing our paper! Regarding the unvailability of prior knowledge of $\|w_\star\|$ and $G$ in practice, let's think about the stochastic optimization setting. In this case, the unavailability of $G$ in practice is *exactly* the problem that popular methods like AdaGrad (the ... | null | null | null | null | null | null |
Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem | Accept (poster) | Summary: The paper studies policy gradient methods for computing a Nash equilibrium in adversarial team Markov games. The authors employ an occupancy measure-based regularization to deal with this non-convex minimax optimization problem. They develop a policy gradient method that allows all team members to take policy ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and recommendations. We are committed to enhancing the quality of our draft and will incorporate your suggestions.
Beginning, could the reviewer specify what they find lacking in our paper's soundness? Soundness in a theoretical paper pertains to the c... | Summary: This paper provides multi-agent RL policy gradient method with polynomial guarantees (iteration and sample complexity) for the adversarial team Markov games problem setting.
Strengths: This paper addresses the main open question from https://openreview.net/forum?id=mjzm6btqgV, that is, developing a policy gra... | Rebuttal 1:
Rebuttal: We thank the author for their comments that motivated us to reflect on our work. Following, we try to address your concerns.
> *"Theorem 3.3, the main result, the polynomial dependence of both sample complexity and iterations on the all parameters is huge! [..] This is disregarding other factors s... | Summary: This papers addresses learning equilibria in adversarial team Markov games, where a team of agents sharing a common reward function competes against a single adversary. Previously, [65] addressed this problem for the model-based case with no sample complexity guarantees. On the other hand, this paper presents ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions and comments and the time they took to review our work.
We would appreciate it if the reviewer reconsidered adding in the strengths of our paper any of the following:
(i) The proof of convergence of inexact projected gradient descent for nonconvex opti... | Summary: This paper investigates the identification of NE in ATMGs. The authors explore the underlying landscape, leveraging optimization theory to effectively solve this problem.
Strengths: Theorem 3.1 stands out with significant contribution. Writing is very clear.
Weaknesses: The other theorems, in my view, merely... | Rebuttal 1:
Rebuttal: We thank the reviewer for their precious time, suggestions, and their encouraging comments.
> *“The other theorems, in my view, merely adapt the MARL problem to a specific instance of an optimization problem (with an observation of hidden structure). The paper does not leverage the special struct... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. We will integrate their suggestions and corrections in our next draft.
As a disclaimer, our focus is on the theoretical advancements in algorithmic game theory and multi-agent reinforcement learning (MARL). We explicitly do not make claims abo... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper considers the problem of independent policy gradient learning in adversarial team markov games (ATMGs). In such games, a team of agents with identical reward functions aim to compete against a single adversary whose reward function is the negation of the team's. The paper consider the setting where ... | null | null | null | null | null | null | null |
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization | Accept (poster) | Summary: This paper focuses on bringing a theoretical understanding to differentiable pruning for neural networks and reveals connections to group lasso.
Strengths: Please see the “Questions” section.
Weaknesses: Please see the “Questions” section.
Technical Quality: 3
Clarity: 3
Questions for Authors: I think thi... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review!
> Does the training curve in Figure 3 represent the entirety of the training? It looks like for the orange curve, the peak around step=48000 is higher than the accuracy at the end of the training.
The peak around step=48000 is due to the use of a dense phase ... | Summary: This paper analyzes sparse training with group sparsity and proposes a general theoretical framework to analyze both hard thresholding (discrete) and scoring (continuous) pruning methods. The proposed theoretical framework of block sparsification encompasses multiple existing sparsification methods via non con... | Rebuttal 1:
Rebuttal: Thank you for the very detailed review!
> The authors discuss that the magnitude is not necessarily the best importance scoring metric for sparsification. However, experimentally it has been observed to be the best performing criteria across multiple datasets for hard thresholding methods with un... | Summary: This work studies the task of neural network pruning. The authors unify the two main directions of the literature: differential pruning and combinatorial optimizations. Specifically, they point out that most differentiable pruning techniques can be considered as non-convex regularization group sparse optimizat... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work!
> Is it just a combination of Sequential Attention and ACDC because they are currently state-of-the-art methods? Or is there a stronger reason and theoretical support behind this?
Based on our experiments, as well as the work of Yasuda et al. 202... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs | Accept (poster) | Summary: This paper focuses on permutation-based methods for causal discovery in Linear Gaussian Acyclic Models (LiGAMs). A new method called QWO is proposed to improve the efficiency of computing a causal graph given a permutation. Compared with baselines, QWO achieves superior performance.
Strengths: 1. According to... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our method's theoretical guarantees, efficient computational complexity, and of our experimental results.
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> The format for definitions is not consistent throughout the paper. Some definitions are directly given in the content body (Lines 64-72), while s... | Summary: The authors present an efficient method for evaluating a score for score-based causal discovery over LiGAMs. Their method uses the whitening matrix $W$, derived from the observed covariance matrix, as a summary statistic. Their method is $\mathcal{O}(n^2)$ faster than the classical *BIC* method, where $n$ is t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their interesting comment regarding incorporating finite-sample uncertainty. We also appreciate the positive feedback on our method's clarity, intuitive design, and superiority in experiments.
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> Can QWO be extended to incorporate finite-sample uncertainty while retai... | Summary: Authors propose a new causal discovery algorithm on permutation-based methods in the context of Linear Gaussian Acyclic Models (LiGAMs). Specifically, authors focus on the computation complexity of existing solutions and propose a novel QW-Orthogonality (QWO) that improve the efficiency of computing a new grap... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comments and are pleased that they found our approach to be a clear and original contribution.
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> Why comparing PDAGs instead of CPDAGs when computing evaluation metrics?
We thank the reviewer for pointing out this issue/typo. In our experiments, we have indeed co... | Summary: This paper considers the problem of speeding up permutation-based causal discovery in linear Gaussian acyclic models. A typical permutation-based causal discovery algorithm includes two components: 1) constructing a DAG permitting a given topological ordering, and 2) a search strategy over the space of permuta... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful comments.
---
> Assumptions needed is not spelled out
We agree with the reviewer that permutation-based algorithms aim to relax necessary assumptions, such as faithfulness. However, for our method, the sparsest Markovian representation assu... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding | Accept (poster) | Summary: The paper presents an interesting idea to make the existing KGE methods achieve better efficiency, termed RecPiece. It is proposed based on two characteristics, i.e., representative ability of cluster centroids and the descriptive ability of the relational facts. Complete experiments are carried out based on f... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions! We respond to your questions carefully one by one carefully, and we hope our responses can address your concerns.
**RQ1. More Description on KGE Baselines, and Evaluation Metrics.**
Thanks for your suggestions. I will add the description of the baselines in ... | Summary: A clustering-guided anchor-based efficient KGE method is proposed in the paper. Concretely, it takes advantages of the clustering, which can be treated as an effective sampling way compared to random selected. Based on such idea, the authors apply the mechanism to different backbones and different downstream t... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions! We respond to your questions carefully one by one carefully, and we hope our responses can address your concerns.
**RQ1. Writing Issues.**
Thanks for your suggestions. I will reorganize our paper into shorter paragraphs, especially for the second and third ... | Summary: To address the computational inefficiencies of conventional knowledge graph embedding models, the authors of this study suggest RecPiece, an anchor selection technique based on relational clustering. RecPiece selects more efficient anchor entities by using the descriptive power of relation types and the repres... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions! We respond to your questions carefully one by one carefully, and we hope our responses can address your concerns.
**RQ1. More Compared KGE Backbones.**
Thanks for your suggestions, and we will answer your questions from three aspects as follows.
**(1)** Ac... | Summary: This paper proposes RecPiece, a novel anchor-based knowledge graph embedding (KGE) model that selects representative anchors via a relational clustering-based strategy. Specifically, RecPiece performs clustering over features of factual triplets instead of entities to generate cluster centroids, and the cluste... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions! We respond to your questions carefully one by one carefully, and we hope our responses can address your concerns.
**RQ1. More Discussion on Trade-off between Efficiency and Effectiveness.**
Thanks, and we will respond from two aspects.
**(1)** The anchor-ba... | Rebuttal 1:
Rebuttal: We thank the SAC, AC, and PCs for their efforts and constructive comments, which are helpful in further improving the quality of our manuscript. We respond to your questions carefully one by one carefully, and we hope our responses can address your concerns.
Note that there are two tables and one... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology | Accept (poster) | Summary: This paper tackles the issue of out-of-domain (OOD) generalization in histopathology, which is complicated by domain shifts due to different scanners, staining procedures, and inter-patient variability. With a focus on single-domain generalization, this paper proposes to prioritize shape features over texture,... | Rebuttal 1:
Rebuttal: Thank you for assessing our paper and recognizing the importance of domain shifts in AI-driven digital pathology and the comprehensiveness of our experimental evaluations and ablation studies.
We are committed to revise the paper so that it is better organized and easier to follow with a more acc... | Summary: The paper proposes a method focusing on nuclei masks, along with an augmentation method, to train a feature extractor to increase
the out-of-domain generalization by directing the model toward learning nuclear features.
The method has been evaluated against 4 different approaches on three different cancer data... | Rebuttal 1:
Rebuttal: Thank you for recognizing the simplicity yet superiority of our approach as well as its biological appeal. We also appreciate your suggestions for additional analyses, which add to our previous experiments to show the strengths of our proposed approach.
> The methods used to benchmark against are... | Summary: The paper "Are nuclear masks all you need for improved out-of-domain generalization? A closer look at cancer classification in histopathology." approaches the task of center classification for out-of-domain histopathology imagery. Motivated by classical shape-based segmentation, the authors suggest utilizing s... | Rebuttal 1:
Rebuttal: Thank you for your comments and for recognizing our approach as interesting and novel.
We are committed to revise the paper to use more accessible language and make it easier to understand our ideas and statements. This will include specifying clearly in the abstract that we analyze cancer classi... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for recognizing the approach we propose as interesting, important, and novel. We also appreciate their input on the language, organization, and clarity of statements in our paper, which we will use to revise the paper thoroughly to make it easier to follow. The... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning | Accept (poster) | Summary: This paper considers preference-guided multi-objective learning from the lens of constrained vector optimization. Cone captures relative preferences. Constraints capture absolute preferences. Under boundedness and smoothness assumptions on the objective function, the paper provides gradient-based methods for t... | Rebuttal 1:
Rebuttal: >W1. In the introduction, the preference cones are not specified for the two examples, which causes confusion that the problem is not vector optimization.
Thanks for the suggestion. In the introduction, the cone is not specified, it can be any proper cone $C$ pre-defined by the user. Here, since ... | Summary: This paper introduces a new algorithm called FERERO, which can handle both relative and absolute preferences in preference-guided multi-objective learning. The problem is formulated as a constrained vector optimization problem. The goal is to find the (approximated) $C_A$-optimal set that satisfies the absolut... | Rebuttal 1:
Rebuttal: >W1. The step sizes in Theorems 2 and 3 depend on iterations $T$, which can be unknown in real-world problems.
Choosing step size depending on the number of iterations $T$ is common in optimization convergence analysis. For example, in [4,10,42], for the convergence analysis of unconstrained MOO... | Summary: This paper introduces FERERO, a novel framework for preference-guided multi-objective learning by depicting the task as a constrained single-objective optimization problem. It incorporates relative preferences, defined by a polyhedral cone, and absolute preferences, defined by linear constraints, into the abov... | Rebuttal 1:
Rebuttal: >Q1. Evidence and importance of "flexible".
Indeed, this is a critical point of our paper!
The "flexibility" is evidenced by that we model **both absolute and relative preferences**. Furthermore, absolute preference considers both inequality and equality constraints. The relative preference co... | Summary: This paper tackled constrained multi-objective optimization problems with Pareto optimality generalized to partial order cone. The authors proposed an algorithm framework to iteratively solving a subproblem to get a update direction and aim at search for Pareto optimal solution in a certain Pareto front region... | Rebuttal 1:
Rebuttal: >W1-1 & Q1. Relative preferences are handled by both partial order and inequality constraints. It is confusing and redundant. Can inequality constraints be converted to the preference cone?
We respectfully disagree. As mentioned in the abstract, relative preference is handled by the partial order... | Rebuttal 1:
Rebuttal: ## General Response
We thank all reviewers for their support and constructive comments. Below we address 3 common questions from the initial reviews.
>1. Comparison of computational cost with baselines.
Following the suggestion, we summarize the per-iteration complexity of different algorithms ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Effective Exploration Based on the Structural Information Principles | Accept (poster) | Summary: This paper aims to tackle the issue in existing RL exploration methods - the ignorance of inherent structure within the state and action space. Therefore the authors propose a new intrinsic reward mechanism that maximizes value-conditional structural entropy and it is adaptable to high-dimensional RL environme... | Rebuttal 1:
Rebuttal: We systematically address each of your queries, labeling weaknesses as 'W' and questions as 'Q'.
Please note that, unless otherwise specified, any table or figure refers to the supplementary results in the Author Rebuttal PDF.
$\bullet$ W1: Additional Experiments.
Our work primarily addresses th... | Summary: This paper proposes a new exploration scheme for RL agents by using structural mutual information for dynamics-aware state-action representation and an intrinsic reward to enhance state-action coverage by maximizing value-conditional structural entropy
Strengths: - constructing intrinsic reward using graph-ba... | Rebuttal 1:
Rebuttal: We systematically address each of your queries, labeling weaknesses as 'W' and questions as 'Q'.
Please note that, unless otherwise specified, any table or figure refers to the supplementary results in the Author Rebuttal PDF.
$\bullet$ W1.1: The entropy maximization strategies maximize the stat... | Summary: In the current field of reinforcement learning, when using representation learning and entropy maximization for exploration, analyses often focus on single variables, making it difficult to capture potential relationships between two variables. Therefore, this paper proposes the SI2E framework. This framework ... | Rebuttal 1:
Rebuttal: We systematically address each of your queries, labeling weaknesses as 'W' and questions as 'Q'.
Please note that, unless otherwise specified, any table or figure refers to the supplementary results in the Author Rebuttal PDF.
$\bullet$ W1: Compared Baselines.
To address this concern, we have in... | Summary: This paper proposes SI2E framework based on structural information principles to overcome inherent limitations in traditional information theory as applied to Reinforcement Learning (RL). The SI2E framework innovatively quantifies the dynamic uncertainties inherent in state-action transitions through a metric ... | Rebuttal 1:
Rebuttal: We systematically address each of your queries, labeling weaknesses as 'W' and questions as 'Q'.
Please note that, unless otherwise specified, any table or figure refers to the supplementary results in the Author Rebuttal PDF.
$\bullet$ W1: Practical Training Time.
We have comprehensively analyz... | Rebuttal 1:
Rebuttal: We are immensely grateful for all reviewers' insightful comments, which have guided a comprehensive refinement of our manuscript.
Please note that supplementary experimental results, including two tables and three figures, are available in the PDF.
Unless otherwise specified, any table or figure... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems | Accept (poster) | Summary: This paper presents a novel algorithm to perform causal inference in time series data based on the idea of convergent cross mappings (CCMs). Similar to Granger causality, the CCM paradigm infers causation by testing whether there exists a map (i.e., a predictor) from the reconstructed (unobserved) state trajec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and consideration. We now address the weaknesses and questions mentioned above.
# Weaknesses
> The explanation of why the correlation coefficient over tangent vectors is better than over trajectories (in CCM) is shaky
One major reason that the use of correlat... | Summary: This paper proposes a novel tangential space causal inference method, TSCI, for identifying causal relationships from time series data generated by dynamical systems. TSCI considers vector fields as explicit representations of dynamic systems and checks for the degree of synchronization between the vector fiel... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and consideration. We now address the weaknesses and questions mentioned above.
# Weaknesses
> It may be unreasonable to use Pearson correlation coefficient in this paper to evaluate the correlation between tangent vectors. As far as I know, Pearson correlatio... | Summary: The authors propose a novel statistic for detecting causality in dynamical systems, which overcomes a key conceptual difficulty in the convergent cross mapping (CCM) method from Sugihara et al. [2012]. CCM is justified by the existence of a surjective "cross map" from the delay embedding of a driving to a driv... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and consideration. We now address the weaknesses and questions mentioned above.
# Weaknesses
> The authors point out that the CCM statistic does not admit a simple decision rule and refer to their correlation coefficients as test statistics. However, the autho... | Summary: The authors propose the Tangent Space Causal Inference (TSCI) method for detecting causalities in dynamic systems. TSCI works by considering vector fields as explicit representations of the systems’ dynamics and checks for the degree of synchronization between the learned vector fields. The authors present bot... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and consideration. We now address the weaknesses and questions mentioned above.
# Weaknesses
> The main weakness of the paper is that the experiments are insufficient to validate the method. Comparison with existing standard methods is missing. Experimental co... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thoughtful comments. We address some common concerns and questions here.
# Comparisons to Granger Causality and Other Methodologies
As mentioned in the introduction, Granger causality (GC) can perform poorly on signals generated by deterministic dyna... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Truncated Variance Reduced Value Iteration | Accept (poster) | Summary: This paper proposes a new faster randomized algorithms for computing an $\varepsilon$-optimal policy in a $\gamma$. discounted MDP The authors give an $\tilde{O}(A_{\text {tot }}\[(1-\gamma)^{-3} \varepsilon^{-2}+(1-\gamma)^{-2}])-$ time algorithm in the sampling setting, where the transition matrix is unknow... | Rebuttal 1:
Rebuttal: Thanks for your comments! We addressed your comment about experiments in the overall response and discuss individual questions here.
**Comment 1: The paper is sometimes hard to follow.**
We would be receptive to refining any areas that were challenging to follow. Could you please clarify where... | Summary: The paper considers the problem of finding an $\varepsilon$-optimal policy of a discounted Markov decision process. Under the generative model, they propose a new algorithm with an improved the sample and time complexity. They also propose an extension to the case where the probability transition matrix is kno... | Rebuttal 1:
Rebuttal: Thank you for your feedack! We’ll carefully address the misspellings/typos ahead of the camera-ready.
**Q1: Can the authors explain how they claim that model-based methods use $\Omega(A_{tot} (1-\gamma)^{-3} \epsilon^{-2})$?**
Good point, indeed, the model-based methods actually only require $... | Summary: The paper provides a randomized algorithm to compute nearly-optimal policies in DMDPs (in the bounded-reward setting) for regimes where the probability transition matrix is either known or unknown. The sample complexities in the paper removes a multiplicative factor of $\frac{1}{1-\gamma}$ on one of the terms ... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
**Comment/Question: Can the constant of $2^8$ in Lemma 2.2 or the constant of $10^4$ be tightened?**
Regarding the comment about the tightness of the $10^4$ constant in $N_{k-1}$, from a quick calculation, we believe it can be lowered to roughly 6500. We think furt... | Summary: This paper introduces Truncated Variance-Reduced Value Iteration (TVRVI), which enhances the previous prior state-of-the-art sample complexity for computing an $\epsilon$-optimal policy in both offline and sampling settings. Specifically, in the offline setting, where the probability transition matrix is known... | Rebuttal 1:
Rebuttal: Thanks for the feedback! We responded about experiments above and discuss your questions here.
**Applying stronger inequalities-- Hoeffding [2], Bernstein [3], Freedman [this paper] has led to improvements.**
As discussed in the main rebuttal, Freedman _alone_ is insufficient to obtain our impr... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and questions! We are encouraged that the reviewers had an overall positive view of our work. In particular, we appreciate that reviewers found our truncated variance reduction idea to be nice as well as novel and felt that the problem we study ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contracting with a Learning Agent | Accept (poster) | Summary: This theoretical paper studies repeated principal-agent contracts where the agent uses no-regret learning algorithms rather than complex strategic reasoning. The main results characterize optimal dynamic contracts against mean-based learning agents:
For linear contracts (including success/failure settings), t... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We address the points raised in the review below.
**Win-win dynamics:** In general, win-win situations arise in “general-sum” games, where the players are not complete adversaries, but rather can increase and share the overall welfare. In the constructions we used fo... | Summary: The paper studies the repeated interaction between a principal and a learning agent. In particular, the authors assume that the agent employes a mean-based learning algorithm. The goal is to design a sequence of contracts that maximizes the principal’s cumulative utility. The main result of the paper is to sho... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We will further extend our discussion of the learning models, and in particular, the comparison between mean-based regret minimization and no-swap regret. Please see also our responses to the other reviews regarding this point.
---
Rebuttal Comment 1.1:
Comment: Than... | Summary: This paper considers the problem of contract design against a (mean-based) no-regret agent. The papers shows several results on the optimal contract design in this dynamic setting. First, with binary outcome, dynamic linear contract, it is optimal to design a free-fall contract. Second, the paper constructs in... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We address the main point raised in the review below.
**Mean-based learning and free-fall contracts:** We completely agree that—knowing now the exploitability of mean-based learning agents—studying different types of learning approaches, such as learning with recency... | Summary: This work studies the problem of contracting with a no-regret learning agent. They show that
- In linear contracts, the optimal dynamic contract against a mean-based learning agent is a free-fall contract.
- dynamic contracts can be win-win. Both principal and agent can benefit from some dynamic contractor an... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We address the points raised in the review below.
**Bandit feedback:** One possibility for bandit feedback is when the agent does not observe the contract $p_t$ but only observes the payoff induced by this contract for the action that was played. A different scenari... | Rebuttal 1:
Rebuttal: We thank the reviewers for the constructive comments and will use this feedback to improve our paper. We address the specific points raised in the reviews in a separate response to each review. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects | Accept (poster) | Summary: The paper proposes novel causal inference framework for closed-loop optogenetics behavioral studies, develops causal inference estimation method for sequential excursion effects that capture local causal contrasts within the same treatment group. The proposed method is robust to accommodate both positivity vio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on our work. The reviewer raises a very interesting methodological/modeling question, and identifies a notational issue, both of which we address below.
1. “The largest weakness I see here is both experimental results uses linear or generalized li... | Summary: This paper introduces a causal inference framework for analyzing close-loop optogenetics experiments. The authors propose using HR-MSMs to estimate the sequential excursion effects (causal effects of specific neural stimulation sequences on behavioral outcomes). Specifically, they develop an IPW estimator with... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful questions, comments and feedback on our work. We believe that the proposed revisions and clarifications in line with the responses below will improve the strength of the paper.
1. “The focus of this paper centers on an application study that refines causal ... | Summary: This work investigate the causal effect estiamtion in the Optogenetics, which is interestimg in causality in science. The paper proposed a nonparametric causal inference framework for analyzing “closed-loop” designs in sequential setting and extends “excursion effect” methods to enable estimation of causal co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions. Most of the major concerns are, we believe, a result of misunderstandings of the goals and claims of our work. We hope that our revisions will make them clearer, and address the questions below:
1. "Some claims are not clear, which makes the... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thoughtful feedback, and were pleased to see agreement on the following positive points:
**Importance & Novelty**:
- z4yk: “This paper proposes the first formal causal inference framework…for closed-loop optogenetics behavioral studies addressing the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Addressing Bias in Online Selection with Limited Budget of Comparisons | Accept (poster) | Summary: The paper studies an extension of the online secretary problems in which candidates from multiple groups arrive. The goal is to find the best candidate, but while within-group comparisons are free, inter-group comparisons are not available a priori and we have a limit of B queries to a comparison oracle for su... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback on our submission. We fully agree with the positive evaluation made, both in terms of strengths (solid theoretical study and introduction of a novel and realistic variant of the multi-color secretary problem) and in terms of future work (rigorous theoretica... | Summary: This paper studies a novel extension of multi-color secretary problem, where comparing candidates from different groups is possible at a cost. With a limited budget total, a Dynamic-Threshold algorithms family is introduced, and the success probability of a special case, i.e. single-threshold algorithm for K g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback on our submission. We fully agree with the positive evaluations made, both in terms of strengths (solid theoretical study and introduction of a novel and realistic variant of the multi-color secretary problem) and in terms of future work (rigorous theoretic... | Summary: This paper tackles an online hiring selection with budget problem. The authors propose a dynamic threshold method and provides theoretical analysis on the algorithm performance. The numerical experiments confirm the findings of the algorithm.
Strengths: 1. This paper is well motivated, and the proposed method... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback on our submission. We generally agree with the positive evaluations made in terms of strengths (solid theoretical study and introduction of a novel and realistic variant of the multi-color secretary problem). We provide a detailed reply to the weaknesses an... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection | Accept (poster) | Summary: In this paper, the authors present DPIC, a novel model for detecting LLM-generated text, which centers on having an auxiliary LLM reverse-generate prompt on candidate text, and then letting the LLM re-generate the answers to the prompt and classify them based on the similarity between the candidate and the re-... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and expertise you have invested in these reviews. We are delighted to receive positive feedback that our work provides a solid contribution to the field. Below we provide point-by-point responses to your comments and questions.
---
- **Question 1**: Lack of det... | Summary: This paper addresses the problem of detecting texts generated by large language models (LLMs), which is a crucial issue considering the potential misuse of such models. The authors propose a novel method, DPIC (Decoupling Prompt and Intrinsic Characteristics), which aims to extract the intrinsic characteristic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and expertise you have invested in these reviews. We are delighted to receive positive feedback that our work provides a significant contribution to the field, especially since our innovative method offers new insights into the essential differences between human... | Summary: This paper proposes a novel method named DPIC (Decoupling Prompt and Intrinsic Characteristics) for detecting texts generated by LLMs. The authors posit that generated texts are a coupled product of prompts and intrinsic characteristics, and suggest that decoupling these two elements can enhance detection qual... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful reviews of our manuscript. Below, we provide point-by-point responses to your comments and questions.
- **Limitations & Weaknesses 3:** The authors did not discuss the out-of-distribution generalization ability of DPIC.
**Response:** Actually, w... | Summary: This paper develops an LLM-generated text detection method by ****. The key idea is to regenerate text based on a prompt reconstructed by an auxiliary LLM from the candidate text so that the regenerated text and the original candidate text can be used to extract similarity-based features, which are used for a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and expertise you have invested in these reviews. We are delighted to receive positive feedback that our work provides a solid contribution to the field, especially since the paper is well-written, the proposed idea is simple and intuitive, and the experiments an... | Rebuttal 1:
Rebuttal: ### **Dear Reviewers,**
We appreciate the constructive and insightful comments from all the reviewers! We sincerely appreciate your time and effort in reviewing our work. We have provided detailed answers to the comments and questions from each reviewer in the different author responses. All the m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense | Accept (poster) | Summary: The paper introduces an adversarial defense method, CausalDiff, which leverages a causal model of robustness combined with a diffusion model to learn label-relevant causative representations. The effectiveness of CausalDiff is specifically demonstrated against unseen attacks, where it surpasses existing advers... | Rebuttal 1:
Rebuttal: **We appreciate the time and effort of the reviewer. In response to the issues raised in the review, we offer the following replies:**
**For Weakness 1**:
- **Similarities**:
Both CausalAdv [1] and DICE [2], like our CausalDiff, model the generative mechanisms of clean data, with causal relatio... | Summary: This work aims to promote the trustworthiness of DNNs through purification. To this end, the authors propose a novel causality view to perform a disentanglement approach using diffusion models. Some experiments are conducted to verify the effectiveness of the proposed method.
Strengths: + This work takes a go... | Rebuttal 1:
Rebuttal: **We appreciate the time and effort of the reviewer. In response to the issues raised in the review, we offer the following replies:**
**Q1**: Adaptive attacks are lacking in this work.
**A1**: **All robustness evaluations in our paper are conducted against adaptive attacks.** This means the att... | Summary: This paper proposed a novel causal diffusion framework based on causal inference to defend against unseen attacks. The causal information bottleneck is interesting to disentangle the target-causative and target-non-causative factors, and then target-causative factors are used for adversarial defense.
Strength... | Rebuttal 1:
Rebuttal: **We appreciate the time and effort of the reviewer. In response to the issues raised in the review, we offer the following replies:**
**Q1**: Some technical details need to be explained. For example, what is the actual meaning of the constraint in Eq. (4)? And why are the positions of z_s and s_... | Summary: The authors propose CausalDiff, a causality-inspired disentanglement approach using diffusion models for adversarial defense, which outperforms state-of-the-art methods on various unseen attacks across multiple datasets.
Strengths: Novel approach combining causal modeling and diffusion models for adversarial ... | Rebuttal 1:
Rebuttal: **We appreciate the time and effort of the reviewer. In response to the issues raised in the review, we offer the following replies:**
**Q1**: Limited discussion on computational complexity and training time.
**A1**: We provide a detailed speed test in Appendix C.4, evaluating the inference tim... | Rebuttal 1:
Rebuttal: **We appreciate the time and effort of all the reviewers.**
Regarding the inference efficiency mentioned by the reviewers, we provide a detailed speed test in Appendix C.4, evaluating the inference time of our CausalDiff and baselines. We will include a discussion on efficiency in the methods sec... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction | Accept (poster) | Summary: The paper addresses the challenge of text-conditioned 3D dynamic human-object interaction (HOI) generation, which has lagged behind advancements in text-conditioned human motion generation due to the scarcity of large-scale interaction data and detailed annotations. The authors propose InterDreamer, a framewor... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful and insightful comments. We address your concerns below:
**W1: handle more complex long interactions**:
- As shown in Figure 4 and in the examples after 00:43 in demo_1.mp4 of the supplementary material, our approach is capable of handling complex and extend... | Summary: This work aims at human-object interaction generation under less supervision. Since existing methods rely on large-scale interaction data, this paper attempts to propose a method that does not require paired data. The proposed method includes three stages, high-level planning by LLMs, low-level control by exis... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful and insightful comments. We address your concerns below:
**W1: Novelty**
We appreciate the reviewer's thoughtful suggestions. While our method incorporates elements adapted from existing approaches, we would like to clarify the novelty of these components, pa... | Summary: InetrDreamer is a framework for synthesizing Human-Object Interactions (HOI) from textual queries. The key feature of InterDreamer is the ability to train without paired text and HOI motion data. To achieve this the work employs a multi-stage pipeline with LLM operating as a high-level planner that defines the... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful and insightful comments. We address your concerns below:
**W1: full template of the query**
- We appreciate the reviewer’s suggestion and have included our detailed query log in Fig. 1 of the rebuttal PDF file. We will discuss related work on this and add t... | Summary: This work aims to address the text-conditioned human-object interaction (HOI) motion generation task. Unlike previous HOI generation approaches that rely on limited existing HOI datasets with text annotations for supervised learning, this work proposes a framework that decouples interaction semantics learning ... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful and insightful comments. We address the concerns below:
**W1: How the world model acts in novel objects**
- The world model employs "contact vertices" as input, which includes features derived from the object distance field. These features encompass the human... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments. We appreciate the recognition that our task is challenging (Reviewer Jz5F) and novel (Reviewer Kjsy), and that our solution is insightful (Reviewer Jz5F), sound (Reviewer Kjsy), and promising, particularly in its potential to scale HOI modeli... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective | Accept (poster) | Summary: This paper investigates iterated learning with large language models (LLMs). The authors present theoretical analyses demonstrating how bias is amplified when LLMs are chained in an iterated learning process. Moreover, they conduct experiments with LLMs that confirm the amplification of bias during iterated le... | Rebuttal 1:
Rebuttal: > 1. The paper devotes extensive text (Sections 3 and 4) …
There is an important misunderstanding in this claim. The Bayesian-IL framework has been discussed in the cognitive science community for decades; Griffith et. al. formally describe it using the Bayesian framework. In that paper, sections... | Summary: The paper discusses how the widespread adoption of Large Language Models (LLMs) and their iterative interactions can lead to an evolutionary process similar to human cultural evolution. It leverages the Bayesian Iterated Learning (IL) framework to explain how subtle biases in LLMs are amplified over iterations... | Rebuttal 1:
Rebuttal: > 1. The framework relies on several assumptions that may not hold …
Thanks for this question. The theoretical proof indeed needs a lot of assumptions which might not be true in practical systems. However, as we stated in Appendix A, we only expect the general trend (i.e., the bias amplification ... | Summary: The main idea of this paper is similar to Xie et al., as both attempt to incorporate in-context learning of Large Language Models (LLMs) into the framework of Bayesian inference. This paper extends the concept to include more general multi-agent, multi-round self-improvement of LLM systems within the framework... | Rebuttal 1:
Rebuttal: > 0. The main idea of this paper is similar to Xie et al. …
We'd like to first highlight some differences from Xie et al. They show that LLM agents behave like Bayesian agents when doing ICL, linking LLM evolution to our Bayesian-IL framework. However, our paper focuses on LLM’s knowledge evoluti... | Summary: The paper explores the evolution of Large Language Models (LLMs) through the lens of Iterated Learning (IL), drawing parallels with human cultural evolution. The authors propose a Bayesian framework to analyze how biases in LLMs are amplified over generations of learning. They introduce the concept of Iterated... | Rebuttal 1:
Rebuttal: > 1. Do you think that the Bayesian framework and iterative learning …
Thanks for this good question. In short, the proposed framework doesn’t care about the implementation and training details of LLMs. That is also the most important benefit of using a highly abstract way (i.e., Bayesian behavio... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Unifying Normative Framework of Decision Confidence | Accept (poster) | Summary: Great paper about confidence with payoffs but maybe not the unifying one yet. They follow the Bayesian Confidence Hypothesis and the optimality assumption to formulate in a POMDP the modelling of perceptual confidence and decision making. The title should be more like modeling decision confidence as soft Q-lea... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and literature suggestions. We will incorporate them into our paper.
We agree entirely that confidence could be different when more than 2 choices are available. In fact, there is a recent work showing that confidence is better modeled by the... | Summary: In this work, the Authors propose a normative model of decision confidence extending the planning-as-inference framework with an optimality variable proportional to the softmax over the reward distribution. The model is fitted to the data from two behavioral tasks featuring the participants’ confidence reports... | Rebuttal 1:
Rebuttal: We thank the reviewer for bringing up great points about testing the model in more and better experiments. We agree that these directions need to be discussed in the paper, and we will do so in the final version, which allows one more page.
Current experiments, mostly including only 2 choices and... | Summary: The present paper presents a normative framework for modeling decision confidence in humans that is generalizable to various tasks and experimental setups. In particular, the authors connect the planning as an inference framework to decision confidence. They validate their model in two different psychophysics ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and questions.
Our framework is normative because the agent maximizes the reward and information entropy of the policy (eq 9). We presented our approach somewhat backward to make it more intuitive for confidence representations. As other reviewers also no... | Summary: The submission proposes a model of decision confidence that is applicable to value-based as well as perceptual decision making tasks, and is generally grounded in normative inference. The approach is applied to two real-world datasets.
Strengths: I think the motivation is compelling, grounding confidence out ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and comments.
Concerns:
About the results, we would like to emphasize that in the first experiment (4.1), subjects were explicitly instructed to report their perceptual confidence (lines 245-247). However, half of them reported their decision c... | Rebuttal 1:
Rebuttal: Content of PDF: Plot of additional analysis for experiment 2. AIC table of Entropy model for experiment 1.
Pdf: /pdf/eb9525274dadc116be5aa0417d1c17acf22604af.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Group Proportional Representation in Retrieval | Accept (poster) | Summary: Authors proposed Multi-Group Proportional Representation, which is a metric to measure representation across multiple intersectional groups (could be many and overlapping) for retrieval. Given a set of representation statistics which quantify group membership in the target groups, MPR measures that group repre... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4ELS
We thank the reviewer for their careful review and valuable feedback and for raising the issue of the computational efficiency of our method. We have addressed this issue in our response below, in the general remarks above, and in the attached pdf.
## WA/B. "This meth... | Summary: This paper introduces the novel metric of Multi-Group Proportional Representation (MPR) designed to address the representational harms in image search and retrieval tasks, which can perpetuate harmful stereotypes, erase cultural identities, and amplify social disparities. Current methods to mitigate these issu... | Rebuttal 1:
Rebuttal: # Response to Reviewer XQou
We are grateful for your review and positive feedback. We will address each of the weaknesses and questions raised (in a combined way) in detail below. We will be happy to answer any further question.
## Q1. "Can you provide more details on the computational requirem... | Summary: This paper introduces a novel metric, Multi-Group Proportional Representation (MPR), to measure and ensure fair representation across intersectional groups in image retrieval. Current methods often ensure representation across individual groups, not intersectional groups. To address this, authors
Strengths: 1... | Rebuttal 1:
Rebuttal: # Response to Reviewer XWMb
We thank the reviewer for their careful reading of our paper and valuable feedback. We address each of the weaknesses and questions raised below. Please let us know if you have any additional questions.
## W1. "...the 'White Male' as an intersectional group raises que... | null | null | Rebuttal 1:
Rebuttal: # General rebuttal
We sincerely thank the reviewers for their thorough assessment of our paper. We appreciate their recognition of our work's contributions to the critical challenge of fair retrieval, particularly in addressing *intersectional representation* (reviewer 4ELS). We are pleased that ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning | Accept (poster) | Summary: This paper focuses on the scenario of continuous RL with high decision frequency. They show that it is hard for distributional RL agents to accurately estimate action values when the decision frequency increases, just like in the case of ordinary RL agents. They propose a distributional analogy of the action g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment of our work, their interest in its results, and insightful comments.
**Re: Discussion on using $(\phi(\eta_1) - \phi(\eta_2))/h^q$ for decision making.**
Thank you for pointing this out. We appreciate your suggestion here. We will be happy to a... | Summary: This paper investigates the action gap in distributional RL, where the decisions are made at high frequency. The authors showed that the distributional RL is also sensitive to the decision frequency. In particular, they proved the action-conditioned return distribution collapse with different rates for the sta... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment of our work, their interest in its results, and insightful comments.
**Re: Motivation and Clarity.**
Thank you for pointing to these potential areas of improvement. We very much appreciate your suggestions. We will be sure to update our revisio... | Summary: The paper investigates the issues around continuous-time distributional reinforcement learning. In traditional RL, the advantage becomes less informative as the frequency of actions increases, vanishing at the limit and making it impossible to distinguish between actions. This work extends this result to distr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment of our work, their interest in its results, and insightful comments.
**Re: Background on SDEs.**
Thank you for pointing this out. We appreciate this suggestion. We will happily expand our background on SDEs. We plan to incorporate discussion on... | null | null | Rebuttal 1:
Rebuttal: # General Response
We are thankful for the interest in our work as well as the time and effort taken to review it.
Reviewers praised our work for the problem that we have highlighted and the completeness of our theoretical treatment of it (TDv5, ZgER, dYRz), our general clarity and rigor of exp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures | Accept (poster) | Summary: The paper introduces a new prompting method, Self-Discover, that enhances the complex reasoning abilities of Large Language Models. Self-Discover consists of two stages. At the first one, the method develops a task-adapted prompt by performing 3 steps: SELECT, when the LLM selects the reasoning modules (RMs) f... | Rebuttal 1:
Rebuttal: Thank you so much for your helpful feedback and suggestions. Our responses to weaknesses and questions are as follows:
__[Task adaptation]__
Thank you for the great point. We’d like to first clarify that though a few task examples are used in meta-prompting, Self-Discover does NOT require any an... | Summary: The paper introduces SELF-DISCOVER, a framework enabling large language models (LLMs) to autonomously identify and utilize intrinsic reasoning structures to tackle various reasoning tasks. The framework is applicable across different model families and aligns with human reasoning patterns.
Strengths: 1. The p... | Rebuttal 1:
Rebuttal: Thank you so much for your helpful feedback and suggestions. Our responses to weaknesses and questions are as follows:
__[Dependent on quality of atomic reasoning modules]__
We would like to point out that the main contribution of this paper is the Self-Discover process. To show its effectivenes... | Summary: This paper introduces SELF-DISCOVER, a framework designed to enhance the reasoning capabilities of Large Language Models (LLMs) such as GPT-4 and PaLM 2. The framework enables LLMs to self-discover and compose intrinsic reasoning structures tailored to specific tasks, improving performance on complex reasoning... | Rebuttal 1:
Rebuttal: Thank you so much for your helpful feedback and suggestions. Our responses to weaknesses and questions are as follows:
__[Simplicity of atomic reasoning modules]__
Thank you for the great point and we agree that the 39 atomic reasoning modules can be improved. We would like to note two points: 1... | Summary: The paper introduces SELF-DISCOVER, a novel framework that enables large language models (LLMs) to self-discover and compose reasoning structures for tackling complex reasoning tasks. The core of SELF-DISCOVER is a self-discovery process where LLMs select multiple atomic reasoning modules, such as critical thi... | Rebuttal 1:
Rebuttal: Thank you so much for your helpful feedback and suggestions. Our responses to weaknesses and questions are as follows:
__[Variety of structures]__
We have included more details on frequency of selected reasoning modules in the attached 1-page pdf. Furthermore, we observe a very diverse set of se... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and insightful comments. We are pleased to receive this positive feedback from reviewers, particularly:
- Significantly improves performance across diverse complex reasoning tasks and demonstrates the value of prompt diversity (Reviewer i8mj, nKMJ, and 5m... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a prompt engineering scheme:
Given a task and 39 prompts for solving tasks,
an LLM is prompted to select which of the 39 prompts are suitable for the task.
The selected prompts are then rephrased to be more specific to the task,
and reformatted in json.
The paper uses GPT-4 Turbo, PaLM 2... | Rebuttal 1:
Rebuttal: Thank you so much for your helpful feedback and suggestions. Our responses to weaknesses and questions are as follows:
__[Specific 39 seed modules]__
Thanks for the very important point. The main contribution of the paper is the self-discover method and we demonstrate its effectiveness with the ... | null | null | null | null | null | null |
AdanCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformer | Accept (poster) | Summary: The work presents present a neural network architecture that combines vision transformers and neural cellular automata. The work shows that this hybrid architecture competitively at image classification task with a downsampled version of Imagenet (224x224). Furthermore, the results show a small improvement on ... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and positive feedback. We now address your questions below.
- **Comparison with ViTCA**
Please refer to the global rebuttal ”AdaNCA and ViTCA”.
- **The difference between MLP processing concatenated channel vectors and Dynamic Interaction**
Please see the g... | Summary: The authors propose the introduction of NCAs into VisionTransformers to improve the robustness against adversarial inputs as well as out of distribution data. This improves basic ViT architecture by up to 10% against specific adversarial attacks. In addition this modification, they propose Dynamica Interaction... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback on our experiments. We will improve the clarity of the sentence in the abstract. We now address your major concerns below.
- **Choice of baselines**
We fully agree with you that the original results reported in the TAPADL paper should be included for compl... | Summary: This paper introduces Adaptor Neural Cellular Automata (AdaNCA), a plug-and-play module designed to enhance the robustness and performance of Vision Transformers (ViTs). The innovation lies in integrating Neural Cellular Automata (NCA) as intermediary adaptors between the layers of ViTs. The paper demonstrates... | Rebuttal 1:
Rebuttal: We thank you for your positive feedback on our work. We now address your questions.
- **The generalizability and scalability of AdaNCA**
We fully agree with you that applying AdaNCA in other computer vision tasks and scaling it to larger datasets and higher-resolution images are worthwhile. Giv... | Summary: This paper proposes a strategy for improving the image classification robustness of Vision Transformers (ViT) through the use of specialized networks that are inserted at strategically placed layers within the ViT model. These networks are called Adapter Neural Cellular Automata (AdaNCA) and are intentionally ... | Rebuttal 1:
Rebuttal: We thank you for the valuable and detailed suggestions as well as the acknowledgment of the originality, significance, and novelty of our work. We now address your questions below.
- **Importance of recurrent updates**
We fully agree with you that the recurrent update scheme of NCA is crucial f... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments and the acknowledgment of our contributions. We are glad to note that all reviewers (h6zC, 3x5z, oE4b, ZhPF) agree on:
- **The novelty and significance of our method in integrating NCA into ViT.**
- **The effectiveness of AdaNCA in improving the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scalable Ensemble Diversification for OOD Generalization and Detection | Reject | Summary: The paper presents SED, a method for scaling up existing diversification methods to large-scale datasets and tasks. SED identifies the OOD-like samples from a single dataset, bypassing the need to prepare a separate OOD dataset. Experimental results demonstrated good performances by SED on the OOD generalizati... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and positive comments about the method/results. The questions/comments are very useful for improving the paper. We added a **number of new results** (in the attached PDF) and made **numerous clarifications** to the paper (summarized below).
---
**W1: ... | Summary: This paper presents a new method which directly encourages ensemble diversification on selected ID datapoints without the need for a separate OOD dataset. They also introduce a new measure of epistemic uncertainty which measures the diversity of the final predictions of each model, and suggest a speedup of com... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and positive comments about the intuitiveness of the method, the extensiveness of the evaluation, and the empirical results. The questions/comments are very useful for improving the paper. We added a **number of new results** (in the attached PDF) and ... | Summary: The paper aims to train a diverse ensemble of models via a framework called Scalable Ensemble Diversification. This framework does not require an additional dataset of OOD inputs, as it identifies OOD samples from a given ID dataset. It then encourages the ensemble to return diverse predictions (disagreement) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and positive comments about the idea/clarity. The questions/comments are very useful for improving the paper. We added a **number of new results** (in the attached PDF) and made **numerous clarifications** to the paper (summarized below).
---
**W1: Ab... | Summary: Ensembles of diverse models have shown promising signs for out-of-distribution (OOD) generalization.
To boost diversity, some methods require a set of OOD examples for measuring the disagreement among models.
The desired OOD examples, however, can be difficult to obtain in practice.
This paper proposes to dyna... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the thorough review and encouraging comments. The questions and suggestions are very helpful to improve the paper, and we propose **several improvements** (details below) that should make the final version much clearer.
We also added a number of **new results** (in the ... | Rebuttal 1:
Rebuttal: We thank the reviewers for recognising the value of our work and for providing constructive feedback to improve the manuscript. Along with the individual rebuttal, we provide here supporting tables, figures, and references in the markdown text and the PDF file. They are referenced as "Table X in t... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes Scalable Ensemble Diversification (SED) to extend existing diversification methods to large-scale datasets and tasks where ID-OOD separation may not be possible, and also propose Predictive Diversity Score (PDS) as a novel measure for epistemic uncertainty. Extensive analysis and experiments... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for recognizing the key values of the paper - clear motivation, clear writing, and experimental results supporting the effectiveness of the method. The questions are also very useful to further improve the paper as detailed below.
We also added a number of **new results** (... | null | null | null | null | null | null |
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation | Accept (poster) | Summary: The study introduces a data-driven variant of LoRA-like Parameter-Efficient Fine-Tuning (PEFT) for Vision Transformers (ViTs), which learns the optimal bottleneck dimension (the rank of the fine-tuning weight matrix) instead of preconfiguring it for each layer. The authors propose learning three additional vec... | Rebuttal 1:
Rebuttal: **Questions: What rank is applied to the LoRA-based baselines in Table 1? It would be beneficial to list these parameters in the appendix.**
In Table 1, the rank for LoRA is set to 8. In subsequent versions, we will follow your suggestion and include the settings for the comparison methods in the... | Summary: This paper proposes a new PEFT method by integrating Householder transformations, which enables the creation of adaptation matrices that have varying ranks across different layers, thereby offering more flexibility in adjusting pre-trained models.
Strengths: S1: This paper proposes a new PEFT method based on ... | Rebuttal 1:
Rebuttal: We agree that all PEFT methods use a reduced number of parameters, and at this level, further reduction in parameter count has limited significance in practical applications. However, compared to previous low-rank-based PEFT methods such as LoRA and Adapter, our approach addresses a key issue: the... | Summary: The paper presents a novel method, HTA, for efficiently adapting pre-trained transformers by applying the Householder transformation to a single vector to approximate the SVD for representing the adaptation matrix. By combining this with an additional rank-1 adaptation matrix, HTA achieves a reduction in the n... | Rebuttal 1:
Rebuttal: **Weaknesses(Originality): Utilizing Householder transformations for matrix decomposition is not a brand-new idea.**
The Householder transformation is indeed not a new concept in matrix decomposition; rather, it is a commonly used method. However, the core of our approach does not lie in the use ... | Summary: The paper presents a new parameter efficient fine tuning (PEFT) technique for vision transformer (ViT) models. The work utilizes the intuition of creating an adaptation matrix for fine-tuning from a popular dimensionality reduction technique named singular value decomposition (SVD) which is named Householder t... | Rebuttal 1:
Rebuttal: **Questions: Do you think the HTA idea is adaptable to other modalities for PEFT?**
To validate the adaptability of HTA in other modalities, we selected the large model Llama from the NLP domain for further testing. Due to time constraints, we only used the smaller commonsense_15k dataset as the ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers and ACs for their thorough and valuable comments on our manuscript and their recognition of our work. We aim to address the shared concerns raised by the reviewers and provide a unified response below. Additionally, we have offered detailed replies to the spec... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors | Accept (poster) | Summary: The topic of using a pre-trained diffusion model as a prior for a Bayesian posterior sampling problem is considered. This formulation is compelling (particularly for images) as it combines the powerful generative capacities of large-scale diffusion models with the coherent inference under certainty provided by... | Rebuttal 1:
Rebuttal: Dear Reviewer GK7d,
We thank you for your time, your feedback and suggestions. Please find our response to your questions below. In the PDF attached to the main rebuttal you can also find an update of Table 2 in the main paper with 4 more competitors suggested by the other Reviewers. These new re... | Summary: This work proposes a new posterior sampling scheme for unconditional diffusion models. The sampling algorithm, named divide-and-conquer posterior sampling (DCPS), combines Langevin Monte Carlo and Gaussian variational inference to operate the transitions between noise levels along the reverse diffusion process... | Rebuttal 1:
Rebuttal: Dear Reviewer af3x,
We would like to thank you for the time you took to review our manuscript and for your feedback and suggestions. Please find below our response to what we believe is the most crucial point in your review. Please consider reading the main rebuttal containing comparisons with n... | Summary: This paper aims to solve the inverse problem within the Bayesian framework. The authors propose DCPS, which approximately samples from the posterior. DCPS creates a sequence of intermediate posterior distributions to approach the target posterior step by step. The authors showcase the effectiveness of DCPS on ... | Rebuttal 1:
Rebuttal: Dear Reviewer xni9,
We would like to thank you for the time you took to review our manuscript and for your feedback and suggestions. Please find below our response to what we believe are the most crucial points.
**On the approximation of $g_k ^{*}$ and design of the intermediate potentials**: We... | Summary: This paper presents a sampling algorithm (DCPS) that leverages diffusion models as priors to solve linear inverse problems. DCPS defines and recursively solves a series of intermediate inverse problems that converge to the original inverse problem. In each iteration, a sample is drawn from the next posterior d... | Rebuttal 1:
Rebuttal: Dear Reviewer vN6C,
We would like to thank you for your time, your feedback and suggestions. We answer your questions below.
**Further experiments:** We have followed your suggestion and proceeded to compare our algorithm with DDNM [1] and DiffPIR [2] on the imaging experiment. We did not compa... | Rebuttal 1:
Rebuttal: Firstly, we would like to sincerely thank the reviewers for taking the time to review our paper and for providing constructive feedback. Here below we address the two recurring comments across your reviews.
**More competitors**: Following the suggestions of Reviewer vN6C, xni9 and af3x we have a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Locally Private and Robust Multi-Armed Bandits | Accept (poster) | Summary: The study examines the interaction between local differential privacy (LDP) and robustness to Huber corruption and heavy-tailed rewards in multi-armed bandits (MABs). It focuses on two scenarios: LDP-then-Corruption (LTC) and Corruption-then-LDP (CTL). They provide a tight characterization of mean estimation e... | Rebuttal 1:
Rebuttal: **Technical flaw in Tao et al. 2022.** Sure, we have provided a detailed discussion on it, which points out the incorrect step in their upper bound proof (see the above general response).
**Novelty of Algorithm 1.** We remark on the novelty (or insight) of Algorithm 1 from the perspectives of alg... | Summary: This paper studies multi-armed bandits where the feedback is a locally differentially private and corrupted version of the true rewards. The main message is that the order in which the rewards are (1) corrupted and (2) made private (i.e., (1) and then (2) or (2) and then (1)) changes the achievable regret rate... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and review. Below is our response to the comments.
**Presentation.** Thanks for the suggestion on the presentation. We will try to incoprate them in the final version, e.g., a summary table.
**Practical scenarios of LTC and CTL** Yes, we have already briefly ... | Summary: This paper considers the heavy-tailed online and offline MAB problem with local differential privacy and Huber corruption, where the CTL, LTC, and C-LDP-C models are considered. By first providing tight high-probability concentration bounds for the mean-estimation problem under the CTL and LTC settings, the au... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and review. We will provide our response below.
**Online MAB.** In this paper, as in previous work (e.g., Wu et al 2023), we treat $\alpha$ as a constant (i.e., it does not scale with $T$). Thus, the third term in big-O of Proposition 4 is a lower order term a... | null | null | Rebuttal 1:
Rebuttal: # Technical flaw in Tao et al. 2022
We thank all the reviewers for their time and insightful comments. Since all reviewers would like to see more discussion of Tao et al. 2022's technical flaw from the perspective of upper bound proof, we will provide a general global response below.
The key fla... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling | Accept (poster) | Summary: This paper proposes WiOR-(C)BO for without-replacement sampling algorithms for (conditional) bilevel optimization. The authors prove the convergence rate for both algorithms under regular assumptions, which are improved upon existing works on the complexity of $\epsilon$.
Strengths: 1. The assumptions are sta... | Rebuttal 1:
Rebuttal: Thanks for spending your valuable time reviewing our manuscript and provide insightful feedbacks. Our responses are provided as below:
**Convergence Rate of SOBA**. We first want to clarify that SOBA [1] has convergence rate of $O(\epsilon^{-4})$ under the notation of our manuscript. In fact, the... | Summary: This paper introduces new algorithms that leverage without-replacement sampling to enhance bilevel optimization. The main contributions are:
1. Introducing more practical bilevel optimization algorithms using without-replacement sampling.
2. Providing comprehensive theoretical analysis demonstrating improved c... | Rebuttal 1:
Rebuttal: Thanks for spending your valuable time in reviewing our manuscript and provide insightful comments.
Firstly, about the upper bound in Assumption 4.3, 4.4 and 4.7, we assume a common bound due to the following reasons. Note that these assumptions measure similar properties, namely the sample esti... | Summary: This paper improves the computational inefficiencies in bilevel optimization algorithms that rely on independent sampling. It proposes a novel without-replacement sampling-based algorithm, which achieves a faster convergence rate than existing methods.
Strengths: This paper develops independent sampling for b... | Rebuttal 1:
Rebuttal: Thanks for spending your valuable time reviewing our manuscript and provide insightful feedbacks. Our responses are provided as below:
*W1: The paper claimed that "Compared to independent sampling-based algorithm such as stocBiO, ... computational cost of training large scale models." Is there an... | Summary: This paper investigates stochastic bilevel optimization. One common practice herein is that the data samples used to calculate all the derivatives are mutually independent, which may however introduce more computational cost compared to the case where the samples are reused. Motivated by this, the authors expl... | Rebuttal 1:
Rebuttal: Thanks for spending your valuable time reviewing our manuscript and provide insightful feedbacks. Our responses are provided as below:
**Technical contributions**. We provide the first theoretical analysis of bilevel optimization that uses without-replacement sampling. A key insight in our analys... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a new algorithm called WiOR-BO, which leverages without-replacement sampling to achieve faster convergence rates compared to traditional independent sampling methods. The algorithm is applied to standard bilevel optimization, conditional bilevel optimization, and specific cases such as min... | Rebuttal 1:
Rebuttal: Thanks for spending your valuable time reviewing our manuscript and provide insightful feedbacks. We provide our responses to your comments below:
**Hyper-parameter Tuning**. Our algorithms include three hyper-parameters: the outer learning rate $\eta$, the inner learning rate $\gamma$ and the le... | null | null | null | null | null | null |
Slot State Space Models | Accept (poster) | Summary: This paper introduces Slot State Space Models where instead of having a single monolithic state, the SSM state is divided into K different - each ideally representing a separate object in the scene. Each SSM state is evolved independently while interacting with each other through self attention. The complete a... | Rebuttal 1:
Rebuttal: ## We sincerely appreciate for your valuable insights!
> The paper has introduced a multi-layer architecture where the number of slots can vary per layer but this architecture has not been used in any of the experiments.
In our current design, only our first layer changes the number of slots from... | Summary: This paper presents SlotSSMs, an extension of SSMs such that their states encourage information separation. This is in contrast to conventional SSMs whose state is monolithic. The authors evaluate slotSSMs in object-centric video understanding and video prediction tasks involving multiple objects and long-rang... | Rebuttal 1:
Rebuttal: ## We sincerely appreciate your insightful feedback!
> Fig 4 predictions do not look good
We would like to rectify an error in Fi.g 4. We mistakenly used Single State SSM (Split)'s figures for "Ours,", and therefore the images does not represent our model's prediction accuracy. We have included a... | Summary: This paper presents Slot State Space Models (SlotSSMs), a novel framework that integrates modular structures and inductive biases into State Space Models (SSMs) to improve sequence modeling. SlotSSMs maintain a collection of independent slot vectors and perform state transitions independently per slot, with sp... | Rebuttal 1:
Rebuttal: ## We sincerely thank you for your positive recommendation and thoughtful comments!
> … the paper does not provide experiments or theoretical analyses in other modalities (e.g., text, audio). … What challenges do you anticipate in these domains, and what benefits might SlotSSMs bring?
>
Thank ... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful and positive feedback! We are encouraged that they find our work **novel** (ERbD, Ae5c), **interesting** (YJW6), and **offers valuable insights** (ERbD). They also highlighted its **potential to facilitate future research** and **lead to more advanced ar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization | Accept (poster) | Summary: The paper addresses the problem of point cloud registration, namely the challenge of outliers that can negatively affect registration quality. The proposed algorithm first constructs a compatibility graph from which local sets of possible correspondences are extracted. The method further involves decoupling t... | Rebuttal 1:
Rebuttal: Dear Reviewer TzsD,
Thanks for the positive assessment of our contributions. We have thoroughly addressed the concerns raised by the reviewer and provided further clarification on our study.
__W1-Time complexity and practicality__
The complex mathematical formulas in the manuscript are intende... | Summary: This study presents a framework for robust inlier identification in multi-model fitting tasks, which is a significant problem in computer vision. The proposed method and its evaluation show promising results.
Strengths: Originality:
-Novel Framework: The paper introduces a new framework for robust inlier iden... | Rebuttal 1:
Rebuttal: Dear Reviewer Z5SY,
Thanks for your precious time and efforts in reviewing our work. We have thoroughly addressed the concerns raised by the reviewer and will revise the paper accordingly.
__W1-Clarify significance and advantage in abstract__
As suggested by the reviewer, we will revise the abs... | Summary: The paper presents an inlier identification algorithm for point cloud registration by reformulating the conventional registration problem as an alignment error ℓ0-minimization problem. This approach focuses on enhancing the accuracy of point cloud registration under conditions with high outlier ratios and nois... | Rebuttal 1:
Rebuttal: Dear Reviewer 9KUM,
Thanks for your insightful comments. We have carefully responded to every comment raised by the reviewer and will make corresponding revisions to the manuscript.
__W1-Efficiency Considerations__
As the reviewer suggests, we provide the figure that changes the axis scaling (F... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers 9KUM, Z5SY, and TzsD,
Thank you for your constructive comments and positive assessment of the manuscript. We have thoroughly addressed the concerns raised. Additional experiments have been conducted and more detailed analyses have been provided. We will also revise the manuscript c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model-based Diffusion for Trajectory Optimization | Accept (poster) | Summary: The paper proposed MBO, a novel method to solve trajectory optimization problems with diffusion models. MBD formulates the optimization problem as a sampling problem and estimates the score function using the dynamic model.
Strengths: - The motivation of the paper is valid. In the trajectory optimization prob... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and suggestions. We address the comments and suggestions as follows:
---
> In the introduction, the authors claimed that existing diffusion-based approaches often require high-quality demonstration data. However, I cannot find any evidence of experiment results... | Summary: This work introduces model-based diffusion (MBD) to solve trajectory optimization (TO) problems without relying on data. The key idea is to explicitly compute the score function by leveraging the model information in TO problems. This paper also shows that MBD has interesting connections to sampling-based opti... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and suggestions. We address the comments and suggestions as follows:
---
> The theoretical derivation and notation in the methodology are quite confusing and hard to follow.
Our approach utilizes Reverse SDE, a technique commonly used in diffusion models, to o... | Summary: This paper presents Model Based Diffusion (MBD), a novel approach to trajectory optimization that leverages a diffusion process. Unlike traditional diffusion models where denoising networks are trained on data using a score matching loss, MBD directly computes the score function for a given target distribution... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and suggestions. We address the comments and suggestions as follows:
---
> The requirement for explicit specification of the cost function, dynamics, and constraints can be a limitation in real-world applications.
The requirement of dynamics and cost is define... | Summary: The paper presents Model-based Diffusion (MBD), a framework to train diffusion models to solve trajectory optimization (TO) problems. In particular, the method assumes that the cost function associated with a TO problem can be evaluated for any trajectory of decision variables (states and control variables), ... | Rebuttal 1:
Rebuttal: We thank reviewer for the comments and suggestions. We address the comments and suggestions as follows:
---
> the paper should better emphasize that the rewards reported in Table 2 are obtained by rolling out RL policies in an online closed-loop fashion.
Thanks for the suggestion.
We will fur... | Rebuttal 1:
Rebuttal: # General Response
We thank all reviewers for their valuable comments.
We want to make the following clarifications and improvements to the manuscript:
## 1. Clarify the Problem Setting when Comparing with RL
We include **PPO/SAC as a performance reference not as a baseline** because there is n... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Calibrating Conformal Prediction | Accept (poster) | Summary: The authors propose a new scheme to obtain prediction intervals that are valid conditioning on the model output. The approach leverages the notion of Perfectly Calibrated Point Predictors and an extension of Venn-Abers calibration to the regression setup.
Strengths: - I like the interpretation of $f(X)$ as a ... | Rebuttal 1:
Rebuttal: ## Response to weaknesses
1. We have added clarification on when our proposed self-calibration objective can approximate feature-conditional validity. Our self-calibration objective aims to provide a relaxation of feature-conditional validity that is feasible in finite samples (Section 2.3). A k... | Summary: The paper proposes a method that jointly calibrates point predictions and provides prediction intervals with valid coverage given the point predictions. This is performed by combining two existing post-hoc processing procedures, Venn-Abers calibration and conformal prediction. The analysis of the method provid... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. **Weakness:** One of the main disadvantages of the proposed approach is the computational complexity... it may not offer significant advantage over other existing methods.
**Response:** While our method has greater computational complexity than the split-CP approach for margi... | Summary: This paper introduces Self-Calibrating Conformal Prediction a novel uncertainty estimation framework combining Venn-abers calibration with conformal prediction. The output provides calibrated point predictions with associated prediction intervals with validity conditional on these model predictions.
Strengths... | Rebuttal 1:
Rebuttal: ## Response to Weaknesses
1. Thank you for this suggestions. In the revised paper, we will include additional real data experiments, specifically the "bike," "bio,", "star", "concrete", and "community" datasets used in CQR reference.
3. In the revised real experiments, we will include CQR as a ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Guiding Exploration for Combinatorial Problems | Accept (poster) | Summary: This paper introduces a self-guiding exploration (SGE) framework that integrates exploration-of-thought, decomposition, and refinement prompting strategies to tackle NP-hard combinatorial optimization problems (COPs). The experiments, conducted on various COPs and several reasoning tasks, demonstrate improved ... | Rebuttal 1:
Rebuttal: __Comment:__ Figure 2 is lacking in detail. A comprehensive example, including detailed prompts and outputs, should be provided.
__Response:__ Thank you for your feedback. Regarding this comment, we aimed to provide a comprehensive textual example, including prompts and model outputs, exactly in ... | Summary: The paper proposes an LLM-based solution for solving standard combinatorial search problems such as TSP and VRP. The proposed solution, called SGE, uses LLMs to (1) propose alternative approaches to solve the problem at hand, (2) decompose a chosen solution approach into subtasks, (3) identify if a given subta... | Rebuttal 1:
Rebuttal: __Comment:__ It is not very clear to me why would one want to use an LLM to solve combinatorial problems.
__Response:__ Thank you for your comment. The use of large language models to solve combinatorial problems offers an advantage by choosing a specific set of heuristics/metaheuristics tailored... | Summary: This paper discusses "Self-Guiding Exploration" (SGE), a new prompting strategy designed to enhance the problem-solving capabilities of Large Language Models (LLMs) in addressing Combinatorial Problems (CPs). The authors demonstrate that SGE leverages the autonomy of LLMs to generate and refine solution paths,... | Rebuttal 1:
Rebuttal: __Comment:__ The computational cost of running multiple explorations and refinements on LLMs is not addressed.
__Response:__ Thank you for bringing this important aspect to our attention. Regarding this comment, we have added a new analysis to address the computational cost of running multiple ex... | null | null | Rebuttal 1:
Rebuttal: __Response to All:__ We would like to extend our sincere thanks to all the reviewers for their valuable work and insightful comments. We carefully considered the feedback provided and made significant improvements to our paper based on your suggestions.
__Addressing Reviewer Concerns:__ One of th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention | Accept (spotlight) | Summary: The authors consider a simplified attention network with shared Query and Key matrices trained with an MSE loss and show a sharp phase transition exists when training this network. In the high-dimensional limit, this paper provides a closed form solution to the training and test loss and shows that a phase tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our work and insightful comments, which we address below.
> Because the results rely on the model reaching the minimum, it is unclear how well these results extend to randomly initialized networks. This is more a problem for studying training dyn... | Summary: This paper introduces a simplifed model of attention and analyzes it theoretically, showing that there exists a phase transition between a paradigm where attention is based mostly on position to one where it is not (which they call "semantic"). I will confess to not being an expert on the methods used and so ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their reading of our work, and the many interesting questions, which we answer below.
> Very dense mathematically, so hard to follow for a reader not intimately familiar with the literature to which it contributes.
We will take advantage of the additional page allowed i... | Summary: The authors state an asymptotic result characterizing the test MSE and training loss in a simplified single-layer model of dot product attention. They apply this result to study a special case in which the target attention function contains a tradeoff parameterized by $\omega$ between positional (i.e. dependen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and their constructive questions, which we address below.
> the stated result applies to a simplified model [...] in terms of [...] the input data.
We have indeed chosen for the sake of clarity to present the results for the simplest insta... | null | null | Rebuttal 1:
Rebuttal: Thank you all for taking the time to read and review our work.
In this global response we post the pdf file containing plots of some preliminary experiments that help to clarify several questions raised in the reviews.
We refer to this global file in the separate responses to each reviewer.
Pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing | Accept (poster) | Summary: The authors propose to extend RFF with parametric transforms of the Fourier features of shift invariant kernels. A path similar to the seminal work of Gretton et al 2007 is followed, where the proposed $\text{HSIC}_\omega$ statistic is shown to converge in distribution to a Gaussian with the true mean (under t... | Rebuttal 1:
Rebuttal: Thank you very much for your positive comments on our work. In what follows, we tried to respond to your concerns, and hope that this feedback is helpful for clear up your concerns.
***W1***: The main weakness of this method is its limitation to translation invariant kernels, which is, however,... | Summary: The paper proposes a novel method of estimating HSIC to determine the independence of two random variables. The original formulation HSIC_b is reformulated and approximated using Monte Carlo integration, HSIC_w, with frequency samples. This reformulation was not derived by the authors but was borrowed from pre... | Rebuttal 1:
Rebuttal: Thanks for your detailed comments. We’ve made a point-point response to your comments. We would appreciate that you can check our feedback. We hope our feedback can clarify most of your concerns, and we are looking forward to your further questions.
***W1***: About criterion J , $c_\alpha$, Typ... | Summary: The paper presents a novel consistent estimator for HSIC, which is computationally efficient, and tries to maximize the testing power under controlled type-1 error. The idea is to begin with a known relation between HSIC and Fourier-based distance between the relevant characteristic functions. The estimator ca... | Rebuttal 1:
Rebuttal: Thanks for your comments. We’ve made a point-point response to your comments. We hope that our feedback can clarify most of your concerns or misunderstandings, and we are looking forward to further discussing with you on any issues about our work.
***W1***: It would have been nice if the introd... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts | Accept (poster) | Summary: This paper proposes a method for upcycling specialized dense models into mixture models. The upcycling is applied to both FFNN and Attention, within a parallel-attention transformer architecture. The evaluation shows their method’s superiority upon a baseline that only upcycles the FFNN layers.
Strengths: * T... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your valuable feedback! We will recap your concerns/questions and address them one by one as follows.
> Concern #1: Specialized models seem not that specialized in their corresponding tasks, making the conclusion less convincing.
Thank you for highlighting this i... | Summary: The paper proposes BAM (Branch-Attend-Mix), a novel approach to improve the training of Mixture of Experts (MoE) models by fully leveraging the parameters of pre-trained dense models. The authors introduce a method to initialize both feed-forward network (FFN) and attention layers from specialized dense models... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your valuable feedback! We will recap your concerns/questions and address them one by one as follows:
> Concern #1: Novelty of this work
We respectfully disagree with the assessment that the combination of BTX and Mixture of Attention is trivial. We list some of the... | Summary: The paper extends previous work (BTX), which combines different expert LLMs into an MoE model by a) building FFN experts, b) averaging the remaining parameters, and c) training a router over the experts. The authors propose BAM, which uses Mixture of Attention (MoA) to consolidate the different attention modu... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your valuable feedback! We will recap your concerns/questions and address them one by one as follows
> Question 1: Can the authors compare FLOP / latency at inference time for both BAM and BTX
Please see our global response for an analysis on inference efficiency. ... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you again for your valuable feedback!
In the attached PDF, we have provided updated experiments and additional ablations.
In addition, see blow for additional analysis on inference efficiency.
# Inference Arithmetic
We analyze the parameter and arithmetic for BTX vs. ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BERTs are Generative In-Context Learners | Accept (poster) | Summary: This paper proposes a finding, indicating that masked language models like BERT can be used as in-context learners. This work suggests that there is potential for a hybrid training, where masked/causal language models can be involved, to take advantage of both objectives. The authors show that masked and gener... | Rebuttal 1:
Rebuttal: Thank you very much for your review!
____
> In Figure 1, the authors demonstrate that DeBERTa outperforms GPT-3 in language understanding. But this is more about the natural advantage of the masked language model. The motivations for providing such experimental data are quite vague to me.
Indeed,... | Summary: This paper argues that masked language models (MLMs) are just as capable as autoregressive language models at in-context learning. To demonstrate this, the authors propose a generative inference technique that allows DeBERTa to generate text, as well as a hybrid autoregressive/MLM pseudo-log-likelihood estimat... | Rebuttal 1:
Rebuttal: Thank you for the review!
____
> The text ranking procedure seems unfair to autoregressive models: the MLMs are given access to the right context, except for the two tokens that directly follow the one being predicted. This would give them unfair advantages on certain tasks that require more long-... | Summary: This paper investigates whether BERT style masked language models can perform in-context learning in multiple LM benchmarks used in the GPT-3 paper, and specifically compares the results of DeBERTa to the GPT-3 model. The paper shows that in multi-choice Q&A, winogrand style quizzes the DeBERTA model can match... | Rebuttal 1:
Rebuttal: Thank you for your review!
____
> The comparison of sampling procedures missing. I would like to see what scores you would get if you calculate logprobs by just summing individual pseudo-likelihoods of the tokens without doing +2 additional mask tokens. Similarly, what happens if you use Wang and ... | Summary: This work proposes a simple modification on masked LMs such that they can be used as a generative way and conduct experiments as generative models. The claim of this work is that, through this modification, masked LMs can be used as in-context learners as GPT-3, adapting to new tasks with further fine-tuning. ... | Rebuttal 1:
Rebuttal: Thank you for the time and effort spent on this review. While we may not completely agree with all your points (see below), they will be very helpful in making the paper more clear in the next version.
____
> The proposed modification may be too simple, and more technical modification is needed to... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toward Semantic Gaze Target Detection | Accept (poster) | Summary: This paper proposes simultaneously detecting a person's gaze location and recognizing the class label of the gaze target. To complete the task, a new architecture is designed. The architecture is efficient when multiple people are present in the image because it processes the scene image once and detects each... | Rebuttal 1:
Rebuttal: **Necessity of $L_{lab}$. It doesn’t improve localization, and the semantics can be predicted in a simpler and more effective way by the baseline**
The $L_{lab}$ loss is necessary for joint training (cf. our reply in the *overall rebuttal* on the motivation for joint training). It is not meant to... | Summary: This paper introduces a novel approach to semantic gaze target detection, extending the traditional gaze following task to include not just localization of where a person is looking, but also identification of what they are looking at. The authors address the limitation of existing gaze following methods that ... | Rebuttal 1:
Rebuttal: **More experimental evidence on computational efficiency. How did we get the 40% decrease in parameters?**
In terms of parameter count, our model features 116M while the baseline has 200M. This is because our model only uses an MLP head (3.3M params) for label prediction while the baseline uses a... | Summary: In this work, the authors propose an enhancement to classical gaze-following prediction, which typically operates on a 2D coordinate level, by integrating semantic label prediction. To this end, they introduce a novel architecture inspired by promotable segmentation, incorporating a multi-input transformer. Th... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the valuable suggestions. Here are our comments:
**Data augmentation for the baseline**
We agree with the reviewer. The baseline variants that are fine-tuned already use data augmentation techniques like flipping and color jittering.
**Use of depth informatio... | Summary: The authors propose an architecture capable of predicting both the gaze target location and the semantic class of a person's gaze target in an image, representing an advancement over traditional methods that only predict the pixel coordinates of gaze fixations. They introduce new benchmark datasets and experim... | Rebuttal 1:
Rebuttal: **Limited improvement compared to Tonini et al. (2023) [46]**
We respectfully disagree with the reviewer. Here are the significant differences
- [46] does not predict a gaze target category (L87-89), it merely uses general object detection as an auxiliary task to improve gaze target localization
... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewers for their thoughtful feedback. We address below the common concerns, and will incorporate the discussion in our final version.
As a reminder, the goal of this paper is to establish the foundation for the novel, significant and challenging task of semantic ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Oracle-Efficient Differentially Private Learning with Public Data | Accept (poster) | Summary: This paper explores methods for private learning by leveraging public unlabeled data. Previous work in this area utilized public data to construct an $\alpha$-covering and then employed an exponential mechanism to output a hypothesis privately. However, the primary drawback of this approach was its exponential... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> If we already have the strong convexity and assume the L is differentiable, can we compute the gradient, run the gradient descent, and reduce the problem to the convex... | Summary: This paper considers the setting of differentially private learning when there is some amount of public data available. A downside of existing algorithms is that they generally use the public data to build a cover which ends up being inefficient. A natural question is to design more efficient algorithms that d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> In Theorem 1, I am not sure what $d$ refers to. Is it a dimension or it refers to something else? In particular, is there a need for the square root since you just wri... | Summary: This paper studies the problem of semi-private learning. In this setting, both public and private data are given while the learning algorithm only has to satisfy differential privacy with respect to the private part. Previous work has showed that leveraging the sample complexity of private learning can be impr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> The resulting sample complexity, though being polynomial, is much higher than previous work.
We agree that the precise polynomial in the sample complexity guarantee ... | Summary: The paper investigates oracle-efficient semi-private learning. It provides a general framework for transforming an efficient non-private learner into an oracle-efficient semi-private learner for smooth data distributions. For convex and Lipschitz loss functions, as well as binary classification loss, they inst... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> Could you discuss whether it's possible to improve this dependence on $\sigma$.
We believe that it is likely that the precise polynomial dependence on problem parame... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper addresses the problem of identifying a particular class of functions which can be learnt efficiently with unlabelled public and labeled private samples maintaining privacy with respect to the private samples only. The authors propose an algorithmic framework for privately learning such function clas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and their helpful comments! Please see responses to individual questions below.
> Why was there a need to create a gaussian process with respect to the auxiliary data in the first place? How would the analysis have gone differently or learnability w... | null | null | null | null | null | null |
Rethinking Fourier Transform from A Basis Functions Perspective for Long-term Time Series Forecasting | Accept (poster) | Summary: The authors propose a Fourier basis mapping model, named FBM, for long-term time series forecasting. FBM embeds the discrete Fourier transform with basis functions, and then introduces a mapping network to replace the inverse discrete Fourier transform. This approach allows FBM to capture implicit frequency fe... | Rebuttal 1:
Rebuttal: We appreciate your constructive suggestions, which will be fully reflected in the final version, including clarifying misreading/misunderstanding. Extra experiment results and the updated Figure 1 are attached in the one-page PDF within the global response for all reviewers.
**Weakness (1)**
Th... | Summary: This work rethinks the discrete Fourier transform from a basis functions perspective, identifying two key issues in existing Fourier-based methods: inconsistent starting cycles and series lengths. To address these, the paper proposes the Fourier basis mapping model (FBM), which leverages Fourier basis expansio... | Rebuttal 1:
Rebuttal: We thank you for your constructive suggestions, which will be fully reflected in the final version. Please refer to the new experiment results in the one-page PDF attached to the global response.
**Weakness W1 and Question Q1**
Table 3 in the PDF shows new short time-series forecasting (STSF) re... | Summary: The paper introduces a novel approach that rethinks the application of the Fourier Transform for time series prediction, proposing a unique Fourier Basis Mapping (FBM) method. It combines both learnable and non-learnable components and demonstrates potential improvements through comprehensive experiments. This... | Rebuttal 1:
Rebuttal: We thank you for your constructive suggestions, which will be fully reflected in the final version. Please also refer to the one-page PDF attached and our reply within the global response to all reviewers for the major changes.
**Weakness 1**
We take your kind suggestion and revise the Introduc... | null | null | Rebuttal 1:
Rebuttal: **Please refer to the one-page PDF file attached here for more information.**
We thank all reviewers for constructive comments and suggestions, and expect positive feedback on the unique innovation, extensive experiments, and competitive performance. During the rebuttal, we have addressed all co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generative Fractional Diffusion Models | Accept (poster) | Summary: The authors introduce a new type of continuous score-based generative models relying on fractional diffusion, a type of diffusion where Brownian Motion BM is replaced by its fractional counterpart fBM, where noise increments or either positively correlated (Hurst index $1 > H > 1/2$) or negatively correlated (... | Rebuttal 1:
Rebuttal: Dear Reviewer kZkj,
Thanks for the insightful comments, valuable feedback and very precise and thorough engagement with our work. See below for detailed answers to your questions and concerns:
>Performance gains are mildly convincing and the effect of the varying hyper-parameters are not that ... | Summary: The work proposes a theoretical framework for training score-based diffusion models based on fractional brownian motion.
Authors provide an approximation framework where fBM being approximated by Markovian noise to derive score-matching in Markov approximation.
Theoretical derivation is supplemented by exper... | Rebuttal 1:
Rebuttal: Dear Reviewer HxVk,
We thank the reviewer for their valuable feedback. Our detailed responses follow:
> It feels that motivation for why we should use fBM is not well elaborated
Having control over the diffusion trajectories are crucial for obtaining a trade-off between generation quality and... | Summary: The paper replaces Brownian motion in diffusion models with fractional Brownian motion.
Strengths: The mathematical description of fractional Brownian motion is clear.
Weaknesses: 1. The paper has very thin numerical experiments. The results do not adequately justify why replacing BM with fractional BM is pr... | Rebuttal 1:
Comment: We appreciate the reviewer's comments and feedback. However, we believe the strong criticism is excessively harsh and unjustified. The absence of a summary or acknowledgment of our method's strengths raises concerns about the reviewer's full understanding of our work. Below, we address the specific... | Summary: The paper proposed diffusion models where Brownian motions are generalized by fractional Brownian motions - a correlated noise process. Due to the non-Markovian property of fractional Brownian motions, the paper suggests approximating it using a linear combination of semimartingale processes. The key insight o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and truly insightful and thoughtful questions, which highlight the potential contributions of our work. We address specific comments and questions below:
>I find the discussion in the experiments in the main text does not give much insight. What ... | Rebuttal 1:
Rebuttal: Dear Reviewers and Respected Area Chair,
We would like to express our gratitude for your feedback.
We appreciate the reviewers' acknowledgement of the novelty of our method for the controlled use of fractional Brownian motion in diffusion models. We are also grateful for the appreciation review... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection | Accept (poster) | Summary: This paper proposes a novel method for finetuning a classifier for both OOD generalization and OOD detection. The paper presents a novel objective function for OOD generalization and detection based on the Bayesian framework and OOD generalization theory. Extensive experimental results suggest that the propose... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the importance of our research problem, clear exposition, sound theoretical analysis, and promising results. We appreciate your support and constructive suggestions and address your concerns as follows.
- **W1.** It is possible that I missed it, but the paper... | Summary: This paper theoretically characterizes the underlying dilemma in SOTA OOD detection method. The authors find that OOD detection performance of state-of-the-art methods is achieved with tradeoff between OOD detection and classification. Accordingly, the authors provide an uncertainty-based strategy which decou... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable comments and appreciate your recognition of the interesting findings, important and enlightening theoretical results as well as sufficient experiments. We provide detailed responses to address your concerns.
- **W1.** I am not sure are there any e... | Summary: This paper reveals the trade-off dilemma of OOD detection and OOD generalization for current SOTA OOD detectors from both theoretical and empirical perspectives. The authors employ a transfer learning framework to analyze the generalization error bound for MSP-based OOD detectors and identify the optimization ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable comments and appreciate your recognition of the clear presentation and effective method. We provide detailed responses to the constructive comments.
- **W1.** The paper only discusses the MSP- and Energy-based OOD detectors, which are both adapted... | Summary: This paper addresses out-of-distribution (OOD) detection and generalization problems. The authors show the sensitive-robust dilemma in learning objectives of OOD detection and generalization and propose a decoupled uncertainty learning (DUL) method to harmonize the above conflict. The proposed method only enco... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and thorough comments on our paper and for recognizing the contribution of our theoretical analysis and the superiority of our DUL method over current SoTA methods.
- **W1.** While analyzing the sensitive-robust dilemma via transfer learning theory is nove... | Rebuttal 1:
Rebuttal: Dear PCs, SACs, ACs, and Reviewers,
Thanks for your valuable feedback and insightful reviews, which greatly improved the paper. We are deeply encouraged that all the reviewers gave positive assessment on our work. This is a **clear** and **well-presented** (Reviewer 31hb, Tcyq) manuscript with a ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Symbolic Regression with a Learned Concept Library | Accept (poster) | Summary: This paper proposes a way to incorporate LLM prompting to improve symbolic regression. They uses PySR, a standard symbolic regression library, as their base SR algorithm. Then they add LLM prompts to different SR algorithm steps: population mutation, crossover, and initialization. They replace the PySR impleme... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work! We address your questions inline:
> **It's not clear how valuable the concept abstraction approach is compared to some "baseline" of simple LLM prompting. For example, one baseline could just use a single concept "This expression is a good formula... | Summary: This paper introduces a method that learns a library of concepts (natural language description of helpful and unhelpful concepts) as a means of guiding genetic search for symbolic regression. The core idea is that such concepts can be used to bias genetic operations through an LLM.
The method was evaluated on... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work! We address all questions inline:
> **Claims that need to be fixed.**
We would be happy to adjust these claims along the lines you suggest. Thank you for pointing this out!
> **In line 83, shouldn't it be P(C) instead of P_C?**
Good catch! Fixe... | Summary: This work focus on symbolic regression. They enhaned the traditional method like genetic algorithms by inducting a library of abstract textual concepts. The algorithm, called LASR, uses zero-shot queries to a large language model to discover and evolve concepts occurring in known high-performing model to disco... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work! We address questions inline:
> **Introduction (...) algorithms.**
We agree that LLMs might require additional compute, which can raise the cost. However, this is a worthwhile tradeoff for scientific discovery for a couple of reasons:
- The expe... | Summary: This paper introduces LASR, a symbolic regression framework that enhances PySR by incorporating Large Language Models (LLMs) to discover and evolve "concepts" from high-performing equations. These concepts are then used to guide the search process. LASR is evaluated on the Feynman equations dataset and a set o... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work! We address questions inline:
> **Could you provide the 7 additional equations that LASR solves ...?**
We’ve added a table containing these equations and the ground truth equation to the global PDF for figures.
> **Are these 7 equations consiste... | Rebuttal 1:
Rebuttal: We’d like to thank the reviewers for their thoughtful comments and suggestions. We've incorporated many suggested changes in our manuscript and will update the PDF whenenver the portal opens up next.
Multiple reviewers expressed concerns about data leakage from LLMs. While this is a valid concer... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits | Accept (poster) | Summary: The paper explores strategies for maximizing the cumulative reward in repeated second-price auctions, where agents draw their values from an unknown distribution. The problem is modeled as a multi-armed bandit (MAB) scenario with structured arms, where the decision-maker selects a subset of agents to compete i... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and questions. As you noticed, our main contributions consist in providing theoretically sound algorithms for the problem that we introduce, under some assumptions that we motivate. Our simulations allow to check
that our theoretical insights are valid when these... | Summary: This paper studies the problem of repeatedly selecting the number of agents to form a coalition against the environment to maximize the cumulative reward in second-price auctions. Specifically, the paper supposes that all bidders are identical with unknown value distribution $F$, and in each round $t$, $n_t$ b... | Rebuttal 1:
Rebuttal: Thank you for your careful review. First, regarding the last
sentence of your summary, we want to precise that for Greedy-Grid (GG) the regret bound we
obtain is **the minimum** between
$\sqrt{(\log_2(N)+|\mathcal{B^\star}|)T}$ and a problem-dependent constant
(independent of $T$). We answer your ... | Summary: This paper studies repeated second-price auctions with ex-ante bidder coalition. There are two groups of bidders, one of size N and one of size p. At each period $t$, a decision maker can choose $n_t$ out of the N bidders to compete with the other p bidders in an auction. Crucially, the decision maker chooses ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and suggestions, as well as for appreciating the key contributions of our work. Regarding the unimodality assumption (W1), we answer in a general comment for all reviewers. Regarding the symmetry of bidders, see the paragraph (Q3) below.
(Q1) We apologize for th... | null | null | Rebuttal 1:
Rebuttal: **General comment** We want to thank all the reviewers for their careful examination of our paper. We appreciate that all reviewers seem enthusiastic about the problem that we introduce, the algorithms that we propose and the theoretical contributions presented in our work.
**Unimodality of the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model Sensitivity Aware Continual Learning | Accept (poster) | Summary: The paper "Model Sensitivity Aware Continual Learning" introduces a novel approach to continual learning (CL) that addresses the trade-off between retaining previously acquired knowledge and excelling in new task performance. Traditional CL models often face the dilemma of catastrophic forgetting or overfittin... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable suggestions and would like to express our gratitude.
**Q1** computational complexity of the proposed method and Fisher Information Matrix (FIM) for large-scale applications.
**A1** Thank you for your question! Although the FIM calculation is costly, our met... | Summary: The paper presents a new method to address catastrophic forgetting and improve the learning ability of a new task in continual learning. They attribute these two objectives to parameter sensitivity. To address this problem, they propose to minimize the performance on the worst-case CL parameter distribution wi... | Rebuttal 1:
Rebuttal: We deeply appreciate the thoughtfulness of your feedback and support!
**Q1** whether the assumption of CL model parameters following a normal distribution could be valid for all models.
**A1** Thank you for your question!
* The assumption that model parameters follow a Gaussian distribution i... | Summary: This paper presents a min-max optimization framework targeting at the model sensitivity for continual learning, where the authors claim that it can mitigate the abrupt change of the model parameters and thus simultaneously alleviates the problem of catastrophic forgetting of the past knowledge and overfitting ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your valuable comments.
**Q1** treating current continual learning objective as a whole without discussing the interplays among the different tasks or the data scarcity problem of the previous tasks. why CL specifically benefits from this algori... | Summary: The paper proposes a new perspective on stability-plasticity trade-off in continual learning, centered on controlling model sensitivity to model updates. The goal is then to ensure that alteration in model parameters does not negatively impact the CL performance of the model.
In order to solve this challengi... | Rebuttal 1:
Rebuttal: We extend our sincere appreciation for your constructive feedback!
**Q1** 5-datasets evaluation
**A1** Thank you for your suggestions! We perform experiments on 5-datasets (memory buffer size of 500) with our method (MACL) as below.
| Method | Class-IL | Task-IL |
| -------- | -------- | ------... | Rebuttal 1:
Rebuttal: # Global Response
**Q1** Online Continual Learning setting evaluation.
**A1** Thank you for your suggestions! We conduct experiment in the online continual learning setting on the SOTA approach, MKD with PCR [1,2]. We follow the same dataset split and hyperparameter setting as [1]. The resu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Genetic-guided GFlowNets for Sample Efficient Molecular Optimization | Accept (poster) | Summary: In this paper, the authors present a sample-efficient molecular optimization method using GFlowNets and a genetic algorithm. The authors demonstrate its effectiveness in generating inhibitors against SARS-CoV-2, with fewer reward calls than other methods, as well as on the PMO benchmark, where the method outpe... | Rebuttal 1:
Rebuttal: ***Thanks for the valuable comments.*** We address concerns as follows.
> Nonetheless, I believe that some of the other methods shown in Figure 3 also have that feature (e.g., REINVENT4 also has a controllable temperature parameter), which many be misleading in the current text/plot that suggests... | Summary: This paper presents a novel approach called Genetic-guided GFlowNets (Genetic GFN) for sample-efficient molecular optimization. The method integrates domain-specific genetic algorithms to guide a GFlowNet policy toward higher-reward molecular samples.
Strengths: - The paper is very pedagogical and easy to rea... | Rebuttal 1:
Rebuttal: ***Thanks for the valuable comments.***
> The proposed method is limited to molecular optimization and is not readily generalizable to other domains.
Our method focuses on integrating strong domain-specific search heuristics into deep neural network policies using the off-policy nature of GFlowN... | Summary: This paper proposed a combination of genetic algorithm (GA) and GFlowNets for molecular optimization, with an emphasis on sample efficiency (achieving high property value by few number of reward calls). The key motivation is that GA can incorporate domain specific knowledge by designing the mutation operations... | Rebuttal 1:
Rebuttal: ***Thanks for the valuable comments.*** We address concerns and questions as follows.
> One weakness is the gap between "domain-specific knowledge" and "GA method" mentioned in the introduction. When I read the introduction, I am expecting the authors will propose some new genetic operators... It... | Summary: This work proposes a variant of GFlowNet, called genetic GFN, for molecular property optimization. Specifically, the authors use genetic search to guide the GFlowNet to explore high-reward regions, addressing the over-exploration issue in GFlowNet. Besides, the authors incorporate some effective training strat... | Rebuttal 1:
Rebuttal: ***Thanks for the valuable comments.*** We address concerns as follows.
> The novelty is somewhat limited, as the proposed Genetic GFN simply combines some existing techniques, that have been studied independently.
This method is novel because it is **the first to combine 1D sequence generation ... | Rebuttal 1:
Rebuttal: ## General Response
We are sincerely grateful to the reviewers for their valuable feedback on our manuscript. We are pleased to hear that the reviewers found our paper **well-written** (4tbn, gNyQ, kTKB, FCer) and supported by **extensive experiments with state-of-the-art performance** (4tbn, gNy... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation | Accept (poster) | Summary: The paper "Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation" introduces a framework called CMCD to address data sparsity in cognitive diagnosis, which affects accuracy and fairness. The authors integrate the monotonicity assumption to ensure data augmentation remains interpretable.... | Rebuttal 1:
Rebuttal: > **Q1: Validation on Diverse Datasets: Have you considered validating your model on more diverse and larger datasets to enhance the robustness and generalizability of your findings?**
A1: Thank you for your suggestion. Firstly, we would like to clarify that the two datasets used in this paper ar... | Summary: This paper focuses on fairness and accuracy issues in Cognitive Diagnosis. Unlike model-based methods, the approach in this paper tackles these issues from a data-driven perspective. Leveraging the unique monotonicity assumption in cognitive diagnosis, the authors propose a general monotonic data augmentation ... | Rebuttal 1:
Rebuttal: > **Q1: The relationship between the two proposed constraints for data augmentation**
A1: Thank you for your explanation. The constraints of these two data augmentations reflect the two states in the monotonicity assumption: when a student answers a question correctly, their ability is assumed to... | Summary: This paper discusses the issues of fairness and accuracy in cognitive diagnostic tasks, which hold significant societal value and impact the fairness of education. Through an experimental perspective, the paper analyzes how data sparsity can lead to unfairness and inaccuracy in cognitive diagnostic tasks. It a... | Rebuttal 1:
Rebuttal: > **W1: I noticed that the experimental results at the end show an improvement in both the fairness and accuracy of diagnostics simultaneously, which seems to contradict the prevailing trade-off between fairness and accuracy. It would be beneficial for the paper to delve deeper into this issue, ex... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' time and efforts in reviewing our paper. We extend our gratitude to each of them for offering constructive and valuable feedback, which we will use to enhance this work. Meanwhile, we are encouraged by the positive comments from reviewers, including:
* **Mo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models | Accept (poster) | Summary: The paper explores the training efficiencies and model performance of latent diffusion models (LDMs) by reimplementing and analyzing various previously published models. The study introduces a novel conditioning mechanism that separates semantic and control metadata inputs, significantly improving class-condit... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and are glad they found our paper a _good advancement_ to the state of the art. In the following, we respond to the points raised:
- **Lack of small-scale experiments.** The objective of our work is to increase understanding of the design choices in state-of-t... | Summary: This paper's contributions can be divided into three main points:
1. Proposing better methods for metadata/semantic level conditioning:
- Instead of using AdaLN, class information is fed into the model through attention.
- Adjusting the strength of meta conditions related to low-level augmentation accor... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. In the following we address it:
- **Unorganized Experiment Result Presentation.** We appreciate the reviewer's suggestion to adopt a table as in Karras et al (2023) to enhance the presentation of our contributions. We have added Table R1 in the att... | Summary: This paper studies how to effectively condition image size and crop information, as done by Stable Diffusion XL, and how to implement an effective coarse-to-fine training strategy. For control conditioning, it is designed to be less entangled than traditional methods, resulting in better performance for the sa... | Rebuttal 1:
Rebuttal: We appreciate the thorough feedback. In the following we address it:
- **Design choices**
- **Noisy replicate padding has minor impact.** In addition to slightly improving the FID and CLIPScore (0.54 and 0.58, respectively), the added noisy replicate can serve as a regularizer with no added c... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for thoughtful reviews and precious feedback. We are glad they appreciated our work under many aspects. In particular, reviewers **cMfV**, **rR4N** highlighted our contribution of a much needed apple-to-apple comparison among SOTA models, while **JPTZ** and **cMfV** emphasiz... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Probabilistic Analysis of Stable Matching in Large Markets with Siblings | Reject | Summary: The paper describes the problem of matching students to daycare centers, with each family allowed to express preferences about the joint allocation of all siblings within the family. The authors present a modified notion of a stable matching, in which a family may choose to withdraw one of its children from a ... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed comments and questions.
Q1
We fully understand your concern regarding the suitability of this paper for a top-tier ML conference. We have chosen the Theory area, specifically Algorithmic Game Theory, where stable matchings have demonstrated significant applicat... | Summary: The authors study a variant of the many-to-one matching problem called the stable matching problem with siblings, which generalizes the stable matching problem with couples. In this problem, some families $f \in F$ may have more than one and at most $k$ siblings, ordered by age $(c_1,\dots,c_k)$. Each family $... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed comments and questions.
Here are the changes based on your suggestions.
Line 97: each child c is associated with one family, denoted as f_c
Line 98: each family f is associated with a set of children, denoted as C_f
Line 99: C_f = (c1, … , ck).
Line 107: I... | Summary: This paper introduces the problem of daycare matching with siblings, an extension of matching with couples. Here, children in families (of size 1 or larger) are matched to daycares. Families have ranked preferences over the tuples of daycares their children end up at (since their preference for one child at on... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed comments and questions.
Q1-Q3:
Once a family f associated with several children is inserted into the market, it may result in the rejection of some children without siblings. If we only check family f’s preferences once, we might overlook a better assignment ... | Summary: This paper studies the existence of a stable daycare-children matching in the presence of siblings from the same families with same preferences over the daycares. The authors particularly study the case when the daycares have similar preferences over the set of children, and the market size is large. They prop... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed comments and questions.
Q1 and Q2:
We are actively collaborating with multiple municipalities in Japan. In current daycare markets, each municipality establishes a unique and complex priority scoring system that is publicly accessible. Typically, children fro... | Rebuttal 1:
Rebuttal: We appreciate the efforts of all the reviewers and their valuable feedback. We are pleased to address their suggestions, which are detailed in our individual responses.
1. One notable feature we observed in the Japanese daycare matching market is the similarity of priority scores for each child a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Differentiable Structure Learning with Partial Orders | Accept (poster) | Summary: This paper studies the problem of learning DAGs from observational data with incorporating prior knowledge represented as partial order constraints by extending from existing continuous DAG learning methods such as NOTEARS and DAGMA. The paper first presents a method that shuts down the corresponding cells of ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful questions. Here are our responses:
1. **Comparison with Eq. 8a:**
We have implemented Eq. 8 and compared it with our augmented acyclicity-based method. The results (average of 3 repetitions) using NOTEARS are reported here. Settings are: ER-... | Summary: This paper contributes interesting new theoretical results for the field of differentiable graph structure learning and showcases how to practically exploit those results for improved structure learning. Differentiable structure learning converts the combinatorial optimisation problem of finding the correct gr... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and invaluable questions. We begin with addressing your major question on the proof part in the paper (point 2 in the Question section). Then, we will respond to your other questions point-by-point.
**Regarding Eq. 8 and the comparison results:** We have impleme... | Summary: The paper provides a solution for imposing partial ordering information into differentiable DAG learning, which is a very important problem. It also proposes an efficient implementation based on rigorous theoretical justification. With this prior information, even with fewer samples, better structural recovery... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable questions. Here are our responses:
**1. Explanation on the Difficulty of Integrating Partial Order Constraints**
The statement "Merely forbidding edges that violate $ \mathcal{O}^+ $ is insufficient for compliance, as it is possible to *walk* from ... | Summary: This paper introduces an approach to integrate partial order constraints into differentiable structure learning for causal discovery. The key contributions are:
* Formulating an equivalent constraint set of path prohibitions to implement partial order constraints in the graph space.
* Proposing an efficient me... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback. We begin by addressing your critical point regarding the analysis of the hyper-parameter $\tau$ as highlighted in Weakness 5. Following this, we will address your other questions and concerns. Here are our responses:
**Weakness 5: Analysis... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization | Accept (poster) | Summary: This work studies distributed model training with a parameter server; contributions are theoretical:
* **Assumptions**: $L$-smooth convex objectives, stochastic gradient estimates, heterogeneous worker distributions, bounded difference in expected gradients of local workers' objectives at a global minima, deno... | Rebuttal 1:
Rebuttal: **Response to Reviewer mxJX**
Thank you for your supportive review, below we address the points that you have raised.
**Regarding weaknesses**
**Q:** adding experiments
**A:** We have added experiments that demonstrate the benefit of our approach and corroborate our theoretical findings. ... | Summary: This paper introduces a new federated learning algorithm called Slowcal-SGD, which basically introduces anytime-SGD into federated learning setting. The authors provide solid convergence analysis and fruitful insights on the new algorithm. They show the algorithm can provably beat both mini-batch SGD and local... | Rebuttal 1:
Rebuttal: **Response to Reviewer 5jSD**
Thank you for your supportive review, below we address the points that you have raised.
**Regarding weaknesses**
**Q:** adding experiments
**A:** We have added experiments that demonstrate the benefit of our approach and corroborate our theoretical findings. P... | Summary: This paper proposes SLowcal-SGD, which is a distributed learning algorithm that builds on customizing a recent technique for incorporating a slowly-changing sequence of query points, which in turn enables to better mitigate the bias induced by the local updates. Theoretical proof is given on the proposed algor... | Rebuttal 1:
Rebuttal: **Response to Reviewer 3Xai**
Thank you for your comments, we address your concerns below and kindly ask you to raise your score accordingly.
**Regarding weaknesses**
**Q:** adding experiments
**A:** We have added experiments that demonstrate the benefit of our approach and corroborate our ... | null | null | Rebuttal 1:
Rebuttal: **Dear reviewers**,
We have now added experimental results comparing our approach to several baselines. Our results showcase the practicality and benefit of our approach and complement our theoretical findings.
We have conducted experiments on the MNIST dataset, which is a widely used benchma... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Matryoshka Query Transformer for Large Vision-Language Models | Accept (poster) | Summary: This paper introduces a new concept called Matryoshka Query Transformer (MQT) that brings in the concept of Matryoshka information packing to make visual tokens flexible and can be used in multimodal vision language models like LLaVA. The reduced tokens tend to reduce the quadratic complexity of the language m... | Rebuttal 1:
Rebuttal: Thank you for your review!
1. Measure of TFLOPs
We measured the TFLOPs by incorporating everything, i.e, including the cost of vision encoder and LLMs. Thank you for pointing out, we would like to incorporate this detail in our revised version.
2. Scenario of just obtain 256 tokens and take t... | Summary: Authors try to use Matryoshka mechanism to guide the learning process of LVLMs such as llava.
Authors show a pretty good scaling curve with different amount of visual tokens.
Strengths: 1. The idea is interesting.
2. The presentation is clear.
3. The attention visualization is interesting.
Weaknesses: 1.... | Rebuttal 1:
Rebuttal: Thank you for your review!
1. What scale to choose for the best trade-off?
The trade-off visualization is from Figure 5 and the complete version is from Figure 8 in our paper’s appendix. For the best tradeoff, as we mentioned in line 215, we observed a ''turning point'' in many benchmarks. This... | Summary: The paper considers multimodal vision transformers, in which we have a stream of both visual and textual tokens. Current architecture typically assume a fixed number $m$ of visual tokens. In contrast, the proposed `MQT` aims to achieves a dynamic number of visual tokens. This is done by integrating a form of c... | Rebuttal 1:
Rebuttal: Thank you for your review!
1. Comparison with dynamic baselines.
For comparison with LLaVA-PruMerge, please refer to our Table 1 in general response. In summary,
Our model significantly outperformed LLaVA-PruMerge in **5 out of 6 tasks**, and by a **2.3** absolute score on average. Moreover, ... | Summary: This paper addresses the challenge of achieving flexibility in the number of visual tokens to suit different tasks and computational resources. Inspired by Matryoshka Representation Learning (MRL), the authors propose the Matryoshka Query Transformer (MQT), which allows for any number of visual tokens during i... | Rebuttal 1:
Rebuttal: Thank you for your review!
1. How many layers does MQT transformer need as compared to Q-Former?
We studied the layers for MQT transformers at the early stage of our research. We didn’t find improved performance with multiple layers. This similar one layer design is also employed by Qwen-VL (... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and their constructive reviews and suggestions. We are encouraged that the reviewers find that:
(1) Our Matryoshka Query transformer is interesting (Reviewer k14Y) and showing **incredible benefits and is a worthy contribution** (Reviewer L7Jp)... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Conditional Independence Test in the Presence of Discretization | Reject | Summary: The authors proposes a method for testing conditional independence in presence of discretisation.
They assume the variables to be jointly Gaussian, and that some of them are accessible only after discretisation; thus the data contain a mix of discrete and continuous variables.
Discretization might remove some ... | Rebuttal 1:
Rebuttal: >Q1: I am skeptical about the specific research question addressed. X1 and X3 are independent given X2; yet they might be not independent given the discretised version of X2.
A1: Thanks for raising the confusion and we would like to use this opportunity to state our motivation. Just as illustrat... | Summary: Authors propose a test for conditional independence in case of discretized variables, i.e. variables that originally were defined over a continuous domain and are then mapped to a discrete domain. In this case, a binary domain. Authors propose to bridge the unobserved continuous variables with the observed dis... | Rebuttal 1:
Rebuttal: >Q1: I would do more experiments
A1: Thanks for your positive feedback. Based on the suggestions from reviewer otB5 and reviewer dvXS, we have supplemented additional experiments to more comprehensively evaluate the power of the proposed test in small sample sizes and included some additional bas... | Summary: This paper presents a novel statistical method for testing conditional independence (CI) when some of the data is discretized. Initially, the authors introduce bridge equations to estimate covariance and establish asymptotic normality, facilitating an unconditional independence test. For the conditional indepe... | Rebuttal 1:
Rebuttal: >Q1: Assumption of a multivariate Gaussian distribution
A1: Thank you for your valuable question. We acknowledge that the assumption of multivariate Gaussian distributions can limit the generality of the proposed test. However, we would like to share a few points regarding its reasonability:
1. *... | Summary: The paper addresses a critical issue in Conditional Independence (CI) testing methods, specifically when the available data is a discretized version of the original continuous data. Traditional CI testing methods often assume that discretized observations can directly substitute for continuous variables, leadi... | Rebuttal 1:
Rebuttal: >Q1 : Assumption of Multivariate Normality, How would the method perform with unkown or non-normal variables
A1: Thank you for your question. We appreciate your insightful feedback. We acknowledge that the assumption of multivariate Gaussian distributions can limit the generality of the proposed ... | Rebuttal 1:
Rebuttal: Dear **Reviewer otB5, szaU, Q2XK, dVXS**, We deeply appreciate the time and effort you have invested in evaluating our work. Your insightful feedback has significantly contributed to improving the quality of our paper. It's encouraging that **Reviewer otB5, Q2XK** acknowledge we are targeting a *... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching | Accept (poster) | Summary: The paper highlights the limitations of current time series forecasting models based on patching techniques. Existing models rely on patching to handle long sequences, but this approach is not suitable for all forecasting tasks. To overcome these limitations, DeformableTST introduces a Deformable Attention mec... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer Qoam for providing thorough insightful comments.
> W1 & Q2: Concerns about the novelty. While the paper is reasonable and convincing, it appears somewhat lacking in terms of novelty, since new attention mechanism has been proposed from past to the presently.
We'd like to... | Summary: The paper presents a novel approach for time series forecasting that relies less on patching. The authors incorporate deformable attention capturing important temporal information and a hierarchical structure that reduces memory consumption. The authors verify the effectiveness of their framework across multip... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer reZp for the detailed and insightful review.
> Q1: Supports for our claims
Due to the page limitation, please refer to **Global Response**.
> Q2: Add an explanation for "important points" in the main text.
+ We will explain the concept of "important points" in Section 1 ... | Summary: The paper introduces a new transformer architecture for time series forecasting that does not necessarily depend on patching, resulting in consistent performance improvements over all baselines. Although the approach primarily extends existing work, its simplicity and potential for widespread adoption in vario... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer fiTw for providing a detailed review and insightful questions.
> W1 & Q4: Experiments on synthetic dataset to prove our model can handle both uniform and clustered attention distribution, especially the clustered ones (centralized attention in localized areas).
+ Thanks fo... | Summary: This paper presents a Time Series Forecasting method DeformableTST that makes patching replaceable in transformer with sparse attention, using a hierarchical architecture to avoid memory issues. Experiments are performed on 8 data sets and the proposed method is compared with variety of SOTA methods.
Strength... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer eY9h for the thorough and detailed comments.
> Concern about contributions.
We'd like to highlight our contributions in the following points:
+ Our method **differs from** previous Transformer-based forecasters for its **better applicability and less reliance on patching*... | Rebuttal 1:
Rebuttal: > **Global Response**
We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for us to improve our paper further.
In this paper, we expose the significant problem of over-reliance on patching in latest Transformer-based forecasters.
Based ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate | Accept (poster) | Summary: The authors proposed a novel regularization approach called Learning from Teaching (LoT) to enhance generalization. The hypothesis that simple correlations are generalizable is the main question for this work. Through a teacher student approach, the authors are capable of provide to the main model more general... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the novelty of our method and the comprehensiveness of our experiments.
> W1: further evidences of the proposed hypothesis.
Thank you for your request for further evidence supporting our hypothesis.
We have provided additional experimental results to validate our hyp... | Summary: This paper proposes the LOT (Learning from Teaching) regularization technique, which employs auxiliary student learners to help the main model capture these more generalizable correlations. The authors hypothesize that generalizable correlations are expected to be easier to imitate, and LOT operationalizes thi... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty of our regularization method and acknowledging that our experiments demonstrate the effectiveness and efficiency of LoT in identifying generalizable information. Below, we provide detailed responses.
> W1: previous studies have shown that more complex data an... | Summary: The paper introduces Learning from Teaching (LOT), a regularization technique to enhance the generalization capabilities of deep neural networks. LOT uses separate student models trained by inmate the prediction of the teacher model to provide feedback, promoting the capture of generalizable and imitable corre... | Rebuttal 1:
Rebuttal: We appreciate your recognition of the novelty and significant improvements brought by LoT, as well as the clearly articulated presentation. Below, we provide detailed responses.
> W1: It will be the best to Include more comparisons with other regularization methods, or other recent advances in k... | null | null | Rebuttal 1:
Rebuttal: **Highlighted General Response**
We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. We also thank all reviewers for the insightful and constructive suggestions, which helped a lot in further improving our paper. In addition to our point-by-point responses below, we pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views | Accept (poster) | Summary: This paper proposes a framework of generalizable 3DGS for 360 degree of NVS. The framework comprises of two main components, one is similar to MVSplat that combines multi-view context image information, the other uses a pre-trained diffusion model for post-processing, which is conditioned on the rendered featu... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer 3TXW (R4)**
### **Q1: Limited technical contributions.**
A1: Kindly refer to the global response to all reviewers for more discussions regarding our contributions.
### **Q2: The scores of quantitative results on DL3DV are smaller than expected.**
A2: The... | Summary: This paper aims to advance 360° novel view synthesis from sparse observations in wild scene scenarios. The key idea is to utilize the improved MVSplat for coarse geometry, refined by a stable video diffusion model to enhance appearance. This differs from prior work that, due to sparse viewpoint inputs, resulte... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer pb1U (R3)**
### **Q1: Unsatisfactory multi-view consistency in complex scenes.**
A1:
1) The setting is extremely challenging: as verified by all provided visual results, MVSplat360 is the only approach that can provide reasonably good results on occluded and inv... | Summary: This paper proposes MVSplat360, a generalized sparse-view novel view synthesis method. MVSplat360 utilizes the Stable Video Diffusion model to guess the novel views besides input sparse views.
Strengths: 1. MVSplat360 achieves better novel view synthesis with sparse input views by introducing the stable video... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer RhQM (R2)**
### **Q1: Limited contributions: engineering via adding SVD**
A1: Please refer to the global response to all reviewers for more detailed discussions of our main contributions.
---
Rebuttal 2:
Comment: Dear Reviewer RhQM,
Did we satisfactorily answe... | Summary: The paper proposes MVSplat360, a method for wide-sweepign or 360-degree novel view synthesis on general scenes from sparse input views. It extends an existing state-of-the-art approach MVSplat to render 3D feature Gaussians as conditioning for a refinement network in form of a pre-trained video diffusion model... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer 4RiL (R1)**
### **Q1: “Benchmarking” in the paper title is inaccurate.**
A1: To make the title reflect our contributions more accurately, we decided to change the title to “MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views”.
Our initial intention ... | Rebuttal 1:
Rebuttal: ## **Global Response to All Reviewers**
We thank all reviewers for their constructive comments. We are encouraged by the appraising comments "the problem definition and main approach explained well", "the evaluation validate the effectiveness of MVSplat360" (**4RiL**), "achieves better NV... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeuMA: Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics | Accept (poster) | Summary: This paper introduces a novel pipeline to train the neural constitutive model via image supervisions. It corrects the existing physical material law with a kind of residual learning, and supervise the parameters by aligning groundtruth images the rendered images which are pictured through differentiable render... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work. Your feedback is instrumental in strengthening our paper. Here are our responses to your concerns.
> W1: The dataset should be described in more detail. For example, the number of views to initialize the scene, and the number of views for the subsequent optimizat... | Summary: This paper proposes the Neural Material Adaptor (NeuMA), a framework which integrates existing physical laws with learned corrections, thus facilitating the accurate learning of actual dynamics, while also maintaining generalizability and interpretability of physical priors. The framework also proposes Particl... | Rebuttal 1:
Rebuttal: Thank you for your supportive evaluation of our work. We provide the answers to your question below.
> Q1: Does your framework involve the same number of particles if the objects do not deform in normal situations?
Yes. Following the common practice in differentiable physics [25,44,50,88], our m... | Summary: The authors propose a new framework to introduce motion dynamics with residuals using a single view on top of a 3D Gaussian splatting based reconstruction of an object obtained from multiple views from the same camera. This approach models motion using physical laws and learned residuals which allows for inter... | Rebuttal 1:
Rebuttal: Thanks for identifying our work and providing valuable comments. We try to address your concerns below.
> W1: The method appears to not be evaluated on real-world data where assumptions about motion dynamics may not hold perfectly. It would be interesting to visualize the motion residuals and qua... | Summary: NeuMA is a technique to learn residuals on top of physics models to better capture intrinsic dynamics of non-rigid materials. The paper uses Gaussian splatting (GS) to obtain differentiable rendering, and update the NeuMA model by minimizing reconstruction error of visual observations. The paper also propose... | Rebuttal 1:
Rebuttal: We are grateful for your positive and constructive comments, and try to address your concerns below.
> W1: It is a bit unclear to me how the generalization to novel objects and object interaction (in section 4.4) should be evaluated ... Can these experiments be quantified and compared to other me... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their time and efforts on reviewing the paper. We are excited to see that reviewers recognized the novelty of our technical contribution (Reviewer TxrR, zoZv, Rwta), acknowledged a better performance achieved by our method over baselines (Reviewer se8S, TxrR, z... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
$\textit{NeuroPath}$: A Neural Pathway Transformer for Joining the Dots of Human Connectomes | Accept (poster) | Summary: In this work, the authors propose NeuroPath, a graph transformer acting on structural (SC) and functional (FC) connectivity matrices. NeuroPath aims to learn the relationship between these pathways and brain functions. The framework uses a twin-branch design to generate coupled features of multi-hop neural pat... | Rebuttal 1:
Rebuttal: **W1, Q1, and Q2:** Consistent thresholds are applied across all experiments and t-tests in our manuscript for all models and baselines. Thresholds for FC and SC are consistently set as 0.5 and 0.1, respectively, for all models and datasets, where SC is normalized to [0, 1] before thresholding.
*... | Summary: Authors propose NeuroPath, a transformer-inspired model that leverages a multi-head self-attention mechanism to capture multi-modal feature representations from SC (structural connectivity) and FC (functional connectivity) graphs. The model is evaluated on well-known large-scale public datasets OASIS, ADNI, UK... | Rebuttal 1:
Rebuttal: ## Weaknesses
The actual computing time of existing brain models, transformers, and our NeuroPath per graph is shown below. According to this table, the computational time to train the model of every experiment can be calculated along with the data number in Table 5 in our manuscript.
| ... | Summary: This paper introduces a novel way of predicting brain diseases with the help of structural and functional brain connectivity. It couples structural as well as functional connectivity from human neuroimaging studies. It comprehensively studies the performance of the new method on a large variety of datasets. In... | Rebuttal 1:
Rebuttal: ## Weaknesses
Equations on line 173 can be rewritten more clearly: a set of learnable parameters $\{ \bar{\mathbf{W}}, \hat{\mathbf{W}} \in \mathbb{R}^{(HC)\times C} \}$ and $\boldsymbol{\bar\alpha}_h, \boldsymbol{\bar\beta}_h, \boldsymbol{\bar\gamma}_h, \boldsymbol{\hat\alpha}_h, \boldsymbol{\ha... | Summary: This paper introduces a transformer model that integrate both structural connectivity (SC) and functional connectivity (FC). It formulates a graph representation learning framework to extract feature from the brain connectome data. The model has two branches to encode SC and FC data separately, and later train... | Rebuttal 1:
Rebuttal: ## Novelty
1. Although there are previous works utilized transformer and graph transformer, our *NeuroPath* is the first model to uncover the SC-FC coupling mechanism between (structural) neural pathways and (functional) neural activities under a new design of MHSA framework that can represent the... | Rebuttal 1:
Rebuttal: # Thank for the insightful comments from all reviewers.
## Performance concern
Since we aim to comprehensively test various scenarios of modeling brain activities, we run baselines and our NeuroPath using multiple experimental settings on each dataset. However, we simply list all numbers to sho... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression | Reject | Summary: This paper proposed LoCoDL, an algorithm than combines communication compression with local training. The authors proved the convergence results under regular assumptions, achieving comparable rate with existing SOTA algorithms. The experimental results also show that LoCoDL behaves best among tested algorithm... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation and acknowledging our contributions.
# Weaknesses
> 1. I believe it important to compare LoCoDL with ADIANA when
is at least 10
or 100
smaller than
to see whether LoCoDL beats ADIANA in these scenarios.
> 2. It's recommended to conduct experiments on... | Summary: This paper proposes LoCoDL, a new GD-based distributed training algorithm that employs both communication compression (CC) and local training (LT). It achieves double acceleration and a SOTA convergence rate for strongly convex problems.
A crux of the algorithmic improvement is maintaining two local estimates... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation and acknowledging our contributions.
# Weaknesses
> 1. LoCoDL does not apply to NNs
This work is indeed mainly theoretical as we develop a new algorithmic framework combining the mechanisms of local training and compression, and validate it with proved ac... | Summary: This paper proposes an algorithm (LoCoDL) that leverages two well-known methods of local training. It reduces the communication load in distributed learning.
Strengths: The paper addresses the interesting problem of distributed learning.
Weaknesses: 1. Generally, compressing the error and feeding it back to ... | Rebuttal 1:
Rebuttal: > 1. compressing the error and feeding it back to the updates is a well-known technique to reduce the variance in distributed learning
Do you mean like in DIANA? We have discussed existing algorithms and techniques in the paper. The contribution is the combination of local training, like in the p... | null | null | Rebuttal 1:
Rebuttal: We have run additional experiments, as recommended by the reviewers, to further compare LoCoDL and the state-of-the-art ADIANA. In all cases, LoCoDL significantly outperforms ADIANA. Attached is the PDF showing some results, including with the MNIST dataset and when d>100n (where ADIANA has a bett... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
End-to-end Learnable Clustering for Intent Learning in Recommendation | Accept (poster) | Summary: This paper introduces an end-to-end learnable clustering framework for intent learning in recommendation systems, termed ELCRec. The current intent recognition methods can be likened to the Expectation-Maximization (EM) algorithm, where the E-step involves clustering to obtain intents, and the M-step uses self... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer py4J [1/3]**
Thanks for your valuable and constructive reviews. We appreciate your insights and suggestions, as they will undoubtedly contribute to improving the quality of our paper. In response to your concerns, we provide answers to the questions as follows in order.
... | Summary: This paper aims to improve the optimization paradigm of the existing intent learning methods for recommendation. A novel intent learning method named ELCRec is proposed by unifying behavior representation learning into the end-to-end learnable clustering framework. Experiments, theoretical analyses, and applic... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer bYUb [1/2]**
Thanks for your valuable and constructive reviews. We appreciate your insights and suggestions, as they will undoubtedly contribute to improving the quality of our paper. In response to your concerns, we provide answers to the questions as follows in order.
... | Summary: In this paper, the authros study the complex optimization issue in the filed of recommendation. It encodes users' behavior sequences and successfully unifies behavior representation learning into a learnable clustering framework. Further, it uses cluster centers as self-supervision signals to highlight mutu... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer WQpX [1/2]**
Thanks for your valuable and constructive reviews. We appreciate your insights and suggestions, as they will undoubtedly contribute to improving the quality of our paper. In response to your concerns, we provide answers to the questions as follows in order.
... | null | null | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to the SAC, AC, and PCs for their dedicated efforts and constructive feedback. Your comments have been invaluable in enhancing the quality of our manuscript. We have meticulously addressed each of your questions and hope our responses satisfactorily address your con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss | Accept (spotlight) | Summary: In this paper, the authors investigate the topic of semi-supervised multi-label learning (SSMLL), and find an interesting problem in SSMLL, namely variance-bias issue, which means that the variance difference between positive and negative samples’ feature distributions for each label in SSMLL is much higher th... | Rebuttal 1:
Rebuttal: First of all, we are very grateful for your time and effort in reviewing this submission. We are encouraged that you agree with the contributions of our paper. Below are the responses to your comments.
**Q1: Lack of quantitative analysis about the variance difference.**
**A1:** Yes, adding quant... | Summary: This paper focuses on the semi-supervised multi-label learning (SSMLL) task and propose a novel and interesting SSMLL method motivated by the variance bias problem, which implies that the variance difference of feature distributions of positive and negative samples for each label in SSMLL is much higher than o... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We are encouraged that you agree with the novelty and contributions of our paper. Below are the answers to your questions.
**Q1: Some efficiency experiments are expected to compare the real running time of the proposed method and other baselines. Does it limit t... | Summary: The paper proposed an interesting idea of using the balanced binary angular margin loss for semi-supervised multi-label learning learning (SSMLL). This is motivated by the empirical observation that the feature distributions of positive and negative samples for each label in SSMLL always suffer from the varian... | Rebuttal 1:
Rebuttal: First, we are very grateful for your time and effort in reviewing this paper. Below are the responses to your questions and comments.
**Q1: How does the negative sampling affect the performance of the proposed method? What happens if the proportion of positive and negative samples of each categor... | Summary: Based on the traditional binary loss function and negative sampling, when using labeled and pseudo-labeled samples for semi-supervised multi-label learning, there is an issue of variance bias between the feature distributions of positive and negative samples for each label. To solve this problem, authors balan... | Rebuttal 1:
Rebuttal: First, we would like to thank you for your time and effort in reviewing our submission. Next, we would like to respond to the main concerns raised in the comments.
**Q1: About the some unstandardized mathematical symbols**
**A1**: Thank you for your correction. We change the ratio of labeled sam... | Rebuttal 1:
Rebuttal: First of all, we sincerely thank all the reviewers for their great efforts in reviewing this submission and providing helpful and valuable comments. Since we cannot revise our paper during the rebuttal period, we plan to make the following revisions in our paper:
- According to most reviewers, we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MVGamba: Unify 3D Content Generation as State Space Sequence Modeling | Accept (poster) | Summary: This work introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor based on the RNN-like State Space Model (SSM). The Gaussian reconstructor propagates causal context containing multi-view information for cross-view self-refinement while generatin... | Rebuttal 1:
Rebuttal: Thank you for your insightful and positive comments! Below, we provide a point-by-point response to address your concerns. We welcome further discussion to improve the clarity and effectiveness of our work.
> **Q1: The performance is continuously increasing as the token length increase.**
**A1... | Summary: This paper introduces MVGamba, a feed-forward, sparse reconstruction model. This model takes a small number of images (e.g., 4 views) to infer 3D Gaussians. Basically, it is a Mamba version of multi-view large reconstruction model. The authors have implemented several strategies to ensure stable training and o... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments! Below, we provide a response to address your concern. We welcome further discussion to enhance the clarity and effectiveness of our work.
>**Q1: How does a non-pixel-aligned transformer model perform?**
**A1:**
- **Non-pixel-align i... | Summary: The authors propose MVGamba, a general and lightweight Gaussian reconstruction model for unified 3D content generation. By replacing the previous LRM work’s transformer architecture with the recent model Mamba. MVGamba can generate long Gaussian sequences with linear complexity in a single forward process, eli... | Rebuttal 1:
Rebuttal: Thank you for the positive and insightful comments! In the following, we provide our point-by-point response and look forward to the subsequent discussion.
>**Q1-1: Input tokens are short.**
**A1-1: 3DGS reconstruction is a long-sequence task.** We analyze the token length of two paradigms in r... | null | null | Rebuttal 1:
Rebuttal: Dear Program Chair, Senior Area Chair, Area Chair, and Reviewers,
We sincerely appreciate the thorough review and insightful feedback provided by each reviewer. The reviewers asked perceptive questions and comments, which are answered in detail in individual responses and have improved our submis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Instance-Optimal Private Density Estimation in the Wasserstein Distance | Accept (poster) | Summary: The paper addresses the problem of differentially private density estimation using the Wasserstein distance. It approaches this problem in a non-parametric manner, meaning it does not assume any specific distribution. Instead of focusing on worst-case error guarantees, the authors aim to design algorithms that... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their review.
**“In Theorem 2.3, the established error includes a multiplicative factor of log|X|, where |X| represents the size of the metric space. I would like to confirm that if X is a finite m -dimensional space, this implies that the error scales with m, whic... | Summary: This paper looks at the problem of private instance optimal density estimation in wasserstein distance. The starting point of their research is the recent result that shows that the minimax rate of eps differentially private density estimation in wasserstein distance scales as (eps*n)^{-1/d}. Therefore, the a... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their review.
**“they can achieve an polylog-factor approximate cost as compared to an algorithm that knows that the unknown distribution is either P or Q, for some distribution Q that exists. Question to the authors, do they mean for every Q or there exists such a ... | Summary: The paper focuses on estimating densities while preserving privacy (differential privacy). Preserving privacy comes with trade-offs in terms of accuracy of their estimates, which is measured using the Wasserstein distance in this paper. One of the main conceptual contribution is introducing a notion of instanc... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for their review.
**“Perhaps not the main focus of the paper, but it would be nice to have all the constants.”**
We agree that constant factors are very important for practical implementations. From the implementation point of view, the constant factors relating algorit... | Summary: The paper studies distribution estimation under Wasserstein distance while requiring the estimator to be differentially private and "instance-optimal," i.e., being competitive against the algorithm optimal on a distribution neighborhood. The derived class of estimators can achieve comparable performance up to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and address their questions below.
**“It seems the reference…distribution.”:**
a) In Wasserstein estimation (unlike TV estimation, for example), it is important to accurately estimate the support of the distribution- putting even a very small amount of ma... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unsupervised Anomaly Detection in The Presence of Missing Values | Accept (poster) | Summary: In anomaly detection, where training data consists only of normal instances, conventional missing value imputation approaches may cause imputation bias, meaning that imputations are inclined to make anomalous incomplete instances appear normal. This study addressed this issue by proposing an end-to-end trainin... | Rebuttal 1:
Rebuttal: We are grateful for your reviews and recognition of our work. Our responses to your questions are as follows.
**Response to Weakness 1 and Question 1:**
The imputer is an MLP network, in which its backbone is designed as follows: $input \rightarrow 512 \rightarrow 128 \rightarrow 128 \rightarrow... | Summary: This study addresses the challenge of anomaly detection in the presence of missing data, which is common in various fields like recommendation systems and bioinformatics. Traditional methods struggle with missing data, leading to biased imputations and ineffective anomaly detection. The study proposes an integ... | Rebuttal 1:
Rebuttal: We are grateful for your reviews and recognition of our work. Regarding your concern about computational time, we supplement comparisons of theoretical time complexity and experimental time cost between our proposed method and the baselines in the following Table 1 and Table 2.
**The notations us... | Summary: This paper introduces ImAD, an end-to-end approach to anomaly detection in the presence of missing data. It addresses the imputation bias observed in the traditional impute-then-detect approaches, where the imputation model trained only on normal data tends to normalize incomplete abnormal samples. The propose... | Rebuttal 1:
Rebuttal: We are grateful for your reviews and suggestions of our work. Our responses to your questions are as follows.
**Response to Weakness 1 and Question 2:**
Since we are studying unsupervised anomaly detection, there is no validation set during the training stage. As shown in Table 8 of our paper, w... | Summary: Paper proposes a unified framework to find anomalies in data with missing attribute values. Instead of relying on impute-then-detect approach, which could lead to imputation bias, authors proposed a multi-objective learning framework in which the imputation and data modeling are done together. The core assumpt... | Rebuttal 1:
Rebuttal: We are very pleased and honored to receive your positive evaluation of our work. Our responses to your questions are as follows.
**Response to Question 1:**
As you are concerned, we have explored the influences of constrained radii $r_1, r_2$ for detection performanc... | Rebuttal 1:
Rebuttal: We appreciate the comments made by all reviewers. We summarize the major work of this rebuttal as follows:
* As requested by reviewer 4yRa, we added time complexity analysis (in the form of $\mathcal{O}(\cdot)$) and running time cost of the compared methods in Table 1 and Table 2 of the attached ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Simple and Fast Distillation of Diffusion Models | Accept (poster) | Summary: This paper presents a fast-training accelerate sampling algorithm that is based on the distillation paradigm. This algorithm performs trajectory matching under all time points from 1 to 0 (t=80-0.006). The approach avoids the huge overhead of bi-level optimization through the ``detach()'' operation in pytorch,... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback! Below we address the specific questions.
**Q: If we extend the training duration, can we achieve results comparable to those of the CTM?**
The remarkable FID results of CTM are mainly attributed to the introduced GAN loss, otherwise the FID would significantly ... | Summary: This paper proposes a fast distillation method for diffusion models. This method simplifies the existing knowledge distillation framework and proposes Simple and Fast Distillation from a global perspective to reduce redundant time steps in training. The SFD framework can achieve good experimental results in a ... | Rebuttal 1:
Rebuttal: Thanks for your feedback! Below we address the specific questions.
**Q: Many of the techniques in the paper lack innovation and are more like technical explorations.**
Here we would like to clarify the main technical contributions of this paper.
(1) In Section 3.1, for the first time, we recogn... | Summary: In this work authors propose a novel diffusion distillation method by unrolling the student model to match unrolled/generated pre-trained diffusion (teacher) model's trajectory. Authors demonstrate effectiveness and compute efficiency of approach on stable diffusion.
Strengths: Paper is easy to read and follo... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback. Below we address the specific questions.
**Q: What is the effect of SFD on diversity?**
We appreciate the reviewer’s suggestion to evaluate the diversity of SFD. Following standard practice, we computed fidelity (measured by precision and density) and diversi... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice | Accept (poster) | Summary: This paper studies the problem of MAB with expert advice and the contextual linear MAB. Specificially, the authors proposed a lower bound of $\Omega(\sqrt{KT\log\frac{N}{K}})$, improving upon previously known lower bound $\Omega(\sqrt{KT\frac{\log N}{\log K}})$ and showing that the previous upper bound $O(\sqr... | Rebuttal 1:
Rebuttal: > one of my concern is that the algorithm only works for the finite context case, which is very restrictive in real applications.
The proposed algorithm (Algorithm 1) can also work for the infinite context case,
in which it enjoys the second regret upper bound in Corollary 1 (line 222):
$R_T = O ... | Summary: This paper studies the problem of bandit learning with expert advice. The main contributions are two refined regret bounds: (1) A matching lower regret bound of $\Omega(\sqrt{KT\log(N/K)})$ for multi-armed bandit problem; (2) $\Theta(\sqrt{dT\log(K,\min\{1,S/d\})})$ regret bound for contextual linear bandits.
... | Rebuttal 1:
Rebuttal: > I also have a question about the proof of Lemma 3. In equation (15), a lemma in the bandit algorithm book is introduced to derive regret bounds. As far as I can see, the lemma only works for fixed $X_0$. However, in the learning process $p_t$ might depends on the historical contexts $X_0, X_1, X... | Summary: In this work, the authors tackle the existing gap between upper and lower bounds in bandits with expert advice. An existing lower bound scaled as $\Omega(\sqrt{KT \frac{\log N}{\log K}}$, while the state of the art of only provided a $O(\sqrt{KT \log (N/K)}$ bound (Kale 2014)
The authors close this gap by pro... | Rebuttal 1:
Rebuttal: > Using this framework is very beneficial in best-of-both-worlds settings rather than in the purely adversarial or purely stochastic regimes. Have you considered generalizing your results to that setting?
Thanks for your suggestion.
We believe that our results can be extended to the best-of-both-... | Summary: The paper investigates two significant extensions of multi-armed bandit problems: multi-armed bandits with expert advice (MwE) and contextual linear bandits (CLB). For MwE, the authors close the gap between previously known upper and lower bounds, establishing a matching lower bound of $\\Omega\\left(\\sqrt{KT... | Rebuttal 1:
Rebuttal: > While the lower bound for the MwE problem improves on prior results, it requires to consider a harder setting than the standard one by restricting the learner.
We deeply appreciate your suggestion.
We agree that this is an important issue,
and we will add this point to the abstract and introduc... | Rebuttal 1:
Rebuttal: # Thank you and future revisions
First of all, we would like to express our deepest gratitude to the reviewers who spent a great deal of time reviewing this paper.
Thanks to the valuable peer review comments we received,
we are confident that the quality of our manuscript will be greatly improved... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents new bounds for regret minimization problem in multi-armed bandits with expert advice (MwE) and contextual linear bandits (CLB). For MwE, This paper bridges the gap between existing upper and lower bounds by establishing a new matching minimax optimal lower bound. In the case of CLBs, the au... | Rebuttal 1:
Rebuttal: > Motivation: The paper lacks an exploration of practical applications of the proposed work, thus indicating a deficiency in motivation. Incorporating a discussion on potential practical applications would significantly add value of the results presented in the paper.
We appreciate your suggestio... | null | null | null | null | null | null |
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields | Accept (poster) | Summary: This paper presents a new pipeline for modeling PDE systems, especially for learning local neural fields. It designs a new encoder-decoder for absorbing any type of input, which avoids the constraint on meshes and cloud points. It can handle diverse geometries. A diffusion-based transformer architecture is use... | Rebuttal 1:
Rebuttal: * **W1** | The motivation of using a VAE-type encoder-decoder is not well justified.
Thank you for your insightful comment. We chose to use a Variational Autoencoder (VAE) over a standard Autoencoder (AE) due to the VAE’s established reputation in the literature for producing compact latent repr... | Summary: The paper proposes a framework using autoencoder and diffusion transformer for predicting the forward dynamics of time-dependent PDEs. Leveraging cross-attention and neural fields, the framework is able to handle different types of meshes and geometries. The authors demonstrate the effectiveness of their propo... | Rebuttal 1:
Rebuttal: * **W1** | Many of the techniques used in the paper are taken from existing models [...] .
Thank you for your feedback. It is true that our paper builds upon established techniques and models, which we have duly acknowledged. However, our contributions lie not only in leveraging these techniques... | Summary: The paper introduces a novel framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. It proposes a flexible encoder-decoder architecture that achieves smooth latent representations of spatial physical fields from various data types and employs a diffusion-... | Rebuttal 1:
Rebuttal: * **W1** | Although the proposed method reduces computational cost, [...] .
Our architecture employs an encoder-decoder structure that includes cross-attention blocks. The computational complexity of these cross-attention operations is $\mathcal{O}(NMd)$, where $N$ represents the number of obser... | Summary: An innovative approach for improving the modeling of partial differential equations (PDEs) using local neural fields is presented in the paper "AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields" (Attentive Reduced Order Model with Attention). Through the provision of a versat... | Rebuttal 1:
Rebuttal: * **Q1** | What are the main drawbacks that AROMA seeks to solve with respect to current neural operator models for PDEs?
AROMA addresses key limitations of existing neural operator models for PDEs. Traditional transformer-based methods, such as those by Li et al. (2023) and Hao et al. (2023), un... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your insightful feedback.
In response to your comments, we have addressed the key concerns by providing additional experimental results. Please refer to the supplementary PDF, specifically:
* An ablation study in Table 1 that compares different processing blocks (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation | Accept (poster) | Summary: In this work, the authors introduce an hierarchical diffusion model for molecular conformer generation. The framework starts with fragment positions initialized by RDKit and designs a diffusion process that generates atomic positions from substructure positions. The reverse diffusion process is modeled by an e... | Rebuttal 1:
Rebuttal: **Q1.** Following #1 in weakness, the final blurring operator (eq. 7) is basically a linear interpolant in Euclidean space between the substructure coordinate space and atom coordinate space. Unlike usual linear interpolant that starts from random Gaussian, here it starts from substructure coordin... | Summary: The paper proposes a novel diffusion method for molecular conformers based on blurring diffusion. The method utilizes RDKit to predict the 3D structure of small molecule fragments and trains a diffusion model to generate the full-atomistic molecule from the RDKit prior, leveraging hierarchical modeling. The me... | Rebuttal 1:
Rebuttal: **W1.** The authors did not consider the molecular conformer fields (MCF) paper, which is the current state-of-the-art approach to molecular conformer generation. The MCF method is more performant than the proposed approach.
**Answer.**
* Regarding the comparison with strong baseline, please che... | Summary: The paper addresses the question by focusing on a fundamental biochemical problem: generating 3D molecular conformers based on molecular graphs in a multiscale manner. It consists of two stages:
1. Generating a coarse-grained fragment-level 3D structure from the molecular graph.
2. Generating fine atomic deta... | Rebuttal 1:
Rebuttal: We would like to thank you for appreciating our work and for providing a great summary. | Summary: - This paper presents a model for small-molecule 3D structure generation, conditioning on its 2D molecular graphs.
- The authors proposed a two-step process to address the problem: 1) first, using an off-the-shelf bioinformatics tool, RDKit, to generate a template scaffold structure; 2) then focusing on trai... | Rebuttal 1:
Rebuttal: **Q1-1.** The superior performance of the proposed models might be due to 1) the accurate generation of fine-grained atomic positions and/or 2) correcting the biases from RDKit-generated scaffolds. However, which component plays a more important role is not clearly addressed.
**Q2.** Related to ... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their constructive feedback and suggestions. Below, we provided general responses to the questions raised by several reviewers.
**G-Q1.** Quality of prior vs Performance.
**Answer.**
* To observe the model's performance based on the quality of fragme... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces Equivariant Blurring Diffusion (EBD), a unique generative model for hierarchical molecular conformer creation. A coarse-to-fine production process is presented by the model, with an emphasis on producing fragment-level structures first and then honing them down to atomic details. The metho... | Rebuttal 1:
Rebuttal: **Q1 (W2).** RDKit is used extensively in the first generation of fragment coordinates. The quality of the initial fragment structures that RDKit provides could limit the model's performance. Have you looked into any other options except RDKit for creating initial fragment coordinates? To what ext... | null | null | null | null | null | null |
Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT | Accept (poster) | Summary: This paper presents an equivariant learning framework that modifies standard invariant-based self-supervised learning methods by integrating differences between object classes (Loss_iSSL) and the changes observed in images before and after identical transformations (Loss_CE-SSL). This adaptation enables the mo... | Rebuttal 1:
Rebuttal: Thank you for your constructive review of our submission. We respond to the questions and limitations point-by-point below:
- Performance on Downstream Classification Tasks (W1, Q3): Please see our general response (Point 3) for our more detailed thoughts on why equivariant training on a signific... | Summary: Summary:
The authors propose a novel approach to self-supervised learning (SSL) by incorporating contrastive equivariant training into existing successful SSL methods based on creating invariance to input transformations. The authors observe that such an incorporation of equivariant contrastive learning produ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful assessment of our paper. Find below is our point-by-point response to the weaknesses and questions raised:
- Marginal Effect on Neural Predictivity: Please see our general response Point 1 and Figure R2 in the rebuttal pdf which directly address this concern about th... | Summary: In their paper ‘Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT’ the authors construct a new kind of contrastive-equivariant loss to optimise ResNet-50 architectures. Their new loss is based on the idea of using both self-supervised learning via object representa... | Rebuttal 1:
Rebuttal: Thank you for your constructive assessment of this submission. Below is our point-by-point response to the weaknesses and questions raised:
- Lack of baselines/context for brain data: We agree that the contribution is clarified by providing more context for the predictivities for a range of model... | Summary: The paper introduces a novel framework, CE-SSL, to address the limitations of traditional self-supervised learning (SSL) objectives, which often result in overly invariant network representations, with a goal to improve the neuronal plausibility of resulting representations. The authors propose a method that i... | Rebuttal 1:
Rebuttal: Thank you for your careful review of our submission. We respond to the listed weaknesses and questions below:
- Introducing a new hyperparameter to tune: we agree that this is a fundamental weakness of our framework. It would be preferable to balance this tradeoff without using two loss functions... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their careful consideration of our contributions and thoughtful questions. Several points were raised in multiple reviews and we respond to these below:
1. Lack of Context/marginal improvements on neural predictivity benchmarks: We agree that we provided in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Auditing Privacy Mechanisms via Label Inference Attacks | Accept (spotlight) | Summary: This paper develops measures for auditing privacy-preserving mechanisms that consume datasets with public features and private labels. The auditing measures compare two attackers whose goal is to infer a private label: a weaker attacker who has access to the dataset’s features and prior knowledge of the correl... | Rebuttal 1:
Rebuttal: * Why are additive and multiplicative measures both needed?
The multiplicative measure is generally stronger, in that a small multiplicative advantage implies a small additive one, but not vice versa. We studied both additive and multiplicative measures for a few reasons. While the multiplicative... | Summary: This paper proposes a number of measures of information gained by an adversary from a mechanism. It presents some theorems and performs a number of experiments.
Strengths: Research on understanding better how much can be inferred when privacy mechanisms are in place is useful.
The text uses mostly correct an... | Rebuttal 1:
Rebuttal: The reviewer’s main criticisms focus on: (a) comparison to related work, and (b) clarity of the mathematical setup and notation.
**Comparison to related work**
* “it is important to understand what we can learn from these measures. [...] Other measures exist, like entropy based. The paper doesn... | Summary: The authors present new auditing tools for privacy mechanisms (differentially private or otherwise) with respect to the threat of label inference. They define measures to measure how much an adversary's posterior belief differs from their prior belief after viewing a dataset processed by a PET method, provide ... | Rebuttal 1:
Rebuttal: * Can you publish/provide the code?
We will publish the code, subject to internal organizational approval. | Summary: The paper introduces new metrics to quantify the amount of label leakage associated with a generic privacy mechanism. It then analyzes two common label privacy mechanisms and quantifies the measures for these two mechanisms. Especially for the mechanism that also has the guarantee of differential privacy, the ... | Rebuttal 1:
Rebuttal: * As the measure is defined as the maximum advantage of all label inference attack and the paper shows how to calculate the measure exactly for two mechanisms, it would be great to see how the existing label inference attack is bounded. This is because the measure is generally hard to compute, e.g... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes two reconstruction advantage metrics to audit label privatization mechanisms. Unlike differentially private (DP) auditing techniques, which focus on worst-case guarantees, the authors of this work focus on distributional guarantees. Concretely, the authors assume the adversary has knowledge... | Rebuttal 1:
Rebuttal: * Explain how to compute proposed metrics or posterior distributions.
We will include more details on how we compute the posterior distributions for each mechanism. As suggested, we do apply Bayes’ theorem to compute the posterior:
$$
Pr(y_i = 1 | x, \mathcal{M}(x,y) = z) = \frac{Pr(\mathcal{M... | null | null | null | null | null | null |
Learning Identifiable Factorized Causal Representations of Cellular Responses | Accept (poster) | Summary: This paper proposes FCR, a causal VAE-based model that aims to decompose and cluster covariates, treatments, and their interactions in the latent space.
Strengths: This paper has a solid and rigorous mathematical foundation, and the assumptions are validated after the model is trained.
Weaknesses: This paper... | Rebuttal 1:
Rebuttal: $\textbf{Readability}$:
Thank you for your feedback regarding the readability of our manuscript. We appreciate your honest assessment and understand the importance of clear and concise writing. We will carefully review the manuscript to eliminate any word redundancy and improve the overall clarit... | Summary: The authors propose a method for disentangling into factorized representations dependent on only covariates, only treatment and the interaction between treatment and covariates. Introducing a set of assumptions, the authors prove the identifiability of these disentangled variables based on previous proofs on n... | Rebuttal 1:
Rebuttal: $\textbf{Hyperparameter selection}$:
Thank you for raising this important question, we appreciate that it is a critical point for evaluating how well the method works. Please check **General Response to All Reviewer b**.
$\textbf{Ablation studies}$:
In addition, as the reviewer suggested, we di... | Summary: The authors present a novel method for causal representation learning in cellular perturbation settings, called Factorized Causal Representation, in which the authors learn disentangled latents for the cellular covariates, the treatment, and the interactions between them. They provide identifiability results f... | Rebuttal 1:
Rebuttal: $\textbf{Dataset size}$:
Due to the limited availability of public datasets and the high cost of single-cell sequencing, single-cell datasets with a large number of drugs are not yet available. However, as demonstrated by the theorems in our paper, incorporating more treatments with well-designe... | Summary: The paper presents a novel method, the Factorized Causal Representation (FCR), which leverages identifiable deep generative models to understand cellular responses to genetic and chemical perturbations across multiple cellular contexts. This method improves upon prior models by learning disentangled representa... | Rebuttal 1:
Rebuttal: $\textbf{Evalution metrics}$:
Please check the **General Response to All Reviewers a**.
For the performance of $R^2$, we assessed the statistical significance of those results between FCR and second best methods using an paired t-test and showed the p-values with the corresponding sample size of ... | Rebuttal 1:
Rebuttal: $\textbf{General Response to ALL Reviewers}$:
We sincerely thank all the reviewers for your thorough evaluation of our paper and your recognition of our contributions. We want to address some of the common questions and update new experiment results here, and we will have detailed responses for e... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors develop a modification of the identifiable nonlinear ICA algorithm of Khemakhem et al. 2020, suited for scenarios with two disjoint groups of auxilliary variables x and t and their interactions, with an application of treatment effect on cell gene expression.
The authors propose a learning objectiv... | Rebuttal 1:
Rebuttal: $\textbf{Comparing the conditional independence results (6.3) with the other baselines}$:
We thank the reviewer for raising this important question. I would like to clarify the experimental setup. The goal of this experiment is to demonstrate that FCR is capable of learning conditionally indepe... | null | null | null | null | null | null |
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs | Accept (poster) | Summary: This paper proposes a method to mitigate the issue of deceptive rewards in Meta-RL, I.e., rewards that may impede further exploration leading to higher cumulative rewards. The authors propose to learn two policies: an exploration and exploitation policy that are conditioned on a common context. During policy t... | Rebuttal 1:
Rebuttal: Thank you for reading, engaging with, and critiquing our work. We greatly appreciate the time you have spent, and your feedback has enabled us to improve the paper. We are delighted that you support sharing First-Explore with the NeurIPS community and consider it well-motivated and addressing “a p... | Summary: This work proposes a novel Meta-RL framework called First-Explore to address the balance between exploration and exploitation. The method learns two distinct policies: one for exploration and one for exploitation. These two policies are then combined to form the final inference policy. The effectiveness of the... | Rebuttal 1:
Rebuttal: Thank you for reading, engaging with, and critiquing our work. We greatly appreciate the time you have spent doing so, and your feedback has enabled us to strengthen the paper. We are delighted that First-Explore tackles “a highly relevant” problem, that the “proposed solution provides valuable in... | Summary: This paper addresses the challenge in meta-reinforcement learning, where agents struggle to perform intelligent exploration across episodes, often failing to avoid repetitive exploration of the same locations. The authors propose a method called First-Explore, which learns two distinct policies: one focused on... | Rebuttal 1:
Rebuttal: Thank you for reading, engaging with, and critiquing our work. We greatly appreciate your time and feedback, which has significantly improved the paper. We are delighted that you consider First-Explore to be “motivated by an important problem” and “an interesting idea.”
We have addressed your con... | null | null | Rebuttal 1:
Rebuttal: Many thanks for reading our work. We are grateful for the feedback provided and for the time you spent engaging with and critiquing First-Explore. We are delighted that First-Explore addresses an "important" [Vncg], "highly relevant" [Erp8] problem "worth solving" [zDt1], and that it is an "intere... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Transformer Reasoning Capabilities via Graph Algorithms | Accept (poster) | Summary: This paper investigates the algorithmic reasoning capabilities of transformers on graph problems and introduces a novel hierarchy that categorizes tasks into solvable classes under different scaling regimes of transformers. In addition, the authors perform experiments on GraphQA to validate their theoretical a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and attention to the utility of the theoretical analysis.
> Limited Generalizability: The theoretical results are based on specific assumptions, parameter scaling regimes, and tasks, which might not be directly related to all real-world scenarios or practi... | Summary: In this work, the authors propose a new representational hierarchy for standard transformers in terms of computing algorithms over graphs. To this end, the authors relate transformers to the massively parallel computing model (MPC), thus establishing the connection to several graph algorithms. The empirical st... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s attention to detail and questions about our experimental assumptions and rigor.
>There is an ambiguity in the empirical results in 4.1 and 4.2. I searched the experimental details in the main paper as well as Appendix E.3 and believe that the authors use standard GNN ... | Summary: This paper studies the reasoning capabilities of transformers by characterizing their representational power to execute graph algorithms. Theoretically, the authors employ a general transformer model previously studied by Sanford et al. (2024), which assumes that the local MLPs can represent arbitrary function... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed responses and their close attention to our assumptions.
> As the authors mentioned at the end of the paper, the assumption of unbounded-size MLPs provides strong results. At the same time, this is an apparent gap between theory and practice. I think that m... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their close reading, detailed feedback, and recognition of the value of this work. Their questions and critique help clarify and improve the messages of this paper.
While we respond to each author’s comments in their corresponding rebuttal, we would to highlight in par... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What does guidance do? A fine-grained analysis in a simple setting | Accept (poster) | Summary: The paper characterizes the distribution from which diffusion guidance samples. It proves that guided diffusion sampling tends towards the edges of the supports of the class-conditional distributions in scenarios involving mixtures of uniform or Gaussian distributions.
Strengths: * The paper clearly proposes ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper as well as the helpful feedback. We hope to address the main concerns below.
**Weaknesses/Questions**
> The paper's structure needs improvement. The experiments should be settled at the end of the paper, rather than in S... | Summary: The paper offers a theoretical investigation of the use of guidance in diffusion models. Through two stylized models, the paper fully characterizes the behavior of using guidance, which violates the commonly adopted intuition.
Strengths: The paper focuses on an important question, i.e., using guidance in diff... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper, as well as the positive encouragement and helpful feedback. We hope to address the main weaknesses/questions below.
**Weaknesses/Questions**
> How general are the phenomena revealed in the stylized examples? Would it be... | Summary: This paper discusses the impact of diffusion guidance, especially when noting that the guided score function does not correspond to that of tilted distributions. The authors theoretically justify that a large guidance scale can lead to low-entropy and "extreme" samples. The authors further discuss score estima... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper and the helpful feedback. We hope to address the main concerns below.
**Weaknesses**
> **The introduction of the paper could be better organized.** The authors organize the paper in a way that the introduction is a bit u... | Summary: Previous authors show that tilting the score at any given noisey time t corresponds to the score of a tilted-at-that-time-t distribution, and they use this to motivate conditional sampling algorithms, but it is shown here that this is not the score of the noised version of the titlted-at-time-0 distribution (w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and for finding our work to be an insightful read with carefully executed theory and experiments. We are encouraged that they agree it is important to question what common empirical choices in the practice of diffusions are actually doing. We will ... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for taking the time to review our paper and for all of the helpful feedback. We hope to address some common points of discussion below.
**Tilting and noising do not commute.**
We use the term “noising” a distribution $p$ to mean the distribution obtai... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper explores the mathematical basis for the principle of "guidance" in generative models built out of dynamical transport of measure and provides a mathematical analysis on why certain effects are empirically observed.
They provide this theoretical analysis for a mixture distribution and then test if ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our paper and for the helpful feedback. We apologize for some of the organizational issues, and hope to address the main weaknesses below.
**Weaknesses**
> There are some statements early on that I found confusing, and without the... | null | null | null | null | null | null |
SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision | Accept (poster) | Summary: This paper proposes SpelsNet, a novel point-to-BRep adjacency representation learning method that integrates conventional Linear Algebraic Representation of B-Rep graph structures into the point cloud domain. SpelsNet consists of two main components: a supervised 3D spatial segmentation head that classifies B-... | Rebuttal 1:
Rebuttal: 1. The CC3D-VEF dataset is a large-scale collection of 3D CAD models and their corresponding 3D scans. Many realistic artifacts, such as missing data, surface noise, smoothing of sharp details are intrinsic to it (see Figure 3 in supplementary material). The CC3D-VEF dataset was considered with ex... | Summary: This paper proposes a method to segment and classify surface primitive elements (B-rep) from point cloud data. Previous approaches independently deal with the surface patches or boundary curves and ignores the full B-rep structure. This leads to inaccurate and disjoint primitive approximation of the surface. U... | Rebuttal 1:
Rebuttal: **Ground Truth Preparation**: Details on data preparation were included in Section 3.1 of the supplementary material due to space constraints. The labeling information of a B-Rep structure is transferred into its mesh representation using a per triangle nearest neighbor assignment under the tolera... | Summary: This paper focused on reverse engineering where cad brep is reconstructed from point cloud. Authors extended the definition of face-edge-vertex incidence matrix to point-face, point-edge adjacency. A new SpelsNet module is added on top of existing pipeline to also predict point assignment w.r.t the primitives.... | Rebuttal 1:
Rebuttal: Firstly, we would like to clarify the difference between SpelsNet and ComplexGen. Both SpelsNet and ComplexGen demonstrate the importance of topology for B-Rep reconstruction, but they differ in their approach.
ComplexGen generates the B-Rep level geometric primitives as parametric curves and su... | Summary: This paper presents a novel model called SpelsNet for surface elements segmentation task based on boundary representation, which uses two heads for predicting B-Rep element types and extracting topological information, respectively. Experiments are evaluated on two extended datasets and the results illustrate ... | Rebuttal 1:
Rebuttal: 1. The input point cloud $\mathbf{P}$ is discretized on a voxel grid with a fixed voxel quantization size $\rho$. The default value is set to $\rho=0.01$ corresponding to the voxel grid size of $100^3$. This value is chosen considering the trade-off between the model's training time and its abilit... | Rebuttal 1:
Rebuttal: First of all, we express our gratitude to the reviewers for their valuable feedback. We retain that our model SpelsNet is "novel"(LGxC, 9eMm). "The results illustrate the efficacy of the proposed method"(LGxC), and "are good versus baseline without the topology prediction module"(js6S). "The appro... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Proportional Fairness in Non-Centroid Clustering | Accept (poster) | Summary: The papers study two notions of proportional fairness in clustering, namely:
1) The core as introduced by Chen et al.
2) Fully Justified Representation (FJR), which is a relaxation of the above and was introduced by Peters et al.
The novelty of the paper lies in studying these problems in clustering sett... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review. Please see our response to Reviewer YYLK regarding our technical novelty.
**Regarding Single-Link HC clustering**
Thank you for pointing out this family of algorithms. We considered it during our search for a better approximation to the core. ... | Summary: This paper studies proportionally fair non-centroid clustering under the fairness criteria core and fully justified representation (FJR).
Both notions are well-studied in the context of centroid clustering. However, the authors claim that they are the first to consider them in the non-centroid setting.
They ... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review. We will correct the typos and use the thm-restate package to restate theorems. And yes, it would indeed be interesting to devise entirely different techniques for non-centroid clustering (although see our response to Reviewer YYLK regarding our ... | Summary: This paper considers the proportional fair clustering for the non-centroid clustering. They consider three types of clustering loss for each agent in a cluster: arbitrary loss, average loss, and maximum loss. The average (maximum) loss is the average (resp. maximum) distance from this agent to other agents in ... | Rebuttal 1:
Rebuttal: Thank you for your review. We will implement your correction. | Summary: This paper considers the proportional fairness in non-centroid clustering, where the loss of an agent is a function of the other agents in its cluster. It is the first work to study the proportional fairness guarantee in non-centroid clustering. It is interesting to consider the non-centroid clustering under t... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review.
**Regarding Q1 (practical background of fairness in non-centroid clustering)**
* Fairness has been studied in clustered federated learning [1-3], where the idea is to be fair to the agents during the grouping. But fairness has also been studie... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their useful feedback. We attach a PDF file with the running times of the three different algorithms.
Pdf: /pdf/bb806277b4c352e6ee8e19a7a055a065ef1e021e.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters | Accept (poster) | Summary: In their paper, the authors introduce JAMBench, a benchmark designed to assess OpenAI's moderation guardrails using malicious questions. JAMBench includes 160 manually crafted questions across hate, sexual content, violence, and self-harm categories, categorized into medium and high severity levels. They also ... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments, and it is very encouraging that you found our general idea to be interesting and important. We believe the mentioned weaknesses and questions can be sufficiently addressed.
### Q1. The impact of the moderation models.
**A1.** Different LLMs have guardrail ... | Summary: This work explores an interesting problem. Although the jailbreak prompt template can induce the LLM itself to generate harmful content, the malicious instruction and harmful content will be filtered out by moderation guardrails (such as input and output detection). How to jailbreak moderation guardrails has n... | Rebuttal 1:
Rebuttal: Thank you for the comments; we feel encouraged that you found the problem we focus on interesting and the motivation of our work reasonable. We believe the mentioned weaknesses and questions can be sufficiently addressed.
### Q1. Is the construction of JAMBench reasonable and necessary?
**A1.** W... | Summary: In this paper, the authors proposed a jailbreak bench and a corresponding method to bypass the moderation guardrail of LLMs.
The proposed JAMBench is proved to be effective in triggering the filtered-out error by the moderation guardrail of LLMs. Besides, the proposed JAM method is effective in bypassing moder... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments and for acknowledging the overall effectiveness of our method. We believe the mentioned weaknesses can be sufficiently addressed.
### Q1. The perplexity of JAM is significantly larger than many baselines.
**A1.** It's true that the prompts containing cipher... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Expanding Sparse Tuning for Low Memory Usage | Accept (poster) | Summary: This paper proposes a method called SNELL for vision model fine-tuning. It extends matrix decomposition from a kernel perspective and designs a novel sparsification mechanism for end-to-end parameter selection. Experimental results show that the proposed SNELL achieves low memory requirements and high performa... | Rebuttal 1:
Rebuttal: We would like to thank you for the detailed comments! We will diligently follow your guidance to further improve our work and manuscripts.
## W1: The utilized kernel function.
Thank you for your suggestion! We will introduce the utilized kernel function in Section 3.2 in the revised paper.
## ... | Summary: This paper focuses on the sparse tuning task of the PEFT methods, by introducing the kernel function modeling applied into the LoRA two low-rank matrices or adapters, which costs low memory usage and leads to better performances. To achieve this target, a Kernelization for LoRA is proposed to map two low-rank ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments that help us provide better clarification and explanation of our work! We hope the following responses can address your concerns.
## W1: Competition-based Sparsification Mechanism Clarification
Thank you for your feedback. We now provide a more deta... | Summary: This paper proposes a method called SNELL for achieving sparse tuning of pre-trained models with low memory usage. The authors employ LoRA to reduce the number of learnable parameters in the optimizer and utilize kernel tricks to ensure the merged matrix maintains a high rank. Additionally, during the sparse t... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback, it has been very insightful for our work. Before addressing your questions, we would like to clarify some misunderstandings regarding Weaknesses 1, 3, and Limitation. All tables are presented in the PDF response due to the limitation of character number.
## ... | Summary: The paper introduces SNELL (Sparse tuning with kerNELized LoRA), a method aimed at reducing memory usage during PEFT of large pre-trained models. SNELL achieves this by decomposing the tunable weight matrices into low-rank matrices and utilizing a competition-based sparsification mechanism, thereby avoiding th... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments that help us extend the applicability and strengthen the novelty! We will diligently follow your guidance to further improve our work.
## W1: Additional Results on Large Language Models.
Following your suggestion, we apply SNELL on LLaMA2-7B to adapt to the co... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their diligent efforts and valuable suggestions, which have greatly contributed to improving the quality of our manuscript.
**Summary of strengths**:
We sincerely appreciate that you find our method:
- novel and promising (reviewers CYCL, hyWE, and 7... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Flexible task abstractions emerge in linear networks with fast and bounded units | Accept (spotlight) | Summary: The paper investigates fast task switching/adaptation in a gated linear network. Specifically, it shows that neuron-like properties including regularization, fast learning, and non-negativity lead the model to demonstrate fast task adaptation and generalize compositionally. They provide a detailed analysis of ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We are glad to hear that you find the theoretical analysis of the underlying mechanism insightful. Moreover, we are encouraged that you value our aim to test our framework against behavioral experiments as well as to demonstrate a basic usefulness beyond syn... | Summary: This paper looks at the problem of having an agent learn a series of tasks sequentially by receiving supervised data for each task. Training a NN on this has the issue of catastrophic forgetting, and learn best with shuffled data. Their model attempts to be more similar to humans, who do best with task data pr... | Rebuttal 1:
Rebuttal: Thank you for your review and thoughtful comments. We are delighted that you appreciate the simplicity of our model and the thorough experiments we provide.
> The idea is to have subnetworks which can learn each task, and then learn a high level router to the subnetworks.
We thank the reviewer ... | Summary: This paper presents a method aimed at learning generalizable task abstractions by constraining neuron dynamics in artificial neural networks. It takes inspiration from biological neurons by constraining artificial neurons to non-negativity, forcing a faster timescale, and regularizing.
The paper goes through... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments. We appreciate that you found the conducted experiments relevant to our claims.
> Investigating these general, simple changes as drivers of flexible task abstractions is creative and exciting.
We are glad that this contrast came across well: Despite the simpli... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments that helped us improve the manuscript. We were glad that the reviewers found “the model remarkably simple and easy to understand” (reviewers 9Ed7 and pBhB), and studying the emergence flexible task abstractions “creative and exciting” (9Ed7, Swa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits | Reject | Summary: The paper addresses a major challenge in biology: identifying evolutionary traits, which are features common to a group of species with a shared ancestor in the phylogenetic tree. Compared to the existing works, this submission proposes new architectures and loss to avoid the over-specification problems. In th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback on our work.
**C1**. “More background: For most of the machine learning conference readers, I guess the proposed problem background is required. Therefore, more related work and background sections should be useful.”
> We thank the rev... | Summary: The authors propose a novel deep learning based algorithm named HComP-Net that can detect evolutionary traits common to groups of species with shared ancestors. Based on earlier studies, they aim to build a model that can accurately isolate common traits of specific species and reject over-specific features.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and positive feedback on our work.
**C1**. “HComp-Net was tested with only 3 datasets, which is understandable, as proper datasets may not be readily available. Still, a more thorough evaluation is desirable in the future.”
> We kindly request... | Summary: The authors propose a method that automatically learns multiple orthogonal embeddings to act as prototypes. This approach helps the discovery of hierarchical similarities by representing data in a structured space.
Strengths: The writing is clear and easy to follow.
The authors conduct experiments that wit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback on our work.
**C1.** “In the comparison with HPnet, the authors modify HComP-Net by removing the final two max pooling layers, resulting in a more detailed 26x26 feature map. In contrast, HPnet produces only a 7x7 feature map as shown ... | Summary: The authors investigate the use of prototype-based explainability (as in ProtoPNet) for the visual discovery of evolutionary traits in biology image repositories.
In particular, the authors aim to find traits that apply to group of species in a hierarchical fashion, according ot the tree-of-life hierarchy.
Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback on our work.
**C1.** “The results are limited to three relatively small datasets. Why are there no result on the iNaturalist dataset which is at least 57x larger than CUB-200- 2011?”
> We kindly request the reviewer to refer to the gl... | Rebuttal 1:
Rebuttal: **General Response to Review Comments**
We sincerely thank all the reviewers for providing constructive feedback. We are encouraged that the reviewers found our work:
- Well-written and easy to follow (Reviewers yoRo, 2ruh)
- Novel and interesting (Reviewers hQyP, 2ruh, Ghgm)
- Shows extensive ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transferable Boltzmann Generators | Accept (poster) | Summary: The article "Transferable Boltzmann Generators" deals with the elaboration of a generative model based on Boltzmann Generators in order to estimate Boltzmann Distributions of molecules on which it has not been trained. The training procedure is based on continuous-time normalizing flow, where the trained model... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and questions.
> It is not clear how different their approach is from [22]. Maybe the authors could comment more on that.
We agree with the reviewer that this aspect of our work is crucial and should be more prominently highlighted.
As noted by the reviewe... | Summary: The paper tackles the challenging and high-impact problem of sampling from the high-dimensional Boltzmann distribution of molecular systems. The proposed method allows to train a Boltzmann generator that is applicable to systems it has not been trained on as demonstrated for a dataset of different Dipeptides. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review, insightful questions, and valuable suggestions. We will now address their comments and questions individually.
> The main methodological innovation consists of including topology information in the embedding. Otherwise, standard methods such as flo... | Summary: The authors proposes Transferable Boltzmann Generators (TBGs), which are transferrable for approximating the target distribution of unseen molecular datasets. TBGs are based on the graph-based continuous normalizing flow, and trained by using the simple flow matching. The authors experimentally demonstrate tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and questions. We now address their comments individually.
> I think the authors should put a more effort on explaining why the proposed TBGs are more transferrable than the previous models, for examples, coordinates-based Boltzmann generators (...) The auth... | Summary: - This work builds upon Equivariant flow matching (Klein, 2023) and proposes a transferable Boltzmann Generator that sample Boltzmann distribution for molecules outside the training set. It is a common scenario that simulation data are often scarce for the system of interest and model transferability is highly... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and questions. We now address their comments individually. To keep the rebuttal within the character limit, we often only cite parts of each question.
> Lack of experiments on the transferability in multiple systems and scalability
We acknowledge t... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their time and insightful feedback on our paper.
In response to the reviewers' request, we have included a comparison of our Transferable Boltzmann Generator (TBG) with the Timewarp model [10]. Unlike our approach, which generates independent samples, the Timewarp ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Causal Context Adjustment Loss for Learned Image Compression | Accept (poster) | Summary: The paper presents a novel approach to learned image compression (LIC) by introducing a Causal Context Adjustment loss (CCA-loss). This method aims to improve the rate-distortion (RD) performance of autoregressive entropy models used in LIC. The proposed approach allows the neural network to adjust the causal ... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and insightful suggestions, which have significantly inspired us and enhanced our work. Our detailed feedback towards your comments is listed as follows.
**Q1: Further Exploration of Causal Context Models (future research direction)**
Leveraging decoded infor... | Summary: This paper proposed Causal Context Adjustment loss (CCA-loss) to explicitly adjust the causal context. However, this paper also proposes an efficient image compression model, which seems to be irrelevant to its main contribution, namely CCA loss. CCA loss and efficient architectures are orthogonal, and there i... | Rebuttal 1:
Rebuttal: Thank you for your comments, our detailed feedback towards your questions is listed as follows.
**Q1: Evaluate CCA-loss on more network architectures**
We follow the reviewer's suggestion and replace the NAF-block in our paper with the residual block and the Swin-Transformer block, respectively.... | Summary: This work proposes a novel causal context adjustment loss to explicitly guide the encoder in prioritizing important information at the early stage of the autoregressive entropy model, which is both interesting and significant compared to the implicit modeling in ELIC. The loss is designed based on the entropy ... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and insightful suggestions. We have conducted additional experiments, and our detailed feedback to your comments is listed below.
**Q1: Ablation study on stronger codecs**
In our submitted paper, we conducted ablation study with small model to facilitate our... | Summary: The paper proposes an auxiliary neural network in conjunction with a loss function during training to improve the better contextual modeling of the data during training phase. The advantage of the proposed technique is that the auxiliary network is not required during inference and hence the complexity is min... | Rebuttal 1:
Rebuttal: Thank you for your positive comments.
Multiply–accumulate operations (MACs) and floating point operations (FLOPs) are two important measurements of model computation. More specifically, MACs calculate the number of multiply-accumulate operations, and each MAC operation consists of a multiplicati... | Rebuttal 1:
Rebuttal: We are sincerely grateful to all the reviewers for their valuable time and expert insights; we truly appreciate their diligent work and constructive feedback.
Pdf: /pdf/d21ea6c47829dc3aba64478a20db5d6ab43f16a9.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Linking In-context Learning in Transformers to Human Episodic Memory | Accept (poster) | Summary: This work establishes a correspondence between 1) induction heads, known to functionally contribute to LLM's in-context learning, and 2) the CMR model of human episodic memory. They showed mechanistic equivalences between the model components, relating the Q-composition realization of induction heads to CMR. T... | Rebuttal 1:
Rebuttal: Thank you for your appreciation and insightful feedback. Below is our point-by-point response:
### Weaknesses
1. You’re right that prior works have linked Transformers to neuroscience. In the introduction, we referenced Whittington et al. (2022) on the relationship between attention mechanisms an... | Summary: The authors examine the relationship between attention heads in transformers and human episodic memory. They demonstrate that induction heads are behaviorally, functionally, and mechanistically similar to the contextual maintenance and retrieval model (CMR) of human episodic memory. In particular, they find th... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the potential of our work and offering us the opportunity to clarify. Below is our point-by-point response.
### Weaknesses
Thank you for raising the point about the target audience of this paper. Please see the global rebuttal, point 1. Given the interdisciplinary natu... | Summary: This paper explores connections between in-context learning (ICL) capabilities of large language models (LLMs) and human episodic memory. Specifically, the authors draw parallels between induction heads in Transformers and the Contextual Maintenance and Retrieval (CMR) model of human episodic memory. They demo... | Rebuttal 1:
Rebuttal: Thank you for your interest and thoughtful feedback on our work. Below is our point-by-point response:
### Weaknesses
1. We used repeated random tokens because: (1) it aligns with human free recall experiments, where random words are presented sequentially (Murdock, 1962); (2) it is a widely ackno... | Summary: The paper compares LLMs to a neuroscience model of human episodic memory, particularly highlighting the similarities between induction heads (responsible for in-context learning in transformers) and CMR.
Strengths: 1. The work is original and offers a deeper understanding of induction heads by linking them to... | Rebuttal 1:
Rebuttal: Thank you for recognizing the value of our work and offering constructive feedback. We provide a point-by-point response below.
### Weaknesses
Please see below.
### Limitations
1. We recognize the need for more clarity, particularly for those unfamiliar with CMR. In the revised manuscript, we w... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments. Here we respond to questions asked by multiple reviewers:
## 1 Clarity and accessibility of our paper
While some reviewers praised the clarity and accessibility of our paper, others felt that there was room for improvement. Given the interdis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding | Accept (poster) | Summary: This paper proposes a domain adaptive semantic segmentation method under the cross-view (front view to top view) setting. It addresses this using vision-language models for additional supervision. It proposes a cross-view geometric constraint to model the structural changes and similarities between two vastly ... | Rebuttal 1:
Rebuttal: Dear Reviewer 29y1,
We would like to express our gratitude for your careful reading and valuable feedback.
We are very happy you encourage that ***our paper addresses an important and practical problem, our proposed approach is well-tailored for the problem and appears to be solid and logical, a... | Summary: The paper introduces a novel method for Unsupervised Domain Adaption to adapt an open-vocabulary segmentation model across different views. To achieve this, the authors introduce a cross-view geometric constraint that captures structural changes between different views. Further, a Geodesic Flow-based Metric is... | Rebuttal 1:
Rebuttal: Dear Reviewer JhPV,
We greatly appreciate your insightful review and valuable feedback.
We are very happy you encourage that ***our paper is well-written and features a good range of contributions, our problem is meaningful, and our idea is well-motivated***.
We appreciate your constructive comm... | Summary: This work proposed a novel unsupervised adaptation method for modeling structural change across different views. Additionally, the paper introduced a new metric for cross-view changes and a new prompting mechanism for cross-view open vocabulary segmentation. Through extensive experiments, the paper shows SoTA ... | Rebuttal 1:
Rebuttal: Dear Reviewer 8Etw,
We are grateful for your careful reading and constructive feedback.
We appreciate your highlighting that ***our proposed approach is efficient and well-designed, our problem is practical, and the method achieves solid performance***.
We appreciate your constructive comments a... | Summary: The paper tackles a problem called cross-view semantic segmentation by using unsupervised domain adaptation methods. The cross-view means from the front-view to top-down view, ie, from car to drone. It recognizes the limitations of existing unsupervised domain adaptation and open-vocabulary semantic segmentati... | Rebuttal 1:
Rebuttal: Dear Reviewer upJj,
We greatly appreciate your insightful review and valuable feedback.
We are very happy you think ***our proposed approach is efficient and achieves significant experimental results***.
We appreciate your constructive comments and would like to address these points as follows.
... | Rebuttal 1:
Rebuttal: ## Global Response
We would like to thank all the reviewers for their careful reading and invaluable feedback.
Reviewer JhPV ***accepts*** our paper;
Reviewer upJj and Reviewer 8Etw ***weakly accept***;
and Reviewer 29y1 consider a ***reject*** at this stage.
We appreciate the reviewers encourag... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases | Accept (poster) | Summary: The paper proposes to match entities in dangling settings, where the entities may not have a link to any other entity. New dataset GA16K is proposed.
Strengths: The task is important in the knowledge graph field.
The method achieves better F1 in the experiments.
There are proofs for the correctness of the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comments and the corresponding discussion will be added in the revision. However, we still would like to clarify some misunderstanding of our work:
1. In terms of baseline comparison, we think our comparison is fair enough since the problem we focus on is the alignmen... | Summary: The paper tries to tackle the challenge of entity alignment (EA) with unlabeled dangling cases in knowledge graphs (KGs), where some entities lack counterparts in another KG.
It presents a framework to detect dangling entities and align matchable entities using a GNN-based encoder and a positive-unlabeled le... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments and address the reviewer's concerns as follows.
```
The discussion on related work appears insufficient. For instance, although the proposed GNN looks similar to Dual-AMN, no explicit discussions regarding this similarity are currently included.
```
... | Summary: This paper elaborates the unique challenges of unlabeled dangling entities in EA task. To address the challenge, it proposes the framework, namely Lambda, for dangling detection and then achieves entity alignment. The main idea is to perform selective aggregation with spectral contrastive learning and to adopt... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewer's invaluable feedback and address the reviewer's concerns as follows.
```
The paper would be better to provide a detailed explanation of why the adaptive dangling indicator is effective.
```
Sorry for not explaining it clearly. We introduce the adaptive da... | Summary: This paper introduces a novel entity alignment framework called Lambda for aligning entities with dangling cases. It includes a GNN encoder, KEESA, to aggregate information within and across KGs, and an iterative positive-unlabeled learning algorithm, iPULE, to detect dangling entities. The authors provide bot... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments and address the reviewer's concerns as follows. The writing issues will be fixed in the revision.
```
The motivation of this paper is not clear enough. There are already many methods leveraging inter-graph and cross-graph GNNs for entity alignment.
`... | Rebuttal 1:
Rebuttal: For reviewer HTtm.
```
Comparing the proposed method with strong baseline models under different ratios of pre-aligned seeds would better demonstrate the method's superiority.
```
Table.1 contains experimental results on different ratios of pre-aligned seeds. The experimental baseline includes M... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Probabilistic Conformal Distillation for Enhancing Missing Modality Robustness | Accept (poster) | Summary: By formulating the missing modality representation as a probability distribution, this work proposes probabilistic conformal distillation (PCD), which is an objective function that encourages 1. consistent latent representation of multimodal embeddings for data points in the same class. 2. Geometric consistenc... | Rebuttal 1:
Rebuttal: > Q1: First, the whole framework relies on a definition of positive and negative points, which is not obvious for a lot of multi-modal learning problems (For example, cross-modal retrieval, missing-modal imputation, etc). Second, as the loss $L\_g$ seems to scale quadratically with batch size, the... | Summary: This paper studied the missing modality robustness problem for multimodal training, by introducing a probabilistic conformal distillation to handle the stringent determinate alignment given the irreparable information asymmetry. Specially, PCD adopts the alignment of the extremum of distribution while maintain... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions.
> Q1: Some equations are misleading. For example, in Eq.(2), it is not clear that whether the authors mean the accumulative probability of $z\_p^\*$ is larger than $z\_n^\*$, or exactly mean each $z\_p^\*$ ... | Summary: Summary:
This paper studies the robustness under missing modality scenarios. In multi-modal learning, missing modality is a very common problem that might hinder the learning performance of many existing strategies. The authors assume that the modalities’ information redundancy could potentially help the learn... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to further comments. We would like to make more detailed explanations to figure out your concerns.
> Q1: Notations are not clearly defined. Moreover, the writing shall be further improved. There is no general logic in formulating the methodology.
**A1**: We really... | null | null | Rebuttal 1:
Rebuttal: We gratefully thank all the reviewers for their devoted efforts and constructive suggestions on this paper. We are glad that the reviewers have some positive impressions of our work, including:
- The overall structure of the paper is **well-organized and easy to follow.** (Reviewer EqMG, UGMA)
- ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QKFormer: Hierarchical Spiking Transformer using Q-K Attention | Accept (spotlight) | Summary: The authors introduce a novel spiking transformer, QKFormer, which incorporates several innovative features: a spike-form Q-K attention mechanism with linear complexity and enhanced energy efficiency, a hierarchical structure that facilitates multi-scale spiking representation, and a spiking patch embedding wi... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. We have carefully studied your comments and have made every effort to address your concerns. We will include the relevant analysis in the revised manuscript accordingly.
### **Weaknesses**
> ***Weaknesses 1**: The authors' assertion that SPEDS can facilitate s... | Summary: This model introduces a spike-form Q-K attention mechanism that efficiently models the importance of token or channel dimensions using binary values, significantly reducing computational complexity .The model is evaluated on the ImageNet-1K dataset, achieving an impressive top-1 accuracy of 85.65%, surpassing ... | Rebuttal 1:
Rebuttal: Thanks for your insightful feedback and your time in reading our manuscript. We hope that the responses below could address your concerns. We will include the relevant analysis in the revised manuscript accordingly.
### **Weaknesses**
> ***Weaknesses 1**: The Q-K attention module in the paper has... | Summary: The author has thoughtfully considered the attention structure of the existing Spiking Transformer (SSA) as well as issues present in other parts. Spiking neural networks are characterized by their high efficiency and energy-saving features. For this purpose, the author has proposed a more efficient attention ... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and your time in reading our paper. We have carefully studied your comments and have made every effort to address your concerns. We will include the relevant analysis in the revised manuscript accordingly.
### **Weaknesses**
> ***Weaknesses 1**: Lack of an ove... | Summary: The authors proposed QKFormer, which pushes SNN to 85% accuracy in ImageNet, becoming a new sota and contributing to the SNN community.
Strengths: 1. The authors propose a model accuracy of Sota, which is 10% higher than the previous spikformer, and the number of parameters is lower than spikformer, which is ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have carefully studied your comments and have made every effort to address your concerns. We will include the relevant analysis in the revised manuscript accordingly.
### **Weaknesses**
> ***Weaknesses 1**: The current accuracy is high, but it still re... | Rebuttal 1:
Rebuttal: Dear ACs and Reviewers,
We would like to first express our gratitude to all the reviewers for their valuable comments. We are encouraged that reviewers have commended the performance of QKFormer architecture and its components including Q-K Attention (Q-K Token Attention and Q-K Channel Attention... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently | Accept (poster) | Summary: This paper studies efficient estimation of probabilistic values. It proposes an algorithm which can approximate any probabilistic value with average convergence $O(n \log n)$. It also proposes an improved algorithm for specific cases.
Strengths: This paper provides a solid contribution over previous work.
Th... | Rebuttal 1:
Rebuttal: We are grateful for your review! Here is our response to your concerns.
**Q: If I’m understanding correctly, the algorithm approximates “any” instead of “all” probabilistic values, i.e. it’s a generic algorithm which can approximate any probabilistic value, but it can’t approximate all values si... | Summary: The paper discusses a novel framework for efficiently approximating probabilistic values, such as Beta Shapley values and weighted Banzhaf values. These values are computationally expensive to calculate exactly, calling for approximation techniques.
Specifically, they propose the One-sample-Fits-All Framework... | Rebuttal 1:
Rebuttal: Thank you for your comments! This response is to address your concerns.
**Q: The variances (and/or biases) of the estimations are not thoroughly examined; instead, they only consider whether the variances will increase based on the range/value of $ m_{s} $.**
A: Please let us know if our unde... | Summary: This paper presents a novel framework called One-Sample-Fits-All (OFA) for efficiently approximating probabilistic values used in feature attribution and data valuation, such as Beta Shapley values and weighted Banzhaf values. The framework maximizes sample reuse and avoids variance amplification, making it ca... | Rebuttal 1:
Rebuttal: We appreciate your review and address your concerns below.
**Q: The paper does not discuss much about the implementation details.**
A: We agree that implementation details are important. We included the pseudocode in the submission and we will release our pytorch code after this work is made p... | Summary: The paper studies efficient estimators for probabilistic values, with applications in data valuation and feature attribution. Since the computation of probabilistic values requires an exponential number of utility function evaluations, efficient estimation of probabilistic values is necessary. Existing approac... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments. We address your concerns below.
**Q: Discuss further the sampling vector q.**
A: Thank you for this suggestion. The optimization problems in Section 4.1 and 4.2 are solved in closed-form, see Proposition 1 and Eq. (4). These closed-form solutions can be c... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations | Accept (poster) | Summary: In this study, the authors developed a new distributed optimization algorithm, Freya Page method, for the non-convex finite-sum optimization problem. The authors provided the corresponding iteration and time complexity bounds. In addition, a lower bound on the time complexity is also provided.
Strengths: The ... | Rebuttal 1:
Rebuttal: We appreciate your comments and are grateful for highlighting the positive aspects of our work. We will now proceed to address the questions raised and provide clarifications.
> I wonder if the results can be extended to the case when the computation time for each gradient $\nabla f_j$ is differe... | Summary: The paper introduces Freya PAGE, a novel parallel optimization method designed for distributed systems where computational resources vary in capability and speed. This method addresses the challenge of stragglers in asynchronous environments by adopting a strategy that adaptively ignores slower computations, t... | Rebuttal 1:
Rebuttal: Thank you for your review and appreciating the strengths of our work. We would like to address your concerns and provide additional explanations.
__Weaknesses__
> Determining the optimal parameters $S$ and $p$ as outlined in Theorem 7 requires knowledge of unknown $\tau_i$ and solving an optimiz... | Summary: The paper introduces Freya PAGE, a new parallel method for large-scale nonconvex finite-sum optimization in heterogeneous and asynchronous computational environments. Freya PAGE, specifically addresses the variability in processing times across different workers due to hardware and network differences. By bein... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for recognizing the strengths of our work. We would like to address each of your comments and provide clarifications.
__Weaknesses__
> The empirical results of this paper are primarily focused on synthetic quadratic optimization tasks and logistic regression probl... | Summary: This paper considers the problem of minimizing a finite sum of non-convex objective terms with multiple computational units that calculate gradient oracles. It introduces two efficient computational subroutines for the main steps of the PAGE algorithm, and provides novel bounds on the real time of computation ... | Rebuttal 1:
Rebuttal: Thank you for your review and appreciating the strengths of our work. We would like to address your comments and provide additional explanations.
__Weaknesses__
> The only limitation for me is that from a practical standpoint, this algorithm might not be optimal as it may introduce a significant... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Batched Best Arm Identification | Accept (poster) | Summary: This paper studies the Batched Best Arm Identification (BBAI) problem in multi-armed bandits. The goal is to design an efficient algorithm that correctly finds the arm with the highest mean with probability $\ge 1 - \delta$, while minimizing: (1) the sample complexity, defined as the total number of arm pulls;... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments. The example you provided perfectly captures our main idea. We greatly appreciate this clear and concise explanation. | Summary: The paper "Optimal Batched Best Arm Identification" introduces Tri-BBAI and Opt-BBAI algorithms to identify the best arm in multi-armed bandit settings. Tri-BBAI achieves optimal sample complexity with only three batches on average as the confidence parameter $\delta$ approaches zero. Opt-BBAI extends Tri-BBAI... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all your questions.
---
Q1: I don't think the regime when $\delta$ approaches 0 is interesting. In a typical scenario, we want to set the confidence relativ... | Summary: This paper considers the problem of BAI in the fixed confidence setting, in the context of finding the best arm in as few batches as possible. That is, instead of observing the reward of each arm pulled in turn, the learner makes multiple pulls in a single batch and only observes all rewards after the completi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and effort in providing detailed feedback on our work. We hope our response will fully address all of the reviewer's points.
---
Q1: When considering finite confidence guarantees the authors could also compare with the recent work "An $\epsilon$-Bes... | Summary: The paper presents two novel algorithms for the batched best arm identification (BBAI) problem. The first is the Tri-BBAI algorithm, which employs three batches with the expectation of achieving the asymptotic optimal sample complexity. Based on Tri-BBAI, the authors conceived the Opt-BBAI algorithm, which ach... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and effort in providing detailed feedback on our work. In the following, we present answers to your suggestions (S) and questions (Q) point-by-point. We hope our response will fully address all of the reviewer's points.
---
S1: Reduce the introductor... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for your insightful comments and for recognizing the strengths of our work. We summarize these strengths as follows:
* Reviewer YCPj, Reviewer 3W3Z, and Reviewer UhZ8 all appreciated that our algorithm achieves the optimal sample and batc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching | Accept (poster) | Summary: The paper introduces GoMatching, a streamlined and efficient baseline for video text spotting that enhances tracking capabilities through a novel Long-Short Term Matching module, while also setting new performance benchmarks on multiple datasets and introducing the ArTVideo test set for arbitrary-shaped text e... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive and insightful comments. Below we address the key concerns and promise to incorporate all feedback in the revised version.
**Q1: An analysis of inference time could be presented and compared with previous work (TransDETR).**
**A1:** We included the compar... | Summary: This work uses a query-based image text spotter for video text spotting to solve the poor recognition issue. To achieve this, they add a rescoring head to restore the confidence of detected text instances and use transformers to enhance the tracking capability in videos.
Strengths: 1. Extend an image text-spo... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful and insightful comments. Below we address the key concerns and promise to incorporate all feedback in the revised version.
**Q1: About the recognition text in qualitative examples and the description of the methodology.**
**A1:** Thanks for your pointin... | Summary: This paper adopts the idea of tracking-by-detection and apply it in the task of video spotting. The proposed algorithm is built on top of a SOTA image text spotting model and the authors contributions lie in the tracking part. They design LST-Matcher to integrate both short-term and long-term matching results.... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive and insightful comments. Below we address the key concerns and promise to incorporate all feedback in the revised version.
**Q1. How much performance improvement was brought by DeepSolo? It would be better to report the quality of detection task.**
**A1:*... | Summary: The paper describes an approach for text spotting in videos. The method uses a text spotting method for still images to detect text instances frame by frame and the output is further processed by a specific module to find association between text instances through adjacent frames. In addition, a new dataset fo... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful and insightful comments. Below we address the key concerns and promise to incorporate all feedback in the revised version.
**Q1. How the proposed method and dataset help to solve the gap between detection and recognition, the optimization conflict, and t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their insightful reviews and kind support for our work. We are encouraged that the reviewers appreciate the interesting contribution and idea (Reviewer 1HqK, Lmjj), the good and impressive results (Reviewer 1HqK, Lmjj, BgWx), and the potential for wider usage w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
4-bit Shampoo for Memory-Efficient Network Training | Accept (poster) | Summary: Quantization is applied to eigen matrix instead of directly to precond. The eigen matrix is then orthonormalized via Björck orthonormalization to orthogonalize V.
Strengths: The method seems to regain performance vs just naively quantizeing the preconditioner. This makes Shampoo require about the same about ... | Rebuttal 1:
Rebuttal: Thank you for the insightful and positive comments. In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which further helps to solve the current issues.
**1) Add experiments on LLMs \& 124M G... | Summary: The paper presents a way to use a second order optimizer with 4-bit quantization, to reduce memory usage. Second order optimizers such as Shampoo use additional memory to store preconditioners and other variables needed for computing the updates. This extra memory can prevent their usage in training very large... | Rebuttal 1:
Rebuttal: Thank you for the insightful and positive comments. In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which further helps to solve the current issues.
**1) Training of large-scale models.**... | Summary: This paper aims to reduce memory usage in second-order optimizers by compressing 32-bit optimizer states to 4-bit. The authors propose a method called 4-bit Shampoo, which quantizes the eigenvector matrix of the preconditioner rather than the preconditioner itself. This approach maintains the performance of 32... | Rebuttal 1:
Rebuttal: Thank you for the insightful and valuable comments.
**1) Evaluation scope.**
Thanks. We trained medium-sized language models, including 124M GPT-2 and LLAMA-2 130M/350M. The results are reported in Table 2 and Figure 1 of the rebuttal PDF, which are consistent with those of vision tasks. Due to ... | Summary: This work introduces a quantized Shampoo method aimed at memory-efficient network training. Shampoo, as a second-order optimizer, incurs additional memory demands due to its optimization states. By implementing 4-bit quantization specifically on Shampoo, this work effectively reduces memory usage. The primary ... | Rebuttal 1:
Rebuttal: Thank you for the insightful and positive comments. In the following, we provide our point-by-point response and hope our response helps address your concerns. We also look forward to the subsequent discussion which further helps to solve the current issues.
**1) Breakdown of memory usage.**
Tha... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments and suggestions. In the attached PDF, we have included additional results to address the reviewers' concerns, specifically in Table 1, Table 2, and Figure 1. We also give Lemma 1 to prove the convergence of our 4-bit Shampoo. We will incorpora... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Causal Model of Theory-of-Mind in AI Agents | Reject | Summary: This paper extends the framework of multi-agent influence diagrams (MAIDs) to explicitly capture complex forms of reasoning corresponding to Theory of Mind (ToM) as required for the interaction of Multi-Agent Systems with human users. It introduces the framework of incomplete information MAIDs (II-MAIDs) for ... | Rebuttal 1:
Rebuttal: Thanks for your feedback! Below we respond to your comments, and point you to the general response in which we address common feedback from all reviewers.
We will update the explanation surrounding the definition of II-MAIDs to make it clearer how we formalise higher-order beliefs.
Regarding you... | Summary: The paper introduces a new framework Incomplete Information Multi-Agent Influence Diagrams (II-MAIDs) for modeling complex multi-agent interactions involving theory of mind (ToM) and higher-order beliefs. The authors prove the equivalence between II-MAIDs and Incomplete Information Extensive Form Games (II-EFG... | Rebuttal 1:
Rebuttal: Thanks for your helpful feedback on the paper – we are glad you appreciated our solid mathematical contribution. Please see our general response which addresses a number of shared concerns.
*“the proposed II-MAID framework appears overly complicated for modeling Theory of Mind“*
Whilst we agree ... | Summary: This work extends the theoretical framework of multi-agent influence diagrams (MAIDs) with incomplete information (II-MAIDs) to explicitly capture this complex form of reasoning. The primary theoretical contribution is the proof of the existence of Nash equilibria, although, in general, these equilibria are im... | Rebuttal 1:
Rebuttal: Thanks for your feedback! We hope that the global response addresses your concerns regarding how this work relates to the literature on safe ML.
## ToM literature
Thank you for these references to the ToM literature – this is extremely useful and we will update our related work section to reflect... | null | null | Rebuttal 1:
Rebuttal: # General response
We thank the reviewers for their feedback on our paper. We are glad the reviewers appreciated our solid technical contribution and relevance to the broader multi-agent literature.
The primary shared concern of the reviewers regards the relevance of our work to the NeurIPS audie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Persistent Homology for High-dimensional Data Based on Spectral Methods | Accept (poster) | Summary: The author(s) propose a metric that is based on combining nearest-neighbor graphs and spectral methods to analyze point clouds of high ambient dimension using Vietoris-Rips filtrations and persistent homology. They show experimentally that the proposed distance better detects the significant topological featur... | Rebuttal 1:
Rebuttal: Dear reviewer QsUc,
many thanks for your review. We are happy that you consider the problem we tackle relevant and our experimental section well-written. We will address your concerns in the following.
**Hole detection score:**
Our hole detection score $s_m = (p_m -p_{m+1})/p_m$ captures the ... | Summary: This work studies a well-known phenomenon in persistent homology, which is that it performs poorly in the presence of noise in the settings of a high-dimensional ambient space. Spectral and diffusion approaches are proposed as a workaround to this problem and shown in an extensive numerical study to perform w... | Rebuttal 1:
Rebuttal: Dear reviewer aHMb,
thank you for your review! We are pleased that you find our experimental setup "thorough and detailed". As the main weaknesses, you listed our discussion of Hiraoka et al. (2024) and our discussion of limitations. We will address these in detail below as well your other concer... | Summary: This paper attempts to mitigate the inadequate performance of persistent homology in high dimensional settings, in particular in the presence of noise. The authors investigate a number of distances and propose to use two kinds of spectral distances, such as diffusion distance and effective resistance, instead ... | Rebuttal 1:
Rebuttal: Dear reviewer C1nu,
we thank you for your review and appreciate that you find the problem we tackle relevant, our experimental setup diverse, and the paper well-written. We will address your concerns in the following.
**W1 & Q3: Theoretical results on spectral methods for persistent homology:**... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers and area chair,
we cordially thank you for the effort invested in assessing our manuscript. We are glad that you found the tackled problem "relevant" (C1nu, QsUc), liked our presentation (C1nu, aHMb), and appreciated both the theoretical contributions (AC) and our experimental set... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Neural ODE Training with Temporal Adaptive Batch Normalization | Accept (poster) | Summary: This paper focusses on adapting standard Batch Normalization to be applied to Neural ODEs. The paper proposes the reason for standard BN failing on Neural ODEs is that the population statistics cannot be meaningfully tracked for continuous $t$. That is, since Neural ODEs can be viewed as having continuous dept... | Rebuttal 1:
Rebuttal: We appreciate the insightful feedback from Reviewer XMSL. Below we respond to each raised concern.
---
**Q0.1: Error bar.**
We had some error bar results in Fig. 10 in the Appendix. We omitted others in the main text, as we observed TA-BN's stability across independent runs. To justify it, we ad... | Summary: The paper presents Temporal Adaptive Batch Normalization (TA-BN) which is tailored for Neural Ordinary Differential Equations (Neural ODEs). This method addresses the limitation of applying traditional Batch Normalization to Neural ODEs by acting as a continuous-time analog. The use of TA-BN in Neural ODEs is ... | Rebuttal 1:
Rebuttal: We sincerely appreciate reviewer 97fY for dedicating time to review our paper. The thoughtful feedback and constructive comments have been invaluable in improving the quality of our work.
---
**Q1: Comparison to related works in the context of SNN and clarifications on novelty.**
Thank you for ... | Summary: The paper proposes a remedy for batch normalisation in NeuralODE training, which proposes using a time grid for estimating the depth-dependent statistics, which leads to improved accuracy of the model.
Strengths: Clarity: the paper is clearly written, with good motivation
Quality: the quality of the paper is... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to Reviewer EgcE for the constructive feedback. We will include the following discussions in the updated manuscript.
---
**Q0.1 Originality: more insights and TA-BN with other ODE solvers**
We have conducted extra experiments using fixed-step solvers like the Eule... | Summary: This work identified the fundamental mismatch between Neural ODEs and traditional batch normalization (BN) techniques. To address this issue, the authors introduced Temporal Adaptive Batch Normalization (TA-BN), incorporating temporal interpolation to accumulate mini-batch statistics during training and use th... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 6xoP's thorough review and valuable insights. Below we respond to each raised concern.
---
**Q1: Overclaim of experimental results about "MobileNetV2-level efficiency"**
We will clarify in our updated manuscript that "efficiency" refers to the accuracy a model can achieve ... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful and constructive feedback on our manuscript. We are encouraged by their positive remarks, noting that our proposed TA-BN is well-motivated (Reviewer 6xoP, Reviewer EgcE), addresses an interesting and important problem (all reviewers), and t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
xLSTM: Extended Long Short-Term Memory | Accept (spotlight) | Summary: This paper introduces the Extended Long Short-Term Memory (xLSTM), which enhances traditional LSTMs with exponential gating and new matrix memory. These improvements address LSTM limitations and enhance their memory storage capability, as well as the ability to revise storage decisions and be more parallelizab... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the excellent score and the follow up questions:
1. It is possible to use a different structure to make the recurrent matrix more parameter efficient. Because of the similarity to Transformer heads and hardware-optimal training on GPUs the block-diagonal fo... | Summary: Proposes some extension to LSTM. Specifically:
* exponential gating with appropriate normalization and stabilization techniques
* sLSTM with a scalar memory, a scalar update, and new memory mixing. This still needs to be calculated sequentially.
* mLSTM that is fully parallelizable with a matrix memory (ins... | Rebuttal 1:
Rebuttal: Thank you very much for your review, which helps to improve our paper!
We are sorry that the scalar notation for LSTM and sLSTM (eqs. 2-14) caused confusion. This notation was chosen to reflect the original LSTM idea of a single cell and to make the distinction to matrix memory cells more pronoun... | Summary: The paper introduces xLSTM, an advanced variant of traditional LSTMs, incorporating two extentions aimed at boosting its memory capacity and performance.
The first enhancement, termed sLSTM, modifies the standard LSTM by integrating exponential input and forget gates alongside a stabilizing normalizer term, de... | Rebuttal 1:
Rebuttal: Thank you very much for your review which helps to improve our work!
Indeed the reviewer is right, GLA and Retention are similar to mLSTM in that sense as they both have a matrix cell state, too. However, we highlight that neither Retention nor GLA have an (input-dependent) input gate and Retent... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments and constructive feedback.
In a potential camera ready version of our paper we addressed all your comments and feedback, which considerably improved our paper.
We thank the reviewers WcbD & ZvGL for appreciating our extensive experiments that we conduct... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA | Accept (poster) | Summary: The paper proposes an alternative method for tokenization in genomic foundation models. Existing approaches adopt methods from natural language processing (NLP) and apply those to tokenize genomic sequences. While these tokenization methods have been validated by human knowledge, there is no basis for their us... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback.
**W1: Clarity in Method Description:**
- We appreciate your feedback regarding the need for a more self-contained and more understandable manuscript. The modified method section with high-level motivation and description added is presented in th... | Summary: DNA language models currently use standard tokenization schemes from NLP that might be unsuitable for modelling DNA sequences. This paper proposes a tokenization scheme called MxDNA that is specifically designed for DNA language modelling. MxDNA presents a learnable tokenization scheme that uses a mixture of c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**W1: Related Works and Motivation:**
- We appreciate your feedback emphasizing the need for clearer motivations behind the modules used in MxDNA. Our approach is fundamentally inspired by the desired properties for genomic tokenization—Meaningful... | Summary: The paper introduces MxDNA, a novel framework for adaptive DNA sequence tokenization. Unlike traditional tokenization methods borrowed from NLP, MxDNA uses a sparse Mixture of Convolution Experts coupled with deformable convolution to autonomously learn an effective tokenization strategy through gradient desce... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**W1: Discussion of VQDNA**:
- We didn't include VQDNA[r1] in our initial submission because it was published very close to the NeurIPS deadline.
- We share a similar motivation with VQDNA. following VQVAE, VQDNA employs a convolutional encoder wit... | Summary: The paper introduces MxDNA, a novel framework designed to autonomously learn effective DNA tokenization strategies through gradient descent. Unlike traditional methods borrowed from natural language processing, MxDNA employs a sparse Mixture of Convolution Experts and deformable convolution to address the disc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback. We have acknowledged the points raised W1/W2/W3 and discussed these in the limitations section of our paper.
**W1: Evaluation on Long-Range Tasks**
- We evaluate our model with pre-trained sequence length of 510 on the long range task proposed... | Rebuttal 1:
Rebuttal: ## **General Description:**
Thanks for the valuable feedback provided by all reviewers. We appreciate all the reviewers JPfb (R1), nT57 (R2), RCJz (R3) and eT4M (R4) for approving our contributions: (1) innovative method (R1, R2, R3, R4), (2) thorough experiments (R1, R3, R4). Besides, the conce... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning | Accept (poster) | Summary: The paper introduces OCTree, a framework that uses LLMs to generate and refine feature generation rules for tabular data. By incorporating decision tree reasoning, OCTree iteratively improves feature generation using feedback from past experiments. The framework enhances the performance of some prediction mode... | Rebuttal 1:
Rebuttal: Dear Reviewer 8DXj,
We sincerely appreciate the time and effort you dedicated to reviewing our manuscript and providing insightful comments. Below, we address each of your points individually.
---
**[W1, 2] The framework is costly and time-consuming (with no training time comparison) and involve... | Summary: The authors propose an automatic feature engineering method called OCTree. The OCTree algorithm uses LLMs to generate new features. The feature is then used in the training of the black box, and the new validation score is stored. The algorithm uses a decision tree to learn rules that show the reasoning behind... | Rebuttal 1:
Rebuttal: Dear Reviewer 245r,
We sincerely appreciate the time and effort you dedicated to reviewing our manuscript and providing insightful comments. Below, we address each of your points individually.
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**[W1] Difficulty following the notation and the algorithm. Authors should release the code.**
Tha... | Summary: This paper proposes a new tabular learning framework called OCTree (Optimizing Column Feature Generator with Decision Tree Reasoning). The framework leverages the reasoning capabilities of large language models (LLMs) to automatically generate new column features based on feedback from decision trees. Experime... | Rebuttal 1:
Rebuttal: Dear Reviewer Pmbp,
We sincerely appreciate the time and effort you dedicated to reviewing our manuscript and providing insightful comments. Below, we address each of your points individually.
---
**[W1] Isn’t it contrary to the original intent of feature engineering if validation scores are not... | Summary: The authors introduce a novel feature engineering technique that leverages LLMs for language-based reasoning and considers the outcomes of past experiments as feedback for iterative rule improvements.
Strengths: - the authors introduce a novel feature engineering technique that leverages LLMs so that past exp... | Rebuttal 1:
Rebuttal: Dear Reviewer 6JM8,
We sincerely appreciate the time and effort you dedicated to reviewing our manuscript and providing insightful comments. Below, we address each of your points individually.
---
**[W1, Q1] The authors did not compare their method to other feature generation techniques, in part... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We sincerely appreciate the time and effort you dedicated to reviewing our manuscript and providing insightful comments. Below, we discuss some of the common points made by reviewers.
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**Comparison with CAAFE [1].**
**Restricted applicability of CAAFE.** We would firs... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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