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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Faster Local Solvers for Graph Diffusion Equations | Accept (poster) | Summary: The paper, "Faster Local Solvers for Graph Diffusion Equations," addresses the efficiency of computing Graph Diffusion Equations (GDEs) such as Personalized PageRank (PPR), Katz centrality, and the Heat kernel, which are essential for various graph-related problems like clustering and training neural networks.... | Rebuttal 1:
Rebuttal: Thank you for taking the time and effort to review our paper. We appreciate your positive perspective. We resolve your concerns as follows.
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**Q1.** What are the potential trade-offs between precision and computational efficiency, and how can practitioners balance these in practical applicati... | Summary: This paper proposes a novel framework for approximately solving graph diffusion equations using a local diffusion process. In addition, the proposed method can effectively localizes standard iterative solvers by designing simple and provably sublinear time algorithms.
Strengths: + The problem is well motivat... | Rebuttal 1:
Rebuttal: Thank you for taking the time and effort to review our paper. We believe there may have been some misunderstandings, and your two concerns can be effectively resolved as follows:
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**Q1.** Can the authors show/provide which local GDE solver achieves better performance on which type(s) of graph... | Summary: This paper proposes a new local iterative framework for solving graph diffusion equations (GDEs). Specifically, the framework approximates GDEs through a local diffusion process, leveraging the strong localization properties of diffusion vectors such as personalized PageRank, Katz centrality, and Heat Kernel. ... | Rebuttal 1:
Rebuttal: We are happy that you like our work. We appreciate your time and effort in reviewing our submission. We addressed your concerns as follows:
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**Q1.** It is unclear how graph structures, such as sparsity and spectral properties, impact the runtime complexity of the local solvers.
**A:** In ter... | Summary: This paper proposes a suite of fast methods to approximately compute graph diffusion vectors such as Personalized PageRank, Katz centrality and the heat kernel. A notable feature of the proposed methods is that they are easily parallelizable and hence can achieve further acceleration on GPU. The authors also p... | Rebuttal 1:
Rebuttal: Thank you for taking the time and effort to review our paper carefully. We appreciate your positive perspective on our paper. Your concerns and our responses are listed as follows.
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**Q1.** The concern of two quantities $ \overline{\operatorname{vol}}\left(\mathcal{S}_T\right) / \overline{\ga... | Rebuttal 1:
Rebuttal: **Our General Responses**
We thank all reviewers for their time and effort in carefully reading our paper. To address some general concerns, we included experimental results in the attached PDF file. Furthermore, some general concerns are worth to response as follows:
---
**Q1. Potential trade-... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm | Accept (poster) | Summary: The authors present a new Swiss Army Knife-like approach that flexibly handles the heterogeneity challenges of a federated multi-task learning framework. The framework uniquely integrates tensor trace paradigms to handle cross-client data, model, and task heterogeneity. The title of the approach is interesting... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions, your positive comments help us a lot. I will respond to your concerns next.
> **Weaknesses 1: Reasonableness of assumptions**
Thank you for your question, and I understand that these assumptions may be overly idealized in some cases, but Lipschitz continu... | Summary: This paper focuses on the issues of heterogeneous federated learning, including data heterogeneity, model heterogeneity, and task heterogeneity. To address these issues, the authors introduce a federated multi-task learning framework based on tensor trace norm.
Strengths: 1. Innovativeness: The authors focus ... | Rebuttal 1:
Rebuttal: Thanks for your affirmative suggestions, which are of great support to us. Next, I will answer your questions one by one.
> **Weaknesses 1: Computational complexity**
The tensor trace norm is defined as the sum of matrix singular values, and its computational complexity on the server is
$O(\min... | Summary: This paper introduces a federated learning method called FedSAK. Compared to existing methods, FedSAK is more flexible and can accommodate data heterogeneity, model heterogeneity, and task heterogeneity. To achieve knowledge transfer between client models in a heterogeneous environment, this method employs ten... | Rebuttal 1:
Rebuttal: Thanks for your recognition and valuable suggestions, I will respond to your questions next.
***
>**Weaknesses 1: Ablation experiments**
We appreciate your valuable comments. We understand the importance of ablation experiments in verifying the validity of a method. Due to specific design choi... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for providing detailed and thoughtful feedback on our submission. We thank the reviewers for appreciating our innovation and value to a large extent while suggesting improvements. We have addressed reviewer comments and questions in individual responses to each reviewer and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation | Accept (poster) | Summary: The paper designs a learnware system to handle the heterogeneous feature spaces. It applies the label information to manage the heterogeneous models. Then it also proposes a strategy to match the model with the additional conditional distribution. The paper also conducts experiments to compare with other learn... | Rebuttal 1:
Rebuttal: **Q1: Clarify the relations between each technical contribution and the heterogeneous feature spaces.**
Thank you for your comments. We will provide a more detailed discussion in the revised version. The following is a brief explanation:
**Subspace learning is a standard method for handling hete... | Summary: In this submission, authors focus on the learnware paradigm, which aims to help users leverage numerous existing high-performing models instead of starting from scratch. They find that label information, including model prediction and user’s minor labeled data, is crucial and previously unexplored and then exp... | Rebuttal 1:
Rebuttal: **Q1: To my knowledge, there have been some works aiming at handling learnwares from heterogeneous feature spaces. However, these works are only cited in reference list while not adequately discussed.**
Thanks for your careful reading. We will discuss more related papers in the revised version. H... | Summary: This paper introduces a novel approach for utilizing heterogeneous models with diverse feature spaces in the learnware paradigm. Key innovations include incorporating label information to improve model specification and subspace learning, and constructing a heterogeneous learnware dock system that uses pseudo ... | Rebuttal 1:
Rebuttal: **Q1: The potential challenges or limitations of the proposed model are suggested to be given.**
Thanks for your advice. We discuss the limitations of the proposed method in the checklist (see Q2: limitations).
In this paper, we consider scenarios where the feature spaces of models in the learnw... | Summary: The paper focuses on the learnware paradigm and finds that label information plays an important role in it, which is both practical and interesting. It proposes a new specification that enhances subspace learning and improves learnware management. Extensive experiments demonstrate the superiority of the propos... | Rebuttal 1:
Rebuttal: **Q1: The method seems complex. Thus, a running time comparison is necessary to show its efficiency.**
Thank you for your suggestion. In the author rebuttal, we provided a brief discussion; here is a more detailed explanation.
**1. Evaluation Setup**
The evaluation setup includes the preparatio... | Rebuttal 1:
Rebuttal: Dear Reviewers,
**Please see the attached one-page PDF with a summary of additional experimental results regarding time analysis and performance on real-world projects.**
We would like to thank all reviewers for their constructive feedback, which has greatly improved our paper. We are encouraged... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling | Accept (poster) | Summary: This paper adds an information bottleneck objective for training a hidden representation and reward head in RLHF. They then empirically investigate advantages in policy optimization using such a reward model: there is less reward model overoptimization as judged by a gold RM, and overoptimization can be spotte... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our strong empirical results. We will address each of your comments below and also in our revised manuscript.
---
> **W1:** Some typos and missing assumptions in variational lower bound derivation.
>
**RW1:** Thank you for carefully checking our derivation process a... | Summary: This paper presents a novel way to train reward models form human preferences using an information bottleneck architecture and training method. They provide a derivation of how to train a reward model with an information bottleneck, and then produce empirical evidence of improved performance when using the Inf... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the novelty, reasonability, and performance improvements of our method. We will address each of your comments below and also in our revised manuscript.
---
> **W1:** Comparison with UWO or WARM.
**RW1**: Thanks for your valuable comment. We would like to clarify that... | Summary: This paper proposes a variational information bottleneck (IB) objective for rewarding modeling in RLHF to mitigate the reward misgeneralization issue, which can cause overoptimization. The authors propose a variational information bottleneck objective to filter out irrelevant information and identify a correla... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the clarity of our paper and recognizing the potential of using IB to address reward misgeneralization in RLHF. We appreciate your positive feedback on the introduction of CSI as a valuable contribution. We will address each of your comments and concerns below and also ... | Summary: The paper proposes a regularization method to mitigate reward hacking using a variational information bottleneck objective. Their experiments show the potential that their method might be an alternative to KL divergence for preventing reward hacking.
Strengths: Reward hacking is an important problem in the fi... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on the use of an information bottleneck objective and your recognition of the insightful derivation of the computable objective. We will address each of your comments and concerns below and also in our revised manuscript.
---
> **W1**: Evidence of solving rew... | Rebuttal 1:
Rebuttal: Dear all Reviewers,
Thank you for your effort in reviewing our paper. The submitted PDF file includes the tables and figures referenced in our responses to your comments. The main contents of this PDF are listed as follows:
- Table 1 presents **the comparison results of RLHF models using differ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GS-Hider: Hiding Messages into 3D Gaussian Splatting | Accept (poster) | Summary: This paper presents GS-Hider, a novel framework for steganography in 3D Gaussian Splatting (3DGS) models. The key innovation is a coupled secured feature attribute that replaces the original spherical harmonics coefficients, allowing the embedding of hidden 3D scenes or images into the original scene without c... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! If there are any additional comments to be added, please continue the discussion with us.
$\textcolor{red}{\textbf{The supplementary rebuttal PDF file can be found at the bottom of the overall response}}$.
> **Weakness #1, Question #1 and Question #2: A... | Summary: The paper "GS-Hider: Hiding Messages into 3D Gaussian Splatting" proposes a steganography framework for 3D Gaussian Splatting (3DGS). GS-Hider embeds messages into 3D scenes by replacing spherical harmonics coefficients with a secured feature attribute and uses decoders to extract hidden and original scenes wi... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! If there are any additional comments to be added, please continue the discussion with us.
> **Weakness #1: Minor Limitations.**
Due to limited space, we only discussed the limitations of our method in terms of rendering quality and speed in the supplementa... | Summary: The paper presents GS-Hider, a novel steganography framework designed for 3D Gaussian Splatting (3DGS). The framework enables the invisible embedding of 3D scenes and images into 3DGS point clouds, ensuring accurate extraction of hidden messages without compromising rendering quality. Extensive experiments dem... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We hope that our responses can address your concerns. If there are still aspects that need further clarification, please feel free to continue the discussion with us!
> **Weakness #1: Limited accessibility and usability.**
- Our method is actually very s... | Summary: The paper introduces GS-Hider, a novel steganography framework for 3D Gaussian Splatting (3DGS). Protecting the security and fidelity of 3D assets while embedding information into transparent 3DGS point clouds is challenging, and the method addresses this by invisibly embedding 3D scenes and images into origin... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We hope that our response will address all of your concerns. All discussions and supplementary analyses will be included in our revised version. If there are any additional comments to be added, please continue the discussion with us.
$\textcolor{red}{\te... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the constructive comments from the reviewers! Below is our brief overall response.
> **First, we are very honored to receive recognition from all the reviewers for various aspects of our work.**
- All reviewers have acknowledged the **soundness, presentation, contribu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SimPO: Simple Preference Optimization with a Reference-Free Reward | Accept (poster) | Summary: This paper presents SimPO, an offline preference optimization method for LLM alignment. SimPO replaces the KL term in DPO with a length-regularized log-probability and adds a margin value for regularization. Extensive experiments in chat benchmarks show that SimPO significantly outperforms DPO and other prefer... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the clarity and comprehensive evaluation of our paper. We address your raised points as follows.
**KL regularization and safety**:
We would like to present our most recent results based on the gemma-2-9b-it model. We measured the original gemma-2-9b-it mode... | Summary: This paper proposes a new offline-RLHF algorithm SimPO, which significantly improves current DPO variants on a collection of benchmarks.
Strengths: The SimPO algorithm is intuitive, simple to implement, and works well, with a good presentation.
A few concrete strong points are list below.
1. The algorithm i... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty, simplicity, and significant empirical results of SimPO!
**Fine-grained case studies**:
Thank you for the suggestion! We refer the reviewer to Figures 8 and 9 in our original submission, as well as Figure 2 in the PDF attached to our general res... | Summary: Simpo introduces a simplistic new method of alignment leveraging the log-probability of the sequence eliminating the need to leverage the SFT policy making the implementation computationally less complex. By introducing a notion of margin in the loss function, SimPO outperforms several existing alignment algor... | Rebuttal 1:
Rebuttal: We'd like to thank the reviewer for the thoughtful feedback. We address your questions as follows.
**Without KL to the SFT, one can violate the constraint and do better with the reward for that specific or similar task, but what about the performance in other tasks that the pre-trained/SFT model ... | Summary: The paper introduces SimPO (Simple Preference Optimization), an extension of Direct Preference Optimization (DPO), by replacing the reference-policy-dependent implicit reward with a reference-free reward.
Specifically, SimPO utilizes the average log probability of a sequence as the implicit reward, aligning it... | Rebuttal 1:
Rebuttal: We’d like to thank the reviewer for acknowledging the novelty and simplicity of our proposed approach. We address your raised points as follows.
**The target reward margin $\gamma$ requires extra tuning**:
We acknowledge that the newly introduced target reward margin requires additional tuning. H... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thoughtful feedback, and we'd like to share some additional analysis and results since the submission!
**Additional Analysis in the attached PDF**
We include the following additional studies:
* KL divergence plots of SimPO vs. DPO
* Qualitative studi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding | Accept (poster) | Summary: This paper presents a comprehensive analysis of leveraging visual foundation models for complex 3D scene understanding. The authors unify three kinds of feature representation: image, video and 3D in a unified paradigm and analyze the effectiveness of those representations on different kinds of 3D tasks.
Stre... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: This work lacks a unified conclusion. The experimental observations are independent (to some extent like an experimental report rather than a research paper). It is better for the authors to summarize the core underlying... | Summary: This paper explores the importance of scene encoding strategies in the context of 3D scene understanding, an area gaining significant attention. The authors investigate the optimal encoding methods for various scenarios, addressing the lack of clarity compared to image-based approaches. They conduct an in-dept... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: New insights from this work? Numerous previous studies [A, B, C] have demonstrated that leveraging foundation models can improve 3D understanding.*
We appreciate the references provided, and will include the discussion ... | Summary: This paper examines various scene encoding methods for 3D scene understanding, encompassing image, video, and 3D models. It explores four distinct tasks: registration, scene reasoning, visual grounding, and segmentation. The experimental results indicate that different encoding techniques excel in different ... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: Details of using 3D feature fields for these tasks should be discussed. What is the baseline model for 3D grounding, QA, and registration.*
* **3D QA**: We use 3D-LLM [35] as our baseline and backbone. We replace the or... | Summary: This paper conducts a large-scale study to answer the unexplored question: which method (among image-based, video-based, and 3D foundation models) performs the best in 3D scene understanding? The results show that DINOv2 demonstrates superior performance, video models excel in object-level tasks, diffusion mod... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: How about Segment Anything (SAM)? Does it perform better than LSeg?*
Thank you for the suggestion. Here we include the performance of SAM as a visual foundation model for our evaluation benchmarks. We use the official p... | Rebuttal 1:
Rebuttal: # General Response
***
We are thankful for the feedback and suggestions from all the reviewers. We are glad that the reviewers recognize our intriguing and meaningful insights for the entire 3D vision and multi-modal community (4P6H, vFsT), representative tasks and wholesome coverage of visual fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Theoretical Foundations of Deep Selective State-Space Models | Accept (poster) | Summary: This work establishes density results of input-controlled differential equations, which formulate different types of state-space models such as S4 and Mamba in the continuous-time idealization. Under this framework, different closures of these models are derived, indicating their distinct inductive biases in t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback on our paper. We appreciate the positive assessment of our work's soundness, presentation, and contribution. Below, we address the raised points.
## Weaknesses
- **Rates of convergence**: While our current theoretical framework in general provi... | Summary: This paper proposes a framework for better understanding key features that allow the success of SSMs. To be specific, the authors first show that recent SSM-based models are linear controlled differential equations (CDEs). Then, the expressive power of linear CDEs are explored, depending on whether the matrice... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback on our paper. We appreciate the positive remarks regarding the timeliness and importance of the research problem we address. We would like to address the points raised concerning weaknesses and questions.
## Weaknesses
- **Preliminaries on Rou... | Summary: This paper proposes a framework of using Rough Path Theory to understand the expressivity of SSMs and Mamba. The paper establishes connections to linear CDEs and then uses tools from Rough Path Theory to explain why gates are so powerful in SSM models.
Strengths: This is a really nice theory explaining SSMs. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback on our paper. We appreciate the positive remarks on the theory's presentation and contribution. We would like to address the points raised regarding “closing the loop”.
Our paper shows how non-diagonal transitions lead to a substantial increase ... | Summary: This work analyses the modeling capability of different SSMs (S4-S6 and others) using Rough Path Theory by viewing SSMs as (input-) controlled differential equations (CDE). To this end, the authors show that SSMs with with dense transition matrices (A) are able to approximate arbitrarily close any continuous f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback on our theoretical work. We are pleased you found our expressivity results important and our proofs accessible. We spent a considerable amount of time making our manuscript easy to parse despite the high technicality of the content.
## Weaknes... | Rebuttal 1:
Rebuttal: We would like to extend our gratitude to all reviewers for their insightful comments and valuable feedback. We appreciate the time and effort invested in evaluating our work. Below, we address the primary clarifications about relevance and practical implications.
- **Our Contribution**. Ours is a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generative Adversarial Model-Based Optimization via Source Critic Regularization | Accept (poster) | Summary: The paper proposed GABO, a novel Bayesian optimization method for offline model-based optimization problems. GABO regulates the surrogate model with source critic actor so that the BO procedure remains in the in-distribution. Experiment results validate that GABO outperforms several baselines in terms of the m... | Rebuttal 1:
Rebuttal: We thank Reviewer yPeb for their thoughtful comments and insights. We address their outstanding questions in our response below.
**Research Scope**
While we focus on the discrete, biological sequence design tasks from Design Bench, it is important to recognize that GABO and Source Critic Regular... | Summary: This work tackles offline Bayesian optimization using adaptive source critic regularization. The authors propose generative adversarial Bayesian optimization, which optimizes against a learned surrogate model without querying the true oracle function during optimization. It utilizes a Lipschitz-bounded source ... | Rebuttal 1:
Rebuttal: We respond to Reviewer AH6U's comments below:
**Rationale of SCR**
To summarize our work and as discussed in our manuscript, **the primary rationale behind our proposed source critic regularization (SCR) method is to stop optimization algorithms from extrapolating against offline surrogate model... | Summary: This paper considered an offline optimization problem where a surrogate objective function instead of the oracle objective can be queried. The surrogate objective function is trained using a reference offline dataset and thus may falsely predict the optimum due to overestimation errors. To resolve this issue, ... | Rebuttal 1:
Rebuttal: We thank Reviewer Yd2B for their thoughtful review of our work, and appreciate that they recognize our proposed source critic regularization (SCR) formulation and strategy as a key contribution of our work.
**Framing of the Manuscript's Narrative**
We agree that our main contribution is Source C... | Summary: The paper considers the problem of offline black-box optimization where only limited (zero-shot or few-shot) online interactions with the objective function is available. Existing approaches commonly train a neural net parametrized surrogate model of the objective using the offline data. The paper proposes to ... | Rebuttal 1:
Rebuttal: We thank Reviewer bMXC for their thoughtful comments and insights, and share their enthusiasm for the proposed significance and strengths of our work. We address their outstanding questions in our response below.
**The Role of the Surrogate NN in MBO**
We thank the reviewer for this comment. We ... | Rebuttal 1:
Rebuttal: # Summary of Revisions Made to the Paper
We thank the Reviewers for their thoughtful comments and consideration of our paper. We are grateful that the Reviewers find our method novel (Reviewer czFU, bMXC, Yd2B, yPeB), well-justified (Reviewer bMXC, Yd2B, yPeB), well-written (Reviewer U2YG), and a... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a novel approach for offline MBO that combines Source Critic Regularization and Bayesian Optimization. Offline MBO aims to train a surrogate model from offline data and subsequently extrapolates it to find an optimal design (as opposed to online MBO which collects more data as it trains the... | Rebuttal 1:
Rebuttal: We thank Reviewer U2YG for their thoughtful feedback on our work, and address their outstanding questions and concerns below.
**Performance of GABO on the 90th percentile metric**
Unlike the other evaluation metrics assessed in our work, GABO indeed does not perform as well on the 90th percentil... | Summary: This paper proposes to use an adversarial regulariser in the BO setting. In particular, the authors propose a systematic way to compute the regularization parameter through a lagrange duality. Overall the regularized method performs on average better than existing methods across a suite of benchmark datasets.
... | Rebuttal 1:
Rebuttal: We thank Reviewer czFU for their thoughtful review of our work, and appreciate that they recognize the technical and empirical contributions of GABO compared with prior work. Please find our responses to their questions below.
**Search for $\alpha$**
Importantly, we confirm that no extra data is... | null | null | null | null |
Spatiotemporal Predictive Pre-training for Robotic Motor Control | Reject | Summary: This paper studies how to extract useful visual features from out-of-domain and action-free human videos to enhance robotic visualmotor control. Specifically, the authors argure that naively extracting spatial features via MAE is insufficient for robotics control, in contrast, jointly captureing spatial contro... | Rebuttal 1:
Rebuttal: **C1: The high costs associated with evaluating real-world tasks using different random seeds make it challenging to report variances. However, assessing the impact of multiple random seeds in simulated tasks could provide more reliable statistical insights. As shown in Table 1, STP's performance ... | Summary: The paper presents a new spatio-temporal pretraining algorithm for representation learning for robotics. The authors propose using masked autoencoding for reconstructing the current frame (for spatial reasoning) and a future frame (for temporal reasoning). The authors provide extensive experimentation across s... | Rebuttal 1:
Rebuttal: **C1: It is unclear where the diverse image data for STP trained with Ego+I in Table 1 is obtained from.**
**R1:** Sorry for the confusion. "STP trained with Ego+I" means that we perform a **hybrid pre-training** using EgoClip and ImageNet data. Specifically, we first initialize ViT with the Imag... | Summary: This paper proposes STP, a visual representation learning method for robotic motor control. Trained on human videos, STP uses masked auto-encoders for spatial-temporal prediction. The spatial decoder predicts the current frame from its representation with 75% of patches masked. The temporal decoder predicts th... | Rebuttal 1:
Rebuttal: **C1: This paper should highlight its differences with R3M, VIP, and V-PTR.**
**R1:** Thank you for your suggestion. We will highlight these differences in the revised paper. Our STP has distinct differences from these works in objectives and techniques.
VIP and V-PTR respectively pre-train the... | Summary: In this paper, we present a self-supervised pre-trained visual representation in robotic motor control, with spatiotemporal prediction with dual decoders, utilizing large-scale video data. The spatial prediction follows a standard MAE pipeline, and the temporal prediction tries to predict the future based on t... | Rebuttal 1:
Rebuttal: **C1: The major concern is the novelty of the previous methods, considering several related papers that leverage human data and visuals pretraining to downstream tasks have been proposed [1-3].**
**R1:** Thanks for your comments. Our STP exhibits some essential differences or advantages with th... | Rebuttal 1:
Rebuttal: We thank all reviewers' efforts in reviewing our paper and giving insightful comments and valuable suggestions. The reviewers' main concerns are concentrated on two primary issues, which we have addressed individually.
**1. There should be a more in-depth discussion on the difference of STP with... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Post-Hoc Reversal: Are We Selecting Models Prematurely? | Accept (poster) | Summary: This paper shows empirical evidence that common approaches to model selection can be improved upon by less greedy alternatives. In particular, authors highlighted what they referred to as post-hoc reversal, when post-training transformations reverse the trends observed in independent training runs. For instanc... | Rebuttal 1:
Rebuttal: Dear reviewer GiJ5,
Thank you for your review
and thoughtful suggestions about writing.
We will take them into account
while revising the manuscript.
We address your concerns regarding experiments below.
> Would it be possible to replicate a subset of the results reported in section 4 and 5 for ... | Summary: The authors investigate applying three post hoc transforms, namely temperature scaling (TS), ensembling, and stochastic weight averaging (SWA), to trained models after training, separately and jointly. They oppose their approach to the "naive approach" and provide empirical observations of a phenomenon they re... | Rebuttal 1:
Rebuttal: Dear reviewer NSoG,
Thank you for your review.
We provide a detailed reponse
regarding the intuitions and explanations for post-hoc reversal (PHR)
in the global rebuttal, along with experimental analyses on CIFAR-10N
and a synthetic dataset to back our claims.
Below we highlight how the global re... | Summary: The paper discusses the phenomenon of *post-hoc reversal*, where the performance trend is reversed after applying post hoc transforms, namely, temperature scaling (TS), stochastic weight averaging (SWA), and ensembling (Ens).
The paper conducts an empirical study to observe the phenomenon across different epo... | Rebuttal 1:
Rebuttal: Dear reviewer vEtm,
Thank you for your review, including suggestions to improve the paper.
We address your concerns below:
> They do not propose any _novel_ mechanism to tackle _post-hoc reversal_.
First, we would like to clarify that
post-hoc reversal (PHR) is not a problem.
On the contrary,
P... | null | null | Rebuttal 1:
Rebuttal: # Overview
We thank all the reviewers for their feedback
and helpful suggestions on improving the work.
We have added extensive explanations and intuitions,
backed by numerous additional experiments and analyses.
We summarize them below:
1. Epoch-, model-, and hyperparameter-wise post-hoc reversa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Flexible Context-Driven Sensory Processing in Dynamical Vision Models | Accept (poster) | Summary: Artificial neural networks are loosely based on biological neural networks. In this study, the authors construct neural networks whose structures are based on general principles of visual signal pathway such as retinotopy. They used this model to ask if the context-dependent feedback mimicking top-down signals... | Rebuttal 1:
Rebuttal: We thank the reviewer for their overall positive comments. We have addressed your comments below including full model and baseline specifications. We hope that we have sufficiently addressed your concerns enough to revise your score.
> **Capture the gist of interplay between low-order sensory are... | Summary: The authors present a convolutional dynamical systems model if visual processing with separate excitatory and inhibitory populations in each layer. They add a low rank modulation of each layer by a factor that is computed as a linear map of the layer activities. This is meant to represent top down modulations ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments. We believe that some of the suggestions raised by the reviewer will increase the quality of the manuscript and we now provide additional analyses and responses to these.
> **Relatively high level of biological detail. The network is image computa... | Summary: The authors introduce DCNet, a hierarchical recurrent vision model that draws inspiration from the structure and function of biological visual circuits. This novel architecture consists of excitatory and inhibitory bottom-up neurons modulated by a (low-rank) representation of a higher area, analogous to the hi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their majorly positive comments. We believe that the suggestions raised by the reviewer will increase the quality of the manuscript and we now provide additional analyses and responses to these.
> **Explicitly highlight DCNet’s anatomical constraints relative to prior wo... | Summary: The paper presents a model for how high levels of (presumably cortical) representation modulate lower levels. A multilayer neural network with recurrent connections both within each layer and between layers is trained on a visual cue-delay-search task. The phenomenology of the model is then studied and chara... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and insightful comments. We provide a point-by-point response below. Your comments have made the manuscript stronger. We hope that we have sufficiently addressed your concerns enough to revise your score.
>**Model mechanisms are motivated by physiology/beh... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time in reading our manuscript and for their extensive feedback. In this general response, we address some common themes across the reviews. We provide detailed answers to specific reviewers' comments in subsequent responses. To go with this rebuttal, we also provi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stylus: Automatic Adapter Selection for Diffusion Models | Accept (oral) | Summary: This work presents a novel system, Stylus, designed to enhance the efficiency and effectiveness of generating high-quality images using diffusion models like Stable Diffusion. The key challenge addressed by this work is the automatic selection and composition of relevant fine-tuned adapters from a vast pool of... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful review and the constructive feedback! We hope the following clarifications address all the questions raised.
**Quality Assurance for Adapters.** We have taken careful preemptive measures to filter out problematic adapters and have outlined in Appendix A.7 a continual ... | Summary: The paper addresses the challenge of selecting and composing relevant adapters for generating high-fidelity images with diffusion models. Stylus introduces a three-stage process: refining adapter descriptions, retrieving relevant adapters based on user prompts, and composing them to create the final image. The... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful and enthusiastic review!
We agree the incorporation of adapters/PEFTs [1] will continue to increase and that automatic adapter selection will be critical for managing and navigating the growing ecosystem of fine-tuned models. Stylus demonstrates improved diversity a... | Summary: The paper proposes Stylus, an approach for automatically selecting and combining fine-tuned adapters on particular tasks to improve the quality of image generation given a prompt. To evaluate Stylus, the paper introduces StylusDocs, a curated dataset containing 75K adapters with pre-computed adapter embeddings... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful and enthusiastic review!
**Reproducibility.** We plan to release Stylus and StylusDocs as open-source resources to ensure transparency and reproducibility.
**Adapter selection and masking.** The composer decomposes the prompt into tasks and maps highly relevant adapt... | Summary: This paper works on post-training optimization for image generation with stable diffusion models. They proposed three stages, refiner, retriever and composer, to personalize a SD model for the prompt and thus to generate the perfect images. The experimental result indicate the potential of the proposed method.... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful review and the constructive feedback! We hope the following clarifications and experiments address your questions.
**Efficiency.** We note that Stylus's efficiency for image generation in terms of CPU and GPU resources is near identical to the base Stable Diffusion ... | Rebuttal 1:
Rebuttal: We are grateful to all the reviewers for their insightful feedback and enthusiastic reviews! To name just a few comments, reviewers acknowledged that Stylus is novel,
> This is a great paper. Original, high quality, clear, and significant. (Reviewer N5AD)
> This work presents a novel system, Sty... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series | Accept (poster) | Summary: It presents a lightweight pretrained model for TS with strong performance.
Strengths: Overall, this is a strong paper with extensive experimental work.
Weaknesses: Adaptive patching is a well-conceived design, although I am unclear why it is termed "adaptive" when the design appears to be fixed and pre-set. ... | Rebuttal 1:
Rebuttal: Thank you reviewer for the constructive feedback. Please find our response below
**Q1: Adaptive patching is a well-conceived design, although I am unclear why it is termed "adaptive" when the design appears to be fixed and pre-set. Perhaps "multiscale patching" would be a more accurate descriptor... | Summary: This paper introduces Tiny Time Mixers (TTM), a new compact pre-trained model for efficient zero/few-shot multivariate time series forecasting. TTM is based on the lightweight TSMixer [1] architecture and incorporates several innovations, which enable effective pre-training on varied dataset resolutions with m... | Rebuttal 1:
Rebuttal: Thank you reviewer for the constructive feedback. Please find our response below
**Q1: The base model TSMixer used for pre-training is not newly proposed.**
Yes, you are correct that TTM builds on the TSMixer foundation. However, TSMixer doesn’t discuss the techniques required to construct a pre... | Summary: The paper introduces Tiny Time Mixers (TTMs), a series of pre-trained models designed for efficient zero/few-shot multivariate time series forecasting. TTMs are built on the lightweight TSMixer architecture and incorporate innovations such as adaptive patching, diverse resolution sampling, and resolution prefi... | Rebuttal 1:
Rebuttal: Thank you reviewer for the constructive feedback. Please find our response below
**Q1: Adding clarification note on the TSMixer architecture**
Thank you for the feedback. Sure. We will clarify the TSmixer used in this paper as compared to the other one.
**Q2: Given that the pre-training dataset... | Summary: This paper proposes a novel time-series pre-trained model TTM that instead of trying to over-parameterize the model, TTM tries to under-parameterize the model for better generalization ability. The model architecture is simple, straightforward, and easy to understand, coming along with advanced training strate... | Rebuttal 1:
Rebuttal: Thank you reviewer for the constructive feedback. Please find our response below:
**Q1: Could the author intuitively explain why such few parameters can work well for time-series forecasting tasks?**
There are three important design choices of TTM that greatly enhance its forecasting accuracy de... | Rebuttal 1:
Rebuttal: Thank you reviewers for your time, effort, and valuable feedback on our paper. We have clarified your queries in the respective sections. We also extend our gratitude to the Area Chairs and all the PC members for investing their valuable time throughout the review process.
**Short summary:** In... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Training Data Attribution via Approximate Unrolling | Accept (poster) | Summary: The paper proposes a training data attribution (TDA) method based on implicit differentiation and unrolling. The goal is to estimate the effect of removing (or changing the weight) of a training example on the final (not necessarily optimal) parameters, accounting for training details influencing the trajector... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive assessment of our paper, acknowledging that it is well-written, technically sound, and presents novel insights. We are grateful for your attention to detail in identifying errors and typos, which we will address in the revised manuscript.
> **Could you point ... | Summary: This paper propose a new TDA method (SOURCE) that combines implicit-differentiation-based methods and unrolling-based approaches. The new method is an extension to the SGD-influence and inherits the advantage to support multi-stage training pipelines while reduce the computation complexity to store and calcula... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer for their thorough evaluation and insightful comments. We are pleased that our paper's presentation and experimental results were well-received. We will address your concerns and questions below.
> **I am not quite convinced and identify the difference between the se... | Summary: The article discusses the limitations of existing training data attribution (TDA) methods, which aim to estimate how a model's behavior would change if specific data points were removed from the training set. The authors propose a new method called Source, which combines the benefits of implicit-differentiatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough evaluation and insightful comments. Our research addresses limitations of influence functions and proposes a practical and novel algorithm to overcome these challenges. Grosse et al. [8], which the reviewer cited as an example of a TDA application, highligh... | Summary: This paper introduces SOURCE, a new data attribution method designed primarily for deep neural networks; more generally, SOURCE is suited for any model class optimized with gradiend-based methods. The authors motivate SOURCE as an approximation to gradient unrolling, i.e., differentiating through the entire tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive and insightful evaluation of our work. We particularly appreciate the recognition of Source’s consistent performance across datasets, the clarity of our paper, and our efforts to balance addressing challenges in realistic scenarios while maintaining co... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all reviewers for their detailed and thoughtful feedback. We are pleased that the reviewers found our work to be well-written, easy to read (**9an4**, **hk4U**, **3Y11**, **5ybi**), addressing an important problem in the field (**9an4**, **3Y11**), technically solid (**9... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding | Accept (poster) | Summary: This paper presents OpenGaussian, a method utilizing 3D Gaussian Splatting for open vocabulary comprehension at the 3D point level. It addresses the limitations of current 3DGS methods that are confined to 2D pixel-level analysis and are inadequate for 3D point-level tasks. The method leverages SAM masks to ma... | Rebuttal 1:
Rebuttal: ## 1. More Detailed Ablation of Two-Stage Codebooks
Thank you for your constructive comments. Following your suggestions, we have incorporated two additional experiments (refer to cases #3 and #4 in the table below) to evaluate the performance when considering xyz coordinate information at the coa... | Summary: In this paper, the authors propose three techniques to enhance the point-level 3D gaussian-based open vocabulary understanding:
1. Intra-mask smoothing loss to draw features within the same mask closer, and inter-mask contrastive loss to increase discriminativeness of the mean feature of each instance.
2. Two-... | Rebuttal 1:
Rebuttal: ## 1. Ablation of Inter/Intra Mask Loss
We truly appreciate your constructive feedback and apologize for any overlook regarding the ablation of the inter/intra mask loss. In response, we have conducted targeted ablation experiments, and the results are presented in the table below. Our analysis is... | Summary: This paper introduces "OpenGaussian," a novel method for 3D point-level open vocabulary understanding using 3D Gaussian Splatting (3DGS). The authors address the limitations of existing 3DGS-based methods that primarily focus on 2D pixel-level parsing. OpenGaussian aims to enhance 3D point-level understanding ... | Rebuttal 1:
Rebuttal: ## 1. Scalability and Generalization
We appreciate the reviewer’s insightful question, which encouraged us to explore the scalability and generalizability of OpenGaussian in other 3D datasets and scenarios.
+ We selected **6 scenes from the Waymo outdoor dataset** captured by vehicle-mounted camer... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
We would like to thank the three anonymous reviewers and the AC for their time and effort in reviewing our paper and providing constructive feedback. We are very grateful for the positive comments from the reviewers, such as “significant advancement over existing methods th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data | Accept (poster) | Summary: The paper proposes the B-ary Tree Push-Pull (BTPP) algorithm for distributed learning over heterogeneous data. The BTPP algorithm introduces 2 spanning directed trees as communication graphs: Pull Tree $G_R$ and Push Tree $G_C$. BTPP has only $\Theta(1)$ communication overhead per iteration and $O(n)$ transien... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide response to your concerns and questions below.
1. **Comparison between BTPP and Push-Pull method.**
There are three main differences between BTPP and the original Push-Pull method. First, the Push-Pull method assumes a general condition on the communic... | Summary: This paper reduces transient time in decentralized optimization algorithms by introducing the B-ary Tree Push-Pull (BTPP) algorithm, derived from the Push-Pull method. The authors assume a setup where any type of communication network can be established among the nodes. They employ two distinct communication n... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide response to your concerns and questions below.
1. **Consensus properties.**
In BTPP, the nodes achieve consensus similar to classical decentralized algorithms. Specifically, noting the definition of $\Pi_{\mathbf{u}}$ on page 7, Lemma 3.3 bounds the su... | Summary: The paper introduces a method for distributed learning, for the common scenario that various "agents" each hold part of a data set locally, and aim to coordinate to find the minimizer of some criterion. Here, the criterion consists of the average of local cost functions, which are assumed to be smooth but non-... | Rebuttal 1:
Rebuttal: Thank you for your comments.
1. **Assumptions on the stochastic gradients.**
The unbiasedness of the stochastic gradients is a result of the sampling strategy.
Note that this work mainly focuses on the scenario where computing the full gradients is expensive due to large datasets. Specifi... | Summary: This paper introduces B-ary Tree Push-Pull (BTPP), an extension of the push-pull framework for distributed learning across a network of agents. The algorithm employs two B-ary tree communication graphs - one for distributing model parameters and one for aggregating gradients. This approach allows each agent to... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide response to your concerns and questions below.
1. **Enhanced experiments.** For the problem of training a CNN on the MNIST dataset, we have further compared the real-time performance of BTPP with other representative methods (see the attachment). The experi... | Rebuttal 1:
Rebuttal: We provide some general feedback corresponding to the common concerns/questions raised by the reviewers. These points will be incorporated in the revision.
1. **Enhanced experiments.** For the problem of training a CNN on the MNIST dataset, we have further compared the real-time performance of BT... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation | Accept (poster) | Summary: This paper examines the prompt learning method used in fair text-to-image (T2I) generation, highlighting its impact on image quality. The authors identify that aligning prompt embeddings with reference image embeddings introduces noise due to unrelated concepts, leading to degraded image quality. They conduct ... | Rebuttal 1:
Rebuttal: We thank Reviewer for detailed feedback. Due to lack of space we consolidate the comments from Weakness(W), Questions(Q), and Limitations (L) into 7 responses.
$ $
>R4Q1: “proposed innovations, Prompt Queuing, and Attention Amplifcation are relatively incremental” (W1)
R4A1: We respectfully re... | Summary: The authors propose FairQueue, a simple and effective solution to solve the quality degradation problem in ITI_GEN through prompt queuing and attention amplification.
Strengths: 1. The paper is well-written and logically structured.
2. The proposed FairQueue effectively solves the quality degradation problem ... | Rebuttal 1:
Rebuttal: >R3Q1: Some of the contribution points of the article can be optimized.
R3A1: We appreciate the Reviewer’s comment and would like to assure the Reviewer that we have already optimized our contributions with further discussion in the supplementary (due to space limitations). Specifically,
- In Su... | Summary: This paper introduces an approach to improving fairness and quality in text-to-image generation models by rethinking prompt learning. The current Inclusive Text-to-Image Generation (ITI-GEN) methods often degrade image quality due to suboptimal training objectives and embedding misalignment between learned pro... | Rebuttal 1:
Rebuttal: >R2Q1:The readability of the paper is poor.
R2A1: We remark that Reviewer 89En scored our presentation excellent, and Reviewer iQtH described it as “well written and logically structured.” We will review the paper once again.
$ $
> R2Q2: I'm curious why the experiments were conducted on only on... | Summary: In the field of fair text-to-image generation, the ITI-GEN paper demonstrated good performance by learning and embedding tSA. However, this paper argues that the embeddings learned in ITI-GEN can include unrelated attributes, resulting in noisy embeddings and significantly degrading the quality of the generate... | Rebuttal 1:
Rebuttal: > R1Q1: “ITI-GEN appears to aggregate all learned tSA embeddings…This difference in implementation may be causing a decline in baseline performance”
R1A1: We thank the Reviewer for their comment, and apologize if it was unclear. We clarify that our code of ITI-Gen as a baseline is **100% identic... | Rebuttal 1:
Rebuttal: Global Response (GR): We thank all the reviewers for their valuable time and effort in reviewing our work. We appreciate the Reviewers' kind comments, such as:
- **Presentation**: Reviewer 89En giving our paper excellent score (4) for presentation; Reviewer iQth for praising our paper as “well-wr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks | Accept (poster) | Summary: This paper studies the impact of feature heterophily, rather than class heterophily, on link prediction tasks with GNN. It introduces the definition of non-homophilic link prediction based on the pairwise feature similarity of connected/non-connected node pairs of the graph. It further shows that the choice of... | Rebuttal 1:
Rebuttal: **W1 Significance of the work**
Thank you for your feedback. We want to point out that going beyond the global characterization of the graphs for the link prediction task, we also provided a zoom-in analysis on the performance on heterophilous edges per method for all the real-world datasets, whi... | Summary: This paper proposes to connect the feature similarity/dissimilarity to the linking possibilities between node pairs. It theoretically demonstrates the necessity of considering the overall feature heterophily of a graph for better link prediction by using a two-dimensional model. On the other hand, the numeri... | Rebuttal 1:
Rebuttal: **W1 Exploring the relationship between feature similarity and structural similarity**
Thank you for raising this valuable suggestion. Exploring the dynamics between feature and structural similarity would indeed be an interesting direction for future research. In this work, our aim is to provide... | Summary: The paper analyze the impact of heterophily in node features on link prediction tasks, and the authors present a theoretical framework that highlights the different optimizations needed for the homophilic and heterophilic link prediction tasks.
Strengths: The paper analyze the impact of heterophily in node fe... | Rebuttal 1:
Rebuttal: **W1, Q1 Clarifications (1) implementation of feature extraction (2) generation of synthetic graphs**
Thank you for your questions.
- For **W1(1)** implementation of feature extraction, we interpret the "feature extraction" part in your comment as how we obtain the node features for our syntheti... | Summary: The paper examines how heterophily in node features affects the performance of Graph Neural Networks (GNNs) in link prediction tasks, which typically do not utilize node class labels. It introduces formal definitions of homophilic and non-homophilic link predictions, proposes GNN designs optimized for feature ... | Rebuttal 1:
Rebuttal: **W1 Theoretical Analysis**
Thank you for your feedback. First, our theorems are derived under reasonable simplifications, which are typical in the heterophily literature (e.g., [1-2]). Second, our empirical analysis extends well beyond these theoretical assumptions, demonstrating that our theory... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for their thoughtful and constructive feedback. We are pleased that all reviewers find the paper clear, most reviewers recognize the novelty of studying the impact of feature heterophily on link prediction with GNNs, and some reviewers (e.g.,... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper analyzes the impact of node features in the link prediction task. Based on the feature similarity, it first categorizes and defines link prediction into homophilic, heterophilic and gated ones then shows the differences among them. Further, it explores the encoder and decoder choices along with the ... | Rebuttal 1:
Rebuttal: **W1&2 Intuitive conclusions & novelty**
We thank the reviewer for the feedback. We note that this is the first work that explicitly characterizes feature heterophily in link prediction with GNNs. Our work pioneers in formalizing the problem, providing concise characterizations, and examining eff... | null | null | null | null | null | null |
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning | Accept (spotlight) | Summary: This paper proposes an unlearning framework based on Langevin dynamics with two key components:
- The initially trained model satisfies some differential privacy guarantee
- Unlearning relies on DP fine-tuning the model on the rest of the dataset (which quickly reduces the privacy loss on the example to be for... | Rebuttal 1:
Rebuttal: We are glad that Reviewer Akdu recognizes our work as a very strong and high-quality paper. We also appreciate the thoughtful comments and suggestions. We address the weaknesses and questions below.
**W1: “Question about LSI constant.”**
Thank you for your valuable suggestion. We will add some i... | Summary: This paper focuses on approximate unlearning under a similar definition of Differential privacy (DP), where privacy is defined as statistical indistinguishability to retraining from scratch. To be specific, they propose Langevin unlearning, which is a new framework based on the noisy gradient descent with priv... | Rebuttal 1:
Rebuttal: We thank reviewer GDGF for their constructive suggestions and positive assessment. We address the weaknesses and questions below.
**W1: “Suggestions for improving the presentation.”**
We appreciate the helpful suggestions for improving the presentation of our manuscript. Following the suggestion... | Summary: This paper proposes a novel framework for machine unlearning through noisy gradient descent with Langevin dynamic analysis. This framework has privacy guarantees for approximate unlearning problems and it unifies differential privacy and machine unlearning process, giving benefits including approximate certifi... | Rebuttal 1:
Rebuttal: We thank reviewer tZ8F for their helpful comments and positive evaluation. We address the weaknesses and questions below.
**W1: “The writing is not easy for readers unfamiliar with differential privacy to follow.”**
We appreciate reviewer tZ8F's insight on making our manuscript more accessible t... | Summary: The paper proposes Langevin unlearning, a new perspective of noisy gradient descent for approximate machine unlearning, which unifies the differential privacy learning process and privacy-certified unlearning process with many algorithmic benefits. The key technique of the proposed method is to carefully track... | Rebuttal 1:
Rebuttal: We thank reviewer 1Nom for their praise and positive feedback. We address their sole question below.
**Q1: “Is it possible to provide experiments on (toy) non-convex problems?”**
Indeed, as mentioned in Section 1.1, the current theoretical bound might not be sufficiently tight for non-convex pro... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization | Accept (poster) | Summary: The paper proposes a multi-resolution network for diffusion model which emphasizes the learning features across resolutions. The method further introduces a time-dependent layer norm to boost the model performance with fewer parameters compared to AdaLN in DiT. This proposed network demonstrates a SoTA perform... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and we carefully address the concerns below.
> W1: The discussion between the method and cascaded methods like "Cascaded diffusion models" and "Matryoshka diffusion models" should be included to highlight the advantages of the method.
... | Summary: This paper proposes to replace the original pull transformer blocks in DiT with multi-resolution network. Detailly, transformer block is employed for low resolution and Conv blocks are used for the remaining higher resolution. An additional time-conditioning block is designed for Conv blocks. The effectiveness... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and we carefully address the concerns below.
> W1: It is unclear what the performance gain comes from. Is the design of transformer in low resolution really necessary?
We thank the reviewer for the suggestion. Below, our experiments in... | Summary: This paper introduces a diffusion model named DiMR, which incorporates a Multi-Resolution Network and Time-Dependent Layer Normalization to enhance image generation quality. Traditional diffusion models, often limited by a trade-off between computational efficiency and visual fidelity, struggle with image dist... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and we carefully address the concerns below.
> W1: The model has a higher FLOPs count compared to a DiT of similar parameter size, raising concerns about slower training speeds.
Thanks for the comment. To address it, we provide a train... | Summary: This paper works on efficient diffusion model backbone architecture by using transformer and ConvNeXt architecture respectively on small and large resolutions of the same inputs, to leverage the strength of both architectures, and alleviate the distortion problem.
Strengths: 1. The writing is easy to read and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and we carefully address the concerns below.
> W1: This work utilizes two standard architectures; more advantageous to propose a new, general, and elegant architecture that combines both.
We thank the reviewer for the suggestion, which... | Rebuttal 1:
Rebuttal: Dear reviewers and ACs,
We thank all reviewers for their valuable comments and feedback, mentioning that our method is "**simple and effective**" (Reviewer 1gRs and MPTv), which "**alleviates distortion of image generation and achieves SoTA FID scores**" (Reviewer Wwps). Additionally, we ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Classification under Performative Distribution Shift | Accept (poster) | Summary: This paper studies the performative learning problem, where the goal is to minimize some measure of *performative risk*, $PR(\theta) := \underset{\theta}{\mathbb{E}}[\ell(Z; \theta)]$, where the difficulty is that random variables come from some distribution the depends on the deployed model parameters $\theta... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her positive review and address his/her questions below. We are also grateful for the four suggestions made in the weakness section to improve the clarity of the manuscript and will made the required changes in the final version of the manuscript.
**Modelling the Per... | Summary: This paper considers a specific performative effect that's characterized as a transformation on the original probability measure on covariate X, which is novel in the literature. The authors propose to restrict the performative effect of deploying model with parameter \theta to such a multiplier function \varp... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her positive review and address his/her questions below.
**Knowledge of $\Pi$** We agree with the reviewer that knowing $\Pi$ entirely is indeed a limitation. In addition to the answers on this point given in response to reviewer PyL2 we point out that, from a theore... | Summary: The authors address the challenge of performative prediction, a scenario in which the predictor's outcomes influence the underlying data distribution. They introduce a novel formulation for the gradient of the performative risk, thereby enabling the implementation of stochastic optimization methods. This new f... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her positive review and address his/her questions below.
**Restriction to Linear Shifts** Theorem 2 proves convexity under general assumptions thanks to the linearity of the shift, and it is unlikely that convexity holds without this assumption. However, our approach... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful assessment of the manuscript and their helpful suggestions to improve the clarity. We are happy to see that reviewers recognize that our "proposed estimator for the performative gradient is novel and innovative" (PyL2) and that our assumptions are "flexible... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive $Q$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning | Accept (poster) | Summary: This paper proposes an offline reinforcement learning framework by combining return-conditioned supervised learning with in-sample Q-learning. By demonstrating their relative merits on offline datasets with different behavior policies, an overall loss function is designed to integrate Q assistance into RCSL. E... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on the strengths of our work. Your valuable suggestions will help us further improve and clarify our work.
### **W1. The definition of degree of Q-aid.**
Thank you for pointing out a good point. We have dealt with environments where the deviations in ... | Summary: Authors of this paper introduce a new algorithm called Q-aided Conditional Supervised Learning (QCS) for offline DRL scenario, which combines the stability of return-conditioned supervised learning (RCSL) and the stitching ability of Q-functions. The primary contributions are 1). Identify the strength and weak... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments that help clarify our algorithm and for the positive feedback on our work. As suggested, we have included further explanations and experimental results to enhance the significance of our study. Due to the rebuttal word limit, **we have addressed the response reg... | Summary: Offline reinforcement learning (RL) has advanced with return-conditioned supervised learning (RCSL) but still lacks stitching ability. Q-Aided Conditional Supervised Learning (QCS) combines RCSL's stability with the stitching capability of Q-functions, addressing Q-function over-generalization. QCS adapts Q-ai... | Rebuttal 1:
Rebuttal: Thank you for providing comments and positive feedback on our work. We hope that our response below will clarify our algorithm and innovations.
### **W1. The innovation of QCS.**
We summarize the innovations of QCS as follows:
* We first analyze the strengths and weaknesses of RCSL and Q... | Summary: This submission proposes an algorithm that combines the stability of return-conditioned supervised learning (RCSL) with the stitching capability of Q-functions. The submission tests their algorithm in the MuJoCo domain with medium, medium-replay, and medium-expert datasets. The performance of the submission’s ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's efforts in providing constructive feedback to improve our work. **Our detailed response to the reviewer's comments is posted below and also in the global response G1-G4.**
### **W1. Comparison with HIQL.**
Thank you for introducing the highly effective algori... | Rebuttal 1:
Rebuttal: We express our deepest gratitude to all the reviewers for their time and effort in evaluating our work and providing valuable advice. Our responses to the reviewers' comments have been left as replies to each review. Moreover, due to the rebuttal word limit, we have posted the responses that could... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Geometric-Averaged Preference Optimization for Soft Preference Labels | Accept (poster) | Summary: Pretrained large language models know information about a wide range of topics, but since pre-training is often done on internet scale data, these models are not aligned with human values. Offline preference learning methods such as DPO are getting increasingly popular for this task.
A key assumption for DPO ... | Rebuttal 1:
Rebuttal: We thank the reviewer for careful reading and thoughtful feedback.
**> What about DPO pushing down probabilities of both positive and negative examples**
As suggested by the reviewer and following https://arxiv.org/abs/2404.14367, **Figure 1 (f)** in additional PDF measures the log ratio $\log \... | Summary: The paper proposes a variation to Direct Preference Optimization which takes into account "soft labels," which aim to reflect that not every evaluator might agree on the relative ordering of a pair of model outputs, or that there may otherwise be a lack of confidence in the ordering of outputs. They model this... | Rebuttal 1:
Rebuttal: We thank the reviewer for careful reading and detailed feedback.
**> W1 (Problem from Bradley-Terry model)**
As the reviewer mentioned, the objective functions stemming from Bradley-Terry model cause "over-optimization" issues, which is inherent in DPO and its derivation from Bradley-Terry model... | Summary: This paper introduces a novel approach to aligning Large Language Models (LLMs) with human preferences by incorporating distributional soft preference labels. The authors argue that existing methods like Direct Preference Optimization (DPO) assume binary, deterministic preferences, which may not accurately ref... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful feedback. Please let us know if our responses in the following address your concerns.
**> Main Weaknesses of Previous Papers & How GDPO Resolves them**
As discussed in Section 3.2, GDPO can avoid (1) "over-optimization" issues (compared to DPO) and (2) objective mi... | Summary: This paper introduces the concept of soft preference labels and proposes leveraging this distributional information, alongside deterministic binary preferences, to enhance Direct Preference Optimization (DPO) losses. By incorporating a weighted geometric average of the LLM output likelihood in the loss functio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
**> W1 & Q2 (Definition of Soft Preference Labels)**
Thank you for pointing this out. We will update the definition of soft preference labels as follows in the revision:
We assume that the binary preference labels ($l(y_1 \succ y_2 | x) = 1$)... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for the detailed and thoughtful feedback. To address the concerns and questions, we provide a one-page PDF for the figures and tables of additional experiments in addition to our response to each reviewer. Here is a brief overview of the PDF contents:
- **(a)** Fi... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Many existing algorithms for aligning large language models (LLMs) with human preferences assume these preferences are binary and deterministic. However, preferences can vary among individuals and should be represented distributionally to capture subtle relationships between responses. This work introduces dis... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback.
**> W1 & Q1 (Can GDPO handle multiple preference labels at once?)**
Following the reviewer’s suggestion, we finetune the LLM the Anthropic Helpfulness and Harmlessness preference datasets simultaneously to study the balance between different real... | null | null | null | null | null | null |
Enhancing Large Language Models through Adaptive Tokenizers | Accept (poster) | Summary: The presented paper proposes an adaptive tokenization scheme that is learned jointly with an LLM and assesses its performance on downstream tasks for smaller model sizes and token budgets.
Strengths: - Interesting idea to include both the loss of the current iteration as well as the loss of the previous itera... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our work and your thoughtful review.
**Q1. Both the model size (up to 410M) and the corpus size (16B) seem to be on the smaller side and it is unclear if these findings would generalize to the billion level parameter level and trillion level token budget.*... | Summary: This study introduces an adaptive tokenizer whose development is integrated with the performance of the LLM. The tokenizer has the particularity that it is fine-tuned based on the model’s perplexity during training. Empirical results show that this approach improves accuracy compared to “traditional” tokenizat... | Rebuttal 1:
Rebuttal: Thank you for your efforts to enhance the quality of our manuscript. We appreciate the issues you identified, and we believe we have thoroughly clarified and addressed them as follows.
**Q1. There are only comparisons with very common tokenizers: BPE, BytePiece, and Unigram. This is enough to gui... | Summary: This paper proposes a method to learn the tokenizer of a language model as part of language model training. The method works by combining a compression loss (which is also used by classical tokenization algorithms) with a language modeling loss, and iteratively removing tokens that contribute the least to the ... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our work and your insightful comments.
**Q1. This ignores the impact that token $x$ has on the language modeling loss as part of the left context. I would have expected a more in-depth discussion.**
Thanks for your insightful feedback. In response to your ... | Summary: Language model vocabularies are typically learned using frequency-based objectives. This objective does not entirely align with the language modeling objective–the task for which these vocabularies are ultimately used–and may therefore cause a bottleneck in language model performance. This proposes a new token... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our paper and valuable comments.
**Q1. The algorithm's repeated LM training and unaddressed runtime issues may limit practicality.**
Our algorithm is feasible in practice, because the optimization phase of the tokenizer only requires a minimal amount of dat... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' time and the valuable feedback they have provided for our paper. These constructive comments have been instrumental in enhancing the quality of our work. Here is one common concern we would like to address:
**General Response: Discussion of Limitations.**
W... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multidimensional Fractional Programming for Normalized Cuts | Accept (poster) | Summary: The paper presents a new fractional programming based algorithm for the multi-cluster normalized cut objective which exploits a multidimensional quadratic transform.
The paper starts by introducing the Ncut problem. Then it discusses previous fractional programming methods such as Dinkelbach's method for the ... | Rebuttal 1:
Rebuttal: Thank you so much for appreciating the presentation and the technical contributions of this work. We would like to address your concerns and questions in the following.
1. **Weakness:** Thanks for the constructive suggestion. We have added two larger datasets: letter recognition (with 20,000 s... | Summary: This manuscript deals with the **Normlaized cut (NCut)** problem by proposing a new algorithm called **fractional programming-based clustering** (FPC). The main idea of FPC is rewriting the original NCut problem to an equivalent one by using a so-called **Multidimensional quadratic transform**. Then, the clust... | Rebuttal 1:
Rebuttal: Thank you so much for the very positive comments on this work. Also thank you so much for providing many constructive suggestions. We have added the convergence rate analysis and many new experiments according to your suggestions, as specified in what follows.
1. **Weakness One:** We would edit... | Summary: The paper addresses the challenge of the Normalized Cut (NCut) problem in unsupervised clustering.
Conventional fractional programming (FP) techniques, especially Dinkelbach’s transform, are inadequate as they only handle single ratios and are limited to two-class clustering.
This paper extends the quadratic... | Rebuttal 1:
Rebuttal: Thank the reviewer for acknowledging the strengths of this paper. We would like to focus on the "Weakness'' and "Questions'' parts in the following.
1. **Weakness One:** Actually, the paper never claims that the reformulation of the NCut problem as a multiple-ratio problem is a contribution o... | null | null | Rebuttal 1:
Rebuttal: First of all, we wish to thank the TPC members for organizing reviews for our paper. The comments from Reviewer MUdC and Reviewer fiXH are quite positive; they both think that the paper is well written and contains sufficient novelty and technical contributions in terms of the NeurIPS criterion. T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multiclass Transductive Online Learning | Accept (spotlight) | Summary: This paper studies transductive online learning in the multiclass setting, where the label space can be unbounded. In the transductive setting, the adversary commits to a sequence of examples and can only adaptively choose labels for a sequence of instances, unlike the pure online setting where the adversary c... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out that our techniques could be of independent interest to the learning theory community and that our result almost rounds out the literature on regret and mistake bounds in the original transductive online learning setting. All minor issues and suggestions will... | Summary: The paper studies the problem of multiclass transductive online learning where the number of classes can be unbounded.
In the transductive setting, the learner receives in advance a sequence of instances $(x_1,…,x_T)$ by an adversary. Then, sequentially at each time step t, it needs to decide a label $\hat{y}... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work to be well written and a nice contribution to a fundamental problem. All minor typos and suggestions will be incorporated in the final version. We address each weakness and question below.
- The algorithm achieving the $\log{T}$ upper bound in the reali... | Summary: This work continues the study of transductive online classification (a learning setting from the 90s recently reviewed by Hanneke et al. [NeurIPS 2023]. The main result is a trichotomy of possible rates for the general multi-class case (even for the infinite label case) in the realizable setting; answering an ... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work timely and interesting. All minor typos and suggestions will be incorporated in the final version. We address each weakness and question below.
- For small $k$ (i.e. $k << 2^{(\log{T})^2}$), the Natarajan bound can be smaller than the upper bound in te... | Summary: This paper addresses the problem of multiclass transductive online learning with unbounded label spaces. The paper extends previous work on binary and finite label spaces to the more general case of unbounded label spaces. The authors introduce two new combinatorial dimensions - the Level-constrained Littlesto... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that our work resolves an open problem in online learning theory and that our paper is very well written and easy to understand. We address each weakness and question below.
- Multiclass learning with unbounded label spaces is a fundamental setting that has been ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LLM Circuit Analyses Are Consistent Across Training and Scale | Accept (poster) | Summary: This paper examines how a few common circuits (IOI, greater than, verb agreement, gendered pronoun prediction) develop at different model scales and timings in training. The main findings are that these circuits develop at the same time across different model scales, that once they develop they do not disappea... | Rebuttal 1:
Rebuttal: Thanks for your thorough review! We respond below, abbreviating your comments for space reasons.
## Weaknesses
> the methods introduced are not scalable to large datasets of auto-discovered circuits
We disagree in part about the scalability of our methods. EAP-IG is efficient, and can now be use... | Summary: This paper studies the stability of circuits in language models during pre-training and across different model sizes. Specifically, it examines the Pythia model suite and a selection of four simple tasks with known circuits: indirect object identification (IOI), subject-verb agreement, greater-than, and gender... | Rebuttal 1:
Rebuttal: Thanks for your helpful review! We agree that many of the issues you bring up are important, and have attempted to address them below.
## Weaknesses:
> While the results demonstrate a significant degree of consistency and stability of circuits across training, the focus on a small number of simp... | Summary: This study explores the emergence and evolution of internal mechanisms in language models of varied sizes during the training process. Specifically, it examines simple tasks such as IOI, Greater-than, Gender Pronoun, and Subject-verb agreement using Pythia models. The findings indicate that models of different... | Rebuttal 1:
Rebuttal: Thanks for your insightful review! These are good points, which we answer below.
## Weaknesses
> 1.1 All the model heads are evaluated, rather than only those involved in the circuit performing the task. It is possible that some heads exhibit certain behaviors without actually being part of the c... | Summary: This paper presents a set of analyses on the dynamics with which internal language models’ mechanisms emerge and change during training. The four mechanisms studied are internal circuits that the model implements to carry out four simple tasks: indirect object identification (IOI), gendered pronoun, greater-th... | Rebuttal 1:
Rebuttal: Thanks for your review and helpful suggestions! We respond to your questions (which contain your stated weaknesses), below.
> 1.1 As a reader, I would appreciate some additional details about the experimental procedure here: what are the exact heads that are being ablated? How are they selected fo... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their thoughtful responses! We are glad to hear that reviewers feel that our paper:
- Presents novel and interesting insights (dE5s, v6cM, wt3i)
- Extends beyond prior work by studying models across scales (865w, wt3i, v6cM)
- Is valuable to the mechani... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Expressive Gaussian Human Avatars from Monocular RGB Video | Accept (poster) | Summary: The paper focuses on improving the expressiveness of digital human avatars, particularly through detailed hand and facial expressions, learned from monocular RGB video. The main contributions are:
- SMPL-X Alignment improves the alignment of the SMPL-X model with RGB frames and aids in accurately recovering av... | Rebuttal 1:
Rebuttal: **Q1: One of the contributions of using and optimizing SMPL-X for expressive avatar is not novel. This has been done in previous works GVA.**
A1: GVA is a concurrent work and has not been accepted. Compared with GVA, our fitting design is quite different, since ours explicitly focuses on the fine... | Summary: The paper introduces a 3DGS-based human avatar generation method from a monocular RGB video. Specifically, the authors first optimize the SMPL-X model to better align with the RGB frames. Then, they propose an adaptive adjustment method for different body parts and use per-pixel confidence to guide 3DGS. It is... | Rebuttal 1:
Rebuttal: **Q1: How can the effectiveness of this confidence be ensured?**
A1: The predictor could leverage the information contained in rendered RGB and depth to adaptively learn the confidence in an end-to-end data-driven manner. The experimental results have validated the effectiveness of CL and we also... | Summary: The paper presents EVA, a model that can recover expressive human avatars from monocular RGB videos. The primary focus is enhancing fine-grained hand and facial expressions using 3D Gaussian Splatting and the SMPL-X model. EVA introduces three key contributions: a plug-and-play module for better SMPL-X alignme... | Rebuttal 1:
Rebuttal: **Q1: Suggestion on simplifying some aspects or providing more intuitive explanations.**
A1: Thanks for this suggestion. We commit to releasing our code upon acceptance. Our work makes a step towards building an expressive avatar from the real-world video. In short, our method aims to answer two ... | Summary: This paper proposes a solution to generate expressive human avatars from monocular RGB videos. The main focus is to improve the expressiveness. To this end, a few ideas such as combining 3d Gaussian splatting and SMPL-X (SMPL+parametric hands), minimizing 2d reprojection error, finegrained density control, et... | Rebuttal 1:
Rebuttal: **Q1: Clarification on reprojection losses.**
A1: We want to clarify that the reprojection losses (keypoint, mask, perceptual loss) are not claimed as our contributions. Instead, we include them in our paper to describe the necessary components of our framework and make sure our paper is self-con... | Rebuttal 1:
Rebuttal: **General Response**
We sincerely appreciate the reviewers for their insightful and constructive comments. We are encouraged by their positive comments and the recognition of the merits of our work. More specifically, the reviewers have appreciated the great importance and challenges introduced b... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: - Given an RGB human video, the paper focuses on photorealistic reconstruction of the human in 3D using SMPL-X and Gaussian rendering.
- The main idea is to align the expressive SMPL-X with the video evidence followed by Gaussian Avatar modeling with better gaussian density control and loss.
- Technical contr... | Rebuttal 1:
Rebuttal: **Q1: Clarification on technical contributions**.
A1: Our proposed three technical contributions are well-motivated and well-formulated to meet the requirement of expressiveness in human avatar modeling, which are validated by extensive experiments and have been acknowledged by Reviewer 4SGk ('th... | null | null | null | null | null | null |
On Tractable $\Phi$-Equilibria in Non-Concave Games | Accept (poster) | Summary: The paper presents new results about no-$\Phi$-regret learning when the utilities are not concave. When $\Phi$ is either finite or in a precise sense "local", it is shown that no-$\Phi$-regret learning is possible, and hence that the corresponding notions of $\Phi$-equilibria can be efficiently approximated at... | Rebuttal 1:
Rebuttal: Thanks for your positive review and constructive comments! We will add a note on the trivial regime where $\delta = O(\epsilon)$. We will update Table 1 to distinguish our contributions better. Below, we address your questions.
Q: *Could you elaborate on Corollary 1? It is not obvious to me how t... | Summary: This paper describes a concept of $\Phi$-equilibrium in non-concave games, which is a rarely discussed notion in the recent literature. This notion of equilibrium is defined over a set of strategy modifications, with specific definitions of $\Phi$, $\Phi$-equilibrium can recover commonly known notions such as ... | Rebuttal 1:
Rebuttal: Thank you for the positive review and constructive comments on the structure and presentation of the paper!
We will incorporate your suggestions and improve the presentation in the revised version of the paper. Since one more page is allowed in the camera-ready version, we will assign more space ... | Summary: This work studies the $\Phi$-equlibrium in non-concave games and discuss when the $\Phi$-equlibrium of a non-concave game can be learned in polynomial time. The theoretical results presented in this paper indicate that if the set $\Phi$ is finite, then there exists an efficient uncoupled algorithm that converg... | Rebuttal 1:
Rebuttal: Thank you for your very positive review. Below, we address your questions.
Q: *I found some papers showing that for Markov games, the CE or CCE can be learned in polynomial time. For example Jin, Chi, et al. "V-Learning--A Simple, Efficient, Decentralized Algorithm for Multiagent RL." arXiv prepr... | Summary: The paper studies nonconcave games and $\Phi$-equilibrium. When $\Phi$ is finite, the paper showed that there exists an uncoupled learning algorithm that can efficiently find the equilibria. Under certain classes of strategy modification, online gradient descent can also approximate $\Phi$-equilibria when $\Ph... | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Better by default: Strong pre-tuned MLPs and boosted trees on tabular data | Accept (poster) | Summary: The authors introduce RealMLP, an improved multilayer perceptron (MLP), alongside improved default parameters for GBDTs and RealMLP. The authors tune RealMLP on a meta-train benchmark with 71 classification and 47 regression datasets and compare them to hyperparameter-optimized versions on a disjoint meta-test... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
Major weaknesses:
1.
- We meant to write “is competitive with GBDTs in terms of benchmark scores”, we will update this sentence. Of course, GBDTs are faster on most datasets with our settings. But note that lower is better in terms of benchma... | Summary: In the paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data", the authors make two major contributions: (i) they propose a multi-layer perceptron configurations that is tuned on a set of training datasets and (ii) investigate in a large scale empirical study how different default p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
Weaknesses:
- Thank you for the suggestion. We have something like this in Figures B.12-B.15, D.2, D.6, D.7, or do you have a specific suggestion for an overview plot?
- Random search 1) is used in the Grinsztajn et al benchmark, 2) is more con... | Summary: The work investigates/provides better default hyperparameter configurations for MLPs and gradient-boosted decision trees. Additionally, it proposes an augmented neural network with a series of enhancements. Experimental results are provided showing that the neural network is on par with gradient-boosted decisi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
Weaknesses:
1. What is the source for your claim? Which AutoML tool would these be in? Are you referring to some of the color-coded improvements or only to the gray ones? There are some non-novel components in Figure 1 (c) since we wanted to s... | Summary: The paper introduces RealMLP, an enhanced Multilayer Perceptron (MLP) designed for classification and regression tasks on tabular data. It also proposes optimized default parameters for both RealMLP and Gradient Boosted Decision Trees (GBDTs). Through benchmarking on diverse datasets, the authors demonstrate t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
Weaknesses:
1. **[1] was uploaded on 13th of July 2024**, so this paper cannot be used to question our novelty. (Besides, it also uses a mix of architectures, while we consider algorithm selection / ensembling.) Regarding ensembles, we do not ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
thank you for the constructive feedback. We identified two main points raised by multiple reviewers:
- 4/5 reviewers asked for more baselines. In total, ten baselines were mentioned (MLP-PLR, ExcelFormer, Net-DNF, TabNet, TabCaps, versions of TabPFN, TabR-HPO, ResNet-HPO, SAINT, FT... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes an enhanced version of the tabular MLP model -- RealMLP. By using multiple tricks over simple MLP, the proposed method becomes much more competitive with GBDTs than a simple MLP. Moreover, the authors provide strong "tuned default" configurations for GBDTs and RealMLP. These configs consider... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
Weaknesses:
1. While we agree that such experiments would be interesting, they would be very costly and are not central to the research question we want to answer in the paper (how good can we make MLPs and GBDTs with default parameters?). We ... | null | null | null | null | null | null |
Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention | Accept (poster) | Summary: The paper presents a novel neural network model designed for supervised graph structure inference. The core contribution is the introduction of a Bilinear Attention Mechanism (BAM) which processes dependency information at the level of covariance matrices of transformed data. This approach respects the geometr... | Rebuttal 1:
Rebuttal: Dear Reviewer wDfW,
Thank you for the insightful and constructive feedback. We sincerely appreciate your recognition of the novelty and effectiveness of our Bilinear Attention Mechanism (BAM) for graph structure inference.
**Research motivation:** We agree that the paper will benefit from a cle... | Summary: This paper studies the problem of graph structure inference using a neural network with bilinear attention mechanism.
Strengths: The proposed framework is novel and can operate in Euclidean and positive semi-definite matrix spaces. Attention mechanism is utilized in an innovative manner to reveal interdepende... | Rebuttal 1:
Rebuttal: Dear Reviewer 2mp1,
Thank you for recognizing the novelty and innovation of our framework. We appreciate the constructive and insightful feedback, on clarifying the advantages of our BAM layer in the SPD space and the theoretical guarantees of our approach.
**Significance and advantages of SPD ... | Summary: This paper proposes to use supervised causal learning approach to learn causal structure. It takes a dataset as input, and outputs a moral graph. The moral graph is an undirected graph with two types of edges: skeleton edge and moralized edge.
In technical space:
architecture: The approach adopts alternating ... | Rebuttal 1:
Rebuttal: Dear Reviewer rbW5,
Thank you for the insightful and constructive feedback. We sincerely appreciate your recognition of the potential impact of our approach.
**Significance of SPD layers:**
We agree that the paper will benefit from a clearer justification of the integration of SPD layers and the... | Summary: This paper proposes a novel neural network model for supervised graph structure inference. The model aims to learn the mapping between observational data and their underlying dependence structure using a bilinear attention mechanism (BAM). The BAM operates on the level of covariance matrices of transformed dat... | Rebuttal 1:
Rebuttal: Dear Reviewer wqVr,
Thank you for the insightful and constructive feedback. We greatly appreciate your recognition of the potential of our approach.
**Efficiency:**
We acknowledge that the level of computational resources required for our method (approximately 82 GiB, as stated in lines 309-312)... | Rebuttal 1:
Rebuttal: We appreciate the time and effort the reviewers have invested in evaluating our work. We are grateful for your constructive feedback and insightful suggestions, which have helped us identify areas for improvement.
We have added a PDF at the end of the author rebuttal, which includes a comparison... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation | Accept (poster) | Summary: This paper explores the potential of the Latent Diffusion Model for Few-shot Semantic Segmentation. The authors study four crucial elements of applying the Diffusion Model to Few-shot Semantic Segmentation and propose several reasonable solutions for each aspect.
Based on their observations, the authors estab... | Rebuttal 1:
Rebuttal: We deeply appreciate your acknowledgment of our motivation and approach. We will try our best to address the questions and weaknesses you have raised.
**W1: Lack of proper references**
Thank you for your feedback. We will add appropriate references in the next version.
**W2: Definition of $\beta... | Summary: This paper explores the potential of diffusion model in Few-Shot Segmentation (FSS) tasks. For achieve better performance, it examines four critical elements of applying the diffusion model to FSS. Building on this research, the paper introduces the DiffewS framework, with experimental results validating its e... | Rebuttal 1:
Rebuttal: We sincerely thank you for your very careful and detailed review. We are delighted that you found our work to be comprehensive and convincing. The questions and weaknesses you've pointed out are incredibly helpful to us, and we will do our utmost to address them below.
**W1: Why should the diffus... | Summary: This paper introduces DiffewS, a novel Diffusion-based generalist model designed for few-shot segmentation. It systematically examines four key components involved in applying Diffusion Models to Few-shot Semantic Segmentation. For each component, the work proposes several viable solutions, which are validated... | Rebuttal 1:
Rebuttal: Thank you very much for acknowledging our motivation and approach. The questions and weaknesses you've pointed out are incredibly helpful to us, and we will do our utmost to address them below.
**Weaknesses: N-shot setting**
Thanks again for pointing out this issue. As we mentioned in the Experi... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to extend our heartfelt thanks to all of you for taking the time and demonstrating professionalism in thoroughly evaluating our work. Your constructive feedback has been immensely helpful in further improving the quality of our work and refining our research. We are ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Multi-dimensional Explanation Alignment for Medical Classification | Accept (poster) | Summary: This work introduces a novel end-to-end concept-based framework called Med-MICN, which is quite inspiring and important for the next XAI era due to its multi-dimensional, powerful interpretability, and efficient performance. Furthermore, they propose an automated process for efficiently and accurately obtainin... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' recognition of our work and your constructive comments. Please find below our detailed responses to the queries you have raised. We hope you could consider increasing our overall score if our response addresses your concerns :)
> W1: "The caption of the picture is not... | Summary: This paper focuses on the explainable classification of medical images with multi-dimensional explanation. The proposed Med-MICN framework contains four modules including feature extraction, auto-annotation, concept embedding, and neural symbolic layer. The incorporation of fuzzy logic rules is novel in the in... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' recognition of our work and your constructive comments. Please find below our detailed responses to the queries you have raised. We hope you could consider increasing our score if our response addresses your concerns :)
> W1: "captions and notations..."
**Response**:... | Summary: This work introduces Med-MICN, an explainable framework for medical image classification. This framework leverages a concept bottleneck framework combined with a neural symbolic reasoning framework to generate simple explanations for its predictions. In general, strong performance is demonstrated.
Strengths: ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' recognition of our work and your constructive comments. Please find below our detailed responses to the queries you have raised. We hope you could consider increasing our overall score if our response addresses your concerns :)
> W1,Q1: "Notations.."
**Response**: We... | Summary: The authors proposed a novel interpretable model called Med-MICN in this work. This method manages medical image classification tasks with multi-dimensional aspects with neural symbolic and concept semantics. With the help of LLMs, the work performed superior on four medical benchmark datasets. In the ablation... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' recognition of our work and your constructive comments. Please find below our detailed responses to the queries you have raised. We hope you could consider increasing our overall score if our response addresses your concerns :)
> W1, W2: "evaluation of the interpretab... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback. We have revised and supplemented our work in the following two aspects.
1. **Notations and Captions**: We have added notations to Figure 2, 3 and also enhanced the captions for Figures 2, 3, and 4 to improve readability. We have placed the mod... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion | Accept (poster) | Summary: This paper proposes a more effective dynamic-k expert selection rule that adjusts the number of executed experts on a per-token basis to reduce the high computational cost of the transformer models. They claim their D2DMoE model outperforms existing approaches.
Strengths: 1. The motivation of this paper is go... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent on our work. We are glad that the reviewer recognizes the proper motivation of our method and the thoroughness of our experiments. Below, we address the concerns raised by the reviewer.
### Weaknesses
> The most important contribution is the dynamic-k rou... | Summary: The paper presents Dense to Dynamic-k Mixture-of-Experts (D2DMoE), a method to convert a dense model into a MoE model, that exploits the fact that activations in Transformer models are typically very sparse. The sparsity can be further improved by the addition of square Hoyer regularization during a light fine... | Rebuttal 1:
Rebuttal: We thank the reviewer for assessing our work and recognizing that our paper is well-written. Below, we address the reviewer's concerns.
> The use of MoEs from scratch is criticized because the training difficulties that these have. However, the training recipes for these have improved significant... | Summary: This paper proposes a method to convert a dense pre-trained transformer network into its sparse counterpart. The approach begins by fine-tuning a pre-trained network to sparsify its activation values. Subsequently, the method clusters the neurons in the MLP layers to form distinct experts and introduces a rout... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our paper. We are pleased to see that the reviewer recognizes the significance of our work and the robustness of our method across different experimental settings. Below, we answer to the issues raised by the reviewer:
### Weaknesses
> This ... | Summary: This paper improves over MoEfication with four innovations: (i) enforcing higher activation sparsity; (ii) directly predict the norm of the output of each expert; (iii) dynamic-k expert selection scheme; and (iv) generalization to any standalone linear layer. The resulting method achieves significant improveme... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our paper. We are pleased that the reviewer recognizes the novelty, applicability, and performance of our method. Below, we address the issues raised by the reviewer.
> The baseline seems weak. ZTW only receives a few citations and is not pub... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the time and effort spent on our work and their valuable comments that helped us improve our work. We appreciate that our work has been praised by the reviewers for its thorough empirical evaluation (reviewers VwY8, R1eJ), novelty (reviewer uQnr), the generality of o... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a method called D2DMoE aimed at enhancing the efficiency of transformer models. D2DMoE implements a dynamic-k routing mechanism that allows the model to select a variable number of experts based on the input. The method leverages the inherent activation sparsity in transformer models to re... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing our work. We are grateful that the reviewer recognizes that our paper presents a thorough empirical evaluation and compares the proposed method against relevant baselines. Below, we present the responses to the issues listed by the reviewer.
> ... | null | null | null | null | null | null |
Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction | Accept (poster) | Summary: NOTE: I have reviewed a previous version of this paper submitted to AAAI 2024. The review here is an updated version of that review reflecting the changes in the paper.
==============================
This paper explores the problem of comparing numerical judgements against each other in order to arrive at a ... | Rebuttal 1:
Rebuttal: Thank you for your recognition and thoughtful comments.
We are grateful that you updated your review reflecting the changes in our paper. Compared with the AAAI 2024 version, we significantly improved our work. Notably, we strengthened our theoretical results \- our algorithm was improved from po... | Summary: This work defines and studies the "Quantitative Relative Judgment Aggregation" problem, which involves asking a set of judge agents to predict the performance of a set of "competitor agents" in some kind of content (e.g., a race). This task is related to recsys style collaborative filtering, ranking systems in... | Rebuttal 1:
Rebuttal: Thank you for your recognition and thoughtful comments.
We are glad that you find our contribution novel and strong, and our presentation extremely clear. We are particularly delighted to see you noted and recognized our documented code for the experiments. Below, we respond to the questions rais... | Summary: The paper studies Quantitative Relative Judgment Aggregation, in which they want to learn a quantitative score for a group of alternatives that align with a series of pairwise quantitative differences between alternatives as much as possible. They extend the result in [Conitzer et al, 2016] from linear loss fu... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments.
We are glad that you find our problem interesting and important, and our theoretical results sound and non-trivial. Below, we respond to the questions raised in your review.
**On the first question**
Conitzer et al., 2015 \[1\] is a short visionary paper ... | Summary: The paper generalizes relative quantitative judgments by Conitzer et al., which aims to aggregate judgments on the relative quality of different candidates from various sources and applies it to a learning-to-rank setting. The authors introduce new aggregation rules QRJA that tries to assign vector $x_1,\dots... | Rebuttal 1:
Rebuttal: Thank you for your recognition and thoughtful comments.
We are glad that you find the topic of our work neat and our theoretical dichotomy results intuitive and interesting. Below, we respond to the questions raised in your review.
**On the first point in Weaknesses**
$\\ell\_p$ QRJA is indeed ... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to read our paper and provide thoughtful comments.
We are delighted to learn that the reviewers find our topic “interesting and important” (Fgap), and the “combination of social choice and ranking prediction” “neat” (iMha). In addition, we are glad that ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
X-Ray: A Sequential 3D Representation For Generation | Accept (spotlight) | Summary: The paper presents X-Ray, a new 3D sequential representation inspired by the penetrating quality of X-ray scans. This technique converts a 3D object into surface frames at different layers, ideal for creating 3D models from images. Experimental findings show that the X-Ray approach outperforms existing methods... | Rebuttal 1:
Rebuttal: # To Reviewer 5c6P
### Question 1: General, accurate, and efficient 3D representations
The author asserts that "General, accurate, and efficient 3D representations are three crucial requirements for 3D generation." However, the citation is missing or the author should provide evidence to support t... | Summary: The paper addresses the problem of missing mesh interiors in image-to-3D generation. The proposed representation, X-Ray, adopts ray casting to encode both visible and hidden surfaces into a video format. With this representation, the authors enable single image-to-3D mesh generation, including the inside of th... | Rebuttal 1:
Rebuttal: # To Reviewer wtWj
We appreciate the reviewer's highly positive rating to our paper! We address the reviewer's further concerns and questions in the following responses.
### Questions 1: Performance of Computed Tomography
Considering Computed Tomography, it is expected that the performance will si... | Summary: The paper proposes a new representation that encodes a 3D mesh into the ray intersection points from a single view point. The position, color, and surface normal at the intersection points are stored as the representation. Poisson reconstruction is used to recover the 3D mesh from the representation. A cascad... | Rebuttal 1:
Rebuttal: # To Reviewer EY92
### Question 1: Multiple viewpoints Extension.
How to extend the proposed representation to include multiple viewpoints to provide better encoding/decoding quality.
### Response 1
we aim to show the advantage of the X-Ray by providing a simple baseline for single-view 3D recons... | Summary: This paper introduces X-Ray, a new 3D representation designed for efficient generation of 3D objects from single images. The key idea is to represent a 3D object as a sequence of 2D "surface frames" capturing hit/miss, depth, normal, and color information along rays cast from the camera viewpoint. This sequent... | Rebuttal 1:
Rebuttal: # To Reviewer xHzv
### Question 1: Comparison to Existing Techniques
A thorough comparison to existing techniques like depth peeling, multi-view depth images, MPI, and the PI3D.
### Response 1
* We discussed multi-view images and MPI in the related work. Furthermore, Multi-view depth cannot sense ... | Rebuttal 1:
Rebuttal: ### Responses to All Reviewers:
We would like to express our sincere gratitude for the valuable feedback provided by the reviewers. It is truly encouraging to see that our X-Ray work has been given positive evaluation by all the reviewers. We appreciate the comments and constructive suggestions gi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation | Accept (poster) | Summary: This paper proposes a plug-and-play diffusion refiner for pre-trained zero-shot feed-forward MDE (monocular depth estimation) models, so that these generalized models can capture fine-grained details. In this paper, the coarse depths output from a pre-trained feed-forward MDE model are used as additional condi... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments. We kindly ask the Reviewer to read the **top-level global response** first. Our detailed responses to the comments in the weaknesses (denoted as W) and questions (denoted as Q) sections are listed below.
> **W-1: Motivation**
Both the training data and the ... | Summary: The paper proposes a simple approach to improve and refine current monocular depth estimation (MDE) methods. Leveraging the strong geometric prior from a state-of-the-art discriminative depth estimation method, and the strong image prior from a generative model, the authors set a new state-of-the-art in MDE. T... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We kindly ask the Reviewer to read the **top-level global response** first. Our detailed responses to the comments in the weaknesses (denoted as W) and questions (denoted as Q) sections are listed below.
> **W-1: Contribution**
Apart from the depth-conditio... | Summary: This paper presents a plug-and-play monocular depth estimator with the diffusion model. In the proposed method, the authors first employ the pretrained monocular depth model (MDE) to estimate a coarse depth map as the condition of the diffusion model. Then, a modified diffusion refiner is used to obtain the fi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We kindly ask the Reviewer to read our **top-level global response** first. Our detailed responses to the comments in the weaknesses (denoted as W) section are listed below.
> **W-1: Novelty**
One of our key contributions is the proposed training strategies... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers and area chairs for their valuable time and comments. We will incorporate all suggestions to improve the revised paper. After providing more results/analyses, we would like to give an overall response and re-emphasize the contribution and performance of BetterDepth... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Emergent Abilities of Language Models from the Loss Perspective | Accept (poster) | Summary: This paper measures a range of language models' pretraining loss and their downstream performance, arguing that emerging capabilities are better measured by losses as opposed to previously proposed model size or compute (FLOPs). The paper also offers evidence against the argument that emergent abilities are a ... | Rebuttal 1:
Rebuttal: For weakness 1, we add results on three tasks on BIG-Bench in the PDF file of global rebuttal.
For question 1, when the language model directly predicts the answer, EM and perplexity give the same trend. However, with chain-of-thought reasoning, the language model first predicts the intermediate ... | Summary: This paper formulates the concept of emergence as a relationship between language modeling training loss and task performance, rather than in relation to model/data scale.
Strengths: The paper is clear. I appreciate the central message, as descriptions that relate the capabilities strictly to the scale of a l... | Rebuttal 1:
Rebuttal: We want to argue that the reviewer misunderstands the difference between our work and Schaeffer et al. In fact, Schaeffer et al do not use perplexity as the evaluation metric for multi-choice tasks. Instead, they propose to use Brier Score, which we also evaluate in Figure 4. The main difference i... | Summary: They demonstrate that 1) pre-training loss is generally predictive of downstream capabilities in language models, rather than models size or number of tokens used during pre-training; and 2) emergent capabilities can also be clearly described in terms of pre-training loss. They also demonstrate that using cont... | Rebuttal 1:
Rebuttal: We agree with the influence of data quality on performance besides compute. We will add the point in the introduction when making the comparison.
Thank the reviewer for appreciating the ablation of learning rate schedule. We will make it clearer in the final version.
The exponential smoothing is... | Summary: The paper investigates the link between the pre-training loss of LLMs and their downstream performance on popular benchmarks. The authors train a series of models ranging from 300M to 32B parameters on English-Chinese datasets of different sizes and study how these models perform on TriviaQA, HellaSwag, RACE, ... | Rebuttal 1:
Rebuttal: For weakness 1, the results on BIG-Bench are presented in the PDF of global rebuttal. The original emergent ability paper evaluates 4 tasks on BIG-Bench. The test set size of the figure-of-speech detection task is too small and the variance is too high. Therefore we evaluate the other three tasks.... | Rebuttal 1:
Rebuttal: The results on BIG-Bench are presented in the PDF file. The original emergent ability paper evaluates 4 tasks on BIG-Bench. The test set size of the figure-of-speech detection task is too small and the variance is too high. Therefore we evaluate the other three tasks. We can observe with pretraini... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper investigates emergent abilities in language models from the perspective of pre-training loss, rather than model size or training compute. The authors challenge recent skepticism about emergent abilities by demonstrating that: (1) Models with the same pre-training loss, regardless of model and data s... | Rebuttal 1:
Rebuttal: We admit the limitations of the work in different model architectures, tokenizers, and pre-training corpus distribution. The main reason is that the work analyzes not only the final checkpoints of different models, but also the intermediate checkpoints, which are not publicly available for many op... | null | null | null | null | null | null |
Follow Hamiltonian Leader: An Efficient Energy-Guided Sampling Method | Reject | Summary: This paper presents a novel parallel sampling method named "Follow Hamiltonian Leader" (FHL) designed to address sampling challenges by leveraging zeroth-order information, particularly when first-order data is unreliable or unavailable. The method incorporates a leader-guiding mechanism to enhance the efficie... | Rebuttal 1:
Rebuttal: **Rebuttal**
Thank you for reviewing our paper and providing insightful feedback. We appreciate your positive remarks and constructive criticism, which will help us improve our work. Below, we address your comments and questions:
1. **Quantitative Experiments on CIFAR-10:**
We understand yo... | Summary: This paper introduces an interesting parallel sampling method that leverages zeroth-order information to address challenges in sampling from probability distributions, particularly when first-order data is unreliable or unavailable. The method incorporates a leader-guiding mechanism, enhancing efficiency and e... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for highlighting both the strengths and areas for improvement in our paper.
**Explanation**
For most sampling algorithms, the objective is to develop a proposal function $Q(x'|x): \mathbb{R}^d \rightarrow \mathbb{R}^d$ that generates a new sample $x'$ from ... | Summary: This work proposes to incorporate the energy $U$ into the gradient-based sampling techniques. In particular, it proposes to choose the lowest energy particle as the leader and then add an extra elastic tension between the leader and followers in the Hamiltonian Monte Carlo method.
Strengths: The idea is simpl... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for providing constructive feedback. We appreciate your comments and have addressed your points and questions below:
1. **Simplicity and Clarity:**
We are pleased that you found our approach simple and clear. The goal was to design a meth... | Summary: This paper presents an interesting new approach for improving sampling methods for energy-based generative models and score-matching models. The key idea is to incorporate zeroth-order information (energy values) in addition to the typical first-order gradient information used by most sampling algorithms like ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed comments on our paper. We appreciate your feedback and are grateful for the opportunity to clarify some aspects of our work. Below, we address your comments and questions:
1. **Novelty of Zeroth-Order Energy Information**
We are glad you found the ... | Rebuttal 1:
Rebuttal: We thank all the reviewers and provide a comprehensive rebuttal to address common questions raised by several reviewers regarding the proposed FHL algorithm. Here is a brief summary:
1. Theoretical Guarantee
2. Quantitative Evaluation
3. Sensitivity to Hyperparameters
4. Parallelizability
5. Lar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Best-of-N Decoding via Speculative Rejection | Accept (poster) | Summary: Best-of-N decoding involves generating multiple responses and then selecting the highest scoring one according to a given metric of interest. Based on the observation that the reward function used for scoring the utterances can distinguish high-quality hypotheses from low-quality ones at an early stage of the ... | Rebuttal 1:
Rebuttal: # Response for the reviewer gUaJ:
We appreciate the reviewer's detailed comments and we have read them carefully. Here are our detailed responses.
---"L36-38: I don’t think that saying that Best-of-N is “essentially” hyperparameter-free makes sense here. As you point out, is a hyperparameter. Al... | Summary: Best-of-N is a decoding-time alignment algorithm that effectively aligns the output of the system at the cost of high inference time. The paper seeks to reduce the computation time by pruning unpromising sequences at early stage using the reward model to estimate the reward on the partial utterance. They empir... | Rebuttal 1:
Rebuttal: # Response for the reviewer qQm8:
We thank the reviewer for providing positive feedback and several good questions of our work. Below are our detailed responses.
---- "Experiments are conducted only on AlpacaFarm-Eval dataset. It would be ideal to have an evaluation of the proposed method in othe... | Summary: The paper proposes an early stopping method to accelerate the Best-of-N method. Experimental results demonstrate its effectiveness.
Strengths: 1. The method has a strong and clear motivation, coupled with easy implementation.
2. Experimental results demonstrate its effectiveness in accelerating best-of-n whil... | Rebuttal 1:
Rebuttal: # Response for the reviewer Qphu:
We thank the reviewer for providing valuable feedback and understanding the value of our work. We have read your comments carefully and below are our detailed responses.
---- "Best-of-N is out-of-date in decoding-time alignment, which weakens the novelty of the ... | null | null | Rebuttal 1:
Rebuttal: # General Response to all reviewers:
We thank all reviewers for the detailed comments and valuable questions. We present additional experiments here---as requested by the reviewers---and also address the reviewers' individual questions separately.
These new experiments include:
- New datasets
- LL... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures | Accept (poster) | Summary: This paper proposes a novel method for graph knowledge distillation. The proposed method incorporates knowledge from both Euclidean and hyperbolic teacher models and transfers it to a student model in a way that leverages the appropriate geometry for different local subgraphs. A SWKT module is used to select ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments! We hope that our response can resolve your concerns. Please feel free to ask any follow-up questions.
---
# W1:
**Notation Definitions:**
- $N$: Number of nodes
- $|E|$: Number of edges
- $D$: Dimension of node features
- $H$: Dimension of hidden ... | Summary: This paper introduces a new graph distillation framework using special teacher networks to consider Euclidean and hyperbolic geometries when performing the distillation to the light-weight student GNN model.
Key techniques include Structure-Wise Knowledge Transfer (SWKT) for selecting appropriate geometric spa... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time and effort in reviewing our paper. We hope that our response can resolve your concerns. Please feel free to ask any follow-up questions.
---
# S3:
In fact, we provided sensitivity analysis of the weight coefficient $\beta$ in Figure 3 (middle). We pe... | Summary: This paper proposes a novel methodology to distill graph neural network (GNN) integrating the various geometries (e.g. Euclidean and hyperbolic). The author first utilizes a structure-wise knowledge transfer module with multiple teacher models from distinct geometric properties. Then, the authors demonstrate ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time and effort in reviewing our paper. We take all comments seriously and try our best to address every raised concerns. Please feel free to ask any follow-up questions.
---
# Q1:
Yes, our experimental settings are strictly following the original papers. ... | Summary: This paper presents a cross-geometric graph knowledge distillation method for graph neural networks. This method employs multiple teacher models, each generating different embeddings with distinct geometric properties, such as Euclidean, hyperbolic, and spherical spaces. The student model is based on Euclidean... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the time and effort in reviewing our paper. We hope that our response can resolve your concerns. Please feel free to ask any follow-up questions.
---
# W1 & Q1:
It is true that the failure of one or more teacher models could potentially impact the student mode... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank the reviewers for the time and effort in reviewing our paper. We take all comments seriously and try our best to address every raised concerns. Please feel free to ask any follow-up questions.
It is encouraging to learn that the reviewers stated that our method... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Retrieval & Fine-Tuning for In-Context Tabular Models | Accept (poster) | Summary: Tabular data is an important yet understudied modality in machine learning. Following recent success of Tab transformer and TabPFN, people have made significant progress on tabular tasks. This paper is an extension of TabPFN, a previous sota tabular learning model on small scale data, and the authors combine w... | Rebuttal 1:
Rebuttal: > There are many limitations on the datasets requirements, e.g. number of features, number of classes, tasks
We agree. There are ways to go beyond them while still using the same architecture though. For instance one can perform feature selection very efficiently as the forward pass of TabPFN is ... | Summary: The authors extend the recently introduced TabPFN to larger and more complex datasets by fetching a relevant context for each test point using a KNN algorithm. The author evaluate two methods, TabPFN-knn, which consists of using the original TabPFN on the fetched context for each test point, and LocalPFN, whic... | Rebuttal 1:
Rebuttal: Thank you very much for your thorough review and helping us improve the paper.
> How are runtimes computed for figure 11? Which hardware? Which hyperparameters?
The time reported is the average time to perform training+inference for a single run (averaged over parameters/datasets/seeds). As such... | Summary: The paper introduces Locally-Calibrated PFN (LoCalPFN), an advanced model for tabular data that enhances the transformer-based TabPFN by incorporating retrieval and fine-tuning techniques. By using k-Nearest Neighbours (kNN) to select a local context for each data point and fine-tuning the model on this retrie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> “Although LoCalPFN shows improved performance, the fine-tuning process increases computational complexity and runtime, especially with large datasets.”
Yes this is true, however note that the complexity added by the finetuning process is not specific t... | Summary: The paper proposes LoCalPFN, a new method that improves the scaling of transformer-based in-context learning for tabular data. It uses retrieval and fine-tuning to adapt the transformer to local subsets of the data, and demonstrates state-of-the-art performance on a variety of datasets.
The paper makes the fol... | Rebuttal 1:
Rebuttal: We appreciate the time you took to review our paper. We hope to clarify some points of confusion below in our response.
> "Recent advancements [...] have struggled to scale to larger and more complex ones." is inaccurate -- depending on the dataset and task definitions.
Can you please clarify th... | Rebuttal 1:
Rebuttal: # General message
We would like to thank the reviewers for their assessment on our work. Overall, reviewers have appreciated our evaluations and experiments (mentioning points such as “extensive” [`j47T`], “robust and comprehensive” [`iFbB`], and “well done, .. principled, … no-cherry picking, … s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings | Accept (poster) | Summary: This paper proposes TEA-GLM to align the GNN representations with LLM token embeddings for zero-shot graph machine learning. TEA-GLM enables cross-dataset and cross-task learning without fine-tuning the LLM. Extensive experiments demonstrate its state-of-the-art performance on unseen tasks and datasets.
Stren... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments! We have addressed your questions as follows:
> The experimental results are somewhat questionable. The vanilla Vicuna-7B even outperformed LLaGA, which leveraged Vicuna-7B as its base model.Furthermore, the results on E-commerce-Children ... | Summary: The paper propose a GNN plus LLM model for zero-shot learning in the graph domain. They first use contrastive learning to pretrain a GNN that can be applied to arbitrary graphs. They added a feature-wise contrastive learning objective on the projected representation to the PCA of LLM token embeddings to bridg... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable suggestions. We have addressed your questions as follows:
> My major concern about the paper is its novelty. Several important components, including using self-supervised learning to generate general node embedding [1], alignning graph representation... | Summary: The motivation of this work is to utilize the zero-shot learning capacity in LLMs for structural data. The basic idea is to connect GNNs with LLMs, by aligning its representations with the token embeddings of an LLM, such that GNNs can encode the structural information and LLMs can do zero-shot inference. The ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's understanding and recognition of the contribution of our work. For the problem regarding
> I do think the presentation of this work can be better. Some parts of the paper (especially section 2) are a little bit difficult to understand,
we apologize for the ... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate the detailed reviews and valuable suggestions provided by the reviewers. Due to the character limit for a separate response, here we will focus on addressing the concerns regarding the novelty of our paper and presenting the results of the additional experiments.
**Novelty... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Does Variance Shape the Regret in Contextual Bandits? | Accept (poster) | Summary: *** In line 12 there is a comment with acronyms of one of the authors, for the AC's consideration if it is a desk reject ***
The paper studies the setting of adversarial contextual MAB under the assumption of access to a realizable general function class for approximating the context-dependent rewards.
The au... | Rebuttal 1:
Rebuttal: Thank you very much for providing these useful feedback and questions.
Weakness:
- *Unclear related work.*
We will improve the related work section based on the suggestion.
- *Difference between the three settings.*
The separation between strong and weak adversary is different from that betw... | Summary: This paper considers contextual bandits with function approximation, and studies the influence of the variance information on the regret bound. It studies three regimes: weak adversary where the variance is revealed before each time step; strong adversary where the variance depends on the chosen action at each... | Rebuttal 1:
Rebuttal: - *Can the authors summarize the main technical novelty used in the paper?*
We have the following technical contributions.
1. Algorithm design through checking disagreement: Our algorithms in Sec 4 and Sec 6 decide whether to use inverse gap weighting based on the degree of disagreement in the ... | Summary: This paper studies contextual bandits with general function approximation, emphasizing the impact of variance information. It proves lower bounds dependent on the Eluder dimension and variance information in both strong and weak adversary settings. It also proposes algorithms for both cases. When the adversary... | Rebuttal 1:
Rebuttal: Thank you very much for providing these useful feedback and questions.
Answers to Weaknesses:
- *Mismatch upper and lower bound for large action space cases*
We have such results for cases where $d_{\mathrm{elu}} < A$ (an upper bound $\tilde{\mathcal{O}}(d_{\mathrm{elu}}\sqrt{T\sigma^2\log|\math... | Summary: This paper studies the problem of obtaining variance-aware regret bounds for realizable contextual bandits with general function approximation. They show that, despite intuition suggested by prior works, there is an unavoidable dependence on the Eluder dimension in getting variance-aware bounds. They investiga... | Rebuttal 1:
Rebuttal: Thank you very much for providing these useful feedback and questions.
Answers to Weaknesses:
- *The main weakness is that the only upper bound results matching the lower bounds require some knowledge of the variance at every single round or under a stronger model class assumption (which then in... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Instance-adaptive Zero-shot Chain-of-Thought Prompting | Accept (poster) | Summary: This paper explores the inside interactions among three components(i.e., question, prompt, rationale) in zero-shot CoT reasoning through the saliency score analysis, and discover the distinct characteristics of good and bad reasoning in terms of information flow. Based on above findings, the authors propose th... | Rebuttal 1:
Rebuttal: Thank you for the constructive advice and comments, which have greatly improved our manuscript, and we are glad to reply to your invaluable suggestions and questions.
### **Response to Weaknesses**
**Weakness 1:**
The proposed method is not clearly explained, some details are missing.
**Respons... | Summary: The paper introduces an instance-adaptive prompting algorithm for zero-shot Chain-of-Thought (CoT) reasoning in large language models (LLMs). Traditional task-level prompts are insufficient for all instances, so the authors propose a strategy that differentiates good and bad prompts based on information flow f... | Rebuttal 1:
Rebuttal: Thanks a lot for your valuable reviews, and we appreciate the time and effort you have taken. Regarding the weaknesses and questions, we would like to elaborate and address your concerns on this work.
### **Response to Weaknesses**
**Weakness 1:**
Limited Scope of Neuron Saliency Score Analysis:... | Summary: This paper analyzed the mechanism of the large language models (LLMs) zero-shot Chain-of-Thought (CoT) reasoning, in which the authors found a pattern to discriminate a good reasoning path and a bad one with the saliency scores. Based on the findings, this paper proposed a set of instance-adaptive prompting ap... | Rebuttal 1:
Rebuttal: Thanks a lot for the time and effort you invested in providing the detailed reviews. Regarding the current weaknesses and questions you pointed out, we are glad to give our responses.
### **Response to Weaknesses**
**Weakness 1:**
Experiments only covered 7B and 13/14B models, involving larger m... | Summary: The authors argue that a single, task-level prompt is insufficient for addressing the diverse needs of different instances within a dataset. To overcome this limitation, they propose an instance-adaptive prompting (IAP) algorithm that differentiates between effective and ineffective prompts for individual inst... | Rebuttal 1:
Rebuttal: We deeply appreciate the time and effort you invested in reviewing our paper, and we are glad to answer your questions.
### **Responses to Weaknesses**
**Weakness 1:**
As the number of available prompts grows, the strategy for selecting the best prompt could become increasingly complex.
**Resp... | Rebuttal 1:
Rebuttal: We want to thank each reviewer for your thoughtful reviews and constructive feedback on our manuscript. We appreciate the time you invested in evaluating our submission, and we are grateful for your detailed suggestions and recommendations. We have carefully considered each of your comments and ha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Scalarizations for Sublinear Hypervolume Regret | Accept (poster) | Summary: This abstract presents a study on non-linear scalarization techniques for multi-objective optimization, specifically focusing on hypervolume scalarizations with random weights. The authors prove that this approach achieves optimal sublinear hypervolume regret bounds and apply it to multiobjective stochastic li... | Rebuttal 1:
Rebuttal: **Novelty of our paper:** Our paper is focused on describing how fast scalarizations can approximate the Pareto frontier under finite samples even with *perfect knowledge of the Pareto frontier (whitebox setting)* and is first of its kind (see Theorem 7). Specifically, we introduce the notion of t... | Summary: This paper shows that the hypervolume scalarization has sublinear hypervolume regret bounds of $O(T^{-1/k})$, and further proves a lower bound of hypervolume regret of $\Omega(T^{-1/k})$. An optimization algorithm for multiobjective linear bandits is proposed. An empirical study is conducted to verify the effe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review.
**Novelty of our paper:** Our paper is focused on describing how fast scalarizations can approximate the Pareto frontier under finite samples even with *perfect knowledge of the Pareto frontier (whitebox setting)* and is first of its kind (see Theore... | Summary: For multi-objective optimization, a common technique is to use scalarizations to reduce the multi-objective to one single objective. While it is easy to use linear function/scalarizations, it does not fully explore a concave region of Pareto frontier. The paper focuses on non-linear scalarization, in particula... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review.
**Scalarizations and Hypervolume Regret:** Our study considers both the scalarization and the weight distribution (see Figure 1 for visualization) and provides a regret guarantees in the worst case for all frontiers. We have included some novel synth... | Summary: The paper addresses the challenge of exploring the Pareto frontier in multi-objective optimization problems, particularly focusing on minimizing hypervolume regret. Linear scalarizations are often inadequate as they fail to explore certain non-convex regions of the Pareto frontier. The authors propose using no... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review.
**Comparison with Other Scalarizations:** The hypervolume regret convergence rate is both a function of both the scalarization and the weight distribution and is a guarantee in the worst case for all frontiers in the whitebox setting. Even the hyperv... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Compact Proofs of Model Performance via Mechanistic Interpretability | Accept (poster) | Summary: This paper makes progress towards the formal verification of model performance by using insights from mechanistic interpretability to reduce proof length. It presents a case study in a simplified setup: a single-layer, single-head attention-only transformer applied to a synthetic task of finding the maximum of... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
We have also rectified the typographical issues.
> You only compare sequence lengths of up to four because the brute-force solution is infeasible beyond that.
Could your proposed proof strategies be used to make guarantees beyond this length? I believe that demonstra... | Summary: The authors apply techniques from mechanistic interpretability to ``compact-ify'' the process required to prove model performance on the max-of-$k$ task, specifically where $k=4$. Through this procedure, the authors claim that (1) there is a positive relationship between mechanisitic understanding and the leng... | Rebuttal 1:
Rebuttal: Thank you very much for the extremely detailed review!
We also appreciate the list of typographic errors, which we’ve corrected in a revised draft of the paper.
We’d like to start by defining what we mean by “compact proof of global robustness” by clarifying our definition on lines 48-69 of Secti... | Summary: The paper aims to generate formal guarantees certifying model’s performance on max of K task. Using brute force gives the bound close to true performance of the model but the complexity cost i.e. compactness is exponential in the size of the vocabulary. The paper then aims to use understanding derived from mec... | Rebuttal 1:
Rebuttal: Thank you for your review!
> The tightness of bounds seem to degrade very quickly as additional insights from mechanistic interpretability are used.
Thus it seems difficult if future works would be able to use insights from mechanistic interpretability to derive tight bounds on performance of the... | Summary: This paper does a detailed and careful case study of the trade-offs and design space of formal verifications for meaningful lower bounds of the accuracy of a transformer model on a chosen task:
- Specifically, a one-layer, one-head, no-MLP, no-layernorm transformer is studied (akin to the model studied in deta... | Rebuttal 1:
Rebuttal: Thank you for your kind review!
> how can this method be carried over to more realistic tasks?
It would be relatively straightforward to carry over the methodology to more realistic tasks if the following conditions were met:
1. A sufficiently detailed mechanistic understanding of the component... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their comments and feedback!
We’re happy that reviewers agree that our approach to verification is novel and that our approach to mech interp is rigorous.
We address the five common questions that reviewers raised.
**(1) Motivation for picking a toy setting (vjrV, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generative Retrieval Meets Multi-Graded Relevance | Accept (spotlight) | Summary: The paper targets the problem of Multi-Graded Relevance in a Generative Retrieval setup.
Generative Retrieval uses a Seq2Seq model to decode relevant “docids” for a given query. Prior work on Generative Retrieval has focused on tasks where there is only one relevant document per query and the degree of relev... | Rebuttal 1:
Rebuttal: Thank you for the constructive and valuable comments. With regard to different comments about weaknesses, our responses are as follows:
**[Comment 1]** While not a strong weakness, the mathematical notation in the paper can use some work to make the paper an easier read. I found it tough to parse... | Summary: The work deals with generative retrieval for cases where documents have multi-graded relevance. The authors propose GR2 a framework for generative retrieval in such cases by tackling two important challenges. First they optimize for relevance and distinctiveness of document IDs by a regularized fusion approach... | Rebuttal 1:
Rebuttal: Thank you for the constructive and valuable comments. With regard to different comments, our responses are as follows:
**[Comment 1]** Some important baselines are missing in the current version of the work. ColBERT (ColBERTV2) in PLAID setup is a powerful dense retriever and would serve as a val... | Summary: The paper proposes a novel QG based docid for generative retrieval, optimising relevance and distinctness of the generated queries jointly. It introduces the MGCC loss with multi-graded labels. Experiments on subsets of Gov2, ClueWeb09-B, Robust04, MS Marco, and NQ with up to 500k documents validate the effect... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive and valuable comments. In response to the various feedback, we address each point as follows:
**[Comment 1]** Limited corpus scale remains a concern for various GR works, further discussion on its potential influence on the proposed method is needed. None... | Summary: In this paper, the authors propose a new generative retrieval model, which utilizes multi-grade relevance labels instead of binary relevance. Using graded relevance labels is not well-discussed in previous works.
Pros:
- The problem itself is interesting and important. Generative retrieval is a hot research t... | Rebuttal 1:
Rebuttal: Thank you for the constructive and valuable comments. With regard to your comments, our responses are as follows:
**[Comment 1]** In the Introduction, the authors introduced the reason why the simple generation likelihood of docids cannot work for graded relevance. It is strange to claim that "Do... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bridging semantics and pragmatics in information-theoretic emergent communication | Accept (poster) | Summary: This study explores how language, combining meaning and context, might evolve. It examines how a shared vocabulary can emerge from interactions that consider the situation. By training agents in an unsupervised fashion to consider both context-specific utility and general communication pressures, the research... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and encouraging feedback!
To address the reviewer’s questions:
> The paper's approach is interesting. To just confirm my understanding: in this specific setting, pragmatic representations will be always more specific (like hyponyms) than semantic representa... | Summary: This paper addresses the co-evolution of pragmatics, which must be interpreted according to context, and lexical semantics, the common meaning independent of context. In this paper neural agents are trained in the pragmatic setting to select the target object from two objects, and evaluated in the semantic set... | Rebuttal 1:
Rebuttal: > The explanation for the significance of the scenario that training in a pragmatic setting and testing in a semantic setting is insufficient.
> What is the significance of training in a pragmatic setting and testing in a semantic setting?
We believe that this concern is somewhat related to the ... | Summary: This paper looks at aligning emergent communication with human language through the joint optimization of the utility, informativeness, and complexity of a communication channel.
This is done in the context of a "pragmatic" signalling game where a speaker agent must refer to an object in an image contrasting i... | Rebuttal 1:
Rebuttal: **Clarity + Questions (major)**
The main goal of the paper is to study the interface between semantics and pragmatics, and specifically, to address the question: how can a shared human-like lexicon emerge from task-specific, context-sensitive pragmatic interactions? As the reviewer noted in the “... | Summary: This paper investigates emergent communication in artificial agents and how properties of the emergent language compares to properties of human language. Specifically, it aims to present a framework to study pragmatic language emergence in systems with different learning objectives, in order to determine const... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. Major: The reviewer assumed that “people should be expected to achieve nearly 100% in performance” and based on that, argued that we should disqualify systems that do not achieve near-perfect task utility. We would like to clarify that the reviewer’s claim is inconsistent with ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their helpful and thoughtful comments. We are excited that the reviews are generally positive and in favor of publication. As we explain in our detailed response to each reviewer and in the summary below, we believe that all the concerns raised by the reviewers can b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions | Accept (spotlight) | Summary: Efficient influence functions (EIFs) for nonparametric estimands are used to construct debiased estimators. Existing methods are primarily estimand-specific and require intricate analytic derivations; existing automated methods don't scale well. This paper proposes general Monte-Carlo estimators of the efficie... | Rebuttal 1:
Rebuttal: We are very grateful to receive such positive feedback that our submission is well motivated, theoretically sound, the evidence is convincing, and that MC-EIF opens up future interesting directions. We also appreciate the actionable feedback.
**(1) Notation**. We thank the reviewer for flagging t... | Summary: The paper establishes a novel method for estimating the efficient influence functions under mild assumptions, called Monte Carlo Efficient Influence Functions(MC-EIF). The method is easy to apply and flexible in many cases, where it can seamlessly equip an existing/popular efficient estimator.
Strengths: It i... | Rebuttal 1:
Rebuttal: We are very grateful to receive such positive feedback on our submission’s exposition, the quality of our theoretical and empirical evidence, and MC-EIF’s generality and ease-of-use. Thank you also for helping us to improve our work with actionable feedback.
**(1) Time comparison relative to othe... | Summary: The paper proposes Monte Carlo Efficient Influence Functions (MC-EIF), an automated technique for numerically computing efficient influence functions using existing differentiable probabilistic programming systems. MC-EIF simplifies efficient statistical estimation for high-dimensional models, achieving optima... | Rebuttal 1:
Rebuttal: We are very grateful to receive such positive feedback on our submission’s exposition, and empirical and theoretical evidence. We are also very grateful to receive actionable feedback we can use to further improve our work.
**(1) Assumption 3.5**. The constant C will not grow with D in Assumption... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring Fixed Point in Image Editing: Theoretical Support and Convergence Optimization | Accept (poster) | Summary: This paper provides important theoretical support and practical optimization in the field of image editing, especially the fixed point problem in the DDIM inversion process. Through the application in image editing and dehazing tasks, the effectiveness and generalization potential of the fixed point theory are... | Rebuttal 1:
Rebuttal: We thank Reviewer eqay for devoting time to this review and providing valuable comments.
**Weaknesses:**
> **W1:** No detailed computational resource requirements (i.e. GFLOPs, param.) are provided.
**A:** Our approach relies on the Stable Diffusion v1.4 model, which is available through the Dif... | Summary: The paper concerns with the fixed points of the DDIM inversion scheme, which is central to many image editing methods. The authors first establish that the DDIM inversion process at any given time step $t$ exhibits a unique fixed point by demonstrating that the corresponding functional has a Lipschitz constant... | Rebuttal 1:
Rebuttal: We thank Reviewer Cq8S for devoting time to this review and providing valuable comments.
**Weaknesses:**
> **W1:** Is Inequality (7) pointwise ineuqliaty? Inequality (7) and (11) are missing $||\cdot||$.
**A:** Inequality (7) is a pointwise inequality. Additionally, Inequalities (7) and (11) sho... | Summary: The paper proves the existence and uniqueness of fixed points in DDIM inversion using the Banach fixed-point theorem. It identifies flaws in existing fixed-point loss functions and proposes optimizations to improve convergence and visual quality of edited images. It also introduces a novel text-based approach ... | Rebuttal 1:
Rebuttal: We thank Reviewer 5f4S for devoting time to this review and providing valuable comments.
**Weaknesses:**
> **W1:** The performance gains the paper proposes are minor.
**A:** The reason why the performance gains are minor in image editing is that across the entire dataset, we used a fixed number ... | Summary: Recent methods treat each step of DDIM inversion as a fixed-point problem to reduce errors, but they lack theoretical support. This paper addresses this gap by making the following contributions: This paper theoretically proves that the Lipschitz constant in DDIM inversion is less than one. By applying the Ban... | Rebuttal 1:
Rebuttal: We thank Reviewer cPeB for devoting time to this review and providing valuable comments.
**Weaknesses:**
> **W1:** This assertion is especially unconvincing when $t$ is large.
**A:** The premise of this assertion is the DDIM deterministic sampling without perturbation. Under this premise, it is ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning | Accept (poster) | Summary: The paper targets the constrained RL problem and provides a primal-dual method C-PG to solve it. The proposed method is extended to C-PGAE and C-PGPE to handle the constraint cases with risk measures. The paper provides a theoretical analysis of the global last-iterate convergence guarantees toward C-PG and e... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our paper and for recognizing that our work is well-written and that our theoretical results are rigorous. Below, we address the raised issues.
**General novelty.** To the best of our knowledge, our paper is the first to introduce a **general fr... | Summary: The paper studies policy-based methods in constrained RL. The author first establishes the last-iterate convergence of the algorithm C-PG under a form a gradient domination assumptions. Then, the author further designs action-based and parameter-based versions of C-PG to handle constraints defined in terms of ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for having appreciated the contribution our work brings and its presentation. In the following, our answers to the Reviewer's concerns.
**Section 4.** We thank the Reviewer for having raised this point. First of all, **Section 3** introduces the **exploration-agnostic** algo... | Summary: This paper proposes a general framework for addressing safe RL problems via gradient-based primal-dual algorithms. The authors show that the proposed algorithm exhibit global last-iterate convergence guarantees under gradient domination assumptions. Additionally, the authors validate their algorithms on severa... | Rebuttal 1:
Rebuttal: We thank the Reviewer for reviewing our work and for appreciating the clarity, the soundness, and the theoretical novelty.
**The role of $\omega$.** The desired accuracy $\varepsilon$ is **decided before** employing the algorithms. As prescribed by theory, $\varepsilon$ appears in the expression ... | Summary: This paper studies the problem of constrained MDP. To solve this problem, this paper adopts the policy gradient methods, and specifically they considered the action-based policy gradient method and parameter-based policy gradient method.
The algorithm proposed in this paper is a type of primal-dual method. U... | Rebuttal 1:
Rebuttal: We thank the Reviewer for appreciating our work and recognizing its novelty. Below, we respond to the Reviewer's questions.
> Do you have lower bounds showing that these rates are tight?
The derivation of a lower bound for Constrained MDPs with **continuous state and/or action spaces** is still ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection | Reject | Summary: The paper "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection" introduces a minimalist reconstruction-based framework for unsupervised anomaly detection (UAD) in multi-class settings. The framework focuses on four main components: Foundation Transformers, Noisy Bottleneck, Line... | Rebuttal 1:
Rebuttal: First, thank you for your valuable reviews and comments.
__W1,2&Q1,2: Justification and discussion of proposed components, and comparison of the difference with prior works.__
In Noisy Bottleneck, we show that the simple Dropout can work as a noise injection module to transformer "reconstruction... | Summary: This paper focuses on the Multi-class Unsupervised Anomaly Detection task and proposes a minimalistic reconstruction-based anomaly detection framework — Dinomaly that consists of only vanilla Transformer blocks. In this framework, four key components (Foundation Transformers, Noisy Bottleneck, Linear Attention... | Rebuttal 1:
Rebuttal: Thank you for your valuable reviews and comments.
__W1: The method relies heavily on transformer architectures, which might limit its applicability to other types of models.__
Transformer architecture has proven its ability as the foundation of machine learning tasks, including NLP (GPT, Llama) ... | Summary: This paper introduces Dinomaly, a simple yet effective anomaly detection framework using pure Transformer architectures. It identifies four key components essential for multi-class anomaly detection: Foundation Transformers, Noisy Bottleneck, Linear Attention, and Loose Reconstruction. Extensive experiments on... | Rebuttal 1:
Rebuttal: First, thank you for your valuable reviews and comments.
__W1: Relevant evidence rather than subjective assumptions for L53-55.__
Previous works on MUAD do make large efforts to design special modules for mitigating "identity mapping". Taking works of NeurIPS as examples, UniAD (NIPS22), the pi... | Summary: This paper introduces Dinomaly, a minimalistic unsupervised anomaly detection (UAD) method designed to bridge the performance gap between multi-class UAD and class-separated UAD. Utilizing pure Transformer architectures with key components such as Foundation Transformers, Noisy Bottleneck, Linear Attention, an... | Rebuttal 1:
Rebuttal: Thank you for your valuable reviews and comments.
We appreciate the concern about the balance between application and theory in our work. While Dinomaly does focus on practical applications and SOTA results, we believe it makes theoretical contributions to the field of unsupervised anomaly detect... | Rebuttal 1:
Rebuttal: First, thank all reviewers for their valuable reviews and comments.
Please post any new questions in the 7-day discussion period.
Pdf: /pdf/97da5aaabad7df74ef6cd078ac93f27b791b79c4.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Dinomaly simplifies the anomaly detection process by eliminating the need for complex designs, additional modules, or specialized techniques. It relies solely on basic Transformer components such as self attention mechanisms and multi-layer perceptrons (MLPs) to perform anomaly detection for multi class images... | Rebuttal 1:
Rebuttal: Thank you for your valuable reviews and comments.
__W1: Decision-making to identify anomalies.__
Dinomaly is based on the assumption that the networks respond differently during inference between seen and unseen input, faithfully reconstructing normal regions while failing for anomalous regions.... | null | null | null | null | null | null |
Subsurface Scattering for Gaussian Splatting | Accept (poster) | Summary: This paper proposes a framework for capturing the geometry, specular, and subsurface scattering appearance of 3D objects using a captured dataset composed of multi-view OLAT images.
The appearance of the object is decomposed into two different models.
First, a 3D Gaussian representation with a spatially vary... | Rebuttal 1:
Rebuttal: ### Additional Relighting Results with Image Based Lighting
> Weakness 3
Please find qualitative and quantitative results for the image based lighting in Fig. 1 and Tab. 1 in the PDF accompanying this rebuttal. We also show additional relighting results together with the other editing capabiliti... | Summary: This paper aims to model the subsurface scattering (SSS) effects under the 3D Gaussian Splatting framework, which is an efficient 3D representation for novel view synthesis. The challenge of this SSS modeling is the complicated light path in the final rendering output. The proposed framework is based on Religh... | Rebuttal 1:
Rebuttal: ### Generalization of MLP / Representational Power of MLP
> Question 2
We add application examples that show interpolation capabilities in both the viewpoint as well as illumination domain. Results on the test set (Fig. 5 in the paper, for example) are outside of the training setting in terms ... | Summary: This paper presents a method for recovering the shape and radiance transfer field (RTF) of an object from multi-view, OLAT data; placing an emphasis on translucent objects that exhibit subsurface scattering (SSS). In particular, the authors extend the framework of Relightable 3D Gaussian [Gao et al. 2024] in t... | Rebuttal 1:
Rebuttal: ### Improvements of Pipeline Overview (Fig. 2)
> Weaknesses
Thanks for the valuable feedback. We realize that the clarity of Fig. 2 in the paper can be improved. Please see Fig. 5 in the PDF for an updated version. We now tried to make clearer that base color, roughness and metalness are propert... | Summary: This paper proposes the 3D Gaussian Splatting for subsurface scattering objects by decomposing the scene into subsurface scattering, diffuse and specular reflections, and object shape. By using multi-view OLAT (one light at a time) data of translucent objects, the proposed method optimizes BRDF parameters attr... | Rebuttal 1:
Rebuttal: ### Limited Novelty due to Recombination of Recent Techniques and Similarity to NeRF-based Approaches
> Weakness 1 & Question 1
To enable the first method for reconstruction of translucent objects that enables relighting and material editing we
> - have performed multiple substantial modificati... | Rebuttal 1:
Rebuttal: R1 pNBB - R2 wfKX - R3 F9dx - R4 uYWZ - R5 uk4p
Find cited works in the original paper, Fig. numbers refer to rebuttal PDF
We thank the reviewers for their constructive feedback and for recognizing our effort to advance an “under-explored research area in inverse rendering” [R1].
We propose a... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes an algorithm to reconstruct relightable objects with subsurface scattering (SSS) effects. It proposes to model SSS as a residual to surface PBR using a neural network. It also proposes to perform shading in image space (i.e. deferred shading) to improve specularity. Lastly, the SSS network ... | Rebuttal 1:
Rebuttal: ### Optimization under Unknown Illumination
> Question 1
Optimization with unknown point light locations adds additional dimensions to an already under-constrained optimization problem which we consider out of scope of this work as we focus on the geometry representation and rendering part.
A... | null | null | null | null | null | null |
SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction | Accept (poster) | Summary: The main contributions of the paper include: 1) proposing a new motion generation framework that combines road and proxy trajectory labeling schemes with a decoder only Transformer for training the next token prediction task; 2) The model demonstrates zero sample generalization ability and scale law across dif... | Rebuttal 1:
Rebuttal: **Q1**: ”The paper explores motion prediction in GPT-style network, which has been done in many works such as StateTransformer.” + “The scale law ability of models based on transformer structure has been proven in many papers, resulting in limited novelty of the method proposed in this paper”
*... | Summary: In this paper, a GPT-style motion generator is developed for scalable multi-agent simulation. Though various techniques such as motion tokenization, factorized agent attention, and next map segment prediction, the proposed SMART framework ranked 1st place on WOSAC leaderboard for meta metric. Further zero-shot... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing thoughtful review and constructive suggestions. We answer the questions as follows.
***
**Q1** : What's the difference in decoder design compared with Trajeglish? Please clarify.” + “Minor in methodology differentiating with Trajeglish[1] and MotionL... | Summary: This paper presents SMART, a model for multi-agent traffic simulation. The approach is based on a decoder-only transformer architecture that predicts all agents motion tokens autoregressively over time. The architecture makes use of factorized attention layers (over map, agents, and time) and relative position... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing thoughtful review and constructive suggestions. We answer the questions as follows.
***
**Q1**: How much does road vector next token prediction contribute to SMART's performance?
**A1**: We have included relevant explanations in our note to all rev... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their reviews. We are incorporating feedback into our paper and will post direct responses to each reviewer's comments and questions. First, we would like to address the common concerns raised by multiple reviewers:
***
**Q1**: How much does road vector next token predic... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Understanding Extrapolation: a Causal Lens | Accept (poster) | Summary: This work addresses the challenge of extrapolation in scenarios where only a few target samples are available. It aims to provide a theoretical understanding and methods for effective extrapolation without needing a target distribution within the training support. The approach involves a latent-variable model ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and the time dedicated to reviewing our work. We address your concerns and questions as follows.
>W1: “Table 2 appears to omit results from TeSLA-s.”
Thank you for the comment. Thanks to your remark, we perform additional experiments to apply our sparsity con... | Summary: The paper addresses the problem of out-of-support extrapolation and presents identification results within a latent variable framework. This framework resembles invariant learning, where one subset of latent variables directly causes the labels, while another subset, termed style latents, undergoes a distribut... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and the valuable time you have dedicated to our work.
It seems that the reviewer might have seen a previous version of this manuscript. In that case, please let us kindly highlight that this submission is significantly different: in the earlier instance, we tr... | Summary: The authors discuss the problem of domain adaptation or distribution shift in the case when only a single point in the shifted domain is available rather than the full distribution. The authors approach such a problem from the perspective of a latent variable model, which assumes that the observations are gene... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable questions! We address your questions point-to-point in the following.
> W1 & Q3: “... how the Eq. (2) and (3) are connected to the learning objective in Sec. 3.3”, “Did I understand correctly that in practice the estimation algorithms (as describe... | Summary: * With regard to extrapolability in classification problems, they introduce a reasonable assumption that the latent factor generating the distribution shift only affects the input x but does not affect the label y is introduced and the identifiability of the latent factor is proved under additional reasonable ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging words and valuable feedback! Below, we address your questions and indicate the changes we’ve made thanks to your suggestion.
> W1: “Methodological improvement and its empirical superiority to the existing test-time adaptation itself is marginal.”
Thank you for your... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for their efforts and helpful comments regarding our paper. We are encouraged that Reviewers **zpbG**, **HS3a**, and **5kN3** find our problem setup relevant and interesting, Reviewers **zpbG** and **gbwX** find our theory interesting and novel, and all Reviewers f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data | Accept (poster) | Summary: This paper introduces a method for inferring policy decisions based on randomized controlled trial data when applied to a target population with new covariate data. The method is nonparametric and makes no assumptions about the distributional forms of the data and certifies valid finite-sample inferences of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and provide our response below. We believe they may clarify some potential misunderstandings.
> [W1] The motivation for problem setup is not clear. Why inferring $L_{n+1}$ for the additional $(n+1)$th data point rather than the new $1...n$?
Bounding $L_{n+1... | Summary: This paper proposes a method for constructing limit curves (upper bounds on the CDF) of an outcome, under a given policy, in a target population, using data from an experimental study. The general goal is to certify that bad outcomes are unlikely in a target population, given experimental data and some knowle... | Rebuttal 1:
Rebuttal: We are very grateful for the reviewer’s past comments, which improved the revision substantially.
> The informal benchmarking approach involves building intuition for plausible impacts of selection bias due to unobserved factors, but it didn't seem to speak to model misspecification or estimatio... | Summary: This paper aims to use trial data to make valid inferences about policy outcomes for a target population. By incorporating additional covariate data from the target population, the sampling of individuals in the trial study is modeled. The authors develop a nonparametric method that provides certifiably valid ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s honesty and trust that the chair will discount this review. | Summary: This paper studies the challenge of generalizing randomized controlled trial (RCT) results to a target population, addressing the potential issue of distributional shift from RCT participants to the intended population. Instead of estimating the expected loss on the target population, the paper proposes a nonp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the question and are pleased that he/she acknowledges the importance of the problem we are tackling.
> If I understand correctly, the policy Pi is given as an exogenous parameter. However, it is a common practice that we learn a policy using the RCT data and aim to know... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar | Accept (poster) | Summary: This paper introduces a novel approach for 3D occupancy prediction in autonomous driving using 4D imaging radar sensors. Traditional methods rely heavily on LiDAR or camera inputs, which are vulnerable to adverse weather conditions. RadarOcc leverages the robustness of 4D radar data, which provides comprehensi... | Rebuttal 1:
Comment: Dear Reviewer XRVn:
We appreciate your detailed summary and positive comments regarding the presentation, novelty, creativity, and the experimental results of our research. Thanks a lot for providing the valuable feedback and raising insightful questions about our work. We address and answer your ... | Summary: This paper leverages recent advancements in automotive radar technology and introduces a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction. The proposed method incorporates Doppler bin descriptors, sidelobe-aware spatial sparsification, and range-wise self-attention mechanisms t... | Rebuttal 1:
Rebuttal: Dear reviewer f9yn:
Thank you for acknowledging our method to be compelling and innovative. We are grateful for your insightful questions and feedback. We provided detailed explanations and responses as follows:
**Q1: Dataset limitation: The experimental analysis is thorough, but it is conducte... | Summary: This paper introduces a 3D occupancy prediction method that, unlike previous radar-based approaches, utilizes 4D imaging radar to leverage additional information. To harness the potential of this under-explored 4D data, the paper tackles challenges such as the large size of raw 4D radar data, inherent noise, a... | Rebuttal 1:
Rebuttal: Dear Reviewer 7z3i:
We are greatly encouraged that you found this work has logical motivation, represents pioneering work in the area, features clear figures and well-organized content, and underscores safety-critical perception tasks for AVs. We acknowledge the concerns you've highlighted and wo... | Summary: It is proposed to utilize 4D imaging 8 radar sensors for 3D occupancy prediction by directly processing the 4D radar tensor, thus preserving essential scene details. RadarOcc innovatively addresses the challenges associated with the voluminous and noisy 4D radar data by employing Doppler bins descriptors, side... | Rebuttal 1:
Rebuttal: Dear reviewer v56z:
We appreciate you for the positive feedback regarding the paper’s approach, innovation and experiment results, and agree that radar raw data could provide more information for perception tasks. We understand your concerns and would like to address your points one by one.
**Q1... | Rebuttal 1:
Rebuttal: Dear reviewers and ACs,
We would like to express our sincere gratitude for the careful inspection and constructive feedback from all the reviewers. We are glad to see that all of the reviewers in general hold a positive attitude towards our paper in the pre-rebuttal period. For positive comments,... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents RadarOcc, a method that enhances 3D occupancy prediction for autonomous vehicles using 4D imaging radar. It directly processes the 4D radar tensor, aiming to overcome the limitations of sparsity and noise associated with conventional radar processing. The methodology introduces techniques su... | Rebuttal 1:
Rebuttal: Dear Reviewer kdzm:
We sincerely thank you for providing valuable comments and raising insightful questions about our work. We are glad that you acknowledge that the utilization of 4DRT data allows for a more comprehensive environmental perception, and our work considers different adverse conditi... | null | null | null | null | null | null |
DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering | Accept (poster) | Summary: This paper presents a new method to learn 3D particle dynamics from sparse 2D observations using inverse rendering.
In constrast to previous work, it does not learn a fully unconstrained model but makes use of known physical priors.
The method learns graph network kernels to model the particle interation force... | Rebuttal 1:
Rebuttal: > **Weakness and Question 2 and 3: Scene and Velocity Initialization**
Thanks for the reviewer pointing out these ambiguities. We explain these details in the following and will add them to our revised manuscript for better readability.
**Scene Initialization.** As we stated in the first paragr... | Summary: This paper proposes to incorporate the neural network with Discrete Element Analysis framework for particle-based simulation. The method adopts GPF as differentiable renderer and is trained through multi-view videos. The proposed model delivers faithful rollouts and outperforms the baselines.
Strengths: * The... | Rebuttal 1:
Rebuttal: > **Weakness 1: Scene Specific**
Thanks for this comment. The proposed approach indeed needs to be trained for modeling different materials. In fact, we prefer to describe it as **material-specific** training rather than scene-specific training. This is because DEL can implicitly encode the mecha... | Summary: The paper proposes a framework to learn 3D dynamics from 2D observations. DEM uses hand-designed kernel functions to model interaction force between particles, which may vary significantly for different material types. The paper changes these kernels to learnable GNN kernels. The time integration still follows... | Rebuttal 1:
Rebuttal: > **Weakness 1: The evaluations are conducted only on synthetic data.**
Thanks for this comment. At present, the proposed method is indeed only evaluated on synthetic data. This is because capturing multiview videos for a dynamic process in the real world is difficult. Because it is hard to **sim... | Summary: The paper considers the problem of physical modeling the dynamics of 3D objects in space using only 2D observations. The authors propose to solve this problem by viewing objects as sets of interacting points and using differentiable rendering of these point clouds. In this pipeline, neural models are used to d... | Rebuttal 1:
Rebuttal: > **Weakness: Polishing Language**
We thank the reviewer for pointing out this. Following this comment, we have thoroughly revised the language and grammar to enhance the overall quality of the text, carefully checked the citation, and optimized the layout. The updated version is fully improved f... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning | Accept (poster) | Summary: This paper introduces a novel framework for cross-domain few-shot learning. The input images are first decomposed into low-frequency content and high-frequency structure using FFT. Then, the PRM-Net includes three branches, low-frequency, high-frequency, and main branch. The PRM-Net includes two priors to regu... | Rebuttal 1:
Rebuttal: ## To Reviewer HqaU :
### Weaknesses (1)
### Response 1 : About computational overhead.
1) Thanks for the comments. The proposed method indeed introduces additional computational cost during the training phase. We will further clarify this weakness in the manuscript.
2) We present the computat... | Summary: The paper introduces an innovative framework that leverages the concept of frequency priors for cross-domain few-shot learning. The novel idea of decomposing images into high and low-frequency components and integrating these into the meta-learning process is a creative advancement in the field.
Built upon est... | Rebuttal 1:
Rebuttal: ## To Reviewer mWuv :
### Weaknesses (1)
### Response :
We present the computational costs during the training and inference phases in Table 2 and 3 (Due to character limitations, please see "To Reviewer SF8W".), respectively. We can draw the following observations.
1) During the training, the... | Summary: The paper introduces a novel framework called Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning, which aims to improve meta-learning's generalization by decomposing images into high- and low-frequency components. This method leverages these components to guide the feature embedding network, en... | Rebuttal 1:
Rebuttal: ## To Reviewer SF8W:
### Weaknesses 1
### Response :
We compared the FFT decomposition results of natural images and EuroSAT images (please see Fig. 1 in Response.pdf). We observed that FFT decomposition of natural images can obtain clear low-frequency and high-frequency information. However, the... | null | null | Rebuttal 1:
Rebuttal: Author Response for ``Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning''
We would like to express our gratitude to the AC and the reviewers for their valuable comments and suggestions. Over the past week, we have responded to all comments mentioned by the reviewers. The response... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MambaLRP: Explaining Selective State Space Sequence Models | Accept (poster) | Summary: The paper presents a method to correctly apply LRP to Mamba models. Through careful analysis, the authors demonstrate that applying LRP directly results in poor performance and propose modifications to recover the propagation rules. The obtained method outperforms the alternatives, grounded by theory, and the ... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments. We address them below and add more discussions as an official comment.
1.1: We use the official code of [1] to produce the results of AttnRoll and G$\times$ AttnRoll (Mamba-Attr). We evaluated MambaLRP's performance against these approaches using flipping and ins... | Summary: ### **Post-rebuttal update**
Given the authors' additional experiments and changes-to-be-made to the manuscript, raising my original score from a 5 to a 6.
### **Original review**
The authors tackle the problem of explainability in Mamba SSMs, which have been recently proposed and widely adapted. Towards thi... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and useful suggestions. We will address your points below.
> Evaluation of Mamba LLMs also on larger 2.8B models
With our empirical evaluations in Table 1, we aimed to cover a representative range of tasks (four different text classification tasks, image classi... | Summary: The paper introduces an LPR framework for Mamba. The method breaks the Mamba architecture into three parts (SiLU activation, selective SSM, and multiplicative gating) and analyzes the layers using relevance scores. The evaluations on languages and images show that the proposed method is more precise and faithf... | Rebuttal 1:
Rebuttal: Thanks for your detailed feedback. We are happy to see that you appreciate the potential of our approach in providing faithful explanations. We will address your points below.
> Regarding the paper being limited to the explainability of Mamba
The class of Selective state space sequence models (M... | Summary: The paper applies Layer-wise Relevance Propagation to Mamba layers. To maintain the relevance conservation law, the authors propose three fixes to SiLU activation, S6 and the multiplicative gating operators respectively with the technique of gradient blocking. The proposed method improves the faithfulness of L... | Rebuttal 1:
Rebuttal: Thank you for your review and useful comments. We will address them in our response below.
> Regarding the main novelty of applying LRP to the Mamba
As new model architectures, such as SSMs and Mamba models, are developed, the field of XAI is challenged to develop faithful attribution methods to... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and valuable feedback. We responded to the comments and made the following changes to our submission. In particular:
- We extended our faithfulness evaluation in Table 1 (main paper) to the larger Mamba-2.8B model trained on SST-2, confirming MambaLRP con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Experimentation When You Can't Experiment | Accept (poster) | Summary: This article studies the pure exploration transductive linear bandit problem in the presence of instrumental variables. The authors assume a linear structural equation model on the instrument, treatment and outcome. The proposed method attempts to estimate the parameters in the structural model while optimally... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our problem setting and ideas, as well as our theoretical contributions.
Regarding your comments:
1. We agree that Sec 3 is dense, making this paper a heavy lift with many theoretical results. We appreciate the suggestion to shorten the cont... | Summary: The paper introduces the confounded pure exploration transductive linear bandit (CPET-LB) problem, which addresses the challenges of conducting adaptive experimentation in environments where direct randomization is not possible. From my understanding, the paper studies the best arm identification problem under... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our thorough theoretical analysis, noting its significance and solid foundation, as well as the strong practical value of our work.
Regarding your comments on weaknesses, we appreciate your detailed concerns:
1. As the reviewers kindly pointed out, we did ... | Summary: This paper addresses the issue of adaptive experimentation using "encouragement" rather than "compulsion instruction," a scenario commonly encountered in industrial applications. The proposed solution integrates linear bandit algorithms with instrumental variables regressions. The authors provide rigorous theo... | Rebuttal 1:
Rebuttal: We are happy to see the reviewer likes our work and find its strong practical value.
Regarding your comment on additional experiments:
Our work is primarily theoretical and to provide an initial solution, which makes the surprising connection between encouragement designs and pure exploration li... | Summary: This paper addresses the problem of pure exploration bandits in the setting of encouragement designs. The authors describe the problem in terms of online instrumental variable regression. Toward this end, the authors derive a finite time confidence interval. Using this as the main tool, the authors then descri... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our problem formulation and the thorough analysis throughout.
Concern on limited experimental evaluation:
Our work is primarily theoretical and to provide an initial solution, which makes the surprising connection between encouragement des... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing insightful comments. As the strengths of our paper,
reviewers Y9pp, oap7, and 4vBb have acknowledged the practical relevance and importance of the problem we introduced, and reviewers Y9pp and byyL acknowledged that our formulation is novel.
Finally, reviewer ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contextual Multinomial Logit Bandits with General Value Functions | Accept (poster) | Summary: This paper considers MNL bandits with a general value function. The authors first examine the case of stochastic contexts and rewards. They suggest an epoch-based algorithm with an offline regression oracle. With uniform exploration, the algorithm achieves a regret bound, specifically $T^{2/3}$ for finite and ... | Rebuttal 1:
Rebuttal: **Q1: Is there any insight about how regret bounds do not include the dependency on 𝜅 for linear class? Or does it include 𝜅 when it is applied to the standard contextual MNL model?**
A: The intuition on why we do not have $\kappa$ dependency is that most previous MNL works (e.g. [7,20,23]) ado... | Summary: This paper addresses the problem of contextual multinomial logit (MNL) bandits with general value functions across both stochastic and adversarial settings. The authors develop a suite of algorithms for different settings and with different computation-regret trade-offs. The application to the linear case surp... | Rebuttal 1:
Rebuttal: **Q1: Computational Inefficiency: The Feel-Good Thompson sampling algorithm, as discussed, lacks computational efficiency, even for linear cases, which could limit its practical applicability.**
A: We acknowledge that the Feel-Good Thompson sampling is not efficient theoretically. However, empiri... | Summary: This paper introduces a couple of algorithms for contextual multinomial logit bandits under two different assumptions: i) stochastic contexts and rewards; ii) adversarial contexts and rewards. The theoretical analysis for algorithms for these two setups is pretty solid. Despite the contribution of this study, ... | Rebuttal 1:
Rebuttal: **Q1: The paper seems incomplete, possibly due to page limits. Some algorithms and results are not fully described. Additionally, many terms and mathematical notations are used without proper definitions or introductions.**
A: We strongly disagree with this comment. Could the reviewer kindly poin... | Summary: The paper presents three primary contributions for the contextual multinomial logit bandits considering both stochastic and adversarial contexts and rewards.
- a suite of algorithms proposed each with a different computation-regret trade-off.
- advances existing regrets by removing the dependence on certain... | Rebuttal 1:
Rebuttal: **Q1: For stochastic contexts and adversarial rewards, [20] show there exists an efficient algorithm achieving $𝑂(\sqrt {𝑇})$ regret. The adversarial reward setup is more general than the stochastic reward. So it is fair to compare it to corollary 3.5. Algorithm 1 has a larger regret upper bound... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma | Accept (poster) | Summary: The authors study the list-replicable coin problem and a closely related underlying geometric problem of constructing ($k,\varepsilon$)-secluded partitions of $\mathbb{R}^d$. The authors resolve the optimal trade-off between $k,\varepsilon$, and $d$ in the latter, and as a corollary give a new set of upper bou... | Rebuttal 1:
Rebuttal: Response to Weaknesses Comment:
The concern regarding the scope to NeurIPS is addressed in the General Response as well as the response to Reviewer W1GL. We agree the main novelty and technicality is in establishing the lower bound. However, the upper bound is critical to complete the picture. Th... | Summary: This paper studies connections between (i) list-replicability, a well-studied relaxation of the standard replicable learning framework and (ii) the design of partitions of the Euclidean space, in particular $(k,\epsilon)$-secluded partitions. A partition $P$ will be called $(k, \epsilon)$-secluded if for any $... | Rebuttal 1:
Rebuttal: Response to Suggestion:
Thank you for the suggestion. We will add the discussion along the lines you propose.
Response to Questions:
Q1: Unit ball in $\ell_p$ norm is a subset of a unit ball in $\ell_\infty$ norm. Thus secluded partitions with respect to $\ell_\infty$ norm are also secluded w... | Summary: This work studies secluded partitions, which appear in the context of list-replicable learning (among other geometric/TCS applications). The complement known bounds on list complexity, the authors present new upper-bounds on the tolerance parameter as a function of the list complexity k and the ambient dimensi... | Rebuttal 1:
Rebuttal: Response to Weaknesses Comment:
This weakness regarding the scope is addressed in the general response, and we reiterate it here. Our work is motivated by the connection of geometric/topological tools to list replicability (secluded partitions and Sperner/KKM Lemmas). Driven by this connection, t... | Summary: In this work the authors study various connections between geometric constructions in $R^d$, in particular various $(k,\varepsilon)$-secluded partitions, and its applications to list-replicable learnability. This connection was originally observed in prior work, and the authors provide stronger quantitative re... | Rebuttal 1:
Rebuttal: Response to the Weaknesses Comment:
We agree with the reviewer that the connection between secluded partitions and list-replicability was already observed. Indeed, that is our main motivation to further investigate the possibility of constructing secluded partitions better than those previously... | Rebuttal 1:
Rebuttal: General Response:
We sincerely thank all the reviewers for their careful reading and thoughtful comments and suggestions.
A common concern raised by the reviewers is the scope and fit of the present work for NeurIPS, which we address here. Our work is motivated by the fundamental connections be... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond task diversity: provable representation transfer for sequential multitask linear bandits | Accept (poster) | Summary: This paper extends the existing work on multi-task linear bandits where the task parameters lie in a low rank subspace. Specifically, this paper assumes $N$ $d$-dimensional linear bandits with parameters lie in an $m$-dimensional subspace. For such a setting, the classical approaches yield a regret linear in $... | Rebuttal 1:
Rebuttal: - Originality: The algorithmic design and the guarantee is closely related to a a few works, including the PEGE by [Rusmevichientong and Tsitsiklis 2010] (related to lemma 3 in this paper), the analysis in Yang et al [2020] (related to lemma 4), EWA algorithm (related to lemma 5). Therefore, the t... | Summary: The paper studies lifelong learning in linear bandits and designs a two-level algorithm with provable low regret without task diversity assumptions. The paper assumes the tasks share a low-rank representation and provides a regret upper bound.
Strengths: The paper uses a two-level approach to solve this probl... | Rebuttal 1:
Rebuttal: - ``In line 109, for a matrix $U$ , isn’t $U_{\perp}$ not unique? This should be defined as a set or stated as one of the matrices.''
**We thank the reviewer for pointing out this impreciseness.** Indeed, the choice of $U_\perp$ is not unique according to our writing in the submitted version.
... | Summary: The paper proposes a strategy for the multi task linear bandit problem, where the tasks are assumed to share a low rank representation, which is ought to be learnt via a new meta learning strategy, where meta exploration and exploitation needs to be balanced. The low rank representation is learnt via optimizin... | Rebuttal 1:
Rebuttal: - ``Shouldn’t a bound on the term $||B_{\perp} -\hat{B}_{n+1,\perp}||$ or a term that showcases the estimation error on the representation, appear in the final regret?''
**Yes.** Indeed, our regret bound has an implicit dependence on $|| \hat{B}_{n, \perp}^T \theta_n ||$ - see Eq. (2).
This is ... | Summary: This paper addresses the problem of representation transfer in a sequential multi-task linear bandit problem. The main objective in the paper is to remove the task diversity assumption made in Qin et al (2022), which places a constraint on any subsequence of tasks observed. The paper proposes an algorithm that... | Rebuttal 1:
Rebuttal: - ``There is a gap to the known lower bound, which is suggested to be due to the task diversity assumption not being met.''
**Before our work, no regret bounds that are $o(N d \sqrt{\tau})$ were known for Sequential representation transfer multi-task linear bandits without Task Diversity assumpti... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback.
In this work, we introduce the first provable representation transfer algorithm for sequential linear bandits without relying on the task diversity assumption. Unlike in parallel settings or sequential settings that assume task diversity,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-language Diversity Benefits Autoformalization | Accept (poster) | Summary: This paper introduces MMA, a large multi-language dataset for autoformalizing theorem statements. MMA employs a back-translation approach using GPT-4 to convert two formal corpora (AFP for Isabelle and mathlib for Lean4) into informal-formal pairs. Experiments demonstrate that LLMs such as LLaMA and Mistral, f... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback and the time they invested in reviewing our paper. We address specific points raised by the reviewer:
> **I think the main weakness of this paper is that MMA is constructed solely based on zero-shot prompting for informalization by G... | Summary: The paper presents a dataset MMA and a model trained on the same. The dataset comprises informal-formal pairs of theorem which are generated using LLM. The experiment shows decent results on the autoformalization tasks of miniF2F and ProofNet benchmarks. The authors also claim that autoformalization performanc... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback and the time they invested in reviewing our paper. We address specific points raised by the reviewer:
> **Even though, authors try manually to sample from the data, the sample size is often small (which is understandable). However, I... | Summary: The authors use backtranslation to create a large (~332k samples) dataset of formal-informal statement pairs in the Lean4 and Isabelle formal proving languages: they take formal samples from a Lean4 and an Isabelle proof library, and ask GPT4 to restate them informally. They use this dataset to finetune two op... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback and the time they invested in reviewing our paper. We address specific points raised by the reviewer:
> **Dataset is automatically ...**
- We thank the reviewer for pointing out that there could be potential improvements to the datas... | Summary: This paper introduces MMA, a large-scale dataset consisting of informal-formal pairs of mathematical statements (i.e., parallel autoformalization data) in two types of formal languages, Isabelle and Lean4. The dataset encompasses statements from multiple domains and exhibits high quality. It was constructed us... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed feedback and the time they invested in reviewing our paper. We address specific points raised by the reviewer:
> **The experiments and the corresponding analysis are not robust enough.**
> **1. The metrics “loss” and “token accuracy” used on ... | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their keen input, which has significantly raised the quality of this paper. You will find a one-page attachment containing two plots we use to demonstrate our points.
Below, we'd like to address a few common points:
1. "Is the evaluation dataset size (100 per l... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Quantitative Convergences of Lie Group Momentum Optimizers | Accept (poster) | Summary: The authors design and analyze momentum-based algorithms on Lie groups. They first study ODEs and provide convergence rates for them. Then they discretize the ODEs (in two different ways -- Lie Heavy Ball and Lie NAG-SC), and show that the second discretization (Lie NAG-SC) has a *locally* accelerated conver... | Rebuttal 1:
Rebuttal: > W1: The requirement that the manifold is a Lie group is very restrictive. There are many manifolds used in practice which are not Lie groups but still have very nice structure. Perhaps the prime example is the Stiefel manifolds. In many applications (especially those related to low rank problems... | Summary: This work first analyzes the convergence rate of the Lie group momentum optimizer by applying the techniques from optimization theory over manifolds to optimization over Lie groups and extending the Lyapunov analysis to Lie group settings. They also provide the convergence analysis of the discrete version of t... | Rebuttal 1:
Rebuttal: > **W1**: In line 280 and the proof in the appendix, the authors mentioned the ''curvature'' but the article does not provide much information about that. I would like to know if this ''curvature'' is an intuitive term related to the second-order information or a rigorous term related the curvatur... | Summary: The authors analyze the momentum method and Nesterov accelerated gradient descent method on the Lie group. With some knowledge of Riemannian geometry, they discuss the computational cost.
Strengths: I have found none strenghs.
Weaknesses: I do not know the author's motivation to write this manuscript.
Cou... | null | Summary: This paper explores the optimization of functions defined on Lie groups using momentum-based dynamics. The authors propose two discretization methods, Lie Heavy-Ball and Lie NAG-SC, and analyze their convergence rates.
The main contributions are as follows:
1. Provide the first quantitative analysis of Lie gr... | Rebuttal 1:
Rebuttal: > W1: The idea of the paper is natural. I think it can be seen as a straightforward extension of the results in [Tao & Ohsawa]. So it may be not novelty.
> Q1: I want to know the novelty of this work compared with [Tao & Ohsawa]
[Tao & Ohsawa] is definitely an inspiration to this work. However, ... | Rebuttal 1:
Rebuttal: **General rebuttal to all**
> **Q1**: More experimental results
A1: We perform more numerical experiments in the rebuttal PDF supplement.
- We add Riemannian GD and Riemannian NAG-SC [R1] into comparison (Fig. 1 in the attached pdf). Of course, our proposed method converges faster than Riemanni... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a new algorithm (Lie NAG-SC) that converges at accelerated rates on Lie groups when initiated close to the true optimum (local convergence). Theoretical analysis and experimental verification shows good performance.
Strengths: The theoretical analysis is quite advanced, and as far as we are... | Rebuttal 1:
Rebuttal: > **W1**: Both the theory and the experimental evaluation only considered local convergence, i.e. cases when the optimizer is initiated near the global optimum. This is a limitation as the relative behaviour of the algorithms further away from the optimum may be completely different.
We agree the... | null | null | null | null | null | null |
OneActor: Consistent Subject Generation via Cluster-Conditioned Guidance | Accept (poster) | Summary: The authors present a generation paradigm called “OneActor” to generate consistent subject in text-to-image generating tasks. The core of this algorithm is called as “cluster-guided score function”, which is based on the concept of score function and created to maintain the consistency of generated images. Add... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful review of our work. For the weakness and questions, we will response to each of them individually.
***
## Weakness 1: Comparison with multi-concept customization pipeline
We provide a qualitative comparison with FreeCustom[1], the current state-of-the-art mult... | Summary: This Study proposes a one-shot tuning paradigm for efficient and consistent subject generation. They introduce two inference strategies to mitigate overfitting, improving generation quality significantly. The semantic space of diffusion mode trained under the proposed method has the same interpolation property... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable reviews. For the weakness and questions, we will response to each of them individually.
***
## Weakness: Novelty of semantic interpolation
We believe our discovery is fundamentally different from previous works. Ordinary diffusion process can be denoted as ... | Summary: This paper proposes OneActor, a one-shot tuning paradigm for consistent subject generation in text-to-image diffusion models, driven solely by prompts and utilizing learned semantic guidance to bypass extensive backbone tuning.
A cluster-conditioned model is introduced to formalize consistent subject generati... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable reviews. For the weakness and questions, we will response to each of them individually.
***
## Weakness 1: Limited styles and objects in the main part of the paper
The 9-page main part of the submission is limited in space so we placed the most crucial comp... | Summary: This paper proposes to formalize the consistent content generation problem from a clustering perspective. By designing a one-shot tuning paradigm with a cluster-conditioned model, the proposed pipeline OneActor can achieve faster tuning while maintaining superior subject consistency. Extensive experiments show... | Rebuttal 1:
Rebuttal: We greatly appreciate your commendation of our work. It is without doubt a tremendous encouragement for us. For the weakness and question, we will response to each of them individually.
***
## Weakness: Typo
We will carefully review our paper sentence-by-sentence and try to correct every type erro... | Rebuttal 1:
Rebuttal: We thank all the reviewers, ACs, SACs and PCs for reviewing so many papers and providing insightful and objective opinions.
We are so grateful that:
- __All the reviewers give our work positive ratings (7556)__, which is a tremendous encouragement for us.
- Reviewer iFKj highly recommends our wor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems | Accept (poster) | Summary: Authors propose ProjDiff which reframes noisy inverse problems with diffusion models as a two-variable constrained optimization by introducing an auxiliary optimization variable. Authors derive a two-variable ELBO as a proxy for the log-prior and solve the optimization problem via projection gradient descent. ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's affirmation of the effectiveness of our method, especially the outstanding performance in music separation and partial generation tasks. Below are our responses.
1. DDNM+ numbers for noisy Deblurring tasks
We are very grateful to the reviewer for pointing out t... | Summary: This paper proposed a new sampling strategy for solving noisy inverse problems using diffusion models. The proposed method is called ProjDiff, which is based on two-step minimization of log-posterior using gradient descent. The sampling procedure is derived by (1) Reparametrization of the noisy measurement as ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's recognition of the innovation and effectiveness of our work. Below are our responses.
1. Reorganizing the theoretical analysis and the proofs of the lemmas.
Thanks for the reviewer's suggestion. We will move Lemma 1 and 2 ahead of the proofs of the Proposition... | Summary: The authors present a new way of solving inverse problems using a diffusion model based prior.
The key idea is that the forward process, which is to be undone, usually involves Gaussian noise.
The authors utilize this by viewing the noisy observation as a as random variable at an intermediate time step t of th... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's recognition of the innovation, effectiveness, and practicality of our work. Below are our responses to the reviewer's questions.
1. Clarification on the motivation and comparison to the established MAP approach.
The core motivation of this paper stems from the... | Summary: This paper proposes ProjDiff for solving inverse problems with pre-trained diffusion models. By deriving a two-variable ELBO as a proxy for the log-prior, this paper reframes the inverse problems as constrained optimization tasks and address them via the projection gradient method.
Strengths: 1. The paper wri... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's suggestions, and here are our responses.
1. ''The assumption for the measurement noise is doubtable.''
We respectfully disagree. Firstly, in practical applications, noise-free inverse problems or inverse problems with Gaussian noise are ubiquitous, and the standard d... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Please refer to the attached one-page PDF for the supplementary experimental results.
We appreciate the valuable feedback provided by all the reviewers on our paper, which has helped us to further refine our work. We are particularly encouraged by the following comments from the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gaussian Graph Network: Learning Efficient and Generalizable Gaussian Representations from Multi-view Images | Accept (poster) | Summary: This paper presents Gaussian Graphs to construct the relations of different Gaussian groups, introduces a Gaussian Graph Network to process Gaussian Graphs.
Strengths: Experimental results are sufficient and convincing. This work is easy to follow.
Weaknesses: 1. As far as I can see, the main work of this pa... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below we address specific questions.
**1. The difference between this paper and other works that combine GNN and GS**
Thank you for pointing out these relevant works SAGS [1] and Hyper-3DG [2]. We would like to highlight our key differences with these works.
O... | Summary: This paper introduces the Gaussian Graph Network (GGN), a novel approach for generalizable 3D Gaussian Splatting (3GDS) reconstruction. The authors identify a problem with previous generalizable 3DGS work: they regress pixel-aligned Gaussians and combine Gaussians from different views directly, resulting in an... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We agree with you that the analysis of training and inference latency of our proposed method is important. We discuss this topic in our general rebuttal. We will add this discussion on training and inference latency in Section 4.2 (line 190) as you suggested. | Summary: This paper proposes a Graph Neural Network architecture to model the relations between multi-view 3D Gaussians predicted by generalizable 3DGS methods. The proposed method works in particularly better for large number (e.g., 4, 8, 16 etc) of input images, compared with previous methods like pixelSplat and MVSp... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below we address specific questions.
**1. The selection of input views**
As the number of input views increases, they are sampled from larger regions of the scene. Thank you for point out this question. We will clarify this setting in Section 4.1 (line 157) in ... | Summary: The paper presents an incremental design built upon existing generalizable GS reconstruction frameworks (e.g., PixelSplat, MVSplat) to fusion overlapped pixel-aligned gaussian points from multiple images with a graph-based operator and pooling. This additional design presents a faster and better rendering effe... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Below we address specific questions.
**1. Training and inference latency**
We agree with you that the analysis of training and inference latency of our proposed method is important. We discuss this topic in our general rebuttal. We will add this discussion on ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful feedback. We tried to address all the questions for each reviewer in the rebuttal session below.
Here we discuss about the **training and inference efficiency** as mentioned by Reviewer sL12, LnYN and zafq. As the point-aligned gaussian points are qui... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs | Accept (poster) | Summary: The paper introduces Chain-of-Preference Optimization (CPO) to improve reasoning in LLMs. CPO utilizes preference data generated during ToT to fine-tune models. This approach improves reasoning without increasing inference complexity.
Strengths: 1. The idea of CPO is interesting.
2. CPO achieves enhanced perf... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions. Below, we respond to the comments in ***Weaknesses (W)*** and ***Questions (Q)***.
---
***W1: CPO performance on MATH benchmark.***
Following your feedback, we included the performance of both CoT and CPO using the LLaMa3-8b-base model in $\\t... | Summary: This paper introduces Chain of Preference Optimization (CPO), a method to enhance mathematical reasoning in large language models by feeding step-level pairs rather than response-level ones into DPO objectives. CPO leverages non-optimal reasoning thoughts from tree-search processes to construct paired preferen... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions. Below, we respond to the comments in ***Weaknesses (W)*** and ***Questions (Q)***.
---
***W1: CPO is fundamentally still DPO.***
Thank you for raising this point. We would like to clarify that while our approach adopts the DPO algorithm to fi... | Summary: This paper presents a method called Chain of Preference Optimization (CPO) that fine-tunes large language models (LLMs) using the tree-of-thought (ToT) method to improve the performance of chain-of-thought (CoT) decoding. CPO aligns each step of CoT reasoning paths with those of ToT by leveraging preference in... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions. Below, we respond to the comments in ***Weaknesses (W)*** and ***Questions (Q)***.
---
***W1: Comparision with ReST and self-rewarding baseline***
We would like to clarify that the settings of ReST and self-rewarding are different from ours, a... | Summary: The paper presents a novel method to enhance the performance of LLMs in complex problem-solving tasks using Chain of Preference Optimization (CPO). The authors propose fine-tuning LLMs using the search tree constructed by tree-of-thought (ToT), allowing CoT to achieve similar or better results without the heav... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions. Below, we respond to the comments in ***Weaknesses (W)*** and ***Questions (Q)***.
---
***W1: Evaluation of the potential impacts of fine-tuning process on LLM's other abilities***
In response to your suggestion, we conducted additional experi... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and we have responded to each reviewer individually. We have also uploaded a Rebuttal PDF that includes:
- $\\textrm{\\color{blue}Figure A}$: Effect of the number of instances in generating paired thoughts;
- $\\textrm{\\color{blue}Figure B}... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents Chain-of-preference optimization (CPO) a self-supervised learning extension of Tree of Thought (ToT). Rather than use ToT during test time, which takes exponentially longer than end-to-end sampling, this paper proposes to use ToT at training time to annotate data for DPO fine-tuning and the... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and questions. Below, we respond to the comments in ***Weaknesses (W)*** and ***Questions (Q)***.
---
***W1: Evaluation using entire test sets***
Thank you for your suggestion. We selected evaluation sets of 300 questions per dataset to manage the high compu... | null | null | null | null | null | null |
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer | Accept (poster) | Summary: This paper aims to address the issue of over-optimization in RLHF. The authors introduce a method named RPO which concurrently minimizes the maximum likelihood estimation of the loss alongside a reward penalty term. Not only do the authors demonstrate that the proposed method is sample-efficient, but they also... | Rebuttal 1:
Rebuttal: **Q1: Additional experiments are required to more comprehensively demonstrate the method's efficiency**
**A1:** Please refer to the **General Response**.
**Q2: The experiments are limited to evaluations using GPT and log probability. It remains unclear whether the observed improvements in perfor... | Summary: The paper introduces the concept of RPO, which combines DPO loss with SFT loss. This approach aims to align the policy with human preferences while simultaneously imitating a baseline distribution, effectively mitigating overoptimization. Empirical results from experiments with LLMs demonstrate that RPO outper... | Rebuttal 1:
Rebuttal: **Q1: The theoretical guarantees provided by the algorithm rely on specific conditions such as *partial coverage*, which might not always hold in practical scenarios, potentially limiting the generalizability of the results.**
**A1:** Thanks for raising the question. We would like to comment that... | Summary: The paper "Provably Mitigating Overoptimization in RLHF" addresses the issue of overoptimization in aligning large language models (LLMs) with human preferences using reinforcement learning from human feedback (RLHF).
The main contributions include:
1. Identification of Overoptimization Source: The paper ide... | Rebuttal 1:
Rebuttal: **Q1: The partial coverage condition lacks discussions since now it's a pair over $(\pi,\pi^{base})$, which is different from the traditional coverage condition. Hence, if we want to compete with a policy $\pi$ better than $\pi^{\mathrm{chosen}}$, how can the direction of $(\pi, \pi^{\mathrm{chose... | null | null | Rebuttal 1:
Rebuttal: **General Response:**
We thank all the reviewers for their time and effort reviewing our paper and we appreciate all your support of our work! We have responded to each of you detailedly.
Here we provide a general response to **Q3** of **Reviewer 9VCJ** and **Q1** of **Reviewer 1DUS** about mor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments | Accept (poster) | Summary: This manuscript aims to tackle the evolving domain generalization (EDG) issue, namely the domain gradually evolves in an underlying continuous structure. The paper introduces the idea of Weight Diffusion (W-Diff), a conditional diffusion model in the parameter space to learn the evolving pattern of classifiers... | Rebuttal 1:
Rebuttal: Sincerely thanks for your efforts in reviewing the paper. Below, we respond to your questions in detail.
> **Q1: Discussion with the two related papers [1, 2].**
Thanks. Our method differs from [1, 2] as follows:
**Focused problem**: G.pt [1] focuses on supervised learning and reinforcement lea... | Summary: The paper proposes a novel method called Weight Diffusion (W-Diff), which employs a conditional diffusion model in the parameter space to learn the evolving patterns of classifiers during domain-incremental training. During inference, the proposed method uses an ensemble of classifiers tailored to the target d... | Rebuttal 1:
Rebuttal: Thanks for your efforts in reviewing the paper and the constructive comments. Below, we have tried our best to address your concerns in detail.
> **Q1: Can this method scale to larger networks and generate the entire network?**
Thanks. Firstly, we have tried larger networks on the fMoW dataset b... | Summary: This paper presents Weight Diffusion (W-Diff), a framework for domain generalization in non-stationary environments. W-Diff leverages a conditional diffusion model in the parameter space to learn the evolving pattern of classifiers during domain-incremental training. Experiments on synthetic and real-world dat... | Rebuttal 1:
Rebuttal: We are grateful for your efforts in reviewing the paper as well as your constructive comments. Below, we do our utmost to address your concerns.
> **Q1: The specific advantages that diffusion models offer for DG.**
Thanks for your comment. Firstly, instead of learning a deterministic classifier ... | Summary: This paper deals with the problem of evolving domain generalization in non-stationary environments, where dynamically changing source domains arrive sequentially, but we only have access to data samples from the current domain (and not the past ones). The main idea is to learn a conditional diffusion model tha... | Rebuttal 1:
Rebuttal: Thanks a lot for your efforts in reviewing the paper. Below, we respond to your questions in detail.
> **Q1: Concerns about the assumption of dataset accessibility in the inference phase.**
Thanks. Firstly, we would like to make it clear that for discriminative tasks, test data (i.e., target dat... | Rebuttal 1:
Rebuttal: Sincerely thank all the reviewers for their efforts in reviewing our paper and providing constructive suggestions. We are greatly encouraged that the reviewers find that
* our framework/idea is **interesting** (*Reviewer EUGW and BuMk*), **novel** (*Reviewer tBNz*), and **pioneering** in applying... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IR-CM: The Fast and General-purpose Image Restoration Method Based on Consistency Model | Accept (poster) | Summary: This paper presents IR-CM, a fast and universal image restoration method leveraging consistency models. The key innovations include a novel linear-nonlinear decoupling training strategy and an origin-estimated consistency function to enhance training effectiveness and inference performance. The proposed method... | Rebuttal 1:
Rebuttal: Thank you for your recognition and support of our work. We will carefully address the issues you raised and make thoughtful revisions.
For Weakness 1 & 2.
answer:
Thank you for your thorough reading and careful review. We will diligently address the issues you identified and meticulously re-exam... | Summary: This paper modifies the consistency model from image generation to image restoration with three modifications. Each module contains robust theoretical proof and shows great performance improvement. Linear-nonlinear decoupling training strategy will motivate future works in using consistency model in image res... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work. We will carefully address your questions and resolve your concerns.
For Weakness 1.
Answer:
We apologize for any misunderstandings. While we adjusted the images in the paper to square shapes to showcase as many comparative experimental results as poss... | Summary: The paper introduces a method called IR-CM (Image Restoration Consistency Model) for fast and general-purpose image restoration.
The key idea is to achieve few-step or one-step inference by employing consistency training on specific mean-reverting stochastic differential equations (SDEs).
A novel linear-nonlin... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work. We will seriously consider your suggestions and address your concerns.
For Weakness 1.
answer:
Our method aims to construct a general-purpose architecture that does not require any prior information. By simply replacing the dataset, it can accomplish d... | Summary: This paper proposes a SDE-based two-stage network to tackle the image restoration tasks, including deraining, deblurring denoising, and low-light image enhancement. Based on the existing stochastic differential equation works, the proposed method focuses on the training efficiency and inference speed. It propo... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work, and we will carefully consider the improvement suggestions you have proposed.
For Weakness 1.
answer:
Thank you for your careful review. We indeed overlooked the introduction of NFE. Number of Function Evaluations(NFE) refers to the number o... | Rebuttal 1:
Rebuttal: We would like to thank the PCs, SACs, ACs, and all anonymous reviewers for their patient responses and valuable suggestions. Your contributions have been invaluable in improving the quality of our work.
We have carefully responded to each reviewer's questions and suggestions. Additionally, we ha... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exclusively Penalized Q-learning for Offline Reinforcement Learning | Accept (spotlight) | Summary: This paper investigates an important problem in offline reinforcement learning (RL), say mitigating unnecessary conservatism in value function. The authors achieve this by selectively penalizing states that are prone to inducing estimation errors, i.e., $f$. The core idea is to train an exclusive penalty $P_\t... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have addressed the feedback regarding Fig. 2 and conducted additional ablation studies to answer the reviewer's concerns related to the proposed prioritized dataset (PD) and the penalty control threshold $\tau$, as detailed in our global response. In respon... | Summary: This paper showed that the popular conservative Q-learning introduces bias into the Q function by its restrictive penalty. It proposed to enforce an adaptive penalty term based on the dataset to avoid bias when the dataset support disagrees with the learned policy. Experimental results supported the authors' c... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have addressed the feedback regarding Fig. 2 and conducted additional ablation studies to answer the reviewer's concerns related to the proposed prioritized dataset (PD) and the penalty control threshold $\tau$, as detailed in our global response. In respon... | Summary: The paper studies the problem of value estimation bias mitigation in offline reinforcement learning. Specifically, the paper takes the well-known Conservative Q-Learning algorithm as a starting point and improves its penalization scheme with an alternative one that applies provably less value underestimation b... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have addressed the feedback regarding Fig. 2 and conducted additional ablation studies related to the proposed prioritized dataset (PD) and the penalty control threshold $\tau$, as detailed in our global response. In response to other concerns raised by the... | Summary: This paper introduces a novel approach to handling distribution shift in
off-policy RL by the means of Q-function regularization. This is
accomplished by modulating a penalty term that is overly conservative in
CQL. The authors argue that CQL overcompensates for the distribution
shift in cases where state-acti... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have addressed the feedback regarding Fig. 2 and conducted additional ablation studies to answer the reviewer's concerns related to the proposed prioritized dataset (PD) and the penalty control threshold $\tau$, as detailed in our global response. In additi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. Based on the reviewers' comments, many expressed difficulties in understanding the figures presented in the paper and suggested that additional ablation studies would be beneficial. Therefore, we provide the following detailed responses to address each of thes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
When Is Inductive Inference Possible? | Accept (spotlight) | Summary: The authors *characterize* possible inductive inference by connecting it to online learning.
I find their work extremely interesting!
Strengths: The choice of topic is excellent, delivery is strong.
Weaknesses: See questions.
Technical Quality: 3
Clarity: 4
Questions for Authors: Page 1:
Can you please sa... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We will address your concerns here.
**Connection to computational complexity (page 1)**: good point! Inductive inference (especially Solomonoff's method) is not only linked to learning theory but also to the theory of computation. We will add some discussion ... | Summary: This paper establishes a novel link between inductive inference and online learning theory. It introduces a novel non-uniform online learning framework and proves a very interesting result on hypothesis class characterization: the authors show inductive inference is possible if and only if the hypothesis class... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We will address your concern here.
**Practical implications**: this work is devoted to a conceptual link between philosophy and learning theory, therefore practical implication is not the primary objective and is beyond the scope of the current work. We belie... | Summary: This paper studies the non-uniform online learning problem, where error bounds can depend on the hypothesis rather than being uniform across hypotheses. In particular, the paper derives theoretical results on (i) conditions for non-uniform online learnability; (ii) regret bounds when the true hypothesis lies o... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We will address your concerns here.
**Significance of the results**: we briefly summarize our contributions here (1) we give a sufficient and necessary condition for inductive inference, a fundamental problem in philosophy, while previous works only provided ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligning Individual and Collective Objectives in Multi-Agent Cooperation | Accept (poster) | Summary: The paper lies in the intersection of multi-agent and game theory. In particular, dealing with "mixed-motive cooperative games". This is, games in which the maximization of individual rewards hampers the maximization of collective rewards. This is, when agents seek to maximize their individual reward/utility, ... | Rebuttal 1:
Rebuttal: **Question 1:** I don't think this is fair or true. The mentioned papers do provide substantial theoretical analysis. This is not the way to place your paper...
**Response:** We apologize for the misunderstanding and appreciate your careful review. We have revised the statement as follows to make... | Summary: The paper proposes Altruistic Gradient Adjustment (AgA) which adjusts the gradients of individual and collective losses to align individual and collective objectives. Besides, the authors prove that AgA effectively attracts gradients to stable fixed points of the collective objective while less sacrificing ind... | Rebuttal 1:
Rebuttal: **Weakness 1:** AgA introduces an additional adjustment term, which needs to be tuned case by case.
**Response:** Thank you for your feedback. Our AgA method indeed includes an gradient adjustment component, however, **the additional adjustment term is derived automatically based on our theoreti... | Summary: This paper introduces a novel optimization method called AGA that employs gradient adjustments to progressively align individual and collective objectives. They prove that this method attracts gradients to stable fixed points of the collective objective while considering individual interests. Their method is e... | Rebuttal 1:
Rebuttal: **Question 1:** Figure 1. It’s not clear how to interpret the reward contours graphs...
**Response:** Thank you for your advice to improve the clarity of the graph description. The x-axis and y-axis represent the actions of the two players, i.e., for player $i$ =\{1, 2\}, $a_i \in \mathbb{R}$, wh... | Summary: The paper investigates the topic of cooperation in a mixed-motive multi-agent setting. They first propose the formulation of a mixed motive game as a differentiable game. Leveraging the structure of the latter, they propose a gradient-based optimization algorithm, AgA. The paper both discusses the theoretical ... | Rebuttal 1:
Rebuttal: **Weakness 1: ... I am specifically referring to ' While PED-DQN enables age ...**
**Response:** Thank you for your thorough and detailed reviews of my paper. We apologize for the unclear sentence and have rewritten it to improve clarity:
*To further promote cooperation, the gifting mechanism—a c... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for your diligent efforts and invaluable feedback. Your comments will greatly contribute to enhancing the quality of our paper. We hope that we have addressed your concerns in our response. If you have any further questions, please don't hesitate to engage in discuss... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model | Accept (poster) | Summary: This paper introduces a new method to make a strong connector between a language model and a vision encoder, in order to build a vision-language model. The method is derived from the Perceiver Resampler, but uses a clever initialization for the queries. The authors validate their approach with a series of abla... | Rebuttal 1:
Rebuttal: We sincerely thanks your comments. We will address your concern below.
**Q1:** About the anchor selection algorithm.
**R1:** Thanks for your suggestion. We will provide you with the pseudocode below.
Assuming the Visual Feature Map is $V \in \mathbb{R}^{B \times N \times D}$, the Visual Attentio... | Summary: This paper propose AcFormer, a novel vision-language connector in MLLMs.
AcFormer is driven by visual anchors observed in vision transformer's PCA of the feature map and attention map of [CLS] token, where high value is observed in both of the maps.
The author use the attention value of [CLS] token to select t... | Rebuttal 1:
Rebuttal: We sincerely thanks for your review. We will address your concern below.
**Q1:** About the inference time.
**R1:** Thanks for your concern of the effectiveness. We show the **inference time** and **training memory** needed below. We report the inference time for each benchmark using the prompt "... | Summary: This paper introduces a novel vision-language connector, Anchor Former (AcFormer), designed to enhance the efficiency and accuracy of multimodal models. By identifying visual anchors within Vision Transformers and utilizing a cost-effective progressive search algorithm, AcFormer leverages these anchors to aggr... | Rebuttal 1:
Rebuttal: We are grateful for your review. And we will address your concern below.
**Q1:** About the effectiveness.
**R1:** Thank you for highlighting this concern.
Our primary motivation in this work is to enhance the efficiency of Large Multimodal Models (LMMs). While various token reduction methods ex... | Summary: This paper proposes a way to select visual tokens, e.g. token pruning, by using attention map scores. This reduces the number of tokens needed in the network which saves compute costs. The method is evaluated on multiple datasets and shows reduced compute while maintaining performance.
Strengths: The paper is... | Rebuttal 1:
Rebuttal: Thanks for your hard work in reviewing! We will address your concern below.
**Q1:** About the novelty.
**R1:**
Thank you for highlighting this issue. Though there are indeed methods for token reduction or token pruning, such as CAbstractor and Perceiver Resampler, ours are different with them i... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dendritic Integration Inspired Artificial Neural Networks Capture Data Correlation | Accept (poster) | Summary: This study explores incorporating channel-wise quadratic neuron, as inspired by dendritic nonlinearity, into artifical neural networks to improve model performance. These models show competitive performance on datasets like CIFAR, ImageNet-1K while maintaining simplicity and efficiency. The theoretical and exp... | Rebuttal 1:
Rebuttal: ## Weaknesses
- "The theortical explaination is limited to highly simplified setting. "
Thank you for your comment. Our theoretical analysis aims to emphasize that quadratic neurons inherently capture second-order information from training samples, which enhances their generalization capabilities... | Summary: The paper introduces a new biologically inspired neural network architecture. Rather than using linear layers followed by a nonlinear activation function, the authors propose a quadratic model instead in the inputs. This form is said to explicitly model the covariance between input features offering better acc... | Rebuttal 1:
Rebuttal: ## Weaknesses
Thank you for the valuable comments, we have provided a biological interpretation of our Dit-CNN in global rebuttal.
## Questions
- "Have the authors look into the structured learned by the quadratic term?"
Thank you for your valuable question. Following your suggestion, we have ex... | Summary: This is an interesting paper looking at quadratic neurons and how they impact performance and/or learning rate of ANNs. The quadratic integration is loosely linked to dendritic integration properties of pyramidal neurons in cortex (though it is unclear whether any real resemblence should be granted). This incr... | Rebuttal 1:
Rebuttal: ## Weaknesses
Thank you for your suggestion, it is indeed an interesting idea. Previous work has shown that the dendritic bilinear integration rule can account for both sub-linear and supra-linear cases, depending on the sign of the quadratic coefficient. In our approach, we directly incorporate t... | Summary: This paper explores the computational benefits of quadratic neurons, which are inspired by the quadratic integration rules of dendrites. The authors first present the theoretical analysis on binary classification for normal distributions, showing the existence and uniqueness of the solution with a single quadr... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. Thank you for your insightful feedback, and I apologize for any lack of clarity in our presentation.
(1.1) Our main analysis aims to emphasizes that quadratic neurons inherently capture second-order information from training samples, which enhances their generalization capabilit... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for their high-quality reviews. To address the reviewers' concerns, we have added more experiments and provided additional details about the model. We hope that our responses effectively address the reviewers' feedback .
We have provided a comprehensive biological ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper theoretically demonstrate that quadratic neurons inspired from dendritic computing inherently capture correlation within structured data. The quadratic rules are integrated with convolution networks and established the so-called Dit-CNNs. Experiments on CIFAR and ImageNet datasets demonstrates compet... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. Thank you for your suggestion. We have indeed demonstrated the uniqueness of critical points under certain assumptions (Theorem 2 in the main text). Under these conditions, it can also be theoretically proven that if the gradient flow algorithm converges, it will drive the paramet... | null | null | null | null | null | null |
Partial Transportability for Domain Generalization | Accept (poster) | Summary: This paper tackles to problem of transportability in domain generalization. Multiple datasets are provided together with graphical information stipulating which causal mechanisms are shared across domains and which mechanisms are going to remain invariant in the target domain. The goal is then to estimate the ... | Rebuttal 1:
Rebuttal: We appreciate the time and effort for reviewing our paper and the positive assessment that our paper “reads nicely”, “the problem appears important” and that our approach is “principled”. Please find below our response to the review, and let us know if you have any further concerns.
> **Q 1.** *”... | Summary: The paper extends the formulation of canonical models to encode the constraints for the transportability tasks. Then it adapts Neural Causal Models for the transportability task and introduces an iterative method Causal Robust Optimization to find a predictor with the best worst-case risk.
Strengths: - The pa... | Rebuttal 1:
Rebuttal: We appreciate the positive assessment of our work, thank you. The following response answers each question sequentially. We would be happy to expand on it if needed.
> **Q 1.** *”Some definitions are not provided, making it difficult for readers to understand certain parts of the paper. For examp... | Summary: This paper studies the problem of domain generalization through the lens of partial transportability, and introduces some new results for bounding the value of a functional of the target distribution, given data from source domains and assumptions about the data generating mechanisms. Authors adapt existing p... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, we appreciate the positive feedback. In the following response we address comments and concerns pointed out by the reviewer. Please let us know if we can help clarify any part of it.
> **Q 1.1.** *The paper organization and presentation seem complicated to co... | null | null | Rebuttal 1:
Rebuttal: In this global rebuttal, we take the opportunity to discuss experimental results of CRO using the figures in the attached pdf as a support.
**Summary.** In the domain generalization task, the source domains $\mathcal{M}^1,\mathcal{M}^2,\dots,\mathcal{M}^K$ ,and the target domain $\mathcal{M}^*$ m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions | Accept (poster) | Summary: This paper investigates an intriguing problem regarding how machine learning models evolve during dynamic retraining using model-annotated samples, incorporating strategic human responses. The authors discover that it becomes increasingly likely for individuals to receive positive decisions as the model underg... | Rebuttal 1:
Rebuttal: Thanks for the comments. We address your questions point by point as follows.
> While the conclusions of the paper help understand the impact of human strategic behavior on ML systems, many of these conclusions are somewhat intuitive and straightforward.
We believe the theoretical results are no... | Summary: This paper addresses the dynamics of machine learning systems when retrained with model-annotated and human-annotated samples in the presence of strategic human agents. It explores how these dynamics affect various welfare aspects, including those of applicants, decision-makers, and the broader social context.... | Rebuttal 1:
Rebuttal: Thanks for the comments. We address your questions point by point as follows.
> Assumptions of the theoretical results and the generalizability
- Our paper focuses on strategic classification settings [1,6,7,11,12,17,25,31,35]. According to the previous literature, human strategic behaviors are ... | Summary: The paper explores a scenario where a machine learning system retrains itself over time by collecting data generated through ML system annotation itself as well as human-annotated data while allowing the distribution of training samples to evolve over time. This evolution is influenced by the strategic behavio... | Rebuttal 1:
Rebuttal: Thanks for the comments. We address your questions point by point as follows.
> Theorem 3.3
The result in Theorem 3.3 (i.e., $a_t > a_{t-1}$) does **not** require $N >> K$ but only needs $N > 0$. Condition $N >> K$ is only used in Proposition 3.4, under which the acceptance rate converges to $1$... | Summary: This paper studies the effects of retraining machine learning (ML) models with data annotated by both humans and the models themselves, especially in social domains where human behavior is influenced by the ML systems. The authors explore how strategic human agents, who adapt their behaviors to receive favorab... | Rebuttal 1:
Rebuttal: Thanks for the comments. We address your questions point by point as follows.
> Empirical evidence or case studies to support the validity of the assumptions
- The monotonic likelihood ratio property (MLRP):
This property is rather standard and has been widely used in literature (e.g., [11,12]... | Rebuttal 1:
Rebuttal: # Global Rebuttal
We thank the reviewers and AC for reviewing our paper. Here we present a global response to the questions shared by multiple reviewers.
## Assumptions and Applicability of Our Model
- The monotonic likelihood ratio property (MLRP)
This property is rather standard and has been... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Overcoming Common Flaws in the Evaluation of Selective Classification Systems | Accept (spotlight) | Summary: The authors introduce a new metric AUGRC for evaluating classifiers under the Selective Classification framework, whereas the classifier
has an option to reject low-confidence predictions. The authors introduce desirable properties for evaluating the Selection Classifiers, and show that the new metric has all ... | Rebuttal 1:
Rebuttal: Thank you again for your valuable comments, and for taking the time to read our general reply, as well as considering our point-by-point comments here:
---
W1. “Fig. 4: the risk vs. coverage curves for AURC vs. AUGRC do not look that different. Sure, there is non-monotonicity in AURC, but that is... | Summary: The paper presents 5 requirements for multi-threshold metric for Selective Classification and a novel metric to evaluate selective classifiers called AUGRC. The proposed metric satisfies 5 requirements that are not met by current approaches. The proposed metric changes the rankings on 5 out of 6 datasets consi... | Rebuttal 1:
Rebuttal: Thank you again for your valuable comments, and for taking the time to read our general reply, as well as considering our point-by-point comments here:
---
W1. “The empirical evaluation can be improved.”
W1.1. “there are some contradictory lines [...]”
* Thank you for pointing out the mistakes i... | Summary: The authors propose 5 requirements that should be satisfied by selective classification (SC) metrics such that they can be successfully used to rank SC models for a task. They then propose a new metric, called AUGRC, which is shown to satisfy all 5 requirements. Finally, the authors show empirically that their... | Rebuttal 1:
Rebuttal: Thank you again for your valuable comments, and for taking the time to read our general reply, as well as considering our point-by-point comments here:
---
W1. “Limited discussion of future works.”
* Thank you for this helpful comment. In response to your feedback, we extended the Conclusion Sect... | Summary: The paper tackles the problem of Selective Classification (SC). The authors show problem with the existing metrics used in evaluating SC and propose to use the Area under the Generalized Risk Coverage curve (AUGRC). Empirical results provide useful insights about the effect of using this metric and shows how t... | Rebuttal 1:
Rebuttal: Thank you again for your valuable comments, and for taking the time to read our general reply, as well as considering our point-by-point comments here:
---
W1. “It is important to add the details of the methods in the main paper. For example, DeepGamblers (DG) is referred to as DG in the main pap... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable comments. The reviewers generally agreed on the added value of our work , noting that “The paper is well written, clear and easy to read” (QM33) , “The metric is simple, yet effective” (xFt7), “The experiments are extensive” (BBe8), and “It is im... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffuserLite: Towards Real-time Diffusion Planning | Accept (poster) | Summary: The paper introduces a lightweight framework employing a Plan Refinement Process (PRP) for generating trajectories from coarse to fine-grained levels. This approach reduces redundant information modeling, significantly enhancing planning efficiency. DiffuserLite achieves an impressive decision-making frequency... | Rebuttal 1:
Rebuttal: Thank you for your effort in reviewing and acknowledging our work. Based on the questions you have raised, it seems you are particularly interested in the implementation details of DiffuserLite and its potential applications. We have already released the initial version of the code on [diffuserlit... | Summary: The paper introduces a method to accelerate diffusion model-based planning called the Plan Refinement Process (PRP). This method divides the planning of the entire trajectory into several temporal spans, focusing on the most recent spans in each planning stage, thereby discarding redundant distant information.... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. The concerns you have raised are insightful, and we aim to address them effectively in the following responses. To be concise, below, we use "Lite" to refer to "DiffuserLite".
---
**Weakness: Limited Novelty**
We aim to elucidate the differences between Lite a... | Summary: This paper introduces DiffuserLite, a lightweight framework that utilizes progressive refinement planning to reduce redundant information generation and achieves real-time diffusion planning.
Key contributions are:
- Introduced the plan refinement process (PRP) for coarse-to-fine-grained trajectory generatio... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions. I am deeply touched by your diligent review and appreciate your dedication. I summarize the questions you raised and respond below:
---
- **Implementation Details**: We have released the code at [diffuserlite/diffuserlite.github.io](https://github.com/di... | Summary: TLDR; Unlike traditional methods that generate the entire trajectory at once, this process gradually refines the plan at each stage, reducing computational costs and improving real-time performance.
DiffuserLite is a lightweight diffusion planning framework designed to increase decision-making frequency in re... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions. I am deeply touched by the diligent review and appreciate your dedication. I summarize the issues and respond below to address your concerns:
---
- **Paper revision:** Following your suggestion, during revision, we will further introduce *Reflow* in main... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data | Accept (poster) | Summary: This paper proposes two stochastic optimization algorithms for instrumental variable regression (IVaR) that operate on streaming data without requiring matrix inversions or mini-batches. When the true model is linear, the paper proves that TOSG-IVaR converges at a rate of $\mathcal{O}(\log T/T)$ and OTSG-IVaR ... | Rebuttal 1:
Rebuttal: Dear Reviewer AcA7,
Thank you for your insightful review. Here we provide response to your questions and concerns.
### **Weaknesses**
**W1** - Please note that many existing IV methods are not applicable for online/streaming setting. We also wish to clarify that in Appendix B Table 1 we have com... | Summary: This paper tackles the instrumental variable regression. The problem setting assumes a model $Y=g_{\theta^*}(X) + \epsilon_1$, but unlike the ordinary regression model, there are correlations between $X$ and $\epsilon_1$. The model assumes in addition an instrument variable $Z$ such that $Y$ and $X$ are indepe... | Rebuttal 1:
Rebuttal: Dear Reviewer WDCF,
Thank you for your review. We provide responses to your concerns in 'weaknesses'.
**W1** - We strongly disagree with your view that the paper's contribution is marginal. As noted in Prop 1, Alg. 1 is not restricted to linear models; it can handle non-linear and non-convex cas... | Summary: The paper shows algorithms for instrumental variable regression that dont need matirx inversions and mini-batches. At the same time, the paper give the rates of convergence.
Strengths: The proposed method offers robust theoretical guarantees and is validated through comprehensive experimental results.
Weakne... | Rebuttal 1:
Rebuttal: Dear Reviewer hwGZ,
Thank you for your comments. Below we provide our response to your questions.
### **Question**
>Q1. Matrix inversion
**Response:**
Please kindly refer to Appendix B for a summary table (Table 1) on arithmetic operations complexity per-iteration. Using the method with matri... | Summary: This paper proposes and analyzes on-line algorithms for instrumental variable regression (IVaR) with streaming data. Specifically, the authors consider the model:
$Y = g_{\theta^*}(X) + \epsilon_1$
where the covariate $X$ and noise $\epsilon_1$ are possibly correlated, but an instrumental variable $Z$ is a... | Rebuttal 1:
Rebuttal: Dear Reviewer E97U,
Thank you for your insightful review. Below we provide our response to your concerns and questions.
### **Weaknesses**
>W1. Motivation
**Response**:
As mentioned in the general response "Adv 3 - Emerging applications", a motivation for developing online/streaming IVaR is t... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your comments and questions. Below we provide our general response. We first present real-data experiments, and then re-emphasize points which were potentially overlooked.
### **Real Data Examples**
We illustrate Alg. 2 on 2 datasets: Angrist and Evans (1998) Child... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing | Accept (poster) | Summary: This paper introduces a custom Guass-Newton method dubbed AGN to solve general over-parametrized matrix sensing problems, and demonstrated that 1) This new method is a descent method under benign assumptions and 2) it achieves Q-linear convergence under restrictive RIP assumptions.
Strengths: 1. Introduced a ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for your positive evaluation of our work, and we also thank you for your valuable and constructive suggestions. As for your concerns, we make detailed responses as follows.
**1. Question: The computational cost of AGN over GD.**
**Answer:** In general, the co... | Summary: This paper focuses on the optimization of overparameterized, non-convex low rank matrix sensing (LRMS)—an essential component in contemporary statistics and machine learning.
This paper introduces an approximated Gaussian-Newton (AGN) method for tackling the non-convex LRMS problem. Notably, AGN incurs a comp... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your valuable comments on our work. As for your concerns, we give detailed response below which we hope can help you fully understand our work. Please feel free to let us know if you have any further concerns. We will deeply appreciate that you ca... | Summary: In this submission, the authors proposed an approximated Gaussian-Newton (AGN) method for overparameterized non-convex low-rank matrix sensing problem. The authors presented the corresponding theoretical analysis and partially explained the reason the proposed AGN method achieves fast convergence rates.
Stren... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for your careful review, constructive suggestions and positive feedbacks. The followings are our responses to your concerns. We will greatly appreciate that you can raise your score if you find our responses resolve your concerns.
**1. Question: About the experim... | null | null | Rebuttal 1:
Rebuttal: Dear ACs and reviewers,
Thank you very much for your valuable comments. We truly appreciate the time and effort you've taken to review our work. We're glad the reviewers found our work valuable and provided positive feedback. Your feedback is important to us, and in accordance with the reviewers'... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series | Accept (poster) | Summary: In this paper, authors propose to convert general time sequences into images by employing invertible transforms and incorporate advanced diffusion vision models to process short- and long-range time-series within the same framework. Through experiments, improvements have been made on multiple tasks, such as un... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of the comprehensiveness of our experiments and the clarity of our writing. We also thank the reviewer for raising concerns and points that helped deepen the discussion. Below, we address these points and are more than happy to respond to any further concer... | Summary: The paper argues for the use of image generative modelling architectures for the time-series generative modelling task. Doing so involves converting a time-series to an image-shaped object, modelling it as an image, and then converting back.
Strengths: - This is a simple idea that is shown to work well in mos... | Rebuttal 1:
Rebuttal: We are thankful to Reviewer SryL for generally identifying the simplicity of our approach and recognizing its ability to solve existing shortcomings and the extensive evaluation where we outperform baselines. We also would like to thank them for their observations, comments, and suggestions that h... | Summary: The paper proposes using invertible transforms to map varying-length time series to images. Using this technique, generative modeling of time series can be done using diffusion vision models. The authors demonstrate state-of-the-art performance on unconditional generation, interpolation, and extrapolation on s... | Rebuttal 1:
Rebuttal: We are thankful to Reviewer iRua for generally identifying the elegancy of our approach and its generalizability. We also would like to thank them for their observations, comments, and suggestions that helped deepen our discussion and improve the paper. Below, we address the reviewer's concerns. G... | null | null | Rebuttal 1:
Rebuttal: We have attached the PDF. References from the rebuttal comments are directed to the tables or figures in this PDF.
Pdf: /pdf/7190df8857151b9fbc3d43c5a2e493ca8e4d685a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning from Noisy Labels via Conditional Distributionally Robust Optimization | Accept (poster) | Summary: This paper studies the problem of learning from noisy labels by using conditional distributionally robust optimization (CDRO) to estimate true label posterior. The authors formulate the problem as minimizing the worst-case risk within a distance-based ambiguity set centered around a reference distribution and ... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript. We appreciate your thoughtful comments and suggestions. We will carefully incorporate the necessary revisions to address your feedback in the new version of the manuscript. Below, we highlight our responses to each of your comments.
1. Notably, building on... | Summary: This paper studied the issue of potential misspecification of estimated true label posterior in learning from noisy labels. To alleviate the impact of this issue, it formulated learning from crowds as a conditional distributionally robust optimization problem, where a robust pseudo-empirical distribution is u... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript. We appreciate your thoughtful comments and suggestions. We will carefully incorporate the necessary revisions to address your feedback in the new version of the manuscript. Below, we highlight our responses to each of your comments.
### 1. About the focuse... | Summary: This work addresses learning from noisy annotations by using conditional distributionally robust optimization (CDRO).
To account for variability in estimating the true label posteriors, the authors propose an approach that minimizes the maximum expected risk with respect to a probability distribution within ... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript. We appreciate your thoughtful comments and suggestions. We will carefully incorporate the necessary revisions to address your feedback in the new version of the manuscript. Below, we highlight our responses to each of your comments.
### 1. About the feedback... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your thoughtful feedback and for the time you dedicated to evaluating our paper. We deeply appreciate your insights and constructive comments. We are pleased to hear that you recognized the strengths and contributions of our paper, which we would like to recap as fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the cohesion and separability of average-link for hierarchical agglomerative clustering | Accept (poster) | Summary: The authors analyse the theoretical properties of the so-called average-link approach for clustering points in metric spaces. They formulate cohesion and separability criteria that capture the goodness of a clustering, essentially formalising the intuition that good clusters should be densely packed and well-s... | Rebuttal 1:
Rebuttal: Thanks for revising the paper and for the positive evaluation!
**Issue 2** *The paper only mentions complete-linkage and single-linkage as alternative linkage methods for clustering. A brief discussion of other clustering methods and how they compare to average-link would have been useful. Speci... | Summary: This paper studies the performance of average-link clustering in metric spaces, focusing on criteria that offer better interpretability than Dasgupta's cost function for cohesion and separability. By investigating how well average-link balances the compactness of clusters (cohesion) with the distinctiveness be... | Rebuttal 1:
Rebuttal: Thanks again for revising our paper and for your positive evaluation!
**Question** *Just out of curiosity, I would only like to know if there are any plans for future directions of the work, as they were not indicated in the paper*
One potential direction for future work is addressing the case i... | Summary: This paper theoretically investigates the effectiveness of average linkage for hierarchical agglomerative clustering. The authors consider the setting where we are clustering in a metric space and consider well motivated definitions of separability and cohesion of clustering. The performance of average linkage... | Rebuttal 1:
Rebuttal: We are glad that you saw several merits in our submission!
**Issue 1**: *I think the presentation of Table 6 could be clearer -- I think directly presenting the results per dataset per method would be clearer*
Reply. I believe you mean Table 1. This table is the best solution we found for the ... | Summary: This paper studies the well-known average linkage algorithm. The paper notes that average linkage has better approximation guarantees with respect to (variants of) Dasgupta's cost compared to complete and single linkage. However, in certain other settings such as metric graphs the approximation factor of avera... | Rebuttal 1:
Rebuttal: We are glad you enjoyed reading our paper and found our results to be solid.
**Issue** *Font size changes from page 3 onwards (line 138)*
Thanks for pointing it out, we will fix it.
**Question** *Are there any downstream settings/tasks where approximation guarantees with regards to max-diam, cs... | Rebuttal 1:
Rebuttal: We thank all the referees for their time and valuable feedback! We are happy that all reviewers are positive about our submission and that it was recognized that our results provide a more clear theoretical picture of why the well-known average linkage method performs so well.
We are attaching a ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distributional Preference Alignment of LLMs via Optimal Transport | Accept (poster) | Summary: The paper proposes a new technique for preference alignment, named Alignment via Optimal Transport (AOT). The proposed technique supports both paired and unpaired alignment settings. The paper introduces a new viewpoint for preference alignment based on stochastic dominance i.e., making the reward distributio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review of the paper and for their insightful questions that we address below:
___
**The evaluation is conducted using Llama3-70B-Instruct instead of GPT4 which is not the standard. However, as described in the paper, the usage of Llama3-70B-Instruct leads... | Summary: The motivation of this paper is that current alignment approaches ensure reward dominance at the sample-level but not on the distributional level. With this in mind, the authors set their goal to design an alignment approach which satisfies First Order Stochastic Dominance (FSD) in rewards for positive example... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments:
___
**Weaknesses**
___
**1) There could be some more motivation for why the FSD condition is practically desirable over the conditional (on x) dominance condition of DPO from Equation (2). Is this more than a theoretical nicety?**
Thanks for... | Summary: This works proposed using Optimal transport in 1D to derive a better alignment guided by the preference data. The main idea is to work with the log likelihood ratio of the marginal distributions of the preference data. The alignment is made through Optimal Transport loss in 1D based on the concept of stoch... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback.
___
**My main concern of this paper is the empirical results in experiment section. Table 1 shows that AOT paired/unpaired do not outperform other methods at least in 4 out 7 cases (ARC, MMLU, Winogrande, GSM8K). When they are versus each othe... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Not Just Object, But State: Compositional Incremental Learning without Forgetting | Accept (poster) | Summary: This paper presents a novel setting of incremental learning, named Compositional Incremental Learning (composition-IL). This setting differs from existing ones as it involves recognizing not only new objects (e.g., a shirt), but also their states (e.g., red) and the resulting compositions (e.g., a red shirt). ... | Rebuttal 1:
Rebuttal: Q1: Many hyperparameters/tuning complexity
A1: Thank you for your constructive suggestions. We consider that assigning weights to different loss terms is a common practice. We highly agree with your suggestion to provide a simplified version of the model. As you suggested, the 'C+S' and 'C+O' in ... | Summary: This work presents a new task called Compositional Incremental Learning (composition-IL).
This new task extends the existing class incremental learning to a more fine-grained scenario for more realistic applications.
This work formulates and designs a new composition-IL benchmark based on Clothing16K and UT-Za... | Rebuttal 1:
Rebuttal: Q1: About the composition-IL
A1: Thank you for your great comment. Firstly, we would like to emphasize that previous prompt-based works have achieved satisfactory results by employing strategies that contradict the paradigm of continual learning. The compared prompt-based methods all involve task... | Summary: This paper introduces a novel task termed Compositional Incremental Learning (composition-IL), which aims to enable models to recognize a variety of state-object compositions incrementally. The authors propose a new model called CompILer, which employs multi-pool prompt learning, object-injected state promptin... | Rebuttal 1:
Rebuttal: Q1: About performance comparison.
A1: Thanks for your question. We are glad to answer it. First, we would like to emphasize that CompILer has achieved SOTA performance across all settings. Notably, on the Split-Clothing dataset, CompILer beats LGCL and CODA-Prompt with an improved average accurac... | Summary: The paper propose compositional Incremental Learning to enable models to recognize state-object compositions incrementally. The paper provides two tailored datasets for composition-IL by modifying two existing datasets in the fashion domain. The paper propose a new prompt-based model comprising of multi-pool p... | Rebuttal 1:
Rebuttal: Q1: About hyperparameter tuning
A1: Thanks for your question. Firstly, we claim that finding the optimal balance between hyperparameters is important yet challenging for continual learning. Since the feature distributions within each incremental session may vary considerably, the optimal hyperpar... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers x9g2 (R1), B271 (R2), KbBF (R3), and EVGX (R4) for their constructive comments and acknowledgements: “the proposed new task composition-IL is novel and welcome”(R1, R2, R3, R4); “the proposed benchmarks are well-constructed”(R1, R3); “CompILer is well-conceived”(R2... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mini-Sequence Transformers: Optimizing Intermediate Memory for Long Sequences Training | Accept (poster) | Summary: The paper introduces the MINI-SEQUENCE TRANSFORMER (MST), a technique designed to enhance the efficiency and accuracy of LLM training, particularly when dealing with extremely long sequences. The core concept behind MST is the partitioning of input sequences into smaller "mini-sequences," which are then proces... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough assessment and insightful comments. Our rebuttal addresses concerns and introduces a significant optimization: chunk-based mini-sequence transformers (MST). This advancement directly addresses the "Potential for Further Optimization" and "Sensitivity... | Summary: The paper targets an LLM-specific but significant challenge: training the LLM model for long-context understanding. The author proposed a Mini-Sequence Transformer method that enables LLM training with extremely long sequences. The method is motivated by the mini-batch, which forwards and backs a chunk of data... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. We have conducted new experiments to address concerns and demonstrate the effectiveness of Mini-Sequence Transformer (MST).
# 1. Addressing Weakness
> Weakness: However, the model's training loss can not effectively demonstrate the method's perfor... | Summary: This paper introduced minibatching along the sequence length for inputs to the MLP and LM-head parts of a Transformer-based model. This method does not change the functionality of the transformer but improves the memory requirement when inputs are of long sequence length.
Overall, while this paper leaves to ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback. We appreciate your support and address your concerns below.
# 1. Addressing Weaknesses
# 1.1 Comparison with lossy methods
We've conducted additional experiments comparing our Mini-Sequence Transformer (MST) approach with quantization te... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thorough and insightful feedback. We appreciate the recognition of our work's potential impact on long-context LLM training. In response to the valuable comments received, we have conducted additional experiments and provided further implementation optimi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection | Accept (poster) | Summary: The authors note that existing physical adversarial attack methods overlook the transitioning from the physical domain to the digital domain, which involves the camera. Therefore, they propose a camera-agnostic attack to enhance the stability of adversarial patches across different cameras. The proposed advers... | Rebuttal 1:
Rebuttal: **Q1: Explanation of hyperparameters and their value ranges for the camera ISP proxy network are needed.**
A1: Thank you for your insightful comment regarding the hyperparameter selection for our camera ISP proxy network.
Camera ISPs consist of multiple processing stages. Tseng *et al.*[1] summa... | Summary: This paper proposed a cross-camera physical adversarial attack, Caera-Agnostic Patch (CAP) attack against person detection.
This method incorporates a differentiable camera Image Signal Processing (ISP) proxy network to compensate for physical-to-digital domain transition gap. Additionally, the camera ISP pro... | Rebuttal 1:
Rebuttal: **Q1: In the experimental setup, only the patch size is provided, while the input image size is missing.**
A1: Thank you for your careful review of our paper. The input image size we used is **640x640**, consistent with the official YOLOv5 repository. It is worth noting that YOLOv5 supports both ... | Summary: The authors present an improved method for human-detection-blocking adversarial patch attacks, with a particular focus on ensuring said attacks are robust to changes in camera. This approach is motivated by a study of the impact of camera Image Signal Processing (ISP) pipelines, which the authors show is a con... | Rebuttal 1:
Rebuttal: **Q1: While the authors experimental evaluation is quite comprehensive [...].**
A1: Thank you for your valuable feedback. We acknowledge that evaluating the performance of our approach on other detectors would be meaningful. To address your concerns, we have conducted additional experiments, as d... | Summary: The paper "Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection" addresses the limitations of current physical adversarial attack methods, which often fail to consider the variability introduced by different camera Image Signal Processing (ISP) pipelines. This oversight... | Rebuttal 1:
Rebuttal: **Q1: Typographical errors [...].**
A1: We have made the following corrections:
- Corrected the citation in line 52: Zhang *et al.*[40] employed [...].
- Corrected the citation in line 129: as Zhang *et al.*[40] demonstrated [...].
- Removed the duplication in line 255.
We will thoroughly proof... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and constructive comments. As summarized by reviewers xDi9, eDby, and ai8f, our work focuses on a critical and previously overlooked aspect—the impact of camera variability on physical adversarial attacks—and proposes an effective and robust adversarial ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization | Accept (poster) | Summary: The paper shows a new lower bound for the generalization error of gradient descent for Lipschitz functions. The bound shows a linear dependency on the dimension that closes the gap between lower and upper bounds in the sample complexity of GD under several regimes. The construction of the function relies on a ... | Rebuttal 1:
Rebuttal: Thank you very much for the review and detailed comments.
- > This could be rewritten in a more concise way with all the improvements under which regimes spelled out explicitly (maybe a table would make sense).
A table is an excellent suggestion. We will follow it, thanks!
- > It should be ment... | Summary: The paper proved a tight lower bound for the sample complexity of full-batch gradient descent. The authors also presented some open questions in this area.
Strengths: I think the paper is not ready.
Weaknesses: - The structure of the paper is not standard. There isn't any conclusion, and it seems that the pa... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review the manuscript.
- The conclusions are clearly presented in the manuscript, but are also present in the other reviews. If, after reading the other reviews, you still feel that certain contributions are not highlighted enough, please point concrete... | Summary: This paper studies the sample complexity of full-batch gradient descent under stochastic convex optimization problem. The main result of this work provides a lower bound of the generalization gap of GD of order $\Omega(\min\\{\frac{d}{m},1\\} \cdot \min\\{\eta d^{3/2}, \eta\sqrt{T},1\\})$, where $d$ denotes di... | Rebuttal 1:
Rebuttal: Thank you very much for the constructive comments and the positive score. Below are answers to concrete questions.
- > For example, it's not clear to me why the oracle 𝑂(𝑡) defined in Eq. 11 satisfies the zero-chain property depicted in Figure 1.
Notice that figure 1 refers to eq. 16 and not... | Summary: ### Summary:
The authors study the generalization error and sample complexity of GD for GD in stochastic CO. Their results show that one can achieve the generalization error of $\tilde{\Theta}(d/m + 1/\sqrt{m)}$ which is the same as the generalization error of ERM. Indeed, they prove that the linear depende... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review the manuscript, and particularly for the positive review.
> - Can you explain how this analysis restricts to full-batch GD and what makes it impossible to obtain results for SGD? I recommend adding a bit of discussion to the paper regarding this.
... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos | Accept (spotlight) | Summary: The paper presents a novel approach to learning task graphs from procedural activities observed in egocentric videos. Task graphs represent the partial ordering of key-steps needed to complete a task and are crucial for developing intelligent agents that can assist users. The proposed method utilizes direct ma... | Rebuttal 1:
Rebuttal: ## Q1: Dependence on Action Recognition Models
**Computational Overhead** while it is true that the action recognition model introduces a computational overhead, at inference, our approach is a lightweight module on top of the detected actions which can be easily plugged into systems already inclu... | Summary: This paper introduced a learning-based task graph generation for procedural actions and mistake detection. Compared to previous approaches which usually consider natural language based descriptions, the proposed approach aims to use backward propagation to adaptively optimize procedural action sequences. To th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work and for their supportive remarks and constructive feedback. In the following, we report our replies to the reviewer's specific queries.
## Q1: The explanations of contrastive loss are somehow hard to follow. For typical contrastive l... | Summary: The paper proposes a differentiable loss function based on the maximum likelihood of generating task graphs from video features. The two proposed methods (DO and TGT) show strong performance, verifying their effectiveness in generating task graphs. Moreover, the predicted graphs are evaluated on downstream tas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of the paper and for providing constructive feedback and suggestions for improvement. In the following, we report our answers to the reviewer's specific queries.
## Q1: L140 describes that the authors separate sequences if the key-step repetitio... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and constructive feedback.
All reviewers appreciated the proposed method. **pcHQ** highlighted that our method demonstrates strong performance on the analyzed datasets, confirming the effectiveness of differentiable task graph generation. **PJ9... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Star Geometry of Critic-Based Regularizer Learning | Accept (poster) | Summary: The paper presents a theoretical analysis of learning regularizers for inverse problems using a critic-based loss. By focusing on a specific family of regularizers (gauges of star-shaped bodies), amenable to theoretical analysis, the authors provide a number of theoretical insights towards existence, uniquenes... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and positive comments that our paper is "very well-written" and that our theoretical framework "opens up a number of new research directions both theoretically and numerically." Below we discuss some of the main questions that were raised.
>How do w... | Summary: This paper explores the learning of task-dependent regularizers using critic-based loss functions in the context of variational regularization for statistical inference and inverse problems. It particularly focuses on a specific family of regularizers, namely gauges of star-shaped bodies, which are common in p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and positive comments that our theoretical framework is "rigorous and methodologically sound" and that our results "address the theoretical gaps in understanding how regularizers are learned." Below we discuss some of the main questions that were rai... | Summary: This paper leverages the star geometry and dual Brunn-Minkowski theory to study the optimal critic-based regularizers. The authors illustrate the optimal regularizer can be interpreted using dual mixed volumes that depend on data distribution. Theorems are proved for the existence and uniqueness of the optimal... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. Below we would like to address concerns/questions that were raised.
>Regarding novelty in relation to [50]
We appreciate the reviewer's concern regarding novelty in relation to [50]. Please see our global comment regarding the novelty and s... | Summary: This submission extends the techniques of [50], i.e., tools from star geometry and dual Brunn-Minkowski theory, to characterize the optimal regularizer under the adversarial regularization framework [51] of inverse problems. \alpha-Divergence as loss functions for learning regularizers is also discussed, with ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and positive comments that our insights offer "an interesting theoretical contribution". Below we would like to address concerns/questions that were raised.
>Regarding novelty in relation to [50]
We appreciate the reviewer's concern regarding novel... | Rebuttal 1:
Rebuttal: We thank all reviewers for their detailed feedback and positive comments that our work is "rigorous and methodologically sound" and that our theory "opens up a number of new research directions both theoretically and numerically". We will address each reviewer's specific questions and concerns ind... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Language Models Encode Collaborative Signals in Recommendation | Reject | Summary: The paper studies an important and open question, how much user behavior knowledge (generally captured by collaborative filtering models) are present in large language models. This has been a topic attracting significant research interest in recent years. The authors propose that simple linear mappings done on... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{orange}{\text{EzpW}}$**
Thanks for your acknowledgment of the importance of the topic we studied. We believe you are an industrial expert with a deep understanding of sequential recommendation. We greatly appreciate your careful reading of our paper and your respons... | Summary: This paper states that LLM encodes collaborative signals that make it easy to connect language representation space with an effective recommendation space. Thus, it proposes an effective collaborative filtering model AlphaRec that takes as input only the transformed LLM representations of textual descriptions ... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{blue}{\text{GXUC}}$**
> **Comment 1: More discussion about the collaborative signals encoded in LMs**
Thanks for your concern and suggestion. We respond to this question as follows.
First. We would like to restate the importance and correctness of the linear map... | Summary: The paper proposes AlphaRec, a novel method to incorporate both knowledge from pre-trained language models and collaborative signals. Authors firstly reveal the advantages brought from pre-trained embedding model, and then propose three modules within AlphaRec. An MLP layer to transform pre-trained embedding t... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{green}{\text{tJs7}}$**
We sincerely thank you for the positive feedback and valuable comments! To address your concerns, we present our responses as follows.
> **Comment 1: Why only encode titles** There is more information within your used Amazon dataset including... | Summary: This paper proposes AlphaRec, an LLM-based recommender system that utilizes language representations of item textual data for recommendations.
Strengths: + Investigating ID paradigm and LLM paradigm is important.
+ The method is simple but seems to be effective.
Weaknesses: - In this paper, what most confuse... | Rebuttal 1:
Rebuttal: **Response to Reviewer $\color{red}{\text{NfrM}}$**
We sincerely thank you for your concerns about our paper.
> **Comment 1: Terminology** The usage of the terminology "collaborative filtering"
Thanks for your question. We would like to kindly point out that collaborative filtering is a common... | Rebuttal 1:
Rebuttal: We sincerely appreciate the efforts of every reviewer to make this paper better. We are delighted to see the importance of the studied topic in this paper is acknowledged by most of the reviewers ($\color{red}{\text{NfrM}}$, $\color{green}{\text{tJs7}}$, and $\color{orange}{\text{EzpW}}$).
We app... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Two applications of Min-Max-Jump distance | Reject | Summary: The paper proposes to use a new distance, Min-Max-Jump, which is the minimum largest distance on any path between two points, to be used in k-means clustering to learn clusters.
Strengths: The distance can overcomes some demerits of the convex ("spherical" in the paper) clusters.
Weaknesses: The distance in ... | Rebuttal 1:
Rebuttal: Question:
"The distance in the paper is, in fact, related to single linkage clustering that assign give a pair of points a distance at which the pair is joint to one cluster. This need to be analyzed to relate to previous work as well as to compute pairwise distances efficiently."
"On evaluation,... | Summary: This paper proposes a new metric, min-max jump distance. Effectively, say we are given a complete graph with vertex set $\Omega$ and edge weights $d(x,y)$ denoting the distance between $x$ and $y$, where $d$ is a metric. Then $MMJ(x,y|\Omega')$ is the minimum, over all paths between $x$ and $y$, of the maximum... | Rebuttal 1:
Rebuttal: Thanks for the detailed and insightful review.
Reviewer vcc5's main concern is the writing quality. We will try our best to improve the writing quality according to all the reviewers' suggestions. We will employ a professional Academic Editing Service if the paper is accepted.
We have open-sou... | Summary: This paper presents the Min-Max-Jump (MMJ) distance concept and two calculation methods, focusing on path optimization in data analysis and clustering. The contributions include introducing MMJ distance, proposing efficient calculation methods, discussing its properties and applications, and offering a user-fr... | Rebuttal 1:
Rebuttal: Reviewer BgWh's main concern is the writing style of the paper. We will polish the writing according to Reviewer BgWh's constructive suggestions. We will employ a professional Academic Editing Service if the paper is accepted.
1. We will revise the introduction section to present a detailed defi... | Summary: Different distance metrics have been introduced in the literature for data analysis. In this paper the authors consider the min-max-jump distance and apply it in the context two applications, namely, k-means clustering and as an internal clustering evaluation index. They also present two algorithms for computi... | Rebuttal 1:
Rebuttal: Question:
"This referee feels that this work is rather incremental." "The work done is incremental with very limited novelty."
Response:
The research is not incremental, but fundamental. The fundamental contributions of the work include:
1. Applying MMJ-based internal clustering evaluation ind... | Rebuttal 1:
Rebuttal: Thanks for all the reviewers' insightful review and constructive suggestions.
The fundamental contributions of the work can be summarized as following:
1. Applying MMJ-based internal clustering evaluation index to the Clustering with Neural Network and Index (CNNI) model, achieves the first ind... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights | Accept (poster) | Summary: The paper shows that in-domain accuracy and Training-free NAS accuracy predictions correlate poorly with out-of-domain accuracy, while characteristics of the model such as Flops, Params and Embed-Dim (number of channels) correlate much better with out-of-domain accuracy. To do this, they train a supernet that ... | Rebuttal 1:
Rebuttal: We appreciate positive feedback and the valuable comments of the Reviewer.
> **Q1:** You show that the embedding dimension has the highest impact among ViT architectural attributes, while network depth has a slight impact on OoD generalization. But the paper lacks an explanation that although em... | Summary: This paper introduces OoD-ViT-NAS, a benchmark designed for evaluating ViT architectures' ability to generalize under Out-of-Distribution (OoD) shifts. This paper reveals that ViT design significantly impacts OoD generalization, In-Distribution (ID) accuracy does not reliably predict OoD performance, and simpl... | Rebuttal 1:
Rebuttal: We appreciate positive feedback and the valuable comments of the Reviewer.
>**Q1:** While the paper introduces a new benchmark, it doesn't sufficiently discuss the practical implications of the findings or potential applications. Including a section on how these insights can influence future ViT... | Summary: This paper studies the out of distribution generalization of vision transformer architecture designs. Specifically the paper studies how in-distribution accuracies relate to OOD accuracies for a set pf 3000 architectures. In addition the paper also studies the correlation between different zero-cost proxies an... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback and valuable comments.
>**Q1:** The paper is more of a study and not a method (and fits better in the datasets and benchmark track of NeurIPS).
**A1:** We justify that our work is suitable for the NeurIPS main track as we develop novel insights leading to a... | Summary: This work presented OoD-ViT-NAS, a comprehensive NAS benchmark with a focus on out-of-distribution generalisation of vision Transformer architectures. The authors created a benchmark with 3000 diverse ViT architectures evaluated on 8 common large-scale OoD datasets, and provided the first comprehensive invest... | Rebuttal 1:
Rebuttal: We appreciate positive feedback and the valuable comments of the Reviewer.
>**Q1:** This study is limited to the Autoformer architecture space, which may limit the generalisability of the findings to other ViT architectures.
**A1:** Autoformer is the most widely used search space in many recent ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable time and effort to review our work. We appreciate Reviewers’ kind comments, such as:
- "The authors conducted a thorough investigation on the impact of ViT architectures in relation to OoD generalisation, covering multiple aspects such as architectural at... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models | Accept (poster) | Summary: The paper introduces "EvolveDirector," a novel framework designed to train a text-to-image generation model that can compete with advanced models using only publicly available resources. The authors aim to address the limitations posed by proprietary data and the inaccessibility of parameters in state-of-the-a... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that our approach is innovative, "EvolveDirector offers a feasible solution to access advanced text-to-image generation capabilities in an open-source environment", "The trained model, Edgen, shows impressive performance", "The paper includes extensive experiments and a... | Summary: This paper investigates how to train the text-to-image model comparable to advanced models using publicly available resources. Specifically, EvolveDirector collects training data with the APIs of advanced models, and further uses a VLM to continuously refine the training dataset. The proposed VLM refinement si... | Rebuttal 1:
Rebuttal: We appreciate your acknowledgment and comments. We will address your concerns as follows.
Notations: W: Weakness, Q: Question
-----
**W.1**:
The detailed instructions for VLM are provided in the supplementary. We structure the outputs of VLM to ensure the generated text prompts can be parsed c... | Summary: This paper explores the effectiveness of training a text-to-image (T2I) model using synthetic examples generated by existing T2I models. The authors find that on the order of 10M image-text pairs are necessary to approach the quality of a good model like PixArt-Alpha, while using only 1M or 100k examples resul... | Rebuttal 1:
Rebuttal: Thank you for your recognition and for providing detailed comments. We will address your concerns as follows.
Notations: W: Weakness, Q: Question
-----
**W.1 & Q.1**:
We will incorporate more details about the source text prompts in the revision. Initially, the text prompts are randomly select... | null | null | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for their insightful comments. We appreciate the recognition of the strengths of our paper, including the novelty and effectiveness of the proposed EvolveDirector, and the recognition of extensive experiments and ablation studies.
We would ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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