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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Risk-sensitive control as inference with Rényi divergence | Accept (poster) | Summary: This paper explores the core research question, "What kind of control problem is solved by control-as-inference (CaI) with Renyi divergence?" The authors characterize the CaI objective that results from replacing Kullback-Leibler (KL) divergence with the more general Renyi divergence. They show that the Reny... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful comments.
> While the work is interesting the paper lacks a strong motivation. . . . risk-sensitive RL approaches, would be necessary.
We apologize for the unclear presentation, which misled the reviewer thinking that our motivation was to improve SAC.
O... | Summary: The paper generalizes the control as inference framework to the risk-sensitive setting using Renyi divergence variational inference. This yields a cost function with an exponential utility and log-probability regularization, weighted by the Renyi divergence parameter $\eta$. From the Taylor expansion of the co... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful comments.
> The experimental evaluation is sparse, with only one simple task and no baselines other than RSAC with $ \eta = 0 $. They also do not evaluate the policy gradient method and contrast it with RSAC.
We sincerely apologize for the lack of explanat... | Summary: This paper considers the control as inference framework of RL. Instead of minimizing the KL which is commonly done in control as inference (and has been shown to be equivalent to MaxEnt RL), they consider minimizing the Rényi divergence. They prove that this minimization is equivalent to minimizing a functiona... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful comments.
> the experiment measures the generalization . . . discuss generalization across environments.
We apologize for the lack of explanation.
Through the experiment, we aim to show the robustness of policies learned by the risk-sensitive soft acto... | Summary: In this paper, the authors consider a risk-sensitive control problem with Renyi divergence. The contributions are primarily theoretical with some rudimentary experiments. The contributions include connection to risk-sensitive control under exponential cost formulation, with Renyi divergence leading to an addit... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful comments. We sincerely appreciate your positive evaluation.
> The policy gradient theorem in Prop 7 has an expectation over trajectories. Does this mean for one update to the policy parameter, an entire trajectory needs to be simulated? In other words, is t... | Rebuttal 1:
Rebuttal: We have attached a PDF file with the experimental results of the empirical distributions of cost under the derived risk-sensitive soft actor-critic.
Pdf: /pdf/147a3077ad9b5938c5b3f3aa018803a0c9a6a86e.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On provable privacy vulnerabilities of graph representations | Accept (poster) | Summary: This paper investigates the ability of the similarity-based edge reconstruction attack (SERA) on attacking both sparse and dense networks, under various configurations of graph neural network (GNN) structures. The authors demonstrate, through both theoretical analysis and experimental results, that SERA perfor... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments, we will integrate your suggestions into revised versions of our paper. Below we address some specific points:
## Q1: About the writing style
Thank you for the advice and we will polish our writing to improve clarity and conciseness.
## Q2: On the limitations... | Summary: The paper studies the performance of similarity-based edge reconstruction attacks (SERA) for graph representations, considering two particular similarity measures (cosine and correlation).
The main contributions are presented in Theorem 4.1 and Theorem 5.1, which analyze the performance of SERA on graph repre... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We appreciate your mentioning the two related works and we will include them in the related works of our paper with careful discusssions. As both of them focus on link prediction, we would like to make the following clarification first:
## In theory, link pre... | Summary: The paper studies to which degree graph representations are vulnerable to similarity-based edge reconstruction attacks (SERA).
SERAs encompass a variety of attacks used to recover the structure of a graph, where a SERA guesses that an edge exists between pairs of nodes that have similar embeddings.
The paper c... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and advices, we will integrate your suggestions into revised versions of our paper. Below we address some specific points:
## Q1: Practicality of assumptions in theorem 4.1
According to our understanding, the primary concern is to what extent the assumption $d... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thought-provoking comments. We believe these valuable comments will lead to improvements of our paper. As we noticed some common concerns among the reviews, we provide some clarifications below:
## About our analysis on linear GNN
In appendix F.3 of our paper, we ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects | Accept (poster) | Summary: The work proposes a transformer architecture for decoding behavior using sEEG neural recording which can account for inter-subject variability that exist due to different placements of sEEG electrodes, different SNRs, inherent biological differences etc. The ability of the proposed transformer architecture to ... | Rebuttal 1:
Rebuttal: Thank you for a very detailed review. Here, we provide our response to your questions. Due to space constraints, for each question we provide only the beginning of your prompt. We apologize for the inconvenience.
_Throughout this rebuttal we refer to single-subject as SS and multi-subject as MS._... | Summary: The authors propose a novel training framework and architecture to predict response time for a color change behavior task from stereotactic electroencephalography (sEEG) data, focusing on integrating data across multiple subjects despite the variability in electrode placement and count. The model tokenizes neu... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and question. We were really excited to read that "The proposed framework effectively addresses the heterogeneity across subjects, a significant challenge in sEEG data processing". Here, we provide our response to your questions and the results from new exper... | Summary: This paper presents a framework and architecture for decoding behavior across subjects using stereotactic electroencephalography (sEEG) data, addressing the challenge of electrode variability. By tokenizing neural activity with convolutions and employing self-attention mechanisms along with a positional encodi... | Rebuttal 1:
Rebuttal: Thank you for your though provoking feedback. We appreciate reading that "combining data from multiple individuals and training a unified model is a substantial step forward compared to traditional single-subject approaches"! Here, we provide our response to your questions and the results from new... | Summary: The paper presents a novel approach to sEEG decoding. Authors highlight the benefit of using data from various subjects for training. However, due to the nature of the sEEG technique, collection of such data is difficult. Authors provide a new deep learning based decoding approach which utilizes spatial positi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and suggestions. We are excited to read that our "method has a value in itself and provides ideas for future research"! Here, we provide our response to your comments/questions and the results from new experiments that we ran to address your concerns. We hope... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. The reviewers pointed out that our unified multi-session, multi-subject modeling approach "is a substantial step forward compared to traditional single-subject approaches" (pdN2) that "provides ideas for future research" (VKBz).
Some highligh... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mind the Graph When Balancing Data for Fairness or Robustness | Accept (poster) | Summary: This paper theoretically studies the applicability of data balancing in achieving fairness or robustness. The paper shows that data balancing may fail depending on the data generation mechanism. The paper introduces conditions for data balancing to produce invariant and optimal models. The paper also shows tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comment and evaluation of our work. We respond to each comment below and provide a new table and figure in the general rebuttal. We hope that these changes provide a more balanced and guiding framing of data balancing for fairness and robustness and that you will co... | Summary: The paper analyses training risk invariant models using data balancing. The authors consider the cases in which data balancing might help obtain risk-invariant models and the cases in which data balancing does not achieve the desired effect. The paper also considers the effect of regularization for robust mode... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and questions. We reply to each below, although the format of the rebuttal doesn’t allow us to provide the proposed text amendments. We prioritized answering all comments but would be happy to provide these suggested changes during the discussion. We hope t... | Summary: The paper focuses on the topic of data balancing for fairness and robustness and uses a causal graph as a tool to analyze the effects of data balancing. In the paper, the paper tries to show both the positive impacts and potential pitfalls of data balancing. For the fairness aspect, the paper focuses on the in... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond to each point below:
**W1: fairness and robustness.** Our work focuses on undesired dependencies between $Y$ and $Z$ and on the use of data balancing to mitigate any bias that might result from these dependencies. This focus dir... | Summary: The paper studies the role of data balancing e.g. on sensitive attributes or class labels on obtaining fair or robust models. It identifies various causal graphs and corresponding independence conditions under which data balancing is expected to succeed (or may fail) to provide recommendations on when to use (... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments on our work. We respond to the comments below:
1. **Necessary and sufficient conditions**: Thank you for this comment. Our results show that Propositions 4.2 and 4.3 are sufficient, while Proposition 4.4 is necessary. Based on the comment from Rev... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments and suggestions. We have answered each point in detail, but wanted to highlight some of the important changes here for visibility:
- We reframed our findings in a comprehensive table to guide the reader for next steps (see pdf).
- We have adde... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval | Accept (poster) | Summary: The paper introduces MolPhenix for tackling the contrastive phenomolecular retrieval problem. MolPhenix employs a uni-modal pretrained phenomics model, an inter-sample similarity-aware loss function, and conditions on the representation of molecular concentration. This approach effectively addresses challenges... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing detailed feedback for our paper. Below, we aim to address your concerns point-by-point
**Concern #1: In Table 2 and Table 4, there is a significant performance increase for DCL, CWCL, SigLip, and S2L compared to other baselines. The source of these improvements... | Summary: The paper makes a significant step forward in the task of phenotype-molecular retrieval–the task to find the molecule applied to perturb a set of cells given a microscopy readout. This problem can be modeled using multi-modal contrastive learning. The proposed MolPhenix method leverages pre-trained foundation ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive, insightful comments and the detailed feedback. We believe your perspective will help shape this into a stronger work. Below, we aim to discuss each of the suggestions and discussion points individually.
**Discussion point #1: “Generating phenotypes or molec... | Summary: The paper introduces MolPhenix, a framework for contrastive phenomolecular retrieval that integrates phenomic data and molecular structures into a joint embedding space. Key contributions include combining phenomic and molecular data for improved retrieval accuracy, proposing effective training guidelines, and... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing detailed feedback on our paper. We aim to address the feedback point by point below:
**Concern #1: The clarity of this work could be significantly improved**
We thank the reviewer for this constructive feedback, we believe this work is best assessed with the ... | Summary: This paper introduces MolPhenix, a model designed to learn a joint latent space between molecular structures and microscopy phenomic experiments, addressing the challenge of contrastive phenomolecular retrieval. The authors point out three key challenges in this domain: limited paired data & batch effects, ina... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing detailed feedback on the paper.
**Concern #1: Additional justification for the S2L loss**
In this section we provide some additional intuition for the S2L loss and further relate it to previous works. We first assess the conceptual similarities between InfoNC... | Rebuttal 1:
Rebuttal: We thank all the reviewers for providing detailed feedback on the paper. We are appreciative of the general support regarding the thoroughness and value of our scientific work:
- “experimental design is robust, demonstrating the framework's superiority in various scenarios and contributing valuab... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work focuses on predicting the molecular impact on cellular functions and investigates the problem of contrastive phenomolecular retrieval. It introduces MolPhenix, a model that leverages a joint latent space between molecular structures and microscopy-based phenomic experiments using contrastive learning... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough and rigorous examination of our paper. Below we aim to address the questions and clarifications to further improve the work.
**Concern #1: For the captions of Table 2~5, it is better to mention the experimental setting.**
Thank you for your feedback. We chang... | null | null | null | null | null | null |
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs | Accept (poster) | Summary: This paper studies the adversarial MDPs, which assume unknown (but stochastic) transition models and adversarial rewards. This paper adopts the linear mixture MDP setting, and they aim to study the dynamic regret, where the comparison policy are allowed to be changed along $K$ steps.
The algorithm this paper ... | Rebuttal 1:
Rebuttal: Thanks for your appreciation of our work! We will address your questions below.
---
**Q1:** "Does the algorithm apply to other types of non-stationary measures, e.g. switching cost, etc?"
**A1:** Thanks for your helpful question. Our algorithm can also be adapted to other types of non-stationar... | Summary: The paper explores reinforcement learning in linear mixture Markov Decision Processes (MDPs) with adversarial rewards and unknown transitions. It analyzes policy-based and occupancy-measure-based methods, identifying their strengths and weaknesses. The paper introduced an algorithm that merges both approaches ... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We take this opportunity to highlight the key contributions of our work.
---
**Q1:** "The paper lacks experimental validation, but could be reasonable given it is a theoretical paper..."
**A1:** The primary goal of this work is to advance the theoretical un... | Summary: This work studies adversarial Linear Mixture MDPs, where the reward function can vary across different episodes, and aims to analyze the dynamic regret, where the baseline policy can also change across different episodes with respect to the dynamic environment. The authors propose a novel algorithm with a theo... | Rebuttal 1:
Rebuttal: Thank you for your constructive review. We will address your questions below.
---
**Q1:** "Why does there exist a $\sqrt{HK \cdot H}$ term in the regret bound? It is directly dominated by the first term $d\sqrt{H^3K}$"
**A1:** Thanks for your careful observation. It is indeed directly dominated... | Summary: Disclaimer: This specific area falls outside my expertise, as indicated by my confidence score. Nevertheless, I have carefully reviewed this paper and the relevant literature to offer the most informed feedback possible.
This paper studies the dynamic regret for adversarial linear mixture MDPs, with unknown t... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments. We will address your questions below.
---
**Q1:** "It seems that the main contribution lies more in the regret analysis rather than the algorithm itself. Could you elaborate if any new techniques are developed in this work?"
**A1:** Thanks for your question. Bo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Pinwheel Centers Act as Geometric Saliency Detectors | Accept (poster) | Summary: This work aims to explain the origin and functional benefits of pinwheel structures in V1 compared to salt-and-pepper configurations. They use a two-dimensional self-evolving spiking neural network (SESNN) model with Hebbian-like plasticity and empirical morphological data to simulate the evolution from salt-a... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and insightful comments on our manuscript.
## Points Raised:
### 1. Bioplausibility of Learning Rules for E>E vs E>I Connections:
**Response:** We recognize the importance of bioplausibility in modeling neuronal networks. In the context of our SESNN model, biolo... | Summary: This paper introduced a novel spiking neural network (SNN) to investigate the functional roles of pinwheel structures in the primary visual cortex of higher mammals and primates. By adjusting a visual RF overlapping parameter, their model can produce the salt-and-pepper and pinwheel organizations observed in l... | Rebuttal 1:
Rebuttal: # Rebuttal for Reviewer FA4k
Thank you for your thorough review and valuable feedback on our paper. We appreciate your recognition of the strengths of our model and its presentation.
## Points Raised:
### 1. Neuronal Response Time to Complexity of Visual Scene:
**Response:** Based on our findings,... | Summary: The authors present a comprehensive model of the primary visual cortex adapted for various mammalian species, demonstrating its ability to reproduce orientation maps and compatibility with experimental data across different factors such as neuron density or RF overlap. Importantly, they provide evidence in the... | Rebuttal 1:
Rebuttal: # Rebuttal for Reviewer Boae
Thank you for your insightful feedback and constructive comments on our paper.
## Points Raised:
### 1. Behavioral Data and Saliency Detection:
**Question1:** Are animals without PCs less efficient in detecting saliency?"
**Response:** We acknowledge the importance of... | Summary: This paper uses a self-evolving spiking neural network model to investigate why some visual systems develop pinwheel structures while others have salt-and-pepper organization of orientation tuning. The simulation shows that the organization depends on the amount of receptive field overlap between neighbouring ... | Rebuttal 1:
Rebuttal: # Rebuttal for Reviewer TFr2
Thank you for your detailed feedback on our paper. We appreciate your recognition of the novelty in our approach and the potential of our modeling technique.
## Points Raised:
### 1. Use of Binary Edge Maps:
**Response:** We indeed used whitened natural images to train... | Rebuttal 1:
Rebuttal: We greatly thank the reviewers for their valuable advice and comments, which are very helpful for us to further improve this work. We are especially encouraged by the recognition from the reviewers:
1. The findings are quite interesting and novel. The modeling approach seems well-designed.
2. This... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MAC Advice for facility location mechanism design | Accept (poster) | Summary: This paper studied facility location games with mostly approximately correct (MAC) predictions. In this setting, there are n agents in a metric space, and k facilities to be located in that space. The cost of each agent is defined as the minimum distance from the facilities to their location. Each agent locati... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We respond to your comments and questions below. If our response is satisfactory, we would greatly appreciate it if you would consider raising your score.
* **Concern**: “One concern is that I am not sure whether the MAC model is natural enough in this problem.”
... | Summary: The authors study variants of the facility location problem from a mechanism design perspective, in which the mechanism receives predictions on the agents' locations. A percentage of the predictions may have an unbounded error, whilst the remainder of the predictions can be incorrect up to a certain bound. Thi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback! We are glad that the reviewer appreciated many aspects of our work. In what follows, we attempt to address the remaining concern and questions.
* **Concern**: “My main concern is on the omission of the epsilon term throughout the paper and slightly vague ... | Summary: This work considers facility location mechanism design in the presence of (pretty good) advice on locations. In this setting, agents report their locations to impact the places facilities are installed or built. In this model, the designer has access to advice that is Mostly and Approximately Correct, a notion... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback! We are glad that the reviewer appreciated some aspects of our work like its usefulness in many situations. We respond to your comments and questions below. If our response is satisfactory, we would greatly appreciate it if you would consider raising your s... | Summary: This paper studies a learning-augmented version of the facilitation location mechanism design problem. In particular, the authors consider a model for predictions they call “mostly approximately correct” in which most points have an estimate close to the true value and a small fraction can be arbitrarily wrong... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and helpful feedback! We are glad that the reviewer appreciated many aspects of our work. We respond to your comments and questions below. If our response to the suspected concern is satisfactory, we would greatly appreciate it if you would consider raising... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to carefully read our work and for their valuable feedback.
We reply to specific points of each reviewer separately. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cross-Modality Perturbation Synergy Attack for Person Re-identification | Accept (poster) | Summary: The paper addresses security concerns in cross-modality person re-identification systems, focusing on systems that use both RGB and infrared images. Traditional ReID systems have primarily focused on RGB images, but the differences between RGB and infrared modalities present unique challenges. The authors prop... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your thorough review and valuable feedback on our paper. Your input has undoubtedly played a pivotal role in enhancing the quality and clarity of the manuscript. Responses to the individual questions below.
**Reviewer’s Comment :**
“ ...... Ar... | Summary: This paper investigates adversarial attacks on cross-modality person re-identification (ReID) systems. It is purportedly the first study to investigate vulnerabilities in cross-modality ReID models, with the goal of evaluating the security of these systems.
To this end, the paper introduces an innovative uni... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your thorough review and valuable feedback on our paper. Your input has undoubtedly played a pivotal role in enhancing the quality and clarity of the manuscript.
Responses to the individual questions below.
**Reviewer’s Comment:**
“The diff... | Summary: This paper is the first to explore the security vulnerabilities of cross-modality ReID models and proposes a universal perturbation attack method for cross-modality person re-identification (ReID) systems, called the Cross-Modality Perturbation Synergy (CMPS) attack. This method innovatively utilizes gradient ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your thorough review and valuable feedback on our paper. Your input has undoubtedly played a pivotal role in enhancing the quality and clarity of the manuscript.
Responses to the individual questions below.
**Reviewer’s Comment :**
“A more tho... | Summary: This paper proposes an innovative strategy called Cross-Modality Perturbation Synergy (CMPS) attack, aimed at revealing security vulnerabilities in cross-modality person re-identification (ReID) systems. These systems are crucial in security applications, typically using RGB and infrared imaging to identify in... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your thorough review and valuable feedback on our paper. Your input has undoubtedly played a pivotal role in enhancing the quality and clarity of the manuscript.
Responses to the individual questions below.
**Reviewer’s Comment:**
“Have the au... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed feedback and valuable time. We are pleased to see that the reviewers found our paper insightful (Zepn, fT1U, Th4d, tAG1), appreciated our experimental validation (fT1U, Th4d), and recognized the theoretical originality and practical value of our work (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space | Accept (poster) | Summary: The paper introduces the metric "Semantic Density" to quantify the uncertainty of LLMs by measuring the distance of response embeddings in a semantic space.
Strengths: - **Structure**: The problem is well-motivated and the paper is clearly and coherently written.
- **Theory**: The adaptation of kernel density... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments, we will answer your concerns follows the order of your original comments, due to the space limitation.
---
Comment: concern regarding theory
Response: Thanks for this insightful comment. We have read reference [1] carefully (it was published in arXiv only a... | Summary: The paper proposes a novel framework for quantifying uncertainty in LLM's responses. The authors introduce the concept of "semantic density" (SD), which measures uncertainty from a probability distribution perspective in semantic space, applicable to any pre-trained LLM without additional training. Experiments... | Rebuttal 1:
Rebuttal: Comment: The comparison with existing methods, although extensive, may not cover all possible alternatives. There are recent or lesser-known methods that also warrant consideration.
Response: Thanks for the suggestions. We tried to include all the mainstream uncertainty quantification methods for... | Summary: This paper addresses the problem of LLM’s lack of uncertainty metric for the response it generates. The authors propose to use semantic density to quantify such uncertainty, as it is not restricted to any specific downstream task. In particular, the approach samples reference responses, analyzes semantic relat... | Rebuttal 1:
Rebuttal: Comment: Only some of the llama and mistral models are tested.
Response: Thanks for the comment. We want to clarify that at the time of submission (May 22), all the open-sourced LLMs released by MistralAI were included in the experiments, e.g., 'Mistral-7B', 'Mixtral-8x7B', and 'Mixtral-8x22B', a... | Summary: The paper proposes an approach for estimating uncertainty of LLM outputs. The proposed approach begins by sampling a diverse set of responses, analyses their equivalence using a NLI classification model, and then computes the semantic density using a kernel density estimate.
Strengths: - The proposed approach... | Rebuttal 1:
Rebuttal: Response: Semantic Entropy (SE) is indeed the baseline to which we compare, aiming to overcome its limitations using a different approach, i.e. Semantic Density (SD). Let us first clarify the difference. although the computation of SE involves analysis of multiple reference responses, only one SE... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for their valuable time and constructive comments. We have considered every comment from each reviewer, and added several experiments as suggested by reviewers. We believe the paper is in a much better form after incorporating constructive suggestions from all th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Where does In-context Learning Happen in Large Language Models? | Accept (poster) | Summary: This study primarily investigates where in-context learning occurs within GPT-style models. Specifically, it explores the stage at which a model transitions from functioning as an in-context learner to a task-specific model. By applying layer masking to the instruction and in-context examples in machine transl... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and comments on our work. Regarding the listed weaknesses of our paper:
> "The practical utility of the findings is questionable."
**We respectfully disagree that our findings hold little practical utility. LLMs are increasingly being adapted to... | Summary: This paper tries to locate where LLMs handle in-context learning (ICL) and interprets how LLMs perform ICL internally. The authors propose a layer-wise attention masking strategy and conclude that LLMs perform ICL in bottom layers.
Strengths: The motivation of this paper is clear, and the research question so... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and comments on our work. Regarding the listed weaknesses and questions:
> Layer-wise masking. The authors only release first j bottom layers and conclude that bottom layers are important.
**Our conclusion is _not_ that bottom layers are importa... | Summary: In-context learning has emerged as an important paradigm in LLMs. In this paper, the authors attempt to characterize where models learn to “recognize” an in-context task.
To do this, in-context portions (examples and/or instructions) are masked out after certain layers and are not included to generate model ... | Rebuttal 1:
Rebuttal: Thank you for the deep appreciation of the work!
> It's not clear why “task recognition” v/s actual task performance was treated as a metric here. It seems that the learnings would be very different between the two. Is task recognition in itself important enough to celebrate over computational w... | Summary: This paper investigates where in-context learning occurs within large language models, focusing specifically on machine translation and code generation tasks. The authors introduce a "layer-from context-masking" technique to identify at which layer an LLM transitions from task recognition to task execution dur... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments on the paper and for the positive view of our work. We provide our response to the weaknesses (the questions are closely related)
> One notable weakness is the limited exploration of why different models exhibit varying behaviors in terms of their "task recog... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation | Accept (poster) | Summary: The paper details a framework for design and control of complex fluidic systems with soft and hard boundaries. The paper specifically develops a fully differentiable pipeline that is able to optimize the design of the mesh to achieve the specified fluid control goal. The proficiency of the proposed framework h... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and insightful questions.
**1. How is the "initial parametric geometry" generated? What are the feasibility constraints...?**
* Thank you for bringing up this important question. Usability is crucial for allowing users to specifying complex shapes en... | Summary: This paper introduces a novel approach to fluidic system design and control using differentiable simulation. The authors propose a method that leverages gradient-based optimization to enhance performance and accuracy in fluid dynamics applications.
Strengths: The paper demonstrates the effectiveness of their ... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s insightful comments and questions.
**1. The majority of the novelty comes from an efficient CUDA kernel implementation but the specifics are omitted. What specific design considerations are taken that enable this method to outperform existing solvers?**
* Becaus... | Summary: The paper proposes a new set of utilities for experimenting with system design and control of viscous fluid flows on deformable domains. The contributions include (a) a differentiable NSE solver, (b) Bezier curve-based geometry parametrization, (c) an algorithm to jointly optimize a control and design objectiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work and raising constructive suggestions.
**1. Proper figures: make the figures on pages 3 and 4 proper figures with captions"**
We will make both inset figures proper figures in the revision.
**2. Solver validation: having some experience implementin... | Summary: The paper aims at a fully automated pipeline for devising neural controls for complex fluidic systems with dynamic boundaries. The system consist of externally driven soft boundaries and internal complex flow behaviors.
The proposed framework contains a differentiable geometry representation, a differentiable... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive suggestions. Below we provide responses to individual questions.
**1. It would be nice to see the framework open-sourced.**
We will open-source our code upon acceptance. We have provided the anonymized version of our code at https://anonymous.4open.scie... | Rebuttal 1:
Rebuttal: We thank all reviewers and the AC for their time and effort in reviewing and for insightful comments to strengthen our work. Besides the responses to individual reviewers, here we would like to highlight our contributions and new quantitative/qualitative results added in the rebuttal.
1. **Contri... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits | Accept (poster) | Summary: This paper discusses Generalized Linear Bandits (GLB) under the self-concordance assumption. The study successfully relaxes the limitations of existing work with an OFU-type algorithm, providing mathematically solid theories for GLB within the self-concordance family. However, the presentation could be signifi... | Rebuttal 1:
Rebuttal: We would like to clarify a misunderstanding of our work that we do not “discuss GLB under the self-concordance assumption”. What is true that our work goes beyond this: we aim to remove this assumption for GLBs with subexponential base distribution.
Now we would like to address concerns pointed ... | Summary: The authors prove that any single-parameter natural exponential family (NEF) with subexponential (subgaussian) base distribution is self-concordant with a stretch factor that grows inverse quadratically (linearly).
Strengths: - Clearly and well-written
- An important theoretical contribution in establishing t... | Rebuttal 1:
Rebuttal: Thanks for your review and suggestions and we really appreciate them!
For the weakness section:
+ We agree that the convex relaxation technique from Abeille et al [1], can be applied to our case. We adapted the proof from Abeille et al [1] and are able to apply the convex relaxation to our confid... | Summary: The paper investigates the self-concordance properties of single-parameter natural exponential families (NEFs) with subexponential tails. It provides two main contributions: first, it demonstrates that NEFs with subexponential tails are self-concordant with polynomial-sized parameters, and second, it applies t... | Rebuttal 1:
Rebuttal: Thanks for reviewing our manuscript and we are delighted at your appraisal of our work. A question was asked “Do the authors believe it possible to lift prior knowledge on parameters such as $S_0$, $L$, $c_1$ and $c_2$ in designing efficient algorithms for GLBs with NEF rewards?”
To the best of t... | null | null | Rebuttal 1:
Rebuttal: We would like to thank you all for the time and effort spent in reviewing our manuscript! We are glad that the reviewers appreciate our theoretical contributions on NEFs (reviewer Ca74 & reviewer n2uQ), our contributions to the bandit literature on second-order regret guarantee for GLBs with subex... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation | Accept (poster) | Summary: Point Cloud Interpolation (PCI) is the task of predicting intermediate point cloud features from a sparser representation, often to construct point cloud representations for intermediate time frames. Contrary to interpolation in other fields, point clouds are largely unstructured and do not preserve consistent... | Rebuttal 1:
Rebuttal: We appreciate your meaningful suggestions and questions, and we will address each of them in our response.
## Q1.1: Non-traditional writing structure of the introduction.
A1.1: We will rigorously revise our introduction structure according to your recommendations. Due to space constraints, please ... | Summary: The paper presents a novel model, “NeuroGauss4D-PCI,” for point cloud frame interpolation, a popular but challenging 3D computer vision task in real-world scenarios such as Lidar point cloud densification. The model outperforms existing PCI methods on the most popular benchmark datasets, DHB and NL-Drive, show... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our paper's organization, literature review, experimental design, and results. Your recognition of our novel approach using 4D Gaussian deformation fields and temporal radial basis function Gaussian residual modules to capture complex spatiotempora... | Summary: The paper introduces NeuroGauss4D-PCI, a model designed to address the challenges of point cloud interpolation (PCI) by using Gaussian soft clustering and a 4D neural field to model complex non-rigid deformations in dynamic scenes. The model excels in capturing spatial and temporal dynamics from sparse data, s... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their insightful questions and provide detailed responses below
## Q1: Poor color scheme.
A1:
We commit to redesigning the color scheme in our main text to improve visibility and readability. For your reference, we've implemented this new color scheme in our PDF... | Summary: This paper proposes a novel method for point cloud interpolation, which aims to address complex non-rigid scenarios. It turns point clouds into 3D Gaussians via iterative soft clustering, and then utilizes several 4D spatio-temporal modules to fuse latent features with neural fields. Specifically, this paper e... | Rebuttal 1:
Rebuttal: Thank you for your insightful critique.
## Q1: Algorithm complexity and limited contributions
A1:
**Due to space constraints, we kindly refer you to Reviewer xq5Q's Q1 for the explanation on model complexity. We apologize for any inconvenience.**
**Design Motivation:**
- Gaussian Soft Clustering:... | Rebuttal 1:
Rebuttal: We sincerely thank the Area Chair for their time and effort in handling our paper, and all reviewers for their detailed and valuable suggestions, which are crucial for improving our work.
The reviewers have positively acknowledged our method's novelty, impact, accuracy, and potential, as highligh... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a method, NeuroGauss4D, to tackle the problem of 4d point cloud interpolation. NeuroGauss4D consists of the following 5 components:
1. "Iterative Gaussian Soft Clustering": a module to encode the scene to Gaussian representation with DGCNN features (map: X, Y, Z, T -> Gaussians(Mu, Cov, Fea... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and valuable feedback. Here are our responses to your questions:
## Weaknesses
### Q1: Is the method overly complex with its 5 components?
**A1:** Our method is designed for efficiency and effectiveness:
1. **Parameter Efficiency:** NeuroGauss4D-PCI uses only abo... | null | null | null | null | null | null |
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes | Accept (poster) | Summary: This paper proposes a statistical theory for off-policy evaluation given observational data from an unknown transition kernel of an original MDP, possibly under policies the same as or different from the target policy. This paper focuses on the situation of environment shift, i.e., the transition kernel of the... | Rebuttal 1:
Rebuttal: Dear reviewer kuGv, thanks for acknowledging the importance of our problem and the strengths of our theoretical results. Please find our responses to your questions below:
**W1: "This paper is purely theoretical and thus it is very hard to evaluate the practicability of the proposed estimation fr... | Summary: This paper studies the problem of evaluating a policy under best- and worst-case perturbations to a Markov decision process (MDP), given transition observations from the original MDP. The first contribution is the robust Q learing algorithm for CVaR problem, the second contribution is the estimation of robust ... | Rebuttal 1:
Rebuttal: Dear reviewer ghkS, thanks for acknowledging the strengths of the theoretical results. Please find our responses to your questions below:
**W1: "The authors seem to be using offline and off-policy interchangably, which is misleading and confusing."**
**Author's Response:** We agree this may be c... | Summary: This paper studies off-policy evaluation problems in robust Markov decision processes, where the environment dynamics may change over time. The authors consider a perturbation model that modifies the transition probability up to a given multiplicative factor. They first propose two new algorithms for learning ... | Rebuttal 1:
Rebuttal: Dear reviewer QdT5, thank you for your review and positive feedback on our work. We appreciate your recognition of the problem's significance, our theoretical contributions, and the alignment of our numerical results with the theory.
**Motivation for perturbation model**
If the cause of the pert... | Summary: This paper studies statistically-efficient robust/optimistic (e.g., worst- or best-case within the uncertainty set $\mathcal{U}$) off-policy evaluation with bounded Radon-Nikodym uncertainty sets centered around known nominal models. It proposes novel algorithms for computing robust/optimistic Q functions, and... | Rebuttal 1:
Rebuttal: **Weakness: Paper is difficult to read**
Thank you for your detailed feedback on how the quality of writing in the paper could be improved. On some level, this is a very difficult research topic to present in a way that is easily accessible without at least some background in stats and/or causal ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search | Accept (spotlight) | Summary: The paper addresses the synthesis planning problem where constrains are taken into account. To that end, the authors propose a double-ended synthesis planning grounded with bidirectional search to ensure that the added constrains are met. They show experimentally that their proposed approach helps with solve r... | Rebuttal 1:
Rebuttal: Thank you for your feedback and questions, they bring up some interesting discussion points that will help improve our work!
---
**Reviewer:** _Proof of soundness and completeness of algorithm 1..._
**Response:**
We agree that some detail regarding algorithm 1 is missing and will add the follo... | Summary: The paper considers computer aided synthesis planning with applications to retrosynthetic analysis in chemistry where the goal is to find a reaction route from purchasable materials to a target molecule. The latter is an important problem with real applications in areas such as drug discovery. In the current l... | Rebuttal 1:
Rebuttal: Thank you for your review and positive reception of our paper! Addressing your comments:
---
**Reviewer:**
_I was not able to find any major weakness in this paper. Perhaps including a small numerical example to show more clearly how the node values are computed and updated during search would ... | Summary: This paper proposes a bidirectional search algorithm for chemical synthesis, in the case where we also have certain "part of the way there" molecules that we would like to include in the discovered synthesis route.
Strengths: The paper is very clearly written, with lots of great notation and detail provided. ... | Rebuttal 1:
Rebuttal: Thank you for your review and the kind words about our work! We address your comments one by one.
---
**Reviewer:**
_By adding bidirectional search, the algorithm gets a bit complicated. Due to my lack of familiarity with the area, I'm not 100% sure how to weigh the complexity vs performance tr... | Summary: the authors propose a new algorithm for double ended synthesis planning. while this problem has received attention from the community a few decades ago, this has recently not been studied at all.
Strengths: - addresses an important outstanding problem in the community
- sound experimentation from the chemistr... | Rebuttal 1:
Rebuttal: Thank you for your review! We address your comments and suggestions as follows:
---
**Reviewer:**
_results from the PAroutes paper and recent work by Tripp et al and Maziarz et al indicate that there are signficantly less or even no differences between the different uni-directional search algo... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful feedback and comments. We have addressed stated weaknesses, questions, and comments in the individual rebuttals. We also include a single page PDF containing additional tables and a figure as referenced in the rebuttals.
Pdf: /pdf/298cab975cce58e6f0da2de... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Principled Graph Transformers | Accept (poster) | Summary: This paper introduces a novel architecture called Edge Transformer (ET) for graph learning tasks. The ET leverages global attention on node pairs instead of individual nodes and demonstrates strong empirical performance without relying on positional or structural encodings. The authors show that ET has the exp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and are happy to address the concerns raised by the reviewer. First, the reviewer states
> The total number of parameters for the model is not provided in the parameter table
>
We provide the parameters for each model in the general response above. As can ... | Summary: In this paper, the authors show that Edge Transformer, a global attention model operating on node pairs, has 3-WL expressive power when provided with the right tokenization. Experiments results also show that the Edge Transformer has competitive performance on molecular regression tasks and algorithmic reason... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and are happy to address the concerns raised by the reviewer. First, the reviewer states
> My major concern is the real usefulness of ET, since the high runtime and memory complexity may offset the importance of ET
>
See our general response for discussion... | Summary: This paper applies Edge Transformer (ET) to the field of graph learning. It is proved that ET has 3-WL expressivity with cubic complexity, which is more efficient than existing 3-WL models. Experiments on BREC benchmark clearly demonstrate its expressive power. Results on the other three molecular datasets and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and are happy to address the concerns raised by the reviewer.
Regarding evaluation, the reviewer says that
> I find it is needed to include more benchmark datasets in graph learning […] to comprehensively demonstrate its practical predictive power compared ... | Summary: This work proposes an Edge Transformer (ET), a global attention model operating on node pairs instead of nodes. Authors theoretically demonstrate that ET has 3-WL expressive power with the proper tokenization. Experimental results demonstrate that the proposed model outperforms other theoretically aligned mode... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and are happy to address the concerns raised by the reviewer. First, the reviewer says
> Previous work [1] has demonstrated that graph transformers (GTs) with proper positional encodings (PEs) can be more powerful than any WL test
>
The work in [1] present... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort and the valuable feedback they provided for our work. Here, we address two common concerns. In addition, we provide parameter counts for each dataset.
### On the novelty and contribution of our work
Here, we address the concern that our work is no... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Association Pattern-aware Fusion for Biological Entity Relationship Prediction | Accept (poster) | Summary: The paper introduces an innovative perspective, namely association pattern, for biological entity relationship prediction (e.g., drug-target protein-adverse reaction). The authors first summarize the characteristics of existing perspectives and emphasize the importance of the association pattern. Next, the ass... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the comments. We believe we have addressed all the concerns and are happy to follow up in the discussion phase.
> W1: The bind-relation feature enhancement module needs extra separate training?
A: Thanks for pointing this out. Our description may not be entirely cle... | Summary: This paper presents a novel approach to predicting relationships among biological entities by addressing limitations in current deep learning methods that focus solely on entity-centric information. The authors introduce an association pattern-aware fusion method that integrates association pattern information... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the comments. We believe we have addressed all the concerns and are happy to follow up in the discussion phase.
> W1.1: Some hyperparameters (such as the number of attention heads) vary on different datasets.
A: We appreciate your valuable feedback. Actually in the... | Summary: The author proposed a deep learning method, termed the Pattern BERP method, designed to elucidate the potential associations among triple-wise biological entities. This method innovativly incorporates association pattern information into the entity representation learning. Moreover, it employs two additional b... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the comments. We believe we have addressed all the concerns and are happy to follow up in the discussion phase.
> W1: The provided interpretation study lacks absolute persuasiveness.
A: Thanks for your insight. There are two additional interpretation cases in Fig. ... | Summary: This work presents a novel approach - Pattern-BERP - for the prediction of biological entity relationships, it utilises entity association patterns as opposed to a lot of existing research that focuses primarily on entity-centric information mapping and aggregation.
The evaluation is done on three different ... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the comments. We believe we have addressed all the concerns and are happy to follow up in the discussion phase.
> W: Possibility to test on more dataset types and relatively huge datasets?
A: We appreciate your insight. Firstly, we clarify that the limited associati... | Rebuttal 1:
Rebuttal: We thank all reviewers for the time spent reviewing the paper and recognizing the **significance** ("with a broad impact" – ugjG, "noteworthy" & "impressive predictive accuracy" & "more efficient" – m3DY, "significant improvements" – 4Nwa), **novelty** ("novel idea" – ugjG, "highly innovative" – m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models | Accept (poster) | Summary: This paper aims to address video inverse problems using image diffusion models. By viewing videos as sequences of warping transformations between frames, the authors propose a novel approach called warped diffusion from a continuous function space perspective. Specifically, warped diffusion includes a Gaussian... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and their high-quality review. We are glad that the Reviewer appreciated the importance of the problem we are solving, the presentation of our work, and our experimental results. In what follows, we do our best to answer the questions raised by the Reviewer.
`... | Summary: This paper addresses the challenge of temporally correlated inverse problems by employing an image diffusion model. The authors introduce a technique termed Warped Diffusion, which incorporates an equivariance self-guidance mechanism. This mechanism ensures that the generated frames maintain consistency when s... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and insightful review. We are very glad that the Reviewer appreciated the presentation of our work, the theoretical underpinnings of our formulation and the strong empirical performance of our method.
`The authors report the noise warping speed at Line 357. We... | Summary: The paper follows a previous work on noise-warping methods for video inverse problem. The authors proposed instead of giving temporally consistent noise maps to the DM, using a continuous function space representation for DM directly can serve more complex spatial transformations and results in better temporal... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and their valuable review. We are glad that the Reviewer appreciated the novelty of our work. In what follows, we do our best to answer some remaining questions.
`The authors argued that the previous work "How I Warped Your Noise" performed badly when the DM i... | Summary: This paper proposes a self-guidance equivariance approach using the Image Diffusion model for generating temporally consistent videos. In previous work HOW I WARPED YOUR NOISE[1], noise is warped across frames to ensure the noise maps are temporally consistent. However, temporally consistent noise maps do not ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their thoughtful review. We are glad to see that the Reviewer appreciated many aspects of our submission, such as the importance of the problem we are addressing and the experimental benefits we are obtaining. In what follows, we do our best to address any remaining conce... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Seeing Beyond the Crop: Using Language Priors for Out-of-Bounding Box Keypoint Prediction | Accept (poster) | Summary: This paper focuses on an interesting and inherent problem in top-down human pose estimation; that is out-of-box prediction in a top-down paradigm. The core of the solution is to utilize the semantic context by giving proper text prompts to CLIP. Specifically, a cropped image is first given to a pre-trained CNN... | Rebuttal 1:
Rebuttal: W1. **Qualitative Results:** More qualitative comparisons with ViTPose and HRNet are shown in Figure 1 of the rebuttal document. From the figure, it is evident that excluding the stick from the bounding box is much more beneficial as it reduces the noise present in the image.
---
W2. **t-SNE v... | Summary: This paper proposes a text-guided keypoint localization method that can detect keypoint that is out-of-input image. The proposed method only crops person area and abandons the object area, then adopts language prior to predict keypoint that is out-of-input image. To verify the effectiveness of the proposed met... | Rebuttal 1:
Rebuttal: W1. (a) **Excluding the sticks:** The concern of abandoning the visible object is addressed in the author's rebuttal.
(b) **Individual localization of human and stick:** We would like to thank the reviewer for raising an insightful question of estimating the pose of human and their extension sep... | Summary: The paper argues that using images including both humans and interacting objects introduces unnecessary visual features that hinder pose estimation performance. To address this, the paper claims that treating objects as unseen to predict interacting object poses can achieve better results and proposes a TokenC... | Rebuttal 1:
Rebuttal: W1. (a) **Custom dataset size:** To the best of our knowledge, this is the first work to address the problem of extension pose estimation and create a dataset for the task. Since pose estimation is a task that is heavily dependent on labels, we spent a lot of time collecting precise manual annotat... | Summary: This paper introduces the problem of estimating the keypoints of humans together with a stick-like object that the human interacts with, i.e. hockey stick or lacrosse stick (which is referred to as "extension" in this paper). The paper claims that prior works on 2D human pose estimation cannot be naively exten... | Rebuttal 1:
Rebuttal: W1. **Training TokenCLIPose with sticks included:** To assess TokenCLIPose's performance on uncropped images, we extended our experiments on the uncropped images (images that contain both players and their sticks). As shown in Table 2 of the rebuttal document, the performance drops by 4.51\% resul... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank all the reviewers for providing constructive feedback that helped us improve the paper. We are delighted that the reviewers recognized the originality of our work in incorporating language to predict out-of-bounding box keypoints (R1, R4), significance of ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Where's Waldo: Diffusion Features For Personalized Segmentation and Retrieval | Accept (poster) | Summary: The authors propose a novel approach without any additional training for Personal retrieval and segmentation tasks, which focus on identifying specific instances within a dataset. The existing methods struggle to locate a desired instance when other instances within the same class are presented. They propose t... | Rebuttal 1:
Rebuttal: Thank you for finding our approach novel and the ides of our method interesting. We address your comments below.
**Q1: The logical flow before the method section needs to be adjusted. The authors should rearrange the related works and the motivation of their work. The authors should list their co... | Summary: The paper introduces Personalized Diffusion Features Matching (PDM), a novel zero-shot approach that utilizes pre-trained text-to-image diffusion models for personalized image retrieval and segmentation without requiring additional training. PDM extracts and fuses semantic and appearance features from an off-t... | Rebuttal 1:
Rebuttal: Thank you for finding our approach insightful, technically sound with great transferability and our paper to be well motivated and easy to follow. We kindly address your comments below.
**Q1: Cite relevant work.**
**A1:** Thank you for your feedback. We will make sure to cite these papers in th... | Summary: This paper explores the use of text-to-image diffusion models for personalized retrieval and segmentation tasks. The authors introduce a novel method called PDM (Personalized Features Diffusion Matching), which leverages intermediate features from pre-trained text-to-image models for personalization tasks with... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We are encouraged that you find our approach innovative, the challenge novel and our paper to be excellently written. We address your comments below.
**Q1: Have you considered more complex ways of combining appearance and semantic features?**
**A1:** We thank the ... | Summary: This paper introduces a novel approach called Personalized Diffusion Features Matching (PDM) for personalized retrieval and segmentation tasks. Most current self-supervised and supervised methods struggle to accurately identify specific instances within a dataset when multiple instances from the same class are... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful feedback. We believe we address all your concerns below.
**Q1: It is not fair to compare PDM to Dinov2 or SAM since those models were not trained with text supervision.**
**A1:** We thank the reviewer for the comment. The paper compared two types of b... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We were happy to see that reviewers found our approach **“novel”**, **“innovative”** (**All**), **“well-motivated"** (**R1, R3**) and recognized its potential as a **“significant advancement in the field of personalized instance retrieval and segmentation”** (**R3**). Add... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-geodesically-convex optimization in the Wasserstein space | Accept (poster) | Summary: This paper introduces and analyzes an optimization scheme, termed "semi-FB Euler", for a particular minimization problem over the Wasserstein space $P_2(R^d)$: $\min_\mu \mathcal{F}(\mu) = \int (G-H) d\mu + \mathcal{H}(\mu)$, where $G$ and $H$ are convex functions over $R^d$ and $\mathcal{H}$ is a functional t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on our work. We hope the reply below can highlight our contribution.
**Strengths**
***"Computational cost..."***: as the answer to reviewer 7EW7, we will discuss the computational cost of the JKO in the revised version.
**Weaknesses**
***``the se... | Summary: This work, of a theoretical nature, considers the problem of minimizing a functional $$\mathcal{F}$$ over the space of probability measures of the form
$$\mathcal{F}(\mu) = \int (G(x) - H(x)) d\mu(x) + \mathcal{H}(\mu)$$
where $G,H$ are **convex** potentials and $\mathcal{H}$ is (typically) the negative entr... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive comments on our work.
**Weaknesses**
***[1.]*** We will put some material from Part B (discussion on the ICNN approach for the JKO as well as Algorithm 2) into the main text as suggested when we have some extra space.
***[2.]*** We agree with this limitat... | Summary: This work focuses on the optimization in the Wasserstein space of a functional which is a sum of an internal energy (convex along a generalized geodesic) and of a potential energy, whose potential is a difference of convex functions. To solve such problem, the authors propose to generalize the semi Forward-Bac... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive and positive comments on our work.
**Weaknesses**
***the scheme involves to compute the JKO operator, which is computationally costly..., toy examples***: Please also refer to our reply to reviewer 7EW7. Since we use [Mokrov21] in our JKO step, our work is... | Summary: This paper studies minimization algorithms for functionals on the Wasserstein space with the following difference of convex functions (DC) structure:
$$
\min_{\mu \in \mathcal{P}_2(X)} \mathcal{F}(\mu) := \int (G(x) - H(x)) d\mu(x) + \mathcal{H}(\mu),
$$ where $G$ and $H$ are convex functions and $\mathcal{H}$... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and positive comments on our work.
**Weaknesses**
***$\bullet$ "The Lojasiewicz condition..."***: We agree that the Lojasiewicz condition is usually used in the case of KL divergence. However, the condition also almost holds for the case of Maximum Mean D... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful, constructive, and high-quality reviews. We reply to each reviewer in their comment section. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RL-GPT: Integrating Reinforcement Learning and Code-as-policy | Accept (oral) | Summary: The authors present a framework to give LLMs the ability to code and train RL agents as a tool for completing tasks. They perform experiments in MineDojo.
Strengths: **Experiments:** Barring the standard deviation issue mentioned in teh weaknesses, the results are a significant improvement over Plan4MC.
**Id... | Rebuttal 1:
Rebuttal: Dear Reviewer ep8Y,
Despite the negative score, we really appreciate your detailed review. We address your questions below.
**Q1. Related works.**
**A1.** Thanks for mentioning these papers. We will add some of them to the related work part in the revision.
- RL-VLM-F introduces visual feedback... | Summary: This paper introduces RL-GPT, a hierarchical framework that uses LLMs to first break down complex embodied tasks into sub-actions that are suitable for coding or learnable through RL, and then write codes or RL configurations to execute the actions. The authors evaluate the framework on MineDojo benchmark and ... | Rebuttal 1:
Rebuttal: Dear Reviewer efny,
Thank you for appreciating our work with valuable suggestions. We address your questions below.
**Q1. Generalization to other environments.**
**A1.** We acknowledged this concern and addressed it to some extent in Appendix Section D. It is difficult to find real-world enviro... | Summary: This work is a variation of the code as policies, which utilizes LLMs to write code robotic policies in code snippets. This work examines minecraft, proposing that certain tasks can be composed into two sets: those solvable using LLM generated code and those best left to be solved using a standard RL agent. ... | Rebuttal 1:
Rebuttal: Dear Reviewer m13r,
Thank you for appreciating our work with valuable suggestions. We address your questions below.
**Q1. Manual design is needed for each specific environment.**
**A1.** Yes, we acknowledge that some manual design is necessary. However, compared to existing agents like Voyager,... | Summary: The paper introduces RL-GPT, a novel framework that integrates Large Language Models (LLMs) with Reinforcement Learning (RL) to enhance the performance of LLM-based agents in complex, embodied environments. The primary goal is to address the limitations of LLMs in executing intricate logic and precise control,... | Rebuttal 1:
Rebuttal: Dear Reviewer LWBp,
Thank you for appreciating our work with valuable suggestions. We address your questions below.
**Q1. Generalization to other environments.**
**A1.** Thanks for this suggestion! We acknowledged this concern and addressed it to some extent in Appendix Section D. It is difficu... | Rebuttal 1:
Rebuttal: Dear all reviewers,
We sincerely thank your effort in the review with valuable comments and suggestions. **We appreciate reviewers _LWBp_, _m13r_, and _efny_ for recognizing our work**. Additional figures are attached in the **_6379_rebuttal_figs.pdf_**, which we will reference in the following s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robot Policy Learning with Temporal Optimal Transport Reward | Accept (poster) | Summary: This paper extends the vanilla optimal transport-based proxy reward method in imitation learning by 1) masking out distant steps within a trajectory in the optimization of the transport plan and 2) considering neighbouring steps in the reward estimate
Strengths: - the paper is well written and organized, with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive comments.
## Questions
> **Q1: Presentation of the central claim**
Due to the importance of this point and limited space, we have clarified the motivation/central claim in the common reply (top) on Clarifications on motivation.
In short, t... | Summary: This work proposes a reinforcement learning method from a few expert demonstrations based on optimal transport. It incorporates temporal information into the framework of optimal transport reward so that the agent can focus on more relevant information in learning. The work proposes two simple tricks to achiev... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed, constructive review and insightful comments.
Your valuable comments helped us to further improve the quality of the paper.
Responses to the questions are below:
> **Q1: Try other design choices for the temporal mask or try to make it a part of the learnin... | Summary: This paper proposes several improvements on top of the Optimal Transport reward for inverse reinforcement learning. The key observation is that the traditional OT-based reward does not consider the temporal information, i.e., it is invaraint to the order of the state-action pairs in a trajectory, and that the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive review and insightful comments!
Your valuable comments helped us to further improve the quality of the paper.
Responses to the questions are below:
| **Q: The proposed method to restrict the temporal information works by masking the transport plan to only... | Summary: The authors introduce TemporalOT, a learning-based proxy reward that incorporates temporal information. via using a mask mechamism and context-embeddings based cost matrix. They implemented the TemporalOT-RL based on ADS implementation, conducted thorough experiments on Meta-world benchmark focusing on the a c... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive review and for the efforts in reviewing our work!
Your comments are valuable in helping us to further improve the quality of the paper.
Responses to the questions are below:
| **Q1: Could the author analyze what potential property of the push... | Rebuttal 1:
Rebuttal: # To all the reviewers: Thanks for the reviews and summary of key paper changes
We thank all the reviewers (**R1**-hYj8, **R2**-FnLe, **R3**-M6BJ and **R4**-GZSS) for their time and efforts in reviewing the paper. The reviewers appreciated that:
- The motivation is clear and the problem is import... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays | Accept (poster) | Summary: This paper studies the best-of-both-worlds (BOBW) algorithms for MAB with delayed feedback. Compared to previous results, it eliminates the need for prior knowledge on maximum delay $d_{\text{max}}$, and the regret scales with the number of “outstanding observations” ($\sigma_{\text{max}}$) rather than with th... | Rebuttal 1:
Rebuttal: **Weaknesses:**
1. We will add a clarification around Lines 48-49, where the term is first used. Technically, the distribution drift is the ratio $x_{t+d_t,i} / x_{t,i}$ of the probability of playing action $i$ at round $t+d_t$, when the observation arrives, to the probability it had when it was... | Summary: This paper proposes a new best-of-both-world algorithm for bandits with a delayed feedback model. The results simultaneously achieve the latest upper bounds for delayed feedback in stochastic and adversarial (up to a factor). The algorithm design utilizes an implicit exploration estimator and the skipping set.... | Rebuttal 1:
Rebuttal: > The algorithm design itself needs to be more novel
It is very common for BOBW algorithms to bear close resemblance to the adversarial algorithms they are derived from, because it allows inheritance of the adversarial regret guarantees. For example, the EXP3++ algorithm (Seldin and Slivkins, 201... | Summary: This paper studied the best of both world algorithms for arbitrary delayed feedback. The proposed algorithm does not require prior knowledge of maximum delay $d_{\max}$ and avoids its linear dependence in the regret bound. To this end, they proposed the implicit exploration that works for the best-of-both-worl... | Rebuttal 1:
Rebuttal: **A detailed explanation of the selection of regularizers or learning rates:** The learning rates and regularizers were taken directly from Zimmert and Seldin (2020), because we had to have the adversarial regret guarantee. The intuition behind the choice of regularizers is that the adversarial re... | Summary: The authors consider the multi-armed bandit problem with delayed feedback, where the loss of a chosen arm is observed several rounds later. In this setting, nearly optimal algorithms have been developed for both stochastic and adversarial environments, as well as best-of-both-worlds (BOBW) algorithms that perf... | Rebuttal 1:
Rebuttal: **Concerning the novelty of our approach to handling excessive delays:** First, we want to emphasize that the work of Masoudian et al. (2022) is unable to cope with excessive delays, because even a single delay of order $T$ renders their regret bound linear in both stochastic and adversarial envi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics | Accept (poster) | Summary: The authors investigate whether dynamical systems models that are statistically fit to neural data recapitulate the same dynamical mechanisms of the circuit. Commonly, neuroscientists only measure activity from a small fraction of neurons within a circuit. Under this constraint, the authors show examples where... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful critiques of our work, which have helped us further strengthen our paper. Below we respond to their comments on Weaknesses of our work:
* The reviewer brings up an excellent point---that both partial observation and the selection of a latent dimension much s... | Summary: The authors address provide a cautionary tale for how data-constrained RNN models can mis-identify the dynamical structure underlying neural population computations when only a subset of the neural population is observed ("partial observation"). Empirical case studies are provided, whereby data-constrained st... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work. Below we address some of the concerns and questions that they raise:
#### Weaknesses:
* We thank the reviewer for pointing out the work of Pandarinath's group, which we unfortunately missed citing in our initial manuscript. We will add the... | Summary: Deriving a mechanistic understanding of neural circuits from observations (neural recordings) is a fundamentally ill-posed problem. This paper explores and exposes this issue in controlled theoretical settings. Specifically, the authors focus on two aspects: the intrinsic biases of data-constrained surrogate m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our manuscript, and are grateful for their positive assessment. Below we respond to their comments on the Weakenesses of our work:
* We are glad the reviewer found our formulation of the problem intuitive, and agree that the settings we consider... | null | null | Rebuttal 1:
Rebuttal: We thank the referees for their careful reading our our manuscript, and are gratified by their positive appraisal of our work.
Here, we would like to address two common points of concern.
## Title and Framing
First, we would like to clarify why we frame the title and abstract to focus on part... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EfficientCAPER: An End-to-End Framework for Fast and Robust Category-Level Articulated Object Pose Estimation | Accept (poster) | Summary: This paper proposes EfficientCAPER, a novel method for category-level articulated object pose estimation from input point cloud. The proposed method first estimates the 6D pose of the free part (or static part), then uses this estimated 6D pose to transform the input point cloud into canonical space, and final... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read this paper and for asking these meaningful questions. Below we respond in detail to your questions.
***W1: The paper misses many important related works such as CAPTRA, Ditto. While these methods do not work in the exact same setting, these methods are releva... | Summary: The paper investigates category-level articulated object pose estimation with a new framework. The framework consists of two stages, including the first for estimation the pose of the free parts using decoupled rotation representation and the second for estimation the pose of the constrained parts by predictin... | Rebuttal 1:
Rebuttal: Thank you for your review, and for your thoughtful comments and questions. They will certainly help improve the revised paper. Below we respond in detail to your questions.
***W1: The proposed method is called EfficientCAPER, which indicates that the running efficiency is a key factor. However, t... | Summary: This paper introduces a new approach for estimating the pose of category-level articulated objects. The proposed framework, EfficientCAPER, aims to address the challenges of kinematic constraints, self-occlusion, and optimization requirements. The method eliminates the need for optimization post-processing by ... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer understands and recognizes the contributions of this work. We address the main concerns as follows.
***W1: The method heavily relies on the accuracy of the free part pose estimation. Any error in this stage can propagate and affect the estimation of the constrain... | Summary: This paper introduces an end-to-end framework designed for category-level articulated object pose estimation. Specifically, it addresses the complexity of articulated objects by using a joint-centric approach that divides the task into two stages: estimating the pose of free parts and then the constrained part... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and evaluation on our submission. Please find our response below.
***W1: Unclear definition of the free part with relative change against the constrained part***.
The "free" and "constrained" parts are indeed interchangeable, however, this does not cause ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
**Please see the attached PDF for a one-page PDF with a summary of added experimental figures**.
We are appreciated by the positive comments of the reviewers on the novelty and significance of our method (Reviewers UxV6, UsR2, cXQd, Gf45), readability (Reviewer UxV6), effecti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning | Accept (poster) | Summary: This paper proposes an parameter-efficient finetuning (PEFT) method, LISA, that only finetunes some sampled layers while freezing the rest for some certain iterations, and resample the finetuned layers later.
To validate LISA's effectiveness, the authors benchmark it on various tasks, like instruction-follo... | Rebuttal 1:
Rebuttal: Thanks for the feedback. To address the raised concerns, we have provided additional clarifications and experiments, the details are listed as follows,
**Weakness 1: Lack of novelty.**
> LISA is very similar to the method proposed in the paper [46] in the references... In addition, the authors do... | Summary: This paper proposes a lighter alternative to LoRA, LISA, based on using importance sampling to periodically choose a subset of layers to optimize. It is motivated by observations on the norm of parameter updates made during training with LoRA, compared to full parameter fine-tuning. Experiments are made compar... | Rebuttal 1:
Rebuttal: We would like to offer our sincere thanks for all your constructive comments and recognition of our contributions. We really appreciate it. Here we will address the raised concerns one by one.
**Weakness: Sampling Strategy.**
> The main issue I have with this paper is that the approach presented ... | Summary: This paper proposes a new optimization algorithm called Layerwise Importance Sampled AdamW (LISA) for large language model (LLM) fine-tuning. The authors observe the skewed weight norms across different layers in the Low-Rank Adaptation (LoRA) method and use this observation to develop LISA, which randomly fre... | Rebuttal 1:
Rebuttal: We would like to offer our sincere thanks for all your constructive comments and recognition of our contributions. We really appreciate it. Here we will address the raised concerns one by one.
**Weakness 1: Open-Source Implementation.**
> I am curious whether the authors will open-source their i... | null | null | Rebuttal 1:
Rebuttal: Thank you very much for all the constructive comments and suggestions! To further address every reviewer's concerns, we have included additional results in the attached PDF file. Please kindly refer to the file for more details.
Pdf: /pdf/fa3c4e412a2f589b4f18b55046ec332437040868.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression | Accept (poster) | Summary: The authors show that when trained on ICL tasks encoding a regression problem, the layers of a transformer implement an implicit higher order optimization method. They contrast this with prior work on this subject that suggest that the transformer in this setting implicitly implements a preconditioned gradient... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed comments and suggestions. We are pleased that the reviewer agrees that our experiments show a better match between the layers of the transformer and iterations of iterative newtons than when compared with gradient descents.
## **Matching between Transformer laye... | Summary: This paper demonstrates that transformers learn to approximately perform higher order algorithms, building on the previous works that demonstrate that transformers can approximate gradient descent which is a first-order algorithm. For the problem of linear-regression, it is empirically shown that that predicti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions. We are pleased they find our work valuable in demonstrating how transformers efficiently solve linear regression problems, including ill-conditioned ones. They appreciate our comparison showing transformers as superior to models li... | Summary: This work investigated the ability of transformers to implement higher-order optimization methods for in-context learning of linear regression tasks. The authors considered a noiseless linear regression setting and compared the output of each layer of TF with few steps on gradient descent (GD), online gradient... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed comments and suggestions. We are pleased that the reviewer thinks our experimental results and their implications are well-discussed. We are happy to see that the reviewer regards our theoretical results novel, our comparison metrics reasonable, and our paper wel... | Summary: In the paper the authors study the nature of in-context learning in transformers. Starting from the hypothesis of previous work on the fact that transformers may internally implement gradient descent algorithms to correctly perform linear regression on test data, the authors put forward the theory that transfo... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed comments and suggestions. We are pleased that the reviewer thinks our ideas intriguing and empirical evidence convincing.
## **Evidence for higher-order is indirect**
Our main evidence is the convergence rate. We do believe that convergence rate is a solid categ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their time and effort in reviewing our work. We are encouraged that the reviewers praise our work for providing a very good understanding of how transformers can efficiently solve linear regression (p5Dq) and find our ideas of higher-order optimization intrigui... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies in-context learning in transformers, and find that gradient descent converges more slowly than transformers/Newton's method for linear regression. Previous works proposed that transformers do in-context learning using gradient-based algorithms, based on experiments with linear regression tas... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed comments and suggestions. We are pleased that the reviewer thinks our experiments are thorough and successfully support our main claim. We are also happy to see the reviewer thinks our paper is well-written.
## **In some cases, Transformers can behave differentl... | null | null | null | null | null | null |
Pessimistic Backward Policy for GFlowNets | Accept (poster) | Summary: The paper successfully identifies and addresses the under-exploitation problem in the flow matching of GFlowNets. The proposed method, Pessimistic Backward Policy (PBP-GFN), adjusts the probabilities of backward trajectories to improve exploration and exploitation, achieving superior performance in various ben... | Rebuttal 1:
Rebuttal: Dear reviewer WPBt,
We express our deep appreciation for your time and insightful comments. In what follows, we address your comments one by one.
---
**W1. The PBP-GFN introduces additional complexity, which may be expensive or inpractical in some settings. Can the authors provide a discussion ... | Summary: The authors present a problem with GFlowNets training that originates from not having seen backward trajectories for a particular terminal state. The authors show that because of the lack of observed trajectories, the backward flows underestimate the probabilities for the observed flows, resulting in a (forwar... | Rebuttal 1:
Rebuttal: Dear reviewer CTvM,
We express our deep appreciation for your time and insightful comments. In what follows, we address your comments one by one.
---
**W1. One can mitigate under-exploitation using a reward-prioritized replay buffer. Adding this as a baseline will be helpful.**
We clarify tha... | Summary: This paper proposes a pessimistic backward policy for GFlowNets (GFN), which maximizes the expectation of observed backward trajectories. This paper points out the under-exploitation of high-reward objects for previous GFN training methods and provides a deep analysis of this problem. Extensive experiments val... | Rebuttal 1:
Rebuttal: Dear reviewer smvU,
We express our deep appreciation for your time and insightful comments. In what follows, we address your comments one by one.
---
**W1. Does the pessimistic training objective affect the original assumption of GFlowNets training objective?**
Our pessimistic training objec... | Summary: This paper addresses the under-exploitation of high-reward objects in Generative Flow Networks (GFlowNets) due to the limited observation of trajectories during training. The authors propose PBP-GFN (Pessimistic Backward Policy for GFlowNets), which modifies the backward policy to maximize the observed backwar... | Rebuttal 1:
Rebuttal: Dear reviewer u41F,
We express our deep appreciation for your time and insightful comments. In what follows, we address your comments one by one.
---
**W1. The paper lacks a strong theoretical analysis to support claims.**
We agree that our paper lacks a strong theoretical analysis since we m... | Rebuttal 1:
Rebuttal: Dear reviewers (**u41F**, **smvU**, **CTvM**, and **WPBt**) and area chairs,
We are deeply grateful for the time and effort you spent reviewing our manuscript. In what follows, we summarize our rebuttal PDF and planned revisions.
---
### Summary of rebuttal PDF
Our rebuttal PDF provides the fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Symmetries in Overparametrized Neural Networks: A Mean Field View | Accept (spotlight) | Summary: This paper studies the Mean-Field limit of generalized shallow neural networks after learning with Wasserstein gradient flow under data augmentation, feature averaging or equivariant architectures as well as standard training. The provided results provide insights into learning with symmetries by covering two ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough reading and the relevant comments and suggestions.
Regarding the detected weaknesses of the paper, we will make sure to address them by providing clearer descriptions of the relevant figures, and correcting the detected typos. For instance, we shall complem... | Summary: The paper investigates the learning dynamics of neural networks with several symmetry-leveraging techniques from a mean-field perspective. The main result indicates that optimizations with data augmentation and feature averaging (and the corresponding Wasserstein gradient flows) are equivalent in the mean-fiel... | Rebuttal 1:
Rebuttal: We thank the reviewer’s insightful feedback. We will address the signaled weaknesses in our final version, including in the Appendix a table with relevant notation and further reference for the main concepts. We will also further stress those aspects that, we believe, go beyond the usual intuition... | Summary: This work studies the symmetric structure of the model and data structures with respect to the action of $G$ and studies the Wasserstein gradient flow (WGF) for learning overparameterized neural networks under the mean-field (MF) regime. In particular, the authors consider data augmentation (DA), feature avera... | Rebuttal 1:
Rebuttal: We thank the reviewer for their relevant feedback about the paper. We will make sure to address the detected weaknesses in our final version, specifically:
**W1: Regarding our work mainly focusing on two-layer neural networks.**
As mentioned in our Global Response, the full MF theory for deep NN... | Summary: This paper presents a mean-field analysis on a class of overparameterized neural networks that are expressed as an ensemble of $N$ units, trained with SGD under symmetries in data distribution and possible use of symmetry-leveraging techniques (data augmentation, feature averaging, and equivariant architecture... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and relevant feedback on the paper.
We will incorporate an explicit Limitations section in the Appendix of the revised version, as well as a simple yet clarifying example of the different notions of symmetric distributions (e.g. if ${\cal Z}=\mathbb... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insights. We here present information to help transversally clarify some of their questions. References from the manuscript are mentioned as presented therein; new references follow the format at the end of this response.
First, for better understandability of th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distribution Learning with Valid Outputs Beyond the Worst-Case | Accept (poster) | Summary: This paper follows up on the line of study initiated in the work "Actively Avoiding Nonsense in Generative Models" by Hanneke et al. 2018. The model studied in that paper is as follows: we are given data generated by a distribution $P$ over a domain $X$. A certain fraction of the domain $X$ is labeled "valid" ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read our work with such attentiveness, and write such a detailed review. We will work to clarify the writing in the next version, and agree that some proof intuition can be added.
Answers to the reviewer's questions can be found below:
1) This is cor... | Summary: The paper proposes a method for vastly improved validation query efficiency in validity-constrained distribution learning by relaxing the requirement for worst case settings of distributions, loss function and true validity function.
Strengths: The paper makes good high level arguments for why regarding commo... | Rebuttal 1:
Rebuttal: We appreciate the review, and will try to address the weaknesses they point out.
We would argue that most practical situations are likely closer to the regime of realizability
explored in the first half of this paper than the case where no model in the model class is a reasonable approximation ... | Summary: The paper considers the problem of learning generative models under validity constraints. Specifically, given data from a distribution and an oracle that tells whether a particular datapoint is valid, the goal is to output a generative model from a hypothesis class Q whose output has a small loss epsilon_1 (co... | Rebuttal 1:
Rebuttal: We appreciate the review, and agree that the next version of the paper should furnish more practical insight.
We mainly see these results as a first attempt at studying the problem of learning a valid generative model from the opposite perspective of [1], which paints learning in this setting as ... | Summary: This paper studies distribution learning with invalidity constraints. It assumes that the algorithm has access to an oracle that provides validity queries, and targets to reduce the total amount of queries to the oracle while achieving comparable learning guarantees to previous counterparts. By specifying the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for looking closely at our work, and apologize for sometimes being less clear than we should.
In line 37 ("while achieving polynomial bounds on the number of validity queries, uses relative large number of validity queries''), what we were referring to is that the improper a... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LoCo: Learning 3D Location-Consistent Image Features with a Memory-Efficient Ranking Loss | Accept (poster) | Summary: This work focuses on the best strategy to pre-train a feature extractor network optimized to be invariant wrt to the viewpoint in the image. To do so the authors use a dataset of paired views from which they extract pairs of positive patches (same 3D point in two different views) and negatives (different 3D po... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review. We appreciate your positive remarks regarding the presentation, efficiency, and potential reusability of our proposed method. We also acknowledge the valuable concerns you’ve raised and address them below.
---
**Weakness (a): Limited experimen... | Summary: The paper introduces a memory efficient loss for location consistent image features. The paper addresses the problem of high memory footprint of previous work, by significantly reducing the memory footprint, by 3 orders of magnitude. Memory efficiency is achieved by sampling the positive pairs from a smaller s... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. We appreciate your recognition of the strengths of our work, particularly in terms of the memory efficiency gains and the analysis of the correction to the loss function. Below, we address your concerns and questions in more detail.
---
**Wea... | Summary: This paper presents a new training scheme for vision foundation models. The key goal of the paper is to enhance the multi-view consistency of vision foundation models. To this end, the paper revisits the idea from soft average precision, and applies the idea for training vision foundation models. Specifically,... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for highlighting both the strengths and weaknesses of our work. We appreciate your feedback, and we would like to address your concerns in detail below.
---
**Weakness 1: Fairness of comparisons and generalization of the proposed method**
We understand you... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their careful reading and thoughtful comments. In this section, we outline the additional ablations and evaluations requested by the reviewers, which are presented in the PDF attached to this response.
## Pixel Correspondence Estimation (Table 1)
### LoCo with DINOv2 Ba... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Simple and Effective Masked Diffusion Language Models | Accept (poster) | Summary: The paper introduces a masked diffusion language modeling (MDLM) framework that enhances the performance of diffusion models in generating high-quality text, closing the gap with autoregressive methods. By applying an effective training strategy and a simplified objective function, MDLM achieves state-of-the-a... | Rebuttal 1:
Title: Response to wXn1 (1/3)
Comment: We want to thank the reviewer for their constructive feedback. We address specific comments and questions below.
---
### ****Concern 1:**** What is “simple”? It was hard to understand the method.
We use the term “simple” because **our algorithm is very similar to BE... | Summary: This paper presents a method for language modeling using simple masked discrete diffusion models. The authors show that a simplified objective combined with optimized training achieves performance improvements over previous diffusion language models. The paper reports state-of-the-art results for diffusion mod... | Rebuttal 1:
Title: Response to DASc (1/2)
Comment: We want to thank the reviewer for their constructive feedback. We address concerns and questions below.
---
### ****Concern 1:**** Novelty of MDLM relative to the D3PM framework and other algorithms.
In addition to attaining ****state-of-the-art diffusion language m... | Summary: While previous works considered diffusion language models less competitive than autoregressive models in text generation tasks, the authors propose a simple framework named masked diffusion language modeling (MDLM), where they claim to have better performance than previous thoughts. The authors derive a simpli... | Rebuttal 1:
Title: Response to BSmY (1/3)
Comment: We thank the reviewer for their constructive feedback. We address the concerns and questions below.
---
### ****Concern 1:**** Adding algorithms for training and inference.
Below we provide pseudocode for MDLM training and inference. We also include these in our rev... | Summary: This paper introduces a new approach to masked diffusion language models (MDLMs) that improves performance over previous discrete diffusion methods. The authors present a simplified, Rao-Blackwellized objective for training MDLMs, which is derived from a substitution-based parameterization of the reverse diffu... | Rebuttal 1:
Title: Response to p1DW (1/3)
Comment: We want to thank the reviewer for their constructive feedback. We address the reviewers comments and questions below.
---
### ****Concern 1:**** Why does MDLM outperform previous methods? There is a need for a deeper theoretical analysis.
Our work outperforms previo... | Rebuttal 1:
Rebuttal: # General Response to Reviewers
Dear reviewers, we thank you all for the useful comments and feedback. In addition to the individual responses we provide directly to each of your comments, we wanted to highlight additional results and clarifications that are common to several of your reviews.
##... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Proximal Causal Inference With Text Data | Accept (poster) | Summary: The situation with unobserved confounding variables is quite common in applied research and makes causal inference complicated. To deal with such settings, the authors propose a new causal inference method that uses multiple instances of pre-treatment text data, estimates two proxies from two zero-shot models ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. We address the reviewer’s questions and comments on our paper's weaknesses.
> Literature review is incomplete. The idea of proximal inference goes back to Zvi Griliches (1977) who should get credit for it. As background reading the corresponding ... | Summary: Causal inference techniques often rely on the assumption that all confounders can be observed or inferred from available data. This paper proposes a method to address the setting where a confounder is entirely unobserved; instead there is available pre-treatment text that can be used to infer proxies, which in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. We address the reviewer’s questions and comments on our paper's weaknesses.
> The significance of the empirical evaluation is limited due to synthetic and semi-synthetic experiments. The impact of the proposed method in realistic settings is hard... | Summary: The paper considers the causal effect estimation setting where a confounder is latent but with unstructured text data that could serve as proxies. Specifically, the paper proposes to incorporate zero-shot classifiers (to operate on text-based proxies), together with a falsification heuristic, into the proximal... | Rebuttal 1:
Rebuttal: Thank you for the reviewer’s comments on questions. We address each below.
>[F]urther assumptions S1 -- S2 are proposed. Then in Section 3 and Section 4, a set of gotcha's, two additional pre-conditions (lines 216 -- 218), and another set of conditions related to odds-ratio based heuristics (line... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and suggestions on improving our paper. We respond to each reviewer individually and address each reviewer’s questions point by point. We also include in our rebuttal a PDF with a figure that shows our proposed proximal causal inference with tex... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning | Accept (poster) | Summary: Inspired by the counter-current phenomenon observed in nature, the authors propose a counter-current learning (CCL) framework that decouples the input and backpropagation information in different circuits, overcoming many biological implausibility issues of backpropagation learning (BP).
Strengths: I am not a... | Rebuttal 1:
Rebuttal: **[W1] Algorithm selection.**
We appreciate the reviewer's question regarding concurrent work. A performance comparison is provided in general response 2 [GR2], demonstrating that our CCL algorithm outperforms other methods. While SoftHebb achieves similar results, it requires 24 hyperparameters... | Summary: In this paper, the authors propose counter-current learning (CCL), a biologically plausible framework for credit assignment in neural networks.
Strengths: The research direction of having new learning algorithms inspired biologically seems relevant and interesting.
Weaknesses: - The authors discuss biologica... | Rebuttal 1:
Rebuttal: **[Q1] In-depth discussion of bIological plausibility for counter-current learning**
We thank the reviewer for the opportunity to further elaborate on the biological plausibility of our method from weight-transport, locality, freezing of neural activity, and update locking perspectives. Although ... | Summary: In this work, the authors proposed the counter-current learning algorithm, a novel algorithm for biologically plausible training of feedforward neural networks. The learning rule is built upon the target-propagation algorithm and its variants. In these algorithms, the backward pathway is typically trained in a... | Rebuttal 1:
Rebuttal: **[W1] Discussion of biological plausibility of the regularization mechanisms.**
We thank the reviewer for pointing out and allowing us to elaborate on this.
- Normalization: Our use of activation normalization during loss computation aligns with divisive normalization observed in biological ne... | Summary: In their paper ‘Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning’ the authors introduce their new ‘counter-current learning (CCL) framework’ which they use to train neural networks of multiple network classes (MLPs, CNNs, Autoencoders) using a learning mechanism which ... | Rebuttal 1:
Rebuttal: **[Q1] Generalization of models**
A: While MLPs and CNNs have established biological plausibility, the biological relevance of Transformers and State Space Models remains unclear and under-explored. For vision tasks, CNNs have been shown to match Transformer performance when computational resourc... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback. We're grateful for their assessment of our work as novel (sTDo, ijwx, myCb), distinctive (ijwx), and empirically supported (sTDo, EBaT), with EBaT noting its 'apparent generality'. We address the main critiques below, focusing on theoretical fo... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The article proposes a learning framework that addresses three major critiques of the backpropagation algorithm regarding its biological plausibility: (i) the weight transport problem, i.e., the error feedback weights being the transposes of the feedforward weights, (ii) the nonlocal update problem, i.e., the ... | Rebuttal 1:
Rebuttal: **[W1] We provide a theoretical extension for the existing framework**
We provide an analytical framework for understanding CCL in General Response 1 [GR1]. In short, we show that CCL can be considered as optimizing an ELBO for a hierarchical model.
**[W4] Validation on large-scale dataset/mode... | null | null | null | null | null | null |
Adversarial Schrödinger Bridge Matching | Accept (poster) | Summary: This paper proposes Discrete-time iterative Markovian Fitting, which is an efficient alternative that greatly reduces NFE from IMF (Shi et al. 2023). This was possible due to the tractability of Brownian bridges for any subintervals, so discretization of IMF does not hurt the performance of if the model can be... | Rebuttal 1:
Rebuttal: Dear Reviewer LCNa, thank you for your comments. Here are the answers to your questions and comments.
**(1) I strongly believe the performance gains are mostly from applying GAN architecture, since D-IMF and IMF indicates essentially the same learning scheme. Therefore, it is indeed an improvemen... | Summary: This paper proposes the Discrete Iterative Markovian Fitting (D-IMF) method. Specifically, this work introduces discretized reciprocal properties and Markovian processes, showing that the optimal plan matches the solution of the static Schrödinger Bridge (SB) problem. Additionally, this work demonstrate that t... | Rebuttal 1:
Rebuttal: Dear Reviewer RgPk, thank you for your comments. Here are the answers to your questions and comments.
**(1) Computational burden and stability study.**
As requested, we provide a study on the effectiveness of ASBM and compare it with the DSBM.
**Number of parameters.** Our ASBM generator has a... | Summary: The paper extends the recently proposed Schrödinger Bridge method, DSBM or IMF, to a discrete-time setup. This extension is non-trivial, requiring appropriate notation for discrete reciprocal and Markovian projections. By leveraging the structure of Brownian bridges used in the original IMF, the paper achieves... | Rebuttal 1:
Rebuttal: Dear Reviewer xGf5, thank you for your comments. Here are the answers to your questions and comments.
**(1) I enjoyed reading the majority of the paper—except, perhaps oddly, the paper title. Over 85\% of the main technical contributions (Sec 3) stem from the construction of the discrete IMF form... | Summary: In this paper, the authors study the Schrodinger Bridge problem and propose an improvement over the Diffusion Schrodinger Bridge Matching (DSBM) methodology [1, 2]. In particular, they propose a discrete counterpart formulation of DSBM. Instead of relying on stochastic processes, they rely on a discrete-time v... | Rebuttal 1:
Rebuttal: Dear Reviewer xtGA, thank you for your comments. Here are the answers to your questions and comments.
**(1) Part of the theory was already done in [1]. ...**
Indeed, the authors of [1] analyze the discrete-time Markovian projection and show that it converges to continuous-time projection in cert... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you all for taking the time to review our paper and for your thoughtful feedback. We are delighted that you found our work to be well-written and that it effectively introduces key concepts (xtga, LCNa). It is encouraging to hear that you see it as theoretically fruitful (RgP... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ACES: Generating a Diversity of Challenging Programming Puzzles with Autotelic Generative Models | Accept (spotlight) | Summary: The paper presents Autotelic CodE Search (ACES), a method for generating diverse and challenging Python programming puzzles using state-of-the-art generative models. Inspired by Quality-Diversity, ACES aims to automate the problem generation process by optimizing for both diversity and difficulty. ACES iterati... | Rebuttal 1:
Rebuttal: We thank Reviewer ESVE for their review, and especially for appreciating the clarity of the paper, and the simplicity and effectiveness of the method. We hope to be able to convince them of the promise of the method for practical applications.
> What are the practical applications of this framewo... | Summary: The authors propose Autotelic CodE Search (ACES) to generate programming puzzles, considering diversity and difficulty, borrowing ideas from intrinsic motivation and evolutionary search, relying on the assistance from LLMs for problem representation, skill label generation, diversity characterization, difficul... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and hope to be able to convince them that our assumptions are sound.
Reviewer NuHw seems to reject the possibility of leveraging LLMs in any kind of systems, based on the fact that LLMs are sometimes wrong. That LLMs are not perfect at any of the tasks we ... | Summary: This paper proposes Autotelic CodE Search (ACES), a method to generate diverse and challenging Python programming puzzles using state-of-the-art generative models. ACES optimizes for both the diversity and difficulty of problems by representing them with semantic descriptors that describe the programming skill... | Rebuttal 1:
Rebuttal: We thank Reviewer qqHU for their review. We thank the reviewer for pointing out the experiments are promising, the research direction interesting, and for noting the method is general and applicable to other domains beyond code. We hope to address the reviewer’s concerns.
> How do the results dif... | Summary: In this paper, the authors propose a method to automate the generation of challenging coding puzzles. By leveraging quality-diversity (QD) algorithms and defining novel metrics, their approach produces coding puzzles that are more diverse and challenging than existing benchmarks.
Strengths: - The proposed alg... | Rebuttal 1:
Rebuttal: We thank Reviewer LkwK for their thoughtful comments, and are glad they find the problem we study important, and the evaluation solid. We now aim to clarify some points in our response.
> The presentation could be improved.
We thank you for this feedback. We have modified section 3.4 by being m... | Rebuttal 1:
Rebuttal: We thank all reviewers for their reviews and the time they spent reading the paper, and we hope this rebuttal and the discussion period will be productive, answer questions and overall lead to a better paper.
There are two ways to read this paper, depending on which background one has. The takeaw... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting | Accept (poster) | Summary: In this paper, the authors propose a dataset distillation approach by decomposing the distillation loss into two terms, the value term and the gradient term. Then, they derive bounds for these two terms that enable more effective dataset distillation for time-series forecasting. Strong performance is demonstra... | Rebuttal 1:
Rebuttal: We apologize for the ambiguity.
**W1:** What does non-optimizable term $\epsilon$ mean?
**A1:**
- Please refer to Eq(4), since test label $x_{t+m:t+m+n}$ is unavailable during dataset condensation, we transform it to the prediction of an expert model $M_f$ on test data $x_{t:t+m}$ for further a... | Summary: This paper explores dataset condensation, which generates a small dataset for training of deep neural networks, for time series forecasting. Specifically, it proposes a one-line dataset condensation plugin designed specifically for time series forecasting. It first formulates the optimization objective of data... | Rebuttal 1:
Rebuttal: **W1:** The demand for dataset condensation for time series forecasting is not urgent. What are the real applications of dataset condensation for time series foundation models?
**A1:**
- Firstly, dataset condensation is also important in other aspects besides reducing computational cost. For inst... | Summary: In this paper the authors study the question of dataset condensation / distillation in the context of time series forecasting. They propose a decomposition that could be used to empirically estimate bound the point-wise forecast MSE loss. Based on this decomposition the authors proposed a condensation plug in ... | Rebuttal 1:
Rebuttal: We apologize for the ambiguity.
**W1:** What are the sample space of $x, s$? Are they univariate time series?
**A1:** Yes, $f, x, s$ are univariate time series. Please refer to Sec.3, l.93-95. A time series dataset can be viewed as a long vector. We cut the dataset into two vectors and obtain tr... | Summary: This paper provides a simple fix on the parameter-matching based dataset condensation methods for time series forecasting tasks. Besides matching the parameters of the model $M_f$ trained on the full training set and $M_s$ trained on the smaller synthetic dataset, it additionally induces the $M_f$ to perform w... | Rebuttal 1:
Rebuttal: **Q1:**
Is the claim "models initialized with different samples of parameter values predict similarly after trained on the full dataset given arbitrary input" in line 162 or Eq(9) supported by any evidence?
**A1:**
We apologize for the ambiguity. Please refer to Figure 3 and line 166-168, we supp... | Rebuttal 1:
Rebuttal: Dear reviewers and area chairs,
We thank all reviewers and area chairs for their valuable time and comments.
We have responded to each reviewer individually to address any comments. We would like to give a brief summary.
1. We clarify some of the meanings of the notations in our paper to addres... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Structure-Aware Framework for Learning Device Placements on Computation Graphs | Accept (poster) | Summary: The paper is about computation graphs, which is an interesting topic. The authors propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit using reinforcement learning. The paper is well written and well organized. However, there are ... | Rebuttal 1:
Rebuttal: We would like to genuinely thank you for your thoughtful comments and your interest in the selected topic. Please find our response to your comments and suggestions below. We have also updated the manuscript in light of your suggestions towards increasing the quality of our paper.
**1. Concatenat... | Summary: The authors have developed a model that automatically places neural network operations on the optimal devices (CPU and GPU) to accelerate model training. They optimize device placement using reinforcement learning and use execution time as the reward. The authors propose using the graph structure as informatio... | Rebuttal 1:
Rebuttal: We would like to thank you for the kind words and we are glad that you liked our paper. We provide clarifications on details that may have been unclear.
**Computation graph.** To convert a neural network model to an OpenVINO computation graph, we first design the model using a deep learning frame... | Summary: This paper introduces an end-to-end framework utilizing policy optimization for device placement during the inference phase of deep learning models. The framework consists of multiple components, including computation graph construction, graph coarsening, node representation learning, and policy optimization. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We hope our response addresses your concerns and highlights the framework’s motivation and rationale.
**TL;DR:** Our high-level motivation is to address the ever-growing capacity requirements for efficient inference on heterogeneous devices. Plea... | Summary: The paper introduces a novel framework for device placement that leverages smaller computation graphs extracted from the OpenVINO toolkit using reinforcement learning. This framework bridges the gap between encoder-placer and grouper-placer techniques by incorporating graph coarsening, node representation lear... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the novelty of our framework, and highlighting its effectiveness and robustness. Below we provide point-to-point responses to your questions/concerns. We hope that our response addresses your concerns.
**Execution time as a reward.** Thank you for this remark.... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful reading and thoughtful comments and suggestions on our paper. We find it encouraging that reviewers have found our work **interesting**, **novel** and **well-organized**! The suggestions have led us to further improve the clarity of the manuscript, impr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models | Accept (poster) | Summary: The paper proposes a theoretical pipeline to inject undetectable backdoors into deep learning models. The pipeline consists of several steps. First, the neural network is trained and converted to a boolean circuit. Then, a non-replicable backdoor can be injected into the binary circuit using pseudo-random gene... | Rebuttal 1:
Rebuttal: [Part 1/n]
We want to thank the reviewer for reading our paper carefully, for appreciating our results and for their constructive feedback and comments.
Q1) To me, the paper's contribution is not quite clear. Since Goldwasser et al. (2022) have already shown how undetectable backdoors can be inj... | Summary: The authors propose a procedure to process a neural network such that it is impossible to efficiently tell whether a backdoor was injected or not. The construction is formal and is based on common cryptographic assumptions. The authors also provide a technical formulation of backdoors for languages models and ... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for reading our paper carefully, for appreciating our results and for their constructive feedback and comments.
Q1: The Section 4.2 is a bit too compressed as it does not even define the steganographic function. The presentation would be more balanced if the language... | Summary: This paper presents a way of inserting backdoors into deep learning and language models, whereupon the resulting backdoored models are not efficiently distinguishable from other perturbed variations of a given model. The paper introduces definitions of "undetectable" and "unreplicable", and rigorously demonstr... | Rebuttal 1:
Rebuttal: [Part 1/n]
We want to thank the reviewer for reading our paper carefully, for appreciating our results and for their constructive feedback and comments.
Q1: While this is a strong theoretical result, this seems to be vanishingly unlikely to be used in practice, since it depends on indistinguis... | Summary: - The paper studies undetectable backdoor attacks for neural networks from a theoretical perspective.
Strengths: - The problem of undetectable backdoor insertion is important for the community.
- The paper considers a more complicated scenario compared to prior work, as it shows undetectability under the whi... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for reading our paper carefully, for appreciating our results and for their constructive feedback and comments.
Q1: It would be good to include a "Conclusion" section in the main body of the paper, as it currently seems to end abruptly.
A1: We would like to thank yo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels | Accept (poster) | Summary: This paper studies the statistical property of Hilbert Schmidt Independence Criterion (HSIC). Specifically, under either Gaussian or continuous bounded translation-invariant characteristic kernel that is defined on $\mathbb{R}^d$, the paper prove that HSIC can be estimated at the optimal rate in the minimax se... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and the kind review.
Below we answer the questions in detail.
- __Technical challenge.__ There are three main tools for deriving lower bounds in the minimax setting: Le Cam's method, Fano's method, and Assouad's lemma. The main technical ch... | Summary: In this work, the authors prove that the minimax optimal rate of HSIC estimation on $\mathbb R^{d}$ for Borel measures containing the Gaussians with continuous bounded translation-invariant characteristic kernels is $n^{-1/2}$.
Strengths: Testing whether a pair of random variables are independent is the centr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and the kind review.
In the following, we answer the questions.
- __Related work.__ The related work can be divided into lower bounding the rate of estimating (1) the kernel mean embedding, (2) maximum mean discrepancy, and (3) the covarian... | Summary: The rate at which HSIC can be estimated is an important and open problem, in this paper, the authors prove that
the minimax optimal rate of HSIC estimation for Borel measures is $\mathcal{O}(n^{-0.5})$ with M>=2 components, which is very important as existing conclusion only holds for M=2. Other byproducts can... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort invested, and for the kind review.
To answer the question regarding experiments: In the minimax framework, one bounds the convergence rate from above and from below.
- The former can be validated empirically in some cases. Indeed, for HSIC, one can ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bayesian Optimization of Functions over Node Subsets in Graphs | Accept (poster) | Summary: This paper addresses the challenge of optimizing functions defined on node subsets in a graph. These functions are combinatorial, black-box, and expensive to evaluate. The proposed solution utilizes Bayesian Optimization (BO), mapping each k-node subset to a node in a new combinatorial graph, and traversing th... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty and potential of our work! Please see our responses to the raised questions below.
> How such approaches achieve provably good performance when we have no assumptions on function $f$? It is likely to fall into no-free-lunch theorem.
We’d like t... | Summary: The present paper proposes an approach based on Bayesian optimization for optimizing a function over subsets of nodes of size $k$ in a graph. At each step of the algorithm, a local neighborhood is constructed and the best node with respect to an acquisition function is chosen as the center for the next step. T... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for acknowledging the contribution of the proposed framework. Please see our response to the review comments below.
> If applying Bayesian optimization to the problem is indeed novel, then I think it is an interesting contribution for the ML community, even if ... | Summary: This paper introduces a framework for optimizing functions of subset of nodes in a graph with Bayesian Optimization (BO). The framework need not know the graph structure beforehand, but does require the knowledge of the cardinality of the considered subsets of nodes $1 \leq k \leq N$ for a graph $\mathcal{G} =... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed feedback which helped us improve the current work. In summary, the reviewer's concerns are (1) If the framework can handle noisy observations, and (2) Comparison with COMBO on small networks. Please see below for our responses.
> Do you account... | Summary: This paper proposes a Bayesian optimization (BO) method for optimizing black-box functions with costly evaluations over subsets of nodes in a graph. The central idea is to use the combo-graph associated with these node subsets from the original graph. This approach maintains the structural information of the o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback that helped us improve our work. Please find our detailed response below.
> The empirical evaluation would benefit from including other BO baselines. I believe that LADDER could be a particularly strong candidate.
To the best of our knowledge, this... | Rebuttal 1:
Rebuttal: We want to first thank all the reviewers for their detailed and valuable feedback, which we find very helpful in improving our work. We are also glad to see they acknowledged the innovation and potential impact in the community (bfWL, Y7Gf, yrTA), the technical soundness (u7DA, Y7Gf), the signific... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Why the Metric Backbone Preserves Community Structure | Accept (poster) | Summary: For weighted distance graphs, the metric backbone corresponds to the union of the shortest paths. This work shows that the community structure of a graph can still be detected from this backbone. This is surprising, since inter-community edges often serve as bridges between communities, so one would expect the... | Rebuttal 1:
Rebuttal: Dear Reviewer bM2e,
We thank you for your time in evaluating our submission and we are grateful for your comments. Please find below responses to the questions raised in your reviews.
* The methodology used in Section 4 is relatively standard, as we took it from previous papers on the metric ba... | Summary: The authors analyze the metric backbone (= all the edges that are on some shortest path) and it's relation to clustering.
They show that under weighted SBM model (with equal expected degree for all nodes), metric backbone
1) the metric backbone approximately maintains the edge probabilities of blocks
2) spec... | Rebuttal 1:
Rebuttal: Dear Reviewer uKT3,
We thank you for your time in evaluating our submission and we are grateful for your comments.
Please find below responses to the questions raised in your reviews.
Answers to the main questions:
* Other sparsification methods can maintain the community structure (as shown in... | Summary: This work focuses on the metric backbone of weighted graphs, which is the union of all-pairs shortest paths. The study analyzes the metric backbone of a class of weighted random graphs with communities and formally proves the robustness of the community structure regarding the removal of non-metric backbone ed... | Rebuttal 1:
Rebuttal: Dear Reviewer Qqxh,
We thank you for your time in evaluating our submission and we are grateful for your comments. Please find below responses to the questions raised in your reviews.
* By dilution of community structure, we mean that the metric backbone could delete a larger proportion of in... | Summary: The authors investigate the shortest-path backbone that is the union of all-pair shortest paths in a weighted graph, and show that it preserves the community structure in weighted Stochastic Block Model (wSBM) with high probability. A key finding in their proof is that the metric backbone maintains the same pr... | Rebuttal 1:
Rebuttal: Dear Reviewer JByP,
We thank you for your time in evaluating our submission and we are grateful for your comments. Please find below responses to the questions raised in your reviews.
* Intuitively, keeping only the shortest paths and deleting everything else could destroy all structures unre... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Noise Contrastive Alignment of Language Models with Explicit Rewards | Accept (poster) | Summary: The paper's main contributions are:
1. Theoretical Integration: It integrates Direct Policy Optimization (DPO) with contrastive learning theories, offering a general framework for using reward and preference data.
2. Value of Suboptimal Responses: Highlights the importance of suboptimal responses in optimizin... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer xAZC
We thank reviewer xAZC for the valuable feedback. It really encourages us to see that the reviewer finds our work to be a good contribution to the field, regarding pointing out the value of suboptimal responses and countering the likelihood decline trend in ali... | Summary: ## Summary
- Typically, RLHF algorithms like DPO use preference pairs dataset.
- The key question, this paper tries to answer is how to incorporate reward datasets annotated with scalar values. Previous approach usually prune the scalar reward datasets by selecting the best response and pairing with a random ... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer WFsu
We appreciate the reviewer WFsu for the very detailed comments on our paper and are glad that the reviewer finds our paper to be helpful. Below we address the reviewer's questions and hope this can help the reviewer increase the confidence of our work.
**Q1: ... | Summary: The authors introduce a general framework for LM alignment, leveraging Noise Contrastive Estimation (NCE) to bridge the gap in handling reward datasets explicitly annotated with scalar evaluations. The framework comprises two parallel algorithms, NCA
and InfoNCA, both enabling the direct extraction of an LM p... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer UYN1
We thank reviewer UYN1 for the praise and suggestions regarding our work. We are pleased to see the reviewer's acknowledgment of our theoretical contribution in bridging the gap between DPO and classic contrastive learning theories. Below we address the review... | Summary: This paper leverages Noise Contrastive Estimation (NCE) to align language models (LMs) with explicit reward data, which can handle both scalar evaluations and multiple preference data. The proposed methods, NCA and InfoNCA, extend current alignment theories by improving upon DPO and addressing the issue of dec... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer EZsK (1/2)
**Q1: The motivation is weak. The description that DPO can only be used for pairwise preference data is wrong (such as Line 31 and 43). In fact, the appendix of DPO says that it can be applied to multiple preference data based on the Plackett-Luce model.... | Rebuttal 1:
Rebuttal: # Rebuttal Summary
We would like to thank all the reviewers for their valuable comments. We are encouraged to see all reviewers recognize the theoretical contribution of our work. Reviewers EyJu, EZsK, and UYN1 highlight the importance of our work in unifying contrastive learning theories (NCE) w... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper addresses a weakness of DPO: it can not deal with preference data whose number of responses is larger than 2. To extend DPO, the author proposes Noise Contrastive Estimation (NCE)-based Alignment algorithms InfoNCA. The author has shown that DPO is one of the special cases of InfoNCA. To further fix ... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer EyJu (1/2)
**Q1: (Major Concern) Fundamental Assumption is we can sample from the optimal policy $\pi^\*$, which is practically impossible because we can only access non-optimal LLM policy $\mu$.**
**A1:** We'd like to clarify that **the availability of** $\pi^\*$... | null | null | null | null | null | null |
QBB: Quantization with Binary Bases for LLMs | Accept (poster) | Summary: This paper proposes a PTQ technique which decompose the model weitghs into a set of binary matrices. An interactive binarization process and a progressive model distillation procedure are conducted to reduce the quantization error. The paper claims to set a new SOTA of a summation-only based approach.
Strengt... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We hope to have addressed their remaining concerns below.
**Q1.** _From the novelty prespective, the proposed binary decomposition method is largely similar to previous nonlinear quantization methods line LQ-Net ([https://arxiv.org/pdf/1807.10029... | Summary: This paper introduces Quantization with Binary Bases (QBB), a novel method designed to reduce the computational complexity of large language models (LLMs) by replacing most multiplications with summations. QBB decomposes original weights into a set of binary (1-bit) matrices through an iterative process, optim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We hope to have addressed their remaining concerns below.
**Q1.** _I believe comparing it with non-uniform quantization methods, such as QuIP#, would enhance this paper, as it does not utilize uniform quantization._
**A1.** Thank you for your su... | Summary: This paper proposed QBB by discomposing the original weights into 4 binary matrics and scaling factor.
To compensate the error, two techniques are further proposed:
1. use gradient descent to find the optimal set of binary matrices and scaling vectors
2. use knowledge distillation to optimize the scaling vec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We hope to have addressed their remaining concerns below.
**Q1.** _On: The technique of decomposing the weights into several binary matrics is similar to BiLLM [1], published on arxiv on Feb. 2024._
**A1.** Thank you for pointing out [1], we wer... | Summary: This research brings the sum of binary bases to the post-training quantization of the large language models. The authors propose a three-step algorithm. In step1: taking the sign of the full-precision weights and use the norm of the weights as the scalings, step2: adjusting the binary bases using gradient desc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. We hope to have addressed their remaining concerns below.
**Q1.** _"On doubts that Table 3 fairly shows the strength&weakness of the method. First explain what W4A16g128 mean._
**A1.** We believe that the comparison is fair, as we align our set... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation | Accept (poster) | Summary: This paper introduces a novel challenge in domain adaptation: the problem of Multi-Source Blended Target Domain Adaptation (MBDA). In MBDA, target domain distributions are blended, necessitating a model that can leverage features from multiple source domains to perform effectively on the blended-target domain.... | Rebuttal 1:
Rebuttal: Thanks for your positive affirmation and constructive comments. Below are our responses to the weaknesses and questions.
>**W1:** The paper mentions various terms related to domain adaptation in Sections 1 and 2, such as UDA, SSDA, MSDA, MMDA, MTDA, and BTDA. What are the main differences and rel... | Summary: This paper works on Multi-source Blended-target Domain Adaptation setting, which learns a model from multiple source domain and evaluates the model in a mixed multi-target domain without access to the domain labels of target data. This paper utilize the style information of the blended-target domain with weigh... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback and questions. We provide our responses below
>**W1:** The style adaptation method is not compared with prior works proposed for the same purpose like MixStyle.
**A1:** We have compared our style adaptation (SA) with some prior works for the same purpose, su... | Summary: The paper proposes a "Style Adaptation and Uncertainty Estimation (SAUE)" method for Multi-source Blended-Target Domain Adaptation (MBDA). The core objective of the SAUE method is to enhance the source domain features by leveraging style information from a blended-target domain, which is a mixture of multiple ... | Rebuttal 1:
Rebuttal: Thanks a lot for your efforts in reviewing the paper. Below, we respond to your questions in details.
>**W1:** Comparison to OCDA.
**A1:** The key characteristic of blended-target domain adaptation (BTDA) is that the target domain is a mixture of multiple sub-domains sharing the same category s... | Summary: The paper addresses the setting of Blended target domain adaptation. The paper proposes style adaptation and uncertainty estimation for multi-source blended target domain adaptation. They propose to utilize the extra knowledge acquired from the blended target domain. Where a similarity factor is utilized to ob... | Rebuttal 1:
Rebuttal: We appreciate the comments and questions you post. Here are our point-to-point responses.
>**Q1:** Experiments on the DomainNet and VisDA datasets.
**A1:** Due to time limitations, we provide comparisons with important and recent multi-source blended-target domain adaptation (MBDA) methods MCD... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chair,
We sincerely thank all the reviewers for their positive comments and helpful feedback, which have significantly improved the quality of this paper. We have uploaded the responses to each reviewer along with the one-page PDF.
In response to the comments, we have car... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual-Perspective Activation: Efficient Channel Denoising via Joint Forward-Backward Criterion for Artificial Neural Networks | Accept (poster) | Summary: This paper introduces a novel activation mechanism, namely Dual-Perspective Activation (DPA), to identify irrelevant channels and thus apply channel denoising for the ANN architecture. The proposed DPA incorporates criteria established and updated online from both forward and backward propagation perspectives ... | Rebuttal 1:
Rebuttal: > **Q1:** It would be helpful if the authors could explore and compare the performance of more complex channel selection techniques to determine the most appropriate method. (Corresponding to **W1:** The authors have not provided a clear justification for why the intersection operation is the only... | Summary: Artificial neural networks apply the principles of the human brain and leverage sparse representations to their advantage. However, existing activation methods still struggle to suppress irrelevant signals, affecting the network's decision-making. To overcome this, a novel Dual-Perspective Activation (DPA) mec... | Rebuttal 1:
Rebuttal: > **Q1:** Please explain why the channels pointed out by red arrows in Fig. 3(a) are potentially irrelevant channels, in order to enhance readers' understanding. Including examples or case studies where similar channels were found to be irrelevant in other contexts might also reinforce the explana... | Summary: The authors argue that sparsity in the activations is a desirable property and should be enforced. They observe that there exist category-specific channels in the network's activations which have a high value only for specific categories while other channels remain low. These low activations are considered as ... | Rebuttal 1:
Rebuttal: > **Q1: (A)** How robust is DPA when other hyperparameters are chosen? The authors state that the $λ$ parameter of the DPA loss was varied depending on network and dataset. **(B)** Was this parameter selection conducted on the test set? **(C)** Were parameters of the other activation functions als... | Summary: This paper addresses the question of how to suppress inputs from irrelevant channels in a deep neural network. The authors develop a novel end-to-end trainable mechanism called Dual-Perspective Activation (DPA) to suppress irrelevant information and increase sparsity. The method is parameter-free, and increase... | Rebuttal 1:
Rebuttal: > **W1:** ... but the work seems lacking in clear demonstration of relevance/impact beyond these benchmark assessments (i.e., less clear how this might impact AI/ML theory, or impact cogsci/neurioscience applications of these models). That said, the noted sparsification of representations suggest ... | Rebuttal 1:
Rebuttal:
## **Global Response**
We thank all the reviewers for their time and constructive feedback, providing us with valuable insights into the areas that require improvement. We have meticulously addressed each reviewer's concerns through our comprehensive responses. In this global response, we... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How to Solve Contextual Goal-Oriented Problems with Offline Datasets? | Accept (poster) | Summary: This paper proposes a novel method to solve CGO problems and proves CODA can learn near-optimal policies without the need for negative labels with natural assumptions. In addition, sufficient experiments prove the effectiveness of the proposed method.
Strengths: 1. The contribution of the proposed method is i... | Rebuttal 1:
Rebuttal: Thanks for your review and the acknowledgement. | Summary: This paper focuses on a new RL task: Contextual Goal-Oriented Offline RL task. This task considers an offline goal-context pair dataset and an unsupervised transition dataset. To address this task, in this paper, Contextual goal-Oriented Data Augmentation (CODA) is proposed to augment a new state $s^+$ and new... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment below.
### 1. “Can goal conditioned methods be used to solve this problem”:
There is no straight-forward way to directly apply typical goal-conditioned methods to our ... | Summary: The paper combines an (unlabeled) dynamics dataset of trajectories, and a (labeled) context-goal dataset in an offline setting to create a combined MDP(Markov Decision Process). They do it by augmenting the dynamic dataset to have fallacious action to the terminal state with reward 1 on goal states given conte... | Rebuttal 1:
Rebuttal: Thank you for the review. We address each comment below.
### 1. Limited novelty:
Could you provide specific reasons why the novelty is limited? We respectfully disagree that our method is trivial since we carefully design the augmented MDP structure and the data augmentation method such that 1) ... | Summary: This paper proposes a simple action-augmented MDP formulation for contextual goal-oriented problems in an offline RL setting. They show that their action-augmented MDP has a regret that is equivalent to the original MDP and any policy can be converted interchangeably without changing the regret. Along with the... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail below.
### 1. “How well the theoretical assumptions and setting of the paper transfers to real world”:
For the assumptions, our assumption **5.1** and **5.2**... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Handle Complex Constraints for Vehicle Routing Problems | Accept (poster) | Summary: This paper introduces a novel framework called Proactive Infeasibility Prevention (PIP) to enhance the capability of neural methods in addressing complex Vehicle Routing Problems (VRPs) with interdependent constraints, such as the Traveling Salesman Problem with Time Window (TSPTW) and TSP with Draft Limit (TS... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive comments and acknowledging that our PIP is innovative, and extensively validated, and our paper is well-organized and clear. We understand the need for more discussion on related works, computational costs, and code release. We hope the response below and the... | Summary: This paper addresses the challenge of predicting feasible solutions for VRPs with complex constraints. It introduces two novel methods to enhance existing algorithms: i) integrating constraints directly into the optimization objective using the Lagrangian Multiplier Approach; ii) PIP framework, which proactive... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s kind effort in providing insightful and detailed feedback. We are delighted that the reviewer finds our work to be novel, well-structured, and effective in addressing feasibility issues. We have conducted additional experiments to address your comments and hope the fol... | Summary: The paper propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs, and further investigates the Lagrange multiplier method for soft objective in VRPs, presenting the PIP (& PIP-D) for the hard case where Lagrange multiplier met... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our work as being with good perspective and very clear description. We understand that the main concerns are the computational cost and applicability towards real-world scenarios. We hope our response below will address them.
---
**[Our PIP addresses shortcom... | Summary: This paper proposes a Proactive Infeasibility Prevention (PIP) framework to enhance the ability of neural methods to handle complex constraints in Vehicle Routing Problem (VRP).
The PIP framework integrates the Lagrangian multiplier to enhance constraint awareness and introduces preventative infeasibility mask... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive and valuable comments. We are delighted that the reviewer found our approach adaptable to complex constraints, effective and flexible. We hope that the following response, along with additional experiments, will address remaining concerns.
---
**[Genera... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts and insightful comments. We are pleased that reviewers found our PIP(-D) framework to be **novel** (#DEtT, #Seke, #Xb4p), **with** **good perspective** (#DEtT), **practical applicability** (#azSu), **and** **effectiveness in addressing the critical constraint h... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Proportional Fairness in Clustering: A Social Choice Perspective | Accept (poster) | Summary: This paper studies three different related notions of fair clustering that have been proposed in the literature for metric centroid clustering: (1) proportional fairness, (2) individual fairness, and (3) transferable core. Prior work has separately defined, motivated, and studied the existence and computation ... | Rebuttal 1:
Rebuttal: Thank you kindly for the minor comment, you are absolutely correct.
We agree that our work has a clear focus on creating relations between the fairness measures.
However, we believe that providing connections and (in-)compatibility between fairness notions is of great importance, and something tha... | Summary: The paper unifies several definitions of fairness in clustering and relates them through approximation ratios. The paper provides theoretical results that 'translate' a definition into another, providing tight bounds for proportional and individual fairness metrics. The paper concludes with a series of results... | Rebuttal 1:
Rebuttal: 1. Yes, we discuss this in section 3.4. Recall that the agent set N corresponds to the point set in k-means, and the centers are chosen from the candidate set C.
In section 3.4, we discuss results for the case that the candidate set is infinite. This also covers e.g. the case when the centers may ... | Summary: The authors study the setting of clustering problem where voters and candidates lie in the metric space and the goal is to elect k candidates representing groups of voters. The authors show interesting connections between this setting and the setting of proportional approval-based committee elections, i.e., th... | Rebuttal 1:
Rebuttal: Thank you for pointing this out. Of course, this deserves a discussion in the conclusion, and we want to add this in the next version.
Indeed, the Expanding Approvals algorithm satisfies the stronger mPJR+.
However, we were not able to show mPJR+ implies stronger bounds than mPJR.
As for EJR and E... | Summary: This paper provides a methodological contribution by bridging three different notions of fairness in clustering: Individual (where every agent is assigned a cluster center no farther from the $\frac{n}{k}$ neighbor), proportional fairness (no group of size $\geq$ $\frac{n}{k}$ should be able to propose a cente... | null | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for taking the time to carefully review our submission.
We address each of the reviewer's comments and questions below and look forward to further discussions in the coming days. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GUIDE: Real-Time Human-Shaped Agents | Accept (poster) | Summary: The paper introduce GUIDE, a RLHF framework for real-time RLHF with online and continous human feedback. GUIDE translates the human feedback to dense reward. Addtionally, GUIDE includes a parallel training model that learns a simulated human feedback. By involving 50 participants annotation, GUIDE solves three... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We would like to address all of your concerns and questions below with point responses:
----
>Practicality: “I find it hard to believe that such a simple continuous feedback model can be applied to real-world scenarios. For example, the paper s... | Summary: The paper proposes a new approach to reinforcement learning with human feedback in simple video games. The method relies on continuous human feedback that is provided by the human observer hovering their mouse over a window with a spectrum of positive and negative rewards. Unlike prior approaches, this method ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We would like to address all of your concerns and questions below with point responses:
----
>“There are three major unstated assumptions: The delay between an event appearing on the screen and the change in human feedback is constant (Question... | Summary: The paper proposes a new framework for human-in-the-loop reinforcement learning, where the human and provide real-time and continuous feedback, and an algorithm where the learning agents uses the human feedback to accelerate policy learning. The paper conducted a user study of 50 subjects to demonstrate the ef... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We are glad that the reviewer found our method to be novel, the scale of our human study to be “a great contribution,” and our human analysis to be insightful. We would like to address all of your concerns and questions below with point response... | Summary: This paper proposes a new framework, GUIDE, for learning from continuous human feedback in complex decision making domains with continuous action spaces. By framing human feedback as a state-action value function, the framework proposes to learn this function and to combine it additively with the (generally sp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. We would like to address all of your concerns and questions below with point responses:
----
>“The assumptions regarding what human feedback represents do not seem consistent between section 3 and 4 (see Questions). ”
We would like to clarify ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Decentralized Noncooperative Games with Coupled Decision-Dependent Distributions | Accept (poster) | Summary: This paper examines endogenous distribution shifts in learning systems, where deployed models influence their environments, altering data distributions. The authors first prove a set of sufficient conditions for the existence and uniqueness of performative stable equilibrium (PSE) and Nash equilibrium (NE). Th... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer for recognizing our contributions and for the constructive comments. Our point-to-point responses to concerns on Questions are given below.**
**Reply to Question 1:** Theorem 3.3 pertains to the existence and uniqueness of the performative stable equilibrium... | Summary: This paper studies decentralized non-cooperative games with endogenous distribution shifts. In the model, each player aims to minimize its expected risk over a distribution that changes with their own and other players’ decisions, subject to a coupled constraint. The paper establishes conditions for the existe... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer for recognizing our contributions and for the constructive comments. Our point-to-point responses to concerns on Weaknesses and Questions are given below.**
**Reply to Weakness 1:**
1. First, we would like to clarify that only two existing works, namely, Na... | Summary: In this paper, the authors introduce the framework of decentralized noncooperative games incorporating the performativity factor and investigate the existence and uniqueness of two equilibrium concepts: Nash equilibrium and performative stable equilibrium. The distance upper bound between NE and PSE is firstly... | Rebuttal 1:
Rebuttal: **Thank you for recognizing our contributions and for the constructive comments. Our point-to-point responses are given below.**
**Reply to Weakness 1:** Given the explicit expressions of the decision-dependent distributions $D_i(\theta)$ for all $i$, we can compute the exact gradient of the perf... | Summary: The paper studies the effect of endogenous distribution shifts stemming from the underlying interaction of the learning system, as formalized in the recent framework of performative prediction. In particular, the paper focuses on the performative effect in a non-cooperative game in which players endeavor to mi... | Rebuttal 1:
Rebuttal: **We sincerely appreciate the reviewer for recognizing our contributions and for the constructive comments. Our point-to-point responses to concerns on Weaknesses and Questions are given below.**
**Reply to Weakness 1:** Thank you for the useful comment. We have provided justifications for the co... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiMSUM: Diffusion Mamba - A Scalable and Unified Spatial-Frequency Method for Image Generation | Accept (poster) | Summary: This paper introduces a state-space architecture for diffusion models that enhances local feature detection in image generation by combining spatial and frequency information. Traditional state-space networks like Mamba struggle with visual data processing, but integrating wavelet transformation improves local... | Rebuttal 1:
Rebuttal: 1. Training longer looks like getting an overfitting curve, an analysis needs to be done. This behavior is contradict to Transformer-based diffusion DiT, MDT, MaskDiT, etc.
Thank you for pointing out, we confirm that our method does have overfitting. After converging to the best FID10K score of 5... | Summary: This paper introduces a novel architecture for diffusion models that leverages spatial and frequency information to emphasize local features in image generation. It integrates wavelet transformation into the state-space networks, such as Mamba, to enhance the awareness of local structures in visual inputs. The... | Rebuttal 1:
Rebuttal: 1. In Line 106, I'm unsure why sigma brings extra computation when the scan path is larger, given that the computation is always fixed.
2. In Line105, it's important to note that flow matching has been utilized in various domains to capture the reader's interest. E.g., boosting diffusion[1], dept... | Summary: This paper proposes a novel Mamba-based diffusion model, DiMSUM, which leverages both spatial and frequency information to enhance visual generation capabilities. Specifically, DiMSUM applies wavelet transformation to the input signals, decomposing them into wavelet subbands. By employing a query-swapped cross... | Rebuttal 1:
Rebuttal: 1. Since the authors argue the incorporation of frequency information can mitigate the problem caused by the scanning order, I urge the authors to conduct an experiment to verify whether the generation performance of DiMSUM model is sensitive to the scanning order.
To substantiate our claims rega... | Summary: This paper proposes to employ a Mamba-transformer hybrid framework with wavelet-spatial scanning for image generation. The authors claim that the scanning in the frequency domain can model the long-range relations of tokens in 2D spatial space. The experiments and ablation studies have shown the effectiveness ... | Rebuttal 1:
Rebuttal: 1. Our architecture might comprise many components such as Mamba, Transformer, and frequency processing. However, each proposed component is well-motivated and vital to the overall framework, as shown in Table 2a (in submission). The simple Mamba network, without frequency processing and transform... | Rebuttal 1:
Rebuttal: We address the reviewers' comments below, referring to them as: R1(RLU7), R2(eitf), R3(nhSz), R4(C5pc). We sincerely thank all reviewers for their valuable feedback. We appreciate the positive comments on clear writing and good flow (R1, R2, R3), well-defined motivation to investigate and integrat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models | Accept (poster) | Summary: The paper proposes a new attack called "privacy backdoors", which introduces backdoors into foundation models, making them more prone to leak fine-tuning data of a victim who is adapting the foundation model for their task. For this attack, the attacker collects a set of possible data points that might be used... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. Below, we address specific points you raised:
> strong assumption
We acknowledge that our threat model differs somewhat from the traditional MIA setting. However, this distinction underscores the signif... | Summary: The authors focus on a new vulnerability that concerns pre-trained models which relies on an adversary modifying the pre-trained model in a way that increases the models vulnerability to membership inference attacks (MIAs). The attack is thoroughly evaluated on both vision-language models and LLMs when fine-tu... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. Below, we address specific points you raised:
> Stealthiness of the attack:
We conducted further evaluations on the stealthiness of the attack. For the vision experiments, as shown below, we observed so... | Summary: This paper introduces a so-called “privacy backdoor” attack. The attacker poisons a pre-trained model to make it susceptible to membership inference attacks (MIA) on an apriori known set of target examples. This poisoning is carried out by continually training the pre-trained model on the target examples and a... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. Below, we address specific points you raised:
> Novelty
Thank you for pointing out the related work. We were not aware of these studies since we wrote this paper in 2023 and submitted it to a previous c... | Summary: This paper proposed a new privacy attack for foundational models like CLIP and large language models (LLMs). The attack's key idea is to ''poison'' the target data (e.g., maximize loss) point into the pretrained models so that the victim's finetuned models uploaded on the open-source platform can reveal what t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and the time you've dedicated to providing it. Below, we address specific points you raised:
> Why would the victim chooses poisoned model instead of the original model
We believe there are several circumstances where the victim might choose the poi... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their valuable feedback and insightful questions. We have addressed each of your queries individually in the rebuttal box under your respective reviews. Please take a look and let us know if you have any further questions.
Pdf: /pdf/288cc4c8c8444ba8304... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Price Homogeneous Data | Accept (poster) | Summary: The paper studies an online learning problem for pricing homogeneous data, where the seller needs to offer a price function/curve based on the data size and learn the arrival probabilities of different customer types to maximize cumulative revenue. The paper first analyzes the structure of the optimal price fu... | Rebuttal 1:
Rebuttal: ### ***Weakness 1. Computational complexity depending on the horizon $T$.***
As mentioned in line 58, achieving sublinear regret in online learning requires choosing $\epsilon$ that vanishes with longer time horizons, i.e., $\epsilon \to 0$ as $T \to \infty$. In Theorems 4.1 and 5.1, we choose $\e... | Summary: Motivated by the emergence of data marketplaces, this paper studies an online data pricing problem involving $ N $ homogeneous data points and $ m $ types of buyers in the market. Specifically, it assumes that each type of buyer has a specific value function $ v_i: [N] \rightarrow [0, 1] $. While the sellers k... | Rebuttal 1:
Rebuttal: ### ***Weakness 1. Main contribution of our paper lies in Section 3, as the techniques in Sections 4-5 seem to be standard.***
See the common response above, Novelty in Sections 4, 5.
### ***Weakness 2. Contribution in Section 4.***
See the common response above.
### ***Weakness 3. Why is it nec... | Summary: This paper considers a data pricing problem in which a seller has $N$ homogeneous data points they wish to sell access to. The seller sets a price curve $p(n)$ which specifies the price a buyer must pay for access to $n$ data points for each $n \in [N]$. Upon arrival, a buyer sees the price curve, and choose... | Rebuttal 1:
Rebuttal: ### ***Weakness 1, Question 2. Lower bounds and lack of tightness.***
See the common response above.
### ***Weakness 2, Question 1. The finite type assumption. Can we remove the assumption that the types are known up front?***
This is an interesting question. We did attempt to solve this problem,... | Summary: This paper addresses the problem of exploration in pricing strategy. It proposes an algorithm and proves that it has lower regret compared to previous algorithms.
Strengths: The setting is interesting, and the results in this paper seem to show improvement over previous algorithms.
Weaknesses: 1. One of the ... | Rebuttal 1:
Rebuttal: ### ***Weakness 1. Insights for discretization scheme.***
First note that in order to get a discretization that approximates any price curve within $\epsilon$, the size of such a discretization should be $\tilde{O}\left( 2^N\epsilon^{-N} \right)$, which is clearly very large.
Our first insight is ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback. First, we would like to address common concerns raised by reviewers.
### ***On technical novelties***
While all reviewers agree on the novelty of our discretization scheme, there is a perception that Sections 4 and 5 simply apply UCB/FTPL to this discret... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BackTime: Backdoor Attacks on Multivariate Time Series Forecasting | Accept (spotlight) | Summary: The paper titled "BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting" introduces a novel method called BACKTIME, aimed at exploring the robustness of multivariate time series (MTS) forecasting models against backdoor attacks. BACKTIME enables an attacker to subtly manipulate predictions by inje... | Rebuttal 1:
Rebuttal: > **Q1. The paper does not adequately address potential limitations or discuss the robustness of the method against countermeasures. A more detailed analysis of the limitations and how they might affect the practical applicability of BACKTIME would strengthen the paper.**
Thanks for the reviewer'... | Summary: This paper proposed a backdoor attach method for multivariate time series forecasting, where only a few works focus on this topic. The injected stealthy triggers are interesting and effective in destroying raw data.
Strengths: The originality of this paper is solid, since this is the first work to consider th... | Rebuttal 1:
Rebuttal: > **Q1. The Trigger Generation and Bi-level Optimization are not clearly presented. Please extend the description of trigger generation and effectiveness.**
Thank you for your attention to the trigger generation and bi-level optimization. These two components are indeed core parts of BackTime. Ho... | Summary: This paper primarily discusses how to conduct a backdoor attack on the multivariate time series forecasting task, proposing a two-stage training approach. The core idea is to identify timestamps with significant differences in MAE of the Clean model and Poisoned model on poisoned data points. Simultaneously, t... | Rebuttal 1:
Rebuttal: > **Q1. The baseline methods in the paper are heuristic, and the worse MAE of these methods compared to the proposed method in the paper does not effectively illustrate the issue.**
Thank you for your concern on the baselines in our papers. We agree with the reviewer that the baselines in this pa... | null | null | Rebuttal 1:
Rebuttal: 1. Figure 1 visualizes three different target patterns.
- Experiments on the response to the reviewer mt9q’s Q2 show that BackTime successfully completes the attack even with complex target patterns.
2. Figure 2 visualizes the loss curve of the two-stage training process in BackTime.
- W... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time | Accept (poster) | Summary: The paper proposes an accelerated sampling method from standard discrete diffusion models like multinomial diffusion and absorbing-state diffusion, using a non-Markovian forward process where the stochasticity is modelled by sampling a single transition time for each token, after which the process is fully det... | Rebuttal 1:
Rebuttal: Thank you for your support and insightful comment.
***
**Q1**. The method is, in practice, somewhat similar to prior work (Campbell et. al. and Hoogeboom et. al.), and in that sense it would be more beneficial for the community if the paper went deeper into analysing potential speed improvements... | Summary: This paper proposes a new formulation for discrete diffusion models whereby the corruption process is defined as a non-markovian process. At each point, a decision is made as to either stay in the current state or switch to a noise sample however crucially, this noise sample is constant throughout the process ... | Rebuttal 1:
Rebuttal: Thank you for your support and valuable feedback. We address your major question as follows.
---
**Q1**. Think there should be some discussion relating to quite a simple baseline that can be implemented for the absorbing state case. $\dots$, however, in the absorbing state case, it seems very s... | Summary: This paper presents the non-Markov process for the discrete diffusion to reduce the sampling time. The authors present the transition time to de-randomize the sampling process and study the non-Markov processes from finite to infinite step sampling. The conditional text generation and unconditional text genera... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We have addressed your questions and provided clarifications below.
***
**Q1**. The authors claim that Eq.(1) and Eq.(6) are different, but they are equal for the absorbing diffusion, which means the proposed non-Markov process is the same as the Markov ... | Summary: The paper introduces a discrete non-Markov diffusion model (DNDM) aimed at accelerating the sampling process in discrete diffusion models. The proposed method reduces the number of neural network function evaluations to speed up the sampling process while maintaining sample quality. The paper explores the tran... | Rebuttal 1:
Rebuttal: Thank you for your support. Below, we address the questions.
***
**Q1**. The method involves a complex process that might be challenging to easily follow and implement. More details or visualizations on how the transition time distribution is determined and whether it can be adapted for differe... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale | Accept (poster) | Summary: The paper proposes a method for synthetically creating a dataset of agent trajectories for navigating websites. They propose using annotated programs, specifically python code interleaved with comments, as a representation of task and motion planning trajectories, where tasks are described in natural language ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper! We are very happy to hear the encouraging words on our motivation, creative usage of data sources, and performance improvement. We believe the comments are resolvable during the rebuttal period. Please see our response below. We would be very happ... | Summary: This paper proposes a demonstration generation approach for learning UI control by mixing two different sources. The first one uses GPT4 to generate scenarios, grounded actions, and observations using WikiHow plans which is initially filtered by GPT-3.5. Another data source is ClueWeb, form which the authors e... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper! We are very happy to hear encouraging words on our domain agnostic data synthesis approach that fills in the current gap. We believe the comments are resolvable during the rebuttal period. Please see our response below. And we would be very happy ... | Summary: This paper investigates turning indirect knowledge from the internet (such tutorial) to the direct knowledge (in the form of textual demonstrations) for LLMs. The algorithm design and the experiments are based on the web browser domain, which is a general interface to various web applications. The proposed met... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper! We are very happy to hear encouraging words about our motivation and effectiveness of our method. We believe the comments are resolvable during the rebuttal period. Please see our response below. We would be very happy to address additional questi... | Summary: This paper studies the problem of dataset generation for the digital task in the context of LLM. The motivation is that the current data collection process often requires human annotation, which is very costly and not scalable. As a result, the paper proposes using large language models (LLMs) to generate a sy... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper! We are very happy to hear the encouraging words about the work being novel and addressing the lack of demonstrations in LLM finetuning for agentic tasks. We believe the comments are resolvable during the rebuttal period. Please see our response be... | Rebuttal 1:
Rebuttal: > ### Comparison with LLMs finetuned with direct demonstrations
Direct demonstrations for web-based tasks are scarce, resulting in a limited number of LLMs fine-tuned with such data. We made our best effort to survey or implement approaches that are fine-tuned with direct demonstrations. We inclu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization | Accept (poster) | Summary: The paper aims to address the inefficiency issue of code generated by LLMs in terms of execution time and memory consumption. The authors propose a framework called Self-Optimization based on OverheAd Profile (SOAP), which improves the efficiency of LLM-generated code by iteratively refining it based on execut... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below:
**W1 & Q1 & W5: Baseline selection can be largely improved.**
Thanks for your suggestion. We provide the evaluation results of SOAP and suggested baselines in **Author Rebuttal Table 1**.
- For... | Summary: This paper studies an important and timely issue: the inefficiency often found in code generated by current Large Language Models (LLMs), which can result in longer execution times and higher memory consumption. To mitigate this issue, the paper proposes a self-optimization framework SOAP, designed to improve ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below:
**W1 & W2 & Q3: The multi-iteration optimization method requires a large token overhead.**
To address the reviewer's concern about overhead and context window limitations, we provide detailed m... | Summary: The paper introduces a novel method for code super-optimization by iteratively refining code using LLMs with profiling information. The focus is on enhancing the efficiency of LLM-generated code, addressing execution time and memory requirements. The proposed methodology involves generating code with an LLM an... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments.
**W1 & Q1: Impact of self-optimization steps**
In our paper, the self-optimization steps are **constraint as 5** Section 4.2, consistent in our experiments. 5 is the default setting for many existing self-optimization methods [1, 2].... | Summary: This paper presents SOAP, a new code generation approach that improves the efficiency of code generated by a LLM. SOAP adopts a self-refinement method that iteratively prompts the LLM to re-generate the code based on the profiling results. Specifically, it uses the line-profiler package in Pyhton to get the ex... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and insightful comments. We hereby address your concerns below:
**W1 & Q1: Novelty and Similarities with Self-Edit and Critic**
To address the reviewer’s concern about the novelty of SOAP in comparison to Self-Edit and Critic, we provide detailed evaluation r... | Rebuttal 1:
Rebuttal: # Tables included in the rebuttal to address the comments made by Reviewer H3VZ, Reviewer VC11, and Reviewer pc3i
|OptimizationProfile|ET(s)|NET|MU(Mb)|NMU|TMU(Mb*s)|NTMU|
|---|---|---|---|---|---|---|
|**GPT-3.5-Turbo-0301**|||||||
|InitialVersion|0.36|2.50|91.25|2.45|157.50|19.75|
|Unsupervise... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation | Reject | Summary: The authors propsed Uni-MoT, a unified structure to align molecules with texts with a VQ tokenizer and the Q-Former in BLIP-2. By treating molecules as new word tokens in the codebook, Uni-MoT aligns the discrete token representation for molecules and texts, while also following the autoregressive manner of LL... | Rebuttal 1:
Rebuttal: > W1. Limited novelty.
We would like to clarify that the Q-Former is only a part of our VQ-driven tokenizer. UniMoT does **not** follow the BLIP-2 structure in its entirety. UniMoT adopts a tokenizer-based architecture, which uses **discrete tokens**, fundamentally different from adapter-based ar... | Summary: This work presents a new molecule LLM that uses a pretrained tokenizer to replace the projection layer. The tokenizer consists a Q-Former and a VQ module, which are trained with consistency loss. The model is evaluated on molecular understanding and generation tasks.
Strengths: - The authors provided a novel ... | Rebuttal 1:
Rebuttal: > W1. Text-to-molecule generation tasks.
As Reviewer d9hA pointed out, adapter-based architectures can also perform text-to-molecule generation tasks. We will revise the manuscript accordingly. However, our contention is that such methods typically do not perform as well as our approach, as demon... | Summary: The authors propose to use a vector-quantized tokenizer that incorporates a Q-Former to connect pre-trained molecule encoder, SMILES encoder, and SMILES decoder so that a language model can integrate molecule and text modalities. Based on the proposed tokenizer, the authors introduce a four-stage training stra... | Rebuttal 1:
Rebuttal: > W1. Limited novelty.
While we use components from existing works like Q-Former, the molecule encoder, and the SMILES encoder and decoder, we adopt a tokenizer-based architecture that uses **discrete tokens**. This is fundamentally different from adapter-based architectures that use **continuous... | Summary: introduce a molecule tokenizer based on Codebooks which gets integrated into UniMoT, a unified molecule-text LLM
Strengths: strong performance on a variety of benchmarks; outperforms/on pair with 3D-MoLM
Weaknesses: - Table 1: include CLAMP [1] as well as standard molecular fingerprints (incl. in said refere... | Rebuttal 1:
Rebuttal: > W1. Including CLAMP and fingerprints baselines.
Thank you for your feedback and for suggesting additional baselines such as CLAMP and standard molecular fingerprints on the molecular property prediction task. We will include these baselines in our revised manuscript to provide a more comprehens... | Rebuttal 1:
Rebuttal: We appreciate the reviewers’ thoughtful and constructive feedback on our manuscript. We have carefully considered each point raised and provide the following detailed clarifications:
**Novelty and technical contribution of UniMoT.**
While we use components from existing works like Q-Former, the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits | Accept (spotlight) | Summary: This paper extends the work of PIC with continuous LVs, proposes to tensorize and to use neural functional sharing to scale the model.
The results show that the proposed approach decreases the trainable parameters as well as the memory usage in training, and also reduces the running time. The approach also wor... | Rebuttal 1:
Rebuttal: We thank the reviewer for deeming our paper to be novel, well-written, and for expecting it to have a high impact in the community. Below we reply to their points.
> It is not clear to me what data set is used in the experiment of "Scaling PICs", and how many RVs are modelled.
In the experiment ... | Summary: This paper presents a pipeline to build probabilistic integral circuits (PICs) more generally as directed acyclic graphs, as opposed to the tree-shaped structure they were limited to before. This significantly increases the expressiveness of the PICs, improving their representation of distributions. The author... | Rebuttal 1:
Rebuttal: We thank the reviewer for deeming our paper to be technically sound, clearly argumented, well-structured and easy-to-follow. Below we reply to their points.
> it is currently not possible to sample from PICs, limiting their impact as a generative model.
It is true that since we use simple uncons... | Summary: This paper introduces a new approach for building probabilistic integral circuits (PICs), a recently introduced probabilistic model that extends probabilistic circuits (PC) with continuous functions of latent variables (in addition to discrete latent variables), with inference performed using a numerical quadr... | Rebuttal 1:
Rebuttal: We thank the reviewer for deeming our paper to be a well-engineered solution delivering strong results. We are also happy that the reviewer enjoyed the reading. Below we reply to their questions.
> The paper is arguably a little weak in terms of novelty
We respectfully disagree as it is non-triv... | Summary: This work extends the probabilistic integral circuits (PICs) from tree-shaped region graphs (RGs) based ones to DAG-shaped ones. While constructing PICs from DAG RGs can lead to more expressive models, it comes with the concern of scalability since the circuit sizes might be increased a lot. To address this co... | Rebuttal 1:
Rebuttal: We thank the reviewer for deeming our paper to be a solid contribution, and a novel and efficient solution to an important problem. Below we clarify the confusion about notation, and respond to their points.
> I'm confused by the definition of the integral unit from Line 58 to Line 62, especially... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful feedback, questions, and kind words. We are glad that the paper seems to be (i) **very well-received** (“The paper was a pleasure to read, with clear and consistent notation” - 6CM1, “transparent and easy to understand” - 6CM1, “The paper is technically... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Information-theoretic Limits of Online Classification with Noisy Labels | Accept (poster) | Summary: The paper addresses the problem of online classification where the true labels are corrupted by unknown stochastic noise and the features are adversarially generated. The main contributions of this paper include: 1. Establishing fundamental limits of minimax risk in online classification with noisy labels by n... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and helpful comments. We address the main concerns raised below:
**Real-World Application:** Our theoretical work is motivated by a number of important real-world applications. These applications involve, e.g., learning under measurement imprecision, ... | Summary: This paper studies online classification with stochastic label noise, where a hypothesis $h^*$ is drawn from some hypothesis class $H$ and the learner wants to learn $h^*$ by observing the noisy labels of the online data.
The standard mistake bound used in online learning counts the number of predictions diffe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and helpful comments. We now address the main concerns raised below:
**Infinite Classes:** In fact, as mentioned in Remark 1, our techniques also work for *infinite classes*. Indeed, for any class $\mathcal{H}$ of finite Littlestone dimension $\mathsf... | Summary: This paper studies online classification where the features are chosen adversarially, but the labels are corrupted by some unknown stochastic noise. The learner makes predictions using the true features and noisy labels, but is evaluated against the true labels (noiseless) in the minimax sense. The authors con... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and helpful comments. We now address the main concerns raised below:
**Adaptivity w.r.t. Noisy Labels:** Indeed, in our setup, the noisy label distributions are selected *adaptively*, while the features are selected obliviously w.r.t. the realization ... | Summary: The paper generalized an agnostic online learning setting from [1]: Nature sequentially produces arbitrary feature vectors, and a noiseless label chosen from an arbitrary hypothesis within a given class. The learner observes the feature vector (exactly), and a noisy label chosen from a distribution chosen from... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and helpful comments. We now address the main concerns raised below:
**Non-triviality of problem setup:** We would like to clarify that the non-triviality of our pairwise testing scheme lies in how to *use* the testing results in an *online* fashion, ... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their helpful comments. We would like to clarify the following two concerns shared by most of the reviewers:
**Infinite Classes:** As mentioned in Remark 1, our techniques also work for *infinite classes*. Indeed, for any class $\mathcal{H}$ of finite Littlestone... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffuLT: Diffusion for Long-tail Recognition Without External Knowledge | Accept (poster) | Summary: This article uses a Diffusion model to generate tail class samples, achieving balance among classes at the data level and thereby addressing the long-tail distribution problem. Additionally, the authors believe that AID samples are the most beneficial in enhancing classifier performance; therefore, they design... | Rebuttal 1:
Rebuttal: ### W1
> The innovation is limited. Essentially, the authors' method designs a reconstruction loss to constrain the generative model to learn a distribution more consistent with real distributions, and many existing studies have proposed similar ideas.
The focus of our approach is *not* on train... | Summary: This paper introduces a novel pipeline for long-tail recognition that differs from conventional strategies by focusing on dataset composition. The method focuses on the dataset and introduces a diffusion model, namely DDPM to generate data for tail classes. The analysis reveals that approximately-in-distributi... | Rebuttal 1:
Rebuttal: ### W1
> Some typos: line 137 ‘am input’
Thank you for your feedback; we'll fix it in the next version.
### W2
> The baseline setup in Table 1,2,3 requires further explanation.
We are sorry for the confusion. Actually, the baseline setup adheres to the code and protocols from [1], with a 200-ep... | Summary: This paper introduces a diffusion based long-tail recognition method termed DiffuLT. DiffuLT uses diffusion model to generate supplement samples in order to balance the long-tailed dataset. The authors first discover that approximately-in-distribution (AID) samples are crucial in enhancing the generative model... | Rebuttal 1:
Rebuttal: ### W1
>It would be better if you show how “centralized” figure 2 is, for example, add support data of the proportion of ID, AID and OOD data. This figure is not intuitive enough.
Thank you for your feedback. We agree that the centralization of data is not immediately apparent in Figure 2. It be... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models | Accept (poster) | Summary: The paper "On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models" introduces a framework that constructs infinite tree-structured probabilistic graphical models (PGMs) corresponding to deep neural networks (DNNs), demonstrating that DNNs perform precise approximations of PGM inference d... | Rebuttal 1:
Rebuttal: We thank the reviewer for this very interesting comment about the computation tree and loopy BP. While the computation tree is certainly a useful tool for precisely characterizing the approximation made by loopy belief propagation, we do not believe that the construction presented in this work i... | Summary: The paper proposes a novel connection between DNNs and PGMs. Specifically, the idea s to unroll the DNN computation graph in the form of a PGM. The main formal result that is shown is that DNN forward propagation corresponds to exact inference in the encoded PGM. More practically, since the PGM is infinite, it... | Rebuttal 1:
Rebuttal: Thank you for the great comment and question. The weakness is addressed in the general response. Regarding your question, we suspect that deep neural network miscalibration may arise from the non-standard infinite width structure of the proposed probabilistic graphical model (PGM). While inference... | Summary: This work bridges the gap between Deep Neural Networks (DNNs) and Probabilistic Graphical Models (PGMs). The authors do so by viewing DNNs as defining a joint distribution over the values of their nodes and showing that forward pass on a DNN is equivalent to exact probabilistic inference on a corresponding inf... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review, great comments on the paper presentation and questions about the experiments. The first weakness about the PGM-DNN construction is addressed in the general response, and we also agree that some rearrangements in Section 5 would be helpful to make the p... | Summary: This paper re-interprets deep neural networks (with sigmoid activations) as probabilistic models. The authors give a construction that converts a DNN to an infinite tree-structured PGM, which has the benefit of yielding probabilstic information about its internal nodes. The key step is to copy input nodes so... | Rebuttal 1:
Rebuttal: Thank you for your detailed and constructive review. We take the comments and questions in order, except that the first criticism of the theory is general and leaves the specifics for later, so we take those later, in the order they are given.
- *"Lack of standard baselines"* The baselines are SG... | Rebuttal 1:
Rebuttal: We thank all reviewers for the thoughtful and thought-provoking reviews. Here we list concerns raised by multiple reviewers and describe our plans to address them. *If a change recommended by reviewers is not explicitly addressed, this implies that we will follow the reviewer's recommendation.*
*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Conformalized Time Series with Semantic Features | Accept (poster) | Summary: The paper proposes producing conformal intervals using non-conformity scores defined in latent space, demonstrating improved performance against existing CP methods that work in output space.
Strengths: Adopting CP in feature space rather than latent space and demonstrating performance gains is novel.
Weakne... | Rebuttal 1:
Rebuttal: We appreciate your valuable and informative suggestions and would clarify the questions and concerns in the following.
Q1: Model-agnostic and f,g splitting criterion.
A1: CT-SSF is model agnostic because CP is naturally model agnostic and any NN-based time series prediction models can be used as... | Summary: The paper presents a novel approach called Conformalized Time Series with Semantic Features (CT-SSF) for improving uncertainty quantification in time-series forecasting.
The authors propose leveraging latent semantic features through deep representation learning to dynamically adjust weights for conformal pre... | Rebuttal 1:
Rebuttal: We appreciate your valuable and informative suggestions and would clarify the questions and concerns in the following.
Q1: Please compare and discuss the impact of switching from output space (single dimension) to the latent space (multiple dimension) on the conformal prediction, e.g., whether an... | Summary: This paper proposes Conformalized Time Series with Semantic Features (CT-SSF), a conformal prediction method that constructs prediction intervals for time series data using nonconformity scores computed in the latent feature space of neural networks. CT-SSF further assigns time-dependent weights to the scores ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback on our manuscript. We appreciate your comments and have taken them into consideration to improve our paper. Below, we do our best to address the reviewer's questions adequately such that we could receive a better score.
Q1: Comparisons with prior works and u... | Summary: The paper presents a conformal prediction approach for time series data in the latent space of the prediction model – Conformalized Time Series with Semantic Features (CT-SSF). Non-conformity scores constructed in the latent space are expected to capture deeper insights of the data and temporal dynamics and im... | Rebuttal 1:
Rebuttal: We extend our sincere appreciation for your valuable feedback and suggestions. Regarding your concerns, we would like to offer further clarification.
Q1: Can the authors elaborate on how $\tilde{w}$ is updated in Algorithm 1?
A1: The weights $\tilde{w}$ are updated based on the attention mechani... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their thorough reviews, helpful suggestions, and constructive comments. To summarize, we made the following main changes to the manuscript to follow the suggestions and comments of the reviewers:
1. To illustrate the generalizability and performance of CT-S... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PointMamba: A Simple State Space Model for Point Cloud Analysis | Accept (poster) | Summary: In summary, PointMamba, an innovative state space model tailored for point cloud analysis, successfully harnesses the global modeling prowess of Mamba, a representative SSM from the NLP domain. By adopting a linear complexity algorithm, PointMamba addresses the computational challenges posed by traditional Tra... | Rebuttal 1:
Rebuttal: - **To weakness 1&2: “… may not take full advantage of the unique characteristics of point cloud data…/…may not be applicable to point cloud…are largely similar to…Point-MAE.”**
**Reply:** Thanks. We respect the reviewer's opinion. However, we believe this paper considers the characteristics of p... | Summary: This paper propose a simple but effective Mamba-based method named PointMamba for point cloud analysis. This paper is the first paper that studies the Mamba-based method for point clouds. The experiments are comprehensive and the paper is in very good shape.
Strengths: 1. This paper demonstrates excellent wri... | Rebuttal 1:
Rebuttal: - **To weakness 1: “(No need for experiments)…encourage the inclusion of indoor segmentation and detection tasks…”**
**Reply:** Thanks for this very constructive suggestion, and we will explore a unified Mamba-based foundation model for various 3D vision tasks (including indoor segmentation, dete... | Summary: This paper introduces PointMamba, an interesting method for point cloud analysis that utilizes a linear complexity state space model (SSM) instead of traditional Transformer architectures. PointMamba employs space-filling curves for point tokenization and features a simple, non-hierarchical Mamba encoder. Addi... | Rebuttal 1:
Rebuttal: - **To Weakness 1: “Some experiments are missing…masking 90% point patches…the performance of max pooling?”**
**Reply:** Good question! We conduct the mentioned missing experiments:
(1) As shown in the table below, masking 90% point patches may harm performance, as a higher masking ratio makes r... | Summary: This work utilizes the Mamba architecture for point clouds. It employs Hilbert and Trans-Hilbert curves to order the point clouds, thus addressing the unidirectional modeling nature of Mamba. Additionally, it replaces transformer blocks with Mamba blocks. The proposed PointMamba demonstrates reasonable perform... | Rebuttal 1:
Rebuttal: - **To Weakness 1:**
**Reply:** Thanks. We would like to emphasize that **simply replacing the Transformer with the Mamba cannot achieve ideal performance** due to the unidirectional modeling (as shown in below table). Thus, we customize the key designs to handle the point cloud data: **1)** The ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We are grateful to the reviewers for their invaluable feedback and the time they dedicated to evaluating our work. We are excited to see that reviewers identified **the novelty of our technical contribution (R2), clear motivation (R2, R3), convincing experiments (R2), superior per... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluation | Accept (poster) | Summary: This paper proposes a new paradigm for evaluating LLM's alignment with human values, which is based on LLM-based agents. It proposes an autonomous agent called ALI-agent, to conduct in-depth and adaptive alignment assessment. The ALI-agent has a memory module, a tool-using module, and an action module to impro... | Rebuttal 1:
Rebuttal: >**Comment 1: Distance metric for memory retrieval --** "In line 125, I notice the evaluation memory uses squared L2-norm as distance, but it seems that cosine similarity is more widely used for retrieval. Are there some insights about it?"
Thanks for the valuable question. For our implementatio... | Summary: The paper proposes an automated pipeline for evaluating the alignment of LLMs with human values. The pipeline involves the generation of scenarios using RAG+ICL, and iteratively revisioning the scenario to create disagreement between LLM judgment and human judgment (i.e. misalignment). The authors did tests on... | Rebuttal 1:
Rebuttal: >**Q1: Comparison with automated red-teaming**
Thanks for your important question, we have conducted a survey as follows:
Automated red teaming is formulated as automatically designing test cases in order to elicit specific behaviors from target LLMs [1-2], mainly categorized as:
1) **Text optim... | Summary: The paper introduces ALI-Agent, an evaluation framework leveraging LLM-powered agents for thorough and adaptive alignment assessments of LLMs. The framework features two stages: Emulation, which automates the creation of realistic test scenarios, and Refinement, which iteratively enhances these scenarios to ex... | Rebuttal 1:
Rebuttal: >**Comment 1: Choice of GPT-4 as the core**
Thanks for bringing up this important issue. We agree that the safeguards of GPT-4 can present obstacles, as we have encountered instances where GPT-4 refused to generate harmful scenarios during experiments. To circumvent this problem, we have designed... | null | null | Rebuttal 1:
Rebuttal: We appreciate all the reviewers for their valuable comments and suggestions to help us improve our work. We are encouraged that the reviewers found that: the topic is important and practical; the method is novel and well demonstrated; the experiments are comprehensive and robust, thus contribution... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? | Accept (poster) | Summary: This work introduces a method for performing concept-based interventions on pretrained black-box neural networks, requiring only a small validation set with concept labels. The paper formalizes the notion of intervenability as a measure of the effectiveness of these interventions and experimentally assesses th... | Rebuttal 1:
Rebuttal: Thank you for the feedback! You will find our point-by-point responses below.
> I could not find the precise training/validation procedure used for the baseline "CBM val".
We have updated the public repository to include the configuration file `config_synthetic_cbm_val.yaml` and code to run the “... | Summary: The paper proposes a novel method for test-time concept-based intervention on black-box models. Starting from the interactive intervention procedure of concept-bottleneck models, the authors devise a novel technique for reproducing it on any pre-trained black-box model. It consists of training a concept probe ... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments! Below, we respond to your concerns point by point.
> Equation 4: presenting Eq. 4 and then only considering the special case of β=1 is not correct.
We believe the formalization of the overall optimization problem is a beneficial contribution to future lines o... | Summary: The paper proposes a method to make any pre-trained black box model intervenable, given a small validation dataset with concept.
This is done by the following procedure:
- Extract the output of a layer of a black-box $g_{ \psi} (z)$ and train a probing network $q_{\xi}(z)$ to extract the concepts from that la... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and questions! Below is our point-by-point response.
> There are two main advantages of using CBMs, interpretability and intervenability. While the proposed method allows for intervenability, this method does not add interpretability to black box model.
As ... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
We thank you for the detailed feedback; we will make sure to address the concerns and incorporate the corresponding changes in the revised manuscript upon acceptance. Below is a summary of the main responses and clarifications addressed in this rebuttal:
- We have provided a more... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffuPac: Contextual Mimicry in Adversarial Packets Generation via Diffusion Model | Accept (poster) | Summary: This paper proposes an adversarial packet generation method, named DiffuPac, to evade detection. DiffuPac integrates BERT and a diffusion model to make the generated packets indistinguishable. To better fit the task, a concatenation strategy and a classifier-free approach are proposed. The experimental results... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ constructive review and insightful suggestions regarding our paper.
Our responses are given in a point-by-point manner for each comment.
**W1.**
We conducted a comprehensive evaluation of different BERT model configurations to identify the most efficient opti... | Summary: This paper proposes a novel strategy named DiffuPac to generate adversarial packets to bypass NIDS detection. DiffuPac encompasses two critical components including the BERT, which captures the semantic meaning of packets and facilitates the embedding and contextual paring. The other one is DiffSeq which makes... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ constructive review and insightful suggestions regarding our paper.
Our responses are given in a point-by-point manner for each comment.
**W1.**
We conducted a detailed malicious functionality evaluation within a controlled, isolated network environment.
We ... | Summary: The paper presents DiffuPac, a novel solution that integrates Bidirectional Encoder Representations from Transformers (BERT) and diffusion models to generate adversarial packets that can evade detection by sophisticated Network Intrusion Detection Systems (NIDS).
DiffuPac leverages the extensive contextual und... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ constructive review and insightful suggestions regarding our paper.
Our responses are given in a point-by-point manner for each comment.
**Q1.**
We appreciate the reviewer's insightful observation regarding the challenges of handling encrypted traffic.
In Dif... | Summary: This work presents DiffPac, a method that leverages BERT and a diffusion model to generate adversarial packets aimed at evading network
intrusion detection systems. Compared to the approach by Han et al. (2021), DiffPac demonstrates superior effectiveness, with most machine learning-based classifiers trained ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ constructive review and insightful suggestions regarding our paper.
Our responses are given in a point-by-point manner for each comment.
**W1.**
We agree that a more detailed discussion on the limitations of existing works and how DiffuPac addresses specific ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful feedback and constructive insights.
Here, we summarized the experiments that we conducted for the ablation study and maliciousness functionality evaluation.
These experiments will be included in the revised version of the paper.
### Ablation Stud... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes DiffuPac, an adversarial packet generation model that aims to evade Network Intrusion Detection Systems (NIDS) without depending on detailed knowledge of NIDS components. DiffuPac combines a pre-trained BERT model with a diffusion model. Experimental results show that DiffuPac outperforms tr... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers’ constructive review and insightful suggestions regarding our paper.
Our responses are given in a point-by-point manner for each comment.
**W1.**
Simpler methods (e.g., [1], [2], [3]) often require surrogate classifiers or access to NIDS components.
This is b... | null | null | null | null | null | null |
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control | Accept (spotlight) | Summary: The research question is whether representation from image generation models are superior to other pre-training paradigms (i.e. contrastive CLIP) for embodied/navigation-type of tasks. The main intuition given is that these tasks require a lot of fine-grained (e.g. spatial) understanding. While CLIP has been u... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see your review mention the novelty of our work and the rigorousness and fairness of our evaluation. We address your concerns below.
---
**1) The mutliple layer results fo... | Summary: This paper studies how diffusion models can be adapted to provide rich representations for training policies. They study how to extract features from a pre-trained Stable Diffusion model in terms of 3 questions: which layers, which diffusion timestep and which text prompts help for downstream task performance.... | Rebuttal 1:
Rebuttal: We thank you for engaging with our work and commending our presentation, comprehensive ablations and the key result of being able to employ a single representation model for navigation and manipulation tasks. We address your questions and concerns below.
---
**1) How do the methods compare when ... | Summary: This paper introduces SCR, a method that extracts representations from a pre-trained text-to-image diffusion model for learning downstream robotics control policies.
Given a noised input image and prompt text, the visual representation is extracted from selected layers of the denoising U-Net. The extraction m... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see your review note our study’s potential for future impact, the thoroughness of our experiments and ablations which demonstrate the strong performance of the approach we s... | Summary: This paper introduces Stable Control Representations (SCR), a novel approach that aggregates intermediate outputs from diffusion models for robotic control tasks. The authors validate its effectiveness on various benchmarks, including manipulation, navigation, and grasp point prediction. The key design space h... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see your review note our study’s innovative exploration, thoroughness of our experiments which demonstrate the strong performance of the approach we study, and the clarity o... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to review our manuscript and for providing thoughtful and constructive feedback.
We were delighted that reviewers recognized that our work studies an “important” and “interesting” problem (**9EUL, QYDZ**) while being presented in a manner which is “easy... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper investigates whether latent representations from a pre-trained tex-to-image generation model are useful for object manipulation or navigation tasks in embodied agents. The paper considers a few design choices in how to extract latent features from Stable Diffusion, that could lead to more versatile a... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see your review mention the clarity of our presentation, the relevance of the problem we are studying, and the comprehensiveness of our evaluation. We address your concerns ... | null | null | null | null | null | null |
Deep Homomorphism Networks | Accept (poster) | Summary: The authors propose a deep graph learning architecture where each layer consists of two key components: (i) the computation of homomorphism count vectors relative to a set of graph patterns, and (ii) a subsequent non-linear transformation. The homomorphisms computed in a layer incorporate features from the pre... | Rebuttal 1:
Rebuttal: Thank you very much for reading our manuscript. In particular, we are pleased to have a review from a specialist of homomorphisms. Below, we address your questions and comments. Especially, we claim our Theorem 3.3 is correct (**[W1]**) because of our definition of generalised homomorphism. Since ... | Summary: The authors introduce Deep Homomorphism Networks (DHNs), which generalize Message Passing Neural Networks (MPNNs) and previous Homomorphism Networks. DHNs explicitly count the number of homomorphisms from pattern graphs to the input graph, calculate representations based on these counts, and update node featur... | Rebuttal 1:
Rebuttal: Thank you very much for the thoughtful review and comments.
# Questions
- **[Q1]** Since real-world dataset comments are common among all reviewers, please refer to our global rebuttal.
- **[Q2]** Thank you very much for giving us a pointer to (Paolino et al., 2024); we will cite and discuss th... | Summary: The authors propose a new neural network architecture for graph data, namely “Deep Homomorphism Network” (DHN). This is constructed as a stacking of “Graph Homomorphism layers”, which compute learnable, generalised homomorphism counts for a set of predefined input patterns.
DHNs extend the following previous ... | Rebuttal 1:
Rebuttal: Thank you very much for reading our manuscript carefully. We revise our manuscript to improve the clarify and readability by addressing your concern. Below, we reply to each comment/question.
# Weakness
**(W1) The presentation of the paper could be improved**
> graph rooted product around Lemma... | null | null | Rebuttal 1:
Rebuttal: First, we thank all three reviewers for their time and detailed comments on our work. Given the submission volume in our community, we are truly grateful that all three reviewers have clearly given our manuscript deep consideration.
In this global rebuttal, we would like to address a common conce... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TopoFR: A Closer Look at Topology Alignment on Face Recognition | Accept (poster) | Summary: This paper proposes a novel framework named TopoFR for face recognition (FR). The authors first discover that existing FR models struggle to effectively preserve the structure information hidden in FR dataset, and provide three specific observations:
(i) the topological structure in the input pixel space bec... | Rebuttal 1:
Rebuttal: We thank the Reviewer GA4X for the careful reading of the manuscript and the related comments, which are helpful to improve our paper.
Our detailed point-by-point responses are provided below.
**W1: It is not clear if this method can be applied to recent proposed loss AdaFace.**
**A1:** Accordi... | Summary: This paper uses the topological structure alignment in face recognition tasks, and it proposes a Perturbation-guided Topological Structure Alignment (PTSA) strategy to align the topology of input image space and latent space. In this paper, Persistent Homology(PH) is used to verify that the complexity of input... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 68Et for the thorough review of the paper and valuable comments that will aid in enhancing our paper.
**W1: Formula 7 may require a more specific explanation.**
**A1:** Based on your valuable suggestion, we provide an explanation of how to estimate the parameter set $\va... | Summary: The paper introduces a new method to improve the topological structures of facial features in the latent space. By exploring the topological structure alignment in face recognition, the authors propose a new structural alignment strategy PTSA to align the structures of origin input space and feature space. The... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer SWuk for the careful reading and the insightful comments, which are helpful in improving our paper. Our detailed point-by-point responses are provided below.
**W1: The results of the method on IJB-B and IJB-C are minor.**
**A1:** Due to the characters limit, we ... | null | null | Rebuttal 1:
Rebuttal: Dear **Reviewer SWuk**, here is our response to some of the concerns you raised.
**W1: The results of the method on IJB-B and IJB-C are minor.**
**A1:** (1) Notably, when using a shallow backbone such as ResNet-50, our method showcases remarkable performance gains on IJB-B, IJB-C and other benc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Blockwise Parallel Language Models with Draft Refinement | Accept (poster) | Summary: The paper analyzes the block drafts generated by multiple independent prediction heads of blockwise parallel language models and observes three key features: consecutive repetitions, confidence of different heads, and efficiency gap with oracle top-k block. To address these issues, the paper proposes two algor... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback in helping refine our work.
- **Note:** Before reading further, **we kindly ask you to check the `Author Rebuttal by Authors` and `the attached PDF`** for detailed explanations and additional results.
# W1. Limited Experimental Scope
We acknowledge the limitat... | Summary: This paper proposes new ways to improve blockwise parallel decoding (BPD), a method to reduce inference latency in large language models. It first analyzes the token distributions produced by multiple prediction heads and then leverages this analysis to develop algorithms to improve BPD inference speed by refi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback in helping refine our work.
- **Note:** Before reading further, **we kindly ask you to check the `Author Rebuttal by Authors` and `the attached PDF`** for detailed explanations and additional results.
# W1. Comparison with Other Latency Reduction Approaches
... | Summary: This paper provides an improved solution for block-drafting, which is a potential useful way to improve the inference efficiency of LLMs. The work begins with observations of the problems of current block drafting, reveals that the consecutive repetition and drafting confidence are related to the quality of th... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We understand your concern regarding the use of different models for the rescoring phase and its potential impact on final performance due to varying token generation distributions.
- **Note:** Before reading further, **we kindly ask you to check the `Author ... | null | null | Rebuttal 1:
Rebuttal: We extend our gratitude to all the reviewers for providing comprehensive and thoughtful feedback on our manuscript. We appreciate your valuable insights into the strengths and areas for improvement of our work.
# Core Contributions of Our Work
- **Novel Findings:** This work explicitly addresses... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Banded Square Root Matrix Factorization for Differentially Private Model Training | Accept (poster) | Summary: The paper proposes the Banded Square Root (BSR) matrix factorization to speed up banded matrix factorization. The authors demonstrate that the workload matrix for SGD with momentum and weight decay can be expressed as a lower triangular Toeplitz matrix. They utilize explicit recursive expressions to compute th... | Rebuttal 1:
Rebuttal: Thank you for the encouraging feedback. In the following we hope to address all your concerns.
**\> Using CVXPY with SCS to compute AOF is not efficient.**
There are, of course, many ways to solve AOF (4). We used SCS, because it is a dedicated SPD-solver which allows for a simple and reproducib... | Summary: This paper proposes the Banded Square Root Matrix Factorization, an efficient approximation of the optimal banded matrix factorization for differentially private machine learning applications. The authors give a closed form expression for the BSR C matrix for any SGD + Momentum + Weight Decay workload by expl... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback. Below we clarify the raised points:
**\> Implementation of AOF seems suboptimal.**
(note that Reviewer VxsQ had a similar question, and for easier reading we provide our answer in both our replies)
There are, of course, many ways to solve AOF (4). We used S... | Summary: This paper addresses optimal matrix factorization mechanism for differential privacy, focussing on stochastic gradient descent (SGD) optimization.
It introduces the banded squared root (BSR) factorization and provide formal lower and upper bounds to the approximation error.
The BSR achieves competitive formal ... | Rebuttal 1:
Rebuttal: Thank you for the encouraging feedback and detailed comments.
**\> Experimental Evaluation.**
We believe there may be a slight misunderstanding. Our main contribution lies in the algorithm and properties of BSR. Specifically, we demonstrate that in the context of MF-SGD, using BSR requires addi... | Summary: The paper considers the problem of adding correlated noise $C^{-1}z$ instead of independent noise $z$ across iterations in continual counting (equivalently, DP-SGD). Past work gave an algorithm to choose $C^{-1}$ that optimizes some objective on the noise under b-min-separated participation, but this algorithm... | Rebuttal 1:
Rebuttal: Thank you for the insightful review. Here, we address the individual concerns:
**\> Relation to arXiv:2404.16706.**
Indeed, this interesting preprint (which we cite as [Dvijotham et al., 2024]) is concurrent work. It studies only the case of MF-SGD without momentum or weight decay (“prefix sum”... | Rebuttal 1:
Rebuttal: Attached is our response PDF; please see the responses to the individual reviews for details.
Pdf: /pdf/e3d27fa7c33ecb040b2ad5c115ea48d2f1cf405d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass | Accept (poster) | Summary: This paper introduces Superposed Decoding, a novel method to generate multiple drafts (k drafts) in a single inference pass. The process involves two iterative steps: (1) running a large language model (LLM) inference on fused tokens and utilizing top-k sampling to produce k candidate tokens, and (2) combining... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review. We are glad that the reviewer found the approach innovative and experiments well designed. Following are the clarifications requested in the review:
**1. Code generation:** We agree with the reviewer and mention in the manuscript that code generation... | Summary: The paper "Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass" introduces a method to generate
k similar responses in parallel using a single forward pass in autoregressive models.
Strengths: The paper validates its method on two open-source LLMs, providing empirical eviden... | Rebuttal 1:
Rebuttal: In the following we provide clarifications that were requested:
**1. Limited scope and restricted scenarios:** We respectfully disagree with the reviewer about the limited scope and scenarios of Superposed Decoding. Short text generation and drafting are common real-world problems and a centerpi... | Summary: This paper proposed a novel decoding algorithm to generate k coherent drafts in one autoregressive inference pass. An additional n-grams model is used to keep the k-drafts coherent. Experimental results show that this method can generate three relatively coherent drafts while achieving a speed-up ratio of more... | Rebuttal 1:
Rebuttal: We are glad that the reviewer found the paper to be well written and are happy to hear that the reviewer appreciated the experiments. Below are the clarifications requested in the review:
**1. Comparison to tree attention masks:** While this paper is interesting and relevant, we do not believe it... | Summary: This paper presents a method to generate multiple sequences from an autoregressive model with a single forward pass. The Superposed Decoding method relies on the approximate linearity of the overall model to additively superpose embeddings for distinct sequences through the model in the same forward pass.
Str... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and glad that the reviewer found the idea novel. We appreciate pointing to related work that we forgot to add in the paper initially and will add it. Below, we provide the clarifications requested:
**1. Understanding the phenomenon:** We agree with th... | Rebuttal 1:
Rebuttal: First, we would like to thank all reviewers for their feedback. We would also like to express sincere appreciation to the AC, SAC, and PCs for the time they have put into the current review cycle. We want to reiterate that Superposed Decoding is a novel algorithm leveraging an interesting phenomen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Task-Agnostic Machine-Learning-Assisted Inference | Accept (poster) | Summary: This paper proposes a post-prediction inference solution (PSPS) that can be adapted into various established data analysis routine and delivers valid and efficient inference for most ML models. In particular, the paper uses both labelled and unlabelled data to derive estimators that are consistent and efficien... | Rebuttal 1:
Rebuttal: **W1:**
Reference [1] focuses on using unlabeled data to improve the estimation of regression function (i.e. E[Y|X]). This is a classic machine learning prediction problem which leverages semi-supervised learning to enhance prediction accuracy. In contrast, our study focuses on improving estimatio... | Summary: This paper proposes a new unified framework for ML-assisted inference that reduces the general problem to essentially one of estimating normal means, and then applies simple operations (and a bootstrap step) to solve the normal means problem. In addition to the unifying framework’s simplicity, a key result is ... | Rebuttal 1:
Rebuttal: **W1:**
Thank you for the comment. We have addressed the variance estimation in Theorem 1 by adding
"
With $\hat{\mathbf{V}}(\hat{\theta} _{\mathrm{PSPS}}) \xrightarrow{P} \mathbf{V}(\hat{\theta} _{\mathrm{PSPS}}), \lim _n \mathbb{P}(\theta _k^* \in \mathcal{C} _{\alpha, k}^{\mathrm{PSPS}})=1-\a... | Summary: The paper proposes a novel statistical framework for ML-assisted inference. It describes how labeled data, together with unlabeled data and a pre-trained ML model, can be used for statistical inference. The paper establishes the asymptotic properties and optimality of the framework and then evaluates it empiri... | Rebuttal 1:
Rebuttal: **Weaknesses, Questions, and Limitations:**
Thank you for the suggestion. One limitation is the computational burden of the naive bootstrap approach. In our original submission, we discussed the future direction of improving the speed of resampling. In the revised manuscript, we have conducted ad... | Summary: The paper introduces a task-agnostic approach to inference with machine learning predictions. The basic idea follows a similar recipe as prediction-powered inference and related recent papers, but it makes use of resampling instead of the CLT with a plug-in estimate of the asymptotic covariance to avoid relyin... | Rebuttal 1:
Rebuttal: **W1**: We first want to highlight that current methods and their implementations typically estimate asymptotic variance using the CLT rather than through resampling (for example, `Angelopoulos et al., 2023` PPI and PPI++). In addition, while resampling-based approaches can bypass the derivation o... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing valuable suggestions that helped us improve our paper. We are particularly encouraged that the reviewers have found that (i) the problem we study in this paper is important (R-DsrD), (ii) our method is simple and elegant (R- TkNf), (iii) our flexible statistica... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Goal-Conditioned On-Policy Reinforcement Learning | Accept (poster) | Summary: After rebuttal
The authors have mostly addressed my minor concerns. I recommend acceptance.
------
This paper aims to improve goal-conditioned RL. Two problems with prior methods (e.g. HER) are discussed, namely (a) their inability to cope with non-markovian rewards; and (b) the necessity of using an off-p... | Rebuttal 1:
Rebuttal: We address the concern about GCPO performance in more RL domains (W1) in Global Author Response, and the other concerns below.
> Q1: Could you run GCPO with only a 1000 demonstrations?
We evaluate the performance of GCPO with 1000 demonstrations on the Reach, PointMaze, and VVC tasks.
**Demonst... | Summary: This paper proposes Goal-Conditioned Policy Optimization (GCPO), an on-policy variant for goal-conditioned RL that can also handle non-markovian reward structures. Common goal-conditioned RL methods are usually related to Hindsight Experience Replay (HER) which however can only solve tasks under the Markovian ... | Rebuttal 1:
Rebuttal: > Response to Q3 and Q4
We consolidate your two questions into three sub-questions.
1. _The effectiveness of GCPO seems to primarily come from the pre-training, and the self-curriculum does not appear to significantly cover more difficult goals._
The objective of GCRL is to achieve more goals u... | Summary: This paper proposes a new on-policy goal conditioned reinforcement learning framework which targets non-markovian rewards, which HER based approaches are unsuccessful. GCPO is a combination of offline pre-training from expert policies using behavior cloning, and online learning from a curriculum which automati... | Rebuttal 1:
Rebuttal: > Q1 & W1: How were the baselines and your method's hyperparameters tuned?
We employ Grid Search to search the following hyperparameters of the evaluated algorithms:
|params|search range|
|:-|:-|
|SAC: Network Architecture|128\*2, 128\*3, 128\*4, 128\*5|
|SAC: ent_coef|$10^{-3},10^{-2},10^{-1}$|... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback and careful review of our paper. We address the two common concerns of many reviewers in the following:
> Q1: How does GCPO perform on other RL domain tasks?
**We conduct two sets of experiments to demonstrate the general applicability of our method... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-Object Hallucination in Vision Language Models | Accept (poster) | Summary: This paper proposes a benchmark of multi-object hallucinations in Large Vision Language Models (LVLMs). Specifically, this paper explores thoroughly on how LVLMs behaves when multiple objects are prompted in user instructions. They introduce Recognition-based Object Probing Evaluation (ROPE), an automated pipe... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful review of our paper. We are happy that you appreciate our novelty and found our analyses thorough. We addressed your questions and concerns below. If any residual concerns remain, we would be glad to discuss further. If no concerns remain, we would apprec... | Summary: Previous evaluations of large vision-language model (LVLM) hallucinations have primarily focused on single objects. This paper introduces a novel hallucination evaluation benchmark named ROPE, which simultaneously assesses multiple objects within a single scene during testing. The authors present several empir... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We are happy that you found our paper well-written, the problem novel and under-explored, and our experiments/analyses extensive and detailed. We addressed your questions and concerns below. If any residual concerns remain, we would be glad to discuss furthe... | Summary: This paper introduce a novel multi-object hallucination evaluation task, which assess model capabilities of classifying multiple objects given visual prompts or spatial tokens. The benchmark dataset contains both commonly seen images and unseen images regarding the instruction dataset. Evaluation results show... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s time and effort reviewing this paper. We thank the reviewer for the positive feedback on the motivation, novelty, and “meticulously designed” experiments. Please see our responses to your questions below.
### W1: Improvement in Writing
Specifically, we descr... | Summary: 1. The work proposes a **new benchmark evaluation method (through modifying existing datasets)** to measure if VLMs can accurately recognize multiple objects in an image simultaneously. This evaluation pipeline, designed by adding 5 bounding boxes to each image in the dataset and creating a fixed prompt, requi... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4TNS
We greatly appreciate your dedicating your valuable time to this detailed review! We address your concerns as follows.
### Weakness
Due to the limited space, we kindly redirect the reviewer to the **General Author Rebuttals**.
### Q3: Seen and Unseen
The Seen and Un... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and thoughtful feedback. We are glad that the reviewers appreciate our motivation, task setup, analysis, and presentation. We hereby respond to the general concerns and update our experimental results.
### General 1: Benchmark Positioning (Reviewer 4TNS)
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalized Fast Exact Conformalization | Accept (poster) | Summary: The authors study the *solution path* of constrained optimization problems when one training label varies in ${\mathbb R}$. In the Conformal Prediction framework, this is equivalent to studying the output of a Full-CP algorithm. The paper extends the approach to optimization problems with general constraints a... | Rebuttal 1:
Rebuttal: # Many thanks for appreciating our work!
Dear reviewer jxtf,
We deeply appreciate your commitment in reviewing our paper and your encouraging words of support for our work! In the following, we will provide a comprehensive response to your review comments.
---
> clarify what "opening the black b... | Summary: This paper proposes a new method to accelerate the computation of full conformal prediction sets, a task that traditionally requires a computationally intensive grid-search approach. The proposed method aims to streamline this process, potentially reducing the need to refit the predictive model for each test p... | Rebuttal 1:
Rebuttal: # Thankful for your constructive feedback!
Dear reviewer KUWB,
We are very grateful for the effort you have put into reviewing our work and for recognizing the importance of our research problem. We have addressed and acted upon your main criticisms, and we are looking forward to further interact... | Summary: The paper introduces a method to compute exact conformal prediction intervals that have statistical guarantees. The method improves from previous work in three ways:
* The conformal interval has exact guarantees instead of approximative
* The loss function family covered is larger than convex, same for the reg... | Rebuttal 1:
Rebuttal: # Many thanks for acknowledging our research!
Dear reviewer x8xk,
We want to express our heartfelt thanks for taking the time to review our paper and for your kind words of appreciation and support for our work! In the following, we will provide a comprehensive response to your review comments.
... | Summary: In the manuscript "Towards fast exact Confomralization of Generalised Parametric Estimation" the authors provide a very interesting generalisation of an approach, fundamental even if a bit disregarded in the mainstream literature on Conformal Prediction, that aims at computing the whole solution path of a regr... | Rebuttal 1:
Rebuttal: # Many thanks for acknowledging our work!
Dear reviewer 4Aeq,
We extend our sincere gratitude for dedicating your valuable time to reviewing our paper and for expressing your appreciation and support for our work! In the following, we will provide a comprehensive response to your questions.
---
... | Rebuttal 1:
Rebuttal: # Gratitude to All the Reviewers 😃
---
Dear Reviewers,
Thanks for the time and effort you have devoted to evaluating our submission #451. We also wish to express our appreciation for your recognition of the strengths of this work, including:
> (ji5B) The method introduced by the authors **work... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors introduce an algorithm to compute prediction intervals for conformal prediction in the context of empirical risk minimization (ERM) with constraints on the parameters. To do so, the authors derive a differential equation solved by the ERM estimator as a function of the last label to compute a solu... | Rebuttal 1:
Rebuttal: # Many thanks for appreciating our research!
Dear reviewer ji5B,
We are truly grateful for your thoughtful review of our paper and for the encouraging support you've shown towards our work! In the following, we will provide a comprehensive response to your comments.
---
> can be used to update t... | null | null | null | null | null | null |
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis | Accept (poster) | Summary: The paper at hand performs a series of ablation studies evaluating the impact of three design decisions on the quality of Text to Image Generation models. Specifically, the authors look at the target of knowledge distillation, training data as well as choice of teacher for distillation. In the end, the authors... | Rebuttal 1:
Rebuttal: Thank you for thoroughly reviewing our work and for your insightful and helpful suggestions for improving our paper. We provide our response in the following. We have made every effort to address all your concerns. Please don’t hesitate to let us know if your concerns/questions are still not clear... | Summary: This paper presents KOALA, a pair of efficient text-to-image synthesis models that reduce computational and memory requirements compared to the base model. This paper achieves this through three key innovations: knowledge distillation into a compact U-Net, the strategic use of high-resolution images and detail... | Rebuttal 1:
Rebuttal: Thank you for thoroughly reviewing our work and for your insightful and helpful suggestions for improving our paper. We provide our response in the following. We have made every effort to address all your concerns. Please don’t hesitate to let us know if your concerns/questions are still not clear... | Summary: This paper presents a set of empirical guidelines to use when distilling Stable Diffusion XL (SDXL) when computational/data resources are limited. The presented guidelines focus on 1) identifying which transformer blocks to drop, 2) what features are the best to distill, and 3) which publicly-available dataset... | Rebuttal 1:
Rebuttal: Thank you for thoroughly reviewing our work and for your insightful and helpful suggestions for improving our paper. We provide our response in the following. We have made every effort to address all your concerns. Please don’t hesitate to let us know if your concerns/questions are still not clear... | Summary: 1. The authors propose two different designs for an efficient denoising U-Net based on SDXL.
2. The authors present three empirical lessons for developing efficient U-Net designs.
Strengths: 1. The authors present empirical findings to justify their design considerations and provide further analyses to exami... | Rebuttal 1:
Rebuttal: Thank you for thoroughly reviewing our work and for your insightful and helpful suggestions for improving our paper. We provide our response in the following. We have made every effort to address all your concerns. Please don’t hesitate to let us know if your concerns/questions are still not clear... | Rebuttal 1:
Rebuttal: # General response
Thank you for thoroughly reviewing our work and for your insightful and helpful suggestions. We have made every effort to address all your concerns. Please let us know if any questions remain unresolved. We are open to discussion and will do our best to address your concerns dur... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model-Based Transfer Learning for Contextual Reinforcement Learning | Accept (poster) | Summary: The paper proposes Model-Based Transfer Learning (MBTL) in Contextual RL, which solves multiple related tasks and enhances generalization across different tasks. MBTL strategically selects a set of source tasks to maximize overall performance and minimize training costs.
The paper theoretically demonstrates th... | Rebuttal 1:
Rebuttal: **The authors truly appreciate the reviewer’s positive feedback on our work. We invite the reviewer to also take a look at our general comments, which include additional experiments on (1) concerns with the linear generalization gap assumption, (2) application of MBTL to tasks with high-dimensiona... | Summary: The paper proposes a new framework to estimate the expected generalization performance across different tasks where the differences have an explicit and model-based structure. To improve the expected generalization performance via training on selected tasks, the paper proposes naive and Bayesian optimization b... | Rebuttal 1:
Rebuttal: **We appreciate the reviewer's thoughtful comments and suggestions. We encourage the reviewer to review our general comments, which include additional experiments addressing (1) concerns with the linear generalization gap assumption, (2) the application of MBTL to tasks involving high-dimensional ... | Summary: The paper introduces a framework called Model-Based Transfer Learning (MBTL) for solving contextual reinforcement learning problems. By modelling the performance loss as a simple linear function of task context similarity, the authors leverages Bayesian optimization techniques and provides theoretical analysis... | Rebuttal 1:
Rebuttal: **We appreciate the reviewer's constructive and insightful feedback. We kindly request the reviewer to also refer to our general comments, which address (1) concerns with the linear generalization gap assumption, (2) the application of MBTL to tasks with high-dimensional visual inputs, and (3) mul... | null | null | Rebuttal 1:
Rebuttal: **The authors appreciate each of the reviewers for their detailed and constructive comments. Here, we first respond to all reviewers before answering each reviewer’s specific question.**
### [GR1] Concerns with the Linear generalization gap assumption
Thank you for your valuable comments. Similar ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Oracle-Efficient Reinforcement Learning for Max Value Ensembles | Accept (poster) | Summary: The paper considers the setting in which several good policies are available for some Markov Decision Process, and the agent has to learn how to combine them in a way that allows to achieve higher performance than following a single of the constituent policies. This problem is quite wide and a large number of ... | Rebuttal 1:
Rebuttal: Dear Reviewer Qu$2$b, thank you so much for highlighting the scaling dependence of our algorithm/its ability to work in large state spaces well and for relating it to our empirical results and the broader multi-task RL area.
**Weaknesses**
> Examples of oracles for the value functions should be ... | Summary: This paper presents an approach to enabling Reinforcement Learning (RL) by improving the process of generating an optimal policy. Their method assumes that there is access to a particular class of Markov Decision Process (MDP). The intuition is having a collection of bases determined as constituent policies. U... | Rebuttal 1:
Rebuttal: Dear Reviewer Z$6$cc, thank you so much for your feedback on our paper and for highlighting the practical nature of our theoretically-grounded algorithm.
**Weaknesses**
> We are unsure what "this class falls under a set of observations provided by the authors that are necessary for the theory to... | Summary: The paper presents an algorithm called MaxIteration for addressing the challenges of RL in large or infinite state spaces. The core idea is to compete with a max-following policy, which at each state selects the action of the constituent policy with the highest value. The MaxIteration algorithm is efficient, r... | Rebuttal 1:
Rebuttal: Dear Reviewer BR1J, thank you for your feedback on our work and highlighting the value of our theoretically motivated empirical algorithm.
**Weaknesses**
> The algorithm assumes access to an ERM oracle, which might not be practical in all scenarios.
Many machine learning problems are known to... | null | null | Rebuttal 1:
Rebuttal: First and foremost, we would like to thank all the reviewers for their time and feedback on our paper. We thank reviewers BR1J and Z6cc for highlighting the usefulness of our theoretically motivated but practically employable algorithm. We also thank reviewer Qu2b for highlighting the simplicity o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance | Accept (poster) | Summary: This paper introduces a new method for Rectified Flow models to perform classifier-guidance sampling without needing a noise-aware classifier. Specifically, the authors leverage a fixed-point method to overcome the need for a noise-aware classifier and anchor the classifier-guided flow trajectory to a referenc... | Rebuttal 1:
Rebuttal: Thank you for the very constructive comments. Below are our responses to the raised concern.
[L1] Generalization to broader diffusion models
* While our classifier guidance is derived based on rectified flow, the same idea can be generalized to some few-step diffusion models by assuming straight... | Summary: The paper introduces a training-free method based on rectified flow and classifier guidance for personalized image generation. The experimental results show that the proposed method performs better than other state-of-the-art baselines in generating personalized images for human faces, live subjects, and certa... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable suggestions, and we would like to address your main concerns as follows:
[W1/Q1] Theoretical justification for Equation (6)
* The intuition for Equation (6) is to shift the velocity toward data regions with higher class likelihood. Formally, we verify this usin... | Summary: The paper introduces a training-free method for subject-driven generation using diffusion models. This approach utilizes a new classifier guidance with off-the-shelf image discriminators and anchors the flow trajectory to a reference, ensuring stability and convergence. The method shows promising results in va... | Rebuttal 1:
Rebuttal: Thank you for providing valuable feedback. Here are our responses to the concerns raised.
[W1/Q1] Domains lacking pre-trained discriminators
* In the short term, we suggest first training a specialized discriminator and then applying our classifier guidance. There are two reasons for doing this ... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for the insightful comments, which are important for improving our work. In response, we have meticulously prepared a PDF file containing figures that effectively address some of the raised concerns. Below is a concise summary of these figures.
* Figure 1: Results for more ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models | Accept (poster) | Summary: The paper presents the data discovery framework. It uses a LLM to iteratively discover and refine interpretable models of pharmacological dynamics. The D3 framework consists of three agents, which work collaboratively to propose, acquire, and integrate new features, validate models, and uncover new insights i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and are glad the reviewer finds our paper interesting and appreciates our innovative application of LLMs to generate interpretable models of pharmacokinetic dynamics. We are also pleased that the reviewer acknowledges the clear writing and comp... | Summary: This paper presents the D3 Data Driven Discovery framework which uses GPT4-1106-Preview in a framework to iteratively consider modifying the features used in the ODE. The dynamical systems are evaluated on MSEs of held-out state-action trajectories.
Strengths: The paper presents an innovative general strateg... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and are glad the reviewer finds our paper's presentation of the innovative general strategy for searching the space of pharmacokinetic models through our D3 framework to be accessible and acknowledges the superior performance of our D3-Hybrid a... | Summary: The paper presents the Data-Driven Discovery (D3) framework, a novel approach that iteratively discovers and refines interpretable pharmacological dynamical models using LLMs. This framework is novel and innovative in its domain, it is designed to address limitations in traditional pharmacokinetic (PK) modelli... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and are glad the reviewer finds our work introducing the Data-Driven Discovery (D3) framework novel, innovative, and significant in its application to pharmacokinetic modeling.
> Cost function considerations: Adding a few sentences or a small ... | Summary: The paper proposes the Data-Driven Discovery (D3) framework, which leverages Large Language Models (LLMs) to iteratively discover and refine interpretable models of pharmacological dynamics. This approach enables the LLM to propose, acquire, and integrate new features, validate, and compare pharmacological dyn... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and are glad the reviewer finds the D3 framework’s ability to iteratively improve models and acquire new features enhances its performance and robustness. We also appreciate that the reviewer acknowledges the clear writing, well-structured meth... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their insightful feedback. The reviewers broadly agree that our approach is novel and effective in leveraging Large Language Models (LLMs) for pharmacological dynamical system discovery.
$\color{red} Re2Z$: “This paper investigates an interesting problem of LL... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper develops an LLM-assisted equation discovery framework especially for pharmacokinetic process. Three agents of Modeling Agent, Evaluator Agent and Feature Acquisition Agent are built to explore, refine and iterate vast model space, including three levels of initial conditions, observed features and p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and are glad the reviewer finds the results promising for using LLMs for pharmacokinetic modeling and the paper well-organized.
> symbolic regression methods like D-CODE [ICLR'22] are not compared.
Thank you for raising additional related wor... | null | null | null | null | null | null |
Score-based generative models are provably robust: an uncertainty quantification perspective | Accept (poster) | Summary: This work studies the influence of different error terms for diffusion models from a continuous perspective under $W_1$ distance. They explain the reason why the early stopping parameter $\epsilon$ would lead to a memory phenomenon of diffusion models. To achieve these results, this work proposes a WUP theorem... | Rebuttal 1:
Rebuttal: **Response to Weakness #1**
Thank you for pointing this out, we should have been more clear in our PDE terminology for the generative modeling community and will make sure to fix it. The *stochasticity* in the generative flow appears as the \emph{Laplacian operator} in our PDEs. Without the Lapla... | Summary: The paper studies the robustness of score-based generative models (SGM) to different sources of error that are relevant in practice, such as the limited expressivity of the score function representation or the choice of reference distribution. Specifically, they upper bound the Wasserstein-1 and L1 distances b... | Rebuttal 1:
Rebuttal: **Response to weakness #1**
Thank you for the insightful feedback. As stated in our rebuttal summary, our goal is to introduce a PDE framework for error analysis of SGMs that are comparable to previous bounds, while being agnostic to situations where the data distribution is supported on a lower-... | Summary: This paper studies the generalization error of diffusion models. The major tool is the Wasserstein uncertainty propagation theorem. With such a result and the regularity analysis in PDE, the authors establish robust analysis for diffusion models with respect to various errors.
Strengths: 1. The authors examin... | Rebuttal 1:
Rebuttal: **Response to weakness #1**
Yes you are correct that for the majority of our results, the bounds are more qualitative than quantitative. We refer the reviewer to our rebuttal summary, that our main goal is to form a bridge between analysis of generative flows and PDE theory. We aim to illustrate ... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful reading, time, and insightful comments on our work. They will be invaluable for improving the current manuscript and for future work.
We emphasize the organizing principle behind our paper. **The primary goal of our work is to establish connections be... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight | Accept (poster) | Summary: This paper introduces Chain-of-Sight (CoS), a novel vision-language bridge module designed to accelerate the pre-training of Multimodal Large Language Models (MLLMs). The key innovation is a sequence of visual resamplers that capture visual details at various spatial scales, allowing for a significant reductio... | Rebuttal 1:
Rebuttal: **Q1.** Ruling out the impact of the scale of training data.
Thank you for pointing this out. We agree that the scale of training data is one of the most crucial contributing factor in the downstream performance of MLLMs.
However, in our humble opinion, it is hard to single out the impact of the... | Summary: This paper proposed a post-pretrain token scaling strategy, Chain-of-Sight, to accelerate the pre-training of Multimodal Large Language Models (MLLMs).
Through the proposed method, the authors were able to achieve a significant improvement in pre-training speed.
The authors confirmed through an ablation study ... | Rebuttal 1:
Rebuttal: **Q1.** Experimental results based on other models.
Thank you for the constructive comment. We provide experimental results based on different language models in the following table, which shows Chain-of-Sight benefits from stronger language models.
| Language model | # PT/SFT tks. | Caps | V... | Summary: 1. This work proposes Chain-of-Sight, a training method of MLLM that leverages global and local visual contexts effectively.
2. To boost efficiency in the pretraining stage, the authors propose a post-pretrain token scaling strategy. During the pretraining state, it requires significantly fewer visual tokens a... | Rebuttal 1:
Rebuttal: **Q1.** The method of hierarchical (multi-scale) visual input has already been explored in many former works, such as LLaVA-NeXT and InternLM-XComposer2-4KHD.
The multi-scale nature is the fundamental characteristic of images manifested by David Marr's pioneering work on vision perception back in... | null | null | Rebuttal 1:
Rebuttal: We genuinely appreciate the reviewers for dedicating their time and effort to review our manuscript and providing valuable comments and insights. We are encouraged by the reviewers' assessment that
1. Our work addresses a crucial challenge in MLLM development: the computational cost of pre-traini... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Causal Imitation for Markov Decision Processes: a Partial Identification Approach | Accept (poster) | Summary: This paper studies causal imitation learning from the perspective of partial identification. First, the authors show a hardness result that when both the transitions and the rewards are confounded, it is not possible to imitate or improve over the expert policy. Going forward, under only reward-confounding or ... | Rebuttal 1:
Rebuttal: > _“extension of the standard imitation algorithm (e.g., GAIL) with the partial identification method”_
Firstly, applying partial identification to IL is nontrivial due to the complex interplay between unobserved confounders and the dynamics of MDPs. Partial identification typically deals with st... | Summary: The paper addresses challenges in imitation learning when the learner and expert have mismatched sensory capabilities and demonstrations are contaminated with unobserved confounding bias. The authors propose robust imitation learning within the framework of Markov Decision Processes (MDPs) using partial identi... | Rebuttal 1:
Rebuttal: We sincerely thank your reviewers and recognize that certain elements of our work might have been misunderstood, which could have influenced the evaluations. Below, we aim to clarify these aspects and remain eager to engage in further dialogue to resolve any lingering doubts. Generally, we have v... | Summary: The paper addresses the challenges in imitation learning when expert demonstrations are contaminated with unobserved confounding bias. It proposes robust imitation learning methods within the framework of MDPs using partial identification techniques. The authors demonstrate theoretically that when unobserved c... | Rebuttal 1:
Rebuttal: We appreciate your reviews and acknowledge that some aspects of our work might have been misunderstood. Below, we provide clarifications and are open to further discussions should there be any residual concerns.
Our experimental design incorporated similar baselines from Zhang et al. (2020), Kumo... | null | null | Rebuttal 1:
Rebuttal: # Overall Response
We appreciate the reviewer’s feedback. We believe that a few misunderstandings of our work led to some of the evaluations being overly harsh and would sincerely ask the reviewers to reconsider our paper given the clarifications provided in the response. We will first address so... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking LLM Memorization through the Lens of Adversarial Compression | Accept (poster) | Summary: This paper proposes a new definition of memorization, the Adversarial Compression Ratio (ACR), based on a compression argument. ACR provides an adversarial perspective on measuring memorization, offering the flexibility to assess memorization for arbitrary strings with relatively low computational requirement... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on our proposed metric for defining memorization in LLMs and its practical implications. We're glad you found our examination of unlearning methods using ACR insightful and our paper clear and well-organized. We acknowledge your concerns and attempt to address ... | Summary: The paper introduces a novel metric Adversarial Compression Ratio (ACR) to assess memorization in LLMs. The authors first contend that the conventional understanding of memorization may not be fully adequate for evaluating the memorization ability. Hence, ACR provides a quantitative measure by proposing that a... | Rebuttal 1:
Rebuttal: Thank you for your positive review! We are pleased that you found our approach innovative and recognized the significance of our contribution to defining and measuring memorization in LLMs. We acknowledge your concerns and attempt to address them below:
### Re: Favoring Longer Sequences
We under... | Summary: The authors propose adversarial compression ratio (ACR) as a novel metric for assessing memorization in LLMs. ACR compares the lengths of the smallest prefix string evoking a target completion from the model with the length of the completion. The shorter the prefix, the higher the compression ratio, and subseq... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review and valuable feedback.
We appreciate your recognition of the strengths in our paper, particularly the well-positioned and motivated overview, exhaustive experimental setup, and the efficacy of our proposed metric in detecting memorized content despite unlea... | Summary: ### Summary
- This paper proposes a new metric for measuring memorization in LLMs.
- Their proposed metric called Adversarial Compression Ratio (ACR) is capable of measuring memorization for arbitrary strings at a reasonably low compute.
- The definition of memorization, the authors propose in the paper is ba... | Rebuttal 1:
Rebuttal: Thank you for your thorough and thoughtful review. We are pleased that you found our definition of memorization consistent with common notions and that you appreciated the clarity and depth of our paper. We acknowledge your concerns and attempt to address them below:
### Re: False Positives
We u... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful feedback and valuable suggestions from all of the reviewers.
In response, we provided further clarity around key assumptions, conducted some experiments, and expanded our discussion in several places in the draft. Specifically, we examined paraphrased versions of th... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes Adversarial Compression Ratio (ACR), a metric for assessing memorisation in LLMs; ACR is defined as the ratio between the length of the generation we need to test memorisation for and the length of the shortest prompt that can elicit such a generation. A question I have is whether "length" ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We appreciate your recognition of our innovative approach to memorization testing. We acknowledge your concerns and attempt to address them below:
### Re: Comparisons with Other Methods
We understand the importance of comparing our Adversarial Compression ... | null | null | null | null | null | null |
SpaceByte: Towards Deleting Tokenization from Large Language Modeling | Accept (poster) | Summary: The authors propose a byte-level architecture called SpaceByte that involves local blocks (lower dimension, windowed attention) and global blocks (higher dimension, global attention) where the global blocks are between chunks of local blocks and only selectively applied. The global blocks are applied to "space... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful and thorough review.
## Weakness 1
We agree that there is not a compelling case to use SpaceByte over a subword Transformer. However, we hope that iterating upon the SpaceByte architecture could eventually yield a compelling byte-level model. And we believe... | Summary: The paper introduces SpaceByte, a byte-level Transformer model that incorporates larger transformer blocks at specific byte boundaries to enhance performance in language modeling tasks. While the approach bears similarities to previous work (e.g. MegaByte), it demonstrates improved performance over traditional... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review of our work and for identifying its key strengths.
## Weakness 3
As discussed in Section 3, our primary contributions beyond prior works is to show how to scale word-boundary byte-level modeling to more diverse text modalities while roughly matching the... | Summary: Byte-level modeling allows transformers to circumvent subword tokenization, thus avoiding the many weaknesses introduced by tokenization. However those models are not very performant compared to subword-based transformers. This paper proposes a novel architecture named *SpaceByte*. The idea is to add an extra ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our work. Although we agree that SpaceByte is more challenging to batch than a Transformer, we would like to emphasize that our work does establish a very significant milestone in byte-level LLMs because SpaceByte is the first byte-level attention-based archit... | Summary: This paper proposes SpaceByte, a byte-level decoder architecture for language modeling. As opposed to comparable models such as MegaByte, SpaceByte applies global transformer blocks after space-like characters, not after patches of a fixed size. The authors show that this approach leads to a substantially impr... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review and for accurately assessing strengths and weaknesses of our work.
We would have loved to include evaluations on downstream tasks, but we unfortunately ran out of manpower and time. | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using? | Accept (poster) | Summary: This paper aims to reduce the dimensionality of brain signals using linear layers to determine the minimal dimension required to preserve most of the reconstruction quality. The study involves experiments with three existing methods: two for brain-to-image reconstruction and one for brain-to-language reconstru... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and extensive feedback. We answer questions and clarify a few points below.
- _The paper suggests that better reconstruction may be achieved with less brain signal input, yet all brain signals are utilized by the reconstruction models._
- Yes, while all brai... | Summary: The authors introduce BrainBits, an information-bottleneck pipeline that measures reconstruction performances from brain signals (fMRI datasets) as a function of bottleneck size by (linearly) projecting the data into a lower dimensional space of controlled dimensionality. The rationale is to disentangle the co... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and good questions, which helped us clarify our interpretations. We're glad that the reviewer found our paper important to the field and easy to follow. Below, we answer the posted questions.
- _Can't the range be set according to data dynamic range?_
- The ins... | Summary: The paper proposes a method called BrainBits which aims at answering if the progress of the fMRI-to-Image/Text field of research comes from a better signal extraction from the brain or from other sources such as having better generative models or exploiting bad metrics. Their method introduces a bottleneck bet... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the importance of our work and for their time and feedback.
- _The main weakness is that the bottleneck introduced in the paper does not definitely answer how much of the brain signal has been exploited in the process. Indeed, the MLP projecting the fMRI data... | Summary: The paper presents a method called BrainBits that aims to assess the extent to which generative image reconstruction based on fMRI data is based on the neural data itself, versus some spurious contribution of the reconstruction model itself (e.g., a stronger prior over natural images, or overfitting to the dis... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback. We're glad you found our work timely, sound, and convincing. We appreciate your questions, which have helped us sharpen our descriptions.
- _What is the effective dimensionality of the actual neural activations? How can we know that the plateau in performance... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and helpful feedback! We address each reviewer's concerns individually.
Pdf: /pdf/6fa6d655b0929644c45af207fe2afbac37b3129a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models | Accept (poster) | Summary: This paper proposes a data selection approach to reduce the sample size required to conduct supervised fine-tuning (SFT) of LLMs for specific domains. The method achieves it by approximating the training gradients on the full data using only a subset of data.
The experiments were done for SFT tasks, including... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate your positive comments on the rationale and thorough analysis of our method. We address your concerns and provide clarifications below:
#### **1. Omitted Related Works:**
Our paper includes related works in the data selection domain, but we will expand t... | Summary: This paper introduces a data selection method called Small to Large (S2L), which uses the training trajectory of a small model to build clusters and select data based on these clusters. The method is shown to be effective in two domains, math and medicine, with superior performance than other data selection me... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and constructive feedback on our paper. We appreciate your positive comments on the uniqueness, intuitiveness, theoretical support, and extensive experiments of our study. We address your concerns and provide clarifications below:
#### **1. Convergence of SFT:**... | Summary: This study presents an innovative technique aimed at improving data efficiency in the supervised fine-tuning of large language models for niche domains. The method leverages the training trajectories of smaller models to inform the data selection for larger models, thereby maximizing the utility of the availab... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate your positive comments on the objective, experiments, structure, and theoretical analysis. We address your concerns and provide clarifications below:
#### **1. Overclaim:**
S2L is generally applicable to various domains without requiring specific adaptat... | Summary: This paper addresses the challenge of data selection in supervised fine-tuning (SFT) of pretrained language models by introducing a novel method called SmallToLarge (S2L). The S2L method involves collecting loss trajectories from a smaller model, clustering these trajectories, and resampling the SFT data to en... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate your positive comments on the motivation, clarity, and extensive experiments of our study. We address your concerns and provide clarifications below:
#### **1. Scalability Concerns:**
The S2L method requires only one additional round of 3-epoch training ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and for recognizing the strengths of our work, including its clear motivation, extensive experiments, and unique contributions to enhancing data efficiency for large language models (LLMs). We have conducted additional experiments and analyses to address the co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field | Accept (poster) | Summary: Paper presents a method for co-training an inference network that learns where points in a NeRF were “seen from” (ie. provenance) in the training data. This allows for quantification of uncertainty of the reconstructed geometry in terms of triangulation (location) and depth error. Mathematically frames these t... | Rebuttal 1:
Rebuttal: Thank you for finding our theoretical extension of IMLE to stochastic processes novel and mathematically rigorous and demonstrating improvements over existing methods. We address the questions raised below.
## Q1 Introduction still leaves me asking “Why is this an important problem?”
Modeling pr... | Summary: The paper describes a method for explicitly modeling the visibility of a point in space in a NeRF model. This is done though modeling provenance which is the space of points where the given point is likely visible. The motivation is that modeling visibility enables the underlying NeRF model to better utilize t... | Rebuttal 1:
Rebuttal: Thank you for finding our work interesting and insightful and our evaluations thorough and compelling. We address the questions raised below.
## Q1 Presentation of the method section.
Thank you for your valuable feedback. We will incorporate your suggestions and promise to improve the presentati... | Summary: This paper addresses gaps in existing Neural Radiance Fields research by modeling the provenance of each point as a stochastic process and enhancing triangulation quality through an extended Implicit Maximum Likelihood Estimation (IMLE) to functional space, resulting in improved novel view synthesis and uncert... | Rebuttal 1:
Rebuttal: Thank you for finding our method interesting, easy to follow, and enriches NeRFs with critical insights on triangulation quality.
We here clarify our experiment setup and avoid confusion. Our main experiments are split into two parts, each corresponding to a different application using ProvNeRF.... | Summary: This paper introduces a way to jointly learn/model provenance during NeRF training, where provenance is defined as locations where a 3D point is likely visible. This design is motivated by the classic idea of modeling triangulation quality, To implement this in NeRF, this paper extends implicit maximum likelih... | Rebuttal 1:
Rebuttal: Thank you for finding our method sound, theoretically complete, and has the potential to inspire follow-up works in the field. We answer the questions raised below.
## Q1 Alternatives in modeling the probability distribution.
Instead of as samples, we can either represent provenances as a discrete... | Rebuttal 1:
Rebuttal: We thank reviewers for their feedback and for finding our approach interesting (u5T3, GM1F), sound (GKL5), and novel (o98v). Our new formulation is classically motivated (GKL5), can be applied to any base NeRF models (GM1F), and leads to “a combination of traditional 3D vision-related fields to no... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Even Sparser Graph Transformers | Accept (poster) | Summary: This paper proposes a two-phase training process for Graph Transformers to address the memory and computational inefficiencies associated with large graph datasets. The first phase involves training a small-width network to estimate attention scores. These scores are then used to construct sparse interaction g... | Rebuttal 1:
Rebuttal: Thanks for your valuable review. Here are our responses. Please also refer to the rebuttal PDF for some extra tables and figures.
> Innovation
As mentioned in lines 97-102, we use Exphormer to interpolate between MPNNs and full-attention Transformers. Lower expander degrees make the model simila... | Summary: This paper proposes a method to reduce the memory complexity of Exphormer's transformer layers by learning a sparser attention pattern. Given some graph learning tasks, the authors first train a small-width, single-head proxy model on CPUs with large memory resources. After training, the attention scores of th... | Rebuttal 1:
Rebuttal: Thanks for your very thorough review; sorry for brevity (character limit). Please also see rebuttal pdf for updated results.
> quadratic memory complexity
FlashAttention computes either full-attention or sparse-block attention mechanisms, with memory-aware loading of relevant parts of data. The ... | Summary: This work studies the topic of sparsifying Graph Transformers which if quadratic is not scalable even on medium sized graphs. It builds on recent works like Exphormer, GraphGPS, SAN, among others and proposes a new two stage procedure with the naming Spexphormer. It is designed to reduce memory complexity for ... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback! Here you can find our response; we also encourage you to check our rebuttal pdf for some new experiments.
> the two stage process
Even for one-stage training, hyperparameter tuning is a significant part of the training. In our setup, the first stage is relative... | null | null | Rebuttal 1:
Rebuttal: # New experiments
All reviewers: please see the PDF linked below, which has some new experimental results.
# Further response to Reviewer X47Y
Due to character limits in responding to your very thorough review, these are continued here; thanks again for your work in reviewing our paper!
> To s... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning | Accept (poster) | Summary: The paper considers the problem of multi-objective RL and performs a rigorous theoretical analysis on the space of optimal policies as a function of the utility functions and preferences. Specifically, in teh case of utility optimal policies, it considers two types of optimality - at the state level and at all... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, suggestions, and feedback. We appreciate that the reviewer agrees with us in the need for addressing in more depth the theoretical aspects of multi-objective reinforcement learning.
We proceed to answer the questions:
**Question 1:
Regarding the types ... | Summary: This paper studies the expressiveness of utility functions in multi-objective reinforcement learning (MORL). In MORL, the reward function of the MDP is a vector of multiple (possibly) conflicting reward functions, and the goal of an agent is to maximize a given utility function (function mapping the vector val... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insights and suggestions.
**Weakness 2:
Yes, Example 3 illustrates a well-known fact in the MORL literature. In case of acceptance, we will state it. However, we wanted to include it before showing Example 4, which builds on Example 3 by providing an example of bo... | Summary: The authors studied preference relations and utility functions, which are the main components of utility-based MORL. Many prior works assumed two things: 1) for a given preference, there exists a utility that captures the preference, and 2) for a given utility, there exists an optimal policy. The authors provi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the provided suggestions and comments.
We hope that our answers will clarify the soundness of our paper. Reviewer's rating of 3 indicates “technical flaws, weak evaluation, or inadequate reproducibility”. We consider that none of them are the case in our paper. We will ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Image Reconstruction Via Autoencoding Sequential Deep Image Prior | Accept (poster) | Summary: The paper proposes Autoencoding Sequential Deep Image Prior (aSeqDIP) an extension to the family of untrained (no training data required) DIP methods.
As in previous work a combination of data consistency loss and autoencoding regularization (a loss between network input and output) is used. However, instead ... | Rebuttal 1:
Rebuttal: **C1 Overfitting robustness for the task of denoising**: Since our method is an unsupervised approach optimizing a single image, our observations suggest that repeatedly setting the network input to the output, along with autoencoding regularization, enables convergence to autoencoding-specific im... | Summary: This manuscript describes a variant of the seminar Deep Image Prior (DIP) work that incorporates aspects and mindset of the iterative prediction workflow in diffusion models.
In particular, the proposed procedure (method?) aSeqDIP is training a network to predict a single and fixed but distorted (e.g. noisy) i... | Rebuttal 1:
Rebuttal: **C1 WHY this approach achieves better results? Strength of the DM-based baselines. Inductive bias discussion**: We thank the reviewer for their constructive comment. The answers to these questions will definitely strengthen our paper. We divided our response to this comment into the following thr... | Summary: The authors in this paper propose an Autoencoding Sequential DIP (aSeqDIP) which aims to address the overfitting issue of DIP while without introducing extra parameters. The idea is very simple, the authors simply feed the output of the DIP into DIP model after each N updates. The authors validate the efficien... | Rebuttal 1:
Rebuttal: **C1 Novelty and Differences with the original DIP**: We appreciate the reviewer's comment. We would like to emphasize that our proposed method differs significantly from other DIP-based methods, including Vanilla DIP. These distinctions, which we believe set our work apart, are highlighted below.... | Summary: This paper investigates how to prevent deep image prior (DIP) from overfitting to the noise or compressed measurements, which is a classic problem of DIP. To address the problem, the authors proposed Autoencoding Sequential DIP (aSeqDIP). The general idea of aSeqDIP is to cuts the overall training process into... | Rebuttal 1:
Rebuttal: **C1+Q1: Comparison with TV-DIP**: We thank the reviewer for their comment. In what follows, we include a comparison with TV-DIP in terms of the average PSNR over 15 scans in MRI (with 4x and the fastMRI test dataset) and 20 images from the CBSD68 dataset for denoising and in-painting. As observed... | Rebuttal 1:
Rebuttal: # Global Response
We thank the reviewers for their constructive comments. Many reviewers raised questions about the limitations and capabilities of the proposed approach, and requested experiments with different tasks, settings, and baselines. Below, we discuss these limitations and summarize the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images | Accept (poster) | Summary: Summary:
The paper addresses the challenge of sparsely annotated object detection (SAOD) in aerial images, a critical task for real-world aerial intelligence systems where annotations are limited. Acknowledging the difficulty posed by the imbalanced probabilities and confidences in predicted aerial objects, th... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions. Here is our detailed response.
(1) Our proposed conformal pseudo-label explorer and multi-clue selection evaluator are meticulously encapsulated classes that can be directly invoked. In practical implementation, they exhibit excellent user-friendliness... | Summary: This paper propose a Progressive Exploration-Conformal Learning (PECL) framework to address the sparsely annotated object detection task, which can adaptively perform the selection of high-quality pseudo-labels. The pseudo-label exploration are formulated as a decision-making paradigm by adopting a conformal ... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions. Here is our detailed response.
(1) H2RBox[1] learns object center localization from horizontal box annotations in the weak supervision branch, utilizes scale and spatial constraints to learn object width, height, and rotation angle information in the s... | Summary: This paper proposes a new learning framework, PECL, for sparsely annotated object detection (SAOD) in aerial images. The framework introduces a conformal pseudo-label explorer and a multi-clue selection evaluator, which can leverage category-specific characteristics and inter-instance contextual relationships.... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions. Here is our detailed response.
(1) Our proposed PECL has improved performance by at least 5.63% compared to supervised baselines. Compared to other state-of-the-art methods, we have also achieved at least 1.35% growth. Given the limited improvement of ... | null | null | Rebuttal 1:
Rebuttal: Accurate annotation is a key factor in ensuring object detection performance. However, the manual annotation process is time-consuming and labor-intensive, especially in remote sensing scenarios with densely arranged objects.
In this paper, we propose the Sparsely Annotated Object Detection (SAOD... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rejection via Learning Density Ratios | Accept (poster) | Summary: The paper proposes a distributional perspective to build abstaining classifiers. By considering an idealized distribution, the authors show that this translates into optimizing a loss risk with a $\varphi$-divergence regularization term. Moreover, they provide results when considering $\alpha$-divergences, a s... | Rebuttal 1:
Rebuttal: We thank the reviewer for sharing their insights and providing useful suggestions on improving our evaluation. Please find the rebuttal below.
> Related Work
We thank the reviewer for their various references. We will make sure to include these in the next version of the paper. We would also be ... | Summary: Classification with rejection emerges as a learning paradigm that allows models to abstain from making predictions. Traditional rejection learning methods typically modify the loss function, enabling models to explicitly reject making predictions when they are inaccurate or uncertain. These methods rely on pro... | Rebuttal 1:
Rebuttal: We thank the Reviewer for praising the interesting nature of our proposed approach and for the insightful questions. We are glad that the Reviewer appreciates the different approach we have taken and hope that the connections to DRO / GVI / distributions can lead to new theories. Please find the p... | Summary: This paper proposes a novel method for classification with rejection based on density ratio estimation. Density ratio is estimated between the data distribution P and the "idealized" distribution Q, where Q is a distribution such that the model can have good prediction performance, while this distribution is s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed reading and many suggestions for improving the readability of the paper. We will change “classification-rejection” to “classifier-rejector” as per the reviewer’s suggestion and space-permitting, will include the broader impact section in the main-body (or a... | Summary: This paper propose a new classification algorithm with abstention by learning a density ratio between an "idealized" distribution to the data distribution: if the density ratio is small, the classifier rejects to predict.
The proposed learning framework is general, and is a function of the choice of $f$-diverg... | Rebuttal 1:
Rebuttal: We thank the reviewer for their praise of the mathematical presentation of the paper and their careful reading. We are happy to change the paper as per the reviewer's suggestions and will ensure that the typos and grammar improves. In what follows, we will answer **Reviewer m8bi** additional quest... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and various suggestions.
We are grateful to the reviewers for recognizing the novelty of our work, providing a “\[...\] more interesting approach to rejection compared to previous papers” (**Reviewer MrTu**). We appreciate the comments that ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models | Accept (poster) | Summary: This work proposes a domain-specific scaling law to optimize mixture ratio between the general domain training corpus and the specific domain corpus. The authors considered several parameterizations and experimented on six different downstream domains such as code, math, and Medical. The result show that D-CPT... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments. I appreciate the time and effort you have put into reviewing our manuscript. Below, I will address your concerns and provide further clarifications.
**Weakness**
We acknowledge that the current status of our works is limited to the Qwen series (as ... | Summary: This work explore the data mixture scaling law ( D-CPT Law) between the general corpus and the downstream domain-corpus for the continual pre-training of large language model.They also extend the fitted standard D-CPT Law on cross-domain settings and propose the Cross-Domain D-CPT Law to predict the D-CPT law... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and constructive suggestions. We will address your concerns shown below in detail.
**Weakness**
From the view of validation loss, continual pre-training (CPT) and pre-training (PT) are consistent, with the main difference being the initial validation loss (CPT: <5... | Summary: For continual pretraining of domain-specific large language models, an important question is how to choose the optimal mixture ratio between the general corpus and the downstream domain corpus. This paper proposes to fit the scaling law for domain-specific continual pre-training (D-CPT Law), using small-scale ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments and questions. I appreciate the time and effort you have put into reviewing our manuscript. Below, I address your concerns and provide further clarifications.
**Weakness**
First, the main motivation of our paper is to address the challenge of determ... | null | null | Rebuttal 1:
Rebuttal: # General Response
Thanks a lot for handling/reviewing our submitted manuscript. We would like to thank the reviewers for their thoughtful and constructive comments and suggestions. By addressing each of the issues raised by the reviewers, we believe that the quality and clarity of our D-CPT Law c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
REDUCR: Robust Data Downsampling using Class Priority Reweighting | Accept (poster) | Summary: This paper proposes a data downsampling method called REDUCR which is robust to class imbalance and distribution shits. In particular, REDUCR reduces the scale of the training data while emphasizing the datapoints with the worst generalization performance. Experiments on 6 benchmarks demonstrate the effective... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review, recognising the strong motivation behind our work, and that our paper is well-written. In response to the weaknesses and questions:
> W1/Q1: REDUCRs Performance under Distribution Shifts
In response to the reviewer’s first question, we direct the reviewer ... | Summary: - This paper introduces a method using an online algorithm with class priority reweighting to downsample data for vision and text tasks. The experiments demonstrate that this approach can achieve robustness and efficiency in situations with imbalanced classes and poor worst-class generation performance.
Stren... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their score and recognising the originality and significance of our approach along with the quality and clarity of our manuscript. In response to the weaknesses, question, and limitations:
> W: Ablation Studies
In Section 5.2 we investigate how the removal of the class ... | Summary: **Problem**: The paper addresses the challenge of efficient online batch selection while training for classification tasks. It identifies a key issue with current batch selection algorithms: they often perform poorly on underrepresented classes in the training data. The paper proposes solutions to this problem... | Rebuttal 1:
Rebuttal: We thank the reviewer for their score recognising the strengths of our paper, including the strong motivation and progress towards solving the problem setting we introduce in this work. In response to the weaknesses and questions:
> W3: Ablation Experiments
We direct the reviewer to Appendix A.7... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers and ACs for their time and work in evaluating our paper. In response to your feedback we have re-run our experiments on the Clothing1M dataset with a new model architecture to test the generalizability of REDUCR. The results can be seen in the attached PDF. REDUCR outperform... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.