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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM | Accept (poster) | Summary: This paper focuses on the video LLMs (Vid-LLMs) area and attempts to improve the fine-grained video understanding. The authors design modules and training data to identify relevant temporal segments based on the given query and then generate outputs based on the identified segments.
Strengths: Overall, the wr... | Rebuttal 1:
Rebuttal: **Q1**: I have concerns about its practicality. Firstly, this reasoning method nearly doubles the inference cost for the same question compared to other methods. Secondly, the accuracy of the final response heavily relies on the accuracy of event localization in the first stage. According to the b... | Summary: This paper focuses on fine-grained video understanding with large language models. Current works suffer from the dilemma between the per-frame token number and temporal sampling frequency to maintain an acceptable sequence length into the language model. The authors propose to sample a global view with low sam... | Rebuttal 1:
Rebuttal: **Q1**: How is the duration of the videos for temporal grounding evaluation? Will the low frequency sampling result in the to lose much information in excessively long videos?
**A**: The duration of temporal grounding tasks typically ranges from 1 to 10 seconds.
To explore this question, we carry... | Summary: This works proposes “SlowFocus” to improve the balance between *tokens per frame* and *frames per video* used in Video LLMs. SlowFocus identifies video segments relevant to a given query and samples selected segments at high frequencies. The high frequency tokens are mixed with low frequency global video token... | Rebuttal 1:
Rebuttal: **Q1**: Motivation not justified: 1) Evaluate the inference speed. 2) The benefit of this approach over uniform sampling at a higher resolution is unclear.
**A**: The additional cost of inference speed is actually minimal, as the low-frequency visual tokens only need to be encoded once.
Moreover,... | Summary: This paper designs a SlowFocus mechanism to allow Vid-LLM's input signals to combine both high frame rate and low frame rate inputs simultaneously. This addresses the issue of maintaining the effectiveness of input information within a limited context window in LLMs. Low frame rate inputs contain global inform... | Rebuttal 1:
Rebuttal: **Q1**: Line 126: Using the same letter "L" to represent both "Low" and "LLM" might appear confusing to readers
**A**: Thanks, we will make a clearer representation in the revision.
**Q2**: Table 5: There is some confusion regarding whether this pertains to the low frame rate parts or the high f... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for the valuable feedback, with consistent recognition for the motivation and innovation.
We are pleased that the reviewers recognized the strengths of our paper:
* This paper is well motivated (**et83**, **uGD4**, **6ATY**, **bGgU**).
* The designed architect... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks | Accept (poster) | Summary: The paper proposes a method to compress large training sets into smaller contexts through soft prompt tuning for prior-data fitted networks. The proposed techniques relax the constraints of PFNs by allowing for an increased number of features, more in-context training examples, and a greater variety of classes... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We appreciate that you found our paper well organized and easy to follow, and our problem well-motivated. We address each of your questions below:
**W1: On novelty**
Thank you for raising this point. We note that our novelty lies in applying prompt tuning to P... | Summary: The paper proposes TuneTables, a method for improving the performance of PFNs on large datasets with a variable number of features and classes. TuneTables uses prompt tuning (fine-tuning) to learn a small set of parameters (context / synthetic datapoints). Depending on the qualities of the dataset, a feature s... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We appreciate that you found our idea interesting, and that you appreciated the large number of experiments we provide. We address each of your questions below:
**W1: Presentation**
We thank the reviewer for a comprehensive set of suggestions on how to improve... | Summary: This paper aims at removing 3 issues with the recently introduced TabPFN model (a transformer pretrained on synthetic datasets, showing great in-context performance on actual small tabular datasets), namely that it can only take as input few samples, few features, and few classes. The authors manage to solve t... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We appreciate that you found our paper important and novel! We address each of your questions below:
**W1: Hyperparameter spaces**
We thank the reviewer for raising this point. We add TuneTables hyperparameters in the PDF attached to the global comment. For al... | Summary: The paper introduces a novel parameter-efficient fine-tuning strategy for prior-data fitted networks (PFNs) called TuneTables. PFNs, similar to large language models, utilize pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass. However, existing PFNs like Tab... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We appreciate that you found our paper to be presented well, and our methodology sound. We address each of your questions below:
**W1: Chosen benchmarks**
We thank the reviewer for raising this clarifying point about our benchmarks. Our main results in Tab. 1 ... | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their valuable feedback. Our work introduces TuneTables, allowing PFNs to scale by orders of magnitude and achieve strong performance on large datasets. We appreciate that the reviewers find our techniques important, interesting and novel (FY4B, 7Ntu, cmgc), with ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Using Surrogates in Covariate-adjusted Response-adaptive Randomization Experiments with Delayed Outcomes | Accept (poster) | Summary: Clinical trials try to achieve the highest statistical precision using the fewest number of enrolled participants. One way to do this is by assigning more participants to the {covariate, arm} combinations with the highest outcome variance. We don't know the outcome variance before running the trial, which moti... | Rebuttal 1:
Rebuttal: Thanks for the careful review and critical comments. Below we give a point-by-point review to your comments.
- *I'm confused by the claim that WHO stage is available immediately, which I took to mean that the experimenters receive the WHO stage for every patient every 6 months. Does that not requ... | Summary: This article studies how to use interminate surrogate outcomes to estimate causal effects when the primary outcome is delayed in clinical trials. The author first proposes a novel Covariate-adjusted Response-adaptive (CARA) design that supports efficient estimation of ATE using both surrogate and primary outc... | Rebuttal 1:
Rebuttal: Thanks for your careful review and critical comments. Below we add a point-by-point response to the weaknesses you listed.
- *The presentation is too messy and lacks explanations in multiple places. It is unclear why EIF and variance bound are presented at the beginning of section 3.*
Re: We ap... | Summary: This paper introduces an approach to optimizing treatment allocation for variance reduction, in the context of adaptive clinical trials. The particular setting is one where there are delays in observing outcomes. These delays that are independent of the outcomes themselves, but they impact efficiency of estim... | Rebuttal 1:
Rebuttal: Thanks for your careful review and critical comments. Below we add a point-by-point response.
- *(W1)*: We agree that our efficiency bound may share some similarities with [1], but also hope to emphasize that deriving a neat decomposition of the tangent space is not straightforward when data come... | Summary: This paper addresses the problem of covariate adaptive experimental design in the presence of time delayed outcomes. More specifically, we assume that participants are enrolled in waves, and the probability of treatment is given conditional on available user covariates. The target estimand is the average treat... | Rebuttal 1:
Rebuttal: Thanks so much for your careful review. We add a point-by-point response to your questions below.
- *It would be useful to have a finite sample analysis.*
Re: Thanks for the suggestion. We pursue asymptotic analysis to establish the distributional convergence and construct confidence intervals b... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the constructive feedback provided by our reviewers. We have made our best efforts to respond to the questions and comments raised by our reviewers.
To answer some of the questions raised by our reviewers, we have attached a pdf file containing the following two figur... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper considers the covariate-adjusted response-adaptive randomization design for settings with delayed outcomes and surrogate outcomes. The paper first characterizes the efficient influence function for estimating the primary outcome under the delayed setting with surrogate outcomes. They then characteriz... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review and critical questions. Below, we add a point-by-point response to your questions:
- _In the numerics, the "No Surrogate" approach seems to perform worse than complete randomization. Is there any insight on why this adaptive approach performs worse than... | null | null | null | null | null | null |
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion | Accept (poster) | Summary: This paper introduces DiffusionFake to address the challenge of generalization in face forgery detection by revisiting the generative process of deepfakes. DiffusionFake reverses the generative process by injecting features into a pre-trained Stable Diffusion model to reconstruct source and target images. The... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive evaluation of our paper, particularly your comments that our work is "logically sound," "novel," and "easy-to-follow." We are committed to further improving our manuscript based on your valuable suggestions. Below, we address each of your questions in detail:
... | Summary: This paper proposes DiffusionFake which can harnesses the power of Stable Diffusion to guide the forgery detector in learning disentangled source and target features inherent in Deepfakes. The features of the detection networks are processed through the target and source transformation modules, and then inject... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive acknowledgment of our method's rationality, as well as the meaningful questions you've raised. Below are our responses to your specific inquiries:
**Q1: Concern about weight module.**
Thanks for your question. We agree that Stable Diffusion's latent represen... | Summary: This paper adopts a novel plug-and-play framework that reverses the generative process of face forgeries to enhance the generalization of detection models. Extensive experimental results from several datasets demonstrate that this method has achieved very competitive performance.
Strengths: - The motivation o... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work and your valuable suggestions. Your feedback is precious to us, and we will carefully revise our paper based on your comments, especially addressing the references you mentioned and correcting any capitalization issues. Regarding your specific q... | Summary: The paper investigates the task of deepfake (especially, face swapping) identification. It proposes to utilize current image generation model to reconstruct source and target profiles from embedded features. The authors did thorough experiments and prove quantitatively that the proposed DiffusionFake method ou... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback on our paper, particularly your comments that our work is "well-motivated" and "simple and intuitive". We are committed to further improving our manuscript based on your constructive suggestions. Below, we address each of your questions in detail:
**... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive and constructive feedback, which will definitely help us improve the quality of this paper. We wish to address their concerns as follows. We have included a PDF with some visualization results mentioned in the rebuttal for your reference.
Pdf: /pdf/b2f3f8b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pruning neural network models for gene regulatory dynamics using data and domain knowledge | Accept (poster) | Summary: The authors proposed an interesting model called DASH. The model can recover the gene interact relationships in a sparsity way and make a high accuracy.
Strengths: The authors proposed an interesting method to model the cells.
The insights are straightforward for biology, and the regulatory network should be... | Rebuttal 1:
Rebuttal: **More biological background**: Thank you for the suggestion. With the additional space, we will add further introductory background including a simple figure of how proteins and genes are interacting, and how such an interaction is represented in the prior matrices, and further explanations on th... | Summary: The authors propose a network pruning approach which is guided by prior biological domain knowledge, which they call Domain-Aware Sparsity Heuristic (DASH). They aim to obtain highly sparse networks, which align with known biology and gene regulatory dynamics. To do so they propose computing pruning scores whi... | Rebuttal 1:
Rebuttal: **Mathematical formulation**: We apologize for the typos in dimensionality. Below we provide a brief note that includes all the corrected notation, definitions of key variables, and motivations behind the mathematical steps, especially pseudo-inverses.
*DASH for $L=1$.*
For a single layer NN, w... | Summary: Gene regulatory network inference is an important, but difficult problem.
The manuscript explores a novel approach to build domain knowledge into a general NODE model for this problem via pruning. The approach could work in other areas.
Strengths: Gene regulatory network inference is an important, but diffic... | Rebuttal 1:
Rebuttal: **Noise levels**: The noise was applied to both the expression data as well as the GRN prior, which we further describe in Appendix B1 and B3. We tested on three different noise levels (0\%, 5\%, 10\%). We included results from the 5\% setting in the main paper and the results for the remaining no... | Summary: The proposed DASH method underscores the importance of interpretability in network pruning for biological discoveries, emphasizing the need for alignment with domain knowledge. Using both synthetic and real data, DASH demonstrates superior performance beyond baselines and offers insights into biological system... | Rebuttal 1:
Rebuttal: **Large scale networks**: For our particular domain, this problem was addressed by the design of the PHOENIX architecture, which scales up to large and complex networks commensurate to the whole human genome (on the order of $10^4$ genes/dimensions). Here we demonstrate that DASH works effectively... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their service and the provided constructive feedback. We are confident that we addressed the remaining concerns. In particular, we
- clarified the validation setting of GRNs, which stemmed from ChIP-seq experiments that were *independent* of the prior informati... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents DASH (Domain-Aware Sparsity Heuristic), a new framework for pruning neural network models by incorporating domain-specific knowledge. The primary goal is to improve the interpretability and biological relevance of models used for gene regulatory network (GRN) inference. Traditional pruning m... | Rebuttal 1:
Rebuttal: **Test set size**: For synthetic data, we know the *ground truth generative model* and evaluate on that. For breast cancer, we use 6\% of data to evaluate the MSE, which we picked as data is really scarce and we need sufficient number of samples to train the model. We do, however, have data of an ... | null | null | null | null | null | null |
Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling | Accept (poster) | Summary: The authors are interested in sequential learning algorithms, within an IID data setup. That is, the ultimate objective is expected loss for a single algorithm output, not the regret incurred by a sequence of candidates. More specifically, they are interested in linear binary classification, and their overarch... | Rebuttal 1:
Rebuttal: > In addition, the use of formal notation in the paper is in my opinion quite sloppy in parts. The technical material feels rushed, and makes it difficult for readers/reviewers to effectively parse the procedure being proposed. I will provide some concrete examples in the next field.
Thank you fo... | Summary: Passive-aggressive algorithm is a seminal method in online learning. However, it may be unstable when outliers arise. This paper uses weighted reservoir sampling to stabilize the linear passive-aggressive online learning. The key idea is that the subsequent number of passive steps can reflect the generalizatio... | Rebuttal 1:
Rebuttal: >The theoretical analysis assumes i.i.d. condition, which is not consistent with the motivation that individual outliers exist and do harm to PA classifier.
Our method falls in a category of techniques that convert algorithms to a low-risk model which generalizes well to unseen data. In order to ... | Summary: This paper resolves the outlier sensitivity problem in online learning. The proposed approach (WAT) can stabilize passive-aggressive online learning algorithms and does not introduce common overheads like hold-out evaluation sets or additional passes.
Strengths: 1. The proposed WAT shows a significant reducti... | Rebuttal 1:
Rebuttal: > While FSOL-WRS shows statistically significant improvements in final test accuracy, PAC-WRS does not consistently show the same level of improvement.
This question has helped us realize we did not fully convey the utility of WRS. The manuscript demonstrates same-or-improved accuracy (ROP, intr... | Summary: The paper proposes a new algorithm for a binary online learning problem where the streaming data consists of i.i.d. samples. The authors propose a variant of the seminal algorithm of Passive-Aggressive classifier. The PAC algorithm only updates its current model when a missclassification occurs. The main idea ... | Rebuttal 1:
Rebuttal: > Also, the comparison with the previous baselines is not very well motivated.
The goal of our proposed method, WRS-Augmented Training (WAT), is to *stabilize the test accuracy over time* of a passive-aggressive base model, like PAC or FSOL, which originally can have massive fluctuations in test... | Rebuttal 1:
Rebuttal: For all reviewers, insufficient time exists for us to run all datasets under all settings. In our responses below, we have results for 14 out of 16 datasets (for $K=64$). The largest two (Criteo and Avazu (Site)) could not be completed in the allotted time. KDD2010 is further a partial result, as ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper seeks to address the issue of instability in passive-aggressive (PA) online learning algorithms, which are highly sensitive to individual data points, particularly outliers. These instabilities can lead to significant fluctuations in the model's accuracy, especially when an outlier is encountered lat... | Rebuttal 1:
Title: Thank you for additional review & response
Comment: Thank you for this additional review. We know it is a bit much to add your review and then see so much other content in reviews/rebuttals, but we are encouraged by the significant overlap in your review and the other positive reviews we have receive... | null | null | null | null | null | null |
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts | Accept (poster) | Summary: This paper proposes a new defensive method for generative T2I models, termed GuardT2I. GuardT2I utilizes a fine-tuned conditional LLM to map text embedding to explicit prompts and detect the presence of NSFW themes. GuardT2I keeps the target T2I model unchanged thus maintaining the generated image quality. Eva... | Rebuttal 1:
Rebuttal: ## **To Reviewer EP5J**
Thank you for your time and effort in reviewing our paper. We greatly appreciate your insightful comments and have addressed each point below.
---
[**Weakness 1**] .. lack of comparison with other defenses, such as SLD and concept removal methods[3].
---
[**Answer ... | Summary: - To defend t2i model from advresarial prompts, this paper presents a novel moderation framework, GuardT2I, that adopts a generative approach to enhance Text-to-Image models’ robustness against adversarial prompts.
- Specifically, GuardT2I uses a large language model to conditionally interpret text guidance e... | Rebuttal 1:
Rebuttal: ## **To Reviewer 1Dsg**
Thank you for your time and effort in reviewing our paper. We greatly appreciate your insightful comments, which we have addressed point by point below.
---
[**Weakness 1**] Lacks evaluation and comparison on the standard text-to-image generation task. I am curious about... | Summary: This paper presents GUARDT2I, a new moderation framework designed to defend against adversarial prompts for text-to-image generation models. Specifically, it uses a large language model to conditionally transform text guidance embeddings into natural language for effective adversarial prompt detection. The exp... | Rebuttal 1:
Rebuttal: ## **To Reviewer 1mLf**
---
[**Weakness 1**] ... __it is important to evaluate the impact of the proposed solutions on normal use cases (e.g., image quality)__ ..., the potential __metrics could be FID__, etc.
---
[**Answer 1**]
We appreciate the reviewer's insightful comment. To address thi... | Summary: The paper introduces a framework designed called GuardT2I to enhance the robustness of Text-to-Image models against adversarial prompts. It allows for the effective detection of adversarial intentions without compromising the performance of the Text-to-Image models.
Strengths: 1. This paper is well-written an... | Rebuttal 1:
Rebuttal: ## **To Reviewer 7kN6**:
Thank you for the time and effort you have invested in reviewing our paper. We greatly appreciate your insightful comments, which we have addressed point by point below.
---
[**Weakness 1**] The effectiveness of the proposed method heavily relies on the performance of ... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their constructive feedback and recognition of our work's strengths. We appreciate your acknowledgment of:
* [__Novel Approach__]: Noted by Reviewers 1mLf, 1Dsg, and EP5J.
* [__Convincing and Comprehensive Experimental Results__]: Praised by all reviewers.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Prompt Optimization Through the Lens of Best Arm Identification | Accept (poster) | Summary: This study focuses on the efficient prompt design under budget constraints, where effective prompts may have to be designed without excessive evaluation of a very large number of candidate prompts.
The paper aims to present a principled framework - called TRIPLE - for tackling this problem, which is achieved ... | Rebuttal 1:
Rebuttal: Thank you for reviewing this work! We are excited to hear your recognition on the potential of the proposed framework and the compelling results. For the raised concerns and questions, we would like to provide the following point-to-point responses.
---
>**Weakness 1.** As the main contribution o... | Summary: This paper studies prompt optimization using BAI-FB. There are several contributions:
1. Draw a connection between prompt optimization with the BAI-FB
2. Benchmark different acquisition function for prompt optimization
3. Extensive experiments are done to show the effectiveness of the proposed prompt optimizat... | Rebuttal 1:
Rebuttal: Thank you for reviewing this work and providing helpful comments. It is our pleasure to hear that you found the presentation is clear, the connection with BAI-FB is clean, and the experiments are extensive. To further address the raised questions and concerns, the following point-by-point response... | Summary: This work proposes an algorithm that adopts the fixed-budget best arm (BAI-FB) identification to search for the best prompt. The authors have considered two variants of BAI-FB including sequential halving (SH) and continuously reject (CR). They then utilize prompt embeddings to enhance the BAI-FB methods via c... | Rebuttal 1:
Rebuttal: Thank you for reviewing this work! The following point-by-point response is provided, where the reviewer's comments are compressed due to the length limit.
---
>**W1.** The presentation could be substantially improved...
**R1.** Thank you for this helpful suggestion! In the revised paper, we wil... | Summary: The authors study prompt optimization, with a focus on finding the best prompt from a pool of proposed prompts under highly limited budgets. They establish a connection to fixed-budget best arm identification in multi-armed bandits (MAB) and explore several algorithms from that problem, applied to prompt selec... | Rebuttal 1:
Rebuttal: Thank you for reviewing this paper and providing the helpful suggestions. We are glad to hear your recognition that this paper is well-presented and well-argued with compelling results. In the meantime, we would like to provide the following point-to-point responses, which hopefully can address yo... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for reviewing this work and giving the helpful comments! We have provided point-by-point responses, which hopefully can address the raised questions and concerns. It will be our pleasure to have further discussions and incorporate any suggestions you may have.
Together ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models | Accept (poster) | Summary: This paper proposes MOHAWK, a three-stage distillation method to transfer knowledge from pretrained transformer models to subquadratic models such as Mamba-2. The key idea is to view both transformers and SSMs as applying different forms of mixing matrices over token sequences. Experiments demonstrate that Phi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer finding our method well-designed and the insights from the ablations valuable. We respond to the reviewer’s questions and concerns, which mainly focus on additional evaluations and comparisons to other distillation methods, below.
> The paper does not provide a detailed... | Summary: The paper distills a Transformer model into the Mamba architecture by using about 1% of the pertaining dataset.
Strengths: 1. The paper successfully distills a Transformer model into the Mamba architecture by using about 1% of the pertaining dataset, reaching best performance on some of the tasks when compare... | Rebuttal 1:
Rebuttal: We are glad the reviewer found our experimental results impressive and liked our extensive comparison to other SSM and sub-quadratic models. The reviewer’s main concern is the presentation, clarity, and terminology. Based on our shared response, we have fixed the issues with the presentation, and ... | Summary: The authors consider the problem of distilling transformers into SSM models (Mamba in particular), which results in the reduction of quadratic complexity at inference to subquadratic complexity. For this purpose, the authors propose MOHAWK, a method consisting of several steps, each aiming to match a different... | Rebuttal 1:
Rebuttal: We appreciate the reviewer finding our analysis thorough and our method novel and important. The reviewer also raised an important point about the entanglement of the architecture, data, and MOHAWK method when looking at the distillation results. We would like to preface by reiterating that our ke... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their time spent reading our paper and providing thoughtful comments and feedback. All reviewers agree that our method enables cost-effective cross-architectural distillation, which is validated by our strong downstream performance, and that the careful ablations support... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ProxyFusion: Face Feature Aggregation Through Sparse Experts | Accept (poster) | Summary: This paper presents a novel face recognition framework that aims to address the challenges of feature fusion in long-range, low-resolution scenes. The authors propose a linear time complexity approach that is compatible with traditional biometric template databases and does not require additional metadata or i... | Rebuttal 1:
Rebuttal: 1. **Optimization:** Thank you for your feedback. For clarity on the optimization of the sparse experts, please refer to section 2.3 titled "Optimization" in our paper. There, we elaborate on the overall optimization objective. Here's a more detailed explanation of our optimization strategy.
Our l... | Summary: The paper proposes a novel feature fusion technique for unconstrained face recognition via sparse experts. The authors propose a expert network selection mechanism using the $k$ proxies, $p_j$, and all the $N$ precomputed face features, $f_i$. The top $\hat{k}$ selected experts are used by the 3d set centers t... | Rebuttal 1:
Rebuttal: We are grateful for your valuable feedback. We have addressed your questions below.
1.a) We define proxies as learnable embeddings / vectors of dimension length 512 (same dimensionality as the face-feature embeddings). These embeddings represent latent information about facial characteristics re... | Summary: The authors introduce a novel approach for face feature fusion, addressing typical scenarios such as low-quality probes and high-quality or different domain gallery sets of faces. They describe alternative approaches and conclude that these are currently limited because they are either not compatible with lega... | Rebuttal 1:
Rebuttal: **Response to Reviewer Comments**
We appreciate the thoughtful critique and the opportunity to enhance the clarity and reproducibility of our work. Below are our responses to the concerns raised:
1. **Code Availability and Implementation Details:**
Thank you for emphasizing the importance of ... | Summary: The authors propose a method for fusing face features extracted from sets of images.
Several proxies are defined that are trained to be specialized for certain aspects of faces, and the most relevant for a given set of face images are obtained.
Importance weights for different samples are estimated for each se... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and providing constructive feedback! Below we provide the answers to your questions. The amends made based on your suggestion and feedback will reflect in the final-version.
1.a) We have provided brief implementation details about the expert netw... | Rebuttal 1:
Rebuttal: We appreciate the detailed and insightful feedback provided by the reviewers. We are thankful that the reviewers recognized the clear motivation and well-written presentation of our method (vG52, waA1), the extensive and rigorous experiments conducted, including empirical evidence on challenging d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Language Generation in the Limit | Accept (spotlight) | Summary: This paper introduces a new theoretical framework for language generation
Strengths: Novel theoretical perspective
Weaknesses: hard to judge
Technical Quality: 4
Clarity: 2
Questions for Authors: hard to judge
Confidence: 1
Soundness: 4
Presentation: 2
Contribution: 2
Limitations: hard to judge
Flag... | Rebuttal 1:
Rebuttal: We would be happy to answer any questions you have about the submission. Given that the current review says, "hard to judge" as the full reply for the Weaknesses, Questions, and Limitations, we do not currently have enough information to proactively provide more, but we would be able to share more... | Summary: In the classical model of language learning in the limit proposed by Gold and Angluin, there is a countable sequence of candidate languages L_1,L_2, ... and a language L* that is equal to L_i for some unknown i. At each time step t, Player 1 draws a string w_t from L*, and Player 2 is required to guess a numbe... | Rebuttal 1:
Rebuttal: Thanks for your review; we would very much like to discuss these points with you, because the simpler argument you propose for the main result --- generation in the limit --- is not correct. Furthermore, our submitted paper contains a description of the approach you describe and an explanation for... | Summary: This paper revisits a classic topic with a new angle that reveals a more positive outlook on a classically pessimistic result.
Namely, the paper that while identification of a formal language from positive examples is generally not possible, even given countably infinite examples, one *can* always learn a (un... | Rebuttal 1:
Rebuttal: Thank you for the review of the paper; we appreciate the comments about the work and the interesting questions.
We agree that with the length constraints of the submission, Section 6 is written in a very compressed format. Indeed, we would plan to provide a more extended discussion of the result... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization | Accept (poster) | Summary: The paper investigates the benign overfitting phenomenon in Vision Transformers. By examining the training dynamics and generalization of a two-layer Transformer model, it establishes a condition to differentiate between benign and harmful overfitting based on the signal-to-noise ratio in the data model. Theor... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
---
**Q1**. The sparsity assumption is too strong, as it relies on the signal being contained within one patch and the noise being contained within another, which may not align with real-world data distrib... | Summary: This paper provides a sharp theoretical characterization of the transition between benign and harmful overfitting regimes for Vision Transformers trained on linearly separable data. The authors carefully analyze the optimization dynamics and provide generalization bounds that depend on the signal-to-noise rati... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
---
**Q1**. Only considers the linearly separable setting, which is already solvable by existing vision and language models, so the conclusions are not very surprising even if initialization and model deta... | Summary: The paper investigates **the benign overfitting phenomenon in Vision Transformers**. The authors adopt a theoretical framework similar to that proposed by Cao et al. (2022), which use **a data model consisting of label-dependent signal and label-independent noise**, but employ **a two-layer Transformer archite... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
---
**Weakness**. Readability issues
**A**. We realize that readability is important for readers to understand and further apply our techniques. In order to enhance readability, we have made the following... | Summary: This study investigates the theoretical aspects of Vision Transformers (ViT) with a focus on their generalization capabilities, particularly under conditions of benign overfitting. Through a detailed analysis of the optimization process involving a self-attention layer and a fully connected layer, optimized us... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
---
**Q**. The current experiments only validate the theoretical results on synthetic datasets. It is recommended that the authors consider adding some experiments on real datasets to test the effects, suc... | Rebuttal 1:
Rebuttal: We kindly thank all the reviewers for their time and for providing valuable feedback on our work.
To validate the practicality of our theoretical results in real-world datasets, we perform some experiments on MNIST dataset. The experimental result shows a transition between benign and harmful ove... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper studies the benign overfitting phenomenon for a two-layer Transformer in vision. The paper characterizes the optimization of the ViT through three different phases in training dynamics and finds a sharp separation condition of the signal-to-noise ratio to distinguish the benign and harmful overfittin... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback! We address your questions and concerns as follows.
---
**Q1**. Lack the introduction of the benign overfitting phenomenon in the first section.
**A1**. In the first section, we first introduce the Transformers and ViT models, and then the empirical and t... | null | null | null | null | null | null |
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting | Accept (spotlight) | Summary: This paper analyzes a linear multi-task regression framework. It derives formulas for asymptotic train and test risk. The formulas provide insights in how the raw data covariances, singal-generating hyperplanes, noise levels, and size of data sets affect the risk. Motivated by the analysis on the linear framew... | Rebuttal 1:
Rebuttal: We thank Reviewer EGU1 for their detailed feedback and for recognizing that our paper is clearly written while providing useful insights.
We address the reviewer's concerns point by point below.
> 1. About the generalization to nonlinear models and the connection between the theoretical analysis... | Summary: The authors analyse the problem of multi-task regression (for a linear model) under the assumption of concentrated random vectors. The results obtained provide a comprehensive description of the performance of the model (including critically its generalisation performance). The authors use this result for hy... | Rebuttal 1:
Rebuttal: We thank the rewiever Zuy6 for their positive feedback and for recognizing that our work is a nice achievement and technically accomplished. This type of feedback is very encouraging.
We answer below the reviewer's questions.
> 1. In the equation for immediately preceding section 4.4 there is ... | Summary: The authors characterise the train and test risk of multi-task regression using random matrix theory. Assessment via Figures 2 and 3 shows a good match between theory and empirical. This motivates a regularised objective for learning mutivariate time-series.
Strengths: The theory contribution in section 4 is ... | Rebuttal 1:
Rebuttal: We thank the reviewer 8z1C for their thoughtful feedback. We are happy to read that the reviewer found the theory contribution significant and original.
We address the reviewer's concerns point by point below.
> A. There are at least two existing works on the theoretical error bounds for multi-... | Summary: The authors derived theoretical insights into the train and test risks of the multi task regression loss using Random Matrix Theory.
Strengths: The closed form solution of the optimization parameter using RMT is novel. The authors set a trend in MTR to gain analytical expressions for optimization parameter an... | Rebuttal 1:
Rebuttal: We thank the reviewer 7acX for their valuable feedback and for recognizing our work's novelty.
We address the reviewer's concerns point by point below.
>1. The paper lacks clarity for instance on line 89, I am not sure what is meant by general optimization and other mathematical tools?
We are h... | Rebuttal 1:
Rebuttal: # **General Comment**
We thank all the reviewers for thoroughly and carefully reading our paper. We are deeply grateful for their recognition of the **novelty** (Reviewer 7acX), **originality and significance** (Reviewer 8z1C) of our contribution, and for acknowledging it as a **really nice achie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Consistency Diffusion Bridge Models | Accept (poster) | Summary: This paper works on efficient sampling of the denoising diffusion bridge models (DDBMs) or similar. In particular, the paper proposes to extend the consistency models (CMs) to DDBMs.
CMs are generative models developed in the context of improving the sampling cost of diffusion-based generative models (DBGMs).... | Rebuttal 1:
Rebuttal: Thank you for your supportive comments and valuable feedback. We would like to address the weaknesses as follows:
> W1: This paper's development deviates from the fundamental assumptions of the It'o integral-based SDEs.
**A**: We respectfully disagree with this statement and would like to addre... | Summary: In this paper, the authors combine Denoising Diffusion Bridge Models (DDBMs), that build a transport map between two arbitrary target distributions (that can be coupled) through a stochastic process, with recent advances on consistency techniques [1], that were originally designed for denoising diffusion model... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contribution and the valuable comments on our work. We would like to provide our responses as follows (in reverse order for better logical flow):
> Q1: The approximation of the score as presented in the current paper (that is evaluating the integrand in only one po... | Summary: Motivated by faster sampling time, the authors investigate consistency model techniques applied to denoising diffusion bridge models, a particular formulation of conditioned diffusion model whereby the forward process is a mixture of diffusion bridge SDEs, with a conditioned terminal point.
In particular, the... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and feedback on our work. We would like to provide our responses as follows:
> W1: The significance of DDBM is exaggerated as a new family of generative models
**A**: Thank you for pointing this out and we agree that DDBM itself can not be called as a family ... | Summary: This paper proposes to combine the consistency model (CM) with diffusion denoising bridge models (DDBMs) to build a consistency diffusion bridge model (CDBMs), which includes two paradigms of consistency bridge distillation and consistency bridge training. The experiments are conducted for image inpainting and... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and feedback on our work. We provide our responses as follows:
> W1: The proposed method may not be very surprising. This work looks more like a direct combination of CM and DDBM, which have been introduced in previous works.
**A**: While the high-level idea ... | Rebuttal 1:
Rebuttal: Dear reviewers:
We add some additional results as a part of our response in the one-page pdf, including:
* The demonstration of sample diversity of the deterministic ODE sampler for DDBM (Fig. 9)
* The qualitative comparison between I$^2$SB (their default setup with officially released checkpoint... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AutoSurvey: Large Language Models Can Automatically Write Surveys | Accept (poster) | Summary: This paper introduces a framework AutoSurvey that uses LLMs to automatically write scientific literature surveys. The process contains mainly four steps with retrieval, content generation and evaluation. The authors compare the framework with human writing and naive rag based LLM generation, in terms of speed,... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1:** As far as I understand, the authors only experiment with the Claude-3-Haiku model in the framework for paper writing. Although the authors employ different models in the evaluation process, the main findings could be biased based on only one model. \
**A1:**
We appreciate... | Summary: This paper presents a methodology for generating automatically surveys called AutoSurvey. AutoSurvey leverages the power of Large Language Models (LLM) and a Retrieval Augmented Generation (RAG) approach using as external resource a database of publications. Based on those publications, an outline is generate... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1:** Database used for the retrieval is not provided.\
**A1:**
We appreciate the reviewer's observation. Due to the large size of the database, which amounts to 17GB even after text extraction from pdf, we are unable to provide it as supplementary material on OpenReview. Howev... | Summary: In this paper, the authors propose an automated system based on LLMs to draft literature surveys on a given topic. The core idea behind the approach involves decomposing the task of writing a survey into multiple smaller subtasks in 4 stages. The first stage focuses on retrieving relevant papers from a databas... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: "The paper lacks a significant novelty component. The concepts of decomposing tasks into smaller subtasks for LLMs, using Retrieval-Augmented Generation (RAG), iteratively refining generated content with LLMs, and employing multiple LLMs as evaluators are well-established in ... | Summary: This paper introduces a fast automating way to write literature surveys based on LLM.It aims to solve the challenges of large volume, complexity, context window limitations, parametric knowledge constraints, and lack of evaluation benchmark.The AutoSurvey pipeline contains initial retrieval & outline generatio... | Rebuttal 1:
Rebuttal: ### Weaknesses
**W1: "About evaluation metric, xxx."**
We appreciate the reviewer’s feedback on the need for a clearer explanation of the evaluation metrics. The citation quality and content quality scores are both obtained using LLMs. The prompts involved in the evaluation can be found in the c... | Rebuttal 1:
Rebuttal: Dear All Reviewers,
We sincerely appreciate all of your thoughtful comments and valuable suggestions on our manuscript. Your expertise and detailed review have been crucial in improving the quality and clarity of our work. We are grateful for your recognition of the strengths in our research, inc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking the Capacity of Graph Neural Networks for Branching Strategy | Accept (poster) | Summary: This submission analyzes the capacity of message-passing graph neural networks (MP-GNNs) in learning strong branching (SB) scores. A class of MILPs, so-called MP-tractable MILPs, are identified as a suitable class, whose SB scores could be accurately approximated by common MP-GNNs. For general MILPs, the autho... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments! Please find our responses below:
* __[Weakness 1] MILP-graph representation:__ The bipartite graph representation is standard in the existing literature and has already been utilized in MILP-related learning tasks. The goal of our paper is to provide... | Summary: In this article the authors present new results regarding the expressivity of GNNs with respect to branching strategies in mixed integer linear programming (MILP). In particular, they explicit a class of MILPs (called MP-tractable) such that for each MILP in the class, there exists a MP-GNN that can ``mimic'' ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments! Please find our responses below:
* __[Question 1, Weakness 1] Complexity of verifying MP-tractability:__ You are correct that verifying the MP-tractability of a MILP data set requires implementing the color refinement on each of the MILP-graphs in a... | Summary: This paper concerns the expressive power of GNN in the context of approximating Strong Branching scores in learning to branch. In this paper, the authors proposed the notion of "MP-tractable" Mixed Integer Linear Programs (MILPs) and analytically proved that all MP-tractable MILPs are distinguishable by messag... | Rebuttal 1:
Rebuttal: Thank you very much for your encouraging comments! Please find our responses below:
* __LP solution with the smallest $\ell_2$-norm:__ One reason we choose LP solution with the smallest $\ell_2$-norm rather than an arbitrary one is to make sure that the LP solution, as well as its resulting SB sco... | Summary: This paper provides a new lens through which the capacity of GNNs can be analyzed---branching strategy. The correspondence between strong branching and GNNs is established and the expressiveness of GNNs are discussed in terms of whether universally approximating strong branching is possible.
Strengths: - the ... | Rebuttal 1:
Rebuttal: Thank you very much for your encouraging comments! Please find our responses below:
* __Practical impact:__ The purpose of the experiment in Figure 3b is to illustrate the difference between 2-FGNN and MP-GNN for MILP problems with symmetry (beyond the MP-tractable class). Our paper is mostly theo... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper investigates the effectiveness of GNNs in approximating strong branching (SB) strategies in mixed-integer linear programming (MILP) problems. SB is a heuristic used in the branch-and-bound algorithm to choose branching variables. SB is among the best-performed heuristics in branch-and-bound algorithm... | Rebuttal 1:
Rebuttal: Thank you very much for your encouraging comments! Please find our responses below:
* __Symmetry of MILPs in practice:__ The symmetry of a MILP problem can be measured by the number of different colors in the output of WL test. For example, (4.1) and (4.2) both admit two different colors, signific... | null | null | null | null | null | null |
DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting | Accept (poster) | Summary: This paper first introduces a new domain-aware FIT baseline called DoFIT-base. DoFIT-base aggregates domain-specific information on the intra-domain server side and domain-agnostic information on the inter-domain server side to reduce interference information from the other domains. By incorporating inter-doma... | Rebuttal 1:
Rebuttal: > Weaknesses:
1: Thank you. FIT_{32qv} is the configuration of the original FIT. We experimented with FIT_{16qvd} and FIT_{32d} to explore the impact of different LoRA configurations on performance in the current domain without increasing computational and communication costs. Adding more LoRA mo... | Summary: This paper proposes Federated Instruction Tuning, aimed at enhancing model's capability and data privacy. The main points lie in enhancing the model of data heterogeneity across dfifferent clients. Specifically, the paper introduces DoFIT, a domain-aware FIT framework, aimed at alleviating the catastrophic fo... | Rebuttal 1:
Rebuttal: > Weaknesses:
**1, 2:** Federated domain adaptation for LLMs is crucial, but no related methods currently exist. Applying existing federated domain adaptation methods like FedGP[1] directly to LLMs yields suboptimal results, as shown in Table 1 of the PDF. Where FedGP/FedGP-g refer to the project... | Summary: This work offers a solution to a problem in the collaborative training of different domains on decentralized data within the FIT paradigm: domain-aware data heterogeneity causes domain-information catastrophic forgetting. The solution relies on two new designs for aggregation and initialization. Specifically, ... | Rebuttal 1:
Rebuttal: > Weaknesses:
1: Thank you. Currently, the experiments show improvements only between two domains. Due to the more complex and variable heterogeneity in multi-domain scenarios, the current framework cannot handle it well. However, as the first federated instruction tuning framework attempting to ... | Summary: The authors propose to utilize intra- and inter-domain server sides in a federated instruction tuning framework to implement discriminative aggregation and initialization strategies. The proposed approach, i.e., DoFIT, is primarily based on FIT of LLM, marking the first solution to address domain-aware data he... | Rebuttal 1:
Rebuttal: > Weaknesses:
1: In our DoFIT, we only added two hyperparameters: top-k and α. As shown in Tables 1 and 2, as well as Figures 4 and 5 of the original paper, the performance differences for different top-k and α values are minimal. Therefore, even when generalizing to new domain data, extensive ad... | Rebuttal 1:
Rebuttal: > We thank the reviewers for their valuable comments. We are glad that the reviewers found:
- The topic we are addressing to be promising and important (Reviewers ZeZJ, Zbxm, DeWw).
- Our experiments to be convincing and showing significant performance improvement (Reviewer Vje9, DeWw).
- Our id... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this work, the issue of domain-aware data heterogeneity is solved, which equally treats intra- and inter-domain data variations but cannot adapt to the challenges of cross-domain training. The paper proposes a novel domain-aware federated instruction tuning (DoFIT) framework for collaborative training acro... | Rebuttal 1:
Rebuttal: > Weaknesses:
1: Thanks. Unlike traditional FL methods that focus on different styles within the same category, the different domains in this paper refer to various application scenarios, such as the financial, general, and medical domains. The proposed framework is suitable for federated instruc... | null | null | null | null | null | null |
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts | Accept (poster) | Summary: The paper addresses automatic red teaming of large language models through open-ended generation of jailbreaks. The key components of their methodology are 1) a categorization of different jailbreak categories to create a diverse archive of possible jailbreaks, 2) a strategy to evolve and mutate jailbreaks, 3)... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and are glad they appreciate the extensive effort we put into our empirical evaluation and hyperparameter descriptions. We also thank them for expressing their concerns very clearly and transparently. We invite them to read our response below.
### **Reproducib... | Summary: This paper introduces RAINBOW TEAMING, an approach for generating diverse adversarial prompts to test and improve the robustness of LLMs) The method uses quality-diversity search to produce a wide range of effective adversarial prompts across different categories. The authors demonstrate its effectiveness on s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback on our work. It is great that the reviewer appreciated the new perspective on adversarial prompt generation through the lense of quality-diversity optimization.
We address the reviewer's concerns below and hope that this will lead to them increasi... | Summary: The authors present Rainbow Teaming, a structured approach to automated redteaming of large language models. Based on a user-specified set of strategies and risk categories, Rainbow Teaming uses a mutator LLM to rewrite several variations of existing prompts, then compares the resulting outputs from the target... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and are glad they found our method clear and effective. However, we believe they missed key points which already address a majority of their concerns. We clarify those points below, and describe new results added to the paper.
### **Novelty**
Our first con... | Summary: The paper proposes a novel method Rainbow Teaming for the automatic generation of diverse adversarial prompts aimed at large language models (LLMs). The goal is to identify and enhance the robustness of LLMs to various user inputs, which is crucial as these models are increasingly used in safety-critical envir... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback on our work. It is great to hear that our proposed method holds significant importance and is straightforward to comprehend.
We address all of the reviewer’s concerns below.
### **Comparison with baselines**
We note that we already perform detai... | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments, and have addressed each of their concerns individually in their respective responses. As a result of their feedback, we have clarified multiple sections of the paper. We have also added the following results:
1. We added a new baseline in Figure 4, which... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: I'd like to thank the authors for submitting their work for review. I found the work insightful, inspiring, and high-quality. In short, the work has been a pleasure to review as a last-minute reviewer.
The manuscript's primary contributions include:
- **Rainbow Teaming Method.** A new methodology for automa... | Rebuttal 1:
Title: Response by Authors (1/2)
Comment: We are very grateful to Reviewer E172 for their extremely detailed review. We are pleased that they found our contributions significant and its presentation exceptional. It is also great that the reviewer appreciated the thoroughness of our empirical results.
We ad... | null | null | null | null | null | null |
Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages | Accept (poster) | Summary: The paper presents results about several forms of masked unique hard-attention transformers by classifying the class of languages recognized by these models using formal languages. Roughly put, a masked unique hard-attention transformer is an encoder that uses unique hardmax as its attention mechanism. In case... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We are glad that you find our work convincing, intriguing, and well-placed.
On B-RASP, please see our global response.
> [T]he paper...makes it challenging for readers to place the results in the broader context of research.
This point is well-taken, and we'... | Summary: This paper presents new theoretical results related to the expressive power of Transformers. The authors focus on Transformers with "hard" attention (a simplifying assumption) and strict future masking (i.e. attention can only attend to positions to the left). The paper develops an equivalence between such Tra... | Rebuttal 1:
Rebuttal: Thank you very much for your review! We're glad you found the paper interesting and see its potential to inspire future work.
> The main results [focus] on architectures with strict future masking and without positional encodings. Both choices are a very uncommon configuration for Transformers.
... | Summary: The paper studies the expressive power of transformer encoders in terms of their ability to recognize regular languages. The main result in the paper establishes that if such encoders are equipped with hard attention, future masking is permitted, and positional encodings are disallowed, then the languages acce... | Rebuttal 1:
Rebuttal: Thank you very much for your review, and for your assessment of our results as "beautiful" (!).
It's true that the equivalence of LTL and masked hard-attention transformers could be proven directly, without going through B-RASP. We have worked out the LTL to transformer direction outside of this ... | Summary: The authors connect masked hard-attention transformers (with single-head attention) with LTL via a Boolean version of RASP, namely B-RASP. Due to established results on LTL, they show that this type of transformer recognizes exactly the star-free languages.
While unique hard attention transformers have been s... | Rebuttal 1:
Rebuttal: Thank you very much for your review and your positive assessment of our paper!
> In my eyes, the main contribution of this paper is therefore the result that this characterisation is exact and the results on strict vs. non-strict masking.
Yes, but we would also remind the reviewer of the other r... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dimension-free deterministic equivalents and scaling laws for random feature regression | Accept (spotlight) | Summary: This paper provides a non-asymptotic bound (Theorem 3.3) on the test error of random feature ridge regression (RFRR) using dimension-free (in the sense of the feature space) deterministic equivalents, which are the solutions to some self-consistent equations, under a concentration assumption (Assumption 3.1) o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the suggestions which will help to improve it. The typos in the appendix raised in the review will be corrected in the revised version of the manuscript.
> *My main concern, however, is the significance of the theoretical contribut... | Summary: EDIT: updated my score after rebuttal.
The authors investigate the excess risk in random feature ridge regression. They give a deterministic expression that approximates the excess risk, with a controlled relative error. The dependence of the deterministic "equivalent" w.r.t. to key quantities in the problem... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the suggestions which will help to improve it. The issues (9, 10) and the typos raised in the minor comment section will be addressed and/or corrected in the revised version. Due to the space constraint in the rebuttals, will answer... | Summary: Prior work on random feature (ridge) regression study the test error in the high-dimensional asymptotic. However, ideally one would hope for a non-asymptotic deterministic characterization of the test error. In this paper, the authors tackle this problem and show that under a concentration assumption, the t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the suggestion which will help improving it.
> "*I suggest the authors expand the discussion around Assumption 3.1 and provide more detailed examples for which this assumption holds.*"
We will expand the discussion around Assumpt... | Summary: The paper studied the non-asymptotic generalization error for random feature ridge regression (RFRR) models. By considering the eigendecomposition of random features with respect to data distribution and weight distribution, the authors proved a feature-dimension-free deterministic equivalence for the generali... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the suggestions which will help improving it. The typos raised (2, 5, 6, 7, 8, 11, 14) will be corrected in the revised version of the manuscript.
Below, we address the other specific comments and questions.
> "*One concern is how... | Rebuttal 1:
Rebuttal: **Assumption 3.1**: We acknowledge Assumption 3.1 can be restrictive, as mentioned in the paragraph 116-122. In fact, it will not be satisfied by some standard examples of random feature models, such as $\varphi (x,w) = \sigma ( \langle x, w\rangle)$ with non-linear activation $\sigma :\mathbb{R}... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Segmenting Watermarked Texts From Language Models | Accept (poster) | Summary: The work makes two contributions: (1) A randomized-based test, including a theoretical analysis of a substring-based watermark detection for two popular distribution-preserving watermarks. The key idea is similar to the base detection methods in their respective base papers but shows some additional theoretica... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We will provide a detailed response to each of your comments below.
### **Weaknesses and Questions:**
*1. The method seems to rely heavily on the hyper-parameter $B$ and would most likely profit from some ablation on the parameter in practical se... | Summary: This paper presents a statistical method for detecting and segmenting watermarked text generated by large language models (LLMs). The key contributions are:
A rigorous analysis of Type I and Type II errors for a randomization test to detect the presence of watermarks in generated text. The authors apply their... | Rebuttal 1:
Rebuttal: We appreciate your comments and we provide a point-by-point response to each of your comments below.
*1. Limited model diversity [...] larger language models would strengthen findings.*
**Ans:** Thanks for the comment. We conducted simulation tests using the Llama model, specifically the Meta-Ll... | Summary: This paper considers the problem of segmenting a watermarked text into watermarked and unwatermarked subsequences. This is achieved using change point detection methods. There is a nice statistical analysis of the single change point detection problem, and a heuristic algorithm (from Kovács et al., 2022) with ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We greatly appreciate your positive feedback. We provide a detailed response to each of your comments below.
*1. This paper could be criticized for novelty, in the sense that it mostly applies standard tools from the change point detection literat... | null | null | Rebuttal 1:
Rebuttal: First and foremost, we express our sincere gratitude to the three reviewers for their invaluable feedback, which has helped us improve the quality of our manuscript. We will revise our manuscript and the supplement, taking all the reviewers' comments into consideration. Before providing point-by-p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Newton Informed Neural Operator for Solving Nonlinear Partial Differential Equations | Accept (poster) | Summary: The manuscript concerns an operator learning technique for elliptic partial differential equations with nonlinear, solution-dependent forcing terms. These PDE can admit multiple solutions, which is a challenge for many existing PDE solution techniques, because they usually only result in a single solution. Th... | Rebuttal 1:
Rebuttal: ## Weaknesses
> Approximation of Newton's Method and Finding Multiple Solutions
- Thank you for your insightful comments.
We acknowledge that a large portion of machine learning research indeed focuses on learning distributions, as seen in generative models like diffusion models, score-based lea... | Summary: The authors presented a new machine learning based technique called the Newton-informed Neural Operator for solving PDEs that have multiple solutions due to nonlinearities. This is an important problem, and the Newton informed neural operator is capable of obtaining these multiple solutions using a single, uni... | Rebuttal 1:
Rebuttal: ## Weaknesses
> Clarity of Exposition:
- We will include a clear architectural diagram of the Newton-informed Neural Operator and provide a step-by-step derivation of the Newton loss function as follows:
For the nonlinear PDEs:
$$
\begin{cases}
\mathcal{L} u(\mathbf{x}) = f(u), & \mathbf{x} \in ... | Summary: In this paper, the authors propose a newton informed neural operator for solving PDEs. Based on the description, it seems the neural operator is designed to predict the solution to the PDE given an initial solution (equation below eq. 2 on pg 3). The neural operator is trained with 2 types of losses, mse loss ... | Rebuttal 1:
Rebuttal: ## Weaknesses
> Data Generation:
- For method 1, we use 500 supervised data samples (with ground truth), while for method 2, we use 5000 unsupervised data samples (only with the initial state) along with supervised data samples.
Here is how we generate the supervised data samples:
1. **Step 1**... | Summary: The paper proposes the Newton Informed Neural Operator, a novel method that leverages classical Newton methods and neural network techniques, to efficiently learn multiple solutions to nonlinear PDEs.
Classical Newton's method iteratively linearizes the nonlinear equation and solves the resulting linear syste... | Rebuttal 1:
Rebuttal: ## Weaknesses
> Comparison:
- The idea behind our proposed Newton Informed Operator Learning is rooted in the computational data generated by classical Newton's methods. When computing multiple solutions of nonlinear PDEs in pattern formation, the initial guesses can be in the millions, especial... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful reading and insightful comments. We would like to clarify our motivation and summarize the pipeline of our method as follows:
**Motivation**
The Newton Informed Neural Operator, focuses on efficiently approximating the iteration of Newton's method fo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SAM-Guided Masked Token Prediction for 3D Scene Understanding | Accept (poster) | Summary: This paper proposes a two-stage SAM-guided pre-training method for 3D scene understanding. The authors present a group-balanced re-weighting method for long-tail representation distillation and a SAM-guided tokenization method to seamlessly align 2D and 3D region-level features. Extensive experiments on variou... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and valuable suggestions. We have addressed your questions as follows.
**Q1, apply on SOTA 3D detectors.**
In the main paper, we did not apply our method to other state-of-the-art detection methods because those works often incorporate specialized d... | Summary: This paper introduces a novel method to enhance 3D scene understanding by addressing misalignment between 2D and 3D representations and the long-tail distribution in 3D datasets. The proposed approach involves a SAM-guided tokenization method for seamless alignment and a group-balanced re-weighting strategy to... | Rebuttal 1:
Rebuttal: Thanks for your positive and insightful comments! We have addressed your questions as follows.
**Q1, visual comparison with other mask generation methods.**
We have added both visual and results comparisons with other mask generation strategies, such as superpoint and superpixel, to justify the ... | Summary: The paper proposes a 3D transformer tokenization technique to align 3D representations with 2D ones, distilling from 2D pre-trained knowledge from SAM. The method achieves favorable performance on 3D object detection and semantic segmentation compared to prior self-supervised learning methods.
Strengths: * Th... | Rebuttal 1:
Rebuttal: Thanks for your positive and insightful comments! We have addressed your questions as follows.
**Q1, why SAM is chosen as the distillation source for tokenization**:
We chose SAM [1] to guide tokenization because it generates great zero-shot masks that provide boundary regularities and effecti... | Summary: The paper proposes a self-supervised method for understanding 3D scenes by predicting the 3D mask of the point cloud. The masks are initialized from Segment Anything (SAM), followed by two stages of the knowledge distillation framework to train the 3D teacher and student networks. The method is evaluated on SU... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and valuable suggestions. We have addressed your questions as follows.
**Q1, innovation of method.**
Please refer to the first part of the general response at the beginning.
**Q2, the insight of the two-stage design.**
Masked token prediction ha... | Rebuttal 1:
Rebuttal: **General Response**
--
We sincerely thank each reviewer for their thoughtful feedback and detailed reviews. We address the main concerns regarding novelty and comparisons with other peer research below.
**Comparison with Seal and the innovation of methodology.**
Thank you for highlighting Seal ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity | Accept (spotlight) | Summary: This work improves the sample complexity of diffusion models by using the parallel sampling technique and achieves $\tilde{O}(\text{poly} \log d)$ results for reverse SDE and PFODE settings at the same time. To achieve these results, this work provides a general version of Girsanov’s theorem to deal with the a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer’s comments and suggestions on our work. In the following paragraphs, we address the reviewer’s concerns.
---
### Regarding comparison with [1, 2]
We appreciate the reviewer’s suggestions to deepen the discussions on our results relative to those in [1, 2]. Alo... | Summary: In this article, the authors propose parallel methods for diffusion models, achieving a poly-logarithmic complexity for the first time under commonly used assumptions. By applying existing Picard Iterations to both the SDE and probability flow ODE of diffusion models, the backward process can be efficiently so... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s insightful comments and appreciation for our work. We have listed our answers to the questions raised in the review as follows:
---
### Regarding comparison with other parallel sampling methods
We appreciate the reviewer’s suggestion regarding the necessity of ... | Summary: Denoising diffusion models generate samples from a complex target distribution by solving stochastic/ordinary differential initialized at the Gaussian noise. Solving these differential equations is typically done by simulating diffusion solutions. This is inherently sequential since simulating the solutions at... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of our work and the valuable feedback. Below are our responses to the questions raised in the review:
---
## Weaknesses
### Regarding numerical experiments
We appreciate the reviewer’s suggestion and recognize the value of empirical verification of ou... | Summary: In this paper, the authors propose to analyse the error in parallel sampling for diffusion models. This represents the first theoretical analysis of parallel sampling for diffusion models. The initial methodology was proposed in ParaDiGMS [1]. In this paper, the authors propose some incremental improvements on... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback, constructive suggestions, and kind affirmation of our work. We would like to address the reviewer’s comments in a one-by-one manner below.
---
## Weaknesses
### Regarding the presentation of results
We thank the reviewer’s suggestions regarding th... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust and Faster Zeroth-Order Minimax Optimization: Complexity and Applications | Accept (poster) | Summary: In this paper, a unified single-loop zeroth-order gradient descent extragradient ascent (ZO-GDEGA) algorithm to solve the nonconvex-concave minimax problem faster and more robustly. The theoretical analysis is provided to guarantee an overall complexity of $O(\epsilon^{-6})$. The experimental results on the da... | Rebuttal 1:
Rebuttal: Thanks for your insightful thoughts and comments! Below we will clarify the two points in the review.
### **Response 1.**
**Related works for first-order methods.**
Existing works mainly focus on first-order (FO) methods for solving NC minimax problems. For the NC-SC setting, GDA [1] and AGDA ... | Summary: They design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to find an ε-stationary point of the function ψ for nonconvex-concave (NC-C) problems. ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems.
... | Rebuttal 1:
Rebuttal: ### **Response 1. The detailed link of code.**
We provide the code of the data poisoning attack experiment on the epsilon\_test dataset. We have sent this code sample as an anonymized link to the AC. We will make our code public.
### **Response 2. The proof of the NC-C problem (5).**
Problem (... | Summary: The paper studies zeroth-order methods for nonconvex-(strongly)-concave minimax optimization. The achieved rates improve previous results and tolerate much larger choice of the smoothing parameters. The proposed methods also perform well for some empirical tasks.
Strengths: Minimax optimization is an importan... | Rebuttal 1:
Rebuttal: We would to thank the reviewer for the insightful comments! Below we will clarify the four points in the review.
### **Response 1. Lower Bounds.**
We will add the following discussion about the lower bound to our revised version.
For minimization problems, the lower bounds on ZO methods justif... | Summary: This paper proposes zeroth-order method called ZO-GDEGA to find a near-stationary point for nonconvex-concave minimax optimization, with complexity guarantee. The proposed method is also extended to stochastic setting, being the first work on ZO method on stochastic NC-C problem. The method has weaker requirem... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the insightful comments! Below we will clarify the three points in the review.
### **Response 1. Complexity (deterministic NC-C)**
$\bullet$ **Our ZO-GDEGA achieved the reduced complexity.** The key reason for different complexity is different complexity m... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the detailed reviews. We were asked by the reviewer 4438 to provide code. Our code can be available at the link https://anonymous.4open.science/r/ZO-GDEGA-2F6E | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper, the authors establish a unified framework of zeroth-order optimization for nonconvex-concave minimax optimization problems in both deterministic and stochastic settings. This framework is based on the gradient descent-extragradient ascent algorithm. They claim that their algorithms require weake... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the insightful thoughts and comments! Below we will clarify the two points in the review.
### **About Assumption 3.**
Firstly, analyzing ZO algorithms has the following two main difficulties compared to analyzing first-order methods. That is, we need to bo... | null | null | null | null | null | null |
MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning | Accept (spotlight) | Summary: The paper studies causal reasoning in video, specifically, causal diagrams in long, multi-event videos. To do this, the authors attempt to define MEDC, a new task for discovering the complete causal relation diagram in multi-event chronological videos (~ 2 minutes). For example, analyzing events from traffic s... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments.
We fully agree that the proposed MECD represents a novel, challenging, and significant task in video understanding.
We also appreciate the acknowledgment of the interest and clarity provided by the VGCM method. | Summary: This paper introduces a new task called Multi-Event Causal Discovery (MECD). Given a video which comprises multiple temporal events that are chronologically organized, the goal is to predict if any previous event has a causal effect on the last event in the sequence. The MECD dataset is filtered and curated ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We’re glad to hear that you found our task interesting and that you see our methods as insightful. Please see below for responses to your comments and questions.
**Q1. Dataset Scale**
We have the following points to this issue:
1. We envision MECD as a lo... | Summary: The paper introduces a new task and dataset, Multi-Event Causal Discovery (MECD) to better understand long videos in a casual perspective. Inspired by the Granger Causality method, the authors devise a framework, dubbed VGCM, to perform the Event Granger Test. Also, VGCM is combined with front-door adjustment ... | Rebuttal 1:
Rebuttal: Thanks for your suggestions and for recognizing the novelty and contribution of our work. Please see the responses to your comments.
**Q1. Model's generalizability**
To the best of our knowledge, our benchmark is currently the first and only one for the video causal discovery task. To further va... | Summary: The paper introduces the Multi-Event Causal Discovery (MECD) task, aiming to uncover causal relationships in videos with multiple events. It presents a novel framework inspired by the Granger Causality method, utilizing a mask-based event prediction model to perform causal inference. The paper also introduces ... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions. We’re glad that you found our work interesting and novel. We address your concerns below.
**Q1. Complexity of implementation**
We have the following points for this question:
1. *The main pipeline of our framework is clear and not overly complex in its impl... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. We are glad to find that reviewers generally recognized our contributions:
- Novelty and contribution of MECD task (Reviewer-9my4, eXFX, 2Vef, 17Ce)
- Innovative and well-motivated framework design of VGCM (Reviewer-9... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization | Accept (poster) | Summary: Building on existing models, the authors used MBO to design promoters in a data-efficient manner, with a particular focus on discovering promoters for similar cell types. This approach was tested on three relatively similar blood cancer cell lines, demonstrating that the method successfully identified numerous... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and address their concerns below. First, we clarify that the novel contribution of our work is our cell-type-specific promoter design workflow. Although we use existing modeling strategies and MBO algorithms, we combine them in a novel way to tackle a diffi... | Summary: The paper outlines a workflow to designing promoter sequences that are specific to cell types, especially closely related cell types. The proposed approach consists of five steps:
1. Pretraining a model on existing massively parallel reporter assay (MPRA) datasets, which are large but restricted to a few well... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and for recognizing the significance of our work. In the responses that follow, we clarify that our data-efficiency claim is due to the pretraining step which is missing in previous promoter design workflows – we will make this clear in the final v... | Summary: The paper presents a comprehensive guide for designing cell-type-specific promoter sequences using a conservative model-based optimization (MBO) approach. The primary goal is to develop promoters that drive gene expression specifically in target cells while minimizing off-target effects in closely related cell... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for recognizing the significance of our work. In our responses below, we first justify the use of gradient ascent vs. other optimizers such as genetic algorithms and simulated annealing, due to its computational efficiency. Then, we clarify that our wor... | null | null | Rebuttal 1:
Rebuttal: In the attached document, we provide an additional table that shows the average Hamming distance to the closest training set sequence for designs from every method. This table illustrates that designs from our workflow are quite distinct from the training set sequences, differing by 125-140 bp on ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HonestLLM: Toward an Honest and Helpful Large Language Model | Accept (poster) | Summary: The paper presents an approach to ensure that LLMs are helpful and honest. The paper curates and releases a dataset that can be used to assess the LLMs honesty and helpfulness. The paper’s evaluation demonstrates that the proposed approach can improve the LLMs helpfulness and honesty by 65% for Llama3-8b and 1... | Rebuttal 1:
Rebuttal: **Q1:** The paper includes a 3d pie chart that significantly hurts the readability of the paper. I suggest to the authors to change the type of plot in Figure 2.
**A1:** Thank you for your feedback regarding the 3D pie chart. We apologize for the readability issues it caused. We have replaced it ... | Summary: In this paper, the authors proposed methods for improving the helpfulness of LLMs while preserving their honesty. To this end, the authors proposed a training-free and a fine-tuning-based method. The main contributions of this paper is the redefinition of Honesty and the proposed improvement methods.
Strength... | Rebuttal 1:
Rebuttal: **Q1.1:** As the construction of the dataset requires human validation and there are 7 human experts. There is no statistical indicator such as agreement provided in the paper.
**A1.1:** Thank you for pointing out the concerns regarding our dataset construction process. Due to word limit constra... | Summary: This paper presents a method to simultaneously enhance the honesty and helpfulness of large language models. The authors start by constructing an evaluation dataset of about 1,000 questions named HONESET. Two types of approaches are proposed: one based on prompt engineering combined with multiple model invocat... | Rebuttal 1:
Rebuttal: **Q1:** It is unclear whether the models' general capabilities are compromised under the two proposed methods. It would be beneficial to include standard benchmarks such as MTBench to observe changes in general metrics.
**A1:** Thank you for highlighting the importance of assessing whether our pr... | Summary: The authors develop a fine-grained dataset and metric for measuring honesty and helpfulness tradeoffs, that consider specific honesty failure modes and
demonstrate prompting and training based techniques to improve along this metric
Strengths: Significance: Honeset is potentially another useful contribution ... | Rebuttal 1:
Rebuttal: **Q1:** Need a data quality reviewer to verify HoneSet more thoroughly
**A1:** Thank you for pointing out the concerns regarding our dataset construction process. We have included a detailed explanation in Global Rebuttal, as shown in Tables 1, 2, and 3.
---
**Q2:** Evolving Dataset Schema.
**... | Rebuttal 1:
Rebuttal: **GQ1:** Further verification of HoneSet construction process. Additional statistical indicator details and roles of human experts in the creation of the HoneSet.
**GA1:** Thank you for pointing out the concerns regarding our dataset construction process. Here is a detailed explanation of data va... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep linear networks for regression are implicitly regularized towards flat minima | Accept (poster) | Summary: The paper explores the behavior of deep linear neural networks in the context of overdetermined univariate regression, which is the setting of a univariate response $y$, more samples than input dimensions, and nonsingular data covariance matrix.
The first result concerns the empirical risk minimizer (this is... | Rebuttal 1:
Rebuttal: * Rigorous connection between theoretical results and GD:
We agree that the connection between our results on gradient flow and the case of GD is not rigorously proven, and we will further emphasize this in the next version.
* Other definitions of sharpness:
We agree that there are several defi... | Summary: This paper studies the implicit bias of gradient flow on deep linear networks for overdetermined univariate regression problems. A lower bound on the sharpness of any minimizer is first derived for the general data covariance matrix, then it is shown that gradient flow with small initialization finds a minimiz... | Rebuttal 1:
Rebuttal: > Tightness of Theorem 2: In Mulayoff and Michaeli (2020), the lower bound on the sharpness is tight for whitened data: there exists a minimizer that achieves the lower bound. However, Theorem 2 in this paper only provides a lower bound. Is this lower bound improvable? (...)
We agree with the rev... | Summary: The paper considers a toy non-convex optimization problem, namely overdetermined univariate regression with a deep linear network. Their main contributions are:
1) A lower bound on the sharpness of the minimizers of the empirical risk. In particular, if the step size is chosen too big, then gradient descent w... | Rebuttal 1:
Rebuttal: > The paper considers a simple setting: deep linear network and underdetermined regime. This allows the authors to characterize interesting and non-trivial behavior. However, it is unclear how much these results can extend beyond this simple setting.
The question on the extension to more complex ... | Summary: The authors show three new results for deep linear networks (DLN). First, they show that any DLN that implements the optimal linear regressor must have a certain "sharpness". This amounts to a lower bound on the largest eigenvalue of the Hessian matrix at that set of weights. They then argue that the weight... | Rebuttal 1:
Rebuttal: > The paper kind of has a mixed message, and doesn't really make the connection to generalization power in a way that is intepretable. Theorem 1 shows that the sharpness has to grow at a rate that is essentially linear in the number of layers. But then they mention that prior work indicates that f... | Rebuttal 1:
Rebuttal: Dear reviewers,
We warmly thank you for your time and relevant comments, which will help us improve our work. If accepted, we will take into account your suggestions, making use of the additional page.
Since several reviewers raised the relevant questions of the link with generalization and of ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Predicting Label Distribution from Ternary Labels | Accept (poster) | Summary: The paper proposes a more cost-effective approach to label distribution inference, i.e., predicting label distributions from ternary labels. Theoretically, the paper elucidates the superiority of the ternary label by analyzing the error of approximating the ground-truth label description degrees by ternary and... | Rebuttal 1:
Rebuttal: ## Responses to Weaknesses
Thank you for your suggestions. Under the space restriction, we will incorporate some background knowledge about three-way philosophies in the revised version. Besides, we will rearrange the Figure 2 and Figure 3, and correct the mathematical formula in Figure 3.
## Re... | Summary: The authors propose a novel multi-label annotation scheme, transitioning from the traditional binary annotation to a ternary annotation. They have validated this new annotation scheme through both theoretical analysis and experimental verification.
Strengths: 1. The authors clearly articulate the problem they... | Rebuttal 1:
Rebuttal: ## Responses to Weakness
We really appreciate your valuable comments. Essentially, partial multi-labels, weakly-supervised multi-labels, semi-supervised multi-labels, and multi-labels with noise are essentially weaker versions of binary labels (i.e., multi-labels) because they either contain nois... | Summary: In this manuscript, the authors explore how to learn the unknown label distribution from given ternary labels (0, 1, and -1). The main contributions of this work include: (1) a new label distribution prediction method is designed for handling ternary labels; (2) the theoretical analysis on the error of approxi... | Rebuttal 1:
Rebuttal: ## Responses to Weakness (1)
(1) Responses to the concern about "Weakly Supervised Multi-Label Learning via Label Enhancement".
We greatly appreciate the constructive feedback you have provided on our paper. __*It should be highly emphasised that the ternary labels in our paper and the WSML (i.e... | Summary: The authors of this paper propose to predict label distribution from ternary labels, i.e., “0” indicating “uncertain relevant”, “1” indicating “definitely relevant” and “-1” indicating “definitely irrelevant”. Besides, they also provide theoretical analysis to show that the ternary label outperforms the binary... | Rebuttal 1:
Rebuttal: ## Responses to Weakness (1) and Question (2)
We are very grateful for your precious suggestions. We additionally perform experiments to analyze the parameter sensitivity. The results are shown in Figure 1 in the submitted PDF in global responses, which will be added to the revised version if spa... | Rebuttal 1:
Rebuttal: We sincerely value the time and thoughtfulness each reviewer has dedicated to enhancing the quality of our paper. We have carefully considered each comment and ensured that each point is addressed. Attached please find a PDF file containing the relevant figures mentioned in the responses for each ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ZeroMark: Towards Dataset Ownership Verification without Disclosing Watermark | Accept (poster) | Summary: This paper proposes a new dataset ownership verification method, ZeroMark, which calculates the boundary gradient between the benign and reconstructed images to verify the authorship. Extensive experiments are conducted on two datasets with four backdoor attack methods to evaluate the ZeroMark.
Strengths: 1) ... | Rebuttal 1:
Rebuttal: Dear Reviewer s8Mh, thank you very much for your careful review of our paper and thoughtful comments. We are encouraged by your positive comments on our **good soundness and presentation**, **extensive experiments**, and **novelty**. We hope the following responses can help clarify potential misun... | Summary: This work presents a method for dataset ownership verification, focusing on confidentiality during the verification phase. The authors highlight that adversaries can remove watermarked data by detecting it during verification. To address this issue, the paper proposes a new verification process based on the co... | Rebuttal 1:
Rebuttal: Dear Reviewer RJcM, thank you very much for your careful review of our paper and thoughtful comments. We are encouraged by your positive comments on our **good presentation** and **novel verification process**. We hope the following responses can help clarify potential misunderstandings and allevi... | Summary: This paper explores how to conduct privacy-preserving dataset ownership verification without directly disclosing dataset watermarks. The proposed method is inspired by the characteristic of boundary gradient of watermarked DNNs. Specifically, it has three main steps, including (1) generate the (closest) bounda... | Rebuttal 1:
Rebuttal: Dear Reviewer RHDz, thank you very much for your careful review of our paper and thoughtful comments. We are encouraged by your positive comments on our **great significance**, **intriguing phenomenon with theoretical analysis**, **novel and interesting method**, **simple and effective method**,, ... | Summary: This paper proposes ZeroMark, a novel scheme for dataset watermark verification. It is based on an observation that the boundary gradients (i.e., gradients of samples near the decision boundary of a watermarked model) of the watermark target class tend to have higher cosine similarities with the watermark patt... | Rebuttal 1:
Rebuttal: Dear Reviewer VV25, thank you very much for your careful review of our paper and thoughtful comments. We are encouraged by your positive comments on our **novel and interesting research problem**, **interesting observation**, **method flexibility**, and **extensive and solid experiments**. We hope... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback, and we have responded to each reviewer individually. We have also uploaded a Rebuttal PDF that includes:
- **Table 1**: The verification performance of ZeroMark for Tiny-ImageNet with SwinTransformer.
- **Tabel 2**: Verification performance ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LLaNA: Large Language and NeRF Assistant | Accept (poster) | Summary: In this submission the authors propose a new pipeline that enables Large Language models to interact with trained object-centric NeRF models. They achieve this by utilising a pretrained meta-network that ingests the weights of a NeRF MLP and outputs a low-dimensional feature vector. This low-dimensional featur... | Rebuttal 1:
Rebuttal: __W1-Q1__
Our choice of using frozen models as baselines has been motivated by LLaNA being the only assistant that works on NeRFs, i.e. whose input modality is a radiance field parametrized as a neural network. Indeed, the papers most closely related to ours -- those proposing object-centric assi... | Summary: The authors propose a method to ingest NeRFs and project them into a language model's latent space for question answering and chat applications on NeRFs directly.
Strengths: S1. Very clear abstract, which nicely frames the paper
S2. Strong related work section
S3. The proposed dataset may be useful to other... | Rebuttal 1:
Rebuttal: __W1__
ShapeNeRF-Text is built upon ShapeNet. 3D shapes are divided into 30939, 3846, and 3859 for the train, val, and test set, respectively. A NeRF is trained for each of these objects. As for text, the dataset features a brief and detailed description and 3 single-rounds and one three-rounds Q... | Summary: This work proposes LLaNA, a multimodal large language model (MLLM) that is capable of aligning language with 3D scene fields embedded with (learned) NeRF models. By projecting the NeRF fields into an LLM’s embedding space, LLaNA utilizes a meta encoder to transform fields seamlessly into the token space of an ... | Rebuttal 1:
Rebuttal: __W1__
The proposed ShapeNeRF-Text is built upon the 3D object dataset ShapeNet. ShapeNeRF-Text has 13 classes of ordinary objects, such as cars, airplanes, and chairs. The shapes are divided into 30939, 3846, and 3859 for the train, val, and test set. A NeRF is trained for each of these objects.... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback, which allowed us to carry out a more extensive investigation, improving the quality of our work.
We reported the results of these extensive experiments in the attached PDF, referred to as the “__rebuttal PDF__”, which we believe provides a clea... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards a Scalable Reference-Free Evaluation of Generative Models | Accept (poster) | Summary: This paper introduces the Fourier-based Kernel Entropy Approximation (FKEA) metric, which efficiently evaluates the diversity of generated samples. The key contributions of this work are twofold: (1) Compared to existing diversity metrics such as VENDI and RKE, the proposed metrics (i.e, FKEA-VENDI and FKEA-RK... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer ymdX for his/her time and constructive feedback and suggestions on our work. The following is our response to the reviewer’s comments and questions.
**1. Variance of estimating entropy scores using FKEA**
Re: To address the reviewer’s question, we measured the sta... | Summary: The work introduces a new method called Fourier-based Kernel Entropy Approximation (FKEA) to evaluate the diversity of data generated by generative models. Traditional evaluation metrics for generative models often rely on reference datasets, which may not always be available or suitable. Recently, reference-f... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer ohKy for his/her time and constructive feedback on our work. The following is our response to the reviewer’s comments and questions.
**1. Datasets in the numerical evaluation**
In the main text, we have discussed the numerical results on the following benchmark d... | Summary: This study proposes a computationally efficient metric for evaluating the performance of recent generative models. It highlights the limitations of using reference data, which can restrict the applicability of evaluation methodologies, and instead suggests a method utilizing kernel functions without references... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer NG2d for his/her time and constructive feedback and suggestions on our work. The following is our response to the reviewer’s comments and questions.
**1. FKEA computational costs compared to VENDI/RKE**
Re: To address the reviewer’s comment, we have measured the t... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback and suggestions. We have responded to each reviewer's comments and questions under the review-box. Here we upload the 1-page PDF including the figures and plots discussed in our responses.
Pdf: /pdf/485773f314e15740ede12c70e94bc44cda36bd7c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting | Accept (poster) | Summary: The authors present a new regularization for Gaussian splatting (GS) method that increases the Shannon entropy of the scale parameters (it was named erank as while rendering the scale is presented as a diagonal matrix, and the aforementioned calculation turns into the effective rank), to better reconstruct 3d ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for acknowledging the contribution of our work. We are grateful for the helpful reviews that could strengthen our paper.
### Tanks and Temples
>Thank you for the suggestion. We conducted the experiments after the submission, and the table below shows a general improveme... | Summary: 3D Gaussian Splatting is a remarkable technique in novel view synthesis. However, it usually degenerates into noodle-like shapes which sometimes bring visual artifacts and inaccurate geometry. This paper analyzes the phenomenon and proposes an effective rank loss to regularize the Gaussians. The technique seem... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for acknowledging the effectiveness and simplicity of our method. Additionally, we are grateful for pointing out the strong analysis and concise writing.
### Supplemental Video
>We promise to share the video results in the project page. Please stay tuned!
### Novel vie... | Summary: This paper performs a statistical analysis on 3DGS for its effective rank distribution of the learned Gaussians. It claims that most of the Gaussian learned are close to rank 1 effectively, giving needle-like artifacts in novel view synthesis and reconstruction. Hence, this paper proposes a regularization loss... | Rebuttal 1:
Rebuttal: We appreciate your high-quality review and detailed understanding of our work. We are also grateful for acknowledging the motivation and effectiveness of our method.
We agree that shedding light on and analyzing the causes of low-rank Gaussians would add value to our paper. Therefore, we conducte... | Summary: The paper identifies a common problem in 3D Gaussian splatting where Gaussians converge to needle-like shapes, with its variance mostly contained in one axis. These needle-shaped Gaussians can cause artifacts and inaccurate surface reconstruction.
To quantify this phenomenon, the paper uses the concept of eff... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for highlighting the importance of the task, as well as the effectiveness and clarity of our method. We are also grateful for suggesting potential baselines and ablations to strengthen our work.
### PhysGaussian
>Initially, we did not consider PhysGaussian due to its fo... | Rebuttal 1:
Rebuttal: We thank the reviewers for acknowledging the effectiveness of our work and highlighting the importance of the task. We are also grateful to all the reviewers for taking the time to read through the paper and providing detailed feedback. Your reviews are immensely helpful in strengthening our work.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding and Improving Training-free Loss-based Diffusion Guidance | Accept (poster) | Summary: This paper explores training-free loss-based diffusion guidance, providing an overview of how training-free guidance functions from an optimization perspective. It analyzes the challenges of training-free guidance, particularly regarding adversarial gradient issues and slower convergence rates. Additionally, t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful and constructive feedbacks. We have added more experiments, baselines, and ablation studies.
> Weakness 1: Assumption of Lipschitz continuity
The assumption of Lipschitz continuity is a standard prerequisite for analyzing diffusion models (e.g... | Summary: This paper explores the theoretical aspects of training-free loss-based guidance mechanisms in diffusion models and improves them from theoretical findings. The first results are in explaining why the guidance strength depended on $\sqrt{\alpha_t}$ worked well in FREEDOM. Then, the authors explore the adversar... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful and constructive feedbacks. Based on the comments, we have added more baselines.
> Weakness 1 and 2: Comparison with [A,B,C,D]
We appreciate the reviewers for highlighting these pertinent references. We have now included [B] (training-based PP... | Summary: This paper examines the mechanisms and limitations of training-free guidance for diffusion models and develops a collection of techniques to overcome the limitations accompanied by both theoretical and empirical results.
Strengths: 1. This paper performs the theoretical analysis on the training-free diffusion... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful and constructive feedbacks. We have added the baselines and integrated the proposed methods into MPGD and LGD-MC.
> Question 1: MPGD and LGD-MC with random augmentation and Polyak step size
We are grateful for your suggestions to enhance our ex... | Summary: The paper studies training-free loss-based diffusion guidance. in comparison to classifier-based guidance. First, the paper examines several drawbacks of loss-based guidance, including (1) while successful minimization of the loss can be achieved, it does not guarantee successful guidance, (2) loss-based gradi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your insightful comments and recognition of this work, especially acknowledging our theoretical contributions.
> Misaligned gradient.
Thank you for suggesting a better name. We agree that the misaligned better captures the behavior and will modify it in the la... | Rebuttal 1:
Rebuttal: # Global Rebuttal
We would like to express our sincere gratitude to all the reviewers for their constructive feedback and recognition of our work. We are particularly grateful for the acknowledgment of the theoretical contributions (Reviewer wPsc, Reviewer P7k4, and Reviewer DfoY), the novelty of... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals | Accept (poster) | Summary: This work presents BeatDiff, a lightweight denoising diffusion generative model for multi-lead ECG signal morphology, addressing the complexities of heartbeat analysis due to noise, missing leads, and limited annotated data. It introduces the EM-BeatDiff algorithm, which leverages BeatDiff as a prior within a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and the appreciation of our effort to apply and propose new methods to use diffusion models (DDM) in ECG. We now address the points raised by the reviewer.
* *Authors reimplement EkGAN, DeScoD, SSSD, WGAN on the PhysioNet...*: We thank the reviewer their comm... | Summary: The manuscript describes a diffusion model that provides a prior to a conditioned linear Bayesian inverse model to produce the heart beat normalized morphology of the 12 lead ECG signal from a single lead ECG measurement. The result is shown very promising results in finding anomalous heart, baseline wander... | Rebuttal 1:
Rebuttal: We would like to first thank the reviewer for taking the time to review our work.
Regarding the question formulated in the weakness section, indeed, it is generally accepted that increasing the number of parameters can improve model accuracy. However, in our case, we found that increasing the siz... | Summary: Authors introduces BeatDiff based on denoising diffusion generative model and EM-BeatDiff combining BeatDiff with an Expectation-Maximization. BeatDiff is used for various ECG tasks, including noise and artifact removal, 12-lead ECG reconstruction from a single lead, and unsupervised anomaly detection. The EM-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback.
* *There is a shortcoming in the selection of comparison models. Although performance evaluations were conducted on various tasks, different models were used for each task. The model should be comparable regardless of the task. A comprehensive co... | Summary: This paper introduces a Diffusion based generator of ECG heartbeat. The proposed technique builds on a very recently introduced sequential approach for diffusion models for linear inverse problems. The authors describe how elegantly the proposed approach can be used to solve several important ECG analysis prob... | Rebuttal 1:
Rebuttal: *… detect pathologies (such as MI) from the reconstructed precordial leads.* In our study, we show that it's possible to generate precordial leads from limb leads for healthy patients. This is because healthy patients have correct electrical signal conduction, and our model, trained on numerous he... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to give their much valued feedback to our work. We believe that the reviews and the discussion will contribute to improve the clarity of our work. We thank the reviewers for pointing out the interest, elegance and applicability of our approach. We have ad... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Constrained Diffusion Models via Dual Training | Accept (poster) | Summary: In this paper, the authors introduce a novel approach termed `Dual Training` for training constrained diffusion models, particularly focusing on scenarios involving `biased data generation`. Initially, the authors adeptly derive the learning objectives for diffusion models within a constrained optimization fra... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and the valuable feedback. We believe that we have fully addressed your concerns and will incorporate the points mentioned below into the final version. We would be happy to address any further questions you might have.
---
> **Major Issue** 1 ... the loss re-we... | Summary: This paper proposes Dual Training, a method designed to constrain the distributions that denoising diffusion models can learn. The authors propose an extension to the standard diffusion model training objective that minimizes the Kullback-Leibler (KL) divergence of the learned distribution with respect to two ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and the valuable feedback. We believe that we have fully addressed your concerns and will incorporate the points mentioned below into the final version. We would be happy to address any further questions you might have.
---
> **Weakness** 1 ... more challenging ... | Summary: This paper studies the constrained diffusion models, motivated by customizing the generation by specific tasks. The idea is to formulate KL divergence-constrained optimization problem (U-KL), which is shown to have zero duality gap. The convergence (rate) of the sampling process is given by assuming a mixture ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and the valuable feedback. We believe that we have fully addressed your concerns/questions and will incorporate all points mentioned below in the final version. If you have any further questions, please feel free to post them, and we would be glad ... | Summary: This paper aims to address the issue of generating biased data based on the training dataset for diffusion models. The authors introduce a constrained diffusion model by imposing the diffusion constraints based on desired diffusions that are informed by requirements/constraints. They propose a dual training al... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and the valuable feedback. We believe that we have fully addressed your concerns/questions, and will incorporate all points mentioned below in the final version. We would be happy to address any further questions you might have.
---
> **Weakness*... | Rebuttal 1:
Rebuttal: We thank the reviewers for recognizing the strengths of our contribution and providing valuable feedback. We believe that we have addressed your concerns and hope that you will reconsider our paper in light of our rebuttal. To better assist your evaluation, we summarize three shared concerns in th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
3D Structure Prediction of Atomic Systems with Flow-based Direct Preference Optimization | Accept (poster) | Summary: The paper proposes to apply the Direct Preference Optimization (DPO) procedure for the 3D Structure Prediction of atomic structure predictions. The approach is applied to crystal and antibody structures, considering different kinds of Gaussian Path for flow matching models, showing a general improvement in the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments, and answer the questions as follows.
> **W1: The significance of the work.**
Thank you for your kindly comment. Our paper, while applying DPO to atomic systems, introduces significant innovations tailored for this domain:
- **Extension of Gaussia... | Summary: The paper introduces a framework called FlowDPO subsuming different Gaussian probability paths for flow matching models predicting the 3D structure of atomic systems. Moreover, the authors propose a method for the automatic creation of a preference pair dataset used for finetuning a pre-trained flow matching m... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments, and answer the reviewer’s questions as follows.
> **W1: Missing motivation for a generative approach to 3D structure prediction of atomic systems.**
> **Q1.1: why would you use a generative approach?**
Thank you for your insightful question. The ... | Summary: This paper introduces FlowDPO, a framework that predicts 3D structures of atomic systems using diffusion flow matching models. To suppress hallucinations and improve sample quality, Direct Preference Optimization (DPO) is adopted to finetune a pretrained model using a preference dataset consisting of winning a... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments! We provide more explanations to address your concerns as follows.
> **W1: The motivation for symmetry preservation and transform invariance of the flow trajectory is unclear and seems not quite relevant to the main idea of using DPO to improve sample accurac... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers and ACs for their time and efforts on reviewing the paper. We are glad that the reviewers recognized the contributions of our paper, and appreciate the reviewers for their insightful comments. We provide additional visualizations and experment results in the supple... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers | Accept (poster) | Summary: In "Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers", the authors propose a novel method for integrating scalar fields, and more generally tensor fields, on trees. Given an input tree and potentially multiple tensor fields, FTFI constructs an auxiliary binary "IntegratorTree... | Rebuttal 1:
Rebuttal: **General comment:**
We would like to sincerely thank the Reviewer for the feedback. **We provide responses below and in the official comment titled: "Additional responses for Reviewer H9MT**.
**Fig. 5: FTFI vs BGFI accuracy-wise:**
Thank you very much for the comment. Given the substantial spe... | Summary: The paper tackles the problem of integrating tensor fields defined on graphs. The paper suggests Fast Tree-Field Integrators (FTFI) that integrate tensors on weighted trees with reduced time complexity, which is based on their data structure called IntegralTrees (IT). The idea is original and new, and the algo... | Rebuttal 1:
Rebuttal: **General comment:**
We would like to sincerely thank the Reviewer for the feedback.
**Improved presentation with the pictorial example in Sec. 3.2 (as Fig. 1):**
Thank you very much for the comment. Following Reviewer’s suggestion, we added a pictorial description of the divide-and-conquer met... | Summary: The authors propose an algorithm for exact polylog-linear multiplication for general weighted trees and cordial functions f, which leads to a fast algorithm for distance-matrix tensor multiplication as used in transformers and graph kernels. The core of the algorithm is a binary tree structure called integrati... | Rebuttal 1:
Rebuttal: **General comment:**
We would like to sincerely thank the Reviewer for the feedback.
**We provide additional responses in the official comment titled: "Additional responses for Reviewer Lag9".**
**A conclusive experiment showing integration into SOTA and positive impact on the quality/efficiency... | Summary: This paper proposes faster matrix multiplication algorithms for a class of structured matrices, namely, f-distance matrices, where the $(i, j)^{th}$ entry of the matrix is $f(\text{dist}(i, j))$, for nodes $i, j$ in a graph. The authors propose an algorithm, referred to as Fast Tree-Field Integrators (FTFI), f... | Rebuttal 1:
Rebuttal: **General comment:**
We would like to sincerely thank the Reviewer for the feedback.
**Processing time of BTFI vs BGFI in Fig. 4:**
Thank you very much for an excellent comment. We have realized that for BTFI we unnecessary applied Kruskal algorithm two times to compute the minimum spanning tre... | Rebuttal 1:
Rebuttal: **Additional pdf with updated plot for Fig. 4 and an additional visualization for the paper**
We would like to sincerely thank all the Reviewers for the very valuable comments and feedback. We summarize our rebuttals in the official comment below and then provide responses to the individual quest... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Elliptical Attention | Accept (poster) | Summary: This manuscript introduces Elliptical Attention, a new approach employing the Mahalanobis distance metric to calculate attention weights. This method delineates a hyper-ellipsoidal neighborhood around each query, amplifying the attention weights of tokens situated in contextually pivotal directions. When compa... | Rebuttal 1:
Rebuttal: **Q1. [Provide results for DeiT and DeiT-Elliptical at additional model sizes]**
For convenience, we combine Tables E.1 and E.2 in the global response attachment into a single Table E below, which shows clean and robust performance of DeiT and DeiT-Elliptical at a larger 22.1M param model consist... | Summary: The paper proposes a new class of self-attention mechanism for transformers. It uses Mahalanobis distance to form hyper-ellipsoidal attention regions around queries, aiming to improve model robustness and reduce representation collapse. This approach is demonstrated to be effective across various tasks like la... | Rebuttal 1:
Rebuttal: **Q1. [Provide further details on experimental setup]**
**Answer:** We thank the reviewer for pointing out missing details on the experimental setup, and we have added to Appendix F additional details on hyperparameters, training procedure, and optimizer specification. Papers using the same exper... | Summary: This paper propose a novel attention mechanism, named Elliptical Attention.
Elliptical Attention use a Mahalanobis distance metric to stretch the underlying feature space in directions of high contextual relevance.
The Elliptical Attention pays more attention to contextually relevant information, rather than f... | Rebuttal 1:
Rebuttal: **Q1. [Provide FLOPs and parameter count]**
**Answer:** We thank the reviewer for pointing out the important consideration of FLOPs and parameter count for a fuller understanding of the comparative efficiency analysis. We refer the reviewer to Tables A and B in the global response for the additio... | null | null | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
Thanks for your thoughtful reviews and valuable comments, which have helped us improve the paper significantly. We are encouraged by the endorsements that: 1) the proposed method of using hyper-ellipsoidal attention regions is a novel and theoretically well-supported approa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities | Accept (poster) | Summary: The paper presents a regularized version of Frank-Wolfe algorithm for monotone variational inequalities for which the authors an $\tilde{O}(T^{-1/2})$ convergence rate of the last iterate. In the stochastic case, using the variance reduction technique, the authors show an algorithm that enjoys $\tilde{O}(T^{-1... | Rebuttal 1:
Rebuttal: >Comment: In Line 808 second equation, $w_t$ does not appear. How would the authors fix this issue? It seems non-trivial to me, although in the easier case when $w_t=0$, the proof will work.
**Response:** We greatly appreciate the reviewer for carefully reading our work. The missing $w_t$ in Line... | Summary: This paper focuses on solving the monotone MVI. To address this problem, the authors propose a new algorithm, which is a generalized variant of the Frank-Wolfe method. They also investigate the $O(T^{-½})$ last-iterate convergence, which matches the best-known results in this context. Additionally, they presen... | Rebuttal 1:
Rebuttal: >Comment: From my understanding, the key idea of the Frank-Wolfe method lies in the linear minimization oracle (LMO) ...
**Response:** We agree with the reviewer that by adding a regularizer, one can no longer use an LMO to find the Frank-Wolfe direction. However, also due to the regularizer, the... | Summary: This paper presents combines Frank-Wolfe algorithm and entropy regularization trick to propose a generalized Frank-Wolfe algorithm for solving MVI problems. The paper proves $O(T^{-1/2})$ last-iterate convergence rate for the deterministic case. And it extends the algorithm to stochastic MVIs with $O(T^{-1/6})... | Rebuttal 1:
Rebuttal: >Comment: I think there's no apparent weaknesses. I suspect the analysis for the stochastic case might not be tight, leaving room for improvement.
**Response:** We greatly appreciate the reviewer acknowledging the contributions of our work. We agree with the reviewer that the convergence rate of ... | Summary: This paper proposes a Frank-Wolfe algorithms for solving monotone VI problems, for both deterministic and stochastic settings, proof that the first one matches the optimal convergence rate, and provide last-iterate convergence guarantees for the second one, which is a novelty if no curvature requirement is imp... | Rebuttal 1:
Rebuttal: >Comment: Despite the theoretical focus of the paper, it is recommended to make experiments more contentful by considering other problems ...
**Response:** We thank the reviewer for the suggestion. In the next version, we will include an additional numerical example to demonstrate the performance... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Base of RoPE Bounds Context Length | Accept (poster) | Summary: The paper investigates the role of RoPE in long-context LLMs. It highlights that the base of RoPE crucially affects the model's ability to handle long contexts. This paper derives a theoretical (and empirical) lower bound for the base value required to maintain long-context capabilities and validates this thro... | Rebuttal 1:
Rebuttal: **Q1:The Desiderata 2. The similar token gets more attention in Section 4 seems intuitively correct but may not be empirically correct. A thorough empirical verification is a must-have.**
A1: In our paper, the similarity between tokens measured by the cosine similarity(A) of their corresponding h... | Summary: ROPE is wildily employed in popular LLMs which encodes positional information with a rotation matrix. Although RoPE is used to enhance long-context capabilities by adjusting its base parameter to address OOD issues, this paper finds that this may result in only superficial long-context abilities. Authors re-ev... | Rebuttal 1:
Rebuttal: **Q1:Regarding the "Desiderata 2 The similar token gets more attention", recently StreamLLM shows that there exists "attention sink" in popular LLMs. Namely most of tokens attend to the first few tokens. This somehow contradicts with the principle that "the similar token gets more attention". Cou... | Summary: This paper investigates the role of Rotary Position Embedding (RoPE) in Large Language Models (LLMs), with a focus on the relationship between RoPE's base and the model's long context ability.
The study looks into the long-context abilities and limitations of current methods that rely on smaller RoPE bases. W... | Rebuttal 1:
Rebuttal: **Q:Extensively test the model using benchmarks such as RULER. Provide more empirical observations on the relationship between the base of RoPE and model performance on those challenging benchmarks.**
A: We greatly appreciate your suggestion. We evaluated Llama2-7b on RULER, and the evaluation r... | Summary: Hi Area Chair, I am not qualified for the review of this paper, this is out of my knowledge scope.
Strengths: n/a
Weaknesses: n/a
Technical Quality: 3
Clarity: 3
Questions for Authors: n/a
Confidence: 1
Soundness: 3
Presentation: 3
Contribution: 3
Limitations: n/a
Flag For Ethics Review: ['No ethics... | null | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, and Program Chairs:
We would like to express our gratitude to all reviewers for taking their valuable time to review our paper. We sincerely appreciate all reviewers for their positive comments on our theoretical analysis, technique soundness and contribution.
Meanw... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning | Accept (spotlight) | Summary: This paper proposes a novel tokenization method for DNA sequence foundatnion model. According to the authors' experiments, their model has similar performances by comparing with other foundation models with larger scale, while their scalability looks better.
Strengths: The tokenization process is interesting,... | Rebuttal 1:
Rebuttal: We appreciate Reviewer tV95's thorough review and valuable comments. We address the reviewer's questions below:
>**1. Their proof majorly focus on arguing the problems of k-mers tokenization. However, some baselines like DNAHyena utilizes single nucleobase as tokens. Is it possible to justify the... | Summary: The paper explores the use of k-mer profiles in genome representation for metagenomic binning. The authors propose a new, scalable model that leverages k-mer profiles to represent DNA fragments efficiently. This model is theoretically grounded and empirically validated against state-of-the-art genome foundatio... | Rebuttal 1:
Rebuttal: We appreciate Reviewer 8HNd 's assessment and valuable comments. We address the reviewer’s questions in detail below:
**1. Comparison Basis:**
In the literature, $k$-mers are widely used as feature vectors, but their effectiveness has not been examined well and is often attributed solely to pra... | Summary: The authors describe an embedding approach for binning metagenomic reads via a k-mer approach. The authors motivate the need for scalable solutions for metagenomic analysis and provide theoretical motivation for k-mer based approaches, which have value on their own. Then they motivate a strategy to learn non-l... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer e86P for the constructive review and helpful recommendations. We address Reviewer e86P’s concerns in detail below.
> **The authors may want to acknowledge a little more in depth that there are also excellent algorithmic solutions for metagenomic binning that are direc... | Summary: The authors provides a theoretical analysis for k-mer-based representation of genomes and then propose a lightweight and scalable model for performing metagenomic binning at the genome read level, relying on the k-mer compositions of the DNA fragments and achieve pretty good results.
Strengths: 1. The logical... | Rebuttal 1:
Rebuttal: We are thankful to Reviewer xd8g for the thoughtful feedback and suggestions. We address the reviewer's questions in detail below:
**1. Limited comparison with the current methods.**
Thank you for your helpful reference, which we have added to the conclusions/future work section. We agree that ... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable feedback and thoughtful insights, which we believe will greatly enhance the quality and clarity of the final version of the paper. While we have addressed each reviewer's questions in their respective rebuttal sections, we would like to provi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation | Accept (poster) | Summary: This paper proposes a novel PEFT method that inspired by the Quantum Circuit. Their method is theoretically supported by the universality theorem and the rank representation theorem to achieve efficient high-rank adaptations on various downstream tasks. The QuanTA method surpasses LoRA, DoRA and even fine-tuni... | Rebuttal 1:
Rebuttal: We are very appreciative of the reviewer's valuable comments and suggestions! We address the raised weaknesses below.
**Weaknesses:**
>I'm curious about the performance of QuanTA on textual understanding (classification) tasks with BERT or RoBERTa. Afterall, all these PEFT models were initially ... | Summary: The paper proposes an efficient fine tuning method inspired from quantum circuits for large scale pretrained models. This is an alternative lorank approximation to weight updates (LoRA). The paper considers decomposing the dimension d of the square weight matrix as d=d1xd2x...xdN) .The hidden vector in d dime... | Rebuttal 1:
Rebuttal: We are very appreciative of the reviewer's valuable comments and suggestions! We address the raised weaknesses and questions below.
**Weaknesses:**
>The paper does not discuss the ordering over the pairs of qubits chosen.
Thanks for the question. While the ordering of pairs in general should no... | Summary: The authors address the issue that low-rank adaptation methods fail when applied to more complex tasks. They clearly present this motivation through experiments on two datasets of varying complexity. Drawing inspiration from quantum information science, the authors propose using the formalism from quantum circ... | Rebuttal 1:
Rebuttal: We are deeply appreciative of the reviewer's valuable comments and suggestions! We address the raised weaknesses and questions below. Due to the rebuttal length limitation, we address the weaknesses in this post and the questions in a new post.
**Weaknesses:**
>Please clarify the start and end l... | Summary: This work proposed a method, QuanTA, that leverages quantum-inspired techniques for efficient fine-tuning of large pre-trained language models. The authors show that it outperforms Low-Rank Adaptation (LoRA) in complex tasks and has significant improvements in common-sense and arithmetic reasoning.
Strengths:... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions! We address the raised weaknesses and questions below.
**Weaknesses:**
>the comparison of related tensor-based approaches is missing, it is unclear the outperformance of the proposed method over related previous works.
We thank the... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable comments! We appreciate all reviewers for noting the novelty of our work as well as our strong theory and experiment results.
Below, we summarize the major concerns of the reviewers and our responses.
>In general, reviewers agree that our results are... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SE(3)-bi-equivariant Transformers for Point Cloud Assembly | Accept (poster) | Summary: This paper presents an end-to-end framework for pointcloud correspondence and relative pose estimation with bi-equivariance to per-part poses. The framework is also equivariant to scaling and swapping part orders. It also designes a transformer architecture with $\mathrm{SE}(3)\times\mathrm{SE}(3)$-equivarianc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. We address the concerns below.
1. I feel "shape registration" is a more proper description of the task in the paper.
We use the word "assembly" following [7], where two pieces of point cloud are matched together. We avoid using the word "registrat... | Summary: This paper introduces a new network to solve the point cloud assembly task, where a 3D transformation is predicted to “align” two point clouds. The proposed network, BTIR, is designed to enforce the symmetries of the point cloud assembly task into the network layers. Specifically, it enforces SE(3) bi equivari... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. We address the concerns below.
1. Robotic manipulation papers including Diffusion-edfs, Tax-pose and Sim3
Yes. Those are related papers. We have now cited them, but as noted in line 889, we are not able to conduct quantitative comparisons, because t... | Summary: This paper proposes an SE(3)-bi-equivariant approach for point cloud assembly, addressing the difficulty of assembling non-overlapping point clouds where traditional correspondence matching methods struggle. The proposed BITR (BI-equivariant TRansformer) solves this problem by exploiting the symmetry of the po... | Rebuttal 1:
Rebuttal: Thanks for the careful reading of our paper.
We are happy you like the extended U-BITR model which is not even included in the main text due to space limitations.
Thanks for bringing up [4,5], we have read through these papers, and they indeed seem useful for accelerating our method.
We now addres... | Summary: The paper addresses the problem of assembling point clouds. Given two partial, potentially unmatched point clouds, the objective is to determine the rigid transformation that best aligns them in relation to the unknown complete shape. The proposed approach is a learning-based method, utilizing an architecture ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. We address the concerns below.
1. The presentation quality could be improved. For example, figure 2 ... The description of the toy experiment..
We apologize for the lack of clarity. We kept all descriptions concise to fit in the limited 9-page const... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a novel architecture that extends the SE(3) transformers to become bi-equivariant for the task of rigid point cloud assembly. That is the authors enforce a single assembly for all rigid transformations of the source or target point clouds that is learnable by the network. They also propose ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort. We address the concerns below.
1. The setup of zero overlap is not addressed properly when equivariant constraints are enforced...
The uniqueness and overlap ratio are completely different concepts. There can be non-unique assembly even in the fully... | null | null | null | null | null | null |
SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models | Accept (poster) | Summary: This work introduces a diffusion-based makeup transfer method named Self-supervised Hierarchical Makeup Transfer (SHMT). The proposed method features a network that extracts makeup features from a distorted makeup image and aligns these features with facial features. The facial features consist of two componen... | Rebuttal 1:
Rebuttal: > 1. It is unclear how the complexity of makeup is judged. Is it determined automatically or by human evaluation? If judged by humans, how is the parameter Laplace feature chosen during training?
Thank you for your question. In our opinion, we believe that each customer who uses a makeup transfer... | Summary: This paper deals with the problem of makeup style transfer given a certain facial image. Current methods usually use synthesized ground truths to guide the model training, which is sub-optimal. This paper proposes to decompose the hierarchical texture details using a Laplacian pyramid and selectively introduce... | Rebuttal 1:
Rebuttal: > 1. In Fig4, it seems the makeup transfer results could be influenced by the features of the reference image. For example, if the given reference image has a darker skin color, then the generated result would have a darker color, which might means the generation results could influenced by some i... | Summary: The paper proposes a method for improving the natural look resulting after makeup transfer. The method is derived from Latent Diffusion Mode and is "destroys" the content to distillate the makeup, that is further difused. The method is evaluated on MT, Wild-MT, LADN datasets.
Strengths: 1. Visual appeal of t... | Rebuttal 1:
Rebuttal: > 1. The technical innovation is limited as mainly there are several prior works put together (although in a non-obvious way) and applied to a new theme (i.e. makeup transfer).
2. Strictly from a machine learning point of view, the paper is not very interesting, as all the models are known.
Than... | null | null | Rebuttal 1:
Rebuttal: Here, we attempt to solve the ethical concerns of Reviewer JmHE as follows:
> 1. Ethical concerns: Potential risk: it has been accepted that "Facial customization for makeup transfer offers an entertaining tool for generating realistic character photos. However, if misused, it could potentially pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection | Accept (poster) | Summary: This paper proposes a radar-camera and temporal fusion method (CRT) for the 3D object detection task. CRT design Multi-View Fusion (MVF), Motion Feature Estimator (MFE), and Motion Guided Temporal Fusion (MGTF) modules. MVF employs radar information for 2D-to-3D projection. MGTF utilizes velocity and occupancy... | Rebuttal 1:
Rebuttal: **W1: The proposed method requires velocity supervision, while some datasets, such as Waymo, do not provide velocity labels.**
The velocity ground truth (GT) for objects is provided as state information in the nuScenes dataset. Without this velocity information, we may need to calculate the deriv... | Summary: This paper presents a method, called CRT-Fusion, for 3D object detection that fuses temporal information with radar-camera features. The CRT-Fusion captures the object motion with three modules: Multi-View Fusion (MVF), Motion Feature Estimator (MFE), and Motion Guided Temporal Fusion (MGTF). The MVF module us... | Rebuttal 1:
Rebuttal: **W1: It is unclear how to address error propagation across frames.**
Error propagation in CRT-Fusion has been addressed by (1) taking a weighted sum of aligned BEV feature maps through the Gated Fusion Network (GFN) and (2) applying MGTF only for absolute velocity predictions above a threshold (... | Summary: In this study, the authors present CRT-Fusion, an innovative framework designed to incorporate temporal information into radar-camera fusion, thereby enhancing the robustness of 3D object detection. The CRT-Fusion framework is composed of three integral modules: Multi-View Fusion (MVF), Motion Feature Estimato... | Rebuttal 1:
Rebuttal: **W1: The paper lacks a detailed description of the loss function used in the proposed framework.**
The total loss function used in CRF-Fusion consists of five loss terms: a standard 3D object detection loss term and four additional loss terms derived from different head networks within our model... | Summary: The paper proposes CRT-Fusion that fuses radar and camera inputs for 3D object detection in BEV space. A radar-camera azimuth attention module is proposed for improving image features for better motion awareness, which further improves the quality of image BEV features. A motion-guided temporal fusion is intro... | Rebuttal 1:
Rebuttal: **W1: The inference latency and required GPU memory will grow linearly with the number of past frames.**
Our model utilizes a memory bank structure where previous BEV features obtained through a cascade of backbone, MVF, and MFE are stored in the buffer to reduce redundant computations. Thus, onl... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews. We have provided responses to the common questions raised by the reviewers below. Additionally, we have attached PDF files presenting qualitative results, as well as the analysis of GPU memory usage, latency, and performance relative to the number of frames.
... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper talks about multi sensor fusion using Motion Information for Bird’s Eye View Object
Detection. Authors have presented a multiple step approach to improve the object detection on nuScenes dataset. The proposed approach comprises of three parts; Multi-View Fusion (MVF) that enhances depth prediction by... | Rebuttal 1:
Rebuttal: **W1: The novelty of the paper is limited.**
We are sorry that the reviewer does not appreciate the contribution of our study, to which we have dedicated much effort. We believe that our work can make a substantial contribution to enhancing the effectiveness of temporal fusion in the field of rad... | null | null | null | null | null | null |
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad | Accept (poster) | Summary: This paper presents a novel algorithm, KATE, which demonstrates impressive scale-invariance properties for Generalized Linear Models.
Strengths: This paper presents a novel algorithm, KATE, which demonstrates impressive scale-invariance properties for Generalized Linear Models. The thorough theoretical analys... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed review. Below, we address the reviewer's questions and concerns.
> **Firstly, the paper could benefit from a more detailed discussion of the limitations of the proposed algorithm, especially in scenarios where certain assumptions may not hold.**
Thank you for... | Summary: This work proposes an optimizer that achieves the optimal convergence guarantee for smooth nonconvex settings and more importantly the scale-invariance property.
Strengths: The paper is very cleanly written. It was very easy to follow.
The main results look sound, and the experimental results are well present... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed review. Below, we address the reviewer's questions and concerns.
> **My only concern is the importance of the problem it tackles. Scale-invariance is definitely a desirable property, but I'm not sure what advances it brings about for ML optimization. What I me... | Summary: The paper introduces a novel optimization algorithm which demonstrate scale-invariance property for generlized linear models unlike Adagrad. The authors analyzed KATE for smooth and non-convex functions and on generalized linear models to obtain the same convergence upper bounds (asymptotically) as Adagrad and... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed review and positive evaluation. Below, we address the reviewer's questions and concerns.
> **I think authors need to emphasize the first point in Strengths by comparing with the diagonal online-newton method (diag-SONew) [1], which is also a scale-invariant al... | Summary: This paper proposes a scale-invariant variant of AdaGrad, called KATE, particularly for generalized linear models. Theoretically, the authors proved a convergence rate of $\mathcal{O}(\log T/\sqrt{T})$ for KATE, matching the best known rates for AdaGrad and Adam. Numerical experiments are used to illustrate KA... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed review and positive evaluation. Below, we address the reviewer's questions and concerns.
> **While it could be harder to demonstrate the scale-invariant property of KATE with real data experiments, is it possible to demonstrate the scale-invariant property of ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and time. We appreciate that the reviewers acknowledged the following strengths of our work:
- Reviewer ZFDF recognises the importance of developing a scale-invariant version of AdaGrad.
- Reviewer uk2H finds the scale-invariance property of KATE very imp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent | Accept (poster) | Summary: This paper studies the problem of learning the $k$-sparse parity problem over the $d$-dimensional boolean hypercube, using a two-layer neural network. The main result is that a specific modification of online SGD, called "sign SGD," can learn the k-parity problem $n = \tilde O(d^{k-1})$ samples and a network w... | Rebuttal 1:
Rebuttal: Thank you for your support and constructive feedback. We address your questions and provide clarifications below.
***
**Q1**. My main issue with this paper is the choice of the Sign SGD algorithm. Unlike vanilla SGD, Sign SGD is not invariant to the choice of the basis, and is implicitly taking ... | Summary: This paper considers the problem of learning a $k$-parity function on the $d$-dimensional hypercube using SGD on a two-layer neural network. They consider a specific choice of activation ($\sigma (x) = x^k$) and training dynamics (correlation loss, online sign SGD, fixed second layer weights) and show the foll... | Rebuttal 1:
Rebuttal: Thank you for your support and helpful comments. Below, we address the questions.
***
**Q1**. I think it was already quite widely believed that SGD on NNs can match the SQ lower bound (this is indicated in many papers on learning sparse functions on the hypercube/multi-index functions on Gaussia... | Summary: The paper addresses the k-sparse parity problem, a fundamental one in computational complexity and algorithmic theory, by using stochastic gradient descent (SGD) on two-layer fully-connected neural networks. The authors demonstrate that SGD can efficiently solve the problem on a d-dimensional hypercube with a ... | Rebuttal 1:
Rebuttal: Thank you for your support and valuable feedback. We address your questions as follows.
***
**Q1**. The results, both in their scope and style, are more aligned with theoretical computer science (CS) than machine learning (ML). While the mathematical contributions are significant, the immediate a... | Summary: This work considers learning k-sparse parities with two layer neural networks trained with stochastic signed gradient descent, where the second layer is fixed at initialization and the first layer is trained. It is shown that with the activation $\sigma(z) = z^k$, a network with width $2^{\Theta(k)}$ can solve... | Rebuttal 1:
Rebuttal: Thank you for your support and constructive feedback. We address your questions as follows.
***
**Q1**. While the goal of this work is to understand the training dynamics of modern neural networks, given the use of a polynomial activation and signed gradients, it is not clear to what extent the ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Kernel PCA for Out-of-Distribution Detection | Accept (poster) | Summary: The paper describes a method for out-of-distribution (OOD) detection. Kernel PCA is applied to the penultimate features of a neural network. Two kernels are chosen motivated by prior work on nearest-neighbour-based detection. Namely the cosine kernel and a cosine-Gaussian kernel. In experiments, both methods o... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments, each of which will be answered pointwisely below.
Due to length limit, we
- use "W", "Q" for Weakness, Question,
- put the references in global response,
- put the mentioned figures in the one-page rebuttal PDF file.
---
### **(W1) The usage of RFF approximatio... | Summary: The paper everage the framework of Kernel PCA (KPCA) for OoD detection, and seek suitable non-linear kernels that advocate the separability between InD and OoD data in the subspace spanned by the principal components. Besides, explicit feature mappings induced from the devoted task specific kernels are adopted... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments, each of which will be answered pointwisely below.
Due to length limit, we
- use "W", "Q", "L" for Weakness, Question, and Limitation,
- put the references in global response,
- put the mentioned figures in the one-page rebuttal PDF file.
---
### **(W1) More com... | Summary: The paper introduces a method that applies Kernel Principal Component Analysis (KPCA) to the output of the penultimate layer of a Deep Neural Network (DNN) for out-of-distribution (OOD) detection. Inspired by previous approaches in OOD detection that utilize K-nearest neighbors (KNN) on $\ell_{2}$ normalized f... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments, each of which will be answered pointwisely below.
---
### **(Weakness 1 & Question 1) Selection of kernels/feature mappings**
We would like to firstly elaborate the effectiveness and novelty of our KPCA method (*Weakness 1*), and then discuss more kernel selectio... | Summary: In this work, the authors leverage the framework of Kernel Principal Component Analysis (KPCA)
for Out-of-Distribution (OoD) detection, aiming to enhance the separability between In-Distribution
(InD) and OoD data within the subspace defined by the principal components. The study introduces
two task-specific k... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments, each of which will be answered pointwisely below.
Due to length limit, we
- use "W", "Q", "L" for Weakness, Question, Limitation,
- put references and mentioned figures in global response and one-page rebuttal PDF file,
- only show the average FPR in tables and ... | Rebuttal 1:
Rebuttal: Dear Program Chairs, Area Chairs, and Reviewers,
First of all, we would like to thank you for your time, constructive suggestions, which greatly help us improve the work. In this global response, we
- provide full results of tables in the response to Reviewer **sSKR**,
- gather the shared referen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective | Accept (poster) | Summary: This article focuses on enhancing time series forecasting capabilities using timestamps and introduces a plug-and-play module called GLAFF. Overall, GLAFF is designed to be simple and lightweight, significantly improving the predictive performance of existing time series forecasting algorithms such as ITransfo... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Some typos in the paper.**
We apologize for the oversight that led to grammatical errors in the paper, causing issues with your reading. These typos will be corrected in the final version.
**Q2: ... | Summary: This paper introduces the GLAFF framework where time series models are adapted to also capture "global" information by using information content in the datetime parsed in components of habitual meaning to complement baseline models ("backbones"). The global information is represented through a Mapper, quantil... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Explanation of the motivation for the component.**
We apologize for the confusion. We will further explain the role of the denormalizer in mitigating data drift. The statistical characteristics (e... | Summary: The paper introduces GLAFF, a novel framework that enhances time series forecasting by modeling timestamps to capture global dependencies and adaptively balancing global and local information, resulting in a significant improvement of 12.5% over existing methods in experiments across nine real-world datasets.
... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We will answer the questions one by one.
**Q1: Comparison with additional baselines.**
Thank you very much for your supplement. All three articles have significant contributions to our field. However, there seems to be some misunderstanding regarding our work. ... | Summary: This paper proposes GLAFF which encodes the time stamps of time series and performs self attention across the encodings of the time dimensions, combining it with the output of a global time series forecaster via a learned weighting scheme. As GLAFF is a set of feature constructions, it’s generally additive in ... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Usefulness of the proposed method in non-regular forecasting.**
The initial point to state is that the timestamp information we integrate has been extensively utilized, though ineffectively, by va... | Rebuttal 1:
Rebuttal: We provide some images and tables **in the PDF attachment of the global response**, accompanied by detailed descriptions in the individual responses for each reviewer.
Pdf: /pdf/915adbffe9fe17fdb1cf701cc0e15a301ebef5ae.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors proposed a plugin to utilize global information from timestamps in time series forecasting tasks. The proposed plugin consists of three main components: attention-based timestamp mapper, robust denormalizer and adaptive combiner. The authors found that using the proposed plugin in combination with ... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Explanation of computation time and memory consumption.**
As stated in Appendix D, higher prediction accuracy is typically accompanied by a higher computational cost. Balancing these needs require... | null | null | null | null | null | null |
Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers | Accept (poster) | Summary: This manuscript addresses the challenge of learning robust feature representations from datasets with heterogeneous channels, where the number and type of channels vary during training and testing. The authors propose a new DiChaViT, a model for Multi-Channel Imaging (MCI) based on the Vision Transformer (ViT)... | Rebuttal 1:
Rebuttal: > Can you provide an in-depth analysis on the effect of losses on the distribution and content of channel and patch tokens?
Thank you for your valuable suggestions! Please refer to Fig. 1a for the effect on channel token content and Fig. 7 the effect on channel token distribution. Additionally, ... | Summary: The authors provided the plug-and-play framework for MCI analysis, considering the classification as a downstream task. To do so, they introduced two-channel and token diversification strategies, and in addition, they proposed a new channel sampling strategy for converging the training faster.
Strengths: - Fa... | Rebuttal 1:
Rebuttal: > The authors repeatedly stress that each modification in DCS and TDL helps ensure the model's robustness. What ablation study did you apply to endorses it? Did you do any statistical testing on your experiments?
Table 3 of our paper presents a leave-one-out ablation study, highlighting that best... | Summary: In this paper, the authors present an improved methodology for modeling hyper-spectral multi channel images that can support a variety of channel configurations at test time. The authors reason that the current baselines treat all channels equally and do not consider the diverse qualities of each channel type.... | Rebuttal 1:
Rebuttal: > 1. It is important to investigate and interpret where the new setup is improving the performance. Additional experiments on interpretations and comparisons of predictions between baseline methods and proposed methodology might help address this weakness.
Thank you for your valuable suggestions!... | Summary: Multiple channel imaging (MCI) is widely used in different application domains, ranging from medical image analysis to satellite imagery. Each channel can contain information that is orthogonal compared to other channels, which can be useful in downstream tasks. Obtaining such orthogonal signals from individua... | Rebuttal 1:
Rebuttal: > **Unseen channels during training**: One of the key limitations of this work, which is also briefly acknowledged by the authors, is that the method is only focused on dealing with missing channels only at inference. Meaning, all channels have to be seen by the model during training. This might n... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments and suggestions on the result analysis!
To get more insights, we conducted some additional analyses and attached the figures and tables in the PDF for the rebuttal.
## 1. How Do Channel-Specific Attention Distributions Differ Between Channel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution | Accept (poster) | Summary: The paper proposes the ImOOD framework to address class imbalance issues in OOD detection. Through Bayesian analysis, the authors identify critical biases and provide a unified regularization technique to improve detection performance. They conduct extensive experiments, demonstrating ImOOD's effectiveness on ... | Rebuttal 1:
Rebuttal: Dear reviewer XZSm:
We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below:
> **W1**: The method relies on external OOD data, which is often difficult to obtain in OOD detection settings.
**A**: We follow PASCL[1]'s setting... | Summary: This manuscript introduces ImOOD, a statistical framework addressing the OOD detection problem in imbalanced data distributions, identifying common issues such as misidentifying tail class ID samples and erroneously predicting OOD samples as head class ID. It reveals a class-aware bias between balanced and imb... | Rebuttal 1:
Rebuttal: Dear reviewer QTK3:
We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below:
> **W1**: It seems that OOD detection under class imbalance is merely an overlap of the two tasks: class-imbalanced learning and OOD detection. Usin... | Summary: The paper addresses the challenge of detecting and rejecting out-of-distribution (OOD) samples by neural networks, particularly when the in-distribution (ID) data is inherently imbalanced. The authors observe that existing OOD detection methods struggle under these conditions primarily because they either misc... | Rebuttal 1:
Rebuttal: Dear reviewer E388:
We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below:
> **W1**: A minor issue is that, when we know in advance that the data are long-tailed, it is a common practice that we will use the long-tailed lea... | Summary: The paper focuses on imbalanced data distribution, and finds that there is a bias term between balanced and imbalanced classification which can be used to explain the performance gap. To account for this bias, the authors introduce a regularization term, and show improved results on benchmarks.
Strengths: - I... | Rebuttal 1:
Rebuttal: Dear reviewer 5Lex:
We thank the reviewer for the valuable time and constructive suggestions, and our point-to-point responses are presented below:
> **W1**: Train-time regularization techniques cannot be applied to existing pretrained models, which may limit their adoption.
**A**: We have also... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable time and constructive suggestions when evaluating our manuscript. We are really encouraged to see **ALL** reviewers find our method **interesting and theoretically grounded** to formulate the gap for imbalanced OOD detection, and **effective and generalize... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization | Accept (poster) | Summary: This paper focuses on generating ligands with desired properties, such as high binding affinity, for the protein-conditioned ligand generation task. The authors propose a Preference Optimization (PO)-based fine-tuning method for pre-trained generative models. They extend DPO (Data-driven Preference Optimizatio... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and questions! The replies to your questions are listed below:
> **[Q1] Practical impact**
Our benchmark results showcase the practical impact of optimizing molecules with user-defined reward functions. A lower binding energy between a protein and a ligan... | Summary: This paper presents ALIDIFF, a framework that aligns pre-trained target-aware molecule diffusion models with desired functional properties using preference optimization. The key contribution of ALIDIFF is the Exact Energy Preference Optimization method, which precisely aligns diffusion models to regions with l... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and questions! The replies to your questions are listed below:
> **[W1] Methodological novelty**
We argue our novelty for not only model design but also method formulation:
1. The DPO framework is originally proposed for optimizing language models. In thi... | Summary: This paper proposes a novel and general alignment framework to align pretrained target diffusion models with preferred functional properties, named AliDiff. AliDiff adjusts the target-conditioned chemical distribution toward regions characterized by lower binding energy and structural rationality. AliDiff can ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and questions! The replies to your questions are listed below:
> **[W1] Alternative dataset**
AliDiff’s training requires the dataset to have multiple ligands with one single protein. CrossDocked2020 is one of the largest synthetic datasets where it has m... | Summary: This paper proposes a novel alignment framework, known as ALIDIFF, to align pretrained target diffusion model with preferred functional properties for structure-based drug design. ALIDIFF shifts the target-conditioned chemical distribution towards regions with higher binding affinity and structural rationality... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and questions! The replies to your questions are listed below:
> **[W1] Lipinski metric should also be reported in Table 1.**
Thank you for your suggestions. To evaluate drug-likeness, we have compared QED across all comparison methods. Lipinski's Rule o... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning | Accept (poster) | Summary: This paper introduces DDFed, which combines FHE (F8lly Homomorphic Encryption) and similarity computation/collaborative selection, to achieve privacy protection and attack mitigation, respectively. The idea of this paper is to leverage FHE within Federated Learning (FL) as a strategy for protecting privacy wh... | Rebuttal 1:
Comment: We appreciate the reviewer's comments and the raised concerns.
**Resp to Q1:**
DDFed moves the comparison task to the client side because no FHE scheme efficiently supports both comparison operations and floating-point numerical computation over encrypted model updates. FHE schemes are generally c... | Summary: This paper proposes a novel Byzantine-robust and differentially private federated learning (FL) framework, named as Dual Defense (DDFed). To guarantee the privacy, DDFed utilizes a secure similarity computation based on fully homomorphic encryption, without leaking client’s privacy to either the server or any ... | Rebuttal 1:
Comment: We appreciate the reviewer's concerns and suggestions.
**Resp to Q1:**
The primary purpose that we initiated the attack at round 50 is to demonstrate the effectiveness of defense mechanisms and clearly show the comparative effects of different defense methods before and after an attack. This setup... | Summary: This paper introduces a Dual Defense Federated learning (DDFed) framework. DDFed simultaneously boosts privacy protection and mitigates poisoning attacks leveraging fully homomorphic encryption (FHE). The experiments with publicly accessible datasets demonstrate DDFed’s effectiveness in safeguarding model priv... | Rebuttal 1:
Comment: We appreciate the reviewer's concerns and suggestions.
**Resp to major concern 1:**
Collusion between the server and clients is beyond the scope of our threat model assumptions in Section 3.1. In PPFL, each solution includes a threat model that defines the adversary's capabilities and behavior, ... | Summary: This paper introduces Dual Defense Federated Learning (DDFed), a framework designed to tackle two major challenges in federated learning: privacy breaches and poisoning attacks. By integrating fully homomorphic encryption, DDFed securely aggregates model updates, thereby enhancing privacy protection without th... | Rebuttal 1:
Comment: We appreciate the reviewer's positive decision and those raised concerns. The challenge of the paper lies in resolving the dilemma where detecting model poisoning requires plaintext model updates from each client, while privacy protection demands safeguarding these updates.
This is our first attem... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Data subsampling for Poisson regression with pth-root-link | Accept (poster) | Summary: the paper considers sampling for Poisson regression, making explicit the dependence of the size of coreset on various parameters, in particular the effect of different link functions. The paper has a theoretical flavor
Strengths: the results seems to be new
Weaknesses: Frankly I am not familiar with this par... | Rebuttal 1:
Rebuttal: In the last line of our abstract (lines 18-20) we state that we show the limitations of our analysis for $p$-th degree root link functions for $p\geq 3$, and state that these limitations show the need for other methods if one aims to generalize our approach to this range of values of $p$. We repea... | Summary: Coresets are a technique in efficient algorithms for data analysis in which a dataset is compressed into a weighted subset of its examples. Typically, coresets are constructed such that a loss function for a given optimization problem (e.g. linear regression, logistic regression, or Poisson regression for this... | Rebuttal 1:
Rebuttal: We do not agree with the statement that Poisson regression with links other than the canonical log link is 'artificial'. The identity link was applied in epidemiology, see e.g. [a], or [b]. The root link function has been applied to forecasting for queueing systems [c], and to account for misspeci... | Summary: This paper demonstrates the theoretical bound analysis on data subsampling for Poisson regression with ID-link and root-link functions based on coreset method and the leverage of $\ell_r$ norms to the loss function. The $\text { ‘ } \rho \text {-complex’ }$ is a novel meaningful parameter for data compressibil... | Rebuttal 1:
Rebuttal: 1. We have difficulty understanding what exactly is lacking in the structure of the paper. We remind the reviewer of the structure of our paper: In Section 1, we provide an introduction that introduces the crucial concepts of a $(1+\varepsilon)$-coreset and sublinear bounds on the coreset size. In... | Summary: For Poisson regression, where the outcome variable is a positive integer, the paper provides sublinear coresets under certain assumption on the data characterized by parameter $\rho$. The link function is the $p^{th}$ root link function with $p = 1 , p=2$. Without any assumptions the authors show that no subl... | Rebuttal 1:
Rebuttal: We will describe some proof-of-concept results that have been obtained since we submitted the paper for review.
We have generated 6-dimensional data that consists of the vertices of a regular simplex (as extreme points on the convex hull) and $n$ further points from a normal distribution rescaled ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and valuable comments on our submission, which we would like to address in individual responses below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Parameter Identifiability of Partially Observed Linear Causal Models | Accept (poster) | Summary: The manuscript proposes novel methods for learning linear structural equation models from partially observed data (i.e., allowing for latent variables). The authors provide graphical identifiability conditions for such models and describe an algorithm for learning structural parameters from data via gradient d... | Rebuttal 1:
Rebuttal: Thank you for the time dedicated to reviewing our paper, the insightful comments, and valuable feedback. Please see our point-by-point responses below.
**Q1:** Regarding writing suggestions about Thm 1, Remark 1, and other related discussions.
**A1:**
Theorem 1 provides a sufficient condition (... | Summary: This paper investigates the problem of parameter identification in linear causal models, which is important and well-studied task in causality. The authors examine models that explicitly include both observed and latent variables. The identification of parameters in such models has not been studied so far and ... | Rebuttal 1:
Rebuttal: Thank you for the time dedicated to reviewing our paper, the insightful comments, and valuable feedback. Please see our point-by-point responses below.
**Q1:** It would be interesting to have sufficient conditions also in structures that do not satisfy conditions 1 and 2: G may be identified even... | Summary: This paper investigates the parameter identifiability of partially observed linear causal models, focusing on whether edge coefficients can be recovered given the causal structure and partially observed data. It extends previous research by considering relationships between all variables, both observed and lat... | Rebuttal 1:
Rebuttal: Thank you for the time dedicated to reviewing our paper, the insightful comments, and valuable feedback. Please see our point-by-point responses below.
**Q1:** Regarding group sign indeterminacy.
**A1:**
The group sign indeterminacy is rather minor with reasons as follows. (i) In practice, we ... | Summary: This paper introduces conditions under which DAGs can be recovered in the linear case where some nodes are observed and some are not. This DAG recovery involves computing edge weights between nodes in a causal graph. Nodes are allowed to be latent or observed, with varying types of edge weight indeterminacy de... | Rebuttal 1:
Rebuttal: Thank you for the time dedicated to reviewing our paper, the insightful comments, and valuable feedback. Please see our point-by-point responses below.
**Q1:** Whether the graph will be dense regarding Eq.3?
**A1:** We note that Eq.3 concerns the estimation of causal coefficients F, given the d... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
STONE: A Submodular Optimization Framework for Active 3D Object Detection | Accept (poster) | Summary: The paper proposes an approach to address challenges in active learning for 3d object detection to select unlabeled point clouds for further labeling. The labeling criteria maximises representativeness of the chosen point cloud with respected to an unlabeled point cloud set and also making sure that classes ar... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments. If any of our responses fail to sufficiently address your concerns, please inform us, and we will promptly follow up.
#### **[W1] Performance boost is marginal. A... | Summary: The paper proposes a novel submodular-based active learning approach STONE for lidar-based 3D object detection. The method then does data balancing using a greedy search algorithm. The method achieves significant improvements on the KITTI and Waymo datasets.
Strengths: + The paper proposes submodular optimiza... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments.
#### **[W1] Quantitaively benchmarking the results with recent detectors like FocalFormer and PillarNet on datasets would strengthen the evaluation.**
We evaluate... | Summary: This paper introduces a framework to reduce the labeling costs of 3D point cloud data in 3D object detection by using a submodular optimization approach. It tries to optimize for data imbalance the distribution of the data like the varying difficulty levels. By the combination of a transformer-architecture and... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments.
#### **[W1] If not intended, in line 113-144 it could be mentioned that the randomly selected number of point clouds D_L in the the beginning is labeled.**
Than... | Summary: This paper introduces a novel framework to reduce labeling costs in 3D object detection. Using submodular optimization, the framework addresses data imbalance and varying difficulty levels in LiDAR point cloud. It employs a two-stage algorithm: Gradient-Based Submodular Subset Selection (GBSSS) for selecting d... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments.
#### **[W1] Generalizability of Submodular Functions: The paper uses specific submodular functions tailored to their problem, but it lacks a detailed analysis of ... | Rebuttal 1:
Rebuttal: Figures for Rebuttal
Pdf: /pdf/5367bb340f1c5d2e339158506e1fb0d25c527cad.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposed an active 3D object framework based on submodular optimization. It focuses on solving data imbalances and covering varying difficulty levels of the point cloud data by using submodular optimization. Extensive experiments show superior results with high computational efficiency.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and valuable suggestions! Below, we provide a detailed response to your questions and comments.
#### **[W1] The paper needs a pipeline figure.**
Please see the attached PDF for the pipeline figure of the proposed approach.
#### **[W2] Compared with the b... | null | null | null | null | null | null |
On Socially Fair Low-Rank Approximation and Column Subset Selection | Accept (poster) | Summary: This paper considers the fair low-rank approximation and fair column subset selection problem. These two problems are similar; they aim to select a subset of vectors that optimize the algorithm's performance across all sub-populations. Formally, given $\ell$ matrices A and $\ell$ matrices B, the goal is to cho... | Rebuttal 1:
Rebuttal: > The main weakness is that the $(1+\epsilon)$-approximation runs in exponential time while for the column selection algorithm, its running time is polynomial but the approximation ratio is linear in $k$. I understand that there is a lower bound under ETH conjecture, but this running time strictly... | Summary: The paper considers low-rank approximation and column selection in a specific setting to which authors refer as socially fair setting. Basically, given $\ell$ matrices $A^{(1)},\dots,A^{(\ell)}$, they consider the problem of approximating solution of $\\min_{U \\in \\mathbb{R}^{k\\times d}} \\max_{i\\in [\ell]... | Rebuttal 1:
Rebuttal: > There are a lot of things to understand in Introduction i.e. section 1.1. Although it gets easier once you are done with it, I would prefer keeping some of the details of section 1.1 until section 3. Furthermore, there is a significant overlap between the two sections, so merging them might give... | Summary: The authors investigate socially-fair low-rank approximation and column subset selection problems. The concept of socially-fair (in the context of clustering problems this fairness notion is well studied) is introduced when the input matrix rows can be partitioned resulting in sub-matrices, the goal is to find... | Rebuttal 1:
Rebuttal: > The paper seems to be written for a specialized audience deeply embedded in the field, rather than for a general audience. The authors frequently cite lemmas and theorems from previous papers without providing sufficient explanations or context, assuming readers already have extensive background... | Summary: The paper studies the socially-fair variants of low-rank approximation and column subset selection. The authors prove hardness results similar to those in the literature for the non-fair versions of the problems. They then propose exponential time close-to-optimal solution for socially-fair low rank approximat... | Rebuttal 1:
Rebuttal: > The paper is not easy to read. The introduction may be shortened.
The nature of our result is theoretical and, in principle, builds on technical results in the area of randomized numerical linear algebra. However, we will provide more background and preliminaries in the next version of our pape... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and valuable feedback. We also appreciate the positive remarks, such as:
- The paper studies two important and relevant problems. (Reviewer icAD)
- The theoretical analysis is well done. (Reviewer icAD)
- The experimental results show that the a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random | Accept (spotlight) | Summary: The paper studies learning half-spaces in the Massart noise setting. That is for $\gamma\eta >0$ and $\eta(x)\leq \eta $ for all $x\in supp(D_{x})$, half space $w^*$ and such that $P( | w^* x | \geq \gamma)$ and $P ( sign( w^* x ) \not= y)=\eta(x)$ for all $x\in supp(D_{x})$ - given such an instance and $\va... | Rebuttal 1:
Rebuttal: Thank you for your careful reading, and all of your detailed feedback. We are glad that you found our explanations clear. Regarding improved dependences on $\mathbb{E}[\eta(\mathbf{x})]$, prior work has shown that such guarantees are likely computationally intractable for statistical query based a... | Summary: The paper considers the problem of PAC-learning $\gamma$-margin halfspaces under $\eta$-Massart noise. The paper provides an efficient algorithm achieving error $\eta+\epsilon$ with sample complexity $\tilde{O}(1/(\epsilon^2\gamma^2))$. The individual dependence of the sample complexity on $\epsilon$ and $\ga... | Rebuttal 1:
Rebuttal: We appreciate that you found our paper well-written. Thank you for your typo suggestions; we will fix these in a revision. Re: your question in Lemma 3, we chose this parameter tradeoff because it yields a $\log(\frac 1 \delta)$ overhead in our runtime. As you suggest, it is also possible to direc... | Summary: This submission studies the problem of learning halfspaces and generalized linear model in the Massart model under a margin assumption. In particular, for the case of halfspaces, the submission gives an efficient algorithm achieving (conjecturally optimal) error $\eta + \varepsilon$ using only $\tilde{O}(\gamm... | Rebuttal 1:
Rebuttal: Thank you for your many helpful comments, and suggested references. We agree with all of your suggestions regarding the introduction and citations, and will fix them in a revision.
We agree additional care can be taken to clarify the technical overview, emphasizing conceptual points and quantitat... | Summary: This paper focused on the fine-grained analysis of learning $\gamma$-margin halfspace with massart noise. The authors designed a new certificate vector $g$ by dividing the gradient vector of leaky ReLU by $|w^\top x| + \gamma$, and showed that when the hyperplane $h_w(x)$ has large 0-1 loss $\ell_{0,1}(w)\geq ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review, and we will make sure to address your typo catches. Re: your question about Massart GLMs, we will definitely provide more clarifying discussion on this point, as it is somewhat confusing. In particular, in a Massart GLM (following Definition 2), the optimal d... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive feedback on our paper!
We would like to point out a technical issue in the current proof of Theorem 4 for learning **Massart GLMs**, which we found after the submission of our paper. The issue can be resolved through a concise extension of the proof of T... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper considers the problem of learning halfspaces with a margin, under the Massart noise model. The Massart noise model generalizes the Random Classification Noise (RCN) Model: while in the RCN model, each label is flipped with fixed probability \eta, in the Massart noise model the the probability of flip... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts; we are glad you found our algorithm natural and simple, and that it was explained well. We mention that beyond our main technical result, from a conceptual standpoint, our paper advances a line of work giving faster learning algorithms under realistic noise mo... | null | null | null | null | null | null |
Novel Object Synthesis via Adaptive Text-Image Harmony | Accept (poster) | Summary: The paper works on novel object synthesis, namely, given an image and text prompt, the proposed method can generate a new image that contains the visual features from the given image and the textual information from the given prompt. The paper proposed a method names Adaptive Text-Image Harmony to tackle the t... | Rebuttal 1:
Rebuttal: **Q1: Dataset and fine-tune baselines.**
**A1:** Thank you for your interest in our dataset and its potential applications.
**Dataset Release:**
We plan to release a dataset of 1,800 text-image pairs to the research community following acceptance. These pairs are created using an outer product... | Summary: This paper aims to generate novel objects based on a reference image and a conditional text prompt (e.g., a bottle (image) with a penguin (text) outlook). To this end, a method called Adaptive Text-Image Harmony (ATIH) is proposed to better align the conditional image and text. Experiments show that ATIH yield... | Rebuttal 1:
Rebuttal: **Q1: Remove the misleading images.**
**A1:** Thank you for your feedback regarding the potential misunderstanding in Figure 2. We acknowledge that the image for text information was intended for visualization purposes only, and we agree that it could mislead readers. Therefore, we will remove th... | Summary: The paper proposes a new method for harmonized image generation conditioned on both text and image conditions, leading to better performance than baselines mentioned in the paper.
Strengths: The idea of using scale factor to combine different conditions is straightforward and reasonable. It is also
The qual... | Rebuttal 1:
Rebuttal: **Q1: Results by the different assumption.**
**A1:** Thank you for your suggestion. Inspired by \[ref-1\], we only inject the late self-attention layers in our method while keeping other settings the same. This simple adjustment enables our model to effectively modify the visual style of generate... | Summary: The paper introduces an innovative method to generate new object images by combining textual descriptions with corresponding images. Addressing the common imbalance in diffusion models between text and image inputs, the authors propose the Adaptive Text-Image Harmony (ATIH) method. This method balances text an... | Rebuttal 1:
Rebuttal: **Q1: More quantitative validation**
**A1:** Thank you for your thoughtful feedback. We recognize the importance of quantitative metrics in validating our algorithm's effectiveness.
We use four widely-used metrics: CLIP-T \[39\], Dino-I \[36\], AES \[46\], and HPS \[56\]. Additionally, we propos... | Rebuttal 1:
Rebuttal: We thank all reviewers and chairs for their time, constructive comments, and recognition of our work. We sincerely hope that all reviewers can support our work, as this paper proposes a novel and reasonable method (**Reviewers xaqc and yinU**) to solve an interesting and challenging task (**Review... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models | Accept (poster) | Summary: This paper proposes FM-Delta, which addresses significant storage overhead caused by fine-tuned LLMs. It maps model parameters into integers and entropy codes their small differences from pre-trained models, reducing cloud storage consumption by about 50% on average.
Strengths: - This paper proposes FM-Delta,... | Rebuttal 1:
Rebuttal: We thank the Reviewer eKhr for the detailed and useful feedbacks. We address your concerns point by point below, and the analyses will be incorporated into our paper.
> **Q1**: Could the author please elaborate on specific scenarios where this method can be applied? What kind of situations requi... | Summary: This paper proposes a novel lossless compression scheme FM-Delta specifically for storing massive fine-tuned models in cloud.
FM-Delta maps fine-tuned and pre-trained model parameters into integers with the same bits, and entropy codes their integer delta. In this way, cloud only needs to store one uncompr... | Rebuttal 1:
Rebuttal: We thank the Reviewer SDxT for the insightful feedback and address your concerns below, and the analyses and clarifications will be incorporated into our paper.
> **Q1**: Did the paper use the symbol frequency as the probability?
**A1**: Yes, we use a quasi-static probability modeler as in [1]\[... | Summary: This paper proposes a method to compress the differences between a pretrained model and a full fine-tuned model to save storage space on cloud servers where the full fine-tuned model is stored. For compression, the pretrained weights and full fine-tuned weights are first converted to unsigned integers and then... | Rebuttal 1:
Rebuttal: We thank the Reviewer yfTq for the insightful feedback and address your concern below, and the analyses will be incorporated into our paper..
> **Q1**: What is the proportion of full fine-tuned models within the entire Hugging Face model repository?
**A1**: Since it is hard to distinguish the fu... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for dedicating their time to review our manuscript.
The uploaded PDF includes an updated workflow figure of FM-Delta for the Reviewer SDxT, and the results of quantizing delta for the Reviewer eKhr.
Pdf: /pdf/8e8761d071d0fd0d3b630e37842dac1b558225a7.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Spectral Graph Pruning Against Over-Squashing and Over-Smoothing | Accept (poster) | Summary: In this paper, the authors propose several variations of a graph pruning/rewiring algorithm, based either on an approximate maximization of the spectral gap, or on a more complex criterion based on Eldan's proof that deleting edges can counter-intuitively lead to an increasing spectral gap (which has been link... | Rebuttal 1:
Rebuttal: We thank reviewer MxtA for the constructive feedback provided and valuable insights. We include answers for each of the points raised.
* W1. We believe the idea of graph lottery tickets is an interesting application since we propose a graph sparsification method. Recent works [Pal, Hoang] sugges... | Summary: This paper investigates the connection between oversmoothing/oversquashing, spectral gap optimization via edge deletions and additions, and the lottery ticket hypothesis. Specifically, they propose that sparsifying a graph can indeed improve its response to oversmoothing / oversquashing. Some theoretical studi... | Rebuttal 1:
Rebuttal: We thank reviewer 1jBY for the constructive feedback provided and valuable insights. We include answers for each of the points raised.
* W1. The ring graph has the specific purpose of providing a counterexample for the hypothesis that oversmoothing and oversquashing have to be traded-off against ... | Summary: The paper addresses the issue of over-squashing and over-smoothing in Message Passing Graph Neural Networks (MPNNs). It proposes a novel spectral gap optimization framework with rewiring, inspired by the Braess phenomenon, to mitigate both over-squashing and over-smoothing. The method is computationally effici... | Rebuttal 1:
Rebuttal: We thank reviewer 1tmm for the constructive feedback provided and valuable insights. We include answers for each of the points raised. Tables are located in the document on the global response.
* W1. The criterion states that if the quantity $g$ is positive, then the Braess paradox occurs. In all ... | Summary: Inspired by the Braess phenomenon, the paper proposes a Greedy graph pruning algorithm (PROXYDELETE) that maximizes the spectral gap in a computationally efficient way to simultaneously address over-smoothing and over-squashing of GNNs. The paper then verifies the empirical effectiveness of the method on long-... | Rebuttal 1:
Rebuttal: We thank reviewer p1uY for the constructive feedback provided and valuable insights. We include answers for each of the points raised. Tables are located in the document on the global response.
* W1. Our theoretical investigations have the purpose of providing a counterexample for the common hypot... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their feedback. We appreciate the overall positive reception of our work and are glad that its novelty, clarity, and thoroughness have been recognized. Below, we summarize our key contributions and address the added material in the attached document:
* Our wor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment | Accept (poster) | Summary: The authors propose to model mixed time series with both continuous variables and discrete variables by constructing latent continuous variables (LCVs) from discrete variables (DVs). Several super-supervised learning constraints are proposed to help improve the effectiveness of LCVs as well as the co-learning ... | Rebuttal 1:
Rebuttal: We deeply appreciate Reviewer nZaK's positive acknowledgment of our work's originality, clarity, and quality. We are especially grateful for the detailed and insightful feedback provided. Rest assured, we are dedicated to addressing your concerns and enhancing our work.
***W1.1&Q1: Smoothness con... | Summary: • This paper introduces a type of spatial-temporal heterogeneity caused by the gap between continuous variables (CVs) and discrete variables (DVs).
• The author introduces latent continuous variables to create a unified continuous numerical space for both CVs and DVs, with the aim to address the heterogeneity... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer ZwAv for the positive evaluation of our work's innovativeness, creativity, and technical quality. Your detailed and insightful comments have helped us improve our work substantially. We hope your concerns are addressed.
***W1&Q3: Mathematical formulas and the LCV Recov... | Summary: The paper "Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment" introduces MiTSformer, a framework designed to address the challenges of mixed time series (MiTS) data, which include both continuous variables (CVs) and discrete variables (DV... | Rebuttal 1:
Rebuttal: We deeply appreciate Reviewer 3NoE's positive acknowledgment of our methodology's innovation, comprehensiveness, effectiveness, and experimental robustness. We are especially grateful for the constructive and insightful feedback provided. Rest assured, we are dedicated to addressing your concerns.... | Summary: The MiTSformer framework includes Latent Continuity Recovery, which recovers latent continuous variables (LCVs) from discrete variables (DVs) using multi-scale temporal context and adversarial guidance, and Spatial-Temporal Attention Blocks, which capture dependencies within and across LCVs and continuous vari... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer GdPs for the favorable recognition of our work's problem setting, effectiveness, and invaluable contribution to designing robust time series foundation models. We deeply appreciate your detailed and perceptive feedback. Rest assured, we are committed to addressing your ... | Rebuttal 1:
Rebuttal: ## Summary of Revisions and Global Response
We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for us to improve our paper further.
**Pioneering the exploration of the mixed time series analysis**, this paper proposes a task-general fram... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic Image Structure | Accept (poster) | Summary: The paper presents a novel approach to unsupervised semantic image segmentation. The authors introduce a novel algebraic methodology that constructs a hierarchy-agnostic semantic region tree, which dynamically identifies scene-conditioned primitives and creates a nuanced and unbiased segmentation of image pix... | Rebuttal 1:
Rebuttal: Dear Reviewer **Xo6d**,
Thanks for your comments and your critical remarks on the parameters.
We make some observations concerning them.
You note that the parameters require *being well set*. However:
1) Table 6 (c) and Table 6 (a) on page 9 of the main paper show that $k_{min}$, $\\lambda_{ma... | Summary: This paper addresses the problem of unsupervised hierarchy-agnostic segmentation by treating it as a graph partitioning problem. Specifically, each node in the graph represents a part, while the weighted edges, measured by similarity scores, reflect the connections between parts. The graph is recursively parti... | Rebuttal 1:
Rebuttal: Dear Reviewer **E2ZP**,
We sincerely appreciate your evaluation and sharp comments. Your feedback is helpful in enhancing the quality and clarity of our work.
As you indicate, we will address the weaknesses.
- Answer to *The pre-trained DINOv2-ViT-B14-REG backbone is a very strong backbone and ... | Summary: The paper proposes a spectral-clustering approach to hierarchically segment an image, in an unsupervised fashion. The method starts from self-supervised features assigned to each pixel. Then, a recursive partitioning is obtained by minimizing a quadratic form for a given level of the hierarchy, and repeating t... | Rebuttal 1:
Rebuttal: Dear Reviewer **47JN**,
Thanks for the valuable comments and the interesting questions.
- Answer to Q1.1
As you fairly suggest, we report in Table R3, attached below, *"an ablation experiment in which another spectral method is used, as in [56], .., with the same superpixels, network, and si... | Summary: The submission presents a way to get hierarchical segmentations from the features extracted by an unsupervised semantic segmentation model. It builds a graph representation from the features or "codes" from the network. Spectral clustering is computed on this graph, followed by recursively partitioning the clu... | Rebuttal 1:
Rebuttal: Dear Reviewer **oEkj**,
Thank you for your interesting comments and for allowing us to review some relevant points.
- Answer to Q1
Table R1, attached below, reports experiments ablating features extracted with different pre-training strategies, namely self-supervised [3,4,5,6] supervised class... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, effort and valuable comments.
We are glad the reviewer found the idea "*somehow simple, yet achieves remarkable results*" (47JN); and that they found the method "*novel and well-motivated, with detailed derivation*" and "*achieves significant improvement com... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making | Accept (oral) | Summary: This paper proposes MDAgents (Medical Decision-making Agents), a LLM collaboration framework for medical question answering. Given a single-modal or multi-modal medical question, MDAgents first classifies its complexity into low, moderate, and high. Based on the complexity checking result, MDAgents assigns a s... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and the valuable insights you provided. Your feedback helps us clarify key aspects of our research and improve the overall quality of our submission.
**W1. The reported scores not consistent with the literature**
Thank you for pointing out the disc... | Summary: This paper introduces a framework called MDAgents, which optimizes collaboration between multiple large language models (LLMs) for medical decision-making tasks. The main technical contribution of MDAgents is the deployment of a moderator agent to assess the complexity of incoming queries, categorizing them in... | Rebuttal 1:
Rebuttal: We appreciate your detailed review and the opportunity to refine our work based on your feedback. Your suggestions are crucial in guiding our efforts to provide a more comprehensive analysis and evaluation.
**W1 & W2. The complexity assessment lacks details and concerns about judgment made by the... | Summary: In this paper, the authors propose MDAgent, a multi-agent framework for medical decision making. In this framework, the complexity of the problem is initially assessed by an agent. Based on this assessment, either a single agent or a group of agents is assigned to solve the problem. The authors evaluate their ... | Rebuttal 1:
Rebuttal: We appreciate your review and the opportunity to address your concerns. We have conducted numerous additional experiments to validate our approach and enhance the robustness of our findings.
**W1. The description of MDT and ICT is unclear.**
* **Agent Initialization:** In our framework, the rol... | Summary: The paper presents MDAgents, a multi-agent LLM system for answering medical questions, ranging from medical question answering and diagnostic reasoning to medical visual interpretation. The main novelty is a crafted collaboration scheme of multiple agents with designated roles, where a medical question is cate... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review and the recognition of our paper’s potential contributions to the field. Your insights are invaluable in guiding us to enhance our work and clarify the findings.
**W1. & Q2. Lack of detailed descriptions and examples**
We recognize the importance of providing... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable and constructive feedback on our submission. We are encouraged that they found the work to be important and novel with a comprehensive evaluation and strong results.
Based on the reviews we have made significant updates to our paper and wou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Preference Alignment with Flow Matching | Accept (poster) | Summary: The paper introduces a novel framework called Preference Flow Matching (PFM) for preference-based reinforcement learning (PbRL). The PFM approach aims to integrate human preferences into pre-trained models without the need for extensive fine-tuning, which is a common requirement in existing PbRL methods. The P... | Rebuttal 1:
Rebuttal: Dear reviewer XK5w, thank you for your careful review and constructive feedback. Our responses to each of your comments are presented below.
- **W1: Exaggerated motivation, limited domain testing and applicability to NLP tasks.**
Thank you for pointing out this concern. We also acknowledge the im... | Summary: This paper presents Preference Flow Matching (PFM), which learns an ODE to transform less-preferred data into more-preferred ones. Due to PFM not explicitly or implicitly relying on reward functions, it avoids reward overfitting. Empirical evidence demonstrates that PFM outperforms DPO in image conditional gen... | Rebuttal 1:
Rebuttal: Dear reviewer 2tji, thank you for your careful review and valuable comments. Our responses are presented below.
- **W1: Main novelty of our method.**
The main novelty of our method comes from its capability of being added on top of any existing black-box models, without the need of fine-tuning th... | Summary: This paper proposes an add-on method named Preference Flow Matching (PFM) to achieve preference alignment without learning a reward model. PFM can transfer $y$ generated from the original model to the preferred $y^+$ through a few flow iterations. Experiments demonstrate its superior performance compared with ... | Rebuttal 1:
Rebuttal: Dear reviewer XhGj, we thank you for your valuable comments and review. We are glad to hear that you have enjoyed reading our paper. Below, we provide our response to your comments and questions.
- **W1: Shifted source distribution during inference.**
As you have pointed out, the shifted source d... | Summary: The paper presents a new framework for preference-based reinforcement learning, relying on Flow Matching. This framework utilizes flow-based models with optimal transport that interpolate between less preferred data and more preferred data, eliminating the need to estimate a reward function implicitly or expli... | Rebuttal 1:
Rebuttal: Dear reviewer yRMJ, we appreciate your invaluable comments and feedback. Below, we provide our response to your comments and questions.
- **Q1. Shifted source distribution during inference.**
As mentioned in our paper, since the source distribution $p_{0}$ is inaccessible during inference, we ins... | Rebuttal 1:
Rebuttal: # (Common Response) New Domain: Added NLP Task
We applied our method (PFM) to the new NLP domain. We adopt a controlled (positive) sentiment review generation task, which is one of the main tasks previously tackled by the DPO paper. As done in the DPO paper, to perform a controlled evaluation, we... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces Preference Flow Matching (PFM), a novel framework for preference-based reinforcement learning that addresses key limitations of existing methods like Reinforcement Learning from Human Feedback (RLHF). PFM leverages flow matching techniques to directly learn from preference data. The core ... | Rebuttal 1:
Rebuttal: Dear reviewer, we appreciate your thorough review and valuable feedback. Below are our responses to your comments and questions.
- **W1: Weak empirical contribution.**
We have obtained promising results in a new domain, please refer to the above common response section for our results in the sele... | null | null | null | null | null | null |
Sample Complexity of Posted Pricing for a Single Item | Accept (spotlight) | Summary: This paper studies the sample complexity of posted pricing problems. In such problems, a sequence of $n$ buyers arrive with valuations drawn from fixed distributions, and the goal is to post prices that maximize either the revenue or the welfare. In particular, the item is sold to the first buyer who accepts i... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! | Summary: This paper studies the problem of learning approximately revenue (welfare) optimal posted-prices single-item auctions from samples. The authors consider the setting where the valuation distributions of the agents are independent and the setting where the distributions are correlated. For both settings and both... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and comments!
- *The conceptual ideas of the work feel a bit incremental, given the long line of work in the setting of learning optimal auctions. The results are limited to a very simple setting of single item auctions.*
We view the simplicity of single item... | Summary: The paper studies a setup of a series posted-price auctions that tries to sell a single item up to the first price acceptance. The authors seeking for the number of samples from buyer value distributions (sample complexity) to be able to setup near-optimal posted-price in the auctions. They consider two object... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and comments!
- *What are the factors (multiplicative constants) in the upper and lower bounds in Theorems 1-4? What is behind O(...) and \Omega(...)?*
We emphasize that because we have normalized valuations to lie in [0,1], the $O(\ldots)$ and $\Omega(\ldots... | Summary: The paper considers the problem of statistically estimating the optimal posted price mechanism for selling one item to multiple buyers. Here, it is assumed that the buyers appear sequentially, a price is presented to each of them and the sale is completed if the price posted to them is lower than their valuati... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and comments!
- *The paper assumes that one obtains independent samples from the valuations of the bidders. It is not clear how such samples are obtained -- it is not clear how one may obtain such samples in practice. More exposition on this point would be hel... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Event-3DGS: Event-based 3D Reconstruction Using 3D Gaussian Splatting | Accept (poster) | Summary: The paper introduces Event-3DGS, the first framework for event-based 3D reconstruction using 3D Gaussian Splatting (3DGS). The method demonstrates superior reconstruction quality, robustness, and efficiency
Strengths: 1. By addressing the challenges of fast-motion and low-light scenarios with event cameras an... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and affirmation. The figures mentioned below can be found in the attached PDF.
W1: Combining 3DGS with Event cameras is somewhat meaningless.
R1: The combination of these two approaches is meaningful:
(1) Pure event data excels in SLAM, 3D reconstruction, and auto... | Summary: The paper introduces Event-3DGS, a framework that leverages event cameras and 3D Gaussian Splatting (3DGS) for efficient and robust 3D reconstruction in challenging real-world scenarios. The authors propose: a high-pass filter-based photovoltage estimation module and a novel event-based 3D reconstruction loss ... | Rebuttal 1:
Rebuttal: The figures (i.e., Fig. R1 and Fig. R2) mentioned below can be found in the attached PDF.
Q1: The paper is not well written and contains many errors, it is very difficult to follow.
A1: Thanks. We will clarify the writing to improve reader understanding in the camera-ready version.
Q1-1: In Eq.... | Summary: This paper proposes a 3D reconstruction method from event cameras using 3D Gaussian Splatting (3DGS). The authors propose an innovative framework that takes a stream of events as input and optimize 3D appearance model with 3DGS. In particular, the authors propose differentiable rendering of event images and ph... | Rebuttal 1:
Rebuttal: Thanks for your positive evaluation.
W1: In the writing, it is better to avoid abbreviations like “it’s” and write “it is”.
R1: we will add the missing words and change “it’s” to “it is” in the camera-ready version.
W2: A word is missing in Line 147.
R2: We missed "event sensor" in Line 147, ... | Summary: This paper proposes a new method for novel view synthesis of intensity images using Event-Camera data via 3d Gaussian Splatting. Event-cameras are a type of camera that captures log intensity changes on the image plane. While 3D Gaussian splatting is a method for novel view synthesis that has been originally d... | Rebuttal 1:
Rebuttal: Thanks for you insightful suggestions and affirmation. The tables and figures mentioned below can be found in the attached PDF.
W1: Rewriting should be clear for the NeurIPS audience.
R1: We will add a high-level introduction to Gaussian Splatting in the main manuscript and provide clearer expla... | Rebuttal 1:
Rebuttal: Thank you to the area chairs and all the reviewers for your valuable comments!
To further verify the effectiveness of our Event-3DGS, we conducted six experimental tests using two event-based NeRF methods (i.e., Ev-NeRF [1] and EventNeRF [2]). The quantitative results and visualization figures ar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning | Accept (poster) | Summary: The paper mainly addresses the problem of obtaining the optimal policies in the distribution correction estimation (DICE) setting, which is one popular offline RL approach. Note that the offline RL assumes that we can’t interact with an environment and only have access to a dataset—sets of (state, action, and ... | Rebuttal 1:
Rebuttal: We are deeply grateful for the reviewer's detailed and accurate summary. We also appreciate the time and effort the reviewer has devoted. As for the weakness, we prepared the following responses, which are presented as follows.
>... However, improvements in the presentation would greatly enhance ... | Summary: The paper introduces a novel offline reinforcement learning approach that leverages diffusion models integrated with DICE-based methods. The proposed guide-then-select paradigm aims to minimize error exploitation. The resultant algorithm achieves state-of-the-art performance on D4RL benchmark tasks.
Strengths... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort dedicated to evaluating our paper, as well as the constructive feedback provided. In response to the concerns and questions raised, we have prepared detailed answers, which are outlined separately below.
>Missing comparison in Table 1 of more optimal G... | Summary: This paper introduces Diffusion-DICE for offline reinforcement learning. Diffusion-DICE motivates from the transformation between the behaviour distribution and the optimal distribution, which inspires the use of generative models for behaviour distribution modelling. Next, Diffusion-DICE decomposes the policy... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort dedicated to evaluating our paper, as well as the constructive feedback provided. In response to the concerns and questions raised, we have prepared detailed answers, which are outlined separately below.
>... The way authors presented the guide-then-se... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions | Accept (poster) | Summary: This paper introduces the task of data mixture inference, i.e. trying to infer what kind of data (e.g. languages, code) a given LLM is trained on. They do so by leveraging the ordered merge rules learned by the LLM's BPE tokenizer. First, they formalize the problem. Next, authors propose a neatly explained so... | Rebuttal 1:
Rebuttal: Thank you for recognizing the “novel” task we study, our “technically developed method,” and the “convincing,” "valuable results" on a "fundamental design decision" made by model developers.
Thank you for the thoughtful questions, and we hope that our response addresses your concerns!
## Elabora... | Summary: This paper proposes a novel data mixture inference attack to uncover the distributional makeup of pretraining data for large language models. By leveraging the ordered vocabulary learned by byte-pair encoding (BPE) tokenizers, the authors formulate a linear program to infer the relative proportions of differen... | Rebuttal 1:
Rebuttal: Thank you for recognizing the “previously understudied” problem we address and the "insightful findings." We have taken care to investigate all the questions you raise (with new experiments) and hope that our response addresses your concerns.
## Today’s LLMs use BPE
While it is true that other to... | Summary: In this paper, the authors studied inference attack on large language models’ (LLM) tokenizer. In particular, they proposed an attack to inference training data sampling weight used to train tokenizer of LLMs, which usually is also the same sampling weight used to train the LLMs. They formulated their attack a... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty of our attack and its “relevan[ce] to key problems in LLM area.” We have taken all of your suggestions into account and hope that our response addresses your concerns.
## Theoretical analysis
Regarding the theoretical analysis, the key observation... | Summary: The paper "Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions" presents a significant contribution to the field of machine learning by introducing a novel attack called "data mixture inference." This attack aims to uncover the composition of pretraining data used in language models... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our work as a “significant contribution to the field” and its “noteworthy practical implications.” We have taken all of your suggestions into account and hope that our response addresses your concerns.
## Today’s LLMs use BPE
While it is true that other toke... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for observing the “novelty” of our data mixture inference attack, its empirical “effectiveness” and the “noteworthy practical implications” of uncovering information about “a fundamental design decision”!
Following insightful reviewer suggestions, we have run several ne... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Emergence of heavy tails in homogenized stochastic gradient descent | Accept (poster) | Summary: This paper analyses the emergence of heavy-tails in homogenized SGD applied on linear models with quadratic loss. Following other works, the authors model SGD through homogenized SGD and show theoretically and empirically that such an equation converges naturally to heavy-tailed distributions, despite the fact... | Rebuttal 1:
Rebuttal: We thank you for your review and the comments on our paper. Below we comment on the Weaknesses and answer to the Questions posed in your review:
* **Lack of Conclusions Section**: In our submission we have omitted a Conclusions section due to space limitations. We will include such a section (di... | Summary: The paper builds up on the theory of heavy tails in Stochastic Gradient Descent (SGD), clearing some aspects of the distribution of the parameters learned during training. Leveraging a combination of the works on homogeneized SGD and diffusion, the author(s) are able to shed light on the role of noise and the ... | Rebuttal 1:
Rebuttal: We thank you for your extensive comments and for your detailed reading of our paper. Regarding the mentioned Weaknesses, Typos and Questions, our replies are as follows:
* **Case of tail-index < 1**: Our results should be interpreted as follows: By Theorem 3.2. hSGD will _always_ have an asympto... | Summary: This paper provides another perspective of the emergence of heavy tails in SGD to the recent literature. Unlike the previous literature, the paper assumes that the SGD can be adequately approximated by homogenized SGD, which is a Brownian-motion driven SDE with a given state-dependent diffusion term.
The pap... | Rebuttal 1:
Rebuttal: Thank you for your comments and your assessment of our paper. To your questions we have the following replies:
**Question (1)**: Our definition of `heavy-tailed' follows the standard definition from the literature on heavy tailed distributions, see e.g. Def. 2.2. in [1]. As we write in l126 a fin... | Summary: This manuscript studies homogenized SGD (hSGD) to characterize the tail behavior of SGD iterates for solving the ridge regression problem. By comparing the homogenized diffusion with a known diffusion process called Pearson diffusion, the authors provide a lower bound on the tail index of the iterates of homog... | Rebuttal 1:
Rebuttal: Thank you for your comments and your assessment of our paper. Regarding the two points raised, we have the following reply:
1. **Difference between hSGD and SGD**: In general, the marginals of hSGD and SGD do not have the same distribution. However, as we mention in line 91f, reference [1] provid... | Rebuttal 1:
Rebuttal: Several reviewers have raised the question if and how our results can be **extended beyond the quadratic loss/to non-linear models**. We think that such an extension is possible along the lines of the method introduced in the paper (and we do have some preliminary results) in the following cases:
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Training an Open-Vocabulary Monocular 3D Detection Model without 3D Data | Accept (poster) | Summary: This paper introduces OVM3D-Det, an open-vocabulary monocular 3D object detection framework that utilizes only RGB images for training. It leverages open-vocabulary 2D models and pseudo-LiDAR to automatically label 3D objects in these images. Additionally, it incorporates pseudo-LiDAR erosion and bounding box ... | Rebuttal 1:
Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following:
---
1. **Performance of Unidepth.**
Thanks for your valuable suggestion. We experiment with different architectures of Unidepth, which exhibit diffe... | Summary: This paper proposes OVM3D-Det, an open-vocabulary monocular 3D object detection framework that trains detectors using only RGB images. It introduces two key designs: adaptive pseudo-LiDAR erosion and bounding box refinement, addressing challenges arising from the absence of LiDAR point clouds.
Strengths: 1. T... | Rebuttal 1:
Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following:
---
1. **nuScenes result.**
We have conducted experiments on nuScenes, and our method has consistently achieved good results, demonstrating its gene... | Summary: The paper presents a novel open-vocabulary monocular 3D object detection framework named OVM3D-Det. This approach aims to train 3D object detectors using only RGB images, making it cost-effective and scalable. The framework utilizes pseudo-LiDAR and large language models to generate pseudo 3D labels, enabling ... | Rebuttal 1:
Rebuttal: We would first like to express our appreciation for your time and insightful comments. Please find our response to your concerns in the following:
---
1. **Dependence on depth estimation quality.**
Indeed, our method relies on accurate depth estimation, similar to most monocular 3D detection me... | Summary: This paper presents OVM3D-Det, a framework for generating 3D bounding box pseudo-labels from monocular RGB images using foundational vision models like GroundingSAM and UniDepth. Using these pseudo-labels, authors train an open-vocabulary version of Cube-RCNN. Authors demonstrate that their proposed approach g... | Rebuttal 1:
Rebuttal: We would first like to express our appreciation for your time and valuable comments. Please find our response to your concerns in the following:
---
1. **Evaluation on OV-3DET and larger-scale datasets.**
Thanks for your suggestion. We show the results **in the global rebuttal** and will add th... | Rebuttal 1:
Rebuttal: We thank all reviewers **[R1,DUxk], [R2,UAne], [R3,Rjjg], [R4,2uq9]** for their constructive comments and helpful feedback.
All reviewers agree on the efficacy of our method, including its broad applicability (R1, R2), innovative design (R2, R3, R4), and clarity of writing (R1, R4). They also hi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Streaming Algorithms for Graphlet Sampling | Accept (poster) | Summary: The paper presents algorithms for sampling a connected subgraph on k nodes, the so called k-graphlets, from a massive graph revealed as a stream of edges in arbitrary order. The algorithms work in the semi-streaming model of computation where one can only use linear space in the number of nodes. The algorithms... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments, which we would like to address as follows.
[Weakness 1 and Limitation]
In the next version of our work, we will highlight the connection between machine learning and the graph mining problem in our paper as follows:
- Graphlet sampling can b... | Summary: Given a graph $G$, a $k$-graphlet is any connected subgraph on $k$ vertices. Orthogonal to sampling instances of isomorphism types of subgraphs (e.g., triangles), graphlet sampling asks to sample a connected subgraph of a specific size. In an offline setting, Uniform Graphlet Sampling (UGS) solves this problem... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments, which we would like to address as follows.
[Response to Weakness]
Our approach can deal with both sparse and dense graphs. The same theoretical guarantees work for sparse graphs and a good performance on sparse graphs by our approach is shown... | Summary: This paper studies uniform sampling of k-graphlets. Authors exttend UGS to the semi-streaming setting, and propose an time and space efficient framework that samples uniform k-grpahlets w.h.p. Authors also provide a lower bound on the memory requirement of any streaming algorithm for uniform k-graphlet samplin... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the comments, which we would like to address as follows.
[Response to Weaknesses]
An efficient UGS sampling algorithm in the standard RAM model is described in the third paragraph of the introduction, which mentions the high level ideas of how topological ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Theory of Optimistically Universal Online Learnability for General Concept Classes | Accept (poster) | Summary: This work considers the theory of universal consistency and optimistically universal learning rules in the binary classification case where, instead of considering all measurable target functions, the analysis is parameterized by a specific class $\mathcal{H}$ of binary classifiers. Two types of classes $\math... | Rebuttal 1:
Rebuttal: We appreciate your helpful comments and we address your questions below.
First, let us re-iterate the definitions of the paper.
For a given concept class $\mathcal{H}$, a process $\mathbb{X} = (X_1,X_2,\ldots)$ admits a universally consistent online learner if there exists a learning algorithm gu... | Summary: This paper considers characterizations for optimistically universal online learnability, a notion that refers to the existence of learning rules that are simultaneously optimal for any data-generating process that satisfies some minimal assumptions, i.e. admitting an optimal process-dependent learning rule. Pr... | Rebuttal 1:
Rebuttal: We appreciate your helpful comments.
We will work to make the notation more readable, such as changing the capital X in the VCL game to make it less confusing.
As for the optimistically universally online learnability problem beyond binary-labeled cases, this is a very interesting question. We ... | Summary: This work continues the line of work by Hanneke and Blanchard et al universal online learning under general stochastic processes (instead of the more common iid or adversarial settings) with binary labels. While previous papers focused on the case of all measurable functions (as an implicit hypothesis spaces),... | Rebuttal 1:
Rebuttal: We appreciate your helpful comments. We will provide more discussion on related works in the final version and help the reader understand our results more easily.
In our work, we are focus on the consistency problem. The quantitative rates problem is an interesting future direction, though will ce... | null | null | Rebuttal 1:
Rebuttal: We want to thank all reviewers for their helpful suggestions and comments. We will polish our paper to make it easier to read and follow.
We here clarify our results again:
First, we would like to clarify the motivation of our work. This line of work on optimistically universal learning is tr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distributionally Robust Performative Prediction | Accept (poster) | Summary: In this paper, the authors propose a new framework and algorithm for finding performatively optimal solutions in the performative prediction framework. Performative optima are in general hard to find, and to do so, a number of approaches start by trying to estimate the underlying distribution map: a task which... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are glad to know that the reviewer appreciated our work from multiple aspects. We address the questions below, and we look forward to interacting with the reviewer during the discussion period.
> “L77: it is easy to see..” Aren’t there cases whe... | Summary: The paper addresses the performative prediction problem wherein the deployment of a model leads to a shift in the true data distribution via a distribution-shift map. In standard performative prediction, the distribution map is unknown - it is either assumed to be of a simple form like a location-scale shiftin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are glad to know that the reviewer appreciated our work from multiple aspects. We address the questions and concerns below, and we look forward to interacting with the reviewer during the discussion period.
> The novelty is simply in bringing to... | Summary: The paper proposes a framework that applies distributionally robust optimization to optimizing performative to obtain the distributionally robust performative optimum (DRPO). Specifically, the framework optimizes the worst case performative risk over the uncertainty set of distribution maps. The paper presents... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We address the questions and concerns below, and we look forward to interacting with the reviewer during the discussion period.
> Connection between numerics and theoretical guarantees.
The numerical experiments and theoretical findings support th... | Summary: Summary: The paper revisits Performative Prediction, a setting proposed in 2020 where the chosen model used to minimize a prediction loss also induces a distribution shift in the data through a distribution map. When the true distribution map is unknown, the learner must use a nominal map to approximate the ch... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are glad to know that the reviewer appreciated our work from multiple aspects. We address the questions and concerns below, and we look forward to interacting with the reviewer during the discussion period.
> What are the pros and cons of such a... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meta-Learning Universal Priors Using Non-Injective Change of Variables | Accept (poster) | Summary: The paper proposes to learn a more expressive parameter prior compared to predefined ones for a meta-learner. The problem is formulated in a Bayesian model-agnostic meta-learning context and its solution leverages a class of non-invective normalising flows to learn the parameter prior (aka Sylvester flows). Ex... | Rebuttal 1:
Rebuttal: Thank you for the questions. The issues raised are addressed one-by-one next.
**Response to weakness 1**
This contribution differs remarkably from probabilistic meta-learning in three key aspects.
- Deterministic algorithm. Our MetaNCoV is a *deterministic* meta-learning approach where task-le... | Summary: This paper proposes a novel approach to meta-learning using normalizing flows for modelling the prior distribution of parameters, with the task solver in the inner loop as the maximum a posterior estimator. Compared with the previous methods, such as MAML, which assume a Gaussian prior, suffering from limited ... | Rebuttal 1:
Rebuttal: Thank you for providing the insightful suggestions. The concerns are addressed one-by-one as follows.
**Regarding Weakness 1**
Our Sylvester NCoV *intentionally* forgoes the injectivity constraint to enhance prior expressiveness, as illustrated in Theorem 3.1. Upon waiving the injectivity, the ... | Summary: One of representative optimization-based meta-learning method is Model-agnostic meta-learning (MAML), where the inner loop can be interpreted as solving MAP. Here, the prior distribution over model parameters is defined as Gaussian pdf and the shared initialization is the mean parameter of the Gaussian pdf. Th... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback provided. The questions raised are addressed one-by-one below.
**Regarding weaknesses 1 and 2**
We would like to kindly bring to the reviewer's attention that the model complexity with meta-parameter dimension $D$ has been already studied extensively in Appendix... | Summary: This paper proposes to use non-injective change of variables theorem to make the prior in meta-learning more flexible than fixed shaped ones that have previously been used. Abundent theoretical analysis and experimental results support their claim that more flexible meta-level prior pdf can significantly boost... | Rebuttal 1:
Rebuttal: Thank you for the interest in this work and the constructive feedback provided, which have been carefully addressed as follows.
**Response to weaknesses**
Due to limited time and computational resources, we are unfortunately unable to provide the results on the full MetaDataset that contains 24... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion | Accept (spotlight) | Summary: This paper proposes FuseFL to address the non-IID data in one-shot federated learning (OFL). Specifically, FuseFL is inspired by the lens of casuality, and fuses each layer of the global model step by step to learn invariant features. Extensive experiments validate that FuseFL achieves SOTA accuracy under vari... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time in reviewing. We appreciate that you find the proposed algorithm novel and well-motivated, the experiments are extensive and the FuseFL is effective. Please see our detailed feedback for your concerns below.
**Q1**: Few-shot FL or one-shot FL.
> Si... | Summary: The authors identify the isolation problem as being the root cause of low accuracy in OFL. The isolation problem arises as the clients locally overfit to spurious features observed in their own local datasets in the absence of knowledge from other clients. To better learn invariant features, the authors propos... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time in reviewing. We appreciate that you find the problem interesting, important, and practical, our idea is clear and performance is great. Please see our detailed feedback for your concerns below.
**Q1**: Experiment Issues.
> Important baseline [1] a... | Summary: The authors provide a causality viewpoint for the data heterogeneity problem in the training of one-shot fed-avg. A block fusing mechanism in the training to provide more information aggregation is designed and the results show a significant improvement for the common tests in ResNet and Cifar.
Strengths: Pro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time in reviewing. We appreciate that you find the problem is interesting, important and practical, our idea is clear and performance is great. Please see our detailed feedback for your concerns below. Some answers can be referred to the global response ... | Summary: This paper proposes a method FuseFL that aims to perform one-shot FL by progressively fusing models from multiple clients. Their motivation is that within each local client, a model might learn spurious features due to the underlying spurious correlations, adversarial attacks, and shortcuts. However, at the gl... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our work. We appreciate that you find the solved problem, our observation, and our core idea are very interesting. Please see our detailed feedback for your concerns below.
**Q1**: Clarity of theoretical analysis.
> Rspu is someth... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for taking the time to review our work. We appreciate that you find that we **address a very interesting and important problem in FL** (Reviewer #mHC6, #FVeg, #V6z4), **our observation is novel** (Reviewer #mHC6), **our trials are significant and can be further exp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bayesian Online Natural Gradient (BONG) | Accept (poster) | Summary: The paper introduces a framework for variational online learning, in which a method is defined by three ingredients: (1) implicit or explicit regularisation (2) type of approximation of the expected gradient and hessian (3) geometry in which the gradient is taken (ie employing natural gradients or standard gra... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback.
We appreciate the suggestion to report a more exhaustive case study and give less space to the motivation from the conjugate case. The reason we prefer the present structure is that it puts more weight on the new algorithms. We’re glad you find the organizing ... | Summary: The authors explore sequential Bayesian inference using variational inference (VI). They propose to remove the regularizing KL term, performing a single natural gradient step using the expected log-likelihood only, and provide relevant ways to approximate the hard-to-compute expectations. They support their ap... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
We were not aware of the Lyu & Tsang paper. We will add discussion of it to Section 2 but also want to point out some important differences: Their goal is optimization, not inference or Bayesian updating. Thus the KL regularizer is not part of their objective whereas... | Summary: The paper proposed a novel parameter update rule. The rule is natural gradient descent on the variational inference loss, where the prior term is dropped. Such rule is motivated in theory under the assumption of likelihood and variational distribution being conjugate, and in practice though experiments on MNIS... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We have a few responses below.
“Proposition 4.1 is nice, but the assumptions are overly restrictive. It feels like this is such a special case that is has no implication on real case scenarios.”
Most efficient VI methods exploit tricks from conjugate Bayesian inferen... | Summary: This paper proposes a new approximate Bayesian technique specifically for online learning whereby posterior distributions over modeling parameters at time $t$ can be achieved with a single natural gradient step evaluated on a model using the previous posterior distribution at time $t-1$ as the prior distributi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments.
As requested, we have attached some figures that plot the ECE (expected calibration error) vs time for the various methods, when evaluated on MNIST using a CNN. Fig 1 shows that the BONG method is (slightly) better calibrated than the other
methods, for smal... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their useful feedback. We give individual responses below.
As requested by reviewer 6NTx, we are also attaching some figures that plot the ECE (expected calibration error) vs time for the various methods, when evaluated on MNIST using a CNN. Fig 1 shows that the BO... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a generalized framework for variational sequential inference of Bayesian neural networks with Gaussian prior distributions by further approximating the KL-divergence and expectations. It presents a very elegant unification of a wide range of (approximate) variational inference algorithms fo... | Rebuttal 1:
Rebuttal: Thank you for your feedback and positive evaluation. We will consider submitting to a journal in the future, but we feel that NeurIPS has higher visibility. We agree the unifying framework makes a good contribution on its own (and we have more theoretical work in progress on these lines) but for N... | null | null | null | null | null | null |
PuLID: Pure and Lightning ID Customization via Contrastive Alignment | Accept (poster) | Summary: In this paper, an ID customization method for text-to-image generation without tuning is studied. In practical applications, the method often encounters the problems of lack of ID fidelity and interference of the original pattern line by inserting ID. To solve this problem, the author proposes a new ID customi... | Rebuttal 1:
Rebuttal: Thanks for your acknowledgment and careful reviews. Our responses are as follows:
**`Q1:` Discuss the limitations and generalization ability of the method.**
The limitations have been discussed in Section 6.1 of the paper. In terms of generalizability of PuLID, the paper has validated its effect... | Summary: This paper introduces Pure and Lightning ID customization (PuLID). It is a tuning-free approach for customizing identities in text-to-image generation. The model is trained over a huge dataset.
Technically, it integrates a Lightning T2I branch alongside a standard diffusion model, incorporating contrastive al... | Rebuttal 1:
Rebuttal: Thanks for your constructive and positive comments. Our responses are as follows:
**`Q1:` Concerns about the internal training dataset.**
**1. Dataset or method – primary source of improvement?**
Table 2 in the main paper illustrates that a baseline, naively trained on the internal dataset, unde... | Summary: This article presents a novel fine-tuning-free ID customization method called PuLID for text-to-image generation tasks. The method introduces Lightning T2I and contrast alignment loss, aiming to minimize the interference with the original model behavior while maintaining high ID fidelity. Experiments show that... | Rebuttal 1:
Rebuttal: Thanks for your helpful advices. We respond to your core questions as follows:
**`Q1:` Lines 133 and 195 both mention that Q is an image feature of UNet, please explain how that image feature was obtained.**
In each cross-attention layer of UNET, the UNET image features are projected into query... | Summary: The paper propose a tuning-free method for customization text-to-image diffusion model. Particularly, the author propose to utilize efficient diffusion model (SDXL-lightning in this case) to generate samples during training. Then, they adopt contrastive alignment loss to preserve the identity of subject in the... | Rebuttal 1:
Rebuttal: Thanks for your positive and valuable comments. Our responses are as follows:
**`Q1:` Since the model requires an efficient text-to-image diffusion model (in terms of number of inference steps), it can hinder the application of the introduced method with other base models.**
Please refer to the ... | Rebuttal 1:
Rebuttal: # Global Response
We sincerely appreciate all reviewers for their insightful and valuable feedback. We are delighted that the reviewers find the paper well-written, the method compellingly motivated, the idea innovative, and the experimental results superior and impressive. Below, we address thei... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Length Optimization in Conformal Prediction | Accept (poster) | Summary: The paper proposes a new method to improve the efficiency and conditional validity of conformal prediction methods by proposing a minimax optimization problem, whereby the length of prediction intervals is minimized while ensuring (approximate) conditional coverage. The proposed method, conformal prediction wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We appreciate the recognition of the strengths of our proposed method, CPL, particularly its novel approach to enhancing predictive efficiency and robustness in conformal prediction. We are glad that the reviewer mentions CPL's potential to be app... | Summary: The paper presents a novel framework for conformal prediction that aims to balance conditional validity and length efficiency. The authors propose CPL to address the challenges of constructing prediction sets that are conditionally valid and have optimal length. This paper is well-written, provides a comprehen... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewer for their thoughtful and detailed evaluation of our submission. We are glad that the introduction of the Conditional Prediction Length (CPL) framework has been recognized as a significant contribution to the field of conformal predicti... | Summary: This paper introduces a new formulation of conformal prediction that not only aims to achieve the coverage property but also explicitly optimises the length of the prediction intervals. The framework is generic and can be applied to various conformity scores. The authors evaluate the approach through extensive... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and positive evaluation of our paper. We appreciate your recognition of the strengths of our work, particularly the formulation of conformal prediction and the insights gained from our motivating example, as well as the thoroughness of our experiments across di... | Summary: The paper introduces a novel length optimization technique for conformal prediction designed to produce the shortest valid prediction intervals.
Strengths: - The paper introduces a novel framework, Conformal Prediction with Length-Optimization (CPL), which effectively balances the need for conditional validit... | Rebuttal 1:
Rebuttal: Thank you for your review and for recognizing the strengths of our paper. We appreciate your acknowledgment of the novel framework introduced in the paper, i.e., Conformal Prediction with Length Optimization (CPL), and the extensive empirical work we conducted to demonstrate its effectiveness. Yo... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to review our submission and providing helpful feedback. We address the reviewers individually below. In addition, a pdf is attached including (i) plots for an additional experiment which we will add to the revised manuscript, and (ii) a sensitivity a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zero-Shot Event-Intensity Asymmetric Stereo via Visual Prompting from Image Domain | Accept (poster) | Summary: This paper addresses the zero-shot event-intensity asymmetric problem. Given an intensity image and the associated events, where the conventional and event cameras are spatially separated by a baseline, ZEST estimates the disparity map between the two input modalities. The key idea is to convert the input even... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We address your concerns below.
**W1. Novelty and contributions of the visual prompting module:**
While we draw inspiration from the EDI model for event-based image *deblurring*, our visual prompting module is not a straightforward rearrangement. We re-purpo... | Summary: The authors propose a novel zero-shot framework to leverage hybrid event-intensity stereo matching (i.e., one frame camera and one event camera) using off-the-shelf stereo models without additional training.
Given the proposed representation alignment, the authors successfully achieve stereo matching using off... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and positive feedback on our work. We address your concerns below.
**S1. Comparison to more methods:**
We appreciate the suggestion to compare with Mostafavi et al. [17]. However, for the slightly different setting where both frames and events are used in... | Summary: This paper propose a zero-shot framework called ZEST, which employs a representation alignment module as a visual prompt for the utilization of off-the-shelf image-oriented stereo models. To further improve robustness, a cue-guided disparity refinement method is proposed.
By comparing the imaging principle of... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and positive assessment of our work. We address each point below.
**W1. Comparison with more methods:**
In the main text, we have compared our approach with several methods (**Table 1**): the deep learning-based method DAEI [33], after obtaining their code ... | Summary: The paper introduces a novel zero-shot framework for event-intensity asymmetric stereo matching. It leverages visual prompts to align frame and event representations and utilizes monocular depth estimation and stereo-matching models pre-trained on diverse image datasets. The key contributions include a visual ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and insightful suggestions. We address each concern below.
**W1. Evaluation on more datasets:**
The DSEC dataset covers a wide range of scenarios, including various lighting conditions, motion patterns, and scene complexities, as illustrated in **Figures 4 an... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable feedback and insightful suggestions. We appreciate the recognition of the novelty and potential impact of our visual prompting technique, and the advancement our work presents in bridging event-based and traditional frame-based vision for stereo ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a novel visual prompting technique for event-intensity asymmetric stereo matching. The key idea is to align event and frame representations using visual prompts, enabling the use of off-the-shelf stereo matching models for event-intensity pairs. The key contributions are: 1) A visual prompt... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and insightful questions. We are pleased that you found our work technically strong, novel, and impactful. We address each point below.
**W1&Q1. Computational efficiency analysis:**
We profiled the computational complexity of our framework's modules on a mach... | null | null | null | null | null | null |
SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow | Accept (poster) | Summary: This paper presents a work for solving semantic image segmentation and image synthesis simultaneously. This work proposed to use rectified flow and adopt a VAE encoder to compress the images and pseudo masks into the latent space. By comparing the proposed method with previous semantic segmentation models and ... | Rebuttal 1:
Rebuttal: Thank you for recognizing our strengths: 1. Our work bridges semantic segmentation and image synthesis with rectified flow framework. 2. Our work shows faster generation speed for high-quality images. We provide more clarifications below.
Q1: The motivation to use the Euler sampler. More discussi... | Summary: The paper presents SemFlow, an approach that binds semantic segmentation and semantic image synthesis using an ordinary differential equation (ODE) model. The motivation is to use rectified flow to enable LDM as a unified framework for both tasks. The key contributions include a unified framework that jointly ... | Rebuttal 1:
Rebuttal: Thank you for recognizing our strengths: The unified framework for joint optimization of semantic segmentation and semantic image synthesis is novel. We provide more clarifications below.
Q1: The necessity of 3-channel pseudo-masks and the insights of Eq.7.
A1: The mask needs to be converted int... | Summary: This paper proposed a unified diffusion-based framework for semantic segmentation and semantic image synthesis. The proposed SemFlow applied the existing ordinary differential equation (ODE) model and modified the transport problem setting. Additionally, it incorporates techniques such as perturbation and stra... | Rebuttal 1:
Rebuttal: Thank you for recognizing the motivation and exploration of our work.
Q1: Lack of citation and comparison with FreeMask in semantic segmentation task.
A1: Thanks for pointing out this. We will cite FreeMask. However, we would like to emphasize that FreeMask is a framework for training data gener... | null | null | Rebuttal 1:
Rebuttal: ## Global Response
We thank all the reviewers for their insightful reviews. We first summarize the strengths of our paper that the reviewers recognized.
1. The unified framework of bridging semantic segmentation and image synthesis via rectified flow is interesting and novel.
2. This work shows f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Siamese Transformer with Hierarchical Refinement for Lane Detection | Accept (poster) | Summary: The paper proposes a Siamese Transformer with hierarchical refinement, named LAne TRansformer (LATR), to enhance lane detection accuracy in complex road environments. LATR integrates global semantic information with finer-scale features using a high-to-low hierarchical refinement structure. Additionally, the p... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback! We address your questions and concerns below. If any other concerns remain, we’ll be happy to discuss them further. If the concerns are addressed well, we would appreciate it if you could consider raising your score.
**[The motivation of this paper is not v... | Summary: The paper proposes a lane detection method based on transformers and utilises a hierarchical refinement of lane queries. The paper uses a high-to-low refinement strategy instead of the traditional low-to-high refinement, which saves the computation cost in transformer attention. The evaluation of three dataset... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! The detailed responses to each concern are given below.
**[Currently, only one LATR module is used for each pyramid level. It is advisable to add more such modules to assess the performance, i.e. whether more LATR modules are redundant or improve the perf... | Summary: This paper introduces a novel Siamese Transformer with hierarchical refinement for lane detection. The core innovation is a high-to-low hierarchical refinement Transformer structure, LATR, which refines lane line key points to integrate global semantic information and finer-scale features fully. Additionally, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback! We address your questions and concerns below. If any other concerns remain, we’ll be happy to discuss them further. If the concerns are addressed well, we would appreciate it if you could consider raising your score.
**[From the perspective of the topic sel... | Summary: This paper introduces LATR (LAne TRansformer), a Siamese Transformer model with hierarchical refinement for lane detection. The model effectively combines global semantic information with finer-scale features to accurately detect lanes, even in challenging scenarios like occlusions and poor lighting. It also i... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! The detailed responses to each concern are given below.
**[Have you considered using self-supervised learning techniques to reduce the dependency on large labeled datasets? ]**
We have considered using self-supervision to reduce the dependence on large l... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack | Accept (poster) | Summary: The paper examines whether backdoors in deep learning models processed by defense algorithms can be reactivated and how to achieve this reactivation. First, it proposes a metric to measure the presence of backdoors, called the backdoor existence coefficient, which ranges from 0 (backdoors are nonexistent) to 1... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's valuable time and thoughtful comments.
**Q1. Details of the surrogate-based attack (i.e., the transfer attack), and the pros and cons of the surrogate-based attack in comparison with the black-box attack (BBA).**
**R1:** Thanks for this constructive comment. W... | Summary: This paper present a novel insight in adversarial robustness, which is posed by the effective removal of backdoor attacks once defenses have been applied. Interestingly, the authors claim that these are still embedded inside compromised machine learning models.
Hence, the authors propose a novel metric, the B... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable time in reading our work and constructive concerns. We are encouraged by the positive comments of very interesting insights and clear description.
**Q1. Whether backdoor re-activation is creating a novel backdoor or not.**
**R1:** Thank you for y... | Summary: Deep neural networks have been demonstrated to be vulnerable to backdoor attacks. Various existing defense strategies have been proposed to remove backdoors. However, this paper observes that backdoors still remain in the models. It introduces a new metric called the backdoor existence coefficient that measure... | Rebuttal 1:
Rebuttal: Thank you for the positive review on the interesting observation. Your insightful questions and concerns are greatly appreciated. Please inform us if these responses effectively address all your inquiries.
**Q1. The differences among our re-activation attack (RBA), the original backdoor attack (O... | Summary: The paper investigates the idea that an attacker could modify the trigger to make it effective against models modified by post-training defenses (to _re-activate_ the backdoor in defended models).
The paper presents a measure of how much the effect of the backdoor shows persists in model activations and shows... | Rebuttal 1:
Rebuttal: Firstly, we would like to show our sincere appreciation to the reviewer for dedicating the valuable time, offering gracious affirmation, and providing constructive.
**Q1. Why is CKA a good choice in Eq. (2) and (3)?**
**R1:** Thanks for this insightful comment. We would like to explain from two ... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable time and constructive comments, and are encouraged by the positive comments of **very interesting insights** (AhAB,ULSi,G6gk,ccvM), **novel ideas and good contribution** (AhAB,G6gk,ccvM), **moderate-impact** (ccvM), **extensive experimental evalu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images | Accept (poster) | Summary: Authors propose MHCDIFF (Multi-hypotheses Conditioned Point Cloud Diffusion) to reconstruct 3D human point could from a single image under occlusion. The key idea behind MHCDIFF is the smart use of image projection features and features from multiple SMPL hypothesis, coupled with point cloud diffusion.
The met... | Rebuttal 1:
Rebuttal: > **Weakness 1-1.** L222: How are SMPL meshes sampled?
>
**Reply:** We sample SMPL meshes via ProPose [16] (lines 178-179). ProPose [16] predicts the distribution parameter of the matrix Fisher distribution as mentioned in Section 3 Preliminary, ProPose [16] in the paper. We sample SMPL pose par... | Summary: The target of this paper is to reconstruct 3D clothed human shape. The conditional point clouds diffusion model is adopted as the main structure of the proposed reconstruction model. This work focuses on designing effective conditioning features, especially for overcoming the occlusions. The conditioning featu... | Rebuttal 1:
Rebuttal: > **Question 1.** Multi-hypotheses was proposed in [8] to model the uncertainty caused by occlusions or depth ambiguities in 3D shape reconstruction. What are the differences between your work and [8] on multi-hypotheses?
>
**Reply:** 3D Multi-bodies [8] learns a multi-hypothesis neural network ... | Summary: This paper introduces method for reconstructing detailed 3D human shapes from single occluded RGB images. The key contributions are:
- A point cloud diffusion model conditioned on projected 2D image features and local features from multiple SMPL mesh hypotheses.
- A multi-hypotheses conditioning mechanism that... | Rebuttal 1:
Rebuttal: > **Weakness 1.** In particular, testing on datasets with more diverse clothing styles and body shapes would be valuable.
>
> **Question 3.** How well does the method generalize to different clothing styles or body shapes not seen during training?
>
> **Question 5.** How does the method perfor... | Summary: This paper investigates the problem of improving the robustness of occluded 3D human reconstruction from a single image. The idea is to achieve better stability under occlusion via two steps: 1. refining pixel-aligned local image feature extraction part with the help of multi-hypothesis human pose and shape es... | Rebuttal 1:
Rebuttal: > **Weakness 1.** Composing the multi-hypothesis human pose and shape with point cloud diffusion seems incremental. The paper lacks solid insight to inspire the readers.
>
> **Question 1-1.** What is the key insight behind the two designs presented in this paper?
>
**Reply:** We design MHCDIFF... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and finding that the proposed method is “simple and intuitive” [vFH7] and shows “significant improvement” [1vtm, V2cx]. We also appreciate [V2cx] from finding that “paper is well written and easy to understand.”
|The number of SMPL sampled|CD (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability | Accept (poster) | Summary: This paper studies the behavior of a single unit of linear self-attention trained via gradient flow on an autoregressive (AR) loss on token sequences generated by a particular class of noiseless (deterministic) linear systems. The first theoretical results state that under some assumptions, the result of gradi... | Rebuttal 1:
Rebuttal: # Response to Reviewer c1Lc
We thank Reviewer c1Lc for the valuable comments.
## Weakness1: key contribution
Thanks for the insightful suggestion. We agree that we do not have direct evidence that "ICL by AR pretraining is more difficult than ICL by few-shot pretraining" because the data model is... | Summary: This paper studies the emergence of mesa-optimization/in-context learning capabilities of Transformers. In particular, it attempts to fill the gap of our understanding of training dynamics and specifically on non-convex dynamics where the sequences are autoregressively generated as $\vec{x}\_{t+1} = W \vec{x}\... | Rebuttal 1:
Rebuttal: # Response to Reviewer WaqB
We thank Reviewer WaqB for the positive support and valuable comments, which can definitely improve the quality of this paper.
## Weakness 1: additional related work
Thanks for the helpful suggestion. We will cite and discuss the mentioned papers in the final version... | Summary: - The authors study the problem of in-context learning an autoregressive (AR) process (defined with a uniformly drawn diagonal unitary transformation) with a one-layer linear causal self-attention model, trained by gradient flow on square loss.
- Under a specific parameter initialization scheme and a distribut... | Rebuttal 1:
Rebuttal: # Response to Reviewer dsNs
We thank Reviewer dsNs for the positive feedback and valuable comments, which can improve the quality of this paper.
## Weakness1: initialization & negative results
Thanks for the nice suggestion. We discuss two concerns respectively.
### Initialization
We argue the dia... | Summary: This paper studies the autoregressive training of a one-layer linear attention model for in-context learning of first-order AR processes. It is shown that under certain distribution of the initial point, the gradient flow on the population next-token prediction loss will converge to a model that makes the pred... | Rebuttal 1:
Rebuttal: # Response to Reviewer ykMD
We thank Reviewer ykMD for the positive score and valuable comments, which can definitely improve the quality of this paper.
## Weakness 1: further highlight the technical contribution
Thanks for the nice suggestion. We consider the novel AR pertaining which makes our s... | Rebuttal 1:
Rebuttal: # Common concerns from reviewers
We thank all reviewers for their valuable and constructive comments. We address the common concerns here and post a point-to-point response to each reviewer as well. We believe the quality of the paper has been improved following the reviewers' suggestions.
## Com... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift | Accept (poster) | Summary: The work aims to bridge an important gap in literature, looking at using public pretraining to improve differentially private model training, even when there is a distribution shift, particularly concept shift, in the public and private data. They report impressive improvements in accuracy. Further, they provi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and for the kind comments regarding the strengths of the work.
> **[W1] Strong assumption that the shared, low-dimensional representation is even learnable across the private and public data.**
> **[Q1] Referencing W1 it might be interesting to look at th... | Summary: There are many contexts where deep learning models trained to preserve differential privacy have much worse performance than models trained without the differential privacy constraint. One common way to improve model quality in these cases is to pre-train a model on publicly available data then fine-tune with ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and comments on our work.
> **The disconnect between the setting for the experiments and the stylized theoretical model is very apparent. It is difficult to say how much of the empirical results from section 4 can be explained by the theoretical... | Summary: The paper proposes a theory for the benefit of transfer learning in DP ML even when public data and private data differ significantly from each other. This theory relies on a subspace shared between public and private data. They empirically verify the algorithm in their theory.
Strengths: The paper is very cl... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to read and provide feedback on our work.
> **The advantage over prior work is mild for linear regression tasks. In linear regression, gradients, input data, or models being constrained to a linear subspace are all roughly the same thing.**
We acknowledg... | Summary: This paper studies the role of using public training data for fine-tuning private tasks. The paper begins by showing, empirically, on three datasets, that fine-tuning significantly outperforms training privately from scratch. The experiments are conducted on several image classification datasets. Then, the pap... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback on our work.
> **The theoretical setup may be too simplistic. For instance, the resulting algorithm 1 is different from what was implemented in the experiments.**
We understand the reviewers' concerns regarding the stylized theoretical model. We ... | Rebuttal 1:
Rebuttal: ## Common response to all reviewers
We thank all of the reviewers for taking the time to read and give helpful feedback on our paper.
As noted by the reviewers, our work provides both theoretical and empirical evidence to show that public pretraining is useful for private finetuning under distr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy | Accept (poster) | Summary: This paper firstly identifies a condition discrepancy in diffusion models that generation results conditioned on the training text prompts can significantly differ membership datasets and hold-out datasets of the model. Based on this observation, the paper proposes a novel method for membership inference that ... | Rebuttal 1:
Rebuttal: Thank you for your recognition of the societal impact of our work and your acknowledgment of our contribution in first identifying the condition likelihood discrepancy. In the following, we address your concerns point by point. (**Refer to the submitted PDF for Tab. A, Tab. B, Tab. C and Fig. A**)... | Summary: This paper proposes a new MIA metric tailored for text-to-image diffusion models. More precisely, they assume that conditional overfitting is more severe that unconditional one. Based on this assumption, a new MIA metric (CLiD) is proposed. The CLiD metric shows superior performance in various text-to-image di... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and efforts in reviewing our paper. Your recognition of the significance of our work and acknowledgment of our experiments’ comprehensiveness is deeply appreciated.
## Weakness (1):
Typos in the paper.
## Answer (1):
Thank you for carefully reviewing our pape... | Summary: In this paper, the authors propose a novel membership inference attack for text-to-image diffusion models. By examining the discrepancy between the text-conditional predictions and unconditional predictions, the proposed method outperforms the SOTA method by a significant margin. In the end, the authors also s... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and your valuable feedback. We are encouraged by your appreciation on our clear motivation, extensive experiments and promising results, as well as the comprehensive experiment of adaptive defenses and good writing. Below we address the detailed c... | Summary: The paper addresses potential unauthorized data usage and the privacy concerns in text-to-image diffusion models. The authors introduce a novel membership inference method, Conditional Likelihood Discrepancy (CLiD), which leverages the identified phenomenon of conditional overfitting in these models. They prop... | Rebuttal 1:
Rebuttal: Thank you for appreciating the novelty and the effectiveness of our work as well as providing valuable feedback. Below we address the detailed comments and hope that you may find our response satisfactory. (**Refer to the submitted PDF for Tab. E and Fig. B**)
## Weakness (1):
The concern about as... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. We are encouraged by your appreciation on our clear motivation and positive societal impact (Reviewers iMS8, gj8y, and k8sd), innovative and pioneering method (Reviewers meQ8, iMS8, and gj8y), comprehensive and practical experiments (Reviewer... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RAGraph: A General Retrieval-Augmented Graph Learning Framework | Accept (poster) | Summary: The paper propose a Retrieval-Augmented method to further assist graph in context learning. With the advancement of graph In-context learning and graph prompting, RAG is a natural technique that could be built upon them. The main contribution of the paper is to develop such a RAG pipeline on graph learning sce... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. We address your concerns and answer your questions below.
**W1: Why RAGraph works on par with RAG in NLP/CV, and how “generation” works. What pattern was learned from retrieved graph pattern?**
**A1**: In NLP, RAG enhances the generation of LLM by retrieving ... | Summary: The paper proposes RAGraph, a framework that enhances GNNs with RAG. RAG allows GNNs to utilize unseen data by retrieving relevant information. Extensive experimental results show the effectiveness of RAGraph.
Strengths: S1. A general and flexible framework.
S2. Extensive experiments on various tasks (both n... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We address your concerns and answer your questions below.
**W1: The diversity of datasets needs to be improved. For link prediction, the paper is evaluated on mostly e-commerce data, where knowledge graph tasks should also be considered. For node classifica... | Summary: This paper aims to leverage the retrieval-augmented generation method to improve the generalisation capability of pretrained graph neural networks (GNNs) to unseen data. To this end, a framework named RAGRAPH is proposed. RAGRAPH first constructs a toy graph vector library (key-value pairs) by chunking from re... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. We address your concerns and answer the questions below.
**W1 & Q1: Unclear of the contribution of (a) inverse importance sampling, (b) augmentation, (c) four similarities, and (d) fusion method to performance improvement.**
**A1**: We conducted four ablati... | Summary: ### Summary:
The paper presents RAGRAPH, a pioneering framework that integrates Retrieval-Augmented Generation (RAG) with pretrained Graph Neural Networks (GNN) to bolster their generalizability on unseen graph data. The authors construct a toy graph vector library capturing key attributes, which aids in the... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We address your concerns and answer your questions below.
**W1: The difficulty to construct and maintain high-quality and diverse graph vector base for different tasks.**
**A1**: We acknowledge the challenge you mentioned of constructing and maintaining hi... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all reviewers for their insightful comments and acknowledging the strengths of RAGraph. We have addressed all the concerns raised and provided comprehensive answers in this rebuttal.
In the attached PDF, we present:
**(1) More ablation experiments menti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs | Accept (poster) | Summary: This paper proposes a `goldfish loss', a loss that excludes some tokens in each training data sequence from the loss computation, with the aim of decreasing verbatim memorization of sequences. Which token is excluded is decided with a function G(x_i). The authors try two different drop functions, one of which... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time and effort in providing this review. Following is our response.
### **Weaknesses**
1. **It is a bit unclear to me why it is necessary to mix in the Wikipedia sequences at such a high rate. RedPajamas is intended to provide a reasonable version of a tr... | Summary: This paper tackles the issue of memorization in large language models (LLMs), where models reproduce verbatim training data, posing copyright, privacy, and legal risks. The authors propose a novel technique called "goldfish loss" to mitigate this problem during training. Instead of calculating the next-token p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and for highlighting the real-world application of goldfish loss. Following is our response.
### **Weaknesses**
**Lacking theoretical guarantees**
- As mentioned in our limitation (Section 7.1), our method is derived from first principles only and our strong r... | Summary: This work presents a new goldfish loss to mitigate memorization during pretraining. Specifically, for every k tokens during training, the loss for one token is skipped to prevent the exact memorization of the entire string. Experiments on 7B llama-2 and 1.1B tinyllama demonstrate that the goldfish loss can eff... | Rebuttal 1:
Rebuttal: We thank the reviewer and below is our response.
---
### **Weaknesses**
1. **Impact of goldfish-loss on downstream performance.**
- This is a good point. We directly discuss this in global rebuttal point 2 (Figure 2).
- To reiterate, using goldfish loss is not a free lunch and see observe... | Summary: This work focuses on the issue of memorization, where a language model can be prompted to exactly repeat a document or sequence from its training data. The authors introduce a loss which masks random tokens in a sequence during training. To ensure the same mask is applied to duplications of a sequence, or a sa... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting simplicity, efficiency, and performance as strengths. Below is our response:
---
### **Weaknesses**
**Results on benchmarks seem to show that while verbatim repetition is avoided, knowledge is still retained (Fig. 4).** Is the output a paraphrase of learned ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable time and effort in providing this review. We are encouraged and we appreciate the reviewer mentioning the simplicity, efficiency, and the strong performance of our approach. Below we provide some "global" comments that address questions shared by multiple ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Spectral Learning of Shared Dynamics Between Generalized-Linear Processes | Accept (poster) | Summary: The work proposes a new linear dynamical system model that aims to model interaction between two time series by explicitly modeling their shared and private dynamics. Furthermore, authors incorporate generalized linear models in their observation model of the proposed dynamical system to handle different kinds... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding that the manuscript is well-written and addresses a gap in the literature. We also thank them for providing very comprehensive and helpful feedback. Below we reply to outstanding questions/concerns inline.
> Generality claim on block form structure described in e... | Summary: In their study "Spectral Learning of Shared Dynamics Between Generalized-Linear Processes", the authors introduce a multi-stage spectral algorithm for learning the parameters of a model for two generalized-linear time-series with shared latent dynamics. Assuming the latent dynamics to contain both shared and p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their close read of our manuscript and for finding it enjoyable with a well-written methods section. Below we address comments/questions inline.
> Feedback on Def 3.1, section A.2, & assumption A.1
We thank the reviewer for their suggestions. We clarify that the WLOG in... | Summary: In this paper, the authors propose spectral learning method for learning shared latent subspaces between multiple observations. The resulting model leverages the novel construction of Hankel matrix between observations from different processes. To promote discovery of shared subspaces, the authors propose the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our manuscript clearly written with comprehensive and convincing experimental results. The reviewer raises a great question regarding initializing EM with our learned parameters. To the best of our knowledge, no existing EM algorithm can perform dissociation of sh... | Summary: This paper introduces a novel multi-step analytical subspace identification algorithm for Generalized-Linear Dynamical Models (GLDMs) to model shared and private dynamics within two time-series data sources. The proposed algorithm effectively decouples shared and private dynamics, demonstrating superior perfor... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our paper well-written, our approach novel and effective, and the experimental section sufficient. Below we address outstanding questions and comments inline.
> The method assumes time-invariant dynamics, which may not hold for all neural data, potentially affect... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to review our submission and for providing helpful feedback, suggestions, and discussion regarding our work. We were encouraged to hear that reviewers found our manuscript “enjoyable” (reviewer m47S) and “well-written” (reviewers Hfka, m47S, and 95d5), wi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Language Model as Visual Explainer | Accept (poster) | Summary: This paper builds an attribute tree using LLMs to explain image classifiers. To this end, the paper designs a framework that expands and prunes nodes for the tree. Using this framework, the paper provides a richly annotated version of CIFAR and ImageNet, which is 5 times more expressive than prior works. With ... | Rebuttal 1:
Rebuttal: We truly appreciate the suggestion and acknowledgment from R-FVKA regarding the dataset and method.
`>>> Q1` **Explanation beyond Attribute Tree**
`>>> A1` Amazing question. We appreciate the valuable papers mentioned by the reviewer and will cite them in our final version. While all [1-6] use n... | Summary: The goal of the paper is to bridge the gap between human comprehension and AI decision. For this purpose, the authors propose a Language Model as Visual Explainer (LVX), an approach to interpret the internal workings of vision models through tree-structured linguistic explanations, without the need for additio... | Rebuttal 1:
Rebuttal: Thank the reviewer for the positive feedback. We are delighted that R-kLjQ acknowledge our novel explainability method using LLMs, our new benchmark, and our evaluation metrics. Those encouraging words means a lot and inspires us to push our research further.
`>>> Q1`**Hierarchical Contrastive Lo... | Summary: The paper suggests a new approach to building a hierarchical tree of visual attributes (represented with language) that matches the decision-making mechanisms of classifiers. The approach is based on using an LLM to suggest a tree of attributes that are related to a specific class, and then refine the tree acc... | Rebuttal 1:
Rebuttal: `>>> Q1`**Explaining Network Mechanism**
`>>> A1`Thanks for the question. Actually, our method explains the network mechanism **by identifying prototype sample and concept**.
Unlike *mechanistic interpretability*, which maps concepts to layers or neurons, our LVX uses `prototype-based explanati... | Summary: This work proposes a method to understand the prediction of an image classification model using a tree of attributes. The tree of attributes is originally constructed in a text-only by querying gpt for identifying attributes of given concepts. They then use an image-to-test model (or retrieval model) to obtai... | Rebuttal 1:
Rebuttal: We sincerely appreciate R-U6GY's thoughtful comments and suggestions. We answer the questions below and will incorporate them into our revised version.
`>>> Q1`**Tree Construction not Affected by Image Model**
`>>> A1`Thank R-U6GY for the insightful comment. In fact, the tree construction is ind... | Rebuttal 1:
Rebuttal: We would like express our sincere gratitude to all reviewers for their constructive comments. We are particularly thankful for the following positive feedback:
- The idea is interesting and novel: `Reviewer UiCA`, `Reviewer kLjQ`;
- The contributions of the dataset and evaluation metrics are... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Manipulation Intention Understanding for Accurate Zero-Shot Composed Image Retrieval | Reject | Summary: This paper focuses on Zero-Shot Composed Image Retrieval (ZS-CIR), which requires retrieving an image matching a reference image while incorporating specified textual modifications. The authors argue that a key challenge in ZS-CIR is training models on limited intention-relevant datasets to understand human in... | Rebuttal 1:
Rebuttal: **W1,Q1. Detailed Explanation of "Intention" in Our Work**
Thank you for your question! In our manuscript (lines 51-57), we define "intention" as the implicit, latent intent within manipulation descriptions. For better clarity, we visualize comparisons between the original captions, rewritten cap... | Summary: This paper introduces De-MINDS, a novel framework for Zero-Shot Composed Image Retrieval (ZS-CIR) that aims to bridge the gap between pre-training and retrieval by incorporating intention-based pseudo-manipulation descriptions. The authors propose intent-CC3M, a dataset featuring these descriptions generated t... | Rebuttal 1:
Rebuttal: **W1. Lacks evidence to support the claim that caption redundancy leads to inaccurate retrieval**
We appreciate your valuable feedback! As demonstrated in Figure 1 of our PDF in Author Rebuttal, caption redundancy presents significant challenges of the SoTA model (i.e., Context-I2W) from two pe... | Summary: This paper introduces an image-text dataset (intent-CC3M) for Zero-Shot Composed Image Retrieval (ZS-CIR) models to make better understanding of human manipulation intentions. Specifically, captions are re-written with LLaVA model to provide more details, and additional manipulation reasoning prompt is applied... | Rebuttal 1:
Rebuttal: **W1. Distinctiveness of the De-MINDS Framework Compared to Other LLM-based CIR Methods**
We appreciate the reviewer's insights about the novelty of our De-MINDS compared with existing LLM-based CIR approaches [1, 2]. Although De-MINDS employs Large Language Models (LLMs), the motivation of our w... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you for your insightful feedback! We are encouraged by the positive comments such as " The proposal is intuitive and clear" (Reviewer CKWd), "The introduced intent-CC3M dataset is innovative and potentially impactful" (Reviewer KSmi) , "the presentation is also ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Non-convex Learning in Dynamic Environments | Accept (poster) | Summary: This paper provides $O(V_T^{1/3}T^{2/3})$ dynamic regret
and $O(\sqrt{\tau\log(T)})$ strongly-adaptive regret guarantees for
online learning with Lipschitz losses in bounded domains.
Strengths: The paper fills a gap in the literature by providing the
dynamic and strongly-adaptive extensions of the FTPL
algori... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
---
**Q1**: What are some examples of interesting losses which are non-convex, but bounded and lipschitz?
**A1**: An illustrative example is the loss function of Generative Adversarial Networks (GANs), which has been extensively discussed by Agarwal et ... | Summary: This paper presents novel algorithms to tackle the challenges of online learning with non-convex loss functions in dynamic settings. The authors extend the Follow the Perturbed Leader (FTPL) algorithm to dynamic environments by proposing two new algorithms: FTPL-D and FTPL-D+. They demonstrate the effectivenes... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
We will add more experiments in the final version. Here, we want to highlight that although each imitation task in our experimental setup is relatively simple, the framework of online constrained meta-learning introduces considerable complexity, especiall... | Summary: The paper proposes two variant algorithms of the FTPL (follow-the-perturbed-leader) algorithm for online learning with non-convex losses in time-changing environments. The authors analyze these algorithms under the settings where the variability of the environment ($V_T$) is both known and unknown. FTPL-D is t... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
---
**Q1**: Going through the proofs: in equation 17 ... if this can be strengthened?
**A1**: In our opinion, it seems impossible to strengthen this proof. The reason is that our analysis of $b_i$ aligns with that used in the convex case, and the derived... | Summary: This paper considers the problem of non-convex online learning in dynamic environments. The authors go on to provide some algorıthmic variants and their respective regret bounds depending on the functional variation. They show that the non-convex dynamic regrets matches the convex ones in the literature.
Stre... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews! Detailed responses have been provided for all the questions raised. In particular, we believe that the major question can be fully addressed by restating our theorems. We hope the reviewer could examine them, and re-evaluate our paper. We are looking forwa... | Rebuttal 1:
Rebuttal: According to the suggestions of **Reviewers fFaA** and **PVQP**, we will reformulate our theorems to explicitly incorporate $\alpha$ and $\beta$. In this way, we obtain the regret bounds below, treating $\alpha$ and $\beta$ as constants.
---
***Theorem 1***. Under Assumptions 1 and 2, and settin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exactly Minimax-Optimal Locally Differentially Private Sampling | Accept (poster) | Summary: Consider the following setting: A public domain $\mathcal{X}$, a family of distributions $\mathcal{P}$ over $\mathcal{X}$, and a mechanism $M$ which give a distribution $P \in \mathcal{P}$ releases an element $x \in \mathcal{X}$, such that $M$ is $\epsilon$-pure local DP with respect to the input, that is - th... | Rebuttal 1:
Rebuttal: We thank Reviewer NxAu for thoroughly reviewing our paper and providing valuable comments.
## Response to the first weakness on comparison with previous work
We agree that a certain part of our results can be seen as a generalization of the previous work [35], as we mentioned in the "Comparison ... | Summary: The paper considers the problem of sampling from a user's distribution under local differential privacy (LDP). Namely, a client has a distribution P in some set of distributions, where the set is known to a data curator. The client wants to perturb P to get Q, such that a sample from Q sent to the data curator... | Rebuttal 1:
Rebuttal: ## Response to the first weakness on alternative mechanism
We thank Reviewer 9St7 for raising an interesting point. We are truly grateful for the thoughtful review of our paper.
Before we discuss about the minimax-optimality of the proposed scheme by the reviewer, we would like to emphasize that ... | Summary: This paper addresses the problem of sampling under local differential privacy (LDP) requirements, focusing on the privacy-utility trade-off (PUT). It defines the fundamental PUT of private sampling in the minimax sense, using f-divergence as the utility measure. The authors characterize the exact PUT for both ... | Rebuttal 1:
Rebuttal: ## Response to the first question on the approximation error
We thank Reviewer KHbV for asking a practically important question. We briefly mentioned the effect of the approximation error in calculating $r_P$ on the privacy budget in Appendix F.2, and we considered this effect in the experiment. ... | Summary: This paper considers the issue of protecting probability distributions with local differential privacy when they are sampled. This problem has been recently studied in view of potential applications to generative models. In particular, the paper studies mechanisms that, given a distribution, produce a "sampler... | Rebuttal 1:
Rebuttal: ## Response to the question on the motivation of protecting distribution
We thank Reviewer D3Yh for thoroughly reviewing our paper and asking an important question regarding practical motivation of protecting distribution.
Let us first introduce an equivalent definition of LDP that illustrates ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for thoroughly reviewing our paper and providing numerous valuable comments. All suggestions have helped us better present our work and delve deeper into our theoretical results. In particular, some comments were especially helpful in improving the paper, and we believe ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EASI: Evolutionary Adversarial Simulator Identification for Sim-to-Real Transfer | Accept (poster) | Summary: The paper introduces an approach to address the challenges of transferring reinforcement learning (RL) policies from simulation to real-world applications. Traditional methods like Domain Randomization (DR) require significant domain-specific knowledge and extensive training time, which often result in subopti... | Rebuttal 1:
Rebuttal: # Response to Reviewer yoqm
`Q1. How sensitive is EASI to the choice of hyperparameters for both the GAN and the ES components?`
`A1.` We conducted a hyperparameter sensitivity analysis for EASI, as detailed in **common response A5**, and provided recommendations for parameter settings. EASI has... | Summary: This work studies the sim-to-real transfer using an evolution strategies with a learned discriminator. The discriminator learns to distinguish the state transition between real and simulation. The evolution strategies aim to optimize the parameters of the simulator so that the data generated by the simulator i... | Rebuttal 1:
Rebuttal: # Response to Reviewer ZLtE
`Q1. The architecture of the used GAN and how are the tuple s, a, s' processed in the training. `:
`A1.` The input to the discriminator consists of a state transition $(s, a, s')$. This $(s, a, s')$ data is combined into a tensor with a shape of $(2 \times \text{dim(s... | Summary: This submission presents an approach to sim2real transfer by predicting better simulation hyperparameters via Evolutionary Adversarial Simulator Identification (EASI). EASI is a combination of a generative adversarial network (GAN) and evolutionary strategy approach to generating simulation hyperparameters. Co... | Rebuttal 1:
Rebuttal: # Response to Reviewer zVKJ
`Q1. How come in ball balance the oracle method performs worse than EASI.`
`A1.` BallBalance is a unique environment where the ball can only be controlled indirectly by a movable table and cannot be directly influenced by actions. In this task, if the ball falls off t... | Summary: The paper tackles the issue of finding correct parametrizations for simulators for robot tasks in order to close the sim-to-real gap. To optimize the simulation parameters, evolutionary strategies (ES) are employed in combination with a discriminator function (trained in a GAN setting). The study shows that th... | Rebuttal 1:
Rebuttal: # Response to Reviewer 2SzS
`Q1. The study lacks some stronger comparison. `
`A1.` In the **common response A4**, we introduce additional comparative experiments, including the FineTune and GARAT. In these experiments, EASI consistently demonstrates the best performance in transferring simulation... | Rebuttal 1:
Rebuttal: # Common Response
We sincerely appreciate the reviewers for their insightful feedback! In the following, we will first address the comments that are shared by multiple reviewers.
`Q1. The significance of this work.`
`A1.` Domain randomization (DR) has become one of the most popular sim-to-real... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces Evolutionary Adversarial Simulator Identification (EASI), a novel sim-to-real method using a combination of Generative Adversarial Network (GAN) and Evolutionary Strategy to bridge the gap between simulation and reality. EASI optimizes simulator parameters by aligning state transitions bet... | Rebuttal 1:
Rebuttal: # Response to Reviewer rASM
`Q1. How do you capture/model the state transition`
`A1.` In this paper, we draw on the training paradigm of GANs to estimate the distance between the simulated and real state transition distributions. According to GAN theory [1], Discriminator D and Generator G play t... | null | null | null | null | null | null |
Divergences between Language Models and Human Brains | Accept (poster) | Summary: The paper studies the differences in language representations in LLMs (GPT-2 XL) and the human brain (MEG activity), by using LLM representations to predict MEG activity. They use an LLM-based approach to identify two domains where LLM representations do not capture brain activity well: social/emotional intell... | Rebuttal 1:
Rebuttal: Thank you Reviewer fRry for the valuable and constructive feedback! Please find our answers to your comments below.
## Best Performing Layer
Regarding the question about why layer 7 (0-indexed) is the best layer at predicting brain responses in our study, which differs from prior literature, we... | Summary: Language models are known to predict MEG signals in humans during reading. In this work, the authors explored for what "kinds" of texts are language models bad at predicting MEG signals. The authors used a novel method to propose possible hypotheses, and found multiple possible categorizations of weak predicti... | Rebuttal 1:
Rebuttal: Thank you Reviewer b2dQ for the positive and constructive review! Please find our answers to your comments below.
>**Many aspects mentioned in the work where the language model are not performant on, for example common sense and social reasoning, have large leaps forward in more modern models. A ... | Summary: This paper explores the differences between LMs and the human brain in processing language. The authors conduct experiments using MEG data from reading and listening tasks to investigate elements of MEG responses that LMs cannot adequately explain. LLMs are used to automatically generate hypotheses, identifyin... | Rebuttal 1:
Rebuttal: Thank you Reviewer jRnr for the valuable and constructive feedback! Please find our answers to your comments below.
## GPT-2 vs Modern LLMs
We agree that more modern models may demonstrate enhanced capabilities in areas like language understanding and social reasoning. To explore this, we replic... | Summary: The authors use a data-driven method to generate hypotheses about particular words/linguistic contexts in which a brain encoding model does not accurately predict the brain response to language. they find that social/emotional/physical complexity, along with linguistic complexity, are all associated with worse... | Rebuttal 1:
Rebuttal: Thank you Reviewer hoiu for the positive and valuable comments! Please find our answers to your comments below.
## Fine-tuning
> **The fine-tuning intervention needs to be appropriately baselined, for example by fine-tuning on other tasks which don't match the hypotheses about what is driving br... | Rebuttal 1:
Rebuttal: Please refer to the attached PDF for 10 additional sentences colored based on prediction error. These sentences are selected from the top 20% most divergent sentences in the Harry Potter dataset. Each of the five colors corresponds to a 20-percentile range of words from the entire dataset.
Pdf: /p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniMTS: Unified Pre-training for Motion Time Series | Accept (poster) | Summary: Overall, this is a paper about human activity recognition from motion time series data. Authors design a unified framework for data generation, pre-training, and evaluation. The data used for pre-training is generated by motion skeleton data. LLM is also involved in text augmentation. Experimental results on 1... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable feedback and for recognizing our idea, promising results and clear presentation. We have addressed the comments as below.
[1] Importance of synthesized data
We add one experiment by incorporating Capture-24 (one of the largest real data from free-... | Summary: This paper proposes a unified pre-training framework using simulated data from body skeleton model for motion time series. The authors rightly pointed out the challenges of collecting motion time series at scale due to privacy concerns despite the fact that the motion time series data have great promises in a ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for all the valuable feedback and for recognizing our technical contributions, novelty of our method, thoroughness of our experiments and the clarity of our paper presentation. We have addressed the comments as below.
[1] Baselines trained on BioBank
We thank t... | Summary: In the context of time series tasks, the paper addresses motion time series data collected from mobile and wearable devices such as smartphones and smartwatches. However, training and testing on the same dataset leads to poor generalizability of the models. The authors propose UniMTS, a unified pre-training pr... | Rebuttal 1:
Rebuttal: We express our gratitude for the reviewer’s constructive feedback, and for recognizing the soundness of our technical claims, the scale and performance of our experiments, as well as the clarity of our paper’s presentation. We have addressed each of the concerns as below.
[1] Contribution of the ... | Summary: This paper introduces a unified pre-training procedure for motion time series that generalizes across diverse device latent factors and activities. A contrastive learning framework that aligns motion time series with text descriptions enriched by large language models and the spatio-temporal graph networks are... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments and for recognizing the effectiveness and clarity of our work. We have addressed each comment as below.
[1] How to obtain text descriptions
We use the original text descriptions in the motion skeleton dataset HumanML3D. As detailed in App... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable comments and feedback. We appreciate that nearly all the reviewers recognized the strengths of our work as follows:
a. **Novelty**: high original approach (4CUw), novel method (jvaP, 6pyv).
b. **Importance**: address a critical gap, signifi... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a novel pretraining method for IMU time series (i.e., acceleration + angular velocity). The authors make use of human skeleton sequences and simulate IMU measurements by calculating the acceleration and angular speed at each joint. These simulated data are then used to pre-train a spatial-t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable feedback, and for recognizing the novelty of our approach and the promising results from our experiments. We have addressed each of the comments as below.
[1] Quality of generated IMU data
UniMTS is aimed for action recognition, so the sampling fr... | Summary: The paper introduces a new pre-training approach designed specifically for motion time series data from mobile and wearable devices. The proposed method, UniMTS, employs a contrastive learning framework to align motion time series with text descriptions, enhanced by large language models. This approach address... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful feedback, and for recognizing the novelty, experimental rigor, and clarity of our presentation. We have addressed the comments as below.
[1] Ego4D and Ego-Exo4D benchmark
a. We acknowledge that these are important action recognition benchmarks; ... | null | null | null | null |
Make Continual Learning Stronger via C-Flat | Accept (poster) | Summary: This paper points out that the current sharpness in continual learning (CL) tends to optimize towards a suboptimal space rather than achieving a global solution for continuous tasks. The proposed C-Flat, smoothly migrates to the global optimal of the joint knowledge space of the current and next tasks, thereby... | Rebuttal 1:
Rebuttal: > Weakness 1: Improve INTRODUCTION.
A: We have done this for the Introduction section to highlight contributions, including highlighting our CL-friendly optimizers, and also discussing how flatness-related works in current CL (flatness in some data/bases/projection, etc).
> Weakness 2: The relat... | Summary: This paper proposes a novel method named C-Flat to mitigate catastrophic forgetting by optimizing for a flatter loss landscape. This method is described as a plug-and-play solution that can be easily integrated into a wide range of existing CL approaches. The paper argues that this approach not only stabilizes... | Rebuttal 1:
Rebuttal: > Weakness 1: Overly strong claims.
A: We appreciate your suggestion! We have revised the manuscript in line 96 to temper these statements.
> Weakness 2: More clear structure for the method.
A: We did shorten that part a lot into one paragraph than a better math environment due to the page lim... | Summary: Continual learning seeks to learn a series of new tasks without forgetting old ones. This paper explores the impact of a flat loss landscape on catastrophic forgetting.
Strengths: - This paper applies loss landscape optimization to multiple categories of continual learning methods.
- The method proposed in th... | Rebuttal 1:
Rebuttal: **NOTE**: Table R1-R4 and Figure R1 are attached to the one-page PDF.
> Weakness 1: Clarification of contributions.
A: For the suggested works, they could further validate the importance of C-Flat on a general and stronger flatness-aware CL optimizer, we have cited them and the detailed discussio... | Summary: The paper proposes a new algorithm agnostic optimisation method, which is tailored specifically for continual learning. This method takes advantage of zeroth order landscape sharpness-aware optimisation and proposes a new method, which improves upon SGD.
Strengths: Originality: the method raises a question wh... | Rebuttal 1:
Rebuttal: **NOTE**: Table R1-R4 and Figure R1 are attached to the one-page PDF.
> Weakness 1: Fix the omissions.
A: Thanks for your kind reminder. We have fixed the omissions and conducted a thorough proofreading for clarity.
> Weakness 2: Significance analysis.
A: i) Note that we set the fixed random s... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments and appreciation of our strengths, e.g.,
+ well-presented and easy to follow (**Reviewer p3Vh/PuVp/Tb4q/475M**);
+ originality and nice novelty (**Reviewer p3Vh/Tb4q/475M**);
+ good generalizability for CL (**Reviewer p3Vh/ PuVp/ Tb4q/475M**).... | 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.