Split (1)

train · 7.63k rows

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
Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes	Accept (poster)	Summary: This paper analyzes the loss landscape of the refusal loss in the jailbreaking problem and proposes a method to detect jailbreaking input. This method has two steps: 1) sample multiple outputs and vote on the results, and 2) take the gradient with respect to the input and measure the gradient norm. Overall, th...	Rebuttal 1: Rebuttal: Weakness 1: The paper does not compare the time and memory efficiency with other baselines. R: We follow the setting in Appendix A.15 to evaluate different methods’ average running time and the required largest GPU memory on Vicuna-7b-V1.5. \|\|Time(s)\|Memory(GB)\|TPR\| \|---\|---\|---\|---\| \|w/...	Summary: This paper introduces the concept of refusal loss function for detecting language model jailbreaking attacks. The refusal loss is defined as $1$ minus the refusal rate, and it is observed that on malicious instructions, the refusal loss tends to have a smaller value and a larger gradient norm. Based on this ob...	Rebuttal 1: Rebuttal: Weakness 1 and Question 1: A discrepancy between the 2-stage introduction of Gradient Cuff in the main text and its description in Appendix A.5, where an additional 3rd step involving sampling and rejection via the JB function is considered. How much does the 3rd JB indicator layer functions f...	Summary: The authors propose Gradient Cuff that uses the gradient norm for response y being a non-refusal (using their refusal loss) towards the prompt x to detect jailbreak attacks. Since the designed loss is non-differentiable (along with the generation process), the authors propose to use a zeroth order gradient est...	Rebuttal 1: Rebuttal: Weakness 1: The argument of batching is somewhat questionable, considering that the LLM operator could batch multiple user requests otherwise. R: We thank the reviewer for pointing out this issue that may cause misunderstanding. We need to argue that our method is designed for model owner...	Summary: In this paper, the authors proposed Gradient Cuff, a novel jailbreak detection method based on the refusal loss landscape. Specifically, 1) the refusal loss is defined as the probabiliy for the LLM to generate a refusal as response; 2) the authors further made the observation that the refusal loss tend to be m...	Rebuttal 1: Rebuttal: Question 1: Is LlamaGuard the more suitable baselines? R: We thank the reviewer for pointing out this baseline for us. We use the Llama-Guard-2-8B as the classifier used in the LlamaGuard method and compare it with Gradient Cuff. The results are shown below. \|Model\|Method\|FPR\|TPR\| \|------...	Rebuttal 1: Rebuttal: We put some questions focusing on clarification in the General Reply * Question: Could the author compare or discuss the proposed method against [2,3,4]? Response**: We thank the reviewer’s recommendation. We carefully read these 3 papers and found that these methods are all quite di...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions	Accept (poster)	Summary: This paper proposed a novel approach to leverage the language inputs to retrieval the scaling factor between relative depth prediction and ground truth metric depth. The task and the approach is novel and interesting. Strengths: (1), To the best of my knowledge this is the first work to use language (text) as...	Rebuttal 1: Rebuttal: Review (W1): My primary concern is the practical applicability of this problem. Is there a significant use case for depth prediction using text descriptions as input? For autonomous driving or AR applications, the user might not be always speaking and describing the environment. Response (W1): Ou...	Summary: The paper proposes a method for learning the scale of single-view observation of scenes from embeddings of text descriptions of the scenes. The method (referred to as RSA model) is of a simple design of MLP layers and is trained on text embeddings extracted from single images. Evaluation is done on various dat...	Rebuttal 1: Rebuttal: Review [1]: Robustness of the method. Despite various design choices and sample images are discussed in the paper, it is not clear how well the method generalizes to difficult images, where there is less context or objects present (e.g. an empty corner of a room, or empty section of a street), or ...	Summary: The paper proposes a method for metric-scale monocular depth estimation called RSA. RSA aims to recover metric-scaled depth maps through a linear transformation, leveraging language descriptions of objects in scenes. The proposed method is validated on multiple standard datasets. Strengths: The use of languag...	Rebuttal 1: Rebuttal: Review: The paper could benefit from a deeper theoretical analysis of why the proposed method works Response: Our method predicts scale from language description to ground relative depth estimation to metric scale. Theoretically speaking, our hypothesis is that certain objects (e.g., cars, trees...	Summary: This paper tackles the challenge of scale ambiguity in monocular scenes. The idea is to leverage the priors in LLMs to estimate the scales of an image. Evaluation is performed on NYU and KITTI datasets in comparison to baseline heuristics. Strengths: - The value of LLMs is shown in a novel domain - code will ...	Rebuttal 1: Rebuttal: Review: The proposed solution seems unreasonable... LLM does not utilize the image but only text alone. Response: There may be a confusion. We do not use an LLM, nor do we mention LLMs in the main paper. The proposed study investigates the hypothesis of whether language, in the form of captions o...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude to each reviewer for their thoughtful and constructive feedback on our manuscript. We sincerely thank each reviewer for highlighting the strengths of our paper on “novel (R-KH1n)”, “outperforms previous methods (R-ZAYV)”, “simple, novel, generalizab...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Space Complexity of Approximating Logistic Loss	Accept (poster)	Summary: This paper gives a d \eps^{-2} bounds for coresets of points/labels that preserve logistical functions. Such a bound is similar to the bounds obtained in some recent upper bounds, and the paper experimentally verifies that a natural complexity measure is likely needed for constructing such coresets. The paper ...	Rebuttal 1: Rebuttal: > The results doesn't cover the `for this solution' sparsification that's weaker than the 'for all' sparsfication, but is still sufficient in many applications. However, that version will likely require significantly different approaches This is a great point, and we agree with the reviewer: it i...	Summary: The paper gives mainly lower bounds against any data compression for logistic regression parameterized by a complexity measure $\mu$. They prove lower bounds $\Omega(d/\epsilon^2)$ as well as $\Omega(d\mu)$. Both are derived by reduction from the indexing indexing problem and a direct sum argument to extend th...	Rebuttal 1: Rebuttal: > state of the art discussion on upper bounds is slightly outdated: updating to the $O(d\mu/\epsilon^2)$ upper bounds https://arxiv.org/abs/2406.00328 will only strengten the contributions. Thank you for pointing out this new result. This Arxiv submission was made after NeurIPS deadline, so we we...	Summary: The paper explores the space complexity lower bounds for data structures that approximate logistic loss with $\epsilon$-relative error in logistic regression problems. The authors provide an $\Omega(d/\epsilon^2)$ space complexity lower bound for datasets with constant $\mu$-complexity, demonstrating that exis...	Rebuttal 1: Rebuttal: > A minor issue, the font of the paper seems different from other papers. Thanks for pointing this out. We have fixed the issue. --- Rebuttal Comment 1.1: Comment: Thanks, I will keep my scores.	Summary: This paper establishes lower bounds on the space complexity for data structures approximating logistic loss with relative error, showing that existing methods are optimal up to lower order factors. It proves that any coreset providing an $(\varepsilon)$-relative error approximation to logistic loss requires st...	Rebuttal 1: Rebuttal: > The paper should clearly articulate how its contributions differ from existing works, especially those by Munteanu et al. and Mai et al. Highlighting specific improvements or unique approaches. Our work is complementary to the works of Munteanu et al. and Mai et al. Both of those papers primari...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer	Accept (poster)	Summary: An image-conditioned 3D generation method with two parts: 1. 3D triplane VAE taking point cloud as input and generate the semi-continuous occulancy, which includes a point2latent encoder and a latent2triplane decoder 2. A direct 3D transformer diffusion model conditioned on single image and generate tokens of...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback and thorough review of the our paper. We carefully respond to each of the concerns and questions below. [Q1] Training on Objaverse dataset and quantitative comparison with state-of-the-art methods. [A1] Thank you for your valuable comment. To ensure...	Summary: This paper proposes an image-to-3D generation model without using SDS optimization. It includes a 3D-VAE that encodes 3D shapes into latent space and a 3D DiT that models the latent distributions. Strengths: - Single-step 3D generation is a relatively unexplored area. - Exploring the DiT model in 3D tasks is ...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback and thorough review of our paper. We carefully respond to each of the concerns and questions below. [Q1]: Similar architectures to image generation and video generation. [A1]: We appreciate the insightful question raised by Reviewer yUww. Our primar...	Summary: Summary: This manuscript focuses on the task of 3D generation, including image-to-3D and text-to-3D tasks. The framework is structured in two stages. In the first stage, a geometry VAE is trained using 3D ground-truth occupancy as supervision. Triplane representations are leveraged as the explicit 3D format, w...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback and thorough review of our paper. We carefully respond to each of the concerns and questions below. [Q1]: Quantitative comparison with state-of-the-art methods. [A1]: To ensure a fair comparison with other methods, we re-train our D3D-VAE and D3D-Di...	Summary: This work presents Direct3D, a new approach for scalable image-to-3D generation using a 3D Latent Diffusion Transformer. This method enables the generation of high-quality 3D assets from text and images without the need for complex optimization techniques. Direct3D introduces a native 3D generative model that ...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback and thorough review of our paper. We carefully respond to each of the concerns and questions below. [Q1]: Training on Objaverse dataset and quantitative evaluation. [A1]: Thanks for your comment. To ensure a fair comparison with other methods, we re...	Rebuttal 1: Rebuttal: We thank all the reviewers for their constructive and insightful suggestions. We are encouraged that all reviewers consider the proposed method novel and interesting: - Reviewer 3ERi: "It is a firm step towards a converged 3D AIGC framework" - Reviewer HuD3: "The algorithm looks novel and i...	NeurIPS_2024_submissions_huggingface	2,024	Summary: Generating high-quality 3D assets from text and images has been difficult due to the lack of scalable 3D representations. This paper, Direct3D, addresses this by introducing a native 3D generative model that scales to real-world input images without needing multiview diffusion models or SDS optimization. The m...	Rebuttal 1: Rebuttal: Thank you for your insightful feedback and thorough review of the our paper. We carefully respond to each of the concerns and questions below. [Q1]: The distinctions between our Direct3D and LN3Diff. [A1]: We thank reviewer 3ERi for bringing up the concurrent work. While LN3Diff was acce...	null	null	null	null	null	null
Unveiling the Bias Impact on Symmetric Moral Consistency of Large Language Models	Accept (poster)	Summary: This paper proposes a framework called tSMC that uses KL divergence to measure the consistency of 12 LLMs on a moral question dataset. They find that the LLMs are more consistent on low-ambiguity questions, and less so on high-ambiguity questions. They also find that LLMs are susceptible to both position and s...	Rebuttal 1: Rebuttal: Thank you for the constructive feedback. > [W0.1] Why value moral consistency? Why is higher consistency better? What if choices are immoral? Moral consistency is desired in AI systems to ensure reliability and predictability in moral reasoning ability. Better refers to the model's consisten...	Summary: This paper presented the impact of bias (position and selection biases) on the symmetric moral consistency of Large Language Models (LLMs) and revealed the inconsistent moral behavior in LLMs. This is significant as LLMs may not adhere to moral principles, which could harm the downstream tasks. A simple yet ef...	Rebuttal 1: Rebuttal: We are delighted that the reviewer found our motivations and ideas interesting and original. Thank you for your positive opinions and insightful comments. > [W1] Explain the framework Thank you for your perceptive question. We appreciate the opportunity to clarify this important aspect of ou...	Summary: The authors propose a “tSMC” framework to evaluate the moral consistency of LLMs. They apply this framework to 12 LLMs with a range of capabilities and find specific biases that significantly impact the consistency of certain models. Strengths: - The authors find a large problem with moral consistency with LL...	Rebuttal 1: Rebuttal: We highly appreciate your high-quality review and valuable suggestions. We hope the following may address your concerns: > [Q1] Selection of parameter α in equation 3. Thank you for this insightful question regarding our choice of α=0.1 in Equation 3. Our selection of α=0.1 was based on bala...	Summary: This paper explores an important problem of symmetric moral consistency of Large Language Models and finds the inconsistency of moral decisions across varied scenarios, which brings the critical concern for the real-world deployment of LLMs. An assessment framework, tSMC, is proposed to address this issue by p...	Rebuttal 1: Rebuttal: We thank the reviewer for recognizing our work's importance and finding our motivations and ideas interesting. We also appreciate the detailed comments posed by the reviewer. Please see below the point-to-point responses to the reviewer's comments. > [W1.1] Clarify lines 124-125 Thank you fo...	Rebuttal 1: Rebuttal: We are deeply grateful to all reviewers for their insightful and constructive feedback. Your comments have significantly enhanced the quality and depth of our paper. We are pleased to hear that our paper is well-structured and easy to follow (`QyMp`, `7hT9`). We also extend our gratitude for reco...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper evaluates selection and position bias in large language models moral consistency. The introduction, which expands over four pages, It is split between motivation case and the contributions. The introduction has one table, one prompt example, a diagram and two figures. It finalizes with an enumerati...	Rebuttal 1: Rebuttal: Thank you for your insightful comments and perspectives. We clarify your concerns below: > [W1] The title Thank you for your feedback on our paper's title. We intended to accurately convey our key finding: the apparent moral consistency of LLMs may not reliably indicate their true capabiliti...	null	null	null	null	null	null
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework	Accept (poster)	Summary: This paper studies data attribution. Unlike the previous works that only quantifies the contribution of each datum, this work also considers the attribution of the cells within every datum. The proposed the joint valuation framework, 2D-OOB, can accurately identify cell outliers, as well as the poisoning attac...	Rebuttal 1: Rebuttal: ### Weakness-1: Section 4.3 Clarification Re.: In line 216 on page 7, we explicitly stated that the poisoned samples are relabeled from the source class to the target class. ---- ### Weakness-2: Difference between 2D-OOB and image feature attribution methods Re.: Thank you fo...	Summary: The paper proposes 2D-OOB, an out-of-bag estimation framework for jointly detecting both samples as well as their cells that are outliers. In other words, 2D-OOB allows attribution of the value of a data sample to its individual features. Evaluation shows that the framework is also able to localize backdoor t...	Rebuttal 1: Rebuttal: ### Weakness-1: Novelty Re.: Thank you for your feedback. We acknowledge that 2D-OOB may initially appear to be a natural extension of existing methods, as it is built on them. However, we believe our contribution is substantial in the field of data valuation, as our proposed method addre...	Summary: This paper proposes a joint valuation framework for not only obtaining data value but also attributing a data points value to its individual features (cells). The framework is build on top of data-OOB. The authors compared the proposed 2D-OOB with 2D-KDD on several tasks, including cell-level outlier detection...	Rebuttal 1: Rebuttal: ### Weakness-1: the OOB framework requires the training algorithm to be a bagging method with weak learners Re.: The primary objective of the joint valuation framework is to evaluate the quality of cells rather than to optimize model performance. The model used serves as a proxy for this ...	Summary: This paper studies data valuation in a fine-grained fashion: determine helpful samples as well as the particular cells that drive them. The proposed method is tested with 12 datasets and has application in backdoor detection. Strengths: - It makes sense to use cells rather than single data samples as the smal...	Rebuttal 1: Rebuttal: ### Weakness: The novelty and contribution may be relatively marginal. Re.: Thank you for your feedback. We acknowledge that 2D-OOB may initially appear to be a natural extension of existing methods, as it is built on them. However, we believe our contribution is substantial in the field ...	Rebuttal 1: Rebuttal: We want to thank all reviewers and AC for their dedicated work. We are appreciative that reviewers agreed our work to be well-motivated, clearly-written, and include extensive experiments. The concept of joint valuation was recognized reasonable (6nYm) and important (4K7L). Our propose...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Optimal deep learning of holomorphic operators between Banach spaces	Accept (spotlight)	Summary: This work examines learning holomorphic operators between Banach spaces. It shows that these learning tasks can be tackled using encoder-decoder approaches with deep neural networks (DNNs), achieving optimal approximation rates up to log factors. The architectures used are problem-agnostic and depend only on t...	Rebuttal 1: Rebuttal: Thank you for your careful review our paper and your positive assessment of it. Here we address the comments in your report. ## 'Perhaps I missed...' Good point. Right now, we do not have a proof that the NSB and Boussinesq problems satisfy the same holomorphy assumption as the diffusion equatio...	Summary: Authors provide theoretical results on sample efficiency for learning holomorphic neural operators $\mathcal{X}\rightarrow\mathcal{Y}$ (results are available for both Hilbert and Banach spaces) with a neural network of a particular structure. The structure of the network is encoder $\mathcal{X}\rightarrow\math...	Rebuttal 1: Rebuttal: We appreciate your effort in reviewing our paper and are delighted by the positive assessment. Here we address the comments in your report. ## '1. After line 102...' Sorry. The term $\|\|\| F \|\|\|$ was missing. We will correct the equation. ## '2. I want to...' ## '1. In (1.3) authors...' Exc...	Summary: The paper addresses the challenge of learning operators between Banach spaces, focusing on holomorphic operators which have many applications. The authors employ standard (DNN) architectures with constant width greater than depth and $\ell_2$-loss minimization. They identify a family of DNNs that achieve optim...	Rebuttal 1: Rebuttal: We greatly appreciate the effort you have put into reviewing our paper and are delighted by your positive opinion of the work. Here we address the comment(s) in your report. ## `I think some of the figures...' Good point. We will use the additional page allowed in the camera-ready version to enl...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Conformalized Credal Set Predictors	Accept (poster)	Summary: This paper proposes a method for predicting credal sets in classification tasks, generating conformal credal sets that are guaranteed to be valid with high probability without any assumptions on the model or distribution. The method is applicable to both first- and second-order predictors. Experiments on Chaos...	Rebuttal 1: Rebuttal: Thank you for your kind words about the paper and for your valuable feedback. We hope our response addresses your comments and questions. - W1. The main similarity between our work and [1] lies in the data assumption we make. However, while the authors in [1] focus on obtaining prediction se...	Summary: Authors propose a new uncertainty estimation method based on credal sets predictors. The new approach is based on the Conformal Prediction framework and shares its finite-sample and distribution-free guarantees. Authors verify the validity of their approach on existing NLP and CV datasets. Strengths: * theore...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for engaging with our work. We tried to clarify two of the mentioned weaknesses/concerns, elaborate on one another, and address their questions. - W1. In our implementations, we ensure that each parameter of the Dirichlet distribution is at least one. We de...	Summary: The paper shows that if access to the (even noisy) estimates of the conditional categorical label distribution is provided, one can define a confidence region within the simplex of conditional probabilities, that is guaranteed (marginally) that the true aleatoric uncertainty (ground truth label distribution) i...	Rebuttal 1: Rebuttal: Thank you for your kind words and feedback. We found your questions very interesting and did our best to elaborate on them. - Q1. Thank you for the reference; it is definitely an interesting and relevant work. To the best of our understanding, the main difference between the two papers is that...	Summary: The authors propose a method for predicting credal sets in the classification task, given training data labeled by probability distributions. The resulting conformal credal sets are guaranteed to be valid with high probability (without any assumptions on model or distribution). The applicability of the method ...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their insightful comments and suggestions. We have made every effort to address and clarify their concerns in the following response. - W1. The comparison with other methods was not feasible primarily due to differences in data assumptions. While the me...	Rebuttal 1: Rebuttal: We want to express our gratitude to all four reviewers for their valuable feedback. We're encouraged that they found our paper important, interesting, and novel, and we appreciate their insightful comments. We would like to offer some general remarks, particularly concerning the evaluation part. ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
HAWK: Learning to Understand Open-World Video Anomalies	Accept (poster)	Summary: This work proposes a novel video-language framework, HAWK, aiming at understanding video anomalies, which incorporates motion modality to enhance its capability. The work generates rich language descriptions for seven different video anomaly datasets, and also generates question-answer pairs to tackle potentia...	Rebuttal 1: Rebuttal: We extend our profound gratitude for your esteemed recognition of our paper. Furthermore, we sincerely appreciate your constructive feedback and suggestions, which have proven to be instrumental in enhancing our manuscript. We are devoted to addressing all of your proposed problems, as shown in ...	Summary: The manuscript proposes a new framework that uses an interactive large-scale visual language model (VLM) to accurately explain video anomalies. The framework can explicitly integrate motion modalities to enhance anomaly identification. The manuscript also constructs an auxiliary consistency loss to guide the v...	Rebuttal 1: Rebuttal: We are deeply grateful for your acknowledgment of our efforts, especially for highlighting our contributions to the framework, dataset, and experimental outcomes. Additionally, we are thankful for the precious suggestions you have offered, which will significantly aid in the enhancement of our man...	Summary: The paper proposes a new variant of the video anomaly detection. Prior methods were vanilla classification methods, and this paper proposes more descriptive anomaly description and also QA along with that. The paper first gives the dataset creation strategy and then introduces the motion model and the video ar...	Rebuttal 1: Rebuttal: We sincerely appreciate the valuable time you have dedicated to providing critical reviews and also are grateful for your recognition of the problem setup, dataset, and experimental evaluation in our work. In response to your issues, we offer the following more clarifications as follows. --- **R...	Summary: This paper presents HAWK, a framework that uses large Visual Language Models (VLMs) to accurately interpret video anomalies. HAWK integrates motion and video modalities through a dual-branch framework, enhanced by an auxiliary consistency loss to focus on motion-related features. The authors annotated over 8,0...	Rebuttal 1: Rebuttal: Thank you for your meticulous and thoughtful review of our work. We greatly appreciate your affirmation of our work's framework, experimental results, and writing quality. We are also thankful for the profound questions you raised regarding our paper and will provide detailed explanations and cla...	Rebuttal 1: Rebuttal: Firstly, we would like to express our heartfelt gratitude to all the reviewers for their insightful and constructive suggestions. These suggestions have been immensely helpful in refining our paper and have provided valuable guidance for our research direction. --- Restating Our Contributions...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Going Beyond Heuristics by Imposing Policy Improvement as a Constraint	Accept (poster)	Summary: In this paper, the authors address the problem of reward design in reinforcement learning. Specifically, it is common practice to add heuristics in the reward to help the training and a lot of manual engineering is needed to balance this heuristic and the main reward. They propose to modify the standard RL obj...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer’s positive feedback on the simplicity of our method, the design and statistical rigor of our experiments, and the clarity of our writing. We have addressed the remaining questions below. > Why doesn’t the algorithm need more data since you need to collect data...	Summary: This paper introduces a constrained optimization approach for reinforcement learning, ensuring that the learned policy outperforms or matches the performance of policies trained solely with heuristic rewards. In simulations, the proposed HEPO method consistently delivers superior task performance across variou...	Rebuttal 1: Rebuttal: We're thrilled that the reviewer love our paper and appreciate their compliments on the clarity of our presentation, the novelty of our proposed method, and the thoroughness of our experimental evaluation. The following are our responses to the remaining comments: > theoretical guarantee on the c...	Summary: The paper presents a novel approach to improve reinforcement learning (RL) by incorporating heuristic signals. The authors propose a constrained optimization method that uses heuristic policies as references to ensure that the learned policies outperform heuristic policies on the exact task objective. The meth...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for appreciating our idea of shifting the focus of optimal policy invariance, as well as our extensive evaluation and clear writing. We address the remaining comments below: > Q1: Demonstrating HEPO's performance with other deep RL algorithms, such as Soft Actor-Cr...	null	null	Rebuttal 1: Rebuttal: We're delighted that the reviewers find our manuscript easy to follow. We sincerely thank them for appreciating the simplicity of our method and the breadth of our experiments. Here, we'd like to summarize the common questions and the new experiments: ### Additional Results - **Generality on SAC...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step	Accept (poster)	Summary: This paper introduces a framework enabling Large Language Models (LLMs) to perform exact arithmetic operations efficiently and securely. By utilizing hidden states from an LLM to control a symbolic architecture named OccamNet, the framework achieves high accuracy and interpretability without compromising speed...	Rebuttal 1: Rebuttal: > Strengths: > >Addressing a Key Weakness: Popular LLMs struggle with simple arithmetic, limiting their understanding of physics and mathematics. This paper overcomes this limitation by integrating a symbolic model, OccamNet, which performs basic arithmetic with 100% accuracy. > >Innovative Integr...	Summary: This paper proposes OccamLLM, an approach to enable models to have better arithmetic abilities. Although LLMs have impressive generative abilities, these models struggle with simple arithmetic. Therefore this paper proposes OccamLLM, which integrates OccamNet, a symbolic netural architecture that can perform a...	Rebuttal 1: Rebuttal: >Strengths: > >S1) The proposed method offers some valuable advantages over existing approaches. It does not need to update the model weights and therefore will not suffer from catastrophic forgetting (like integrating LLM tools may), while being more computationally efficient than code generation...	Summary: The paper tackles the well known issue of performing arithmetic operations from LLMs. In particular, authors showed the following 1. Llama + OccamNet model can be trained to perform a single arithmetic task 2. Train the OccamNet model and Occam switch using synthetic data, both pure arithmetic task and simple ...	Rebuttal 1: Rebuttal: >Strengths: > >Adding an external OccamNet helps on avoiding finetuning the LLM model (which exposes the model to the risk of catastrophic forgetting or becoming less safe). The manual data collection is either cheap or completely non-existent, with the result that the idea can be replicated for o...	Summary: The paper "OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step" introduces a novel framework for enabling exact arithmetic within large language models (LLMs) by integrating a symbolic architecture called OccamNet. This framework, termed OccamLLM or OccamLlama when using the Llama model, allows...	Rebuttal 1: Rebuttal: > The integration of OccamNet with LLMs for single-step arithmetic is a novel approach... We appreciate your kind remarks! > Limited Novelty in Approach: The paper stitches two existing architectures together to solve confined arithmetic tasks. While this combination is innovative, it does not i...	Rebuttal 1: Rebuttal: # Responses to general concerns We would like to thank all reviewers for their thoughtful comments. Below we address general concerns. > We only tried small models To demonstrate OccamLLM's scalability, we trained OccamLlama using Llama 3 70B Instruct (OccamLlama 70B) and evaluated it in Figure ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper introduces OccamLLM, which is a novel framework that enables exact arithmetic in a single autoregressive step and enhances the arithmetic capabilities of LLMs. In detail, OccamLLM adds several additional decoders to the original LLM to help it conduct arithmetic tasks using a symbolic model named Oc...	Rebuttal 1: Rebuttal: > The idea of adding OccamLLM Decoders to LLM and using it to predict the weights of OccamNet for arithmetic tasks is novel. The paper is generally well-written. We appreciate your kind remarks! > The improvements brought by OccamLLM on general reasoning tasks are limited. While OccamLlama outpe...	null	null	null	null	null	null
LG-VQ: Language-Guided Codebook Learning	Accept (poster)	Summary: This paper points out that most of existing vector quantization methods learn a single-modal codebook, resulting in suboptimal performance when the codebook is applied to multi-modal downstream tasks. To address this issue, the authors propose a new language-guided codebook learning framework, called LG-VQ, wh...	Rebuttal 1: Rebuttal: We are thankful for the reviewer’s time and valuable feedback. We are glad that the reviewer found that our paper is easy to follow and well-written. Please see below for our responses to your comment and questions. > Q1: The details of the relationship alignment module are not clear. Fig. 3 is n...	Summary: This paper considers the limitations in learning an expressive codebook and proposes a novel multi-modal codebook learning method. The authors propose two semantic supervision modules and three losses. Experiments demonstrates the effectiveness of their method. Strengths: 1. This work is easy to follow, and ...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and valuable feedback. We are glad that the reviewer found that our paper is easy to follow and motivation is interesting. Please see below for our responses to your concerns and questions. > Q1: It is not easy to obtain major objects and relations between dif...	Summary: This paper proposed a new codebook learning method to improve the performance of multi-modal downstream tasks. The proposed method can be easily integrated into existing VQ models and shows performance improvements in various cross-modal tasks such as Image Generation and Visual Text Reasoning. Strengths: The...	Rebuttal 1: Rebuttal: We thank the reviewer for their time and valuable feedback. We are glad that the reviewer found that our paper is well-written and easy to follow. Please see below for our responses to your comments. > Q1: The main concern about this paper is its novelty. We would like to highlight the novelty ...	Summary: This paper addresses the issue that current VQ codebook lacks multimodal information, and proposes to introduce language guidance to learn a more comprehensive one. It develops semantic alignment module and relationship alignment module with multiple additional losses to regularize the codebook with additional...	Rebuttal 1: Rebuttal: We thank Reviewer 8ZRy for the positive review and valuable feedback. We would like to address your questions below one by one. > Q1: The proposed method involved additional language information (not only the pretrained CLIP model but also the raw language materials) during training. This might r...	Rebuttal 1: Rebuttal: ## Global Response We sincerely thank all the reviewers for the thorough reviews and valuable feedback and will incorporate their suggestions into our next revision. We are glad to hear that the motivation is interesting (Reviewer YeAE), the paper is well-written (Reviewer YeAE) and easy to follo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning from Uncertain Data: From Possible Worlds to Possible Models	Accept (poster)	Summary: The paper introduces a new approach for uncertainty quantification of linear models. The proposed approach is interesting, but the empirical comparison to other means of uncertainty quantification is quite limited. Strengths: * The proposed approach for propagating the uncertainty about the correct linear mod...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful review. Due to space limitations, we have summarized each question and numbered them from Q1 to Q9. Q1. Are possible models more important than possible predictions? We did not mean to argue that understanding possible models is more important than ...	Summary: The paper introduces a method called ZORRO that allows for the over-approximation of the set of ridge regression models learned over a set of possible datasets. The authors use abstract interpretation, propose abstract gradient descent algorithms, and develop efficient techniques to learn the set of models. Th...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful review and suggestions. We will mention the numbers on figures on page 5 represent the iterations, and we will add a brief example of uncertain data around Definition 2.1 to make this more concrete. In response to the reviewer's other questions/comments: ...	Summary: This paper attempts to represent the uncertainty from dataset variations using a type of convex polytope that can compactly represent high-dimensional spaces, as dataset multiplicity from imperfections like missing data may scale exponentially. The paper develops techniques to ensure the tractability of gradie...	Rebuttal 1: Rebuttal: We thank the reviewer for their thoughtful review. Below are the response to reviewer's questions/comments. Weaknesses: The techniques introduced in the paper are only applicable to linear models. Please see our response in the main rebuttal. **Are there scenarios where the overapproximatio...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their thoughtful reviews. We have responded to each reviewer in the individual responses and use the general response to present experimental results to justify some of our claims made in the responses and to respond to common concerns and concerns that require more exte...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Analysing the Generalisation and Reliability of Steering Vectors	Accept (poster)	Summary: This paper investigates the generalisability and stability of using steering vectors for controlling behaviour of LLMs. The authors demonstrate that steerability is highly variable across inputs and is effected by spurious concepts. The author also demonstrate the brittleness of the steering vectors to changes...	Rebuttal 1: Rebuttal: We thank the reviewer for their sincere appreciation of our work! The reviewer’s main specific concern is about investigating whether low steerability is due to a nonlinear representation of a concept (as opposed to more mundane reasons). Generally, we think that this is a very difficult questio...	Summary: This paper primarily analyzes the generalization of the reliability of steering vectors. The authors first focus on multiple-choice questions and introduce a new metric, the steerability score, to measure the effectiveness of steering. The experimental results indicate that for many behaviors, steering is unre...	Rebuttal 1: Rebuttal: We thank the reviewer for their comments, and are glad to hear that they consider our work both timely and insightful. We agree that open-ended steering is a more practical and useful setting, and we believe our work can be generalised to this setting with minor modifications. Steerability in ou...	Summary: The authors analyze and present many problems with steering vectors. Strengths: The papers presents quite a few problems of steering vectors that deserve wide recognition. The unreliability and propensity of steering behaviors, high variance from spurious factors, and the argumentation that steerability is a ...	Rebuttal 1: Rebuttal: We thank the reviewer for their sincere appreciation of our work! We agree that the highlighted failure modes and challenges of steering vectors deserve broad attention, and are excited for future work to analyse these failure modes in more detail. We are grateful to the reviewer for the suggest...	Summary: The paper investigates the effectiveness of steering vectors (SVs) in guiding language model behavior by intervening the intermediate model activations at inference time. SVs have shown potential for improving model capabilities and alignment, but their reliability and generalization properties are not well un...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and insightful comments! We are glad to hear that the reviewer finds our work original, clear, sound, and significant. Responding to comments raised in ‘Weaknesses’: 1. We define steerability with respect to an abstract ‘propensity’ quantity. In our pape...	Rebuttal 1: Rebuttal: We thank the reviewers for their insightful comments and feedback. We are glad to see that most reviewers think our paper addresses a timely and important question, that our experiments are regarded as technically sound, and that our writing is clear and coherent. During the rebuttal period, we ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Measuring Dejavu Memorization Efficiently	Accept (poster)	Summary: this paper proposes a method to measure memorization in representation learning models without the need for training two separate models. The proposed method aims to address the computational challenges and practical limitations of the existing déjà vu method by using simpler alternative approaches to quantify...	Rebuttal 1: Rebuttal: Thank you for reviewing our paper and raising those important questions! Weakness 1 - Sample-level granular analysis In our paper we obtain dataset-level statistics by aggregating sample level results. Appendix C.1 provides examples for Vision and C.2 for Vision language models. We ran a...	Summary: This paper proposes methods to gauge the ability of a model to memorize Strengths: 1. Originality: to my knowledge, this is an original work with novel results. 1. Clarity: The writing is clear. 1. Significance: The memorization of foundation models is critical to study, these methods and findings are signifi...	Rebuttal 1: Rebuttal: Thank you for the detailed review of our paper and the questions! Question 1- Clarification on Section 4.1.2 Vision Representation In our work we are not stating that there is a causal relationship between training size and memorization. We hypothesize that pre-trained models memorize less ...	Summary: The paper introduces a method to measure memorization in representation learning models without the need for training multiple models. Previous déjà vu memorization estimation methods require two models to estimate dataset-level correlations and memorization, which is computationally expensive. The authors pro...	Rebuttal 1: Rebuttal: Thank you for the thoughtful comments! Novelty of the work Original Déjà Vu memorization [2] work requires training two different models with the same SSL architecture on two disjoint splits of the training data and is computationally expensive. The novelty of our work lies in proposing crea...	null	null	Rebuttal 1: Rebuttal: On the lack of sample-level memorization scores ---- Reviewers Aswd and Uwm9 brought up an important question about the lack of sample-level memorization metrics and scores. In our work we obtain dataset-level metrics by aggregating sample-level memorization results. Hence, it is straightfor...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness	Accept (poster)	Summary: This work develops the first algorithm that achieves the near-optimal oracle complexity $\widetilde{O}(\epsilon^{-3})$ (the number of evaluations to gradient or Hessian/Jacobian vector product) to achieve an $\epsilon$-stationary point of a stochastic bilevel optimization problem with unbounded smooth nonconve...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We have addressed each of your concerns below. A1. Assumption 4.2 used in Section 4.3.1 is indeed problem-related and can be verified when applied to the bilevel optimization setting. Note that the main goal of Section 4.3.1 is to pro...	Summary: The authors consider bilevel optimization problems under the unbounded smoothness assumption of the outer function. They propose AccBO, an AID-based method that uses Stochastic Nesterov Accelerated Gradient to approximate the lower-level solution and Neumann approximations to estimate the inverse Hessian-Vecto...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We have addressed each of your concerns below. W1. Code issue. A1. Please see A5 to A7 for details. W2. AccBO comes with many hyperparameters. A2. Please note that existing literature on bilevel optimization with va...	Summary: This paper investigates a class of stochastic bilevel optimization problems where the upper-level function is nonconvex with potentially unbounded smoothness, and the lower-level problem is strongly convex. To improve the convergence rate, the authors propose a new Accelerated Bilevel Optimization algorithm na...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We have addressed each of your concerns below. **Q1. The convergence analysis for Option I of the algorithm is restricted to the lower-level problem being a one-dimensional quadratic function. This limitation reduces the generality and applic...	Summary: This paper considers a class of stochastic bilevel optimization problems where the upper-level function is nonconvex with potentially unbounded smoothness, and the lower-level problem is strongly convex. Their novel algorithms achieve an oracle complexity of $O(1/\epsilon^3)$ to find an $\epsilon$-stationary p...	Rebuttal 1: Rebuttal: Thank you for taking the time to review our paper. We have addressed each of your concerns below. **Q1. In the algorithm, for the update of the normalized stochastic gradient, it seems that it can be reformulated to tune the learning rate by dividing by the norm of the stochastic gradient. Th...	Rebuttal 1: Rebuttal: General Response to All Reviewers Thank you to all the reviewers for taking the time to review our paper and provide valuable feedback. We have addressed each of your concerns individually, and below is a summary of the key changes made during the rebuttal phase. 1. We would like to clarify ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework	Accept (poster)	Summary: The paper proposes a carbon-efficient neural architecture search (NAS), CE-NAS, to reduce the carbon emitted during the NAS process by dynamically allocate GPU resources based on the predicted future carbon intensity. CE-NAS leverages reinforcement learning for generating the probability of choosing which mode...	Rebuttal 1: Rebuttal: We greatly appreciate the positive comments and insightful suggestions from the reviewer. Below are our answers to the reviewer's questions. --- Experiments are California only We will demonstrate more results for different locations in our future work. --- **Compared to the work proposed...	Summary: Neural architecture search (NAS) is known to be extremely compute intensive, which also increases the corresponding energy consumption and the carbon costs due to energy production. In this work, the authors attempt to reduce the carbon cost of performing NAS. To this end, they propose a reinforcement learning...	Rebuttal 1: Rebuttal: We sincerely appreciate the reviewer for a thorough review and valuable comments. Below are our responses to the reviewer's concerns. --- Spatial carbon intensity variations Thank you for the suggestion. Considering the spatial variation of carbon intensity is an intriguing idea, and it is i...	Summary: This paper introduces a NAS framework, named CE-NAS, which focuses on reducing the carbon footprint of architecture search. To do this, CE-NAS decides when to use which NAS evaluation method depending the current carbon intensity, and how to allocate available GPU resources. Their carbon forecasting model also...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for your insightful comments, please check our answers below. --- How easy is it in practice to access live information about carbon efficiency? There are third-party providers, such as ElectricityMap and WattTime, that allow users to query carbon intensity th...	Summary: The paper introduces CE-NAS, a novel framework for neural architecture search that prioritizes carbon efficiency in the model design process. It addresses the high carbon cost associated with NAS by dynamically adjusting GPU resources based on predicted carbon intensity and search results. The framework integr...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for your insightful comments and suggestions, please check our answers below. --- Comparison between CE-NAS and zero-shot NAS We first thank the reviewer for pointing out the missing related work on zero-shot NAS in our paper. We will add the following disc...	Rebuttal 1: Rebuttal: Reference [1] On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloud, Sukprasert et al. EuroSys 2024. [2] Anthony L F W, Kanding B, Selvan R. "Carbontracker: Tracking and predicting the carbon footprint of training deep learning models". arXiv preprint arXiv:...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models	Accept (poster)	Summary: This paper introduces Lumen, a large multimodal model designed for versatile vision-centric tasks. Lumen features a decoupled architecture that initially learns a shared heatmap representation to address various vision-related tasks such as object detection and instance segmentation. Subsequently, this represe...	Rebuttal 1: Rebuttal: Q1: The origanization of the paper A1: Sorry for the confusion caused by the paper organization, we will reorganize the paper following your suggestion in the revised version. Q2: The inference speed of the model A2: We have discussed the inference speed of our model in the ***A...	Summary: 1. This study proposes a new Large Multimodal Model architecture(Lumen) that prioritizes a vision-centric approach and addresses the limitations of current methods, which oversee the unique features of various visual tasks. 2. It separates perception capability learning into task-specific decoders, leading to...	Rebuttal 1: Rebuttal: Q1: The design of Lumen A1: As discussed in the Common Question Q1, it is nontrivial to extend the LMM with versatile vision-centric capabities. We further provide proofs below to further validate this claim: 1. Compared with Griffon that refactorizes box coordinates into language ans...	Summary: The paper proposes Lumen, a new LMM architecture that splits perception learning into task-agnostic and task-specific stages. Lumen finely aligns vision-language concepts, creating a shared representation for all visual tasks. This is then decoded with lightweight task-specific decoders requiring minimal train...	Rebuttal 1: Rebuttal: Q1: Comparison with UniPerceiver-v2 and VisionLLM A1: There might be a misunderstanding. Our major contribution is that we *use the heatmap as the effective intermediate representation to prevent the LMM being trapped by the inductive biases of different vision-centric tasks* (i.e, th...	Summary: The paper introduces Lumen, a novel architecture for Large Multimodal Models (LMMs) that enhances vision-centric capabilities by decoupling task-agnostic and task-specific learning stages, thereby improving performance across various vision tasks while maintaining general visual understanding. This architectur...	Rebuttal 1: Rebuttal: Q1: VQA performances compared to prior methods A1: Since our major motivation lies in *extending versatile vision-centric capabilities while keeping the general-purpose coversational capabilities of the LMM*, we do not meticously select the VQA data for training at present. Thanks for...	Rebuttal 1: Rebuttal: We sincerely appreciate all reviewers for their efforts and valuable comments. We first address some common questions here, and then respond to each reviewer separately. We hope our responses can clarify the concerns of reviewers. ### Common Questions Q1: Comparison with LMM generalists **A...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper proposes to augment the output of a Large Multimodal Model (LMM) with three tasks, namely detection, segmentation, and pose estimation. To that end, Lumen first predicts heatmaps conditioned on the encoded input image and instruction. This heatmap is then used by task-specific decoders to predict gro...	Rebuttal 1: Rebuttal: Q1-1: The value of the LLM A1-1: In the workflow of Lumen, *the LLM plays a crucial role in jointly comprehending the open-ended instruction and visual content (which is LLM's strength), and its understanding of the inputs is further condensed into the hidden state of the [LOC] token....	null	null	null	null	null	null
Learning to Cooperate with Humans using Generative Agents	Accept (poster)	Summary: This work proposes a novel method to train agents to coordinate with humans in team tasks. The main contribution is that instead of training an RL cooperator policy based on a limited number of human models, the authors use a generative model to capture the latent variable representation of human policy. The l...	Rebuttal 1: Rebuttal: Thank you for your detailed review and positive feedback on our comprehensive real-human study. We're glad you find it has high ecological validity. > There are a few experiment results (Fig 5a and 6b) where GAMMA + HA (FFT) is not good on Multi-strategy Counter. We acknowledge that the FFT meth...	Summary: This paper proposes GAMMA, a generative model that learns a latent representing partner agents' strategy. Through interpolation, the latent can be used generate diverse partners to train ego agents for human-AI cooperation. Authors studied training the generative model with both simulated data and real-human d...	Rebuttal 1: Rebuttal: Thank you for your review! For typos, we will correct them in the next version. We address your concerns below. > The baselines are outdated. Thanks for the suggestion, we have run MEP (see Fig 2 in the rebuttal PDF) and show that GAMMA can still improve its performance. We agree all the metho...	Summary: The paper addresses the challenge of training AI agents that can effectively coordinate with diverse human partners in cooperative tasks without prior interaction. This work demonstrates that using generative models to create diverse training partners can significantly improve an AI agent's ability to coordina...	Rebuttal 1: Rebuttal: We thank you for your detailed review. We address your questions below. > Results lack statistical significance - First of all, for the most important human experiment, GAMMA is statistically significantly better (p < 0.05). See the common response in detail. - For Cramped Room and Forced Coord...	Summary: The authors propose a novel method for training agents that effectively cooperate with humans by leveraging generative models that can sample the space of agent behaviors Strengths: - Well-written and easy to follow - Experimental setup is clear, well-documented, and expansive - Code and even a live demo are ...	Rebuttal 1: Rebuttal: We thank the reviewer for your detailed and insightful feedback. We are grateful that you took the time to check out our demo. We hope the following responses address your concerns. > Does the proposed method GAMMA fairly consistently outperform baselines statistically significantly? GAMMA does ...	Rebuttal 1: Rebuttal: We thank all the reviewers for their detailed reviews and feedback. To summarize the positive feedback, reviewers recognize the importance of the problem we studied and find the paper well-written with well-designed experiments. We are thankful to reviewer igMC for taking the time to explore the...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Adam with model exponential moving average is effective for nonconvex optimization	Accept (poster)	Summary: In this paper, the authors analyze the online learning framework 'discounted-to-nonconvex conversion', and propose a bound for the expectation of the exponential average of the output gradient. Moreover, they find that if the online learner follows a specific loss structure(FTRL), then the output of this onlin...	Rebuttal 1: Rebuttal: Thank you for your detailed comments! We answer your questions one by one below. 1. You are correct that $\mathbf{g_t}$ is computed at $\mathbf{x_t}$ in practice. In our new version, we actually fix this issue, and in our new main results, $\mathbf{g_t}$ is computed at $\mathbf{x_t}$, and the...	Summary: The authors show that with clipping and model exponential average, (Clipping) Adam can perform better than SGD. Strengths: Give the analysis of (Clipping) Adam with model exponential average. Weaknesses: 1. The paper is hard to follow. (i) In Algorithm 1, it would be better to add "output: $\bar{w}_T$"...	Rebuttal 1: Rebuttal: Thanks for your comments. Please see the other responses for some clarifications to the mathematics that we will include in the final paper. We hope this will make the paper easier to follow. Regarding Lemma 7: it is indeed an important part of our analysis, and as we show it does indeed allow us...	Summary: This paper proposes a variant of Adam involving per-iteration clipping of the updates (which look like Adam's updates) and EMA-based weight averaging. More specifically, two versions of this variant are proposed -- one is a global (w.r.t. all the coordinates) version and one is a per-coordinate version. The tw...	Rebuttal 1: Rebuttal: Thank you for your constructive comments! Let us address your questions one by one. - (Weakness 1 / Question 1) We indeed need to choose $\mathbf{\bar{w}}_t = \mathbb{E}[\mathbf{y}_t]$ as the output, and this is crucial to achieve a $(\lambda,\epsilon)$-stationarity, our main notion of converg...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields	Accept (poster)	Summary: This paper presents GeoNLF, a new approach to pose-free large-scale point-cloud registration (PCR) and LiDAR novel view synthesis (NVS). To address the limitation of both ICP-based and NeRF-based methods, the authors proposed a hybrid approach involving both robust ICP and global optimisation of NeRF. Experime...	Rebuttal 1: Rebuttal: We greatly appreciate your positive feedback on our paper's presentation and the novelty of our method. Below are the responses to your concerns. [Q1]: The issue of the algorithm's complexity and the role of the components(SR and geometric constraints). 1. Complexity of Our Algorithm. Ou...	Summary: In this submission the authors propose a novel strategy to jointly optimize a Neural LIDAR Field and the corresponding LIDAR poses. They achieve this by utilizing a hybrid strategy of alternating pure graph-based geometric optimization and global optimization of the Neural Field. Additionally they introduce a ...	Rebuttal 1: Rebuttal: We greatly appreciate your careful reading and your questions provide valuable insights. We are pleased to address your concerns and engage in further discussion with you. [Q1]: Initialization of Poses. We follow previous works BARF[32] and NeRFmm[59], which are classical in the pose-free NeR...	Summary: The paper proposes GeoNLF, a method for pose-free Lidar point cloud novel view synthesis. They propose optimizing point cloud poses simultaneously through a bundle-adjusting neural lidar field as well as through ICP-inspired geometric optimization. The neural lidar field is supervised with ground truth range i...	Rebuttal 1: Rebuttal: We greatly appreciate your positive feedback on our method's novelty and the significance of our new task to reduce the impact of noisy estimated LiDAR poses during LiDAR NVS. The issues you raised have been effectively addressed as follows. Regarding weaknesses 2,5 and your concern about the time...	null	null	Rebuttal 1: Rebuttal: We would like to thank all reviewers for their constructive comments on our work. We identified several comments that are common across more than one reviewer. Thus, we highlight them here. [Q1]: The issue of variable reuse in Eq.(13). Sorry for the confusion. Since we introduce geometry-base...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Vision-Language Navigation with Energy-Based Policy	Accept (poster)	Summary: To attempt to solve the catastrophic failure issues that arise from behavioral cloning, the authors introduce Energy-based Navigation Policy (Enp) which uses an energy-based model to learn the expert policy from demonstrations. The authors claim that by doing so Enp prioritizes learning the full trajectory ins...	Rebuttal 1: Rebuttal: Q1: The improvement of ENP is lower than that of semantic grid maps.\ A1: First, respectfully, the improvement of 1-2% on both SR and SPL is non-trival in VLN. This point was acknowledged by the other reviewers, such as "work pretty well" (Reviewer UeCa) and "achieving obvious impr...	Summary: This paper studies vision-and-language navigation (VLN) by addressing the problems of error accumulation in the Markov decision process and the difficulties in designing an optimal reward function, respectively, in the commonly applied behavioral cloning (BC) and reinforcement learning (RL) approaches. It prop...	Rebuttal 1: Rebuttal: Q1: Evaluation on ScaleVLN.\ A1: Per your request, we conduct additional experiments on ScaleVLN [49] using the original hyperparameters of ENP (L226-228). \|Models\|Training Data of R2R\|val unseen SR\|val unseen SPL\|test unseen SR\|test unseen SPL\| \|:-:\|:-:\|:-:\|:-:\|:-:\|:-:\| \|DUET [6]...	Summary: This paper proposes to learn a better Vision-and-Language Navigation agent by jointly optimizing the action and the state. Specifically, they propose an energy-based expert policy, where they adds an explicit objective for navigation exploration and incorporates prior knowledge from the expert demonstrations. ...	Rebuttal 1: Rebuttal: *Q1: "... whether the proposed approach also benefits VLN agents when having large-scale data."*\ A1:** Thank you for your comments. The proposed ENP is a general framework to mitigate the accumulation of errors in the sequential decision-making process of VLN. To demonstrate the effectivene...	null	null	Rebuttal 1: Rebuttal: To all reviewers: Thank you very much for your valuable time and suggestive comments. In response, we have meticulously addressed each point raised and provided point-to-point clarifications. Also, the final version of our manuscript will be updated accordingly. We appreciate the positive ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML	Accept (poster)	Summary: The paper address the challenges of Byzantine-robust training in asynchronous distributed machine learning systems, aiming to enhance efficiency amid massive parallelization and heterogeneous compute resources, by introducing a novel weighted robust aggregation framework. Quantify the difficulty in asynchronou...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We wish to address some points raised to provide further clarification and insight into our work: Regarding the Weighted Approach Experiment: We would like to clarify that we addressed the comparison with weighting methods in Figure 2 of our paper. This...	Summary: This work introduces a weighted robust aggregation framework and extends traditional robust aggregators to asynchronous Byzantine environments. By integrating the proposed framework with a recent double momentum mechanism, the authors achieve optimal convergence rates in asynchronous Byzantine ML under the con...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We wish to address some points raised to provide further clarification and insight into our work: Regarding the Empirical Evaluation: We agree that including experiments on additional datasets would strengthen the empirical validation of our proposed ap...	Summary: The paper addresses the challenges of Byzantine-robust training in asynchronous distributed machine learning systems. The authors propose a novel weighted robust aggregation framework to mitigate the effects of delayed updates and enhance fault tolerance in such environments. They incorporate a recent variance...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We wish to address some points raised to provide further clarification and insight into our work: Regarding the Complexity of Methodology: While our approach may not be trivial to code, conceptually, it can be implemented as a combination of several qu...	Summary: The paper introduces the Asynchronous Robust μ^2-SGD algorithm, designed to address the challenges of Byzantine-robust training in asynchronous distributed machine learning (ML) systems. The method extends the standard μ^2-SGD by incorporating a robust weighted aggregation framework that mitigates the effects ...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We wish to address some points raised to provide further clarification and insight into our work: Regarding the Assumptions on Byzantine Behavior: “... it raises questions about whether alternative, potentially simpler solutions could be explored” This...	Rebuttal 1: Rebuttal: Dear reviewers, We appreciate your valuable feedback and have included additional results on the CIFAR-10 dataset. CIFAR-10 is a more complex dataset consisting of 60,000 32x32 color images in 10 classes, with 6,000 images per class. These results demonstrate the effectiveness of our proposed...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The authors investigate asynchronous training of convex models based on a parameter server in the Byzantine failure setting, evaluating their method on MNIST Strengths: - Author's method achieves the optimal convergence rate, which they claim prior work in asynchronous Byzantine ML has not achieved. - Authors...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback. We wish to address some points raised to provide further clarification and insight into our work: Regarding the Convex Setting: The convex case is important for two main reasons: It captures classical ML problems like l...	null	null	null	null	null	null
Skinned Motion Retargeting with Dense Geometric Interaction Perception	Accept (spotlight)	Summary: The paper proposes a data-driven method for character animation retargeting. The authors introduce Semantically Consistent Sensors (SCS), which are a set of surface points determined by casting rays from bones. Additionally, a Dense Mesh Interaction (DMI) field is proposed to measure the relative positions of ...	Rebuttal 1: Rebuttal: We express our gratitude to Reviewer YXjG for acknowledging our extensive experiments and state-of-the-art performance. Your assistance in refining metric definitions is highly appreciated. # Q1: Contact Error depends on absolute scale. We agree that Contact Error should not depend on the absolu...	Summary: * The paper proposes a new motion retargeting method based on dense geometric information. This dense geometric information is gathered as dense interaction fields by pre-generated pseudo sensors, which are semantically embedded on the skin. The interaction fields are capable of representing the semantic infor...	Rebuttal 1: Rebuttal: We extend our appreciation to Reviewer utNP for recognizing the novelty of our method and the effectiveness of the proposed DMI field. We sincerely value your recommendation to include additional experimental results. # Q1: More ablation studies on different sensor arrangements. We provide addit...	Summary: The author proposes MeshRet, a new solution that achieves geometry-aware motion retargeting across various mesh topologies in a single stage. The author introduces semantically consistent sensors (SCS) and dense mesh interaction (DMI) field to guide the training of MeshRet, effectively achieving semantic alig...	Rebuttal 1: Rebuttal: We are grateful to Reviewer WToy for acknowledging our novel technical contributions and the effectiveness of our experimental evidence. We also appreciate your guidance in clarifying the notation in the DMI field description. # Q1: What are the number $S$ and $K$? We apologize for any confusion...	Summary: To address the issues such as jittery, interpenetration, and contact mismatches issues caused in the motion retargeting task, this paper proposes a new framework named MeshRet, directly modeling the dense geometric interactions in motion retargeting. It initially establishes dense mesh correspondences using se...	Rebuttal 1: Rebuttal: We extend our gratitude to Reviewer yJ9L for recognizing the novelty and state-of-the-art performance of our method. Your guidance on balancing proximal and distal sensor pairs has been particularly insightful. # Q1: How to choose the “cls”, “far”, or “dm” version? We appreciate your suggestion ...	Rebuttal 1: Rebuttal: We sincerely thank all the reviewers for their valuable comments. We extend our gratitude to Reviewer yJ9L for recognizing the novelty and state-of-the-art performance of our method. Your guidance on balancing proximal and distal sensor pairs has been particularly insightful. We are grateful to ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Achieving Domain-Independent Certified Robustness via Knowledge Continuity	Accept (poster)	Summary: In this work, the authors propose a novel definition of "continuity": knowledge continuity, which measures the continuity of a model with respect to its hidden representations instead of the inputs. The knowledge continuity can be used to certify adversarial robustness across domain modality. Then, they provid...	Rebuttal 1: Rebuttal: Dear Reviewer `7Euo`, Thank you for your review. We appreciate your recognition of the theoretical depth of our work, as well as its applications. We would like to address your concerns as follows: > Some typos Thank you for pointing these out! They will be fixed in the final manuscript. > In...	Summary: This paper proposes a generalization of adversarial robustness and certified robustness for continuous, discrete, or non-metrizable input domains. The authors introduce _knowledge continuity_, a novel definition inspired by Lipschitz continuity which aims to certify the robustness of neural networks across inp...	Rebuttal 1: Rebuttal: Dear Reviewer `Cz6j`, Thank you for your valuable feedback. We are glad you appreciated the timeliness of our work and its theoretical soundness. We hear your concerns, and hope to clarify the following: > While knowledge continuity could in theoretically be used on any architecture where the hi...	Summary: This paper proposes a new concept inspired by Lipschitz continuity, aimed at certifying the robustness of neural networks across different input domains, such as continuous and discrete domains in vision and language. This certification guarantees based on the loss function and intermediate learned metric spac...	Rebuttal 1: Rebuttal: Dear Reviewer `nGPb`, Thank you for your time and review. We are glad you appreciated some of the key contributions of our paper, especially that our approach is a rigorous, domain-independent extension of Lipschitz continuity. We hear your concerns, and would like to address them here: > experi...	Summary: This paper proposes "knowledge continuity", a metric inspired by the Lipschitz continuity that aims to certify the robustness of neural networks. Compared to existing methods, the proposed metric yields certification guarantees that only depend on loss functions and the intermediate learned metric spaces of ne...	Rebuttal 1: Rebuttal: Dear Reviewer `88sL`, We appreciate your thoughtful feedback. We are glad you noticed the elegance and novelty of our metric, and appreciated the way our theoretical results tie nicely into practical applications. We address your concerns as follows: > A lot of details of the empirical results a...	Rebuttal 1: Rebuttal: Dear All Reviewers, Thank you for your time and valuable insights into our work. In particular, we are glad that multiple reviewers appreciated the rigor of our theoretical work, the way our definitions generalize various robustness metrics across domains, and the importance of our practical expe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Edit Visual Programs with Self-Supervision	Accept (poster)	Summary: This paper studied the task of visual program induction (VPI), which involves inferring programs given visual targets. Compared to existing methods using one-shot generation (iteratively predicting program tokens until completion), this work proposed an Edit Network that can predict local edits that improve vi...	Rebuttal 1: Rebuttal: Thank you for your review and interesting suggestions. We hope to address your concerns below: # Baseline Comparisons ```This paper only compares two models, with and without the edit network, both of which are implemented by the authors.``` It’s true that we compare primarily against a ‘one-sh...	Summary: This paper focuses on a problem in visual program induction. It proposes a new method for visual program editing, which trains an edit network along with a one-shot network with boostrapped finetuning. Strengths: - The authors motivate and define the problem well. - The paper is well-written with clear illus...	Rebuttal 1: Rebuttal: Thank you for the positive review. We are glad you found our presentation “clear” and our experiments “comprehensive”. ```The proposed method is somewhat but not significantly novel.``` While novelty is hard to quantify, we do believe that our submission makes a significant impact that would be ...	Summary: The paper proposes a learning framework for editing visual programs. The learned edit network can be combined with the classical one-shot network, achieving better visual program induction performance under the same compute budget. Strengths: * The paper is well-motivated, with the insight that editing operat...	Rebuttal 1: Rebuttal: Thank you for your positive review and interesting questions. ```How to scale up the method to more complex data? For example, other categories from ShapeNet``` Thank you for the suggestion! Please see our general response for new experimental results on more challenging test-set tasks. ```Ho...	null	null	Rebuttal 1: Rebuttal: We thank the reviewers for their encouraging and helpful comments. We are glad that reviewers found our proposed framework “well-motivated” (2AbF) and insightful (reBN). Reviewers remarked that our submission was “well-written” (2AbF) and “technically sound” (WhD9), and further commented that our...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation	Accept (poster)	Summary: This paper is concerned with the task of 3D segmentation in medical images. Motivated by the high cost of annotations by radiologists, the authors propose a novel active learning method based on the coresets. Here, instead of using the euclidean metric for defining the similarity between different samples, the...	Rebuttal 1: Rebuttal: We thank the reviewer for their great feedback. We hope to address the main concerns for the reviewer here. *3D vs. 2D segmentation models* Thank you for your feedback related to this. It’s true that generally 3D models outperform 2D models. However, based on our empirical results, 3D models...	Summary: The paper introduces a novel active learning approach based on group-based contrastive learning, aiming to optimize data selection and model efficiency in medical image segmentation tasks. By combining NT_Xent loss with various group contrastive losses, it addresses the Coreset problem in active learning, part...	Rebuttal 1: Rebuttal: We thank the reviewer for their great feedback. We hope to address the main concerns for the reviewer here. *Model performance and annotation time trade-offs for weakly and fully-supervised 2D slices and 3D volumes* Thank you for your feedback related to this. As stated in the general respon...	Summary: The authors propose a new metric learning approach to improve the Coreset method (an optimization framework to seek the most diverse samples for model learning in active learning) with applications for slice-based active learning in 3D medical segmentation. The main idea is to utilize data groups extracted fro...	Rebuttal 1: Rebuttal: We thank the reviewer for their great feedback. We hope to address the main concerns for the reviewer here. *Method novelty* Thank you for your feedback related to this. We addressed your concerns related to the method novelty in the general response and here further elaborate our novelty co...	Summary: The paper presents an active learning framework based on deep metric learning for 3D medical image segmentation. The proposed approach extends the CoreSet algorithm for active learning by computing distances on features learned via a contrastive loss. This contrastive loss exploits low-cost labels to group sam...	Rebuttal 1: Rebuttal: We thank the reviewer for their great feedback. We hope to address the main concerns for the reviewer here. *Method novelty* Thank you for your feedback related to this. We have addressed several of your concerns related to method novelty in the general response when discussing how our appro...	Rebuttal 1: Rebuttal: Thank you all for reading our paper and your great feedback. I will address some of your general questions in the following response. Please note that we have attached a PDF with an additional table and figure that we reference in our response. *Method novelty* We want to emphasize the uniqu...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals	Accept (poster)	Summary: The authors proposed Du-IN, a speech decoding framework that incorporates a self-supervised pre-training stage and a classification stage. Experiments are conducted on 12 subjects with a task of word classification. Comparisons are conducted to demonstrate effectiveness. Strengths: * (a) The paper is clearly ...	Rebuttal 1: Rebuttal: We are deeply grateful to you for the thorough review and constructive comments, which have greatly assisted us in improving the quality and presentation of our manuscript. Please see below for the point-by-point responses to your comments. Answer to W1: It’s true that the BEiT framework is p...	Summary: SETTING: Decoding spoken speech from invasive neural recordings, to wit, sEEG. In particular, the MS aims to classify 61 independently produced (Chinese) words. APPROACH: The focus of this study is on unsupervised pretraining via autoencoding, with either (a) vector-quantization or (b) masking. Masks are 10...	Rebuttal 1: Rebuttal: We are deeply grateful to you for the thorough review and constructive comments, which have greatly assisted us in improving the quality and presentation of our manuscript. Please see below for the point-by-point responses to your comments. Answer to Q1: Thank you for the helpful suggestion. ...	Summary: The authors introduce Du-IN, a novel decoding technique for speech production the leverages sEEG data. To achieve their goals, the authors gathers a novel dataset. The Du-IN model learns multi-variate (across channel) representations from sEEG signal using a discrete cookbook and a masked modeling based self-s...	Rebuttal 1: Rebuttal: We are deeply grateful to you for the thorough review and constructive comments, which have greatly assisted us in improving the quality and presentation of our manuscript. Please see below for the point-by-point responses to your comments. Answer to W1 & W2: Thank you for the valuable feedba...	Summary: The authors introduce a neural network architecture, a self-supervised learning (SSL) method for pre-training models for sEEG decoding and a novel dataset. - The architecture starts with segmenting the sEEG signal along the time dimension, followed by a linear module combining information across the different ...	Rebuttal 1: Rebuttal: We are deeply grateful to you for the thorough review and constructive comments, which have greatly assisted us in improving the quality and presentation of our manuscript. Please see below for the point-by-point responses to your comments. Answer to Major W1: Thank you for the helpful sugges...	Rebuttal 1: Rebuttal: # Global Response to AC and all reviewers Thanks to all reviewers for careful reading and thoughtful feedback. We are grateful for acknowledging our method "looks promising as a general technique for sEEG data" and that our dataset “is likely to become the basis of benchmarks for future research i...	NeurIPS_2024_submissions_huggingface	2,024	Summary: Due to the locality and specificity of brain computation, existing models that learn representations of a single channel yield unsatisfactory performances on more difficult tasks like speech decoding. This paper tries to build a stereoElectroEncephaloGraphy (sEEG) foundation model with Vector Quantization (VQ)...	Rebuttal 1: Rebuttal: We are deeply grateful to you for the thorough review and constructive comments, which have greatly assisted us in improving the quality and presentation of our manuscript. Please see below for the point-by-point responses to your comments. Answer to W1: 1. It’s true that the BEiT framework ...	null	null	null	null	null	null
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment	Accept (spotlight)	Summary: This paper proposes a dynamic adjustment mechanism to enhance the variation component in batch normalization loss so that increase the diversity of synthetic images. By adjusting the weights, the synthesized images achieve sufficient diversity to cover the widespread distribution of the real dataset. Strength...	Rebuttal 1: Rebuttal: Thank you for your insightful comments! We answer your questions as below. Q1 (Weakness 1 & Question 1): A few equations, such as (14), need more explanation. The current text does not adequately inform the reader on how to reproduce the results, as there is insufficient connection to the im...	Summary: This paper proposes a new method to enhance dataset distillation by improving the diversity of synthesized datasets. The authors introduce a dynamic and directed weight adjustment technique to maximize the representativeness and diversity of synthetic instances. Theoretical and empirical analyses demonstrate t...	Rebuttal 1: Rebuttal: Thank you for your constructive comments! We sincerely appreciate the time and effort you dedicated to reviewing our work. We will incorporate the mentioned state-of-the-art methods [2,3,4,5] in the related work section and include the downstream experiments in our revised paper. Below are our res...	Summary: This paper introduces a random directed perturbation strategy to the teacher models for further strengthening the diversity of the existing sample-wise data synthesis method. Through intuitive empirical and theoretical analysis of the diversity factors, the authors proposed a simple yet overlooked weight adjus...	Rebuttal 1: Rebuttal: We appreciate your valuable comments and answer your questions in order. Q1 (Weakness 1 & Question 1): Considering that the recent State-of-the-art models such as ViT are not leveraging BN, the proposed approach might not be directly applicable to these vision transformer-based models. *A1...	Summary: This paper introduces a novel method for dataset distillation (DD): DD through Directed Weight Adjustment (DWA). The authors first identify that the current dataset distillation methods often fail to ensure diversity in the distilled data, which leads to suboptimal performances. To address this issue, they pro...	Rebuttal 1: Rebuttal: Thank you for your constructive comments! We give point-to-point replies to your questions in the following. Q1: an ablation analysis. A1: We conducted an ablation study on these three components using the CIFAR-100 dataset under the ipc=50 setting, as you suggested. The experimental re...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
AI Sandbagging: Language Models can Strategically Underperform on Evaluations	Reject	Summary: The proposed work reveals that large language models may be susceptible to strategic underperformance on an evaluation benchmark. In particular, they investigate this claim on a variety of frontier models, through a myriad of tactics from simple prompting to more involved fine-tuning strategies. Their results ...	Rebuttal 1: Rebuttal: We are grateful for your positive review – we’re glad that you appreciate our motivation and experimental contributions! > consideration of only MCQA benchmarks is limiting, but can be addressed in future work We indeed agree, and invite work which moves beyond the regime of the MCQA setting. #...	Summary: The paper discusses an interesting concept of sandbagging in LLM, with practical results using available models and existing datasets. Wide range of models are used in this work, and sub topics primarily include password-locking LLMs. The transition and explanations need improvement, some of the things in this...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. > The paper is difficult to understand, the delivery and presentation needs improvement, the main motive of the paper is not so clear to understand. We note that other reviewers (TYgF and qZwJ) gave “excellent” scores for presentation, and qZwJ wrote “th...	Summary: The paper explores the concept of strategic underperformance, termed "sandbagging," in AI models, focusing on two scenarios: selective underperformance and emulating specific capability levels. It investigates the implications of these strategies on both general capabilities and safety. Strengths: - the conce...	Rebuttal 1: Rebuttal: Thank you for your constructive feedback and for your questions which will help increase the clarity of the paper. We’re glad that you found the motivation and implications for model deployment interesting. ## Limited datasets used You suggest that we conduct additional experiments on existing da...	Summary: The authors assess the chance of AI systems strategically underperform on "dangerous capability evaluations" in order to hide their risks, a la Volkswagen emissions testing cheating, by having awareness of the fact that they're being evaluated and that the evaluation is intended to test safety. They use MMLU ...	Rebuttal 1: Rebuttal: Thanks for your feedback and for taking the time to engage with the paper! We’re glad you found the calibration experiments interesting. On the discussion of “goals” and “beliefs” – please see the global response. Given the context and motivations of our paper (see global response), we do not b...	Rebuttal 1: Rebuttal: # Global Rebuttal We thank the reviewers for their feedback and engagement which will help us improve the paper. As a reminder, our work introduces the problem of sandbagging in the context of evaluations, and supports this with empirical results regarding the capability of LMs to selectively unde...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations	Accept (spotlight)	Summary: The paper develops 2nd-order methods for variational inequalities under inexact Jacobian information. Existing methods requires exact derivative information which can be prohibitively expensive for large-problems limiting their practicality. To address this issue, the paper analyzes the impact of inexact deriv...	Rebuttal 1: Rebuttal: Dear Reviewer Vrno, Thank you for your detailed and thoughtful review of our paper. We are pleased to hear that you found our theoretical analysis and practical developments compelling. Your positive feedback on the clarity and readability of the writing is greatly appreciated. We also value your...	Summary: This paper studies the impact of inexactness Jacobian in second-order methods for solving variational inequalities. This article is very informative， including a novel inexact second-order framework which achieves a convergence rate that matches the proposed lower bound, feasible solution to the sub problem, ...	Rebuttal 1: Rebuttal: Dear Reviewer jz9w, Thank you for your thorough and insightful review of our paper! We are grateful for your recognition of the strengths of our work. We greatly appreciate your constructive feedback on areas needing further discussion. Below, we try to address the related works that prompted you...	Summary: This paper presents a new second-order algorithm for variational inequalities that is applicable to inexact Jacobian. The complete convergence analysis and a theoretical lower bound are provided in the paper. Strengths: 1. A novel second-order algorithm is proposed for variational inequalities that is able to...	Rebuttal 1: Rebuttal: Dear Reviewer 7Fy2, Thank you for your valuable feedback! We appreciate your acknowledgment of the strengths of our work, including the tightness of the proposed upper and lower bounds. We also value your constructive feedback regarding areas that need further discussion, particularly in relation...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Adaptive Labeling for Efficient Out-of-distribution Model Evaluation	Accept (poster)	Summary: This paper studies model evaluation under distribution shifts. It formulates a planning problem to select inputs for labeling from a large pool of unlabeled data. The paper then proposes an auto-differentiable planning framework to compute reliable policy gradients and update the sampling policy accordingly. T...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and valuable feedback on our manuscript. Please refer to the global response for our detailed answers to the concerns raised and the changes we intend to make in the camera-ready version based on all the reviews. We also address the concerns raised poi...	Summary: This work formulates the process of data labeling as a Markov Decision Process (MDP). It introduces three key components: K-subset sampling, an Uncertainty Quantification (UQ) module, and a differentiable simulator. These components enable the application of the REINFORCE policy gradient algorithm to solve the...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and valuable feedback on our manuscript. Below, we address the specific questions raised by the reviewer. Additionally, please refer to the global response for the changes we intend to make in the camera-ready version based on all the reviews. **The o...	Summary: This paper focuses on sampling efforts on out-of-distribution (OOD) examples to minimize uncertainty in the prediction model's performance over T periods. The main idea is based on a novel view of MDP, which considers beliefs as states and samples as actions. The authors also demonstrate how to make the entire...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and valuable feedback on our manuscript. Below, we address the specific questions raised by the reviewer. Additionally, please refer to the global response for the changes we intend to make in the camera-ready version based on all the reviews. **I do...	Summary: This paper addresses the issue of selection bias in previously collected data when ground truth labels are expensive. It introduces a computational framework for adaptive labeling, aiming to provide cost-efficient model evaluations under severe distribution shifts. The problem is formulated as a Markov Decisio...	Rebuttal 1: Rebuttal: We thank the reviewer for the thorough review and valuable feedback on our manuscript. Below, we address the specific questions raised by the reviewer. Additionally, please refer to the global response for the changes we intend to make in the camera-ready version based on all the reviews. **Line ...	Rebuttal 1: Rebuttal: We thank all the reviewers for the valuable feedback. We appreciate the constructive comments and will incorporate all discussions below in the camera-ready version of the paper. Summary of contributions: Our work introduces a novel problem formulation for evaluating model performance when ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
BendVLM: Test-Time Debiasing of Vision-Language Embeddings	Accept (poster)	Summary: This paper presents to address the bias problem in CLIP models from a novel angle - directly debiasing CLIP without fine-tuning. To begin with, this work defines two metrics: CCF and CCFD, which can quantify the bias effect of CLIP embeddings. The authors then propose a method involving two approaches to addre...	Rebuttal 1: Rebuttal: Thank you for your review and thoughtful questions. We address them below: Why Does Performance Not Degrade After Debiasing? Yes, part of this is likely due to the fact that we do not perform any finetuning and find the embedding that is maximally similar to the initial embedding while mainta...	Summary: This paper focuses on debiasing VLM embeddings and proposes a fine-tuning-free method for online open-set embedding debiasing and tailors the debiasing operation to each unique input, which is advanced over the previous one-size-fits-all linear approach. The paper assumes that the debiased embeddings should sa...	Rebuttal 1: Rebuttal: Thank you for your review and constructive feedback. We address your questions below: The “Open Set” Ability of Bend-VLM: We don’t need to know the class of the instance prior to training (and there is not a distinct training phase at all, since we do test time adaptation). We do need to kn...	Summary: The paper introduces a novel approach named BEND-VLM (Bias Elimination with Nonlinear Debiasing of Vision Language Models), designed to debias vision-language model embeddings at test time. The key innovation lies in a fine-tuning-free, nonlinear debiasing method that is adaptive to each specific query, enhanc...	Rebuttal 1: Rebuttal: Thank you for your review and thoughtful questions. We address them below: W1: Whether Bend-VLM works when there is a distribution shift between the reference and target domains is an excellent question. We have conducted a new experiment that shows that Bend-VLM **still outperforms the compe...	Summary: This paper proposes a two step test-time debasing method for VLMs. In the first step, an orthogonalizing approach is applied to text embeddings. In the second step, a constraint is utilized to equalize the distances between the debiased embeddings and images from a reference dataset. Experimental results show...	Rebuttal 1: Rebuttal: Thank you for your thoughtful feedback. We address your concerns below: Missing Ablation Study: We’ve added an ablation study based on your suggestion. The ablation study results are in Table1 in the PDF attached to the general rebuttal. Results show that while most of the Worst-Group Accurac...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable time and feedback, and are very happy to see the largely positive reaction to our work. We address the common questions from the reviewers below: Does an Ablation Study show the necessity of Bend-VLM’s Step 1 And Step 2? [GYMR, xaVq] Yes, both Step ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Mixture of Demonstrations for In-Context Learning	Accept (poster)	Summary: This paper proposes MoD (Mixture of Demonstrations), which partitions the demonstration pool into clusters using embeddings to enhance the diversity of demonstrations. For each cluster, an individual retriever is trained with the few-shot scoring from the LLM to maximize the combinational effect of the selecte...	Rebuttal 1: Rebuttal: > W1: Comparison of total FLOPs of MoD compared to previous work. RW1: Hereby we compare the FLOPs of MoD with the SOTA baseline CEIL. We follow the same hyperparameter and encoder settings as CEIL. Thus, the total FLOPs is linear to the cost of each training sample. Denote the FLOPs ...	Summary: The paper presents a new method for creating a dense retriever for ICL for LLMs performing tasks. The proposed combines a mixture of experts model, coordinate descent (for alternating optimization to the experts), and contrastive loss to create the retriever. The paper then goes on to show the method's perform...	Rebuttal 1: Rebuttal: >W1: The method still requires labeled data in the validation set in order to work. The value of LLMs, particularly for Labeling tasks, is their ability to work in zero-shot settings. Thus, having to have labeled data examples to get labeled data examples lowers the significance of the propose...	Summary: The paper introduces the Mixture of Demonstrations (MoD) framework for the demonstration retrieval for In-Context Learning (ICL) in Large Language Models (LLMs). Current methods for selecting demonstrations are hindered by large search spaces and insufficient optimization. MoD addresses these issues by partiti...	Rebuttal 1: Rebuttal: > W1: Lack of complexity analysis. RW1: We would like to provide the following analysis of search space and complexity. - Search Space: CEIL precomputes a small relevance set using a pre-trained retriever and optimizes the search process within this narrowed space. While this appro...	Summary: This paper presents a framework called Mixture of Demonstrations (MoD) for selecting demonstrations to improve in-context learning (ICL) performance of large language models (LLMs). Their method partitions the demonstration pool into groups, each managed by an expert, and applies an expert-wise training strate...	Rebuttal 1: Rebuttal: > W1: The computational complexity of the proposed method seems very high. Since in-context learning itself is an inference-based method without training, it would be much more expensive to conduct a training-based method before doing in-context learning on a new task. Moreover, the proposed m...	Rebuttal 1: Rebuttal: Dear Reviewers, We sincerely thank you for your efforts in reviewing our paper. We hope that our detailed clarifications address your concerns. Below is a summary of common issues raised. - Complexity. We emphasize that MoD reduces computational complexity in practical applications. Mo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
On the Necessity of Collaboration for Online Model Selection with Decentralized Data	Accept (poster)	Summary: The paper studies the problem of regret minimization $ \min_{f_1, \ldots, f_T} \left( \sum_{t=1}^T \ell(f_t(x_t), y_t) - \min_{f \in \mathcal{F}^*} \sum_{t=1}^T \ell(f(x_t), y_t) \right)$ in distributed settings with $M$ clients. The paper proves lower bounds on regret and introduces a federated algorithm with...	Rebuttal 1: Rebuttal: We are grateful to the reviewer for taking the time to evaluate our paper and for providing encouraging feedback. We will continue our research in this area to make more controbutions.	Summary: This paper discusses the necessity of collaboration among clients in a decentralized data setting for online model selection. The authors analyze this necessity from the perspective of computational constraints and propose a new federated learning algorithm. They prove the upper and lower bounds of this algori...	Rebuttal 1: Rebuttal: We thank the reviewer for detailed comments and valuable suggestions. The suggestions will make our presentation more clear. Next we answer the questions. [Question 1] The motivation for federated learning in the context of OMS-DecD is not clearly articulated. It would be beneficial to provid...	Summary: The paper proposes a new online multi-kernel federated learning algorithm. The proposed method works based on estimating the gradients by the clients to make collaborations among clients for online federated learning helpful. The paper analyzes the regret bound of the proposed algorithm. Strengths: 1. The pap...	Rebuttal 1: Rebuttal: We thank the reviewer for valuable comments and suggestions. Next we answer the questions. [Question 1] The proposed algorithm improves the regret of online multi-kernel learning by constant. Maybe adding some assumptions can help to achieve tighter regret bounds. Finding such conditions can ...	Summary: This paper looks at the problem of Online Model Selection in a Decentralized context (OMS-DECD). The authors show the relationship of this problem class with multiple problem classes included Online Model Selection, federated learning, online multi-kernel learning etc. They consider two aspects of the problem ...	Rebuttal 1: Rebuttal: We thank the reviewer for offering valuable comments and suggestions. Next we answer the questions. [Question 1] The paper is difficult to follow at times due to the mathematical density. It would be better presented by motivating the actual theorems in the paper. [Answer] We will pro...	Rebuttal 1: Rebuttal: We thank the reviewers for taking the time to review our paper and offering valuable comments and suggestions. In this paper, we address the fundamental problem of whether collaboration is necessary for online model selection with decentralized data (OMS-DecD). All reviewers acknowledged our theo...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Fine-Grained Complexity of Gradient Computation for Training Large Language Models	Accept (poster)	Summary: This paper studies the fine-grained complexity of gradient computation for training attention networks. In particular, the paper shows that under SETH assumption, when the norm of certain matrices are bounded by $B$, the gradient computation lower bound is almost linear. an almost optimal algorithm is used in ...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. Indeed our focus is on the theoretical foundations of these LLM computational tasks, although we are optimistic that these results will help future empirical work as well. For example, the prior work on the theory of these problems which we extend here [AS23] h...	Summary: This paper studies the complexity of gradient computation in training models with attention layers. The paper claims that their results support the same complexity as the forward computation for the backward/gradient computation. The results show the impact of the size of the entries of the attention metrics o...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. We aimed to organise the paper by first introducing the problem we study (Definition 1.4 in section 1.1) then explaining our two main results and how they relate to each other (section 1.2), explaining the context of other work in this area (section 2), and the...	Summary: This paper presents a comprehensive analysis of the fine-grained complexity involved in gradient computation for training large language models (LLMs). Building on previous work that characterized the complexity of forward computations in LLMs, this study extends the analysis to backward computations. The auth...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. Indeed our focus is on the theoretical foundations of these LLM computational tasks, although we are optimistic that these results will help future empirical work as well. For example, the prior work on the theory of these problems which we extend here [AS23] h...	Summary: The authors study Approximate Attention Loss Gradient Computation (AALGC) (e.g. complexity of training LLMs with backprop): - they show that the magnitude of entries of certain matrices (layer inputs times QKV matrices) affect whether LLM backprop is "almost-linear" or not. This connects techniques like quanti...	Rebuttal 1: Rebuttal: Thank you for the thoughtful review. We especially appreciate the suggestions in the ``weaknesses'' section, and we will make these suggested changes in the final version. To answer your questions: $\bullet$ You’re right, our main problem definition (Definition 1.4) defines these as separate par...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Class Concept Representation from Contextual Texts for Training-Free Multi-Label Recognition	Reject	Summary: The paper supplies a post hoc method to tune the ResNet based CLIP method on multi-label recognition task. Firstly, the method includes class concept representation, which is an alternative of the default prompt “The photo of a {class}”. It is the average of class description sentence embedding from a text des...	Rebuttal 1: Rebuttal: We appreciate your valuable comments and suggestions. >### [W1] Clear explanation for difference between TaI-DPT and our method. To clarify, our method differs from TaI-DPT in two key ways: * Training-free approach: Unlike TaI-DPT, our method performs multi-label recognition without any train...	Summary: This paper proposes a class concept representation for zero-shot multi-label recognition in a label-free manner and introduces a context-guided visual feature that enhances the alignment of the visual feature of VLM with the class concept. Strengths: 1. This paper presents a novel class concept representation...	Rebuttal 1: Rebuttal: We appreciate your valuable feedback and comments! >### [W1] Additional multi-label few-shot method in Table 3. As you mentioned, Tip-adapter is a training-free few-shot method proposed in 2021, primarily designed for single-label classification. To incorporate a recent few-shot method for evalua...	Summary: The authors propose a method to adapt without training a large vision-language model for the task of multi-label recognition. They introduce a class concept representation, based on averaging the representation of image descriptions relevant to each class, to replace simple hand-crafted text prompts (e.g., “a ...	Rebuttal 1: Rebuttal: Thank you for your instructive comments and suggestions. >### [W1 & Q1] Clarification on Sec.3.2 Context-Guided Visual Feature. We appreciate the reviewer's careful reading of Section 3.2. Iterative reduction of the number of groups $G$ is formalized in Eq.2 and 3. For a detailed explanation of t...	Summary: This paper proposes class concept representation for zero-shot multi-label recognition. The paper also proposes context-guided visual representation, which is in the same linear space as class concept representation, with sequential attention. Experiments show the proposed methods improved the performance of z...	Rebuttal 1: Rebuttal: We appreciate your insightful feedback and suggestions. >### [W1] Effectiveness of class concept representation $t^{concept}_{c}$. (Verification of class concept): To evaluate the class concept representation, we compared the closest text descriptions in the MS-COCO caption dataset to both (...	Rebuttal 1: Rebuttal: We sincerely appreciate the constructive feedback from the reviewers. Their insightful comments and suggestions have significantly enhanced the quality of our work. We are pleased to the highlighting that * Training-free zero-shot method (7zr3, jAJ4, TJWi, xF3A) * We introduce a trainin...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios	Accept (poster)	Summary: he paper addresses visual reasoning problems. It introduces a fully neural iterative and parallel reasoning mechanism (IPRM) that combines iterative computation with the ability to perform distinct operations simultaneously. Evaluation is performed on visual reasoning datasets such as CLVER, AGQA, CLEVR-Humans...	Rebuttal 1: Rebuttal: Thank you for your informative review and insightful questions. We provide point-by-point clarifications below: Can proposed architecture be scaled to a video-language model that can provide zero-shot results on multiple video-datasets? Scaling our architecture (IPRM) to a video-language mode...	Summary: This paper introduces the Iterative and Parallel Reasoning Mechanism (IPRM), a novel neural architecture designed to enhance visual question answering (VQA) by combining iterative and parallel computation. The IPRM aims to address the limitations of existing methods that rely solely on either iterative or para...	Rebuttal 1: Rebuttal: Thank you for your informative review and useful suggestions. We provide point-by-point clarifications below: Technical details can be dense and challenging to follow: To enable easier readability and understanding of the method, We have currently provided pseudocode mapping to each equation...	Summary: The paper proposes an iterative and parallel reasoning mechanism (IPRM) for VQA tasks, combining step by step iterative computation with parallel processing of independent operations, and also maintaining an internal memory of operation states and results states. Firstly at each reasoning step, previous operat...	Rebuttal 1: Rebuttal: Thank you for your thorough review, questions and suggestions. We provide point-by-point clarifications below: Reason behind applying a MLP with nonlinear activation to get query in eq. 4: In applying a nonlinear activation for eq.4, we drew inspiration from the cross-attention transformer ar...	Summary: This paper introduces the Iterative and Parallel Reasoning Mechanism (IPRM), a neural architecture for complex visual reasoning and question answering tasks. IPRM involves a three-step iterative process: Operation Formation, Operation Execution, and Operation Composition. In Operation Formation, IPRM generates...	Rebuttal 1: Rebuttal: Thank you for your careful review and questions. We provide point-by-point clarifications below: Some notations need further clarifications: Thank you for pointing these out and we apologize for the resulting confusion. - Yes, K in fig.2 is equivalent to N_op (which denotes number of paralle...	Rebuttal 1: Rebuttal: We thank all reviewers for their time in reviewing the paper and for their positive and thoughtful feedback. We are encouraged that they found our proposed architecture and underlying motivation to combine iterative and parallel computation for complex visual reasoning tasks to be novel (nzfV, 3tY...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Data Debugging is NP-hard for Classifiers Trained with SGD	Reject	Summary: The paper studies the computational complexity of data debugging, defined as finding a subset of the training data such that the model obtained by retraining on this subset has better accuracy. The paper focuses on linear models and investigates various loss functions, showing that in some cases, the debugging...	Rebuttal 1: Rebuttal: We appreciate the viewer for providing the valuable feedback. -The model studied in the paper is limited to linear classifiers, which is very restrictive. This paper studies the complexity lower bound of the data debugging problem. To this end, the studied model should be as simple as possi...	Summary: This work investigates the computational complexity of the data debugging problem, i.e. the problem of identifying a subset of training data that, when removed, improves model accuracy on a given test point. Via standard complexity theoretic reductions, it establishes the NP-hardness of this problem for linear...	Rebuttal 1: Rebuttal: We appreciate the viewer for providing the valuable feedback. ===REPLY TO THE WEAKNESSES=== 1. Theorem 3.1 implies the intractability of a model-agnostic ``universal data debugger'', where the loss function, the learning rate, the model dimension, etc. are given as the input. The majority of p...	Summary: This paper addresses the computational task (coined as "data debugging") of finding training data subsets that yield different test point predictions. The focus of the paper is the SGD learning algorithm with linear classifiers. The paper shows that: - For general loss functions (to transform yw^Tx to a loss ...	Rebuttal 1: Rebuttal: We appreciate the viewer for providing the valuable feedback. ===REPLY TO THE WEAKNESSES=== Relationship of scoring based methods to data debugging: since it is reported that score-based selection approaches (i.e., score each training sample in the first phase and delete training samples greedi...	null	null	Rebuttal 1: Rebuttal: 1.High-level ideas of the proof of Theorem 3.1 The proof of Theorem 3.1 is illustrated in the attatched pdf file. In a MONOTONE 1-IN-3 SAT instance, the truth values of each clause and variable are stored in a parameter. The process of gradient descent converging precisely simulates the process o...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input	Accept (poster)	Summary: This submission studies the training dynamics of a two-layer neural network in the mean-field limit, when the data is isotropic Gaussian and the target function is a low-dimensional multi-index model. The authors consider the population gradient flow on the squared error, and identify a "reflective property" t...	Rebuttal 1: Rebuttal: Thanks a lot for your helpful and insightful comments and questions. Please find our responses below: * __Connection/difference between the reflective property (equations (3.1)-(3.2)) and $\textup{IsoLeap}(h^)>1$ (Abbe et al. 2023):__ Our conditions (3.1) and (3.2) are more general than $\t...	Summary: This manuscript extends previous work on learning sparse polynomials on the hypercube to Gaussian space. To achieve this, the authors introduce a generalization of the merged staircase property studied in previous work. Specifically, they demonstrate that their "reflective property" is necessary to learn polyn...	Rebuttal 1: Rebuttal: Thanks a lot for your helpful and insightful comments and questions. Please find our responses below: * __Connection/difference between the reflective property (equations (3.1)-(3.2)) and $\textup{IsoLeap}(h^)>1$ (Abbe et al. 2023):__ Our conditions (3.1) and (3.2) are more general than $\t...	Summary: This paper considers learning a subspace sparse polynomial with Gaussian data, using a two-layer neural network trained in the mean-field regime. Their main results are two fold: 1)They introduce a reflective property that generalizes the non- merged-staircase property from [1] to Gaussian input. They show tha...	Rebuttal 1: Rebuttal: Thanks a lot for your helpful and insightful comments and questions. Please find our responses below: * __Connection/difference between the reflective property (equations (3.1)-(3.2)) and $\textup{IsoLeap}(h^)>1$ (Abbe et al. 2023):__ Our conditions (3.1) and (3.2) are more general than $\t...	Summary: This paper studies how well two-layer neural networks can learn subspace-sparse polynomials using stochastic gradient descent when the input is Gaussian. It proposes a necessary condition for learning, showing that if the target function has certain symmetries, the neural network cannot learn it perfectly. The...	Rebuttal 1: Rebuttal: Thank you very much for your encouraging comments! We appreciate your time and consideration.	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DOFEN: Deep Oblivious Forest ENsemble	Accept (poster)	Summary: The paper introduces DOFEN, a deep learning method for tabular data inspired by oblivious decision trees and random forests. DOFEN combines a novel differentiable oblivious tree model with a novel forest ensembling stage. On a well-established tabular benchmark, DOFEN performs close to boosted trees and mostly...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. Following are our responses to each individual comment. > Weaknesses 1: I found the description of the method sometimes confusing, especially the mathematical notation (see below). Maybe writing the method as an algorithm could help? I was also missing so...	Summary: Tabular data is widely used in various fields. This paper studies the current situation that DNN still lags behind Gradient Boosting Decision Trees (GBDTs) in tabular data. This paper introduces Deep Oblivious Forest Ensemble (DOFEN), which is inspired by unbiased decision trees and implements on-off sparse se...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. Following are our responses to each individual comment. > Weakness 1: Compared with other DNNs, the inference time of DOFEN may be relatively long, which may affect its practicality in application scenarios that require rapid response.\ > Question 1: The ...	Summary: This paper proposes Deep Oblivious Forest ENsemble (DOFEN), a novel neural network-based architecture for tabular deep learning. DOFEN extends Neural Oblivious Decision Ensembles (NODE) by relaxing the interaction level between a sample and the conditions on nodes in oblivious decision trees. In particular, wh...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. Following are our responses to each individual comment. > Weaknesses 1: The paper is not very well-written, and I found it quite hard to follow. In future revisions, we will add a Table of Contents (ToC) in front of the Appendix section and update the eq...	Summary: This paper primarily investigates DNN models for tabular data. Current DNN models have limited performance and do not outperform GBDTs. The authors believe the key lies in the need for sparse feature extraction from tabular data. Existing attention-based models lead to dense selection of columns, while tree-ba...	Rebuttal 1: Rebuttal: We thank the reviewer for their feedback. Following are our responses to each individual comment. > Weaknesses 1: Implementation Complexity: The architecture of DOFEN may be more complex than traditional tree models and some DNN models, which could increase the difficulty of implementation and un...	Rebuttal 1: Rebuttal: We thank all the reviewers for their time and insightful comments which have helped improve our paper. We found all reviewers mentioned the issue of relatively long inference time of DOFEN, and several reviewers mentioned the interpretability of DOFEN and the readability of the paper. Hence, we de...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
AlterMOMA: Fusion Redundancy Pruning for Camera-LiDAR Fusion Models with Alternative Modality Masking	Accept (poster)	Summary: This paper introduces Alternative Modality Masking Pruning (AlterMOMA), a pruning framework utilizing alternative masking on each modality to pinpoint redundant parameters. It detects redundant features and relevant parameters through the reactivation process. These redundant parameters are then pruned using a...	Rebuttal 1: Rebuttal: Thank you for your positive feedback and insightful comments. We will answer your questions in the following: *Weakness 1: Lack of the computataional overhead of pruning process.* - Thank you for reminding us of the importance of detailing the cost associated with the proposed pruning me...	Summary: This paper addresses the problem of feature redundancy in camera-LiDAR fusion models. The authors propose a novel pruning framework, AlterMOMA, which employs alternative masking to identify and prune redundant parameters in these models. The paper demonstrates the effectiveness of AlterMOMA through extensive e...	Rebuttal 1: Rebuttal: Thanks for your insightful comments and suggestions. We will address your concerns in the following answers: *Weakness 1:* Generalizability of AlterMOMA to additional modalities and tasks. - We appreciate the inspiring comments. We currently focus on camera and LiDAR modalities, as well as ...	Summary: This paper introduces a novel and effective pruning framework (AlterMOMA) and outperforms existing pruning methods on multimodal 3D detection/segmentation tasks, based on a novel insight : "The absence of fusion-contributed features will compel fusion modules to ’reactivate’ their fusion-redundant counterparts...	Rebuttal 1: Rebuttal: We appreciate the professional and insightful comments. We address each comment as follows: *Weakness 1:* The computataional overhead of pruning process. - We agree that providing the computational overhead for the proposed pruning framework would significantly enhance the quality of this p...	null	null	Rebuttal 1: Rebuttal: Thank you to all the reviewers for your efforts in reviewing our work. Your insightful suggestions and comments are greatly appreciated and will significantly enhance the quality of our paper. We are grateful for the positive feedback on various aspects and are encouraged that the reviewers pointe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization	Accept (poster)	Summary: To tackle the optimization problem where the objective is a weighted sum of losses from multiple domains, an improved optimization algorithm is designed by incorporating past history and appropriate regularization. Merits of the proposed algorithm are demonstrated by a rigorous theoretical analysis of converge...	Rebuttal 1: Rebuttal: Thank you for your concrete comments. We address them below. >I do not follow where the $bd$ term comes from in the per iteration complexity. Is it possible to provide some details? The quantity comes from the fact that $b$ evaluations of $(\ell_i, \nabla \ell_i)$ in the computation of $v^{P...	Summary: The authors provide a state-of-the-art optimization algorithm with convergence guarantees for f-divergence DRO. The numerical study is also extensive Strengths: 1. Techincal results are strong and to the best of my knowledge, I haven't find mistakes 2. Numerical study is extensive Weaknesses: As the author h...	Rebuttal 1: Rebuttal: Thank you for your comments. We address the main concern below. >As the author has commented, they only solve for convex optimization formulation. The extension to non-convex setup also seems interesting and important. Thank you for raising this point. Broadly, the study of optimization appr...	Summary: This paper proposes DRAGO, a variance-reduced optimization method for distributionally robust optimization (DRO). In particular, the authors consider the penalized DRO framework and view DRO as min-max optimization with a simple finite-sum structure. Their algorithm maintains explicit histories for the individ...	Rebuttal 1: Rebuttal: Thank you for your insightful comments. We address them below. >SAG-type methods usually have high memory requirements as $d$ increases. Thank you for identifying this important point. We have updated the manuscript to include a discussion on space complexity, which in many cases can be redu...	Summary: The paper considers the DRO problem with a dual penalization term and primal regularization term. Their core contribution is a new primal-dual algorithm for this problem: - It achieves a new best first-order oracle complexity in certain parameter regimes. In particular, it beats previous algorithms when the du...	Rebuttal 1: Rebuttal: Thank you for your detailed suggestions—we have incorporated the comments about comparisons, formal statements of complexity results, and minor typos into the manuscript. >The cases with $\mu=0$ or $\nu=0$ are handled in Appendix C.4, but not in a very formal way. Thank you for raisi...	Rebuttal 1: Rebuttal: We thank the reviewers for their hard work reviewing our paper and providing insightful and concrete comments! We have revised the manuscript to incorporate these suggestions. A summary of the main points of concern is listed below, and all remaining comments are addressed in individual responses....	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper studies stochastic variance-reduced algorithms for regularized distribution robust optimization problems, formulated as a minimax problem. The paper proposed a novel primal-dual algorithm, and analyzed its iteration complexity. Under Lipschitz and smoothness assumptions, the algorithm achieves a line...	Rebuttal 1: Rebuttal: Thank you for your thorough comments. We address them below. > Is it possible to extend the analysis to non-strongly convex settings, or the case with convex individual functions but strongly convex summation? Thank you for raising this important point. We discuss in Appendix C.4 modificatio...	null	null	null	null	null	null
Octopus: A Multi-modal LLM with Parallel Recognition and Sequential Understanding	Accept (poster)	Summary: The paper presents Octopus, a combined MLLM for joint localization and visual chat tasks. The authors insert the DETR decoder into the LLM to perform visual grounding to replace LLM’s outputs for more precise localization. The bottom LLM layers are utilized for parallel recognition and the recognition results ...	Rebuttal 1: Rebuttal: Q1: The motivation of combining DETR with MLLM is not new. Several previous works have explored the nearly same design. In particular, LLaVA-Grounding have the same design by introducing the Detr decoder into the output of LLM. A1: Our motivation is not combining DETR with MLLM. Our moti...	Summary: This paper introduces Octopus, a multimodal LLM that employs parallel recognition and sequential understanding. Inspired by the human brain, the authors aim to address the efficiency issues inherent in existing methods by proposing this novel approach. The paper makes three key contributions: (1) Identificatio...	Rebuttal 1: Rebuttal: Q1: The comparison in this paper are not comprehensive and sometimes unfair to some baseline methods. It would be better to compare Octopus and LLaVA using the exact same training data. Moreover, in Table 3, some methods from Table 2, such as LLaVA and Qwen-VL, are not included. A1: Than...	Summary: This paper introduce a new “parallel recognition → sequential understanding” framework for MLLMs. Specifically, the bottom LLM layers are utilized for parallel recognition and the recognition results are relayed into the top LLM layers for sequential understanding. The parallel recognition in the bottom LLM la...	Rebuttal 1: Rebuttal: Q1: Missing many experiments. This paper propose a framework for both detection and understanding. However, the authors only report the performance of results on VQA MLLMs. They only report inference time and fps on RefCoCo and Flickr30k, not performance. A1: Table 1 and Section 4.2 in t...	Summary: Octopus first identify unifying visual recoginition and understanding is not optimal from two aspects, 1) parallel recognition could achieve better effiency, 2) recognition results can help understanding. Based on this insight, this work built a recognition then understanding pipeline for MLLM. Extensive expe...	Rebuttal 1: Rebuttal: Q1: More experimental investigations shall be conducted to support the motivation of this paper, especially more quantitative analysis. A1: Thanks. During rebuttal, we conducted one more experiment according to your suggestion. This experiment along with the already-existing ones in Tabl...	Rebuttal 1: Rebuttal: We thank all the reviewers for their valuable comments. We are grateful to Reviewer Up36 and Reviewer aS3o for recognizing the novelty of our method, and to Reviewer hBAZ and Reviewer u3Ag for acknowledging its effectiveness and straightforwardness. In the rebuttal, we provide detailed responses t...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models	Accept (poster)	Summary: The paper proposes distilling the data synthesis ability from the strong (and large) model to the small model and using the small model to synthesize (continual) pre-training data, focusing on mathematical problem-solving with natural language reasoning and tool-integrated reasoning. Specifically, the paper 1...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the insightful suggestion and positive feedback, and list our responses for the weaknesses 1 below: W1. It might need further consideration about whether continual pre-training on large-scale synthetic instruction data before fine-tuning is compatible with ...	Summary: This work proposes a low-cost yet efficient method to synthesize QA pairs using a small LLM (e.g., DeepSeek-Math-7B model) instead of GPT-4, significantly reducing cost and improving performance in mathematical reasoning. Concretely, it begins by initializing the data synthesis LLM through distillation from G...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the insightful suggestion and positive feedback, and list our responses for the weaknesses below: W1. The core idea involves distilling GPT-4’s synthesis capability into a smaller yet robust 7B LLM (e.g., Deepseek-Math-7B), which remains within the scope of...	Summary: This paper proposes a framework for training large-scale mathematical reasoning models using synthetic data, introducing JiuZhang3.0 based on this framework. Compared to manually collected or LLM-generated training data, this framework significantly reduces resource costs. The authors trained three different s...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the insightful suggestion, and list our responses for the weaknesses below: W1. Novelty: Overall, I believe the innovation of this work is limited. The core of this work is a framework for generating high-quality training data. However, using GPT-4 to gener...	Summary: The paper addresses the high cost of training large language models (LLMs) for mathematical reasoning by proposing a more efficient method for generating high-quality pre-training data using a smaller LLM. Instead of relying on extensive pre-training data or using powerful models like GPT-4 for large-scale syn...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the insightful suggestion and positive feedback, and list our responses for the weaknesses 1-5 below: W1. Table 1 shows that as the model size increases, the performance does not improve significantly, and some metrics even decline. - Reply to W1. As sh...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FIFO-Diffusion: Generating Infinite Videos from Text without Training	Accept (poster)	Summary: This article proposes a training-free diagonal denoising method aimed at extending video diffusion models, which typically generate only short clips, to the production of long videos. The authors have validated their method across multiple baselines and demonstrated that it can be extended to different VDM arc...	Rebuttal 1: Rebuttal: [W1] Scaling-up and FIFO-Diffusion Thank you for your insightful comments. We agree that scaling up in deep learning is crucial, particularly for video generation. However, we argue that FIFO-Diffusion offers benefits even for scaled-up models as an universal inference technique, and may aid ...	Summary: The authors introduce FIFO-diffusion, a method to enable pre-trained video diffusion models to generate infinitely-long videos via the proposed diagonal denoising. In diagonal denoising, in contrast to the conventional denoising, the noise levels of the frames in a window form a decreasing sequence. This way, ...	Rebuttal 1: Rebuttal: [W1] Prior work: Relevance to Tedi and Rolling Diffusion Model Thank you for bringing this to our attention. Since we became aware of Tedi [R5] and Rolling Diffusion Model [R1] after the ICML conference, which was subsequent to the submission, we gladly plan to cite [R5] and [R1], acknowledgi...	Summary: Paper Overview: This paper introduces the FIFO-diffusion pipeline designed for training-free autoregressive video generation. - The core method, *diagonal denoising*, processes latents at varying denoising levels sequentially. - To enhance the video generation capabilities with more extensive denois...	Rebuttal 1: Rebuttal: [W1] Relevance to Rolling Diffusion Models Thank you for bringing this to our attention. We gladly plan to cite Rolling Diffusion Models [R1], acknowledging its relevance to our work. Please understand that we became aware of Rolling Diffusion Models [R1] after the ICML conference, which was ...	Summary: Given a “base” diffusion based video generation model, this paper shows how one can use “diagonal denoising” to generate arbitrarily long videos which have similar visual quality as the base model and obey conditioning. Notably, the proposed method does not require any training which makes it applicable in pr...	Rebuttal 1: Rebuttal: [W1] Long-video FVD Evaluation We appreciate your invaluable recommendation. Including long-video evaluations on specific datasets would definitely strengthen our paper by evaluating general performance of our techniques for video generation. In response, we have evaluated the class-conditio...	Rebuttal 1: Rebuttal: # Global Rebuttal 1. Relevance to Rolling Diffusion Models We became aware of Rolling Diffusion Models [R1] after the ICML conference, which was subsequent to the submission. We gladly plan to cite [R1], acknowledging its relevance to our work, as both techniques process latents with differe...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent	Accept (poster)	Summary: The authors study the effect of exponential symmetries on the learning dynamics of SGD. They establish the theorem that every exponential symmetry implies the existence of a unique and attractive fixed point in SGD dynamics, which could explain phenomena in machine learning such as matrix factorization and pro...	Rebuttal 1: Rebuttal: Thank you very much for the feedback. Please first see our summary rebuttal above. Weaknesses: C is not guaranteed to exist. (Line 70) Thanks for this comment. This is a well-known fact in the study of symmetries and the Noether theorem. It is required that $J$ needs to be conservative....	Summary: This paper considers the relationship between the behavior of SGD for solving empirical risk minimization and the exponential symmetry. Strengths: The strength of this paper is to study the relationship (Theorem 4.3) between the behavior of SGD for solving empirical risk minimization and the exponential symme...	Rebuttal 1: Rebuttal: Thank you for your criticisms. We believe that your criticisms are due to misunderstanding of mathematics and the related literature and not paying attention to the details given in the immediate context. We will answer concisely due to space constraints. However, please ask us to elaborate on eve...	Summary: The paper takes important steps toward showing that the noise in SGD pushed the dynamics along symmetry directions toward a unique fixed point. This differs in an important way from GD and gradient flow (GF) where one can show that in the presence of continuous symmetries (called exponential symmetries in the ...	Rebuttal 1: Rebuttal: Thanks for your review. Please first see our summary rebuttal above. We will answer concisely due to space constraint. However, please ask us to elaborate on every point that you need more detail. Ref [1]... Ref. [1] shows the existence of an invariant set, mainly due to the permutation symm...	null	null	Rebuttal 1: Rebuttal: # Summary Rebuttal We thank all the reviewers for their constructive feedback, which we will rely on to further improve our manuscript. We are encouraged to see that both reviewers rhMv and QY9h rate our contribution as "excellent." We believe some questions and criticisms may arise from...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery	Accept (poster)	Summary: The paper presents a new training paradigm for better test-time optimization performance at test time. Specifically, two strategies are proposed. (1). integrate the test-time optimization into the training procedure, which performs test-time optimization before running the typical training in each training ite...	Rebuttal 1: Rebuttal: Question 1: The biggest concern is that the performance improvement is not significant when compared with the existing method. The comparison of 3DPW is not fair, since the results of the last two lines use 3DPW for training, while the reported PLIKS does not use 3DPW for training. PLIKS train...	Summary: This paper targets on the Human Mesh Recovery task. The authors adopt a test-time per-instance optimization method similar to EFT, but proposes a meta-learning-like framework to pre-optimize the pretrained human pose estimation network, aiming to provide a better starting point for test-time optimization. In a...	Rebuttal 1: Rebuttal: Question 1: In Fig. 3, Why EFT gets worse and which design of the proposed approach alleviates this issue? Our Response: This is probably because EFT is only finetuned with 2D reprojection loss and is more *sensitive to the errors in the 2D joints*. At the first several optimization s...	Summary: Human mesh recovery (HMR) is improved by incorporating test-time optimization into the mesh prediction model’s training loop, allowing the learned parameters to potentially be more readily adapted to examples at test time. This is referred to as a meta learning approach, and it departs from prior work that als...	Rebuttal 1: Rebuttal: Question 1: Rephasing “key observation”, “rethinking”, “proposes”, etc., as Kim et al. 2022 have used meta learning already. Our Response: Thanks very much for the suggestion. Yes, Kim et al. 2022 also adopted meta learning, as referenced in our paper. The difference is that their method ...	Summary: This paper introduces a novel method to enhance Human Mesh Recovery (HMR) from images. The approach integrates test-time optimization into the training process, inspired by meta-learning. The dual-network structure is designed to align training and test-time objectives, improving the starting point for test-ti...	Rebuttal 1: Rebuttal: Question 1: There remains a discrepancy between the training loss (using ground truth labels) and the testing-time loss (using pseudo labels). Our Response: Thanks a lot for the insightful question which deserves further interpretations. The design of dual networks is one of the core con...	Rebuttal 1: Rebuttal: Dear ACs and Reviewers: We sincerely thank you and all reviewers for your time and review. All reviewers endorse the novel idea of this paper. For example, Reviewer PPKd says "The authors introduce *an innovative approach*", Reviewer Cdc4 says "Adding to the meta learning approach an auxili...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies	Accept (poster)	Summary: This paper focuses on accelerating flow-based imitation learning algorithms by extending the existing work of Rectified Flow to the field of imitation learning, proposing AdaFlow. The paper leverages probability flows to train a state-conditioned action generator and introduces a variance-adaptive ODE solver t...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback, which has helped us improve the clarity and comprehensiveness of our work. Complexity of expert behavior and variance The complexity of expert behavior and the variance are intertwined. Since we perform offline learning with demonstration data, the model...	Summary: This paper introduces AdaFlow, a Flow-based policy for imitation learning. The key innovation is a variance-adaptive solver that employs smaller step sizes when complexity is high and reduces to one-step generation when straightforward. The authors provide theoretical analysis, offering an error bound based on...	Rebuttal 1: Rebuttal: Thank you for your valuable feedback, which has helped us improve the clarity and comprehensiveness of our work. Possibility of extending to higher-dimensional robotics tasks We agree that applying AdaFlow to more complex and high-dimensional tasks like humanoid control would be interesting....	Summary: In this paper, the authors propose a new step schedule for sampling from a gradient flow that they claim accelerates the sampling process. While they focus on the setting of Imitation Learning, in principle, their algorithm samples from some conditional distribution $\pi_E$ of actions given observations on ...	Rebuttal 1: Rebuttal: We believe the reviewer might have some misunderstandings, and we will ensure to further clarify our statements in the updated version of the paper. - In Line 147, $x_t$ is a random variable that follows the marginalized probability at time $t$. Thus, $x_t$ is not conditional on the path up t...	Summary: This paper proposes the AdaFlow algorithm for training fast and accurate imitation learning agents. AdaFlow learns a policy based on the flow generative model and makes use of the connection between the conditional variance of the training loss and the discretization error of the ODEs. In practice, it uses the...	Rebuttal 1: Rebuttal: We thank Reviewer df3a for the thoughtful questions and suggestions. 1. What is the advantage of AdaFlow over Rectified Flow with reflow? - AdaFlow can learn action generation and variance estimation together, as shown in Appendix A.6, where joint and separate training yield similar pe...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
FilterNet: Harnessing Frequency Filters for Time Series Forecasting	Accept (poster)	Summary: This paper introduces FilterNet, a novel neural network architecture for time series forecasting that leverages learnable frequency filters inspired by signal processing techniques. This paper proposes two types of filters within FilterNet: a plain shaping filter that uses a universal frequency kernel for sign...	Rebuttal 1: Rebuttal: We appreciate your review and the positive comments regarding our paper. We would like to respond to your comments as follows, and we hope that we address all your concerns below: **W1. The paper mainly introduces two filters. The connection and difference between the two filters is not highlight...	Summary: This paper explores a novel direction that leverages frequency filters directly for time series forecasting, and proposes a simple yet effective framework, namely FilterNet, from a signal processing perspective. It designs two types of learnable frequency filters corresponding to the linear and attention mappi...	Rebuttal 1: Rebuttal: Many thanks for your positive comments and constructive suggestions. We provide a point-by-point response to your comments as follows, and we hope that we address all your concerns below: **W1. The visualization setting for Figure 1 appears to be missing in Appendix C.4, and I recommend adding so...	Summary: The paper proposes to use Fourier transforms with parametrized filters as layers for time series forecasting models. Filters can either be learnable parameters directly (called "plain shaping filter") or be a function in the inputs, gates (called "contextual shaping filter"). In experiments the authors show th...	Rebuttal 1: Rebuttal: We appreciate your review regarding our paper. We would like to respond to your comments and hope that we address all your concerns below: W1. Empirical comparison with results from a closely related baseline, FiLM are not shown, and FiLM reports better results for all but one dataset. A1. ...	Summary: This paper investigates an interesting direction from a signal processing perspective, making a new attempt to apply frequency filters directly for time series forecasting. A simple yet effective architecture called FilterNet is proposed, utilizing two types of frequency filters to achieve forecasting. Compreh...	Rebuttal 1: Rebuttal: Many thanks for your constructive comments and suggestions. We provide a point-by-point response to your comments below: W1. The marginal improvement over existing approaches, as shown in Table 1, raises doubts about the compelling rationale for adopting the proposed method. A1. The primary ...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models	Accept (spotlight)	Summary: This paper proposes an approximate MPE inference scheme for multiple *discrete* probabilistic models that guarantee efficient log-likelihood computations. The approach is based on training a carefully-designed neural network to answer these queries, building upon three key ideas: 1) self-supervised learnin...	Rebuttal 1: Rebuttal: ## Response to Reviewer jEuh ### Comment 1 > The limitations of the approach are not clearly stated. Response: We have discussed our approach's limitations in the Conclusion and Future Work section. We summarize the limitations below: The primary limitations are the inability to answer compl...	Summary: This paper proposes a neural network-based approach to approximate the most probable explanations (MPE) on probabilistic models. The basic idea is to train a neural network that takes as input an encoding of an assignment to an arbitrary subset of evidence variables and outputs an assignment to the remaining q...	Rebuttal 1: Rebuttal: ## Response to Reviewer 1g2T ### Comment 1 > It appears that the inference time optimization is doing a lot of the heavy lifting compared to pretraining. Response: The any-MPE task is challenging due to exponential input configurations and variable divisions. Training a single network for al...	Summary: This paper proposes a novel neural network-based method to solve the computationally challenging task of finding the Most Probable Explanation (MPE), which is known to be NP-hard. The proposed method involves an inference-time optimization process with a self-supervised loss function to iteratively improve the...	Rebuttal 1: Rebuttal: ## Response to Reviewer q7PK ### Comment 1 > The presentation of the main experimental results could be improved. The captions of Figure 1 and Figure 2 are quite simple and do not provide enough details about the information conveyed in the figures. It would be better to include more descriptive ...	null	null	Rebuttal 1: Rebuttal: # Author Response to Reviews for Paper #12727 We appreciate the reviewers' constructive feedback, which will enhance our paper's quality. We have addressed all points raised and additionally provide the following responses to common questions and concerns below: ## Presentation of experimental re...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sample-Efficient Agnostic Boosting	Accept (poster)	Summary: The paper studies the agnostic boosting problem. That is the learner is given access to a weak learner that given $m_0$ samples form any distribution $D'$ returns a hypothesis $h$ from some baseset $\mathcal{B}$ such that it is $corr_{D'}(h)\geq \gamma corr_{D'}(h^*)-\varepsilon_0$ with probabilty $\delta_0$, ...	Rebuttal 1: Rebuttal: We thank the reviewer for their time, and their positive appraisal of our work. We address the reviewer’s questions below. Epsilon0: Here essentially following the precedent set by KK and BCHM, we state our main results (e.g., Theorem 4) for a fixed value of weak learner’s tolerance $\varepsi...	Summary: This work proposes a new agnostic boosting algorithm whose sample complexity is shown to be polynomially better than that of previous methods. Compared to existing techniques, the advantage of the proposed algorithm comes mainly from the novel concept of reusing samples across multiple rounds of boosting. Thro...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough review, and for their positive comments regarding the rigor and the significance of our work. Experiments: The runtime used for all experiments was a Google Cloud Engine VM instance with 2 vCPUs (Intel Xeon 64-bit @ 2.20 GHz) and 12 GB RAM. The python ...	Summary: The authors propose a method of classifier boosting that improves the sample complexity bound in agnostic settings. To achieve it, they propose an algorithm carefully reusing the sample across iteration for updating pseudo labels. They also demonstrate the usefulness of the method with the agnostic halfspace l...	Rebuttal 1: Rebuttal: We thank the reviewer for their time, and their appreciation of the technical novelties of our work. Comparisons to ERM via SGD: The boosting framework (including the celebrated Adaboost algorithm) is founded on the premise that it is often easy to construct weak learners both in theory and p...	Summary: This paper continues the study of agnostic boosting for binary classification tasks. They provide a new agnostic boosting algorithm that, by re-using samples across multiple rounds of boosting, is more sample-efficient than existing algorithms. As corollaries, they derive improved sample complexities for agno...	Rebuttal 1: Rebuttal: We thank the reviewer for their time, and for their appreciation of the work’s technical contributions. Technical Presentation: Thanks for the suggestion. We plan to implement the following changes to address your concerns: + Lemma 9 is indeed important both for understanding the algorithm an...	Rebuttal 1: Rebuttal: We thank all the reviewers for their comments and editorial suggestions. We hope that we have adequately addressed each reviewer’s questions in the space for one-to-one responses. In this unified response, we hope to highlight two changes of common interest. Experiments: Following Reviewer 3L...	NeurIPS_2024_submissions_huggingface	2,024	Summary: The paper proposes a new agnostic boosting algorithm that improves the sample complexity of a previous algorithm by a factor of $\gamma^{-1}\epsilon^{-1}$ at the expense of an increase in the computational (oracle) complexity by a factor of logarithmic size or VC dimension of the base learning class. Here, $\e...	Rebuttal 1: Rebuttal: We thank the reviewer for their thorough review, and for their positive comments regarding the significance of our work and the presentation. Large number of parameters: Our current presentation is set up to prioritize clean statements of learning guarantees; having independently denoted cons...	null	null	null	null	null	null
Calibrating Reasoning in Language Models with Internal Consistency	Accept (poster)	Summary: This paper investigates the correlation between CoT reasoning and internal representations in Transformer models. It shows that there are inconsistencies between model’s internal representations in different layers. Such inconsistencies can be used to distinguish correct and incorrect reasoning paths, and ther...	Rebuttal 1: Rebuttal: Thank you for rating our paper positively\! Your efforts in reviewing our paper and providing profound advice are sincerely appreciated. We would like to address your remaining concerns in the following response. W1: Marginal improvement of `SC+IC` over `SC` Thank you for your constructive ...	Summary: This paper first reveals that inconsistencies emerge between the internal representations in middle layers and those in final layers, which might undermine the reliability of reasoning processes, and then proposes internal consistency to measure the model’s confidence. Furthermore, the authors propose a method...	Rebuttal 1: Rebuttal: Thank you for rating our paper positively\! We sincerely appreciate your observant review and insightful suggestions and aim to address your concerns in the following response. W1: Motivation behind internal consistency We would like to clarify that we do not expect latent predictions to be...	Summary: This paper investigates the use of internal consistency as a measure for calibration, and to improve the reasoning capabilities of LLMs. The internal consistency is a measure of the model’s confidence by examining the agreement of latent predictions decoded from intermediate layers, and this measure does not r...	Rebuttal 1: Rebuttal: Thank you for the comprehensive and insightful feedback\! We appreciate your effort in reviewing our paper and are committed to addressing your concerns in the following response. W1: Internal consistency is rather straightforward Indeed, internal consistency (IC) serves as a straightforwar...	Summary: The paper explores the concept of "internal consistency" in Large Language Models (LLMs), mainly focusing on chain-of-thought (CoT) reasoning, where models articulate step-by-step rationales. The authors probe internal representations of LLMs to assess the alignment between middle and final layer outputs and p...	Rebuttal 1: Rebuttal: Thank you for your thoughtful reviews\! We appreciate your recognition of our method as “novel and promising”. We aim to address your concerns in the following response. W1: The proposed techniques could be computationally expensive The proposed techniques incur minimal computational overhe...	Rebuttal 1: Rebuttal: We greatly appreciate the detailed feedback from the reviewers. Your insightful suggestions have significantly inspired us to enhance our draft. We are committed to address the reviewers’ concerns on a point-by-point basis. Regarding the common concerns raised by the reviewers, we have conducted a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval	Accept (poster)	Summary: The paper introduces EINSMATCH, an early interaction graph neural network for subgraph matching and retrieval that maintains and refines explicit alignments between query and corpus graphs. EINSMATCH has several novelties: computing embeddings guided by injective alignments between graphs, updating alignments ...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful review. The weaknesses raised in the review are addressed below. > The datasets used in the experiments are relatively small, with at most ~20 nodes. In early experiments, we found relatively small size of query graphs to actually present more challenge...	Summary: This work proposes a GNN architecture for graph matching problem. The innovations in the work mainly lie in the early interaction between the query graph and the corpus graph, and the node-pair matching setting, as well as the lazy update technique in each round. Empirical results show that this work significa...	Rebuttal 1: Rebuttal: We thank the reviewer for their insightful feedback. The questions raised in the review are addressed below. > Any related work indicating early interaction is good? The GMN paper itself provides some evidence: GMN-match (early cross-graph interaction) is slower but convincingly superior in terms...	Summary: This paper proposes an early interaction network for subgraph matching. The proposed method enables (1) early interaction GNNs with alignment refinement at both node and edge levels, where the alignments are refined by GNNs; (2) eager or lazy alignment updates, and (3) node-pair partner interaction, instead o...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive feedback. The questions raised in the review are addressed below. >ablation study (or a hyperparameter sensitivity study).. how did the authors tune hyperparameters? We used hyperparameters from the code of IsoNet, which, thanks to the authors, also...	Summary: The paper proposes EinsMatch, a neural approach for graph retrieval, i.e. the problem of finding the best candidates of a corpus of graphs that contain a subgraph isomorphic to a given query graph. EinsMatch is based on a Graph Neural Network architecture and introduces technical improvements over existing me...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and insightful review. > paper hard to follow...only one single figure In the global rebuttal PDF, we include another diagram (Figure A) to distinguish between node-pair interaction (earlier work) vs node-pair partner interaction, and also contrast the...	Rebuttal 1: Rebuttal: We are thankful to all the reviewers for taking out time to provide us with detailed reviews. Through this rebuttal, we aim to address questions that came up across multiple reviews. Figures have also been shared through the rebuttal PDF attached herewith. > Why is Lazy update better? (Reviewers ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Convergence Analysis of Split Federated Learning on Heterogeneous Data	Accept (poster)	Summary: Split federated learning combines the advantage of split learning and federated learning. However, there is a lack of theoretical analysis. This paper provides the first comprehensive analysis on split federated learning. The settings include strongly convex, general convex and non-convex. This paper discusse...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! Our response to your comments is as follows. We use "W" to tag responses to comments in the Weakness and "Q" for responses to comments in the Questions. 1. Latency of SL and SFL (for W1) Split learning has higher latency than centralized learning due ...	Summary: Split Federated Learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, where clients train one part in a parallel federated manner, and a main server trains the other. Despite recent research on SFL algo...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! Our response to your comments is as follows. 1. Related work We enriched the related work as follows. In particular, we have added more discussions on the references. There are many convergence results on FL. Most studies focus on data heterogeneity...	Summary: This paper analyzes the convergence of split federated learning (SFL) on heterogeneous data and gives theoretical convergence rates under different assumptions on the convexity of the loss function and the participation schemes of the clients. Strengths: The paper considers the problem comprehensively, includ...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! Our response to your comments is as follows. 1. More comparing methods We have included the benchmarks, i.e., FedProx and FedAdam, as suggested. We have used the same hyper-parameters, and trained the models on both CIFAR-10 and CIFAR-100. The results ar...	Summary: The paper proposes the first convergence analysis of the two variants of split federated learning under different set of hypotheses. Strengths: To the best of my knowledge, as also stated by the authors, there is no theoretical analysis of split federated learning. This is a serious knowledge gap, given the p...	Rebuttal 1: Rebuttal: Thank you for your valuable comments! Our response to your comments is as follows. 1. Diminishing stepsize for $\mu$-strongly convex loss functions We use a diminishing stepsize of $\frac{4}{\mu\tilde{\tau}(\gamma+t)}$ and update the conditions specific to $\mu$-strongly convex loss functio...	Rebuttal 1: Rebuttal: We would like to thank all of you for your time and comments on our paper. We have carefully revised our paper and prepared the responses. The added figures are in the attached file. Pdf: /pdf/a398882f48646fe7e1e2c1e921d3a8730aa259c6.pdf	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper provides theoretical convergence analysis for split federated learning (SFL) methods (SFL-V1 and SFL-V2) under full and partial client participation for strongly convex, generally convex and non-convex settings. These bounds help build fundamental understanding of the settings in which SFL, federate...	Rebuttal 1: Rebuttal: Thank you for your valuable comments. Our response to your comments is as follows. 1. Choice of $L_c$ and tradeoff We use the ResNet-18 model in our experiments, which contains four blocks. This means the candidate for $L_c$ is in {$1,2,3,4$}. Thus, $L_c=4$ is already the maximum $L_c$ that ...	null	null	null	null	null	null
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning	Accept (poster)	Summary: This paper introduces Collaborative World Models (CoWorld), a novel approach to offline reinforcement learning (RL) with visual inputs. CoWorld conceptualizes offline RL as an online-to-offline transfer learning problem, utilizing an auxiliary online simulator to address the challenges of overfitting in repres...	Rebuttal 1: Rebuttal: > Q1: The availability of an online environment. There are many real-world scenarios (such as robot control, autonomous driving, and healthcare) where existing simulators can be employed as source domains. See General Response (Q1) for details. Further, our method is specifically designed to...	Summary: This paper studies model-based offline reinforcement learning with visual observations. This work focuses on the overfitting problem in state representation and the overestimation bias for the value function and introduces the environment model from related tasks to present an offline-online-offline solution. ...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. Our responses are provided below. If you have any further questions, please feel free to let us know. > Q1: Not sure if taking 10 episodes over 3 seeds is sufficient. We extended the results from 3 random seeds to 5 random seeds and tested the performanc...	Summary: This paper studies CoWorld, a model-based RL method that learns the policy through an offline-online-offline framework. Specifically, CoWorld assumes that, in addition to the offline dataset, there is another (simulated) environment for online interaction. Through the offline dataset and interactive environmen...	Rebuttal 1: Rebuttal: We appreciate your great efforts in reviewing our paper and hope that the following responses can address all of your concerns. > Q1: Practical situations where the proposed method is applicable. Good question! CoWorld can be applied in various real-world scenarios such as robot control, autono...	Summary: This work proposes a model-based algorithm for offline visual reinforcement learning using domain transfer learning. The proposed method demonstrates superior performance over existing approaches in several baselines. Strengths: S1. The paper presents the necessity of proposed modules in the algorithms throug...	Rebuttal 1: Rebuttal: Thank you for your comments. We have conducted new experiments to discuss the training cost of our model. Please refer to the results presented in the Rebuttal PDF. We hope our responses will address your specific concerns regarding training efficiency. > Q1: How does the performance look whe...	Rebuttal 1: Rebuttal: ## General Responses to All Reviewers In addition to the specific responses below, we here reply to the general questions raised by the reviewers. >Q1: Practical situations where the proposed method is applicable. As demonstrated in Table 1 of our manuscript, our method can be used in scenarios ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer	Accept (poster)	Summary: The paper introduces a masked encoder based transformer model for time series forecasting. It performs masking in the observation space, replacing the prediction horizon with 0s. It further applies an attention mask such that tokens only attend to the context, and do not attend to the masked tokens. The paper ...	Rebuttal 1: Rebuttal: Thank you for your in-depth reviews and constructive suggestions. We appreciate the opportunity to clarify the distinctions between our approach and MOIRAI, as well as highlight the key contributions of our paper. While there are similarities between our model and MOIRAI due to shared influences...	Summary: This paper introduces ElasTST, which aimed at addressing the challenge of varied-horizon time-series forecasting. ElasTST integrates a non-autoregressive architecture with placeholders for forecasting horizons, structured self-attention masks, and a tunable rotary position embedding. Furthermore, it employs a ...	Rebuttal 1: Rebuttal: Thanks for your in-depth reviews, insightful questions, and constructive suggestions. We hope the following responses can help to address all your questions and concerns. ## Response to Weakness 1 > A comparison with methods such as no positional embedding, vanilla positional encoding, trainable...	Summary: The paper introduces ElasTST, a novel approach designed to enhance robustness in forecasting across various horizons. The proposed architecture dynamically adapts to different forecasting horizons, addressing a significant challenge in the literature. Previous methods typically relied on recursive approaches o...	Rebuttal 1: Rebuttal: Thanks for your in-depth reviews, insightful questions, and constructive suggestions. We hope the following responses can help to address all your questions and concerns. ## Response to Weakness 1 We will expand our literature review to provide a more comprehensive discussion of recent time seri...	null	null	Rebuttal 1: Rebuttal: We sincerely thank the reviewers for their insightful comments. In response to your suggestions, we have conducted additional experiments, which are detailed in the attached PDF. The ablation study further confirms that each proposed design element is crucial for achieving robust varied-horizon f...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Unifying Homophily and Heterophily for Spectral Graph Neural Networks via Triple Filter Ensembles	Accept (poster)	Summary: This paper proposes a novel spectral GNN architecture, the TFE-GNN, which integrates homophily and heterophily using a triple filter ensemble (TFE) mechanism. The model aims to overcome the limitations of existing polynomial-based learnable spectral GNNs by adaptively extracting features from graphs with varyi...	Rebuttal 1: Rebuttal: We are grateful for your comments and suggestions, and we will explain and answer your concerns point by point!. > Weakness 1: The potential computational overhead introduced by the triple filter ensemble. W-A1: Thanks for pointing out what we need to strengthen. The triple filter en...	Summary: This paper proposes a spectral GNN with triple filter ensemble (TFE-GNN). As the ensemble of two diverse and simple low-pass and high-pass graph filters, TFE-GNN achieves good generalization ability and SOTA performance on both homophilic and heterophilic datasets. Strengths: S1. This work is motivated reason...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable comments and suggestions and we answer your concerns point to point! > Weakness 1: More theoretical and empirical results are expected to explain why TFE-GNN has better generalization. W-A1: Thanks for your meaningful comments. We further explain...	Summary: This paper proposed a spectral GNN with triple filter ensemble (TFE-GNN) that aims to retain both the ability of polynomial based spectral GNNs to approximate filters and the higher generalization and performance on graph learning tasks, when most existing GNNs can only retain one of the two capabilities. Theo...	Rebuttal 1: Rebuttal: We appreciate your valuable suggestions and we will explain them line by line. > Question 1: I recommend the authors to run additional experiments on two more up-to-date heterophilous datasets, such as those introduced in the papers [1, 2]. A1: Thank you for your valuable comment. W...	null	null	Rebuttal 1: Rebuttal: Global Response We sincerely thank all reviewers for their thoughtful and constructive feedback and their recognition of our work! We have made significant improvements to our initial results in response to their comments. We add a new figure and two tables in the one-page PDF and update the ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction	Accept (poster)	Summary: This paper introduces the Sign-based Stochastic Variance Reduction (SSVR) algorithm, which enhances the convergence rate of the traditional signSGD method. By incorporating variance reduction techniques with sign-based updates, the authors achieve a convergence rate of $O(d^{1/2}T^{-1/3})$ for general stochast...	Rebuttal 1: Rebuttal: Thank you very much for your constructive comments! --- Q1: The authors should include more experimental results to demonstrate the dependency on batch size for the proposed methods. This would clarify whether the proposed method also necessitates large batches for convergence in practice. *...	Summary: The paper introduces an enhanced sign-based method called SSVR, designed to improve the convergence rate of signSGD with variance reduction techniques. SSVR achieves an improved convergence rate of $O(d^{1/2}T^{-1/3})$ for stochastic non-convex functions. For non-convex finite-sum optimization problems, the SS...	Rebuttal 1: Rebuttal: Thank you very much for your constructive comments and suggestions! --- Q1: The authors introduce the bounded gradient assumption (Assumption 4) for distributed settings. Why is this assumption necessary? Is it commonly used in previous literature for this setting? A1: This assumption ...	Summary: This paper considers application of variance reduction to sign-based optimization, i.e., a setting where the algorithm can only access to $\mathrm{sign}(\nabla f(x^t; \xi^t))$ at step $t$, in the smooth and nonconvex setting. Because $\\{\pm 1\\}$-valued vectors can be transmitted more efficiently than real-va...	Rebuttal 1: Rebuttal: Thanks for your constructive comments! We will revise accordingly. --- Q1: Variance reduction is quite common in the optimization field so I am afraid this paper might be a bit incremental. Section 3.3 looks redundant given Theorem 2, as there are no technical difficulty about weakening the a...	Summary: This paper investigates the stochastic optimization problem and a special case of finite sum optimization problem. They propose the Sign-based Stochastic Variance Reduction (SSVR) method, leveraging variance reduction techiniques, which improves the convergence rate of Sign stochastic gradient descent (signSGD...	Rebuttal 1: Rebuttal: Many thanks for the constructive feedback! We will revise our paper accordingly. --- Q1: How did you get the convergence rate of $\mathcal{O}(m^{1/2}d^{1/2}T^{-1/2})$ for the SignSVRG? It seems that in their paper they claim $\mathcal{O}(T^{-1/2})$ convergence rate without $m$ and $d$ in the...	Rebuttal 1: Rebuttal: ## Global Response ## --- In response to the request of reviewers, we provide additional experimental results in this part. Figure 1 \& Figure 2: According to the suggestion of Reviewer 2Gd9, we provide the performance of our SSVR method with different hyper-parameters. First, we fix the...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints	Accept (poster)	Summary: This works addresses a bilevel problem where the lower-level has constraints that couple both the upper- and lower-level variables. The proposed method leverages a penalty approach and Lagrangian duality theory that transform the original bilevel problem into a single-level minimization problem where the x and...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our presentation and recognizing our work. Our response to your comments follows. Q1. Bounded optimal Lagrange Multiplier assumption for all $x\in\mathcal{X}$. We appreciate the reviewer for highlighting this issue. We apologize for the incorrect refere...	Summary: This paper proposes an algorithm named BLOCC for solving a particular class of bilevel optimization problems where so-called coupled constraints are present. Coupled constraints are constraints w.r.t. the upper and lower level variables which are applied in addition to simple constraints like bound/ball constr...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our contributions and recognizing our algorithm on large scale experiments "particularly appealing". Q1 More clarification for large-scale problems and textualizaing in a broader BLO literature view. e.g. as in value function reformulation [1] We apprec...	Summary: The paper proposed a fully first-order algorithm named BLOCC for the bilevel optimization problems with lower level coupled constraints. They provide convergence theory for the algorithm and demonstrate its effectiveness through numerical experiments in SVM-based model training and infrastructure planning in t...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our work and the constructive review. Our response to your comments follows. Q1.1 Stating strongly convexity in introduction Thank you for your suggestion. We have noted this in line 43 and will clarify it further in the introduction, such as around lin...	Summary: This paper presents an algorithm to solve bilevel optimization problems with coupled constraints using a primal-dual-assisted penalty reformulation. The study establishes rigorous convergence theory and demonstrates the algorithm's effectiveness through real-world applications, including SVM model training and...	Rebuttal 1: Rebuttal: We thank the reviewer for appreciating our contribution and the constructive review. Our response to your comments follows. Q1. Computational complexity and scalability of BLOCC Thank you for raising this question. We answer this in General Response G1. **Q2. Limited baselines in the ex...	Rebuttal 1: Rebuttal: # General Response We sincerely thank the reviewers for their constructive feedback. We appreciate their recognition of our work in tackling the challenging constrained bilevel problem with an efficient algorithm, rigorous convergence theory, and demonstrating effectiveness and robustness through...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Motif-oriented influence maximization for viral marketing in large-scale social networks	Accept (poster)	Summary: The paper introduces a motif-oriented influence maximization (MOIM) problem under the linear threshold model, where motifs, rather than individual nodes, are the primary targets for viral marketing. The authors propose an algorithm to optimize the influence function by establishing submodular upper and lower b...	Rebuttal 1: Rebuttal: Q1: Unclear Motif Definition Rationale: The primary issue with the paper is the rationale behind defining a motif as a strongly connected group. The authors do not provide sufficient justification for this choice, and it is unclear if other works have adopted a similar definition. Limitations...	Summary: The paper proposes a novel approach to influence maximization (IM) by targeting motifs—interconnected subgraphs of nodes—instead of individual nodes. The authors propose the motif-oriented influence maximization (MOIM) model under the linear threshold (LT) and independent cascade (IC) models. They demonstrate ...	Rebuttal 1: Rebuttal: Q1: The paper lacks an approximation hardness result, which is important for understanding the limits of the proposed algorithm's performance. Additionally, the NP-hardness of the problem is derived straightforwardly from traditional influence maximization, offering little new insight into the...	Summary: The paper on motif-oriented influence maximization for viral marketing in large-scale social networks introduces a novel approach that focuses on targeting motifs - strongly connected subgraphs of users - to maximize influence (i.e., targeting motifs rather than individual users for viral marketing campaigns)....	Rebuttal 1: Rebuttal: Q1: The term "Motif" in Network Analysis is traditionally defined as recurrent sub-patterns in the graph. It is better to stay consistent with the literature to avoid confusion for some readers. Reply: Thank you for your valuable feedback. The term "motif" is commonly employed in the anal...	Summary: This paper proposes the motif-oriented influence maximization problem based on the traditional influence maximization problem. This paper proves that the problem is NP-hard and proposes a new method, LBMOIM, based on upper and lower bounds to solve it. The experimental results demonstrate that LBMOIM can achie...	Rebuttal 1: Rebuttal: Q1: The paper is filled with numerous symbols… Adding intuitive explanations … Reply: Thank you for your feedback. To improve the readability and flow of the main text, we will move the detailed proofs of the upper and lower bounds (currently on Page 4) to the supplementary material in th...	Rebuttal 1: Rebuttal: The appendix file includes a figure that shows the influence of motif size on the performance. Pdf: /pdf/bb09475e6f8f49e07ece07155a745b5033c805fb.pdf	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models	Accept (poster)	Summary: This work considers Bayesian inference via MCMC methods (specifically: Hamiltonian Monte Carlo) in linear mixed-effects models (LMMs). Due to linearity and Gaussianity, the random effects can be analytically integrated out so that the MCMC algorithm needs to only target a distribution on a smaller space which ...	Rebuttal 1: Rebuttal: ``` I don't think the title (and the emphasis on HMC in the abstract and introduction) is entirely adequate. ``` Thanks for this comment. Please see the general response. Briefly, we propose to change the title and revise the introduction to clarify that the method is generally applicable, but mai...	Summary: The paper addresses the challenge of marginalizing latent variables in linear mixed-effects models (LMMs) to improve Hamiltonian Monte Carlo (HMC) sampling. The authors propose an algorithm for automatic marginalization of random effects in LMMs, leveraging fast linear algebra techniques. The experiments evalu...	Rebuttal 1: Rebuttal: Thank you for the questions about the experiments. We respond to your questions below, where we will reference additional results uploaded in the PDF response. Broadly, these additional results show additional metrics and experiments consistent with our original findings that marginalization impro...	Summary: The paper studies marginalization in linear mixed-effects models with a Gaussian likelihood. Marginalizing out variables is a common trick to improve the convergence of MCMC. As pointed out, for the linear mixed-effects models with a Gaussian likelihood, naively doing so will actually increase the computation ...	Rebuttal 1: Rebuttal: Overall, reviewer UJHx suggests that similar techniques are known or standard and faults the paper for not comparing to them, but provides no details or citations. It is impossible to accept these claims without justification. We respond to specific comments below. ``` Such techniques are widely...	Summary: This paper considers the problem of Bayesian inference in linear mixed-effects models. This is a popular class of models used throughout many areas of application. Inference in such models depends on being able to determine values of latent variables (fixed and observed effects) given the observed data. This i...	Rebuttal 1: Rebuttal: ``` What sort of prior structures allow for this to be applied? ``` In the context of LMMs, the distributions of the effects are assumed to be normal. We do not restrict the priors of other variables. ``` Are there methods that can be developed that perform approximate marginalization in some way...	Rebuttal 1: Rebuttal: # General response ## Title of the work It’s a good point that the method is not limited to HMC. We will adjust the title to better reflect our contributions. We propose the alternative "Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models". In our revision, we will also...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices	Accept (poster)	Summary: This paper proposes a neural operator (PACE) with improved accuracy, efficiency, and speed for electromagnetic field simulation. PACE is a network block in the surrogate machine learning model. Strengths: This paper conducted extensive experimental evaluations for the proposed method. Also, the accuracy and e...	Rebuttal 1: Rebuttal: Thank you so much for your time in reviewing our paper and providing valuable feedback on our work! Please see our responses below: Q.1 Incremental work over NeurOLight with little improvement? We would like to emphasize that our work is not simply incremental over NeurOLight but...	Summary: The authors propose PACE operators, which perform much better than existing methods on predicting optical fields for real-world complicated optical devices. The work builds up on and is compared to the NeurOLight framework which represents the SOTA in this field. The benefits of physics-agnostic PACE operators...	Rebuttal 1: Rebuttal: Thank you so much for your valuable feedback on our work! Please see our responses below: Q.1 Narrow audience and other benchmarks We'd like to emphasize that our paper aligns with NeurIPS’s interests and offers novel ML contributions to the AI for PDE community (check common response Q.1) ...	Summary: This paper propose a novel cross-axis factorized PACE operator with a strong long-distance modeling capacity to connect the full-domain complex field pattern with local device structures. Additionally, the authors proposed a two-stage model learning method for extremely hard cases, with a first-stage model lea...	Rebuttal 1: Rebuttal: Thank you so much for your appreciation of our contributions and for providing valuable feedback on our work! Please see our responses below: Q.1 Use “coarse to fine” instead of “divide and conquer” Thank you for your suggestion. We agree that our learning flow could be more precisely descri...	Summary: This paper develops a computationally efficient learning based PDE simulator for modeling optical fields within photonic devices. The proposed method builds upon NeurOLight [7] and adds a novel factorization of the integral kernel and a two-stage approach to prediction---produce a rough initial estimate that i...	Rebuttal 1: Rebuttal: Thank you so much for your valuable feedback on our work! Please see our responses below: Q.1 Somewhat narrow audience We would like to stress that our paper aligns well with NeurIPS’s interest and has essential machine learning contributions to AI for the PDE community. Please check the det...	Rebuttal 1: Rebuttal: We sincerely appreciate the constructive feedback provided by all the reviewers. We are truly inspired by their acknowledgment of our paper’s contribution and strong accuracy compared to various baselines as a new state-of-the-art in AI for optic simulation. We appreciate the chance to address co...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery	Accept (spotlight)	Summary: This work presents a new neural network for tackling PDEs' forward and backward problems, especially for small physics systems. The forward problem simulates the outcome of a physics system given the inputs, while the backward problem estimates the parameters of a physics system given the inputs and outputs. T...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable time and constructive suggestions. We have generated more families of kernels, and performed additional tests on three different training settings (diverse tasks, single task with diverse function pairs, and diverse tasks with a reduced number of samples). ...	Summary: This paper explores using large-language deep-neural network to build a foundation physical model that can solve inverse PDE problem. It proposes Nonlocal Attention Operator model with a kernel map that is dependent on both input and output functions. The intermediate proofs help people understand the underlyi...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable time and constructive suggestions. Our source code, trained models, and datasets will be made publicly available on Github upon acceptance of the paper, so as to guarantee reproducibility of all results. Our other responses: Limited baselines: We did n...	Summary: This paper introduces a novel approach called the Nonlocal Attention Operator (NAO) for simultaneously solving forward and inverse PDE problems across multiple physical systems. The key innovation is using an attention-based kernel map that learns to infer resolution-invariant kernels from datasets, enabling b...	Rebuttal 1: Rebuttal: We thank the reviewer for the valuable comments and suggestions. Our response: Number of trainable parameters and computational complexity: Denoting $(N,d,d_k,L,n_{train})$ as the size of the spatial mesh, the number of data pairs in each training model, the column width of the query/key weig...	Summary: This paper proposes a novel neural operator architecture called Nonlocal Attention Operator (NAO) for learning both forward and inverse problems in physical systems from data. The key contributions are: 1. A new attention-based neural operator that can simultaneously learn the forward mapping (physics modelin...	Rebuttal 1: Rebuttal: We thank the reviewer for the insightful comments and questions. Our response: Interpretability for physical understanding: The physical meaning of the learned kernel is application-dependent: in the context of material modeling, the learned kernel characterizes the interaction between materi...	Rebuttal 1: Rebuttal: We thank the reviewers for the constructive comments and for recognizing the novelty and broad impact of our work, the soundness and solid theoretical foundation of our formulation, the effectiveness and meaningful physical interpretability of our model, as well as the comprehensive experimental s...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition	Reject	Summary: This paper proposes a denoising framework to alleviate the influence of noisy text descriptions on open-vocabulary action recognition in real scenarios. A comprehensive analysis of the noise rate/type in text description is provided and the robustness evaluation of existing OVAR methods is conducted. A DENOISE...	Rebuttal 1: Rebuttal: W1 In Table 2, I would like to see the performance of other correction methods (e.g., GPT3.5/4/4o) for a more comprehensive comparison. A1 We present additional results tested on UCF101 with ActionCLIP, using GPT4o and GPT4 as denoising model. We will update table 2 with result of GPT4/4o...	Summary: This paper tackles the challenge of noisy text descriptions in Open-Vocabulary Action Recognition (OVAR), a task that associates videos with textual labels in computer vision. The authors identify the issue of text noise, such as typos and misspellings, which can hinder the performance of OVAR systems. To addr...	Rebuttal 1: Rebuttal: W1 The baseline models are outdated and not tailored for OVAR. The authors failed to reference recent OVAR papers such as OpenVCLIP[1] (ICML 2023), FROSTER (ICLR 2024), and OTI (ACM MM 2023). A1 Please note that in this paper, we are motivated is to draw public attention to the poor robus...	Summary: This paper deals with the problem of Open-Vocabulary Action Recogniton (OVAR). Specifically, it focuses on the issue that the action labels provided by users may contain some noise such as misspellings and typos. The authors find that the existing OVAR methods' performance drops significantly in this situation...	Rebuttal 1: Rebuttal: W1 This paper actually focuses on text denoising and does not involve any specific action recognition technology. The author just chose the field of OVAR to verify the effectiveness of the proposed text-denoising method. The title is somewhat misleading. I think the author should regard text-d...	Summary: This paper addresses the challenge of noisy text descriptions in Open-Vocabulary Action Recognition. It introduces the DENOISER framework, which combines generative and discriminative approaches to denoise the text descriptions and improve the accuracy of visual sample classification. The paper provides emp...	Rebuttal 1: Rebuttal: W1 Textual description noise does exist, but it can be corrected with offline language model, what are the advantages of the proposed approach? A1 Thank you for acknowledging the presence of noise in text descriptions. We agree that offline language models can mitigate these noises to an...	Rebuttal 1: Rebuttal: We thank the reviewers for their valuable advices. We would like to underline that the contribution of this paper is two fold. First, we verify the robustness of existing OVAR models during the inference period under noisy textual descriptions. Our study spans from classic models like ActionCLIP...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors	Accept (poster)	Summary: - Proposes method for parameter efficient fine-tuning using Singular Value Decomposition of the pretrained weights - Fine-tuning is done by adding a learned residual $\Delta W = UMV^\top$, where $M$ is a sparse trainable matrix, which results in fine-tuned weigts $\hat{W} = U\Sigma V^\top + UMV^{\top}$ - Propo...	Rebuttal 1: Rebuttal: We sincerely thank the reviewer for the rigorous review and valuable feedback. We have addressed all the weaknesses (W) and questions (Q) with extensive experiments and clarifications. W1: Please refer to our global response G1 for a comprehensive answer. W2: 1, 4. We appreciate yo...	Summary: The authors propose a parameter-efficient fine-tuning method with singular vectors (SVFT). SVFT modifies the parameter matrix based on the structure of the original parameter matrix $\mathbf{W}$. By training the coefficient of a sparse combination of outer products of $\mathbf{W}$'s singular vectors, SVFT reco...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback. We address the weaknesses (W) and questions (Q) raised by the reviewer below. W1: We thank the reviewer for pointing us to these papers (SAM-PARSER [1], SVDiff [2]). Indeed we are not the first one to explore the singular vectors/values space...	Summary: This paper presents a new PEFT method to enhance the fine-tuning efficiency of large language models (LLMs). The approach uses SVD to decompose pre-trained weights, initializing adapters with eigenvectors and eigenvalues, and adjusts within the eigenvalue space. Extensive experiments show that the proposed met...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback. We address the weaknesses (W) raised by the reviewer below. W1: We emphasize that our main contribution is making off-diagonal elements in $M$ learnable; this allows us to smoothly trade-off between the number of trainable parameters and exp...	Summary: This paper proposes a parameter-efficient fine-tuning (PEFT) method named SVFT. Instead of imposing learnable parameters directly on the pre-trained weight matrices, it applies singular value decomposition to the pre-trained weight matrices and then tunes only the coefficients of the singular values. The metho...	Rebuttal 1: Rebuttal: We thank the reviewer for their valuable feedback. We address the weaknesses (W) raised by the reviewer below. W1: Recall that VeRA injects frozen low-rank random matrices with learnable scaling vectors resulting in weight updates independent of the pre-trained weights. SVFT$^P$ derives its ...	Rebuttal 1: Rebuttal: We thank all reviewers for their valuable feedback. We address overlapping concerns below. --- G1: Missing Discussion of Related Work.\ SVF, SVDiff, SAM-PARSER are equivalent to SVFT$^{P}$ barring minor implementation differences. We compare these methods against SVFT$^{P}$ on GSM-8K and Mat...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood	Accept (poster)	Summary: This paper studies the problem of sequential probability assignment under logarithmic loss with context and assumes that both the contexts and labels are generated adversarially. The main contribution is a complete characterization of the minimax regret via a new complexity measure named "contextual Shartkov s...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed and knowledgeable review. We appreciate your recognition of our main results, and hope you will help us defend the merit of our work. We first address your comments on the weaknesses of our paper and then answer your questions. # Clarification on our technic...	Summary: The authors explore the fundamental problem of sequential probability assignments, specifically focusing on log-loss online learning with an arbitrary, possibly non-parametric hypothesis class. They introduce a new complexity measure and demonstrate that the worst-case contextual Shtarkov sum equals minimal re...	Rebuttal 1: Rebuttal: We thank the reviewer for their constructive review. Below, we address your comments on experimental results and our cNML algorithm. # On experimental results and our cNML We would like to elaborate on the statement of our results. Theorem 3.2 says that if we compute the worst-case regret of all a...	Summary: In this work, the authors consider the problem of sequential probability assignment, also known as online learning with the log-loss. While earlier results have shown that in the context-free or transductive settings, the Shtarkov complexity is minimax optimal (and attained with normalized maximum likelihood)...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed review and, in particular, acknowledging the importance of the problem being studied and praising our presentation. To begin, we address your concern about the novelty in our global rebuttal. We will address your remaining comments on the weaknesses of our pa...	Summary: This paper studies minimax regret in the online learning setup. The paper extends prior works to nonbinary labels and proves improved bounds on the regret. The analysis is through studying the Shtarkov sum that characterizes the minimax regret. The authors develop a data-dependent variant of the contextual Sh...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed review and, in particular, your recognition of our technical contributions. To begin, we address your concern about the novelty in our global rebuttal. We will address your remaining comments on the weaknesses of our paper and answer your questions. # On cNML...	Rebuttal 1: Rebuttal: Multiple reviewers question the novelty of this work, but also express some uncertainty by not expressing complete confidence. Here we give a full throated defense of our work's significance and novelty. We hope this settles the matter. # On the significance of the problem First, we want to highli...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut	Accept (poster)	Summary: The paper introduces an approach to data-driven algorithm design by leveraging neural networks to select the most appropriate algorithm for a given instance of a computational problem. The motivation is clear. This paper decision to extend the paradigm from selecting a single best algorithm to a context-specif...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful review. Weaknesses: 1. While training can be expensive, the neural network can then be directly used on all future instances from this distribution. There is no need to do any further analysis on future instances. Compare this to the use of (weighted s...	Summary: This paper studies data-driven algorithm design, which learns a class of algorithms to optimize the expected performance scores given i.i.d. samples of instances. In particular, the paper studies the sample complexity of learning (the weights of) neural networks computing the size of certain branch-and-cut tre...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful review. Weaknesses: - We think this opens up a whole new field of investigation at the intersection of integer programming and learning theory. We are currently working on other papers where the theory is extended to other families of cutting planes, a...	Summary: The authors study the problem of algorithm selection for specific instances of a problem. In particular they provide theoretical results for the learnability of a mapping from problem instances to algorithm parameters with application to branch-and-cut. They establish sample complexity bounds for learning a fe...	Rebuttal 1: Rebuttal: Thank you very much for your thoughtful review. Weaknesses: - A useful insight to draw from the sample complexity bounds is that the number of data samples is linear in the number of neural network parameters. This means that if the practitioner wishes to use a larger neural network for even...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data	Accept (poster)	Summary: This paper studies federated learning on a compact smooth submanifold using distributed Riemannian optimization methods. The proposed methods were examined on PCA and low-rank matrix completion tasks. Strengths: The proposed proximal smoothness of a manifold is intuitive and interesting. The notation can be ...	Rebuttal 1: Rebuttal: Thank you very much for your careful review and valuable comments. Weaknesses We have addressed all the comments accordingly. Please see the response to Questions for details. Questions 1) Thank you very much for your comments. We have provided a comparison with [ICML 2020] and [Neurips 2021...	Summary: The authors introduce a submanifold into the setting of federated learning. Utilising Riemannian gradients for optimisation, they show different properties of the proposed method, such as avoiding client drift and being computationally cheaper. Strengths: - The computational cost is advantageous, which could ...	Rebuttal 1: Rebuttal: Thank you very much for your careful review and valuable comments! Weaknesses 1) Thank you for your comment and the opportunity to clarify this point. In our algorithm design, we not only utilize projection but also exploit the manifold geometry, specifically the Riemannian gradient, to create ...	Summary: This article proposes a new algorithm for non-convex optimization on smooth manifolds in the federated learning setting. The algorithm is proved to have a sub-linear convergence guarantee, and numerical experiments on kPCA and low-rank matrix completion demonstrate the convergence and efficiency of the propose...	Rebuttal 1: Rebuttal: We would like to express our sincere gratitude for your careful review and positive assessment of our work! Weaknesses 1) The projection operator can be explicitly calculated for many common submanifolds, as discussed in [SIOPT 2012]. For the Stiefel manifold, the closed-form expression for the ...	Summary: This paper addresses several computational challenges in federated learning on manifolds, by using a simple projection mapping to bypass the need for exponential mappings, logarithmic mappings, and parallel transports needed in prior work. The proposed technique also enables multiple local updates and full cli...	Rebuttal 1: Rebuttal: Thank you for your careful review and positive assessment of our work! Suggestions Nonconvexity 1) All the concepts related to convexity and nonconvexity are defined in the ambient space, which is the Euclidean space for the considered submanifolds. We will state this clearly in the revised man...	Rebuttal 1: Rebuttal: We sincerely appreciate the careful reviews and valuable comments from all the reviewers. We have provided point-by-point responses to each comment. In particular, for Reviewer 3dV9's Q1 regarding the comparison with related Riemannian methods and Q4 regarding the comparison with Euclidean baselin...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
PiCO: Peer Review in LLMs based on the Consistency Optimization	Reject	Summary: The paper introduces a novel unsupervised evaluation method for large language models (LLMs): it uses a peer-review mechanism of a models' anonymized answers by other models. The approach assigns a (learnable) capability parameter to each LLM and solves a constrained optimization problem to maximize the consis...	Rebuttal 1: Rebuttal: Thank you for your thoughtful comments, which prompt us to improve our manuscript. Q1: What is the benefit of eliminating the weaker ones rather than keeping them around? how did you come up with the threshold of 60\% to remove? can you learn this threshold automatically? A: The benefit ...	Summary: This paper studies how to estimate LLMs' performance ranking without human preference annotations. In particular, it proposes to leverage three metrics (PEN, CIN, LIS) to evaluate the estimation quality, gives an estimation mechanism that first asks a list of LLMs (called "reviewers") to rank pairwise answers ...	Rebuttal 1: Rebuttal: Thank you for your very detailed comments and suggestions for improvement. And we also appreciate your recognition of our interesting idea and assumption. Next, we will answer all your questions one by one. **Q1: Unclear implication of ground truth ranking: What is the physical meaning of th...	Summary: In this paper, the authors propose a more reliable evaluation system to rank the abilities of different large language models (LLMs). Previous evaluation methods typically suffer from two main drawbacks: (1) benchmark data leakage and (2) cost-intensive and potentially biased human evaluations. To address thes...	Rebuttal 1: Rebuttal: We appreciate your detailed comments. We would like to address your concerns below. **Q1: Reliance on Consistency Assumption: The effectiveness of the method relies on the consistency assumption. However, a natural concern is, "Does this assumption always hold true in practice?" I suggest the aut...	Summary: The highlight of the work is the proposed method of evaluating Large Language Models without relying on human feedback. Strengths: - The proposed evaluation method is a novel attempt of automating the LLM improvement process. Such method worth further exploration. It could be adapted to many of the LLMs and p...	Rebuttal 1: Rebuttal: Thank you for your recognition of our interesting observations, good presentation, and great visualization. And now we will explain and discuss the questions you raised. Q1: How do you define unlabeled questions in line76, as in to what extend is a question unlabeled? A: The "unlabel...	Rebuttal 1: Rebuttal: ## To All Reviewers We thank all reviewers for their thoughtful and constructive comments on our works. We have responded to all reviewers' comments and uploaded an additional rebuttal material (PDF). The PDF includes four figures about, learning stability (Figure R1 for Review \#8Eov), weight non...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning Formal Mathematics From Intrinsic Motivation	Accept (oral)	Summary: This work targets the problem of formal theorem-proving, in the particular setting where a learning agent starts only from axioms (contrary to most other works which use models trained on a plethora of mathematics data already) similar to an AlphaZero style setup. They use a single neural model (transformer) t...	Rebuttal 1: Rebuttal: We thank the reviewer for the through assessment of our work, and the comment on the significance of our research question! We provide responses below, and additional examples in the global response. > Can you report numbers measuring problem difficulty as length of proofs? Can you include exampl...	Summary: The authors propose a novel approach to formal theorem proving (FTP) that leverages a language model's self-improvement capabilities by framing mathematical theorem proving as a sequential game involving generating a conjecture to be proven, and then proving it, and so forth. The approach consists of two prima...	Rebuttal 1: Rebuttal: Thank you for the through review and encouraging comments! We agree that several of the clarifications you requested should be addressed in the paper, and will do so (including the additional examples in the global response). > There is a substantial body of work on the self-improvement of LLMs t...	Summary: This paper proposes to create mathematical agents by jointly learning to posing challenging problems (conjecturing) and solving the problems (theorem proving). Specifically, they use a randomly initialized Transformer to perform both conjecturing and proof search in a loop. Strengths: The proposed method is d...	Rebuttal 1: Rebuttal: We thank the reviewer for the encouraging review of our work! We provide clarifications below, and would be happy to answer any questions that remain. > Further justification of using log-likelihood as the main evaluation in this paper. - It can be seen as a sort of continuous version of the suc...	Summary: The authors investigate a novel setting for ML-based formal theorem proving, where the proving agent in addition to learning to prove theorems at the same time learns to propose conjectures. The process is composed into a self-improving feedback loop that starts just from axioms and continues to prove increasi...	Rebuttal 1: Rebuttal: We thank the reviewer for the through and encouraging assessment of our work! We provide clarifications below, and would be happy to answer any questions that remain. > How the conjecture generation is conditioned on the difficulty level? Once we attempt to prove a conjecture, we obtain a diffic...	Rebuttal 1: Rebuttal: We thank all reviewers for the detailed evaluations of our submission. We appreciate the general comments on the novelty of our conjecturing and proving loop. One common request was to show more examples of conjectures and proofs as they evolve during training. We provide examples here (all *sampl...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper presents a new method for training LLM agents in mathematical reasoning, starting only from basic axioms. The main idea is to make the agent learn two skills together: coming up with hard math problems (conjecturing) and solving them (theorem proving). The authors use a language model to do both tas...	Rebuttal 1: Rebuttal: We thank the reviewer for the through review and encouraging comments about our work, especially about not relying on human-made examples. We answer the questions below, but are happy to engage further. > The current approach does not accumulate a growing library of proven theorems, which limits ...	null	null	null	null	null	null
Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits	Accept (poster)	Summary: The paper addresses causal bandits problem without prior knowledge of causal graph and in the presence of latent variables. Previous work shows that the best possible interventions form a set known as POMISs. The paper theoretically demonstrates that a partial discovery of the causal graph is sufficient to fin...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed review and comments. Below we address the reviewer’s concerns: Proposed regret bound can become very large when $\epsilon$ , $\gamma$ is small. How is it possible to know $\epsilon$ , $\gamma$ in-advance? We agree with the reviewer ...	Summary: The authors extend the methodology in Lee and Bareinboim [2018] (L&B) for regret minimisation in a causal bandit setting with initially unknown causal structure and possible latent confounding. They identify the necessary and sufficient structural knowledge of latent confounding to identify possibly optimal mi...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed review and comments. Below we address the reviewer’s concerns: **(Latent structure assumption) Role and how restrictive it is? Can these assumptions be relaxed , say if latent confounders are allowed to confound up to n (e.g., 3) observed va...	Summary: This paper ooks into the problem of causal bandits without graph information and with unobserved variables. The paper presents both graph learning algorithms and causal bandit algorithms. Strengths: This paper tackles the novel problem of causal bandits (CB) with unknown graphs and unobserved variables. Theor...	Rebuttal 1: Rebuttal: We would like to thank the reviewer for their detailed review and comments. Below we address the reviewer’s concerns: Transitive reduction forces $\mathcal{G}$ to be connected? Transitive reduction of a DAG $\mathcal{G}$ is another DAG with the same transitive closure as $\mathcal{G}$ but ...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Disentangled Representation Learning in Non-Markovian Causal Systems	Accept (poster)	Summary: This paper attempts to disentangle latent variables from high-dimensional observational data (e.g., EEG data) as much as possible, based on knowledge of the causal structure between latent factors, high-dimensional observational data obtained by different interventions in several domains, and knowledge of the ...	Rebuttal 1: Rebuttal: > W#1 “The practicality of the proposed method is questionable…” We address this question directly in Appendix I. FAQ Q2 (pg 46) and provide some details in author’s rebuttal section II. Firstly, expert knowledge can help infer causal structures. For instance, in Appendix D.1 (pg 31), it's ...	Summary: The authors propose a method called CRID for Disentangled Representation Learning in non-Markovian Causal settings (i.e. the latent variables are dependent and there might be unobserved confounders). The two sets of variables A and B are understood to be disentangled if the learned representations of A are a f...	Rebuttal 1: Rebuttal: ## New Experiment Details The images are a modification of the popular MNIST dataset with a bar and color added (to both the digit and the bar). Each digit in the original database contains many examples with different writing styles. The goal is to demonstrate that a disentangled representation ...	Summary: This paper tackles the problem of causal disentangled representation learning in non-Markovian causal systems, which include (1) unobserved confounding and (2) arbitrary distributions from various domains. The authors introduce new graphical criteria for disentanglement and an algorithm to map which latent var...	Rebuttal 1: Rebuttal: > W#1 “There is a gap …experiments” We want to clarify that the proposed work aims to identify if a learned representation will exhibit disentanglement. This is a separate question from estimating the disentangled representations, and using said representations in a downstream task, s...	Summary: This paper extends the previous work on learning disentangled representation from heterogeneous observational data with known hidden structures (e.g., Causal Component Analysis). Unlike the previous work assuming Markov conditions, the proposed theory can deal with non-Markovian causal systems. The identifiabi...	Rebuttal 1: Rebuttal: We thank the reviewer for the feedback. We feel that a few misreadings of our work make the evaluation overly harsh, and hope you can reconsider the paper based on the clarifications provided below. ## W#1 > W#1 (cont) “I'm not completely sure about the significance of the theoretical results, es...	Rebuttal 1: Rebuttal: We thank the reviewers for providing valuable feedback. We address common questions here. ## I. Novelty and value? (XxiF, j7y2, fxDa) As illustrated in Table 1 in the manuscript (L50-64) and Fig. 1 in pdf rebuttal, the literature can be interpreted through the following axes: (1) input assumptio...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This study considers the problem of causal representation learning in the presence of confounding, a direction that wasn’t previously explored. This translates to finding the unmixing function and disentangling a set of target variables from another given the knowledge of the latent causal graph and an input s...	Rebuttal 1: Rebuttal: > W#1 “The contributions seem a bit overstated…” Thank you for pointing out additional relevant works. Broadly, please refer to Section I in the author’s rebuttal and Fig.1 in the pdf for a global view of this setting. Specifically, we have added a comparison of these papers [1-4] in the ...	null	null	null	null	null	null
MoVA: Adapting Mixture of Vision Experts to Multimodal Context	Accept (poster)	Summary: The paper proposes a MoVA to extract and fuse task-specific knowledge from different visual encoder. Besides, the paper designs a coarse-to-fine mechanism, which dynamically selects the most suitable vision experts, then extracts and fuses task-specific knowledge from various experts. The experimental results ...	Rebuttal 1: Rebuttal: Dear Reviewer FVeU, Thanks for appreciating our work and your advice. We will address your concerns below. Q1: The number of components in line 102 does not match the serial numbers. There are four components in MoVA. We will fix this typo in the revised version. **Q2: The process involves...	Summary: This paper proposes MoVA, a powerful and novel MLLM, adaptively routing and fusing task-specific vision experts with a coarse-to-fine mechanism. MoVA first leverages the tool-use capabilities of LLM, routing the most appropriate experts from expert candidates and then uses the MoV-Adapter module to enhance the...	Rebuttal 1: Rebuttal: Dear reviewer gqRq, Thanks for your comments. We will address your concerns below. Q1: Is there an adapter structure in MoVA? Why do we call it MoV-Adapter? Similar to conventional MLLMs, MoVA comprises a pretrained LLM, a base vision encoder, multiple vision experts, and a projector that c...	Summary: This paper considers a critical issue in vision language models, i.e., the vision encoder design. The authors proposes MoVA, a coarse to fine routing strategy, adaptively routing and fusing task specific vision experts which best suit the task. Experiments conducted on a wide range of MLLM benchmark demonstrat...	Rebuttal 1: Rebuttal: Dear Reviewer dPiw, Thanks for giving so many constructive suggestions for our paper, I will clarify the settings. Q1: The rationality of using task-specific vision models. 1. We have conducted extensive experiments to benchmark different vision encoders in Table 1. We find a single CLIP en...	Summary: In this paper, the authors propose MoVA, a powerful MLLM composed of coarse-grained context-aware expert routing and fine-grained expert fusion with MoV-Adapter. Based on multimodal context and model expertise, MoVA fully leverages representation from multiple context-relevant vision encoder experts flexibly a...	Rebuttal 1: Rebuttal: Dear Reviewer Lntx, Thank you for appreciating our approach. We will address your concerns below. Q1: Leveraging multiple visual encoders in MLLM design does not introduce novelty. Additionally, the design of MoV-Adapter also lacks innovation. Leveraging multiple visual encoders is not our ...	Rebuttal 1: Rebuttal: Global response: We would like to extend our sincere gratitude to all reviewers for devoting their valuable time and expertise to reviewing our paper. We are delighted to hear that the reviewers have generally acknowledged and appreciated the contributions made in our work, which include 1. Clea...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks	Accept (poster)	Summary: The authors calculate the likelihood ratio (LR) norm (=E_Q[L^2]), of the LR L, for a class of non-gaussian distributions. In particular they show in their Theorem 2 that LR norm diverges as n=d^c for c>1, where d is the dimension and n is the sample size. Note that LR diverges when the distributions are disti...	Rebuttal 1: Rebuttal: We thank you for your comments. We hope that our response can answer your questions; if so, we would appreciate it if you could revisit the rating of the paper. If not, we are happy to give further explanations during the discussion period. First we would like to address a possible source of conf...	Summary: The paper develops a likelihood ratio (LR) for a LR test to distinguish between two distributions, a "spiked" model and a Gaussian one related to higher order cumulants. Using the LR, the authors prove that in an ideal scenario the minimum number of samples needed to distinguish the two distributions is linear...	Rebuttal 1: Rebuttal: We thank you for the very positive feedback, and for carefully reading the manuscript. Your summary perfectly captures the main ideas of our paper, and we thank you for pointing out the typo. --- Rebuttal Comment 1.1: Comment: Having given a re-read of the paper, reading the other reviews, and y...	Summary: The paper provides an in-depth examination of the efficiency of neural networks in extracting features from higher-order cumulants in high-dimensional datasets. Higher-order cumulants, which quantify non-Gaussian correlations among multiple variables, are crucial for neural network performance. The study focus...	Rebuttal 1: Rebuttal: We thank you for your useful comments and questions that will undoubtedly help to improve the paper. We will now address all the points one by one. > Assumption of a Rademacher prior for the special direction u. Following your suggestion, we will move the discussion of the prior on $u$ that is...	Summary: The authors study the computational-to-statistical gap when learning from higher-order cumulants. In particular, they consider binary classification of the “spike cumulant model” introduced in [38]: inputs in both classes are drawn from a standard Gaussian distribution and are distinguished only by the additio...	Rebuttal 1: Rebuttal: We thank you for your kind comments and the useful suggestions that will certainly improve the quality of the paper, and in particular our presentation of our experimental results. We reply to each suggestion and question in the following. ### Experimental setting In a nutshell, the idea behind ...	Rebuttal 1: Rebuttal: # Global response We thank the area chair and the reviewers for their for their time spent reviewing our work, where we analyse the difficulty of learning from higher-order data correlations from the statistical point of view (via the likelihood ratio, LR), the computational point of view (via th...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context	Accept (poster)	Summary: This paper investigates the ICL abilities of transformers for learning nonlinear function classes. The authors study transformers with nonlinear MLP layers and demonstrate that such transformers can efficiently learn single-index target functions in-context with a prompt length dependent on the function's low-...	Rebuttal 1: Rebuttal: We thank the reviewer for the helpful feedback. We address the technical concerns below. --- "pre-training algorithm involves setups that deviate from standard LLM training approaches" We agree that Algorithm 1 deviates from the most standard training procedure in practice. We make the fol...	Summary: This work studies in-context learning on nonlinear functions, which are Hermite polynomials, using Transformers with linear attention and nonlinear MLP. When the nonlinear target function depends on a low-rank dimension, the ICL risk trained by gradient descent is proven to be a function of the rank. The proof...	Rebuttal 1: Rebuttal: We are thankful for your helpful comments. We address the concerns and answer the questions below. --- "The theoretical result seems not to be tight." Note that although we have an MLP block in our architecture, it is not trainable during the inference time. Indeed a sample complexity of $...	Summary: * The authors theoretically study the capacity for (nonlinear) transformer models to perform in-context learning with a low-complexity function class. * The set-up involves pretraining transformers on sequences encoding input-output examples from a reduced-rank Gaussian single-index model. * The use of G...	Rebuttal 1: Rebuttal: We thank the reviewer for the thoughtful comment and constructive feedback. We address the technical concerns below. --- Potentially misleading baseline and prior works Thank you for the insightful comments. Indeed there are empirical examples of transformers achieving better statistical ef...	Summary: This work shows that a single layer transformer can learn low dimensional structure in pretraining, and use it to learn a single index function in-context with a number of samples that scales in the size of the low-dimensional structure. Algorithm: The MLP layer is trained for one step of GD and the attention ...	Rebuttal 1: Rebuttal: We thank the reviewer for the helpful feedback. We address the technical concerns below. --- Nonstandard architecture and training procedure We agree that Algorithm 1 deviates from the most standard training procedure in practice. We make the following remarks on our architecture and optim...	Rebuttal 1: Rebuttal: Dear Reviewers and Area Chair, We appreciate your time and effort to provide detailed comments on our paper. In response to the suggestions and comments on the experimental verification, we conducted the following experiments (the results of which are reported in the attached .pdf). --- First, ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
BAKU: An Efficient Transformer for Multi-Task Policy Learning	Accept (poster)	Summary: The paper combines a series of architectural modifications made by prior work in offline imitation learning and shows that the new, composite architecture performs well in multi-task simulated settings and in data scarce, real-world robotics scenarios. Comprehensive ablations show the impact of each design cho...	Rebuttal 1: Rebuttal: We thank you for your positive response to our paper and we are glad that you like the paper writing and the results. We provide clarifications to your questions below. “No variance or confidence intervals reported”: We have included the results for multiple seeds with standard deviation in ...	Summary: This paper presents a model for multi-task behavior cloning. The paper analysis several different existing architectural options across three simulated environments to find the best options. The best model is then tested in the real world across a series of tasks on real robots. Strengths: The paper tests o...	Rebuttal 1: Rebuttal: We thank you for your positive response to our paper and we are glad that you found the paper clear and understandable. We provide clarifications to your questions below. “... more extensive experiments … real world”: We include a new experiment studying the performance of BAKU on long-horiz...	Summary: This work comprehensively investigates the architecture for multi-task policy learning on robotics manipulation tasks. Considering imitation learning with a limited number of demonstrations, the authors study the design choice of the network architecture, including encoders for observation with multi-modality,...	Rebuttal 1: Rebuttal: We thank you for your insightful comments about the paper and we are glad that you found the paper easy to understand and the experiments extensive. We provide clarifications to your questions below. “... combination of existing architecture/design choices”: You are correct that we try to fi...	Summary: The paper presents a novel transformer architecture, BAKU, designed for efficient multi-task policy learning in robotics. BAKU consists of three components: sensory encoders, an observation trunk, and an action head. According to the experimental results, BAKU is showcased to be effective in both simulated and...	Rebuttal 1: Rebuttal: We thank you for your comments about the paper and we are glad that you found the paper well organized and the experiments extensive. We provide clarifications to your questions below. “... novelty of the algorithm … limited”: We have addressed the novelty of the algorithm in the global resp...	Rebuttal 1: Rebuttal: We thank the reviewers for their constructive feedback. The reviewers have requested important clarifications and additional experiments. We have provided these as a detailed response to each review along with a summary of some shared concerns in this global response. **Concerns about novelty (Re...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper presents a multitask BC model for robot learning. They systematically compare several components of recently proposed BC models and combines their best aspects into a common architecture. Key design choices include: observation trunk, model sizes, action heads, goal modalities, action chunking, obse...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed comments about the work. We are glad that you found the study systematic and would like to provide clarifications to the questions below: “The statistical significance of the experiments is questionable”: We have conducted additional experiments and rep...	null	null	null	null	null	null
Automatic Outlier Rectification via Optimal Transport	Accept (poster)	Summary: A framework for regression tasks with outliers (see robust statistics) is introduced, based on an optimal transport cost function. A novel concave cost function directly facilitates the rectification of outliers. Mathematical proofs are given and evaluation is performed on two simpler and one real world task w...	null	Summary: The paper presents a new framework for outlier detection using optimal transport with a concave cost function. Unlike traditional methods that separate outlier detection and estimation, this approach integrates both into a single optimization process, improving efficiency. The concave cost function helps accur...	null	Summary: The paper presents a novel method for outlier rectification in robust statistics. In particular, taking inspiration from distributionally robust optimization (DRO) methods, this paper proposes to extend the formulation to the field of robust statistics. To this end, a rectification set is constructed using opt...	null	Summary: This paper is, essentially, about robust model fitting. In this paper, it is proposed to optimize an optimal transport problem. As loss function, the authors choose concave functions \|\|z-z‘\|\|^r with r \in (0,1). The idea behind choosing this shape of functions is that it allows to move outliers farther due to ...	null	Rebuttal 1: Rebuttal: We sincerely thank all reviewers for their positive feedback and appreciate the effort to review our work. We respond below to individual points. ## fFC9 + Material, neural nets. While there is significant material in our work, we note that other papers at NeurIPS have large appendices as well. A...	NeurIPS_2024_submissions_huggingface	2,024	Summary: A new approach for robust estimation is proposed based inspired by optimal transport. The general approach is introduced and then new estimators are derived for three particular cases : mean estimation, linear fitting and surface regression. The estimators are compared numerically with standard estimators on s...	null	null	null	null	null	null	null
SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning	Accept (poster)	Summary: This paper considers the problem of offline imitation learning from sub-optimal demonstrations. The paper proposes the SPRINQL algorithm, which automatically weights the demonstrations, regularizes the loss with a reference reward function, and then performs inverse soft-Q learning. Experimental results show t...	Rebuttal 1: Rebuttal: We thank the reviewer for carefully reading our paper and for the constructive feedback. --- > In Equation (3), a modified objective is presented. However, it seems that this objective depends largely on the quality of weights, and it does not necessarily recover the ground truth reward functio...	Summary: This paper considers the problem of imitation learning on suboptimal demonstrations given coarse trajectory expertise rankings. It introduces SPRINQL, a method combining inverse soft-Q learning, conservative Q learning, and reward learning from ranked trajectory preferences. SPRINQL estimates a reference rewar...	Rebuttal 1: Rebuttal: We thank the reviewer for reading our paper and the positive feedback. --- > Suboptimal trajectories are sourced from adding random noise to expert actions. While this is a reasonable proxy, it would be more convincing to see them generated from under-trained RL policies instead. How does SPRIN...	Summary: This paper presents SPRINQL (Sub-oPtimal demonstrations driven Reward regularized INverse soft Q Learning), a novel algorithm for offline imitation learning with multiple levels of expert demonstrations. SPRINQL leverages distribution matching and reward regularization to effectively leverage the mixture of no...	Rebuttal 1: Rebuttal: We thank the reviewer for the positive feedback and the insightful comments. --- > The authors mention that this setting (having several sets of data labeled with relative quality scores) is more general than two quality levels: expert and sub-optimal. Could the authors provide further support f...	Summary: This work presents an offline imitation learning (IL) method that leverages both expert and suboptimal demonstrations. Distinct from previous methods, this approach assumes access to suboptimal demonstrations across a spectrum of performance levels, with a known ranking to the learners. To utilizes suboptimal ...	Rebuttal 1: Rebuttal: We thank the reviewer for carefully reading our paper and providing us with constructive feedback. --- > Significant concerns with this submission are the questionable reporting on the performance of baseline methods and issues related to reproducibility. The experimental results presented are ...	Rebuttal 1: Rebuttal: We thank the reviewers for carefully reading our paper and providing constructive feedback. We have made efforts to address all the points raised. Please find here, some key major points we wish to emphasize: Comparison to DWBC, DemoDICE, TRAIL: Our key contribution is an offline imitation le...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation	Accept (poster)	Summary: Authors introduce a method that overcomes the forward method to generate "new and up to date latent representations" of a chest X-ray using a diffusion based model. It addresses an important question in healthcare regarding differing time scales (EHR and CXRs) and how to leverage information from the EHR to in...	Rebuttal 1: Rebuttal: Thank you for your insightful review and positive feedback on our paper. As suggested, we will include relevant discussions and citations on cross attention for EHR and change all "PRAUC" to "AUPRC" for consistency in the final version. We hope the following responses address your concerns. --- *...	Summary: The paper aims to address the asynchronicity problem in multimodal clinical data, such as EHR and CXR, for predicting clinical tasks. The asynchronicity in multimodal clinical data comes from the CXR, which is generally taken with a much longer interval compared to the continually collected EHR. The authors pr...	Rebuttal 1: Rebuttal: Thank you for your insightful review and positive feedback on our paper. We hope the following responses address your concerns. --- Weaknesses: Clinical usage is limited. When the time interval is longer than 36 hours, the best clinical practice would be to take a new CXR. Thank you for the...	Summary: The paper proposes a new method, DDL-CXR, to generate chest X-ray images that address asynchronicity in multi-modal clinical data for predictive tasks. The authors leverage latent diffusion models for patient-specific generation, conditioned on a previous CXR image and EHR time series. The experiments using MI...	Rebuttal 1: Rebuttal: Thank you for your constructive comments. We hope the following responses address your concerns. --- Weakness 1: Related works leveraging the temporal structure of images and the claim of contribution. The mentioned paper (BioViL-T) is indeed relevant. BioViL-T learns spatiotemporal features...	Summary: The paper proposes a Diffusion-based Dynamic Latent Chest X-ray Image Generation (DDL-CXR) to address the asynchronicity in multimodal clinical data fusion. The primary objective is to generate up-to-date latent representations of chest X-ray images based on existing X-ray data and EHR. The method leverages la...	Rebuttal 1: Rebuttal: Thank you for your comments and insights. We hope the following responses address your concerns. --- 1. Description of latent diffusion model could be more precise. The latent diffusion model (LDM) used in this work has the same architecture as the original LDM paper. Due to space limit, i...	Rebuttal 1: Rebuttal: We sincerely appreciate all reviewers for their valuable comments and constructive suggestions. We are glad that reviewers appreciated the new method for clinical multimodal fusion via generative models to address asynchronicity. We summarize the strengths from the reviewers as follows: 1. I...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation	Accept (poster)	Summary: This paper studies the problem of dataset valuation, a variant of data valuation, where the contributions of multiple datasets, instead of single data points, to a metric such as performance of a machine learning prediction model are quantified. The authors analyze the computation of the Shapley value, where e...	Rebuttal 1: Rebuttal: We thank the anonymous reviewer for the remarks and questions. They are all addressed in detail in the following. 1. The baselines in the experimental evaluation of the Shapley value approximation are limited. Please remark in Table 2 that we have also considered KNN-Shapley and LOO. In addit...	Summary: The paper proposes a novel proxy for the Shapley value (SV) which is called discrete uniform (DU) Shapley. Unlike the SV, this proxy does not suffer from the exponential complexity problem of the SV. Of course the DU Shapley, therein does not capture all information entailed in the SVs for a cooperative game. ...	Rebuttal 1: Rebuttal: We thank the anonymous reviewer for the remarks, questions, and suggestion. They are all addressed in detail in the following. 1. Weak Baselines. We thank the reviewer for the references. We have conducted experiments concerning the theoretical probability at which SVARM can guarantee an erro...	Summary: The paper studies dataset evaluation with Shapley value. They assume that each dataset contains i.i.d. data points from a distribution and exploit the knowledge about the structure of the underlying data distribution to design more efficient Shapley value estimators. Based on asymptotic analysis of Shapley val...	Rebuttal 1: Rebuttal: We thank the anonymous reviewer for the remarks and questions. They are all addressed in detail in the following. 1. Concerning the computational efficiency improvements of our method, we have conducted new experiments. Please refer to the general answer for the results. 2. **Distributional ...	Summary: This paper proposes a general dataset valuation method which only requires N number of utility function evaluations, where N is the number of players. The approach is motivated by a homogeneous setting for linear regression, but experiments also show the effectiveness of such an approach in general settings. ...	Rebuttal 1: Rebuttal: We thank the anonymous reviewer for the remarks and questions. 1. Regarding the computational complexity discussion, please refer to the general answer. 2. Regaring your question, the MC estimator used to estimate the Shapley values is the one presented in Equation (6), also coined DataS...	Rebuttal 1: Rebuttal: We thank the anonymous reviewers for the useful reviews. As the question concerning the complexity of computing the Shapley value of all players arose several times, we address it as a general reply. We provide several answers. a) Approximation schemes such as Monte Carlo (DataShapley), DU-Shaple...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Training Compute-Optimal Protein Language Models	Accept (spotlight)	Summary: The main objective of the paper is to explore the scalability of Protein Language Models (PLMs) and to provide models to determine the optimal number of parameters, pre-training dataset size, and transferability between pre-training tasks as functions of the available computational budget. The study contains ...	Rebuttal 1: Rebuttal: About the Weakness-1. Thank you for your valuable feedback. Based on your suggestion, we will adjust the x-axis range and include the trained maximum models for 1022 FLOPs, namely MLM 10.7B and CLM 7.2B in section 5. About the Weakness-3: the evaluation of protein generation.** We explai...	Summary: The authors fit scaling laws to determine how to train protein language models compute-optimally, for both causal LM and masked LM tasks. They also consider a setup to consider possible effective data transfer when training on one task and transfer training on the other. Finally, they use their derived scaling...	Rebuttal 1: Rebuttal: About the Question-1: the re-organization of the paper. We appreciate your insights and have taken them into consideration for our revisions. Although we cannot directly modify the paper PDF during the rebuttal period, we demonstrate how we will reorganize and improve the writing style of our ...	Summary: In this work authors propose to explore training compute-optimal protein language models and aim to derive scaling laws. Specifically, they (i) explore new pre-training data for protein language models (ii) derive scaling laws for protein language models using these datasets for both Masked Language Modeling a...	Rebuttal 1: Rebuttal: About the Weakness-1: the lack of discussions. Thank you for your insightful suggestions. We will make the following modifications to significantly enhance the clarity and impact of our manuscript. - Adding conclusions or takeaway findings at the end of each main section. - Moving the detaile...	Summary: This paper studies scaling law for two types of protein language models: masked language modelling and causal modelling. The paper finds that both types scale sublinearly but the coefficients of the scaling law are very different. Based on the derived scaling law, the paper then reevaluates the allocation of r...	Rebuttal 1: Rebuttal: About Weakness-1: the protein understanding tasks and missing experimental details. We have provided a comprehensive task performance comparison across 8 protein understanding benchmarks, as adopted from the protein understanding benchmarks available at biomap-research on Hugging Face. These b...	Rebuttal 1: Rebuttal: Dear Reviewers, Thank you for your valuable feedback on our manuscript. We have taken your comments seriously and have made several important revisions to enhance the quality and readability of our work. Below is a summary of the key changes: ### Additional Experiments - **Expanded Understandin...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination	Accept (poster)	Summary: The paper introduces a post-processing method using a joint classifier-discriminator model to imporve the generation quality of consistency models. This model is trained adversarially, with the robust classifier providing gradient based on class assignment and the discriminator score. This post-processing meth...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed review and insightful questions. These constructive comments provide valuable perspectives to enhance our work. Additionally, we are grateful for your recognition of the efficiency of our method. Below, we address the reviewer’s specific concerns: >Althoug...	Summary: This paper proposes a post-processing method to improve the generated image quality of consistency models. The method mainly includes a joint classifier-discriminator, which contains an adversarial training objective and a gradual training approach. Experimental results show the effectiveness of the proposed m...	Rebuttal 1: Rebuttal: Dear reviewer, we sincerely appreciate the time and effort you dedicated to reviewing our paper. Your thoughtful and constructive feedback has been invaluable in highlighting the strengths of our work. We address your concerns below: >The evaluations are limited. Only ImageNet at resolution 64x64...	Summary: The paper proposes a method to mitigate the quality degradation of images generated by consistency-based generative models compared to diffusion models. The main idea is to adapt the generated image based on the logits of an adversarial real/synthetic image discriminator to maximize the image resemblance to...	Rebuttal 1: Rebuttal: Dear reviewer, we sincerely appreciate the time and effort you dedicated reviewing our paper. Your thoughtful and constructive feedback has been invaluable in highlighting the strengths of our work. Your insightful reviews and acknowledgment of our contributions inspire us to continue refining our...	Summary: The manuscript proposes a method for refining consistency-based image generation using a joint-trained classifier-discriminator. The method helps to improve the quality of the fast-sampling consistency models. Strengths: 1. The paper is well written 2. the performance is significant Weaknesses: 1. The motiv...	Rebuttal 1: Rebuttal: We thank the reviewer for the detailed review and insightful questions. These constructive comments provide valuable perspectives to enhance our work. Additionally, we are grateful for your recognition of the significant performance of our method. Below, we address the reviewer’s specific concern...	Rebuttal 1: Rebuttal: We appreciate the reviewers' constructive feedback and their recognition of our method's novelty and simplicity (R2), as well as its potential for integration into various generative models (R2, R3, R4). We have carefully considered the main issues raised by the reviewers and have taken steps to a...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
The Mamba in the Llama: Distilling and Accelerating Hybrid Models	Accept (poster)	Summary: The paper focuses on distilling transformers into SSMs for fast inference after training. Specifically, the authors initialize states of SSMs by pretrained transformer components and utilize speculative decoding for multi-step predictions. Experiments show that the distilled hybrid model can achieve competitiv...	Rebuttal 1: Rebuttal: Thanks for your comments! Please find our responses below. $\mathcal{Q}.$ In Section 2.2, Why is A set as fixed over time? $\mathcal{A}.$ The equation after line 92 has a fixed-over-time $A$ because it is the continuous SSM, which is consistent with the Mamba paper. However, after discretizati...	Summary: The paper presents a mechanism to distill from llama to mamba. Strengths: (1) Good paper writing with clear figures. (2) The method is easy to understand. (3) Good experiment design on speculative decoding and other distillation strategy. (4) Good performance (Table 1, 2) Weaknesses: The experiments are a bi...	Rebuttal 1: Rebuttal: We sincerely appreciate your feedback. Please see our responses below. $\mathcal{Q}.$ The experiments are a bit limited, only performed on chat corpora. Further data mixture is needed to understand the effectiveness of the approach $\mathcal{A}.$ Please refer to 2 in general response where we ho...	Summary: The paper proposes a way to convert a Transformer architecture to a Mamba architecture by first initializing a Mamba layer based on a pre-trained attention layer in order to facilitate faster convergence followed by knowledge distillation from the Transformer to the Mamba model. The training procedure only inv...	Rebuttal 1: Rebuttal: Thank you very much for your comments! We kindly ask you to find our responses below. $\mathcal{Q}.$ No comparison is performed with random initialization for task specific performance. $\mathcal{A}.$ We added a comparison with random initialization. Please refer to 3a in the general response. ...	null	null	Rebuttal 1: Rebuttal: We appreciate each reviewer's insightful feedback. The excellent feedback has helped us improve our work and obtain new experimental results. We hope these clarify the main questions from the reviews. 1. Scaling up the traning data to 20B tokens, adding Llama-3, 1/8 attention: We include new ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Protecting Your LLMs with Information Bottleneck	Accept (poster)	Summary: This work proposes IBProtector, the first defense against LLM jailbreak attacks based on the IB principle, which aims to extract minial and sufficient relevance information necessary for downstream response task. Several experiments show that this method has high effectiveness and adaptability without requirin...	Rebuttal 1: Rebuttal: Dear Reviewer, We sincerely appreciate your positive feedback and encouraging comments on our paper. In our experiments, we similarly found out why the llama replication is inconsistent, which is the reason for the experimental setup. As mentioned in line 569, the template for each model uses Fas...	Summary: This paper introduces the IBProtector, a novel defense mechanism designed to safeguard large language models (LLMs) against jailbreak attacks. Grounded in the information bottleneck principle, IBProtector compresses and perturbs adversarial prompts using a lightweight, trainable extractor, ensuring that only e...	Rebuttal 1: Rebuttal: Dear Reviewer, We thank the reviewer for the detailed constructive feedback on our work and answer the questions below: 1. Defending against jailbreak attacks uses only benign words. Response: Thanks for your constructive comment. It is important to note that PAP is not entirely comp...	Summary: This paper proposes a defense against jailbreak attacks on large language models (LLMs) using the principle of information bottleneck. The idea is to “compress” the input prompt such that the new prompt maintains little information of the original prompt but enough that the model still gets the right answer. ...	Rebuttal 1: Rebuttal: Dear Reviewer, Thank you for your insightful suggestions. We answer the questions below: 1. Training set of the baselines. (Some misunderstandings) Response: For training data, we are definitely setting the SAME. In addition, the test dataset is DIFFERENT from training data. The firs...	Summary: This paper introduces a defense mechanism based on the Information Bottleneck (IB) principle, i.e., IBProtector. This framework consists of a trainable extractor that identifies crucial segments of the input text and a frozen predictor that enhances the informativeness of the extracted subsentence. A challenge...	Rebuttal 1: Rebuttal: Dear Reviewer, We greatly appreciate your insightful comments! Here are our responses to the comments. 1. Potential Bias. Response: Thanks for pointing this question out. The low-entropy problem comes from minimizing the mutual information term $I(X; X_{sub})=H(X_{sub})-H(X_{sub}\|X)$...	Rebuttal 1: Rebuttal: ## General Response Dear AC and Reviewers, We would like to sincerely appreciate the reviewers for their positive feedback and highly constructive comments. To improve the clarity and readability of the paper, the following changes have been made and the manuscript will be revised accordingly in...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs	Accept (poster)	Summary: This paper proposed a Domain Shift diffusion-based SR model, namely DoSSR, which capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images. In addition, authors advance their method by transit...	Rebuttal 1: Rebuttal: Q1: Novelty against ResShift A1: Conceptually, our method is similar to ResShift, as both involve the design of a diffusion bridge between the LR and HR images. The main differences between the two diffusion schemes are that ours are carefully designed in order to better leverage the g...	Summary: The existing diffusion-based image restoration models do not fully exploit the generative prior of the pre-trained diffusion models and produce high-quality images starting from Gaussian noise, leading to inefficiency. To address these issues, the paper a Domain Shift diffusion-based SR model (DoSSR) which can...	Rebuttal 1: Rebuttal: Q1: Overall model structure and implementation details A1: Thanks for the valuable suggestion! Please find the figure and descriptions about detailed model structure in the attached PDF. Also, Figure 2(a)(b) in the main text shows training and inference pipelines and may help better und...	Summary: This paper proposes a Domain Shift diffusion-based SR model, named DoSSR, that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images. Introducing the SDEs to describe the process of domain...	Rebuttal 1: Rebuttal: Q1: Regarding the reference index. A1: For real-world image super-resolution tasks, using reference metrics is not always an effective way to measure the quality of the generated results, as the 'groundtruth' image is merely one of many plausible outcomes. For methods based on diffusion ...	Summary: This paper presents DoSSR, a novel framework for diffusion-based image super-resolution that aims to balance efficiency and performance. A domain shift equation that integrates with existing diffusion models, allowing inference to start from low-resolution images while leveraging pretrained diffusion priors. A...	Rebuttal 1: Rebuttal: Q1: Regarding interpolation between LR and HR. A1: We want to clarify the necessity of interpolation between LR and HR from the following aspects. 1. _HR is necessary: All diffusion-based SR method consturct training samples using HR images._ This is because that our objective is to ...	Rebuttal 1: Rebuttal: # General Response We thank all reviewers for their positive feedback: solid theoretical foundation (Reviewer 7zor), outperforming existing methods on multiple benchmarks (Reviewer QMdK), better performance using few evaluation steps (Reviewer tfJj), and well-organized, clear to understand (Revie...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Graph Edit Distance with General Costs Using Neural Set Divergence	Accept (poster)	Summary: This paper studies the approximation of non-uniform Graph Edit Distance (GED) using graph neural networks. It considers four types of edit operations with different costs: edge deletion, edge addition, node deletion, and node addition. The paper proposes explicitly accounting for the costs assigned to each typ...	Rebuttal 1: Rebuttal: > differences between 𝑞 and 𝜂 Yes, we apologize for the mistake, $q$ = 1-$\eta$. The notation was redundant; only one is needed -- we will correct it. > some related work [1] is missing. Since NeurIPS allowed including the Appendix with the main paper, we placed the related work there. ...	Summary: This paper proposes GRAPHEDX, an innovative neural model that deftly handles Graph Edit Distance (GED) calculations with customizable edit costs. By representing graphs as rich sets of node and edge embeddings and using a Gumbel-Sinkhorn permutation generator, GRAPHEDX captures the subtle nuances of graph stru...	Rebuttal 1: Rebuttal: > The current model may not fully address richly attributed graphs with complex node and edge features. We did not perform any experiments on node and edge features, but we can encode node feature and edge features in our models. Specifically, node features can be encoded during feature initia...	Summary: This paper proposes GRAPHEDX, a neural model that learns to estimate the graph edit distace among a pair of graphs, not only in the case of equal and symmetric costs for edit operations, but in the case of unequal costs specified for the four edit operations. The core of the proposal represents each graph as a...	Rebuttal 1: Rebuttal: > What is the cost of training and estimation? In Appendices F.11 and F.12, we discuss the computational cost of GED estimation. The following table presents the total training time and the amortized per-graph pair inference time (in milliseconds) for different methods on all dataset. The train...	null	null	Rebuttal 1: Rebuttal: > Evaluation on larger graphs As has been correctly pointed out, obtaining training data for GED is nontrivial for larger graphs, and baselines have often used noisy GED values for supervision. Since our work addresses asymmetric cost-sensitive GED for the first time, we preferred to use exact ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously	Accept (spotlight)	Summary: This paper considers the problem of tracking regret (aka. switching cost). The previous start-of-the-art result has a $\ln T$ dependence on the final regret bound. This paper claims to remove the extra $\ln T$ (more specifically, an $\sqrt{\ln T}$ factor) and achieve the optimal switching regret. Strengths: T...	Rebuttal 1: Rebuttal: Thank you. We appreciate that you found our solution impressive and will improve the presentation of our paper, based on your feedback, as we detail below and in our comments to the other reviewers. “However, neither the reason why this weighted combination…” - We will add a sketch of the proof w...	Summary: This paper provides an algorithm which achieves a strongly-adaptive* switching regret guarantee, removing the logarithmic penalty typically associated with efficient algorithms achieving strongly-adaptive guarantees. --- Update after rebuttal: The results are only for switching regret, not* strongly-adapti...	Rebuttal 1: Rebuttal: Thank you, we appreciate that the importance of our work as a potential breakthrough was noted. We will improve the presentation of our paper, based on your feedback, as we detail below, and in our comments to the other reviewers. “If the result is correct, it seems like it could be a breakthroug...	Summary: This paper studies online convex optimisation under a non-stationary environment. The authors focus on the switching regret, which evaluates the regret on every possible segment of the trials. This paper demonstrates that the additional logarithmic factor $O(\log T)$ shown in previous results can be improved, ...	Rebuttal 1: Rebuttal: Thank you for your review. We will improve the presentation of our paper based on your feedback, as we detail below, and in our comments to the other reviewers. “it would be beneficial for the descriptions of theorems to be more precise...” - All the conditions on $\mathcal{X}$ and the loss funct...	Summary: The paper considers the problem of switching and dynamic regret in online convex optimization (OCO). Given any segmentation of $[T]$, the switching regret is equal to the sum of static regrets on each segment. For the switching regret, the best-known bound obtained by prior works was $\mathcal{O}(\sum_{k}\sqrt...	Rebuttal 1: Rebuttal: We appreciate that the theoretical importance of our results was acknowledged and thank you for your review. “I suggest the authors use appropriate punctuation” - We will ensure appropriate punctuation is used throughout the final camera-ready version of our manuscript. “Minor typo…” - We have f...	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers	Accept (poster)	Summary: In this work, the authors present GCFormer, a new graph transformer (GT) architecture for improved node classification on homophilic and heterophilic graphs. To this end, the authors propose to sample a fixed number of both positive and negative tokens for each node $v$ in the graph and then restrict the atten...	Rebuttal 1: Rebuttal: Thank you for the detailed comments and valuable questions. We provide details to clarify your major concerns. >Q1. I expect the authors to clearly motivate their approach and demonstrate the benefits GCFormer offers. In addition, I hope that the authors can provide supporting evidence to the...	Summary: This paper proposes a new graph Transformers for node classification. Different from previous graph Transformers that only select nodes with high-similarity to construct the token sequences, this paper proposes GCFormer, that considers both high-similairity and low-similarity nodes as tokens. Moreover, GCForme...	Rebuttal 1: Rebuttal: Many thanks for the positive evaluation and insightful questions that help us improve this work. We provide the following detailed responses to your questions. > Q1. I suggest authors add recent representative graph Transformers, such as VCR-Graphormer [1], as baselines to make the experiment...	Summary: This paper proposes a token sequence-based graph transformer method named Graph Contrastive Transformer (GCFormer). They first sample top-$k$ similarity nodes as positive token sequence and regard the rest as negative token sequence. Then, they use transformer to obtain the positive and negative representation...	Rebuttal 1: Rebuttal: We appreciate the reviewer for providing valuable feedback and comments on our paper. Following are our detailed responses to your questions. >Q1. The method is not new to the community and the intuition behind getting node representation by subtracting negative embedding from positive embeddi...	null	null	Rebuttal 1: Rebuttal: Here, we present the experimental results to address the concerns of Reviewer rUu7 and Reviewer pga6. ### Reviewer rUu7 we first report the performance of GCFormer and three representative GCL-based methods for supervised node classification. The results are as follows: \| ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding	Accept (poster)	Summary: The authors develop graphical characterizations and estimation tools to bound the effect of policies given a causal graph and observational data collected in non-identifiable settings. Strengths: Strengths and contributions: (1) derive analytical bounds for general probabilistic and conditional policies that ...	Rebuttal 1: Rebuttal: Thank you for your review. Please consider the following descriptions and explanations to address your question. 1. *”Can the authors highlight the main challenge/novelty of the proposed bound for policy compared to bounds for treatment effect?”* We would be happy to summarize what we consid...	Summary: This paper proposes a partial identification method for the value of policies that intervene on a potentially multivariate set of variables in a known causal graph. It searches for adjustment sets or IVs that apply to one set of variables intervened on conditioned on others. Strengths: Expanding the reach of ...	Rebuttal 1: Rebuttal: Thank you for your review and positive feedback. In the following we hope to address the mentioned weaknesses. Please let us know if we can expand on any of it. 1. ***”It would be valuable to have more intuition about what the method is doing. My heuristic understanding right now is that it is co...	Summary: This paper proposes a new method to partially identify policy effects using a known causal graph and observational data. In theory, the paper derives general bounds for probabilistic and conditional policies, generalizing the existing results on discrete atomic policies. To estimate the bounds, the authors ext...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review and positive assessment of our work, we appreciate the feedback. Please find our response to specific concerns and questions in the following. 1. ***”I think the paper can discuss a bit more about the cases in which the policy effect is identifiable, given a k...	Summary: The paper proposes a new framework that uses structural causal graphs for estimating the effect of policies under unobserved confounding. Their approach first derives tighter analytical bounds on the estimates of the effects of the policies under the structural causal graph setting and the paper uses the resul...	Rebuttal 1: Rebuttal: Thank you for your thoughtful review. The typos and missing / broken references have been corrected, thank you. In the following, we aim to address your concerns point by point. 1. ***“The paper is somewhat hard to follow as the authors of the paper assume readers are familiar with existing notat...	Rebuttal 1: Rebuttal: we thank the reviewers for their time reviewing our work. In this global rebuttal, we include an additional experiment that compares the bounds obtained according to the different propositions in Sec. 3 of the paper. This addresses specifically a comment from Reviewer LyZM. The details of the exp...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Explaining Datasets in Words: Statistical Models with Natural Language Parameters	Accept (poster)	Summary: The authors propose a general framework to generate explanations of different text datasets, by parameterizing the data distribution with textual predicates. The textual predicates, along with their weights, can be viewed as an explanation of the data. Three different tasks are used as examples: clustering, ti...	Rebuttal 1: Rebuttal: Thanks for your review and recommended experiments! ## Summary of Our Response ### Re: pure LLM prompting baseline. - This baseline is similar to our no-refine and no-relax baseline, and we showed that both refinement and relaxation were helpful (Takeaways 1 & 2). - We **directly ran the naïve ...	Summary: This paper proposes to use "natural language predicates" to explain text datasets. Authors develop a model-agnostic algorithm that optimizes continuous relaxations of predicate parameters with gradient descent and discretizes them by prompting language models. With this method, the authors can characterize the...	Rebuttal 1: Rebuttal: Thanks for your thoughtful review. We will - Clarify our motivation to explain datasets - We compared our method to the approach you have recommended (representing clusters with verbs/nouns). We find that natural language predicates can provide much better explanations than individual words. ...	Summary: The paper introduces a a framework for a family of models parameterized by natural language predicates. This models allow for a language-based interpretation of text distribution. Such framework can easily instatiate models include clustering, time-series, and classification models. The authors develop a model...	Rebuttal 1: Rebuttal: Thanks for appreciating the strength of our paper! We will address each of your questions below. ### Checking whether our conclusion generalizes to other embedding models We report the performance of another embedding model, all-mpnet-base-v2, and report the results in Table 1 & 2 in the author ...	null	null	Rebuttal 1: Rebuttal: Thanks to all the reviewers for the feedback! We are glad that overall the reviewers like our formulation, method, and writing. In this response, we supplement new experiments to strengthen our paper. - Comparison against naive prompting (reviewer krnw): while our paper has implicitly compared to...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization	Accept (poster)	Summary: The paper provides new lower bounds for the uniform stability of UD-based and IFT-based algorithms in hyperparameter optimization. Strengths: 1. The general setting is described clearly. 2. The uniform stability lower bounds presented in the paper are novel and clear. Weaknesses: 1. In my view, the technique...	Rebuttal 1: Rebuttal: # Response to Reviewer 3C12 We thank Reviewer 3C12 for the thoughtful and constructive comments. ## W1: Technical challenges of establishing the lower bounds We agree that the instability of gradient-based algorithms on non-convex loss functions is intuitively expected, but rigorously establishing...	Summary: The authors consider gradient-based algorithms for hyperparameter selection. They derive lower stability bounds for UD and IFT-based algorithms that solve this problem. To get this lower bound, the authors introduce the notion of lower-bounded exansion property of the update rule of the hyperparameters. Stren...	Rebuttal 1: Rebuttal: # Response to Reviewer bA9a ## W&Q: Extension of the lower bound analysis to algorithms with warm-starting strategy. We thank Reviewer bA9a for the positive comments and thoughtful questions. We are uncertain about the precise meaning of the "warm-starting strategy" you mentioned, as we do not ...	Summary: This paper studies the generalization bound of the hyper-parameter optimization problem. It provides a uniform lower-bound for the validation argument stability, which proves that the upper-bound of the validation argument stability in existing works is tight. Strengths: The paper is well-written and easy to ...	Rebuttal 1: Rebuttal: # Response to Reviewer NBuR ## W&Q: Interpretation of the current generalization bound w.r.t. validation and training sample sizes. We thank Reviewer NBuR for the insightful question. Intuitively, the expected population risk can be divided into the generalization error and the empirical validat...	null	null	null	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization	Accept (poster)	Summary: The authors study a new framework for learning under distribution shift caused by a family of `invariance maps’ $\mathscr{T}: X \to X$. Informally, one may think of this as generalizing e.g. settings such as learning under invariance to rotation, where the maps may be rotation matrices and the learner should p...	Rebuttal 1: Rebuttal: We thank you for your detailed review. We address your comments below. > When $H$ is unknown, the authors give a similar result in the realizable case using data augmentation, and a weaker variant for the agnostic setting for finite which ensures low error non-uniformly (that is, for each fixe...	Summary: This paper studies a theoretical model of out-of-distribution (OOD) generalization, where the possible distribution shifts are encoded by a collection $\mathcal{T}$ of transformations on the domain. The goal in this setting is given a distribution $D$ to learn a hypothesis $h$ from some hypothesis class $\math...	Rebuttal 1: Rebuttal: We thank you for your detailed review. We clarify below how our work is different from multi-distribution learning, and how the lower bounds on multi-distribution learning do not contradict our upper bound result in Theorem 3.1 / Remark 3.2. > Could you explain the main differences between ...	Summary: The authors investigate transform-invariant binary classification problems in which the learner observes transformed features during the prediction phase and aims to construct a classifier robust against test-time transformations. Their first contribution is the derivation of a generalization error bound on th...	Rebuttal 1: Rebuttal: We thank you for your detailed review. We address your comments below. We emphasize that our contributions are conceptual and theoretical. At the conceptual-level, to the best of our knowledge, this is the first work to model the OOD/distribution shift problem through a transformation function cl...	Summary: The work focuses on theoretical aspects of distribution shift, considering various frameworks that expand the standard PAC model of learning. The paper considers a hypothesis class $\mathcal{H}$ and a class of transformations of the domain $\mathcal{T}$. The learning algorithm is given some data drawn i.i.d. ...	Rebuttal 1: Rebuttal: Thank you for your detailed review. We address one your comments below. > One could argue that one of the most important use cases is when $\mathcal{T}$ form a group of symmetry transformations. As discussed on page 9, in this setting the technique of data augmentation was studied previously. A...	Rebuttal 1: Rebuttal: We thank all the reviewers for their helpful input and feedback. We emphasize that our contributions are conceptual and theoretical. At the conceptual-level, to the best of our knowledge, this is the first work to model the OOD/distribution shift problem through a transformation function class, ...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper proposed a transformation-based invariant learning framework for OOD generalization. The authors initiated a theoretical study for this framework, investigating learning scenarios where the target class of transformations is either known or unknown. Upper bounds on the sample complexity in terms of ...	Rebuttal 1: Rebuttal: Thank you for your detailed review. We address your comments below. > When proposing a new learning rule, it is better to demonstrate its utility by comparing it to existing and similar ones, such as ERM (at least) and DRO (similar). ERM can be viewed as concerned only with the identity transfo...	null	null	null	null	null	null
DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM	Accept (poster)	Summary: This manuscript introduces a new denoising autoencoder for cryo-EM micrographs based on vision transformers (ViTs) which is trained on masked pairs of images with a noise2noise loss function paired combined a reconstruction loss. In addition to the model itself, an important contribution of the work is the cur...	Rebuttal 1: Rebuttal: # Reviewer AE1Z (5) We sincerely thank the reviewer for the detailed review of our work. ## On the Clarification of Details in the Method We truly appreciate your detailed suggestions for our method. Here, we clarify the details in below: 1. Similar to MAE [He K et al, 2022], the mask ratio $\...	Summary: In this paper, the authors introduce DRACO, a Denoising-Reconstruction Autoencoder for Cryogenic Electron Microscopy (cryo-EM) inspired by the Noise2Noise (N2N) approach. DRACO aims to address the high-level noise corruption in cryo-EM images, which are often overlooked by other foundation models in computer v...	Rebuttal 1: Rebuttal: We thank the reviewer for their questions and suggestions on denoising. Based on this, we will further clarify the questions related to denoising baseline selection and metric evaluation. ## On the Choice of Denoising Baselines Noise2Clean baseline. Supervised image denoising methods like No...	Summary: In this paper, the authors introduce an autoencoder-based model to denoise cryo-EM micrographs. By splitting aligned movies into even and odd images, the authors obtain two images with similar signal and two different realizations of noise. Following Noise2Noise and Topaz-Denoise, the denoising target is to pr...	Rebuttal 1: Rebuttal: We appreciate the reviewer's suggestions regarding the writing of the article. We will carefully consider your feedback and make the necessary improvements in the revision. ## Clarification of Denoising Section We apologize for the confusion and re-structure the paper. The reviewer is correct th...	Summary: This work proposes a foundation model for Cryo-EM tasks. The technique consists of pretraining a masked autoencoder on a curated dataset, with a Noise2Noise-like self-supervision scheme. The learned representations are then fine-tuned for various tasks, such as denoising, particle picking and micrograph curati...	Rebuttal 1: Rebuttal: Thanks for your appreciation and insighful comments. ## Clarification of Supervision Target We thank the reviewer suggests that the supervision signal of visible patches can be replaced by the original micrograph to fully ultilize the high-quality information. In our paper, we followed the pract...	Rebuttal 1: Rebuttal: # Global Response Please see the uploaded PDF for additional DRACO results on unseen cryo-ET data and a comparison with Blind2Unblind. We are encouraged that all reviewers recognize DRACO as a potentially beneficial foundation model adaptable to a broad range of cryo-EM downstream tasks. We ...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
What Are Large Language Models Mapping to in the Brain? A Case Against Over-Reliance on Brain Scores	Reject	Summary: The paper studies the existing "brain score" approach of evaluating how similar LLM representations are to human brain activity. First, they show that when using shuffled train-test splits on the Pereira dataset, which some prior studies use, a trivial temporal auto-correlation model performs similarly to GPT2...	Rebuttal 1: Rebuttal: W1: Thank you for raising this point. Indeed, the percentages we reported in the submitted draft suggest that the relative increase in variance explained by OASM+GPT2-XL above OASM alone is fairly small (13.6% (EXP1) and 31.5% (EXP2)). While this does explicitly show that the variance that GPT...	Summary: The authors investigate the simplest set of features that can explain variance in neural recordings (fMRI, ECoG) during language processing. The authors focus on the surprisingly high alignment ("brain scores") of untrained LLMs, but also investigate trained LLMs. The authors conclude that the predictivity per...	Rebuttal 1: Rebuttal: W1: We agree, and will incorporate a related works section into our paper. W2: Thank you for pointing this out. We have replicated all our key findings in Pereira using the design choices made in Schrimpf et. al 2021, specifically we used last token pooling, computed Pearson correlation...	Summary: This paper paper studies the topic of neural - brain representation mappings. They focus on three neural datasetse commonly used in LLM-to-brain mapping studies: Pereira fMRI, EcoG and Blank fMRI. Specifically, the study investigates the assumptions underpinning previous positive reports about the existence of...	Rebuttal 1: Rebuttal: W1: Thank you for pointing this out, we will make sure to expand on our description of how shuffled train-test splits are constructed in our updated draft. We include this expanded description below: Expanded description of shuffled train-test splits: In neural encoding studies, datasets ...	Summary: There is a large body of research focused on measuring the similarity between language processing in the brain and in language models. Recent studies have shown that representations from Transformer-based language models exhibit a higher degree of alignment with brain activity in language regions. However, the...	Rebuttal 1: Rebuttal: W1: All our fMRI analyses on Pereira and Blank, with the exception of the glass brain plots in Figure 2d and 3d, are focused on the language network as defined by the same procedure used by Fedorenko. Since the language network is defined by selecting voxels that are more selective to se...	Rebuttal 1: Rebuttal: We thank the reviewers for all the feedback. We have performed several new experiments to address the following concerns. 1: In our original draft, we departed from the design choices made in Schrimpf et. al 2021. Specifically, we used banded ridge regression instead of vanilla linear regres...	NeurIPS_2024_submissions_huggingface	2,024	Summary: This paper investigates the similarity between large language models (LLMs) and human brain activity by analyzing brain scores, which measure how well a model predicts neural signals. The authors question the validity of using brain scores as a measure of similarity between LLMs and human cognitive processes. ...	Rebuttal 1: Rebuttal: W1: We believe there may be a misunderstanding of our core findings. We clarify our three core contributions here as well as their novelty + uniqueness, which we state in our abstract as well as throughout our paper: 1: Shuffled train-test splits, which have been used by multiple previous ...	null	null	null	null	null	null
RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness	Accept (poster)	Summary: This paper addresses the challenge of training Machine Learning (ML) models that show good robustness against multiple l_p attacks and accuracy. This problem has already been studied in previous work [33,6], but the papers discuss the problem from the lens of distribution shift. This point of view represents t...	Rebuttal 1: Rebuttal: - Analysis of time per epoch: We present additional results demonstrating the fact that RAMP is more expensive than E-AT and less expensive than MAX. These results, recorded in seconds per epoch, were obtained using a single A100 GPU. RAMP consistently supports that fact in all experiments. ...	Summary: The paper proposes a framework called RAMP to boost the adversarial robustness against multiple $l_p$ perturbations. Compared with existing methods, RAMP can better retain the robustness against the original type of perturbations when fine-tuning the model. The authors provide theoretical and empirical justifi...	Rebuttal 1: Rebuttal: - Novelty in the algorithm: Firstly, $p_r$ represents the predictions of $l_r$ adversarial examples, not the probability of the clean input. Our logit pairing method differs from TRADES as we only regularize the logits on the correctly predicted $l_r$ adversarial example subset, which TRADES d...	Summary: This work proposes a training framework RAMP, to boost the robustness against multiple $\ell_p$ perturbations, through connecting NT with AT via gradient projection. Strengths: - Easy to follow and clear presentation - Enough details - Some theoretical analyses - Outwardly, this is a good paper Weaknesses: M...	Rebuttal 1: Rebuttal: - The essential meaning of studying this problem: a) In real-world settings, adversarial attacks often involve multiple types of perturbations, necessitating models to be robust against $l_1$, $l_2$, and $l_\infty$ attacks. For instance, in image recognition: $l_1$ attacks modify key pixels ...	Summary: The paper introduces a new addition to the adversarial training literature -- it proposes to augment standard adversarial training [Madry et al.] with a specific logits pairing loss and, the existing technique, of gradient project. The paper shows strong results on the union of lp-norm adversarial accuracy (...	Rebuttal 1: Rebuttal: - The one main weakness is that Table 1 must be re-generated using all the test set points in CIFAR-10, not just 1000. Thank you for the valuable suggestion. We have re-evaluated both RAMP and E-AT using the entire CIFAR-10 dataset. The results demonstrate that RAMP consistently enhances union ...	Rebuttal 1: Rebuttal: We would like to thank the reviewers for their valuable feedback and constructive suggestions. We hope our responses have adequately addressed the questions and concerns raised by the reviewers. Here we summarize our response to the main concerns of the reviewers. - **The essential meaning of thi...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
YOLOv10: Real-Time End-to-End Object Detection	Accept (poster)	Summary: This paper further advances the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. Specifically, the paper presents the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. ...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the speed-accuracy trade-offs of YOLOv10, the effectiveness of NMS-free training, and the design intention of efficiency-accuracy driven architectural optimizations. We provide the response to each comment...	Summary: This paper presents a one-stage object detector that introduces a consistent dual assignments strategy, effectively eliminating the need for NMS and significantly accelerating inference speed with minimal impact on accuracy. Additionally, the paper explores a series of model designs aimed at balancing efficien...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the consistent dual assignments strategy, efficiency-accuracy driven model designs, and the improved performance and speed. We provide the response to each comment in the point-to-point manner below. Pleas...	Summary: The authors further improve the YOLO series detector to the tenth generation, through the following two points. The first part is the post-processing improvement, and the authors propose consistent dual assignments for NMS-free training. The second part is the effective model architecture design, where the aut...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the end-to-end framework, well-organized writing, and solid experimental validation. We provide the response to each comment in the point-to-point manner below. Please feel free to contact us if you have f...	Summary: The paper presents YOLOv10, an advancement in the YOLO series for real-time object detection. The authors have focused on eliminating the need for Non-Maximum Suppression (NMS) and optimizing the model architecture for efficiency and accuracy. The paper includes a novel training strategy and architectural enha...	Rebuttal 1: Rebuttal: We sincerely appreciate your valuable feedback and insightful comments. Thank you for liking the new training strategy, comprehensive model optimization strategy, and extensive experiments. We provide the response to each comment in the point-to-point manner below. Please feel free to contact us i...	Rebuttal 1: Rebuttal: Discussion 1: About the technical novelty. Thanks. We would like to discuss more about our motivation, findings, and contributions. Our motivation stems from optimizing the detection pipeline of YOLOs, including the NMS post-processing and model architectural design. (1) For the post-proces...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Conditional Outcome Equivalence: A Quantile Alternative to CATE	Accept (poster)	Summary: The paper introduces a new estimator to obtain conditional quantiles of a treatment effect and argues that it is superior to the pre-existing "conditional quantile treatment effect" (CQTE) estimator. The main difference is that accurate CQTE estimates require accurate conditional quantile models even though th...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and salient comments and aim to address them now. ___ # Clarification on the separate estimator We appreciate that we did not provide enough information on this separate estimator and will ensure to include its explicit form in the paper. We do not quite t...	Summary: The paper studies an important problem -- treatment effects often have distributional effects and considering conditional mean outcomes is often not the correct objective for evaluating prescriptive interventions. One popular approach to consider heterogeneous effects across a population which allows researche...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and salient comments and aim to address them now. ___ # Interpreting the CQTE We appreciate how crucial it is to properly justify and explain a new estimate and will make sure to provide more intuition and interpretation for our estimator in the final docum...	Summary: The authors propose the Conditional Quantile Comparator (CQC), a function that maps an outcome from the control group in a binary treatment setting to an outcome in the treatment group, such that they represent the same conditional quantiles in their respective distributions. The estimation procedure consists ...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and salient comments and aim to address them now. ___ # Justification of our DR estimator in the context of other estimators We thank the reviewer for pointing us towards these works and acknowledge that they do provide robust methods for estimating the CQT...	Summary: The authors propose a new estimand called the Conditional Quantile Comparator (CQC), which computes the quantiles of the outcome distributions of the treatment effect, hence improving on the (usual) conditional average treatment effect. The CQC is defined as a measurable function that maps an untreated outcome...	Rebuttal 1: Rebuttal: We thank the reviewer for their detailed and salient comments and aim to address them now. ___ # Complexity of using quantiles Regarding the indirect estimation of the quantiles: due to the monotonicity of the CCDFs and $h^*$, we incur only a trivial increase in our error bounds for estimating the...	Rebuttal 1: Rebuttal: We would like to thank all of the reviewers for their thoughtful questions and valuable feedback. We address most points in the reviewer-specific responses but will highlight a few key points here as well. ___ # Comparisons to other approaches In line with feedback from multiple reviewers, we hav...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering	Accept (poster)	Summary: The authors have made a series of contributions to the field of Audio-Visual Question Answering (AVQA). Firstly, the introduction of the innovative dataset MUSIC-AVQA-R, along with its associated evaluation metrics, stands out. This dataset and metrics are crucial for a comprehensive evaluation of AVQA models'...	Rebuttal 1: Rebuttal: Reply for Q1. For a truly robust AVQA model, it should accurately answer questions that have the same meaning but are presented in different forms. In future work, we will devise more fine-grained evaluation metrics. The metric is as follows: $$ ACC=\frac{\sum_{i=1}^{n_g} \operatorname{sgn}(i)...	Summary: Prevalent Audio-Visual Question Answering (AVQA) methods often learn biases from the dataset, which can lead to reduced robustness. Additionally, current datasets may not accurately assess these methods. To address these issues, the authors introduce MUSIC-AVQA-R, a dataset created in two steps. First, they re...	Rebuttal 1: Rebuttal: We appreciate your constructive comments and valuable suggestions. * Reply for Q1. Please see the above author's rebuttal attached with a "response.pdf". * Reply for Q2. Please see the above author's rebuttal attached with a "response.pdf". * Reply for Q3. We will explain the MCCD fro...	Summary: This paper first analyzes the bias in the existing benchmark (MUSIC-AVQA) on Audio-Visual Question Answering (AVQA). They find MUSIC-AVQA testing set is created using templates and only has 9k QA pairs with limited vocabulary, leading to the potential correlation between words in questions and answers. Therefo...	Rebuttal 1: Rebuttal: Thanks for your valuable and constructive comments. * Reply for Q1. We fine-tune the VisualBERT and linear classifier simultaneously in the ablation study, consistent with the main experiment. The experimental results without training MCCD are shown in the following table. We observe that Vis...	null	null	Rebuttal 1: Rebuttal: * Reply for dataset quality and bias difference between AVQA and VQA. First, we believe the rephrasing quality of MUSIC-AVQA-R can be guaranteed from three aspects: (1) professional annotators, (2) a strict screening mechanism, and (3) strong support from statistical indicators. For exampl...	NeurIPS_2024_submissions_huggingface	2,024	null	null	null	null	null	null	null	null
Derandomizing Multi-Distribution Learning	Accept (poster)	Summary: Multi-distribution learning is an extension of the agnostic learning setup which defined as follows. Given a collection of k distributions $(D_1, …, D_k)$ over the domain $\mathcal{X} \times \\{\pm 1\\}$ and baseline hypothesis class $\mathcal{H}$ find a (potentially randomized) classifier $f: \mathcal{X} \to ...	Rebuttal 1: Rebuttal: Thanks a lot for the review. Let us answer question 1 last as it has the longest reply. Question 2: See also the discussion with reviewer QSLG on remark 3. Furthermore, you only have Markov's inequality to bound the probability of performing significantly worse than your expected error. Indeed,...	Summary: This paper explores the setting of multi-distribution learning in the binary label setting, where the goal is to output a classifier that does well with respect to a set of distributions, rather than just a single distribution. In the agnostic case, all existing learning algorithms output a randomized classifi...	Rebuttal 1: Rebuttal: We thank the reviewer for the review. For question 1 on predictor for the conditional distribution: If we interpret your question correctly, you are asking whether it would be sensible to ask only that on a given x, we output Pr[y = 1 \| x] and Pr[y = -1 \| x] instead of a prediction of the class. ...	Summary: This paper considers the multi-distribution learning setting of Haghtalab et al. (2022), where, given $k$ unknown data distributions $\\{\\mathcal{D}_1, \\dots, \\mathcal{D}_k\\}$, the goal is to find a classifier $f: \\mathcal{X} \\rightarrow \\{-1, 1\\}$ such that $f$ performs almost as well as $\underset{...	Rebuttal 1: Rebuttal: Thanks a lot for your review. To answer your questions: For remark 3 regarding "best we can guarantee": Well, from the guarantees of a randomized multi-distribution learner alone, the claim in 42-44 indeed seem to be roughly the best possible. Consider for instance k distributions on a domain X ...	Summary: The paper studies the problem of multi-distribution learning in the agnostic setting. The authors provide a a computational lower bound for deterministic classifiers (based on some mild assumption on the hypothesis class, and the assumption $\text{BPP}\neq \text{NP}$), as well as an upper bound: an improper de...	Rebuttal 1: Rebuttal: Thanks a lot for your review. Regarding label consistency, please note that lines 81-86 compare it to the previous definition of deterministic labels by Ben-David and Urner. According to their results, in the case of a single distribution, deterministic labels are statistically almost as hard as t...	null	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.