title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE | Accept (poster) | Summary: The paper introduces Uni-Med, a medical generalist foundation model designed for multi-task learning across six different medical tasks. The proposed CMoE module leverages a mixture of projection experts to align visual and language embedding spaces effectively. The model demonstrates significant performance i... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments!
**Q1:** The ablation studies show that certain configurations (e.g., using a high number of projection experts) might lead to overfitting. This aspect could be discussed in more detail, including strategies to mitigate overfitting.
**A1:** Thank you for... | Summary: The paper presents Uni-Med, a medical generalist foundation model designed to perform multiple medical tasks efficiently through multi-task learning. This model introduces a Connector-Mixture-of-Experts (CMoE) module to mitigate the tug-of-war problem in multi-modal, multi-task optimization, which is a common ... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments!
**Q1:** The model currently supports only 2D images, whereas most commonly used medical imaging modalities, such as CT and MRI, are in 3D.
**A1:** Thank you for your advice. Same as most medical MLLMs, we input **2D slices and corresponding questions fo... | Summary: The authors propose to build a medical generalist multi-modal foundation model using a novel "connector mixture of experts" module to solve the problem of "multi-task" learning. Their connector-MOE technique introduces a projection and routing module from the visual encoder into the LLM that is explicitly cond... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments!
**Q1:** I disagree with the claim that there is limited research on connecting modalities in multi-modal models.
**A1:** There is no doubt about that researches about connecting modalities in multi-modal models is popular and extensive. As mentioned in ... | Summary: This paper introduces Uni-Med, which applies mixture of experts at the connector level for efficient training toward a unified medical multi-modal foundation model. The contributions of this work include 1.) curation of indexes to quantify tug-of-war problem in multi-modal multi-task modal; 2.) a novel perspec... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments!
**Q1:** While the reviewer appreciates the acknowledgment of several limitations of this work, it would be better to mention them in the main text, especially limitation no. 5 in lines 662-663. If the space is not sufficient, at least they should be briefly... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to express our heartfelt gratitude for your invaluable time, expertise, and meticulous attention in reviewing our manuscript. The insightful comments and constructive feedback have immensely enriched the quality and rigor of our work.
We appreciate that the revie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploration by Learning Diverse Skills through Successor State Representations | Accept (poster) | Summary: This paper proposes a new skill-based exploration method that leverages successor states. The authors start by arguing the inadequacy of maximizing mutual information as objective for learning diverse skills that also encourage exploration. Then the authors define the mutual information in terms of successor s... | Rebuttal 1:
Rebuttal: # Answer: Reviewer 8kZQ
We thank the reviewer 8kZQ for their very positive comments on the paper presentation and for their insightful comments on C-learning. We hope to address their questions in our response.
We did find that the application of C-learning is limited based on the dimensionalit... | Summary: The paper introduces LEADS, an algorithm to learn diverse skills that additionally encourages exploration. LEADS is motivated by the observation that common mutual information-based diversity-seeking algorithms cannot effectively encourage exploration. LEADS instead proposes a new objective based on successor ... | Rebuttal 1:
Rebuttal: # Answer: Reviewer axfE
We thank Reviewer axfE for their comments. In our general response, we address their specific concern regarding a more comprehensive comparison of LEADS with other baselines. Below, we will address the remaining questions and concerns about our study.
### Beyond the maxim... | Summary: The authors aim to solve the problem of exploring to learn a diverse set of skills. To do this, they modify a commonly used mutual information objective in two ways: first, they apply it to the successor measure, and second, they change the sampling distribution to focus on states with high uncertainty. They t... | Rebuttal 1:
Rebuttal: # Answer: Reviewer 98FT
We thank Reviewer 98FT for their comments. In the following, we address their specific questions and concerns about our study.
### Hand environment:
Indeed, the environment used in our tests is HandReach. We will make this clear in the experimental section.
### Theoretica... | Summary: Having an agent learn a set of diverse skills potentially affords the agent better environment exploration. A lack of diversity among the learned skills may reduce the agent's ability to discover newer informative states in the environment. This paper formalizes a method for an agent to learn a set skills by e... | Rebuttal 1:
Rebuttal: # Answer: Reviewer QVxs
We thank Reviewer QVxs for their comments. In our general response, we address several of the concerns raised by the reviewer. Below, we specifically address their additional questions and concerns about our study.
### Statement in line 164:
The sentence "natural interpl... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful feedback, which has been crucial in refining our paper and pinpointing areas that require further clarification. In this general response, we address the concerns raised by reviewers regarding the paper's clarity. We believe these concerns will lead ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space | Accept (spotlight) | Summary: The paper introduces a novel framework for understanding concept learning in text-conditioned generative models. The experiments are carefully designed (although most of them look a bit toyish) and support the analyses well. The three key findings, i.e., concept signal levels determine the learning dynamics; c... | Rebuttal 1:
Rebuttal: Dear reviewer o8es,
Thank you so much for thoroughly understanding, and positively evaluating our work. We are glad to hear that you enjoyed our work, and found all of our three main findings to be “quite novel and could provide many insights to the community”. We thank you also for your very ins... | Summary: This paper introduces a new framework, "Concept Space," designed to study the learning dynamics of capability and behavior in generative models. It uses the concept signal to measure the rate of concept learning. They trained a diffusion model on a simplified synthetic dataset to validate their hypotheses that... | Rebuttal 1:
Rebuttal: Dear reviewer uzj6,
We thank you for your detailed feedback! We are glad you found our concept space framework and the robust emergence of capability insightful and interesting. We are especially happy to hear that our work provides “insights into understanding and controlling the development of ... | Summary: This manuscript investigates the distinction between capabilities and behaviors in diffusion models. Using a toy task, the authors analyze at what point during training the model begins generating objects with correct specific features (in particular color and shape). They find that increasing the salience of ... | Rebuttal 1:
Rebuttal: Dear reviewer G81k,
We thank the reviewer for their very detailed feedback. We are happy you found our findings in 4.4 “surprising and notable”, just the way we felt, and 5. and 4.2 “helpful” and “useful”. In response to your comments and concerns, we have added:\
(i) A clear definition and clari... | Summary: The paper introduces "concept space", a framework for analyzing the learning dynamics of generative models, focused especially on compositional generalization. The key contributions are:
- Introducing the concept of "concept signal" that governs the rate of concept learning (and in particular determine learni... | Rebuttal 1:
Rebuttal: Dear reviewer cQgr,
We thank the reviewer for their detailed feedback. We are excited that you found our work provides a novel lens for analyzing learning dynamics of diffusion models, yielding claims that are interesting, clear, and well-backed. In response, we have added:\
(i) A major experimen... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank the reviewers for their thoughtful feedback and for recognizing the value of our work. We are pleased that all reviewers found our main contributions, particularly the introduction of the “concept space” framework and the studies of hidden capabilities to be... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits | Accept (poster) | Summary: This article focuses on the preference-based evolutionary multiobjective optimization. First of all, such problems are widely found in real engineering application scenarios, thus becoming one of the most popular research directions in the field of multi-objective optimization.
Overall, in terms of the prese... | Rebuttal 1:
Rebuttal: **Response to weaknesses and questions**
We address the reviewer’s concerns one by one as follows.
1. The choice of benchmark test problems follow the existing literature [1] and it also adheres to the criteria outlined in [2,3]. This ensures that the chosen benchmark test problems have differen... | Summary: This paper focused on the problem of multi-objective optimization in the dueling bandits settings. A clustering-based stochastic dueling bandits algorithm was developed and analysis. The performance is further validated via experiments.
Strengths: - A new framework via combining dueling bandits and evolution... | Rebuttal 1:
Rebuttal: **Response to W1: Choice of winner**
We address the reviewer’s concerns from the following three aspects.
1. The Copeland winner is more universally applicable than the Condorcet winner. The Copeland winner includes the Condorcet winner. That is to say the Condorcet winner is always a Copeland w... | Summary: Preference-based evolutionary multi-objective optimization (PBEMO) methods involve optimization (explore the space), consultation (learn human preference), and elicitation (guide evolutionary search). Existing PBEMO methods may suffer from inaccurate reward models, which is likely to happen given that human fe... | Rebuttal 1:
Rebuttal: **Response to W1: Choice of $K$**
We agree with the reviewer that $K$ can impact the crowdedness of solutions within a subset. In particular, the larger the $K$ is, the smaller the distances between solutions within each subset are. As for the choice and sensitivity of $K$, our justifications ar... | Summary: One challenge and potential advantage of multi-objective optimization (MO) is to adapt the dynamic human preferences while outputting an optimum. Although Preference-based evolutionary MO (PBEMO) is a promising framework, current approaches are inefficient and may not interpret the decision makers' true intent... | Rebuttal 1:
Rebuttal: **Response to Q1: Is the Gaussian mixture model computed**
Thank you for this question. First of all, the Gaussian mixture model is not computed. Instead, it is a model assumption which we contend to be reasonable to represent user preference distribution within the solution of interest (SOI) reg... | Rebuttal 1:
Rebuttal: # Response to reviewer hDtX about the choice of $K$
We agree with the reviewer hDtX that $K$ can impact the crowdedness of solutions within a subset. In particular, the larger the $K$ is, the smaller the distances between solutions within each subset are. As for the choice of $K$, we justify this ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CALANet: Cheap All-Layer Aggregation for Human Activity Recognition | Accept (poster) | Summary: This paper designs CALANet, a cheap all-layer aggregation network designed for real-time sensor-based HAR. The main objective of CALANet is to improve the accuracy of HAR while maintaining a low computational costs on edge devices for real-time applications. The authors argue that existing CNN models for HAR ... | Rebuttal 1:
Rebuttal: We are grateful for the constructive comments of the reviewer. Below, we provide specific answers and explanations regarding those comments.
**(W1) One of my concerns is the baselines. Why choose these models as baselines? I believe there are many more advanced models in sensor-based HAR. I sugge... | Summary: The CALANet describes a technique to aggregate the features from all the neural network layers for human activity recognition (HAR).
Because HAR is a common application for wearable devices, the model needs to be lightweight to be deployed on the edge for example on an Apple Watch.
Existing studies are often l... | Rebuttal 1:
Rebuttal: We are grateful for the constructive comments of the reviewer. Below, we provide specific answers and explanations regarding those comments.
**(W1) The notations of the proofs can be better clarified so that the readers don't need to refer to the appendix. For example, $D$ is not explained in eq.... | Summary: This paper proposes an all-layer aggregation network, CALANet, to improve model accuracy while maintaining the efficiency of lightweight models. Specifically, the authors have exploited the features from all layers for classification, as a kind of aggregation.
Strengths: 1. The motivation of this work is clea... | Rebuttal 1:
Rebuttal: We are grateful for the constructive comments of the reviewer. Below, we provide specific answers and explanations regarding those comments.
**(W1) My major concern is about the aggregation. When I first read the title of this paper, I assumed that the authors have trained a neural network and th... | Summary: The problem of Human Activity Recognition (HAR) is considered in this paper where the border between different activities can differ depending on the type of activity. For example, one activity can be “just sitting”, and another can be “sitting while speaking”. To this end, the authors argue that we need to le... | Rebuttal 1:
Rebuttal: We are grateful for the constructive comments. Below, we provide specific answers and explanations regarding those comments.
**(W1) The weakness of this paper is its relevance to the NeurIPS. This work gets more attention and appreciated by people in the Mobile or UbiComp community as the novelty... | Rebuttal 1:
Rebuttal: # General Response
We thank the reviewers for their detailed feedback and valuable comments. We are glad the reviewers find that
- Our paper deals with a novel and interesting question and has a clear motivation.
- "This paper spots a very interesting problem in HAR and offers an effective solu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From an Image to a Scene: Learning to Imagine the World from a Million 360° Videos | Accept (poster) | Summary: The paper introduces a large-scale, real-world, multiview, 360-degree outward dataset designed for static novel view synthesis and 3D reconstruction. To capture these data, the study develops a pipeline capable of identifying corresponding multiview frames from 360-degree outward videos with a fixed camera tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We are happy that they find our dataset crucial for modern data-driven methods and find our correspondence search pipeline to be innovative. We have addressed comments and questions below and are happy to engage in further discussion. Note that we c... | Summary: This paper considers novel view synthesis from a single image. The main contribution is a dataset sourced from 1 million 360 videos from youtube, which is used to create about 380 million pairs of images with corresponding relative poses. With this dataset the authors train a diffusion model similar to zero-1-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We are glad they appreciated the diversity and scale of our proposed dataset and the new capabilities of our resulting model. We provide responses to their comments and questions below and are happy to engage in further discussion.
**A few things ... | Summary: This paper introduces a new approach for efficiently finding corresponding frames from diverse viewpoints from YouTube videos at scale. The resulting 360-1M dataset contains over eighty million frames from approximately one million videos. Additionally, the paper also builds on the 360-1M dataset by proposing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We are glad they found our algorithm for transforming 360° video into multi-view data to be especially significant and see its potential for scaling 3D datasets. We provide responses to their comments and questions below and would be happy to engage... | Summary: This paper addresses the challenge of training models with 3D object understanding using large-scale real data, proposing the use of 360-degree videos as a scalable and diverse data source. Its main contributions include a 360-1M video dataset, an efficient method to convert 360-degree videos into multi-view d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We are glad they find our model demonstrates important capabilities for large-scale scene understanding, the idea of using 360° video is interesting, and our dataset will be useful for future researchers. We provide responses to their comments and q... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful reviews and feedback. We are glad that the reviewers found our dataset to be crucial for modern data-driven methods [whPn] and our proposed pipeline for constructing multi-view data from 360° video to be innovative [whPn], interesting [8VWi] and especial... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge | Accept (poster) | Summary: The generalization of DeepFake detection can be addressed by enhancing training data with synthetic fake faces, known as pseudo-fakes. Traditional methods generate these faces by spatial blending operations. However, the limitations of these methods are that they ignore to simulate the frequency distribution, ... | Rebuttal 1:
Rebuttal: Thank you for the positive review on the significance of our topic, novelty and experimental results.
**Q1: Could you provide more details on why placing the center of the circular annular aperture at the top-left corner of the frequency map results in a one-dimensional spectrum diagram?**
**R1... | Summary: This work studies generalizable deepfake detection. The proposed method is motivated by a new data augmentation method that blends real and fake faces in the frequency domain. The paper claims the forgery can be found in three different frequency bands and proposes an unsupervised learning method, Frequency Pa... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable time and comments.
**Q1: The proposed method does not achieve state-of-the-art detection performance in table 1.**
**R1:** We would like to highlight that **our method achieves the highest number of top-1 rankings compared to all others (best perf... | Summary: This paper proposes FreqBlender, a new method to generate pseudo-fake faces that effectively simulate the frequency distribution of wild fake faces. Unlike common blending techniques done in the spatial domain, their method blends frequency knowledge. An analysis is conducted, showing that three frequency comp... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback regarding the originality, quality, clarity, and significance of our work, and are grateful to the constructive comments.
**Q1: Some references are missing, e.g., [1, 2, 3, 4].**
**R1:** Thanks for the suggestion. We will include these related references in th... | Summary: This paper have introduced an effective way to improve the generalization of DeepFake detection via generating pseudo-fake faces by blending frequency knowledge. The proposed approach achieves state-of-the-art (SOTA) results on various deepfake detecion datasets.
Strengths: 1) This paper attempts to combine ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive comments on the novelty, writting and experiment configuration, and for the constructive suggestions.
**Q1: The idea of this paper is similar to some already published papers, such as [1], [2] and [3]. I hope this paper can cite and further analyz... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable time and professional comments. We are encouraged by the positive feedback and suggestions on the following aspects:
* **Soundness, Presentation, and Contribution**:
* **All reviewers** rate these sections as **Good** in their reviews.
* **N... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries | Accept (poster) | Summary: This paper proposes a new occupancy predictor that utilizes the octree structure to reduce the total number of occupancy query. The octree tree is initialized with semantic information and recursively rectified, so that it can accurately represent the structure of the driving scenes.
Strengths: 1. This work g... | Rebuttal 1:
Rebuttal: W1. **More details of the segmentation model**
Segmentation models are trained using homologous data and then integrated into the network as fixed components, being inferred together rather than as additional separate modules. In this setup, a UNet architecture is used, where the encoder part ... | Summary: This paper introduces OctreeOcc, aiming to tackle the heavy computational demands of the dense and regular grid representations employed by the previous methods. Instead of randomly initializing the octree structure, OctreeOcc incorporates the semantic priors of images as guidance. The octree structure are fur... | Rebuttal 1:
Rebuttal: W1. **The image resolution and visible mask should be marked**
In Table 1 of original paper, we use "$\star$" to indicate whether a method uses a camera mask during training. Methods marked with "$\star$" are the latest SOTA methods.
The image sizes used in training for each method are as ... | Summary: This paper introduces OctreeOcc, an innovative 3D occupancy prediction framework that leverages the octree representation to adaptively capture valuable information in 3D, offering variable granularity to accommodate object shapes and semantic regions of varying sizes and complexities. The authors incorporate ... | Rebuttal 1:
Rebuttal: W1. **Too many manually set hyperparameters affect generalisability**
Octree-related hyperparameters focus on selecting the top-k operation’s K during octree query sparsification and rectification.
These hyperparameters are statistically derived from the dataset and are not difficult to d... | Summary: The authors aim to tackle the problem of high memory usage in dense occupancy prediction for 3D scenes. They introduce OctreeOcc, a method that uses octree structures to make predictions more efficiently. Experimental results show that OctreeOcc reduces computational load and achieves competitive performance.
... | Rebuttal 1:
Rebuttal: W1. **The proposed method seems difficult to optimize and relies on segmentation and historical information**
Regarding optimization, our method uses the same optimizer settings as other methods and is trained for 24 epochs as well. Under **the same training conditions**, our approach achieves... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment | Accept (poster) | Summary: This paper proposes an approach to aligning Large Language Models (LLMs) with human preferences by integrating Inverse Reinforcement Learning (IRL) into the supervised fine-tuning (SFT) stage. The proposed IRL-based method simultaneously builds a reward model and a policy model during the SFT stage, enhancing ... | Rebuttal 1:
Rebuttal: > The paper does not address the scalability of the proposed methods to even larger models or different types of LLMs beyond the ones tested.
**Response**: We thank the reviewer for the suggestion. Here we believe provide a theoretical analysis would be enough since we know exactly how much memor... | Summary: This paper proposes to study if IRL techniques can be used for aligning large language models. They propose two different algorithms: one that explicitly learns a reward model and one that implicitly learns the reward in the policy. These reward models are learned by contrasting expert generations and the poli... | Rebuttal 1:
Rebuttal: > The experiments are not very convincing. For the Open LLM leaderboard experiments, the gain is really small (around 1 or 2%). It seems likely that this gain is due to variance in the training process, especially since IRFT has ~1% variance in different hyperparameter settings.
**Response**: To ... | Summary: The authors propose using inverse reinforcement learning (IRL) on demonstration data in place of supervised learning, as is typically done for LLMs. The intuition is that human preferences are also encoded in demonstrations collected for SFT. Concretely, the authors propose a bilevel optimization approach with... | Rebuttal 1:
Rebuttal: > The experimental results seem to support the efficacy of the proposed algorithms, however the gains across various tasks with IRFT seem fairly marginal (it would help if Table 2 and 3 contained error bars, perhaps with policies finetuned with different seeds). As it stands, it seems like differe... | Summary: This paper proposes two methods focusing on RLHF, namely RFT and IRFT. The takeaway message is that the SFT stage also significantly benefits from learning a reward model instead of using the human demonstration data directly via supervised learning.
Strengths: 1. The paper is clear, illustrating the differen... | Rebuttal 1:
Rebuttal: > 1. The training procedure is complicated, meaning there are many issues in tunning. Is there any computation cost analysis for better understanding the limitations of this method?
**Response**: We thank the reviewer for bringing this issue up. For Algorithm 1 in the paper, we need to maintain a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for valuable comments. In this general rebuttal, we provide answers and results to questions that are raised by multiple reviewers. We wish our extra analysis and results could help you better understanding the contribution of the work, which we believe is significant to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Compositional 3D-aware Video Generation with LLM Director | Accept (poster) | Summary: This paper presents an LLM-involved three-stage pipeline for text-guided compositional 3D-aware video generation. In the first stage, an LLM is employed as director to decompose input textual prompts into sub-prompts including scene, object and motion. Subsequently, it leverages multi-modal LLM to make an init... | Rebuttal 1:
Rebuttal: Dear Reviewer BLtC, thank you for taking the time to review our work and for your positive and insightful feedback. We are pleased to hear that you found our idea interesting and that it demonstrated improved performance. We hope the following comments address your concerns.
**Q: Criteria for cho... | Summary: This paper proposes a method to synthesize dynamic 3D videos, with moving objects and camera. It treats the tasks compositionally, generating the (static) background and foreground figures separately. The generation process is orchestrated by an LLM, which provides prompts to separate models that specialize in... | Rebuttal 1:
Rebuttal: Dear Reviewer 7svJ, thank you for your thoughtful feedback and for looking into every detail of our work. We are pleased that you found our idea elegant, novel, clearly motivated, and well-organized. We hope the following comments address your concerns.
**Q: More qualitative examples should be pr... | Summary: In this work, the authors propose a framework for 3D-aware video generation using guidance from LLM. Specifically, this work follows previous studies on LLM for video generation that takes language model as a director to do below sub-tasks:
1) expand and decompose the prompt into different aspects, and then us... | Rebuttal 1:
Rebuttal: Dear Reviewer N95p, thank you for taking the time to review our work and for providing thoughtful feedback. We are pleased that you found our setting novel and the pipeline coherent. We address your concerns as follows.
**Q: Novelty of this work.**
A: As noted by Reviewers 7svJ and BLtC, our wor... | Summary: The paper presents a pipeline for 3D-aware video generation by composing scenes, objects, and motions. One key idea is to use existing LLM to provide coarse guidance on the scale and trajectory of objects, and then refine the coarse rendering with SDS. The method is compared with multiple methods.
Strengths: ... | Rebuttal 1:
Rebuttal: Dear Reviewer 6K4s, we are grateful for your careful review and the valuable feedback that you provided for our paper. We appreciate that you found our paper easy to read and our ideas explainable. We hope the following comments address your concerns.
**Q: Main contributions of this paper.**
A: ... | Rebuttal 1:
Rebuttal: We extend our sincere thanks to all the reviewers for their time and effort. We appreciate your positive feedback, noting that our work was described as "novel and elegant" (N95p, 7svJ, BLtC), "effective and achieving better results" (N95p, 7svJ, BLtC), and "easy to read and well-organized" (6K4s,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal ablation for interpretability | Accept (spotlight) | Summary: This paper presents an alternative approach to perform component ablations for neural network interpretability. Specifically, the authors consider a neural network as a causal graph and propose *optimal ablations* which simulate component removal by setting the value of a node in the computational graph to a c... | Rebuttal 1:
Rebuttal: Thank you for the positive and helpful feedback, and we’re glad you appreciated our work!
> Some of the applications lack depth as a result of the number of different methods studied. For example, it would have been interesting to further investigate the observations on factual recall.
## Comme... | Summary: This paper explores a new approach to replacing features in the forward pass of a neural network, for purposes of interpretability. The approach is, rather than zeroing out features or replacing them with some pre-specified constant or random variable, to optimize a replacement constant to minimize the loss of... | Rebuttal 1:
Rebuttal: Thank you for the positive and helpful feedback, and we appreciate your optimism about our work!
>First, the argument in the paper, while being mathematically clear, is not philosophically rigorous
We agree and have rewritten this section (see global reply). The term “total ablation” clarifies w... | Summary: - Introduces a notion of "optimal component ablation" for activation patching methods in mechanistic interpretability. Specifically, they propose an ablation method that involves setting a component’s value to the constant value that minimizes ablation loss over a chosen distribution.
- Shows that optimal abl... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback.
>Writing and paper structure can be significantly better
We’ve revised the writing so that the structure makes more sense, improving flow and clarifying differences between sections. In particular, we’ve substantially rewritten section 2 to better context... | Summary: Different intervention techniques aim to "ablate" parts of the representation of the model to infer their causal function, e.g. by adding gaussian noise. the paper suggests to derive a notion of "optimal" ablation. Particularly, instead of zeroing out or replacing the ablated part with its mean, it is proposed... | Rebuttal 1:
Rebuttal: Thank you for your generous feedback, and for recognizing the value of our work! | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback. We use this space to address questions shared between multiple reviewers.
**Reviewers note our motivation for minimizing ∆ could be clearer.** We agree and propose the following changes. We add the following definition to section 2.1:
>For ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space | Reject | Summary: The authors proposes a method to create a universal feature space using brain fMRI response prediction as a training objective. The key idea is that deep networks trained with different objectives share common feature channels that can be clustered into sets corresponding to distinct brain regions, revealing v... | null | Summary: The paper proposes a method to align different vision models' features to a common space, and to discover interpretable features as clusters in this space.
The alignment is done by learning linear mappings from features to fMRI activations, the intuition being that the human visual cortex provides a meaningfu... | null | Summary: In the domain of interpretability research, this paper aims to make a mark by proposing AlignedCut, a method to discover shared and expressive visual feature spaces across networks by aligning those spaces with neural responses in human brains. The method is quite interesting - channel-wise responses to images... | null | Summary: The paper introduces a new method to interpret deep learning models using brain data. The two apparent contributions are that (1) this new model is able to align channels activations from different layers of different models into a universal feature space, and (2) a Nystrom-like approximation is introduced to ... | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning | Accept (poster) | Summary: This paper revisits the core challenges of Online Continual Learning (OCL) in high-speed data stream environments, identifying two significant obstacles: the model's ignorance and myopia. It then introduces a non-sparse classifier evolution framework (NsCE) designed to effectively address these issues. Additio... | Rebuttal 1:
Rebuttal: _Respected Reviewer abk8,_ We first thank you for your valuable and insightful feedback, and for recognizing our analysis from a Pac-Bayes perspective and proposed method. Below, we address your concerns in a point-by-point manner and welcome further discussion if anything remains unclear.
Q: _Di... | Summary: This paper identifies two previously overlooked challenges in online continual learning (CL): model ignorance and myopia. In response, it introduces a new framework called Non-sparse Classifier Evolution (NsCE). NsCE features non-sparse maximum separation regularization and targeted experience replay technique... | Rebuttal 1:
Rebuttal: _Respected Reviewer u1kw,_ We first thank you for your valuable and insightful feedback, and for recognizing our empirical evaluation and theoretical insights. Below, we address your concerns in a point-by-point manner and welcome further discussion if anything remains unclear.
Q: _How the pre-tr... | Summary: The paper identifies and formalizes main challenges specific to OCL. Notably the authors highlight the need for a stronger focus on ignorance (the inability of the online learner to fully converge) and throughput. Similarly, the authors identify Myopia, which corresponds to learning sparse feature, as a potent... | Rebuttal 1:
Rebuttal: _Respected Reviewer WX6u,_ We first thank you for your valuable and insightful feedback, and for recognizing our motivation and theoretical analysis. Below, we address your concerns in a point-by-point manner and welcome further discussion if anything remains unclear.
Q: _Justification behind the... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate your time and effort in reviewing our manuscript and offering valuable suggestions. Based on some of the reviews, we provide a pdf including a figure showing the redefined weight sparsity value during the training with and without the sparsity-regularizati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality | Accept (spotlight) | Summary: This work considers a two player game with externalities imposed from one agent (“upstream”) to the other (“downstream”) agent. Equilibrium in the absence of taxation or payments between players would result in heavy efficiency loss due to the externalities.
The Coase theorem tells us that in these situations... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback. All the comments will be taken into account in the new version of the paper. You can find below our answers to each of your concerns.
> It will be nice to see a discussion about possible notions of optimality of mechanisms that incentivize particip... | Summary: The paper explores the field of mitigating externalities in economic interactions by applying the Coase Theorem with hindsight rationality. The Coase Theorem, a fundamental concept in economics, suggests that in the presence of externalities, property rights, and bargaining strategies can be utilized to achiev... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback. All the comments will be taken into account in the new version of the paper. You can find below our answers to each of your concerns.
> W1. One significant weakness of the paper is the lack of empirical validation or experimental results to back up... | Summary: The paper presents a two-player sequential game with bandit feedback where one player's actions create externalities that affect the other player's outcomes. In this model, the utility of only one player (the downstream player) is impacted by the collective actions of both the upstream and downstream players. ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback, that will be taken into account in the new version of the paper. We answer below your different questions.
> It is not clear whether the regret incurred by the downstream player is optimal.
See our general answer on optimality of the regret bound for BELGI... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed and insightful feedback. All their comments will be taken into account in the revised version of our work. We answer individually to each reviewer's concern. Besides, a couple points were raised by multiple authors: we answer these points below.
## Ab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Policy Mirror Descent with Lookahead | Accept (poster) | Summary: The paper studies policy mirror descent (PMD) where the policy improvement step is modified to include an action-value with $h$-step lookahead (contains $h-1$ applications of the Bellman optimality operator on the value of the policy from the previous iteration). In the exact tabular setting where the value fu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on our work and for their useful comments. We are glad the reviewer found the paper "quite well written and easy to follow" and the idea of using lookahead in PMD "interesting". We address their concerns in the following.
**It would be good to e... | Summary: The authors proposed a version of the policy mirror descent algorithm that uses a multi-step greedy policy improvement operator. Afterward, the authors showed the theoretical benefits of the proposed method by a better contraction rate $\gamma^h$ instead of $\gamma$-contraction for a usual 1-step greedy policy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our work and for appreciating our theoretical contributions. We reply to their remaining comments in the following.
**The experimental part of the paper might be improved by running continuous control experiments.**
Thank you for the sugge... | Summary: The author propose a multi-step greedy approach for Policy Mirror Descent. Combing PMD with multiple greedy policy updates results in a faster $\gamma^{h}$ rate improved the previously thought optimal $\gamma$ rate. Additionally, the authors extend their analysis to the stochastic setting and when using functi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and for acknowledging the novelty and well-founded motivations of our paper as well as its presentation. We hope that the following discussion fully answers your questions.
**It seems that the analysis is done in the functional representation of the policy... | Summary: The paper introduces h-PMD, an extension of the Policy Mirror Descent (PMD) algorithm, which incorporates multi-step lookahead to improve policy updates in reinforcement learning. PMD is a general framework that includes several policy gradient methods and relates to advanced algorithms like TRPO and PPO. Reco... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time for assessing our work, for their feedback and questions. We reply to each of one of their questions (also covering their weaknesses and limitations comments) in what follows. We will be happy to address any further concern.
**1. Can you provide more detailed ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable time and feedback. We address the remaining concerns of the reviewers in our individual responses below. We have also performed additional experiments to support our responses. Please find figures relating to these experiments attached. These simulatio... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper applies the idea of lookahead in policy improvement procedures under the PMD setting. They prove the $\gamma^h$-linear convergence rate. They also propose the inexact version of h-PMD and extend to the function approximation case.
Strengths: The writing of the paper is pretty good. The main idea an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We are glad that the reviewer finds the writing "pretty good" and the "main idea and the results of the paper easy to follow". We address their remaining concerns in the following. Please let us know if you have any further questions, we will be happy to a... | null | null | null | null | null | null |
Shaping the distribution of neural responses with interneurons in a recurrent circuit model | Accept (poster) | Summary: This paper proposes a normative recurrent circuit to solve an optimal transport problem, focusing on the problem of Gaussianization of natural image statistics.
Strengths: This paper was a pleasure to read. The writing is clear, the formulation of the problem elegant, and the results represent a clear advance... | Rebuttal 1:
Rebuttal: Thank you for your review, we are pleased that you enjoyed reading our work!
### Weaknesses
We agree the plausibility of the activation functions warrants further discussion. The interneuron activation functions indeed resemble two-sided rectified-power law activations. A potentially more plausi... | Summary: This paper investigates a crucial question in neuroscience: how do neural circuits convert inputs into a target distribution, specifically focusing on how local interneurons transform natural image statistics into a spherical Gaussian distribution. The authors approach this problem through the lens of optimal ... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will revise our paper in accordance with your suggestions.
### Weaknesses
- We will explicitly define these distributions in our revision.
- This is a great point. Interestingly, the learned weights $W$ are approximately shared across images, whereas the optimal $g... | Summary: Authors propose an online algorithm that solves the problem of optimal transport. In particular, assuming a spherical distribution of stimuli, the goal of the algorithm is to generate neural responses such that their distribution best approximates the distribution of stimuli. Authors find that a neural network... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our paper. We take your concerns quite seriously and have revised our paper accordingly. Please find our responses to your listed weakness and your questions below.
### Weaknesses
- We tested a modified version of our algorithm in which we enforce Dale's law.... | Summary: This paper introduces a method that utilizes multiple inhibitory neurons and the Hebbian learning rule to transform a signal into a representation that conforms to a specified target distribution. Specifically, the model incorporates Hebbian synaptic plasticity to establish connections that optimally match thi... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our paper and for your thoughtful comments. We are pleased that you find the paper clear and appreciate the models online capabilities! We understand your concerns about the biological realism of our model and have addressed these concerns in our general autho... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and helpful comments. Here we respond to two important concerns and provide individual responses below.
## Biological realism
Reviewers **zp8S**, **Voaj** and **txmi** listed the biological realism of our model as a primary concern that limits the applicability... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Discrepancy Testing for Learning with Distribution Shift | Accept (poster) | Summary: The paper extends the recently introduced Testable Learning with Distribution Shift (TDS) model and makes contributions in three main aspects: Universal TDS Learners, Optimal Error Guarantees via L1 Sandwiching, and Fully Polynomial-Time Testing. It presents universal TDS learners that perform well across a wi... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for their constructive feedback and for appreciating our work.
- The fully polynomial-time testers we propose in this work apply to the class of balanced halfspace intersections. There are two important implications of our results related to current large pre-trained... | Summary: Discrepancy distance is crucial in domain adaptation. The paper proposes the first set of provably efficient algorithms for testing localized discrepancy distance. This approach can generalize and improve prior work on TDS learning, and further extend to semi-parametric settings. By separating learning and tes... | Rebuttal 1:
Rebuttal: We wish to thank the anonymous reviewer for their feedback.
1. Our Definition 1.1 is a generalization of the localized disparity discrepancy w.r.t. 0-1 loss as described in (Zhang et al., 2020). In particular, they use a specific notion of neighborhood (which corresponds to the disagreement neigh... | Summary: This paper considers the problem of designing Testable Learning with Distribution Shift (TDS learning) algorithms. Through proposed algorithms for testing localized discrepancy distance, the authors give a set of efficient TDS learning algorithms. These algorithms improves all prior work in the sense that they... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
We would like to clarify that we propose 3 different discrepancy testers, one for each of the sections 3, 4 and 5 (see proposition 3.2, appendix D.1 and appendix E1). The relevant discussion can be found in lines 103–109 and high-level descriptions for th... | Summary: The paper investigates the problem of learning under distribution shift in the recently introduced framework of testable learning with distribution shifts (TDS) [Klivans et. al. 24]. In this framework, the learner receives labeled samples from the train distribution D and unlabeled samples from the test distri... | Rebuttal 1:
Rebuttal: Thanks for your question about "strong assumptions". The goal of this line of work is precisely to remove the strong assumptions inherent in all prior work in the fields of distribution shift and domain adaptation. More precisely, all prior work requires an assumption on both the train distributi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Distribution shift is a well known problem where the classifier is trained on a particular data distribution encounters an input distribution far from the training set(OOD). In such a scenario it is desirable that the model performance not degrade too much, and one way to ensure this to estimate the discrepan... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for their comments and for appreciating our work.
- We agree that experimental evaluation of our algorithms would be interesting, but we believe that a thorough, dedicated evaluation would be preferable and more suitable for future work, since the scope of this pape... | null | null | null | null | null | null |
NRGBoost: Energy-Based Generative Boosted Trees | Reject | Summary: The authors propose a energy-based generative boosting method. They try to maximize the log-likelihood functional delta with a second-order expansion. This lead to a boosting algorithm with deltas as steps in the log-likelihood. Instead of scaling the steps with a fixed predefined value (as a anti-overfitting ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and encouraging words about our work, as well as for providing helpful context about our method's ability to produce passable image data as a tree-based generative model.
We address the reviewer's two concerns below.
# Distribution Metric
While w... | Summary: This paper proposes a boosted tree algorithm that performs distribution learning using an energy-based formulation. Inspired by methods like XGBoost, it is claimed to achieve high performance not only in generative ability but also in discriminative performance.
Strengths: The proposed method incorporates tec... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We will try to address all their concerns below.
Regarding the minor point about separately reporting averages and the respective standard deviations, we agree that this was a bad choice and will change the paper to present them in the same format... | Summary: This paper proposed an energy based generative boosting algorithm analogous to XGBoost, which can be used as generative model as well as be applied to discriminative tasks.
Strengths: The energy-based boosting is novel. The proposed method is capable of both generative sampling and discriminative tasks, enabl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Below are our responses to each individual question.
# Question 1
Our main goal is to compare generative methods and we mark as bold the best **generative** model on each dataset. We will add a note to the caption of Table 1 to make this clear since XGB... | Summary: The paper proposes to extend the success of tree-based methods in discriminative tasks to generative modelling, which is implemented via an energy-based generative boosting algorithm (NRGBoost). Specifically, NRGBoost directly extends the tree-based tabular models by replacing the discriminative objectives wit... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and careful review of our work and for the detailed feedback provided.
We will try to address all the weaknesses pointed out below.
# Missing Methods
**ARF:**
As another tree-based density method we agree that this is a very valuable comparison to make. We have thus ma... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to review our work.
We have been working diligently to incorporate their feedback in order to improve the paper.
Below is a list of the main changes we have done as a result.
We kindly ask the reviewers to also check the attached PDF which includes additi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CSPG: Crossing Sparse Proximity Graphs for Approximate Nearest Neighbor Search | Accept (poster) | Summary: This paper proposes CSPG as a novel graph index for vector similarity search. CSPG divides the dataset into sub-datasets with some common routing vectors. It builds a separate graph for each sub-dataset; during search, graph traversal is first conducted on one sub-dataset to quickly approach the neighborhood o... | Rebuttal 1:
Rebuttal: We would like to thank you for carefully reviewing our work and for providing such insightful suggestions. We have provided a Rebuttal PDF in the global Rebuttal, which contains all additional experimental setup and results.
## For Q1: The Relation between HNSW and CSPG
Our framework shares simi... | Summary: The article considers the task of graph-based approximate nearest neighbor (ANN) search. It provides a general method for building several sparse overlapping proximity graphs, and a method for searching them. The proposed algorithm can be used in combination with any kind of existing proximity graph. The exper... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our work and for your insightful suggestions and interesting topics. We have provided a Rebuttal PDF in the global Rebuttal, which contains all additional experimental setup and results. Regarding of reproducibility, We have provided our source code for all experi... | Summary: This paper studies the problem of approximate nearest neighbor search (ANNS), and proposes an optimized solution for existing proximity graph-based solution. The main idea of this solution is to decompose the original dataset into several partitions, build a proximity graph for each partition, and consider cro... | Rebuttal 1:
Rebuttal: Thank you for reading our work and providing detailed suggestions. We have provided a Rebuttal PDF in the global Rebuttal, which contains all additional experimental setup and results.
## For Q1 and W1: Novelty and Contributions
Most existing methods that adopt the idea of partitioning data ofte... | Summary: The manuscript proposes a novel schema called "Crossing Sparse Proximity Graphs" (CSPG) which defines an proximity graph (PG) for the graph-based approximate nearest neighbor search (ANNS) problem. Searching on top of the CSPG is provably faster than existing proximity graphs (which derived from other ANNS alg... | Rebuttal 1:
Rebuttal: We sincerely thank you for reading our work and providing helpful suggestions. We have provided a Rebuttal PDF in the global Rebuttal, which contains all additional experimental setup and results.
## For Weakness: Additional Explanation for Section 5.4
CSPG is a framework designed to accelerate ... | Rebuttal 1:
Rebuttal: We sincerely appreciate all four reviewers for their instructive suggestions on our submitted paper. Their suggestions have been immensely helpful in improving our work and enhancing our experiments. To response the relevant points of concern, we have conducted additional experiments and included ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction | Accept (poster) | Summary: This paper proposes a framework that integrates 3D brain structures with visual semantics using a Vision Transformer 3D. By aligning fMRI features with multiple levels of visual embeddings, it eliminates the need for subject-specific models and allows extraction from single-trial data. The extractor consolidat... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thorough review and positive feedback on our work. Your comments are invaluable for the improvement of our method and the revision of the manuscript. Let us address each individual comment/question below.
1. **Benchmarking with Real-World Datasets:**
We ful... | Summary: This paper presents an innovative framework that leverages Vision Transformer 3D (ViT3D) to integrate 3D brain structures with visual semantics, enhancing visual reconstruction and language interaction from fMRI data. By aligning fMRI features with visual embeddings through a unified feature extractor and inte... | Rebuttal 1:
Rebuttal: We appreciate your detailed and insightful review. Your positive feedback on the soundness, presentation, and contribution of our work is highly encouraging. We are pleased that you found our framework innovative and robust in integrating visual and neural signals. We will now list your concerns o... | Summary: The paper introduces a new, subject-agnostic visual reconstruction pipeline. They introduce a way to integrate across fMRI readings from different subjects and enhance their integration using LLMs. Through this integration they see a consistent improvement of high level semantic feature baselines in their reco... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments. Your feedback has helped us enhance the quality of our manuscript. Below, we will provide detailed responses to your comments.
### **Concerns about Preprocessing and Inter-Subject Variability:**
We appreciate your concern regarding inter-subject variability.... | Summary: - This model focuses on the task of reconstructing image stimuli from fMRI readings
- Instead of training a subject specific model, a subject-generic model is trained
- The model is built around a pre-trained LLM core, which is finetuned to take the fMRI as input and then:
- engage in dialogue about the imag... | Rebuttal 1:
Rebuttal: We appreciate your detailed and insightful review. Below, we provide a brief summary of your questions and our responses:
1. **Impact of $\hat{z}_c$ on image reconstructions: How much does the reconstruction depend on $\hat{z}_c$? What if $\hat{z}_c$ were replaced with random noise?**
We condu... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful feedback. We have carefully reviewed your comments and provided specific responses to each point raised. We appreciate the opportunity to discuss our research in more depth and clarify any concerns through these responses.
In the attached response docume... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One-Step Diffusion Distillation through Score Implicit Matching | Accept (poster) | Summary: This paper proposes a distillation-based accelerated sampling method for various score-based diffusion models, such as EDM and Stable Diffusion. The authors have named this method Score Implicit Matching (SIM), which is designed to compress information from a diffusion-based teacher into single-step generator ... | Rebuttal 1:
Rebuttal: Thank you for your useful feedback. We will address your concerns one by one. Before that, we first give a summary of our work.
In this work, we introduce score implicit matching (SIM), a novel distillation algorithm that achieves competitive performances on CIFAR10 generation tasks, and **lossle... | Summary: The authors study the distillation of score-based models in one-step generators. They propose a new objective function for score distillation coined Score-Based Divergence. This divergence measures the mean distance between the pre-trained score-based model and a score-based model learned on the distribution i... | Rebuttal 1:
Rebuttal: We are glad that you like our work. We appreciate your valuable suggestions, and we will incorporate them in our revision. In this paper, we introduce score implicit matching (SIM), a theoretically sound distillation method that secretly minimizes a general family of score-based divergences when d... | Summary: This work proposed Score Implicit Matching (SIM) to distill diffusion models into a one-step generator. The core idea is to use the “score-gradient theorem” to transform the minimization of score-based divergences between generator and real score functions into an implicit and tractable minimization problem. T... | Rebuttal 1:
Rebuttal: Thank you for your useful suggestions. We will address your concerns one by one. Before that, we first give a summary of the main contributions of our work.
In this work, we introduce score implicit matching (SIM), a novel distillation algorithm that achieves **competitive performances on CIFAR10... | Summary: This paper proposes a new distribution matching loss between the one-step generator and the pre-trained diffusion model. The reverse KL divergence proposed in Diff-Instruct is generalized through the Score-divergence gradient Theorem.
Strengths: 1. The Score-divergence theorem is well adapted to generalize Di... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We will address your concerns one by one. Before that, we first give a summary of the main contributions of our work.
In this work, we introduce score implicit matching (SIM), a novel diffusion distillation algorithm that achieves **competitive performanc... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for your valuable feedback. In this cell, we address some common concerns.
As the **Reviewer G1sA** and **Reviewer Jq2C** wish, we run two additional experiments to demonstrate the wide applicability of SIM: (1) text-to-3D generation using text-to-2D diffusion; and (2... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper ‘One-Step Diffusion Distillation through Score Implicit Matching’ introduces a novel framework Score Implicit Matching (SIM), to distill pre-trained diffusion models into single-step generator models. This approach achieves almost no performance loss compared to the teacher diffusion model while bein... | Rebuttal 1:
Rebuttal: We are glad that you like our work. We appreciate your valuable suggestions, and we will take them in the revision. In the following paragraphs, we will address your concerns one by one.
**Q1**. Doing a broader comparison with other distillation and generative modeling techniques would provide a ... | null | null | null | null | null | null |
RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees | Accept (poster) | Summary: This paper introduces a novel image watermarking technique that injects signals into both the frequency and pixel domains of images. The watermark is specifically designed for the detection of AI-generated images and incorporates smoothing techniques to provide provable error guarantees against attacks under c... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for devoting their valuable time to reviewing our paper and offering insightful suggestions for improving it.
> Q: The paper lacks a comprehensive analysis of performance against image manipulations. While Table 4 illustrates performance on various attacks, it is u... | Summary: This paper proposes a Robust, Agile plug-and-play Watermarking (RAW) framework, which adds learnable watermarks directly on the original image and employs a classifier to detect the presence of the watermark. This design enhances both adaptability and computation efficiency, providing an model-agnostic for rea... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for devoting their valuable time to reviewing our paper and offering insightful suggestions for improving it.
> Q: The proposed method still embeds the watermark into the carrier image and is not specifically designed for AI-generated images.
**R**: Thank you for ... | Summary: This paper proposes a post-processing watermarking strategy that embeds watermarks into images after generation. The strategy is designed to be computationally efficient and model-agnostic. It involves training a learnable watermark and embedding it into both the frequency and spatial domains of the original i... | Rebuttal 1:
Rebuttal: > Q: The survey of existing works in the "Related Works" section is incomplete. Numerous works on watermark frameworks for diffusion models are not included.
**R**: Thank you for your questions regarding the related works section. We will include more works on watermark frameworks for diffusion m... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Carrot and Stick: Eliciting Comparison Data and Beyond | Accept (poster) | Summary: This paper studies elicting comparison data with truthfulness guarantees, specifically strict Bayesian Nash equilbrium. The authors utilize strong stochastic transitivity to define a Bayesian strong stochastic transitivity model and generalize several existing models. Under this model (i.e., problem setting), ... | Rebuttal 1:
Rebuttal: Thank you for your valuable input!
***
**Question 1**: What is the randomness of $T_\theta$?
**Answer**: $T_\theta$ is a stochastic comparison function that outputs random comparison given parameter $\theta$. The randomness of $T_\theta$ captures noise in observing comparisons. For instance, in t... | Summary: This paper considers the problem of eliciting pairwise comparisons truthfully from strategic agents in the absence of ground truth. It considers the popular framework of peer prediction which is a class of mechanisms where reports from "peer" agents are used as a proxy for ground truth in order to design a pay... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable input! Below, we will address your question in this paper.
***
**Question**: Weak stochastic transitivity.
**Answer**: This is an interesting question. We conjecture that weak stochastic transitivity alone is not sufficient. Our proof requires $\Pr[T_\theta(... | Summary: * This paper proposes a peer prediction mechanism for elicitation of comparison data.
* In the model, there is a collection of items $A$, and a set of agents $N$ which privately observe noisy comparisons between items. Comparisons are characterized by a stochastic comparison function $T_\\theta$, parameterized... | Rebuttal 1:
Rebuttal: Thank you for your valuable input.
***
**Question 1**: What are the relations between Mechanism 3 and Mechanisms 1 and 2? Is it possible to think about Theorem 3.1 and Theorem 5.1 as corollaries of Theorem 5.2?
**Answer**: Mechanism 3 offers a general scheme for designing peer prediction mechanis... | null | null | Rebuttal 1:
Rebuttal: Please find our additional experiment results attached. Thanks!
In the first three figures, we run our experiments, including the new group strategical behavior, on a new dataset: the HuggingFace H4 Stack Exchange Preference Dataset, which is a dataset used to align LLMs with human preferences. T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Recovering Complete Actions for Cross-dataset Skeleton Action Recognition | Accept (poster) | Summary: This paper presents an innovative recover-and-resample augmentation framework to tackle the domain generalization challenge in skeleton-based action recognition. Utilizing the concept of a complete action prior, this method reconstructs entire action sequences from partial observations and resamples them to ge... | Rebuttal 1:
Rebuttal: Dear Reviewer UadT:
Thanks for your effort for reviewing our paper and giving kind suggestions. We really appreciate your positive comments on our paper.
[Q1] Can the authors elaborate more on why domain generalization is critical in the context of skeleton-based human action recognition, despit... | Summary: The work proposes a novel recover-and-resample augmentation framework for domain generalization with application to skeleton-based action recognition. The authors aim to tackle a specific issue when moving from one dataset to another, i.e. the temporal misalignment of actions of the same class. In the experime... | Rebuttal 1:
Rebuttal: Dear Reviewer Vnjk:
Thanks for reviewing our paper. Due to space limit, we answer main questions regarding the soundness of the paper. Feel free to raise further questions.
Introduction
[Q1.1] What do you mean by "human performs generally complete actions within large datasets" and "in terms o... | Summary: This paper proposes a novel recover-and-resample augmentation framework to address the skeleton action generalization problem across different datasets. The framework utilizes a complete action prior to recover full action sequences from partial observations, employing boundary pose-conditioned extrapolation a... | Rebuttal 1:
Rebuttal: Dear Reviewer V13L:
Thanks for your effort for reviewing our paper and giving kind suggestions. We really appreciate your positive comments on our paper.
[Q1] Dependency on Clustering Algorithms: The approach heavily relies on clustering algorithms for learning boundary poses and linear transfor... | Summary: In this paper, the authors address the issue of generalizing skeleton-based action recognition across different domains. They propose a novel recover-and-resample augmentation framework based on the concept of complete action prior. The approach is validated on different cross-dataset settings and demonstrates... | Rebuttal 1:
Rebuttal: Dear Reviewer UxEw:
Thanks for your effort for reviewing our paper and giving kind suggestions.
[Q1] I am content with the current experimental setting
[A1] Do you mean you are not content with the current experimental setting? Since the generalizability of skeleton-based action recognition is... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Classifier-guided Gradient Modulation for Enhanced Multimodal Learning | Accept (poster) | Summary: This paper proposes a balanced multimodal learning method. Compared to existing methods that only consider the gradient size, it also considers the direction of the gradient.
Strengths: The experiment includes multiple data sets and multiple tasks.
Weaknesses: There is less visualization analysis of the expe... | Rebuttal 1:
Rebuttal: Thanks for your valuable time and comments.
**(W1):Theoretical analysis.**
We provide an analysis of why the combination of improvement can balance the training. In Sec 3.2, we know the dominant modality will be updated faster than others, which makes the gradient $\partial\Omega/ \partial\phi$ ... | Summary: This paper proposed a balanced multi-modal learning method with Classifier-Guided Gradient Modulation (CGGM), considering both the magnitude and directions of the gradients, with no limitations on the type of tasks, optimizers, the number of modalities.
Strengths: 1. Balanced multi-modal learning considering ... | Rebuttal 1:
Rebuttal: Thanks for your valuable time and constructive comments.
**(W1): Difference and comparison with previous methods.**
The differences between CGGM and [1] are:
- [1] considers a fixed loss term for direction during the training process while CGGM employs a dynamic loss term to balance the trainin... | Summary: This paper focuses on the notorious modal imbalance problem in multi-modal learning. To alleviate the modality imbalance, the proposed method modulates gradient magnitude and the directions of the gradient simultaneously. Experiments on various multi-modal datasets demonstrate the efficiency.
Strengths: - Thi... | Rebuttal 1:
Rebuttal: Thanks for your valuable time and constructive comments.
**(W1): The explanation of how the gradient direction between the specific modality and their fusion influences the modality update.**
In Eq(3) and Eq(4), we know that the parameter $\theta$ and the term $\partial \mathcal F, \partial \mat... | Summary: This paper proposes CGGM, a novel strategy to balance the multimodal training process. Compared with existing methods, it can deal with the unbalanced multimodal learning problem with different optimizers, takes, and more than two modalities.
Strengths: The motivation is sufficient and the experiments on dif... | Rebuttal 1:
Rebuttal: Thanks for your valuable time and constructive comments.
**(W1): Computational complexity of the additional classifiers experimental results.**
The additional classifiers will need more computational resources during training. However, during inference, the classifiers will be discarded. Therefo... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their great effort and constructive comments on our manuscript. During the rebuttal period, we have been focusing on these beneficial suggestions from the reviewers and doing our best to add several experiments and revise our manuscript.
According to the revie... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalization Bounds via Conditional $f$-Information | Accept (poster) | Summary: This paper introduces novel generalization bounds using the conditional f-information framework. It first derives conditional f-information-based generalization bounds for bounded loss, then examines mutual information generalization bounds as a specific example of their general theorem. This analysis helps to... | Rebuttal 1:
Rebuttal: We thank you sincerely for the valuable feedback. Our responses follow.
>- For the definition of the expected generalization error (line 101 on page 3), should the correct definition be $\mathbb{E}\_S[\mathbb{E}\_W[L_\mu(W)-L_S(W)]]$ instead of the current formula in the paper? If I understand co... | Summary: This paper presents a general framework for generalization bounds using a careful application of the Donsker-Vardaran representation of the conditional $f$-information, and show that a suitable quadratic Taylor expansion of the bounds, for various $f$-divergences, improves over the state of the art.
Strengths... | Rebuttal 1:
Rebuttal: We thank you sincerely for your valuable feedback and the positive comments on our paper. Our responses follow.
>- I am not an expert in the state-of-art guarantees for generalization, so please take these concerns with a grain of salt. However, my only concern would be that these guarantees do n... | Summary: Understanding generalization using information-theoritic measures of dependence between input and output of a learning algorithm is an important area of study. The main focus of this line of work is using the KL divergence as a measure of dependence and providing generalization bounds. In this work, the author... | Rebuttal 1:
Rebuttal: We thank you sincerely for your constructive comments on our paper. Our responses follow.
>- 1- The point that one can replace KL with an arbitrary f-divergence was shown in the following paper:
Lugosi G, Neu G. Online-to-PAC conversions: Generalization bounds via regret analysis. arXiv preprint ... | Summary: This paper extends conditional mutual information bounds to other $f$-divergences. A list of bounds involving various $f$-information terms are established. The results are derived by evaluating a previously established variational formula for f-divergences at a specific function. Analysis tailored to various ... | Rebuttal 1:
Rebuttal: We thank you sincerely for your valuable feedback on our paper. Our responses follow.
>- The main weakness of this work is in the motivation ...
**Response.** As noted in Lines 159-160, when a bound contains $\mathbb{E}[G_i]$, we refer to it as an "oracle" bound, such as Theorem 3.1. Obtaining t... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspective | Accept (poster) | Summary: The paper analyzes the features of a deep equilibrium model (DEQ) using neural collapse metrics under class-balanced and imbalanced settings. In particular, it is shown that under imbalanced conditions, the class means of features in DEQ are relatively closer to a simplex ETF orientation than in the case of an... | Rebuttal 1:
Rebuttal: > Q1: The paper suffers from major presentation issues. In particular, a lot of notation/formulation errors lead to unclear results.
A1: Thanks for pointing out these typos and errors! We have made the following corrections in the revised version:
* In Line 74, we have adjusted the domain as $\p... | Summary: This paper studies the neural collapse (NC) phenomenon in Deep Equilibrium model (DEQ), a competitive implicit neural network model to standard explicit model. The authors compare the theoretical property of DEQ and NN on the layer-peeled model, a simplified model that include the last two layers only. It was ... | Rebuttal 1:
Rebuttal: >Q1: My major concern of this paper is its contribution and significance. Since the discovery of minority collapse, there has been tons of literature on how to mitigate the minority collapse. Therefore, my opinion is that neural collapse analysis can provide limited insight about the advantage of ... | Summary: This paper investigates the representation of Deep Equilibrium Models (DEQ), highlighting their memory efficiency and competitive performance. Using NC, it shows that DEQ exhibits NC under balanced conditions and maintains advantages in imbalanced settings. Theoretical findings are validated through experiment... | Rebuttal 1:
Rebuttal: > Q1: The analysis is limited to simple imbalanced scenarios and DEQ models, restricting the generalizability of the findings to more complex real-world situations.
A1: Thanks for your comment! Currently, our work primarily focuses on theoretical aspects, and discussing real-world issues will be... | Summary: The author analyzes the DEQ from the prospective of Neural Collapse to demonstrate the reason why DEQ is effective. The Nerual collapse means that at the final phase of training (training error is close to zero), the feature and classifier vector converges to a simplex Equiangular Tight
Frame. Neural collap... | Rebuttal 1:
Rebuttal: > Q1: The smaller lower bound of loss function may cause overfitting, which may affect the performance in the test phase. Add discussions on this part can be helpful.
A1: Thanks for your valuable suggestion! We agree with you that lower bound of loss function can lead to overfitting.
We would li... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of our paper and help with improving our manuscript. We sincerely appreciate that you find our work:
- adds to the understanding of the behavior of DEQ (C4pT, a3Sj),
- conducts extensive and solid experiments (C4pT, a3Sj, Zj9t),
- addresses an nove... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting the Potential of Large Language Models with an Intelligent Information Assistant | Accept (poster) | Summary: The paper introduces AssistRAG, a framework that integrates an intelligent information assistant within LLMs. AssistRAG employs a two-phase training approach involving Curriculum Assistant Learning and Reinforced Preference Optimization, focusing on memory management and knowledge management. Experiments on th... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback. We have responded to each of the weaknesses (W), questions (Q), and limitations (L) you raised. We hope the following responses clarify the contributions of our work and address your concerns.
***
**R to W1:**
While memory and knowledge manag... | Summary: To address the limitation of LLMs generating factually incorrect information, the authors have introduced AssistRAG, an intelligent information assistant with LLMs, building upon existing retrieval-augmented generation (RAG) strategies. The system operates in two main categories: memory management and knowledg... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback and recognition of our paper. We hope that the following responses will address your concerns:
***
**R to W1:**
To further demonstrate the effectiveness of our model, we have included additional experiments with two agent solutions on the 2wiki data... | Summary: This paper proposes AssistRAG, an architecture for augmenting LLMs with a separate, trainable agent that helps with information retrieval and memory/knowledge management. The authors motivate such an architecture (as opposed to, say, fine-tuning the main LLM for RAG) and describe how to build and train it. The... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our paper. We sincerely appreciate all the feedback from the reviewers and will make revisions to enhance the paper's impact and applicability based on your valuable suggestions.
Regarding the datasets, we have compiled the publication years of all commonly used ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Measuring Per-Unit Interpretability at Scale Without Humans | Accept (poster) | Summary: The paper proposes a novel method for measuring per-unit (e.g. per-neuron) interpretability of vision models, which is based on a DreamSim-based automation of the 2-AFC task. The method, which is called the Machine Interpretability Score (MIS) is found to be highly correlated with human measures of interpretab... | Rebuttal 1:
Rebuttal: Dear Reviewer gaJB, \
Thank you for your valuable feedback and for praising our paper as a *“triumph of the genre”* with *”outstanding quality”* and finding it *”solve[s] a longstanding problem”*. Please let us know whether our responses below addressed all of your questions or whether there are f... | Summary: The paper presents a method to automate a per-unit (e.g. individual neuron, channel in a conv layer) interpretability score for vision models that was previously computed via an expensive human study [50]. They demonstrate that their automated scores are highly correlated with the human measures, and then appl... | Rebuttal 1:
Rebuttal: Dear Reviewer j42j, \
Thank you for your detailed feedback. Please let us know whether our responses below addressed all of your questions or whether there are further questions we can answer so that you can confidently increase your score
**Q:** “find the underlying task to easy”, “does not acc... | Summary: The authors introduce a computational metric for interpretability. Their proposed metric is a computational version of an evaluation metric introduced in previous literature which measure human perceived interpretability. Crucially, they find that this metric correlates well, and since it does not require huma... | Rebuttal 1:
Rebuttal: Dear Reviewer P5jG,\
Thank you for your valuable feedback and for praising our paper as a *“significant work”* with *”interesting results”*. Please let us know whether our responses below addressed all of your questions or whether there are further questions we can answer so that you can confident... | Summary: The paper suggests a new automatic measure to asses how interpretable individual units inside vision models are (called MIS). The per-unit metric assesses the similarity of two query images (one should maximize unit activation and one minimize it) to two groups of representative exemplars (top-activating and l... | Rebuttal 1:
Rebuttal: Dear Reviewer 8Jb5,\
Thank you for reviewing our paper. Please let us know whether our responses below addressed all of your questions or whether there are further questions we can answer so that you feel confident in increasing your score.
**Q:** *“I do not find any of the conclusions very excit... | Rebuttal 1:
Rebuttal: Dear reviewers,\
Thank you for your valuable feedback. We are delighted that you praise our paper as a *“triumph of the genre”* with *”outstanding quality”* (Rev. gaJB) and finding it’s results *”overall interesting”* (Rev. P5jG) and *“potentially very useful”* (Rev. j42j) for an *”important”* top... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SparseLLM: Towards Global Pruning of Pre-trained Language Models | Accept (poster) | Summary: This paper tries to improve the pruning technique for LLM to enhance computational and memory efficiency. The proposed SparseLLM, circumvents the scalability problem of global pruning and suboptimal performance due to local pruning. It breaks down global pruning into subproblems
Strengths: 1. Pruning LLM rema... | Rebuttal 1:
Rebuttal: Dear Reviewer K3H3,
Thank you for finding our method interesting and practically useful. Please refer to our response below for details:
> *"It appears the pruning procedure differs from model to model. If the model architecture changes, the pruning needs to be adjusted. Therefore, I have doubts... | Summary: In this paper, the author proposes a method to globally prune large language models (LLMs) without consuming significant memory. By using auxiliary variables, the LLM can be pruned separately while maintaining dependencies. The evaluation demonstrates that the proposed method outperforms previous approaches in... | Rebuttal 1:
Rebuttal: Dear Reviewer 35o1,
We are grateful for your recognition of the novelty of our method. Please find our detailed response below:
> *"The use cases appear tricky regarding model size. For smaller models (<7B), they can fit into GPU memory (A100), allowing global pruning. For larger models (>70B), ... | Summary: This paper introduces SparseLLM, a novel pruning technique targeted at the FFN layers in LLMs. By treating global and local (layer-wise) pruning as special cases in the proposed formulation, SparseLLM can circumvent the limitations of both extremes. The proposed method introduces auxiliary variables and soft c... | Rebuttal 1:
Rebuttal: Dear Reviewer snMo,
We sincerely appreciate that you found our paper and method interesting with solid results. Please refer to our response below for details:
> *"The proposed approach appears to obtain accuracy figures on par with SparseGPT at 70% unstructured sparsity. In higher sparsity reg... | Summary: This paper presents SparseLLM, a framework to prune large language models by decomposing the global pruning objective into multiple sub-problems, each of which can be solved with low resources, when combined, solve the global pruning objective. The method reformulates LLMs as a chain of modular functions and u... | Rebuttal 1:
Rebuttal: Dear Reviewer FMFD,
We sincerely appreciate that you found our paper and method interesting with solid results. Please refer to our response below for details:
> *"The experiments are a bit weak in model choice. Older models like OPT and Llama-2 are chosen, when many new and better-performing mo... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank all your professional and constructive comments, especially given the time and workload for this year's NeurIPS. We have provided detailed responses to each individual comment and hope we have addressed all your concerns. Below is a brief summary of the new expe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Implicit Regularization Paths of Weighted Neural Representations | Accept (poster) | Summary: Neural networks are a powerful tool for extracting features from data, but these features can be very high-dimensional. This high dimensionality can be a bottleneck for training machine learning models, requiring computational power and memory. This manuscript investigates implicit regularization through subsa... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time and valuable feedback and acknowledging the strengths of our paper!
Thanks also for a nice reference pointer!
Below, we expand more on this reference and connections to our work.
**Weakness** and **Question**
- **[W1] and [Q1] Related work on iterat... | Summary: This paper studies the weighted linear regression problem where the feature matrix is left-multiplied with a random matrix $\mathbf{W}$ denoting the sample weighting. Under the assumption of the asymptotic freeness between this weighting matrix and the feature matrix, the paper shows that the weighted ridge re... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and providing valuable suggestions!
Thank you also for the nice questions!
We appreciate all the feedback and have addressed the weaknesses and questions below.
**Weaknesses**
- **[W1] Practical applicability.** Both Theorems 1 and 2 provide "dat... | Summary: This paper investigates the implicit regularization effects of weighted pretrained features. It establishes a path of equivalence between different weighting matrices and ridge regularization with matching effective degrees of freedom. The study extends results to structured features and ensembles, providing t... | Rebuttal 1:
Rebuttal: Thank you for the encouragement and comments! While we appreciate all the feedback, we believe that the main concerns raised are related to the clarity of exposition and can be easily addressed easily. Below, we address all the weaknesses and questions on a point-by-point basis.
We respectfully re... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for taking the time to review our paper. We appreciate the constructive comments and valuable feedback.
All three reviewers have acknowledged several key strengths of our paper.
- Reviewer **cp5Y** liked the clear overview of previous works provided in Table ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions | Accept (poster) | Summary: The paper investigates the problem of differentially private stochastic convex optimization (SCO) under the heavy-tailed setting and achieves a nearly optimal rate of $G_2 \cdot \frac{1}{\sqrt{n}}+G_k\left(\frac{\sqrt{d}}{n \varepsilon}\right)^{1-\frac{1}{k}}$. Specifically, it first provides results using Cli... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts and feedback.
Section 3: We apologize for the lack of clarity in Section 3. In Section 3.1, we propose Algorithm 1, which achieves good performance on an empirical loss assuming the dataset satisfies a property (bounded $b_{\mathcal{D}}$, see (6)). In Section ... | Summary: This paper addresses differentially private stochastic convex optimization (DP-SCO) with heavy-tailed gradients, where previous assumptions of uniform Lipschitz constants are relaxed to bounded k-th moments. The authors introduce a new reduction-based framework that adapts strategies from the uniform Lipschitz... | Rebuttal 1:
Rebuttal: Thank you for your careful reading; we address your questions here.
Re: clarity, we agree with your suggestion, and will add a description of the algorithm and the specific guarantees we are using about it to help the reader. Thanks for pointing this out.
Re: population-level localization, we w... | Summary: .The paper studies DP-SGD under the assumption that the gradients have heavy-tailed phenomenon. This has been recently motivated and studied a lot by recent works. The authors claim to achieve optimal rate for this problem.
Strengths: They obtain the first optimal rates (up to logarithmic factors) in the heav... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts. We appreciate your positive feedback. Please feel free to let us know if you have any questions or suggestions. | Summary: This paper gives three main results.
The first is a nearly optimal (losing a few logarithmic factors) excess loss rate for differentially private stochastic convex optimization (DP-SCO) when the gradient norms have $k$ bounded moments. In particular, they achieve the optimal excess loss under $\rho$-concentra... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts, and we appreciate your positive feedback. Please feel free to let us know if you have any questions or suggestions. | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the problem of differentially private stochastic convex optimization with heavy-tailed gradients. This paper points out that in typical optimization research, the assumption of uniformly G-Lipschitz, while convenient for bounding sensitivity, does not always hold. Based on this weakness, the... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. We answer your questions below.
Experiments: The focus of our work is theoretical in nature: developing algorithms that address known gaps in the literature on DP-SCO, a problem of interest in both theory and practice. We believe this goal has significant merit... | null | null | null | null | null | null |
BiDM: Pushing the Limit of Quantization for Diffusion Models | Accept (poster) | Summary: This paper proposes BiDM, which focuses on quantizing both weights and activations of diffusion models (DMs). Specifically, the authors introduce:
- Cross-timestep feature connection to enhance the accuracy of noise prediction in binarized DMs.
- Space-patched distillation, a novel variant of distillation loss... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our work. Here are our responses to your concerns:
> Q1: The experimental ...
Thank you for your suggestion. We have added experiments and discussions on EfficientDM. You can check the Global Rebuttal (1) for more details.
> Q2: Figure.4 ...
Thank you fo... | Summary: The manuscript proposes a method for fully binarizing both the weights and activations of diffusion models, named BiDM. Structurally, it introduces an improved XNOR method for scaling factors of activations and high-level feature connections across time steps, based on observations of existing temporal phenome... | Rebuttal 1:
Rebuttal: Thank you very much for your high recognition of our work and the valuable suggestions you provided. Our response is as follows:
> Q1: The description of the cross-time step connection during the training phase is somewhat vague. During the inference phase, the impact of this connection is iterat... | Summary: This paper aims to fully binarize weights and activations of diffusion models (DMs) to achieve storage saving and inference acceleration. To this end, the paper proposes timestep-friendly binary structure (TBS), which employs learnable activation binarizers and cross-timestep feature connections to capture the... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript and providing valuable suggestions. Here are our responses to some of the concerns you raised:
> Q1: The main weakness of the paper is that the proposed method, BiDM, increases the training time of DMS compared to the original process. This issue should be m... | null | null | Rebuttal 1:
Rebuttal: ## Global Rebuttal
We appreciate all reviewers for their careful reviews and the constructive feedback provided on our work, BiDM. Here is a summary of the main contributions of BiDM:
We propose BiDM, the first method to achieve an accurate fully binarized diffusion model, aiming for extreme com... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stepwise Weighted Spike Coding for Deep Spiking Neural Networks | Reject | Summary: This work belongs to ANN2SNN and proposes a novel coding scheme and neuron model to enhance the efficiency and accuracy of Spiking Neural Networks (SNNs) while reducing energy consumption. The Stepwise Weighted Spike (SWS) coding scheme improves information encoding by stepwise weighting input signals and intr... | Rebuttal 1:
Rebuttal: Thanks for your constructive and thoughtful comments. We are encouraged that you have recognized the novelty of our encoding scheme, the completeness of our experiments, the clarity of our presentation and the significance of our research. We would like to address your concerns and answer your que... | Summary: This paper proposes a novel Stepwise Weighted Spike (SWS) coding scheme designed to improve the efficiency of Spiking Neural Networks (SNNs) by compressing spikes and weighting their significance in each step of neural computation. This method addresses the issues of high delays and energy consumption associat... | Rebuttal 1:
Rebuttal: Thanks for your constructive and valuable feedback. We are encouraged that you found our approach innovative and the performance satisfactory. We would like to address your concerns and your questions in the following.
> 1. Clarity and Detail:
Thank you for your constructive feedback. We apolog... | Summary: The paper proposes a new coding scheme called Stepwise Weighted Spike (SWS) coding scheme for spiking neural networks to enhance the efficiency and reduce the number of operations and thus energy consumption. The SWS coding scheme tackles challenges associated with temporal and rate coding, such as heightened ... | Rebuttal 1:
Rebuttal: Thanks for your constructive and thoughtful comments. We are encouraged that you found our proposed coding scheme effective. We would like to address your concerns and answer your questions in the following.
> 1. Which model of a spiking neuron is being employed in equation 3 (line 120)? What is ... | Summary: The authors introduce a novel encoding method called Stepwise Weighted Spike (SWS) and a corresponding new neuron model named Ternary Self-Amplifying (TSA) for classification tasks utilizing the ANN2SNN training method. The proposed SWS encoding method assigns weights to the importance of spikes at each time s... | Rebuttal 1:
Rebuttal: Thanks for your valuable and constructive feedback. We are delighted that you found our analysis of the method comprehensive and the experimental results satisfactory. We would like to address your concerns and answer your questions in the following.
> 1. Effectiveness of SWS: Various encoding me... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors introduce a new spike coding scheme, which allows them to directly convert quantized ANN to their coding scheme. They demonstrate the effectiveness of their conversion on several pre-trained ANN with minimal loss in performance at the cost of an increase in latency.
Strengths: - strong experimenta... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We are encouraged that you found our proposed coding scheme to have strong experimental performance. We would like to address your concerns and answer your questions in the following.
> 1. Could you compare your approach more explicitly to *ref. 30*?
We first d... | null | null | null | null | null | null |
Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators | Accept (poster) | Summary: The authors presented Universal Physics Transformers (UPTs), a framework for efficient learning and scaling neural operators. UPTs offer flexibility in handling various data types, whether grid-based or particle-based. UPTs compress data into a low-dimensional latent space and perform dynamics propagation with... | Rebuttal 1:
Rebuttal: We appreciate your review and respond to the raised concerns below.
**Experiments with other PDEs**
UPTs are neural operators known to be applicable across various PDE types. Due to the nonlinear nature of the NS equations, they are notoriously more challenging to solve than parabolic or hyperbo... | Summary: This paper introduces Universal Physics Transformers (UPTs) to provide a unified learning paradigm for grid- or particle-based structures, enabling scalability across meshes and particles. UPTs mainly follow Encode-Process-Decode paradigm and allow queries at any space-time point through perceiver-like cross a... | Rebuttal 1:
Rebuttal: Thank you for your review and helpful comments which were very useful to improve the paper. We address your points individually.
**Comparison to other transformer methods**
The fundamental building principles of UPT are (i) an encoding that is designed to handle irregular grids of various... | Summary: This paper introduces a transformer-based architecture to scale neural operators to larger and more complex conditions involving spatiotemporal modeling. The novelty of this architecture is the use of the so-called latent rollout, which performs autoregressive modeling on the latent space, as opposed to the de... | Rebuttal 1:
Rebuttal: Thank you for your comments which helped to improve the paper. We addressed all your comments and followed all your suggestions.
**Memory and time complexity of different architectures**
We provide theoretical complexities in Appendix C.6 in Table 3. Note that when scaling only the input size t... | Summary: The authors present a new framework for unifying PDE surrogate modeling across domains. The proposed encoder, approximator, and decoder structure can accommodate PDEs of different discretizations and simulation types. Solutions are approximated in latent space, which aids in scalability and reducing computatio... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and helpful comments which were very useful to improve the paper. We address your points individually.
**Why UPT is a better scalable latent space model**
Latent Spectral Model uses geo-FNO (which could be consider as predecessor of GINO) to handle irregular g... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive feedback and for their constructive comments and suggestions.
We are pleased to see that the reviewers highlighted the clarity of our paper and appreciated our detailed and thorough experiments. Several reviewers recognized the efficiency of our approach b... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper a framework for efficiently scaling neural operators is introduced under the name of Universal Physics Transformers (UPTs). It is a novel paradigm that scales neural operators across diverse spatio-temporal problems without relying on specific grid or particle-based structures. Leveraging transfo... | Rebuttal 1:
Rebuttal: Thank you for your profound review and suggestions that helped us to improve our paper a lot. We addressed all your comments and followed all your suggestions. Please let us expand a bit on your comments.
**Clarity and detail in methodology**
In order to make the paper more accessible we -- as... | null | null | null | null | null | null |
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level | Accept (poster) | Summary: The paper studies Graph Injection Attacks on text-attributed graphs. The study presents three attack designs: Vanilla Text-level GIA (VTGIA), Inversion-based Text-level GIA (ITGIA), and Word-frequency-based Text-level GIA (WTGIA). The key contributions include demonstrating the effectiveness of text level pert... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our paper. We have addressed your concerns and provided clarifications below.
---
**W1:** *More about the Embedding-Text-Embedding Process.*
**Why Embedding-to-Text:** Traditional GIAs included only the text-to-embedding process, with the pipeline as fo... | Summary: This paper investigates an interesting topic. The authors first discuss the practicality of Graph Injection Attacks (GIA), arguing that previous approaches, which merely inject harmful embeddings, are unrealistic. It introduces three text-level GIA methods: ITGIA, VTGIA, and WTGIA, and conducts a thorough exam... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. Please find our detailed responses below:
---
**W1:** *Citations of TDGIA, ATDGIA, MetaGIA, and AGIA.*
We have cited TDGIA in Section 2.
The other methods (ATDGIA, MetaGIA, and AGIA) are introduced in [1].
In our experiments, we followed... | Summary: The paper studies GIAs, particularly focusing on text-attributed graphs (TAGs). It introduces a method for GIAs by injecting textual content directly into the graph, as opposed to the traditional method of embedding-level attacks. The authors propose three new attack designs: Vanilla Text-level GIA (VTGIA), In... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback on our paper. We appreciate your insights and have addressed your concerns below.
---
**W1 and W5:** *Exploration of Robustness Against Defense Mechanisms*
In the **author rebuttal**, we included classic methods like GAT and GraphSAGE, as well as the EGNNGua... | Summary: This paper explores the vulnerability of GNNs to Graph injection Attacks (GIAs), which involve injecting malicious nodes into a graph. This paper explores GIAs at the text level, presenting three types of GIA designs that inject textual content into the graphs. The significance of text interpretability in atta... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback on our paper. We have addressed your concerns and provided clarifications below.
---
**W1:** *Why previous works cannot be applied to this task.*
We would like to clarify that by “Raw Feature,” we are referring to raw text, which represents the unembedded ... | Rebuttal 1:
Rebuttal: **Summary of Rebuttal**
Thanks to the reviewers for their diligent efforts and thorough evaluation.
We are glad to receive such positive feedback.
Notably, all reviewers acknowledged that conducting text-GIA is novel and meaningful.
Reviewers nfHh and Da4q recognized our method's technical con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A theoretical design of concept sets: improving the predictability of concept bottleneck models | Accept (poster) | Summary: This paper provides a theoretical analysis of properties of concept sets in CLIP-based CBMs. Specifically, the authors focus on the effect of concept sets on the empirical performance of CBMs in the low-resource regime and under distribution shifts. Towards this, the authors identify two characteristics of co... | Rebuttal 1:
Rebuttal: Thank you for your in-depth review and for highlighting this theoretical work's high relevance to the NeurIPS community.
## New experiments
The purpose of the experiments in our paper is to illustrate the theoretical insights, and we believe the current experiments serve that purpose well. However... | Summary: This paper presents theoretical contributions related to CBM, which delves into the impact that the choice of concept set has on CBM performance. It identifies advantageous conditions for CBMs, offering an orthogonal and meaningful perspective compared to most other works on CBMs.
Strengths: - Overall, the th... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful evaluation and appreciate your recognition of our theory's insightfulness and clarity, as well as our results' unique perspective.
### The use of LLMs for concept generation
The core objective of our method and experiments is to illustrate and validate our theoreti... | Summary: This paper addresses an important research question in CBM — understanding the properties of concept sets and their connection to the performance of CBMs.
A theoretical framework for concept sets is proposed, focusing on two desiderata for concepts: expressiveness and model-aware inductive bias.
Their theoret... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad you find the research question important and the insights interesting.
## Notation
We agree with all the suggestions and have incorporated them. Specifically:
* Changes in the introduction:
* We remove $\\theta$.
* L22: we introduce $\\hat{g}$... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thoughtful and insightful feedback. We appreciate their recognition of the relevance and importance of our research on understanding the properties of concept sets and their impact on Concept Bottleneck Models (CBMs). We value their appreciation of the pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identify Then Recommend: Towards Unsupervised Group Recommendation | Accept (poster) | Summary: This paper addresses the group recommendation task and innovatively proposes an unsupervised approach to automatically infer user-group distributions and make suitable recommendations.
For the group identification stage, a heuristic-based merge-and-split method is developed to facilitate this inference. For g... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer x7i3**
Thanks for your valuable and constructive reviews. We appreciate your insights and suggestions, as they will undoubtedly contribute to improving the quality of our paper. In response to your concerns, we provide answers to the questions as follows in order.
### ... | Summary: The research topic of this paper is group recommendation, i.e., recommend items to groups of users. The authors point out two issues of existing models including the fixed number of groups and the supervised training schema. To solve these problems, they propose a novel unsupervised group recommendation model ... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer pdQ8T**
Thanks for your valuable and constructive reviews. We appreciate your insights and suggestions, as they will undoubtedly contribute to improving the quality of our paper. In response to your concerns, we provide answers to the questions as follows in order.
... | Summary: This paper tackles the group recommendation problem by proposing an unsupervised group recommendation framework named ITR (Identify Then Recommend). Specifically, the paper first identifies the area and density of each region automatically, then combining with a heuristic strategy to identify groups. Then, per... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer hAA1**
Thanks for your valuable and constructive reviews. We appreciate your insights and suggestions, as they will undoubtedly contribute to improving the quality of our paper. In response to your concerns, we provide answers to the questions as follows in order.
### ... | Summary: This study pointed out two issues in group recommendation in the context of industrial applications. First, the group label can be dynamic and may require constant training. Second, the annotation cost for the supervised learning is great. To address these two issues, the study proposed an unsupervised group r... | Rebuttal 1:
Rebuttal: ## **Response to Reviewer GTSo [1/2]**
Thanks for your valuable and constructive reviews. We appreciate your insights and suggestions, as they will undoubtedly contribute to improving the quality of our paper. In response to your concerns, we provide answers to the questions as follows in order.... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to the SAC, AC, and PCs for their dedicated efforts and constructive feedback. Your comments have been invaluable in enhancing the quality of our manuscript. We have meticulously addressed each of your questions and hope our responses satisfactorily address your con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis | Accept (poster) | Summary: This paper proposed a 3D Gaussian Splatting method to render novel views with sparse inputs. This paper first use a pre-trained keypoints matching network to generate dense point initializations, and propose a consistent loss between the warped binocular stereo image. Meanwhile, they introduce a opacity penalt... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer gh5j for the invaluable feedback and time invested in evaluating our work. We respond to each question below.
**_Q1: The opacity penalty guides the remaining Gaussians closer to the scene surface._**
Opacity penalty prune the Gaussians that far from the scene... | Summary: This paper proposes a new method related to the problem of novel view synthesis in a sparse input setting. The authors propose to exploit stereo consistency as a self-supervision signal in contrast to the use of priors such as diffusion which tends to produce less precise geometry. Specifically, the work propo... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer UVce for the invaluable feedback and time invested in evaluating our work. We respond to each question below.
**_Q1: Comparison to generalizable methods._**
Thank you for listing some of the latest generalizable novel view synthesis methods. We run the state-... | Summary: This paper proposes 3D Gaussian splatting from sparse views aided by pre-trained key points matching initialization, binocular stereo constraints, and opacity regularization. Binocular stereo constraints utilize perspective projection to warp synthetic stereo images into the training images for self-supervisio... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer sng2 for the invaluable feedback and time invested in evaluating our work. We respond to each question below.
**_Q1: Opacity regularization does not seem enough to consider it a contribution._**
Although opacity regularization is a very simple strategy, it si... | Summary: This paper introduces a novel method for 3D Gaussian-based sparse view synthesis. Specifically, initialized from dense point clouds, the depth-warping loss and the opacity penalty strategy are introduced to obtain accurate 3D Gaussians. Extensive experiments on the Blender, LLFF and DTU dataset have demonstra... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer q3N3 for the invaluable feedback and time invested in evaluating our work. We respond to each question below.
**_Q1: Whether depth-warping loss can be viewed as a contribution_**
Although some papers regard unseen views or adjacent training views as source vi... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their invaluable feedback and the time they dedicated to evaluating our work. We are pleased that the reviewers appreciated the representation and significance of the paper. We have addressed each reviewer’s comments separately, providing detailed analyses and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems | Accept (poster) | Summary: This paper analyzes the implicit regularization effect of Sharpness-Aware Minimization (SAM), focusing specifically on scale-invariant problems. While existing research emphasizes sharpness, this study introduces a new concept called Balancedness, demonstrating both theoretically and empirically that SAM promo... | Rebuttal 1:
Rebuttal: We appreciate the time and efforts from the reviewer put into this review. We also want to thank the reviewer for recognizing the strength of our work. We will update our draft and code repo to further improve the quality of this work.
**W1.** *While LoRA training is performed with AdamW in the o... | Summary: This paper investigates the dynamics of SAM when the loss is of the form f(xy^T) or f(x^Ty). This formulation includes interesting scenarios such as LoRA. This paper shows that SAM will promote balancedness, which is the difference between the squared norms of two variables. Based on this new analysis, this pa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the nice questions. We will add these discussions to the draft.
**W1.** *Arguments including that SGD will make balancedness unchanged are only correct for infinitesimal learning rate and claims like this should be made rigorous (for example at line 137).*
Thank y... | Summary: This paper investigates the implicit regularization effects of sharpness-aware minimization (SAM) on scale-invariant optimization problems, introducing "balancedness" as a new metric for analysis. The authors provide theoretical results showing that SAM promotes balanced solutions for both non-overparameterize... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts devoted to this paper. Below please find our responses to weakness and questions.
**W1.** *The scope is limited to scale-invariant problems, and LoRA types optimization problem.*
We agree with the reviewer that the major application is on scale-inva... | Summary: ### Summary:
This work introduces balancedness (instead of sharpness) and proposes Balancedness-Aware Regularization (BAR), used for scale-invariant problems (e.g., LoRA). Given an objective such as $f(xy^T)$ or $f(x^Ty)$ with parameters $x,y$ and a fixed function $f$, the balancedness is defined as $B_t = \f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and efforts devoted to this paper. Below please find our point-to-point responses. Please let us know if there are other questions or unclearness during discussion. We are always glad to improve the quality of our submission.
**W1.** *The paper is not well-writt... | Rebuttal 1:
Rebuttal: We thank the ACs and reviewers for handling this submission. Your comments are appreciated, and the manuscript will also be updated accordingly. Our point-to-point responses can be found below, and a .pdf file is also attached as graphical illustration to questions from Reviewer t2ze.
Pdf: /pdf/04... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Score matching through the roof: linear, nonlinear, and latent variables causal discovery | Reject | Summary: This paper present novel theoretical results to identify causal effects in restricted ANMs even in case of unobserved confounders.
Strengths: **The paper provides novel contributions to the field of score-based causal discovery by extending previous works to confounded restricted ANMs. Based on these contribu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review, the constructive comments and the stimulating question. We will go through these points in what follows.
## Weaknesses
We thank the reviewer for the constructive comments: we propose to implement all the 4 main points suggested, as we agree they wil... | Summary: The authors propose AdaScore, a method for causal discovery that generalizes previous work based on score matching for SCMs with possibly latent nodes. They combine connections of the score to conditional independence as well as to additive noise SCMs and show that a NoGAM-type procedure works to recover the d... | Rebuttal 1:
Rebuttal: We thank Reviewer TL8k for their thorough review and the valuable comments therein. One important criticism exposed by the reviewer is that the contribution of our work is limited in relation to the existing literature: in this regard, we address to general response and the first point of our resp... | Summary: The paper extends theoretical results about causal discovery through score matching to encompass both linear and non-linear SCMs and lift the sufficiency assumption. The theoretical results relax the non-linearity assumption of Montagna et al 2023 by swapping it with the less restrictive one of restricted ANM ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reading our paper. One important concern unaddressed by our general response is about the limits of our experimental evaluation: we point to the first bullet in our response below, and the experimental results in the PDF of the rebuttal.
## Weakne... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for the time spent reading and understanding our paper, as well as for the insightful comments and questions. Our paper is **well received in terms of clarity** - with Presentation scores 3 from all reviewers - **and soundness** - with scores 3, 3, 4 from R TL8k, R mmww, R ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Theoretical Limits of Learning with Label Differential Privacy | Reject | Summary: This paper investigates the theoretical boundaries of learning with Label Differential Privacy (Label-DP) in both central and local models.
Label-DP is a weakening of standard differential privacy, where only the privacy of the "label" of each example is to be protected (an example is a pair (feature vector, l... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and valuable suggestions.
**Table 1**
Thanks for this comment, which is also raised by another reviewer kpNG. We will add citations directly in the table.
**Line 122**
Thanks. We will correct this typo.
**Line 135**
Thanks. $\eta^*(x)=\max_k \eta... | Summary: This work studies the minimax rates for classification and regression under (pure) label differential privacy in both the local and central models. They prove that rates of convergence for classification and regression with bounded label noise in the local label DP model are comparable to those for the non-pri... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review and detailed comments.
Firstly, thanks for finding these grammatical errors. We will correct them during revision.
**The main challenge and the techniques to overcome them as stated in the abstract aren’t clear to me as a reader at this point. It’s n... | Summary: The paper considers the problems of classification and regression under the constraint of local/central pure label DP. The authors derive upper and lower bounds on the excess risk (compared to the non-private Bayes classifier/regression) for these problems, under somewhat standard assumptions on the 'ground tr... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable suggestion on improvement of presentation.
We agree that Lemma 1 in the appendix is important. In our revised paper, we will move this lemma to the main body of the paper. Moreover, we will also provide a better idea of the proof.
The outline of proving theo... | Summary: This paper investigates the minimax risks of classification and regression (with both bounded and heavy-tailed noise) under label differential privacy (DP) in both central and local models.
Strengths: The paper provides a comprehensive analysis by considering both upper and lower bounds for the minimax risks.... | Rebuttal 1:
Rebuttal: Thanks for your review and valuable suggestions. We will update our paper in our revised version accordingly.
**1. Around line 178, the output of the mechanism for classification is unclear. Why is it not a one-hot vector, or at least why is the L1 norm not equal to 1?**
As long as the privacy r... | Rebuttal 1:
Rebuttal: Thanks all the reviewers for your careful reading and valuable comments. We are encouraged to know that reviewers are positive about the novelty and value of our works. We have also received some detailed comments about definitions, notations and presentations that can be further improved. Some of... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Penalty-based Methods for Simple Bilevel Optimization under Hölderian Error Bounds | Accept (poster) | Summary: This article presents a nice extension to the broad class of penalty method problems, particularly for the case when the objective functions have a Hölderian error bound. This assumption for this class of problems appears new, and a thorough coverage of interesting results are claimed.
Strengths: - New result... | Rebuttal 1:
Rebuttal: We acknowledge the valuable insights you have provided for our paper.
# Weakness 1:
Thank you for pointing this out. Our definition of the subdifferential is derived from convex analysis. We use the subdifferential for a convex function $f$: $\partial f(x) = \\{ g : f(y) - f(x) \ge g^T(y - x) \... | Summary: This paper deals with a simple bilevel (the lower-level problem has no dependence on the upper-level variable) optimization problem, where both the upper- and the lower-level objectives are convex and potentially non-smooth. To simplify the original bilevel problem the authors consider a penalty-based single-l... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable insights and thoughtful feedback regarding this paper.
# Weakness 1:
We should emphasize that the problem class considered here receives a lot of interest in the literature.
- Simplicity: Although bilevel problems may appear straightforward, they have numer... | Summary: This work proposes a penalty based algorithm for simple bilevel optimization problems. The paper studies the relationship between the solutions of the penalized problem and the original bilevel problem. It extends the existing results on general bilevel optimization problem, which are established under PL cond... | Rebuttal 1:
Rebuttal: Your expert insights are crucial to our research, and we greatly appreciate your guidance and suggestions.
# Weaknesses 1:
We should point out that for simple bilevel optimization (SBO), there is no existing penalty method, although there exists a closed related method that is based on Tikhonov r... | Summary: The authors propose algorithms to solve simple bilevel optimization problems of the form $\min_x F(x)$ s.t. $x$ minimizes $G$, in the case where $F$ and $G$ are composite convex functions (i.e. some of 2 convex functions, one of which is also smooth), and assuming Lipschitz continuity of $F$ on the set of mini... | Rebuttal 1:
Rebuttal: Thanks for providing these valuable suggestions.
# Weakness:
Solodov [1] applied Tikhonov regularization (TR) [2] to solve simple bilevel optimization problems. Although the formulation of TR (l.47-48 in our paper) is similar to our method (l.36-37 in our paper), their origins and theories differ.... | Rebuttal 1:
Rebuttal: In this global 'Author Rebuttal', we upload our additional experimental results, as well as the clearer and more readable experimental results of Sections 4.1 and 4.2 in our paper.
# Linear regression problem
The first experiment is the sparse linear regression problem on the data ($3,000$ instan... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Referencing Where to Focus: Improving Visual Grounding with Referential Query | Accept (poster) | Summary: This paper focuses on the visual grounding task, which proposes a query adaption module that can be seamlessly integrated into CLIP. By strategically inserting this module into different layers of CLIP, the learnable query can adaptively learn target-related information from multi-level image feature maps, an... | Rebuttal 1:
Rebuttal: > Q1:It is better to provide the experiment results without using auxiliary loss, which can further observe the influence of auxiliary loss on the referential query.
We conduct the ablation study with auxiliary loss on RefCOCOg, and the results demonstrate the effectiveness of auxiliary loss. By ... | Summary: This paper addresses the generation of queries for the decoder. The authors propose RefFormer to generate the referential query with the prior context. A query adaption module is proposed to capture extensive target-related context and provide valuable referential knowledge for the decoder. Extensive experimen... | Rebuttal 1:
Rebuttal: >Q1: This idea seems straightforward but lacks some innovation. The method mentioned in the paper has been applied in other fields, such as R2-tuning.
Thank you for your question. We analyze the differences between our approach and R2-tuning [a] from two aspects: motivation and implementation:
... | Summary: The existing one-stage visual grounding methods suffer from cross-modal learning difficulty and focus simply on the deepest visual features. This paper designs a query adaption (QA) module to provide target-related referential queries for the decoder. The proposed architecture Reformer is based on a CLIP model... | Rebuttal 1:
Rebuttal: > Q1: How does the paper select the number of inserted QA modules? How does the paper select the indices of inserted layers? Why do more layers or lower layers hurt the performance?
We provide more experiments on layer selection on RefCOCOg below. We categorize [1-4], [5-8], and [9-12] as low-le... | Summary: This paper proposes a novel visual grounding framework, called RefFormer, aims to improve the learning process of learnable queries. Specifically, it introduces a query adaption module (QA) that can be seamlessly integrated into different layers of CLIP, which can not only provide prior information to the dec... | Rebuttal 1:
Rebuttal: > Q1: Line 72 should correct "RefFormer" to "QA".
Thank you. We will correct the typos in our paper.
> Q2: A more detailed description is needed for the text side in the QA module, such as how the interaction with visual features is initiated.
In the CAMF block, we take the language features... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation | Accept (poster) | Summary: This paper proposes a safe RL algorithm that adjusts the sample number during one on-policy update based on the conflict between the reward gradient and the cost gradient. When the policy's cost value is near the constraint threshold and there is a gradient conflict, a larger sample number is used; otherwise, ... | Rebuttal 1:
Rebuttal: # Reply to Reviewer qrLu
We appreciate the reviewer for recognizing our contributions in both practice and theory, and for providing constructive suggestions.
> **Q1:** The sample manipulation is an interesting mechanism with the gradient conflict as a signal. Is it solely applied to PCRPO or c... | Summary: The paper introduces an approach, Efficient Safe Policy Optimization (ESPO), aimed to improve the efficiency of safe reinforcement learning (RL). ESPO tries to enhance sample efficiency through sample manipulation, addressing the challenges of sample inefficiency in safe RL, which often requires extensive inte... | Rebuttal 1:
Rebuttal: # Reply to Reviewer by8k
Many thanks to the reviewer for recognizing our contributions in terms of theory and practice.
> **Q1:** Adding evaluations on more diverse, complex, or real-time environments would strengthen the generalizability claims.
**A1:** Thank the reviewer for insightful commen... | Summary: The paper introduces Efficient Safe Policy Optimization (ESPO), an approach that enhances safe reinforcement learning by dynamically adjusting sample sizes based on gradient conflicts. ESPO optimizes reward and safety, improves convergence stability, and reduces sample complexity. The experiments shows the pro... | Rebuttal 1:
Rebuttal: # Reply to Reviewer oXwv
> **Q1:** The paper experimented on SafetyReacher-v4, SafetyWalker2d-v4, and SafetyHopper/AntVelocity. Those safety tasks do not test generalization, such as those with safety gym.
**A1:** We appreciate the reviewer's insightful comments. To the best of our understandin... | Summary: This paper presents a novel algorithm for safe reinforcement learning, ESPO,
which independently collects gradient information for reward optimization and
constraint satisfaction. It then makes a dynamic choice about how to combine
these two gradients in order to find an optimal safe policy. In addition, the
p... | Rebuttal 1:
Rebuttal: # Reply to Reviewer qn1v
> **Q1:** More discussion of the critical coefficients $x_t^r$ and $x_t^c$ of the algorithm's performance. How are the coefficients $x_t^r$ and $x_t^c$ computed?
**A1:** Thanks for raising this point! We conduct new ablation experiments and have added a detailed discussio... | Rebuttal 1:
Rebuttal: # General Response:
We thank the reviewers for their careful reading of the paper and their insightful and valuable feedback. Here, we provide **new experimental results** and discussions to answer some common questions raised by reviewers.
**We attached a pdf file to show the required new expe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Label is Worth A Thousand Images in Dataset Distillation | Accept (poster) | Summary: The paper studies the effect of synthetic image soft labels on training performance, and show that the success of DD methods is attributed to the use of informative labels. The authors showed that the structured information in soft labels is important, and there is a tradeoff between knowledge and data. Genera... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s constructive comments and are glad they found our work interesting. We respond to their specific comments below:
__Response to Weakness__
> *Figure 4, regarding performance gain when data are wrongly labeled*
The argmax certainly contains a lot of information. ... | Summary: This paper investigates the importance of soft labels for dataset distillation methods, conducting detailed ablation experiments on the role of labels under various settings. It deeply explores the impact of labels on learning, providing an in-depth analysis and study of the intrinsic properties of labels. The... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s constructive comments and thoughtful insights. We respond to their specific comments below:
__Response to Weakness__
> *According to Table 7, the expert model (epoch) used to produce soft labels in the soft label baseline appears to be carefully selected. Does ... | Summary: This paper introduces soft probabilistic labels to the dataset distillation task. Specifically, it finds that the labels should consider structured information and perform unequally. Experiments on diverse datasets demonstrate its effectiveness.
Strengths: 1) This paper proposes the introduction of soft label... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback! We respond to their specific comments below:
__Response to Weakness__
> *The motivation needs to be stated more clearly. Why are label-level methods regarded as superior to image-level methods?*
To our best understanding, the dataset distillation communi... | Summary: This paper analyses the role of soft labels used in dataset distillation. Experiments with different ablation studies show that the performance of soft labels based data distillation approaches is primarily attributed to the use of soft labels. Secondly, the authors study the various types of soft labels and t... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback! We are glad that you found our work interesting. We respond to their specific comments below:
__Response to weakness__
> *The paper could benefit from a theoretical analysis of why soft labels are effective. The generalizability of the findings to data di... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for the detailed and thoughtful response! We are glad that reviewers have found our work interersting and scientifically sound. We have carefully read through all the comments and we believe that all your feedback will bring improvements to our work!
We address... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models | Accept (poster) | Summary: The paper presents Prompt-Agnostic Adversarial Perturbation (PAP), a method to enhance privacy and security in customized text-to-image diffusion models like Stable Diffusion. These models, while enabling high-quality image synthesis, pose risks of privacy breaches and unauthorized artwork usage. Existing ad... | Rebuttal 1:
Rebuttal: Dear Reviewer Q3Lh,
Thank you for your thorough review and constructive feedback. Your perceptive comments and suggestions have helped us improve our work.
Q1: a) PAP seems too complex? b) Simply expand prompts with LLM and gradient ensemble?
> a) The final implementation of PAP, as presented in... | Summary: This paper proposes a novel adversarial training method for text-to-image diffusion models, enhancing robustness against prompt-agnostic attacks. Specifically, the authors utilize prompts (embeddings) from a prompt distribution rather than a specific prompt for adversarial training. The proposed method is eval... | Rebuttal 1:
Rebuttal: Dear Reviewer nNzN,
We appreciate the time and effort you put into providing feedback on our work. Your insightful comments have contributed to the enhancement of our paper.
Q1: Demonstrate the connection between the test prompt and the prompt distribution
> We demonstrate the robustness of PAP ... | Summary: - This work is about a method to craft perturbation images to protect users/artists from personalized text to image diffusion methods (specifically DreamBooth and Textual inversion), that generalize better to unseen prompts than previous works.
- The core algorithm is "Prompt-Agnostic Adversarial Perturbation"... | Rebuttal 1:
Rebuttal: Dear Reviewer JLDf,
We are grateful for your constructive criticism and insightful comments on our work. We greatly value your feedback and have made significant improvements based on your suggestions:
Q1: Apply PAP to other methods including AdvDM + PAP/Diff-Protect
> Per your advice, we have ... | Summary: The authors propose a prompt-agnostic adversarial perturbation (PAP) method for customized diffusion models. They first use Laplace approximation to model the prompt distribution. Then they derive the attacks by maximizing the disturbance expectation. Extensive experiments on three datasets validate their perf... | Rebuttal 1:
Rebuttal: Dear Reviewer JA4c,
Thank you for your valuable feedback and insightful comments on our work. We appreciate your suggestions and have made the following improvements:
Q1: Potential semantic gap between the estimated prompts and natural language
> a) We emphasize the importance of considering pr... | Rebuttal 1:
Rebuttal: Dear AC and all the reviewers,
We would like to express our sincere gratitude to you for your comprehensive evaluation of our manuscript, as well as your insightful feedback and constructive suggestions.
We have tried our best to answer all questions of the reviewers about our paper. We wander i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models | Accept (poster) | Summary: This paper proposed a Self-TAught Recognizer (STAR) that leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) systems in diverse target domains. The proposed method includes a novel indicator that empirically integrates step wise information during decoding to assess the tok... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer 8Y8C for the valuable and constructive comments. Please find detailed responses below:
- ***Weakness 1 & 2***
Thanks for your feedback. We clarify that the true-label models use all labeled data from the training set of each domain. Therefore, how good this up... | Summary: The paper proposes STAR, a novel algorithm for unsupervised domain adaptation (UDA) of speech foundation models (e.g., one that has a decoder like Whisper). The UDA setting is the semi-supervised setting where unlabeled data from the target domain is available, and the speech foundation model is available. The... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer zXq9 for your valuable and constructive comments. Please find detailed responses below:
- ***Q1: References to previous work***
We sincerely appreciate your feedback. While this is not a completely new topic, our proposed method only requires a downloadable fo... | Summary: This paper investigates the use of audio-only data to enhance ASR performance for domain adaption in Speech Foundation Models. The approach is straightforward: recognition results are used to compute a confidence score for each token (e.g., BPE in this paper), which then weights the loss function, as shown in ... | Rebuttal 1:
Rebuttal: We appreciate Reviewer LswZ for valuable and constructive comments, and we believe our detailed responses below can solve your concerns on missing details. Please let us know if you have further questions or recommendations.
- ***Q1: Several missing details: Are the attention weights from cross-... | Summary: The paper proposes STAR, a novel ASR domain adaptation technique that requires no labeled data and only a few unlabeled samples. STAR utilizes the confidence score and self-attention score obtained during decoder inference to calculate the reliability score (STAR indicator) for each token. The score of each to... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer vnSx for valuable and constructive comments. Your suggestion is also instructive for further analysis and our future work.
Please find detailed responses below:
- ***Q1: Appling STAR for CTC or RNN-T-based ASR models.***
Thanks for your feedback, As the illu... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation | Accept (poster) | Summary: The PeakConv (PKC) model specialized for radar signal analysis effectively characterizes the target signatures of radar signals. However, the fixed predefined peak receptive field limits the performance of PKC due to significant variations in target features and associated interference within radar signals. To... | Rebuttal 1:
Rebuttal: Thank you for your constructive and thoughtful comments. We appreciate the recognition of the strengths of our work: the innovation of our method, technically sound paper and overall clarity in writing. We are glad to answer all your questions.
**Q1**: The correspondence between interference in r... | Summary: This paper presents a radar semantic segmentation method, AdaPKC, which combines PeakConv and Adaptive Peak Receptive Field (APRF) concepts. The author demonstrates extensive applicability of AdaPKC in radar perception including autonomous driving, drone surveillance, and ocean monitoring. The method significa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We appreciate the recognition of the strengths of our work: superior performance than SoTA, solid evidence of the method's efficacy by rigorous ablation studies, contribution to the development of radar semantic segmentation. We are glad to answer all your q... | Summary: This paper proposes an idea of adaptive peak receptive field, and upgrades PKC to AdaPKC based on this idea. Beyond that, a novel fine-tuning technology to further boost the performance of AdaPKC-based RSS networks is presented.
Strengths: The adaptive version of PeakConv (PKC) is motivated by the adaptive se... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We are glad to answer all your questions.
**Q1:** The adaptive version of PeakConv (PKC) is considered to be incremental work.
**A1:** Please see our response to all authors that clarify the **[Novelty]** of our work.
**Q2:** AdaPKC performance improvemen... | Summary: This paper works on the improvement of Radar semantic segmentation. Motivated by the limitation of learning ability of the SoTA PKC method due to the fixed peak receptive field (PRF), an adaptive version named AdaPKC is proposed. The method can be metric-based and learning-based. The advantages of the propos... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments on our work. We appreciate the recognition of the strengths of our work: the practical motivation and good presentation, extensive experiments and analysis, and superior performance than SoTA. We are glad to answer all your questions.
**Q1:** The novelty of ... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewers for their valuable feedback. In this section, we commence by tackling the concerns that have been collectively raised. These shared concerns correspond to the three keywords in the title:
**[Novelty] What is the novelty of this work compared to existing PK... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Masked Pre-training Enables Universal Zero-shot Denoiser | Accept (poster) | Summary: The paper proposes a novel zero-shot image denoising method named Masked Pre-train then Iterative fill (MPI). This method leverages a pre-trained model on vast natural images using a masking strategy to learn generalized image distributions, enabling effective denoising without prior knowledge of the specific ... | Rebuttal 1:
Rebuttal: $\textbf{Q1: More comparison with zero-shot modifications of blind spot methods}$
$\textbf{A1:}$ Thank you for your thorough consideration. We have revised AP-BSN, MM-BSN, and PUCA (as shown in the overall rebuttal to all reviewers), and the results are listed in $\textit{Table 1}$ in provided PD... | Summary: This paper proposes a method that could handle image denoising regardless of the noise types and intensity. To achieve this goal, the proposed method includes two crucial steps: first, the model will be pretrained on a large amount of images (with masking); second, the pretrained model will be fine tuned on th... | Rebuttal 1:
Rebuttal: $\textbf{Q1: Similarities and differences compared to MetaDIP and DGP}$
$\textbf{A1:}$ Thank you for your pointing out. Our method shares some similarities with MetaDIP and DGP. MetaDIP learns denoising by obtaining initial weights beneficial for downstream tasks, while our method uses masked tra... | Summary: This paper proposes a zero-shot image denoising method called Masked Pre-train then Iterative fill (MPI). The key idea is to pre-train a model on natural images using masked image modeling, then apply this pre-trained model to denoise new images in a zero-shot manner through an iterative optimization process. ... | Rebuttal 1:
Rebuttal: $\textbf{Q1: Lack of essence of our method}$
$\textbf{A1:}$ Thank you for your pointing out. The core of our method lies in the inherent denoising capability of a model pre-trained with masked natural images. This motivation is demonstrated in main text. The pixel-level random masking we employ c... | Summary: The paper introduces a novel zero-shot image denoising paradigm called Masked Pre-train then Iterative fill (MPI). The key contributions are:
MPI first pre-trains a model on a large dataset of natural images using a pixel-wise masking strategy. This allows the model to learn the underlying distribution and re... | Rebuttal 1:
Rebuttal: Thank you for your suggestions. We studied the three works you provided carefully. The first is a score-based denoising algorithm, the second is an improvement on blind-spot networks (PUCA), and the third is a diffusion-based image restoration method (DDPG). The first two seem to be unsupervised d... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and efforts of all the reviewers, as well as their valuable suggestions provided during the review process. We are encouraged by the reviewers' recognition of our work and acknowledge that there are still many weaknesses in our current work. We carefully considered... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Grounding and Validation of Algorithmic Recourse in Real-World Contexts: A Systematized Literature Review | Reject | Summary: This paper provides a review of previous works that study "algorithmic recourse", i.e. conceptual and practical approaches for giving people actionable recommendations to change how they are impacted by algorithmic systems. This literature is deeply connected with counterfactual explanations and understanding ... | Rebuttal 1:
Rebuttal: Thank you for this insightful review and feedback!
As mentioned in the "global rebuttal" we cut some parts of Section 5 to fit within the nine-page limit. As we see it, we are able to address your points using the data that we have already collected and processed.
---
> If a version of this pa... | Summary: The paper provides a comprehensive review of the algorithmic recourse research literature, concentrating on understanding the recourse research "in the wild", by focusing on the practical application of these techniques in real-world scenarios. The authors then provide some suggestions to practitioners to push... | Rebuttal 1:
Rebuttal: Thank you for this feedback, we are very happy that you perceive our work as useful for the community!
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> I feel NeurIPS is not the right venue for this kind of contribution, since this paper does not provide the level of technical novelty required by the conference.
We understand your reaso... | Summary: The paper is a review of algorithmic recourse (AR) literature. The authors deploy a systematic framework to investigate research trends in algorithmic recourse and evaluate their incorporation of practical concerns like societal and institutional considerations of AR, or lack thereof. The review finds that cur... | Rebuttal 1:
Rebuttal: Thank you for the review and feedback points, we appreciate it!
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> As mentioned in Section 4.6, there are papers (albeit in smaller numbers than we would want) that already provide real-world examples and attempt to discuss ethics within recourse.
We believe that the main novel contribution ... | Summary: The authors present a survey regarding algorithmic recourse scientific literature. In their work, the authors analyze what types of contributions do the authors choose to make to the AR research, what are the criteria covered in the authors’ definitions of AR, what are the criteria covered in the authors’ defi... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our paper!
---
> (1) - the authors in their abstract mention multiple times the actionable component of algorithmic recourse, which is present in some of the subsections, but in others, the link to this aspect is less clear and could be enhance... | Rebuttal 1:
Rebuttal: Dear Reviewers,
First and foremost, we would like to thank you for your insightful and comprehensive comments. We know that the review process tends to be time-consuming, and we are grateful that you took this time to read our paper in depth and produce reviews of such high quality.
We are a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products | Accept (poster) | Summary: The authors propose the use of the tensor product to model the interactions between object properties and their values, in contrast to the usual concatenation-based fusion for compositional representations. Extensive experiments on a large variety of image datasets are performed, where performance gains are of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive and insightful review.
**W1: Scalability Issues - Multiplicative Dimensionality of (Soft) TPR**
As noted, the Soft TPR lives in a $D_{F} \cdot D_{R}$ dimensional space, which grows multiplicatively. However, several factors mitigate scalability conc... | Summary: This work explores compositional representations -- considered a crucial capability underlying intelligent human behaviour in deep learning systems. It argues that there is an incompatibility between discrete symbolic compositional representations—e.g. as obtained through traditional disentanglement approaches... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed and thoughtful feedback.
**W1, Q1, Q2: Motivation for approach and model**
Thank you for the detailed questions on the motivation. We clarify these points in the General Response.
**W2: Limited Domains**
Applying Soft TPR to language is an intriguing... | Summary: This paper introduces a novel framework for representation learning known as Soft Tensor Product Representations (Soft TPR), aimed at capturing the compositional structure of data more effectively. The authors propose a continuously-valued compositional representation that contrasts with traditional symbolic m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and thoughtful review.
**W1**: To our best knowledge, MPI score is not established in disentanglement learning literature [19,23-26,31,32,34,38].
Consistent with VCT [34], we evaluate disentanglement using 3 datasets (Cars3D, Shapes3D, MPI3D) and 4 metr... | null | null | Rebuttal 1:
Rebuttal: (Please note all rebuttal tables are included in the 1-page PDF)
**1) Incompatibility between disentangled representations and deep learning’s continuous vector spaces (Reviewer 93av Q1)**
Traditional disentanglement methods, although producing continuously-valued representations $\psi_{d}(x)$, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeuralSteiner: Learning Steiner Tree for Overflow-avoiding Global Routing in Chip Design | Accept (poster) | Summary: This paper proposes NeuralSteiner, a two-phase global routing scheme to optimize both wirelength and overflow. It also demonstrate capability of generalization on unseen large-scale circuits. Experiments on public benchmarks show NeuralSteiner reduces 99.6 \% in overflow with a wirelength increase of 1.8 \%.
... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. Please note our global comment with additional experimental results. Below we will address specific questions.
W1: Thanks again for your meticulous review, and we will correct typos in the revised version.
> **W2: NeuralSteiner is not an ... | Summary: This paper tackles the challenging task of global routing in VLSI design, with the aim of addressing the overflow limitation inherent in current learning-based methodologies. The authors introduce NeuralSteiner, a novel approach that builds upon a previous method known as HubRouter. Like HubRouter, NeuralStein... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and valuable feedback. Please note our global comment with additional experimental results. Below we will address specific questions.
> **W1&W2: Analysis of time complexity in the construction stage.**
We understand the reviewer's concerns regarding NeuralStein... | Summary: The paper presents NeuralSteiner, a method to improve chip design routing by minimizing overflow. Using a neural network to predict Steiner points and a graph-based algorithm for selection, ensures connectivity and reduces congestion. NeuralSteiner outperforms existing methods, achieving up to a 99.8% reductio... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Please note our global responses with additional experimental results. Below we will address specific questions.
W1: Thanks again for your meticulous review, and we will correct typos in the revised version.
> **W2: The paper should be ... | Summary: This paper presents a learning-driven approach for overflow avoiding Steiner tree construction. The authors propose a two-stage framework that initially predicts the locations of potential Steiner points, followed by a post-processing algorithm that constructs the Steiner tree based on these predicted points. ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments. Please note our global comment with additional experimental results. Below we will address specific questions.
> **W1/Q1: Novelty and contribution of the method**
Thank you for your valuable comments. Here, we further elaborate on the ... | Rebuttal 1:
Rebuttal: Dear Area Chairs and Reviewers,
We would like to thank all reviewers for providing constructive feedback that helps us improved the paper. We are encouraged that the reviewers acknowledge the novelty (4X1w, 1MAW), effectiveness (4X1w, 1eER, 1MAW) of our approach, thoroughness of our experiments (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Weight decay induces low-rank attention layers | Accept (poster) | Summary: The paper studies the effect of weight decay on losses where the trained parameters include two matrices that are multiplies, and specifically the bias of such losses towards low rank. It is shown theoretically that under certain conditions, local minima of the L_2 regularized loss coincide with minima of the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and constructive criticism. Below, we address your concerns point by point.
> The theoretical results seem to apply more for compressed sensing or matrix completion, where the loss is $L(A^\top B)$ for some differentiable $L$. However, the paper (... | Summary: This paper studies the effect of weight decay on the product of matrices, which appear in the attention layers. This paper shows that weight decay will have an effect of reducing the rank hence hurting the generalization. The theoretical results are verified in extensive experiments.
Strengths: 1. Understandi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We address your concerns point by point below:
> Some theoretical results have already been shown
We stress that while Wang and Jacot [2023] show a similar result, their result does not apply to transformers where $AB^\top$ is not full rank due to an ar... | Summary: This paper explores how applying weight decay to matrices affects the rank of their product. They show that L2 regularization of the operands is equivalent to regularizing the nuclear norm (sum of singular values) of the product which could result in a lower rank. The attention block of transformers contains s... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for providing us with valuable feedback. We address your concerns and questions point by point below.
> The theory relies on analyzing the converged solution with gradient flow. I’m not sure how well this corresponds to real training (it would be nice to discuss th... | Summary: The authors investigate the landscape of two different optimization problems. For a general objective function $L$ defined on a matrix space, they consider two regularized objectives $\mathcal L_*(B, A) = L(AB^\top) + \lambda ||AB^\top||_*$, $\mathcal L_2(B, A) = L(AB^\top) + \frac{\lambda}{2} (||A||_F^2 + ||B... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and interest. Below, we address your concerns point by point.
> Lack of an additional lemma addressing the effect of time discretization in the gradient flow scenario. The stochastic case is studied through SDEs, which may differ significantly... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their efforts in evaluating our work. Please find your personalized responses addressing your specific concerns. We welcome any further questions and remain open to continued discussions. | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper, the authors study the role of the weight decay in training of matrices especially when they appear in multiplicative form. First estabilshing the equivalence between the L2 regularization and nuclear norm regularized loss at stationary points and local minima, they establish how even training on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback and constructive criticism. We address your concerns point by point below.
> The writing and exposition could be greatly improved
We really appreciate all the feedback about the writing. We fully agree with all the above points, all of which a... | null | null | null | null | null | null |
Self-playing Adversarial Language Game Enhances LLM Reasoning | Accept (poster) | Summary: This paper explores an adversarial language game named Adversarial Taboo, where an attacker and a defender engage in a conversation centered around a target word visible only to the attacker. The attacker's goal is to prompt the defender to unconsciously utter the target word, while the defender strives to avo... | Rebuttal 1:
Rebuttal: We appreciate the suggestions and comments from Reviewer 1AP3. To address the reviewer's concerns:
1. About **why reasoning ability improves by training within the game**: We provided a brief explanation about why self-playing adversarial games can improve the LLM's reasoning in the abstract and ... | Summary: The paper introduces a self-play method, Adversarial Taboo, to bolster the reasoning ability of LLMs. By engaging in a two-player game that requires strategic communication around a target word, LLMs demonstrate improved performance across several reasoning benchmarks. The method leverages reinforcement learni... | Rebuttal 1:
Rebuttal: We thank reviewer R1Zh for the constructive comments and suggestions. To address the reviewer's concerns:
1. About the **evaluation on general tasks**: we agree with the reviewer that evaluating general language capacities is important to the SPAG models. We actually have reported the general lan... | Summary: This paper explores the effects of fine-tuning LLMs on adversarial language games on standard NLP benchmarks such as MMLU, BIG-Bench Hard, etc. The primary game studied is that of "adversarial taboo", an adversarial variant of the well known game Taboo that was first introduced in prior work. This variant is f... | Rebuttal 1:
Rebuttal: We appreciate reviewer ib6k's detailed comments and review. To address the reviewer's concerns:
### 1. About **RL results**:
we did utilize the MDP/RL formalisms to introduce our SPAG objective in equation (13), which is a policy-gradient-based off-policy RL loss. Then we did conduct the RL tr... | Summary: This research investigates a novel self-play training method for Large Language Models (LLMs) using an adversarial language game called Adversarial Taboo. In this game, one LLM acts as an attacker trying to get another LLM (acting as a defender) to say a secret word without revealing it directly. Both sides ne... | Rebuttal 1:
Rebuttal: We express many thanks for the reviewer's supportive comments. For the reviewer's concerns:
1. About **the second model choice**: we choose Baichuan-13B as the second base model mainly because the full self-play experiments (imitation learning, SPAG-1, SPAG-2, SPAG-3) of one base model are alread... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GAVEL: Generating Games via Evolution and Language Models | Accept (poster) | Summary: The paper presents a method to generate board games in a domain specific language using an LLM tuned on that language. An LLM is finetuned using fill-in-the-middle training to complete descriptions of board games from a previously gathered dataset. This LLM is used as a mutation operator in a quality-diversity... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful comments and critiques. We hope to address their primary concern through additional baseline experiments that were performed during the response period.
Specifically, we compare GAVEL against directly sampling from our fine-tuned language model and few-shot... | Summary: The paper presents GAVEL (Games via Evolution and Language Models), a system designed for automated game generation. The authors utilize the Ludii game description language (L-GDL) to encode game rules and leverage a combination of evolutionary computation and large language models (LLMs) to generate new and n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful comments and critiques and we hope to address their general concerns in our response. We also appreciate the pointers to additional related work and will include them in a camera-ready version of our paper.
While it is the case that the results we present a... | Summary: The paper targets on generating interesting games automatically. To do this, it proposes an evolution-based algorithm, which iteratively mutates the game components using the generalizability of an LLM. The work is based on a previous large-scale game datasets Ludii.
Strengths: 1. It is an interesting task to... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We acknowledge the reviewer’s points about the difficulty of evaluation and will include a more thorough discussion of these challenges in a camera-ready version.
We also provide individual responses to the specific questions raised:
- Q1: Is... | Summary: The authors consider the problem of generating sets of diverse and interesting multi-player games. They instantiate this problem in the subspace of board-game like games using a recent domain-specific language called Ludii. They then use a quality-diversity algorithm (specifically, map-elites with a language-m... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to carefully appraise our work. We hope we can adequately address their comments here.
First, while it is the case that our results are specific to games and the Ludii description language, we feel that our approach is general enough to apply to most dom... | Rebuttal 1:
Rebuttal: We would like to thank each of the reviewers for taking the time to consider our work. In the attached PDF we include the results of additional baseline experiments performed during the response period that compare GAVEL to a pure sampling approach and few-shot prompting with GPT-4o. The details a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exact Random Graph Matching with Multiple Graphs | Reject | Summary: This paper considers the graph matching problem, where the goal is to produce a mapping between vertices of multiple graphs which maximizes similarities among them. The authors study graph matching from a theoretical perspective, in which one observes multiple (appropriately correlated) Erdös-Rényi (ER) graphs... | Rebuttal 1:
Rebuttal: Thank you for your review. Our original submission uses $(n,p) = (10^3,0.1)$ in the simulation, and we have now included details such as error rates before and after boosting through transitive closure in the PDF file accompanying our global response. These results are for the simulated output of ... | Summary: This paper studies the information theoretic limits for matching
multiple correlated random graphs. Based on a correlated Erdos-Renyi
random graph model, the authors provide both lower bound and achievable
bound for the condition to correctly match all nodes with high
probability. These bounds match each other... | Rebuttal 1:
Rebuttal: Thank you for your review. We address the two questions below:
1. The theoretical guarantees for the $k$-core estimator hold for any constant $k \geq 13$ (this is an artifact of the analysis). In the PDF file (global response) with simulation results, we use $k \in \lbrace 13,14 \rbrace$ because ... | Summary: This theoretical paper gives tight conditions for exact graph matching with multiple correlated random graphs. This problem has been extensively studied recently for the case of 2 graphs, and it is shown here that with more than 2 graphs, there is a regime where pairwise alignment is not possible, but with the... | Rebuttal 1:
Rebuttal: Thank you for your review. Please see our response to reviewer Yg8a for a note on contextualizing our work with respect to NeurIPS, and the PDF in our global response for experimental evaluation of our algorithm in ER and non-ER models.
---
Rebuttal Comment 1.1:
Comment: Thank you for your rebut... | Summary: The paper aims to find out alignments between G_1 and G_2,....G_m, under the assumptions that they all are essentially sampled from ER graph distribution. The paper presents one impossibility result (or necessary condition to estimate such alignment) and two sufficiency results to solve the underlying problem... | Rebuttal 1:
Rebuttal: Thank you for your review.
**On relevance to NeurIPS:** NeurIPS and other learning conferences have been the venue of choice for other works in graph matching that establish information-theoretic recovery limits in various settings, such as [1]-[4] below. Since graph matching is an important dat... | Rebuttal 1:
Rebuttal: We would like to address experimental evaluation of our proposed algorithm in the global response, since this was raised by multiple reviewers.
As Reviewer tQgp pointed out: the $k$-core estimator is not efficient, but its output can be efficiently simulated by computing the $k$-core of the true ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model | Accept (poster) | Summary: This paper argue that I2V-DMs tend to overly rely on the conditional image at large time steps, resulting in videos with less dynamic motion.
To address this, they propose using the KL divergence between the initial noise distribution and the actual marginal distribution to enhance the amplitude of motion whil... | Rebuttal 1:
Rebuttal: # Author Response to Reviewer f9AW
We thank reviewer f9AW for the valuable and constructive comments. We address the concerns as follows.
## Q1: Claim. The conditional image and the diversity of content changes are, in fact, a matter of trade-off. The essence of image-to-video generation is to l... | Summary: The paper points out that existing image-to-video diffusion models can lead to videos without significant/desired amount of motion. Some evidence is presented to show that this is due to what the authors call "conditional image leakage" where the model places too much emphasis on the conditional image and all ... | Rebuttal 1:
Rebuttal: # Author Response to Reviewer hjyn
We sincerely thank Reviewer hjyn for the constructive and valuable comments. The concerns are addressed as follows.
## Q1: Issues with derivation.
We clarify that our objective is to optimize $\mu_p$ and $\sigma_p^2$, the parameters of $p_M(X_M)=N(X_M; \mu_p, ... | Summary: This paper investigates and proposes solutions for the problem of “conditional image leakage” in image-to-video generation. The authors claim that existing image-to-video diffusion models over-rely on the conditional image at large diffusion timesteps when the inputs are too noisy, leading to static video outp... | Rebuttal 1:
Rebuttal: # Author Response to Reviewer NwFJ
We thank Reviewer NwFJ for the valuable comments.
## Q1: Add comparisons.
### Q1-1: Add comparison with related noise initialization methods.
(1) As suggested, we add comparisons with FreeInit[a], Progressive Noise[b] and FrameInit[c]. Our Analytic-Init outp... | Summary: This paper presents an approach for image-to-video (I2V) generation. The authors start with a conditional image leakage problem in existing works where the conditional image significantly influences the generation process thereby impacting the dynamism of the video. The authors propose an inference time and a ... | Rebuttal 1:
Rebuttal: # Author Response to Reviewer iEBW
We sincerely thank Reviewer iEBW for the recognition of our work and for providing constructive comments.
## Q1: Details about the ImageBench dataset.
Our ImageBench dataset is designed based on two key aspects: breadth of categories and logical complexity. Fo... | Rebuttal 1:
Rebuttal: # Common Concerns from reviewers
## Common Concern 1 (from NwFJ and f9AW): Incorporating additional guidance signals can achieve significant motion and control the motion degree. Show the input motion score against the output motion magnitude.
We clarify that **our method is orthogonal to the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Attention Temperature Matters in ViT-Based Cross-Domain Few-Shot Learning | Accept (poster) | Summary: This paper deals with ViT-based cross-domain few-shot learning. It first proposes an observation regarding ViT-based model. In cross-domain learning, the attention module in ViT seems to be hurting the model performance in the target domain. Based on this observed phenomenon, this paper proposes some fixes by ... | Rebuttal 1:
Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns.
## W1. Implementing with other baseline methods.
We implemented our method with ProtoNet. Our method continues to be effective with this learning method and improves the performance on the target domain.
... | Summary: This paper investigates the application of Vision Transformer (ViT) in Cross-Domain Few-Shot Learning (CDFSL). It fully analyzes the effectiveness of attention to CDFSL performance through experiments and identifies a method to enhance ViT's transferability across domains by adjusting attention mechanisms thro... | Rebuttal 1:
Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns.
## W1. Random attention initialization
Since random attention initialization tends to produce a uniform attention map, we can view our attention abandonment method as producing a randomly initialized atte... | Summary: This paper investigates the effectiveness of the attention mechanism in Vision Transformer (ViT) for solving cross-domain few-shot learning tasks. It finds that the traditional query-key attention operation is more on the side of discriminability than transferability in their trade-off balance, thus leading to... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work!
## W1. Fig. 2a and Fig. 5a
We have added the color bar to the attention map and enlarged the class toke for a clear observation. Please refer to the global rebuttal PDF Fig.1 and Fig.2.
## W2. Formatted references
We have checked the references an... | Summary: This paper studies the effectiveness of Vision Transformer (ViT) for Cross-domain few-shot learning (CDFSL). In particular, the authors found that by simply multiplying a temperature to the attention in ViT blocks, the target-domain performance consistently increases, even though the attention map is downgrade... | Rebuttal 1:
Rebuttal: We truly appreciate your valuable comments. In the following, we respond to the concerns.
## W1. Theoretical insights.
Please refer to the global rebuttal Q1; we have conducted a theoretical analysis.
## W2. Retrain ViT on the source domain
We would like to point out that following current ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable input.
## Q1. Theoretical insights
#### 1. Our method reduces the sharpness of the model's loss landscape.
Theoretically, we analyze our findings from the sharpness of the loss landscapes (Foret 2021). That is, each value of model weights is viewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff | Accept (poster) | Summary: This paper studies the code refinement problem: given a candidate program and the reason why the program fails to satisfy the user specification, an LLM is called to generate an improved program. The paper frames this as an arm-acquiring bandit problem and solves this using Thompson sampling. The evaluation is... | Rebuttal 1:
Rebuttal: Thank you for the review and for the suggestions. We have run new experiments that we really believe can address your concerns. Please see below.
> only GPT-4 is studied. Would REx give better results in the large-compute setting when the self-repairing capability of the base model is weaker and ... | Summary: This paper explores a variety of code generation tasks, taking the approach of using a LLM to generate solutions, and conditioning the generation of each solution on the repair of a previously generated solution. It frames this as an arm-acquiring bandit problem, where each solution generated is an arm, and pu... | Rebuttal 1:
Rebuttal: Thank you for the helpful review! Please see below for our responses, and see the global response PDF for new experimental results motivated by your suggestions.
> only GPT-4 is tested
Thanks or the suggestion of testing other models. Please see the global response PDF for new results on other L... | Summary: This paper proposes to improve the iterative code refinement process by prioritizing the “good” programs, where the goodness is defined by a heuristic estimator -- the program that passes the more test cases is better. To balance exploration (explore a lesser refined program) and exploitation (refine the best ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful input and for your support. Please see below our responses.
> REx often brings only marginal improvements given a large enough budget (Figure 4)
We agree! REx isn't magic: It is simply a more hyperparameter-robust, cost-saving refinement policy that also modestly imp... | Summary: The paper identifies that LLM refinement process can be formulated as (arm-acquiring) non-contextual bandit problem which can be solved optimally (in the limit) using principled bandit-algorithms like Thompson Sampling against heuristic based solutions. It applies this idea to three code refinement tasks and d... | Rebuttal 1:
Rebuttal: Thank you for the feedback and the supportive review. Below we answer your main questions.
> Choice of LLM. As authors mentioned, choice of the LLM might play a role in the performance of this work. It might be useful to study this axis further
Great idea! In the global response we have attached... | Rebuttal 1:
Rebuttal: Multiple reviewers raised the concern that we only evaluated our method on GPT4.
To address this concern we are in the process of running our experiments on GPT3.5, Llama3, and Claude3. The attached PDF shows preliminary in-progress results.
Although the results are still preliminary, the advant... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate | Accept (poster) | Summary: This paper proproses LookHere, a novel positional encoding method for Vision Transformers for dealing with high-resolution images. Specifically, LookHere explicitly constratin attention heads to attend certain directions via 2D masks. With comprehensive experiments. LookHere demonstrates strong performance acr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. In particular, their recognition of our clear ideas, comprehensive experiments, and writing, as well as suggestions to improve the paper. The review asks two questions, which we address in this rebuttal.
$\textbf{Q1)}$ Have you considered a discu... | Summary: This paper introduces Lookhere, a novel mask-based positional encoding designed to address the performance degradation of ViT when resolution changes. Lookhere restricts the receptive field of each head in the attention mechanism and enhances the diversity of information perceived by each head. Extensive exper... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. In particular, their recognition of our writing and illustrations, extensive experiments, and the importance of resolution generalization in ViTs, as well as suggestions to improve the paper. The review lists one concern, which we address in this ... | Summary: The authors explore an alternative to existing positional encoding methods for vision transformers. Within attention operations, 2D attention masks (subtracted from attention matrix i.e. key-query inner product, prior to softmax) limits feature mixing to fixed fields of view pointed in different directions. Vi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. In particular, their recognition of our writing, proposed method, rigorous experimentation, extensive ablations, and ImageNet-HR, as well as suggestions to improve the paper. The review lists two concerns, which we address in this rebuttal.
$\tex... | Summary: Vision transformer is known for its constrained scalability across various image resolutions and this work is designed to address the generalization ability of ViT at high-resolution images. Specifically, the authors propose LookHere, a drop-in replacement for the positional encoding of standard ViTs, which re... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. In particular, their recognition of our extensive experiments, LookHere’s performance, and our submission’s presentation, as well as suggestions to improve the paper. The review lists three concerns, which we address in this rebuttal.
$\textbf{Q1... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful comments and are pleased with the positive reception of our submission. In particular, we appreciate the recognition of our writing, presentation, and extensive experiments from all reviewers. Additionally, we appreciate the suggestions to improve our pa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Constrained Sampling with Primal-Dual Langevin Monte Carlo | Accept (poster) | Summary: This paper aims to solve the constrained sampling problem via primal-dual method. The authors proposed a new sampling method PD-LMC and provide detailed convergence analysis. Several numerical experiments were conducted to verify the sampling method.
Strengths: The structure of paper is easy to follow. The au... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments on our results, particularly our theoretical guarantees. As the reviewer correctly notes, in contrast to [22] ([1] in the reviewer's comments), our analysis handles two levels of approximation: time- and space-discretization. We next address their ... | Summary: The paper studies constrained sampling schemes. The objective function is the KL divergences with a target distribution, while the constraint set is given as some expectation equations or inequalities. The authors rewrite the problem into a saddle point formulation and study the Wasserstein gradient descent an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their enthusiastic opinion of our paper. We next address their questions one-by-one.
**Weakness** The reviewer has a point. We focused on regular Langevin dynamics and its literature, but this paper is indeed part of the large line of work of sampling as an optimization ... | Summary: In this work, the focus is on a constrained optimisation problem in the space of measures. Specifically, the goal is to obtain a distribution / samples from a distribution which is close in KL to a target distribution while also satisfying a set of statistical constraints. The paper discusses this somewhat aty... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments on our paper.
**Weakness** The reviewer has a point that to obtain a practical algorithm we perform stochastic updates in both the primal (step 3) and dual (steps 4-5), which complicates things. This in fact marks an important distinction with the... | null | null | Rebuttal 1:
Rebuttal: We thank the editors and reviewers for their time and for the positive comments on our paper.
In the sequel, we respond to each of their questions individually. Throughout our responses, we refer to references and equations as numbered in the submitted version of the manuscript. We also refer to ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Measuring Goal-Directedness | Accept (spotlight) | Summary: The paper proposes a measure of evidence for goal-directedness based on the maximum causal entropy principle, MEG. It is operationalized as the ability to predict variable D based on the hypothesis that it is optimizing a utility function U, where U represents the goal and D the agent’s decisions. Specifically... | Rebuttal 1:
Rebuttal: Thanks for the review.
**Lower bound.**
It’s true that our algorithm can only guarantee a lower bound in the unknown-utility case, but this is true whenever one performs SGD on a nonconvex loss function to estimate some quantity. With neural networks, we often get good estimates of the quantity ... | Summary: * The work introduces a framework for quantifying the goal-directedness of a
decision-making system with respect to a particular utility function or a
family of utility functions.
* The method is grounded in Dennett's *intentional stance* and Jaynes'
*maximum entropy inference*. The authors have made som... | Rebuttal 1:
Rebuttal: We appreciate your extensive and careful review.
**Motivation.**
Agreed emergent goal-directedness is a core AI safety problem. Updated to be more explicit.
**Terminology.**
We agree our approach is not a unique interpretation of Dennett, and his view not the only one in the literature. We have ... | Summary: The paper introduces maximum entropy goal-directedness (MEG), a measure of how much an agent is goal directed towards a given utility function. The authors extend the theoretical framework introduced to the case where the utility function that is being optimized is not known. Moreover, they propose an algorith... | Rebuttal 1:
Rebuttal: Thanks for the review.
**Requiring a causal model**.
A full causal model is not strictly necessary for computing MEG. What is needed is a simulator where you can measure the variables of interest under different policies. We have updated the paper to make this clearer. That said, investigating ho... | Summary: The paper is studying a problem that is both mathematical and philosophical in nature: how can we measure quantitatively, whether an observed behaviour is goal-directed or not (or to what extent it is). It tackles this problem by starting from a causal model of the world, where there are explicit utility varia... | Rebuttal 1:
Rebuttal: Thanks for your review. We agree that the problem of measuring goal-directedness is of great significance and we’re glad you feel we have made a novel contribution here.
**Societal implications**.
We agree this should be discussed in more detail. Thanks for the suggestion of an extended discussio... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their feedback, which has already allowed us to improve the paper. We are pleased that the reviewers agree that the problem we are tackling is an important one and that our approach is novel.
We have responded to each reviewer’s points individually. Minor presentat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provable Partially Observable Reinforcement Learning with Privileged Information | Accept (poster) | Summary: This paper offers valuable insights into the use of privileged information in partially observable reinforcement learning (POMDP), presenting theoretical advancements and practical algorithms. However, challenges remain in ensuring the effectiveness of expert distillation, applicability to real-world problems,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and believe there are several important misunderstandings we would like to clarify. We address the reviewer's concerns and questions below:
---
## Regarding the potential sub-optimality of the expert
**Firstly, the focus of our paper is to study ho... | Summary: This paper has several contributions. The authors examine using privileged information for partial observability in RL. The paper looks at two empirical paradigms, expert distillation and asymmetric actor-critic, analyzing their computational and sample efficiency under specific conditions.
In addition, the a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Please see our responses below:
## Regarding the examination of existing empirical paradigms
We agree with the reviewer that understanding existing empirical paradigms is important for theory-oriented research. We believe our paper indeed examin... | Summary: This paper presents a novel theoretical characterization of certain kinds of
POMDP's which admit efficient learning. First, related characterizations are
explored and theoretical results show that these classes of POMDP's suffer from
certain drawbacks when trying to learn policies. Based on this analysis, a ne... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We believe there are several important misunderstandings we want to clarify. Please see our responses below:
---
## Response to Point 1 of weakness
We are thankful for the suggestions on the organization of the paper. We will re-organize the paper... | Summary: The submission addresses both statistically and computationally efficient reinforcement learning (RL) in Partially Observable Markov Decision Processes (POMDPs). The training phase has access to hidden states, while the goal is to learn the optimal policy at test time without such access. The authors propose a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback, and noticed that there were several important misunderstandings we would like to clarify.
## Response to Point 1 of high-level critic:
**For sample complexity, we respectfully disagree with the claim that with privileged information, statistical ha... | Rebuttal 1:
Rebuttal: ## Additional experimental results
In response to the reviewers, to make our experimental evaluation more sufficient, we **have added new results** by testing our algorithms on more POMDP problems of larger size than the original problems in the paper. Meanwhile, we also addressed the problem of ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Evaluation of LLMs via Branching Preference Learning | Reject | Summary: In this work, the authors conceptualize the evaluation process as a decision tree, where each node represents an evaluation action, and each path from the root to a leaf node represents a trajectory of evaluation reasoning. The authors demonstrate that within a limited search space, there exist better decision... | Rebuttal 1:
Rebuttal: Thanks for your review comments.
**For Concerns**:
> Q: The authors missed a lot of key related works
1. We clarify our task and related work (refer to common responses 1, 2, and 4 for more details):
- Our work has broader application scenarios; it is not only for benchmarking model capabiliti... | Summary: This paper proposes a novel approach to efficiently evaluate LLMs using branching preference learning. The authors conceptualize the evaluation process as a decision tree, where each path represents an evaluation reasoning trajectory. They introduce a tree-based data sampling method and preference learning bas... | Rebuttal 1:
Rebuttal: Thank you very much for appreciating the novelty of our work. We hope the following responses can further address your concerns:
> Q: If in-distribution evaluation performance is mediocre but out-of-distribution does better, then doesn't the most gain come from a better dataset?
1. We try to exp... | Summary: They present an approach to improving LM evaluation by having models first generate an evaluation criteria, then a scoring guideline, and then finally a final judgement. They then develop a procedure for collecting training data corresponding to these three steps by applying branching/pruning approach (sample ... | Rebuttal 1:
Rebuttal: Thank you very much for appreciating our idea of transforming the evaluation task into a tree search problem. We hope the following responses can further address your concerns：
> Q: The paper is honestly pretty hard to follow. There's a lot of moving parts and it's not explained in an easy to dig... | Summary: The paper investigates how to improve the quality of automated evaluation through fine-tuning (SFT and DPO). The main algorithm proposed by the paper is to construct an search tree which consists of node of (criterion, scoring guide, and judgment). This tree is later pruned and modified and the different paths... | Rebuttal 1:
Rebuttal: Thank you very much for your appreciation of our work. We hope the following responses can address your concerns:
> Q: It seems that no human preference/judgment label is used.
1. You are right that we do not use any human labels (see common response 4). As you can see, many research fields and ... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer cKwh, GhxJ, and RFGW for appreciating the novelty and interest of our approach. We also appreciate the acknowledgment of our writing and presentation by Reviewer YdUv and sKgh.
We need to make the following clarifications: how our research differs from other work (1 an... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this work, the authors propose a tree-based data sampling method to conceptualize the evaluation process as a decision tree, where each node represents an evaluation action, and each path from the root to a leaf node represents a trajectory of evaluation reasoning. The proposed method involves generating su... | Rebuttal 1:
Rebuttal: Thanks for your review comments, as well as your appreciation of the importance and writing quality of our work.
**For Concerns：**
> Q: Potential Biases
We believe that incorporating synthetic data is essential for the future development of LLMs. Of course, reducing bias is also a crucial issu... | null | null | null | null | null | null |
Is Value Learning Really the Main Bottleneck in Offline RL? | Accept (poster) | Summary: The paper presents an empirical analysis to determine the main challenge in offline RL for control among value function learning, policy extraction, and policy generalization to test-time states. With various deep learning-based experiments, it reaches the conclusion that policy extraction and policy generaliz... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback about this work. We especially appreciate the reviewer's feedback about statistical significance and the clarity of our claims. Following the reviewer’s suggestion, we have added more seeds and variance metrics to improve the statistical ... | Summary: This paper attempts to understand the relative importance of policy learning and value learning in offline reinforcement learning. The analysis is broken into two parts: (1) when decoupling the policy and value learning steps the authors test the relative data efficiency of the two steps, and (2) the authors t... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback about this work. It appears that we were not entirely clear about our main messages in the initial draft, which we believe may have caused some confusion about our claims. Below we describe how we have revised our paper to prevent potenti... | Summary: This paper empirically analyzes the bottlenecks in offline RL from three aspects: value learning, policy extraction, and policy generalization at evaluation time. Through the empirical evaluation, two observations were made: 1) the policy extraction algorithms affect the performance of offline RL significantly... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback about this work. We especially appreciate the reviewer's feedback about statistical significance as well as the question on our claim about value learning. Following the reviewer’s suggestion, we have added variance metrics as well as fou... | Summary: This paper addresses the question of why offline RL often underperforms imitation learning. They formalize the question they choose to ask, " is the bottleneck in learning the value function, the policy, or something else? What is the best way to improve performance given the bottleneck?", and provide three po... | Rebuttal 1:
Rebuttal: Thank you for the positive review and constructive feedback about this work! We especially appreciate the question on discrete-action MDPs. Please find our answer to the question below.
---
* **“... most of the empirical results are in environments with continuous action spaces. Do you expect th... | Rebuttal 1:
Rebuttal: We appreciate all four reviewers’ detailed feedback and suggestions. We would like to highlight the additional results we provide in the new 1-page PDF.
* **Adding $\mathbf{4}$ more seeds ($\mathbf{8}$ seeds in total) and standard deviation metrics:** Following the reviewers’ suggestions, we have... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
If You Want to Be Robust, Be Wary of Initialization | Accept (poster) | Summary: This paper provides a theoretical study on the impact of number of epoch and initialisation to adversarial attack for GNN, potentially generalise to DNN. The theoretical evidence is supported by some empirical results.
Strengths: The study of how number of training epoch and initialisation affect GNN robustne... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and we would like to answer their main concerns and questions as follows:
**[W1 - Regarding the provided references]** We thank the reviewer for these references that we weren’t aware of and we will include a complete analysis and discussion i... | Summary: This paper studies the impact of weight initialization (and training epochs) on adversarial robustness of a GNN model, both theoretically and empirically. The analysis is also extended to DNNs in general, although this is not the focus of this paper. The theoretical and empirical analysis both suggest that inc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. We would like to answer their main concerns and questions as follows:
**[W1 - General comment]** We feel slightly misunderstood by this comment and therefore want to apologize for any confusion that may have arisen from a possible lack of clari... | Summary: This work investigates the relationship between weight initialization and adversarial robustness, specifically in Graph Neural Networks (GNNs). In this setting, a defender wants to train a GNN for which, given an input graph X, and adversary cannot find a similar graph which induces a different output from th... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for their feedback and their comments.
We are grateful for your comment on the novelty of our theoretical analysis and direction. Regarding the generalization of the results, we have drafted a complete response in the “General Rebuttal” as all the re... | Summary: The paper investigates the under-explored impact of weight initialization on the robustness of Graph Neural Networks (GNNs) against adversarial perturbations. The authors present a theoretical framework linking weight initialization strategies and training epochs to the model's resilience to adversarial attack... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for their feedback and comments.
As detailed in our “General Rebuttal”, the main focus of our research is related to GNNs, hence why the experimental setting was rather focused on this side. The extension to other models (such as DNNs in the paper or... | Rebuttal 1:
Rebuttal: **General Comment to all the Reviewers:**
We are grateful to all the reviewers for their comments on the potential novelty of our theoretical insights and direction and we are also happy that our “generalization to other model” section has caught their attention. We would like to point out that o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks | Accept (poster) | Summary: This paper proposes a novel framework for model disassembling and assembling. A component locating technique is introduced to disassemble task-aware components from the models. And an alignment padding strategy and a parameter scaling strategy are also designed to assemble these useful components.
This work i... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We are pleased that you find our work to be novel and that you appreciate the clarity of our proposed pipeline and the convincing nature of the results. Below are our responses to each of your comments (your comments are highlighted in italics).
> Q1: *In s... | Summary: This paper draws inspiration from the information subsystem pathways in biological vision systems and proposes a Model Disassembling and Assembling (MDA) approach.
- For model disassembling, the authors introduce the concepts of contribution aggregation and contribution allocation within convolutional filters... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We are pleased that you find our work on model disassembly and reassembly to be novel and consider it to be inspiring for model interpretability and architectural design. Below are our responses to each of your comments (your comments are highlighted in ital... | Summary: This paper proposes the Model Disassembling and Assembling (MDA) task for CNN classifiers, introducing techniques for extracting and reassembling task-aware components. Experiments show reassembled models perform comparably or better than original models. The approach offers new applications in decision route ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We are pleased that you found our work to be novel and our techniques for solving the problem to be innovative. Below are our responses to each of your comments (your comments are highlighted in italics).
> Q1: *The definition of sub-task is strongly tied t... | Summary: The paper introduces Model Disassembling and Assembling (MDA), a novel method inspired by the biological visual system to create new deep learning models without retraining. By disassembling pretrained CNNs into task-aware components and reassembling them, the approach maintains performance while enabling effi... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and comments on our work. We are pleased that you found our work to be novel and interesting, and that you found the experimental results impressive. We are also delighted that you pointed out how our method allows for the arbitrary creation of new models, simila... | Rebuttal 1:
Rebuttal: Dear Reviewers FhUd, UTXG, gfoc, and 17d9,
Thank you for the time and effort you have invested in reviewing our paper. We are particularly grateful for your recognition of the novelty and insight of our work and are pleased that our proposed model disassembly and reassembly approach has been deem... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nonlinear dynamics of localization in neural receptive fields | Accept (spotlight) | Summary: This paper investigates when localized receptive fields arise in supervised neural networks. Extending a recent work of Ingrosso and Goldt, the authors propose that simple single-neuron models learn localized receptive fields when trained on data with sufficiently negative excess kurtosis, while if the excess ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their extremely helpful feedback, in
particular for making us aware of relevant work and clarifying minor
misconceptions. We also appreciate your insistence to more
comprehensively quantify and replicate our analyses.
> **Lemma 3.1's treatment of time is not sufficiently... | Summary: SETTING: Olshausen & Field famously showed that requiring natural image patches to be constructed from a small number of independent elements from an overcomplete dictionary populates that dictionary with spatially localized feature detectors. But localization also appears in DNNs trained on discriminative-le... | Rebuttal 1:
Rebuttal: We thank the reviewer for their excellent feedback. We especially
appreciated your precise and astute questions with regard to the
validity and generalizability of our model, and also for reading closely
enough to identify a typo in our proof.
> **how these results can be generalized beyond this ... | Summary: The authors analytically derive the learning dynamics of (extremely) simple neural network models and characterize conditions under which units learn localized receptive fields. This builds on celebrated work in computational neuroscience on sparse coding and independent components analysis, offering a new per... | Rebuttal 1:
Rebuttal: We thank the reviewer for their well-formulated concerns and
suggestions, especially with regard to improving the interpretability
and readability of our work.
> **Figures 4, 5, 6, and the right hand side of Figure 3 are hard to
see. It could be helpful to add color and show fewer lines.**
We th... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their excellent feedback. In particular, we
thank each of the reviewers for providing specific, actionable questions
and concerns. We have done our best to address each of these in our
responses.
### Minor corrections to errata
We also thank the reviewers for reading o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction | Accept (poster) | Summary: This paper proposes a method for object orientation estimation from images. The method predicts object orientation in the frequency domain of SO(3), by using SO(3) equivariant layers that operate on coefficients of Wigner-D matrices. The network is trained with a MSE loss between the predicted Wigner-D coeff... | Rebuttal 1:
Rebuttal: We thank reviewer f5v8 for constructive comments and suggestions.
**[W1. Clarifying contributions]**
Please see our general response above
**[W2. Claims of structural guarantee of 3D rotational equivariance]**
The statement "structurally guarantee 3D rotational equivariance" is indeed mislead... | Summary: The paper tackles 3D pose estimation from a single image. It builds on Image2Sphere which maps CNN features to the sphere which are then processed by spherical CNNs to produce a distribution over SO(3). The submission proposes using an MSE loss function in the spectral domain instead of cross-entropy in the sp... | Rebuttal 1:
Rebuttal: We thank reviewer HCET for constructive comments and suggestions.
**[W1. Clarifying our contributions compared to I2S [34]]**
Please see our general response above.
**[W2-a. Conversion from Euler angles to Wigner-D during training (L245)]**
The conversion from Euler angles to Wigner-D matrice... | Summary: The paper proposes a method for regressing the rotation of an object from an image, where several (~10s) of training views of the object with known rotations are available. In particular, the approach proposes a rotation-equivariant network that predicts continuous Wigner-D matrix coefficients in the frequency... | Rebuttal 1:
Rebuttal: We thank reviewer HnJh for constructive comments and suggestions.
**[W1.1, W1.2, Q1, Q2 clarifying the contributions]**
Please see our general response above.
**[W1.3, W4.4, Q3. Continuity of rotations, Sensitivity analysis of the SO(3) HEALPix discretization]**
We carefully claim that our l... | Summary: In this work authors predicts SO(3) poses for objects by predicting Wigner-D coefficients in frequency space. Similar to other work [1], it first lifts 2D features to 2-sphere using pre-defined grid and orthographic projection to sphere using this grid, convert them to frequency domain, applies SO(3)-equivaria... | Rebuttal 1:
Rebuttal: We thank reviewer iMp8 for constructive comments and suggestions.
**[W1. Comparison of pose visualization]**
We present a comparison of pose visualizations in Figure R2. This visualization method is the same to those used in Figures A2 and A3 in the appendix.
We compare to the I2S [34] baseline... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for their constructive comments and recognition of the strengths of our paper:
* Reviewer iMp8: We appreciate your positive feedback on the paper's structure, figures, equations, detailed method section, and ablation studies.
* Reviewer HnJh: Thank you for highlighting ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data | Accept (poster) | Summary: Mainly in the setting of imbalanced data, this paper proposes a probabilistic federated prompt-tuning pre-trained model from two aspects: local prompt generation and global prompt aggregation. In local prompt generation, each local set is assumed to be a random set of prompts distributed by a hierarchical gene... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing our contribution.
Your questions are addressed below:
**Q1. Workflow Diagram.** We apologize for the confusion. Our workflow diagram illustrates two different phases of our algorithm as annotated in the caption of Fig. 4. These two phases are i... | Summary: This paper studies the problem of prompt tuning in FL to address the data imbalance problem. The topic is interesting and broad enough for the community. The motivation and problem setting are good and promising. Experiments are sufficient to support the effectiveness of the proposed method.
Strengths: 1. The... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed questions which we have addressed below.
**Q1. Real-world application to support this setting.** We would like to emphasize that our work has first showcased its improved performance on standard non iid settings simulated with Dirichlet prior -... | Summary: This paper introduces a novel approach to prompt-tuning within the federated learning (FL) framework, focusing on enhancing the adaptability of pre-trained models across diverse clients using a probabilistic method. They proposed a hierarchical approach to model the generation and aggregation of local prompts.... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contribution and for the constructive feedback, which are addressed below.
**Q1. Discussing other fine-tuning approaches.**
We agree with the reviewer that a more comprehensive discussion on PFPT will make our contribution position clearer. However, we w... | Summary: This paper address the challenges of prompt-tuning pre-trained models in federated learning scenarios with diverse local data distributions. Specifically, it formulates the prompt summarizing procedure as a probabilistic set modeling task, treating each local set as an independent sample of a random point proc... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for recognizing the strength of our paper and for helping us catch a format issue.
We thought the broader impact section is counted as part of the checklist content which is not counted towards the page limit. It is possible that we had misunderstood the policy... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments. We summarize below our responses to the reviewers’ questions and concerns, as well as additional results to support our method.
**Reviewer HV8G** requested several clarifications and minor adjustments of our manuscript, which we have thoroug... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discovering plasticity rules that organize and maintain neural circuits | Accept (poster) | Summary: This paper introduces meta-learned plasticity rules for sequence generating neural circuits. While existing works demonstrated that the dynamics in HVC is generated by both excitatory and inhibitory synaptic updates, their approaches are based on a guessed rule. Motivated from this, the authors experimentally ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for the additional references.
We agree with the reviewer that the novelty of this work primarily lies in the application of an existing technique to a specific question within neuroscience: how might the organization of useful circuit dynamics that ac... | Summary: The authors investigate the development and persistence of intrinsic connectivity structures within a
neural region without external input. They propose that a local plasticity rule can lead to self-organising connectivity motifs, creating inductive biases beneficial for subsequent information processing. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful evaluation of our manuscript. We have worked to address all comments and rewrite our paper accordingly.
We now provide a rationale for why the second term in Eq. 4 loses its significance in the perturbed context. We have added the following to Sec. 2.2:
“W... | Summary: This work applies a meta-learning procedure for plasticity rule discovery to a neural network model of sequence generation. Plasticity rules are constructed from parameterized basis functions, and the parameters are found through evolutionary search based on a fitness function quantifying how accurately the pl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments. We have worked to address their suggestions and make changes to the manuscript accordingly.
We agree with the reviewer that activity-independent wiring plays an important role in determining the capacity of a network to self-organize (see Lakshmin... | Summary: This paper proposes a method for discovering plasticity rules in spiking neural networks to achieve sequence generation using both excitatory and inhibitory dynamics. Rules are parametrized with basis function. The biological approach in the model involves also considerations homeostasis and robustness to pert... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful consideration of our manuscript.
We address the reviewer’s concerns regarding novelty in the common response. Briefly, our unique contribution is the exploration of unsupervised and unrewarded plasticity via meta-learning that organizes and maintains a spec... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful feedback. We appreciate their overall support of the manuscript and their constructive criticism. Below, we address their major concerns.
R1 and R4 noted that the paper lacked a demonstration that the learned plasticity rule accelerated the ability of t... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a conceptually simple set-up to learn plasticity rules that result in neural networks that perform a specific task. The set-up of the paper is elegant and made up of simple but effective elements, such as linear-threshold neurons and evolutionary optimisation. The authors obtain some intrigu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our paper and their comments.
We agree with the reviewer that this promising methodology should be used to study the self-organization of other computational motifs, such as line and ring attractors, and we hope that this paper provides a path fo... | null | null | null | null | null | null |
Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models | Accept (poster) | Summary: This paper explores the potential of using the rank of model hidden states for evaluating model capabilities. Specifically, the authors propose calculating the rank difference between trained and untrained models as a measure of model performance. The core idea is that the rank difference can reflect the model... | Rebuttal 1:
Rebuttal: Q1: A larger dimensionality of hidden states usually means a larger rank difference when the effective rank proportion remains the same. In other words, although larger models have a higher rank difference, the proportion of noise reduction might be smaller compared to smaller models. Does this su... | Summary: This paper introduces “rank difference” that measures the reduction in the rank of LLM’s representations. It evaluates the quality of LLMs, which could be used in addition to the reduction in the cross-entropy loss. The idea is based on the assumption that LLM’s representations (e.g., the hidden states of each... | Rebuttal 1:
Rebuttal: Q1: Although the idea is novel, its practical usability is limited. To evaluate the quality of systems (e.g., LLM, ASR, etc), downstream metrics such as accuracy, ROUGE, BLEU, WER, etc. are used as they better align with real use cases. While, a metric like cross-entropy is used as it is different... | Summary: The article presents a measure known as "rank difference" to assess the effectiveness of Language Models (LLMs) by analyzing their internal representations. This metric is based on information theory and geometric principles aiming to quantify how LLMs eliminate unnecessary information post training. The autho... | Rebuttal 1:
Rebuttal: Q1: The paper fails to delve into how the rank difference evolves throughout training missing insights, into its behavior as the model progresses.
A1: Thanks for your question. To address your concern and further investigate how "rank difference" changes during training, we continually train OPT... | Summary: The paper proposes a rank-based evaluation metric that quantifies the amount of redundant information in the hidden representations of a model and applies it to both text-only and multi-modal models. The effective rank is obtained by the rank of its covariance matrix and is interpreted using information theory... | Rebuttal 1:
Rebuttal: Q1: It is unclear what additional information the absolute rank or the rank difference brings to the table apart from a new interpretation. The rank differences are hard to interpret given a lack of detail on how they are computed for models of varying sizes.
A1:
Thanks for your comments. We wou... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks | Accept (poster) | Summary: The paper proposes a model that supports a wide range of multimodal tasks, beyond text generation. The approach leverages an LLM, modality specific encoders and task specific decoders to effectively handle different tasks. The LLM communicates with task decoders via “super link”, which consists of special to... | Rebuttal 1:
Rebuttal: Dear reviewer nisa,
Thanks very much for your valuable comments. We hope our responses can address your concerns and clarify our contribution.
**Q1: The paper title is a bit misleading because the model is an agglomeration of many powerful pretrained models instead of a single model.**
**A1:** ... | Summary: This paper introduces an advanced multimodal large model (MLLM) that integrates visual perception, understanding, and generation in a unified framework. Unlike traditional models limited to text outputs, it expands its capabilities to tasks like object localization, pose estimation, and image generation and ed... | Rebuttal 1:
Rebuttal: Dear reviewer W77r,
Thanks a lot for your insightful reviews and support for our work! We hope our responses can address your questions.
**Q1: Complex training process.**
**A1:** We kindly invite the reviewer to refer to the common question Q2 for the details.
GIT [1] mainly focuses on visua... | Summary: This paper proposes an end-to-end generalist Multimodal Large Language Model (MLLM) for a variety of vision-language tasks, including captioning, detection, segmentation, and image generation. It introduces the concept of "Super Link" for triggering different tasks and attaches corresponding task decoders to t... | Rebuttal 1:
Rebuttal: Dear reviewer x7n8,
Thanks so much for your constructive comments and support for acceptance. We hope our responses can address your concerns.
**Q1: More related works.**
**A1:** Thanks for pointing out the missing related works. AnyGPT builds a multimodal text-centric dataset for any-to-any mu... | null | null | Rebuttal 1:
Rebuttal: Dear all reviewers and ACs,
We sincerely thank you for all the time and effort in reviewing our paper and giving valuable comments. We are really encouraged that all the reviewers appreciate the good motivation, extensive experiments, strong performance, and clear representations. We will first a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks | Accept (poster) | Summary: This paper studies the trajectory of a certain iterative scheme in the high-dimensional limit.
In each step, there're two update steps on a fresh batch: 1) regularized least squares; 2) element-wise nonlinear transformation.
This type of iteration is motivated and encompasses several interesting iterations su... | Rebuttal 1:
Rebuttal: Thank you for the thorough review of our technical results and constructive feedback. We agree that obtaining non-asymptotic guarantees for this problem would be a very interesting technical challenge for future work. Here, we rely explicitly on a distributional characterization of the iterates, r... | Summary: The paper considers a general algorithm of the form shown in Lines 66-67, as it includes several interesting methods as special cases.
The main result is Theorem 1, which proves asymptotic convergence in probability of any function $g\in PL(2)$ to the corresponding expectation. One important special case of t... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback. We address your individual questions and concerns below.
*(1) “Could the authors please provide experiments similar to Figure 1 and allow the algorithm to reuse the data? (it is not very clear to me whether Figure 1 takes fresh batches ... | Summary: In this paper, the authors propose theoretical analysis of the high-dimensional dynamics of the reweighted least-squares methods in the context of "linear diagonal networks".
The general algorithm is given in Equation (4) and includes alternating minimization (AM), reparameterized IRLS, and linear recursive f... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and constructive feedback. We address your individual questions and concerns below.
*(1) “It seems that the problem under study is of interest, but it would be great if the author would better motivate the study and setting, e.g., by providing some take-home mess... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and helpful feedback. We will carefully consider and incorporate all the suggestions into the next revision of this paper.
We address here some of the main comments:
**(a) Sample-splitting assumption:** We agree that the sample-splitting assumption devia... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Membership Inference Attacks against Large Vision-Language Models | Accept (poster) | Summary: This paper focuses on the membership inference attack (MIA) of large vision language models. It creates multiple MIA datasets and proposes a new method to identify whether an image or a text belongs to the training dataset.
Strengths: 1. The authors have proposed an intriguing research problem, which focuses ... | Rebuttal 1:
Rebuttal: We thank the reviewer rMMx for the insightful feedback. We address the concerns below.
---
> **Q1:** [The setting for detecting a description sentence is a little confusing for me. Why do you feed the model with a black image rather than others, like those generated by DALL-E? Also, why do you s... | Summary: This paper presents an interesting idea of detecting training data in large vision-language models (VLLMs) through membership inference attacks (MIAs). The authors introduce the following interesting points :
- An MIA benchmark, specifically designed for VLLMs, called Vision Language MIA (VL-MIA). This benchm... | Rebuttal 1:
Rebuttal: We thank the reviewer e87W for the insightful feedback. We address the concerns below.
---
> **Q1:** Is there any underlying bias caused by the nature of differences between natural and synthetic data (in VL-MIA/DALL-E)?
**A1:** This is an interesting question and thanks for pointing it out. It... | Summary: The paper introduced a benchmark for membership inference attack on VLMs, proposed a pipeline for token-level image detection, and proposed a target-free metric for image MIA detections. The pipeline relies on the fixed sequence of the VLM output to obtain the output image, instruction, and description segment... | Rebuttal 1:
Rebuttal: We thank the reviewer FZ3m for the insightful feedback. We address the concerns below.
---
> **Q1:** Clarification on image embeddings but not image tokens. Why do we not have access to the image tokens?
**A1:** As in the illustrative [Figure 1 of the PDF](https://openreview.net/forum?id=nv2Qt5... | Summary: The rise of large vision-language models (VLLMs) has significantly advanced multi-modal tasks but also brought forth concerns about data security and privacy. This paper introduces a novel membership inference attack (MIA) benchmark specifically designed for VLLMs to detect training data, addressing the lack o... | Rebuttal 1:
Rebuttal: We thank the reviewer M6Nm for the insightful feedback. We address the concerns below.
---
> **Q1:** Small Evaluation Dataset.
**A1:** We extend both VL-MIA/Flickr and VL-MIA/Text to 2000 samples. The results in the extended datasets can be found in [Table 2 and Table 3 of the PDF](https://op... | Rebuttal 1:
Rebuttal: Dear reviewers,
We appreciate your insightful comments. The attached PDF contains the necessary figures and tables corresponding to each individual response below.
During the rebuttal period, we expand our benchmark by incorporating new diverse datasets, as motivated by reviewers **e87W** and **... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-Euclidean Mixture Model for Social Network Embedding | Accept (poster) | Summary: This paper proposes NMM-GNN, a non-Euclidean mixture model that captures both homophily and hierarchies in social networks for embedding.
Strengths: 1.The paper is well-structured, clearly written, and easy to follow.
2.In the experiments section, the author compared baselines from different categories on mu... | Rebuttal 1:
Rebuttal: Thanks a lot for your insightful comments and feedback. Please find our response below to your questions.
Q1: The related work section does not cover most existing social embedding works. For example, many works, in addition to RaRE, also consider both the similarities and the social impact of no... | Summary: The authors understand why links are generated through node-embedded representations of social networks. Specifically, spherical space is utilized to represent the homogeneity of nodes and hyperbolic space is utilized to represent the hierarchy and influence of nodes. By mixing these two spaces together, the c... | Rebuttal 1:
Rebuttal: Q1: Idea is not novel. Hyperbolic/spherical spaces are are commonly used in social networking. [1] Network geometry. Nature physics
A1: We would like to clarify and highlight that the novelty of our work lies in representing the factors in network science that explain how links are generated in t... | Summary: This paper proposes a new Graph-based non-Euclidean mixture model for social networks.
Under the assumptions that social network links are formed due to either homophily or social influence,
the homophily factor is modeled in spherical space and the social influence factor is in hyperbolic space.
The homophil... | Rebuttal 1:
Rebuttal: Thanks a lot for your insightful comments and feedback. Please find our response below to your questions.
Q1: The motivation of the space unification is unclear: Why is the space unification necessary? Each node has two coordinates, in the spherical and the hyperbolic space. The link between node... | Summary: This work addresses the embedding of social networks with downstream tasks such as link prediction in mind. In this manuscript the authors propose to model the link as the mixture of two factors of node embedding, Spherical and Hyperbolic. Concretely, two kinds of node embedding are unified into a single loss ... | Rebuttal 1:
Rebuttal: Thanks a lot for your insightful comments and feedback. Please find our response below to your questions.
Q1: The major issue is on limited datasets in experiments. I would suggest the authors consider other datasets used in previous works, such as synthetic datasets, citation networks (PubMed, w... | Rebuttal 1:
Rebuttal: Dear Reviewers: Thank you for your time in reading our paper and for your useful comments/questions/suggestions on our paper. We have responded individually to each reviewer, however, we are also including a general summary answer to some common questions:
Datasets: In the main paper we specifica... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Compute-Optimal Solutions for Acoustic Wave Equation Using Hard-Constraint PINNs | Reject | Summary: The paper attempts to train PINNs which solves acoustic wave equations. They do so by using hard-constrained PINNs which can enforce IC and BCs, and propose a collocation point sampling method (DAFS) based on the amplitude of the solution at different regions.
Strengths: The paper considers an interesting pro... | Rebuttal 1:
Rebuttal: We thank Reviewer \textbf{AwP6} for their constructive comments and appreciations of our strengths such as "The paper considers an interesting problem in acoustics".
\issue{Weaknesses}
% The paper itself feels less coherent, and seems like just an application of many existing PINN training techni... | Summary: The manuscript treats the one dimensional wave equation with a PINN approach and discusses the imposition of boundary and initial conditions directly into the network, as common practice in PINNs. The authors then propose a quadrature scheme based on a coarse finite difference discretization of the wave equati... | Rebuttal 1:
Rebuttal: We thank Reviewer \textbf{cEZM} pointing out. We acknowledge the concern about the focus on the one-dimensional wave equation. While the 1D wave equation serves as an initial validation, our intention is to propose a general framework for imposing hard constraints in PINNs, including for the first... | Summary: This paper explores to solve the acoustic wave equation in the context of PINNs. Hard boundary and initial conditions are enforced by employing continuous functions within the PINN ansatz to ensure that these conditions are satisfied. A Dynamic Amplitude-Focused Sampling (DAFS) method is introduced to improve ... | Rebuttal 1:
Rebuttal: We thank Reviewer \textbf{U3hW} for their constructive comments and appreciation of our strengths, such as "The hard constraint imposition formula are general."
\issue{Weaknesses (1)}
% Only the wave equation is discussed.
The wave equation is the focus of our study. We are proposing a general fr... | Summary: This paper improves the training efficiency of original physics-informed neural networks to solve the 1D wave equation threefold: first by extending ansatz to also take the first derivative into account, second by a sampling method that focuses on high-amplitude regions, and third by a framework for domain dec... | Rebuttal 1:
Rebuttal: We thank Reviewer \textbf{QJWv} for their constructive comments and appreciations of our strengths such as "The related work is well presented" and "The evaluation of the six candidate functions for $\tau$ in section 4.2 provides interesting insights. The authors explore an advanced selection meth... | Rebuttal 1:
Rebuttal: We thank Reviewer \textbf{QJWv}, \textbf{U3hW}, \textbf{cEZM} and \textbf{AwP6} for their constructive comments and appreciations of our strengths such as "The related work is well presented", "The evaluation of the six candidate functions for $\tau$ in section 4.2 provides interesting insights. T... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ActAnywhere: Subject-Aware Video Background Generation | Accept (poster) | Summary: The task addressed by this paper is, given the the appearance and segmentation mask of a foreground subject in a video, for example a human running, to synthesize video backgrounds that are plausible and realistic, both in content and motion. For example, for a person running, if the ground is wet there should... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and for acknowledging that the introduced problem possesses originality, the high quality of the results demonstrates the effectiveness of the proposed model, and that the paper is clearly written. We next address the reviewer’s questions and com... | Summary: This paper introduces ActAnywhere, a video diffusion model designed to generate video backgrounds that adapt to the foreground subject's motion. By utilizing a sequence of foreground subject segmentation and a background image, the model produces realistic videos with coherent foreground-background interaction... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback from the reviewer and thank them for acknowledging that our introduced problem is novel, the proposed method is effective and makes a significant contribution to the movie and VFX industries, and that our paper is well-written. We address the reviewer's individu... | Summary: This paper studies a new topic: automatic background generation of moving foreground subject. Different from video inpainting/outpainting and other video editing methods, the method in this paper can maintain the consistency of foreground moving subject, and maintain reasonable and realistic interactions, came... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and for acknowledging that our introduced task is novel, and that the experimental comparisons are under a fair setup. We address the reviewer’s particular questions and comments below.
> Training resolution is low… The method of using reference i... | Summary: This paper study to automatically generate video background that tailors to foreground subject motion. It proposes ActAnywhere, a video diffusion model that takes as input a sequence of foreground subject segmentation and an image of a novel background and generates a video of the subject interacting in this b... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback from the reviewer and thank them for acknowledging that our introduced task is interesting and that our proposed model achieved competitive results. We next address the reviewer's individual questions and comments.
> CLIP encoder alone cannot contain detailed i... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the AC and the reviewers for their hard work and time in reviewing our submission. We appreciate the positive feedback and recognition of the novelty and significance of the introduced problem, the high quality of the results and the effectiveness of the proposed m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Constrained Binary Decision Making | Accept (poster) | Summary: The authors proposed an optimization problem which has specific form of solutions that can be leveraged to solve various types of binary statistical decision making problems.
Strengths: The formulation of the optimization is general and there are many applications in binary statistical decision making problem... | Rebuttal 1:
Rebuttal: R: *The motivation for characterizing the optimal solution is quote the authors: "This example underscores the advantages of understanding the structure of the optimal solution to underlying BDM problems". So it seems reasonable to me to add some experiments to validate the claim.*
A: Recent pape... | Summary: The paper titled "Constrained Binary Decision Making" presents a comprehensive framework for binary statistical decision making (BDM), a critical area in both classical statistics and modern machine learning. The authors formulated BDM problems as constrained optimization tasks and provide a detailed character... | Rebuttal 1:
Rebuttal: R: *The authors claimed "Conversely, skipping the optimal strategy derivation and using heuristic rules, such as the SIRC strategy from the original SCOD paper [23], can lead to sub-optimal performance". I think it would be interesting (but not required) to conduct experiments to compare the propo... | Summary: This paper studies a class of optimality criteria for what it calls "binary decision making" which is basically binary classification but with a randomized classifier. It characterizes the solutions for thes criteria, recovering some known results and also establishing new ones. The paper is entirely theoretic... | Rebuttal 1:
Rebuttal: **Priority questions**
R: *What is the impact of the contribution? Does it help us train selective classifiers? Some optimal solutions seem to require knowledge of the underlying distributions. In line 165 it is stated that the results are “potentially useful for specific applications”, but no co... | Summary: This paper characterizes optimal solutions to a class of binary statistical decision-making (BDM) problems. The problem recovers as special cases the likelihood-ratio problem and variants of classification with rejection problems. The optimal solutions characterized in this paper coincide with the known soluti... | Rebuttal 1:
Rebuttal: **Weaknesses**
R: *In general, problem (17) is an infinite-dimensional linear program (ILP). There is a huge literature on this.*
A: We agree that BDM is an instance of ILP, enabling the use of tools like duality and KKT conditions. However, it is unclear if these tools can provide the same cha... | Rebuttal 1:
Rebuttal: We thank all reviewers for their efforts and valuable comments. Our responses are submitted separately for each review. | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a framework for binary statistical decision-making (BDM), where decisions are made between two states based on statistical evidence. The authors introduce a constrained optimization problem formulation for BDM, involving integrals of Lebesgue measurable functions, and provide a detailed char... | Rebuttal 1:
Title: Authors answers to reviewer's questions
Comment: R: Have the authors considered conducting empirical experiments to validate the proposed framework? ...
A: Our paper focuses solely on providing a mathematical tool for deriving optimal strategies in various BDM problems. Our theorem implies these opt... | null | null | null | null | null | null |
Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression | Accept (poster) | Summary: The paper explores linear regression with Gaussian design corrupted by noise that has bounded first moments, focusing on the 'high-dimensional' regime where the feature dimensionality $p$ scales proportionally with the number of training data $n$. The authors propose two estimators that, aside from an additive... | Rebuttal 1:
Rebuttal: Thank you for the comments, we provide our response below.
> **Q1:**
In the case of the square loss and full-batch gradient descent, do the authors recover exactly the same theoretical results in [5]? If not, what are the differences in this case?
**A1:**
If the squared loss and full-batch gra... | Summary: This paper focuses on deriving consistent estimators of the generalization error for robust regression with Gaussian design and heavy tailed noise along the SGD trajectory. They propose two estimators, $\hat r$ (which requires knowledge of the covariance) and $\tilde r$ (which does not) and prove consistency o... | Rebuttal 1:
Rebuttal: > **Q1:**
(...) dependencies on $T, \eta_{max}$ in numerators of Theorems 2.6,2.7?
**A1:**
The dependence on $T$ is $T^T$ and the dependence on $\eta_{\text{max}}$ is $\eta_{\text{max}}^{T}$, as can be seen in Lemma D.4. We do not expect this bound to be tight. Simulation results confirm that th... | Summary: This manuscript aims at finding computational efficient measure of generalization performance for high-dimensional robust regression with regularization. In this scenario, when loss function is not quadratic, estimating the out-of-sample error $\| \Sigma^{1/2}(b_t - b^* ) \|^2$ (where $\Sigma$ is the covarianc... | Rebuttal 1:
Rebuttal: Thanks for the comments and the opportunity to clarify our contributions.
> **Weakness:**
Despite being more general, this paper seems to constitute limited progress towards the community. When comparing it with the work by Bellec and Tan [5], it is not hard to notice the estimator in Theorem 3.... | Summary: #### Summary
This paper examines the generalization performance of iterates produced by Gradient Descent (GD), Stochastic Gradient Descent (SGD), and their proximal variants in high-dimensional robust regression problems. The paper introduces estimators that accurately track the generalization error of the ite... | Rebuttal 1:
Rebuttal: Thank you for the insightful and encouraging feedback. Here, we respond to your comments point by point.
> **Q1**: Could you provide more details on the computational complexity of the proposed estimators? How do they scale with increasing dataset size and dimensionality?
**A1:**
We first note t... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SS1: Accelerating Inference with Fast and Expressive Sketch Structured Transform | Accept (poster) | Summary: The paper introduces a structured randomized parameter sharing scheme (SS1) for computational complexity reduction. A couple of coalescing techniques are suggested, where the chunk size does not affect the approximation error. The proposed method is easy to combine with the quantization method to achieve furth... | Rebuttal 1:
Rebuttal: We thank you for the suggestions to improve the paper. Incorporating the suggestions, we have made several writing changes to the paper that are listed in the common rebuttal. We hope to have addressed your concerns and incorporated your suggestions to your satisfaction. Specific concerns are addr... | Summary: The paper introduces the Sketch Structured Transform (SS1), a novel approach designed to enhance the efficiency of tensor multiplication in deep learning models. SS1 leverages structured yet random parameter sharing to reduce computational load while maintaining the expressive power of the models. The authors ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the support of our paper. We have made writing changes to improve the manuscript and additional experiments as suggested by other reviewers. They are listed in common rebuttal. Specific clarifications are answered below,
**K- and N-coalescing, add complexity to the imple... | Summary: the authors propose the Sketch Structured Transform (SS1), a randomized parameter-sharing method that reduces FLOPs and latency while maintaining model quality. SS1 ties parameters within a single neuron weight and can be implemented to reduce input dimensions before multiplying with a compressed weight vector... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support of our paper. We apologize for writing issues and assure the reviewer that we have taken due care to fix them both in the main paper and appendix. The list of changes made are presented in the common rebuttal. We clarify some specific concerns below
**Nota... | Summary: This paper introduces Sketch Structured Transform (SS1), a hardware-efficient structured matrix for improving the latency of linear layers in neural networks. The paper theoretically analyzes the effectiveness of SS1 in random projections, optionally combined with quantization. Experiments show favorable perfo... | Rebuttal 1:
Title: Request for sharing Banded Matrix details
Comment: Dear reviewer, we would be happy to perform comparative experiments on banded matrix while we prepare for rebuttal as time permits.
Can you please share links to the fast implementation of Banded Weight matrices? In our preliminary search, we have ... | Rebuttal 1:
Rebuttal: While the paper's scores are borderline, the consensus of reviewers on its good soundness and contribution of the paper is encouraging.
Several suggestions on improving the presentation of the paper were made, which we have incorporated in our updated manuscript. Reviewers AurA and ZoDN are will... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces the sketch structured transform (SS1) method for fast inference. SS1 is a form of randomized parameter sharing (RPS) with connections to low-rank/dimensionality-reduction methods. Theory is used to elucidate SS1’s compatibility with quantization -- it is also compatible with other RPS met... | Rebuttal 1:
Rebuttal: We thank the reviewer for the support of our paper. We have made writing changes to improve the manuscript and additional experiments as suggested by other reviewers. They are listed in common rebuttal.
Please find the clarifications requested below:
**Line 285**: In table 2 (right), we can see... | null | null | null | null | null | null |
Partial Gromov Wasserstein Metric | Reject | Summary: This paper introduces the an unbalance Gromov-Wasserstein distance, which adopted the formulation of unbalance optimal transport into the Gromov-Wasserstein with total variance (TV) penalty instead of KL. This new distance allows comparison of probability measures from different space with partial amount of ma... | Rebuttal 1:
Rebuttal: ## It's worth noting in the literature review the similar works formulating...
**Answer:**
The authors thank the reviewer for their point. These two references will be added in the Introduction.
## The proof of the metric properties is not well displayed in the main text, as this is the main hig... | Summary: The paper considers an unbalanced version of the Gromov-Wasserstein distance, where the discrepancy terms correspond to the total variation between certain product measures for the given marginal and the marginal of the solution, respectively. Different (re)formulations of this distance is considered in both t... | Rebuttal 1:
Rebuttal: ## One weakness with the methods in the paper...
**Answer:**
The authors apologize for the misunderstanding regarding the English term "Partial Gromov-Wasserstein solver" in the main text. As discussed in the paper, GW and its variants UGW/MPGW/PGW are non-convex problems. To the best of our know... | Summary: This paper introduces the Partial Gromov-Wasserstein (PGW) metric as a means to handle unbalanced Gromov-Wasserstein problems between non-probability mm-spaces. The authors develop and demonstrate two computationally efficient variants of the Frank-Wolfe algorithm for solving the PGW problem. They establish th... | Rebuttal 1:
Rebuttal: ## While the paper provides a comparison with existing methods...
**Answer:** The baseline methods we selected follow from the paper [45] [44] and [Beier et al., 2023](https://arxiv.org/abs/2112.11964). As the main topic of this paper is to introduce a partial GW formulation that defines a metric... | Summary: This paper proposes a partial Gromov -Wasserstein (PGW) formulation, which relaxes the original constraints present in Gromov-Wasserstein (GW) formulation. In PGW, the marginal equality constraints of GW are replaced by marginal inequality constraints. Following existing works in partial GW setting, the paper ... | Rebuttal 1:
Rebuttal: ## It is unclear what the paper implies by "mathematically this equivalence relation is not verified." in line 955?
**Answer**:
The authors thank the reviewer's point. The sentence "mathematically this equivalence relation is not verified" will be removed. The section "relation between PGW and... | Rebuttal 1:
Rebuttal: # Relation between PGW and MPGW
### Statement 1: The relation between PGW and MPGW can be described as follows (Proposition L.1. will be updated):
- For each $\lambda \ge 0$, there exists $\rho \in [0, \min(|\mu|, |\nu|)]$ such that:
- For each $\gamma \in \Gamma_\leq(\mu, \nu)$ with $|\gamma| =... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Wings: Learning Multimodal LLMs without Text-only Forgetting | Accept (poster) | Summary: This paper introduces the text-only forgetting phenomenon, where multimodal large language models (MLLMs) experience a significant decline in performance on text-only evaluations. The authors claim that this phenomenon is related to the attention shift of cross-layer MLLM-LAWS (Layer-level Attention Weights) b... | Rebuttal 1:
Rebuttal: Dear Reviewer LUvX,
We sincerely thank Reviewer LUvX for the keen observations and suggestions, as well as the recognition of the attention shift and Wings effectiveness, and the overall flow of the writing. We will update all modifications in the final version. Thank you.
* **Q1:** "MLLM-Laws w... | Summary: Multimodal large language models (MLLMs) are initiated with a trained vision encoder and LLM, then fine-tuned on multimodal mixed inputs. In this process, the LLM catastrophically forgets the text-only instructions. This paper first reveals that text-only forgetting is related to the attention shifts from pre-... | Rebuttal 1:
Rebuttal: Dear Reviewer TqCY,
We sincerely appreciate reviewer TqCY for acknowledging our analysis of attention shift and the effectiveness of Wings. All updates will be included in the final version. Thank you!
* **Q1:** "how the Wings module improves the correlation"
* **A1:**
* **From a structural... | Summary: This paper addresses a significant challenge that when Multimodal Large Language Models (MLLMs) as they expand LLMs’ capabilities to include vision tasks. Specifically, it highlights the issue of "text-only forgetting," which occurs when MLLMs trained with visual inputs struggle to effectively process text-onl... | Rebuttal 1:
Rebuttal: Dear Reviewer 3jFi,
Thank you very much for Reviewer 3jFi's detailed feedback. We appreciate the reviewer's recognition of Wings' motivation, writing, and overall performance. All updates will be incorporated into the final version. Below are our responses to the clear comments and questions rais... | Summary: The paper addresses how to solve the well-known problem of multimodal large language models (MLLMs), text-only forgetting referring to the phenomenon of MLLMs showing drastic performance drops on text-only instructions. The paper first observes based on the analysis over 100 MLLMs that the performance drop is ... | Rebuttal 1:
Rebuttal: Dear Reviewer GnKS,
We appreciate Reviewer GnKS's thoughtful feedback and support, especially regarding the motivation, structure, and performance of our Wings. In response to these valuable insights, we have conducted additional experiments and enriched our descriptions to reinforce our approach... | Rebuttal 1:
Rebuttal: Dear Reviewers,
**Thank you for your meticulous observations and analyses.** We are thrilled that you have recognized our work, Wings. We appreciate your acknowledgment of Wings’ **effectiveness on** text-only, multimodal, and Interleaved Image and Text (IIT) benchmarks. We are particularly pleas... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes | Accept (poster) | Summary: The paper proposes a method to handle the errors when reducing the voltage of SRAM to reduce the power consumption. The method is based on preprocessing the input data into error-resilient forms. Experiments show a tradeoff with a reduction of 24% of power and 2-3% accuracy loss in CIFAR10-ResNet settings.
St... | Rebuttal 1:
Rebuttal: We thank the reviewer for your effort and time in reviewing our paper. Our responses to your concerns are as follows:
**1. (More Motivation)** First of all, thank you for recognizing the value of our work in recovering accuracy under 1% bit errors. In fact, while energy savings are a beneficial a... | Summary: This paper presents NeuralFuse, a module that produces error-resistant data representations by learning input transformations in order to solve the accuracy loss of deep neural networks (DNNs) brought on by low-voltage-induced bit errors in SRAM, allowing DNNs to continue operating accurately even at low volta... | Rebuttal 1:
Rebuttal: We thank the reviewer for your effort and time in reviewing our paper. Our responses to your concerns are as follows:
**1. (Latency)** We understand the reviewers' concerns. However, we respectfully disagree that our evaluation would change drastically if we assume that Neuralfuse operation is pe... | Summary: The paper presents NeuralFuse, a novel approach to address the accuracy degradation of deep neural networks (DNNs) in low-voltage regimes. The core idea is to learn an input transformation module that can generate error-resistant data representations, thereby protecting DNN accuracy even when bit errors occur ... | Rebuttal 1:
Rebuttal: We thank reviewer for recognizing the novelty, practicality, and effectiveness of our work. We address your comments in the following:
**1. (Complex Models and Tasks)** As an algorithm designer, we choose CNN-based models with classification, which may be a representative problem to prove our ide... | Summary: The authors train an input pre-processing module which aims to counteract the effects of random bit errors induced by low-voltage SRAM operation. They demonstrate the ability to avoid most accuracy drops on a handful of CNNs while operating in a 0.5-1% error regime.
Disclosure: I have reviewed this paper in t... | Rebuttal 1:
Rebuttal: Thank you so much for recognizing our work as unique and valuable and especially for pointing out its potential to inspire a number of follow-up works. We are thrilled that you enjoyed reading our paper and provided such encouraging reviews and constructive comments.
We address the answers to yo... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' valuable feedback and the efforts of the program chair and area chair. We are particularly pleased that reviewers found our paper well-written (aRGH), featuring a novel idea (aRGH, nPtF), highlighting energy efficiency benefits (nPtF, LgKi), providing thorou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding and Minimising Outlier Features in Transformer Training | Accept (poster) | Summary: This paper is doing a lot and is a rare case of the abstract/title really underselling what the paper contains. Basically, the paper investigates outlier feature emergence (OFE) in LLMs and some potential fixes for it. The paper argues that such a study is important for both practical reasons (preventing OFE a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in carefully reviewing our work. We are pleased that the review demonstrates a clear understanding of our contributions, and in particular how our work builds on existing work to relate OFEs to areas such as signal Propagation and entropy collapse. W... | Summary: The paper tackles outlier features (OF), i.e. neurons with activation magnitudes that are significantly larger than average which can cause issues with quantization and low-precision training. The paper introduces several metrics for quantifying the existence of OFs and uses them to explore which design choice... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review. We are pleased the reviewer feels we “made a sufficiently large contribution to the research of OFs”, appreciates our experimental design, and finds our paper “well written and relatively easy to understand considering its topic”. We address the... | Summary: The paper focuses on Outlier Features (OF) in neural networks, particularly transformers, where certain neurons exhibit significantly higher activation levels than others. OFs hinder model quantisation and their emergence during training is poorly understood. The study introduces quantitative metrics to measur... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing our work. We are pleased that the reviewer writes that our “findings are supported by empirical validation across various datasets, demonstrating the effectiveness of the proposed metrics and strategies”. The reviewer’s concerns largely ... | Summary: This paper addresses the issue of Outlier Features (OFs), which are neurons with activation magnitudes significantly exceeding the average during neural network training, particularly in transformers. These OFs are undesirable to model quantization, leading to high quantization errors. The authors propose quan... | Rebuttal 1:
Rebuttal: We thank Reviewer Lb1W for their time. However, the reviewer has made several assertive yet unsubstantiated criticisms of our work, which oppose all the other reviewers. We are concerned by these criticisms as they lack constructive feedback and could be perceived as overly critical from our view.... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time in reviewing our work. We are encouraged that three out of four reviewers gave scores leaning towards accept, with an average score of 6 across all four reviews. In particular, reviewer cUUo gave the highest possible score of 10, commenting: “this work will be... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models | Accept (poster) | Summary: This work focuses on understanding supervised finie-tuning (denoted SFT, where parameter weights are changed) and in-context learning (denoted ICL, where weights remain frozen and the adaptivity is done through a few shots in prompts), in the context of modern LLM. In doing so, the authors propose to apply the... | Rebuttal 1:
Rebuttal: Thank you for the time spent reading our manuscript and the concerns you raised, which will help improve our study and make its exposition clearer. We will address all your points below.
> *This manuscript has several issues in clarity. [...]*
We agree with you that the description of the ADP al... | Summary: The paper explores the internal dynamics of LLMs when subjected to ICL and SFT strategies. Despite achieving comparable outcomes, the paper reveals that these methods result in distinct representation landscapes within the model. ICL fosters a hierarchical organization of representations based on semantic cont... | Rebuttal 1:
Rebuttal: Thank you for the time spent reading our manuscript, for your general appreciation of our work, and for your questions. We will answer point by point below.
> *1. Do few-shot examples significantly impact the analysis results? If so, in what aspects are these impacts manifested?*
The order and i... | Summary: In this paper, the authors analyze the probability landscapes of the hidden representations of LLMs when they perform in-context learning (ICL) and supervised fine-tuning (SFT) using the MMLU question answering benchmark, and the Llama-3, Llama-2, and Mistral-7B LLMs are used for this purpose. The Advanced Den... | Rebuttal 1:
Rebuttal: We are grateful for your constructive and thoughtful comments and for the time spent on our manuscript. We will address all the main concerns below:
> *Is it possible to extend some of this experimental analysis to other benchmarks?*
In our work, we analyzed MMLU because it includes a wide varie... | null | null | Rebuttal 1:
Rebuttal: We thank the chair and the senior area chair for their time spent reviewing our work.
*Summary of our contribution*:\
In this study, we compare fine-tuning (FT) and in-context learning (ICL) in LLMs by
analyzing the evolution of the probability density in the hidden layers of state-of-the-art LLM... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning | Accept (poster) | Summary: This paper introduces DEMO, a text to video diffusion approach that aims at enhancing the movement in the video produced by text to video models. In additional to the classical diffusion loss, DEMO introduces:
* __A text motion loss__: the CLIP text encoder used for the temporal cross attention is fine-tuned ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and insightful questions. Below are our responses to the concerns raised:
**Q1**: The comparison with other approaches is not 100\% fair since additional parameters (a CLIP encoder) are trained.
**Response**: We appreciate your concern about the fairness of c... | Summary: This paper introduces novel framework called DEcomposed MOtion (DEMO), which enhances motion synthesis in T2V generation by decomposing both text encoding and conditioning into content and motion components. Authors investigate sensitivity of CLIP text encoder to motion descriptions and propose to condition th... | Rebuttal 1:
Rebuttal: **Q1**: We strongly recommend to incorporate a user study in the research to obtain statistical qualitative results.
**Response**:
Thank you for your excellent suggestion. We conducted a user study to compare our method with other video generation models. We selected 50 prompts from EvalCrafter [... | Summary: This paper proposes a method for enhancing motion synthesis in T2V generation by decomposing both text encoding and conditioning into content and motion components, called Decomposed Motion (DEMO). To address the issue of inadequate motion representation in text encoding, they decompose text encoding into cont... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. Below are our responses to the concerns raised:
**Q1**: The related work section contains too little content.
**Response**: We acknowledge that the related work section is limited due to page constraints. In the revised paper, we will expand this section an... | Summary: This paper aims to improve the motion dynamics generation of the text-to-video generation models. It proposes a framework to decompose both text encoding and conditioning into content and motion components. For text encoding, a CLIP encoder is fine-tuned to encode the motion information in the text prompts bet... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback. Below are our responses to the concerns raised:
**Q1**: In the pilot study, employing only one kind of sentence template may not be sufficient to draw the conclusion that the text encoder is less sensitive to the motion instructions in the text prompt.
**Res... | Rebuttal 1:
Rebuttal: **Q1**:The presentation of Fig.3 (qualitative results) is not good. The improved motion are hardly to observe and it is difficult to compare DEMO with other methods.
**Response**: We thank all the reviewers for highlighting this concern. Qualitative comparison of videos generated by different mod... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dual-Personalizing Adapter for Federated Foundation Models | Accept (poster) | Summary: This paper focuses on personalization and test-time adaptation in federated learning of foundation models. The authors propose two solutions for this setting and show its performance on two splits of FLAN dataset.
Strengths: - The paper is generally easy to read.
- The constructed experimental setup is genenr... | Rebuttal 1:
Rebuttal: **W1: Cannot clearly see the significance of considering personalization and test-time adaptation at the same time.**
Our proposed method is **not doing test-time adaptation**. In general, the test-time adaptation methods [1] usually involve fine-tuning steps to align the model parameters with ne... | Summary: The authors use two set of adapters to personalize a model with federated learning. The idea is to use FL to learn the global adapter whereas each device has a local adapter to personalize the model for each client.
Strengths: - The paper is well written and easy to understand
- The overall approach is sound... | Rebuttal 1:
Rebuttal: **W1: The paper would benefit for a much stronger motivation.**
Unlike traditional FL (especially personalized FL) which primarily addresses heterogeneity among clients during training, our setting also considers heterogeneity within each client during testing, building on insights from previous ... | Summary: This paper proposes a novel dual-personalizing adapter to tackle the test-time distribution shift for federated foundation models (FedFM). FedFM is a new research domain to enhance foundation models by leveraging many fine-tuning tasks on many protected datasets for end users. The solution is essentially to ta... | Rebuttal 1:
Rebuttal: **W2 and W3: Design of trade-off mechanism and large-scale experiment.**
Thanks for your insightful advice. We will further explore better mechanisms from the perspective of generalization theory and more datasets in real applications.
**Q1: Choice of similarity function.**
We select cosine sim... | Summary: Federated Foundation Models (FedFM) is an emerging research domain to study collaboratively fine-tuning the pre-trained foundation models. This paper studied a test-time distribution shift problem on FedFM by proposing a new dual-personalising adapter.
Strengths: 1) The proposed method is novel. The targeting... | Rebuttal 1:
Rebuttal: **W1: Discussion of assumption.**
Yes, our main experiments are based on the assumption that all possible distributions are included in all clients. We already discussed this in the first paragraph of section 4 and Appendix C. To enhance clarity, we will rewrite this part and emphasize keywords i... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable reviews. We also appreciate their recognition of the key contributions of our work and the efficacy of our method.
1. **Contributions** to federated foundation models:
- "The targeting problem is significant to the emerging domain of FedFM." (Rev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Fourier Prompt Tuning | Accept (poster) | Summary: This paper introduces Visual Fourier Prompt Tuning (VFPT), a novel approach to parameter-efficient fine-tuning (PEFT) for large-scale Transformer-based vision models. VFPT integrates Fast Fourier Transform (FFT) with prompt tuning, allowing the model to adapt to new tasks effectively by utilizing both spatial ... | Rebuttal 1:
Rebuttal: Dear Reviewer TrZ7,
We sincerely appreciate your time and effort in reviewing our paper and providing valuable comments. We provide explanations to your questions point-by-point in the following.
**Q1: Regarding the discrepancy of tuned parameter rate.**
**A1:** Sorry for the confusion. We want... | Summary: This paper introduces Visual Fourier Prompt Tuning (VFPT), an approach for parameter-efficient fine-tuning of large vision models. VFPT integrates Fast Fourier Transform (FFT) operations into visual prompt tuning, allowing it to incorporate both spatial and frequency domain information. The method demonstrates... | Rebuttal 1:
Rebuttal: Dear Reviewer 9y4B,
We sincerely appreciate your time and effort in reviewing our paper and providing detailed comments and suggestions, which are crucial for improving our work. We provide explanations to your questions point-by-point in the following.
**Q1: Regarding theoretical analysis.**
*... | Summary: This paper proposes Visual Fourier Prompt Tuning (VFPT) to address performance degradation in parameter-efficient finetuning (PEFT) methods caused by dataset disparities. VFPT integrates Fast Fourier Transform (FFT) into prompt embeddings, enhancing performance with minimal parameter usage. This work is built ... | Rebuttal 1:
Rebuttal: Dear Reviewer PyKu,
We sincerely appreciate your time and effort in reviewing our paper and providing valuable comments. We provide explanations to your questions point-by-point in the following.
**Q1: Regarding the VFPT performance with larger models.**
**A1:** Thank you for the great suggesti... | null | null | Rebuttal 1:
Rebuttal: To All Reviewers:
We sincerely thank all reviewers for your valuable suggestions and constructive feedback. We have revised our paper accordingly. The major changes are as follows:
- We’ve conducted additional experiments on the large model (i.e., ViT-Huge), vision-language model (i.e., CLIP), an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DistrictNet: Decision-aware learning for geographical districting | Accept (poster) | Summary: This paper propose an ML-based method to solve districting problems that are known to be challenging. The core idea is to first convert the districting problem to a simpler capacitated minimum spanning tree problem and then leverage the existing learning to optimize method to perform imitation learning. Such c... | Rebuttal 1:
Rebuttal: Thank you very much for your in-depth review, valuable suggestions, and overall positive appreciation of the paper.
> Some benchmarks from the OR/optimization community are missing. When the demands are uniformly distributed, I believe the second-stage TSP objective value has some very nice close... | Summary: This paper tackles the problem of geographical districting through decision-aware learning. The problem is challenging due to the large combinatorial number of possible district designs. By generalizing the GNN-based method (similar to [Ferraz et al.2024 arXiv]) based on decision-aware learning through the Fen... | Rebuttal 1:
Rebuttal: We thank you very much for your review and detailed evaluation of our paper.
> Some unclear explanations of using GNN, compared with the existing work [Ferraz et al. 2024]; some parts just follow [Ferraz et al. 2024]. These points raise the insufficient explanation of the technical contributions... | Summary: The paper discusses the development and evaluation of a new method called
DISTRICTNET for addressing districting and routing problems using neural networks.
The approach focuses on minimizing the Fenchel Young loss and generalizing to large
out-of-distribution instances from training on smaller instances. Th... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review of the paper and the helpful suggestions. We respond to your concerns and questions below.
> The supplementary material is essential and the authors should consider [...] bringing some of the appendix to the main paper. For example, I was lost about th... | Summary: The authors use CMST to solve the problem of districting and routing in large scale scenarios. Finding the relationship between CMST and partitioning is quite beneficial for researchers engaged in related research.
However, it is worth noting that the authors only report the performance of the model on the '... | Rebuttal 1:
Rebuttal: Thank you for your review and insightful comments. We answer your questions below and clarify the concerns discussed in the report.
> The authors only report the performance of the model on the 'cost' metric, and do not investigate the performance of the method on the common metrics of traditiona... | Rebuttal 1:
Rebuttal: ## We thank the reviewers for their constructive comments and feedback
Dear Reviewers, Dear Area Chair,
We want to express our sincere thanks for the detailed reviews of our work and the constructive feedback. The reviewers have appreciated the value of our decision-aware approach to solve large... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation | Accept (poster) | Summary: This paper presents a procedure for synthesizing hard UNSAT formulas from a given distribution of UNSAT formulas. Concretely, a formula is generated by 1) extracting a core from a seed instance; 2) adding random new clauses; and 3) refining the formula to become harder. In step 3), a GNN is trained to predict ... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer's generous acknowledgement of the
elegance and performance of our method. In addition, the reviewer's
comments about scalability and publicly available data have led us to
make significant additions to the depth of our work's experimental
setting and analysis. Fin... | Summary: This paper proposes a novel method for generating hard UNSAT problems. The method targets the "core identification" problem and iteratively performs refinement using a GNN-based detection procedure, which preserves the key aspects of the original instances that impact solver runtimes. The experimental results ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and critical comments. These
comments have prompted several new explorations, including an
examination of the progression of hardness during refinement, and the
addition of a new dataset to the experimental setting. These improve
both the strength of our res... | Summary: This paper introduces HardCore, a novel method for efficiently generating hard Unsatisfiable (UNSAT) Boolean Satisfiability (SAT) problems, addressing the critical challenge of data scarcity. The approach combines a Graph Neural Network (GNN) for rapid core prediction with an iterative core refinement process,... | Rebuttal 1:
Rebuttal: We wish to express our gratitude to the reviewer for their
acknowledgement of our extensive experimentation and innovation. The
reviewer's comments have guided us to explore meaningful improvements to
the presented work: complete results on an additional public dataset, a
deeper analysis of scalin... | Summary: This paper addresses the scarcity of practical data (industrial satisfiability problem instances) for training deep learning methods for SAT solving. The existing data augmentation methods suffer from either the limited scalability or the collapsed hardness of the generated instances. Therefore, the authors in... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful and
constructive review. In following the reviewers comments we have
uncovered new, valuable experiments (Circuit-Split LEC) as well as
short-comings in the description of certain details of the experimental
setting.
## Weaknesses
1. *GNN... | Rebuttal 1:
Rebuttal: We thank the reviewers for the insightful and thoughtful reviews. All
reviewers have stated that the proposed method is novel and stands as a
meaningful and innovative contribution to the field. Several reviewers
agreed that the experimentation was extensive and demonstrative of
improvements over ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks | Accept (poster) | Summary: The proposed approach uses the block diagonal form of the Fischer Information Matrix (FIM) which is achieved by computing this FIM with every layer in the network sequentially. In addition, the space complexity is limited by consider the diagonal form of this FIM matrix for each layer and its respective gradie... | Rebuttal 1:
Rebuttal: Q1: What is $s_l$ in line 136 ?
A1: Sorry for the unclear description, $s_l= W_l a_{l-1}$. We will correct it in the final version.
Q2: This reveiwer would like to know what's the difference between the proposed approach and https://ojs.aaai.org/index.php/AAAI/article/view/16867?
A2: Thanks fo... | Summary: The authors propose a novel way to approximate the Fisher information matrix. They do this by starting with the block diagonal approximation of the Fisher information matrix which they compute using a novel layer-wise sampling method without performing a complete backpropagation. Then they approximate each blo... | Rebuttal 1:
Rebuttal: Q1: The first part of weaknesses.
A1: We are sincerely thankful for the valuable suggestions. Our primary objective is to propose and optimizer and achieve an optimal balance between the speed and accuracy of model training. Expanding upon established methodologies in the field of NGD, we propos... | Summary: The authors propose a computationally feasible second-order method for training neural nets, layer-wise natural GD, which includes an Adaptive Layer-Wise Learning Rate scheme. The method eliminates the backprop pass by using a layer-local sampling approach to approximate the Fisher information matrix; thus pro... | Rebuttal 1:
Rebuttal: Q1: Weaknesses.
A1: We would like to express our gratitude for the insightful suggestions provided. Our primary aim is to establish an optimal equilibrium between model training speed and accuracy. Building upon the existing approaches in NGD, we propose a layer-wise sampling methodology to effic... | Summary: The paper introduces a new optimization algorithm called LNGD (Layer-wise Natural Gradient Descent). This optimizer aims to enhance the training efficiency of deep neural networks by approximating the Fisher information matrix in a computationally efficient manner and introducing adaptive layer-wise learning r... | Rebuttal 1:
Rebuttal: Q1: Assumption of Gaussian distribution.
A1: Thanks for your valuable suggestion. We have added Figure 1 to illustrate the validity of the Gaussian distribution assumption. Please refer to the submitted pdf file. We collect the output of two layers of the ResNet-18 network on CIFAR-10. Figure 1 ... | Rebuttal 1:
Rebuttal: We are very grateful to the four reviewers for their constructive comments and valuable suggestions on our manuscript.
The tables and figures mentioned in the reply are given in the the submitted pdf file. Please see it for details.
In our manuscript, we aim to propose an optimizer that can ach... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory | Accept (poster) | Summary: In this paper, the authors discuss the impact of random initialization of deep neural networks in the neural tangent kernel (NTK) regime.
Precisely, the authors prove that in the case of standard random initialization, the network function in the finite wide limit, converges to the NTK predictor uniformly.
The... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and for raising these questions. We will summarize our main technical contribution and address the questions:
### **Summary of our technical contribution**
*"The result in Theorem 4.2 appears novel and interesting, but it is a bit difficult to position this in th... | Summary: The paper aims to study the impact of standard random weight initialization - as opposed to mirror initialization - on NTK theory. The key observation is that, for standard initialization, the operation of the network at initialization $f_0$ acts as a bias on the regression function $f^*$. When analyzing the... | Rebuttal 1:
Rebuttal: Thanks a lot for your valuable suggestion. It greatly helps us re-interpret our main results. More precisely, Theorem 4.3 and 4.4 actually tell us that the generalization ability depends on the smoothness of the target function $f^*$ and the initialization function. In particular, given the target... | Summary: This paper explores the standard random mode of initialization of neural networks through the lens of the neural tangent kernel (NTK). This connection is made by showing that a randomly initialized neural network does indeed converge to the NTK uniformly during training thus allowing to analyze the generalizat... | Rebuttal 1:
Rebuttal: ### Minors
Thank you for your thorough review and for pointing out the typos and grammatical errors in our paper. We appreciate your feedback, and we have made the necessary corrections as follows:
1. **Proposition 2.2:** The typo "satisfis" has been corrected to "satisfies".
- Original: *".... | Summary: This paper studies various kernel theories of neural networks, both random feature and neural tangent kernels. The main approach is to use the decay rates of the target function and kernel eigenvalues to get generalization error rates. The paper's novel theoretical contribution is to show uniform convergence o... | Rebuttal 1:
Rebuttal: ### Brief Introduction of Mirror Initialization
*"In the introduction and experimental sections, the standard and mirror initializations are contrasted. ...... what is known about the mirror initialization."*
Thank you for your suggestion. Here we provide a brief introduction of the existing the... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Power of Decision Trees in Auto-Regressive Language Modeling | Accept (poster) | Summary: This paper contributes both theoretical and empirical evidence that autoregressive decision trees (ARDTs) are an expressive machine learning model. On the theoretical side, the authors show that a family of ARDTs with concatenated inputs can, in theory, efficiently (usually polynomial in size of input sequence... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their thorough and constructive review of our paper. We appreciate your acknowledgment of the novel and enlightening theoretical analysis, clear writing, and the non-trivial application of ARDTs to language modeling.
Q1: Confusions regarding the proof
A1: ... | Summary: This paper explores the idea to use AutoRegressive Decision Trees for language modelling. From a theoretical perspective, ARDTs are shown to model systems such as finite automata and more generally Turing machines. From an experimental point of view, ARDTs are shown to be able to generate grammatically correct... | Rebuttal 1:
Rebuttal: We first want to thank the reviewer for their thorough review and positive assessment of our paper. Specifically, we appreciate your acknowledgment of the paper's novel ideas, good balance between performance and interpretability, and interesting theoretical analysis.
Q1: The difference between t... | Summary: * This work studies theoretical and practical applications of auto-regressive decision trees (ARDT) in language generation and reasoning tasks.
* Through theoretical analysis, the authors show that ARDTs can learn more sophisticated functions than previously known, such as automata, Turing machines, and sparse... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and very positive assessment of our paper. Specifically, we appreciate your acknowledgment of the paper's clarity, informativeness, and its contribution to model interpretability.
Q1: Performance changes with the text length. What will the performance be in gene... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their comprehensive and constructive feedback on our submission. We are pleased that our work is recognized for its novelty (Reviewers o5To, 97Y1, and qHp8), its novel and enlightening theoretical analysis (Reviewers o5To and 97Y1), and a nice balance between performance... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice | Accept (poster) | Summary: This paper uses a state-of-the art-models model from psychology for preference learning. To make learning tractable, the authors use variational inference to approximate unknown distributions (and expectations). The model is shown to outperform existing approaches, suggesting that it better predicts human pref... | Rebuttal 1:
Rebuttal: Thank you for the review of our paper and thoughtful feedback. We address your main questions and feedback below:
>I find this work to be a nice example of cross-disciplinary ML project: model from psychology, use of variational methods to make it computationally feasible, diverse set of experimen... | Summary: The paper introduces two models for learning preferences from human choice behaviors: the Computationally Rational Choice Surrogate (CRCS) and a variant (LC-CRCS) that considers the context effect of the choice set. Both models are based on a state-of-the-art cognitive model of human decision-making. The paper... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We address specific questions below:
>Limited Applicability: The models require data that includes the orderings of each feature […] and the true utilities
This is incorrect.The data we used was straightforward choice data consisting of the options presented ... | Summary: In this paper, the authors present a new model which approximates an existing intractable Bayesian model for preference learning. The paper describes a generative model for choice, and shows how inference can be approximated using two different neural networks. The proposed model is then augmented using a cros... | Rebuttal 1:
Rebuttal: Thank you for the review and feedback on our paper. We address specific questions below:
>While it was easy to follow the theory, I found it a little difficult to deeply understand the application section. A little more focus on methodology on at least one experiment might have been more helpful. ... | Summary: The submission proposes an approach to preference learning using a model inspired by findings in cognitive science. Specifically, it uses an amortized inference variant of a previously proposed model to enable tractable inference of preference values, and applies it to a number of case studies, where it is sho... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper and your feedback. We have addressed the main questions and concerns you have raised below.
>the policy network takes in $x, w, \tilde{u}$ and $\tilde{o}$, and a sufficiently flexible surrogate should be able to learn arbitrary functions of x and w already. W... | Rebuttal 1:
Rebuttal: We thank all reviewers for the time and effort dedicated to review of our work and for the helpful and constructive feedback.
## Motivation and focus of the paper
Our paper presents a cross-disciplinary approach to preference learning. Humans are known to exhibit a number of context effects when ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection | Accept (spotlight) | Summary: This paper proposes a new way of harnessing unlabeled LLM generation as the training set for fact verification. The key assumption is "LLM generates factually correct statements more than hallucinogenic statements", which guarantees the existence of clear subspace between hallucination and non-hallucination st... | Rebuttal 1:
Rebuttal: We thank you for recognizing our work as interesting and for studying an important challenge. We appreciate the reviewer's comments and suggestions, which we address below:
**A1. The effect of adding more unlabeled data**
Thank you for the suggestion! Our ablation on the number of unlabeled da... | Summary: The paper attempts to address the popular problem of hallucination in texts generated by today's LLMs (large language models). Hallucination refers to false or misleading text generated by LLMs. The paper attempts to address hallucination by proposing a truthfulness classifier, HaloScope, that operates on the ... | Rebuttal 1:
Rebuttal: We are deeply encouraged that you recognize our method to be novel, welcome, and beneficial to the research community.
Your summary and comments are insightful and spot-on :)
**A1. Clarification on the problem setup**
You raise a great point! We agree with you that the truthfulness of an LLM g... | Summary: This paper presents a technique for detecting hallucinations by leveraging unlabeled data generation. Instead of relying on human annotation, the method automatically distinguishes between truthful and untruthful generations using network embeddings and their projection onto singular vectors with high singular... | Rebuttal 1:
Rebuttal: We are glad to see that the reviewer recognized the strengths of our work from various perspectives. We thank the reviewer for the thorough comments and suggestions. We are happy to clarify as follows:
**A1. Clarification on the BLEURT metric**
Thank you for pointing this out! BLEURT [1] is des... | Summary: The paper is very clear in the problem it is facing and the solution it proposes is also clearly described. Specifically it is looking at detecting hallucinations produced by generative language models. It does so by taking internal representations of the LLM, projecting these onto SVD factorisation which iden... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. We are encouraged that you recognize our approach to be clear and with interesting experiments and thorough ablations. We address your questions below:
**A1. Effectiveness of our approach on additional tasks**
Thank you for the suggestion! ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and valuable comments. We are encouraged to see that all reviewers find our approach **interesting, clear, simple, new, welcome**, and **scales well** (smZn, 6VqW, tABH, JMXE), and our results **very interesting, excellently presented**, with **thorough, w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SDEs for Adaptive Methods: The Role of Noise | Reject | Summary: This work derives SDEs for adaptive gradient methods and study the role of gradient noise. The analysis starts from theoretically driving the SDE for SignSGD and highlight its significant difference from SGD. The work further generalize the SDE analysis for AdamW and RMSpropW, two popular adaptive optimizers w... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for the significant effort put into this review: We appreciate the acknowledgement of the value of our research. We thank you for the questions as they stimulated us to include some more references and dig deeper to showcase the explanatory power of our SDEs even mo... | Summary: The authors derive SDE for signSGD and Adam(W). The experiments show that the algorithm will converge toward the limit of the theorem indicates.
Strengths: The authors propose "accurate" SDEs for algorithms Sign-SGD and Adam(W).
Weaknesses: 1. In Remark after Lemma 3.6, the authors claim that Sign-SGD is (al... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer: We appreciate the questions as they stimulated us to clarify certain aspects and dig deeper to showcase the explanatory power of our SDEs even more.
**Weakness 1:**
*"In Remark after Lemma 3.6, the authors claim that Sign-SGD is (almost) linear in $\sigma_{\text{... | Summary: This paper derives SDEs for SignSGD, RMSprop, and Adam.
The analysis offers insights into the convergence speed, stationary distribution, and robustness to heavy-tail noise of adaptive methods.
Strengths: - The derived SDE for SignSGD exhibits three different phases of the dynamics.
- The analysis reveals th... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their thorough and thoughtful review. We appreciate the questions posed, as they motivated us to delve deeper and further showcase the explanatory power of our SDEs. However, we would like to clarify that **contrary to what is mentioned under "Limitations", none of our SD... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate your thorough reviews, insightful comments, and interesting questions regarding our paper: Your feedback has helped enhance our work.
The considerable time and effort we devoted during this rebuttal period were rewarding, as we derived new interesting insi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection | Accept (poster) | Summary: This paper proposes OneDet3D, a universal one-for-all model that addresses 3D detection across various domains, including both indoor and outdoor scenes. It tackles two primary issues: data-level interference caused by differences in the point clouds themselves and category-level interference caused by label c... | Rebuttal 1:
Rebuttal: **Q1: In lines 60-62, the authors claim that 3D sparse convolution is better than point-based feature extractors due to its robustness to domain gaps. Could the authors elaborate on this in detail?**
A1:
Table 3-1: Comparison with point-based feature extractor
| | SUN RGB-D | ScanNet | KI... | Summary: This paper proposes OneDet3D, which is a multi-domain jointly trained point cloud object detector for universal 3D object detection. It is the first 3D detector that supports point clouds from both indoor and outdoor scenes simultaneously with only one set of parameters. The experiments are conducted on multip... | Rebuttal 1:
Rebuttal: **Q1: The performance in Table 2 for the nuScenes dataset is strange.**
A1: The reason is that we train and evaluate only on the car category of the nuScenes dataset. Since only the car class is involved in training, we do not need to use the CBGS sampler for class balance optimization during tra... | Summary: This manuscript introduces OneDet3D, a universal point cloud-based 3D object detector designed to address the challenges of multi-domain joint training. The primary motivation is to overcome the limitations of existing 3D detectors, which are typically trained and tested on single datasets, restricting their g... | Rebuttal 1:
Rebuttal: **Q1: It is recommended to make a clearer comparison and have additional discussions with closely related works**
A1:
* Our main contribution is that we propose **a universal 3D detector that can directly generalize across various indoor and outdoor point clouds**, once trained. Existing works on... | null | null | Rebuttal 1:
Rebuttal: We include the flowchart of our method in the PDF file here.
Pdf: /pdf/0c00c14ae92d913851451fc839bd03d6526cedc6.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold | Reject | Summary: This paper addresses the issue of the flow matching method's lack of dependence on the data population. It proposes incorporating the initial population density into the vector field through amortization—using a Graph Neural Network (GNN) to embed the populations and adding this embedding to the input of the v... | Rebuttal 1:
Rebuttal: >The paper could explain more about the meta-learning aspect of this method. Specifically, what makes it meta-learning?
Our work follows the naming convention of *Meta Optimal Transport* [1] and is "meta" in the sense of amortizing learning over multiple input distributions. This is related to ... | Summary: This paper proposed an extension of the Conditional Generative Modeling via Flow Matching (CGFM) framework. Taken inspiration from the theory of Wasserstein Gradient Flow, this new framework, Meta Flow Matching, proposed to learn the push-forward mapping of multiple measures in the same measures-space. This is... | Rebuttal 1:
Rebuttal: >The first part of the methodology section seems to be phrased as a new methodological contribution, but if I'm not mistaken this is just more or less restating the already established theory of W2 gradient flow and continuity equation (eq 14). I think the authors should put Section 3.1 into the b... | Summary: The paper discussed the novel problem setup of generative modeling of the dynamics of probability distributions. The paper proposed Meta Flow Matching (MFM), an extension of the flow matching framework for implicitly learning the vector fields on the Wasserstein manifold of probability distributions. The paper... | Rebuttal 1:
Rebuttal: >Such an idea has already been applied in various diffusion or flow matching models including image generation, protein co-design [1], and peptide design [2].
The reviewer is correct in that MFM is a conditionally trained flow matching model. Indeed, there are many conditionally trained flow matc... | Summary: This paper introduces Meta Flow Matching (MFM), a flow matching framework modeling interacting samples evolving over time by integrating vector fields on the Wasserstein manifold. The authors leverage a Graph Neural Network to embed populations of samples and thus generalize the method over different initial d... | Rebuttal 1:
Rebuttal: >In Table 1 of the synthetic experiment ... MFM doesn't seem to beat existing methods on the metrics and from the visualizations, it's hard to tell MFM is actually doing better than FM.
We thank the reviewer for the opportunity to further clarify our results. Metric-wise from Table 1, MFM outperf... | Rebuttal 1:
Rebuttal: ## Feedback Summary
We thank all the reviewers for the time they invested in reviewing our paper and for their valuable and constructive feedback that will help improve our work's overall quality.
In this work, we introduced **Meta Flow Matching (MFM)**, a novel framework for learning the dynam... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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