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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BitDelta: Your Fine-Tune May Only Be Worth One Bit | Accept (poster) | Summary: This paper introduces BitDelta which quantizes the aggregated weight updates (the authors call it “delta”) to 1-bit after full fine-tuning. The paper claims that the approach has two applications: 1. First, it shows that the delta is highly redundant and 2. It is useful in the multi-client-single-server applic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind review! Please find below our point-by-point response regarding your feedback:
> 1, The author claim that BitDelta shows the potential redundancy of information added during fine-tuning. However, this is not a new finding and almost all PEFT approaches (for exam... | Summary: Aiming at storage and serving overhead caused by multiple finetuned LLMs for various downstream tasks, this paper proposes a memory-friendly model compression method namely BitDelta which binaries the delta of each weight matrix and uses self-distillation to learn optimal scaling factors.
Strengths: BitDelta ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind review! Please find below our point-by-point response regarding your feedback:
We would first like to clarify a misconception that significantly impacts the evaluation of our work: **we do not fine-tune our own models** in this paper. Rather, we take existing po... | Summary: This paper introduces Bitdelta, a method that enables quantization with just 1 bit. The main idea is to compress the weight delta into a scalar and a binary matrix. The experimental results demonstrate that Bitdelta achieves better performance compared to other techniques.
Strengths: - Easy to read and well-s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind review! Please find below our point-by-point response regarding your feedback:
> A significant contribution of Bitdelta lies in its ability to reduce memory consumption through 1-bit quantization. However, the paper lacks detailed evidence to support this claim.... | Summary: The paper proposes to quantize the weight delta of a fine-tuned LLM to 1-bit and observes that the model quality only drops a little.
During the binarization step, it requires calibrating the scaling factor with a few hundreds of distillation steps. This is less than a full fine tuning. Evaluation show that th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind review! Please find below our point-by-point response regarding your feedback:
> While requiring low storage size, the proposed method introduces an extra binary-float matmul during inference. Although it is indeed a special kernel and can have much lower inferen... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unraveling the Gradient Descent Dynamics of Transformers | Accept (poster) | Summary: This paper proves that with appropriate initialization GD can train a Transformer model with either Softmax or Gaussian kernel to achieve a global optimal solution. Besides, this paper highlights the Gaussian attention kernel exhibits much favorable behavior than Softmax in certain scenarios.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: >>**Your comment**: The new insight we can gain compared to Wu et al [2024]; the fixed $W^O$ in optimization.
**Response**: See the general rebuttal at the top of the page **Comment 1**.
>> **Your comment**: The comparison between the Gaussian and Softmax kernels only updates $W^Q$.
**Response*... | Summary: The authors establish different convergence theorems on the training of a single layer transformer with different trainable weight matrices and kernel functions. They prove that, under certain conditions, a one-layer Transformer with a Gaussian kernel converges faster than one with a Softmax kernel. Finally, t... | Rebuttal 1:
Rebuttal: >>**Your comment**: The difference in convergence speed in Fig. 2 seems like constant level with same rate.
**Response**: Thank the reviewer for the comment. We need to clarify that it is **reasonable** that the difference in convergence rate in Fig. 2 is constant level. Further, we provide **em... | Summary: This paper analyzes the convergence behavior of Transformer models with different attention mechanisms, specifically comparing Softmax and Gaussian kernel attention. The authors provide theoretical results on the conditions for global convergence and empirically demonstrate differences in optimization landscap... | Rebuttal 1:
Rebuttal: >>**Your comment**: The empirical evaluation is limited and doesn't fully validate the theoretical claims in practical settings. The experiments use simplified Transformer models on relatively small datasets (IMDB and Pathfinder). It would be more convincing to see results on larger, more complex ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for the comments and suggestions. We will summarize the strength of our work from reviewers as following:
1. The analysis of Transformer is under realistic setting, and requires no strict data assumption.
2. We delve into each variables within attention kernels. The analysis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models | Accept (poster) | Summary: The paper proposes to mitigate task interference during multimodal instruction tuning with a mixture of experts, in both the language and the encoder side. The paper is well written, contains insightful analysis and shows improvements over baselines.
Strengths: - Mixture of experts is an important topic that ... | Rebuttal 1:
Rebuttal: **Q1:** Lag behind LLaVA with more parameters, vision encoders, and training time.
**A1:** The original LLaVA includes much fewer tasks than our MoME for training and evaluation, we reported only the results on the shared tasks in Table 3 of the manuscript. **Thus, it is improper and inaccurate t... | Summary: The paper proposed a MoE design for MLLM, it utilize MoE in both visual encoding procedure and LLM decoding procedure. The paper utilized a dynamic routing module to mix visual features from different experts, and adopted a multi-adapter structure to combine the knowledge of differnent language experts. Widely... | Rebuttal 1:
Rebuttal: **Q1:** Key design philosophy has already been brought out in many previous works.
**A1:** The motivation and capabilities of MoVE are completely different from the previous work.
1. “**Eyes Wide Shut**” introduced an additional DINO encoder and chose to interleave visual features, which ended u... | Summary: In this paper, the authors introduce a mixture of multimodal experts (MoME) to reduce task interference and develop a generalist MLLM. MoME consists of two main components: a mixture of vision experts (MoVE) and a mixture of language experts (MoLE). MoVE can adaptively adjust features transformed from differen... | Rebuttal 1:
Rebuttal: **Q1:** Reasons for notable improvements in Table 1.
**A1:** When mixing visual representations from different vision experts, they are first transformed into a unified-length sequence of feature vectors and then aggregated, each step of which will severely damage the visual information.
- Tran... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers (R#1g6k, R#cdtp, R#ZwrT) for their time and efforts in providing constructive feedback. We are very encouraged that reviewers found our work effective (R#1g6k, R#cdtp, R#ZwrT), with a clear analysis of task interference (R#1g6k), comprehensive experiments (R#1g... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Sampling for Efficient Softmax Approximation | Accept (poster) | Summary: This paper introduces an efficient algorithm called AdaptiveSoftmax, designed to compute the top k outputs of the softmax function more effectively than the traditional full softmax computation. The key innovation lies in its adaptive approach, which leverages multi-armed bandit techniques to prioritize comput... | Rebuttal 1:
Rebuttal: We thank Reviewer wft8 for their careful reading of our discussion of empirical and theoretical improvements, and for highlighting this relevant reference. We discuss this paper, and the assumption of sub-gaussianity below.
1. **Sub-gaussian assumption:** this is a very fair question, which we di... | Summary: This paper focuses on the efficient approximation of the softmax function. The authors propose an algorithm named AdaptiveSoftmax, which aims to reduce the computational cost of the softmax function in high-dimensional environments. Inspired by the multi-armed bandit problem, the algorithm adaptively allocates... | Rebuttal 1:
Rebuttal: We thank Reviewer kEa9 for their in depth review: we wholeheartedly agree that this method of adaptive computation holds the potential for significant computational improvements across a wide range of applications. We respond to the specific questions below.
1. **Ablation study:** The theorems re... | Summary: The softmax function is a widely used tool, e.g., as an activation in the final layer of a classifier. Hence, cutting down on its computational costs can have a significant impact across the AIML field. This paper aims at this by introducing an adaptive variant of computing the softmax for the top $k$ values b... | Rebuttal 1:
Rebuttal: We thank Reviewer qUNV for their helpful feedback regarding improving the exposition of the improved algorithmic performance of our method.
We respond to their two main points below:
1. **Varying parameters:**
The user-desired parameters can take a wide range.
We kept $\epsilon = 0.3$ (i.e. 30\%)... | Summary: This paper introduces an algorithm named AdaptiveSoftmax, designed to efficiently compute the top-k softmax values rather than the full softmax computation. This algorithm is particularly useful in high-dimensional settings where the computation of the full softmax function can be prohibitively expensive. The ... | Rebuttal 1:
Rebuttal: We thank Reviewer qEfP for their insightful and detailed feedback.
We provide a point-by-point response to Reviewer qEfP’s comments below.
a) **Sub-gaussian assumption:** This assumption is minimally restrictive, and is borne out in practice.
We provide an in depth discussion in main response poi... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their careful reading of our manuscript.
We were pleased to see that all reviewers appreciated the novel PAC guarantees provided by this work for efficient, instance adaptive softmax computation.
We have addressed all the comments and suggestions made b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems | Accept (poster) | Summary: The paper proposes a white-box approach to human-machine teaming, in which human teammates can see their virtual counterparts’ policies and adjust them accordingly. The framework is built on top of differentiable decision trees, and the authors propose contextual pruning as a means of simplification for traini... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that they enjoyed reading our paper, that the paper's contribution to the field is significant, and that our paper is well-written and the ideas are clear. We have responded below to the weaknesses and questions noted by the reviewer.
**Contextual Pruning Results ... | Summary: In this paper the authors present an approach to human-AI teaming in the common Overcooked domain via Interpretable Discrete Control Trees (IDCTs), which are differentiable decision trees which the authors visualize and make controllable. The authors present two examples of where existing blackbox models may d... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that our study in Human-AI teaming would be beneficial to the community.
**Unsupported Claims and Design Decisions** -- As the field of Human-AI Teaming is still relatively new, some motivations are positional. For example, in considering lines 129-132, some may ... | Summary: In this manuscript, it focuses on the collaboration in human-machine teaming (HMT) based on interactive and explainable systems. In order to address the existing issues, such as decoupling, the author(s) explored an interesting paradigm in HMT and proposed some guidelines based on the study. Also, the author(s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. We have responded to the weaknesses and questions noted by the reviewer.
**Instability of L1** -- We thank the reviewer for this comment. The L1 regularization is only applied to the action leaf nodes of the tree policy. This regularization serve... | Summary: This paper focuses on developing strategies to enhance transparency and interpretability in human-AI teaming settings. Based on my understanding, two collaboration contexts have been considered: human-preferred collaboration and AI-preferred suboptimal teaming strategy. The authors have implemented specific st... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that our paper is well-motivated, provides valuable insight into designing human-AI collaboration interfaces, and studies a relatively underexplored domain. We have responded to the weaknesses and questions noted by the reviewer.
**Comparison to Explainability App... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their insightful reviews and valuable feedback on our paper. We have included a rebuttal document with additional figures as well as provided rebuttals to each reviewer below.
Pdf: /pdf/3b8a150973800409209615a357eccfb2ebee69af.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors | Accept (poster) | Summary: It is proposed to solve image inverse problems with diffusion priors by sliced Gibbs sampling (SGS). To sample from the product of prior and likelihood distributions, SGS assumes a pair of variables $x,z$ coupled by a Gaussian and alternates conditional sampling steps: on $x$ using the prior density multiplied... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Below we provide point-by-point responses to your comments.
> Weakness 1.1
We provide the confidence intervals of all the results in Tables 1 and 2 in the attached PDF. Based on these results, we claim that our method achieves comparable or superior outcomes o... | Summary: This paper proposes a novel posterior sampling algorithm for solving inverse problems with Diffusion models. The proposed algorithm is based on the Split Gibbs sampling scheme and consists in, first introducing an extended distribution $\pi_\rho(x, z)$ that admits the original posterior $\pi$ as $x$ marginal a... | Rebuttal 1:
Rebuttal: Thank you for your evaluation of our work. Below we provide point-by-point responses to your comments.
> **Computational cost**: I believe that the proposed method is much slower than existing alternatives. This is not that much of an issue for me (but of course this depends on the reader and pra... | Summary: The paper introduces a method to address Bayesian inverse problems in computational imaging. It leverages the generative capabilities of diffusion models (DMs) to sample the posterior distribution over all possible solutions from noisy and sparse measurements. The method combines a Markov chain Monte Carlo (MC... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our work. Below we provide point-by-point responses to your comments.
> While the method demonstrates superior performance, the computational cost and efficiency are not thoroughly discussed. A comparison of computational resources required compared to other methods... | Summary: This paper proposes a Markov Chain Monte Carlo algorithm for posterior sampling in both linear and non-linear inverse problems. The core of the proposed method is based on a Split Gibbs Sampler that alternates between two steps: one involving the likelihood and the other the prior. Additionally, the paper conn... | Rebuttal 1:
Rebuttal: Thank you for your evaluation of our paper. We respond to your comment below.
> There are no apparent weaknesses. However, I am curious about the reconstruction speed comparison (seconds/image) between DPS and the proposed method. It would be practically very attractive for the community to use t... | Rebuttal 1:
Rebuttal: ## **Response to all reviewers**
We thank all the reviewers for their careful reviews and constructive feedback. We are glad that our method was recognized as "rigorous", "well-executed" (reviewer G2Ty), and “unlike some previous works...effective for both linear and non-linear inverse problems” (... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper, the authors treat the problem of sampling from posterior of image inverse problems. Their formulation is based on the Split Gibbs Sampler (SGS) algorithm, which alternates between sampling from Moreau regularized versions of the prior and of the likelihood. The main contribution of the paper is ... | Rebuttal 1:
Rebuttal: Thank you for your efforts on reviewing our paper. Below we provide point-by-point responses to your comments:
> Weakness 1
We believe that our work has several significant contributions over the existing works [1] and [2]. Moreover, we highlight that according to [NeurIPS 2024 policy](https://n... | null | null | null | null | null | null |
A Combinatorial Algorithm for the Semi-Discrete Optimal Transport Problem | Accept (poster) | Summary: The paper presents a primal-dual algorithm for approximately solving the semi-discrete optimal transport problem. The algorithm runs $\log \Delta/\epsilon $ scales, starting with the scale $\delta = \Delta^2$ and halving it in each round. The idea is to maintain a $\delta$-feasible weight function during the c... | Rebuttal 1:
Rebuttal: We appreciate your thorough review. We answer your questions below.
----
>Could the authors provide some more background on the construction of Voronoi diagram, ie, the complexity of the construction?
**Response:**
For 2 dimensions, the weighted Voronoi diagram under the squared Euclidean dista... | Summary: The paper studies the semi-discrete optimal transport problem, i.e. a generalization of bipartite matching where one side of the graph is not finite, but instead a probability distribution on some subset of the R^d.
It gives an algorithm that computes an optimal transport plan (up to some additive $\varepsilo... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review. We address your concern below.
> The work is largely a reworking of other recent results in the field, in particular the paper of Agarwal et al. at SODA24. It's not clear that improving the polynomial dependence here is highly relevant.
**Response:** Improve... | Summary: This paper proposes a novel combinatorial algorithm for the problem known as semi-discrete optimal transport. The proposed method constructs a residual graph by considering the cells of a $\delta$-expanded Voronoi diagram, a relaxed concept of a weighted Voronoi diagram, as vertices. The algorithm performs aug... | Rebuttal 1:
Rebuttal: We appreciate your insightful review. We answer your question below.
>Is this method an implementable and practically applicable algorithm, or is it challenging to apply in real-world scenarios due to large constant factors, thus possessing only theoretical value?
**Response:**
The main contribu... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation | Accept (poster) | Summary: This paper introduces E2ENet, a 3D medical image segmentation model designed for efficiency and performance. E2ENet incorporates Dynamic Sparse Feature Fusion (DSFF) to adaptively fuse informative multi-scale features and a Restricted Depth-Shift mechanism in 3D convolution to maintain low model complexity. Ex... | Rebuttal 1:
Rebuttal: ## To Reviewer SYnH
We would like to thank the reviewer for your thoughtful and detailed comments. We are glad that you appreciate the efficiency improvements and robust validation presented in our paper. We address your comments below.
> Unclear Backbone Network: The backbone network of E2ENet ... | Summary: The paper introduces E2ENet, a novel neural network designed for 3D medical image segmentation, which emphasizes efficiency in computational resource usage without compromising accuracy. This paper introduces a Dynamic Sparse Feature Fusion (DSFF) mechanism that adaptively learns to integrate multi-scale featu... | Rebuttal 1:
Rebuttal: ## To Reviewer JUDh
Thank you very much for taking the time to review our paper and for your helpful comments. We provide detailed responses to your constructive feedback below.
> **For Section 3.2 Table 3, it seems that for the different shift size, the mDice score does not vary much from each ... | Summary: In this paper, the authors propose a novel architecture that addresses the challenges observed in increasing the model size and computational complexity of neural network architectures. This leads to concerns in the deployment stage, mainly because of resource-limited hardware. The authors propose a 3D medical... | Rebuttal 1:
Rebuttal: ## To Reviewer xwUb
We sincerely thank you for your time and effort in reviewing our paper. We are glad that you find our work interesting and novel. We address your comments below.
> Tables 3 and 4 don’t provide consistent results. It seems that the combination that works for CT does not optima... | null | null | Rebuttal 1:
Rebuttal: ## To All Reviewers:
We thank the reviewers for their constructive suggestions and in-depth analysis, which is helpful for our work. We are humbled by such a positive response, and we truly appreciate it.
We are delighted to note that all reviewers recognized the novelty of our research, found t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity | Accept (poster) | Summary: This paper applies quality diversity (QD) optimization (evolutionary method) to the problem of diverse task generation for (meta) reinforcement learning (RL). It argues that QD could be used in settings where an open-ended simulator’s parameterization is unlikely to produce tasks that are diverse in high-level... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review our work and for appreciating its merits. We have addressed the noted weaknesses below, as well as their question about parameterizations.
> [W1] It is still necessary to hand-craft the high-level features, which needs expert knowledge (or at lea... | Summary: This paper introduces DIVA, a technique for exploring the parameter space of parametrisable environments. The technique uses a variant of MAP-Elites to explore the environment parameter space, finding exemplar points spread across the parameter space, as measured with respect to some user provided features. Th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and attention to detail. We have made all of the minor changes suggested under “Questions”, which will be present in the camera-ready draft. We address both major concerns below.
**(1) On the domains being “toy”**
> [W1] [...] domains [...] are all very simp... | Summary: The paper identifies the limitation that hand-crafting a sufficiently diverse set of simulated training tasks to bridge any significant sim-to-real gap is labor-intensive. It then proposes DIVA, a new evolutionary approach for generating diverse training tasks in the absence of well-behaved simulator parameter... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review of our work, and for engaging us with curiosity to better understand the paper. Below we address the two main weaknesses noted by the reviewer, and clarify certain aspects of the work in response to the reviewer’s questions.
> [W1] [...] The pre... | Summary: This paper describes an approach for learning a QD-archive to be used as a proxy for samples of test environments, and shows that this results in improved performance in producing a set of meta-learning tasks over DR and UED baselines.
Strengths: The introduction of QD approaches into UED algorithms is a prom... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to write such a thorough review, and for not only appreciating the merits of this work, but for identifying points of weakness to strengthen our manuscript. We have responded to each of the weaknesses listed and questions posed below, and look forward to f... | Rebuttal 1:
Rebuttal: We thank the reviewers for the time they have taken to engage constructively with our work. Reviewer _kFbG_ appreciates the “novel [...] connection between QD and UED for meta-RL training” we have developed, along with our work’s explicit “focus on [relevant] high-level features”—in contrast to th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer | Accept (poster) | Summary: The authors propose am method for predicting contacts between proteins and aptamers based on frame averaging transformers. They showcase their methods on contact prediction and unsupervised aptamer screening, showing improvements over some baselines. The authors also compare to RoseTTAFoldNA, which seems to ou... | Rebuttal 1:
Rebuttal: ```
Weakness: Comparing to a pre-trained structure prediction method is clearly something to be explored well, and the authors do that in section 4.4 (with additional results with AF3 in the appendix). At the same time, this part seems to me not very well developed in the paper. I am especially co... | Summary: This paper mainly focuses on protein-nucleic acid contact prediction and unsupervised aptamer virtual screening. The latter is based on an unsupervised learning approach by predicting the contact maps. An equivariant architecture that integrates frame averaging and transformer blocks is proposed for this task... | Rebuttal 1:
Rebuttal: ```
Weakness: The novelty of the proposed architecture is limited. The proposed architecture simply combines frame averaging and transformer blocks. The idea that builds the attention module based on SE(3)-invariant features is proposed in Equiformer. The dataset and benchmark track may be more su... | Summary: The authors propose a novel equivariant model, FAFormer, which leverages the frame averaging operation as an integral geometric component within the Transformer. The authors prove the invariance and equivariance of the architecture. They further conduct experiments showing that FAFormer performs well in contac... | Rebuttal 1:
Rebuttal: ```
Weakness: The F1 score for contact map prediction tasks is about 0.1, which is very low. I question whether this score can be used to judge which model is better. I suggest involving other node-level and graph-level prediction tasks for comparing FAFormer with other equivariant neural networks... | null | null | Rebuttal 1:
Rebuttal: We appreciate the reviewers for noting that we propose a novel model (DGm4,npWJ) to address a meaningful problem (DGm4,qqru,npWJ) with a comprehensive evaluation (DGm4,npWJ). We further summarize our key contributions as follows:
1. We explore a new angle to conduct aptamer screening in an unsupe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds | Accept (poster) | Summary: This paper provides a new adaptative learning rate framework for hard problems, called Stability-Penalty-Bias matching learning rate (SPB-matching). Using this SPB-matching learning rate, the paper proposes a Best-of-both-worlds (BOBW) algorithm framework for hard online learning problems. It not only achieves... | Rebuttal 1:
Rebuttal: We are grateful for your valuable time and detailed review.
Below are our responses to the review.
> The authors should consider including an optimization problem aligned with (1) to better illustrate the hard problems.
Thank you for the comment regarding optimization problems.
But, we may not u... | Summary: The paper aims to develop a new adaptive learning rate framework for the Follow-the-Regularized-Leader (FTRL) algorithm that addresses online learning problems with a minimax regret of $\Theta(T^{2/3})$.
It specifically targets problems with indirect feedback, such as partial monitoring and graph bandits, an... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to carefully review our paper and for providing many questions. Below are our responses to your review.
> The effectiveness of the SPB-matching framework depends on the proper tuning of parameters, which might limit its practical applicability without furth... | Summary: In this work, the authors propose a simple and adaptive learning rate for FTRL, which can achieve the minimax regret of $O(T^{2/3})$ for some hard online learning problems. Specifically, they have applied their algorithm (FTRL with the proposed learning rate) to achieve the best-of-worlds regret bounds for par... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough and thoughtful review of our work. Here are our responses to your feedback.
> The proposed learning rate is highly inspired by an existing adaptive learning rate in a recent work [26]. ... which does not bring enough challenges.
Yes, as the reviwer pointed ou... | Summary: This paper considers the online problems with a minimax regret of $\Theta(T^{2/3})$ and proposes a new learning rate tuning based on the FTRL algorithm to tackle these problems. The development of the learning rate is straightforward and simpler compared to previous works. The proposed algorithm is versatile i... | Rebuttal 1:
Rebuttal: We appreciate your valuable time and helpful comments.
Below is our response to your comments.
> With a quick glance at the cited paper [26], in Section 5.1, this paper also employs the 'forced exploration' strategy, except that the balance ratio is fixed. It would be beneficial to further discus... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable time and thorough, insightful reviews.
While we have replied directly to each reviewer, there are some important points that we could not fully explain due to space constraints.
Therefore, we are addressing these points as global comments here. Please feel fr... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a novel adaptive learning rate framework for Follow-the-Regularized-Leader (FTRL) tailored to online learning problems characterized by a minimax regret of Θ(T^2/3). This new learning rate, termed Stability-Penalty-Bias (SPB) matching, is designed by balancing stability, penalty, and bias ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable time and thorough review.
Regarding the minor comments and typos, we will address and correct them in the revised version.
Below is our response to the review:
> the regret bounds in Corollaries 9 and 11 are not fully clear because of the presence of $\kappa$... | null | null | null | null | null | null |
Amortized Planning with Large-Scale Transformers: A Case Study on Chess | Accept (poster) | Summary: This paper introduces an open dataset comprised of real chess positions annotated with a evaluation score, the best move to play, and a score for each legal move according to StockFish 16 using 50ms for each action. They then train large transformers over this dataset, and include in-depth analysis through mak... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and positive feedback.
**Is the dataset of poor quality if you use Stockfish with only 50ms evaluation time (as there is a significant Elo disparity between 0.05s and 1.5s for Stockfish)? Also, why does Stockfish with 0.05s evaluation time take 1.5s to ... | Summary: This paper aims to solve the challenging problem in the chess game. The main contributions include building a large-scale dataset and training a large-scale transformer model with the collected dataset. The proposed approach has shown a significant outperformance over the baselines.
Strengths: The writing is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback.
**Is the transformer or the dataset more important for performance improvement?**
Although this cannot be determined with certainty given our results, we investigated this question in Table A2, where we ablated model architecture and the time li... | Summary: The paper introduces a large dataset of chess board states along with annotations for best moves and state-(action)-values. It demonstrates that transformers trained on this dataset with supervised learning can achieve significant performance and generalization. This adds to the growing evidence showing that n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful study of our paper and, in particular, for all their clarifying remarks on lc0, which have helped us to improve the description of that baseline.
**Lc0’s T82 network is a domain-specific transformer (rather than a ConvNet), trained using supervised learnin... | Summary: The paper is well-written and demonstrates that supervised learning with Stockfish evaluations can create a strong chess engine. The methodology is solid, and the inclusion of an LLM as a baseline is noteworthy. However, the motivation is ambiguous and the use of transformers over CNNs is questioned. Additiona... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback.
**The paper claims to aim at approximating Stockfish by distilling its board evaluation into a transformer but evaluates performance w.r.t. chess play rather than Stockfish’s centipawn score, which invalidates this claim. Instead, the paper a... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed comments and positive feedback.
We are pleased that the reviewers consider our paper well-written (`R-wU89`, `R-LZwX`, `R-Ye4U`) and a “timely addition to the literature of learning non-trivial algorithms” (`R-LZwX`, `R-tXJ6`), our methodology solid (`R... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length | Accept (poster) | Summary: The paper introduces Megaladon, an improvement on the existing technique Mega. This technique uses a diagonalizable complex moving average to allow for integration of information across a longer context efficiently.
Strengths: - The technique is validated against modern models across a large variety of benchm... | Rebuttal 1:
Rebuttal: Thanks for your time and constructive comments! We appreciate your positive feedback on the good motivation, novelty of Megalodon and its strong empirical results. We address your concerns and questions below and please let us know if you still have concerns after you read our response.
> W1: The... | Summary: Megalodon i.e. Mega2 improves over Mega by using (1) complex-valued EMA; (2) improved normalization schemes (e.g. timestep normalization, attention normalization, pre-norm with 2-hop residuals).
Strengths: - Timestep normalization is simple and reasonable. A highly-optimized CUDA kernel provided in this work ... | Rebuttal 1:
Rebuttal: Thanks for your time and constructive comments! We appreciate your positive feedback on the good motivation, novelty of Megalodon and its strong empirical results. We address your concerns and questions below and please let us know if you still have concerns after you read our response.
> W1: The... | Summary: This is an empirincal paper. The paper presents MEGALODON, a neural architecture designed to overcome the quadratic complexity and weak length extrapolation of Transformers. By extensive experiments, this paper demonstrates MEGALODON's ability to efficiently handle unlimited context lengths and its superior p... | Rebuttal 1:
Rebuttal: Thanks for your time and constructive comments! We appreciate your positive feedback on the good motivation, novelty of Megalodon and its strong empirical results. We address your concerns and questions below and please let us know if you still have concerns after you read our response.
> W1: The... | Summary: The paper proposes Megalodon, which introduces three advancements over Mega: complex EMA, timestep normalization, and normalized attention. These advancements address the limitations of chunk-wise attention and architecture divergence across different tasks and data types. The new model is evaluated alongside ... | Rebuttal 1:
Rebuttal: Thanks for your time and constructive comments! We appreciate your positive feedback on the good motivation, novelty of Megalodon and its strong empirical results. We address your concerns and questions below and please let us know if you still have concerns after you read our response.
> W1 & Q1:... | Rebuttal 1:
Rebuttal: # Ablation studies on CEMA and Timestep Normalization
**Ablation on LRA**
We first conducted ablation studies on LRA to demonstrate the effectiveness of CEMA and Timestep Normalization components in Megalodon. The results are shown in the following table:
| Models | ListOps ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding | Accept (poster) | Summary: This paper proposes the random cycle coding that achieves optimal rate by bits-back coding techniques for cluster assignments. In addition, the newly proposed algorithm requires less computing resources, making it more practical.
Strengths: For the cluster assignments, this paper proposes an optimal compressi... | Rebuttal 1:
Rebuttal: > This paper borrows bits-back coding techniques and random order coding, making this paper quite incremental in the view of techniques. The difficulty in combing these two techniques is not clearly presented.
RCC is a very different algorithm from ROC.
- ROC is a cluster-compression method, and ... | Summary: ## Summary
* This paper propose an entropy coding technique named RCC, which is the first one to achieve the optimal rate for cluster assignment. Theoretical and empirical results show that the rate saving and speed up of the proposed approach over previous suboptimal work ROC is evident when the number of clu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the relevant practical questions.
Most questions center around a comparison to ROC. Indeed there is a regime where the compression gain our method provides, RCC, is marginal over ROC (when $k$ is small). However, there are regimes where the gain is substantial (when $k$ ... | Summary: This paper study the problem of cluster assignments of datasets, the goal of which is encoding datasets along with their cluster information. The authors propose Random Cycle Coding method that utilizes cycle representation of permutation as indicators for cluster. The cycle is formed by a sequence of numbers ... | Rebuttal 1:
Rebuttal: > What are the differences between RCC and ROC in cluster assignment problem? From my understanding, RCC stores cluster information by permutation. Then, how ROC-1 and ROC-2 store the clusterings?
That is correct, RCC stores the cluster information in the disjoint cycles of the permutation. ROC-1... | Summary: This paper proposes a coding scheme for lossless compression of cluster assignments. The proposed coding scheme is based on random order coding (ROC) with bits-back coding. Analysis and experiments show that it outperforms two variants of ROC in complexity and compression rate.
Strengths: The presentation and... | Rebuttal 1:
Rebuttal: > After carefully reading this work, I believe it is out of the scope of NeurIPS. [...] does not include any discussion or consideration on the topics listed on NeurIPS 2024 Call For Paper.
This work fits in the call for papers under “Infrastructure”, in the sub-category “improved implementation ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces Random Cycle Coding (RCC), a method for lossless compression of cluster assignments in data sets. RCC encodes data sequentially, representing cluster assignments as cycles in permutations, which eliminates the need for artificial labels. The method is shown to achieve the Shannon bound in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestion. An example of encoding and decoding a cluster assignment has been added to the appendix to further clarify the algorithm.
> Why does the encode + decode time for RCC in Fig. 2 decrease as 'k' increases?
This can be understood by noting that, in the exper... | null | null | null | null | null | null |
A test of stochastic parroting in a generalisation task: predicting the characters in TV series | Reject | Summary: The authors present an analysis of logistic regression on sentence embeddings as a way to predict the speaker of a particular line of dialogue in the Big Bang Theory. Specifically, they fit a PCA model to embeddings obtained from a sentence transformer and then use each PCA dimension as a linear feature. The a... | null | Summary: The authors are focused on whether or not LLMs can be thought of as stochastic parrots or contain "Sparks of AGI". They look into what kind of data is recoverable from internal LLM representations. Specifically, the authors investigate to what extent the task of identifying TV personalities (e.g. Penny vs Shel... | null | Summary: This paper’s main contribution is to apply a logistic regression on the principal components of the LLM embeddings for classifying TV series characters based on their dialog lines. The main finding is the logistic regression approach does worse than GPT-4 in predicting TA characters, but is comparable to human... | null | Summary: The paper aims to prove LLMs work as "stochastic parrots" (Bender et al) rather than "sparks of agi" (Bubeck et al). To prove this claim, the paper presents an experiment where a task can be solved by training a linear model (logistic regression) on top of PCA of the LLM output. The authors then claim, based o... | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning | Accept (poster) | Summary: The paper addresses the issue of missing modality information in multimodal systems and proposes solutions for two key problems:
1. Excessively complex feature interactions lead to information redundancy and cumulative errors.
2. Previous works did not align representations semantically.
In enhancing multimod... | Rebuttal 1:
Rebuttal: **Q1**: About symbolic representation in figures and texts.
**A1**: Thank you for the reminder! It is necessary that we use symbols in figures and text to represent the data flow and workflow of each component in the framework. We will improve the representation in the revision to make it easier ... | Summary: The paper addresses the challenge of data incompleteness in Multimodal Sentiment Analysis (MSA). It introduces a novel approach called the Language-dominated Noise-resistant Learning Network (LNLN). The LNLN leverages the dense sentiment information in the language modality, considered the dominant modality, t... | Rebuttal 1:
Rebuttal: **Q1**: About model applicability in different scenarios.
**A1**: Many thanks to the reviewer for the constructive suggestions. We would like to clarify several points.
(1) In the MSA task, the language modality contains more refined and rich sentiment semantics than the other modalities, and thu... | Summary: The paper presents the Representation Factorization and Alignment (ReFA) framework for Multimodal Sentiment Analysis (MSA) under uncertain missing modalities. ReFA employs a fine-grained representation factorization module to extract sentiment-relevant and modality-specific representations through crossmodal t... | Rebuttal 1:
Rebuttal: **Q1**: About modality translation and sentiment semantic reconstruction.
**A1**:
* Both modality translation and sentiment semantic reconstruction are designed for MSA tasks. Specifically, the core idea of modality translation is to utilize transitions among different modalities to achieve an e... | Summary: The paper addresses the challenges of multimodal sentiment analysis (MSA) in real-world applications, particularly when some modalities may be missing, which can hinder the effectiveness of the analysis. The authors propose a framework called Representation Factorization and Alignment (ReFA) to tackle the issu... | Rebuttal 1:
Rebuttal: **Q1**: About computational complexities.
**A1**: Thank you for your comments! We need to clarify that we have compared and discussed the proposed framework with the existing methods in terms of the number of parameters, FLOPs, and performance in three testing situations, as shown in Section A.4 ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their suggestions and thoughtful comments.
Pdf: /pdf/6daf91580339650fa181d30a385e1a2e39bdb5d3.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalized Multimodal Fusion via Poisson-Nernst-Planck Equation | Reject | Summary: The paper introduces CrossCheckGPT, a novel method for assessing hallucination robustness in multimodal foundation models without requiring reference standards. Utilizing cross-system consistency, the proposed method aims to provide a universal evaluation framework capable of being applied across various domai... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and provide feedback. However, it seems there might have been a mix-up, as your comments appear to address a different paper.
---
Rebuttal 2:
Title: Not relevant review!
Comment: Dear TT1j. The end of the discussion period is close. It seems that... | Summary: The paper proposes a novel multimodal fusion method named Generalized Multimodal Fusion (GMF), which leverages the Poisson-Nernst-Planck (PNP) equation from physics to manage the feature fusion process in multimodal learning tasks. By treating features as charged particles, the method allows for a dynamic sepa... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Due to word count limitations, please refer to the "rebuttal to all" for some common issues. Here are our simplified responses:
## Key Concepts
### Feature Charge
Features are treated like charged particles in an electric field, moving in specific spaces for fusion.... | Summary: The paper introduces a Generalized Multimodal Fusion (GMF) method using the Poisson-Nernst-Planck (PNP) equation to address challenges in multimodal fusion, such as feature extraction efficacy, data integrity, feature dimension consistency, and adaptability across various downstream tasks. The GMF method lever... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments and constructive feedback. Due to the word limit, please refer to rebuttal to all for some content. Here are our responses:
### 1. Sensitivity of Hyperparameter b(j)
| Method | Boundary | mAP@10 | mAP@20 | mAP@50 | mAP@100 | Params | F... | Summary: In this paper, the authors combined the Poisson-Nernst-Planck (PNP) equation with information entropy theory and proposed a generalized multimodal fusion approach, which disassociates modality-specific and modality-invariant features, thereby reducing the join entropy of input features and meanwhile decreasing... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive comments. Because of word limit constraints, you can refer to a rebuttal to all for answers to some questions. Here are our responses, which we hope will address your concerns:
### 1. Labeling Errors in Section 5.1
Thank you very much for your ... | Rebuttal 1:
Rebuttal: Thanks for the reviewers' constructive comments. For the common questions of some reviewers, we have uniformly answered:
## 1. Wilcoxon Signed-Rank Test
We propose two hypotheses:
1. H0: No significant difference between our method and the comparison algorithms.
2. H1: Significant difference be... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Continual Learning in the Frequency Domain | Accept (poster) | Summary: Inspired by the human visual system (HVS), this paper proposes a new framework called Continual Learning in the Frequency Domain (CLFD) for edge devices. For the input features of the feature extractor, CLFD employs wavelet transforms to map the original input image to the frequency domain, thereby reducing th... | Rebuttal 1:
Rebuttal: Firstly, we extend our gratitude for the time and attention devoted to reviewing our paper. Below, we have carefully addressed each of your concerns to the best of our knowledge to improve the overall contribution of the paper.
# The necessity of utilizing frequency space for continuous learning ... | Summary: Based on the research that human visual system (HVS) exhibits varying sensitivities to different frequency components, this paper proposes to do continual learning in the wavelet frequency domain to reduce the size of inputs. The proposed CLFD module includes feature extractor and feature encoder, where the fe... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude for your thorough review of our paper. Below, we have diligently addressed each of your concerns to the best of our understanding, aiming to enhance the paper's overall contribution.
# It is better to include larger databases.
We agree with the reviewer that intro... | Summary: In this paper, the authors proposed a novel replay-based continual learning, which is named continual learning in the frequency domain (CLFD). The framework consists of two main modules, frequency domain feature encoder (FFE) and class-aware frequency domain feature selection (CFFS). FFE utilizes discrete wave... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for dedicating time to thoroughly review our work. We value the positive feedback provided on our manuscript. Below, we address the weaknesses and queries raised:
# More ablation studies about hyperparameters.
We appreciate the feedback from the reviewers... | Summary: This paper introduces a novel framework designed to enhance the efficiency and effectiveness of continual learning (CL) systems by leveraging frequency domain representations, inspired by the human visual system's varying sensitivity to different frequency components. This approach aims to address the limitati... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to the reviewer for offering thoughtful feedback and providing a constructive evaluation of our work. The valuable input has significantly contributed to the improvement of our paper.
# The paper does not thoroughly discuss its performance across a broader range o... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' valuable comments and concerns. In the response below, we hope to have addressed all the points raised. Should there be any further questions or clarifications needed, please inform us so we can fully address any aspects of the paper during the rebuttal perio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What If the Input is Expanded in OOD Detection? | Accept (poster) | Summary: In previous OOD detection methods, extracting discriminative information from OOD data relative to ID data is challenging with the representation of a single input. This paper provides a novel perspective for out-of-distribution (OOD) detection by leveraging multiple types of corruptions to expand the original... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **W1, Q1:** Lack explaination and comparison. Different types of corruptions are used in the method, which is similar to the idea of Watermarking, where a ... | Summary: This paper first identifies the shortcoming of previous out-of-distribution (OOD) detection methods: the single-input paradigm limits the representation dimension for extracting valid information. To address this issue, a novel method CoVer that expands the original input space with multiple types of corruptio... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **W1, Q1:** Although the proposed method can improve the performance of the SOTA methods, whether they face the same challenges and how does the proposed m... | Summary: Authors ntroduce a new approach to out-of-distribution (OOD) detection by expanding input representation dimensions using common corruptions. Traditional methods focus on single-input representations, limiting their effectiveness. This work identifies "confidence mutation," where OOD samples' confidence levels... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
> **W1:** Omission of Data Depths and Information Projections in Related Work
Thanks for your valuable suggestions and bringing us those insightful works o... | Summary: The paper aims to identify Out-Of-Distribution (OOD) samples by applying common image corruptions (noise, blur, etc.) to the input. The phenomenon is referred to as confidence mutation, where original inputs, along with corruptions, increase the confidence of in-distribution (ID) data, while the confidence of... | Rebuttal 1:
Rebuttal: Thank you for your time devoted to reviewing this paper and your constructive suggestions. Here are our detailed replies to your questions.
>**W1:** About Novelty
We would like to reclaim that our work is novel in introducing a new perspective, i.e., expanding the dimension of input with corrupt... | Rebuttal 1:
Rebuttal: ## General Response
We appreciate all the reviewers for their thoughtful comments and suggestions on our paper.
We are very glad to see that the reviewers find **our focused problem is important** (R3,R4) within the OOD detection research, and the method is **novel** (R1,R2,R3,R4) and **simple b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention | Accept (poster) | Summary: This paper develops a new image-to-multiview diffusion model with two key highlights. First, it proposes a novel method for estimating the focal length and elevation. Second, it introduces a new cross-view attention mechanism. Experiments demonstrate that the method outperforms previous SOTAs.
Strengths: Prev... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and insightful comments! We have tried to address your concerns in the updated manuscript and our rebuttal text:
**Q1: More results of perspective input.**
We appreciate this suggestion and have accordingly expanded our results section. In Figure 2 of the glob... | Summary: The authors proposed a method that can estimate the camera intrinsic matrix of the render of a given object, which attempted to solve the problem of other image-to-3d methods that only trained on fixed camera intrinsic matrix.
Strengths: The proposed method is the first work that considers the change of the c... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and insightful comments! We have tried to address your concerns in the updated manuscript and our rebuttal text:
**Q1: Row-wise attention is just epipolar attention and is not the contribution of this paper.**
We respectfully disagree with this characterizati... | Summary: This work introduces a novel take on multiview diffusion models, highlighting the potential to realize high-resolution images from one image. The method comes with a new design for the diffusion-based camera prediction module, focal length, and elevation of the input image elevation, together with row-wise att... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and insightful comments! We have tried to address your concerns in the updated manuscript and our rebuttal text:
**Q1: Comparison with LRM-based methods.**
Tab.1 showcases the comparisons of Era3D with OpenLRM and CRM on the GSO dataset, which shows that Era3D e... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. In summary, the reviewers are positive about the novelty, performance, and potential of our method: **"address several significant challenges associated with MV Diffusion"**(R-5Kpg), **"the first work that considers the change of the camera intri... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback | Accept (poster) | Summary: The authors present a current problem (research gap): if humans give model answer feedback in a partially observable environment, such feedback may lead to deceptive inflation and overjustification. For example, if humans purely depend on the error reporting result, this will encourage the model to hide the er... | Rebuttal 1:
Rebuttal: Thank you for the detailed review of our work!
> Given the formalization, the reader would expect the authors to (1)explain the question within the formalization, (2)give an experiment to empirically reveal the question or justify the proposed solution, and (3)give metrics to judge the effectiven... | Summary: This work studies the impact of partial observability in RLHF. Two failure cases are defined: deceptive inflation and overjustification, and under them, concrete conditions are provided to impact the learned policy. Moreover, the ambiguity induced by the partial observability is further measured that the feedb... | Rebuttal 1:
Rebuttal: Thank you for your detailed review!
> This work discusses the partial observability mostly in the theoretical sense, with a few hypothetical examples. It would be nice to see or verify the impact of partial observability in real experiments.
Our work is indeed theoretical. For our general philo... | Summary: The paper discusses the challenges that arise when human annotations for RLHF are based on partial observations. They formally define deceptive inflation and overjustification as failure cases caused by partial observation, and theoretically prove that standard RLHF is guaranteed to result in either or both of... | Rebuttal 1:
Rebuttal: Thank you for your review!
> The proofs are based on assumptions of a specific MDP structure and a particular human belief function, which might not be easily generalized to realistic, complex environments.
**It is not true that we assume a specific MDP structure.** Our work applies to any MDP, ... | Summary: The paper addresses the problem of accounting for humans having only partial observability of their environment when providing feedback. They outline two natural issues that can arise from such partial observability - deceptive inflation and overjustification - and provide examples of both. They then explore w... | Rebuttal 1:
Rebuttal: Thank you for your thorough review!
>The paper would greatly benefit from carefully designed experiments that unambiguously demonstrate the existence of such problems in practice.
Our work is theoretical and we advocate for judging it on a theoretical standard. We explain our viewpoint in more d... | Rebuttal 1:
Rebuttal: # Global Rebuttal
In this global answer, we want to advocate for the inclusion of our purely theoretical work. The reviewers highlight the following (and more!) positive aspects of our work:
- *On the problem setting*: Our setting is “vital” (LhS3) and has “significant value” (zgLN).
- *On decep... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pure Message Passing Can Estimate Common Neighbor for Link Prediction | Accept (poster) | Summary: This paper studies the problem of encoding pairwise structural features to enhance link prediction (LP). They note that previous literature has shown that standard MPNNs cannot encode the necessary pairwise information needed for LP. The authors argue that with careful design, MPNNs can in fact estimate pairwi... | Rebuttal 1:
Rebuttal: We first want to appreciate the reviewer's extensive feedback, which is extremely valuable to us given the large volume of review loads this year. We also very appreciate the reviewer's positive feedback on the importance of the problem we address, especially acknowledging the importance of trade-... | Summary: The authors of this work introduce the Message Passing Link Predictor (MPLP), a link prediction model that uses pure message passing - as originally proposed by Gilmer et al. - to estimate structural similarities between nodes. The method is motivated by the fact that a single round of message passing using on... | Rebuttal 1:
Rebuttal: We first want to express our gratitude for the reviewer's comment that the reviewer "enjoyed reading this work". For us, it is the most rewarding feedback to know that our work is well-received. We will address the following points in the rebuttal:
## Q1
The weight matrix $W$ in theorem 3.1 does ... | Summary: This paper first shows that pure message passing can count common neighbor. Based on the proposed theory, this paper develops MPLP for link prediction, where the common neighbor is an important heuristic feature. Experiments on link prediction demonstrate the performance gains of MPLP over baselines.
Strengt... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We will address the following points in the rebuttal:
## W1
We apologize for any confusion regarding MPLP. MPLP aims to count the number of nodes at varying distances from a target node pair. Compared to pre-computed heuristics like Common Neighbor, MPLP is... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We appreciate your valuable feedback and constructive suggestions when reviewing our paper. Here, we want to address a concern raised by **Reviewer CYaw** regarding the difference between MPLP(+) and ELPH/BUDDY[1]:
## W1: Comparison with ELPH/BUDDY
We appreciate the reviewer's qu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting | Accept (poster) | Summary: This paper presents a new algorithm for matrix completion in the semi-random setting, where each entry is revealed independently with a probability at least $p = \frac{\mathrm{poly}(r, \mu, \log d)}{d}$. The paper provides a promising approach for designing fast matrix completion algorithms that are robust to ... | Rebuttal 1:
Rebuttal: Thank you for your questions about practicality; we addressed these in our meta-comment. We hope our response clarified the motivations and focus of our work.
In terms of how much additional work would be necessary for our work to become practical, it is hard to make such a prediction, but we d... | Summary: This paper proposes an algorithm that is able to achieve high-accuracy in semi-random matrix completion with nearly linear time. The key innovation lies in using a flow-based adaptive reweighting scheme to mitigate the bias introduced by the adversary. This technique effectively identifies and downweights the... | Rebuttal 1:
Rebuttal: Thank you for your questions about practicality; we addressed these in our meta-comment. We hope our response clarified the motivations and focus of our work. We agree that to further develop this line of research, it is an important open direction to achieve simpler and more practical instantiati... | Summary: This paper considers the semi-random matrix completion problem. Given an unknown rank-r matrix M, each entry of the matrix Is observed independently with a probability p_{ij} at least p. The goal is to find a matrix close the matrix M.
The sap-based algorithm can achieve the nearly-optimal sample complexit... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts. We appreciate that you found our insights interesting and our exposition clear. | Summary: This paper consider semi-random matrix completion via flow-based adaptive reweighting.
The main result is the first high-accuracy nearly-linear time algorithm for solving semi-random matrix completion, and an extension to the noisy observation setting.
I am not familiar with the area of matrix completion.
... | null | Rebuttal 1:
Rebuttal: We thank all of the reviewers for their reviewing efforts and feedback.
Several reviewers, e.g., cp6D and RnDV, asked us to address our method’s practicality. We acknowledge that our work’s primary contribution is theoretical. However, we note that previous practical matrix completion algorithms ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper, the authors study semirandom matrix completion. In their models, entry $ij$ of a $\mu$-incoherent, symmetric ground truth rank-$r$ matrix $M^{\star} \in \mathbb{R}^{d \times d}$ are revealed independently with probability $p_{ij}$, where $1 \ge p_{ij} \ge p$ for some parameter $p$ that is a func... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review and kind comments. We appreciate that you found our paper well-written and its ideas of general interest.
The rank of the output as currently written can be quite large, we estimate the exponent in the $\text{poly}(r)$ as $\geq 30$. The losses stem from the ... | null | null | null | null | null | null |
Polyhedral Complex Derivation from Piecewise Trilinear Networks | Accept (poster) | Summary: The paper proposes a theoretical link between the tropical algebraic interpretation of ReLU-based neural networks and surfaces represented by signed distance functions trained on those networks. Based on that theoretical interpretation, a surface extraction algorithm is proposed.
Strengths: (1) The mathematic... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive feedback. We appreciate your recognition of the theoretical soundness of our framework. We would like to address your concerns regarding the presentation of our paper.
**W1. I do not like the current presentation of the paper.**
- We understand... | Summary: The paper extends the idea of extracting the polyhedral complex from ReLU networks to extracting a more general cell complex from ReLU networks with a positional encoding, specifically, trilinear encodings, such as HashGrid and TensoRF, commonly used in neural field representations of geometries or scenes.
It ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts and the detailed comments you provided. We are particularly pleased that the theoretical aspects of the work are considered a significant contribution.
**At least briefly describing the former would make for a more rounded story.**
- In Sec. 1, we introduce m... | Summary: The paper presents a theoretical and practical framework to extract meshes from piecewise trilinerar networks using hash grid representation. They further show that hypersurfaces within the trilinear regions become planes under the eikonal constraint. A method for approximating the intersection between 3 hyper... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive feedback. We explain our rationale for the evaluation and additional note for the angular distance we used in quantitative evaluations.
**W1. While there are some improvements over marching cubes, the improvements are not quite substantial, and ... | Summary: The work addresses the problem of exact iso-surface extraction given an implicit surface represented by a neural network. The idea behind this line of work is to closely analyze the architecture of the neural network to efficiently extract the true (or at least an approximate of) learned iso-surface. Previous ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for pointing out the effective rebuttal points. We appreciate your time and effort in going through our work multiple times.
**W1. Simplification of Language**
We are committed to making our paper more accessible. Based on your feedback, we will revisit th... | Rebuttal 1:
Rebuttal: Please refer to the **rebuttal PDF (below PDF button)** attached for **Figures A-F** mentioned in the author's feedback. This PDF is high-resolution; please enlarge the PDF if you needed.
* **Fig. A.** Detailed mesh visualizations for Ours, MC, MT, and NDC.
* **Fig. B.** A visual explanation for... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No Free Delivery Service: Epistemic limits of passive data collection in complex social systems | Accept (poster) | Summary: This work shows that the classic paradigm of training and testing in ML has validity flaws that makes it unreasonable to generalize about performance from test-set performance. They use the no free lunch theorems to theoretically demonstrate this, and then show empirical lens on the MovieLens benchmark dataset... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. I agree that it is important to clarifying the prescriptive argument and the role of participatory methods. I hope that the response to all authors as well as the below additional clarifications can alleviate your concerns. Since everything is addressable vi... | Summary: Building advanced and very large general-purpose ML models using extremely large datasets from sources such as the Internet has gained a lot of recent attention.
Particularly, when the training set is sampled from a distribution S while the target distribution is T, the paper introduces the notions of (\epsilo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. I agree that W1 is important to clarify and hope that the response to all authors as well as the below additional clarifications can alleviate your concerns. Since everything is addressable via minor clarification updates, I hope this will also allow you to ... | Summary: The paper addresses the critical issue of model validation in AI systems, especially those deployed in complex social environments. It argues that the prevalent train-test paradigm, commonly used for model validation, is often invalid in these settings due to the inherent assumptions it violates. The paper pre... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. I agree that your comments address important question and hope that the response to all authors as well as the below additional clarifications can alleviate your concerns. Since everything is addressable via minor clarification updates, I hope this will also... | null | null | Rebuttal 1:
Rebuttal: Thanks to all reviewers for their insightful feedback, it will help me to clarify important aspects of the paper and improve its impact. Before addressing the reviewers' concerns in detail, I am happy to acknowledge the overall very positive feedback from all reviewers on soundness, contribution, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks | Accept (poster) | Summary: The present manuscript concerns the study how gradient descent achieves feature learning in a certain class of two-layer neural networks.
The main idea of the manuscript, which builds heavily on previous line of works, is to consider how the population loss is minimized during the first steps of gradient desce... | Rebuttal 1:
Rebuttal: We would like to thank reviewers for the feedback. For the concern about sample complexity, please refer to the general response where we address the questions. | Summary: This paper studies the learning properties of networks trained with gradient descent. More precisely, the authors focus on the late stages of the dynamics where the algorithm learns the ground truth directions. These findings extend the usual ones in the literature that are focused on the early stages of the d... | Rebuttal 1:
Rebuttal: We would like to thank reviewer's efforts to provide detailed feedback. We will incorporate suggestions on the presentation of the paper and discuss more related works in the revision. Below we try to address reviewer's concerns.
> In equation (3) you preprocess the target network to remove its f... | Summary: The present paper studies feature learning in the end phase of training. The authors show that when the loss is small, gradient steps capture relevant directions.
Strengths: - The problem of feature learning studied in the paper is important.
- From a technical point of view, the analysis of phase 3 of the al... | Rebuttal 1:
Rebuttal: We would like to thank reviewer's efforts to provide detailed feedback. We will try to address reviewer's concerns below and incorporate the suggestions in the revision.
>1. In the abstract, ...
Theorem 2 is a combination of early-stage feature learning (Stage 1 and 2) and final-stage feature le... | null | null | Rebuttal 1:
Rebuttal: We appreciate the reviewers' detailed feedback and their efforts in providing constructive comments. One common question raised by reviewers is about sample complexity. We try to address it below.
First, we would like to emphasize that even the analysis on population loss is highly non-trivial an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Explicit Flow Matching: On The Theory of Flow Matching Algorithms with Applications | Reject | Summary: The paper proposes a loss for training flow matching (rectified flows, stochastic interoplants) that is based on integrating the target velocity over the available data as the regression target. This reduces the variance of the gradient estimator and can also be applied in the stochastic variant of flow matchi... | Rebuttal 1:
Rebuttal: Thank you for your detailed analysis of our work.
**Let us discuss your comments:**
* ``The notation is overly...``
We've changed the notations and listed changes in the 1-page PDF in the "all reviewers answer".
Wherever we refer to the probability density function,we use the symbol $\rho$ (or... | Summary: The paper proposes an analytic formula for the vector field satisfying the continuity equation for the given change of density which interpolates between two distributions in the sample space. This is a common setting in the Flow Matching model [6] (Rectified Flows [1], Stochastic Interpolants).
The authors ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the specific notes. We will be happy to answer any additional
questions you may have.
**Let us discuss your comments:**
* `` ... formula is already well-known...``
Thank you for the links and the comment.
We want to emphasize that we do not present formula (10) (or (11)... | Summary: The paper proposes a novel approach to training flow-based generative model by deriving the conditional flow matching objective function with respect to the flow function. The author argues that this new method of training will reduce variance, add stability, and ultimately lead to faster convergence. Addition... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and comments.
**Let us also discuss your comments:**
* ``The paper is difficult to follow...``
We have carefully revised the manuscript to enhance its readability and coherence. We make several changes in the notation which we put in one-page PDF ... | Summary: This works aims at producing a lower variance loss for flow matching. This is done by using the formula for the ground truth marginal velocity field, estimating it using self-normalized importance sampling, then regressing onto this estimated marginal velocity field.
Strengths: Variance reduction for CFM is a... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you very much for your analysis of our work and the specific notes you
formulated! If you have any additional questions, we will be happy to answer them and make
additional edits to the text of the manuscript to improve the quality of the presentation.
**Let us discuss your c... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and insights. To enhance the clarity and comprehensiveness of our work, we have identified key areas where multiple reviewers expressed similar questions or critiques. We will provide detailed explanations and refinements in the updated version of... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gradient Rewiring for Editable Graph Neural Network Training | Accept (poster) | Summary: Model editing involved fine-tuning a pre-trained model specifically on training examples where it cannot predict the correct output in order to correct these errors. While model training has been explored for CV and NLP models, GNNs pose a unique challenge due to the unordered data type and node-level classifi... | Rebuttal 1:
Rebuttal: Thanks for the constructive and insightful comments. We carefully revised the manuscripts based on all reviewers' comments. Please see the revised manuscript at https://anonymous.4open.science/r/Gradient_rewiring_editing-E16E/GRE_NeurIPS24.pdf.
**[W1: Presentation on GRE and GRE+]**
Ans: hank y... | Summary: The authors propose a model editing technique motivated by an observed inconsistency between the gradient of target and training nodes' cross-entropy losses. Their proposed method, Gradient Rewiring for Editable Graph Neural Networks (GRE), stores the anchor of the gradients of the training nodes and uses the ... | Rebuttal 1:
Rebuttal: Thanks for the constructive and insightful comments. We carefully revised the manuscripts based on all reviewers' comments. Please see the revised manuscript at https://anonymous.4open.science/r/Gradient_rewiring_editing-E16E/GRE_NeurIPS24.pdf.
**[W1: Add comparison in terms of editing time and ... | Summary: The work introduces a novel method called Gradient Rewiring (GRE) to address the challenge of editable training in graph neural networks. Traditional fine-tuning approaches often struggle with maintaining performance for both target and training nodes. GRE aims to overcome this limitation by rewiring gradients... | Rebuttal 1:
Rebuttal: Thanks for the constructive and insightful comments. We carefully revised the manuscripts based on all reviewers' comments. Please see the revised manuscript at https://anonymous.4open.science/r/Gradient_rewiring_editing-E16E/GRE_NeurIPS24.pdf.
**[W1: This general approach can be tested on vario... | Summary: This paper tackles the challenge of editing GNNs. The authors highlight a key issue in GNN editing: the gradient inconsistency between target and training nodes, which can degrade performance when the model is fine-tuned using only the target node’s loss. To address this, they introduce the Gradient Rewiring (... | Rebuttal 1:
Rebuttal: Thanks for the constructive and insightful comments. We carefully revised the manuscripts based on all reviewers' comments. Please see the revised manuscript at https://anonymous.4open.science/r/Gradient_rewiring_editing-E16E/GRE_NeurIPS24.pdf.
**[W1: The significance of editing graph neural net... | Rebuttal 1:
Rebuttal: # A summary of our rebuttal.
We thank all reviewers for your valuable time and comments. We are glad that many reviewers found the following:
* **Our paper is well-motivated and well-organized**
* R `VRYA`: This paper is well-motivated and well-organized, addressing a research question that r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts | Accept (poster) | Summary: This paper proposes a training-free open-ended object detector that leverages VLM(CogVLM) to recognize and roughly locate objects(through the attention map) and prompts SAM by the coarse points results. To generate accurate point prompts, VL-SAM utilizes techniques like head aggregation, attention flow, attent... | Rebuttal 1:
Rebuttal: ### Q1: Speed and parameter.
As we discussed in the limitation, the speed and parameter problem can be gradually overcome by recent lightweight models. Besides, the main purpose of this paper is to provide a **feasible solution** to address the open-ended perception challenge; thus we do not cons... | Summary: This work proposes a novel approach for the so-called open-ended detection (and segmentation) problem, which is about localizing and naming objects in a given image without the user having to specify any pre-defined label space. This is relevant for scenarios where it is hard to define a complete list of objec... | Rebuttal 1:
Rebuttal: ### Q1: The performance of OWLv2.
Thanks for reminding. We will add it to the paper.
### Q2: Results for non-rare classes in LVIS.
The following table provides results for common ($AP_c$) and frequent ($AP_f$) classes of LVIS. We can find that, though Close-set and Open-set methods achieve better ... | Summary: In this paper, the authors proposed to combine vision language models and segment anything model (SAM) for open-ended object detection. Attention maps generated from vision language models were used to prompt SAM. Experiments on multiple benchmark datasets demonstrated better performance over several baseline ... | Rebuttal 1:
Rebuttal: ### Q1: Evaluation is still on predefined object category names.
The inference of VL-SAM **does not** rely on predefined object category names; we only use them to calculate the evaluation metrics.
Specifically, as Lines 211-213 mentioned, we use VL-SAM to generate object categories by itself. H... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments. They recognize that our work can "lead to lots of useful applications" (71UW, 6VtQ, bgFd), "achieves good results" (71UW, 6VtQ), and is "well explained" (71UW, 6VtQ) and "effective" (71UW, bgFd). We address the reviewers' concerns in the rebu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks | Accept (poster) | Summary: This paper introduces Optimus-1, a retrieval augmented generation method that enables minecraft agents to excel in long-horizon tasks. The proposed method is based on a Hybrid Multimodal Memory Module that consists of an Abstracted Multimodal Experience Pool and a Hierarchical Directed Knowledge Graph. Those t... | Rebuttal 1:
Rebuttal: > Q1: The writing of this paper can be improved.
A1: Thank you for your valuable suggestions. In the method section, we first introduce the core contribution of this paper, Hybrid Multimodal Memory, including its motivation, innovations, and components. Based on it, we developed a novel agent fra... | Summary: This paper presents Optimus-1, a multimodal agent that focuses on Minecraft tasks. Specifically, Optimus-1 is equipped with a Hybrid Multimodal Memory including: 1) a Hierarchical Directed Knowledge Graph that stores the world knowledge through free exploration and teacher guidance; 2) an Abstracted Multimoda... | Rebuttal 1:
Rebuttal: > Q1: In the construction of AMEP, given the video stream, how to adaptively update the abstracted frames in the buffer? How is the threshold of MineCLIP determined?
A1: (1) **As described in Section 2.1.1**, for video streams, we filter video streams at 1-second intervals and store them in a var... | Summary: The paper proposes "Optimus-1" a Mulit-Modal LLM based agent. They evaluate their agent extensively on MineCraft and demonstrate superior performance compared to previous work.
The key components of the approach are:
1. A memory module consisting of structured memories (DAG) and multi-modal experience repla... | Rebuttal 1:
Rebuttal: > Q1: How are teacher demonstrations obtained? How costly is this? How many human annotators and hours are needed for this?
A1: (1) We obtain teacher demonstrations from Minecraft Wiki. For each task group, we randomly select 5 tasks that are not included in the benchmark. We then create correspo... | Summary: The paper tackles the long-horizon tasks in Minecraft by building a pipeline based on multimodal LLM. Specifically, it proposes to store multimodal memory during agent exploration and a knowledge graph that stores the causal relations between objects and tasks. Additionally, a self-reflection mechanism is used... | Rebuttal 1:
Rebuttal: > Q1: There seem to be too many individual components in the paper, most of which are claimed to be important components in the paper, which makes it unclear to what extent the claimed contribution generalizes to broader settings beyond Minecraft (or even just beyond the evaluated tasks in Minecra... | Rebuttal 1:
Rebuttal: **Response to all Reviewers**
We would like to thank all reviewers (#3HCc, #397y, #mEzT, #AfEc, #wzp9) for their time and efforts in providing constructive feedback. We are very encouraged that the reviewers found the manuscript is well-written and easy to follow (R#397y, R#AfEc), proposed Hybrid... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Optimus-1 introduces an innovative Hybrid Multimodal Memory module that combines a Hierarchical Directed Knowledge Graph (HDKG) and an Abstracted Multimodal Experience Pool (AMEP) to address knowledge and experience management in long-horizon tasks.
Strengths: * Proposes a hybrid multimodal memory module, whi... | Rebuttal 1:
Rebuttal: > Q1: Improving long-horizon task performance through memory modules is common in LLMs; applying this directly to VLMs is not very novel.
A1: **Incorporating multimodal memory into VLMs presents significant challenges compared to apply unimodal memory to LLMs.** Long-horizon tasks require the mod... | null | null | null | null | null | null |
MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Experts | Accept (poster) | Summary: This paper aims to address the quality issues in human-centric text-to-image generation by constructing a large-scale dataset of millions of portraits. Additionally, the authors propose training experts for generating facial and hand details and efficiently integrate these experts using the MoE architecture in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comment, which is truly invigorating and encouraging! Below is our pointwise response, hoping to address your concerns.
**Q1:** The text description process is inadequate, particularly regarding the manual filtering of data generated by the LLaVA model.
**A... | Summary: This paper enhances these Text-to-image diffusion models by introducing a curated dataset of over a million human-centric images and a novel method, MoLE, which utilizes specialized low-rank modules to improve facial and hand image quality in diffusion processes.
If the rebuilt dataset can be race-balanced an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging comment! Below is our pointwise response, hoping to address your concerns.
**Q1:** An analysis of the ratios of different races in one prompt (A Beautiful Woman)? Provide a discussion that if the ratios of different races are the same, will the generation... | Summary: This paper aims to explore human-centric text-to-image generation, particularly in the context of faces and hands, the results often fall short of naturalness due to insufficient training priors. The authors alleviate the issue from two perspectives. 1) The authors collect a human-centric dataset with two spec... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comment! We hope our response presented below can address your concerns.
**Q1:** Provide and analyze the distribution of the local weight sent to each expert by the sigmoid function.
**A1:** We thank the reviewer's advice. We provide the distribution of the... | Summary: The authors propose a large-scale dataset for human image generation, comprising over one million images, including close-up face and hand subsets. They also introduce MoLE (Mixture of Low-rank Experts), a novel framework that utilizes two low-rank experts to learn face and hand representations. This approach ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comment! Below is our pointwise response, hoping to address your concerns.
**Q1:** Comparison with existing datasets like CosmicMan is essential to contextualize its value.
**A1:** We thank the reviewer's advice. Below, we give a comparison of the differenc... | Rebuttal 1:
Rebuttal: ## General Response
Thank all reviewers for their time and effort in reviewing our paper. We also thank all reviewers for their valuable feedback, which is instrumental in enhancing the quality of our work. We hope our pointwise responses below can clarify all reviewers’ confusion and alleviate a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Maia-2: A Unified Model for Human-AI Alignment in Chess | Accept (poster) | Summary: The paper introduces a unified modeling approach called Hermes for aligning human and AI performance in chess. Hermes effectively captures human chess styles across different skill levels, providing a coherent reflection of player improvement. The approach incorporates a skill-aware attention mechanism that dy... | Rebuttal 1:
Rebuttal: **Design choices (W2)**
We agree that we didn’t sufficiently explain our architecture choice. The rationale behind our design is that each channel (feature map) of the ResNet output represents different aspects of a chess position, and the attention blocks actively select and interact with the fe... | Summary: This work explores developing a unified model to predict human moves in Chess. To address the coherence challenges, the authors propose to use skill-aware attention with channel-wise patching to encode skill levels and board positions into a neural network model. Experimental results show their proposed model ... | Rebuttal 1:
Rebuttal: **Skill level encoder (W1)**
Maia implicitly encodes both players’ skill levels by only selecting the games between the same-strength players for model training. In Table 1, we have equated the active and opponent skill levels to ensure fair comparisons, and our model outperforms Maia in this set... | Summary: The paper proposes a unified modeling approach named Hermes for aligning human and AI behaviors in chess. It addresses the limitations of previous models by integrating a skill-aware attention mechanism that dynamically adapts to various skill levels of players. The Hermes model aims to enhance AI-guided teach... | Rebuttal 1:
Rebuttal: **Contribution**
To the best of our knowledge, we are the first to emphasize coherence in human behavior modeling. It is important to reconsider the assertion that there are "no fundamentally new concepts," as our work introduces the first human behavior model that is coherent across various skil... | Summary: This work tackles the problem of modeling chess agents at varying skill levels. Prior work learns separate models for each skill level, so the authors introduce their method “Hermes” which uses skill-aware attention to adapt the predictions based on the skills i.e. chess ratings of both players in the game. Te... | Rebuttal 1:
Rebuttal: **Main focuses & Broader Impact (L1, L2, W1)**
We contribute an approach to human move prediction that is not only the new state of the art for accuracy, but our model achieves **coherence** in its predictions. To power algorithmic teaching tools, we believe that it is not enough to treat differe... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful reviews and constructive suggestions for our work. Our work has been recognized as addressing "an important problem of skill-aware modeling of human behavior" (Reviewer 1), introducing an "innovative skill-aware attention mechanism” (Reviewer 2), and being "very solid... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL | Accept (poster) | Summary: The paper proposes a general offline-to-online (O2O) reinforcement learning method that can work with any offline RL algorithm. It addresses evaluation and improvement mismatches between offline datasets and online environments by (1) Re-evaluating the offline critic optimistically; (2) Calibrating the critic ... | Rebuttal 1:
Rebuttal: Thanks you for the high praise and the comprehensive review of our paper.
**Q1:** There are several minor presentation issues, not very critical, but they significantly affect the visual quality of the paper.
**A1:** Thanks for your suggestions. We have revised these language mistakes. We will... | Summary: The paper addresses the offline-to-online (O2O) reinforcement learning problem with the goal of improving online performance by leveraging offline data. The primary contributions of this paper are twofold. First, it identifies and elaborates on two key challenges in O2O RL: evaluation and improvement mismatche... | Rebuttal 1:
Rebuttal: Thanks for your high praise and comprehensive review of our paper. We appreciate the questions you raised and are committed to delivering a comprehensive response to address the issues.
**Q1:** The method consists of three components. The first two components are developed to solve two mismatches... | Summary: This paper proposes to handle evaluation and improvement mismatches in offline-to-online RL. To this end, the authors suggest to re-evaluate the pessimistic critic and calibrate the misaligned critic with the reliable offline actor. Then, they perform constrained fine-tuning. They evaluate the performance in t... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our paper. We are glad that you consider our work “theoretical analyzed”. We are glad to answer all your questions.
**Q1:** Difficult to follow up on technical novelties. I suggest adding concept figures or algorithm tables to highlight core contributions.
**... | Summary: The paper proposes a general framework to bridge offline-to-online RL. It first studies two types of issues in O2O RL: evaluation mismatch and improvement mismatch. The proposed method addresses these issues by combining policy re-evaluation, value alignment, and constrained online fine-tuning. Unlike prior me... | Rebuttal 1:
Rebuttal: Thanks for your insightful review and positive recognition of our paper. We are glad that you consider our work “interesting, solid, well-written”. We appreciate the questions you raised and are committed to delivering a comprehensive response to address the issues.
**Q1:** Suggest mentioning the... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for your thorough evaluation of our paper. Your constructive comments have been invaluable in helping us enhance our work.
Pdf: /pdf/ab828a1400e5467ba6cd8fd5cbb2e410784f3354.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize | Accept (spotlight) | Summary: This paper studies constant step-size stochastic approximation algorithms with Markovian noise. It is known that in this case, the error of a stochastic does not vanish asymptotically, even when consider averaging. This is \theta_n has a bias. Previous work have show how to study the bias of such algorithm fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the strengths and contribution of our paper, and for the constructive comments. Below we provide our responses to the comments. In the following, we use [1] [2] etc. to refer to papers cited in our submission, and [a] [b] etc.
for new references, with bibliogr... | Summary: The present paper obtains a representation for the asymptotic bias of constant step-size nonlinear stochastic approximation with Markovian noise. In particular, this characterization makes the hindering effect of memory and nonlinearity explicit in terms of the algorithm's performance. Moreover, the authors es... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our paper’s contribution and strengths, and for the constructive comments. We provide our detailed responses below. In the following, we use [1] [2] etc. to refer to papers cited in our submission, and [a] [b] etc. for new references, with bibliographic inform... | Summary: This paper considers a nonlinear stochastic approximation (SA) problem with Markov noise (MC). It is assumed that the MC is uniformly geometrically ergodic. Instead of the standard iterative procedure, a projection onto a bounded set is additionally introduced (the latter can be relaxed under the additional as... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our paper’s contribution and strengths, and for the constructive comments. We provide our detailed responses below. In what follows, we use [1] [2] etc. to refer to papers cited in our submission, and [a] [b] etc. for new references, with bibliographic informa... | Summary: This paper investigates stochastic approximation (SA) with Markovian data and nonlinear updates under constant stepsize, and establishes the weak convergence of the joint process $(x_t, \theta_t)$. It also presents a precise characterization of the asymptotic bias of the SA iterates.
Strengths: I find this pa... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our paper’s contribution and strengths, and for the constructive comments. We provide our detailed responses below.
**Comment: Numerical experiments.**
We thank the reviewer for the suggestion. We conduct a set of experiments, running SGD on $L_2$-regularize... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful feedback. In this global rebuttal, we discuss smoothness, strong monotonicity, the use of projection, and the minorization assumption of the noise sequence. We also supplement our theoretical results with a set of numerical experiments, with results in t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging | Accept (spotlight) | Summary: This paper addresses the challenge of spatially varying Point Spread Function (PSF) in lensless imaging and introduces a two-stage approach for reconstructing lensless images.
Strengths: This paper is well-written, presenting a detailed statement of the problem, the physical imaging model, an innovative recon... | Rebuttal 1:
Rebuttal: **1. Real-world experiments**
The DiffuserCam and PhlatCam datasets utilized in our study are collected from real-world lensless camera prototypes. These datasets are widely recognized and extensively used in lensless imaging literature, providing robust and reliable benchmarks for evaluation. Th... | Summary: This paper proposes a method for reconstructing photorealistic images in lensless imaging. The reconstruction process, which aims to be consistent with observations while achieving photorealism, is based on range-null space decomposition. To accommodate realistic cameras, the method introduces SVDeconv, which ... | Rebuttal 1:
Rebuttal: **1. Details of the training process**
In the first stage, SVDeconv is trained using range-space content derived from ground truth images. SVDeconv consists of two main parameter components: a learnable deconvolution kernel initialized with known PSFs, and a U-Net initialized with standard weight... | Summary: This paper proposed a deep learning-based approach for lensless imaging. To address the problem of model mismatch that simple convolutional models cannot accurately describe the lensless imaging process, this paper introduced a spatially-varying devolution module that reweights the deconvolution results from m... | Rebuttal 1:
Rebuttal: **1. Comparison with DDNM**
Our paper presents notable advancements compared to DDNM, in both the reconstruction quality and inference speed. Training-free methods like DDNM depend on an accurate imaging model to recover null space, but acquiring such a model for lensless imaging is difficult. In... | Summary: This paper proposes a novel two-stage approach for lensless image reconstruction. The approach ensures data consistency with a spatially varying deconvolution method and enhances photorealism using pre-trained diffusion models. This method outperforms existing techniques in data fidelity and visual quality on ... | Rebuttal 1:
Rebuttal: **1. Choice of the number of deconvolution kernels**
The rationale for using 3x3 PSF kernels is based on the assumption that the central PSF effectively represents the region of the field of view (FoV) where PSFs change smoothly, which is typical in lensless settings. The 3x3 choice balances perf... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort dedicated by the ACs and reviewers in evaluating our work. We have carefully considered all comments and suggestions, and our detailed responses can be found in the rebuttal box below.
Pdf: /pdf/f562ca218a7df507c715c8de4e3a51f6946ac42c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Continuous Product Graph Neural Networks | Accept (poster) | Summary: The authors propose a new spectral GNN for Cartesian product graphs. The graph filter is chosen as Laplacian heat diffusion, which is separable across factor graphs. The stability and over-smoothing of the proposed model are studied. The authors perform synthetic experiments to validate the analysis. Experimen... | Rebuttal 1:
Rebuttal: **We’ve expanded some points of our rebuttal in additional comments. We kindly invite the reviewer to read them if required.**
**W1**. We should have mentioned that, first, we need to define the Dirichlet energy for the tensorial data, $D_T(.)$, as the summation of the factor-wise Dirichlet energ... | Summary: This paper proposes tensor PDE for graph neural networks temporal graph prediction. The construction is seems to be good and theoretical analysis for oversmoothing and stability are provided. Experiments show good performance with the proposed method compared to several baseline methods.
Strengths: 1) improve... | Rebuttal 1:
Rebuttal: **Q1**. The training time comparison with the most relevant baseline, GTCNN, has been provided in the **uploaded PDF** on the main rebuttal (Tab. R2) for the NOAA and MetrLA datasets. We observe that our model requires less time per epoch to be trained.
**Q2**.
- Based on Eqn. (18), we can expec... | Summary: The paper proposes CITRUS, a novel model for jointly learning multidomain couplings from product graph signals based on tensorial PDEs on graphs (TPDEGs). By modelling these representations as separable continuous heat graph kernels as solutions to the TPDEG, the paper shows that the underlying graph is actual... | Rebuttal 1:
Rebuttal: **W1**. We’ve significantly simplified the notations in our paper. For example, we removed the unnecessary tildes from the notation in the revised manuscript.
- Regarding the lower bar for tensors, we find it important to differentiate tensors (higher-order data) from regular matrices as in [9], b... | Summary: The authors propose Tensorial Partial Differential Equations (TPDEG) to model multidomain data. Then they propose Continuous Product Graph Neural Networks (CITRUS) as a continuous solution. They provide theoretical and experimental analysis of their proposed approaches. They test their approach on spatiotempor... | Rebuttal 1:
Rebuttal: One example where real-world graphs cannot be represented as a Cartesian product of factor graphs is the case where we have time-varying dynamic graphs, which might lead to some approximation errors by assuming the resulting graph is a Cartesian product graph. Indeed, this comment relates to the g... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewers for their thoughtful and constructive feedback. We are encouraged by their recognition of several strengths in our work. In particular, the reviewers found that: "*The proposed methods are backed by theory*" (reviewers wQ9d, QFxx, and Y9UP), "*The experime... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fine-Tuning is Fine, if Calibrated | Accept (poster) | Summary: The paper proposes a simple post-training calibration technique for classifying missing classes after fine-tuning. For example, assuming the pre-trained model can classify 1000 classes and is fine-tuned on a subset of these classes from a different image domain, the proposed method improves the classification ... | Rebuttal 1:
Rebuttal: We appreciate your detailed review and positive assessment of the strengths of our paper. We address your concerns as follows.
Weakness:
**W1: Statements are not precise. … The extent of forgetting and degradation depends on the fine-tuning procedure. ….**
We apologize if our statement in the ab... | Summary: The authors argue that fine-tuning doesn’t forget the features for classes not participating in it, but rather downscales their logits as a result of which the model ends up being overconfident for the fine-tuning classes. Counter-intuitively, the authors claim that fine-tuning also enhances the discriminative... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments. We are pleased that the reviewer recognize several key strengths of our paper. We respond to your concerns (weaknesses and questions) as follows.
**W1. NCM classifier’s accuracy and discriminating features.**
Thank you for the comment. Our paper f... | Summary: It is commonly believed that fine-tuning zero-shot models on seen classes will lead to a decrease in performance on unseen classes.
In this paper, the authors systematically examine the issue that find that (1) the fine-tuned feature extractor is not damaged: NCM improves the absent class accuracy without cat... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback. This project has been a challenging journey, yet rewarding. We respond to your valuable comment below.
**W: “I suggest that the authors include more experiments on CLIP models, …”**
Thank you for the suggestion. We will certainly include more experiments an... | Summary: The paper unveils the improved features of absent classes when a pre-trained model is fine-tuned on a subset of all classes. The paper presents an empirical study on three datasets to demonstrate this finding and proposes a calibration method to post-process the logits after fine-tuning to improve the classifi... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and valuable feedback on our work.
**Q1: … the improvement does not always hold when the fine-tuned classes have features that are not helpful or even harmful ...**
Thank you for your insightful question, and we will include more discussions in our camera-... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments. We are glad that the reviewers found our findings and motivations “interesting” (amJX, 5T9R, YzGv), our study providing “insights” correcting prior belief (9Ei3), our solution “simple and easy to use” (amJX, YzGv) with “impressive” gains (5T9R), ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics | Accept (poster) | Summary: This paper develops a novel model of latent neural dynamics that builds on existing work in switching and time-varying linear state space models. In particular, the authors propose a full probabilistic formulation of decomposed LDS models and provide a variational EM algorithm to do inference in these models. ... | Rebuttal 1:
Rebuttal: We thank Reviewer mAN7 for their thoughtful feedback and suggested improvements to our experiments. We are encouraged that they found our work to be "high-quality" and a "positive contribution to the modeling of neural dynamics".
**W1:** While p-dLDS does require more complex mathematical concept... | Summary: The Authors propose a probabilistic extension to the "Decomposed Linear Dynamical Systems (dDLS)" method by Mudrik et al. (2024). The proposed p-dLDS belongs to a family of models specifically designed to describe neural activity from high-dimensional dynamical data. The effectiveness of p-dLDS is evaluated on... | Rebuttal 1:
Rebuttal: We thank Reviewer 3hfN for taking their time to review our work.
**W1:** We report the standard deviation across independent runs in Tables 3, 4 and 5 in the supplementary materials. We omit these values in the main text due to limited space.
**W2:** While we do not perform an ablation study for... | Summary: The paper proposes the probabilistic decomposed linear dynamical systems (p-dLDS) model, which extends the existing dLDS model. With the time-varying offset term and probabilistic formulation, p-dLDS improves upon the dLDS model in terms of robustness to noise and the ability to capture systems that orbit mult... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our submission and are encouraged that you find our work to be "well-written" and "adds value to the recently developed dLDS".
**W1. Sweeping dynamics noise. Advantage of p-dLDS in a realistic setting.**
We agree that it's important to understand our model's... | Summary: The paper presents a probabilistic version of the dLDS model, first presented by Mudrik et al (2024). The primary impetus for this model was to present a version of the dLDS that was more robust to noise, although the authors here also included a slowly-evolving offset term meant to capture evolving fixed poin... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper thoroughly. We are encouraged that you find our work to be "original", "a pleasure to read", "well-conceived", and of "interest to the computational neuroscience community".
**Q1 and Q2: Factorization of the coefficient transition and Definition o... | Rebuttal 1:
Rebuttal: Thank you to all four reviewers for their insightful feedback. We respond to reviewers individually below, but provide a description of our additional experiments here. Please see our attached pdf for figures relevant to the experiments below.
**Experiments:**
1. **Increasing the number of rSLD... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Linear Uncertainty Quantification of Graphical Model Inference | Accept (poster) | Summary: This paper proposes an uncertainty quantification method for graphical models that uses linear propagation to model uncertainty. Experiments show that it can achieve competitive results and fast convergence in downstream tasks.
Strengths: 1: The paper is well-written. The motivation for the paper and the prop... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive reviews.
## Regarding the Formatting Issues
>The formatting of the paper is less professional, especially in the appendix. In B.3.2, there are large blanks between the title and Table 2. Also, Table 3 is oversized.
Thank you for pointing out these forma... | Summary: [Edit: After discussion, the Authors have addressed my concerns on the presentation and discussion of their results. I have accordingly increased my score (previously 5).]
The paper proposes an alternative algorithm for message passing for uncertainty quantification in graphical models. The method is linear, ... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive reviews.
>Some areas were hard to understand. E.g. the motivating discussion of why some (but not all) existing methods could only reduce uncertainty.
The sentence you mentioned in the original text is: “However, this results in **any neighbor of a node ... | Summary: This paper considers the problem of calculating uncertainty in the infererence results on probabilistic graphical models. Their method is based on a previously published linearization of the belief propagation method, to provide scalability, interpretability, and unbiasedness. The benefits of the new algorithm... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive reviews.
>The proposed method is based on a previously developed linearization method, making their innovation limited.
Indeed, we did use a conclusion from LinBP (Centered BP) in our derivation. However, LinUProp fundamentally differs from LinBP in its... | Summary: A method for quantifying the uncertainty in the graphical model is proposed. The authors claimed that the method is superior over prior methods including NETCONF and Monte Carlo in the aspects of scalability, interpretability, and unbiasedness. Active learning that uses node uncertainty estimation for unlabele... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive reviews.
>I have doubt about the interpretability of LinUProp, as it involves high-order terms such as $T^2$,$T^3$, etc. that are not easy to be understood.
When we say that LinUProp is interpretable, we mean that for the posterior uncertainty of each no... | Rebuttal 1:
Rebuttal: Tables 2 and 3 have been updated with annotations for the standard deviation and t-test results.
Pdf: /pdf/77fe82473d1ed7d2d458119547c7d4b3d530418c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Bayesian Persuasion Without a Clue | Accept (spotlight) | Summary: The paper studies online Bayesian persuasion with "no clue", i.e., the sender knows nothing about the prior distribution or the receiver's utility function a priori (it is however assumed that there exists a prior distribution and states are drawn iid from it). The main results are (1) in the online model whe... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the insightful feedbacks and for pointing out some typos. We will incorporate these suggestions and correct the typos in the final version of the paper.
> Overview part of sec 4: my first instinct is that explore-then-commit strategies normally give regret bounds like $... | Summary: This paper studies repeated Bayesian persuasion where the sender does not know the prior distribution of the state of the world and the receiver's utility (while the receiver knows the prior and their utility and myopically best responds in each period). The authors design online learning algorithm for the sen... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the insightful feedbacks. We will also incorporate the two Reviewer's suggestions in the final version of the paper.
> The proposed algorithm seems to have an exponential running time in the worst case. Specifically, the number of vertices $\mathcal{V}$ of the polytopes ... | Summary: The paper studies Bayesian persuasion in a learning setting with minimum knowledge about the receiver: neither the receiver's prior nor their utility function is known. In the model, the sender can commit to different signaling strategies and acquire the receiver's optimal response to each signal they send. Th... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the insightful feedbacks and for pointing out some typos. Specifically, we will do our best to improve the part below Definition 1 and the comparison between previous models at Line 296 in the final version of the paper.
> Is there any justification how the receiver get... | Summary: The paper studies online Bayesian persuasion problems in which an informed sender
repeatedly faces a receiver with the goal of influencing their behavior through the provision of payoff-relevant information. The paper considers a setting where the sender does not know anything about the prior and the receiver’... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the insightful comments. We will adopt them in the final version of the paper and we will better discuss the points suggested by the Reviewer.
> It is indeed a bit interesting to me that the unknown of both prior and receiver’s utilities make the learning problem signifi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Data Diagnosis and Debiasing with Concept Graphs | Accept (poster) | Summary: The paper introduces a method for addressing the issue of inherent bias in data during the training process. This bias can lead to unreliable predictions from the model. The proposed method, called ConBias, is a new approach for identifying and mitigating Concept co-occurrence Biases in visual datasets. The me... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments.
**Clarification on the ablation studies:**
Since the concept graph phase in ConBias is not a learning mechanism, we circumvent the need for initialization or training strategies. This is beneficial as we achieve both controllability and interpreta... | Summary: The authors present ConBias, which is a novel approach for diagnosing and de-biasing co-occurrence bias in datasets. Unlike previous works, ConBias addresses both diagnosis and de-biasing / balancing strategy, to target the data augmentation specifically to address spurious concept co-occurrences. The main com... | Rebuttal 1:
Rebuttal: Thank you for the encouraging review and the detailed feedback.
**Extension to multi-class tasks and scalability:**
ConBias can be conveniently extended to the multi-class setup. For this, we note the definitions in line 142 and line 146. While the common clique computation remains the same, for ... | Summary: This paper points out inherent biases in visual dataset. To address this issue, the paper proposes a new framework called ConBias, which proceeds in three steps: (1) Concept Graph Construction, (2) Concept Diagnosis, and (3) Concept Debiasing. Using concept metadata in the dataset, concept graph is constructed... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments.
**Metadata:**
As we discuss in the main paper (lines 324-325), we assume the availability of reliable ground truth concept sets. Such annotations already exist for the datasets we investigate - Waterbirds, UrbanCars, and COCO-GB. We agree that unre... | Summary: This paper introduces a concept graph-based framework to diagnose and mitigate biases in visual datasets by representing datasets as knowledge graphs of object co-occurrences. The approach involves constructing a concept graph, diagnosing concept imbalances, and debiasing by generating images with under-repres... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback.
**Intuition and advantage of constructing knowledge graph:**
ConBias is not simply a statistical co-occurrence counting mechanism. In fact, in lines [87-90], we mention that there are diagnostic tools that exist today which compute such object co... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing valuable feedback on our work. We are glad that they found our idea "original" (R3); our diagnosing and debiasing method "structured and controllable" (R1), "novel, significant" (R3); our graph-based framework "a new approach to generate unbiased images" (R2), ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
High-probability complexity bounds for stochastic non-convex minimax optimization | Accept (poster) | Summary: This paper proves a high probability convergence bound and almost sure convergence for stochastic smoothed AGDA method for minimax problems in nonconvex-PL setting. This is the first high probability guarantee and almost sure convergence guarantee in this nonconvex setting.
Strengths: This is the first high p... | Rebuttal 1:
Rebuttal: Thanks for all the insightful comments. Below are our responses:
**1-** [68] analyzes sm-AGDA in the deterministic nonconvex-concave setting, and introduces a Lyapunov function to establish a complexity result. In a follow-up work, [63] leverages the same Lyapunov function to adapt the analysis t... | Summary: This paper investigates the stochastic non-convex minimax optimization problem $\min_x \max_y f(x,y)$ where the function $f$ is smooth, then nonconvex in $x$ and PL in $y$. The authors presented a high probability analysis for the smoothed alternating gradient descent ascent (sm-AGDA) method. This analysis is ... | Rebuttal 1:
Rebuttal: We thank the referee for their time invested in reviewing our paper. Below are our responses to the questions raised.
**Weaknesses**
**1-** Please see our general response to the reviewers above in the window titled **General response: Tightness of our high-probability bounds,** where we explain... | Summary: The authors build upon earlier works to prove a high probability upper bound, that is linear in variance of the stochastic gradients, on the number of stochastic gradient calls of smoothed alternating GDA method.
Strengths: The paper is well written with clear theorems, proofs and numerical results.
Weakness... | Rebuttal 1:
Rebuttal: We thank the referee for their time invested in our paper. Below are our responses to the weaknesses raised.
While our concentration inequality (Thm 9) seems tailored to the analysis of sm-AGDA, we can argue that it can also aid in deriving high probability bounds for many other nonconvex first-o... | Summary: This paper considers the important open problem of stochastic smooth nonconvex minimax optimization. This paper proposes single-loop stochastic GDA method, which was known to be practically desirable but had no theoretical complexity compared to other non-single-loop methods with better complexies on nonconvex... | Rebuttal 1:
Rebuttal: We thank the referee for their time invested in reviewing our paper. Below are our responses to the questions raised.
**Weaknesses**:
**1-** The smaller font size is a typo due to misplaced bracket after a displayed equation. We will fix this issue; thanks for the good catch.
**2-** Our work ex... | Rebuttal 1:
Rebuttal: ### General response: Tightness of our high-probability bounds
We thank the referees for their time invested in reviewing our paper. Following the request from several referees, we discuss below the benefit of our approach against naive expectation-to-high-probability conversions using Markov's i... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors derive a high probability bound for convergence of a stochastic gradient descent-ascent method over a nonconvex (PL class) of functions. Specifically, they analyze the sm-AGDA algorithm, which previously only had a bound in expectation, and show a similarly tight high probability bound when gradien... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the feedback. Below we provide a point-by-point response to each of the weakness/question raised in order.
**Weaknesses**
**1-** The only known last-iterate results for nonmonotone VI problems require more restrictive conditions like local *quadratic growth* around ... | null | null | null | null | null | null |
Sample Complexity of Interventional Causal Representation Learning | Accept (poster) | Summary: Interventional causal representation learning aims to recover the causal graph for latent variables and simultaneously recover the latent variables. While identifiability has been established for the infinite sample regime, this is not practical. This paper aims to provide PAC-style bounds on identifiability (... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind review and thoughtful questions. We address the raised questions as follows.
**Causal order estimation.** The reviewer is correct in that since the causal graph and variables are latent, we can choose any ordering of the nodes and latent variables first and then... | Summary: This paper establishes finite sample guarantees for recovering the latent causal graph under stochastic soft interventions and observations under a unknown linear transformation. Experiments are conducted and code is given.
Strengths: The paper is well motivated and the contributions are clear.
Weaknesses: I... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind review and thoughtful questions.
- ${\bf P}_I$ is the permutation matrix for permutation $I$, the intervention order. We will include the definition in the notations paragraph.
- The current theorem statements are given for specific choice of thresholds that r... | Summary: This paper provides finite-sample identifiability results for recovering the latent causal graph and the generating latent variables given observations in a high-dimensional space that have been generated by the latent variables by a linear transformation and single-node soft interventional data with intervent... | Rebuttal 1:
Rebuttal: We thank the reviewer for a thorough review and thoughtful questions. We hope our answers address the main concerns of the reviewer.
**Setting the thresholds.** We are grateful for the reviewer bringing this up. We recognize that our choices of thresholds could have been presented differently an... | Summary: This paper provides finite sample analysis for causal representation learning with general latent causal models, single-node soft interventions, and linear mixing functions. Sample complexity for identifying graphs up to ancestors and identifying latent variables up to mixing with parent variables have been st... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our results interesting and the thoughtful questions. We address the questions as follows.
- **Source of error in experiments.**
To assess the source of errors in experiments, we have the following analysis: In Appendix B, we establish the identifiability guaran... | Rebuttal 1:
Rebuttal: In the attached PDF, we provide a figure to update Figure 1b of the submitted paper with the updated experiment results. Specifically, there are two changes:
1) Each data point in old Figure 1b corresponds to a specific tuple $(N,n,d)$ (listed in Appendix G). For each $(N,n)$, the old plot has two... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SPO: Sequential Monte Carlo Policy Optimisation | Accept (poster) | Summary: This paper maximizes the expected reward wrt the policy by rewriting it as a log marginal likelihood in a probabilistic model where latents comprise of states, actions, and “optimality” observations.
The prior over states is the world model and the prior over actions is the policy which being optimized. The l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review which will be used to clarify details of the paper
1. We aim to sample from the target policy over sequences $\tau= (s_0, a_0, s_1, a_1, ..., s_t, a_t, s_{t+1})$ similar to [60]. In the following notation we switch to using $\tau$ from $x$ and give... | Summary: This paper propose SPO: Sequential Monte Carlo Policy Optimisation, a RL algorithm with the Expectation Maximisation (EM) framework for MDP. Experiments on both discrete and continuous environments show that SPO outperforms baselines in terms of sample efficiency and scalability.
Strengths: Quality: The pape... | Rebuttal 1:
Rebuttal: **On Novelty:** Re: "limited novelty'', we respectfully disagree with the reviewer. We highlight three contributions (all included in the manuscript lines 27, 39, 38):
1. This is the first instance of Sequential Monte Carlo being used as a policy improvement operator for RL. To date, MCTS has be... | Summary: This paper introduces a novel iterative, particle-based approach to sequential Monte Carlo planning by combining expectation maximization with importance sampling. Their approach uses a model to sample real state-action trajectories in a problem domain then, after importance-weighting said transitions, compute... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review.
Re: evaluation of SPO with an imperfect model. Our primary goal was to focus on the novel planning aspects of SPO and its use within training. We believe that the results demonstrate that SMC is a valuable planning method in its own right demonst... | null | null | Rebuttal 1:
Rebuttal: In this section we provide additional results/experiments in response to reviewer xUvG and reviewer X2nr.
**Additional Results**
**Figure 1**
While we give in depth details regarding how the KL constraint arises within SPO and the method through with it is enforced (see Appendix G.2). In the o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk | Accept (poster) | Summary: The authors propose a sampling framework from a convex body with a barrier.
Strengths: The main strength of the paper lies at the novelty and the significance of the results, especially that of Theorem 1.1.
Weaknesses: See questions below.
Technical Quality: 3
Clarity: 4
Questions for Authors: Page 1:
Can... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments and questions, and we would be happy to answer your questions.
**Q**: Can you motivate the problem a bit better, for a broader CS audience?
**A**: Yes. Sampling is a fundamental problem in computer science and machine learning. Consider any constrained opti... | Summary: The paper gives a Markov chain Monte Carlo algorithm for estimating the probability of a high dimensional polytope under a log-concave probability distribution. The algorithm improves the mixing time of previous results, while maintaining the best known per-iteration cost. More specifically, in conjunction wit... | Rebuttal 1:
Rebuttal: We thank you for your review. We respectfully disagree with your point that the improvement we obtain is incremental, as the prior best algorithms for $d$-dimensional polytopes with $n$ hyperplanes mixes in $nd$ steps, while ours mixes in $d^2$ steps. For the regime where $n\gg d$, this is a huge ... | Summary: In this paper, the authors give improved mixing times for a Markov chain whose goal is to approximately sample from distributions whose densities are proportional to $\exp(-f(x))$, where $f$ is $L$-Lipschitz and convex, restricted to a convex set defined by the intersection of $n$ hyperplanes that lies in a Eu... | Rebuttal 1:
Rebuttal: We deeply appreciate your thoughtful and positive feedback for our work. We thank you for pointing out the convexity of the Lewis weights program for all $p>0$ as indicated in [LS19], and this is particularly the case for our application when $p$ is $\mathrm{poly}\log n$. For your questions,
**Q*... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices | Accept (poster) | Summary: The paper "TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices" presents a combination of test-time Adaptation of pre-trained models and early-exist strategies for the efficient inference of Deep Learning models on edge devices. The authors introduce the ideas of test-time Adaptati... | Rebuttal 1:
Rebuttal: Thanks for all your positive comments. Please see below our response to the specific weakness and questions.
>Q1: Unclear how to use activation memory to split the network. How did you analyze the activations for grouping the models into sub-modules? Did you apply some principle here, or was it a... | Summary: The paper "TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices" presents a framework designed to enable test-time adaptation (TTA) on resource-constrained IoT devices. TinyTTA utilizes a self-ensemble and batch-agnostic early-exit strategy to adapt to data distribution shifts effic... | Rebuttal 1:
Rebuttal: Thanks for all your positive comments. Please see below our responses.
>Q1: Other type of data other than vision data?
A1: We tested on the Musan Keywords Spotting audio dataset [2] using a pretrained MicroNets [1] (86% accuracy on Speech Commands V2), which includes 35 speech commands with real... | Summary: This work presents a test-time adaptation framework for tiny deep neural networks. Specifically, the proposed framework partitions a specific model based on the memory usage of each layer, clusters adjacent layers with similar memory usage into a submodule, and adds an early exit header for each module. To avo... | Rebuttal 1:
Rebuttal: Thanks for all your positive comments. Please see below our response to the specific weakness and questions.
>Q1: Lack of discussion on design choices: why can't the "fine-tune bias only" technique from TinyTL [28] be used in test-time adaptation? What is its performance compared to only fine-tuni... | Summary: In this work, the authors focus on enabling test time adaption on resource limited edge devices. To achieve that, the authors first to train a self-ensemble network where the sub-networks are partitioned according to memory usage. After that, the authors further adopt WS normalization to improve adaption capac... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments. Please see below our responses.
> Q1: In Fig.3, initial layers occupy much higher memory than later layers. Is it better to avoid pass through some of the initial layers and active more later layers?
A1: Memory usage primarily concerns activations, which st... | Rebuttal 1:
Rebuttal: Dear reviewers and meta reviewers,
We appreciate all the positive comments of our work:
- Reviewer yW6M: First time using WS and self-ensemble for on-device test time adaptation.
- Reviewer DHsU: Well-motivated, impressive results, and real device deployment on low-end IoT devices with only 512 K... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing | Accept (poster) | Summary: This paper proposes a new method for multi-view consistent inpainting. The input includes a video and a sequence of masks. The first image could be manipulated by any 2D editing approach, while the remaining images need to be inpainted using the proposed method. The method is built upon the SD1.5-inpainting mo... | Rebuttal 1:
Rebuttal: We rearrange and classify similar questions together.
**W1. No 3D representation? Tuning down "3D editing" in title.**
Thanks.
First, we would like to clarify that our paper has included a detailed discussion on the explicit 3D representation (3DGS) within our method in Section C of Appendix (... | Summary: The paper formulates the 3D object editing task as a multi-view 2D in-painting task. Firstly, MVInpainter-F is employed to remove the object and obtain the background scene. Then, MVInpainter-O generates multi-view images based on the reference view.
Strengths: 1. Solving the 3D object editing task as multi-v... | Rebuttal 1:
Rebuttal: **W1, Q2. Whether mask adaption is enough robust? How to deal with different masks and backgrounds?**
Thanks for this good point. As long as the 'basic plane' where the object is placed can be approximated to a plane, the proposed mask adaption is robust enough with the theoretical basis of persp... | Summary: This paper proposes a new 3D editing method by regarding it as a multi-view 2D inpainting task. It ensures cross-view consistency through video priors and concatenated reference key/value attention and controls camera movement without explicit poses using slot attention. It shows effectiveness in object remov... | Rebuttal 1:
Rebuttal: **W1. Heuristic assumption of mask adaption?**
Thanks for this point. We should clarify that the planar assumption of mask adaption in Line 232 is reasonable and straightforward to implement across diverse real-world cases.
First, this strategy roughly decides the mask location rather than provid... | Summary: The paper studies the task of multi-view consistent real-world object removal and insertion, enabled by learning a model, MVInpainter, trained to perform multi-view 2D inpainting. The paper demonstrates its effectiveness on both object-centric and scene-level datasets with the task of object removal and object... | Rebuttal 1:
Rebuttal: **W1. How does the method perform with unordered multi-view inputs?**
Thanks. Although our work mainly focuses on the sequential multi-view inputs to better leverage the video prior from video components, it can also achieve comparable performance with unordered inputs as exploratively verified i... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments from all reviewers. We thank the positive comments of 'interesting and effective', 'novel and reasonable design' of slot-attention based flow grouping (jssh, VJEq); 'important yet less explored', 'interesting and innovative' of multi-view generation (n43z, NjjA)... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MMSite: A Multi-modal Framework for the Identification of Active Sites in Proteins | Accept (poster) | Summary: The manuscript proposes MMSite, a multimodal framework combining amino acid sequences and textual descriptions to predict active sites of proteins at a residue-level. For that, the authors train an attention-based architecture in two stages. In the first stage, the objective is to align sequence and text embed... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback and constructive suggestions. Below is our point-by-point response to your comments.
**Re - Weaknesses 1**
It's true that MMSite rely on an external generative model to produce textual context for inference. This decouples the training of prot... | Summary: This paper introduces MMSite, a multi-modal framework to improve the identification of active sites in proteins by leveraging protein sequences and textual descriptions. The authors build the ProTAD, a dataset containing over 570,000 pairs of protein sequences and detailed textual descriptions. The MMSite uses... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback and constructive suggestions. Below is our point-by-point response to your comments.
**Re - Weaknesses 1**
Thanks for your concern regarding the inference efficiency of our model! During inference, if the protein has corresponding multi-attrib... | Summary: The paper constructs a Protein-attribute text dataset, ProTAD, and proposes a multi-modal framework, MMsite, that enhances PLM with biomedical language models. During the inference stage, the authors propose generating biomedical text with Prot2Text, which is then fed into the MMsite module. The paper demonstr... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback and constructive suggestions. Below is our point-by-point response to your comments.
**Re - Weaknesses**
To the best of our knowledge, ProTAD is currently the only dataset that simultaneously includes amino acid-level labels and detailed prote... | Summary: The paper proposes a framework to improve the active site prediction for the protein representations by fine-tuning the model on the text function descriptions.
Strengths: * The paper addresses active site prediction, which is an interesting and understudied problem in protein science.
* The study compares i... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback and constructive suggestions. Below is our point-by-point response to your comments.
**Re - Weaknesses 1**
We agree that using text descriptions to enhance protein representations may seem similar to existing works. However, our innovation lie... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We wish to extend our sincere gratitude for your time and insightful feedback on our manuscript. Your insights are greatly helpful in improving the quality and clarity of our work. The following is a summarized response to some of the valuable suggestions and common issues raised.... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the problem of identifying active sites in proteins.
The paper proposes a novel multi-modal framework, MMSite that aligns and fuses textual and sequential modalities of protein sequences ("First Align, Then Fuse" strategy). The authors leverage pre-trained protein language models and biomed... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback and constructive suggestions. Below is our point-by-point response to your comments.
**Re - Weaknesses Evaluation 1**
Regarding # of trainable params, the difference between MMSite and baselines is not very large, as the former has 9 trainable... | null | null | null | null | null | null |
Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations | Accept (poster) | Summary: This paper proposes a new method for parallelizing the sampling process of diffusion models, offering a novel way to trade compute for speed. In particular, it relies on the parareal iteration in this setting, where the main idea is to split the solving timesteps into multiple groups and then update them in pa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review, and are excited to hear that the reviewer finds the novel ideas presented to be very interesting, and that the experiments indeed demonstrate the efficacy of this approach! We respond to the concerns below:
> Baselines
Please refer to the combined response t... | Summary: This paper proposes Self-Refining Diffusion Samplers (SRDS), which draws inspiration from the Parareal algorithm, aims to solve the reverse process accurately without retraining models, balance the tradeoff between sample quality and sampling speed. lower latency for requiring fewer sampling steps to reach con... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review, and are happy to hear that the experimental results are convincing! We respond to the concerns below:
> ParaDiGMs baseline
Please refer to the combined response to all reviewers, where we clearly demonstrate the superiority of SRDS over ParaDiGMS. For instan... | Summary: Inspired by the parallel-in-time ODE integration literature, especially Parareal method, this paper introduces SRDS as a fast sampler for diffusion models enabling efficient parallelization. Experimental results demonstrate that the proposed SRDS reduces the number of steps required to synthesize samples.
Str... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review, and appreciate that they recognize the practical applicability of our work! We respond to the concerns below:
> ParaDiGMs baseline
Please refer to the combined response to all reviewers, where we clearly demonstrate the superiority of SRDS over ParaDiGMS. Fo... | Summary: This work proposes SRDS, a sampler for diffusion models that applies the Parareal algorithm, to reduce the overall sampling latency by introducing extra but parallelizable network evaluations compared to the fully sequential fashion. With higher device utilization or device parallelism through batched inferenc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and are glad to hear the reviewer’s agreement of the broad applicability of SRDS! We respond to the concerns below:
> ParaDiGMs baseline
Please refer to the combined response to all reviewers, where we clearly demonstrate the superiority of SRDS over ParaDiGM... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful reviews! We are thrilled to hear that the reviewers find that our paper is “well written and easy to follow”, “demonstrates a very interesting direction”, provides “a neat baseline and rich directions for future technical improvements”, and “will benefit... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a new approach to speed up (improve latency) the generation of samples from diffusion models. The approach is orthogonal to many other approaches present in the literature for the same task. It leverages Parareal algorithm for the task by getting a quick coarse approximate of the sample and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review, and appreciate that they find the paper well written and easy to follow! We respond to the remaining concerns below:
> The authors argue on theoretical grounds that this approach can be combined with other approaches to reduce latency of diffusion models. Thi... | null | null | null | null | null | null |
Absorb & Escape: Overcoming Single Model Limitations in Generating Heterogeneous Genomic Sequences | Accept (poster) | Summary: The authors propose to generate DNA sequences using pre-trained DMs and then modifying the segments (randomly selected), through autoregressive models.
Strengths: The problem that the authors are trying to address, i.e. heterogeneity of the sequences due to existing multiple different element is valid questio... | Rebuttal 1:
Rebuttal: We appreciated for your feedback. We have provided additional experiment results in response to your questions, this includes applying A\&E algorithm on the Dirichlet Flow Matching in the new task and answers to common questions from other reviewers. In the following, we respond to your questions ... | Summary: This paper addresses limitations in existing methods for generating genomic sequences, which struggle to capture the heterogeneous nature of DNA. The authors propose a new framework called Absorb & Escape (A&E) that combines the strengths of autoregressive (AR) models and diffusion models (DMs) to generate mor... | Rebuttal 1:
Rebuttal: We appreciate your feedback. Following your suggestions, we have provided additional results comparing A&E with DDSM and DFM in the global response, along with clarifications to address common questions from other reviewers. Below, we provide detailed responses to each of your questions.
### Q1. ... | Summary: The authors introduce a novel approach, called Absorb & Escape (A&E), for generating DNA sequences by combining the strengths of autoregressive (AR) and diffusion models (DMs). The authors argue that existing single-model approaches struggle with the heterogeneous nature of genomic sequences, which consist of ... | Rebuttal 1:
Rebuttal: We appreciate your feedback on the paper. In our general response, we included additional results comparing A&E with other methods on additional datasets, a sensitivity analysis of the hyperparameter $T_{absorb}$, an explanation of how to define token-level probability from the diffusion model, a... | Summary: This paper presents a sampling approach called Absorb and Escape (A&E) that combines the strengths of diffusion models (DMs) and autoregressive models (AR models) for generating DNA sequences. The authors rightly point out that DNA sequences are generally composed of segments that do not follow the same distri... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful reviews. Following your suggestions, we have presented additional experiments on DDSM dataset and more detailed description on the dataset in the general response and uploaded PDF. Hopefully our general reply addresses your concerns regarding the selection o... | Rebuttal 1:
Rebuttal: We appreciated the valuable feedback from all reviewers. Overall, the reviewers agree that our proposed algorithm, A&E, is well-motivated and clearly illustrates the limitations of AutoRegressive (AR) models and Diffusion Models (DMs).
Most reviewers (crFN, m7HE, hZwW) recognize the significant ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers | Accept (poster) | Summary: This paper discovers that many existing neural network classifiers share a strong correlation between input margin (distance to decision boundary) and output margin (difference between top-2 logits). The paper further proposes to use this property for local robustness estimation.
Strengths: Efficient local ro... | Rebuttal 1:
Rebuttal: Thank you for your review! Please find below our responses to your concerns.
**a/ Lipschtz Smoothness vs. Margin Consistency**
Thank you for raising this point. Lipschitz smoothness is an essential property of robust models since a small Lipschitz constant $L$ guarantees the network's output can... | Summary: The authors propose using the distance between the two max values of a model's output as a proxy for the input margin to efficiently identify samples vulnerable to adversarial attacks. This proposed margin consistency is formally defined and shown to work across many robust models on the CIFAR10 and CIFAR100 d... | Rebuttal 1:
Rebuttal: Thank you for your review! Please see our comments in the general response about standardly trained models with zero adversarial accuracy and results on ImageNet. Below are our responses to your other concerns.
**a/ (W2) No additional analysis for why margin consistency fails for the 2 CIFAR10 mo... | Summary: It is currently difficult to determine how susceptible a given input to a model is to adversarial perturbations. The distance from the input to the model's decision boundary in the input space (input space margin) is a reasonable metric, but it is intractible to compute for many deep neural networks and not al... | Rebuttal 1:
Rebuttal: Thank you for your review! Please see our comments in the general response for answers to your questions (Q1 and Q2 are addressed in the third point of the general response, and Q3 is addressed in the last point). Below are our responses to your other concerns.
**a/ Bias across subpopulations**:
... | Summary: The paper addresses the problem of efficiently detecting vulnerable inputs to a robust deep classifier at test time without the need for running adversarial attacks or formal verification. They introduce the idea of margin consistency of a classifier to connect the input-space margin and feature-space margin (... | Rebuttal 1:
Rebuttal: Thank you for your review! We will take the corrections into account. Please see our comments in the general response about defining the margin consistency in terms of the logit margin and results on the $\ell_2$ norm.
**a/ The proof of Theorem 1.**
Thank you for reading the proof carefully; w... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their appreciation and thoughtful comments! The points raised by the reviewers are certainly very useful for clarifying and improving the presentation of our work while also bringing interesting avenues for future exploration. Below are some general comments ab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
fMRI predictors based on language models of increasing complexity recover brain left lateralization | Accept (poster) | Summary: The paper studies how well 28 large language models (LLMs) predict fMRI activity of human subjects listening to an audiobook. First, they observe a scaling law; the neural predictivity of LLMs increases linearly with the logarithm of the number of model parameters, a result consistent with prior work. Second, ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Here is a point-by-point response to your comments.
> The growing L-R asymmetry result may suggest qualitatively interesting differences between larger vs smaller LLMs, as briefly mentioned in Lines 256-260 in the paper. However, an alternative reason could be that ri... | Summary: The authors investigate whether larger-parameter language models better predict left versus right hemisphere brain responses (recorded during listening of a naturalistic story, via fMRI), motivated by left-lateralized processing of language in most individuals. They indeed find that larger models better predic... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Here is a point-by-point response to your comments.
> Pertaining to the issue of group-level averaging, is it true that the left-hemisphere generally has higher reliability? Was that controlled for in any way in the analyses (as far as I understand, the authors analyz... | Summary: Recent studies on language processing in the human brain using fMRI data and language model embeddings have shown that both hemispheres are involved in language processing although many previous studies have indicated a left lateralization. The authors aim to reconcile these findings. They use embeddings from ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Here is a point-by-point response to your comments.
> The second paragraph of the introduction (and the abstract) mentions that many recent works have discovered strikingly symmetric brain maps for language processing. To the best of my knowledge, many of these studie... | Summary: The paper studies whether encoding fits with LLMs onto fMRI data can be used to find left lateralization, the idea that language is lateralized to the left hemisphere. This is a well-known property of language localization in many humans. They also show that increasing model size improves fit from their encodi... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Here is a point-by-point response to your comments.
> The paper should make an argument about why lateralization is an important property of brain fits with LLMs. I think this was missing.
Studies correlating word embeddings or LLMs activations and fMRI data have pro... | Rebuttal 1:
Rebuttal: We would like to sincerely thank all four reviewers for their detailed and valuable reviews of our manuscript. We think they have helped to clarify our contribution and strengthen our paper by adding some checks, as described below.
An important point, raised by most reviewers, concerns the poten... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection | Accept (poster) | Summary: This paper proposes a purely textual-based training method for detecting out-of-distribution or hateful images. The authors train an additional embedding layer over frozen CLIP encoders using text data. The authors propose the use of a novel loss function for this training. The method improves performance over... | Rebuttal 1:
Rebuttal: Firstly, we thank you for your thorough review of our paper. We particularly appreciate your recognition of our innovative approach using only textual data for hate detection, which eliminates the need to source hateful images and addresses ethical concerns. Additionally, we value your acknowledgm... | Summary: This paper introduces an efficient and effective text-only training method for detecting undesired visual content. Its key contributions include a theoretical demonstration of how text data can substitute visual data, a new loss function, and a method for synthesizing textual data. These efforts aim to segrega... | Rebuttal 1:
Rebuttal: Firstly, we thank you for your thorough review of our paper. We particularly appreciate your recognition that our method is the compelling approach of utilizing text-only mode for detecting data in other modes and that the training and inference processes are highly efficient. We are also immensel... | Summary: This paper focuses on textual training methods to remove undesirable (such as biased or offensive) visual content and proposes a method for detecting unwanted visual content using only synthetic textual data to partition visual data. The classifier trained on textual content can be successfully transferred to ... | Rebuttal 1:
Rebuttal: Firstly, we thank you for your thorough review of our paper. We particularly appreciate your recognition that our paper has no significant weaknesses and is well-organized. We have made our best efforts to address your remaining concerns as follows. If our responses meet your expectations, we woul... | Summary: This paper proposes an objective function for CLIP-based architecture to enhance out-of-distribution (OOD) detection. Instead of relying on OOD image data, the approach extracts OOD words from various sources and updates some trainable embeddings using predefined text embedding. Results show that the proposed ... | Rebuttal 1:
Rebuttal: Firstly, we express our gratitude for your thorough review of our manuscript. Particularly, we appreciate your recognition of our method as valuable and useful in practice, as well as your acknowledgment that the justification of our method is well-motivated and clearly presented. Furthermore, we ... | Rebuttal 1:
Rebuttal: We genuinely appreciate the reviewers' dedicated time and their valuable feedback. Before addressing each reviewer's specific concerns in detail below, we would like to summarize here the contributions of our research that have been recognized and the aspects that have been enhanced in our study.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prospective Learning: Learning for a Dynamic Future | Accept (poster) | Summary: In this work, the authors formalize the notion of “prospective learning”, which considers that the data to be learned are sampled from a stochastic process, rather than being sampled from a fixed distribution. Perspective learning considers time by giving a set of hypotheses for each time step during inference... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and valuable suggestions. We are glad that they have found the theoretical and experimental fronts of our work to be thorough. We also appreciate the positive aspects of our work, they have mentioned: (1) prospective learning as a useful theoretical... | Summary: The paper focuses on new paradigm of learning called "prospective learning" oppose to Probably Approximately Correct (PAC) paradigm which is how current AI systems are being designed. PAC is time agnostic given the data while PL is time-aware. The paper clearly outlines different scenarios of PL with examples ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments on our work. We are glad that they recognize our theoretical contributions.
> **Paper fails to explain how training on unlabeled data would be yield in the proposed paradigm.**
This is a good question. We have been inspired by the work of Steve Hanneke \... | Summary: Update: I read the rebuttal and I found it convincing, especially the explanation of the main theorem of the paper. Additionally, the time-aware ERM idea based on time-conditioning sounds nice.
---
This paper proposes a prospective learning framework in which a sequence of future hypotheses is produced usin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We are glad that they find our theoretical contributions to be important for learning in non-stationary environments. We believe we have addressed all your concerns in the response below. If you think these responses are satisfactory, we would be very grat... | Summary: The paper develops a new theoretical framework to address machine learning problems where data distributions and objectives evolve over time. Unlike the traditional PAC learning framework that assumes static distributions, this paper introduces "Prospective Learning" (PL), which models data as a stochastic pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and valuable suggestions. We are glad that they recognize that prospective learning addresses a fundamental problem. And we are glad that they recognize the theoretical and experimental contributions of our work. If the Reviewer thinks our responses... | Rebuttal 1:
Rebuttal: ### **Common response to all Reviewers**
We thank the reviewers for lending their expertise to assess our work and for helping us improve it. We are glad that the reviewers are positively inclined towards this work. The find that the problem is fundamental, and our paper presents a general framew... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits | Accept (spotlight) | Summary: This is a solid paper for approximating Hölder smooth functions using parameterized Quantum Circuits (PQC). The results show that using PQC for approximation can achieve better results than those in Lu's paper, especially when $K$, the length of the local region of Taylor expansion, is not large and the dimens... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable comments. We would like to provide a detailed response to the insightful questions raised by the reviewer.
> 1. For continuous and Lipschitz continuous functions, the author only establishes the Universal Approximation Theory. Can the author impro... | Summary: The authors explore the power and limitations of parameterized quantum circuits (PQCs).
They show that a large class of multivariate polynomials and smooth functions can be efficiently (approximately) represented by PQCs.
More importantly, they show that the requirements of such PQCs compare favorably to their... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the contributions and novelty of our work! Here we respond to the insightful comments and questions raised by the reviewer.
> 1. Page 1: Maybe you can early on present a figure depicting some PQC (and a deep ReLu net, for comparison)? This could help the read... | Summary: In this work, the authors aim to build a theoretical understanding of parameterized quantum circuits via non-asymptotic approximation error performance analysis. In particular, they demonstrate the advantages of PQCs over classical ones if specific smoothness criteria can be satisfied. The simulation results c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable comments. We would like to provide a detailed response to the questions raised by the reviewer.
> 1. The authors' theoretical analysis of PQCs relies on the assumption of continuous or smooth target functions, which hinders the theoretical usage f... | Summary: This paper studies the expressiveness of parameterized circuits to perform multivariate function approximation. This serves as a quantum counterpart to the theoretical results of classical machine learning, namely the universal approximation theorem. Theoretical results provide bounds on the approximation erro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of the paper and inspiring feedback. Here we respond to the insightful comments and questions.
> 1. The use of ideas from quantum signal processing is a bit of a double-edged sword. While Theorem 3 provides a non-asymptotic error bound, the quantum resou... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning | Accept (poster) | Summary: The proposed inter- & intra-modality modeling (I2M2) framework addresses the limitations of conventional approaches in supervised multi-modal learning. By considering both inter-modality dependencies and intra-modality dependencies, it achieves superior performance in predicting labels. The I2M2 framework offe... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your review and your thoughtful comments. We are glad that you find our method versatile and adaptable to different scenarios. We address your concerns below:
> I am confused about the implementation details of the proposed method, particularly the simulation of q_{x... | Summary: The authors present a novel framework, I2M2 (Inter- & Intra-Modality Modeling), designed to enhance supervised multi-modal learning by effectively leveraging multiple modalities. This framework captures both the relationships between different modalities (inter-modality dependencies) and within a single modali... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your review and your thoughtful comments. We appreciate your recognition of our paper as providing a fresh perspective on multi-modal learning. We address your concerns below:
> Further experimental analyses could be strengthened, especially for the selection variabl... | Summary: Previous supervised multi-modal learning involves mapping multiple modalities to a target label, with previous studies focusing separately on either inter-modality or intra-modality dependencies. This approach may not be optimal, so the proposed inter- & intra-modality modeling (I2M2) framework captures and in... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your review and thoughtful comments. We are glad that you found our paper well-written, easy to understand, and potentially applicable to the broader field. We address your concerns regarding computational complexity and comparison with CLIP below.
> It would be bett... | Summary: This paper proposes a framework for multi-modal learning called inter- & intra-modality modeling (I2M2). I2M2 can simultaneously capture inter-modality dependencies (relationships between different modalities) and intra-modality dependencies (relationships within a single modality). This approach aims to impro... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your review and your thoughtful comments. We appreciate you finding our paper well-motivated and claims verified through the experiments. We address your concerns below.
> In the introduction, the author states that existing inter-modality modeling methods can techni... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies supervised learning for multi-modal data. Previous works can be categorized into inter-modality learning and intro-modality learning. Inter-modality learning aims to learn multi-modal data jointly by techniques such as feature fusion. Intral-modality learning focuses on learning uni-modal da... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your review and your thoughtful comments. We appreciate you finding our paper well-motivated and simple to use with thorough experiments across different domains. We clarify your concerns below.
> For the proposed model, are the inter-modal classifier and intra-modal... | null | null | null | null | null | null |
FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention | Accept (poster) | Summary: The paper introduces a SpectralBlend Temporal Attention (SB-TA) mechanism, which blends low-frequency and high-frequency components from attention features together to enhance consistency and realism in generating long videos. The authors tested the proposed algorithm using 25 text prompts on LaVie and VideoCr... | Rebuttal 1:
Rebuttal: > **My first concern is that 128 frames itself cannot be considered long video generation. Therefore, it would be more convincing to see how effective the authors' proposed method is on longer videos, such as generating 1-minute videos like Sora.**
>
Thank you for your feedback regarding the len... | Summary: This paper proposes a training-free method to generate 8x longer videos based on 16-frame pre-trained video diffusion models. It observes that extended temporal attention has a negative effect on high-frequency generation. Thus, it proposes an SB-TA module to fuse global low-frequency temporal features and loc... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We have carefully considered your comments and suggestions to improve our paper. Below are our responses to each of your concerns:
> **The related work section is not reader-friendly for those unfamiliar with the field**
>
We apologize for the lack of clari... | Summary: The auther propose FreeLong, a training-free method for long video generation. This paper identifies that the problem with long video generation lies in the scope of attention and the distortion of high-frequency components. Based on the observation, the auther proposes a novel method to blend different freque... | Rebuttal 1:
Rebuttal: > **what is the computational cost compared to previous method (like freenoise) ?**
>
Based on your feedback, we conducted a comparison of inference times between our method, FreeLong, and other methods, including FreeNoise, the sliding window method, and direct application. The results are summ... | Summary: The paper presents FreeLong, a novel training-free method for generating extended videos (128 frames) using pre-trained short video (16 frames) diffusion models. The key component is the SpectralBlend Temporal Attention (SB-TA), which fuses low-frequency global video features with high-frequency local features... | Rebuttal 1:
Rebuttal: > **Inference time comparisons**
>
Thank you for your question. Following your advice, we conducted a comparison of inference times. As shown in the table below, our method achieves faster inference speeds than multi-pass methods, such as FreeNoise.
| Method | Inference Time |
| --- | --- |
| D... | Rebuttal 1:
Rebuttal: We thank all reviewers for engaging in the review process. Our code will be made public upon acceptance.
We are deeply encouraged by positive comments from the reviewers. We appreciate the recognition and endorsement of our proposed training-free pipeline, such as acknowledging its analysis and m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Why Transformers Need Adam: A Hessian Perspective | Accept (poster) | Summary: This paper explores the performance gap between SGD and Adam when training transformers. The authors analyze the eigenspectrum of the Hessian in different neural network architectures and attribute the failure of SGD to the different Hessian spectra in transformers compared to CNNs and MLPs. They empirically s... | Rebuttal 1:
Rebuttal: Thanks for the insightful question and careful evaluation of our paper. We provide response as follows.
**1. In theoretical analysis, the momentum part of Adam is fully ignored ( $\beta_1=0$ in Algorithm 3).**
Thanks for the comment. The current analysis ignores momentum because we want to focu... | Summary: This paper investigates why Adam outperforms SGD in Transformers by examining Hessian estimations. While the overall Hessian spectrum appears similar in both Transformers and other neural networks, the block-wise Hessian spectrum is heterogeneous across blocks in Transformers and homogeneous in other neural ne... | Rebuttal 1:
Rebuttal: Thanks for the careful review of our work. Here is our respectful response.
**1. It is unclear whether this correlation implies causation beyond intuitive understanding.**
We provide the following evidence on causation.
**I: Theory:** On quadratic models, our theory suggests that: when heter... | Summary: The paper investigates why SGD performs worse than Adam in Transformers from the lens of Hessian. They first find that Transformers are "heterogeneous", that is, the Hessian spectrum across parameter blocks varies dramatically. Then they conduct various tasks on Transformers, CNNs, MLPs, and quadratic problems... | Rebuttal 1:
Rebuttal: We are grateful for the careful review of our paper and the great questions. Please find our respectful reply below.
**1. For the same Transformers model, when the initialization changes to induce different block heterogenous levels, how would the performance of SGD compared with Adam change?**
... | Summary: In this work the authors assert that the Adam optimizer works well on Transformers while Stochastic Gradient Descent (SGD) does not, and they attempt to explain this phenomenon by inspecting the spectrum of Transformers (i.e., the eigenvalues of the model's Hessian matrix) and other models, such as convolution... | Rebuttal 1:
Rebuttal: Thanks for the careful evaluation of our paper. We respectfully provide our response as follows.
**1-1. Are there other publications that make these claims (SGD worse than Adam on attention-based model)?**
Thanks for raising this comment. Yes, it is widely reported that SGD largely underperforms ... | Rebuttal 1:
Rebuttal: Dear reviewers and AC:
We attached a PDF with the following four figures. Please check.
**Figure 1:** On ViT, BERT, and GPT2-nano, we carefully grid search the learning rate for SGD and report all the results. We find that on all these tasks, SGD (even after careful tuning) is significantly wors... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing | Accept (poster) | Summary: This work first studies Randomized Smoothing in Deep Equilibrium Models. To combat the prohibitive computational cost, this work designs a procedure called SRS to speed up certification, mainly relying on the fast convergence of multiple predictions with DEM. The certification theorem is revised accordingly to... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you so much for your valuable comments and recognition of the novelty, effectiveness, and presentation of our work. We are happy to address your concerns and questions with the following results and illustrations.
**Weakness 1**: In the evaluation, the authors only show the d... | Summary: Due to the inability of existing deterministic methods for providing certified robustness to Deep Equilibrium Models (DEQs) to be applied to large-scale datasets and network structures, this paper provides scalable certified robustness for DEQs through the probabilistic method of random smoothing. To avoid the... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you so much for your valuable comments and recognition of the novelty, effectiveness, and presentation of our work. We are happy to address your concerns and questions.
**Weakness 1**: In the process of correlation-eliminated certification, this paper requires the use of stan... | Summary: This paper provides a method for randomized smoothing for DEQs. Given the computational challenges associated with DEQs, the authors propose a method that is intended to speed up the process of creating a smooth classifier for a DEQ model based on fixed-point reuse. Given that fixed-point reuse introduces depe... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you so much for your valuable comments and recognition of the novelty, effectiveness, and presentation of our work. We are happy to address your concerns and questions.
**Question 1**: What is the effect of using a model trained with Jacobian regularization on your method? Do... | Summary: This paper develops the first randomized smoothing certified defense for DEQs, termed as Serialized Random Smoothing (SRS). To address the scalability issue of randomized smoothing, To reduce computational redundancy, SRS leverages historical information and a new certified radius estimation. The proposed meth... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you so much for your valuable comments and recognition of the topic, effectiveness, and presentation of our work. We are happy to address your concerns and questions with the following results and illustrations.
**Question 1**: Can the authors clarify the difficulty and novel... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces a novel method based on random smoothing to improve the certified robustness of Deep Equilibrium Models (DEQs), a promising type of implicit neural network.
Directly applying random smoothing to DEQs incurs high computational costs. To overcome this issue, the authors leverage the proper... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you so much for your valuable comments and recognition of the novelty, effectiveness, and presentation of our work. We are happy to address your concerns and questions with the following results and illustrations.
**Question 1**: On line 52, "Due to the conservative certifica... | null | null | null | null | null | null |
Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks | Accept (poster) | Summary: In this study, the authors established the convergence rate of the gradient descent and Nesterov's accelerated gradient descent methods for the asymmetric matrix factorization and the 2-layer linear network. The authors proved that an unbalanced initialization can lead to linear convergence of both methods, an... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and constructive comments. Our responses to each of the comments are listed below.
> Q1: I think the following two papers also discussed the asymmetric low-rank matrix optimization problem.
We thank the reviewer for pointing out these related works... | Summary: This paper considers the convergence of the first-order optimization method, which includes the gradient descent and the nesterov's acccelerated gradient
for the marix factorization and the linear neural network.
In Section 2.1, they analyze the gradient descent algorithm on the matrix factorization (c.f. T... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing the paper and helping us improve.
Our responses to each point of the Weaknesses and Questions are listed below.
> W1: The major concern is the potential impact of this paper. Generally speaking, this paper studies a well-studied problem with quite standard te... | Summary: The paper calculates convergence rates of the gradient descent and the Nesterov's accelerated gradient descent algorithms for factorization of rectangular matrices- a nonconvex optimization problem. Their analysis is for algorithms when the factor matrices are initialized as follows: one matrix is initialized... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and constructive comments.
> W1: The experimentation is done on very small matrices. In practice much larger rectangular matrices are often encountered. It would give more insights if the experiments were also performed with moderate and large size ... | Summary: In this papers the authors analyze the convergence of Nesterov Accelerated Gradient algorithm for a) rectangular matrix factorisation and b) linear neural networks. By using imbalanced initialisation, the authors come up with linear rates of convergence impoving upon state-of-the-art regarding dependence of ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and constructive comments. Our responses to each of the comments are listed below.
> W1\& Q1: The authors didn't mention relevant works studying imbalance effect on the similar problems e.g. [1]. In [1] the authors provided rates of convergence of g... | Rebuttal 1:
Rebuttal: We thank all the reviewers for dedicating their time to reviewing our paper and providing valuable feedback.
In response to the comments involving the size of the matrices (by reviewer t3Co), the performance of GD/NAG with different values of $c$ (by reviewer u7PK), and the amount of imbalance (by... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encoding | Accept (poster) | Summary: The paper studies how to optimize multi-vector retrieval under the MaxSim similarity function (commonly used in NLP literatures like ColBERT). This is done by mapping queries and item embeddings asymmetrically to fixed dimensional embeddings (FDEs), such that in the mapped embedding space, the inner product si... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions. We reply to the main questions and concerns below.
> W1:
We first note that the FDE technique, like the SV Heuristic, is an approach for multi-vector retrieval that must be followed by a re-ranking step with the exact Chamfer si... | Summary: Efficient vector retrieval to maximise inner product similarity is well studied, and this paper explores the issue of multi-vector retrieval to support late interaction models like Colbert. The core idea is to use SimHash to generate clusters of multiple representation of documents, represent each document's v... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions. We reply to the main questions and concerns below, and attempt to clarify several points.
> W1: “SimHash theory focuses on EMD minimisation while the focus here is on Chamfer similarity maximisation”.
We would like to point out t... | Summary: The paper proposes a method of speeding up text retrieval approximating ColBERT multi-vector ranker with very high dimensional single vector using projections.
Authors demonstrate that the proposed approach is better compared to a colbert-based single vector heuristic and is comparable to PLAID in terms of a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions about the paper. Below, we address both points of weakness mentioned by the reviewer.
> W1:
We thank the reviewer for the reference to this paper. Using a re-ranker or general similarity metric in the graph Beam Search is an interesting ... | Summary: Multi-vector representation can greatly help retrieval systems work efficiently and accurately, transforming these sets of multiple vectors into a single vector representation that can still encapsulate the information from the multiple vectors, allowing efficient search using traditional single-vector search ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond now to the specific questions of the reviewer.
> “Computational overhead: as introduced by the FDE creation and query processing, the computational overhead may be high as the dataset grows. The MUVERA may have problems in prep... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments and suggestions. We reply to the questions and comments of each reviewer individually in the corresponding rebuttal fields. First, we would like to emphasize here several global points which will address common concerns of the reviewers.
Fi... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a method of converting multi-vector query-document retrieval problem to a single query-document vector-based retrieval problem. The basic idea is to project the multi-vector queries and documents into fixed-dimensional embedding through random projections. Further efficiency in storage of th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond now to the specific questions of the reviewer.
> “It seems to me in the current age of LLMs, this technique appears to be a bit dated. There wasn't any mention of SpladeV2 and its variants either that are actively being used in... | Summary: This paper aims to improve the search efficiency of multi-vector retrieval models, such as ColBERT. Specifically, the authors propose MUVERA framework, which reduces the multi-vector (MV) similarity of a query/document pair to the single-vector (SV) similarity by constructing Fixed Dimensional Encoding (FDE) o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions. We respond now to the specific questions of the reviewer.
> Q1(a):
We first clarify questions about the index size of MUVERA. The reviewer is correct that we need to store both (some representation of) the FDE’s and original multi-vecto... | null | null | null | null |
Mixture of Link Predictors on Graphs | Accept (poster) | Summary: This paper proposes an ensemble model of different link prediction methods. The authors find that different node pairs on the graph can form a link due to different pairwise representations, and there is no single link prediction model that can capture all of them. Then the author proposes to combine the outpu... | Rebuttal 1:
Rebuttal: Dear Reviewer SDXC,
We appreciate your constructive feedback. We are pleased to provide detailed responses to address your concerns.
**W1:The novelty of the paper is limited.**
**A1:** The novelty of Link-MoE: Our Link-MoE model stands out due to its innovative approach, leveraging the compleme... | Summary: The paper proposes a mixture of experts model, Link-MoE, for link prediction on graphs. The motivation behind the proposed approach is that different node pairs within the same dataset necessitate varied pairwise information for accurate prediction, while existing methods consider the same pairwise information... | Rebuttal 1:
Rebuttal: Dear Reviewer 758a,
Thank you so much for your support and recognition of our framework. We are pleased to provide detailed responses to address your concerns.
**W1: The figures are hardly readable sometimes (e.g., Fig. 8), the font size (in the legend) should be increased.**
**A1:** Thank you ... | Summary: Link prediction in graphs is a fundamental task in graph machine learning and multiple heuristics and ML algorithms have been designed in research to leverage the pairwise and structural information to predict links between nodes. This work takes inspiration from the success of MoE models across various vertic... | Rebuttal 1:
Rebuttal: Dear Reviewer fyCt,
We sincerely appreciate your recognition of our framework and insightful comments. We are pleased to provide detailed responses to your questions.
**W2: It solves for a very specific task - Link Prediction in an inductive setting only. Not clear to me how it could be leverage... | Summary: This paper presents a mixture of experts model, termed Link-MoE, for link prediction on graphs. Link-MoE individually trains various link prediction models as experts and selects the most appropriate experts for different node pairs. The prediction results from the selected experts are then weighted to produce... | Rebuttal 1:
Rebuttal: Dear Reviewer bwf6,
We appreciate your constructive feedback. We are pleased to provide detailed responses to address your concerns.
**W1:Despite its empirical effectiveness, the technical novelty appears limited.**
**A1:** The novelty of Link-MoE. Our Link-MoE model stands out by intelligently... | Rebuttal 1:
Rebuttal: # Global Response
We thank the reviewers for the valuable comments and suggestions. In this global response, we are willing to provide information about tables and figures in the rebuttal pdf file.
**Table 1** presents the results of traditional MoE, a few experts, and experts used as input fea... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion | Accept (poster) | Summary: The authors investigate a popular reverse-diffusion sampling process, when the scores are estimated not from target samples but using the target's unnormalized density, which is the setup in energy-based modelling. Specifically, the authors consider a recent Monte-Carlo estimator of the scores which requires s... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable advice and comments and greatly appreciate the positive evaluation.
>The authors are clear about the limitations of their method, for example the complexity in Corollary 3.1. is exponential not only in the dimension d but also in the smoothness L... | Summary: In this paper, the authors are interested in the problem of sampling from an arbitrary non-logconcave probability distribution (namely, multi-modal distribution) with only access to its unnormalized density. While most of popular sampling methods rely on queries of the score of the target, i.e. the gradient of... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable advice and comments.
> The comparison to the related work is incomplete. The authors do not cite the work [1] (although it was published approximately at the same time as [2]), where the authors propose a 1st-order sampling method, SLIPS, based ... | Summary: This paper proposes a novel zero-order sampling algorithm (ZOD-MC) when the target distribution is beyond the log-concavity and even isoperimetry. Different from first-order diffusion-based Monte Carlo proposed previously, this paper only requires the zero-order information. Besides, this paper shows the good ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable advice and comments.
> Some related work is missed in the authors’ survey, e.g., [1]. Theorem 7 of [1] provides a similar zeroth-order complexity O(exp(d)log(1/\varepsilon)) for achieving a minimal optimal error. I suggest the authors compare the ... | Summary: This paper considers the problem of sampling from an unnormalized density by combining techniques developed from score-based generative modeling and non-log-concave sampling. Specifically, based on the Reverse Diffusion Monte Carlo (RDMC) framework proposed in [1], which is a meta-algorithm based on an oracle ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable advice and comments and greatly appreciate the positive evaluation.
> One possible drawback of the proposed sampler, just as the authors stated in the paper, is that its iteration complexity depends linearly on the data dimension. Hence, it will... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for the helpful comments. Results of newly added experiments are included in the pdf file.
Pdf: /pdf/6f7a8c16bb61fc0e4cfb43d74c7fbdbda4bbe6f8.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics | Accept (poster) | Summary: This paper implements a Lorentz-equivariant transformer and applies it to several problems in particle physics. The main contribution of the paper is not the definition of a conceptually different model. At least it doesn't claim to define a model significantly different from previously proposed models. But it... | Rebuttal 1:
Rebuttal: Thank you for the thorough and constructive review. We are particularly happy that you appreciate the open-source release of our library. Thanks as well for the questions and criticisms, which we address in the following.
> The paper is not self-contained.
Thank you for this feedback. Due to the... | Summary: The paper proposes a Lorentz equivariant transformer (L-GATr) based on geometric algebra for high energy physics. It generalizes the Geometric Algebra Transformer (GATr) from $E(3)$ equivariance to the Lorentz group. The proposed transformer is then developed into a generative model based on Riemannian flow ma... | Rebuttal 1:
Rebuttal: Thank you for the thorough and constructive review. We are glad to hear that you appreciated how we generalized the GATr architecture from E(3) to the Lorentz symmetry and the development of the first Lorentz-equivariant architecture. We were particularly happy that you found the paper easy to fol... | Summary: The paper proposes the Lorentz Geometric Algebra Transformer (L-GATr) for high-energy physics tasks. This model extends the Geometric Algebra Transformer by incorporating relativistic considerations. Specifically, L-GATr supports partial and approximate symmetry for symmetry-breaking inputs and is applied to g... | Rebuttal 1:
Rebuttal: Thank you for the thorough and constructive review. We are glad that you liked the motivation of our work, found our contributions significant, and appreciated the generative modelling part. Thanks as well for the questions and criticisms, which we address in the following.
> The model's performa... | Summary: The authors propose an architecture for high-energy physics events – the Lorentz Geometric Algebra Transformer, which is equivariant under Lorentz transformations. The architecture is based on the Geometric Algebra Transformer architecture, and generalizes to relativistic scenarios and the Lorentz symmetry. Th... | Rebuttal 1:
Rebuttal: Thank you for the thorough and constructive review. We are happy to hear that you found our architecture a valuable contribution and the paper well-written. Thanks as well for the questions and criticisms, which we address one by one.
> significant computational overhead
It's true that L-GATr ha... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their detailed feedback and questions.
We are excited to read that the reviewers found the work a "valuable contribution to the field" (reviewer **Pf88**), that they appreciated the "significance of [the] contributions" (reviewer **srsp**), and that they f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FuseAnyPart: Diffusion-Driven Facial Parts Swapping via Multiple Reference Images | Accept (spotlight) | Summary: This paper introduces a facial parts swapping framework based on diffusion models, named FuseAnyPart. Unlike traditional methods that swap entire faces, FuseAnyPart allows for the swapping of individual facial features. The framework fine-tuned a pre-trained CLIP model to extract features from different facial... | Rebuttal 1:
Rebuttal: **[W1. The problem of skin color change.]**
FuseAnyPart may encounter skin color changes, particularly when there is a significant difference between the skin colors of the source images and the target image.
This issue arises because the source and target images are fused in the latent space, and... | Summary: This paper delves into the strategy of facial parts swapping which are not studied before, their method aims at fusing facial parts from different sources into the overall background/face images. It involves masking facial landmark areas(i.e. eyes, mouth, nose) and fusing with mask-based operation. Finally, th... | Rebuttal 1:
Rebuttal: **[W1. Details about OSim.]**
Osim measures the similarity between the swapped facial parts (eyes, nose, mouth) in the generated image and those in the reference images.
For example, we utilized the CelebA-HQ dataset and employed the grounding-dino detection model to identify and extract bounding ... | Summary: This paper explores the partial face swapping problem. Rather than swapping the whole face from A to B, partial face swapping aims to swap some specific area (or organ) of A to B. In this paper, a diffusion based partial face framework is proposed. Besides, two modules are designed to better fuse the extracted... | Rebuttal 1:
Rebuttal: **[W1. The primary challenge lies in the fusion mechanism.]**
Traditional face-swapping methods first perform face reenactment and then paste it onto the target image in pixel space, as illustrated in FSGAN and DiffFace.
However, operations in pixel space often result in unnatural images with vis... | Summary: "FuseAnyPart: Diffusion-Driven Facial Parts Swapping via Multiple Reference Images" introduces a novel framework for swapping individual facial parts using a diffusion model that effectively utilizes multiple reference images. The paper outlines the methodological innovation and superiority of FuseAnyPart over... | Rebuttal 1:
Rebuttal: **[W. The weakness of FuseAnyPart.]**
1) Diffusion models typically have high computational complexity due to the need for recursive iterations, which limits their applications to real-time or on lower-end devices.
2) More diverse testing results are illustrated in Figure 1 in the newly submitted... | Rebuttal 1:
Rebuttal: Dear reviewers and meta reviewers,
We appreciate all reviewers for their valuable comments and suggestions.
We have carefully addressed the comments and added details and comparisons as follows:
- We have provided solutions to accelerate inference and improve low data quality.
- We have added ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Can Simple Averaging Defeat Modern Watermarks? | Accept (poster) | Summary: This paper introduces a study on the vulnerabilities of digital watermarking techniques to steganalysis attacks. Extensive experiments are conducted to demonstrate the effectiveness of steganalysis in detecting and removing watermarks from images, especially when targeting content-agnostic watermarking methods... | Rebuttal 1:
Rebuttal: > Why Tree-Ring conforms to the additive Simple Linear Assumption model?
1. Addition in spatial domain equates to addition in frequency domain: $x(t)+y(t) \xleftrightarrow{\mathcal{F}} X(j\omega)+Y(j\omega)$.
2. Tree-Ring's pattern added to the initial noise propagates through the generation pipe... | Summary: The paper introduces steganalysis techniques targeting watermark removal and forgery. The authors demonstrate through experiments that existing content-agnostic watermarking methods are unable to resist steganalysis attacks, advocating for the adoption of content-adaptive strategies.
Strengths: This paper is ... | Rebuttal 1:
Rebuttal: > Why the reported PSNR for Tree-Ring is low (17dB)?
**Short Answer: Our method achieves 29.79dB/34.58dB (blackbox/graybox, n=5000) PSNR when removing Tree-Ring watermarks, indicating minimal visible alteration. The 17dB PSNR value mentioned in the paper measures a different aspect.**
$$
\text{C... | Summary: The paper addresses vulnerabilities in digital watermarking methods, especially those that are content-agnostic. These methods, which embed fixed watermark patterns regardless of image content, are susceptible to steganalysis attacks that can extract and manipulate these patterns, potentially removing or forgi... | Rebuttal 1:
Rebuttal: > The method is fundamentally general and not restricted to images. Providing its effectiveness on other modalities, such as audio, would further validate and broaden the impact.
**You are correct that the issue extends beyond images.** After conducting steganalysis on Audioseal [1] invisible aud... | Summary: This paper introduces a steganalysis-based attack aimed at image watermarking methods. The attack is effective in both gray-box and black-box scenarios and focuses on identifying repeating patterns present in watermarked images. These patterns can be exploited to either remove watermarks or add them to non-wat... | Rebuttal 1:
Rebuttal: > Similar ideas have been explored in existing works [1, 2].
We argue that our work fundamentally differs from [1, 2] with distinctive advantages.
**Differences with the spoofing attack in [1]**
The spoofing attack described in [1] requires access to the watermark encoder (white-box) and does n... | Rebuttal 1:
Rebuttal: ### Common Response
We appreciate the reviewers' comments and the opportunity for rebuttal. Here we would like to clarify the significance and contributions of our work.
**Our contributions**
- **We introduce the first blackbox, training-free method** that successfully removed and forged... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gradient Guidance for Diffusion Models: An Optimization Perspective | Accept (poster) | Summary: This paper investigate gradient guidance for adapting or fine-tuning pre-trained diffusion models from an optimization perspective.
The author proposed a look-ahead loss based gradient guidance and two variants of diffusion-based generative optimization algorithms utilizing it.
The author provided theoretical ... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our theoretical contributions and your valuable suggestions! We've added more detailed explanations of the derivations in the proof. | Summary: This paper proposes a new approach to the problem of gradient-guided generation for diffusion models. The main challenge of gradient-guided generation is to maintain the generated sample within the support of the sample distribution. To address this issue, this work starts with a simplified model, using a line... | Rebuttal 1:
Rebuttal: >**Q1:** The proposed method is prohibitively slow due to gradient backpropagation over the neural network. Other approaches like [1][2] do not require gradient backpropagation over the neural network.
**A1:** There seems to be some confusion. Your understanding is incorrect. Both [1] and [2] req... | Summary: Under the assumption of the data belonging to a low-dimensional linear subspace, the authors investigate two common gradient-based guidance techniques of diffusion models, encouraging the use of one of them (computing the gradient at the estimate of x0 given xt, as done in many works).
For concave reward, an... | Rebuttal 1:
Rebuttal: Thank you for appreciating our effort in providing theory for guidance. We first respond to your main concerns.
>**Weakness 1** Assumptions made are strong. Any idea how can Theorem 1 be generalized to low dimensional manifold rather than low dimensional linear subspace?
**A:** Given the challen... | Summary: This paper rethinks the gradient-based guidance methods through the optimization perspective. Similar to the manifold assumption, the authors first rely on the assumption that the observed data is from the lower dimensional space. Then, they claim that the naive gradient guidance does not maintain the data su... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our theoretical value and other comments! We first respond to your main concerns.
>**Weakness 1:** While the proposed algorithm demonstrates considerable theoretical value, its practical implementation appears to be quite slow.
**A1:** During rebuttal, we add a new ru... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their valuable feedback!
### **New Theory, Experiments, and Running-Time Analysis**
**Theory for the failure of Naive gradient:** We construct a rigorous counterexample Sec 3 in [pdf][pdf-link] showing that the generated samples will suffer at least a constant er... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Invariant subspaces and PCA in nearly matrix multiplication time | Accept (poster) | Summary: The paper analyses complexity of generalized symmetric eigenvalue problem computation.
Strengths: - Solid analysis of the computations involved in generalized eigenvalue problems.
- Excellent survey of related works.
- Relevant applications in ML (PCA).
Weaknesses: - The assumption of H being symmetric needs... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback and for their suggestions to improve the readability of our manuscript. Below we provide replies to the specific questions that were raised:
1. **Question:** The assumption of $H$ being symmetric needs to be in the Abstract as well, as it is quite si... | Summary: This paper considers the following optimization problem: given Hermitian $H$ and Hermitian, positive definite $S$, find matrices $C$ and $\Lambda$ such that $HC=SC\Lambda$ where $C$ is the eigenvectors and $\Lambda$ is the eigenvalues. The important application is when the eigenvectors of the interest form an ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive feedback and for taking the time to review our manuscript. The reviewer is absolutely right about the rectangular FMM. Actually, our comment about rectangular FMM only applies to the analysis of the Block-Krylov method in Section 4.2 and i... | Summary: The paper tackles the fundamental problem of GEP subspace approximation (with forward error approximation). The problem is very relevant to different areas of Machine learning. This paper improves the time complexity of this approximation from cubic in n to matrix multiplication time, which is a significant im... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and carefully examining our manuscript as well as Appendix B. Our replies to their comments can be found below:
1. **Question:** Could the authors please elaborate on the difference between their result and the forward error approximation in T... | Summary: The paper presents a novel approach to approximating invariant subspaces of generalized eigenvalue problems (GEPs), which are fundamental in many applications such as Principal Component Analysis (PCA) and Density Functional Theory (DFT). The authors introduce an algorithm that computes a spectral projector $ ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive feedback and for carefully reviewing our manuscript! Below we provide answers to the specific questions that were asked:
1. **Question:** Parameter Selection: How should the hyperparameters (e.g., spectral gap, condition number) be chosen in ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the computational cost of invariant subspaces for generalized eigenvalue problems (GEPs) which is a fundamental computational problem with applications in machine learning and scientific computing. The authors propose a novel method that approximates the spectral projectors of GEPs, and give... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive feedback. Below we elaborate on the specific questions that were raised:
1. **Question:**
In the statement of Theorem 1.1 and Proposition 2.1, is $||H||\leq 1$ a proper assumption?
**Answer:**
$||\cdot||\leq 1$ is typically a ... | null | null | null | null | null | null |
Generalized Protein Pocket Generation with Prior-Informed Flow Matching | Accept (spotlight) | Summary: In this paper, the authors proposed PocketFlow, a protein-ligand interaction prior-informed flow matching model for protein pocket generation. The flow matching for backbone frames, sidechain torsion angles, and residue/interaction types are appropriately defined. To enhance the structural validity and binding... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and appreciation!
**Comment 1**: The backbone model of PocketFlow is modified from existing work such as FrameDiff and is less novel.
**Response 1**: Thanks for the question! This paper is an application-driven paper and would be of great inter... | Summary: This paper studies the task of generalized ligand-binding protein pocket generation. To tackle the challenges of existing works, the authors proposed PocketFlow, a generative model that incorporates protein-ligand interaction priors based on flow matching. PocketFlow explicitly learns the protein-ligand intera... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and appreciation!
**Comment 1**: What is the time cost of PocketFlow and baseline methods in generating protein pockets? Would the prior knowledge-based guidance bring an extra burden to pocket generation?
**Response 1**: Thanks for the detailed qu... | Summary: The paper explores methods for generating protein pockets given a ligand using a flow matching generative approach. Unlike previous methods, the proposed approach integrates additional constraints into the flow matching learning process to guide the search for relevant pockets. Two types of constraints are con... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments! Our replies are listed below:
**Comment 1**: The authors did not specify which dataset was used to train their predictor or address the potential for information leakage between the training dataset and the test set used for pocket finding.
**Res... | Summary: The paper proposed PocketFlow, a generative model for designing protein pockets that bind with ligands. It aims to overcome limitations in existing methods by incorporating protein-ligand interaction priors and utilizing flow matching. PocketFlow is designed to handle multiple ligand modalities and demonstrate... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and appreciation!
**Comment 1**: The method only considers interactions between protein and ligand, potentially neglecting interactions between protein sidechains within the pocket region.
**Response 1**: Thanks for the insightful comment! In P... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stochastic Optimal Control for Diffusion Bridges in Function Spaces | Accept (poster) | Summary: The paper uses a stochastic optimal control to derive Doob's h-transform in infinite dimensions, and it shows the relation between solving the optimal control problem and learning diffusion generative models. The approach applies both to bridge sampling and for generative modelling. The approach is demonstrate... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewer for their thorough evaluation of our work. We appreciate your recognition of the merits of our research.
-----
**1.Clarification on prior work**
- We agree that clarifying the relationship between previous papers on Doob's h-transform and o... | Summary: The authors investigate the notion of h-transform in infinite dimensional state spaces and provide a novel representation (Theorem 2.3) based on connections to stochastic optimal control.
The authors introduce two approaches to using this h transform derivation - firstly in something resembling bridge matchin... | Rebuttal 1:
Rebuttal: We appreciate the recognition of our paper's strengths and extend our thanks to the reviewer for their comprehensive review and insightful comments. Below, we provide detailed responses to address each valuable comment.
-----
**1.Motivation, Comparison with baselines**
- We agree with the revie... | Summary: This article proposes a perspective on diffusion-based generative models based on stochastic optimal control, with objective functions based on the log density ratio between objectives.
Strengths: As far as I could evaluate, the mathematics are correct, and this particular mathematical perspective is new (to ... | Rebuttal 1:
Rebuttal: We gratefully thank the reviewers for their valuable feedback and suggestions. Here, we address the concerns raised by the reviewer.
-----
**1.Early-on explanation and clarification**
- Following the reviewers’ suggestions, we have further clarified our motivation in the general response. Pleas... | Summary: The paper presents stochastic control in function spaces with applications in diffusion bridges and Bayesian learning. Since the Lebesgue measure does not exist in infinite dimensional space, the authors derive Doob-h function with the Radon-Nikodym density with respect to a suitable Gaussian measure and con... | Rebuttal 1:
Rebuttal: We sincerely appreciate your interest in our research and acknowledgment of its significant contributions. We are also grateful for the insightful questions raised by the reviewer, to which we have provided detailed responses in the subsequent text.
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**1.Comparison with finite-dimensional b... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort the reviewers have dedicated to evaluating our paper. In response to their valuable and insightful feedback, we have provided some general responses that address comments common to all reviewers. **The attached PDF file includes relevant figures and tabl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO | Accept (poster) | Summary: The paper investigates the phenomenon of feature collapse in the popular on-policy RL algorithm PPO. It demonstrates that increasing the sample reuse (number of epochs) in PPO deteriorates the feature rank and plasticity. It also finds that the clipping operation for the conservative update in PPO does not pre... | Rebuttal 1:
Rebuttal: >**Q1.** … leads to feature collapse, but … it is uncommon to see PPO run for 6 or 8 epochs (Figure 1, 2, and 3).
**A1.** **The collapse phenomenon we show is certainly not exclusive to running with more epochs. The phenomenon does happen with the “standard” hyperparameters of the environments we... | Summary: The paper examines the loss of plasticity and its connection representation collapse in policy networks in online reinforcement learning (as opposed to previously studied value networks in offline reinforcement learning). The paper establishes the problem in the Atari game domain including the growth in the no... | Rebuttal 1:
Rebuttal: >**Q1.** The toy setting was good demonstration of the problems with trust region constraints. It may help to introduce this sooner … The paper would benefit from outlining the trust region insight sooner as it is a core idea that only gets explained in detail on page 7 of the paper.
**A1.** We t... | Summary: The paper addresses non-stationarity in RL and its impact on deep learning networks, focusing on PPO. It identifies that networks in PPO, like those in off-policy methods, suffer from representation rank deterioration, leading to performance collapse. The authors propose Proximal Feature Optimization (PFO), a ... | Rebuttal 1:
Rebuttal: >**Q1.** The authors claim to open data and code; however, I could not locate them. Therefore, I apologize if I overlooked their presence.
**A1.** **Yes, these are available on an anonymous GitHub repo mentioned in Appendix line 578.** The GitHub repo contains further links to the Weights&Biases ... | Summary: This work provides an empirical study of the feature rank deterioration and loss of plasticity of the Proximal Policy Optimization (PPO) algorithm on Atari and Mujoco tasks. Then links the deterioration of the performance to representation collapse and hence the break of the trust region. From there, the autho... | Rebuttal 1:
Rebuttal: >**Q1.a** Figures can be plotted with better quality, don't overlap the labels (in Figure 3),
**A1.a** Indeed. We will correct this in the camera-ready version.
>**Q1.b** and maybe set the titles to the quantity of interest.
**A1b.** Can the reviewer please elaborate on this or give an example?... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough and insightful reviews. We are glad that the reviewers appreciate the novelty and impact of assessing loss of plasticity in on-policy optimization and its connection to PPO’s trust region and acknowledge our thorough experimental setup.
We have **addresse... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper investigates the phenomenon of loss of trainability and performance collapse with PPO agents in standard RL benchmarks. The same phenonema from other settings is found to hold with PPO agents and, moreover, increasing the number of training epochs exacerbates the effect.
Further investigation finds ... | Rebuttal 1:
Rebuttal: We address the key points in the rebuttal and the remaining ones in a comment.
>**Q1.** line 193 feels a bit out of place … investigation is not mentioned earlier … suggest removing or summarizing the paragraph …
**A1. This information is relevant to practitioners in diagnosing the kind of colla... | Summary: This paper presents a series of experimental studies to diagnose the learning issues of PPO under non-stationarity in Atari-5 and MuJoCo tasks. Based on the results, this paper establishes a connection among feature rank/norm, plasticity loss and trust region violation and learning performance. To mitigate the... | Rebuttal 1:
Rebuttal: >**Q1.** … PFO is closely related to DR3 and RD …. More discussions are necessary.
**A1.** We thank the reviewer for pointing these out. Although **these regularizations emerge from value-based offline-RL challenges**, a discussion of their similarities with PFO can be valuable to our audience. W... | null | null | null | null |
Graph Learning for Numeric Planning | Accept (poster) | Summary: This paper proposed new learning-based methods for numeric planning. Numeric planning is formalized with the PDDL language. The proposed approaches are based on graph neural networks, and are evaluated in a lot of domains, e.g., blockworld, childsnack.
Strengths: The experiment section seems solid, and the pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestion for related work.
## Questions. (Applicability to Different Scenarios)
The proposed approaches are suitable for various different scenarios such as reinforcement learning (RL) and more general graph representation learning (GRL) problems.
- ... | Summary: The paper proposes a new method for learning a heuristic function to guide search for solving numeric planning problems. In contrast to classical planning, the states in numeric planning may involve numeric variables while the state transitions are defined by mathematical expressions over these kinds of variab... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions identifying which parts of the paper can be made clearer.
## Weaknesses
We agree that the paper presentation can be improved and found it challenging to fit all of the material in the page limit. We will make good use of an extra page to add... | Summary: The paper tackles numeric planning problems by proposing two heuristics for numeric planning. The first one is based on graph kernels for graphs and addresses both continuous and categorical attributes. The second uses graph neural networks. The authors experimentally show the effectiveness of the two proposed... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestions and questions for helping clarify the paper.
## Weaknesses
We agree that the paper presentation can be improved and found it challenging to fit all of the material in the page limit. We will make good use of an extra page to address the presentation issu... | Summary: The authors introduce a method to generate features for planning tasks that involve numerical variables. These features can then be used with machine learning to learn a heuristic function from a set of training examples. Architectures used for learning include Gaussian processes and graph neural networks. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions for helping us identify where we could improve our paper’s clarity.
## Weaknesses clarifications
> This makes it appear as if ranking is contributing to the overall success and not the numerical representation and learning.
- We would first l... | Rebuttal 1:
Rebuttal: We thank all reviewers for their reviews and suggestions for improving our paper.
We noticed that the common weakness pointed out by reviewers is that our paper could make use of additional details or illustrations to better explain our methods. We agree and believe it was difficult to fit this ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Network Lasso Bandits | Reject | Summary: The authors propose to use network Lasso to learn a multi-task bandit problem with given network structure. More specifically, the network structure has pre-defined unknown clustering structure, where within each cluster all the bandit tasks share the same model. The authors propose a bandit algorithm that can... | Rebuttal 1:
Rebuttal: ## Providing a real-world example
We would like to point out that the bulk of the contributions are theoretical. They lie especially in the establishment of the oracle inequality, and ensuring the RE condition for the empirical covariance matrix.
With that being said, the cluster structure is wel... | Summary: In this paper, authors work under the multi-task contextual bandit settings by representing the task correlations through the graph structure. To solve this problem, authors propose an algorithm that utilizes a linear regression formulation with Lasso constraint in terms of the node connectivity. Theoretical a... | Rebuttal 1:
Rebuttal: ## Assumption on the arm-generating process
Kindly refer to the general rebuttal where we address this point.
## Comparing the theoretical outcomes
We will take the advice and add comparisons of theoretical results to the used baselines. Here we will compare with previous works in clustering and ... | Summary: This paper addresses the multi-task bandit problem using graph information. The given graph represents the relationships between tasks. Assuming that the preference vector of clustered tasks is constant, the problem is formulated as a network lasso problem to estimate the lasso estimator.
A modified restricte... | Rebuttal 1:
Rebuttal: ## Explaining the RE condition and relating it to its counterpart in high-dimensional statistics
For our RE condition, if we do not restrict the signal $Z$ to the cone $\mathcal{S}$, and if we replace the RE norm with the Frobenius norm, we obtain a non-null minimum eigenvalue condition. If we ass... | Summary: The paper introduces a multi-task contextual bandit algorithm that leverages a graph structure to model relationships between tasks. The algorithm assumes that the preference vectors of the tasks are piecewise constant over the graph, forming clusters. By solving an online network lasso problem with a time-dep... | Rebuttal 1:
Rebuttal: ## Limited novelty
Despite the similarity in the technical tools used in the analysis to those in Oh et al. 2021, we respectfully disagree. Indeed, such techniques have also been used in [3,4,5,6] but we still faced the challenge of formulating a suitable RE condition (Definition 2), ensuring that... | Rebuttal 1:
Rebuttal: We would like to express our deep gratitude to the reviewers for the substantial effort they put into reading and evaluating our work.
Upon recognizing that several reviewers have raised common concerns, we will address these in a general rebuttal. Specific responses to individual concerns will ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers | Accept (poster) | Summary: In order to improve the efficiency of Large Language Models, the authors explore the use of three structured approximations in the FFN blocks of the Transformer: LowRank, BlockShuffle, and BlockDense. They consider both pre-training and decoding, which have distinct requirements and bottlenecks and a range of ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful review and insights on this paper. We have added the necessary experiments as suggested and provided a detailed response below. We hope our response addresses the reviewer’s questions.
* **Q1**: The models were trained between 2B and 25B tok... | Summary: This paper mainly focues on using structured matrices to substitute dense matrices in FFNs for training from scratch tasks. The authors propose BlockDense as a combination of low-ranked dense and block diagonal matrices (Figure 2), and to address the loss spike issues in low-ranked training process, the author... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for providing a constructive review and detailed comments.
Before responding in detail, firstly we would like to clarify our paper's focus. We investigate the performance of three structured matrices in modern LLM training from efficiency, optimization, and s... | Summary: The paper studies efficient Transformer variants. Unlike most existing works on efficient attention, this work proposes methods to enhance the efficiency by focusing on feedforward networks (FFNs). It explores several efficient linear layer designs, and proposes techniques to address the training issues and de... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable suggestions and careful reading of this paper. We will fix the minor issues in the revision and list the detailed responses to the questions below. Hope our reply can address the concerns.
* **Q1**: The experimental study can be made more solid... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive feedback. Before replying to the comments one by one, we would like to highlight our contributions and clarify common questions in this general response:
In this paper, we investigate the performance of three structured parameterizat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications | Accept (poster) | Summary: The authors of this paper built PedCorpus, a Chinese pediatric dataset, and PedistrcsGPT, the first Chinese pediatric LLM assistant. Their model was built via continuous pertaining, full-parameter supervised fine-tuning (SFT), direct following preference optimization, and parameter-efficient secondary SFT. Per... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our new preference optimization and thorough evaluations. We present our detailed responses below.
**Q1**: Clarification of technical novelty.
**A1**: As stated in lines 33-43, this paper addresses the shortcomings at the dataset and framework level of the L... | Summary: - This paper introduces PediatricsGPT, a Chinese AI assistant for paediatrics
- They created a large dataset (PedCorpus) with 300k+ medical instructions
- The training process is pretty involved - includes pre-training, fine-tuning, and preference alignment
- They came up with some new techniques, like hybrid ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our comprehensive datasets, new techniques, and thorough evaluations. We present our detailed responses below. The common questions regarding the ethical issues can be found in the **global response**.
**Q1**: Explain the dataset construction including textbo... | Summary: This paper tried to build an AI-powered pediatric consultation system. Their motivation is current Chinese conversation LLMs for healthcare underperform in pediatric applications due to insufficient instruction data and bad training procedures. To tackle this problem, the authors proposed PedCorpus, a high-qua... | Rebuttal 1:
Rebuttal: Insightful comments! Below are some specific responses. The common questions regarding the ethical issues can be found in the **global response**.
**Q1**: Evidence on the effectiveness of DFPO.
**A1**: We provide evidence both quantitatively and qualitatively.
* Since the degree of preference ... | Summary: This paper introduces a Chinese pediatric LLM assistant, PediatricsGPT. It follows a standard pretraining and SFT pipeline to incorporate the general medical knowledge schema into the models. Specifically, they optimize the response to enhance the generation of pediatrician-like humanistic responses.
Strength... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our large-scale dataset, open-source resources, and extensive experiments. We present our detailed responses below.
**Q1**: About the evaluation of model responses.
**A1**: Insightful comments! To rigorously evaluate whether the in-context learning (ICL) str... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort. Here we clarify common questions about ethical issues and open source details of the datasets.
**Q1**: Ethical issues with datasets.
**A1**: As stated in lines 101-124, the proposed PedCorpus consists of three parts, including pediatric data, rea... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents PediatricsGPT, a specialized large language model (LLM) designed to assist in pediatric medical consultations in China, where there is a significant shortage of healthcare resources. PediatricsGPT is built upon PedCorpus, a high-quality dataset containing over 300,000 instructions from pedia... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our high-quality dataset, multi-phase training process, and comprehensive experiments. We present our detailed responses below.
**Q1**: Highlight the novelty of this paper and how it differs technically from other works.
**A1**: Thanks for the constructive c... | Summary: This paper proposes the first Chinese pediatric Large Language Model (LLM) assistant, designed through a robust training pipeline including continuous pre-training, full-parameter supervised fine-tuning, and direct following preference optimization. The model is shown to outperform existing Chinese medical LLM... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contribution of significant milestone, novel domain adaptation paradigmm, and innovative training pipeline. Below are some specific responses. The common questions regarding the ethical issues can be found in the **global response**.
**Q1**: About the eva... | null | null | null | null |
Fairness-Aware Estimation of Graphical Models | Accept (poster) | Summary: This paper proposes a novel method for estimating graphical models, particularly Gaussian, Covariance, and Ising models, from data, taking fairness into consideration. Fairness is defined on the graph disparity error, i.e., the difference between the loss of the model for a particular group and the minimum los... | Rebuttal 1:
Rebuttal: > **Weakness 1:** The optimality analysis requires the convexity of the loss function, and it remains unclear how the performance would be affected if the loss function were non-convex, which is common in machine learning. For example, if we use an encoding network to convert the data X into a rep... | Summary: This paper explores the issue of fairness in the estimation of graphical models (GMs), specifically Gaussian, Covariance, and Ising models. The authors introduce a comprehensive framework aimed at mitigating bias in GM estimation concerning protected attributes. This framework integrates pairwise graph dispari... | Rebuttal 1:
Rebuttal: > **Weakness 1:** In the introduction and related works section, the paper does not provide complete information about GMs. Additionally, the paper does not clearly explain why it focuses on only three types of GMs instead of providing a general framework. The author needs to clearly highlight any... | Summary: This paper introduces a framework to address fairness in the estimation of graphical models, particularly focusing on Gaussian, Covariance, and Ising models. The motivation stems from the potential bias in standard GMs when handling data involving sensitive characteristics or protected groups. The proposed fra... | Rebuttal 1:
Rebuttal: > **Weakness 1:** Fairness metric: The choice and justification of fairness metrics used in the evaluation could be more thoroughly discussed. This would provide a clearer understanding of how fairness is quantified and the implications of these choices.
> **Question 1:** Could you elaborate more... | Summary: The paper investigates the issue of bias in 3 particular graphical models: Gaussian, Gaussian Covariance, and Binary Ising. In this regard, the authors propose a framework to enhance fairness in the estimation of graph models. They incorporate the difference of loss between the protected groups and the accura... | Rebuttal 1:
Rebuttal: > **Weakness 1:** The authors only consider one criterion of fairness, which is achieving equal loss among subgroups. How do the authors justify focusing solely on this criterion? Additionally, how might incorporating other fairness criteria impact the results?
> **Weakness 5:** Why do the author... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time in providing feedback and questions about our submitted paper. Below, we summarize the main issues that the reviewers raised, along with a summary of our responses. Furthermore, we provide **new real experiments** to address the reviewers' comments, which ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stabilized Proximal-Point Methods for Federated Optimization | Accept (spotlight) | Summary: The authors of the paper extend the DANE algorithm to the stabilized-DANE (S-DANE) algorithm based on intuition from stabilized proximal point method. They also enhance the proposed S-DANE method with Monterio-Svaiter acceleration. The algorithms proposed by the authors allow for partial participation and vari... | Rebuttal 1:
Rebuttal: We thank the reviewer for the great evaluation of our paper. Your every comment is important to us. We did our best to
understand and reply to your constructive feedback as follows:
> W1
Thanks for the great question. Here are some justifications.
1. Interestingly, we do not use any smoothness... | Summary: An algorithm for distributed convex optimization with partial participation is proposed, under a similarity assumption.
Strengths: The proposed algorithm has complexity Acc-S-DANE has claimed communication complexity O(sqrt(delta/mu)log(1/epsilon), for the first time. (I did not check the details of the proof... | Rebuttal 1:
Rebuttal: We thank the reviewer for the great evaluation of our paper. Your every comment is important to us. We did our best to
understand and reply to your constructive feedback as follows:
> W1
Thanks for the nice question.
1. This statement only (appears and) refers to the accuracy condition written... | Summary: The paper introduces a stabilized version of DANE (S-DANE). It replaces the proximal point step with an extragradient-type step. With the well-designed subproblem criterion, the number of local gradient oracle queries improves over DANE in logarithmic terms. It further combines Monteiro-Svaiter acceleration wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the great evaluation and the support of our paper!
> Q1
Thanks for the interesting question. The geometric meaning of equation (3) can be found in [1]. Previously, we derived this equation directly from the proof. Since we want to have the one-step recurrence of the fo... | Summary: This paper considers the problem of distributed optimization under second-order similarity under (strong) convexity and smoothness. The paper proposes a new algorithm, Stabilized DANE, which (a) matches the best-known communication complexity under Hessian similarity while (b) requiring that local computation ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the great evaluation of our paper! Your every comment is important to us. We did our best to
understand and reply to your constructive feedback as follows:
> W1
Yes, this sentence is confusing! We will rewrite it.
- This sentence (that was first written in [22]) partic... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive evaluations of our manuscript and we appreciate all the help from the reviewers for improving the paper.
In this work, we aim to develop federated optimization algorithms that 1) minimize the number of required communication rounds to reach the desire... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a new variant of the existing DANE algorithm for federated learning. The paper integrates the stabilized proximal point method into DANE to form S-DANE. Convergence analyses are provided showing that S-DANE has the same rate as DANE but with better dependency on the communication round. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the great evaluation of our paper. Your every comment is important to us. We did our best to
understand and reply to your constructive feedback as follows:
> W1 \& Q1
Thanks for the question. The description can be found from line 158 to 163. Specifically, during each ... | null | null | null | null | null | null |
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting | Accept (poster) | Summary: This paper considers a strategic variant of the multi-armed bandit problem with payments. It thereby builds upon a problem studied by Braverman et al. The paper formally introduces the problem formulation and proposes an algorithm, S-SE, that combines successive elimination with a meticulously chosen payment r... | Rebuttal 1:
Rebuttal: Dear Reviewer VQPm,
Thank you very much for your detailed and insightful review. We greatly appreciate the time and effort you dedicated to it.
We are computing regret with reference to $\mu_{2}$, which involves incentivizing the best arm with $O(T \Delta_{12})$. This approach is not harmful, as... | Summary: Multi-armed bandit problems capture explore-exploit scenarios under different reward structures including stochastic and adversarial. In this paper, the authors consider bandit problems where the arms report rewards strategically. To tackle such a problem, the authors devise a successive elimination scheme, wh... | Rebuttal 1:
Rebuttal: Dear Reviewer 3dvn,
Thank you for your insightful feedback and for the time you dedicated to reviewing our paper.
In the current version of our paper, we did not include experimental results, which is indeed quite common in game theory, due to the fact that the dynamics of the game are intrinsic... | Summary: The paper addresses a strategic bandit setting. Specifically, the player (algorithm) can select between K arms as in standard bandits but each arm can choose the reward it reports instead. Because of the debt-free assumptions, the reported value cannot exceed the realized reward. The papers gives an algorithm ... | Rebuttal 1:
Rebuttal: Dear Reviewer JBtJ,
We appreciate your review and the time you've dedicated to it.
In this paper, we concentrate on strategic arms under debt-free reporting, driven by various real-world applications such as interactions on e-commerce platforms and repeated trades with budget constraints [9, 10]... | Summary: This paper studies a multi-armed bandit setting with stochastic arms but where the arms are strategic agents -- when an arm is pulled it can choose what fraction of the reward to keep for itself and what fraction of the reward to pass on to the principal (the learning algorithm picking the arms). The principal... | Rebuttal 1:
Rebuttal: Dear Reviewer byTp,
We express our sincere gratitude for your invaluable review and the time you have dedicated to it.
This paper, motivated by various real-world applications such as e-commerce and repeated trades with balanced budgets, delves into strategic arms under debt-free reporting. Our ... | Rebuttal 1:
Rebuttal: Here we present the complementary experimental results to our theoretical analysis, as suggested by Reviewer 3dvn.
Pdf: /pdf/973db4ab320d286e19573c8cd4e83c3bfae0a674.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Prevalence of Neural Collapse in Neural Multivariate Regression | Accept (poster) | Summary: The paper introduces a novel extension of neural collapse to neural regression collapse, demonstrating that a similar phenomenon exists in multivariate regression. It treats the last layer feature vectors as free variables when minimizing the loss function and derives results similar to those of traditional ne... | Rebuttal 1:
Rebuttal: We would like to thank you for the detailed review and helpful comments as well as for recognizing that our work “derives results similar to those of traditional neural collapse”.
We acknowledge that the analysis for NRC bears some similarities with traditional NC for classification tasks with M... | Summary: This paper investigates Neural Regression Collapse (NRC), a new form of Neural Collapse observed in multivariate regression tasks. NRC is characterized by three phenomena: (NRC1) last-layer feature vectors collapsing to the subspace spanned by the principal components of feature vectors, (NRC2) feature vectors... | Rebuttal 1:
Rebuttal: We would like to thank you for the detailed review and helpful comments. We also thank you for recognizing NRC as a “new form of Neural Collapse observed in multivariate regression tasks” and that “this study extends Neural Collapse to regression, suggesting a universal deep learning behavior”.
*... | Summary: This paper studies the neural collapse phenomena in neural multivariate regression. The authors rigorously analyze the neural collapse behavior using a simplified model that only includes the last two layers. It was shown that in the multivariate regression task, the last layer of the neural network would coll... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed review and helpful comments. We also thank you for recognizing that the “study is novel,” “provides a new understanding of neural multivariate regression,” and that the “theoretical results and experimental results are solid and well-organized”.... | Summary: The authors explore a new notion of neural collapse which has been formulated to accommodate multivariate regression tasks. NC was originally introduced and recognized as an artifact of multi-class classification tasks. While there has been extensive research into the phenomena of neural collapse for classific... | Rebuttal 1:
Rebuttal: We thank you for your very detailed and insightful review, and for all the positive comments you made regarding our work. We also fully agree that the presentation of the experimental results can be improved. During the rebuttal week, we have worked very hard, running additional experiments and re... | Rebuttal 1:
Rebuttal: Since 2020, when [Papyan et al., 2020] published their seminal paper on neural collapse, there has been a flurry of activity in the area, with at least a dozen papers on the topic of neural collapse published in major machine learning venues. To our knowledge, this entire stream of important resea... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts | Accept (spotlight) | Summary: This paper aims to overcome potential distribution shifts from a single training distribution to any testing distribution and adapt to different user preferences for trade-offs between head and tail classes. The proposed method leverages hypernetworks to generate a diverse set of expert models, enabling the sy... | Rebuttal 1:
Rebuttal: **W1:**
Thank you for raising the question. Let me try to further clarify some key concepts based on the content of the paper:
- **Concept of environment:**
In Section 3, "environment" refers to a dataset with different class prior probability distributions. Each environment Em has its own class p... | Summary: This paper addresses long-tailed learning with a focus on tackling distribution shift and accommodating different user preferences for the trade-off between head and tail classes. The authors propose a method called PRL, which generates a set of diverse expert models via hypernetworks to cover all possible dis... | Rebuttal 1:
Rebuttal: **W1:** Thank you for your correction. $R = (\theta, \phi)$ represents our preference vector, where $\phi$ is the radian representation mentioned in Equation (14). Figures 3 and 4 depict the control of different preference vectors on model performance.
**W2:** $R = (\theta, \phi)$ represents our ... | Summary: This paper addresses the crucial and challenging problem of long-tailed learning under distribution shifts between training and testing data, which is highly relevant to real-world applications. The authors propose a novel and insightful learning paradigm that aims to obtain a set of diverse expert classifiers... | Rebuttal 1:
Rebuttal: **W1:** You raised a very insightful question. The probability density function of the Dirichlet distribution is:
$$f(x_1, \ldots, x_K; \alpha_1, \ldots, \alpha_K) = \frac{1}{B(\alpha)} \prod_{i=1}^K x_i^{\alpha_i - 1}$$
where $\alpha=(\alpha_1,\ldots,\alpha_K)$ are the hyperparameters of the di... | Summary: The paper addresses the problem of learning long-tailed distributions, with the imbalance of head and tail classes. The paper introduces a long-tail learning paradigm based on diverse set of experts and hypernetworks. The proposed method can meet personalized user preferences and can adapt to wide range of dis... | Rebuttal 1:
Rebuttal: **W1 and Q1:** Thank you for your question. The four perspectives in the **public response section** are intended to answer this question. Please refer to the four perspectives in the public response due to space limitations.
**W2 and Q2:** Thank you for your question. The explanation of this par... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for their valuable comments. Your feedback has helped us improve the quality of the paper and strengthen the arguments. We are pleased that most reviewers have a positive attitude towards our work :).
We are very grateful to the reviewers for acknowledgi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards a Unified Framework of Clustering-based Anomaly Detection | Reject | Summary: The paper addresses unsupervised anomaly detection by proposing a method named UniCAD. The authors aim to enhance anomaly detection performance by establishing a theoretical connection between representation learning, clustering, and anomaly detection. They introduce a unified framework that jointly optimizes ... | Rebuttal 1:
Rebuttal: > The ablation study on the hyperparameters 𝑘 and 𝑙 is insufficient. The authors only present results from a single dataset, satimage-2, where their method achieves an almost perfect score. It would be more informative to perform ablation studies across all 30 datasets or at least a subset where... | Summary: This paper proposes UniCAD, a theoretically unified framework for representation learning, clustering, and anomaly detection. This paper first introduces the mixture of Student-t distribution $p(x|\Theta, \Phi)$ with degree of freedom $\nu=1$ based on a representation learner $f_\Theta$ using NN. Then, this pa... | Rebuttal 1:
Rebuttal: > The comparison with DeepSVDD and DIF is excellent, but I think the paper also needs to be compared with other Deep anomaly detection methods. For example, DROCC \[1\].\[1\] Goyal, Sachin, et al. "DROCC: Deep robust one-class classification. "International conference on machine learning. PMLR, 20... | Summary: This paper introduces a novel probabilistic mixture model for unsupervised anomaly detection (UAD) that unifies representation learning, clustering, and anomaly detection into a single theoretical framework. The proposed UniCAD model addresses the lack of a unified approach in existing methods, which often con... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We will address each of the weaknesses and suggestions you mentioned.
> The connection between force analysis and anomaly detection, particularly between Equations 7 and 8 in Section 3.2.1, could benefit from further justification. While the analogy is interesting, it ... | Summary: The authors propose UniCAD to jointly model representation learning,
clustering and anomaly detection. The main objective is maximizing
the product of anomaly indicator (1 is normal, 0 is anomaly) and the
joint probability of instance x_i and cluster c_k given parameters for
representation learning theta and ... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' valuable suggestions for our work. We hope the following responses will clarify any doubts and enhance the quality of our paper.
> The clustering part is similar to a typical Gaussian mixture model for clustering via EM, except for t-distribution instead of Gaussian a... | Rebuttal 1:
Rebuttal: We sincerely appreciate the positive feedback from most reviewers on our paper, as well as the very useful suggestions from different aspects for further improving the quality of our work.
In the rebuttal, we have carefully read the reviews and provided corresponding answers in each individual re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification | Accept (spotlight) | Summary: The paper introduces a logical regularization method called L-Reg, aimed at enhancing the generalization ability of image classification tasks through a logical reasoning framework. L-Reg effectively simplifies the complexity of the model by ensuring that the generated atomic formulas align with the logical re... | Rebuttal 1:
Rebuttal: We really appreciate your insightful comments, and we address your weaknesses and questions point-by-point.
**W1.** Thank you for your insightful comment. As discussed in Paper Lines 344-358, L-Reg relies on the precondition that each dimension of the semantic features represents independent sem... | Summary: This paper addresses the multi-domain generalization (mDG), generalized category discovery (GCD), and the more challenging mDG+GCD task. The authors introduce a logical reasoning-based regularization term called L-Reg, which bridges logical analysis with image classification to enhance model interpretability a... | Rebuttal 1:
Rebuttal: We really appreciate your insightful comments, and we address your weaknesses and questions point-by-point. Some points of weaknesses and questions are combined because they are very associated with each other.
**W1&Q2.** Thank you so much for your comments, and sorry for any confusion caused by ... | Summary: This work introduces a sample-based regularization technique, L-Reg, which goes beyond techniques like parameter-based L2 regularization by being more interpretable and demonstrating better generalization ability. The work formalizes the notion of semantic support to force the model to learn minimal sufficient... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments, and we address your weaknesses and questions point-by-point. Some points of weaknesses and questions are combined because they are very associated with each other.
**W1.** We really appreciate this insightful comment. In our study, we adopted the commonly ... | Summary: The paper proposes a novel logical regularization termed L-Reg for visual classification. L-Reg encourages models to focus on the salient semantics and thereby emerges interpretability. The theoretical analysis provides clear connections between logical reasoning and L-Reg. Extensive experiments demonstrate th... | Rebuttal 1:
Rebuttal: These insightful comments are highly appreciated. We believe these two questions are very related; therefore, please allow us to address them together.
As discussed in Reply to All Reviews 1, the L-Reg's interpretability is rooted in learning the good general atomic formulas. Specifically, L-Re... | Rebuttal 1:
Rebuttal: **Reply to All**
We sincerely appreciate the reviewers' insightful comments, which have helped us refine and improve our paper. We have identified common concerns across the reviewers and address them collectively here. Detailed responses to individual reviewers are provided separately. Please no... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper mainly focuses on two problems: 1) How does logical reasoning relate to visual tasks such as image classification? 2) How can we derive a logical reasoning-based regularization term to benefit generalization?. Then, this paper proposes a method called Logical Reasoning Regularization based on the an... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and we address your weaknesses and questions point-by-point.
**W1.** We humbly believe L-Reg delivers consistent and evident gains. Please refer to Reply to All for the highlighted improvements. **Moreover, as you suggested, we further validate L-Reg's effi... | null | null | null | null | null | null |
Conjugate Bayesian Two-step Change Point Detection for Hawkes Process | Accept (poster) | Summary: The paper proposes a new Bayesian inference method for change point detection in Hawkes Processes using conditionally conjugate priors and Gibbs Sampling. In their experiments, their new method turns out to perform better than competing methods both in terms of accuracy and speed.
Strengths: The authors do a ... | Rebuttal 1:
Rebuttal: > Q: The stress test in the ablation study seems to vary the difficulty of the problem, which makes sense. However, the authors only show the results of their own method there, although I see no reason to not also show the results of the other methods for these cases.
A: Thank you for your valuab... | Summary: The paper aims to detect change points (in terms of model parameters) in point processes and proposes a conjugate Bayesian two-step change point detection method for Hawkes processes. This is achieved by applying data augmentation and a novel Gibbs sampler for closed-form updates for model parameters. For both... | Rebuttal 1:
Rebuttal: > Q: Could you help explain the statistical significance of results for synthetic data, e.g., how many runs to average the results for the std.? It seems to me that 1 std. is relatively large compared to the average, and there are large overlaps of 1 std. between different models.
A: Thanks for... | Summary: This paper proposes a conjugate Bayesian two-step change point detection method for the Hawkes process using data augmentation. It addresses the computational inefficiency of existing methods by providing analytical expressions. The new method proves to be more accurate and efficient, as demonstrated by extens... | Rebuttal 1:
Rebuttal: > Q: The implementation details have not been adequately discussed ...... as these concepts may not be familiar to readers outside this domain.
A: Thank you for your suggestion. Due to page limit, some content had to be placed in the appendix. However, we appreciate your feedback and will consid... | Summary: This paper considers the Bayesian two-step change point detection model for Hawkes process. Through data augmentation techniques such as the use of Polya-Gamma random variables and the marked Poisson process, conditional conjugacy is achieved and an efficient Gibbs sampler can be designed for posterior computa... | Rebuttal 1:
Rebuttal: > Q: The method is only Bayesian in the first step, ...... more computationally challenging.
A: Thank you for your valuable suggestion. As you mentioned, we are indeed only using Bayesian in the first step at the moment. In future work, we will consider using a complete Bayesian approach.
> Q: ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their efforts in providing insightful comments and constructive feedback.
We are pleased that the reviewers have recognized the significance of our paper in solving an interesting change point detection problem in Hawkes process [R1, R2, R3, R4], conducting comprehensiv... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gliding over the Pareto Front with Uniform Designs | Accept (poster) | Summary: This paper attempts to address a challenging problem in the field of MOO: how to generate a uniform set of solutions on the PF. The authors try to directly characterize uniformity and propose fill distance to quantitatively measure it. Given that fill distance is difficult to optimize directly, the paper furth... | Rebuttal 1:
Rebuttal: We thank the reviewer for the exhaustive feedback and, especially, for acknowledging that our work and contributions are significant to the community. Due to space limit, we will **directly take the advice on writing** and incorporate them into the next revision.
**W1. (1) Minimizing the fill dis... | Summary: The paper presents a new approach to effectively represent the entire Pareto front in multi-objective optimization problems. The authors propose using fill distance as a new metric for uniformity in MOO, addressing the challenge of quantifying the representativeness of design points.
Strengths: - The paper pr... | Rebuttal 1:
Rebuttal: We thank for the detailed feedbacks from the reviewer. We hope our following response can address your concerns more or less.
Since W1 and Q1 are related. We combine the answers here.
**W1. The proposed method relies on a neural network to approximate the Pareto front. The performance of the me... | Summary: In this paper, the authors focus on finding K uniform Pareto-optimal points that are capable of representing the entire Pareto front and propose a new metric, namely fill distance to quantify the effectiveness of these K points. To minimize the fill distance easily, the authors adopted a surrogate model, calle... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out key passages that could be complemented with the necessary background for a broader audience.
**W1. What are the drawbacks of the existing methods for generating diverse solutions? You need to have a simple summary.** \
For indicators, please refer our respo... | Summary: This study addresses the problem of generating K uniform Pareto-optimal solutions for multi-objective optimization problems. It introduces a new metric, fill distance, to evaluate the uniformity of the solution set on the Pareto front. This metric is then used as the objective for evolutionary optimization, wh... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable reviews and hope our response addresses your concerns.
**W1. Fill distance (FD) is not discussed and compared with other metrics for achieving and access uniformity.**
We beg to argue some discussion has been provided.
1. **Hypervolume (HV)**: As noted in ... | Rebuttal 1:
Rebuttal: We sincerely appreciate all helpful feedback and comments from the reviewers. In this part, we first address some general comments raised by the reviewers.
**Q1 (Asked by 5qjZ and N5xS). Relationship between the convergence of IGD and the uniformity of Fill Distance (FD). The advantages of two i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback | Reject | Summary: The work studies linear contextual dueling bandits with adversarial feedback. In each round $t$ the agent observes a context $x_t$ and chooses two actions $(a_t,b_t)$. The environment generates a binary preference label $\ell_t = \mathbb{I}(a_t > b_t)$. The underlying assumption is that there exists a linear r... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions! We answer your questions as follows.
---
**Q1**: Technical novelties
**A1**: We want to emphasize that we study the dueling bandit problem, which is different from the standard linear bandit problem and incurs several challenges when using... | Summary: This paper investigated the contextual dueling bandits with adversarial feedback, where the adversary can corrupt the binary feedback of the agent to a certain level. A new algorithm named RCDB has been proposed. The key idea lies in the utilization of uncertainty-weight MLE. Regret analysis of RCDB was provid... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We answer your questions as follows.
---
**Q1**: Challenges to extend the linear reward to more general settings.
**A1**: It might be possible to consider the corruption problem for a nonlinear reward function class with finite Eluder dimension, like in [1]... | Summary: The author proposed an algorithm, coined robust contextual dueling bandits (RCDB) for advarial feedback, using uncertainty-weighted maximum likelihood estimation. The algorithm guarantees $\widetilde{O}(d\sqrt{T}+CT)$.
Strengths: 1. Their algorithm is not limited to a finite number of arms.
2. Their algorith... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We answer your questions as follows.
----
**Q1**: Hard to follow the paper.
**A1**:
Thank you for pointing out these issues. We will address your concerns one by one:
(1) $\Sigma_t$ appeared without introducing.
Our definition of $\Sigma_t$ is provided in... | Summary: This paper studies the Contextual Dueling Bandits from Adversarial Feedback problem, in a linear reward setting. The authors propose an algorithm named robust contextual dueling bandits (RCDB), which is designed based on uncertainty-weighted regression and MLE. The authors prove that the proposed algorithm ach... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. We will address your questions one by one.
---
**Q1**: The title may be a little bit misleading, I think the setting of this paper is the adversarial corruption setting, not the setting with completely adversarial feedback. And the setting is the linear rewa... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Personalized Federated Learning via Feature Distribution Adaptation | Accept (poster) | Summary: Federated Learning (FL) combines data from multiple clients to train a global model but struggles with heterogeneous data. Personalized Federated Learning (PFL) creates individual models for each client, addressing this issue. Traditional methods face challenges with bias-variance trade-offs and rely on limite... | Rebuttal 1:
Rebuttal: **W1 (Distinction with Prior PFL Methods)** Our method falls under the personalization framework of shared representation learning with personalized classifiers (discussed in our related work, L85-107).
The distinction of our work is in our formulation of generative client classifiers. Notably, t... | Summary: This work introduces pFedFDA, a novel approach to personalized federated learning that conceptualizes global representation learning as a generative modeling task. Specifically, the method involves shared representation learning, guided by a generative classifier characterized by a low-variance global probabil... | Rebuttal 1:
Rebuttal: **W1 (Performance Comparison of pFedFDA with Prior Works)** We believe this might be a misread of our results. While the performance improvements are not as significant in Tab. 2, pFedFDA is still competitive (achieving top-2 performance in 5 of the 7 scenarios). In particular, it beats FedBABU in... | Summary: The paper introduces a personalized Federated Learning (FL) method that adapts global generative classifiers to local feature distributions. The authors show that their method can handle complex distribution shifts for computer vision tasks.
Strengths: - The paper proposes a personalized FL method that uses a... | Rebuttal 1:
Rebuttal: **W1/Q4 (Privacy Concerns of Sharing Gaussian Sufficient Statistics)** We appreciate the reviewer's comments and think it is important to discuss the privacy implications in our work.
The transmission of client feature statistics to the parameter server does not immediately raise high privacy ri... | Summary: This paper uses Class-Conditional Gaussian Model to formulate the latent representation of the global generative part; for the other part, a personalized federated learning algorithm pFedFDA is designed via Federated Distribution Adaptation. This paper then proves a bound on the bias-variance trade-off of pFe... | Rebuttal 1:
Rebuttal: **W1 (Robustness to Data Heterogeneity)** We note that while Tab. 1 and Tab. 3 are based on CIFAR datasets, these evaluations introduce the additional challenges of client covariate shift (via natural image corruptions) and data scarcity. Our strong performance in these settings indicates that pFe... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their detailed comments and feedback. We will revise the paper accordingly to further clarify our work and address the points brought up in these discussions.
In our attached rebuttal PDF, we have provided the following additional experimental results:
Ta... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces pFedFDA, a personalized Federated Learning (pFL) method designed to address the issue of client heterogeneity in federated learning. pFedFDA combines global knowledge through server aggregation with local knowledge through client-specific training and distribution estimation, enhancing th... | Rebuttal 1:
Rebuttal: **W1 (Choice of Statistical Measures)** We selected the mean and covariance as the class-conditional Gaussian model is uniquely defined by these parameters. While it would have been possible to communicate estimates of the inverse-covariance matrix, this adds unnecessary computation which we avoid... | null | null | null | null | null | null |
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition | Accept (poster) | Summary: This paper tackled the issue of inter-entity distribution discrepancies in multi-entity action recognition. The authors proposed convex hull adaptive shift method to minimize the cross entity discrepancies, where CLB and MPMMD are proposed to assist the learning procedure. The method is verified to be effectiv... | Rebuttal 1:
Rebuttal: Thanks for your encouraging and constructive comments. We appreciate your recognition of the methodology, experimental results, and contributions. We understand that your concerns may arise from the need for greater clarity and detail in presenting our motivations and methodology. Below, we addres... | Summary: This paper proposes CHASE, a multi-entity skeleton data augmentation/preprocessing technique, to mitigate inter-entity distribution gaps and improve the multi-entity action recognition. Specifically, the authors formulate a new constraint called ICHAS, design a lightweight block CLB to learn the nonlinear mapp... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive comments. We appreciate your recognition of the writing, motivation and experiments. We understand that some of your concerns may stem from clarity and experimental details. Below, we address these issues one by one and have clarified them in the revised p... | Summary: This paper focuses on the interesting problem of the normalization strategy for multi-entity skeletons in skeleton-based action recognition. The proposed method is intuitive, and the authors provided detailed implementation details for reproduction. However, this work is unclear, and the experiments are unconv... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. We appreciate your recognition of the motivation behind CHASE and the extensive implementation details for reproducibility. We understand that your concerns may stem from some misunderstandings and the presentation of ablation studies. Below, we address these i... | Summary: The paper proposes a normalization method for skeleton-based multi-entity recognition based on finding the center of mass within the convex hull of the spatio-temporal domain of the point cloud defined by the skeletons over a sequence. The main idea is to "center" the world of skeletons to unbias the subsequen... | Rebuttal 1:
Rebuttal: We appreciate your recognition of the methodology of CHASE, proper mathematical derivation, and extensive experiments. We understand that your concerns may stem from clarity and presentation of our contributions and motivation. Below, we address these issues one by one and have clarified them in t... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to all the reviewers for their time, insightful suggestions, and valuable comments. We deeply appreciate the positive recognition from the reviewers regarding our paper’s motivation (hFtj, 16iH, WotV), the elegance and simplicity of our methodology (N... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Augmentation Invariance Pretraining | Accept (poster) | Summary: This submission proposed to accelerate the training of ViT by two methods: 1) Randomly drop tokens for input. 2) Dynamically resize patches into different dimension. The second method is published in previous works.
Strengths: N.A.
Weaknesses: 1. The novelity of the sumission is limited: In first propsoed me... | Rebuttal 1:
Rebuttal: We are sad that the reviewer completely misunderstood the contributions and the significance of the proposed work. We made clear both in the related work and method sections that Token Dropout and Patch Scaling are NOT contributions of our work, by explicitly citing the origins of each technique. ... | Summary: This paper presents an acceleration framework for Vision Transformers in contrastive learning. It utilizerandomized token dropout and patch-scaling to reduce the sequence length to accelerate training. Based on an analysis of the gradient estimation error, this paper proposes an automated procedure to identify... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback. Below we answer the main concerns, and we will revise the paper accordingly. If there are any remaining concerns we can clarify or provide additional results/analysis, please let us know. We will be responsive during the author-reviewer discussion phase. Given ... | Summary: This work focuses on speeding up contrastive learning with vision transformers. Two methods, specifically tailored to ViTs, are investigated for making pretraining more efficient: randomised token dropout and flexible patch scaling. Additionally, the authors analyse the gradient estimation errors from these me... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback. We are glad to see the originality of the proposed method and the significance and quality of our empirical results appreciated. We will revise the paper to add experiments and clarify unclear points, as described below. If there are any remaining concerns we c... | Summary: The paper presents a framework to speed up the pre-training of Vision Transformers (ViTs) in a self-supervised contrastive learning setup. The proposed method incorporates randomized token dropout and flexible patch scaling. The authors leverage this framework to analyze estimated gradient errors and its downs... | Rebuttal 1:
Rebuttal: We appreciate the valuable feedback. We are glad the reviewer found the noticeable improvements in ViT contrastive pre-training convergence valuable. This is indeed the flagship result of the paper, which has not been explored in any other prior work. Given the importance of the topic (contrastive... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis | Accept (poster) | Summary: This paper introduces Amplified SCAFFOLD, which is an optimization algorithm for federated learning under periodic client participation. The authors prove that it achieves reduced communication cost, linear speedup, and resilience to data heterogeneity. Numerical experiments are provided to evaluate the perfor... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments on our paper. Below we have responded to your comments and questions.
Weaknesses:
1. **Detailed description of the algorithm.** Thank you for the feedback on our presentation. Section 4.1 is meant to describe the key algorithmic components of Amplified Scaffol... | Summary: The paper proposes a new algorithm named Amplified SCAFFOLD for federated learning in environments with periodic client participation and heterogeneous data. The authors address the realistic setting where clients (e.g., mobile devices) are not always available for participation. The proposed algorithm aims to... | Rebuttal 1:
Rebuttal: Thank you for you effort in reviewing our paper. Below we have responded to your thoughts and answered your questions.
Weaknesses:
1. **Meaning of Assumption 1(a).** We addressed this point in the general response: our Assumption 1(a) contains a typo. Actually, the correct version of our Assumpt... | Summary: This work addresses the limitations of federated learning under realistic client participation patterns, specifically focusing on nonconvex optimization. The proposed algorithm, Amplified SCAFFOLD, achieves linear speedup, reduced communication, and resilience to data heterogeneity without requiring strong ass... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions. We have responded to your thoughts and questions below.
Questions:
1\. **Table of notation.** Thank you for this suggestion. We agree that it would make the presentation more clear and we will provide a table of notation in the updated version.
2\. **M... | Summary: In this paper, the authors examine realistic participation scenarios, including cyclic client participation and arbitrary participation patterns. They focus on a non-convex optimization setting, which is common in practical applications but challenging to address. To tackle this, they introduce a novel method ... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments on our paper. Below we have responded to your questions and concerns.
Weaknesses:
1. **Missing related work.** Thank you for pointing out these works. We agree that including these works in the discussion of our paper will establish a broader context of the FL... | Rebuttal 1:
Rebuttal: Thank you to all of the reviewers for your time and effort in the review process. Here we describe additional experimental results that we have added to address the reviewer comments, and give answers to common questions. We have also responded individually to each review below.
1. **Experiments ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning | Accept (poster) | Summary: This paper presents a method which discretizes action trajectories from offline data into skills through a BPE-inspired tokenization method. These discrete action trajectory skills are given directly to a high-level agent to utilize for solving downstream tasks.
Strengths: **Presentation:** Writing is clear a... | Rebuttal 1:
Rebuttal: Thank you for your care in reading our paper. We appreciate the positive feedback on the clarity, and the comment on efficiency and the strength of the results, including a “pretty comprehensive” evaluation. We respond to your concerns below.
1. Method cannot be state-conditioned
Though it is tr... | Summary: This paper uses Byte pair encoding to create a discretised action space for RL from demonstrations. The authors show that Byte pair encoding can:
* improve exploration in sparse reward settings
* creating the skill action space is computationally cheap compared to methods that train deep learning models
* thei... | Rebuttal 1:
Rebuttal: Thank you for your time and care in reviewing our paper. We appreciate your feedback and share your excitement regarding the intuitiveness of the method and the computational efficiency. We answer your questions below.
1. How many demonstrations are used?
For these tasks we use the existing D4RL... | Summary: This paper presents a novel method for skill discovery in reinforcement learning by leveraging tokenization techniques from Natural Language Processing (NLP). The approach involves discretizing the action space through clustering, and then using byte-pair encoding to generate temporally extended actions. The m... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate that you highlighted the creativity of the approach as well as the breadth of the study. We respond point by point to your concerns below.
1. Range of baselines
We would be happy to cite and discuss these methods in the paper. LOVE... | Summary: The paper titled "Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning" introduces a novel method for skill extraction from demonstrations to address the challenge of exploration in sparse-reward reinforcement learning (RL). Inspired by the Byte-Pair Encoding (BPE) algorithm used in natura... | Rebuttal 1:
Rebuttal: Thank you for your time and consideration spent reviewing. We appreciate that you highlight the novelty and significance of the approach, as well as the clarity in presentation and the challenge of the tasks that we choose to evaluate in. We respond to the weaknesses and questions below.
1. Stoch... | Rebuttal 1:
Rebuttal: We appreciate the time and care all reviewers have taken in reading our paper and offering feedback, and thank them for their input.
We are particularly happy to see reviewers appreciate the novelty and creativity of the proposed method (bwJh, iKv2), the extreme efficiency of the approach (bwJh, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding | Accept (poster) | Summary: This work presents a new multi-modal large language model, named OMG-LLaVA. This model builds on previous work OMG-Seg and combines a LLM (LLaVA-like) into one simple framework. Compared with previous MLLMs, this work unifies lots of image-level, object-level, and pixel-level segmentation and reasoning tasks i... | Rebuttal 1:
Rebuttal: **Q1. More detailed designs or ablation should be carried out for perception prior embedding design.**
**A**: Thanks for your suggestion. We have added more detailed ablation studies on perception prior embedding strategy. We have conducted ablation studies on different strategies for assigning ... | Summary: This paper proposes OMG-LLaVA, an elegant MLLM framework. OMG-LLaVA achieves pixel-level, object-level, and image-level understanding and reasoning with only a perception model and a language model. OMG-LLaVA exhibits more comprehensive capabilities than current MLLMs, such as LLaVA, Osprey, and GLaMM. The exp... | Rebuttal 1:
Rebuttal: **Q1. The paper needs to include more ablation experiments to help readers better understand the work. For example, what would happen if the visual projector design shared an MLP for pixel- and object-centric visual tokens?**
**A:** Table R4 presents the results of ablation studies on the project... | Summary: This paper presents OMG-LLaVA, which facilitates pixel-level, object-level and image-level understanding tasks within a unified framework. Within the OMG-LLaVA, an OMG decoder and perception prior embedding approach are proposed to enhance object-centric comprehension. Comparison with state-of-the-art methods ... | Rebuttal 1:
Rebuttal: **Q1. The proposed method is not very elegant as too many designs customized for different tasks are introduced in the framework.**
**A**: OMG-LLaVA boasts an elegant model architecture and more streamlined workflows across tasks than GLaMM. For instance, in GLaMM, visual prompt encoding and seg... | Summary: This work proposes, OMG-LLaVA, a unified framework for image-level, object-level, and pixel-level vision-language comprehension. In particular, OMG-Seg, a universal image segmentation model, is integrated with a LLaVA-like multimodal large language model (MLLM), so that various image-level (e.g., image caption... | Rebuttal 1:
Rebuttal: **Q1. [Performance] Although this work claims OMG-LLaVA as a generalist model, it cannot outperform prior state-of-the-art models on any specific task.**
**A**: We have updated the performance of OMG-LLaVA, as shown in Table R1. OMG-LLaVA outperforms LISA, PixelLM, and GSVA on the RES benchmarks.... | Rebuttal 1:
Rebuttal: # General Responses
---
Dear Reviewers,
We thank all the reviewers for the detailed suggestions. All reviewers acknowledge the technical contributions of our work, including the new unified designs for MLLM and comprehensive benchmark evaluation. We listed additional important experiments and an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to be Smooth: An End-to-End Differentiable Particle Smoother | Accept (poster) | Summary: This paper proposes an end-to-end differentiable particle smoother and presents its application to global localization of a subject moving through real-world city-scale environments. Although there exists prior work in differentiable particle *filtering*, such as Mixture Density Particle Filters (MDPF), *smoot... | Rebuttal 1:
Rebuttal: Thank you for the positive comments and constructive feedback.
Please see our global response to all reviewers for discussion of training time and paper figure formatting.
We agree that adding some discussion of accuracy vs compute demands of MDPF and MDPS would be useful, and will do this. In... | Summary: My understanding is that this work seeks to learn a state-space model by differentiating through a regularised version of the sequential Monte Carlo (SMC) approximation of the the (generalised) two-filter smoother.
Strengths: I am not very familiar with the applications used as numerical examples but they see... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback.
**To address your concerns:**
- Our MDPS method learns both the measurement model and the dynamics model via end-to-end training. The “Training Details” paragraph at the end of section 4 was intended to highlight the 3-stage training approach. Additio... | Summary: The author's propose the first differentiable particle smoother, building on the MDPF of Younis and Sudderth which replaces the multinomial resampling step with a sampling from a KDE mixture. Their smoother is a two filter smoother where both the forwards and backwards filters are MDPFs. Instead of using the f... | Rebuttal 1:
Rebuttal: Thank you for your praise and feedback. We are grateful for the careful review of our work and appreciate your highlights on the novel nature of our differentiable particle smoother, and the strength of our experimental results compared to scenarios considered by related work.
**To address some o... | Summary: The paper proposes a learnable differentiable particle smoother system that extends an existing differentiable particle filter to a smoother. The method utilizes two independent particle filters for the two smoothing directions with importance sampling to address computation scaling issues.
Strengths: - Propo... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback. Please see our global response for discussion of training time and paper figure formatting.
With regard to comparison with existing works, we would like to make the distinction between local localization and global localization. Local localization estim... | Rebuttal 1:
Rebuttal: Thank you all for your feedback and helpful suggestions. We want to address a few topics that were referenced by multiple reviewers.
First, we would like to emphasize that our work is the first and only generalization of state-of-the-art differentiable particle filters to the more challenging pa... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control | Accept (poster) | Summary: This paper introduces Efficient Diffusion Alignment (EDA), which draws inspiration from the optimization paradigm of DPO to align the distribution of Diffusion policies with the optimal solutions of constrained policy optimization. Experimental results demonstrate the outstanding performance and fine-tuning ef... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer FQV2
We completely agree with the reviewer's assessment of our paper, regarding both the praise and the pointed-out limitations. We thank the reviewer for providing valuable feedback and the reviewer's expertise in delivering such an insightful review, even though s... | Summary: This paper introduces Efficient Diffusion Alignment (EDA) for offline continuous control, combining the preference alignment theories with reinforcement learning. Specifically, EDA bridges the alignment finetuning by representing the diffusion score as a derivative of a scalar neural network with respect to ac... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer y7D4
We truly appreciate the reviewer for the valuable suggestions and comments. Concerns are mainly about the computational complexity of EDA and the experimentation/application of the algorithm.
**Q1: Could provide some explanations to the actual training time ... | Summary: This paper introduces Efficient Diffusion Alignment (EDA) for solving offline reinforcement learning problems. The approach involves first training a behavior cloning model using only state-action data, without reward information. Subsequently, the model is fine-tuned with rewards using DPO. During the reward-... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer jAma
**Q1: Does EDA require backpropagation to compute the predicted noise and then another backpropagation to update the network? Would this involve computing higher-order gradients?**
**A1:** Yes. The pretraining of BDM requires two backpropagation steps for a si... | Summary: Diffusion models have shown impressive results in solving real-world problems. This paper builds on the success of large language models (LLMs) to enhance the development of diffusion models. Specifically, they introduced Efficient Diffusion Alignment (EDA), a pipeline to train diffusion models in two stages s... | Rebuttal 1:
Rebuttal: # Official Response to Reviewer 2HKR (1/2)
We thank the reviewer for the very detailed feedback! The reviewer's concerns are twofold: 1. confusion regarding what the BDM model is trying to model/learn and how to train it; 2. the comprehensiveness of the experiments. We hope our explanations and th... | Rebuttal 1:
Rebuttal: # Rebuttal Summary
We would like to thank all the reviewers for their valuable comments. We are encouraged to see all reviewers recognize the theoretical novelty of our work. Reviewers FQV2 and y7D4 highlight the critical importance of the problem with diffusion models that we are trying to solv... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Graph Pooling with Persistent Homology | Accept (poster) | Summary: The paper introduces topological graph pooling, a novel method that leverages persistent homology to enhance general graph pooling techniques. The use of persistent homology serves two primary purposes, preserving important topological features in the coarsened graphs and generating edge weights for them. For ... | Rebuttal 1:
Rebuttal: Figure 1 (a), second row, is strange
>Figure 1(a) illustrates that PH and pooling naturally share a similar hierarchical structure, thereby aligning well. It does not imply that PH must progress from smaller to larger graphs.
Typographical errors and incorrect expressions
>We thank Reviewer TU8... | Summary: This paper proposes a TDA-based mechanism for storing phase information in the pooling layer of a GNN. The proposed approach resamples the connections in the graph and scales the edge weights using persistence information from a one-dimensional persistence diagram. Experiments on artificial data for concept ev... | Rebuttal 1:
Rebuttal: W1: The proposed method uses a 1-dim betti number, but its validity is not known. All 1-dim death times seem to be the same (maximum time for filtration) and seem to be only information about the last connection of the cycle. Only limited information on the cycle seems to be stored and is less con... | Summary: This paper proposes a topology-based graph pooling layer, TIP. TIP fits easily with the current graph pooling frameworks. Once the pooled graph is obtained from any existing graph pooling techniques, the authors make this pooled graph adaptable to persistent homology. They consider the PH of this graph and opt... | Rebuttal 1:
Rebuttal: W1: Theorem 4.1 is incorrect as stated. Only the 1-dimensional topological features computed by PH cannot be as expressive as 1-WL test in distinguishing non-isomorphic graphs. Consider two graphs with no loops, with different numbers of vertices. 1-WL test will be able to distinguish these graphs... | Summary: This paper proposes a new and systematic way to integrate persistent homology into GNNs for improved performance. Rather than applying PH in a brute force manner as much existing work does, this work adapts the method based on the coincidence between the graph pooling mechanism and the filtration of PH. The ... | Rebuttal 1:
Rebuttal: Questions: Is the approach adaptable to these special cases of GNNs? Does the performance also hold up in machine learning tasks, such as image classification? Would the theoretical guarantees also need to be adapted?
>The proposed approach is adaptable as long as the data can be represented as g... | Rebuttal 1:
Rebuttal: **General Response:**
We would like to express our sincere gratitude for your thorough review of our manuscript and for providing valuable feedback and suggestions. Your expertise and insights have been instrumental in improving the quality and clarity of our work.
During the rebuttal period... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces TIP (topology-invariant pooling), a PH-based pooling layer. The proposed approach involves resampling graph connections from soft-cluster assignment matrices and adjusting edge weights using persistence information derived from 1-dimensional diagrams. TIP leverages a loss function designed... | Rebuttal 1:
Rebuttal: W1:Concerns about theory and filtrations.
>The theoretical analysis of the expressive power and other properties of graph pooling is crucial in this field, as it aids in selecting between existing pooling operators or developing new ones [1]. Beyond TOGL [2] that proves the expressivity of PH usi... | null | null | null | null | null | null |
Spiking Transformer with Experts Mixture | Accept (poster) | Summary: The manuscript presents an innovative approach to integrating SNNs and MoE methodologies into a cohesive framework. It introduces the Spiking Experts Mixture Mechanism (SEMM), which leverages the sparse spiking activations of SNNs and the conditional computation of MoE. The proposed SEMM has been adapted int... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions for improvement.
> ***Weakness 1**: Several Unclear Notations.*
**AW1**: Sorry for the confusion caused by unclear notations. To clarify: __(1)__ $N$ denotes the length of image patches. __(2)__ Thanks for your advice. We will change the abbreviation ... | Summary: The authors introduce MOE into SNN, propose the SEMM structure, and use this structure to enhance the attention and MLP modules in the Spikeformer-like architecture. They transform these elements into the EMSA and EMSP modules. The new network architecture achieved better performance under similar parameter se... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments. We would like to address your concerns below.
> ***Weakness 1**: The author needs to provide a more detailed explanation of the spike-driven advantage, especially on asynchronous hardware.*
**AW1**: As you mentioned, deploying SEMM on a fully asynchronous ne... | Summary: The paper presents a novel integration of Spiking Neural Networks (SNNs) with Mixture-of-Experts (MoE) to form the Spiking Transformer, introducing the Spiking Experts Mixture Mechanism (SEMM). The SEMM enables dynamic sparse-conditional computation by having both experts and routers output spiking sequences, ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We will explain and discuss your concerns.
> ***Weakness 1, Question 1, Limitation 1**: More experiments across diverse datasets to generalize the findings.*
**A1**: __These datasets in experiments possess distinct characteristics and scales, which verify the e... | Summary: This research introduces the Spiking Experts Mixture Mechanism (SEMM), a paradigm that combines Spiking Neural Networks (SNNs) with Mixture-of-Experts (MoE) to enhance the capabilities of Spiking Transformers. The proposed SEMM leverages the energy-efficient, sparse spiking activation characteristic of SNNs, r... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and suggestions for improvement. We would like to address your concerns and answer your questions below.
> ***Weakness 1**: The application of SEMM on the most state-of-the-art Spiking Transformers such as Spike-driven Transformer V2.*
**AW1**: Following you... | Rebuttal 1:
Rebuttal: Dear ACs and Reviewers,
We sincerely appreciate the valuable time and feedback provided by each reviewer. We have responded to each of their comments individually.
In the rebuttal, we use
> ***Weakness/Question/Limitation**: ...*
to summarize the reviewers' comments. Responses start with '__... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token | Accept (poster) | Summary: This paper proposes a training-based confidence calibration method, named IDK-tuning, for improving LLM factuality. Specifically, a special `[IDK]` ("I Don't Know") token is added to the model's vocabulary and an objective is introduced which shifts some probability mass of wrong predictions to the `[IDK]` tok... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their time reading our work, writing a thorough review and bringing up thoughtful comments.
We are encouraged that the reviewer finds our method intuitive, novel and well-motivated, that our experiments are deemed extensive, and that the paper is considered wel... | Summary: The paper proposes calibrating LLMs during a continued pertaining phase via an added [IDK] token to model uncertainty.
Strengths: - The paper is structured well and written coherently.
- The introduction of the $\texttt{[IDK]}$ token to explicitly model uncertainty in LLMs is a novel approach
- Through ablat... | Rebuttal 1:
Rebuttal: We are very thankful to the reviewer for their time reading our work and writing a thorough review.
We are encouraged that the reviewer finds our paper to be well written, our approach to be novel, and our ablation experiments to be extensive and to properly demonstrate the behavior of our approa... | Summary: To allow LMs to express their uncertainty for generative tasks, the authors introduce a new special IDK token. The authors modify the cross-entropy training objective to assign part of the probability mass to the IDK token in cases where the model gets the prediction wrong. The token embedding is randomly init... | Rebuttal 1:
Rebuttal: We first express our sincere gratitude to the reviewer for their time reading our work, writing a thorough review, and bringing up thoughtful comments.
We are encouraged that the reviewer finds our paper to be well written, our approach to be novel, and our experiments to be extensive and to prop... | Summary: It introduces a novel method to address the issue of hallucinations in Large Language Models (LLMs). These models, despite their proficiency in capturing and generating human knowledge, can sometimes produce factually incorrect text. To combat this, the authors propose a calibration method that incorporates an... | Rebuttal 1:
Rebuttal: We first highly thank the reviewer for their time reading our work, writing a thorough review and bringing up thoughtful comments.
We are encouraged that the reviewer finds our method a creative and novel method to handle an existing and important problem current LLMs have - which is generating m... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort put into providing a thorough review of our work. We briefly highlight the strengths of our work as identified by the reviewers:
- Our [IDK] token approach is a “novel approach” deemed “an original contribution to the field of natural language proce... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning | Accept (poster) | Summary: Extending context length is a fundamental challenge for LLMs. Unlike previous approaches that focus on efficiently handling text tokens, this paper introduces a novel method: encoding lengthy text information into image renderings. These image tokens effectively increase the context length and enhance the perf... | Rebuttal 1:
Rebuttal: **Q1. Information loss from image rendering and trade-off between performance and computation cost:**
**1.Information Preservation in Rendering:**
The rendering process preserves all the words in the text image, ensuring that no textual information is lost during this step.
**2.Information Encod... | Summary: The paper proposes a method to increase the context size of multi-modal large language models. The goal is to increase the context with minimal GPU memory for both training and inference as well as floating pointing operations. Finally, the authors show that the method obtains good results on in-context learni... | Rebuttal 1:
Rebuttal: **Q1. Questions related to line 75-76.**
_i. Clarification on Token Concatenation:_
Notice that <visualx> is used as `a placeholder to indicate the position of an image` within the token sequence. It has a `token length of 1`. For example, in a sequence of 256 tokens, the structure could be <visu... | Summary: This paper introduces a method called Visualized In-Context Text Processing (VisInContext) to address the challenge of processing long in-context texts in multimodal learning, which arises due to the high GPU memory and computational costs associated with lengthy textual content. VisInContext converts long tex... | Rebuttal 1:
Rebuttal: **Q1. Complexity of Implementation and Barrier to Adoption:**
It is not true, and we would like to emphasize that our implementation is not only simple but also easy to adopt for future works.
**i. Text Rendering:** This step is performed during the preprocessing phase on the CPU and can be impl... | Summary: The paper introduces Visualized In-Context Text Processing (VisInContext), a novel technique designed to enhance multi-modal learning models by efficiently expanding their in-context text length. This method transforms extensive text into visual tokens, substantially lowering GPU memory consumption and computa... | Rebuttal 1:
Rebuttal: **Q1. The motivation of “text-only in-context few-shots experiment” is not clear:**
The motivation behind the “text-only in-context few-shots experiment” aligns with the common practices in mainstream few-shot learning architectures like Flamingo, IDEFICS, and EMU2[1]. These models often include ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Disentangling Linear Quadratic Control with Untrusted ML Predictions | Accept (poster) | Summary: This paper presents a novel policy, DISC, designed to manage uncertain perturbations in dynamical systems by learning a confidence parameter online. The focus is on integrating predictions from machine learning (ML) tools, which are often unreliable, into linear quadratic control (LQC) frameworks. The key inno... | Rebuttal 1:
Rebuttal: Thank you for all the comments and we appreciate your positive feedback on our work!
Regarding the issues pointed out in the weakness part, here are our explanations and future plans:
***Assumptions:*** We acknowledge the limitations of assuming the continuity and bijectivity of the mixing func... | Summary: The submission considers LQC with perturbations. The considered problem is interesting with potential applications on LQC with noisy/unreliable ML predictions.
The submission improves the existing method in Robustness and consistency in linear quadratic control with untrusted predictions, where there is a co... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate your comments on the strengths and acknowledge the concerns regarding the clarity of our writing and presentation. Please find our clarification below.
1. ```Clarification of Basic Concepts in Abstract``` The detailed meanings of "*best-of-both-worlds*" an... | Summary: The paper considers the problem of LQR where the added perturbations may not be iid Gaussian noise but depend on some latent variables (which are themselves predicted using an ML model). In particular, the authors consider the dynamics given by
x_{t+1} = A x_{t} + B u_t + f(s_t, \theta)
where s_t and \thet... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novel aspects and strengths of our work, especially the dual focus on consistency and robustness in the use of machine learning predictions within control systems. We also appreciate your acknowledgment of the importance of the problem we are addressing and the novel ... | Summary: The paper investigates the setting of linear quadratic control with latent perturbations. In particular, the state evolution does not only depend on the current state and the control input, but also on various latent variables that are mixed through a linear or nonlinear mixing function of unknown parameteriza... | Rebuttal 1:
Rebuttal: Thank you so much for your high-quality, very detailed, and insightful comments. They are very helpful and we sincerely appreciate a lot for what you dedicated in the reviewing process. Below are our responses to the questions:
***Question 1***. Thank you for pointing out the typo. The matrix $ H... | Rebuttal 1:
Rebuttal: We greatly appreciate all the questions received. In this global rebuttal, we provide more detailed discussions.
***Nonlinear Mixing Function:***
Regarding Question 6 from F6Jr, we provide a simple experiment involving two latent variables to offer preliminary insights. While our current experim... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm | Accept (spotlight) | Summary: The paper studies the performance of the Quantum Approximate Optimization Algorithm (QAOA) for a classical average case problem from high dimensional statistic: tensor principal component analysis (tPCA), which exhibits a computational-statistical gap. The paper investigates if this algorithm can achieve a qua... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments on the importance of the algorithm and the problem under study. To make the motivation more concrete: We believe that tensor PCA is a promising avenue for demonstrating quantum advantage because the computational-statistical gap in the spiked tenso... | Summary: The paper investigates the performance of the Quantum Approximate Optimization Algorithm (QAOA) on the spiked tensor model problem. The authors demonstrate that QAOA's weak recovery threshold aligns with that of tensor power iteration and show through heuristic calculations that multi-step QAOA could potential... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on the novelty of our results. Regarding the weaknesses mentioned, we acknowledge that the quantum advantage is modest. Before our work, the extent of quantum advantage that the QAOA could provide on the spiked tensor problem was unknown, especially ... | Summary: The quantum approximate optimization algorithm is analyzed for the spiked tensor model. Weak recovery
of 1-step QAOA is rigorously shown to matche that of 1-step tensor power iteration. Heuristic calculations for p-step QAOA
matche that of p-step tensor power iteration.
Strengths: There have been many works o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments on the importance of understanding the power of quantum algorithms for statistical inference problems. Regarding the weakness mentioned, we remark that our analysis and derivation of the sine-Gaussian law is completely rigorous at p=1. Additionally, ... | Summary: This submission proposed to use quantum approximate optimization algorithm (QAOA) to compute the maximum likelihood estimator in the statistical estimation problem of the spiked tensor model.
Using the overlap between the estimated vector and the original vector, the author(s) obtained rigorous analysis for t... | Rebuttal 1:
Rebuttal: We thank the reviewer for a positive assessment of our paper. We refer the reviewer to the global rebuttal for our response regarding the weakness of using heuristic calculation. Moreover, our asymptotic results in the $n\to\infty$ limit also show good agreement with numerical experiments at finit... | Rebuttal 1:
Rebuttal: In this global rebuttal, we address the concern raised by Reviewers 2, 3, and 4 about our use of physics-style heuristics and the rigor of theoretical results. We emphasize that our result at depth $p=1$ is fully rigorous. While the analysis for $p>1$ is not fully rigorous due to the use of Dirac ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies the performance of 1-step and multi-step quantum approximate optimization algorithm (QAOA) for spiked tensor problem. In this problem one observes a q-dimentional tensor which is a properly normalized linear combination of q-th tensor power of unknown vector $u \in \{+1, -1\}^n$ and Gaussian... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our paper and agree on the importance of studying quantum algorithms applied to the spiked tensor.
Regarding the mentioned weakness, we would like to point out that the various celebrated classical hardness results for the spiked tensor problem... | null | null | null | null | null | null |
Embedding-Aligned Language Models | Accept (poster) | Summary: The paper proposes a method for prompting LLMs to generate content that optimizes an objective defined in a latent space through externally provided embedding spaces. To this end, they define a reinforcement learning agent (EAGLE) as follows: Given an entity (e.g. a movie description), an LLM is prompted to ge... | Rebuttal 1:
Rebuttal: Thank you for your positive review and helpful feedback. We appreciate the fact that you found our work interesting and novel. Please find our response to your comments and suggestions below.
Re clarity:\
We’ll elaborate further on the reference policy and its use. We’ll also move more informatio... | Summary: This paper presents a method to steer an LLM’s generation towards optimal regions of a latent embedding space using reinforcement learning. The technique involves a language model to guide an LLM by modifying the textual representation of an entity. This work builds off previous work on embedding language mode... | Rebuttal 1:
Rebuttal: Thank you for your review and your helpful comments. We appreciate you finding our paper novel and a strong improvement over ELM. Please find our response to your suggestions and comments below.
- We will add additional experiments using the public Amazon dataset, using user profiles and embeddin... | Summary: This work proposes training language models so that they follow objectives or utility functions which are defined in the embedding space. They define it as a reinforcement learning problem so that the EAGLE agent uses an actions prompt to probe the environment which is an LLM. The changed entity is embedded in... | Rebuttal 1:
Rebuttal: Thank you for your review and your helpful comments. Please find our response to your comments and suggestions below.
Re Prompt Generation:
1. Much recent work (see, e.g., [1,2,3,4]) has shown the importance of designing task-specific prompts for solving tasks. While our work aligns LLMs with emb... | Summary: This paper proposes an algorithm to train an LLM-based agent *EAGLE*, that can align itself with existing domain-specific *latent embedding spaces* (e.g. embedding vectors in recommender systems, personalized advertising, and content creation) to discover novel content gaps and recommend new entities. It defin... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and helpful feedback. We appreciate your positive assessment of our formulation and its strengths in surfacing content gaps using latent embeddings and the generative capabilities of LLMs. We address your concerns and questions below.
As you correctly point out,... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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