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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Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times | Accept (poster) | Summary: The authors propose a dual multi-head cross attention mechanism to extract problem features represented by the inputted uncertainty sets for the robust vehicle routing problem with uncertain travel times under the min-max regret criterion. The experimental results on the robust TSP and VRP demonstrate the effi... | Rebuttal 1:
Rebuttal: **Q1:** The algorithm has not been compared to other existing neural combinatorial optimization works.
**A1:**
To the best of our knowledge, this paper represents the initial application of neural combinatorial optimization to address RTSP/RVRP. Nonetheless, we have tried to compared different n... | Summary: The paper concerns robust routing problem with uncertain travel times under the min-max regret criterion, which represents an extended and robust version of the classic traveling salesman problem (TSP) and vehicle routing problem (VRP). The authors proposed a dual multi-head cross-attention mechanism to extrac... | Rebuttal 1:
Rebuttal: **Q:** I've also found a minor writing issue: it seems that in definition 3.1, it is not clear what exactly $x_{ij}$ is (one can guess, but it would be good to clarify).
**A:** Thank you for your advice. We will include the explanation for $x_{ij}$ in the revised version.
Here, $x_{ij}$ is a b... | Summary: The min-max regret criterion is employed, and the routing problems are solved when the moving cost of an edge varies (perhaps uniformly) within a certain range. The authors have discussed two (possibly) uncertain classes and proposed a theoretical analysis and a learning-based solver (on TSP and CVRP).
Streng... | Rebuttal 1:
Rebuttal: **Q1:** In contrast to [S1], there appear to be some gaps concerning the uncertainty of edge times (for example, another standard way is modeling them by some distributions).
**A1:** In the field of robust optimization, the modeling of robust uncertainty sets can exhibit significant variation. Ap... | Summary: This paper proposes a neural combinatorial optimization (NCO) method to solve robust routing problems under uncertain travel times under the min-max regret criterion - a common occurrence in real-life scenarios in which one wants to minimize worst-case scenarios. The proposed approach employs the transformer-... | Rebuttal 1:
Rebuttal: **Q1:** Equation 6: why is the loss function minimizing the expected reward? Shouldn’t it maximize it? Moreover, the gradient of the loss is defined in Eq. 10, which is different from the loss (with a different notation).
**A1:** In order to maximize the expected reward, we invert the reward to a... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a neural network based approach and applied it to the robust version of the traveling salesman problem (TSP) and the related vehicle routing problem (VRP) where interval uncertainty is taken into account and the goal is to minimise the max-regret value. The architecture (encoder, decoder) is... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will strive to broaden the literature review to incorporate more recent studies. Additionally, we will carefully review and adjust the table font size and correct any errors.
**Q1:** The formulation of the robust TSP presented in Section 3 seems to be the same to t... | null | null | null | null | null | null |
Almost Surely Asymptotically Constant Graph Neural Networks | Accept (poster) | Summary: This paper theoretically analyze the phenomenon that GNN-based probabilistic classifiers almost surely converge to a constant. A formal approach based on term language is proposed. It provides a unified framework to study different GNN models.
Strengths: 1. The paper is well-organized.
2. The theoretical stud... | Rebuttal 1:
Rebuttal: Thank you very much for your review and for finding our work solid, well-organised, and interesting. We reply to each of your comments below.
> The use of ``term language'' is abstract and can be difficult to understand. It is preferable the authors can explain more and provide simple (numerical)... | Summary: The paper presents a theoretical study on the expressive power of some popular GNNs (MPNNs with the mean aggregator, GAT, and GPS+RW) as multi-class classifiers with random featured graphs. It shows that their output converges a.a.s. to a constant function from various random graph models, including Erdős-Re... | Rebuttal 1:
Rebuttal: Thank you very much for your review and for pointing out the strength and applicability of our term language. We respond to each point below.
> *Unclear Contribution to Expressiveness Analysis*. The expressiveness power is usually used to describe the GNN’s ability to approximate functions, which... | Summary: In this paper, the authors show that GNNs for graph classification applied to several classes of random graphs (with any node features) converges asymptotically almost surely to a constant. This is similar to several recent results in the literature on graphon-like graphs, but with a potentially stronger notio... | Rebuttal 1:
Rebuttal: Thank you very much for your review and for finding our approach original and acknowledging the handling of Barabasi-Albert graphs in our work — an important difference to existing related works. We respond to each of your comments below.
> the presentation of language, terms, and all associated ... | Summary: This paper introduces a term language to express many common GNN architectures as probabilistic classifiers. Using this term language, the authors provide asymptotically almost sure convergence results for dense and sparse random graph models (Erdos-Renyi variants, Barabasi-Albert, Stochastic Block Model). The... | Rebuttal 1:
Rebuttal: Thank you very much for your review and for finding our work novel, useful, easy to follow, and elegant. We respond to each of your comments below.
> The term language is not explained in sufficient detail. E.g., are there any specific assumptions that $\pi$ and $\tau$ need to satisfy? Could you ... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and constructive comments. We are grateful that the reviewers appreciated the novelty of our use of a term language to capture a wide class of GNN architectures, and the fact that our results apply to distributions beyond those typically cons... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions | Accept (poster) | Summary: This paper addresses the theoretical question of whether, through repeated interactions, strategic agents can overcome the uncertainty about the exact payoff structure of the game being played to achieve outcomes they could have achieved in the absence of uncertainty. The authors specifically consider a Stacke... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback on our work.
> same action spaces for all G in the support of D
Yes, we agree with your point. We do require all the games in the support of D to have the same action spaces for both agents to avoid the issue that you proposed. We will make sure to add this cl... | Summary: In this paper, the authors study whether players can achieve the Stackelberg value when they have uncertainty about the game. Specifically, the authors consider a two-player setting where players can repeatedly interact with the environment. They consider the pure Nash equilibrium in the meta game where the st... | Rebuttal 1:
Rebuttal: Thank you for the insightful questions.
> further clarification on section 3.2, why cannot thee less-informed player estimate the game for the first o(T) rounds and then use the same strategy as the perfectly informed player for the rest of the rounds
Thank you for the question. We believe this ... | Summary: The paper studies two-player repeated games where both players use (no-regret) learning algorithms to choose strategies simultaneously in each stage, which are called meta-games. The authors define the pure Nash equilibria of the meta-games and explored the players' equilibrium utilities based on their initial... | Rebuttal 1:
Rebuttal: Thank you for the feedback.
> Reason for using Stackelberg value as a benchmark
Thank you for your question. We agree that more information on choosing Stackelberg value as a benchmark will strengthen the paper. We plan to include more details, as we briefly discuss below.
We view our work as p... | Summary: The paper explores the impact of information asymmetry on the ability of agents to achieve their Stackelberg optimal strategy in repeated games. It investigates whether agents can overcome initial uncertainty through strategic interactions alone. The authors propose a meta-game model where players' actions are... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions.
> While the paper discusses the inability of uninformed players to achieve their Stackelberg values, it could provide more insight into the learning dynamics and the rate at which players converge (or fail to converge) to these values.
In our paper, we ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Aligning LLM Agents by Learning Latent Preference from User Edits | Accept (poster) | Summary: In this paper, the author formulates a new task that the user may want to edit the agent's response to make its later responses more personalized. The author proposes a method called PRELUDE to learn preference descriptions of users from users' previous edited contexts in an interactive framework. The author a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback. Please find our responses below in the order of the review. We start with an important misunderstanding.
> In practice, will someone take a lot of time to give feedback to the agent? ...
**Major Clarification on the naturalness of user edits:** Th... | Summary: This paper investigates interactive LLM alignment by analyzing user edits to an agent's responses. The proposed framework, PRELUDE, enhances the agent's alignment with user preferences without extensive fine-tuning, thus avoiding high costs and potential performance degradation on other tasks. PRELUDE infers u... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback. Please find our responses below in the order in the review.
> What is (and how to decide) the granularity of each edit? .... So are you treating each single modification as one edit? More discussions on this part is needed.
**Clarification on edit... | Summary: The paper discusses interactive learning of LLM-based language agents based on user edits on the agent’s output. It first proposes the framework that infers a description of the user’s latent preferences based on historical edit data, and then uses an LLM to infer these user preferences. The proposed solution ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback. Please find our responses below in the order of the review.
There are complementary improvements happening in the general field of LLM prompting, inference, and planning. Some of the future work directions in which we can incorporate these ideas to... | Summary: The paper first proposes, PRELUDE, session level personalization for a writing assistance task. Here a model must learn natural language user preferences from edits made by the user to outputs generated a model during a session. Next the paper proposes an algorithm, CIPHER, which leverages an LLM to infer natu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback. Please find our responses below in the order in the review.
> The number of rounds in a session appears to be very high...
**Evaluation at different T:** Since our setup is an online learning setting, we can evaluate the method and baseline at any... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contextual Decision-Making with Knapsacks Beyond the Worst Case | Accept (poster) | Summary: This paper considers the problem of dynamic decision making with resource constraints. They show that under certain conditions they can achieve O(1) regret with respect to the time horizon. However, I am not sure whether this is true regret (i.e. with respect to the best policy in hindsight) or regret with res... | Rebuttal 1:
Rebuttal: Thanks for your review and comments.
We now respond to your concerns and questions.
**Theorem 3.1.**
It is *completely correct* for you to say that in the equation of Theorem 3.1, $V^{\mathrm{FL}}$ could be replaced by $V^{\mathrm{ON}}$.
In other words, we are talking about the *true regret*,... | Summary: This paper studies contextual decision making with Knapsack constraints assuming that the requests and the contexts follows some distributions. It studies the full information setting when each context is revealed after the decision is made and the partial information setting when the context is revealed only ... | Rebuttal 1:
Rebuttal: Thanks for your comments and questions. We will now answer them.
**Why uniqueness and non-degeneracy is important.**
The corresponding discussion is located in *Lines 233--243* in our paper.
In short words, under this assumption (Assumption 3.1), we have the stability property that if all est... | Summary: This paper studies an online contextual optimization problem with resource constraints. The paper provides a sufficient condition (worst case condition) under which the fundamental limit on the regret bound is reached. The paper further provides an algorithm that can achieve \tilde O(1) regret when the worst c... | Rebuttal 1:
Rebuttal: Thanks for appreciating our work.
We will now answer your questions.
**Known $T$.**
$T$ is known beforehand in this work, which is a common assumption in the literature, as supposed by the survey of Balseiro, Besbes, and Pizarro (2023).
We have not yet considered the problem of unknown $T$, ... | Summary: This paper considers a new contextual decision-making model with knapsack constraints, which is highly related to the CBwK setting but features a different information feedback structure. Under this model, the authors nearly characterize the conditions under which $\tilde{O}(1)$ regret can be achieved based on... | Rebuttal 1:
Rebuttal: Thanks for your kind comments and suggestions.
We now respond to your concerns and questions.
**Stronger assumptions on the randomness.**
Indeed, our model requires an explicit form of randomness $\gamma$ for the external factor.
This explicit model is crucial for us in this work to learn it... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Animal-Bench: Benchmarking Multimodal Video Models for Animal-centric Video Understanding | Accept (poster) | Summary: This paper introduces Animal-Bench, a video question answering benchmark focused on animals, which is usually overlooked in previous video benchmarks. Animal-Bench is sourced from six datasets and includes 13 tasks. Eight video-language models are evaluated on the benchmark and the results reveal shortcomings ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and thoughtful suggestions, and hope our response will address your concerns.
**W1: Missing dataset statistics**
For each task, the included videos correspond to only one question. For example, for the object recognition task, we only selected videos containi... | Summary: Propose of an automated pipeline for animal-centric large-scale multimodal video benchmark, Animal-Bench, that simulates real-world conditions such as snowing via diffusion-based video editing approach. This data is sourced from 6 dataset and multiple filtering have been applied to ensure diversity and lack of... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and thoughtful suggestions, and hope our response will address your concerns.
**W1: Potential negative impacts**
Please kindly refer to "Author Rebuttal" Q3.
**Q1: Option-setting methods**
Please kindly refer to "Author Rebuttal" Q2.
**Q2: Limitations of t... | Summary: 1. This paper introduces Animal-Bench, an animal-centric video understanding benchmark. The benchmark includes 13 tasks, spanning 7 major animal categories and 822 species.
2. The authors collect data primarily from 6 open datasets, such as TGIF-QA and Animal Kingdom, and apply data filtering based on diversit... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and thoughtful suggestions, and hope our response will address your concerns.
**W1: Class imbalance**
Firstly, the size shown in Table 3 represents the number of data points for each task, rather than the number of animal class or behavior class. Our evaluati... | Summary: This work introduces Animal-Bench, a novel benchmark for evaluating multimodal video models in animal-centric video understanding. The benchmark covers 13 tasks spanning 7 major animal categories and 822 species. It proposes an automated pipeline for data filtering and question-answer pair generation, reducing... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and thoughtful suggestions, and hope our response will address your concerns.
**W1, W2: Question and answer quality**
In fact, although the examples in the “Time” task in Table 2 show multiple objects, only one object is performing an action. The other object... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable comments.
Due to the word limit for responses to each reviewer, we respond to common questions that were mentioned more than once here, and respond to other questions in the individual reviewer responses. In the numbering, "W" indicates a response to the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HiCoM: Hierarchical Coherent Motion for Dynamic Streamable Scenes with 3D Gaussian Splatting | Accept (poster) | Summary: This paper proposes a novel online training framework for multi-view dynamic scenes. A HiCoM framework is introduced for online learning of dynamic scenes. To initialize a better 3D Gaussian, the authors propose adding noise because it may overfit in the forward-facing multiview setups. Modeling motion by a sp... | Rebuttal 1:
Rebuttal: Thank you for positive assessment of adding noise to 3D Gaussian representation learning, the effectiveness of our hierarchical coherent motion, and the simple deformation data structure to save memory. We now address the main concerns you have raised as follows.
### 1. The performance of HiCoM i... | Summary: This paper proposes a novel framework for online reconstruction of dynamic scenes based on 3DGS. The main contribution lies in its Hierarchical Coherent Motion, which gives a more compact motion representation. Results show its effectiveness and rendering improvement.
Strengths: 1. The method is straightforwa... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive feedback on the clarity and straightforward nature of our method. We are particularly encouraged by your recognition of the effectiveness of our perturbation smoothing strategy, as well as your appreciation of the simplicity. Your acknowledgment of the thoroughn... | Summary: This paper deals with the online reconstruction of dynamic scenes from multi view video input. The authors build their method on the popular 3D Gaussian Splatting technique. To tackle this setting, they propose 1. A perturbation smoothing that introduces small perturbations to the 3D positions of the Gaussians... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of our work, particularly the perturbation smoothing strategy, the exceptional performance on N3DV and Meet Room datasets, and the quality of our writing. In the following, we address each of your comments and questions in detail.
### 1. The impact of motio... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thorough evaluation and valuable feedback on our manuscript. We are pleased that all reviewers recognized strengths of our proposed method and find it simple and effective. Reviewers 1 (oHau), 2 (QKxo) highlighted the effectiveness of our perturbation... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting | Accept (oral) | Summary: The paper presents a method to distill knowledge about a given task or domain from text based knowledge into a form that can be used train a RL policy. The method extracts knowledge from text with a LLM, which is represented in a pseudocode-like textual form, and uses the knowledge to turn the LLM into a dynam... | Rebuttal 1:
Rebuttal: **Q1: It was difficult for me to follow what exactly what does in the method.**
We are sorry that the current presentation of URI is not straightforward enough. As URI’s implementation involves interactions among several components from different research domains, it indeed might look complex, ... | Summary: The paper introduces a novel approach to policy learning, termed "Policy Learning from Books," which leverages existing textual knowledge, such as books and tutorials, to develop policy networks without the need for extensive real-world interactions. This method is inspired by how humans learn new skills from ... | Rebuttal 1:
Rebuttal: **Q1: add baselines: (1) distilled policy from the imaginary dataset generated directly by the GPT without the information from the books; (2) compare it with some other policies learned with conventional RL algorithms**
Thanks for the valuable suggestions. In the rebuttal period, we implement t... | Summary: The paper introduces an intriguing approach to reinforcement learning (RL) through the concept of Policy Learning from Books (PLfB), which leverages textual resources like books and tutorials to derive policy networks. This methodology represents a interesting departure from traditional RL techniques that rely... | Rebuttal 1:
Rebuttal: **Q1: Prompting Strategy Details & Exploration: Could you elaborate on the specific prompting strategies used to generate the imaginary dataset? How do you believe different strategies might influence the quality and effectiveness of the policy learned?**
We agree that the design of prompts matte... | Summary: This paper introduces Policy Learning from Books (PLfB). This framework leverages the knowledge encoded in textual books and tutorials to train decision-making policies, specifically for playing football, without requiring direct interaction with the environment. This method is a three-stage framework Understa... | Rebuttal 1:
Rebuttal: **Q1: If there are more domains where the authors could experiment with different data sources, the readers would have better expectation of the model.**
We agree that applying URI to more domains would strengthen readers' understanding, expectation, and belief in it. Thus, we build a new proof-o... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive and thoughtful feedback. We appreciate all the recognition and kind comments on our work, including **conceptual novelty and enlightenment** (R1, R2, R3, R4); **realistic, extensive, and well-motivated experiments** (R1, R2, R3, R4); **promising re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Identifying Spatio-Temporal Drivers of Extreme Events | Accept (poster) | Summary: In this paper, the author investigates a novel, significant, and practical problem: how to efficiently identify extreme anomaly events from climate data. To address the temporal delays between anomalies and extremes and the spatially uneven response of anomaly events, the author first innovatively constructs t... | Rebuttal 1:
Rebuttal: We appreciate and thank the reviewer for recognizing and appreciating the efforts behind this work. Please see our responses to the questions below.
> Code and datasets
Please note that we will release the code and datasets along with the documentations upon publication. We describe the datasets... | Summary: This work aims to identify the atmospheric drivers of extreme droughts. For this, they assume that for every impact of extreme droughts measurable with remote sensing, there is a precursor signal in assimilated land surface and meteorological data. The work proposes to identify these precursor signals with inh... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and the thoughtful feedback. We are glad that you found our work interesting and important for future research.
> Terminology
Thank you for this suggestion. We will follow your suggestion and define the terminology in the introduction section of the paper in the... | Summary: The paper proposes a novel approach to identifying spatio-temporal anomalies correlated to extremes such as drought. Using neural network, to predict extreme events by learning spatio-temporal binary masks of anomalies identified in climate data. The network is trained end-to-end to predict both anomalies and ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback for this work and that the reviewer recognized the novelty of this work. We respond to the questions below.
> The method only shows results on droughts. Have you tried the method on other extreme events other than droughts?
We tested the algorithm on 9 types... | Summary: This paper proposes an approach to learning the spatio-temporal relationships between events with spatial differences and temporal delays. Specifically, they propose a method that identifies spatial-temporal anomalies in multivariate climate data that are correlated with extremes. The authors conduct experimen... | Rebuttal 1:
Rebuttal: Thank you for your time and for reviewing our work. We are glad that you found the task and the problem we address in this work important. In the following, we answer your questions.
> Difference between anomaly and extreme in this paper is not clear. What is the difference between anomaly and ex... | Rebuttal 1:
Rebuttal: We thank all reviewers for their efforts and the valuable comments. We appreciate the positive and encouraging comments by the reviewers that we briefly summarize:
* Reviewer fRrQ acknowledges that the proposed task is crucial for climate science and acknowledges the experiments on real and synthe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers | Accept (poster) | Summary: The authors consider resource-scare RL. In the considered setup, the agent is restricted to a small replay buffer, or single sample updates (incremental RL). The authors observe that traditional on-policy and off-policy algorithms (PPO, SAC, and TD3) fail to learn performing policies when the replay buffer siz... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time and commitment to evaluating our paper. We're glad the reviewer noted the importance of the problem setting, the diversity of tasks, and the use of 30 seeds per task. They also appreciated the clarity and writing quality. We're also pleased to read th... | Summary: The paper address the problem of incremental reinforcement learning in low compute regimes. It introduces an algorithm Action Value Gradient (AVG), that uses the entropy regularized actor-critic learning method for solving the task in an incremental fashion. The $\textbf{re-parameterization}$ (RP) trick is use... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time and commitment to evaluating our paper. We are pleased to read that the reviewer thinks our paper is well-written and that the problem we focus on is important to the continual learning and adaptive learning communities as well as for applications in ... | Summary: In this paper, the authors propose a deep RL algorithm named AVG (Average Value Gradient) which uses incremental learning. This allows eliminating storing experiences in replay buffer for training models. They also combine tricks like penultimate normalization and return scaling for robustness. AVG also does n... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time and commitment to evaluating our paper. We are pleased that the reviewer recognizes the value of eliminating replay buffers and target Q-networks to develop simpler, more computationally efficient learning algorithms. Below, we address the points rais... | Summary: This work presents a well-grounded algorithm advancement in incremental deep policy gradient learning, which builts on Action Value Gradient Theorem. Extensive simulated and real-world experiments and ablations demonstrate the superiority of the proposed AVG algorithm.
Strengths: 1. This work is a novel metho... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time and commitment to evaluating our paper. We are pleased that the reviewer thinks our proposed method, AVG, is a novel, "well-grounded algorithm advancement" that tackles an important challenge in real-world applications. We are also happy that the revi... | Rebuttal 1:
Rebuttal: We thank our reviewers for their insightful comments and questions.
We are pleased to see that reviewers recognized several notable strengths of our paper: 1) the novelty and well-grounded approach of AVG, 2) its relevance to continual and adaptive learning, with potential applications in search... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Are Language Models Actually Useful for Time Series Forecasting? | Accept (spotlight) | Summary: This paper questions the effectiveness of LLMs in time series forecasting. Through a series of ablation studies on 3 LLM-based forecasting methods, the authors find that removing or replacing the LLM component often improves forecasting results. They conclude that LLMs do not provide significant benefits for t... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful and positive review! We've responded to each of your points below.
**W1: Can our work extend to other uses of LLMs for time series?**
We agree this is an exciting, natural direction for our work. While it’s beyond the scope of one paper, we hope our work inspires the co... | Summary: A recent surge of papers have popularised the usage of pre-trained LLMs for time series forecasting. The paper analyses the claim that LLMs are useful for time series forecasting, by performing a series of ablation studies. Their conclusion is that LLMs bring little to no benefit for the task, and are signific... | Rebuttal 1:
Rebuttal: Thank you for the positive and encouraging feedback and for your endorsement of our work! We've addressed your questions below.
**W1: Why does RQ3 only consider LLaTa?**
Thanks for suggesting we extend RQ3 to include all methods. We completely agree this would strengthen this RQ and our choice w... | Summary: This paper explores the effectiveness of language models in time series forecasting. The authors substantiate their claim by performing three straightforward ablations of three popular and recent LLM-based forecasting methods. After extensive experiments, the authors find that patching and attention structures... | Rebuttal 1:
Rebuttal: Thank you for your focused and actionable review. We are glad you agree our work provides interesting and insightful observations! As detailed point-by-point below, **we’ve addressed your remaining concerns by running your suggested experiments**.
**W1: Including state-of-the-art forecasting mode... | Summary: This paper presents an extensive empirical study on the effect of pre-trained LLMs in time series forecasting tasks. By ablating popular LLM adaptations on widely adopted time series benchmarks, experiments in the paper show LLMs do not benefit from pre-training on text data to gain improvement in forecasting ... | Rebuttal 1:
Rebuttal: Thank you for the encouraging feedback and the very positive review! We've fixed the typo and responded to your query about Table 5 below.
**Why does "woPre+woFT" perform well in Table 5?**
This is a great observation! We were initially surprised by this, too. But we’d like to clarify that “woPr... | Rebuttal 1:
Rebuttal: Thank you to all reviewers for your thoughtful feedback—we are thrilled to see such positive reception!
To summarize this feedback, the reviewers emphasize our study's importance, noting we present **"very significant findings regarding a recent trend in time series forecasting"** [wccd], **"very... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication | Accept (poster) | Summary: The paper introduces a framework for communication in multi-agent RL. The communication space is aligned with the embedding space of natural language. The loss for training the underlying RL algorithm is augmented with a cosine similarity loss to align the communication message generated to the embeddings gene... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work, and we seek to clarify our experiment design with the following responses.
> Q1: Figure 1 can be better presented
Your suggestion is well received. Thank you for pointing it out. We have remade the framework illustration in Figure D in the PDF an... | Summary: This paper proposes a pipeline for aligning multi-agent communication with natural language by using an LLM to collect a synthetic dataset of natural language interactions. Within a MARL communication pipeline, this dataset is used to align the learned communication space with word embeddings of reference comm... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the motivation and generalizability of our work, and we seek to clarify the framing of our research.
> Q1: Missing comparison with CICERO
Thank you for pointing out this paper. We indeed came across it but decided not to include it in the comparison becaus... | Summary: The paper presents a novel computational pipeline aimed at aligning the communication space of Multi-Agent Reinforcement Learning (MARL) agents with an embedding space of human natural language. The authors propose grounding agent communications on synthetic data generated by embodied Large Language Models (LL... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the originality and soundness of LangGround, our proposed method for MARL agents with human-interpretable communication. We seek to clarify your questions with the following responses and additional experimental results.
> Q1: No theoretical proof
We do ... | Summary: The paper presents a method of using LLMs with synthetic data to generate human interpretable multi-agent communication protocols using zero-shot learning. The model, called language-grounded multi-agent communication (LangGround) aligns the communication of the multi-agent model with the LLM based communicati... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty and soundness of LangGround, our proposed method for MARL agents with human-interpretable communication. We appreciate your constructive comments and suggestions and seek to clarify them with the following responses and additional experiment resu... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and effort in helping us improve the paper. We appreciate your acknowledgment of the novelty and soundness of LangGround, our proposed method for MARL agents with human-interpretable communication. In this rebuttal, we seek to clarify a few common question... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning | Accept (poster) | Summary: This paper studies RL in the federated setting, where each agent only receives a local reward and communicates with other agents in a networked graph. The authors develop federated natural actor-critic in the tabular setting and the linear function approximation setting, with exact and inexact policy evaluatio... | Rebuttal 1:
Rebuttal: ## Response to Reviewer vHsi
Thank you for your time in reviewing our paper. We appreciate your positive feedback. Below We address your points. If our responses adequately address your concerns, we would be grateful if you could consider increasing your current score. And we are happy to answer ... | Summary: This paper studied federated multi-task reinforcement learning (RL), where multiple learning agents interact with different RL problems (different rewards) and communicate through an arbitrary communication graph. This paper proposed a federated natural policy gradient algorithm and a federated natural actor-c... | Rebuttal 1:
Rebuttal: ## Response to Reviewer LgxC
Thank you for your review and positive comments. Below we address you questions point-by-point. If our responses resolve your concerns, we'd appreciate your consideration of increasing your current score. Certainly, please also let us know if you have further questio... | Summary: A decentralized policy-gradient algorithm is introduced. The setting is that multiple agents are operating in environments with identical states, actions, and dynamics but different reward functions; the goal is for the agents to collaboratively find a common policy that maximimzes the aggregate value across ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer tByy
Thank you for your comments. Below we answer your questions point-by-point. If these clarifications address your primary concerns, we'd appreciate your consideration of increasing your score. Certainly, please don't hesitate to request any further clarification.
> ... | Summary: The paper studies the federated multi-task reinforcement learning in a decentralized setting. In the problem, the agents share the same transition kernel but have different reward functions. The communications of agents are defined on a prescribed graph topology. The authors first consider tabular setting and ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer bXwg
Thank you for your feedback. Below We address your concerns. If our responses resolve your questions, we'd appreciate your consideration in raising the score. For any remaining issues, please don't hesitate to let us know.
> **(Weaknesses) how to obtain the mixing ma... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor | Accept (poster) | Summary: The paper proposes a camera pose synchronization method based on trifocal tensors. The method first estimates trifocal tensors given a set of calibrated images. A HOSVD runs on the trifocal tensors to project them into a set of tensors with multilinear rank (6, 4, 4). The edge scales are then obtained from pro... | Rebuttal 1:
Rebuttal: We would like to thank you for the encouraging comments regarding our theory, which indeed is the main contribution of this work. We also appreciate the observations regarding our experiments.
Q1. The EPFL dataset (I used to know it as the Strecha dataset) is extremely simple, with all recent S... | Summary: The paper studies the characterization of the block tensor of trifocal tensors. The paper shows that under the assumption that the scales are known and the block tensor is complete, the global camera parameters can be extracted using Tucker factorization. Since those assumptions don't apply to real-world probl... | Rebuttal 1:
Rebuttal: We would like to thank you for your generally positive feedback.
Q1: The main weakness of the paper is its contribution to real-world applications in terms of runtime, accuracy, and input size. Due to the usage of block trifocal tensors, the algorithm is limited in the number of input cameras (... | Summary: This paper proposes a method to recover rotation and translation from the trifocal tensor, which encodes the three-view geometric parameters. The basic idea is based on Tucker factorization of the trifocal tensor, revealing a low multilinear rank of (6, 4, 4), independent of the number of cameras. A synchroniz... | Rebuttal 1:
Rebuttal: We would like to thank you for your time in reviewing our manuscript, and hope that you will consider our rebuttal to your concerns.
Clarification for the reviewer
We want to stress that our paper is about synchronizing (or denoising) a set of multiple noisy trifocal tensors coming from n views,... | Summary: The paper presents a novel method for camera synchronization using a tensor-based approach, specifically focusing on the block trifocal tensor. The authors discovered that the block tensor of trifocal tensors has a (6, 4, 4) core tensor in the Tucker factorization (HOSVD), independent of the number of cameras ... | Rebuttal 1:
Rebuttal: We would like to thank you for your generally positive feedback, and the suggestions for further research.
Q1: Notation Clarification
Response: We will better clarify the notation in the published version. We intentionally didn't distinguish between contravariant and covariant components to avoi... | Rebuttal 1:
Rebuttal: We would first like to thank all reviewers for all their valuable comments and time. There are some common issues brought up by you, and we would like to respond to some of them in this common rebuttal.
1. We would like to clarify the intent of this work. Our main goal is theoretical: we would ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Demystifying amortized causal discovery with transformers | Reject | Summary: This paper studies the level of generalization achievable when training a predictor to classify “X causes Y” vs. “Y causes X” from observational data. Motivated by recent works performing causal discovery using a pretrained transformer model, the works explores which cases result in predictors that generalize ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time dedicated to our paper. One important criticism is that we should better highlight our contribution in comparison to Lopez et al. (2015): this is addressed in the first bullet of our response to the Weaknesses section.
## Weaknesses
- *“The paper should be upfr... | Summary: This paper explores why causal discovery from observational data, particularly with CSIvA, a transformer-based model, can achieve competitive performance despite seemingly avoiding the explicit assumptions that traditional methods make for identifiability. The authors demonstrate that constraints on the traini... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in analyzing our work.
## Weaknesses
The only comment present in the weaknesses section is that “The presentation can be significantly improved. Since the paper aims to offer novel insights, it is crucial to organize the arguments, theoretical resu... | Summary: The paper studies the behaviour of amortised (supervised) causal discovery methods based on different training data distributions and its relation to more traditional causal discovery and the related identifiability theory. The authors empirically validate the intuitions about supervised causal discovery and g... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and effort in understanding our paper. Before proceeding further, we note the conciseness of the Weaknesses section, where two generic criticisms are expressed in four lines of text. In absence of more articulated concerns, we respond at our best to the com... | Summary: This paper conducts an empirical study of the performance of supervised causal discovery methods, its generality, and the learnability vs. causal structure identifiability. The scope is the bivariate case, and with controlled mechanism and noise to establish the SCM for training and testing data.
In my opini... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and the time taken analysing our paper. Before proceeding further, we note that one important criticism from the reviewer appears to be that “the current findings are still very limited, need to be further consolidated”. We point to the first bullet of our ... | Rebuttal 1:
Rebuttal: We thank the reviewers for the time spent reading and understanding our paper, as well as for the insightful comments and questions. Our work is well received in terms of soundness and presentation quality (with scores ranging from 2 to 3). In contrast, we notice a more polarized view regarding th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Recursive Introspection: Teaching Language Model Agents How to Self-Improve | Accept (poster) | Summary: This paper proposes a general paradigm for fine-tuning LLMs such that the LLM can iteratively refine from its previous outputs in an in-context learning fashion. Experiments are conducted on math tasks where the learned model can self-improve with multi-turn outputs.
Strengths: 1. The proposed RISE framework ... | Rebuttal 1:
Rebuttal: Thanks for your feedback and for a positive assessment of our work. We are glad that you think there is no major weakness and we appreciate the suggestions for future improvement. We fully agree that the literature on in-context RL is quite related, we discuss below and will add a discussion. We p... | Summary: This paper introduces RISE: Recursive IntroSpEction, a novel approach to fine-tuning Large Language Models (LLMs) for self-improvement. The core idea is to enable LLMs to introspect, reason, and correct their mistakes over multiple turns. This is achieved by treating the fine-tuning process as a multi-turn Mar... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for a positive assessment! To address your concerns, we **add new results below on MBPP[1] and CoNaLa[2] (Table 3 in 1-page PDF), two coding benchmarks that show the efficacy of RISE on coding tasks**. We also present results showing the efficacy of RISE with weak m... | Summary: The manuscript tries to solve the problem that existing large language models (LLMs) dono't have the ability of continually improving their responses sequentially, even in scenarios where they are explicitly told that they are making a mistake. The authors propose a fine-tune approach, so-called RISE (Recursiv... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for a positive assessment! To address your concerns, we have added a new result running RISE for > 5 turns (Table 1 in the 1-page PDF), and find that RISE still continues to outperform other methods. We address your questions below & will update the paper. **Please ... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed feedback and reviews! We are glad that all the reviewers had a positive assessment of our work and we believe that addressing the reviewers’ feedback in this rebuttal period has made the paper stronger.
**We have added several new empirical results** in ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GVKF: Gaussian Voxel Kernel Functions for Highly Efficient Surface Reconstruction in Open Scenes | Accept (poster) | Summary: This paper presents Gaussian Voxel Kernel Functions (GVKF) for 3D surface reconstruction. The authors establish a continuous signed distance function derived from discrete 3D Gaussians, achieving high-fidelity open-scene surface reconstruction. They claim that the proposed method has high reconstruction qualit... | Rebuttal 1:
Rebuttal: > **Q1: Discussion or validation of memory consumption.**
The quantitative comparison of memory usage is already presented in Table 2 of the main text. Compared to explicit GS methods (3DGS, 2DGS), our approach significantly reduces training memory consumption and storage, thanks to the introdu... | Summary: The paper presents interesting combintation of implicit and explicit representation that achieves efficient and high-fidelity open-scene reconstruction. Basiclaly, they combine the sparse voxel representation attached with per-voxel Gaussian splatting representation and proposes formulation for 3D surface reco... | Rebuttal 1:
Rebuttal: > **Q1: The geometric reconstruction is inferior to Neuralangelo as shown in Table 3. Can you elaborate more on this?**
Implicit methods, such as those based on NeRF, typically utilize a global fitting approach for SDF, which allows them to fully leverage the universal approximation capabilitie... | Summary: The paper introduces Gaussian Voxel Kernel Functions (GVKF), a novel approach for efficient 3D surface reconstruction in open scenes, leveraging 3D Gaussian Splatting.
The approach aims to combine the strengths of both implicit representations (NeRFs, Neural SDFs) and explicit representations (2D/3D Gaussian ... | Rebuttal 1:
Rebuttal: > **Q1: How does the method perform without voxel grids? What is the impact on quality and VRAM?**
We conducted an ablation study on the Tant dataset to evaluate the impact of voxel representation and SDF mapping. The results are presented in the table below:
| Ablation |PSNR |F1 | Mem | S... | Summary: This paper introduces a method for 3D Gaussian reconstruction and surface extraction from the 3D Gaussians. The method can render scenes with low memory usage through a voxel organization. Additionally, the authors derived a continuous implicit field on top of the 3D Gaussians and extracted the surface on this... | Rebuttal 1:
Rebuttal: > **Q1: Flowchart is too abstract**
Thank you for your suggestion. We have provided more figures in the rebuttal file to illustrate our pipeline.
> **Q2: The explanation of Eq 5**
As shown in Fig 3 of the rebuttal file, the fourth row shows three 1D Gaussian kernel functions, where $t_i$ denote... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Please see the attached PDF page, which includes additional experimental results and formula illustrations, to help clarify our approach. We are deeply grateful for the constructive feedback provided by all reviewers, which has significantly helped improve our paper. We are please... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection | Accept (poster) | Summary: The authors propose a novel approach for architecture for the task of subset selection that is based on developing layers (HORSE) which satisfy a notion of an identity property (leveraging learning representations that connect a set V with its subset S) in a permutationally-invariant way, leveraging key-query-... | Rebuttal 1:
Rebuttal: Thank you very much for appreciating our work and helping us correct the typos! We would like to address your insightful questions with the following responses.
***What is the role of the random partitioning of the set V?***
We consider scenarios where the ground set V is so large that a single ... | Summary: The paper studies an interesting and important problem that is general in machine learning, focusing on motivations (the coexistence of the set-to-set interaction and the large-scale setting issues) that are crucial in this field. While people may like to see the real-world practical analysis of this method, t... | Rebuttal 1:
Rebuttal: Thank you very much for your appreciation of our work and for posing such interesting and promising questions! We will first offer some clarifications and then proceed to response to your insightful questions.
***Some confusion regarding the experimental settings***
As stated in the Introduction... | Summary: Paper is interested in subset selection. Given ground-set $V$, how to choose a subset $S \subseteq V$ that maximizes utility function $F(S, V)$. The core contribution of the method is in (randomly) partitioning $V$ into multiple subsets $S_1, S_2, \dots, S_m$, computing some matrices (based on this random part... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have invested! Your suggestions regarding notations and typos have greatly assisted us in revising our manuscript. For the remaining concerns, we offer the corresponding clarifications below.
***The difference between neural subset selection and (co... | Summary: The paper introduces HORSE, a method for neural subset selection. It addresses the limitations of existing methods by introducing the concept of Identity Property and utilizing an attention-based mechanism to capture complex interactions within the input set. HORSE demonstrates superior performance on various ... | Rebuttal 1:
Rebuttal: We greatly appreciate the time and effort you have invested! In response to your concerns and insightful questions, we have provided detailed clarifications and additional experimental results.
***Are there any state-of-the-art GNN-based or deep sets models that utilize attention mechanisms?***
... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BOLD: Boolean Logic Deep Learning | Accept (poster) | Summary: This paper proposes a novel mathematical concept, termed Boolean variation, and demonstrates that it can be used to define optimization algorithms for training deep neural networks (NNs) consisting of Boolean weights and activations. This is in stark contrast to contemporary binarized NNs, which achieve effici... | Rebuttal 1:
Rebuttal: Many thanks for your positive feedback and insightful comments.
---
**Q1. Methods / Experimental setting: What logic gate/s were used for L? Were different gate types tested, and were there significant performance differences? Is a mix of gate types better than a single gate type? Do different a... | Summary: The paper introduces an innovative approach to deep learning by flipping the binary weights and inputs. This method optimizes convergence in the Boolean domain, promising significant improvements in training efficiency and energy consumption. The extensive experiments conducted validate the effectiveness of th... | Rebuttal 1:
Rebuttal: Many thanks for your positive feedback and insightful comments.
---
**Q1. Assumption A6 (E[Q0(w) | w] = w): The paper assumes E[Q0(w)∣w]=wE[Q0(w) | w] = wE[Q0(w)∣w]=w. However, the justification for this assumption is unclear and needs further explanation.**
Thank you the reviewer for this in-d... | Summary: Proposal of a training method for binary (or ternary) neural networks (inputs and weights are 2-valued, -1,+1, or 3-valued, -1,0,+1), which works without latent floating-point weights, but directly adapts the binary (or ternary) weights, thus improving training (especially in terms of memory consumption). A ki... | Rebuttal 1:
Rebuttal: Many thanks for your insightful comments.
---
**Q1. The presentation of the theory is extremely condensed, with a lot of specifics delegated to the appendix...**
Thanks for your comment. We strongly believe that a self-contained and detailed paper is beneficial. Therefore, we aimed to provide c... | Summary: The paper explores advancements in binary neural networks (BNNs), particularly focusing on Boolean-weighted methods. It highlights the limitations of current BNN approaches and proposes a new method, B⊕LD, that operates directly on Boolean weights, improving energy efficiency and accuracy without relying heavi... | Rebuttal 1:
Rebuttal: Many thanks for your positive feedback and insightful comments.
---
**Q1. The method's reliance on Boolean weights may limit its adaptability to new data and tasks that require more flexible representations.**
Thanks for your comment. It is true that low-precision neural networks often raise co... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank the Reviewers for having carefully reviewed our paper and provided insightful comments, which help to improve the paper. We are strongly encouraged by the endorsements and highly positive feedbacks from all the reviewers in the initial review on several aspects:
**1) Pot... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a strategy for 1-bit backpropagating through the logic expressions of binary neural networks, drastically improving training speeds of BNNs.
Strengths: It is great to see a paper tackling 1-bit precision training.
This is a really important topic, and tackling it on the level of logic is v... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's effort to analyze our paper. Due to the 6000 character limitation, our response is split in two sets. Below we address the main questions (Q: 6, 9, 12, 13, 14). The remaining questions are addressed in the “Official Comment” following the review. Note that this is in c... | null | null | null | null | null | null |
Pretraining with Random Noise for Fast and Robust Learning without Weight Transport | Accept (poster) | Summary: The authors argue that networks trained with feedback alignment can be pre-trained with random input-output pairs. They demonstrate that this allows for faster learning (after the input-output pairs). They also show that the effective dimensionality of the network activity decreases if the pretraining is used,... | Rebuttal 1:
Rebuttal: > One is that the authors do not train until convergence, neither for the random pre-training nor for the task related data.
**: Please find our response in Global Response, issue 1**
We acknowledge the suggestion that comparisons should be made after ensuring full convergence during network tra... | Summary: This paper explores the idea that the brain uses spontaneous prenatal activity to optimize its structure. This is done by showing the benefits of pre-training on random noise for artificial neural networks that are trained with the feedback alignment method - a training strategy that is more biologically-plaus... | Rebuttal 1:
Rebuttal: > I am unclear as to what the Definition in lines 147-149 is trying to convey.
We acknowledge that the definition in Lines 147-149 regarding alignment between forward and backward weights is unclear.
- \mathbb{E} represents the expectation over the parallels between W and B across independent neu... | Summary: The paper explores how pretraining neural networks with random noise can improve learning efficiency and generalization without relying on weight transport, inspired by spontaneous neural activity in developing biological brains.
Key Findings:
1. Random noise training aligns forward weights with synaptic feed... | Rebuttal 1:
Rebuttal: > The bulk of the experiments only studied one-hidden neural network trained on MNIST
**: Please find our response in Global Response, Issue 1**
**We expanded our experiments by increasing the depth of the network and confirmed that even with deeper architectures, the accuracy of the randomly no... | null | null | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' thorough evaluation of our manuscript and their constructive feedback. We have revised our manuscript based on their comments, including additional analyses and simulations. We are confident that our revisions address all concerns raised, further validating o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability | Accept (poster) | Summary: The work studies heterogeneous and non-stationary client availability in FL, showing that it can have a significant impact on the performance of FedAvg. As a solution, the work presents the FedAWE algorithm, which compensate the missed computationd due to unavailability with minimal additional overhead w.r.t. ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation for the motivation, principled algorithm and good results of the paper. We hope that our response can address your concerns.
**W1: on pointers to the supplementary material.** We apologize for not being clear enough in the main text. If the paper gets accepted, we ... | Summary: To address intermittent client availability, the authors study heterogeneous and non-stationary client availability, highlighting the significant impact of such heterogeneity using FedAvg. They propose FedAWE, which (i) compensates for missed computations due to unavailability and (ii) evenly diffuses local up... | Rebuttal 1:
Rebuttal: We are truly grateful for your appreciation for our paper flow and extensive numerical experiments. We believe that many of your concerns can be easily addressed, and we apologize for our lack of clarity on these points. We hope that our response can address your concerns, and the insights discuss... | Summary: This paper primarily focuses on addressing the issue of intermittent client availability in federated learning, where the problem scenario involves heterogeneity in participation and non-stationary client availability.The authors draw on ideas from other federated learning algorithms, including asynchronous fe... | Rebuttal 1:
Rebuttal: Thank you for your appreciation for the novelty and generality of our problem setup. Please find our responses to your questions below.
**W1/Q1: reference Theorems 1-2 in the FedAU paper [53] for a theoretical analysis of Section 4.** Yes, we are happy to reference Theorem 1 and Theorem 2 in the ... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive and thorough reviews, and for their careful evaluation of our paper.
In this global response, we would like to provide a brief summary of the reviews, based on our understanding. Overall, the reviewers confirm that
- our research topic is ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multiple Physics Pretraining for Spatiotemporal Surrogate Models | Accept (poster) | Summary: This paper introduces the Multiple Physics Pretraining (MPP) model, a pretraining approach for physical surrogate modeling of spatiotemporal systems using transformers. MPP uses a backbone model to predict the dynamics of several heterogeneous physical systems simultaneously. The authors include a shared embed... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and expertise. The reviewer raised a number of interesting points that we'd like to address. Some of our responses are more discussion than rebuttal, but we hope we can ease the reviewer's concerns in certain areas and allow them to feel confident in their revi... | Summary: This paper introduces a multiple physics pretraining approach for surrogate modeling, which learns general useful features across diverse physical tasks with a shared embedding and normalization strategy. The experiment results show the proposed MPP-pretrained model outperforms task-specific baselines on all p... | Rebuttal 1:
Rebuttal: To begin, we'd like to thank the reviewer for the effort they put forth in reviewing our work. Their feedback will help us strengthen the submission and we are grateful for their expertise. We hope that we can address their concerns through our rebuttal. If we are able to address any of your conce... | Summary: This article introduces a transformer model which turns across multiple special temporal physics. Using a clever choice of standardization and scaled training, they managed to train a model that can predict the next step given context of snapshots. In the article, they show that a single model can learn dynami... | Rebuttal 1:
Rebuttal: We'd like to thank the reviewer for their thorough reading of our paper. The reviewer raises some great questions and discussion topics which we aim to address below.
#### Q1 -
The finetuning settings are provided in C.3 for 2D and C.4 for 3D. However, while the instructions for initializing n... | Summary: The work proposes the idea of pre-training models on multiple PDE problems and demonstrates that such a pretrained model can be effectively fine-tuned on a target PDE when the pre-training and fine-tuning PDEs are similar. The authors carefully constructed the scalable transformer architecture by employing an ... | Rebuttal 1:
Rebuttal: First off, we’d like to thank the reviewer for their time and effort in reviewing our paper. Your feedback will make the paper stronger. There are a few points raised in the review that we hope to clarify.
---
## W1 - Only transferable to very similar PDEs.
The hypothesis we explore in this wor... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation | Accept (poster) | Summary: The authors apply the concept of prompt optimization to reinforcement learning by pre-training a transformer-based policy on a tasks-agnostic (offline) dataset, injected with subgoals, to fine-tune a meta-policy optimizing the trajectory of goals to accomplish a given task.
Strengths: The authors propose an e... | Rebuttal 1:
Rebuttal: ## Thanks for your review! Here, we respond to your comments and address the issues. We hope to hear back from you if you have further questions!
**Q1:** Paper writing issues.
**A1:** Thank you for your constructive feedback! We've added a running example in Figure 2 of our attached PDF to clari... | Summary: The paper proposes a pretrain-and-prompt-tuning paradigm to tackle the generalization challenge in RL. It pretrains a goal-conditioned transformer from task-agnostic datasets, and during fine-tuning, it constructs a goal sequence as a prompt and tunes that prompt via multi-arm bandit algorithms.
Strengths: - ... | Rebuttal 1:
Rebuttal: ## Thanks for your review! Here, we respond to your comments and address the issues. We hope to hear back from you if you have further questions!
**Q1:** Using BC to train skills will limit the performance when the dataset quality is not high.
**A1:** Indeed, the dataset quality impacts the perf... | Summary: This paper addresses the fast adaptation of pre-trained policy from task-agnostic datasets. The authors propose to avoid RL interactions on new tasks through the combination of Transformer-based policies to model multiple goals and efficient online adaptation through prompt optimization. The experiments demons... | Rebuttal 1:
Rebuttal: ## Thanks for your review! Here, we respond to your comments and address the issues. We hope to hear back from you if you have further questions!
**Q1:** Why the hindsight relabeling is required?
**A1:** We use hindsight relabeling in the pre-training stage to learn from an offline dataset with... | null | null | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for your insightful comments and constructive feedback. Here, we provide a summary of the reviews and our responses to the key points raised.
### Summary of positive feedback
- All reviewers: The proposed method (multi-goal pre-training and prompt optimization) is p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Posterior Label Smoothing for Node Classification | Reject | Summary: This paper proposed label-smoothing to improve the transductive node classification in GNN.
Strengths: Label-smoothing and knowledge distillation are applied for node classification performance.
Weaknesses: 1. The paper could benefit from discussing related works that combine label-smoothing with Graph Neura... | Rebuttal 1:
Rebuttal: We sincerely appreciate your effort in the review process and your constructive feedback to improve our paper. We particularly recognize the issue of lacking related work. We have conducted additional experiments and strengthened the related work section. Detailed responses to your questions are p... | Summary: The paper proposes PosteL, a label smoothing method utilizing posterior distribution for node classification in graph-structured data. It is basically a preprocessing method for GNNs, generating soft labels based on neighborhood context and global label statistics before the training phase.
Strengths: 1. The ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your effort in the review process and recognition of the strengths of our work. We have carefully considered all of your comments, and detailed responses to your questions are provided below. We hope this helps address any concerns you may have.
**W1: Sensitivity analy... | Summary: This paper introduces Posterior Label smoothing (PosteL), an innovative approach to enhance node classification on graph-structured data. PosteL integrates local neighborhood information with global label statistics to generate soft labels, aiming to improve model generalization and mitigate overfitting. The a... | Rebuttal 1:
Rebuttal: We sincerely appreciate your effort in the review process and your constructive feedback. We have carefully reviewed our paper based on your comments, and detailed responses to your questions are provided below. Finally, thank you for highlighting the editorial issues regarding the duplicated refe... | Summary: This work proposes a preprocessing step to refine labels of nodes in a structured graph that can benefit different graph-related transductive classification tasks. Inspired by the success of label smoothing in other machine learning tasks, the authors propose a label smoothing procedure based on a Bayesian inf... | Rebuttal 1:
Rebuttal: Thank you for your dedicated effort in the review process. We appreciate the constructive feedback and your recognition of the strengths of our work. We have carefully considered all the points you mentioned and provided detailed responses to each question below. Additionally, we will address the ... | Rebuttal 1:
Rebuttal: ### **General response**
We sincerely appreciate the effort all reviewers dedicated to the review process. We are also grateful for the constructive feedback and have carefully considered all the comments we received. There are two questions that most reviewers asked. We address these questions i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling | Accept (poster) | Summary: The paper introduces a novel explicit and structured 3D representation - GaussianCube - used in combination with U-Net diffusion models for 3D generation. While 3D Gaussians have a lot of advantages, they are not directly compatible with efficient architectures for generative modeling because of their unstruct... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We address the reviewer's concerns below:
> Q: Parameter distribution of 3D Gaussians used for diffusion process.
A: Although the parameters of 3D Gaussians have very different distributions, we observe that after applying data normalization,... | Summary: The paper is about 3d object generative model. The generative objects are represented with gaussian splatting. Thus the main obstacle is the large point clouds. It is difficult to generate such large point clouds. The authors proposed a way to put point cloud to a structured grid using optimal transport. Some ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We address the reviewer's concerns below:
> Q: Time and memory analysis of our method.
A: Thanks for your suggestions. As detailed in the supplementary material (Lines 501-503), the proposed densification-constrained fitting requires approxim... | Summary: The paper proposes an approach to 3D generation at the object level with the help of diffusion models. The main challenge in using diffusion model to generate 3D is the choice of 3D representation that fits well with the denoising network. The paper proposes using fixed number of Gaussians lying on a regular 3... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We address the reviewer's concerns below:
> Q: Given that many 3D generative models are based on images or videos due to the abundance of 2D data, how data scalability can be achieved for our approach?
A: We acknowledge the reviewer's point t... | Summary: The paper proposes a new structured and explicit representation based on 3D Gaussian Splatting for 3D generation. The key idea is to properly organize the 3D Gaussians into a fixed-size volume, allowing for the use of the standard 3D UNet for diffusion. First, the paper uses a densification-constrained fitting... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We address the reviewer's concerns below:
> Q: Parameters of Triplane.
A: In Table 2 of the main paper, we assigned the size of the Triplane as $3\times256\times256\times32$. We set the size of Voxels to $32\times32\times32\times14$ with the ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We express our sincere gratitude to all reviewers for their valuable feedback, which has immensely contributed to the enhancement of our paper. We are greatly encouraged by the reviewers' acknowledgment that our paper:
- propose a novel method to address the irregularity of 3D Gau... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Intervention and Conditioning in Causal Bayesian Networks | Accept (poster) | Summary: This paper builds off recent work showing that Pearl's proposed approach for calculating conditional probabilities involving interventions is incorrect. Using Pearl's concept of autonomy (independence of mechanisms), the authors build a series of formalisms to calculate arbitrary conditional probabilities in ... | Rebuttal 1:
Rebuttal: lines 17 and 33: we will clarify the issues you mentioned.
lines 45-46: We think there's a natural reading of this material this
is consistent with what we said. Suppose that you intervene to set
X=1. To find out the effect of this, you consult the structural
equation. That makes the equation t... | Summary: The paper introduces a condition under which the counterfactual probabilities can be computed from a Causal Bayesian network (CBN). This is not the case in general and functional causal models such as SCMs are often required for counterfactual reasoning. Specifically, the paper shows that when the outcomes are... | Rebuttal 1:
Rebuttal: Thank you for your positive comments. We
will provide more examples where the independence assumptions do and
do not hold. | Summary: This paper shows the identifiability of interventional formulas in Causal Bayesian Networks (CBNs) under an independence-of-machanism assumption. It also formalizes the construction of Balke and Pearl (1994) of how to convert a CBN to a Structural Causal Model (SCM).
Strengths: 1. This paper considers an impo... | Rebuttal 1:
Rebuttal: - With regard to the second assumption needed to identify the
probabilities in a CBN, we agree that it is nontrivial. We will
expand the discussion on lines 55-63 and provide more examples.
Given our view of exogenous variables are simply variables whose
values are determined "from the outside", ... | Summary: They define counterfactual probability for causal Bayesian networks as the probability of i-compatible causal models.
In the appendix, they show how to calculate counterfactual probabilities and necessary/sufficient probabilities.
Strengths: One needs definition like in this paper before doing any kind of re... | Rebuttal 1:
Rebuttal: We thank the reviewer for several insightful and detailed comments
(even from the appendix!).
p. 2: footnote: This should indeed be p(u \mid e). Thanks! According
to our reading of Pearl, the procedure as we've described it is
Pearl's. He does not require disjointness. Our method, even without
... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Variational Delayed Policy Optimization | Accept (spotlight) | Summary: The paper re-examines the delayed RL framework in terms of an equivalent variational inference problem. The authors theoretically motivate maximising the return of a reference policy that does not feature delays using an argument of improved sample complexity and performance that improves with smaller delays. ... | Rebuttal 1:
Rebuttal: We genuinely appreciate the positive feedback provided by Reviewer io6Z.
The typos will be corrected in the revised version.
This work aims to present a new delayed RL method from the perspective of variational inference which can effectively improve the sample complexity without compromising the ... | Summary: The paper proposes a new algorithm (VDPO) for delayed RL which first learns a reference policy in the delay-free MDP using standard RL tools and then uses behavior cloning to encourage visitation of optimal trajectories in the delayed MDP.
The paper shows theoretical guarantees on sample complexity and perform... | Rebuttal 1:
Rebuttal: We sincerely appreciate the positive comments provided by Reviewer wuNr, and our responses are as follows.
# Weakness 1: It would be interesting to see an extension of this approach to stochastic delays.
We appreciate that the importance of addressing stochastic delays is recognized. Though this... | Summary: The paper proposes a novel delayed rl algorithm called variational delayed policy optimization, which reformulates delayed RL as a variational inference problem and solves it with a two-step iterative optimization. Both theoretical and empirical results show that VDPO achieves better performance in sample effi... | Rebuttal 1:
Rebuttal: We thank Reviewer FFJG for the comment. Before replying to all the concerns and questions in detail, we want to clarify that this work adopts commonly used evaluation metrics, sample efficiency and performance (return), and conducts fair comparison with existing works [1, 2, 3]. Our detailed respo... | null | null | Rebuttal 1:
Rebuttal: # General Response
We sincerely appreciate the insightful comments and feedback from all the reviewers.
The main contribution of this work is to address the sample efficiency issue in delayed RL by first introducing variational inference to reformulate the original problem and then solving the hi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interpreting the Weight Space of Customized Diffusion Models | Accept (poster) | Summary: The paper proposes to learn a manifold over customized diffusion model weights as a subspace for interpretable downstream applications such as sampling, editing, and inversion. At the technological level, the authors collect the weights of over 60,000 models as the dataset and fine-tune those weights via LoRA... | Rebuttal 1:
Rebuttal: **“...editing and inverting have been widely studied in the field (see Weaknesses)”** and **“These methods have covered the scenarios including latent sampling, editing, and inverting and thus should be discussed”** and **“The methodological level design is also not entirely new and intriguing.”**... | Summary: The paper investigates the weight space spanned by a large collection of customized diffusion models, proposing a novel subspace termed weights2weights (w2w). The study populates this space with a dataset of over 60,000 models, each fine-tuned to encode a different person's visual identity. The paper demonstra... | Rebuttal 1:
Rebuttal: **“The construction of w2w spaces needs over [60,000 models]...The compute resources should be provided…”**
Training a single identity LoRA with rank 1 for our dataset of models requires ~8GB VRAM, and takes 220 seconds on a single A100 GPU. Please refer to Appendix C for details.
**“And is the... | Summary: This paper constructs a dataset that contains LoRA models trained on images of 60,000 different people. A weight manifold is determined based on the parameters of these 60,000 models using PCA. Sampling, editing, and inversion can be performed on this manifold, and the rationality of this manifold is demonstra... | Rebuttal 1:
Rebuttal: **“...some recent works present the way of generating identity…given only one reference image…provide the comparison between this paper and these works.”**
Thank you for your suggestion. In the rebuttal PDF Table 1, we compare against Celeb-Basis [1] and IP-Adapter FaceID [2], following the same ... | Summary: This paper explores the latent space of weights in customized diffusion models, introducing the weights2weights (w2w) space, a subspace encoding different human identities. By fine-tuning over 65,000 models, each representing a distinct human identity, the authors model this weight space using low-rank adaptat... | Rebuttal 1:
Rebuttal: **“The overall idea of this paper is more similar to [1]...I suggest the authors could discuss more about the differences between this paper and [1].”**
Although these two papers share the use of PCA and the application to personalization, there are fundamental differences. Many works have found ... | Rebuttal 1:
Rebuttal: # Global Response:
We sincerely thank the reviewers for their feedback. We are glad that the reviewers found our creation and analysis of a large dataset of model weights “interesting” (abHC, 215A), and the concept of modeling the manifold of diffusion model weights “novel” with “broad applicati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors | Reject | Summary: The paper examines prediction and estimation risk of ridgeless least squares estimator in the setting of a general error structure. The iid assumption on the error structure is often not valid in settings such as time series data , panel data, grouped data etc. The current paper introduces a theoretical framew... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We respond to the concerns and questions below:
> [Q1] (...) how do the prediction risk and the estimation risk behave in Figure 1 and 2 respectively as $n$ and $p$ increase? In other words can we infer any pattern from the results shown in Theorem 3.4 and ... | Summary: The paper explores the prediction risk and estimation risk of the ridgeless least squares estimator under more general assumptions on regression errors. It highlights the benefits of overparameterization in a realistic setting that allows for clustered or serial dependence. The paper establishes that the estim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We respond to the question below:
**On large-scale validation**
- Please see our top-level comment [General Response] and Fig S1 in the PDF attached to it.
- We additionally tested a wide range of $(n,p)$ pairs including $(500,5\text{k})(500,50\text{k}),(... | Summary: The paper investigates the properties of minimum norm (ridgeless) interpolation least squares estimators, analyzing prediction risk and estimation risk under broader regression error assumptions, including clustered or serial dependence. This diverges from the typical assumption of i.i.d. errors with zero mean... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and comments.
We also believe that it is important to understand the real-world regression challenges with even more intricate patterns of error dependence. Even though it is extremely difficult to fully grasp the complexity of real-world problems, the paper... | Summary: The paper considers the ridgeless least-squares estimator, and derives its prediction and estimation risk. One of the assumptions used is that the expectation of the noise variance matrix is finite and positive-definite. This is more general than the assumption that this expectation is some positive multiple o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We respond to the concerns below:
**On the technical contributions**
- We have a concise proof. A compact and special technique made it possible.
- The main technical difficulty is that we generally cannot directly factor out $\Omega$ from $\text{Tr}(X^\dag... | Rebuttal 1:
Rebuttal: # **[General Response]**
We would like to thank the reviewers for the thorough examination of the paper and their insightful and valuable comments.
We appreciate that all the reviewers recognized the strengths of our paper with positive ratings, saying "the presentation is clear", the introduct... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Segment Any Change | Accept (poster) | Summary: This paper proposed a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions. The proposed method called AnyChange is built on the segment anything model (SAM) via our training-free adaptation method. By revealing and exploiting in... | Rebuttal 1:
Rebuttal: ## To Reviewer n4V1
**W1: I doubt whether SAM has the ability to detect some very minor changes.**
As you suggested, we demonstrate the case of tiny/minor changes, e.g., small vehicle changes. Please check **Figure 4 in the rebuttal PDF**.
The main observation is that directly applying AnyChange... | Summary: The authors propose AnyChange, a novel framework for zero-shot change detection in remote sensing imagery. This framework leverages the Segment Anything Model (SAM) and introduces a "bitemporal latent matching" method to identify changes between images taken at different times. AnyChange identifies changes by ... | Rebuttal 1:
Rebuttal: ## To Reviewer eUYB
**W1: How is the framework able to compute pixel level features for the mask proposals? Is there an interpolation step?**
There is a bilinear interpolation to upsample the feature map back to the original image size. Then we compute the mask embedding by averaging each positi... | Summary: The authors address the problem of zero-shot change detection. While some models focus on zero-shot semantic segmentation, there hasn't been much work in the area of zero-shot change detection. The lack of availability of large change detection datasets makes it non-trivial to train such models from scratch us... | Rebuttal 1:
Rebuttal: ## To Reviewer SmiM
**W1: ablation study for matching direction.**
We have added this ablation study. The results are as follows:
| | LEVIR-CD | S2Looking | xView2 | SECOND ... | Summary: This paper introduces the AnyChange model, aimed at enabling zero-shot change detection in remote sensing imagery. The model builds upon the SAM, utilizing a training-free adaptation method called bitemporal latent matching. This method leverages semantic similarities within and between images captured at diff... | Rebuttal 1:
Rebuttal: ## To Reviewer MDhn
**W1.1: The novelty and uniqueness of this approach compared to existing cosine similarity-based methods (e.g., RaVAEn) are questionable.**
Our AnyChange is fundamentally different from existing methods.
- **Zero-shot vs. unsupervised**. Existing unsupervised methods need to ... | Rebuttal 1:
Rebuttal: We are sincerely grateful for the reviewer's efforts and their constructive feedback. We appreciate the reviewers’ acknowledgment that
- [**pioneer**] Our work is one of the first works to propose zero-shot change detection of remote sensing imagery (eUYB).
- [**novel**] Our work is novel and inte... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Distinguishable Trajectory Representation with Contrastive Loss | Accept (poster) | Summary: This paper presents a contrastive approach for learning diverse policies in multi-agent reinforcement learning (MARL). It maximizes the mutual information between trajectory representations and identity representations, formulating this maximization as an InfoNCE loss function. The methodology is tested across... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We clarify your concerns and problems below:
Weakness 1: Technical Novelty ... contribution.
We discussed the CIA method in the related works in our original paper. The differences between our method and CIA are shown below:
First, our main id... | Summary: This paper proposes a novel Contrastive Trajectory Representation (CTR) method based on learning distinguishable trajectory
representations to encourage multi-agent diversity. It introduces contrastive learning to maximize the mutual information between the trajectory representations and learnable identity ... | Rebuttal 1:
Rebuttal: Thank you for your careful review and for providing us with detailed and helpful feedback. We response to your concerns below:
Weakness 1: The paper lacks a detailed description of the identity representation, only saying that it is a learnable vector.
Previous mutual information-based methods t... | Summary: The paper proposes using a contrastive trajectory representation to improve diversity and exploration in decentralized multi-agent reinforcement learning. Experimental evaluations show the positive impact in a small-scale environment and improved performance in various SMAC scenarios.
Strengths: The authors p... | Rebuttal 1:
Rebuttal: We greatly value your expertise and the effort you put into reviewing our paper. Here are the responses to your concerns:
Weakness 1:The .. being learned.
In our paper, we discuss a decentralized learning scenario where agents share the same policy network parameters but learn different decentra... | Summary: This paper introduces a novel approach to learning in multi-agent reinforcement learning (MARL) environments by focusing on distinguishable trajectory representations to encourage agent diversity. The proposed method, termed Contrastive Trajectory Representation (CTR), leverages a contrastive learning loss to ... | Rebuttal 1:
Rebuttal: We appreciate your time and the valuable insights you provided during the review process. We clarify your concerns and problems below:
W1: Dependency ... different settings.
The values for the weight of the contrastive loss in different scenarios are listed in Table 4 in our paper. To investigat... | Rebuttal 1:
Rebuttal: We greatly appreciate the time you have taken to review our paper. The PDF attachment presents the Tables and Figures referenced in the responses. We hope to receive your feedback soon so that we can further improve our paper.
Pdf: /pdf/7ec18ba04d1c31a3dfec211b68aabfd82c499b9c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose a method to maximize mutual information between trajectory representations of different agents in the multi-agent reinforcement learning setting. Rather than comparing policies on a state-by-state basis, they instead learn a representation of the entire trajectory using sequence models and ... | Rebuttal 1:
Rebuttal: We thank you for taking the time to review. We agree with your comments. Our method can be integrated with a variety of MARL methods based on the CTDE framework including value-based and policy-based methods. Since our method simply incorporates the trajectory encoder with the decentralized policy... | null | null | null | null | null | null |
FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection | Accept (poster) | Summary: This paper introduces a FL framework FOOGD aimed at addressing the simultaneous challenges of the out-of-distribution (OOD) generalization and OOD detection in decentralized environments. FOOGD estimates the data distribution of different clients through SM3D and introduces SAG to ensure the consistency of fea... | Rebuttal 1:
Rebuttal: Thanks for your professional and detailed review!
W: The training log is not provided.
A: To validate the effectiveness and implementation, we release a well-trained FedAVG-FOOGD model on Cifar10 $\alpha=0.5$ in supplemental materials. And we are willing to release our project as well as the ... | Summary: This paper introduces FOOGD, a federated collaboration framework designed to achieve both out-of-distribution (OOD) generalization and detection. FOOGD estimates the probability density of each client to obtain a reliable global distribution and incorporates the SM3D model and SAG module to enhance the detecti... | Rebuttal 1:
Rebuttal: We appreciate it a lot for your insightful reviews.
W1: Although this section provides performance metrics for multiple datasets, the description of the chosen statistical analysis methods (such as whether hypothesis testing or confidence interval calculation was performed) is insufficient, affec... | Summary: This paper addresses various OOD (Out-of-Distribution) shifts that may occur in federated settings by proposing a unified framework to simultaneously tackle OOD generalization and detection issues. Specifically, this paper introduces FOOGD, which estimates arbitrary client probability densities to create a rel... | Rebuttal 1:
Rebuttal: Thanks for your constructive review! For your concerns, we explain them in the following.
W1: It is essential to clearly define the relationships among FOOGD, $SM^3D$, and SAG, in the abstract and introduction to enhance the readability of the entire paper.
A1: FOOGD is the overall federated ... | Summary: This paper focuses on the federated learning setup that non-IID, semantic-shift, and covariate-shift take place in the same time. And the authors propose a framework of FOOGD with a unnormalized distribution estimation method, i.e., SM3D, to release the distribution assumption and constraints in heterogeneous ... | Rebuttal 1:
Rebuttal: Thank you for appreciating the contributions of our work. For the weakness concerned by the reviewers, we provide the discussion as below.
W1: How to understand IsOUT() function with negative threshold in Eq. (9)?
A1:IsOUT() function is the OOD detection function in our work. When the norm of ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms | Accept (spotlight) | Summary: This paper introduces DeSparsify, a novel adversarial attack targeting the availability of vision transformers that employ token sparsification techniques. The authors demonstrate that TS mechanisms, which aim to improve computational efficiency, create a new attack surface that can be exploited to compromise ... | Rebuttal 1:
Rebuttal: Thank you for your time, effort and comments.
**Q1**: "..limited to vision transformers.."
**A1**: Our study focuses on vision transformers because the token sparsification methods we examine are specifically designed and optimized for this domain. We agree that exploring the applicability of De... | Summary: The authors investigate the scenario of an adversary forcing a vision transformer to operate less efficiently. This class of availability attack focuses on ramping up the cost of operation for the model host, who is assumed to use token sparsification (TS) to make operation cheaper. The authors formulate an at... | Rebuttal 1:
Rebuttal: Thank you for your time, effort and comments.
**Q1**: "The submission is mainly held back by the writing quality..."
**A1**: Thank you for your detailed comments. We fixed the presentation issues and corrected them in the paper.
**Q2**: "The main technical drawback of the attack.."
**A2**: Ple... | Summary: In this paper, the authors propose an availability-oriented attack method called DeSparsify, targeting vision transformers that utilize token sparsification (TS) mechanisms.
To perform an effective attack, a custom loss function is introduced to three different ViT sparsification techniques. This approach not... | Rebuttal 1:
Rebuttal: Thank you for your time, effort and comments.
**Q1**: ".. This objective conflicts with the operations described in Eqs. (2-3)."
**A1**: Please note that the novel loss attacking components we propose should be minimized and not maximized. That is, our optimization process follows the computed ... | Summary: The paper presents DeSparsify, an adversarial attack on vision transformers utilizing token sparsification (TS). It highlights the vulnerability of TS techniques due to their dynamic nature and shows how DeSparsify can deplete system resources while preserving the model's original classification accuracy. The ... | Rebuttal 1:
Rebuttal: Thank you for your time, effort and comments.
**Q1**: "..add some examples and visualizations for the adversarial examples"
**A1**: visualizations for the adversarial examples, including baselines and our attack variants, can be found in Appendix D. We will also include the perturbations in the ... | Rebuttal 1:
Rebuttal: First, we thank the reviewers* for their time, effort and comments.
We are pleased to see that the reviewers find that the proposed research problem is important (R1, R2, R3, R4, R5, R6), and the results and analysis are thorough (R1, R2, R3, R4, R5, R6).
We are equally glad that the reviewers h... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes DeSparsify, an adversarial attack against token sparsification methods for ViTs. Such attacks aim at modifying input images to increase the inference time and cost while preserving the original classification. In particular, the paper designs specific losses against three existing sparsifica... | Rebuttal 1:
Rebuttal: Thank you for your time, effort and comments.
**Q1**: "sparsification methods are not robust to adversarial attacks is not surprising"
**A1**: While it may seem unsurprising now that sparsification methods are not robust to adversarial attacks, this understanding was not trivial prior to our wor... | Summary: Token sparsification uses input-dependent strategy to discarded uninformative tokens, improving the resource efficiency of vision transformers. This paper propose DeSparsify, to attack vision transformers that use token sparsification. The attack aims at exhausting the operating system’s resources.
Strengths:... | Rebuttal 1:
Rebuttal: Thank you for your time, effort and comments.
**Q1**: "..Compared to methods that do not use TS.."
**A1**: In theory, the attack's upper bound corresponds to the model's performance when no sparsification is applied, i.e., a "vanilla" model that utilizes all tokens during inference.
In practice... | null | null | null | null |
Grokking of Implicit Reasoning in Transformers: A Mechanistic Journey to the Edge of Generalization | Accept (poster) | Summary: Recent work has shown that LLMs are bad at implicit reasoning over parametric knowledge, and this work asks whether that's a fundamental limitation of the transformer architecture or not. Through thoroughly investigating the performance of an 8-layer transformer trained from scratch on 2-hop reasoning tasks, t... | Rebuttal 1:
Rebuttal: Thank you for the informative and constructive comments! We will incorporate them to improve our work in the revised version.
**[Causal tracing - W2]** First of all, as you mentioned, we believe this is not a weakness of our work and more of a general question about causal tracing. To explain her... | Summary: The work mainly focuses on the systematic generalization (specificially, in the paper, two implicit reasoning types: composition and comparison of facts) of the grokked (i.e., training far beyond overfitting so that the model can finally learn some specific generalization skills and achieve high testing perfor... | Rebuttal 1:
Rebuttal: Thank you for the informative and constructive comments! We will incorporate them to improve our work in the revised version.
**[Abstract nature and connection to practice - W1]** Yes, we do admit that one limitation of our work is its synthetic and abstract nature (Appendix H). Still, we believe... | Summary: This paper investigates whether Transformer models can learn implicit reasoning through the phenomenon of "grokking" focusing on two types of reasoning: composition and comparison. Also, the paper reveals the mechanisms behind grokking and the differences in systematic generalization across different reasoning... | Rebuttal 1:
Rebuttal: Thank you for the informative and constructive comments. We will incorporate them to improve our work in the revised version.
**[Tokenization and loss - W1]** If the “(1)(2)” here means the rules in Equations (1) and (2), these rules are latent and the model only sees the atomic and inferred fact... | Summary: This paper explains the reasoning ability in LMs is acquired through grokking, which requires extended training beyond overfitting. Through analytical experiments, the authors explore the mechanisms behind grokking, the formation of generalizing circuits, and the impact of systematicity in the configuration o... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments! We will incorporate them to improve our work in the revised version.
**[Limited scope and dependence on synthetic data]** We do admit these limitations in the paper (Appendix H), however, we believe that despite these, we are taking the initial steps towar... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the constructive comments. We have provided detailed responses individually, with additional figures/tables (referred to as the "added PDF") attached here. There are two recurring topics across the reviews which we would like to briefly reiterate here.
**[Synthetic ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper explores the ability of transformers to learn two synthetic tasks when trained from scratch. The tasks consist of rigidly structured data and effectively represent: 1. following a path of length 2 in a graph (the "composition" task), and 2. looking up + comparing two items in a dataset ("comparison"... | Rebuttal 1:
Rebuttal: Thank you for the very informative and constructive comments! We will incorporate them to improve our work in the revised version. Overall, we believe that all the major technical concerns raised here are due to some misunderstandings of our paper, and we will group your comments into different to... | null | null | null | null | null | null |
ACFun: Abstract-Concrete Fusion Facial Stylization | Accept (poster) | Summary: The paper introduces a new method for face stylization. The model uses a pretrained diffusion model (Stable Diffusion 1.4) and the proposed Abstract and Concrete Modules (AFun and CFun modules) to achieve the goal. The former extracts the style details to be imbued into the generative process. The latter condi... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work and pointing out our issues in detail. Thank you for taking the time to review our work. We would like to provide the following answers to the questions you have pointed out.
W1: You have pointed out a crucial issue, and we have added quantitative experiments bas... | Summary: This paper proposes a novel facial stylization method called ACFun, which achieves high-quality stylization effects by combining abstract and concrete visual elements. It contains the Abstract Fusion Module (AFun) and Concrete Fusion Module (CFun) to learn the abstract and concrete features. A new loss functio... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work and pointing out our issues. We have supplemented the experiment you pointed out in Weakness. It can be seen that fewer steps will make the image more realistic, while more steps will make it more stylized. For different text descriptions, it can be seen that we ... | Summary: This paper deals with the problem of facial stylization using one style image and one facial image. Specifically, the authors design an abstract fusion module and a concrete fusion module to learn the abstract and concrete features of the style and face separately. They further design a face and style imagery ... | Rebuttal 1:
Rebuttal: Thank you for admiring our work and pointing out our issues. We respond to the weaknesses and questions you pointed out as follows.
W1: We quantify style, content, and overall through user study. We selected 50 people through a survey questionnaire, and the results showed that our method achieved ... | Summary: This article introduces a generative model ACFun for facial stylization, which designs two modules AFun and CFun to learn the abstract and concrete features of styles and faces. The authors design a Face and Style Imagery Alignment Loss to align the style image with the face image in the latent space, using th... | Rebuttal 1:
Rebuttal: Thank you for pointing out the problems in our paper. We respond to the weaknesses and questions you pointed out as follows.
W1: I'm very sorry. Due to a layout problem, the content of section 4.1 is in the second paragraph of section 4.2.
W2: Due to the excellent performance of InstantStyle, we o... | Rebuttal 1:
Rebuttal: We have added experiments on text-guided generation and ablation and related experiments on different diffusion steps, different levels of text description detail, and different genders. We also conducted quantitative experiments based on user study, providing 40 pairs of style facial images and t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving | Accept (poster) | Summary: The paper investigates the presence of metacognitive knowledge in large language models (LLMs), specifically focusing on their ability to reason and apply skills in mathematical contexts. The authors develop a prompt-guided interaction procedure that enables a powerful LLM to assign skill labels to math questi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review.
**Multiple skills per question**
The reviewer has asked for preliminary experiments showcasing multiple skill labels. We refer the reviewer to the common rebuttal for this experiment. We show decent improvements on the MATH dataset with a multi-s... | Summary: This paper studied how metacognitive knowledge can improve LLM's performance in two math datasets. Author asked LLM to solve math questions and identify a skills needed within a provided list of skills. The author used pedagogy research and use experiment to show improving LLM's metacognitive knowledge can im... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review.
**Comparison to RAG**
The reviewer has mentioned that the proposed method is similar to RAG. While we agree that the proposed approach has a similar flavor as RAG and can be considered as one instatiation of RAG, we would like to point that there ... | Summary: This paper proposes a novel framework for extracting metacognitive knowledge from LLMs. Specifically, the training examples are firstly assigned with skill names. Then they are clustered with in semantic perspective. Finally they group the training examples as skill exemplars for the use of inference phase. Ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and appreciating the novelty of our method.
**On the hyperparameters required for skill clustering and analysis of granularity of skill names**
There are no hyperparameters used in the skill labelling, skill clustering, or the skill relabelling pha... | Summary: This paper investigates whether large language models (LLMs) possess metacognitive knowledge, or an understanding of their own thinking and reasoning processes, particularly in the context of solving mathematical problems. Authors introduce a method to extract and utilize this metacognitive knowledge to enhanc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review.
**On Assigning multiple skills per question**
We would like to point the reviewer to the common rebuttal for the experiment on multiple skills per question.
**Discussion on overfitting**
The reviewer has raised the question of overfitting on th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and comments. In this common response we present new experimental results which we obtained during the rebuttal phase which help clarify some of the questions and concerns raised by the reviewers:
## Multi-Skill Labelling
Reviewers ADDk and xadG have menti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CosAE: Learnable Fourier Series for Image Restoration | Accept (poster) | Summary: This paper formulates the latent space of the autoencoder as a set of Fourier series space, and the encoded images are represented as corresponding amplitude and phase coefficients, which formulates a highly compressed latent space with faithful reconstruction ability. Extensive experiments on natural images ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. In regards to your questions, see our responses below:
**Q1: It is not clear what is the necessity to formulate a highly compressed latent space (or so called information bottleneck) with detailed reconstruction ability for image restoration, as th... | Summary: The paper introduces the cosine auto encoder method for image restoration. CosAE encodes frequency coefficients to enable high spatial compression. Experiments on flexible resolution super-solution and blind image restoration demonstrate its effectiveness and generalization.
Strengths: 1. Nicely presented pa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our idea, paper presentation, and solid experiments, as well as their valuable feedback. In regards to the weaknesses and the questions, see our responses below:
**Q1: The paper lacks theoretical interpretations about the superiority of Fourier space ov... | Summary: This paper proposed CosAE, a novel autoencoder architecture integrated with the Fourier series for image restoration tasks. Unlike traditional autoencoders that use spatially compressed latent spaces, CosAE encodes images using frequency coefficients, which allows for significant spatial compression while pres... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's acknowledgment that our approach is simple but insightful. We also thank the reviewer for the valuable feedback. In regards to the questions, please see our responses below:
**Q1: Is the LPIPS loss function also adopted, considering that the original LIIF does not inc... | Summary: The paper introduces a novel autoencoder that represents an input image using a series of paired Fourier coefficients, representing amplitude and phase. Each pair corresponds to a specific frequency, with all frequencies being learnable parameters of the autoencoder, shared across all images. During decoding, ... | Rebuttal 1:
Rebuttal: We appreciate Reviewer `Gqnq`’s positive assessment of our work regarding the presentation, the technique contribution, and the soundness of our experiments. We address the questions and concerns in the following.
**Q1: More justification for the decoding part.**
The Fourier inverse transform is... | Rebuttal 1:
Rebuttal: We thank the reviewers for recognizing the technique contribution of our work (Reviewer `Gqnq`), the good-quality presentation (Reviewer `Gqnq`, `Yyg3`, `gTQ7`), simple and intuitive idea (Reviewer `Yyg3`, `gTQ7`), and acknowledging its potential impact (Reviewer `Gqnq`).
While we address the ind... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MSA Generation with Seqs2Seqs Pretraining: Advancing Protein Structure Predictions | Accept (poster) | Summary: The paper introduces MSA-Generator, a novel self-supervised generative protein language model designed to address the limitations in protein structure prediction due to shallow multiple sequence alignments (MSAs). This model, pre-trained on a sequences-to-sequences task using an automatically constructed datas... | Rebuttal 1:
Rebuttal: We really appreciate your feedback and advice on improving the work, here we provide more discussion:
1. **Generated MSA for Traditional Models**
This is indeed an interesting and valuable question. We adopted CCMpred [1], one of the leading graphical models for protein contact map predi... | Summary: This work introduces MSA-Generator to generate virtual, informative MSAs. The generated MSAs can advance protein structure prediction.
Strengths: - The MSA generation and protein structure prediction problems studied in this work are important.
- The writing is clear and the method is easy to follow.
Weaknes... | Rebuttal 1:
Rebuttal: 1. **Novelty**
The innovation of our work lies in our pioneering approach to **self-supervised pretraining for Multiple Sequence Alignment (MSA) generation**. While tied-row attention and self-column attention are similar to the mechanisms in the MSA Transformer, it's important to note th... | Summary: This paper introduces a method for generating multiple sequence alignments (MSAs) using a self-supervised seq2seq task. By leveraging large-scale protein databases, this approach produces virtual, informative MSAs that enhance the performance of protein structure prediction models such as AlphaFold2 and RoseTT... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and would like to clarify the following points:
1. **Orphan proteins**
We have included results for proteins with only single sequences and presented these findings in our global response. Please refer to it for detailed discussion.
2. **Criteri... | Summary: The paper proposes a method to generate MSA sequences to provide more alignments of the MSA. MSA-Generator can increase the depth of the MSA input, and thus incorporate more information. MSA-Generator demonstrates its capacity to synthesize higher-quality MSA via experiments on the CASP dataset.
Strengths: - ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and would like to clarify the following points:
1. **CAMEO Results**
Thank you for suggesting the inclusion of CAMEO as an additional benchmark. We have taken this into consideration and conducted further experiments. For the CAMEO benchmark, we sear... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' efforts and feedback. We noticed a common interest in whether the proposed method could benefit single protein sequences, also referred to as orphan protein sequences.
To address this, we conducted experiments using the entire CASP14/15 dataset (the dataset used in Se... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences | Accept (poster) | Summary: This paper analyzes compressed Federated Learning algorithms in the setting of Random Reshuffling. It first provides theory for RR with compression (Q-RR), and several variants (based on existing methods for compressed SGD) which have improved convergence guarantees. The paper sets the hypothesis that naive Q-... | Rebuttal 1:
Rebuttal: >**On the main hypothesis that reducing the compression variance brings improvement, it is yet unclear whether this is also a necessary condition. The paper does provide a convergence result for Q-RR that does not improve over Q-SGD; but what would be preferable is a lower bound that shows how Q-R... | Summary: The authors propose communication-efficient distributed learning algorithms with faster convergence rates than previous methods. Their starting point is mini-batch stochastic gradient descent (SGD): Previous works have shown that random reshuffling (RR), i.e. sampling mini-batches without replacement (in other... | Rebuttal 1:
Rebuttal: >**From a technical perspective, my main concern is that I am unsure how challenging it is to realise random reshuffling in practice.**
RR is a well-known technique exploited in many applications: for ML training, for SGD, Monte Carlo methods, etc. For example, in many ML frameworks, such as Tens... | Summary: Gradient compression is a popular technique for improving the communication complexity of stochastic first-order methods in the distributed training of machine learning models. This work points out that existing work only considers with-replacement sampling of stochastic gradients, while in practice stochastic... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and a very positive evaluation of our work.
>**While the experiments conducted are thorough, they are limited to certain types of tasks (logistic regression and ResNet-18 on CIFAR-10). The generalizability of the results to other models and datasets... | Summary: This paper provide analysis of methods with gradient compression and without-replacement sampling. Based on this analysis, this paper proposes several new algorithms for distributed optimization with communication, including Q-RR and its vairants Q-NASTYA, which compress gradient differences in the scenario of... | Rebuttal 1:
Rebuttal: >**... including Q-RR and its variants Q-NASTYA, which compress gradient differences in the scenario of data random shuffling.**
Just to clarify, Q-NASTYA is not a variant with compressed gradient differences. The variants that utilize compressed gradient differences are DIANA-RR and DIANA-NASTYA... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and time. We addressed all the questions, comments, and concerns raised by the reviewers in separate messages.
Following **Reviewer r1qn** question about the importance of the usage of multiple shift vectors in DIANA-RR, we conducted additional experiment... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PAC-Bayes-Chernoff bounds for unbounded losses | Accept (poster) | Summary: The authors propose a new oracle PAC-Bayesian bound that has two main features: it is valid for unbounded losses (or at least under assumptions weaker than bounded loss) and it allows for an exact optimization of the free parameter $\lambda$ appearing in most PAC-Bayesian bound, with only the cost of a penalty... | Rebuttal 1:
Rebuttal: We are grateful for your detailed feedback, it will definitely improve the quality of our paper.
> It should be made more clear how the main results compare to existing results [...]
Thank you for the suggestion. You are correct that our discussion is somewhat incomplete and scattered throughou... | Summary: This paper gives novel PAC-Bayes oracle bounds using the Cramer transform's basic properties under a bounded exponential moment condition.
The benefit of using the Cramer transform is that the bound allows exact optimization of the free parameter $\lambda$ incurring in a $\log n$ penalty without resorting to... | Rebuttal 1:
Rebuttal: We sincerely thank you for your review, we are happy that you appreciated the clarity and the insights of our work. We address your questions below.
> I don't understand the author's claim in the first paragraph of the Section Limitations and future work: An apparent limitation of our approach i... | Summary: The authors present a novel PAC-Bayes bound tailored for unbounded losses, akin to a PAC-Bayes version of the Cramér-Chernoff inequality. The provided bound allows exact optimization of the free parameter across various PAC-Bayes bounds, and leads to more informative and tighter bounds by incorporating "model-... | Rebuttal 1:
Rebuttal: Thank you very much for your review, we are happy to see that you appreciated our contributions and had a pleasant reading. We address your doubts below:
> The only fully empirical bound is Theorem 16. However, the Lipschitz constant L is unknown in practice and has to be estimated. Does that af... | Summary: The paper presents a PAC-Bayes bound for the unbounded loss setting, improving on some of the main drawbacks of previous work on such bounds. The first such drawback discussed is the dependence of the tightness of the such bounds on a priori chosen free parameters, something which can usually only be partially... | Rebuttal 1:
Rebuttal: Thank you very much for your review, we are happy to see that you appreciated the clarity of our writing and the motivation for our contribution. As for the weaknesses and questions, we address them individually below.
> My main concern would be the extent to which this work will be interesting ... | Rebuttal 1:
Rebuttal: We thank all four reviewers for their helpful questions and suggestions. We are happy to see that there is certain consensus on the clarity, the soundness and the contributions of our work. We hope we clarified your doubts, and we are willing to implement the suggested changes on the camera-ready ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Spike-based Neuromorphic Model for Sound Source Localization | Accept (poster) | Summary: This study draws inspiration from the intrinsic mechanisms of sound source localization in biological auditory systems to design an efficient and robust SSL model. The core contributions include two primary aspects: firstly, replacing the energy-intensive Fourier Transform(FT) operations with RF neurons in the... | Rebuttal 1:
Rebuttal: Thank you very much for your appreciation of our paper. In response to the issues you raised, we provide the following responses:
# Q1: Mathematical Equivalence of RF Neurons and FT Operations
**A**: The mathematical proof demonstrating the **equivalence** of RF neurons as an efficient substitut... | Summary: This paper constructs a sound source localization model by leveraging efficient spiking neural networks and biologically-inspired auditory localization mechanisms. Although numerous studies have previously approached this subject from a biomimetic perspective, this paper commendably considers the balance betwe... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition of our work. In response to the questions you raised, we provide the following replies:
# W1: Enhance the Background Introduction
We strongly agree with your perspective. In the background section, we will incorporate additional research on SSL tasks and c... | Summary: This paper introduces a spike-based neuromorphic model for sound source localization. It utilizes the RF-PLC methods for auditory-like spectral analysis and encoding. Additionally, it is supported by the MAA module, which simulates attention mechanisms in specific biological frequency bands. These technologies... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback and for taking the time to read our paper. We hope the following responses will address your questions.
# W1: How to Design Loss Function
**A**: To achieve a $1\degree$ resolution in SSL tasks, the output layer comprises 360 neurons, each representi... | Summary: This paper introduces a spike-based neuromorphic model designed for sound source localization (SSL), capitalizing on the inherent properties of Resonate-and-Fire (RF) neurons. By encoding sound via phase-locking to leverage the resonance characteristics of these neurons, the model efficiently represents intera... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the strengths and quality contributions of our paper. In response to the questions you raised, we are providing further details and insights to clarify the points mentioned in your review.
# Q1: Generalization
**A**: As per your suggestion, we evaluated our mode... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes a SNN-based model for SSL. To achieve efficient processing of raw
speech signals, they introduce a phase-locking coding (RF-PLC) method using Resonate-and-Fire (RF)
neurons and detection neurons.
Strengths: This work proposes a SNN-based model for SSL. To achieve efficient processing of r... | Rebuttal 1:
Rebuttal: Thank you very much for your review comments. In response to the issues you have raised, we offer the following replies:
# Q1: Limited Improvement
- **Limited Improvement**: In SSL tasks, Acc and MAE are the most important metrics. As shown in the following table, extensive comparative experiment... | Summary: The paper presents a novel neuromorphic model for sound source localization (SSL) inspired by biological auditory systems. The model integrates spike-based neural encoding and computation, employing Resonate-and-Fire (RF) neurons with a phase-locking coding (RF-PLC) method. The RF-PLC method leverages the reso... | Rebuttal 1:
Rebuttal: We greatly appreciate your recognition of the innovative aspects and motivation of our work. In response to the weaknesses and questions, we will provide further detailed explanations:
# W1: Compare Analysis
**A**: Following your suggestion , we rigorously evaluated our work against previous SOTA... | null | null | null | null |
TAPTRv2: Attention-based Position Update Improves Tracking Any Point | Accept (poster) | Summary: This paper proposes TAPTRv2, an improved version of TAPTR, which addresses the Tracking Any Point (TAP) task. TAPTRv2 introduces a novel attention-based position update (APU) operation that leverages key-aware deformable attention to refine point query positions. This operation removes the need for cost-volume... | Rebuttal 1:
Rebuttal: ## Summary of review ratings.
| Reviewer | yaYP | wowB | zD8r | zGVx |
| :--- | :---: | :---: | :---: | :---: |
| Rating | Weak Accept | Weak Accept | Borderline Accept | Borderline Reject |
| Confidence | 4 | 4 | 4 | 3 |
## Rebuttal - zD8r
We thank the reviewer... | Summary: The paper proposes an improved version of TAPTR, a DETR-based approach for point-based tracking in videos. TAPTR-v2 removes the dependency of TAPTR on the cost-volume, using local window features to define the keys and values to be used within the attention blocks of the DETR's transformer decoder. This simpli... | Rebuttal 1:
Rebuttal: We thank the reviewer's kind suggestions and questions. We also thank the reviewer for recognizing our writing, motivation, deep analysis, and appreciation of our design in APU block.
### Q1. The initialization of point queries.
__A1__ We appreciate the reviewer’s thorough review and pointing ou... | Summary: The paper introduces TAPTRv2, an enhancement of the TAPTR framework, which is akin to a DETR-based point tracking approach. It critically examines the reliance on cost-volume in traditional Tracking Any Point (TAP) challenges and questions its necessity, particularly how it affects the query's content feature ... | Rebuttal 1:
Rebuttal: ## Summary of review ratings.
| Reviewer | yaYP | wowB | zD8r | zGVx |
| :--- | :---: | :---: | :---: | :---: |
| Rating | Weak Accept | Weak Accept | Borderline Accept | Borderline Reject |
| Confidence | 4 | 4 | 4 | 3 |
## Rebuttal - yaYP
We thank the reviewer ... | Summary: The paper presents TAPTRv2, an improved Transformer-based approach for the Tracking Any Point (TAP) task. Building on TAPTR, which utilizes designs from DEtection TRansformer (DETR), TAPTRv2 addresses a critical issue related to the reliance on cost-volume. This reliance was found to contaminate the point quer... | Rebuttal 1:
Rebuttal: ## Summary of review ratings.
| Reviewer | yaYP | wowB | zD8r | zGVx |
| :--- | :---: | :---: | :---: | :---: |
| Rating | Weak Accept | Weak Accept | Borderline Accept | Borderline Reject |
| Confidence | 4 | 4 | 4 | 3 |
## Rebuttal - zGVx
We thank the reviewer ... | Rebuttal 1:
Rebuttal: We are sincerely grateful to the reviewers for dedicating their time and effort to review our work thoroughly. The constructive suggestions and thoughtful concerns raised by the reviewers are very helpful in improving our camera-ready version of the paper. We will respond to each reviewer’s commen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Simplifying Latent Dynamics with Softly State-Invariant World Models | Accept (poster) | Summary: This paper proposes a Parsimonious Latent Space Model (PLSM) as a latent world model method. The main idea of PLSM is twofold: (1) the use of an additional hidden variable $h_t$, which is bottlenecked to have parsimonious information (for better latent predictability) and (2) the use of difference prediction i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful review and we are glad that the reviewer enjoyed reading our paper. The reviewer raised several important points regarding the effect of our regularization and how it compares to other regularization methods in RL. In response. we show that ou... | Summary: This paper presents an information bottleneck principle to regularize the latent dynamics, which makes the effect of the agent’s actions more predictable. This approach minimizes the mutual information between latent states and the change that action produces in the agent’s latent state, in turn minimizing the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for the helpful suggestions. To address the concerns raised in the review, we have added another set of experiments showing that PLSM improves robustness in visual control tasks with distracting background videos (see Supporting Figure A), as... | Summary: This paper introduces a method to enforce parsimonious latent dynamic models. The key idea is that if we can minimise the influence of states on the dynamic, i.e. the conditional mutual information $I(z_t, \Delta_t | a_t)$, the dynamic can generalise better to unseen states during prediction. In order to achie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and helpful suggestions. We have taken steps to address the concern that performance improvements are relatively modest: We evaluated PLSM in environments with visual distractions and show that parsimonious dynamics can offer considerable perfo... | Summary: The paper addresses learning a world model with state-invariant dynamics. To this end, it proposes Parsimonious Latent Space Model (PLSM), which introduces an information bottleneck to the additive dynamics residual. The influence of the state on the dynamics is summarized in the bottleneck variable, whose nor... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and detailed review of our paper. Based on the reviewer’s suggestions, we have clarified the details of the regularization in the appendix, and performed several analyses to show that our regularization works in a principled way. In short, we find that our ... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for the time and effort put into providing thoughtful and constructive feedback on our paper. Reviewers generally found our method novel and interesting.
* Reviewer PXJB wrote that ‘The authors performed extensive experiments’ and that our method ‘appea... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose an approach to learn latent dynamics from high dimensional observations. Their method seeks to minimize the mutual information between the current latent state and the change in the latent state conditioned on the next action, which they argue minimizes the dependence that the state represe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review, and pointing out ways in which we can improve the paper. To address the reviewer’s concern about the low number of training steps, we extended the training runs for the DMC tasks where performance had not converged, and see that PLSM retains its adv... | null | null | null | null | null | null |
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs | Accept (poster) | Summary: This paper aims to improve KG-augmented LLMs by introducing a self-correcting adaptive planning paradigm. PoG uses three important mechanisms: Guidance, Memory, and Reflection. Guidance leverages LLM to decompose the query into subqueries; Memory stores historical retrieval and reasoning information for reflec... | Rebuttal 1:
Rebuttal: Many thanks for your valuable feedback on our paper. We appreciate your recognition of the method design, excellent performance, and generalization. In response to your concerns, we would like to address the following points:
- **[W1: Query decomposition]**: Thank you for your constructive sugges... | Summary: This paper proposes a new self-correcting adaptive planning paradigm for KG-augmented LLM named Plan-on-Graph (PoG). It has three important mechanism: Guidance, Memory and Reflection. Experiments on three knowledge graph question answering datasets show good results.
Strengths: 1, The paper is presented well.... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback on our paper. We appreciate your recognition of the good presentation, excellent performance, and method design. In response to your concerns, we would like to address the following points:
- **[W1 & Q1: Difference with GoT]**: We would like to emph... | Summary: This paper proposes a self-correcting adaptive planning paradigm for KG-augmented LLM called PoG. It consists of four components: task decomposition, path exploration, memory updating, and evaluation. Experiments on three datasets demonstrate the effectiveness of PoG, outperforming previous methods.
Strengths... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback on our paper. We appreciate your recognition of the method design, well presentation, and superior performance. In response to your concerns, we would like to address the following points:
- **[W1: Result analysis]**: Thank you for your constructive... | Summary: The paper introduces Plan-on-Graph (PoG), a new paradigm for integrating LLMs with KGs to enhance their reasoning capabilities. The main innovation lies in PoG’s self-correcting adaptive planning mechanism, which addresses the limitations of existing KG-augmented LLMs that rely on predefined exploration spaces... | Rebuttal 1:
Rebuttal: Many thanks for your valuable feedback on our paper. We appreciate your recognition of the novel framework, great performance, and extensive experiments. In response to your concerns, we would like to address the following points:
- **[W1: Complexity of implementation and optimization]**: Regard... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces Plan-on-Graph (PoG), a novel self-correcting adaptive planning paradigm for Knowledge Graph-augmented Large Language Models (KG-LLM). PoG aims to address limitations in existing KG-augmented LLM approaches by decomposing questions into sub-objectives and iteratively exploring reasoning pa... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback on our paper. We appreciate your recognition of the novel approach, comprehensive context, and extensive experiments. In response to your concerns, we would like to address the following points:
- **[W1: Missing prompt details & Implementation ambi... | null | null | null | null | null | null |
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning | Accept (poster) | Summary: This paper proposes Large Language Models-guided Dynamic Adaptation (LLM-DA) to leverage LLMs to extract temporal logical rules for TKGR. Experimental results demonstrate LLM-DA significantly improves reasoning accuracy without the need for fine-tuning the LLM.
Strengths: The paper is well-structured and easy... | Rebuttal 1:
Rebuttal: # Comment for Reviewer CXWD:
Thank you very much for your professional review and valuable suggestions. We have carefully considered and responded to the questions you raised.
$\color{blue}{W.1:}$ The writing could be improved. Some figures should be polished.
$\color{blue}{Re:}$ We really appr... | Summary: This paper explores the use of Large Language Models (LLMs) for Temporal Knowledge Graph Reasoning (TKGR). Specifically, the paper leverages LLMs for rule-based TKGR to identify temporal patterns and enable interpretable reasoning. Additionally, it introduces a dynamic adaptation strategy that iteratively upda... | Rebuttal 1:
Rebuttal: # Comment for Reviewer thzY:
# Weaknesses:
$\color{blue}{W.1:}$ There is limited analysis of the constrained Markovian random walks.
$\color{blue}{Re:}$ Thanks for your suggestions. In the paper, we have provided theoretical analysis of the constrained Markovian random walks in Appendix A from ... | Summary: This paper introduces Large Language Models-guided Dynamic Adaptation (LLM-DA), a novel approach for Temporal Knowledge Graph Reasoning (TKGR). LLM-DA leverages LLMs to extract temporal logical rules from historical data, providing interpretable reasoning. It also incorporates a dynamic adaptation strategy to ... | Rebuttal 1:
Rebuttal: # Comment for Reviewer 9wEQ:
Thank you very much for your professional review and valuable suggestions. We have carefully considered and responded to the questions you raised.
$\color{blue}{W.1:}$ Long-horizon forecasting concerns.
$\color{blue}{Re:}$ Thank you for your insightful comments regar... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Beyond accuracy: understanding the performance of LLMs on exams designed for humans | Reject | Summary: This paper shows the use of psychometric modeling techniques to measure the reasoning ability of LLMs on human exams. Specifically, the author(s) use Item Response Theory (IRT) to evaluate a Brazilian college-entrance exam, and demonstrate that IRT can provide a more informative evaluation of LLMs , including:... | Rebuttal 1:
Rebuttal: The results analysis would benefit from a more detailed and clearer/deeper analysis, some statements made (eg. L293-298) are high level observations based on the results, but lack further insight into why certain LLM behaviors occur. Performing more detailed analyses into the specific subset of qu... | Summary: The paper focuses on evaluating LLM abilities on a dataset of 8 college-entrance exams in Brazil (translated to English) measuring Item Response Theory instead of Accuracy. It highlights how such metric is useful to better understand models' performance.
Strengths: I have found the work very well structured a... | Rebuttal 1:
Rebuttal: While the paper is well structured, I felt it was missing a "what now?" message. The authors wrote a convincing argument in favour of using IRT, how do we convince now the field of ML / AI to use it more extensively? What are its limitations in comparison with accuracy-based metrics (given there a... | Summary: This paper initiates the empirical study of the performance of LLMs using Item Response Theory (IRT) models from a large college-entrance exam.
Strengths: - The question of construct validity of LLM evaluations (based on scores in exams designed for humans) is very important. This paper addresses this questio... | Rebuttal 1:
Rebuttal: (a) What are "outlier models" (line 237)? We cannot see from Figure 1 that "outlier models ... have higher accuracy and/or lower IRT scores..." - how is this statement supported?
__We agree that this statement lacks a quantitative interpretation. We have removed this sentence (and its paragraph... | Summary: This paper provides a fresh perspective to evaluating LLMs by arguing for a stronger emphasis on psychometric methods particularly Item Response Theory (IRT) when evaluating them on exams designed for humans, rather than the reliance on traditional metrics such as accuracy. The authors postulate that IRT provi... | Rebuttal 1:
Rebuttal: 1) For instance for the questions in Math and Natural Sciences wherein the models show fluctuating performance it would be useful to know what those questions aim to test.
__We appreciate the referee's comments. An important difference between the Math/Natural Sciences and Languages/Humanities ex... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fourier-enhanced Implicit Neural Fusion Network for Multispectral and Hyperspectral Image Fusion | Accept (poster) | Summary: The paper provides a novel INR-based fusion framework tailored for the MHIF task, effectively capturing high-frequency details and global information through innovative architectural components. The key contributions are transforming the latent features into the frequency domain to enhance high-frequency infor... | Rebuttal 1:
Rebuttal: **A1.** Thank you for your detailed review. We have revisited the sections that were considered complex and have worked to simplify the descriptions. For example, we have included foundational knowledge on INR and Fourier Transforms directly in the manuscript. Although the theoretical aspects invo... | Summary: The paper introduces a novel Fourier-enhanced Implicit Neural Fusion Network .The core innovation lies in the integration of Fourier transformations within an Implicit Neural Representation framework to address the loss of high-frequency information—a common limitation in existing INR approaches. The effective... | Rebuttal 1:
Rebuttal: **A1.** Thank you for your comments. We have revisited the sections that were considered overly technical and dense and have made efforts to simplify the descriptions. For instance, **we have included preliminary on INR and Fourier Transforms directly in the main text. The background on these topi... | Summary: The paper proposes a novel Fourier-enhanced Implicit Neural Fusion Network (FeINFN) specifically designed for the Multispectral and Hyperspectral Image Fusion task. The paper identifies the unique characteristics of the amplitude and phase of the latent codes in both HRHSI and LRHSI, and proposes to enhance hi... | Rebuttal 1:
Rebuttal: **Q1.** What does 'current neural network-based methods are insensitive to high-frequency information' mean, and how does the proposed model demonstrate sensitivity to high-frequency information?
**A1.** Thank you for your careful review. **(1)** High-frequency insensitivity is a common issue wit... | Summary: This paper proposes a quite interesting hyperspectral and multispectral image fusion framework via implicit representation. Moreover, the authors introduce the Fourier transformation to decouple the amplitude and phase domain.
Strengths: 1. Introducing implicit model into the task of hyperspectral multispectr... | Rebuttal 1:
Rebuttal: **Q1.** Improvement of Fourier design. The results from table 3 and figure 7 seem not consistent.
**A1.** Thank you for your insightful comments. We apologize for any confusion caused by our unclear presentation. To clarify:
- **Inconsistencies Between Tab. 3 and Fig. 7:** The results in Tab. 3 ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DAGER: Exact Gradient Inversion for Large Language Models | Accept (poster) | Summary: The authors proposed a method to recover user training data from gradients in federated learning. The key observation is if a linear network component exists in the overall neural network, the gradient of the linear component parameter can be shown as a linear combination of the input to this linear component.... | Rebuttal 1:
Rebuttal: $\newcommand{\Rj}{\textcolor{green}{ju5t}}$$\newcommand{\RL}{\textcolor{red}{L4TG}}$We would like to thank reviewer $\Rj$ for the positive review and the thorough and insightful questions. We are happy they found our work effective, and the empirical results impressive, highlighting the significan... | Summary: This is the first paper to use low rank decomposition to attack the gradient of the self-attention layer to extract information for LLM. It also provides a fast algorithm to recover the correct token first, then the sequence.
Strengths: Solid experiments and math proof. It’s a good innovative finding, especi... | Rebuttal 1:
Rebuttal: $\newcommand{\RU}{\textcolor{blue}{Ujeg}}$We would like to thank reviewer $\RU$ for their very positive review, the provided insights and helpful recommendations. We are happy they found our experiments and proofs to be solid and our method innovative. Further, we are glad that the reviewer credit... | Summary: In this paper, the authors propose the DAGER algorithm which leverages the low-rankness of self-attention layer gradients in order to filter out incorrect embedding vectors and recursively reconstruct true input text. DAGER works within the Centralized Federated Learning setting, where the server is honest in ... | Rebuttal 1:
Rebuttal: $\newcommand{\RL}{\textcolor{red}{L4TG}}$We would like to thank reviewer $\RL$ for the very positive review and feedback. We are happy to read that the reviewer finds our work very smart, practical and novel. Further, we are glad they assessed our paper as easy to follow and our approach as highly... | null | null | Rebuttal 1:
Rebuttal: $\newcommand{\RL}{\textcolor{red}{L4TG}}$$\newcommand{\RU}{\textcolor{blue}{Ujeg}}$$\newcommand{\Rj}{\textcolor{green}{ju5t}}$$\newcommand{\bm}[1]{\mathbf{#1}}$$\newcommand{\dl}{{\partial\mathcal{L}}}$$\newcommand{\dldz}{\frac{\dl}{\partial\bm{Q}_l}}$We would like to thank the reviewers for their ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Scaling Law for Time Series Forecasting | Accept (poster) | Summary: The authors propose a set of scaling laws on time series based relating model size, data size, and the "history dependence" of the time series. They end with a power law which they then try on different datasets.
Strengths: The topic is interesting and it would be a good thing to study.
Weaknesses: The prese... | Rebuttal 1:
Rebuttal: Dear Reviewer 7rY6,
Thank you for a detailed review! We truly appreciate your reviews and questions, especially for our theories, which aim our theory towards an omnipotent theory. We would like to briefly summarize your concerns, and our rebuttal and then expand upon this summary.
**1.Theory As... | Summary: This paper introduces scaling laws for DNN analysing MTS data, involving dataset sample size, model size, look-back horizon, dataset covariate size (dimension), and noise/uncertainty in the dataset. It relies on an axiomatization of the intrinsic information space (linearity, isomorphism) and bayesian optimal ... | Rebuttal 1:
Rebuttal: Dear Reviewer oD7J,
Thank you for the detailed and constructive review! We truly appreciate your reviews and questions. Here are our responses:
**1. Theory assumptions**
Let us give a sketch of our proof, where our assumptions are marked as **bold text**:
Suppose the true sample is $x[-H:S]$,... | Summary: This paper proposes a theory for scaling law in time series forecasting that accounts for dataset size, model complexity, and data granularity, with a particular focus on the look-back horizon. This paper empirically evaluates various models across diverse time series forecasting datasets to verify the validit... | Rebuttal 1:
Rebuttal: Dear Reviewer SJaN:
Thank you for the review! We truly appreciate your ideas and questions. Here are our responses:
1.**Novelty and contribution related to the 'Main Argument'**
1.1. ‘Longer horizon gives worse performance’ **is an important fact that could bring insight to the community, and t... | Summary: This paper proposes a theory that explains why complex models do not necessarily outperform simpler models even under the presence of larger amount of data, and why longer inputs hurt performance of some models. The authors consider the data size, model complexity, data granularity and the look-back horizon. T... | Rebuttal 1:
Rebuttal: Dear Reviewer Vwbb,
Thank you for a detailed and inspiring review! We truly appreciate your concerns and adivce! Here are our responses:
**1. Fitting experimental results with our derived formula**
As displayed in **Figure 1,2 and 5** in our original submission, the solid lines are the fitted ... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for the detailed reviews! In this global Rebuttal section, we would like to further clarify **(1) Contribution and Novelty of our work**, **(2) contents in the Extra Page Pdf** (which mainly contains more experimental results validating Zip-f law for more complicated cas... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a theoretical framework for scaling laws in time series forecasting, focusing on the impact of dataset size, model complexity, and look-back horizon on model performance.
The authors make two main contributions:
They propose a novel theory that explains scaling behaviors in time series ... | Rebuttal 1:
Rebuttal: Dear Reviewer urDc,
Thank you for a detailed and inspiring review! We truly appreciate your feedback and concerns! Here are our responses:
**1.How the proposed theory extends to multi-variate?**
While our deduction is primarily written in a single-variable manner, it can be easily adapted to th... | null | null | null | null | null | null |
The Limits of Differential Privacy in Online Learning | Accept (poster) | Summary: The paper studies online learning of concept classes under DP (differential privacy) constraint. The paper makes progress towards understanding mistake bounds (mostly) in the realizable case in a few settings. Concretely, The paper shows that:
1. If the adversary is oblivious, then PAC pure DP learnability imp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and our detailed responses are listed below.
> figure/meta-theorem that neatly explains all the relationships.
We will add a figure to explain the relationships in the revision.
Our main results imply that there exists a hypothesis class such... | Summary: The paper demonstrates that any function class that is offline PAC learnable with pure DP is also online learnable with pure DP against an oblivious adversary. In this context, a hypothesis class is considered online learnable in the realizable setting if there exists an algorithm with a sublinear mistake boun... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and our detailed responses are listed below.
> The proof of Theorem 4.3 is not clear
We will polish the proof of Theorem 4.3 to make it more clear in the revision.
> It looks like Lemma E.2 shows the concentration assumption in Dmitriev et al... | Summary: This paper studies limits of pure DP and approximate DP in the context of online learning (with oblivious and adaptive adversaries).
Strengths: The research questions are interesting and the results may have fundamental value.
I'm not an expert in all topics covered by the paper, but the contributions seem no... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments, and our detailed responses are listed below.
> no conclusions, discussion, further work, limitations
We state our main conclusions in Section 1.1, which are the separation results between no DP, pure DP, and approximate DP online learning.
Th... | Summary: Various results are proved about online learning of private learning algorithms, contrasting no DP, pure DP and approximate DP.
Strengths: The paper present some interesting new results on online learning with DP. I read up to section 3 and the writing is very clear and the results are important and purported... | Rebuttal 1:
Rebuttal: Thanks for the review and comments, and we have further clarified a few places to make it more accessible. | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Metalearned Neural Circuit for Nonparametric Bayesian Inference | Accept (poster) | Summary: The authors present a recurrent neural net (RNN) that learns to mimic a Bayesian non-parametric (BNP) approach to classification with (potentially) heavy-tailed data and an a priori unknown number of classes. The method is evaluated on three experimental setups.
Strengths: The paper itself is well-written and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Regarding reproducibility, please see the attached PDF (under general author rebuttal), where we have added results for neural circuits trained using 5 different random initializations. Please note that the variation in results across these ra... | Summary: This paper proposes a novel method for inference for Bayesian nonparametric models through an amortized approach. After simulating data from a Dirichlet Process Mixture Model, a Recurrent Neural Net is trained to characterise the relationship between the simulated data and the parameters, and consequently char... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and constructive comments. In particular, we are grateful for the suggestion to improve our analysis by training models with multiple random initializations. Please refer to the PDF under the general author rebuttal above, where we show the results of train... | Summary: This paper presents an approach for nonparametric Bayesian inference using metalearning to train a recurrent neural network (RNN) to perform sequential inference over an unbounded number of classes. The key contributions are: (a) method to extract the inductive bias from a Dirichlet process mixture model (DPMM... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and constructive feedback. The reviewer is correct that in our experiments we assume the use of a pretrained featurizer. Our initial motivation was to demonstrate that neural networks originally trained on a fixed number of classes can easily be adapted to ... | Summary: This paper proposes to use recurrent neural network (RNN) for classification with an open (non-fixed) number of class labels. This is motivated by non-parametric Bayesian models such as Dirichlet process mixture model and the iterative update on exponential family sufficient statistics in its particle filter m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. Visualizing the weight matrix W and examining the relationship between the expected number of clusters and the number of observations are both excellent suggestions. The probability of predicting a new cluster can be estimated in the synthetic... | Rebuttal 1:
Rebuttal: We thank each of the reviewers for their feedback. In response to the suggestions provided by the reviewers, we have prepared several new results in the attached PDF.
We appreciate the suggestion to improve intuitions about the approach through visualizations. We have added plots visualizing the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings | Accept (poster) | Summary: This paper proposes a method of rasterizing and optimizing 3DGS for omni directional cameras. Built on top of the original 3DGS perspective camera rendering, this paper approximates the rendering of each 3D Gaussian as a perspective camera pointing towards each Gaussian. By limiting the size of individual Gaus... | Rebuttal 1:
Rebuttal: ### **Q1) The details about the maximum size of the Gaussian**
**A) We applied different maximum size thresholds depending on the elevation angles.**
Looking at Eq. (7), in omnidirectional projection, the Gaussian located in the polar region is rasterized to a wider area of the image than the equa... | Summary: This paper implements a rasterization module for 3D Gaussian Splatting (3DGS) for omnidirectional images. The rasterizer assumes local affine approximation and projects Gaussian primitives to the unit sphere centered by the camera position, which may be reasonable for relatively small Gaussians. Based on the C... | Rebuttal 1:
Rebuttal: ### **Q) Are there any specific technical novelty we (readers and reviewers) should care about?**
**A) We hope that our work will not be dismissed as merely an engineering effort.**
The idea of applying a local affine approximation to a sphere may seem simple at first glance. However, aside from ... | Summary: The paper introduces a novel approach for 3D scene reconstruction from 360 omnidirectional imagery that the authors make compatible with a 2D Gaussian Splatting representation [x].
The proposed method is evaluated on 3 public datasets against 4 Radiance Field variants including NeRF derivatives and 3D Gaussia... | Rebuttal 1:
Rebuttal: ### **Q1) Inconsistencies between key sections and the contents**
**A) We strongly contend that the iEY4's review is based on a substantial misapprehension of our study, thus leading to erroneous conclusions.**
Our work is based on 3DGS [25] and is not related to 2DGS at all.
Eq. (9) describes a d... | Summary: This submission tackles the problem of extending 3D Gaussian Splatting (3DGS) to omnidirectional imagery. 3DGS and specifically its proposer rasterizer is limited to perspective camera. While omnidirectional images can be decomposed into perspective cameras, this typically introduces severe distortion artifact... | Rebuttal 1:
Rebuttal: ### **Q1) How to implement alpha blending?**
**A) We conducted our work based on the 3DGS framework, utilizing the same tile-based rasterization and alpha-blending pipeline.**
Our work suggests how to render with an omnidirectional (omni in short) camera model instead of a perspective (persp in sh... | Rebuttal 1:
Rebuttal: # Statements to All Reviewers
We appreciate all reviewers for their valuable comments.
We have thoroughly examined the reviews and hope to address all questions and misunderstandings through this rebuttal.
We have responded to each reviewer's queries in a question-and-answer format.
We will also f... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
From Instance Training to Instruction Learning: Task Adapters Generation from Instructions | Accept (poster) | Summary: This paper tackled zero-shot learning of LLMs to acquire cross-task generalization. The authors focused on the LoRA adapter, one of the methods used for LLMs for parameter-efficient fine-tuning. They generate task adapters by feeding the task instruction to the hypernetwork. Experimental results based on T5-LM... | Rebuttal 1:
Rebuttal: Dear reviewer 2ySK:
We greatly appreciate your professional review of our article. Here is our feedback.
### Response to W1:
> W1. Because the proposed method underperformed the standard fine-tuning...
1. **Training Phase**: **Gradient computations for each sample are unnecessary**. Our method... | Summary: The paper addresses limitations of current instruction fine-tuning approaches for large language models, which rely heavily on instance training with extensive task data. This limits adaptability to real-world scenarios where labelled task instances are scarce and broader task generalisation is needed.
The co... | Rebuttal 1:
Rebuttal: Dear reviewer Wqw9:
We greatly appreciate your professional review of our article. Here is our feedback.
### Response to W1:
> W1. Limited model size exploration: The authors primarily focus on models up to 3B parameters, with only limited experiments on an 11B model. This leaves questions abou... | Summary: This work proposes a new learning paradigm to train large language models (LLMs) for its better task adaptation and generalization ability. Specifically, they propose the method called TAGI, which follows a two-staged teacher-student fashion by firstly learning a set of task-specific LORA weights and then util... | Rebuttal 1:
Rebuttal: Dear reviewer UQYV:
We greatly appreciate your professional review of our article. Here is our feedback.
### Response to W1:
> W1. While I do appreciate the meta-train varying experiments, the distribution shifts of tasks in meta-train or test datasets seem to be consistent (i.e., I did not fin... | Summary: The authors introduce Task Adapters Generation from Instructions (TAGI), which converts instructions into task-specific adapters using a hypernetwork. They employ the Knowledge Distillation framework and a two-stage training process: first, hypernetwork pretraining on standard text pretraining data, followed b... | Rebuttal 1:
Rebuttal: Dear reviewer z6rG:
We greatly appreciate your professional review of our article. Here is our feedback.
### Response to W1:
> W1. Following a method that assigns indices to the LoRA layer, the flexibility of the LoRA architecture structure is reduced.
Our method dynamically generates LoRA wei... | Rebuttal 1:
Rebuttal: ### Q1:
> Q1. Instruction Length Analysis
We supplement the instruction length analysis of the experimental dataset here, theoretically supplementing the effectiveness of the method and the savings in reasoning costs.
Median sequence length, given in the number of T5 tokens, for Super-Natural Ins... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models | Accept (poster) | Summary: This paper proposes a slow and fast parameter-efficient tuning method for continual learning. Slow learner is learned on the first session and fixed with a transfer loss. Fast learner is continually updating for new tasks. Slow learner and Fast learner are further restricted to avoid forgetting.
Strengths: Th... | Rebuttal 1:
Rebuttal: We will include these experiments and provide detailed explanations of the results in the revised version.
Q1: The performance with only fast learner (FL) is already as good as the proposed method SAFE, which may not support the main idea.
A1: **First**, to further validate the effectiveness ... | Summary: The paper proposes a novel method named SAFE (Slow And Fast parameter-Efficient tuning) to tackle challenges in continual learning. SAFE introduces a unified framework that combines slow parameter-efficient tuning (S-PET) for inheriting general knowledge from pre-trained models (PTMs) and fast parameter-effici... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. We will include the mentioned experiments and detailed explanations in the revised version.
Q1: Training the slow learner (SL) only in the initial session may restrict its ability to adapt to new tasks, potentially limiting the overall flexibility of the model. Why... | Summary: This paper proposes a novel paradigm for continual learning called SAFE, which utilizes both slow and fast parameter updates. The method focuses on continual learning with pre-trained models, using slow updates to preserve the generalization capability of the pre-trained model while employing fast updates to a... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. We will include the mentioned experiments and analysis in the revised version.
Q1: The slow learner (SL) assumes that the data distribution of the first task in the continual learning (IL) scenario is roughly similar to subsequent tasks, which is not always true in pr... | Summary: The paper introduces the SAFE (Slow And Fast parameter-Efficient tuning) framework for continual learning using pre-trained models (PTMs). The proposed approach combines slow parameter-efficient tuning (S-PET) to inherit general knowledge from PTMs and fast parameter-efficient tuning (F-PET) to adapt to new ta... | Rebuttal 1:
Rebuttal: Thanks for your comments. We will include these experiments and provide detailed analysis in the revised version.
Q1: What are the specific effects of the cross-correlation matrix and its specific motivation? This design is quite similar to the de-correlation operation process in RanPAC.
A1: The... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable comments from all the reviewers. We diligently provide detailed explanations for the questions raised in the respective comments section **point-to-point**.
In addition, **supplementary experiments and theoretical analyses** are incorporated into the one-page ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models | Accept (poster) | Summary: The authors propose a new method for coherence free entrywise estimation of eigenvectors in signal plus noise model. Namely, entrywise estimation error usually depends on incoherence of the underlying matrix and can significantly increase error bounds for coherent matrix estimation. In this work, authors show ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their praise and for their thoughtful suggestions.
Specific responses to their concerns and questions are given below.
1) The main theorem is proven only in rank-$1$ setting, and rank-$r$ setting is only empirically tested.
Please see our discussion in the global rebutt... | Summary: The authors consider the spiked Gaussian Wigner matrices, where the main goal is to estimate the (low-rank) spike. Since the known performance of the spectral method for the estimation (of the spike) deteriorates as the maximal entry of the spike (more precisely, the incoherence parameter) increases, the autho... | Rebuttal 1:
Rebuttal: We thank the referee for their careful reading.
We must politely disagree with their correctness concerns, which mostly relate to citations in the literature review.
These provide context and background to our paper and are unrelated to our proofs.
We have clarified these points below and will edi... | Summary: The paper studies the low rank matrix estimation problem. It aims to find an estimator that is good with respect to the $\ell_{2,\infty}$ norm. In general, such errors depend on incoherence parameters. The authors propose and prove a spectral algorithm that does not depend on the coherence parameters when the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment and thoughtful suggestions. We address their concerns and questions below.
1) [The authors] prove a nice rate of convergence for [Alg. 1]. Unfortunately, the proof relies on some technical assumptions to simplify the proof. [remainder elided for... | Summary: This paper proposes an algorithm to estimate the eigenvector of a low-rank matrix under Gaussian noise. The algorithm provides a $\ell_{\infty}$ guarantee that is coherence free for rank-one matrices, at the cost of worsening the dependence on $\log n$ and some technical assumptions. The main idea is to utiliz... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words.
A brief response to their concerns is below.
1) The main weakness is Assumption 2 and Assumption 3, which are a bit weird and could significantly worsen the bound in some cases.
We agree that Assumption 2 is ungainly. Please see our discussion of this... | Rebuttal 1:
Rebuttal: We thank the referees and area chairs for their time, efforts and for their helpful comments, which have greatly improved the paper.
Two common themes among the reviewers' reports were Assumption 2 and the extension of Theorem 1 to the general $r \ge 1$ case.
In addition to these two points, two ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper mainly studies the problem of eigenvector estimation in low-rank signal-plus-noise matrix models and some new lower bounds for estimation rates in such models are derived. Specifically, the entrywise estimation error of the proposed procedure has no dependence on the coherence $\mu$ for the rank-one... | Rebuttal 1:
Rebuttal: 1) The theoretical results [...] only fit for the scenarios where the low-rank signal matrix is symmetric, thereby limiting its practical use.
We note that there are many applications (see, e.g., network analysis, neuroimaging, covariance estimation) where the target low-rank matrix to be estimat... | null | null | null | null | null | null |
Distributional Reinforcement Learning with Regularized Wasserstein Loss | Accept (poster) | Summary: This paper proposes a new RL algorithm that leverages Sinkhorn divergence, which they claimed as a regularized Wasserstein loss. Theoretically, they showed the contraction properties that align with the interpolation nature of Sinkhorn divergence between Wasserstein distance and MMD. Empirically, it outperform... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your positive assessment and insightful feedback, and we would like to address the concerns you raised in your review.
>My question is more intuitive---why should we pick Sinkhorn over others at a high level of intuition?
We summ... | Summary: • This paper proposes a novel distributional RL algorithm, called SinkhornDRL, which interpolates between Wasserstein distance and MMD. The authors aim to estimate the distribution using unrestricted statistics, enhancing stability and facilitating extension to multi-dimensional reward settings. The authors al... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your positive assessment and insightful feedback and would like to address the concerns you raised in the Weakness and Question parts of your review.
>Weakness 1: The text and figures in the paper are quite dense and difficult to re... | Summary: This paper introduces Sinkhorn Distributional Reinforcement Learning (SinkhornDRL), a new algorithm designed to address the limitations of current distributional RL methods, particularly those relying on quantile regression. Existing methods often struggle with accurately capturing the characteristics of retur... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. We appreciate your positive assessment and insightful feedback and would like to address the concerns you raised in your review.
>Question 1: Are there existing works on distributional reinforcement learning that use the entropic regularized Was... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank all the reviewers for their thoughtful and constructive feedback on our paper. We deeply appreciate your positive assessment and have thoroughly provided our response to address each of your concerns. We are committed to enhancing the quality of our work an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fast Proxy Experiment Design for Causal Effect Identification | Accept (poster) | Summary: This paper provide a computationally efficient algorithm for finding the sets of variables $\mathbf{Z}_1,\cdots,\mathbf{Z}_m$ that achieve the minimum intervention cost, allowing for $P(\mathbf{y} \mid \operatorname{do}(\mathbf{x}))$ to be identifiable from $\{P(\mathbf{V} \mid \operatorname{do}(\mathbf{Z}_i))... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough reading of our paper and their detailed feedback. We have addressed each comment and question below.
## Weaknesses:
### W1:
We acknowledge this comment. We have made our statements more precise to clarify that data fusion for causal identification... | Summary: This paper contributes to connecting the MCID problem—finding the minimum-cost interventions to identify a given causal effect, which has been proven to be NP-Complete—with four well-known problems, such as weighted maximum satisfiability and integer linear programming. These reformulations allow the original ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s comments and feedback. We respond to your individual comments below.
## Weaknesses:
### W1:
Based on our extensive simulations given in Section 5 and Appendix A, the WPMAX-SAT reformulation, when paired with a high-performance MAX-SAT solver like RC2, consistently out... | Summary: In this paper, the authors consider how to introduce interventional data based on observational data to make causal effect identifiability with the minimal cost. A high-dependent method is proposed by Akbari et al. [2022], which needs a very large computational cost. In this paper, by converting the problem to... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and positive comments. We respond to your question below.
## Questions:
### Q1:
Could the authors provide more clues about the reason that the SAT-based method performs better than the existing method?
**Response:**
At a high level, the previously existing m... | Summary: The problem of finding a lower cost intervention to identify causal effects has been shown to be NP-complete.
This paper provides many new reformulations to the problem in terms of a partially weighted maximum satisfiability (in the main paper), integer linear programming (in supplementary), submodular functio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and positive assessment. Taking your comments into account, we have rewritten and restructured the relevant sections of the paper to improve the presentation and clarity. We respond to your comments and questions below.
## Weaknesses:
### W1:
We acknowledg... | Rebuttal 1:
Rebuttal: Thank you to all reviewers for your valuable feedback. We have carefully reviewed each comment and addressed all questions and concerns in our individual rebuttals. We welcome further questions or comments and look forward to engaging with you during the discussion period. We have made the followi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections | Accept (poster) | Summary: This paper proposes a simple kNN-based label noise correction strategy to improve the performance of two-stage last-layer retraining methods for group robustness under moderate label noise. The authors show that the performance of RAD and SELF deteriorates quickly when label noise is present in the held-out da... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments, especially their acknowledgment of the strength of our evaluation and method in general. We would like to address each question (Qx) and weakness (Wx) individually. Note that our references continue numbering from the review.
(W1) Regarding th... | Summary: This paper addresses the challenge of improving worst-group accuracy (WGA) in machine learning models, particularly in the presence of noisy labels. The authors focus on last-layer retraining (LLR) methods, which have emerged as an efficient approach for correcting existing base models to ensure fairness acros... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thorough analysis of our submission and hope that we can answer some of the questions presented. We answer each question (Qx) and weakness (Wx):
(Q1/W3) Regarding the need for clean embeddings, as we point out in the discussion and in section 3 of our submission, Is... | Summary: This paper examines the recently introduced last-layer retraining (LLR) method, which focuses on reweighting features to ensure fairness and improve worst-group performance with minimal group annotation. The authors point out the shortcomings of the LLR method, particularly when label noise is present. To addr... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s considered response to our submission. We would like to politely push back on a few points and answer the reviewer’s questions (Qx) and weaknesses (Wx) in turn. Note that our references continue numbering from the review.
(Q1/W1) Regarding the need for clean embeddin... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful comments, and are grateful that they found our work well-written and sound. We would like to address the most common concerns in a general comment and we hope that this demonstrates the strong, and sometimes unexpected, contribution of our method.
-... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Universal Exact Compression of Differentially Private Mechanisms | Accept (poster) | Summary: A new technique for compressing local differential privacy (LDP) reports is presented, based on Poisson functional representations, a tool from information theory that allows encoding a random variable in close to information-theoretically minimum number of bits (expected) in a "universal" manner that does not... | Rebuttal 1:
Rebuttal: We thank Reviewer GP8R for the constructive feedback.
We are pleased to hear Reviewer GP8R thought our PPR technique is interesting and the paper is very well-written.
Please find our responses to the questions and comments below.
**Regarding a more self-contained and accessible version on a sp... | Summary: The paper addresses the problem of reducing the communication cost of messages that are shared under differential privacy (DP) guarantees. This is an important problem in privacy preserving machine learning, where parties that share obfuscated large models could incur in significant communication overhead.
... | Rebuttal 1:
Rebuttal: We thank Reviewer U6Tv for the constructive and detailed feedback. We are pleased to hear that Reviewer U6Tv appreciate the universality of our proposed method. Please find our responses to the comments below.
**Shared randomness and privacy:** Whether shared randomness weakens or prevents privac... | Summary: The paper investigates the problem of compressing the output of differentially private algorithms, particularly focusing on the local model. Given a Local Differential Privacy (LDP) algorithm $ A $ that induces a conditional distribution $ p_{Z \mid X} $ where $ Z = A(X) $, the objective is to generate a messa... | Rebuttal 1:
Rebuttal: We thank Reviewer VD1w for the constructive feedback. We are pleased to hear that Reviewer VD1w find the idea of viewing the compression of the output of LDP algorithms as channel simulation inspiring, and our technique can serve as a fundamental primitive to DP algorithm designers. Please find ou... | Summary: The paper designs Poisson private representation (PPR) to compress and simulate any local randomizer while ensuring local differential privacy. PPR exactly preserves the joint distribution of the data and the output of the original local randomizer, and also achieves a compression size within a logarithmic gap... | Rebuttal 1:
Rebuttal: We thank Reviewer NbQx for the constructive feedback.
We are pleased to hear Reviewer NbQx thought our manuscript is well-organized and easy to follow.
Below, we clarify the weakness and address the question pointed out by the reviewer
**Regarding small $n$:** Firstly, as noted in footnote 6,... | Rebuttal 1:
Rebuttal: Dear Reviewers and ACs,
We would like to thank all the reviewers for carefully reviewing our paper, their patience and also their valuable and constructive feedback.
We observed that the feedback from all four reviewers is generally positive.
Most reviewers mentioned the novelty of introducing ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fair Kernel K-Means: from Single Kernel to Multiple Kernel | Accept (poster) | Summary: This paper focuses on the fairness in the kernel k-means. It designs a new fairness regularized term, which has the same form as the kernel k-means. Then it plugs this term into the kernel k-means and extends it to the multiple kernel k-means. Some theoretical analyses are provided to help to tune the hyper-pa... | Rebuttal 1:
Rebuttal: W1. We will revise the related work to introduce the fair clustering methods in more detail.
W2. Since our method has the same form as the standard kernel k-means, we do not increase much overhead. In contrast, in our method, instead of using the eigenvalue decomposition which is used in conventi... | Summary: The authors design a novel fair kernel k-means method and a fair multiple kernel k-means method. The main part is the fairness regularization term. By minimizing this term, the optimal fairness, which is defined in Definition 1, can be achieved. The authors also derive the generalization error bound and discus... | Rebuttal 1:
Rebuttal: W1. Bal is a very strict evaluation metric that considers the worst case. Notice that $\mathrm{Bal}\left(\mathcal{C}\right)=\min_{k} \left(\frac{N_{k}^{\min}}{N_{k}^{\max}} \right)\in[0,1]$. As long as in one cluster, there are no instances of one protected group, according to its definition, Bal ... | Summary: This paper proposes a novel Fair Kernel K-Means (FKKM) framework to address the fairness issue in kernel k-means clustering. The authors introduce a fairness regularization term that can be seamlessly integrated into the kernel k-means objective function. They extend this approach to multiple kernel k-means, r... | Rebuttal 1:
Rebuttal: W1. The methods can be used in some applications involving humans which need fairness. For example, in the clustering of the customers of the banks, we wish to partition the customers into several groups to make the decisions for each individual. However, when doing the partition or making the dec... | Summary: The paper introduces a new framework called Fair Kernel K-Means (FKKM) aimed at addressing fairness issues in kernel K-means clustering. By incorporating a fairness regularization term, the method ensures fair data partitioning and avoids discrimination against specific groups. Additionally, the paper extends ... | Rebuttal 1:
Rebuttal: W1. Our methods can be used in applications involving humans which need fairness. For example, in bank system, we make decisions without considering the gender of customers to avoid sexism. In experiments, we use Credit Card data in this scenario. The data is to predict whether a customer will fac... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Can Large Language Model Agents Simulate Human Trust Behavior? | Accept (poster) | Summary: This paper studies the trust behaviors of LLM-based agents, which are important for agents to simulate humans. The authors focus on (1) how LLM-based express trust behaviors, and (2) the similarity/alignment between agent trust and human trust. They find that LLM-based agents generally exhibit trust behaviors ... | Rebuttal 1:
Rebuttal: We are sincerely thankful for the valuable and constructive feedback and are more than willing to provide more responses in the reviewer-author discussion session if the reviewer has any further questions.
> C1: I think more experiments can be added to repeated trust games, because in the real w... | Summary: This paper proposes a framework utilizing behavioral economic paradigms to investigate LLM's trust behaviors and compare them with human behaviors. This paper considers multiple LLMs (from small size to large and commercial size), as well as multiple tasks that circle the behavioral factors (Reciprocity Antici... | Rebuttal 1:
Rebuttal: We are grateful for the valuable and constructive feedback and are more than willing to provide more responses in the reviewer-author discussion session if the reviewer has any further questions.
> C1: In the Repeated Trust Game, as I found in the appendix, the prompts only provide the last roun... | Summary: This paper investigates whether Large Language Model agents can effectively simulate human trust behavior. The authors explore trust behaviors using the Trust Game and its variations, comparing the trust exhibited by these agents with that of humans. They find that GPT-4 shows a high degree of behavioral align... | Rebuttal 1:
Rebuttal: We genuinely appreciate the valuable and constructive feedback and are more than willing to provide more responses in the reviewer-author discussion session if the reviewer has any further questions.
> C1: Trust games simplify real human …
R1: Thanks for the suggestion. First, we would like to ... | Summary: The paper targets an important issue for adopting LLM agents as simulation tools in social and economic sciences and in role-playing application, namely if LLM agents can really simulate human trust behaviors. More specifically, they adopt the well-known framework of Trust Games and they discover that LLM agen... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable and constructive feedback and are more than willing to provide more responses in the reviewer-author discussion session if the reviewer has any further questions.
> C1: The structure of the paper could be improved. …
R1: Thanks for the suggestion. **We ackno... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable and constructive feedback from all the reviewers and would like to humbly emphasize the following points:
1. We have multiple novel findings, supported by extensive empirical experiments and comparative analysis with existing human studies:
- We discover th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes | Accept (poster) | Summary: This work proposes a model to reconstruct a clean and sharp NeRF from a set of hand-held low-light images. The authors recognize the implicit order of the degradations (blur, noise, and low visibility), and sequentially decouple and remove each degradation in the network training. An SND module is proposed for... | Rebuttal 1:
Rebuttal: ### W1 Part1: Error in Eq.4 & 5 and the missing derivation.
**R:** We thank the reviewer for the careful reading and apologize for the mistakes in Eq.5, which **omitted the deblurring process**.
The **missing derivation** is also added to Eq. 5. The revised Eq. 5 should be:
$ C_{noisy}(r) = CTP(C... | Summary: The paper proposed a method to train a NeRF with blurry (due to camera motion), low-light scene images. After training the method allows the recovery of enhanced, sharp images. To solve the problem two modules are proposed:
1) A SND module for noise modeling, which includes both a noise-prediction NeRF (N-NeR... | Rebuttal 1:
Rebuttal: ### W1: Insufficient Dataset Contribution.
**R:** Actually, we did **not just** take images from the LOL-Blur dataset and then ran an off-the-shelf SFM method to obtain the camera parameters. To build an effective dataset, we did the following works:
(1) **Scene selection**: We went through the ... | Summary: The authors propose LuSh-NeRF, a model that reconstructs a clean and sharp NeRF from handheld low-light images by sequentially modeling noise and blur. LuSh-NeRF includes a Scene-Noise Decomposition (SND) module for noise removal and a Camera Trajectory Prediction (CTP) module for estimating camera motions bas... | Rebuttal 1:
Rebuttal: ### W1: Ablation studies regarding the roles of proposed modules.
**R:** Thanks for your positive feedback on our work, the **visualization** of the ablation experiments can be found in **Fig.6** in the main text. To better demonstrate the effectiveness of the different modules in LuSh-NeRF, we pe... | Summary: This method proposes a solution for NeRF optimization under low light settings by resolving 3 different forms of degradation: low intensity, camera noise, and motion blur. Low intensity is effectively resolved by scaling up the image, camera noise is resolved by proposing a consistency loss between different v... | Rebuttal 1:
Rebuttal: ### W1: Ablation studies with quantitative results.
**R:** As suggested, we have conducted detailed ablation studies on all the synthetic scenes in the following table:
| Scene | Dorm | | | Poster | | | Plane | | | Sakura | ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their comments and suggestions. We are glad to see that reviewers comment our idea/work as novel (4gW7), sound (54cf), appreciated (4eod), and appealing (m3Lt). We address the raised concerns below and will revise our paper according to all comments. Please let us know i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Spiking Neural Network as Adaptive Event Stream Slicer | Accept (poster) | Summary: This paper proposes to use spiking neural networks (SNNs) to slice the event stream in an adaptive manner before passing the voxelized events to the downstream inference model. The first step of the proposed method divides the input event stream into voxelized event cells with the same temporal interval. An SN... | Rebuttal 1:
Rebuttal: **Q1:** *While SNNs are efficient and consume less energy than ANNs, SNNs are also less capable than ANNs. Since the speed of the entire SNN+ANN prediction pipeline is going to be slow anyway, it may be worthwhile to investigate whether using an ANN as an event slicer can lead to better prediction... | Summary: This work proposes a novel method for adaptively sample event data and subsequently preprocess it, utilizing a spiking neural networks (SNNs) as module.
The sampling method involves a feedback mechanism that triggers the activation of the SNN.
Strengths: Tests are done on dataset with different lighting cond... | Rebuttal 1:
Rebuttal: **Q1:** *The experiments conducted do not contain tasks such as optical flow, object detection, or image reconstruction. The type of tasks tested is limited.*
**A1:**
Thank you for your suggestion. Due to limited time and resources, we have endeavored to incorporate a variety of task types. Speci... | Summary: The authors designed a plug-and-play event processing method, SpikeSlicer, to split event streams with an adaptive amount. The proposed method is a lightweight SNN, constrained by a custom Spiking Position-aware Loss (SPA-Loss) to regulate neuron states. Additionally, a downstream ANN refines the slicing decis... | Rebuttal 1:
Rebuttal: **Q1:** *The comparison algorithm for event-based object tracking, DiMP, is from 2019. Why not try the latest methods? In recent years, many studies have focused on improving the effectiveness of event stream representation to enhance the performance of event vision tasks; Many methods for object ... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their thoughtful comments and feedback. We appreciate that all reviewers agreed that the idea of using spiking neural network (SNN) for dynamic event slicing is interesting and evaluated the paper with positive scores. Below, we address the primary concerns rai... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reward Machines for Deep RL in Noisy and Uncertain Environments | Accept (poster) | Summary: The authors present an extension of the general “Reward Machine” framework to partially observable reinforcement learning environments. In particular, they consider cases where an agent does not have direct access to a labeling function which maps from state transitions to the relevant propositions needed to u... | Rebuttal 1:
Rebuttal: Thank you for your review and for the strong endorsement of our work. We are glad that you recognize its merits across all the major criteria. Please see our response to your feedback and questions below.
> **is there an account for why TDM performance is substantially better on than the baseline... | Summary: This paper focuses on the automatic design of reward machines in reinforcement learning, which holds potential for interpreting instructions, enforcing safety constraints, and more. It is particularly relevant in the real world, especially in the era of large language models (LLMs), where defining reward funct... | Rebuttal 1:
Rebuttal: Thanks for the review. First, we’d like to clarify that our focus is not the “automatic design of Reward Machines” (we assume the RMs are specified by a human). Rather, the work is about whether we can effectively follow task specifications (expressed via RMs) even when the vocabulary cannot be in... | Summary: The paper proposes the use of Reward Machines (RMs) in deep reinforcement learning (RL) for noisy and uncertain environments, characterizing these settings as Partially Observable Markov Decision Processes (POMDPs). The contributions include:
- Proposing framework for using RMs in deep RL in partially observab... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation and for recognizing that this work addresses an innovative problem, introduces rigorous definitions and insights, and presents well-supported claims in a clear and organized manner. We address your main questions and concerns below.
> **… to what extent coul... | Summary: This paper investigates the use of Reward Machines in Deep Reinforcement Learning (RL) for handling noisy and uncertain environments. It frames the problem as a Partially Observable Markov Decision Process (POMDP) and proposes a set of RL algorithms leverage the task structure under uncertain interpretation o... | Rebuttal 1:
Rebuttal: Thank you for your constructive review. We take seriously the issues you raised regarding clarity and will revise the manuscript accordingly.
> **Should the abstraction model be the method or the problem?**
The abstraction model is part of the problem. It captures the agent’s uncertain prior ove... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their time and for their detailed and informative reviews. Reviewers found “the combination of RMs with deep RL algorithms to handle noisy and uncertain environments [to be] innovative” (ed91), noting that the work was “broadly applicable across many fields as it doe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems | Accept (poster) | Summary: This paper addresses the problem of ensembling in low precision number systems, where quantized models act as members of the ensemble. The authors suggest that quantization errors can be leveraged to enhance ensemble diversity. Based on this concept, they propose a method called LPE-BSR. Through extensive expe... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We hope our response below addresses any remaining concerns. If you have any further questions, please let us know. Otherwise, we kindly request that you reconsider your assessment accordingly. Thank you again for your valuable feedback!
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> In line 174, w... | Summary: This paper presents a new way to generate an ensemble of models without the need for training multiple times and with the extra advantage of using low-precision representation which inherently saves memory.
The idea is to build an ensemble of models starting from stochastic variations of a single model. Those ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the potential and interest in our idea. We appreciate your detailed and constructive feedback. We have addressed your comments below and are confident that incorporating these revisions into the final version will significantly strengthen our work. If you have any additio... | Summary: This paper addresses the scalability challenge in ensembling deep neural networks for large models by introducing a novel low precision ensembling method. The approach generates an ensemble of models from a single model using low precision number systems in a training-free manner. Empirical analysis shows that... | Rebuttal 1:
Rebuttal: Thank you for recognizing the significance of the problem we addressed, the clarity of our writing, the technical robustness of our proposed method, and the effectiveness of our experimental results. We have addressed your comments below and believe that incorporating these revisions into the fina... | Summary: The paper proposes that ensembles of quantized low-precision instances of large models outperform the source models on image classification and MMLU tasks. The low precision models are generated using Bernoulli stochastic rounding. The authors support their claims by presenting empirical results for several mo... | Rebuttal 1:
Rebuttal: We are pleased with the positive feedback that highlights our work as both inspired and interesting. We hope the responses provided below address any remaining concerns. Please let us know if there are any further issues. Thank you for your valuable comments!
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> The paper presents NLL compari... | Rebuttal 1:
Rebuttal: # Global Response
First and foremost, we would like to thank all the reviewers for their time and effort in reviewing our paper. We are pleased to note that all the reviewers agreed our paper is of high quality. In particular, they noted that it tackles an important problem (yx79), is well-writte... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces | Accept (poster) | Summary: The paper presents a novel approach to deriving convergence using two 'stability' properties or assumptions. Improved bounds are derived which show faster convergence than predicted by traditional bounds.
Strengths: 1. The authors present a new framework for analyzing RL convergence properties. The authors pr... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our work. We really appreciate it!
- $Weakness$
RE: Thank you for your helpful suggestion. We will add toy examples and include more discussions to improve the clarity of the paper.
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Rebuttal 2:
Title: Please respond to the authors
Comment: Hello revi... | Summary: In the context of continual control reinforcement learning, this paper presents a new analysis of how a good Q-function estimate (measured in terms of the Bellman residual norms) induces a good greedy policy (measured in terms of the value gap compared to the optimal policy). The key contribution is the formal... | Rebuttal 1:
Rebuttal: Thank you for recognizing the value of our research! Below, we will address each of the points you’ve raised.
- $Weakness 1$
RE: Thank you for your valuable feedback. We will work on shortening Section 3 and adding more discussions of stability conditions and toy examples to make the intuitions ... | Summary: This paper demonstrates how sample complexity can be improved under some conditions in offline and online RL.
It considers an episodic MDP framework with episodes of length H.
One of the contributions of the paper is that, under specific stability conditions for the MDP, getting Bellman residual errors < \ep... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our paper. We are grateful for your recognition of our theory as both well-motivated and general. Below, we will address each of the points you’ve raised.
$Weakness$
That is a good point; we will be more cautious about the rigor of the discussions.
$Question$
... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Spatio-Spectral Graph Neural Networks | Accept (poster) | Summary: The authors are proposing Spatio-Spectral Graph Neural Networks ($S^2GNNs$), a hybrid model that combines (Spatial) Message Passing Neural Networks (MPNNs) and Spectral Graph Neural Networks. The authors argue that, by combining message-passing with spectral filters, the model can better model both local and g... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable points and positive feedback. We will use the extra space in the camera-ready version to address the reviewer's points.
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## High-resolution filters for low frequencies
We truncate the spectral filter for efficiency reasons mainly, whereas the special ch... | Summary: This paper proposes Spatio-Spectral Graph Neural Networks (S²GNNs) to address the limitations of ℓ-step Spatial Message Passing Graph Neural Networks (MPGNNs), such as limited receptive fields and over-squashing. S²GNNs combine spatial and spectral graph filters for efficient global information propagation, of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and for acknowledging the theoretical justification along with the ubiquitous possibilities of lifting GNNs to S$^2$GNNs. Furthermore, we thank the reviewer for highlighting that the paper contains a lot of intuitive analysis with examples.
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## W1: Pse... | Summary: The paper presents Spatio-Spectral Graph Neural Networks (S$^2$GNNs), a novel paradigm that combines spatial and spectral parameters to overcome the limitations of $\ell$-step MPGNNs, notably their restricted receptive fields and over-squashing issues. S$^2$GNNs achieve global information propagation efficient... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and for acknowledging the theoretical justification along with the ubiquitous possibilities of lifting GNNs to S$^2$GNNs.
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## W1: Ablation on the combination of spatial and spectral filter
We agree with the reviewer that different combinations may yie... | Summary: The proposed method Spatio-Spectral Graph Neural Networks (S2GNNs) combine the spectral GCN and spatial GCNs embeddings linearly. The paper considers a deep dive into spectral filter properties and attempt to motivate the combination with spatial filtering. There are well known results repeated in the method s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their critical thoughts about our work! We are convinced that we conclusively resolved all points brought up by the reviewer. We would highly appreciate a major reevaluation of our submission.
Before going into detail, we want to highlight that our empirical results yiel... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and valuable feedback! Notably, we thank reviewers EZD5, H69b, and rAa9 for uniformly acknowledging our theoretical foundations/analysis of our Spatio-Spectral Graph Neural Networks (S$^2$GNNs), along with our method's general applicability and strong empi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UV-free Texture Generation with Denoising and Geodesic Heat Diffusion | Accept (poster) | Summary: The paper proposed a new way to represent the texture to replace the UV-map and trained a model for texturing generation of objects that are not limited on a specific category.
Strengths: The proposed new representation avoid per-category training and seams on UV maps.
Weaknesses: 1. Missing the experiments ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and efforts invested in our paper. Our detailed responses are given below.
> Fig. 3 is not detailed enough to describe how the entire pipeline work.
We thank the reviewer for suggesting us to include the full pipeline figure, which we provided in the suppleme... | Summary: This paper proposes to apply denoising diffusion generative model directly on mesh surfaces, with a focus on texture generation.
It achieves this goal by utilizing the DiffusionNet method which utilizes heat diffusion to enable 'convolution' and message passing within the surface.
This work modify and extend t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and encouraging feedback. We are glad the reviewer believes our work has greater potential than just mesh texturing. We agree that our approach can easily adapt to other applications requiring the generation of signals that reside on geometric structures th... | Summary: This paper proposes UV3-TeD, a 3D mesh texturing method without explicit UV mapping. To circumvent the challenges of using explicit UV mapping, the authors propose to represent texture as a point cloud with color features. While there were some methods that used similar representation, the authors emphasize th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive and interesting comments. We start by addressing two related points and then proceed to provide a detailed response to the remaining comments.
> The visualized results for the generation results seem to be too low-frequency. The concept of heat diffusi... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their constructive feedback. We appreciate that reviewers find our work interesting, valid, and fresh (Yhxo), with greater potential than just mesh texturing (cPWU), clearly presented (Yhxo, cPWU), and with nice and effective visualisations (Yhxo, cPWU). We... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OccFusion: Rendering Occluded Humans with Generative Diffusion Priors | Accept (poster) | Summary: This paper presents OccFusion, a 3D human avatar creation system that combines 3D Gaussian Splatting (3DGS) and 2D diffusion models to effectively render occluded regions. To this end, three stages are designed. First, in the initialization stage, complete human masks (without occluder) are obtained using a pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and the helpful comments! We address your concerns below.
> Rendering quality seems blurry
With only 10 mins of training time, OccFusion surpasses state-of-the-art occluded human rendering methods by a significant margin qualitatively and quantitatively, as il... | Summary: This paper introduces OccFusion, a method for rendering occluded humans.
Similar to other 3DGS-based human rendering methods, OccFusion optimizes a set of 3D Gaussians to improve training and rendering speed.
OccFusion proposes adopting generative diffusion priors to ensure complete and high-quality render... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our work and the helpful comments! We address your concerns below.
> Lack of discussion about NeRF in the Wild, Ha-NeRF, Gaussian in the Wild and NeRF On-the-go
Thanks for the suggestion. We will add the following sentences to section 2.2 of o... | Summary: This paper proposes a method for reconstructing gaussian-based human avatars from occluded captures. The gaussian avatar model is based on GauHuman, which is optimized in multiple stages, including using a diffusion based prior in the canonical space to recover the complete human. In the first stage, a consist... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and the helpful comments! We address your concerns below.
> Incompleteness of Figure 6
Thanks for the suggestion. The reason why we do not include results for all experiments for both subjects in Figure 6 is to showcase the benefits of our proposed components ... | Summary: OccFusion proposes an approach to model human bodies that fail under occlusion in monocular videos. The authors utilize 3D Gaussian splatting for efficient rendering and leverage pretrained off-the-shelf image diffusion models as 2D priors. Their approach involves a three-stage training process, sequentially r... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and the helpful comments! We address your concerns below.
> Discussion of off-the-shelf models
Thanks for the suggestion. We will include the following edited text in the Introduction of the final version:
```
In this work, we introduce OccFusion, an efficien... | Rebuttal 1:
Rebuttal: We would like to thank all of the reviewers for their thoughtful feedback and helpful suggestions! We agree that occluded human reconstruction from a monocular video is an “important and unexplored problem” (Reviewer BvJA) that is “relatively new and practical” (Reviewer gVDG). By proposing a “nov... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work presents a Gaussian-based approach to reconstruct 3D human poses from occluded monocular video. Building upon the success of previous methods like SDS, the paper suggests introducing a pre-trained diffusion prior to complete the occluded areas and further refine the missing appearance. The framework ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your time and the helpful comments! We address your concerns below.
> Proof that applying SDS on RGB images causes appearance inconsistency
In Fig. 3 of the rebuttal PDF, we include additional experiments comparing the rendering results of applying SDS on RGB vs. on hum... | null | null | null | null | null | null |
Quantum Deep Equilibrium Models | Accept (poster) | Summary: The technique of deep equilibrium models, which were introduced to efficiently handle classical sequential data, is here applied to networks consisting of quantum circuits. The performance is compared to both direct solvers and baseline algorithms (VAE and PCA) for datasets derived from MNIST-4, MNIST, and Fas... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s positive assessment and thoughtful comments on our work. In response to their questions, we offer the following answers.
**Classical vs Quantum Data**
Firstly, we would like to state that we are not intending this work to make any strong statements about quant... | Summary: In this paper the author present a quantum version of the Deep Equilibrium models. These networks approximate iterative approach through many layers by approximating it with a single set of parameters that would have converged if there were an infinity of layers. The paper is written well and the method is ver... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and constructive critique of our work. In response to their concerns, we have provided the following clarifications and additional information:
**Scalability**
We agree with the reviewer that scaling is an important question, and one that is very difficult t... | Summary: This paper introduces a new paradigm for training quantum machine learning models using Deep Equilibrium Models (DEQ). The authors propose Quantum Deep Equilibrium Models (QDEQ) to enhance the performance of parametrized quantum circuits (PQC) while addressing issues related to circuit depth and parameter scal... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments and are thankful for the positive feedback. In response to the reviewer’s questions, we have provided the following answers:
**Influence of noise**
We agree that this is a very interesting and relevant question that we opted to defer to further re... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for spending time on our paper and providing valuable feedback. We are glad that the reviewers find our generalization of deep equilibrium networks to quantum circuits novel and interesting for real-world applications.
We have addressed the following points in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks | Accept (poster) | Summary: In this work, the phenomenon of asymmetric valleys in deep neural networks (DNNs) minima, first observed and described in [1], is systematically examined. By studying different types of (random) directions around a given minimum, the authors discovered that the degree of sign consistency between the chosen dir... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper and finding our paper "**well structured and easy to follow**" and stating "**the claims are clearly formulated and well supported empirically**".
Also, the authors thank you for **your recognition that the contributions are novel and significant when compared to the... | Summary: This paper explores the factors affecting the symmetry of DNN valleys, encompassing (1) the dataset, network architecture, initialization, and hyperparameters that influence the convergence point; and (2) the magnitude and direction of the noise for 1D visualization. The major contribution is the observation t... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper and **finding our observation reasonable**.
########
Q1: The asymmetry is not unexpected and has been studied in [24].
A1: Surely, our work is majorly motivated by the proposal of asymmetry valley in [24], and we have declared this relation in lines 28-29 and 67-7... | Summary: This paper investigates the characteristics of the asymmetric valley in deep neural networks (DNNs) for classification. The authors perform a perturbation analysis around the local minima, considering the direction of the injected noise. The asymmetric valley demonstrates that DNNs exhibit smaller fluctuations... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper and finding our paper's strengths, e.g., "**the paper is well-structured and clear**", "**the theoretical insights are convincing**", and "**the proposed method is simple and effective**".
########
Q1: The different architectures in Section 6.1.
A1: As stated in l... | null | null | Rebuttal 1:
Rebuttal: Thanks for all the reviewers' efforts in reviewing our paper!
We are delighted that the three reviewers found our strengths.
The reviewer MUA2 advocates "**the paper is well-structured and clear**", "**the theoretical insights are convincing**", and "**the proposed method is simple and effective... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in LLMs | Accept (poster) | Summary: This paper presents the Uncertainty of Thoughts (UoT) algorithm designed to enhance large language models (LLMs) by enabling them to actively seek information through effective questioning. UoT incorporates three key components: an uncertainty-aware simulation approach to model possible future scenarios, uncer... | Rebuttal 1:
Rebuttal: We highly value the feedback you provided. Your suggestions have prompted us to refine certain aspects of our work. We hope the following clarifications will satisfactorily address the points you raised.
> Q1: The paper lacks analysis and comparison of inference times, which are crucial for user ... | Summary: The authors propose an uncertainty-aware information-seeking framework. This approach involves having the LLM simulate future scenarios and select the question that maximizes information gain. They evaluate their method using the latest LLMs across various benchmarks and introduce new datasets specifically des... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed feedback and suggestions. Your comments have been very helpful in guiding us to enhance our work. Please find our clarifications below, which we hope will resolve your concerns.
> Q1: The authors should include human-based experiments
As you suggested, we ru... | Summary: The paper addresses the problem of how to guide an LLM to find the right answer to a given question, in cases additional information must be elicited from the agent (possibly human) asking the question before knowing the right answer. The proposed algorithm, called Uncertainty of Thoughts (UoT), works by simul... | Rebuttal 1:
Rebuttal: Thank you very much for recognizing the value of our work and providing valuable suggestions. Your feedback is instrumental in enhancing the quality of our research. Below are some clarifications that we hope will address your concerns.
> Q1: For the case where the answers are open ended, I do ... | Summary: The paper introduces Uncertainty of Thoughts (UoT), aimed at enhancing the ability of large language models (LLMs) to actively seek information by asking effective questions. UoT integrates an uncertainty-aware simulation method, uncertainty-based rewards motivated by information gain, and a reward propagation... | Rebuttal 1:
Rebuttal: We are grateful for your thorough review and insightful comments. Your feedback has encouraged us to refine and improve our work. We hope the clarifications below will adequately address the issues you mentioned.
> Q1: There's a significant performance gap between baseline models (CoT and ToT) a... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their helpful comments and suggestions. Here is a summary of our responses to address the major concerns of reviewers.
**1. Elaboration of Open Set Setting (Proposed by reviewer1[Ho3e15])**
In the closed set setting, our algorithm starts with a known poss... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents an approach known as “uncertainty of thoughts (UoT)”, which builds upon related ideas of creating a tree of responses to answer a question, such as the “tree of thoughts” and related approaches. The key components of the system include 1) an approach to ask different types of questions and ... | Rebuttal 1:
Rebuttal: We greatly appreciate your insightful feedback. Below, we provide clarifications to address the concerns, which we will incorporate in next version.
> Q1: The open set case seems important but was not covered in sufficient detail in the paper.
Sorry for any confusion. We explain the open set set... | null | null | null | null | null | null |
SceneCraft: Layout-Guided 3D Scene Generation | Accept (poster) | Summary: This paper proposes a layout-guided method for room generation. The proposed method uses 3D scene layout as a prior and leverages semantic and depth maps as 2D conditions. Specifically, the authors start by training a 2D conditional diffusion model named DreamScene2D, which utilizes ControlNet to incorporate p... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: The key insight of the proposed method is similar to the recently released paper UrbanArchitect [A]. Although two works can be seen as concurrent works, I hope the authors can include a discussion.*
We appreciate the r... | Summary: This work tackles the problem of 3D scene generation from bounding box scene, text prompt and camera trajectory. The core contribution of the work stems from a 3D scene generation method where the method is able to produce a complex scene, which the previous works could not generate due to using a panoramic re... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: Overall, the scenes look more synthetic compared to MVDiffusion and text2room.*
We'd like to address this concern of the reviewer by providing context for our approach:
* **Prioritizing 3D Consistency**: Our primary fo... | Summary: DreamScene proposes a method for 3D indoor scene generation with text and layout as input. For this, they propose using a 3D bounding box scene representation as the means to provide layout guidance. This is then used as an input, along with text prompt, to a 2D diffusion model, dreamscene2D, which is capable ... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: Evaluation: Authors should provide quantitative results.*
We appreciate the reviewer's suggestion for quantitative results.
* Our initial decision to prioritize qualitative evaluation was based on the challenges in def... | Summary: This paper addresses the task of 3D-consistent indoor scene generation using 2D diffusion models. As input the user provides a 3D bounding-box layout of the scene and a text prompt coarsely describing the scene. The proposed method contains a two-stage training phase. In the first stage, a 2D diffusion model i... | Rebuttal 1:
Rebuttal: We appreciate your comments, and address your concerns as follows:
***
1. *Q: The proposed method might not be very novel. The contribution of the paper seems mainly to be fine-tuning the diffusion model for semantic-guided image synthesis. The distillation stage is mostly similar to the existing... | Rebuttal 1:
Rebuttal: # General Response
We greatly appreciate the thoughtful feedback and suggestions from all reviewers. We are pleased that the reviewers recognize the strengths of our approach, including more detailed and accurate scene generation (DRMD, Nerf), excellent controllability and user-friendly design (G... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding | Accept (poster) | Summary: To capture the long-term relationship in cardiac motion, the authors proposed GPTrack, a novel unsupervised framework crafted to fully explore the temporal and spatial dynamics of cardiac motion. They proposed employing the sequential Gaussian Process in the latent space and aggregating sequential information ... | Rebuttal 1:
Rebuttal: ***Dear reviewer Tej6***: Thank you for your kind comments and suggestions, which help us improve our paper's quality. Here are our responses to weaknesses and questions. New experiments are included in the uploaded PDF file to better illustrate your questions.
**Q1. Method applied to com... | Summary: 1. This paper presents GPTrack, an unsupervised framework designed to thoroughly investigate the temporal and spatial dynamics of cardiac movement.
2. GPTrack refines motion tracking by utilizing sequential Gaussian Processes within the latent space and encoding statistical data with spatial information at ea... | Rebuttal 1:
Rebuttal: ***Dear Reviewer bQtH:***
Thank you for your valuable feedback, we would like to address your questions point by point in the following.
**Q1. Line 26, Can optical flow ...**
The optical flow (OF) is also able to capture the temporal coherence. In *section Appendix A1*, we discussed the... | Summary: The authors proposed a latent modeling framework for cardiac motion tracking. They introduced the GPTrack module for image encoding, which considers both forward and backward information flow. A Gaussian process was integrated to describe the motion prior. Extensive experiments were performed on both echocardi... | Rebuttal 1:
Rebuttal: ***Dear reviewer ssxr:*** Thank you for your efforts and valuable feedback, we would like to address your questions point by point in the following.
**Q1.Are there any results showing the impact of varying the number of frames?**
Table R.4 shows the ablation study of varying frame numbe... | null | null | Rebuttal 1:
Rebuttal: ### **We first thank all reviewers for their valuable feedback to help us improve our work**.
### **Below, we will address the concerns of reviewers about the experiments and details of our proposed method. In our uploaded one-page PDF, we provide more experiments and ablation studies to better i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently | Accept (poster) | Summary: The goal of this work is to study optimal transport (OT) distances between pairs of finite Markov chains, providing a novel relation between OT distances and probabilistic bisimulation metrics. The proposed linear program builds on ideas from optimal control in Markov decision processes, and the designed algor... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work and your very detailed reading! We will address your main points below, and will take the remaining minor comments into account when working on the final version of the paper.
Q1: It is not entirely clear why they need to duplicate the variables ... | Summary: This submission
Namely, the authors define a notion of optimal transport distance between Markov chains on state spaces with a ground metric. This notion of distance differs from standard optimal transport, as the set of couplings is restricted to the set of so-called bicausal couplings. Using the results of... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and your critical reading of our work! Regarding your questions:
1. Note that the argument $\mu^*$ achieving the infimum exists, and an optimal transition coupling $\pi^*$ can be decoded from it. Concretely, given the joint distriibution $\mu^*$ over $\mathcal... | Summary: This work integrates optimal transport with Markov chains by proposing an alternative joint distribution between Markov processes, namely "discounted occupancy couplings". They show that optimal transport distances can be computed as a a linear program (LP) in reduced space. This improves the computational eff... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work, as well as your insightful remarks! We respond to your questions below.
Re weakness: We agree that the analysis working only for the case $m=\infty$ is the biggest limitation of our results. By analogy with modified policy iteration (that bridge... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling | Accept (poster) | Summary: The paper proposes a method to accelerate discrete prompt optimization algorithms. In addition to the target model, a smaller draft model is used to reduce the number of candidates to be evaluated by the target model based on an agreement score between the two models. When applied to Greedy Coordinate Gradient... | Rebuttal 1:
Rebuttal: Dear Reviewer hJdQ,
Thank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:
> Concern: Transferability of Probe Sampling
We appreciate your concern regarding the trans... | Summary: The authors propose using a significantly smaller draft model compared to the target LLM to filter candidate suffixes, thereby accelerating the training process of GCG-based algorithms.
The results demonstrate a faster training speed with enhanced ASR.
Strengths: 1. I appreciate the innovative approach of uti... | Rebuttal 1:
Rebuttal: Dear Reviewer Jngk,
We appreciate the time and effort you have put into providing valuable feedback. However, we respectfully believe there might be some misunderstanding regarding our work. We would appreciate the opportunity to clarify a few points and address your concerns as follows:
> Mis... | Summary: This paper presents a novel algorithm called "Probe sampling" to accelerate the Greedy Coordinate Gradient (GCG) method for optimizing adversarial prompts against large language models (LLMs). The key idea is to use a smaller "draft" model to filter out unpromising candidate prompts, reducing the number of exp... | Rebuttal 1:
Rebuttal: Dear Reviewer bSGi,
Thank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:
> Concern #1: Compare with more related works
We appreciate your concern about comparing pr... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime | Accept (poster) | Summary: The paper focuses on the minmax rate for goodness-of-fit testing for discrete distributions under bandwidth and differential privacy constraints in a distributed setting, leveraging Le Cam’s theorem. The main distinction from previous literature lies in the consideration of the distributed setting.
Strengths:... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort dedicated to evaluating our paper and we thank you for your thoughtful review. We appreciate your recognition of the paper’s mathematical rigor, organization, and clarity. We would like to address the concerns and the question you raised regarding the di... | Summary: This paper explores distributed goodness-of-fit testing for discrete distributions under bandwidth and differential privacy constraints. The authors extend results from multivariate Gaussian models using Le Cam’s theory of statistical equivalence. They derive matching minimax upper and lower bounds for the goo... | Rebuttal 1:
Rebuttal: Thank you for the time and effort invested in evaluating our work, the kind words, the constructive feedback and the interesting questions raised. We respond pointwise below.
*"The paper addresses key challenges in distributed settings, specifically under bandwidth and privacy constraints, which ... | Summary: This paper investigates the problem of Goodness-of-fit testing for multinomial distributions in federated learning in the case where the number of samples n per federated agent is large, and under a bandwidth or privacy constraint. Under certain scaling regimes, the authors characterize the number of samples n... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort spent on evaluating our paper, the positive feedback and the insightful question. Below, we address the suggestions and questions raised by the Reviewer.
*"The paragraphs after Theorem 1 and 2 respectively could be expanded somewhat. It would be intere... | Summary: The paper addresses distributed goodness-of-fit testing problems under user-level communication and local differential privacy (DP) constraints. In this scenario, each of the m users receives n samples, and a central server aims to test whether the underlying discrete distribution is uniform. This classical pr... | Rebuttal 1:
Rebuttal: We express our sincere thanks to the Reviewer for taking the time and effort to thoroughly review, the insightful comments and constructive feedback on our paper. The Reviewer identifies areas for improvement, which we will address point-by-point below.
### *Limited contribution:*
"*The main te... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the Reviewers for carefully reading our paper and their interest in our work. We are happy to hear that the majority of the Reviewers found our paper "very well written" (aGfz), "well-written, well-organized" (3Xr9) and the theory derived "rigorous and thorough... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space | Accept (poster) | Summary: This paper demonstrates the attack vector using soft prompt tuning (prompt optimization in the token embedding space) for jailbreaking aligned LLMs and for “breaking” unlearned LLMs.
Strengths: ### Significance
I believe that the problem studied in this paper is well-motivated. Soft prompts are a threat that... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and agree with the perspective on the trade-off between the benefits and potential negative impacts of open-source foundation models in the context of open-source threats. In the following, we address weaknesses and questions and try to keep the response c... | Summary: The paper discusses a new adversarial attacking approach called "embedding space attacks" targeting open-source large language models (LLMs). Overall, traditional adversarial methods focus on discrete input manipulations at the token level, effective in closed-source environments accessed via APIs. However, wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We try to keep the response concise and are happy to discuss any follow-up questions.
**W1: Does the attack really elicit knowledge of the model, or are we effectively
doing finetuning?**
**A1:** This is an interesting question. In our experiments, we wa... | Summary: This paper proposes a new white-box adversarial attack on large language models (LLMs). The attack is the first to be performed directly in the embedding space of the model; as such, the chosen threat model mainly targets open-source LLMs. The proposed methodology is applied to two goals: (i) removing guardrai... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We try to keep the response concise and are happy to discuss any follow-up questions.
**W1: Why are open-source attacks relevant when models without safety guardrails exist**
**A1:** This is indeed a relevant question worth discussing. If we believe that... | Summary: This paper introduces embedding space attacks as a novel threat model for open-source large language models (LLMs). The authors demonstrate that these attacks can efficiently circumvent safety alignments and extract supposedly unlearned information from LLMs. The paper presents two main applications: 1) breaki... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We try to keep the response concise and are happy to discuss any follow-up questions.
**W1: Recommendation to use other methods to calculate ASR**
**A1:** We thank the reviewer for bringing up this topic. We agree that more reliable methods have been de... | Rebuttal 1:
Rebuttal: We thank the reviewers for their effort and feedback! We've made several improvements to our work and are happy to discuss any open questions. The following experiments have been added to the paper (see PDF):
## Additional Experiments
### **1) More reliable ways to calculate ASR**
We thank the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Surprising Effectiveness of SP Voting with Partial Preferences | Accept (poster) | Summary: Surprisingly popular voting allows to value expertise by eliciting both judgments and predictions over others' judgments. Its application to ranking is however challenged by the combinatorial size of reporting predictions over others' judgments. This paper provides practical solutions to adapt surprisingly pop... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. They are very helpful and we will incorporate them in the next version of the paper.
> “Do I understand correctly that, in Partial-SP, we compute for each voter a set of pairwise comparisons $a\succ b$ , each of which unbiases non-expertise with SP... | Summary: The paper generalizes the surprisingly popular algorithm to the partial ranking setting. The prior method that considers this generalization only works when every agent provides her signal over the full ranking. The current paper considers how to elicit only partial information from agents and aggregate them u... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful feedback. Below we provide detailed responses to the questions.
> “First of all, the prior paper [25] seems to greatly discount the contribution (and effort) of this work. In particular, they use the same datasets, very similar experimental designs, and the sa... | Summary: This paper studies the problem to recover the ground truth ordering over a large number of alternatives. The assumption is that the ground truth ranking is drawn from a prior, and each voter observes a noisy version of the ground truth. It was previously shown that the surprisingly popular (SP) algorithm could... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and insightful questions. Below we provide answers to the questions.
> “In the introduction, the authors mention that the Surprisingly Popular Voting algorithm can recover the ground-truth ranking. I wonder if this is a theoretical guarantee or empirical / ... | Summary: The paper extends the previous work of (Hosseini et al. 25) by overcoming a major weakness: eliciting a full ranking and a prediction about the ranking is too costly. The paper designed a method that elicit partial preferences and recover the full ranking by aggregating partial rankings. They empirically test ... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your feedback and insightful comments. If you have additional questions, do let us know and we will be happy to answer them. | Rebuttal 1:
Rebuttal: Dear reviewers,
Many thanks for your feedback and insightful comments. Upon re-evaluating our parameter inference approach, we identified that the scipy.stats.kendalltau function we used computes Kendall's tau correlation, and not Kendall's tau distance. This distinction led to discrepancies in t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HYDRA: Model Factorization Framework for Black-Box LLM Personalization | Accept (poster) | Summary: The paper proposes HYDRA, a learning-based model factorization framework that captures both user-specific and shared behavior patterns to enable effective personalization within black-box LLMs. The framework involves training a reranker to prioritize the most useful information from top-retrieved relevant hist... | Rebuttal 1:
Rebuttal: Thank you for your detailed suggestions. Please find our responses below:
> W1&Q1: Time complexity.
**A:** We summarize (1) the time complexity for the different stages and (2) the time consumed for training and inference on 100 training users and 50 test users in **Table R3**. Please see **off... | Summary: This paper provides a black-box LLM personalization framework that explores global and local knowledge from user’s historical behaviour through model factorization.
Strengths: Strengths:
1. The paper is straightforward. The method is reasonable
2. This is good to see the authors provided many details includi... | Rebuttal 1:
Rebuttal: Thank you for your detailed suggestions and comments. Please find the corresponding responses below:
> W1: The performance is not convincing.
**A:** We appreciate your thorough observations regarding the performance of ICL-Random in comparison to other baselines and HYDRA. We would like to expla... | Summary: The paper introduces HYDRA, a model factorization framework designed to personalize large language models (LLMs) without modifying their internal parameters. HYDRA addresses the challenge of personalizing inherently opaque, black-box LLMs through a retrieval-augmented workflow. This method enhances personaliza... | Rebuttal 1:
Rebuttal: Thank you for your detailed suggestions and comments. Please find the corresponding responses as follows:
> W1: Limited Evaluation Metrics.
**A:** In line with previous personalization research [1]- [3], LaMP serves as **a standard personalization benchmark that has been widely used ** in evalua... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely appreciate the time and effort dedicated to evaluating our work. We have summarized the additional experiments and analyses conducted during the rebuttal phase, and we are committed to incorporating them in the revised manuscript.
Our newly added main experiments an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neuro-Symbolic Data Generation for Math Reasoning | Accept (poster) | Summary: This paper introduces a methodology for generating mathematics data in a neurosymbolic fashion. Starting with existing math problems, they perform two different mutation operations: simplification and complication. The simplification operation performs variable and expression unfolding, whereas the complicatio... | Rebuttal 1:
Rebuttal: **Dear Reviewer XHjb:**
Thank you for the valuable feedback on our paper. We appreciate the time and effort you have put into reviewing our work and we are grateful for encouraging comments such as nice idea, significant work, and clear writing. We have carefully read your review and addressed yo... | Summary: This paper proposes a neural-symbolic framework to generate valid and diverse mathematical training data at scale. The framework consists of three steps: formalization, mutation, and reformalization. The first two steps are achieved using symbolic solvers, while the last step is accomplished using large langua... | Rebuttal 1:
Rebuttal: **Dear Reviewer upsU:**
Thank you for the valuable feedback on our paper. We appreciate the time and effort you have put into reviewing our work and we are grateful for encouraging comments such as the clear paper structure, novel method, solid experiments, and interesting findings. We have caref... | Summary: To solve the dilemma of diversity and validity involved in current math problem generation methods, this paper proposes a neuro-symbolic framework that initially generates formal mathematical problems and then informalizes them back into natural language versions. By casting the data generation into the formal... | Rebuttal 1:
Rebuttal: **Dear Reviewer bEBk:**
Thank you for the valuable feedback on our paper. We appreciate the time and effort you have put into reviewing our work, and we are grateful for encouraging comments such as promising framework and effective approach. We have carefully read your review and addressed your ... | Summary: This paper describes a framework to transform natural language math problems into a formal setting (e.g., in SMT-LIB format), mutate the problems in a user-specified way, and auto-informalise those mutated problems into natural language ones. Through this pipeline, a larger synthetic dataset can be generated t... | Rebuttal 1:
Rebuttal: **Dear Reviewer BS8K:**
Thank you for the insightful feedback on our paper. We appreciate the time and effort you have put into reviewing our work, and we are grateful for encouraging comments such as good writing, good performance, and promising scalability. We have carefully read your review a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their in-depth comments, which urge us improving our paper. We will revise the paper accordingly. Here, we summarize our responses to the major issues raised by the reviewers.
Reviewers **BS8K**, **bEBk**, and **XHjb** request further discussion about tool-use frame... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mitigating Object Hallucination via Concentric Causal Attention | Accept (poster) | Summary: The paper attributes hallucinations in Large Vision-Language Models (LVLMs) to Rotary Positional Encoding (RoPE). It observes that LVLMs inherit a long-term decay issue from RoPE, where the inner-product of two tokens decays relative to their distance. This results in weaker visual-text interactions when the t... | Rebuttal 1:
Rebuttal: Thank you for your meticulous reading and giving credit to our novelty and analysis of Rotary Position Encoding (RoPE) and LVLM hallucination. We appreciate you pointing out some additional references, which we would include to make our research more complete. Please find our responses as follows... | Summary: The paper shows that object hallucination in LVLMs is linked to the commonly adopted Rotary Position Encoding (RoPE) strategies. The long-term decay in RoPE causes hallucinations when important visual tokens are distant from visual instructions. To address this, the authors propose the Concentric Causal Attent... | Rebuttal 1:
Rebuttal: Thank you for your valuable insights. Please see our responses to your questions below.
**W1: Alternative scanning method.**
**A:**: We justify the design of our method by providing new comparative studies for different position encoding schemes and alternative scanning methods. We first compare... | Summary: This paper analyze the long-term dependency between text token and visual token in LVLMs from a novel positional encoding perspective by replace the RoPE method. The analysis shows that RoPE introduce clear long-term decay regarding the attention scores. The authors propose a novel concentric causal attention ... | Rebuttal 1:
Rebuttal: Thanks for your detailed and insightful suggestions. Please find our responses as follows.
**W1-a: Pretraining setup.**
**A:** Thanks for mentioning this concern. We would share that it is a typo in line 227 where we claim we use CC-595K dataset [42] for pre-training stage. In fact, our pre-trai... | Summary: This paper explores how current LVLM's hallucination appears through analyzing the impact of RoPE long-term decay on vision information attenuation during flow. It gives clear visualization results and theoretical evidence to prove the basic point, revealing that the causal attention mask and RoPE embbeding i... | Rebuttal 1:
Rebuttal: Thanks for your detailed and thorough suggestions. Please find our replies as below.
**W1: CCA and OPERA [23].**
**A:** We ground our design on analysis of information flow in LLaVA model. This shares commonalities with OPERA which analyzes information flow in LVLM autoregressive decoding. Thank... | Rebuttal 1:
Rebuttal: We sincerely appreciate reviewers `9xbc` and `1NX2` for acknowledging clear motivation behind our work, and reviewers `5Msc` and `5R3r` for recognizing novelty of our study, along with thoughtful and kind suggestions for improving our paper. Please find new figures in attached `pdf`. New figures i... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Supervised Adversarial Training via Diverse Augmented Queries and Self-Supervised Double Perturbation | Accept (poster) | Summary: The authors aim to further close the gap between the clean generalization gap and the robust generalization gap for SSL models. Several engineering improvements have been made to the SAT pipeline, such as including strong data augmentation, adversarially perturbing weight, and using separate BN. The resulting ... | Rebuttal 1:
Rebuttal: Dear reviewer iqHN,
We appreciate your positive comments! Here is our response:
1. We added the results with ResNet18 and ResNet50 on CIFAR10 (larger model is hard for us to train in limited time). The results of ResNet18 are not included in our initial submission for it is not as obvious as in ... | Summary: The paper introduces the DAQ-SDP (Diverse Augmented Queries Self-supervised Double Perturbation) method to solve the problem of large robust generalization gap and clean accuracy degradation in self-supervised adversarial training. The experimental results demonstrate the effectiveness of the DAQ-SDP method.
... | Rebuttal 1:
Rebuttal: Dear reviewer 6QGn,
We appreciate your efforts and time in reviewing our paper. The following is our response.
First, the assumption in line 56 is also supported by [38]. This work includes the following description for self-supervised adversarial learning:
1. “A challenging problem due to its ... | Summary: The paper proposes a method to improve self-supervised adversarial training. This method consists of two stages. First, a standard self-supervised model is trained on clean images to learn a feature extractor network F-1. In the second stage, a robust feature extractor F_2 is trained based on the features gene... | Rebuttal 1:
Rebuttal: Dear reviewer dErS,
We appreciate your time and efforts in reviewing our paper. We are sorry the phrases of “unified perspective” and “unified understanding” cause confusion. First please let us explain the following points:
a) There are actually two types of generalization in the self-supervis... | Summary: This paper proposes a method to solve the robust generalization problem for self-supervised adversarial training in general. Starting by showing the generalization gap in existing self-supervised adversarial training framework, it proposes to solve the problem from the aspects of data complexity and model regu... | Rebuttal 1:
Rebuttal: Dear reviewer mcfF,
We appreciate your positive comments. Here is our response for your questions:
1. We have added new experiments on ResNet18 and ResNet50 to prove the effectiveness of our method.
ResNet18:
| Method | Clean | PGD | AA |
|---|---|---|---|
| DynACL+AIR| 78.08 | 49.12 | 45.17 |
... | Rebuttal 1:
Rebuttal: We have uploaded a one-page pdf file containing unclear figures in the original submission and additional visualizations.
Pdf: /pdf/21bf2e38fc62307b2bd2fe362fba7606469c14cc.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cross-Scale Self-Supervised Blind Image Deblurring via Implicit Neural Representation | Accept (poster) | Summary: This paper proposes a blind image blurring method which reparametrizes the latent images the blurring kernel by the implicit neural representations (INRs). In addition, the authors also propose a cross-scale consistency loss. The authors validate the effectiveness of their method on several datasets.
Strength... | Rebuttal 1:
Rebuttal: Thanks the reviewer for the comments. See below for our responses to the concerns and questions.
**[W1]** *The idea of using a deep neural network for latent images and blurring kernel reparametrizatrion is not new. The current proposed method is very similar to the one in ref1. But I do not see ... | Summary: This paper introduces a self-supervised method for BID that does not require GT images. By leveraging an exact relationship among the blurred image, latent image, and blur kernel across consecutive scales, this paper propose an **effective cross-scale consistency loss** implemented by representing the image an... | Rebuttal 1:
Rebuttal: Thanks for the comments. Please see below for the responses.
**[W1]** *The INRs framework and progressive learning mechanism on which the paper is based are common. The degree of innovation is average except for cross-scale estimation consistency constraint:*
We agree that the cross-scale consi... | Summary: This paper proposed a self-supervised method for blind image deconvolution (BID). The main idea is to introduce the implicit neural representation (INR) technique for representing both the blur kernel and the image, such that they can be parameterized at different scales using a single model. With such a INR r... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Please see below for our responses.
**[W1]** *The presentation of this manuscript should be improved.*
Thanks for the feedback, we will improve the organization by moving some parts of Sec. 1.2 into Sec. 3.
---
**[W2.1]** *Comparison with Recent Self-Sup... | Summary: This paper presents an approach to solve blind image deconvolution. The authors use multiscale Implicit Neural Representations (INRs) to depict both the latent image and the blur kernel. In addition, they propose a method that incorporates a cross-scale consistency loss and a progressive scale optimization pro... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work and your valuable comments. See below for the responses to the concerns and questions.
**[W1]** *There exist prior works on Multiscale Implicit Neural Representations. The authors should provide a more detailed discussion on the similarities and differences be... | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
We sincerely appreciate the reviewers for their constructive comments, as well as their time and effort in evaluating our manuscript.
Please find below our clarifications on some common concerns and questions.
---
**Main contributions**
Our work presents two main cont... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Convergence of No-Swap-Regret Dynamics in Self-Play | Accept (poster) | Summary: The paper proves that in "almost all" symmetric zero-sum two-player games (excluding a measure zero set), if both players apply the same no-swap-regret algorithm with the same initialization, then the players' iterates satisfy frequent-iterate convergence to Nash-equilibrium.
The main two lemmas forming the p... | Rebuttal 1:
Rebuttal: Thank you for your review! We are happy to hear that you found our paper to be self-contained, well written and easy to read!
We view the main contribution of our paper mapping the landscape of which algorithms/settings it is possible to obtain last-iterate convergence. Note that in terms of algo... | Summary: This paper studies the convergence properties of no-swap-regret learning dynamics in symmetric two-player zero-sum games. The paper's main result is that in almost all symmetric zero-sum games with symmetric initializations, if both agents run identical no-swap-regret algorithms, their joint strategy profiles ... | Rebuttal 1:
Rebuttal: We thank you for your support. We are very happy to hear that you found the subject matter interesting and that you appreciate the simplicity of our proofs!
The reviewer asks “why is this an advantage over using OGDA/OMWU?” We don’t think OGDA/OMWU is inherently better or worse than NoSwapRegret... | Summary: This submission studies no-swap-regret dynamics in two-player zero-sum games. In particular, they make the novel observation that no-swap-regret dynamics provably converge in a last-iterate-like sense ("frequent iterate convergence") to Nash equilibria in symmetric two-player zero-sum games. In order to show t... | Rebuttal 1:
Rebuttal: We thank you for your support! We are very encouraged to hear that you find our results to be both important as well as interesting!
Currently, we do not have any stronger analysis for the case of specific no-swap regret algorithms such as e.g., the Blum-Mansour initiated with MWU. Empirically, w... | Summary: The paper studies the convergence of no-swap-regret dynamics in zero-sum games. In particular, it is shown that in almost all symmetric zero-sum games and under a symmetric initialization, no-swap-regret dynamics are guaranteed to converge in a last-iterate sense to a Nash equilibrium; all of the previous assu... | Rebuttal 1:
Rebuttal: We thank you for your support! We particularly appreciate that you point out that you find our contribution to be both important as well as surprising!
We believe that the relative simplicity of our proof (when e.g. compared against the analysis of optimistic mirror descent) should be seen as an ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful and thoughtful reviews of our paper. We respond to each review individually below. (The PDF attached to this global rebuttal contains a figure accompanying a response to a question of Reviewer r1mG).
Pdf: /pdf/78589834a31cab305d4b258f2d0cdf16c02f5048.pd... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models | Accept (poster) | Summary: The paper explores the spatial reasoning capabilities of LLMs. To this end, the paper introduces novel testbeds including multiple tasks and, importantly, a novel approach called Visualization-of-Thought (VoT). This method aims to enhance the spatial reasoning capabilities of LLMs by visualizing their reasonin... | Rebuttal 1:
Rebuttal: Thank you for your feedback, and we appreciate your support!
Spatial reasoning in LLMs is a less explored research topic, we only scratch the surface of it. Despite this testbed is relatively simple to humans and real-world spatial reasoning challenges, it's still challenging for LLMs. It covers ... | Summary: The paper proposes a new prompting method, "Visualization of Thought" (VoT) prompting, to enable LLMs to perform better on spatial reasoning tasks. In VoT, the LLM is prompted with instructions on performing a multi-step spatial reasoning task, followed by the text "Visualize the state after each reasoning st... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful and constructive review of this paper. Your insights are invaluable, and we have carefully considered each of your comments.
> **Weakness 1: Unclear details**
We'll improve clarity of *visuospatial sketchpad*, *caption of figure 4 and 10* in the camera-read... | Summary: This paper focuses on enhancing the spatial reasoning capabilities of Large Language Models (LLMs) by introducing Visualization-of-Thought (VoT) prompting. Inspired by the human cognitive ability to visualize unseen objects - a process known as the Mind’s Eye - VoT visualizes reasoning processes to guide LLMs ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, which are invaluable in helping us refine and clarify our work. We are grateful for the opportunity to address the points raised and provide further clarification on the aspects that may have been misunderstood.
> **Weakness 1: Unclear descriptions**
There ... | Summary: The paper presents a novel approach called Visualization-of-Thought (VoT) prompting, aimed at enhancing the spatial reasoning abilities of Large Language Models (LLMs). Inspired by the human cognitive process known as the “Mind’s Eye,” the authors propose a method where LLMs visualize their reasoning steps to ... | Rebuttal 1:
Rebuttal: Thank you for your thorough evaluation and constructive comments of our work. We'd like to clarify:
> **Weakness 1: Limited Task Diversity**
We fully appreciate the commonly adopted benchmark such as bAbI[1], StepGame[2], SpartQA[3], SPARTUN[4] etc., which lay solid foundations for spatial reason... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distributional Monte-Carlo Planning with Thompson Sampling in Stochastic Environments | Reject | Summary: The authors explore the use of distributional reinforcement learning within Monte Carlo Tree search. They propose two algorithms CATS and PATS a categorical distribution and particle distribution based approach respectfully. They perform a theoretical analysis of the methods and show analysis of regret. They t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thorough reading and the positive feedback and criticism of our work. We would like to ask the reviewer to read the above response to all the issues raised. In addition, we respond to each reviewer's concerns below.
Lack of Referencing and Novelty
We ... | Summary: The paper propose two algorithms, Categorical Thompson Sampling for MCTS (CATS) and Particle Thompson Sampling for MCTS (PATS). These algorithms extend Distributional Reinforcement Learning (RL) to Monte-Carlo Tree Search (MCTS) by modeling value functions as categorical and particle distributions, respectivel... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoroughly reading and reviewing our paper with positive feedback and criticism. We would like to ask the reviewer to read the above answer to all the questions raised. In addition, we provide answers to each reviewer's concerns below.
Empirical Validation and Benchmark ... | Summary: The paper introduces distributional return estimates to MCTS-based planning. For this the authors borrow from work on distributional Q-Learning and show how to adapt the MCTS value back-up and action selection steps to compute and utilise these distributions. They formulate two approaches based on different di... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful and detailed feedback with both positive and constructive criticism. We would ask the reviewer to see the overall answer above. In addition, we would like to reply to the reviewer's concern line by line below.
Empirical Evaluation and Toy Domain
We understan... | Summary: This paper introduces Categorical Thompson Sampling for MCTS (CATS) and Particle Thompson Sampling for MCTS (PATS) algorithms, which incorporate distributional reinforcement learning into Monte Carlo Tree Search (MCTS) to handle value estimation in stochastic settings. By modeling value functions as categorica... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and constructive feedback. We would like to answer the main concern below:
Computational Complexity
We think that the computational overhead may not be a significant issue because it only occurs in CATS when we increase the number of atoms for bett... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed feedback and constructive criticism.
The main contribution of our paper is to introduce a novel distributional \textbf{planning} approach that goes beyond distributional reinforcement learning (RL), which primarily focuses on \textbf{learning}. Our theore... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks | Accept (poster) | Summary: The paper presents a novel Signed Graph Augmentation (SGA) framework designed to enhance the performance of Signed Graph Neural Networks (SGNNs). The primary focus is on addressing two persistent issues in SGNNs: graph sparsity and unbalanced triangles. The authors demonstrate that the commonly used DropEdge m... | Rebuttal 1:
Rebuttal: **For Weakness 1 and Q1 on more downstream tasks:**
Thanks for your constructive comments. Existing SGNN methods focus primarily on the link sign prediction task, and they ignore the performance on other tasks. We experiment with the performance (metric: Average Accuracy± standard deviation) of S... | Summary: This work addresses the scarcity of effective data augmentation strategies tailored for signed graphs, especially considering the dearth of auxiliary information in real-world datasets. By presenting the generalization error bound for SGNNs and disproving the universal benefit of random DropEdge, this paper in... | Rebuttal 1:
Rebuttal: **For Weakness 1 on other augmentation methods:**
Thank you for the reviewers' suggestions. We carefully reviewed the recommended papers [1-4]. Although the data augmentation methods discussed in those articles are not specifically designed for the link sign prediction task, their underlying conc... | Summary: Link sign prediction is a significant downstream task in graph data analysis. Current graph data augmentation methods seldom explore this task. This paper analyzes, both theoretically and experimentally, why existing graph data augmentation methods perform poorly on this task and proposes a new data augmentati... | Rebuttal 1:
Rebuttal: (1) **For Weakness 1 on some training samples removed :**
Our theoretical analysis indeed demonstrates that reducing the number of edges during training can degrade model performance. However, the SGA method does not reduce the overall number of edges. Specifically, we take a cautious approach to... | Summary: This paper proposes a new research subfield focusing on data augmentation methods for signed graphs. Unlike the widely studied unsigned graph augmentation, this method targets the downstream task of link sign prediction rather than the mainstream node classification [1] or graph classification [2]. As far as I... | Rebuttal 1:
Rebuttal: (1) **For weakness 1 on application scope:**
Compared to more generalized graphs, signed graph analysis has its exclusive downstream task (i.e., link sign prediction) which are important and very interesting, such as product reviews [10], bill votes [11], paper reviews, polarization study [14], e... | Rebuttal 1:
Rebuttal: Thank all reviewers for their valuable and constructive comments. We address the common concern here and believe the quality of the paper has been improved following the reviewers' suggestions.
**Common Concern: Time and Space Complexity of SGA**
Suppose we are given a signed graph $\mathcal{G}=... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes | Accept (poster) | Summary: This paper focuses on solving linear systems arising from large-scale Gaussian process hyperparameter optimisation.
The first technique proposed in this paper is a reformulation of the linear system for log determinant gradient estimation, which is called the "pathwise estimator".
The authors argue that this ... | Rebuttal 1:
Rebuttal: We thank Reviewer upE1 for their time to read and review our work, and are delighted to hear that our "writing is very clear", and "empirical evaluations are extensive". In the following, we want to address their specific concerns and questions:
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*There exists something similar to the pathwise... | Summary: The paper considers the problem of simultaneously fitting a Gaussian process (GP) to data along with determining the hyperparameters (kernel width, noise variance) for the GP. The overall algorithm is standard, consisting of an outer loop, which is a simple gradient update to the hyperparameters, and an inner... | Rebuttal 1:
Rebuttal: We thank Reviewer 89S9 for their time to read and review our work, and are excited to hear that our experiments are "thorough" and "carefully done". In the following, we want to address their specific concerns and questions:
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*It is hard for me to see what the really new ideas are in this pape... | Summary: The paper presents approaches that can speed up solving linear systems arising in the GP regression problem. The two basic ideas are a warm start and limiting the computation budget. Another novelty is the pathwise gradient estimator, which leads to fewer iterations needed for convergence. The detailed numeric... | Rebuttal 1:
Rebuttal: We thank Reviewer Xgts for their time to read and review our work, and are grateful to hear that our "motivation and suggestions are clear", our manuscript is "well-prepared", and our experimental evaluation is "extensive". In the following, we want to address their specific concerns and questions... | Summary: This paper investigates several iterative techniques for solving linear systems when applied to the problem of finding GP hyperparameters. Specifically, the following modifications to the method are suggested:
- A "pathwise" sampling estimator for the Hutchinson trace estimator
- Warm starting linear systems s... | Rebuttal 1:
Rebuttal: We thank Reviewer NcMH for their time to read and review our work. In the following, we want to address their specific concerns and questions:
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*The main weakness of this paper is that it does not compare the results of the approximate methods to a reliable exact method. This makes it impossib... | Rebuttal 1:
Rebuttal: We thank all reviewers, ACs, SACs, PCs, organisers, and other volunteers for their time. NeurIPS 2024 would not be possible without their generous time commitments! In the following, we first give an overview of reviewer comments and then discuss the main concerns raised by reviewers.
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**Overv... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models | Accept (poster) | Summary: The work investigates the design choices and analysis components in the recently proposed white-box transformer (CRATE). The work studies and analyzes the design rolling objective (SRR) in CRATE-like architecture. The work also proposes using SRR as layerwise constraints and shows the SRR will further improve... | Rebuttal 1:
Rebuttal: **It will benefit the paper if the authors can provide more insights and investigation from section 5. For example, examining using SSR to measure other pretrained transformer-like architectures.**
Evaluating $R^c(Z;U)=\sum_{k=1}^K \frac{1}{2}\log \operatorname{det}(I+\gamma (U_k^TZ)^T (U_k^TZ))$... | Summary: The paper investigates a Transformer-like deep network architecture CRATE based on algorithmic unrolling of the sparse rare reduction objective function. It points out some pitfalls of the approximated layer operation where it does not decrease the R^c as it should, studies some alternatives, and also points o... | Rebuttal 1:
Rebuttal: **W1: Lines 125 and 135-136 make conclusions based on how (6) is related to (7), but these conclusions are not justified.**
The derivations in (6-7) are rephrased from the original CRATE paper [1]. The softmax introduced after omitting the first-order term is quite intuitive, which converts auto-... | Summary: This paper considers a recent line of transformer-like models called CRATE where each layer is designed to approximate a gradient-based optimization step of an information-theoretic objective function called sparse rate reduction. The contributions of the paper are: (1) investigating whether CRATE actually imp... | Rebuttal 1:
Rebuttal: **W1:Unclear motivations/soundness of variants**
CRATE-N aims to counteract issues in CRATE-C where the update can increase $R^c$, opposing SRR principle. By moving in the opposite way of CRATE-C, CRATE-N implements decrease in $R^c$ more faithfully, aligning better with SRR principle. CRATE-T ad... | Summary: This paper conducts an in-depth of study of CRATE, a previously proposed Transformer-like architecture to make deep learning more white-box. CRATE was motivated by sparse rate reduction (SRR), and it is a multi-layer architecture designed to optimize the SRR objective iteratively layer by layer. The authors fi... | Rebuttal 1:
Rebuttal: **W1: What is the point of proposing CRATE-N and CRATE-T? What problems do they solve?**
The goal of developing CRATE-N is to address the potential issue of CRATE-C. As we pointed out that the update (7) in CRATE-C, which performs a gradient descent only for the second-order term, could maximizi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the thoughtful reviews and comments provided by all reviewers. Below, we address the main points raised, details can be found in corresponding blocks for each reviewer:
- Reviewer RBPW questioned the role of different variants and the behavior of the SRR objective. We clar... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multistep Distillation of Diffusion Models via Moment Matching | Accept (poster) | Summary: This paper presents a new method for making diffusion models faster to sample. The method distills many-step diffusion models into few-step models and extends recently proposed one-step methods to the multistep case by moment matching. By using up to 8 sampling steps, the obtained distilled models outperform n... | Rebuttal 1:
Rebuttal: Thanks for your review. Please find our response to your comments below:
> Moment Matching Distillation Part is difficult and too mathematical to understand.
Appendix A of the paper contains the mathematical details we felt we could reasonably omit from the main text. In our updated version of t... | Summary: This paper proposes an approach to distill a diffusion model into a multi-step generator. Building on previous works that use distribution matching to train a few-step student generator, the paper introduces a novel method of matching the conditional expectation of clean data given noisy data along the samplin... | Rebuttal 1:
Rebuttal: Thanks for your review and kind words. Please find our response to your comments below:
* Requests for additional ablations:
> Exclusion of the z_s dependence on the parameters when calculating gradients of the moment matching loss (line 90).
We’d be happy to include this ablation in the camer... | Summary: The authors proposed a fast sampling method by distilling a diffusion model to model $q(x|z_t)$. This is achieved by matching moments, with two novel approaches proposed to implement it in practice.
Strengths: - The idea of distilling to model the conditional distribution $q(x|z_t)$ by using matching the mome... | Rebuttal 1:
Rebuttal: Thanks for your review. Please find our response to your comments and questions here:
> Notation
Thanks! We’ll clarify the notation issues you identified in the paper.
$g_{\theta}(z_t, t)$ and $g_{\theta}(z_t)$ indeed refer to the same denoising model. Dropping the dependence on $t$ is customary... | Summary: This paper proposes a diffusion distillation algorithm based on moment matching. The starting point of the paper is to achieve distribution consistency by ensuring that the denoising process $\widetilde{x} = g_\eta(z_t,t)$ with fewer steps conforms to the true distribution, incorporating moment matching. To es... | Rebuttal 1:
Rebuttal: Thanks for your review. Please find our response to your remarks below:
> 1. [...] It is worth exploring the values of L(ϕ) at different timesteps after training.
The instantaneous version of L(ϕ) is indeed informative, and we investigate its value over different training steps in section 5.4 (... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents a novel method for distilling diffusion models to require fewer function evaluations. The method is based on moment-matching, and two practical algorithms are presented and evaluated. Empirical results are state-of-the-art in few-step regimes.
Strengths: The paper presents a well-motivate... | Rebuttal 1:
Rebuttal: Thanks for your kind words.
Please find our answers to your questions below:
> I would have liked to see more discussion of why the 1- and 2- step regimes do not perform as well as other approaches. Is this a characteristic of the moment-matching approach?
As the number of sampling steps is decr... | null | null | null | null | null | null |
Stabilizing Zero-Shot Prediction: A Novel Antidote to Forgetting in Continual Vision-Language Tasks | Accept (poster) | Summary: The paper introduces a novel continual learning (CL) method called ZAF (Zero-shot Antidote to Forgetting) designed to enhance the retention of previously learned skills in vision-language (VL) models without replaying historical data. The proposed approach leverages zero-shot stability as an indicator of anti-... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful summary and for acknowledging the contributions of our work with the ZAF model. We greatly appreciate your recognition of the paper's clear presentation, significant performance improvements, and its empirical and theoretical rigor. Your appreciation for the introduct... | Summary: This paper tackles continual pretraining / training for vision-language models by introducing both exponentially moving averaged LoRA, and more importantly, replay during training on additional unaligned text and image data. In doing so, the authors show that higher performance on standard continual VL tasks c... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and for recognizing the clarity and performance improvements of our manuscript. We have addressed the common questions separately and will now respond to the specific concerns you raised.
**1. The relation of zero-shot prediction stability, absolute zero-shot ... | Summary: The paper presents a novel continual learning (CL) method named ZAF (Zero-shot Antidote to Forgetting) aimed at enhancing the performance of pre-trained vision-language (VL) models in zero-shot prediction tasks. The authors identify zero-shot stability as a key indicator of a model’s ability to retain previous... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and valuable suggestions. We have addressed the common questions separately and will now respond to the unique concerns raised.
1. **Differences between the evaluations in our Figure 1 and ZSCL[49]**
Our approach diverges from ZSCL in both objectives and eval... | null | null | Rebuttal 1:
Rebuttal: Thank you to all reviewers for your insightful feedback. We will first address common concerns about our zero-shot stability and EMA-LoRA architecture, followed by detailed responses to each reviewer’s specific comments.
**Q1: How zero-shot prediction stability indicates anti-forgetting capabilit... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization | Accept (poster) | Summary: The paper presents a compelling investigation into the effects of reshuffling resampling splits on the generalization performance of hyperparameter optimization (HPO) strategies. Through a mix of theoretical analysis and empirical studies, the authors argue that reshuffling can lead to statistically significan... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review of our submission and your suggestions on how to improve our submission. Below you can find the answers to your criticism and questions:
> Generalizability of Results: The experiments are somewhat limited in scope, focusing on specific types of data se... | Summary: The standard protocol evaluates various hyperparameter configurations using a resampling estimate of the generalization error to guide optimization and select a final hyperparameter configuration. The authors argue that reshuffling the splits for every configuration often improves the final model’s generalizat... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review of our submission and for assessing our study as “large-scale, realistic hyperparameter optimization experiment”. Below you can find detailed answers to your questions and criticism:
> The effectiveness of reshuffling depends on dataset characteristics... | Summary: This paper studies the effect of reshuffling the splits over which hyper-parameter optimiazation is performed. Specifically, the authors provide theoretical guarantees on the generalization performance achieved via reshuffling and empirically demonstrate its impact both via simulation and benchmark experiments... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review and the suggestions, which will help us to improve our paper. We are very happy that you find our paper well-written and that you appreciate our up-front discussion of the limitations of the study. Below you can find detailed answers to your questions.
... | Summary: In hyperparameter optimization (HPO), individuals often use the same resampling for different configurations to ensure fair comparison. However, this fixed split may introduce bias into the optimization process, particularly after numerous evaluations, as it tends to favor configurations that align well with t... | Rebuttal 1:
Rebuttal: Thank you very much for your suggestions on the presentation and the experimental setup, which will help us make our paper more convincing. In the following, we provide detailed answers to your questions:
>The authors use a large number of symbols in the paper, adding a notation table may be cons... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time, constructive feedback, useful suggestions, and positive evaluation. We want to use the space here to address two points that were raised by multiple reviewers.
**1. (DL64, sYf9, QU3x, rcFG) Additional experiments**
Some reviewers suggested various additiona... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper suggests the idea that reshuffling the splits for every configuration during the hyperparameter optimization can improve the generalization property. The paper derives the theoretical analysis to shows how reshuffling affects the asymptotic behaviour of the validation loss surface, and the paper also... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful feedback on the presentation of our theoretic results, which will help us to improve their presentation. Below, we give detailed answers to your suggestions:
**Concerns regarding the theoretical analysis**
> In Theorem 2.1, the “regularity conditions” need to... | null | null | null | null | null | null |
Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery | Accept (oral) | Summary: This paper targets the task of generalized class discovery (GCD), and argues that the existing teacher-student learning framework suffers from three challenges: 1) learning gap between old and new classes, 2) feature discrepancies between augmented images, and 3) attention inconsistency between teacher and stu... | Rebuttal 1:
Rebuttal: Thanks for your thorough review. We appreciate your attention to detail regarding the potential extra computation overhead compared with conventional self-attention block.
**Response to Q1**
> ***Potential extra computation overhead (e.g., theoretical/empirical analysis) compared with the convent... | Summary: The paper introduces FlipClass, a novel method addressing the challenges of Generalized Category Discovery (GCD) in open-world scenarios. It identifies the misalignment of attention between teacher and student models as a key issue hindering effective learning, especially when new classes are introduced. To ta... | Rebuttal 1:
Rebuttal: We appreciate your meticulous review and valuable feedback.
**Response to W1**
> ***The content regarding "the Hopfield Network" and the underlying motivation could be enhanced.***
Thank you for the suggestions! We have moved the **state-query capacity of the Hopfield Network** from Appendix A.1... | Summary: This work proposes an attention alignment technique based on the Hopfield network energy function. Specifically, this work proposes to update the teacher model to increase the posterior teacher likelihood given the current student, which is modeled with the Hopfield network energy-based model. The teacher upda... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback. Before addressing your inquiries, we wish to clarify certain weaknesses highlighted in the review that we believe require further elucidation.
**Response to W1**
> ***Put the related work in the main content rather than in the appendix.***
Thanks for your su... | Summary: The paper introduces FlipClass, a dynamic teacher-student attention alignment strategy designed to address the challenges of Generalized Category Discovery (GCD) in open-world scenarios. Unlike traditional teacher-student frameworks, FlipClass updates the teacher’s attention to align with the student’s evolvin... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and constructive comments. Before addressing your inquiries, we wish to clarify certain weaknesses highlighted in the review that we believe require further elucidation.
**Response to W1**:
> ***Certain details in the ablation study, like the impact of str... | Rebuttal 1:
Rebuttal: ## **General Response to All Reviewers**
We sincerely thank all reviewers for the time they spent reviewing our manuscript and for their thoughtful feedback. We appreciate that the reviewers found our paper theoretically and methodologically novel, with strengths such as:
- the **idea** of dynami... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Diffusion Spectral Representation for Reinforcement Learning | Accept (poster) | Summary: This paper proposes an efficient and novel method for integrating diffusion models into reinforcement learning (RL). One major drawback of existing diffusion models is the high computational cost at inference time due to iterative sampling. This paper utilizes a diffusion model to learn representations of late... | Rebuttal 1:
Rebuttal: * **W1: Table 1 performance**
We would like to note that our method surpasses or parallels existing methods for 7 out of 10 tasks from the MBBL benchmark. We keep the same architecture and hyper-parameters when benchmarking our approach, so we expect better performances for the rest of the tasks ... | Summary: ## Main summary
The authors propose Diff-Rep, an algorithmic framework leveraging diffusion models for learning spectral representations of MDPs, from which relevant quantities such as the Q-function can be linearly decoded.
The main methodological contribution of the paper is showing that spectral represen... | Rebuttal 1:
Rebuttal: We apologize for any ambiguity regarding certain claims in the paper. Below, we will offer additional justifications for them and revise the corresponding sections. We also appreciate the grammar corrections and will ensure that the final version is free of such errors.
* **W1: SVD decomposition... | Summary: This paper proposes Diffusion Representation (Diff-Rep). This approach leverages diffusion models to learn representations for value functions in reinforcement learning while avoiding the high inference cost of sampling from diffusion models.
Strengths: 1. Diff-Rep provides a novel and principled approach to ... | Rebuttal 1:
Rebuttal: * **W1 \& Q1: Limited theoretical analysis.**
The major contribution of this paper is developing an efficient algorithm for sufficient representation through diffusion, with intensive empirical evaluation.
We would like to emphasize that our paper aligns with the existing algorithms [1][2] in le... | Summary: The paper proposed a representation learning method based on diffusion model. The paper developed the method using the EBM setting of the transition probability, and proposed a finite dimension approximation of the state-action representation by minimizing an orthormal regularization term. The performance of t... | Rebuttal 1:
Rebuttal: * **W1: Some discussions on the representation quality are expected, e.g. to check whether Diff-Rep can recover latent state representations.**
We would like to emphasize that Diff-Rep focuses on extracting representations that are sufficient to represent the $Q$-functions, rather than the laten... | Rebuttal 1:
Rebuttal: We would like to thank all our reviewers for their effort and time in providing constructive suggestions for our paper. We are delighted that our reviewers recognized the relevance of our problem and the novelty of our method.
We note that both the reviewer adAM and iUqH raised concerns regardin... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mamba State-Space Models Can Be Strong Downstream Learners | Reject | Summary: This paper explores the capabilities of Mamba state-space models (SSMs) in comparison to Transformer large language models (LLMs) in various downstream learning tasks. Despite Mamba's success in some areas, the paper identifies challenges and limitations in achieving performance parity with Transformers on sta... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. Please find responses to your primary concerns below.
### ***Is it possible to extend this analysis directly to the output? Since the stability of the hidden state does not necessarily imply the stability of the output.***
We thank the reviewer ... | Summary: This paper explores Mamba's downstream learning capabilities through two primary aspects: (i) fine-tuning and (ii) in-context learning. Specifically, it examines the training stability and robustness of fine-tuning when mixed precision is applied, as well as Mamba's ability to perform in-context learning. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and review. We first address the reviewer's main claims from their review, then their additional questions.
### ***The solution is also standard, and the improvements are also expected since once trains well, Mamba should be able to perform in-context learnin... | Summary: This paper looks at improving start space models or Mamba by enabling mixed precision handling to improve inference and fine-tuning. The results show similar performance with a significantly reduced memory requirement
Strengths: There are extensive results compared to full-precision models
The authors provide... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback in reviewing our paper. We address the reviewer's main concerns below.
### ***Despite memory savings, the largest Mamba models are not evaluated due to memory limitations***
We note, while this is true for full fine-tuning Mamba-{1.4B, 2.8B} mode... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback and time reviewing our paper. In what follows, we summarize and address the main reviewer concerns.
# The paper's conclusions may be directly drawn from the conclusions of [1]
Our submission's presented conclusions may not be directly drawn from the conc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VLMimic: Vision Language Models are Visual Imitation Learner for Fine-grained Actions | Accept (poster) | Summary: This work introduces VLMimic, a framework to acquire robotic skills by imitating from videos.
The system uses VL foundation models to ground human-object interaction videos.
Hierarchical representations are used to learn robotic skills and recorded in knowledge bank.
In unseen environments, the skill adapter i... | Rebuttal 1:
Rebuttal: **Q1. System scalability.**
We greatly appreciate your constructive suggestions! The computational time and memory requirements are estimated as follows:
**Computational time of iterative comparison**. The majority of computational time in iterative comparison is allocated to awaiting responses ... | Summary: The paper proposes a system that can perform imitation learning based on human demonstration videos. The authors design a system using many pre-trained components like VLMs and hand and object tracker and 3D reconstruction and pose estimation tools. They show that by combining these existing tools they can ach... | Rebuttal 1:
Rebuttal: **Q1. Give a lot of credit to VLMs**
Thanks for your valuable suggestions! We give credit to VLMs since the majority of **other foundation models serve as data generators** within the human-object interaction grounding module, providing motion data for VLMs to analyze, while **the acquisition and... | Summary: This paper introduces a novel paradigm named VLMimic, which employs Vision-Language Models (VLMs) and multiple vision tools to ground object-centric information from demonstrations into fine-grained actionable skill sets. VLMimic exhibits a remarkable success rate in manipulation tasks and demonstrates signifi... | Rebuttal 1:
Rebuttal: **Q1. The proposed pipeline encompasses multiple foundation models/vision tools.**
Thanks for your feedback! We hope to address your concerns through the following two points.
(1) **Our VLMimic demonstrates strong robustness against the cumulative error.** Motivated by your insightful feedback, ... | Summary: This paper proposes a method to learn a policy from human videos utilizing advances in vision-language models. The authors first parse human videos into several keyframes, then detect and track hand-object interactions. From the segmented interactions, they learn low-level actions by following the 3D hand-obje... | Rebuttal 1:
Rebuttal: **Q1. The tasks lack complex interactions.**
Thanks for your valuable feedback! We extend our evaluation to include complex tasks, and **demonstrate strong performance on FMB [1], which demands precise manipulation**. FMB evaluates complex robotic manipulation tasks encompassing the grasping and ... | Rebuttal 1:
Rebuttal: # General Response
We sincerely appreciate all reviewers’ time and efforts in reviewing our paper. We are glad to find that reviewers generally recognized our contributions:
- **Method Development**. Presenting an innovative and intuitive approach, introducing a novel application of VLMs [ovGy, Y... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler | Accept (poster) | Summary: In this paper, the authors identify a new problem in open-set domain generalization, proposing that dynamically adapting the domain scheduler used for data partitioning based on specific criteria could lead to a more targeted training strategy and improved outcomes. They introduce a novel training strategy nam... | Rebuttal 1:
Rebuttal: We appreciate the review effort from Reviewer tsHR and provide the point-to-point responses as follows. All the explanations will be included into our final paper.
**W1**: Training order scheduler is very important in deep learning for some specific techniques, e.g., meta learning and curriculum... | Summary: The paper addresses the challenges of Open-Set Domain Generalization (OSDG) and introduces the Evidential Bi-Level Hardest Domain Scheduler (EBiL-HaDS), which adaptively sequences domains based on their reliability, assessed through a follower network. The authors verify EBiL-HaDS on three benchmarks: PACS, Di... | Rebuttal 1:
Rebuttal: Thank you for recognizing the motivation and contribution of the proposed method.
**W1:** The anonymous link to the source code has been submitted in the official comment to AC which can be helpful for the results reproduction. The source code will be released publicly in the final version.
**... | Summary: In this paper, an observation is proposed, that an adaptive domain scheduler benefits more in OSDG compared with prefixed sequential and random domain schedulers. A follower network is proposed to strategically sequences domains by assessing their reliabilities, which is trained with confidence scores learned ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the motivation and contribution of our proposed method. We have conducted an additional 48 experiments using the ResNet152 and ViT base models. The results, detailed below, compare our proposed method with MEDIC and other challenging baselines such as ARPL, MLDG, SWAD, an... | Summary: The paper presents an adaptive domain scheduler with ability to adjust the training order dynamically according to model’s current performance and domain difficulty to address OSDG, short for Open-Set Domain Generalization (OSDG), task where the model is exposed to domain shift and category shift.
Strengths: ... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the motivation behind our work and the effort put into the review. We will address the mentioned formatting and citation issues in our final version submission. The source code has been shared in the official comment to the AC, including a sample for result reproduction... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference | Accept (spotlight) | Summary: Diffusion models rely on the use of the reverse SDE (or ODE) for sample generation, but the discrete-time simulation of the reverse SDE, when regarded as solving a sequence of subproblems, is potentially less efficient compared to the usage of better MCMC samplers for solving each subproblem. This paper sugges... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and helpful suggestions. We will response to your concerns point by point in the following.
> W1: (1). Writing Issues, (2). The paper suggests numerical experiments in the abstract, but only includes the experiments in the appendix. (3). Should the paper clarify ... | Summary: This paper presents a framework for accelerating the inference process in denoising diffusion probabilistic models (DDPMs) by optimizing the balance between the number and complexity of sampling subproblems.
Strengths: - The proposed Reverse Transition Kernel (RTK) framework allows for a more efficient decomp... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and helpful suggestions. We will response to your concerns point by point in the following.
> Q1: While the paper focuses on diffusion models, it raises the question of whether the RTK framework can be applied to other generative models or machine learning tasks.... | Summary: The manuscript is on accelerating or improving the inference in diffusion models. This denoising process corresponds to the discretization of an ODE or SDE. In this work, the authors view this process as multiple reverse transition kernel sampling subproblems. They introduce a general framework for the reverse... | Rebuttal 1:
Rebuttal: Thanks for your comments. We will response to your question in the following.
> W1: The manuscript lacks of experiments and especially comparison with existing inference methods. Theoretically, these methods seem to improve on existing one, but it would be great to show it in practice as well, e.... | Summary: This paper takes a fresh perspective on simulating the backward SDE in diffusion modelling, proposing to replace the usual Euler-Maruyama discretisation (i.e. based on Gaussian approximations) with a more accurate approximation (based on Metropolis--Hastings and related techniques). The approach is explored i... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and helpful suggestions. We will response to your concerns point by point in the following.
> W1: The principal justification for this strategy is theoretical, and it is not yet clear whether it is practically beneficial.
AW1: In this paper, we mainly focus on ... | Rebuttal 1:
Rebuttal: Thanks for your valuable suggestions. To supplement numerical experiments and demonstrate the benefits of our RTK-based methods in practice, we have conducted the following new experiments.
### Synthetic Data Experiments
---
We performed additional experiments on various Mixture of Gaussians (M... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper discusses an acceleration method for the inference of the diffusion models. It considers the denoising diffusion process as a sequence of reverse transition kernel (RTK) sampling subproblems. Then, the paper integrate the RTK subproblems to the Metropolis-Adjusted Langevin Algorithm (MALA) and Under... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review. We will answer your concerns one by one in the following.
> W1: The convergence results in section 4 are built upon a particular choice in the step size...
AW1: Sorry for the confusion. The particular step size choice in Section 4 is to simplify our t... | null | null | null | null | null | null |
Out-Of-Distribution Detection with Diversification (Provably) | Accept (poster) | Summary: The authors propose DiverseMixup, a Mixup data augmentation technique applied to auxiliary OOD data to improve the OOD detection capacities of classifiers trained with the Outlier Exposure technique. They provide a theoretical analysis justifying their approach and demonstrate the superior empirical performanc... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our novel method and satisfying experiments. We appreciate your support and constructive suggestions and address your concerns as follows.
---
## W1. The quantities $h^*_{ood}$ and $h^*_{aux}$ are never defined. Why would an argmin be a set in that case?
Th... | Summary: The paper proposed Diversity-induced Mixup for OOD detection (diverseMix), which enhances the diversity of auxiliary outlier set for training in an efficient way.
Strengths: 1. The paper is written well and is easy to understand.
2. The studied problem is very important.
3. The results seem to outperform st... | Rebuttal 1:
Rebuttal: Thank you for your reviews. We are encouraged that you appreciate our studied problem and state-of-the-art result. We address your concerns as follows.
## W1. My biggest concern is that there are already some papers that theoretically analyze the effect of auxiliary outliers and proposed some co... | Summary: This study aims to explore the reasons behind the effectiveness of out-of-distribution (OOD) regularization methods by linking the auxiliary OOD dataset to generalizability. The researchers show that the variety within the auxiliary OOD datasets significantly influences the performance of OOD detectors. Moreov... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your valuable comments and appreciate your recognition of the effective method as well as sufficient theoretical guarantees. We provide detailed responses to the constructive comments.
## W1. The reviewer has some concerns regarding the empirical evaluations o... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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