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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach | Accept (poster) | Summary: This paper proposed an interesting model to integrate high-order clusters into the TKG representation learning. A cluster-aware unsupervised alignment mechanism is introduced to ensure the alignment of soft overlapping clusters across timestamps. An implicit correlation encoder is also proposed to capture late... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s meaningful comments and insightful questions.
>Q1: Lack of clarity in introducing and defining entity graph and cluster graph concepts
Thank you for raising this concern. We acknowledge that these crucial concepts were not sufficiently introduced or defined... | Summary: The paper addresses Temporal Knowledge Graph (TKG) representation learning, which aims to embed temporally evolving entities and relations into a continuous low-dimensional vector space. Existing methods struggle to capture the temporal evolution of high-order correlations in TKGs. The authors propose a novel ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s meaningful comments and insightful questions.
>Q1: Absence of experimental results on ICEWS05-15 and GDELT datasets
ICEWS05-15 dataset shares the same source as ICEWS and has a similar scale to ICEWS18, which we included in our initial experiments, so we do... | Summary: This paper studies the temporal knowledge graph representation learning task and proposes a temporal evolution-aware framework DECRL.
By assigning different entities to distinct clusters at each timestamp and modeling the evolution and shifts of these clusters, cluster-aware information is explicitly incorpor... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s meaningful comments and insightful questions.
>Q1: Unclear motivation for introducing a cluster graph at each timestamp to capture temporal shift information at the clustering scale
We consider the complex dynamics of international alliances and conflicts, ... | Summary: The paper proposed a deep evolutionary clustering method for TKGE to capture the temporal evolution of high-order correlation in TKGs. A cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters
across timestamps. Extensive experiments ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s meaningful comments and insightful questions.
>Q1: Lack of intuitive case study demonstrating temporal evolution of high-order correlations
**We would like to draw attention to the comparison between Figure 2d (Final DECRL) and Figure 2f (Final DECRL-w/o-fu... | Rebuttal 1:
Rebuttal: Summary of Revision:
We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for us to improve our paper further.
The reviewers generally held positive opinions of our paper, in that the proposed approach is **“technically reasonable”, “well... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR | Accept (poster) | Summary: This paper proposes a training framework for unsupervised speech recognition models, building on top of wav2vec-u. The authors note that the performance in wav2vec-u is hindered by the quality of segment boundaries, as they show that the phoneme-error-rate can be improved significantly with ground truth segmen... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> The authors propose a very complex methodology. **These are not a weakness per se**, but point towards the overall complex picture of the proposed method.
To ensure th... | Summary: This paper addresses the challenging problem of learning speech recognition without parallel recordings and transcriptions. They build upon state-of-the-art approaches of Wav2Vec-U(2) in this area and propose to refine a pretrained model using a reinforcement learning approach. The main idea is to split the pr... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> The author’s (mis)use of the word “phoneme” is problematic. What the authors may have had in mind is phonetic segmentation instead.
We thank the reviewer for pointing ... | Summary: The paper proposes an improvement over wav2vec-U for unsupervised speech recognition, specifically for the task of predicting phonemes. For the final WER performance, a given lexicon is used to get from the phoneme sequence to words.
So the paper focuses on improving the segmentation/boundaries of the phoneme... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> How do you get from phonemes to words? ... it needs a lexicon. If lexicon is used, is it fair to call this unsupervised ASR? I think this should be made much more clear... | Summary: This paper proposes a new UASR approach which explicitly learns both segmentation (phoneme boundary) prediction and phoneme class prediction. To do segmentation, the paper proposes some reinforcement-learning objectives. The phoneme class prediction follows an existing approach. It turns out the proposed appro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
> ASR don't need accurate segmentation, e.g. CTC model is peaky -- the peak of a phoneme can appear at any frame for this phoneme.
While recent **supervised ASR** does n... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QT-ViT: Improving Linear Attention in ViT with Quadratic Taylor Expansion | Accept (poster) | Summary: In this paper, the authors propose QT-ViT models, which improve the traditional linear self-attention methods by using a second-order (quadratic) Taylor expansion to approximate the original softmax attention and then accelerate this process using a fast approximation algorithm reducing computational complexit... | Rebuttal 1:
Rebuttal: **Q1: In Table 1, the top 1 acc of the proposed QT-ViT models over EfficientViT appears to diminish. This trend raises concerns about the scalability and robustness of QT-ViT models as they are scaled up. The paper should include a detailed analysis of this trend.**
A1: First of all, the difficul... | Summary: This paper proposes a novel method to compute the kernel function in linear attention. They decompose the softmax attention with Tayler expansion and utilize the first two items to approximate the exponential function. The Kronecker product is used to decompose the quadratic Taylor expansion into two kernel fu... | Rebuttal 1:
Rebuttal: **Q1: What is the dimension of $\alpha$, $\beta$ and $\gamma$ used in Eq.11? Are there any ablation studies using different parameters?**
A1: $\alpha$, $\beta$, and $\gamma$ are all scalars as shown in Line 164 in the original paper. All of them are learnable parameters and are initially set to 1... | Summary: This paper introduces QT-ViT models, which enhance linear self-attention using quadratic Taylor expansion. The similarity function is decomposed into the product of two kernel embeddings via the Kronecker product. By employing a fast approximation algorithm, the computational cost is reduced while maintaining ... | Rebuttal 1:
Rebuttal: **Q1: Overall improvement is incremental. Add results on ImageNetv2 and real.**
A1: EfficientViT is currently the state-of-the-art method according to the accuracy-efficiency trade-off. Also, note that the difficulty of increasing the classification accuracy from a baseline of 79% is different fr... | Summary: This paper proposed a new linear complexity sequence modeling strategy for image modeling. To achieve this, the authors first replace the softmax attention with second-order Taylor expansion, then accelerate its computation with a fast approximation algorithm. The effectiveness of the proposed method is valida... | Rebuttal 1:
Rebuttal: **Q1: It is unclear why directly using the first-order Taylor expansion is worse than using the second-order Taylor expansion with linear approximation. The author should provide a section to discuss this.**
A1: In fact, the disadvantages of first-order Taylor attention are mentioned in the secti... | Rebuttal 1:
Rebuttal: The PDF contains the answer to Q7 of Reviewer QZCU that adds the latencies of different models on NVIDIA-V100 GPU, and the answer to Q3 of Reviewer dQG7 that adds the results of QTViT 1-3 and EfficientViT-L series into the figure.
Pdf: /pdf/2ba18fdecae8c979ef39cbbc6db3f63af08dcf87.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction | Accept (poster) | Summary: This work introduces Text2NKG. A proclaimed first method of extracting n-ary facts from text for construction a KG. The focus on n-ary is a more complex task than standard RE as an n-ary fact can hold more entities than the standard RDF {subject, relation, object}.
Candidate 3-ary span tuples are formed from... | Rebuttal 1:
Rebuttal: Thank you for a careful review of our work. We appreciate that you find our work concrete in motivation and pleasant to read. We respond to your specific concerns and questions below.
---
**For Weakness 1 (Code makes mention of ACE dataset, but this does not seem to have made it into the end-work... | Summary: Text2NKG is a novel framework for fine-grained n-ary relation extraction aimed at constructing n-ary relational knowledge graphs (NKGs). Unlike traditional binary relational knowledge graphs, NKGs encompass relations involving more than two entities, making them more reflective of real-world scenarios. Text2NK... | Rebuttal 1:
Rebuttal: Thank you for a detailed review. We are delighted that you think our work have significant potential applications and technical effectiveness. We answer your questions below.
---
**For Weakness 1 (The comparison settings with the large language models need to be clearer.):**
Thanks for your sugg... | Summary: The paper introduces a novel framework for fine-grained n-ary relation extraction aimed at constructing n-ary relational knowledge graphs (NKGs). Traditional KGs primarily focus on binary relations, but this work targets n-ary relations which involve more than two entities, aligning more closely with real-worl... | Rebuttal 1:
Rebuttal: Thank you for your insightful evaluation of our paper. We are happy that you find our paper clear to follow and the results impressive. We respond to your concerns and questions below.
---
**For Weakness 1 (The paper does not thoroughly address the computational efficiency and scalability.):**
A... | Summary: The paper presents Text2NKG, a new framework designed for fine-grained n-ary relation extraction aimed at constructing n-ary relational knowledge graphs (NKGs). By introducing a span-tuple classification method combined with hetero-ordered merging and output merging strategies, it achieves extraction across va... | Rebuttal 1:
Rebuttal: Thank you for a detailed review. We appreciate that you find our work effective for n-ary relation extraction and consist with practical applications. We answer your concerns below.
---
**For Weakness 1(i) (Vital specifics regarding input prompts to language models like GPT-4.):**
Our ChatGPT an... | Rebuttal 1:
Rebuttal: We thank all four reviewers for carefully reading our paper and providing constructive feedback. We appreciate the recognition of our work's strengths, which are summarized as follows.
**1. Novelty in Motivation.**
To address challenges such as the diversity of NKG schemas, determination of the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models | Accept (oral) | Summary: This work proposes a maximum entropy inverse reinforcement learning (IRL) approach for improving the sample quality of diffusion generative models, especially when using a small number of generation steps.
Strengths: 1. The paper presents a novel and principled approach for improving diffusion models by formu... | Rebuttal 1:
Rebuttal: Dear Reviewer YBX1,
We deeply appreciate your thorough and constructive comments. We will do our best to answer your questions.
**Q. What is the connection to KL-regularized RL fine-tuning, particularly in papers such as [1-4]?**
Thanks for pointing out an interesting connection. The sampler up... | Summary: The authors propose learning a denoising diffusion model without using the denoising loss. Instead, they propose first training an energy based model and then treating the diffusion denoising sampler as an RL trajectory with the energy based model as the reward. To learn the energy based model, however they pr... | Rebuttal 1:
Rebuttal: Dear Reviewer yjrf,
We appreicate your time and effort in reviewing our work. Here, we are happy to address your concerns and questions.
**Q. Is the objective function stable?**
> The objective in Eq (5) seems somewhat hard to be confident of, in the sense that given any $\pi$ and $p$, the opt... | Summary: This paper seeks to improve diffusion models by employing inverse reinforcement learning methods of imitation rather than (more myopic) behavioral cloning methods, which prevalent existing diffusion models can be viewed as using. It trains an energy-based model using maximum entropy inverse reinforcement learn... | Rebuttal 1:
Rebuttal: Dear Reviewer Nqik,
Thank you for taking the time to review our work. We deeply value your feedback and are happy to address your questions.
**Q. Is theoretical analysis possible?**
> Is there potential for theoretical analysis or guarantees using this approach?
Thanks for bringing up an import... | Summary: The authors introduce a maximum entropy inverse reinforcement learning (IRL) approach for
enhancing the sample quality of diffusion generative models, especially with limited generation
time steps. Named Diffusion by Maximum Entropy IRL (DxMI), the approach involves joint
training of a diffusion model and an e... | Rebuttal 1:
Rebuttal: Dear Reviewer EhWr,
We appreciate the comprehensive feedback on our manuscript. All the comments and questions raised have been considered below.
**Q. Complexity of Implementation**
> The implementation of DxMI, particularly the joint training of diffusion models and EBMs, might be complex and... | Rebuttal 1:
Rebuttal: ## General Comments to AC and All Reviewers
We appreciate all reviewers for their thoughtful comments and remarks. We thank the reviewers for their insightful feedback and constructive comments and for providing suggestions that would improve our paper.
First of all, we are encouraged that the r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FairWire: Fair Graph Generation | Accept (poster) | Summary: In this submission a graph diffusion model with fairness correction is introduced for graph generation. The model is also applied for link prediction. The authors introduce a graph regularizer for fairness based on a theoretical bound for subgroup distance and representation distance. This analysis is newly in... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the Reviewer for the raised points. We have addressed all points raised by the Reviewer, and presented our responses below.
**Weakness:** We thank the Reviewer for this comment under Weaknesses.
In [24], it is stated that:
“For clarity and simplicity, we consid... | Summary: This work considers the problem of fairness in learning over graphs. Namely, it adopts a criterion for fairness that quantifies the probability of a relationship existing between nodes whose sensitive attribute value matches (intra-edges) versus not (inter-edges). It first derives theoretical results that boun... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the supportive and constructive remarks, as well as valuable comments. We have presented our responses to the Reviewer’s questions below.
**Weakness:** Regarding the comment under Weaknesses, we agree with the reviewer that “individuals tend to form connect... | Summary: The impact of generative learning algorithms on structural bias is investigated in this paper. The authors provide a theoretical analysis on the sources of structural bias which result in disparity. Then a novel fairness regularizer is designed to alleviate the structural bias for link prediction tasks over gr... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their insightful comments regarding our work. We have addressed the Reviewer’s comments, and placed our responses to each comment below. Please note that references written in [R#] format are provided at the end of this rebuttal, whereas the ones in [#] format correspond ... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the Reviewers for their detailed reviews and constructive suggestions. We have addressed the questions raised by the Reviewers, and presented the detailed responses in a point-to-point manner.
Pdf: /pdf/5154905b7d07d3a03ad5d5a8bc02cf7ec9fd2279.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers | Accept (poster) | Summary: This paper proposes a way to analyze the interplay of attention paths. Statistical analysis is provided, and experiments are performed to verify the theory.
Strengths: The idea considered in this paper is interesting, and the experiments seem to align with the theoretical justifications.
Weaknesses: [1] This... | Rebuttal 1:
Rebuttal: We thank the reviewer for the precious feedback, which helped us to improve the communication of our findings.
At the moment, however, we believe that our contributions have not been evaluated in the proper context.
In particular, the reviewer seems to interpret our work as providing a method of... | Summary: The paper investigates Bayesian learning of the value weight matrices of a deep multi-head attention network without MLPs employing the back-propagating kernel renormalization (BPKR) [1] technique in the linear regime, where the training set size $P$ scales with the width $N$, i.e., $P/N = O(1)$. It finds that... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time reviewing our work and for many positive comments. We’d like to address the reviewer’s remaining concerns as follows.
1. *The analysis relies on several very strong assumptions: (i) linearity of the network output in the value weights, (ii) applying t... | Summary: This paper provides an interpretation of wide (large embedding dimension, $N$) multi-head attention-only transformers solving in-context learning tasks as performing high-dimensional kernel combining. The paper derives the exact statistics of the predictor, when the number of training examples $P$, and $N \to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable time reviewing our work, for the many positive comments, and for the intriguing questions.
Below are our replies to the reviewer’s questions, as well as minor concerns.
1. *One minor weakness is that the experiments are limited to binary classification.*... | Summary: This work studies the mechanism of attention in deep Transformers. The theory shows that the prediction process can be expressed as a combination of kernels of different attention paths. Experiments are conducted to verify the theory, which also motivates a pruning method on different attention heads.
Strengt... | Rebuttal 1:
Rebuttal: We thank the reviewer for the precious feedback, which helped us to improve the communication of our findings.
At the moment, however, we believe that our contributions have not been evaluated in the proper context. Before reading this response, we kindly ask the reviewer to read our global respo... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable time and their constructive comments. All reviewers agree on two main points:
- Our theoretical contribution is “interesting” (15FG and 5PZg), “novel” (Y9xT and 9bWs) and “technically sound” (Y9xT).
- Experiments thoroughly validate our theoretical results... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Uncovering Safety Risks of Large Language Models through Concept Activation Vector | Accept (poster) | Summary: The paper introduces a Safety Concept Activation Vector framework designed to identify and exploit vulnerabilities in LLMs by accurately interpreting their safety mechanisms. The authors develop an SCAV-guided attack method that enhances the success rate of generating harmful prompts and embedding-level attack... | Rebuttal 1:
Rebuttal: Thanks for your insightful and detailed comments, which enable us to better clarify our contributions and ethical considerations.
> **Weakness 1: Interpretability used in attacks by [1]**
>
We acknowledge that previous works like [1] have explored the use of interpretability to assist attacks.... | Summary: This paper introduces the Safety Concept Activation Vector (SCAV) framework, which guides attacks on LLMs by interpreting their embedding-level safety mechanisms. This work estimates the likelihood that an embedding is considered malicious by the LLM, utilizing a linear classifier based on Concept Activation V... | Rebuttal 1:
Rebuttal: > **Weakness 1 and Question 1: Linear interpretability assumption**
>
Thanks for explaining your concern in such a detailed and constructive way. Per your suggestion, we further justify the assumption by providing:
1. **Empirical evidence across models and layers**. As shown in Rebuttal Table ... | Summary: This paper proposes a jailbreak attack (SCAV) inspired by the Concept Activation Vector (CAV) on neural networks. For safety concept, this concept vector is essentially defined as a direction orthogonal to the decision boundary of a linear classifier trained to distinguish safe and harmful instruction on embed... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and questions.
> **Weakness 1: Optimizing only the last layer in token-level attack**
>
We optimize only the last layer to ensure
1. **A fair comparison with baselines**: our baselines (e.g., [1]) use only information from the last layer so we follow their s... | Summary: This paper introduces a new framework named Safety Concept Activation Vector (SCAV) for jailbreaking LLMs. This framework is built upon LLM interpretability work. Specifically, the paper utilizes an interoperability approach called Concept Activation Vector, which can linearly separate safe and unsafe instruct... | Rebuttal 1:
Rebuttal: Thanks a lot for your insightful and constructive comments. We really appreciate the efforts that you have made in helping improve our paper.
> **Weakness 1: Interpretability used in attacks by [1].**
>
We acknowledge that previous works like [1] have explored the use of interpretability to ass... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for the insightful comments and recognition of our work. We are encouraged that reviews think our paper has the following strengths:
- Well-written and easy to follow (Reviewers 6xey, CRgs, xmcv)
- Comprehensive experiments (Reviewers 6xey, CRgs, xmcv)
- Superior perfo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse | Accept (poster) | Summary: This work investigates the implicit bias of both multi-task learning (MTL) and fine tuning pre-trained models (PT+FT) in diagonal linear networks and shallow ReLU neural networks. The contributions are as follows:
1. The authors prove that both MTL and PT+FT bias the networks to *reuse features*. This holds fo... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful review. Below we respond to your comments and questions. (Due to the character limit on the rebuttal, we focus here on the most important responses and have relegated additional responses to a comment.)
First, in response to the reviewer’s summary of the paper... | Summary: Abstract
The goals of the paper are very clear from the abstract, as are the results.
Introduction
Lines 23-42: The authors do a great job of summarizing the applications of MTL (using it here loosely to capture MTL and PF+FT) while recognizing that very little work has been done to explore exactly why it w... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and your helpful comments. We will make sure to make the notation more accessible and really appreciate your detailed notes on this. Below we respond to the questions and comments you raised in your review. (Due to the character limit, we focus on the... | Summary: In this study, the authors explore the inductive biases associated with multi-task learning (MTL) and the sequential process of pretraining followed by finetuning (PT+FT) in neural networks. Specifically, they analyze the implicit regularization effects in diagonal linear networks and single-hidden-layer ReLU ... | Rebuttal 1:
Rebuttal: Thank you very much for your helpful review. Below we respond in detail to your comments.
> Although the findings are promising, the article notes that more work is needed to test these phenomena in more complex tasks and larger models. This suggests that the research's applicability and universa... | Summary: This paper studies the implicit bias of gradient descent on the linear diagonal model and two-layer ReLU networks but instead of looking at the standard single output regression / classification setting, the authors study multiple outputs. In particular, the dataset X is associated with labels y and another da... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and your helpful comments. Below we respond to your questions.
> This paper seems somewhat related to the PT + FT setting. Mainly out of curiosity, as I think the settings are a little different, do you see any connections?
We agree that this paper ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful feedback and comments. In particular, the reviewers have suggested a number of relevant papers that we will add to the related work section. In addition, they have provided valuable feedback on clarity, which we will take into account in revising the manusc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Renovating Names in Open-Vocabulary Segmentation Benchmarks | Accept (poster) | Summary: Open-vocabulary models use class names as text prompts for unseen categories’ generalization during training. In this paper, the issue of imprecise or even wrong class names from existing datasets is specially studied. One simple and general framework is proposed for automatic dataset renaming, that is, first ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our paper to be “on the right path” and that our presentation to be “easy to follow” and have “clear logic”. We address the rest of the comments below.
**1. Noise types and rates.**
Besides the noise types we visualized in Figure 1, there are indeed many ... | Summary: The paper explores modifying the class names associated with mask annotations in the segmentation datasets in the context of open-vocabulary segmentation. A method, RENOVATE, is described to perform such renaming in an automated pipeline. RENOVATE leverages an image captioning model to generate contextual word... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our paper and our efforts in making our writing “engaging”. We address additional comments as the following:
**1. On the renaming model and downstream segmentation task.**
Thanks for the suggestion, we will work on making this part more stre... | Summary: The paper considers the problem of open-vocabulary segmentation. The task requires segmentation models to recognize categories outside of the training taxonomy. This usually means learning a joint image-text feature space and classification via similarity matching between class text embeddings and segmentation... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out that we addressed an “important but underexplored” problem and that our approach is “technically sound”, “properly explained”, and “would have positive impact on future research”. We address the remaining comments below.
**1-(a). Batch collation to train the... | null | null | Rebuttal 1:
Rebuttal: We’d like to thank all reviewers again for their valuable feedback.
We are pleased to see that reviewers found that our work is **novel** (Reviewer aQgc) and **technically sound** (Reviewer fwux), **considers an important and underexplored problem** (Reviewer fwux, Reviewer CT7Q) and would **posi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels | Accept (poster) | Summary: The paper introduces SAMI, an iterative algorithm designed to align LLMs with behavioral principles (constitutions) without the need for preference labels or demonstrations. SAMI achieves this by optimizing the conditional mutual information between principles and self-generated responses given queries. The ap... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work!
As mentioned in our responses to our first reviewer LYCL, SAMI, like other alignment methods such as DPO, suffers from over-optimization (e.g., non-coherent outputs if trained for too long). We regularize against this “forgetting” by always star... | Summary: This work presents a method to align the model towards a set of principles. The general idea is to sample responses from the model with different constitutions and optimize the matching between responses and constitutions via an infoNCE-type contrastive loss. The whole process is done iteratively to improve th... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work. We completely agree that comparing to other baselines than instruct-finetuned models and base models directly is an important limitation of our work and we plan to address this in future extensions.
Regarding your specific questions:
- We agree ... | Summary: The authors propose a technique to improve the ability of language models (LM) to abide by constitutions without using human labels. First, they ask a principle writer LM to construct detailed constitutions and inverse versions of them (called “antitheses” in this work). Then the main LM, which is the LM to be... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work and providing the additional length correction reference. We will make sure to report both statistical significance in addition to confidence intervals in our revision. Moreover, we will revise figures for clarity as requested.
Please see our at... | Summary: This paper introduces SAMI (Self-Supervised Alignment with Mutual Information), an iterative algorithm for aligning language models to follow behavioral principles without using preference labels or demonstrations. The key idea is to finetune a pretrained LM to increase the mutual information between constitut... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work!
We completely agree that future work should include a wider range of tasks. Widening both the range of tasks and principles is crucial for training a general constitution-following model, and we plan to address this limitation in future extensi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their positive evaluation of our work and their helpful suggestions for revising our paper.
We have included length-corrected dialogue win rates based on logistic regression (as requested by reviewer eqUp) in the attached .pdf.
Point-by-point responses to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Safe and Efficient: A Primal-Dual Method for Offline Convex CMDPs under Partial Data Coverage | Accept (poster) | Summary: This paper formulates offline convex MDPs under safety constraints and proposes a linear programming-based primal-dual algorithm for solving it. The authors make a partial data coverage assumption yet achieve a sample complexity of $\mathcal{O}(\frac{1}{(1-\gamma)\sqrt{n}})$ while the current SOTA is $\mathcal... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our paper! We appreciate your support and comments. We'd like to respond to the major comments in the following.
**The experiments are toy examples and have limitations when extending to complicated continuous state-action space.**: As our paper is primaril... | Summary: This paper investigates batch RL with safety constraints and function approximation, which is a question of both theoretical and practical importance.
Strengths: The assumptions considered in this paper are less restrictive compared to previous works. In particular, most previous works consider linear objecti... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We would like to address your concerns point by point.
**The class $\mathcal X$ is not well-explained**: In Assumption 3, operator $\phi(w)$ or $x$ aims to calculate the $l_1$ norm of the constraint function $Kw - (1-\gamma)\mu_0$. It assumes that for all $... | Summary: This paper proposes a novel linear programming based primal-dual algorithm for convex MDPs which incorporates “uncertainty” parameters to improve data efficiency, while requiring only partial data coverage assumption. The authors provide theoretical results achieve a sample complexity of $O(1/((1-\gamma)\sqrt{... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and detailed comments on our paper. We will respond to the major concern in the following.
**Need more explanations on Assumptions 2-4 and the reason for introducing the set $\mathcal W$ and $\mathcal X$.**: Assumption 2 assumes the optimal policy $\pi^*$ $(w^... | Summary: This paper studies the offline safe reinforcement learning (RL) problem and proposes a primal-dual algorithm to solve this problem. The key contributions are that the paper focuses on the more general setting of convex Markov Decision Processes (convex MDPs), where the objective function is a convex utility fu... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers' detailed comments. Please find our point-by-point response to your questions below.
**Concern of the novelty of the contributions**: We would like to emphasize that we are the first to study offline convex CMDPs and provide state-of-the-art theoretical result... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Regression under demographic parity constraints via unlabeled post-processing | Accept (poster) | Summary: This paper considers post-processing regressors to satisfy the group fairness criterion of demographic parity, in particular, under the attribute unaware setting.
1. The authors begin by analyzing the constrained optimization problem (\*) for fair post-processing, and showing in lemma 3.1 that the solution, i... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful reading and fully agree with the evaluation regarding the strengths and potential directions for future investigation. We will address the questions raised below.
*Weaknesses*
**W1:**
* Regarding lipschitzness of $F$. The reviewer feels that some discussions... | Summary: This paper proposes an algorithm that takes in an fitted regression function and a sensitive attribute predictor and output a prediction function satisfying the demographic parity constraint. It designs a smooth convex objective function with discretization to solve for a prediction function with small risk an... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for the careful reading and evaluation. We agree with the feedback and will correct the suggested typos in the revision. We will also address the question raised below.
**Q**: In practice, how do we pick algorithm parameters such as L, B and $\beta$?
**A**: In practice... | Summary: This paper presents a post-processing algorithm designed to enforce demographic parity in regression tasks without access to sensitive attributes during inference.
Strengths: The solution is versatile solution for enforcing demographic parity as it can be applied on different optimizers.
The paper provides a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and effort in reviewing our work, but we respectfully disagree with the evaluation.
*Weaknesses*
The reviewer has criticized several stylistic choices in our paper. While we recognize that these choices may not align with the reviewer's preferences, it is worth... | null | null | Rebuttal 1:
Rebuttal: We conducted an ablation study to observe the behaviors of other algorithms, as suggested by Reviewer qMMa. A figure that illustrates the comparison is included in the attached pdf file.
In conclusion, all algorithms perform similarly in the middle to high unfairness regime, while those based on ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Interventionally Consistent Surrogates for Complex Simulation Models | Accept (poster) | Summary: In their use by practitioners, complex simulators may be run under intervention scenarii. To reduce the overall cost of running many scenarii, relying on cheaper to compute surrogate models is a standard practice. The authors thus propose surrogates that can additionally learn these interventions effect, relyi... | Rebuttal 1:
Rebuttal: **Improving the discussion of results**
We will use some of the additional space in the revision to provide further discussion. In particular, we will:
- state and discuss the relative computational costs of our complex simulators and our surrogates (briefly: we see that our surrogates run approx... | Summary: This work presents a method for learning surrogate models of simulations.
Both the simulation and the surrogate model are treated as causal models, where the surrogate is an element of a parametric family of causal models.
The map from simulation variables to surrogate variables is assumed to be known.
For tra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time & kind comments ("paper is a pleasure to read..very clear", "well-defined, consistent and easy to follow", "bridges the gap between.. causality.. and useful applications").
The reviewer identifies as a single weakness the novelty of our paper wrt a very recent... | Summary: The paper proposes a novel framework for learning surrogate models of expensive simulators by formulating them as structural causal models. Specifically, the authors focus on learning interventionally consistent surrogate models for large-scale complex simulation models. The authors' claims are supported by th... | Rebuttal 1:
Rebuttal: Thank you for your review and suggestions for improving our paper. We provide itemised responses to your two questions below.
**a.** To expand our empirical assessment of the framework we propose, and to provide practitioners with a further example of how they may use our framework, we have prepa... | Summary: The paper introduces a framework for learning surrogate models for complex simulations that preserve simulation behavior under changes in the structural parameters of the underlying model (i.e. the intervention) using causal inference. The framework is tested on epidemiological agent-based models of disease sp... | Rebuttal 1:
Rebuttal: Q1 & W4: Generally, we expect $\eta$ to be informed by domain experts/policymakers based on downstream tasks, perhaps accounting for economic/political constraints. E.g. in a pandemic, economic constraints may preclude lockdowns of length >2 weeks, and political pressure may demand action soon; he... | Rebuttal 1:
Rebuttal: We thank all reviewers for their feedback. We have responded to each of your points individually & are happy to expand on anything in the discussion period. We address some common points here.
**Additional experiment**
A common recommendation was to test our method in another experiment. We have... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning | Accept (poster) | Summary: This work presents a transformer architecture inspired by predictor-corrector methods for solving ODE problems. It adopts the Adams-Bashforth-Moulton method and utilizes an exponential moving average (EMA) method to compose the predictor, discussing two correctors (the EMA-based and the simple backward Euler m... | Rebuttal 1:
Rebuttal: Following your suggestion, we have scaled up our large language model (LLM) experiments, focusing on how our PCformer performs in the LLM setting. We aim to demonstrate the capabilities of PCformer from both model capacity and data volume perspectives. The updated results are as follows:
| Model(... | Summary: This paper takes inspiration from established high-order approaches in numerical analysis for solving differential equations to improve the architectural design of Transformers. Prior work has shown that residual networks can be seen as discrete approximations of Ordinary Differential Equations (ODE) and explo... | Rebuttal 1:
Rebuttal: We thanks for your recognition for our motivation and the organization of our method.
We also appreciate for your constructive feedback towards the shortcomes of the current manuscript, and we think all the concerns would be well addressed in our improved version. Here, we would like to show the d... | Summary: This paper presents an approach to improve the performance of Transformer models for conditional natural language generation (machine translation and summarization). The authors introduce a predictor-corrector framework, inspired by numerical methods for
solving ordinary differential equations (ODEs), to enh... | Rebuttal 1:
Rebuttal: Thanks for your constructive advice and we think all the conerns would be well addressed in our improved version. We would like to address your concerns as follows:
> W1: The paper does not compare its results with state-of-the-art models, which could have provided a more comprehensive evaluation... | Summary: The paper presents advancements in Transformer architecture to minimize errors in approximating solutions to Ordinary Differential Equations (ODEs).
The contributions are:
- Introducing a learning paradigm with a high-order predictor and multistep corrector to reduce truncation errors.
- Proposing an exponen... | Rebuttal 1:
Rebuttal: Thanks for your recognition on our writting and the style to present our idea. We would like to answer your questions as follows:
> W1: The complexity of implementing these methods might be a barrier for practical adoption. The paper could benefit from providing more detailed guidelines or code t... | Rebuttal 1:
Rebuttal: Thank you to all four reviewers for your efforts and instructive comments on our paper. We believe these updated results address your concerns regarding the efficiency and effectiveness.
> W1: about the computation overhead, inference and training cost comparison.
| Model | La... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing | Accept (spotlight) | Summary: This paper presents a method by which an MLLM (gpt 4) is used as a task planner for image editing tasks. A catalog of off the shelf models is used as a set of tools and the planner constructs a task tree, executes proposed tasks, performs verification, and optionally backtracks.
The authors claim the followi... | Rebuttal 1:
Rebuttal: **Q1: Probably should cite Gupta, et. al. "Visual Programming: Compositional Visual Reasoning Without Training", CVPR 2023.**
A1:
Thank you for your suggestion. This paper uses language models to generate visual programs for compositional visual tasks, which are then executed. In concept, this pa... | Summary: This paper introduces a new multi-modal agent for image generation and editing that break down these tasks into subproblems to solve with external tools, including self correction module with verification feedback.
Strengths: - While the idea of augmenting a large (multimodal) language model with tools and t... | Rebuttal 1:
Rebuttal: **Q1: The paper would be stronger if it performed a deeper analysis, for example, on common error cases, or via more finegrained ablation studies on the tools / positive-aware tool execution.**
A1:
* error cases: We show two error cases in the Fig. 2 and Fig. 3 of the rebuttal document. As can be... | Summary: This paper presents GenArtist, a system that utilizes MLLMs as agents for image generation and editing, especially for complex language descriptions. The key idea is to first use MLLM to decompose the generation task as an execution tree of various tools such as SDXL and LMD, and then utilize all the tools in ... | Rebuttal 1:
Rebuttal: **Q1: My major concern is about the possible limitation in the decomposition tree.**
A1:
* Thank you for your suggestion. We have indeed considered this issue. Therefore, during verification, in addition to verifying the accuracy of the generated images, the agent is also required to assess their... | null | null | Rebuttal 1:
Rebuttal: We include some essential images for the rebuttal in the PDF file here, mainly comprising the regenerated images for the hot dog case and some analysis about error cases.
Pdf: /pdf/7bf66d245cdeb3ecee8358f0df44996956ace38a.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
End-to-End Ontology Learning with Large Language Models | Accept (poster) | Summary: This paper proposes a new methodology of Ontology Learning using Large Language Models and ontology paths which is called OLLM. After introducing two ontology-datasets for Wikipedia and ArXiv, they train an LLM using the titles, summaries, and ontology paths to allow for sub-graph generation. The method is add... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback.
> While the OLLM results are better than most of the baseline methods that is not the case using the motif distance metric (Wikipedia: Finetune = 0.05 while OLLM = 0.080, Arxiv: Memorisation = 0.037 while OLLM = 0.097). That is not well explained... | Summary: The paper is well-written, addresses a valuable real-world problem, and proposes a simple, intuitive, and effective method. The experiments are rigorously designed with comprehensive evaluation metrics. Overall, this is a high-quality work with notable contributions.
However, there are concerns about whether ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback.
> W1: The paper's main claim is that end-to-end modelling is better than pipelined methods. However, it is unclear whether the improved performance is credited to the end-to-end modelling approach or the capabilities of LLMs. Although I am convin... | Summary: The paper introduces a novel method called OLLM (Ontology Learning with Large Models) for automating the construction of ontologies using large language models (LLMs). Ontologies are crucial for structuring domain-specific knowledge, but their manual construction is labor-intensive. The authors aim to address ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback.
> The paper focuses on building ontologies with concepts and taxonomic relations, which is only a part of the full spectrum of ontology components. A comprehensive ontology includes not only hierarchical relationships but also properties, semanti... | Summary: The paper aims to address the challenge of constructing ontologies, which traditionally require substantial manual effort. Ontologies are structured representations of domain knowledge used for automatic machine processing. Previous methods for ontology learning (OL) using large language models (LLMs) focused ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback.
> Using embedding similarity is not a good fit here, as for difficult samples, it would likely be wrong. There is also bias that comes from pretraining (so the metric can't really be used in 'unusual' domains for which such embeddings are not ava... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time. The feedback is constructive and helpful.
We are happy to see that most reviewers found the end-to-end OL task interesting, and agree that our core contribution, OLLM, is a novel approach to leveraging LLMs for OL. Reviewers also agree that the paper is ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EReLELA: Exploration in Reinforcement Learning via Emergent Language Abstractions | Reject | Summary: The work presents the idea of employing emergent languages (EL) abstractions combined with count-based approaches for exploration, to improve exploration in sparse reward reinforcement learning (RL) settings.
Strengths: Overall, the major strength of this paper comes from its **novelty**.
* The idea of emer... | Rebuttal 1:
Rebuttal: Once again we thank the reviewer for their time and thorough review.
We will try to address most review points below.
## Regarding the lack of insights on the learned representations:
We thank the reviewer for this comment and their curiosity towards these results. Our paper initially did not in... | Summary: This paper investigates using an emergent communication protocol as a auxiliary reward in navigation reinforcement learning setting, particularly those where exploration is a difficult (i.e., sparse reward settings). The experiments show that certain emergent communication games can be effective in solving th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thorough review. We will try to address most reviewing point below.
## Regarding the three characteristics that are particularly important to EmeCom:
We appreciate the characteristics highlighted and tend to agree with their evaluations, with the exceptio... | Summary: This paper proposes to leverage the Emergence Communication paradigm via the use of referential games to learn state abstractions for a Reinforcement Learning domain. The authors claim that using this approach, their proposed method is able to learn abstractions that boost exploration for an RL agent, and lead... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thorough review, and are grateful for their appreciation of the work. We address below the review points:
## Q1: Could we gain readability from moving ln101-113 into Section 3?
Thank you for mentioning this, ln101-113 was indeed a remainder from a previou... | Summary: EReLELA investigates whether by asking the agent to learn and describe the environment through emergent language (EL) can help with hard exploration tasks, compared to using natural language (NL) description alone.
I personally find this angle interesting and refreshing -- and the connection to count-based ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thorough review. Please refer to the global rebuttal answer extensively as most of the reviewing points raised were addressed there. We address below the remaining one.
Please let us know if there is improvement you could think of in light of our rebuttals ... | Rebuttal 1:
Rebuttal: We thank reviewers for their time and thorough reviews. We believe that their advice will greatly improve the paper.
In this global rebuttal answer, we will address the main review points, shared among reviewers. We will address the remaining points in individual rebuttals to each reviews.
## Cl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Spectral Adapter: Fine-Tuning in Spectral Space | Accept (poster) | Summary: In summary, this paper investigates advancements in Parameter-Efficient Fine-Tuning (PEFT) for pre-trained neural networks by integrating spectral information from pretrained weights, aiming to enhance the classic LoRA approach. By employing Singular Value Decomposition (SVD), the authors introduce two spectra... | Rebuttal 1:
Rebuttal: Thanks for the careful reviews. Given the rebuttal character limit, we address $\textbf{most important questions}$ here, followed by some $\textbf{other comments later}$.
$\textbf{Weaknesses 4.}$ Additionally, if the spectral space is modified, the rank will also change. This implies that the opt... | Summary: The paper proposes fine-tuning pretrained model weights in the spectral space for parameter efficiency. It explores two spectral adaptation mechanisms: additive tuning and orthogonal rotation of top singular vectors. The authors introduce these methods, providing theoretical analyses on rank capacity and robus... | Rebuttal 1:
Rebuttal: Thanks for the careful reviews. Given the rebuttal character limit, we address $\textbf{most important questions}$ here, followed by some $\textbf{other comments later}$.
$\textbf{Weaknesses 1.}$ The analysis of spectral adaptation robustness in Section 3.2 could be clearer. It would be helpful ... | Summary: In this paper, the authors proposed to modulate top-r singular vectors after performing SVD on the pretrained weights. Both theoretical analysis and experiments have shown that the proposed two types of spectral fine-tuning methods can improve the representation capacity of low-rank adapters.
Strengths: 1. Th... | Rebuttal 1:
Rebuttal: Thanks for the careful reviews. Here are our answers to the questions.
$\textbf{Weaknesses 1.}$ The proposed method have two versions, including spectral adapter-A and spectral adapter-R. However, the application boundaries of these two methods are not clear. Some experiments are applied by adapt... | Summary: The paper presents a new low rank adapter for large models. The idea is to apply the adapter in the SVD decomposition of a weight matrix. Two methods are proposed. First, train parameters that get added to top r columns of U and V matrices. Second, train parameters that rotate top r columns of U and V matrices... | Rebuttal 1:
Rebuttal: Thanks for the careful review. Given the rebuttal character limit, we address $\textbf{most important questions}$ here, followed by some $\textbf{other comments later}$.
$\textbf{Weaknesses 1.}$ LLM evaluations are few while diffusion evaluations seem to be mostly quantitative.
$\textbf{Answer t... | Rebuttal 1:
Rebuttal: Since there are both similar questions that different reviewers have asked about and some points we want to make clear to all reviewers, we summarize several important points here. We also reply individually to each reviewer with respect to their own concerns in more details. We have highlighted t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models | Accept (poster) | Summary: This paper discusses the impact of the common practice of filtering for English-only data on training vision-language models, showing that this negatively impacts performance on tasks covering diverse cultural regions and backgrounds. The work proposes foregoing this filtering step to improve this representati... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We especially appreciate their positive assessment of the importance and presentation of our work. We provide our answers to the reviewer’s raised questions and concerns below, are looking forward to their response, and h... | Summary: The paper shows that vision-language models trained solely on English filtered data displays a bias towards western-centric benchmarks. They then present a simple solution for training models that perform well on globally diverse datasets while not sacrificing performance on gold standard datasets such as Imag... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We especially appreciate their positive assessment of the originality, quality, significance, and clarity of our work. We hope that our rebuttal below addresses all of the reviewer’s questions, are happy to provide more d... | Summary: For the field of training contrastively learned-based VLM, this paper firstly displays that training from English data would lead to worse cultural diversity in zero-classification evaluation. By discarding the influence of languages in evaluation, this paper proposed a geo-localization task, which could obser... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We provide our answers to the reviewer’s raised questions and concerns below, are looking forward to their response, and hope for a favorable reassessment of our scores.
**Q1: How is the paper structured?**
We are sorry... | Summary: The paper compares performance of VLMs trained on just English data, multilingual data and the English translations of the multilingual data, on a range of benchmarks including Western-centric ones as well tasks that involve geographically diverse inputs. The paper finds that (i) training on just English data ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We especially appreciate their positive assessment of the thoroughness, importance and practicality of our work. We hope that our rebuttal below addresses all of the reviewer’s questions, are happy to provide more details... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the detailed and constructive questions and comments. We especially appreciate the positive feedback on the thoroughness of our experiments, significance of findings, and clarity of work. In the below, we answer the concerns shared by multiple reviewers. We... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AGILE: A Novel Reinforcement Learning Framework of LLM Agents | Accept (poster) | Summary: This paper jointly introduces an evaluation environment for LLM agents and an LLM agent architecture called AGILE.
The environments is a Question-Answering environment concerning the characteristics of commercial products sourced from Amazon. The benchmark itself is composed of 3 tasks: 1) a fact retrieval t... | Rebuttal 1:
Comment: We sincerely thank you for your effort and valuable comments in reviewing our paper. We appreciate your recognition of our contributions and your insightful feedback. We have addressed these concerns in the Rebuttal Section within the given time constraints to the best of our ability.
---
Please ... | Summary: This work presents a novel framework for LLM agents named AGILE. The entire AGILE system is trained end-to-end using reinforcement learning. A key feature of AGILE is its ability to seek advice from external human experts. Additionally, the authors have developed ProductQA, a challenging dataset of complex QA,... | Rebuttal 1:
Rebuttal: We sincerely thank you for your positive feedback and effort in reviewing our paper. Thank you for your constructive questions. In our response, we will quote each question and provide our answers accordingly.
## Response to comments
---
> **Q1 (Weakness 1):** It is somewhat like WebGPT (in terms... | Summary: This paper proposes AGILE, a reinforcement-learning based framework for finetuning LLMs for conversational QA tasks. The models are initially trained using imitation learning, then further finetuned with RL. Once finetuned, the models show strong performance, surpassing GPT-4 while using much smaller models.
... | Rebuttal 1:
Rebuttal: We genuinely appreciate your positive feedback and the time you invested in reviewing our paper. Thank you for your insightful questions. In our response, we will address each question individually, quoting them and providing our answers accordingly.
## Response to comments
---
> **Q1 (Weakness... | Summary: The paper "AGILE: A Novel Framework of LLM Agents" introduces a new framework for Large Language Model (LLM) agents designed to handle complex conversational tasks. The framework, named AGILE (AGent that Interacts and Learns from Environments), incorporates LLMs, memory, tools, and interactions with experts. A... | Rebuttal 1:
Comment: We sincerely appreciate your effort and valuable comments in reviewing our paper. Your recognition of our contributions and your insightful feedback are greatly valued. We have addressed your concerns in the Rebuttal Section to the best of our ability within the given time constraints.
---
Please... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback from all reviewers. We have tried our best to address each question raised in the respective reviews.
Additionally, we have conducted supplementary experiments to address certain concerns and incorporated the results in a one-page PDF file attached to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving | Accept (poster) | Summary: The paper proposes a prompt engineering method to address generalizability and consistency issues in existing prompting approaches for LLM-based problem solving. It utilizes four LLM-based agents including strategy generator, executor, optimizer, and evaluator. The proposed method works by generalizing knowled... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We address each question as follows:
> The intuition explained in the paper relies on generalization and consistency as the main traits of the proposed method. It would have been helpful to use a concrete example showing how alternative methods such as CoT ... | Summary: This work uses an LLM to describe the strategy that it should use to solve classes of problems, and then applies that strategy to the different test cases as required. In addition, there is a caching and evaluation process whereby these strategies are improved during in an in-context learning prompt training/... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We address each question as follows:
> Regarding the actual prompt and text tweak
We appreciate your suggestions. We will incorporate these revisions in the new version of our paper.
> Is the Strategy Generator the same LLM as being 'tested' - or is the s... | Summary: This paper introduces a novel prompting strategy for large language models called StrategyLLM, which it employs to solve a range of tasks in math, commonsense reasoning, word sorting, and last letter concatenation. The approach involves (1) prompting a language model to generate task-specific instance-general ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We address each question as follows:
> A simpler approach that still tests the core idea of the paper would be to keep the strategy generator and inference steps, but skip the intermediate grading and optimization of the strategies. Would the method work wi... | Summary: This paper proposes StrategyLLM, a pipeline for improving the few-shot reasoning performance. The main intuition is that when solutions to few-shot exemplars are inconsistent in terms of the reasoning process, the performance can be suboptimal compared to those with consistent solutions. Based on this intuitio... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We address each question as follows:
> The paper says that a strategy is applied when it passes an accuracy threshold, which is <1. Does it mean that when using the few-shot prompt constructed by StrategyLLM, some examples have wrong solutions?
Yes, we wou... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ProTransformer: Robustify Transformers via Plug-and-Play Paradigm | Accept (poster) | Summary: This paper proposes a robust attention mechanism to improve the resilience of transformer-based architectures. The method does not need additional training or finetuning with only four lines of code, which is simple yet effective. To show the effectiveness of the proposed mechanism, the paper conducts experime... | Rebuttal 1:
Rebuttal: Thanks for your recognition of the efficiency and effectiveness of our method. We are glad to solve134
your concerns and answer your questions with the following illustrations.
**W1**: As the paper points about one of the advantages of the method is the efficiency (efficient Newton-ISLR algorithm... | Summary: This paper proposed a robust transformer architecture named ProTransformer through a plug-and-play paradigm without further training or fine-tuning. The authors include robust token estimators in the self-attention blocks which are more resilient to the dominating impact of input tokens, and apply Newton-ISLR ... | Rebuttal 1:
Rebuttal: Thanks for your recognition of the novelty and effectiveness of our method. We are glad to solve33
your concerns and answer your questions with the following illustrations.
**W1**: Lack of analysis of clean performance in jailbreak attacks. I want to know if ProTransformer would hurt the generati... | Summary: This paper proposes an interpretable robust attention layer to robustify transformer architecture via a plug-and-play paradigm.
Strengths: The proposed method is practical and can be plugged into the given transformer as a plug-and-play layer.
The experiments are robust and conclusive, signifying the efficacy... | Rebuttal 1:
Rebuttal: Thanks for your recognition of the efficiency and effectiveness of our method. We are glad to solve134
your concerns and answer your questions with the following illustrations.
**W1**: It seems that the complexity of the proposed ProAttention is still greater than that of linear attention [1] [2]... | Summary: In this paper, the authors intend to robustify transformer architectures against adversarial attacks to enhance their resilience across various machine learning tasks. Specifically, they propose the ProAttention mechanism. They use a novel interpretation of the self-attention mechanism as a weighted least squa... | Rebuttal 1:
Rebuttal: Thanks for your recognition of the efficiency and effectiveness of our method. We are glad to solve134
your concerns and answer your questions with the following illustrations.
**W1**: We want to clarify that the proposed ProAttention is plugged into fixed pre-trained models without finetuning, ... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for the recognition of the novelty and effectiveness of our method. Since several reviewers have the concern about the efficiency of ProTransformer, we will elaborate the response as follows.
## **Response for the concern of efficiency of ProTransformer**
We want to p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ad Auctions for LLMs via Retrieval Augmented Generation | Accept (poster) | Summary: The paper considers the integration of ads into large language model (LLM) generation, as well as the design of a mechanism for ad allocation and pricing that comes with this integration. The paper introduces the notion of a "segment auction", where the output discourse is break down into various segments. Fol... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for thoughtful and detailed comments.
> The paper uses strong assumptions on the retrieval component
We agree that implementing a performant, calibrated retrieval component is a challenging engineering task in practice. However, it is well studied in the literature. We... | Summary: This paper studies an interesting and timely application of ad auctions for LLMs via retrieval augmented generation. They propose a segment auction that takes the bid and relevance as the input and outputs the price by a randomized second price auction. This auction maximizes the logarithmic social welfare tha... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable comments.
> Independent assumption
We in fact extend our independent segment auction to a more general segment auction with history dependent relevance measure in Appendix C. We will make sure to add a pointer to this part in the main body of the paper. In t... | Summary: In this paper, the authors integrate the auction mechanism into RAG LLMs for computational advertising. They propose a novel segment auction method where an auction is run to integrate single or multiple ads into each segment output of LLMs. Experiments on several auction scenarios are conducted to verify the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for detailed review. We hope that our rebuttal can address your concerns.
Note that our framework consists of several components, each of which could be further researched to improve real-world deployment. Our focus was on proposing the framework, analyzing it theoretica... | Summary: The work studies ad auctions integrated in LLM output powered by retrieval-augmented generation. The authors propose an ad auction (so-called segment auction) where an ad is put in retriever with some probability. An efficiency-fairness balance is maximized (through logarithmic social welfare). An extension to... | Rebuttal 1:
Rebuttal: We first appreciate the reviewer for insightful comments.
> About unclear selection of optimization function
The reviewer is correct that it is chosen after the allocation function of segment auction is determined. We remark that our selection of optimization functions stems from the probabilist... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback. In this section, we address the concerns regarding design choices and engineering components in our proposed framework, along with a recap over our main contributions, and further results for multi-ad segment auction.
------
### Segments an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cross-modal Representation Flattening for Multi-modal Domain Generalization | Accept (poster) | Summary: This paper extends the analysis of unimodal flatness to the domain of multi-modal domain generalization (MMDG) for the first time, and proposes the Cross-Modal Representation Flattening (CMRF) method. By constructing a shared representation space and cross-modal knowledge distillation, it addresses the issues ... | Rebuttal 1:
Rebuttal: We thank reviewer for valuable comments. We respond below to each of the concerns.
>1. The paper mentions using simple moving average to build the teacher network, but how to extract knowledge from the mixed representation and how to transfer this knowledge to each modality is not clear enough.
... | Summary: The paper addresses the problem of multi-modal domain generalization (MMDG). The authors identify two main challenges in MMDG: modality competition and discrepant uni-modal flatness and propose a novel method called Cross-Modal Representation Flattening (CMRF). This method optimizes the representation-space lo... | Rebuttal 1:
Rebuttal: We thank reviewer for valuable comments. We respond below to each of the concerns.
> 1. Are there any visualization or quantitative results to show that after CMRF training the loss landscape of different modalities get consistent flat region?
**Response**: Thanks a lot for the valuable comments... | Summary: In this paper, the authors identify two primary limitations in multi-modal domain generalization (MMDG): modality competition and discrepant uni-modal flatness. To address these challenges, they propose a novel approach called Cross-Modal Representation Flattening (CMRF). CMRF constructs interpolations by mixi... | Rebuttal 1:
Rebuttal: We thank reviewer for valuable comments. We respond below to each of the concerns.
> 1. This paper lacks a detailed discussion on the practical implementation aspects of the proposed method, such as computational requirements and scalability.
**Response:**
**1) Computational requirements:** Fi... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive feedback:
1. A fresh perspective of modality competition and discrepant uni-modal flatness for MMDG [eDBu, iu3y, 8XoV];
2. Flatness analysis within multi-modal domain generalization (MMDG) and the proposed method for flatting representation loss landsca... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health | Accept (poster) | Summary: The paper titled "A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health" introduces a novel method for improving public health resource allocation. By combining Restless Multi-Armed Bandit (RMAB) models with the interpretive power of Large Language Models (LLMs), the au... | Rebuttal 1:
Rebuttal: 1) Simulated Environment Reliance: “The paper relies on simulations for validation. Real-world trials would strengthen findings. A plan for field testing DLM in actual public health settings, including partnerships for pilot studies and addressing ethical and logistical challenges, is needed.”
Th... | Summary: This paper proposes using a decision-language model for restless multi-armed bandit tasks (RMAB) in the public health domain. The authors evaluated their method in a simulation environment developed from a real-world dataset. The authors conducted experiments with 16 different prompts and compared their approa... | Rebuttal 1:
Rebuttal: 1) Abstract Citations: “Avoid citing sources in the Abstract.”
We thank the reviewer for the suggestion. We will remove this citation from the abstract. We include this citation at the end of the introduction.
2) Real-World Validation: “The approach provided in this paper lacks real-world valid... | Summary: Restless multi-armed bandits (RMAB) are effective for resource allocation in public health but lack adaptability to changing policies. Large Language Models (LLMs) have potential in healthcare for dynamic resource allocation through language prompts but are understudied in this area. This work introduces a Dec... | Rebuttal 1:
Rebuttal: 1) “The related works section is not comprehensive…”
We thank the reviewer for the comment. There is growing research in LLMs for healthcare (see Sec. 1). In particular, a question summarization framework in healthcare domains has been proposed [1, 2]. Methods based on contrastive language image ... | null | null | Rebuttal 1:
Rebuttal: **Thank You**
We thank the reviewers for their insightful feedback and comments. We are encouraged to find that the reviewers recognized the paper as novel, introducing "a new method in this application" (R1), and specifically highlighting its “impressive originality” (R3) and “pioneering” (R1) u... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Faster Repeated Evasion Attacks in Tree Ensembles | Accept (poster) | Summary: The paper proposes a method to speed up the robustness verification of tree-based classifiers for all examples in a dataset. Previous methods solve the robustness verification problem for tree-based classifiers, which is NP-Complete, for each instance separately. However, the paper highlights that finding adve... | Rebuttal 1:
Rebuttal: **Why l-inf only?**
We choose to work with the l-inf norm as recent works on approximate evasion
attacks primarily focus on this scenario.
This favours the experimental evaluation as these methods are the most efficient in literature, while still working in a perfectly reasonable scenario where t... | Summary: This paper studies adversarial attacks on tree ensemble models. The main contribution of this paper is to speed up (compared to *kantchelian* and *veritas*, please let me know if there is any misunderstanding) the process of crafting adversarial examples of tree ensemble models. It is claimed, under the settin... | Rebuttal 1:
Rebuttal: **NNs vs Tree Ensembles**
Just because "neural network is the most popular model in modern machine learning" does not imply that all research should solely focus on them; we believe that diversity is also important.
Tree ensemble models like XGBoost are extremely popular, very easy to apply and s... | Summary: This paper proposes a new mechanism for performing computationally-efficient adversarial attacks on decision tree ensembles. Specifically, it considers the setting where an attacker wants to attack *many* samples in a dataset at the same time, and considers the *average* time to attack each sample. (The paper ... | Rebuttal 1:
Rebuttal: Thank you for the two extremely interesting and insightful suggestions! We are in the process of exploring these and we will mention these possible variations in the final version of the paper. We agree with the reviewer and we think they could be effective in some specific use cases of our algori... | Summary: This paper proposes a new method for a faster generation of adversarial attacks on tree ensembles such as XGBoost and random forest. The proposed approach has two parts: first, a subset of relevant attributes in a tree is identified. This step is inspired by an empirical. observation that on tree ensembles, mo... | Rebuttal 1:
Rebuttal: **Assume access to a subset of test examples during attack**
We specifically look at the case where somebody wants to generate **many** adversarial examples.
There are several scenarios where this is the case. In areas like phishing (or fake webshops), attackers need to generate and register many... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The main goal of this paper is to show that it is feasible to speed up the generation of adversarial examples against tree ensembles by perturbing only a small subset of the feature space. Their approach expands the current literature by offering an algorithmic alternative that takes advantage of the smaller s... | Rebuttal 1:
Rebuttal: **Originality / Impact**
The paper's impact and originality lie in the insight that we can efficiently perform evasion attacks by only perturbing a restricted set of features.
To our knowledge, there is little work on exploiting the sequential nature of performing evasion attacks, i.e., existing ... | null | null | null | null | null | null |
Improving Adaptivity via Over-Parameterization in Sequence Models | Accept (poster) | Summary: This paper investigates the influence of over-parameterization on the adaptivity and generalization of sequence models. The work highlights the significance of eigenfunctions in kernel regression and introduces an over-parameterized gradient descent method to analyze the effects of varying eigenfunction orders... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and your comments.
We appreciate that you recognize that "the theoretical results are compelling".
We would like to address your concerns in the following:
> One significant weakness of the paper is the limited experimental validation.
>
> 2.The t... | Summary: The authors analyze the generalization error for kernel regression sequence models in the overparameterized regime.
They rigorously show that due the overparameterization, the learned parameters are better adapted to the underlying structure.
They also provide some numerical experiments corroborating their fin... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and valuable comments.
We will address your concerns in the following:
> The main concern here, is that the setting is too constrained, in that the kernel map is assumed to be fixed.
In fact, the goal of our paper is to investigate the **impact of the dynamic... | Summary: This paper proposed an overparameterized gradient descent method. Its benefits on generalization has been verified both theoretically and empirically.
Strengths: 1.The paper is well-written with a clear structure. Motivations are well-explained on why the authors study the problem, and the illustrative exampl... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and recognizing the contribution of our work.
We will address your concerns in the following:
1. > The insightful explanations for why the proposed method can improve generalization are lacked.
We are sorry for not presenting it clearer.
Generally speak... | Summary: This paper investigates how over-parameterization can enhance generalization and adaptivity within the non-parametric regression framework. Drawing on insights from kernel regression and over-parameterization theory, the authors focus on the sequence model, which can approximate a wide range of non-parametric ... | Rebuttal 1:
Rebuttal: Thank you for your efforts in reviewing our paper and recognizing the contribution of our work.
We appreciate your constructive feedback and will address your concerns in the following:
> Narrow/Restrictive Settings
>
> Dynamically Evolving Kernels
We fully agree with you that the dynamic evolvi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Authors consider the problem of fitting data with given a kernel but with a different estimator than kernel regression, which is based on an overparameterized version of the gradient flow for the squared loss. This estimator is inspired by the behavior of first-order algorithms in the area of deep learning the... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable comments.
Following your advice, we will add the related papers that you mentioned.
> My major concern is that similar results to this one already exist in the literature and the authors have not compared
> their results to them and how they diffe... | null | null | null | null | null | null |
DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models | Accept (poster) | Summary: This paper proposes DreamSteerer, a plug-in method that enhances existing text-to-image personalization techniques by improving the editability of source images.
DreamSteerer proposes a novel Editability Driven Score Distillation (EDSD) objective to improve the structural alignment between the source and edit... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedbacks. Following are responses to your questions.
>**W1 Further comparison study**
We are targeting image editing with personalized concepts (not exist in vocabulary); thus, editing baselines outside of personalization are not applicable for our task.
**Compar... | Summary: The paper introduces DreamSteerer, a method to enhance the editability of source images using personalized diffusion models in text-to-image (T2I) personalization. Existing methods often fail to maintain edit fidelity when applied to new contexts due to limited reference images and adaptability issues. DreamSt... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedbacks. Following are responses to your questions.
> **W1 Comparison to Object Driven Image Editing works**
We thank the reviewer for the constructive suggestion and acknowledgement on the novelty of our method. For a detailed discussion on how our approach differs ... | Summary: Aiming at addressing unsatisfactory editability on the source image, this paper proposes a novel plug-in method for augmenting existing T2I personalization methods. Specifically, this framework finetunes the personalization parameters by training a novel Editability Driven Score Distillation objective under t... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions. Following are responses to your concerns.
> **W1 Insufficient related work of T2I personalization**
Thank you for the suggestion. We provide an additional literature review related to T2I personalization mostly from this year below:
Recent... | Summary: DreamSteerer is a proposed fine-tuning pipeline for personalized diffusion models, designed to enhance the custom editability of these models. The authors point out that naively incorporating existing image editing and personalization methods—such as employing score distillation and DDS where the differentiabl... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and questions. Following are responses for your questions.
> **W1 Benchmarking on more extensive personalization datasets**
Thank you for your feedback. Our decision to evaluate based on 16 concepts from the open repository[1] was driven by the availability... | Rebuttal 1:
Rebuttal: ## Global comment
We thank all reviewers for their great effort and suggestions. Some general clarifications are provided below for better understanding of both our task and our method. We will include these details in our revised paper.
> **Part 1**
***Connection with text-driven image editing*... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design | Accept (oral) | Summary: After rebuttal
I have upped my score to 7. I think this paper is good, as it makes a small, easy to implement and simple to understand change to existing UED methods, and it delivers improved empirical results.
-----
This paper uses a GMM-based method to quantity novelty of levels in UED. It uses the stat... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful attention to detail and the positive feedback provided. The following clarifications will hopefully address your concerns and strengthen the case for our paper:
### Weaknesses
**Q: While the focus on unordered transition tuples is mentioned as a benefit, I wo... | Summary: This paper focus on the unsupervised environment design (UED) problem, whereby a student trains in an adaptive curriculum of environments proposed by a teacher. The authors propose GENIE, a method for assessing novelty of environments, which essentially means the teacher prioritizes environments with high expl... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's feedback and the recognition of the key strengths of our method. The following clarifications will effectively address your concerns and further enhance our paper:
### Weaknesses:
**Q: There is a strong assumption that all transitions are independent, which... | Summary: This paper proposes using novelty quantification in an unsupervised environment design for training a more generalizable policy. Built on an intuition that environments with unfamiliar states are novel environments, their proposed algorithm uses Gaussian mixture models to allow an RL agent to explore novel env... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and valuable feedback. We believe the concerns raised generally pertain to broader problems in RL and are not inherent problems of GENIE. The following clarifications will explain our stance and strengthen the case for our paper. We kindly request the reviewer to ... | Summary: This paper proposes adding a domain-general metric for promoting novelty to state of the art UED methods in order to help the environment generator better explore and cover the space of environments. The novelty bonus is based on a surprise of a (state, action) pair model which is a learned Gaussian mixture m... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful comments and recognition of the valuable direction of novelty-driven autocurriculum that GENIE is pushing for the UED field. Also, it is always refreshing to engage with a reviewer who has in-depth knowledge of UED. The new results within the globa... | Rebuttal 1:
Rebuttal: # Global Rebuttal
## 1. Addressing GENIE's Name
There is a general consensus among the reviewers that "GENIE" might be confused with the method recently introduced by Bruce et. al. (2024). We agree that it would probably be wise to choose a different name for the framework, allowing both importan... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hierarchical Uncertainty Exploration via Feedforward Posterior Trees | Accept (poster) | Summary: The paper proposes a method to obtain a hierarchical representation of samples from the posterior distribution of inverse problems. This method can be integrated into any existing approach to learn and sample from the posterior distribution. It involves learning a tree structure where each node represents a pr... | Rebuttal 1:
Rebuttal: **Tree loss function and training scheme**
Thanks for pointing out this is not clear enough. We will include an appendix with the explicit training algorithm to better clarify the loss function and our overall training scheme. Kindly note that we trained our models from scratch on a standard da... | Summary: This work proposes a technique to predict a tree-structured hierarchical summarization of a posterior distribution using a single forward pass of a neural network. The technique is an amortized hierarchical version of the oracle loss in multiple choice learning. Experiments show the method is effective at hier... | Rebuttal 1:
Rebuttal: **Comparing runtime in neural function evaluations vs GPU seconds**
Please see Table 1 in the rebuttal PDF where runtime is reported in seconds. As mentioned in checklist item 8 (Experiments Compute Resources), we reported neural function evaluations (NFEs) as an architecture-agnostic measure. H... | Summary: This work proposes to solve the problem of quantifying and visualising the uncertainty in the solutions of ill-posed inverse problems like image-to-image translation, image re-construction, inpainting, etc. The paper proposes to do so by using 'Posterior-trees' where the authors make use of the result that opt... | Rebuttal 1:
Rebuttal: **Providing more background on CVT and the results of \[45\]**
We thank the reviewer for bringing this to our attention. In the camera-ready version, we will include an additional appendix summarizing the main results of \[45\] upon which we build.
**Tradeoff between breadth and depth of the con... | null | null | Rebuttal 1:
Rebuttal: Updated runtime table in seconds
Pdf: /pdf/25fe6a74ceb9d960e6beb85ea192cb6d004a607d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Direct Unlearning Optimization for Robust and Safe Text-to-Image Models | Accept (poster) | Summary: The paper proposes a new method for unlearning diffusion-based generative models. The proposed method uses the basic idea of reinforcement learning with human feedback. To collect pairs of images to be unlearned and corrected for preference optimization, the author uses SDEdit to modify the NSFW content. Exper... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for recognizing the novelty and effectiveness of our approach in using SDEdit for curating paired images.
---
> *[W1] Novelty of Technical Contribution*
We want to clarify that our main contribution is proposing **a new image-based unlearning framework** t... | Summary: This paper proposes a diffusion unlearning optimization framework to achieve NSFW visual content removal in T2I models. Specifically, the authors develop an image-based unlearning method that utilizes curated paired image data (unsafe images and their corresponding safe images generated by the SDEdit model) fo... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our paper's clarity and the novelty of connecting preference optimization to unlearning.
---
> *[W1] Applicability of DUO to Diffusion Transformers*
We appreciate your inquiry about DUO's applicability to other architectures. We have successfully extended our evaluat... | Summary: The authors introduce synthesized image data and preference optimization for concept unlearning in diffusion models. Additionally, they consider the regularization of model preservation performance to ensure a balanced approach.
Strengths: 1. The presentation is clear and well-structured.
2. There is a solid ... | Rebuttal 1:
Rebuttal: First, thank you for acknowledging that our paper is clear and well-structured.
---
> *[W1] Impact of the number of synthesized image pairs on DUO performance*
Thank you for your constructive comment. Figure R7 from the global rebuttal PDF demonstrates how varying the number of synthesized imag... | Summary: The authors address the issue of adversarial attack to diffusion model generating inappropriate image contents in this paper, critiquing the previous work on unlearning technique unlearns harmful prompt but making themself vulnerable to adversarial prompt. Instead they propose the image-based unlearning techni... | Rebuttal 1:
Rebuttal: First, thank you for your recognition of our paper's clear structure and strong evaluation results.
---
> *[W1] Construction of Violence Unlearning LoRA*
We appreciate the opportunity to clarify this process. As you correctly surmised, we applied DUO independently to four subcategories of viole... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback, which we will integrate into the final manuscript.
We appreciate the acknowledgment of our paper's clear structure and presentation (Ub4z, tvVC, rmhF), the novelty of connecting preference optimization to unlearning (rmhF), the use of SDEdit ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AverNet: All-in-one Video Restoration for Time-varying Unknown Degradations | Accept (poster) | Summary: This paper studies the time-varying unknown degradations in videos and proposes an all-in-one video restoration network to recover corrupted videos. Specifically, the network consists of two modules named PGA and PCE, which are designed to address the pixel shifts issue caused by time-varying degradations and ... | Rebuttal 1:
Rebuttal: **Q1：Evaluation on degradations with variable intervals.**
As suggested, we conduct new experiments by synthesizing new test sets with variable degradation intervals. The results show that our method effectively handles degradations with variable intervals. Specifically, the test sets are synthes... | Summary: The authors propose a prompt learning based framework for all-in-one video restoration with time-varying degradations. Their work employs a prompt-guided alignment module to overcome pixel shifts caused by time-varying degradations. Multiple unknown degradations are learned through a prompt-conditional module.... | Rebuttal 1:
Rebuttal: **Q1&Q2：Increase the severity of degradations over time.**
As suggested, we conduct new experiments by synthesizing four test sets with progressively worsening degradations and the results prove the effectiveness of our method. Specifically, in the first test set, different types of degradations ... | Summary: The paper considers the problem of all-in-one restoration in videos, which is fundamentally different from images due to time-varying notion of degradations affecting the videos. The paper proposes prompt based modules to condition the restoration of frames on.
Strengths: **S1.** The paper extends the problem... | Rebuttal 1:
Rebuttal: **Q1：Effectiveness of prompts in longer video Set8 with complex degradation changes.**
Actually, our prompt-based AverNet is more effective in dealing with complex degradation changes in longer video Set8. To highlight how well the prompts can adapt to changing degradations, we synthesize new tes... | Summary: This paper presents a video restoration method capable of addressing time-varying unknown degradations (TUD) with a single model. The proposed method employs two modules, i.e., the prompt-guided alignment (PGA) module and the prompt-conditioned enhancement (PCE) module in the propagation to leverage the tempor... | Rebuttal 1:
Rebuttal: **Q1: Evaluations on the realistic video dataset.**
We further evaluate the effectiveness of our pipeline and network on realistic video dataset VideoLQ [1]. The results show that the models trained on our pipeline generalizes well on the realistic degradations. As shown in Table 1, our network ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Variational Flow Matching for Graph Generation | Accept (poster) | Summary: This paper proposed a variant of flow matching for graph generation from a variational perspective, called CatFlow. The main idea is not to estimate the marginal velocity, but instead to estimate the posterior first and then evaluate the marginal velocity as the expectation of the conditional velocity w.r.t. t... | Rebuttal 1:
Rebuttal: Dear reviewer vH9B,
We would like to thank you for the useful questions and positive words about our work. We will first answer each question and then propose concrete changes to the paper to address them in the final version.
> The main weakness of the paper is that the core idea of learning t... | Summary: This work presents variational flow matching framework VFM and introduces CatFlow, an application to generation of categorical data such as graph. The paper reformulates flow matching using variational perspective and with linear assumption on the conditional vector field achieves tractable objective. The auth... | Rebuttal 1:
Rebuttal: Dear reviewer WAZn,
We would like to thank you very much for your positive comments and thoughtful questions about our work. We will first answer each question and then propose concrete changes to the paper to address them in the final version. Since some questions are related, we grouped them to... | Summary: This paper introduces a new variational inference framework of flow matching with a focus on applying the framework on discrete data generation. Instead of using the squared norm in standard flow matching, the paper proposes a variational distribution to the conditional path, which is used in the vector field.... | Rebuttal 1:
Rebuttal: Dear reviewer 5EeZ,
First of all, we would like to thank you very much for your positive words and useful points about clarity. We will first answer each question and then propose concrete changes to the paper to address them in the final version.
> In standard flow matching, one needs to minimi... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thoughtful reading of the manuscript and their detailed comments. We are happy to hear that reviewers overall appear in agreement that this is a clearly written paper that provides a novel variational perspective on flow matching, develops useful con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
State Space Models on Temporal Graphs: A First-Principles Study | Accept (poster) | Summary: The paper proposes an approach for processing discrete-time temporal graphs via an extension of state-space models (SSMs). In this direction, the main contribution of the paper is a generalization of the HIPPO framework for graph structured data (named GHIPPO), which defines how the state of different nodes sh... | Rebuttal 1:
Rebuttal: We appreciate your valuable and insightful comments, and we provide our responses below.
**W1: heterophilic graphs issues**
Thank you for your question. We agree with you that equation (3) reflects a prior belief that the node representation generated by GHiPPO should be homophilic under the rep... | Summary: The paper introduces GRAPHSSM, a novel state space model framework for temporal graphs. GRAPHSSM extends state space models (SSMs) by incorporating structural information through Laplacian regularization to handle the dynamic behaviors of temporal graphs. The framework aims to overcome limitations of recurrent... | Rebuttal 1:
Rebuttal: **W1: Missing comparison of related works [1,2] and advanced methods**
Thank you for your suggestions. We have throughly reviewed the literature in temporal graph learning and have awared of several advanced works such as [3] and [4]. However, to our best knowledge, they are focusing on the **con... | Summary: This paper investigates SSM theory to temporal graphs by integrating structural information into the online approximation with laplacian regularization term.
Strengths: 1. The proposed method has the theoretical support to show the effectiveness of the proposed method.
2. The experimental results show that th... | Rebuttal 1:
Rebuttal: We appreciate your positive reviews. Your concerns are addressed as follows:
**W1 & Q1: Parameter size of GraphSSM and baseline models**
Thank you for your insightful suggestion. Per your suggestion, we have now included the comparison of parameter sizes between GraphSSM models and baselines on ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Tractable Inference Perspective of Offline RL | Accept (poster) | Summary: This paper considers the RvS setting, where a sequence model is learned and used for a control-as-inference policy extraction. Naive sequence models fail due to overly optimistic reward-to-go in stochastic environments. It is possible to alleviate this with rejection sampling, but this suffers from the curse o... | Rebuttal 1:
Rebuttal: ### Comment #1: whether beam search is important in Trifle
we will first clarify the relationship between Trifle and beam search. Then we conduct a comprehensive ablation study on beam search algorithms to confirm Trifle's superiority.
As mentioned in the general response, the key insight of Tri... | Summary: The paper provides empirical evidence that sequence models are able to identify promising actions, but that their policies at inference-time can be suboptimal. The paper proposes to use a tractable probabilistic model to bias the generated action sequences towards optimal ones. The paper provides fairly extens... | Rebuttal 1:
Rebuttal: ### Comment #1: comparison with models augmented by Q-function learning components (e.g., QDT)
We compare the TT-based Trifle with QDT in Appendix 4.1, and DT-based Trifle with QDT in **Table 3(a)** of the rebuttal PDF. The results demonstrate that DT-based Trifle significantly outperforms QDT, s... | Summary: This paper studies offline reinforcement learning and argues that tractability, e.g., the ability to answer probabilistic queries, is important for performance improvement. As a result, the authors propose a model that utilizes modern tractable generative models to answer arbitrary marginal/conditional probabi... | Rebuttal 1:
Rebuttal: ### Comment #1: connection between rejection sampling and the correction terms, and the corresponding computational complexity
We thank the reviewer for their constructive feedback. As mentioned in the general response, Trifle can be applied to many existing RvS algorithms (e.g., TT, DT) to mitig... | Summary: The paper introduces Trifle (Tractable Inference for Offline RL) that leverages Tractable Probabilistic Models (TPMs) to enhance the performance of offline RL tasks. The paper emphasizes that beyond the expressiveness of sequence models, tractability--efficiently answering probabilistic queries--is crucial for... | Rebuttal 1:
Rebuttal: ### Comment #1: practical implications of the theoretical guarantees in Secs. 4.1 and 4.2
Thanks for the constructive comment. The theoretical results are used to elaborate two key inference-side challenges. Both challenges are introduced in the general comment, and we provide further details in ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback and for acknowledging our paper as novel, well-presented, and comprehensively evaluated. We summarize common questions and concerns raised by the reviewers in the following.
**Key technical differences of Trifle compared to other RvS or offl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification | Accept (poster) | Summary: The authors presents a study on the RM in RLHF. Theoretical results of the paper assume that the reward from the RM consists of a true reward plus a noise term which is modeled as a random variable. Under this model the authors show that
- If the noise terms are heavy-tailed there exists policies with vanishi... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful criticism. We considered writing more about how asymptotic results relate to real life, and will do so in the next version of the paper. We additionally hope that this addresses your concerns:
* *Asymptotic vs bounded/clipped:* With a bounded RM the asymptotic results wou... | Summary: This paper analyzes a phenomenon called "catastrophic Goodhart": in RL training, suppose the learned reward function is the sum of the true utility function and some noise, then if (1) the true utility and the noise are independent and light-tailed, maximizing reward with KL regularization can also give high u... | Rebuttal 1:
Rebuttal: Thanks for the positive feedback. To address the weaknesses and questions:
* *Implicit regularization:* we acknowledge this as a weakness, and have some ideas for how implicit regularization can prevent Goodharting. But there is not really any evidence yet, and we would be excited to see follow-up... | Summary: This paper investigates the effectiveness of using KL divergence for regularization in reinforcement learning from human feedback (RLHF), particularly when dealing with reward functions that have misspecified errors. It introduces the concept of "catastrophic Goodhart," a scenario where policies can achieve ex... | Rebuttal 1:
Rebuttal: Thank you for your review, which gives several intriguing suggestions for experiments, as well as presentation improvements.
As for readability and clarity, we have made various edits in the latest revision of the paper, including streamlining the background section for readers unfamiliar with AI... | Summary: The paper first considers a theoretical stylized model of jointly distributed utility and (mis-specified) rewards and proves a novel result that for any heavy tailed reference distribution, it is possible to find another distribution that arbitrarily approximates it in terms of the KL divergence, yet has an un... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising several questions and highlighting where the paper lacks clarity.
1. *On reparameterizing rewards to change whether they are heavy-tailed:*
Reward can be reparameterized; however, in settings where the true reward is heavy-tailed, making reward artificiall... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for their thoughtful feedback, constructive criticism, and recognition of our paper's contributions. We appreciate the time and effort invested in evaluating our work and suggesting improvements.
The reviewers unanimously agreed the paper is technically sound and th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Coupled Mamba: Enhanced Multimodal Fusion with Coupled State Space Model | Accept (poster) | Summary: This paper extends the state space model Mamba into the multi-modal domain. The authors propose utilizing separate Mamba blocks to process each modality and suggest conditioning the state of each modality on the others to facilitate modality interaction. They further introduce a parallelism technique for the c... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful assessment of our paper; we are deeply grateful for the time and expertise you dedicated.
**W1: Parallelism through global convolution:**
S4 relies on a global convolution kernel to achieve parallelism, while Mamba iterates on Equation 3 to compute int... | Summary: This paper proposes the coupled mamba to address the problem of multimodal data fusion. The core architecture of the coupled mamba is derived in the form of Equation (6) by improving upon Equation (5). Extensive ablation experiments validate the effectiveness of the proposed coupled mamba.
Strengths: The arti... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments and feedback, and we are deeply grateful for the time and expertise you dedicated to reviewing our paper..
**W1: Incomplete and inconsistent references**
We thank the reviewer for pointing out this issue. We will carefully proofread and correct... | Summary: The paper addresses the challenge of multi-modal fusion in deep learning. Current fusion methods struggle to capture complex intra- and inter-modality correlations. Recent state space models like Mamba show promise but are limited in fusing multiple modalities efficiently. The paper propose Coupled Mamba, key ... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments and feedback, and we are deeply grateful for the time and expertise you dedicated to reviewing our paper..
**W1: Provide insights over combining Mamba branches for multi-modal fusion**
Effective multi-modal fusion hinges on balancing inter-moda... | Summary: This work proposes Coupled SSM (State Space Models) to fuse multiple modalities effectively with SSM. Instead of fusing multi-modal features directly, the proposed method couples state chains of multiple modalities while maintaining the independence of intra-modality state processes. Specifically, they first p... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful comments and feedback, and we are deeply grateful for the time and expertise you dedicated to reviewing our paper..
**W1: The improvement in the regression task on CMU-MOSEI seems marginal**
The CMU-MOSEI dataset mostly consists of short sequences: most... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback and appreciate the time and expertise they have dedicated to evaluating our work. We are encouraged by the positive comments highlighting the strengths of our approach: "superior and consistent performance" (JRDw, pbQ3, FSJZ, qhp2, nXDG), "demon... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a coupled mamba model for multi-modal fusion. The multi-modal hidden states are fused inside of Mamba, so the current state learns not only from a single modality but the correlation of all modalities. The experiments on multi-modal sentiment analysis show that the proposed model outperfor... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful feedback. We genuinely appreciate the effort and expertise you invested in reviewing our paper.
**W1: Lack of speed and memory analysis**
We thank the reviewer for this insightful feedback. In our rebuttal PDF, we have included a comparison of memory us... | null | null | null | null | null | null |
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization | Accept (poster) | Summary: This paper introduces QVPO, a new model-free algorithm that trains a diffusion policy online. It proposes a Q-weighted VLO loss by weighting the original diffusion model objective with Q-values. To address the issue of negative Q-values, the algorithm uses advantage instead. QVPO also encourages exploration by... | Rebuttal 1:
Rebuttal: We thank the reviewer for your valuable feedback and comments! We itemize the weaknesses you mentioned and answer them.
> **Q1**: While Figure 2 is informative in demonstrating the ideal effect of the entropy term, experimental results on toy examples would provide stronger evidence than an illus... | Summary: The paper proposes a novel model-free online reinforcement learning (RL) algorithm called Q-weighted Variational Policy Optimization (QVPO), which leverages the expressiveness and multimodality of diffusion models. By introducing Q-weighted variational loss and entropy regularization, the authors aim to overco... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her careful reading and valuable suggestions. Below we will answer your concerns point-by-point.
> **Q1**: The experiments are limited to D4RL datasets, which do not cover the full spectrum of possible RL environments. Can the proposed algorithm be adapted for tasks ... | Summary: The paper uses a diffusion policy to address online RL. The method works by weighting the diffusion model VLO loss using advantages computed with a Q function. Additionally, they add an entropy maximization term to aid with exploration. The authors present results indicating strong performance on a variety of ... | Rebuttal 1:
Rebuttal: > **Q1**: You make claims about DIPO and QSM that don't seem to be proven anywhere. You really should compare to QSM.
**A1(DIPO)**: **We did compare QVPO with DIPO in Figure 4 (original paper)**. Besides, as mentioned in lines 110-114, DIPO creates a dedicated buffer for diffusion policy, update... | Summary: The method proposes using Q-weight diffusion loss to train agents in online RL. Instead of approximating log probability, the paper uses advantage weight tuning to maintain the entropy of the actor.
Strengths: The paper is well-organized, and the method is straightforward but effective. I found Theorem 1 inte... | Rebuttal 1:
Rebuttal: > **Q1**: My main concern is that the design of equivalent Q-weight transformation functions is heuristic and may need some theoretical intuition or backup.
**A1**: Thank you for raising the concern. Here is the theoretical proof to show the convergence of QVPO with qadv weight transformation fun... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful reading of considerate and meaningful suggestions to help us improve our paper. We sincerely appreciate that the reviewers find our work "straightforward but effective" (nUs6), "a significant work" (Kypj, Qp9e), "novel and innovative as the first to apply t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning | Accept (poster) | Summary: This work concerns deep learning for survival analysis. Deep learning models for survival analysis are typically desired to have good discriminative performance, where a model can differentiate between patients of different risk profile, and calibration performance, where the time-to-event is accurately predic... | Rebuttal 1:
Rebuttal: # [ Response to Reviewer VjbS ]
We thank the reviewer for the valuable suggestions on our work. We have addressed the reviewer’s comments in our response below.
## (Details of Table 1)
- Mean and Standard Deviation: The values in Table 1 represent the mean performance metrics, with standard deviat... | Summary: The authors study discrete-time survival analysis, proposing to train models using a loss function that combines the NLL loss and a modified NCE loss. This NCE loss is modified to take the survival outcomes (event times) into account, mitigating the effect of potential false negatives that have small event tim... | Rebuttal 1:
Rebuttal: # [ Response to Reviewer 1ZZS ]
We thank the reviewer for the positive feedback on our work. We have addressed the comments in the updated manuscript and provided a point-by-point response below.
## (Technical Novelty about ConSurv)
ConSurv enhances the discriminative power of survival models by ... | Summary: The paper proposes a contrastive learning loss to regularize maximum likelihood learning of discretized survival times. The main contribution of this work is the utilization of the Laplace kernel to weigh negative pairs inversely proportional to their time difference from the anchor sample, while also accounti... | Rebuttal 1:
Rebuttal: # [ Response to Reviewer Lfcy ]
We thank the reviewer for the valuable suggestions on our work. We have addressed the reviewer’s comments in our response below.
## (Sensitive Analysis of the $\alpha$)
Introducing a margin prevents a comparable pair of samples with distant (unobserved) event times ... | Summary: This paper presents an approach to handle survival analysis datasets using a method to improve both calibration and discrimination. This adds a bunch of novelties to the field like handling right-censoring and an SNCE loss to handle calibration and ranking simultaneously (NLL + contrastive loss). This paper pr... | Rebuttal 1:
Rebuttal: # [ Response to Reviewer RNpF ]
We thank the reviewer for the positive feedback on our work. We have included the details of censoring in the General Response Section.
## (Missing Experiments on Unstructured Data)
Unfortunately, despite the potential of the contrastive learning framework utilized... | Rebuttal 1:
Rebuttal: # [ General Response to the Reviewers ]
We thank the reviewers for taking their valuable time to provide insightful comments and suggestions for the paper. We believe the thoughtful reviews and recommendations have substantially improved the quality of the paper. In this response, we aim to addres... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization | Accept (poster) | Summary: The authors propose a zero-th order algorithm for Stochastic Compositional Optimization (SCO) problems. Such problems are given in the form of a composition of two functions, each of them being random depending, and the corresponding objective should be minimizer in expectation. The authors consider the nonsmo... | Rebuttal 1:
Rebuttal: We thank the reviewer VEqR for your insightful and detailed review. Here we would like to address your concern.
**Q1:** What are the precise references to the paper of Lin et al.?
**A1:** Lemma 3.7 refers to Proposition 2.3 of Lin et al. [30], and Lemma 3.8 refers to Lemma D.1 of Lin et al. [30... | Summary: This paper investigates stochastic compositional optimization (SCO) problems, which are popular in many real-world applications. The authors focus on nonconvex and nonsmooth SCO, and propose gradient-free stochastic methods for finding the $(\delta,\epsilon)$-Goldstein stationary points of such problems with n... | Rebuttal 1:
Rebuttal: We thank the reviewer nNfG for your detailed review.
**Q1:** The authors utilize variance reduction technique to accelerate their algorithms. However, they do not review some important related work on variance reduction in this paper, e.g., SVRG, STORM and so on.
**A1:** Thanks for the suggestio... | Summary: This work proposes two zeroth-order methods (including one variance-reduced method) for solving non-convex non-smooth stochastic compositional optimization (SCO). These two methods are further extended to solving convex non-smooth SCO. Theoretical analysis are provided to show the convergence guarantee of all ... | Rebuttal 1:
Rebuttal: We thank the reviewer ZyfL for your insightful review.
**Q1:** My main concern is in the novelty of the proposed methods and their convergence analysis. Base on my understanding, the proposed four methods are extensions of the existing work [31].
**A1:** This is the first work that proposes st... | Summary: This paper studied the zero-order method for computing an approximately stationary point for a Lipschitz function with a composition structure. The main difficulty lies in the function value evaluation. The composition structure involves multiple expectations, requiring multiple rounds of sampling to obtain a ... | Rebuttal 1:
Rebuttal: We thank Reviewer iZH7 for your careful review and insightful suggestions.
**Q1:** I would recommend specifying the distribution $P$ in the main text, rather than in the statement of Lemma 3.7. It seems the definition of $f_\delta$ appears in L134, with a very general distribution P following. T... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks | Reject | Summary: The paper titled "Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks" proposes a new approach to enhancing the security of Federated Learning (FL) systems. The paper identifies that existing FL defenses are inadequate against adaptive and mixed attacks. To address thi... | Rebuttal 1:
Rebuttal: 1. computationally intensive
We stress that our proposed method deals with mixed attacks of unknown and uncertain types, which is beyond the scope of other baselines that focus on specific attacks. Since our problem setup is more complicated, it is not surprising that more computational resources... | Summary: The paper presents a game theoretic model for robust federated learning. The technique is composed of pre-training and online adaptation. During pre-training, a meta-policy for the defender is solved as a Bayesian Stackelberg Markov game. The defense policy is further polished during the online adaptation stag... | Rebuttal 1:
Rebuttal: Please refer to rebuttal to Reviewer a9ia for privacy concern. We have address the minor comments. We currently didn't calculate the statistical significance (need more computation resources). In most experiments, we fix initial model and all random seeds for fair comparisons. Client-side defense ... | Summary: The authors propose a defense mechanism in federated learning that has adaptability inspired from meta learning. The authors formulates a Bayesian Stackelberg Markov game (BSMG), focusing on addressing poisoning attacks of unknown or uncertain types. The authors propose an equilbrium inspired by meta learning ... | Rebuttal 1:
Rebuttal: 1. privacy concern
Please refer to rebuttal to Reviewer a9ia
2. meta-Stackelberg equilibrium
Indeed, the core of the proposed meta-Stackelberg framework lies in the meta-Stackelberg equilibrium (meta-SE). The essence of meta-SE is to create the strategic adaptation in the interim stage (online)... | Summary: This paper considers the problem of backdoor/poisoning attacks in federated learning (FL). In this setting, a single attacker has control over all malicious clients trying to employ different attack types (on each controlled client). This paper aims to create a defense mechanism against such adaptive attackers... | Rebuttal 1:
Rebuttal: 1. practicality of the assumptions
(1) In the conclusion of the paper, we have discussed the potential privacy issue of our approach, pointed out our initial efforts to mitigate this concern, and outlined a future direction to address it in a more principled way (i.e., client-side defense). We al... | Rebuttal 1:
Rebuttal: We extend our heartfelt gratitude to the reviewers for their invaluable questions, insightful comments, and constructive suggestions. We look forward to your inspiring thoughts. While the detailed responses are attached to reviewers' comments, we summarize some key updates and revisions here.
W... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper addresses the vulnerabilities of Federated Learning (FL) systems to various adversarial attacks, including model poisoning and backdoor attacks. The proposed solution is a Meta Stackelberg Game (meta-SG) framework designed to offer robust and adaptive defenses against these attacks. The approach form... | Rebuttal 1:
Rebuttal: 1. meta-SE vs BSE
We compare the BSE policy $\theta_{BSE}$ and the meta-SE $\theta_{meta}$ from an information feedback viewpoint. The BSE policy uses the current global model $s^t=w^t_g$ to determine the defense action: $\pi_\mathcal{D}(a^t_\mathcal{D}|s^t, \theta_{BSE})$. This policy is Markovi... | null | null | null | null | null | null |
Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images | Accept (poster) | Summary: This paper proposed a new image cloaking approach, which adds adversarial noise on single-view image and makes TGS-based 3D reconstruction fail. This can be served as a watermark for protecting copyright image assets.
Strengths: The topic is popular and needs more investigation by the community. The paper wri... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your valuable feedback and suggestions.
**Response to W1: Extending to other methods**
Our method is designed to utilize the explicit geometry feature in GS-based single views to 3D methods, which is fragile and susceptible to disturbances in the reconstruction pro... | Summary: This paper proposes a novel method to protect copyrighted images from unauthorized 3D reconstruction using Triplane Gaussian Splatting (TGS). This topic is very interesting and highly valuable for preventing the misuse of copyrighted images. Their proposed method achieves protection by incorporating invisible ... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your recognize and valuable suggestions.
**Response to W1: Impact on image quality**
Our geometry cloak is designed to be invisible so legitimate users can have visually consistent results with the original image quality. All perturbations are controlled within a... | Summary: The paper introduces a novel approach to protect copyrighted images from unauthorized 3D reconstructions using Triplane Gaussian Splatting (TGS). The method involves embedding invisible geometry perturbations, termed "geometry cloaks," into images. These cloaks cause TGS to fail in a specific way, generating a... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your recognition and valuable suggestions.
**Response to W1: Extending to other methods**
Our method is designed to utilize the explicit geometry feature in GS-based single views to 3D methods, which is fragile and susceptible to disturbances in the reconstruction ... | Summary: The paper introduces a novel image protection approach called "Geometry Cloak" to prevent unauthorized 3D model generation from copyrighted images using single-view 3D reconstruction methods like Triplane Gaussian Splatting (TGS). The Geometry Cloak embeds invisible geometry perturbations into images, which ar... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your recognition and valuable suggestions.
**Response to W1: Extending to other methods**
Our method is designed to utilize the explicit geometry feature in GS-based single views to 3D methods, which is fragile and susceptible to disturbances in the reconstruction p... | Rebuttal 1:
Rebuttal: Dear reviewers,
We would like to thank all the reviewers for their time and for writing thoughtful reviews of our work.
In this work, we introduce the geometry cloak, which can effectively manipulate the process of 3DGS-based single-image to 3D methods by adding invisible perturbation. We reveal... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a method for copyrights in 3D reconstruction, specifically targeting novel-view synthesis, rather than traditional 2D images. Recently, advancements in 3D reconstruction have been driven by neural radiance fields (NeRFs) and 3D Gaussian splatting (3D GS), both of which maintain 3D consisten... | Rebuttal 1:
Rebuttal: Dear reviewer,
We express our gratitude for your recognition and valuable suggestions.
**Response to W1: Extending to other methods**
Our method is designed to utilize the explicit geometry feature in GS-based single views to 3D methods, which is fragile and susceptible to disturbances in the... | null | null | null | null | null | null |
Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization | Accept (poster) | Summary: This paper introduces a novel approach to gradual domain adaptation using distributionally robust optimization (DRO). The core idea is to adapt models across successive datasets by controlling the Wasserstein distance between distributions and ensuring they lie on a favorable manifold. The authors apply the... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback. The reviewer's main concern is the applicability of our method in real-world tasks which we have addressed in the camera-ready version. Below, we provide detailed responses to each of the reviewer's comments and concerns:
----
**Weaknesses*... | Summary: This paper studies the theoretical aspect of gradual domain adaptation (GDA), where the knowledge of labeled source domains is supposed to be transferred to a sequence of target domains. The main results show that the gradual adaptation process can be well characterized by the distributionally robust optimizat... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback. The reviewer's main concern was the lack of sufficient discussion on certain aspects of the paper, which we have addressed in the revised/camera-ready version. Below, we provide detailed responses to each of the reviewer's comments and concer... | Summary: This paper proposes an optimization paradigm for gradual domain adaptation by iteratively performing manifold-constrained Wasserstein DRO and pseudo-labeling on the sequence of domains. The error propagation is theoretically investigated by a compatibility measure $g(\eta)$ between the manifold of distribution... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive feedback. We have addressed the remaining concerns as follows:
**Weaknesses**:
- **At the end of proof of theorem 3.1, the author says that one can only upper bound $\min\\{\mathbb{E}[\ell(x, y)], 1- \mathbb{E}[\ell(x, y)]\\}$, which is much... | Summary: The paper presents a new approach to gradual domain adaptation using distributionally robust optimization (DRO). This approach provides theoretical guarantees for model adaptation across successive datasets by bounding the Wasserstein distance between consecutive distributions and requiring that these distribu... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive feedback. We have addressed the remaining concerns as follows:
**Weaknesses**:
- **Although this is a theoretical paper, the experimental study is very limited**: We have expanded the experimental section significantly (please refer to the g... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their thoughtful comments and feedback. As some questions and concerns were raised by multiple reviewers, we have provided a global response.
Some reviewers expressed concerns regarding the implementability of our method and its perf... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Value of Reward Lookahead in Reinforcement Learning | Accept (spotlight) | Summary: The paper investigates theoretically the advantage that RL agents get from reward lookahead. In a tabular MDP setup, the reward is postulated as being a random variable $R_h(s, a)$, whose value is, by default, revealed to the agent after taking the action $a$ in state $s$ at a time-step $h$. Authors calculate ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and apologize for any clarity issue; we will consider adding a central example to improve the clarity of the introduction.
Sadly, due to the page limit, we had to move to the appendix some parts of the proof that we also find essential - we intend to use all... | Summary: This paper examines the value of having lookahead information about future rewards in reinforcement learning. Specifically, it analyzes the competitive ratio between the optimal agent under no lookahead versus agents that can see reward realizations for some number of future timesteps. This competitive ratio i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback.
* **Perfect future information:** as stated in the conclusions section, we agree that situations with noisy/imperfect predictions are of great interest and should be further investigated in future work. Nonetheless, we believe that the case of perfect infor... | Summary: This paper aims to quantifiably analyze the the value of future reward lookahead in Reinforcement Learning settings where future reward information is available before-hand. The authors utilize competitive analysis, and characterize the worst-case reward distribution while also deriving exact ratios for the wo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments.
1. Thanks for the comment. To the best of our knowledge, there are two types of rollout approaches that are applied in control/RL: i) Rollout as a tool to perform planning: in this case, it is a computational scheme, and no future information is ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Night-to-Day Translation via Illumination Degradation Disentanglement | Reject | Summary: This paper presents an approach, namely N2D3, for night-to-day image translation. Specifically, the proposed pipeline involves two stages: illumination degradation disentanglement and degradation-aware contrastive learning. The first stage decomposes an image into darkness, well-lit areas, light effects, and h... | Rebuttal 1:
Rebuttal: ***Q1: Omission of a key citation.***
**R1:**
We apologize for the omission of this key citation, which has caused confusion. We will ensure that the key citation is included in the revised version.
***Q2: Why are four types of degradation considered?/What is the rationale behind categorizing d... | Summary: The paper proposes a new framework N2D3 for solving night to day image translation problem. Their framework consists of a physics-based disentanglement module and a contrastive learning module for preserving semantic consistency. Their method shows improved performance in terms of FID and downstream task perfo... | Rebuttal 1:
Rebuttal: ***Q1: Clarity of the method sections can be improved. For instance, including more rigorous definitions or visualizations of what well-lit and different light effects mean and provide a motivation why it is helpful to disentangle those illumination causes separately.***
**R1:** Thanks for the ad... | Summary: This paper presents a comprehensive solution for Night2Day image translation by leveraging physical priors, photometric modeling, and contrastive learning, leading to state-of-the-art performance in visual quality and downstream vision tasks.
Strengths: The authors develop a photometric model based on Kubelka... | Rebuttal 1:
Rebuttal: ***Q1: The writing is difficult to understand. The explanations and derivations for Eqs (1) to (5) lack logical coherence and necessary references, making them hard to follow. The derivations for Eqs (7) to (9) also lack supporting references, casting doubt on their validity.***
**R1:** We apolog... | Summary: This paper proposes to address the night-to-day translation problem in which its learning basically can be briefly described by two steps: 1) illumination distribution as well as the physic priors built upon the Kubelka-Munk photometric model are firstly adopted to separate/disentangle the image regions into f... | Rebuttal 1:
Rebuttal: ***Q1: Although experimentally shown to be effective, the mechanism and the basic ideas behind leveraging the illuminance distribution as well as the physic priors for realizing disentanglement of four degradation categories (i.e. darkness, well-lit, light effects, and high-light) are not well ex... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for meticulously evaluating our paper.
First of all, we promise to add the missing citation in the revised version and apologize for our oversight. While we employ the color invariant from [1], we are the first to discuss its characteristics in nighttime scenes and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Cryptographic Hardness of Score Estimation | Accept (poster) | Summary: This paper investigates the difficulty of distinguishing a Gaussian pancake distribution from a Gaussian distribution from the perspective of score estimation. The authors show that, by assuming the $L^2$-error of the score estimation is of the order $\log(d)$, there is a polynomial-time algorithm that solves ... | Rebuttal 1:
Rebuttal: We thank the reviewer for clearly communicating points of confusion and giving us the opportunity to clarify our results. Before providing any clarifications, we would like to mention that the reviewer’s summary below accurately and succinctly captures the main contribution of our paper.
> *I gue... | Summary: This paper shows that $L^2$-accurate score estimation, a crucial primitive in the theory of diffusion models and sampling, is computationally hard in the worst-case. The main theorem is a negative result that provides a statistical-computational gap: if computationally efficient $L^2$-accurate score estimation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the detailed review of our paper. The reviewer's summary effectively and accurately captures the essence of our results. We address elements of the review below, which highlight the strengths of our paper and pose insightful questions that deserv... | Summary: Without knowing data distribution, it is computationally hard to estimate score function from data samples, such as the reverse step of diffusion models. One previous work shows that L^2-accurate score estimation along the forward process can help efficiently sampling from arbitrary data distribution. However,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and rating. We are grateful for the reviewer’s appreciation of the significance of our main result as a bridge between score estimation and the cryptographic notion of computational indistinguishability, as well as the future research directions we h... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion | Accept (poster) | Summary: ### NeurIPS Review
#### Summary:
This submission presents an innovative 2D hairstyle editing pipeline leveraging latent diffusion models (LDM). The key component of this method is the Multi-Stage Hairstyle Blend (MHB), which facilitates separate control over hairstyle and hair color. By integrating structural... | Rebuttal 1:
Rebuttal: Thank you for your suggestions. We have included additional failure cases in Figure 1 of the PDF, where we comprehensively demonstrate the effects of multi-view hair color transfer and discuss the reasons for poor performance.
In Figure 1 of the PDF, we have added discussions on the limitations o... | Summary: This paper presents a new framework for hair editing tasks, which includes editing hair color and hairstyle using text descriptions, reference images, and stroke maps. The proposed approach leverages Latent Diffusion Models (LDMs) and introduces the Multi-stage Hairstyle Blend (MHB) technique to effectively se... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for pointing out the weaknesses.
**W1:** This study is the first to propose a diffusion-based pipeline in the hair editing field, addressing the issue of **multi-color hair structure** in **text2img** and **img2img** scenarios. Previously, no methods specifically focuse... | Summary: The paper introduces an approach for hair editing using Latent Diffusion Models (LDMs). A warping module ensures precise alignment of the target hair mask and enables hair color structure editing using reference images. The proposed Multi-Hair Basis (MHB) method within LDMs decouples hair color and hairstyle. ... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for pointing out the weaknesses.
1. We have reviewed the HairNet video and paper. The issue of transferring hair across diverse/different poses is not the problem addressed in our paper. HairNet can effectively control different facial angles, which would be highly benef... | Summary: This paper presents a novel approach called HairDiffusion for editing hair in images using latent diffusion models. The main contributions of the work are:
1. Introduction of the Multi-stage Hairstyle Blend (MHB) method for effectively separating control over hair color and hairstyle in the latent space of th... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
1. We will correct the typos and missing text, and supplement the finetune hyperparameter settings in the future version.
2. We have showed the limitations of our method's dependency on masks in the supplementary materials. We have supplemented the limitations... | Rebuttal 1:
Rebuttal: **G1: More Examples of the Limitations:** In Figure 1 of the PDF, we have added discussions on the limitations of the warping module in extreme cases of hair color transfer, including significant pose differences, complex textures, and large discrepancies in hairstyle regions. To visually illustra... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficiency for Free: Ideal Data Are Transportable Representations | Accept (poster) | Summary: This paper considers data set distillation to a more concise form for purposes of representational efficiency, partly as an attempt to consider what forms of deployment might benefit from such forms and more importantly seeking to offer a universal form of translator to extract such compact representations fro... | Rebuttal 1:
Rebuttal: > [W1] The way the paper is written it almost appears that authors are approaching the concepts of dataset distillation considerations (eg in Table 1) as if such matters are unexplored in literature, which I would sort of find surprising. I think the paper would have been better structured as lead... | Summary: This paper proposes to accelerate the training of self-supervised learning with a pre-trained teacher model and use the prediction of this teacher as a target. The method is motivated from a toy example that the training convergence is dominated by the variance of the random variable. Then, the authors conjure... | Rebuttal 1:
Rebuttal: > [W1] […] I recommend introducing the technical solution first and then discussing the underlying motivations.
>
[R1] Thank you for your feedback. We will revise the paper to present the technical solution earlier, enhancing clarity and focus.
> [W2] Technically, my major concern lies in the m... | Summary: The paper addresses the scalability constraints in representation learning by proposing a novel Representation Learning Accelerator (RELA). Current paradigms, focusing separately on self-supervised learning and dataset distillation, overlook the potential of intermediate acceleration. The authors define ideal ... | Rebuttal 1:
Rebuttal: > [W1] The paper lacks generalization ability to high-resolution datasets, such as 1Kx1K, which are common in practical datasets like clinical and aerial images.
>
[R1] Thank you for your feedback. We have conducted additional experiments using the CelebA-HQ dataset (1024 $\times$ 1024). The res... | Summary: The authors propose a theoretically motivated dynamic distilled datasets. With use of these transportable representation the authors show that they can outperform the performance of the original dataset and in some cases even the complete dataset.
Strengths: - The methods have a sound theoratical basis, the p... | Rebuttal 1:
Rebuttal: > [W1] The writing style can improve and make the paper approachable. The notations are often terse and lack details about symbols until later. Especially for proofs in appendix.
>
[R1] Thank you for your feedback. We will revise the paper to improve its clarity and make it more accessible. Spec... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you again for your constructive comments, which have been very helpful in improving our paper.
During the rebuttal period, we have addressed your concerns in detail in our responses. We have also provided additional experimental results in the attached PDF.
Thank you very ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene | Accept (poster) | Summary: The author proposed a novel, time-efficient, template-free NeRF-based method for 3D dynamic scene reconstruction, focusing on capturing detailed explicit geometry for each entity in the scene. Extensive experiments demonstrate the efficiency of the proposed method.
Strengths: 1. The paper is well-organized, a... | Rebuttal 1:
Rebuttal: **Time consumed per iteration:** Thank you for bringing this to our attention. Our approach takes around 0.3 sec/frame with INN while 0.9 sec/frame with the Broyden approach used in TAVA. We will include this information in our final paper.
**Information on how we obtain semantic masks:** Please ... | Summary: This paper propose TFS-NeRF to solve the semantic reconstruction of dynamic scenes.
Strengths: Experimental results on multiple entity and deformable entity reconstructions are good.
Weaknesses: It is very hard to follow the storyline of the introduction.
Unclear contributions. In Lines 81 and 91, the paper... | Rebuttal 1:
Rebuttal: **Hard to follow introduction:** Thank you for your feedback regarding the introduction. We apologize if the storyline was challenging to follow. We will carefully review and revise the introduction to ensure it is clearer and more cohesive. Your input is valuable, and we appreciate your efforts t... | Summary: The paper addresses the problem of reconstructing dynamic environments for arbitrary rigid, non-rigid, or deformable entities. The authors propose a template-free 3D semantic NeRF for dynamic scenes, which employs an Invertible Neural Network (INN) for LBS prediction, and optimizing per-entity skinning weights... | Rebuttal 1:
Rebuttal: **How is the semantic masks calculated:** Please refer to the *Author Rebuttal* section.
**Limiting novelty and distinctiveness w.r.t TAVA, INN:** Our method focuses on the research gap of producing semantic 3D reconstruction of dynamic scenes under multiple object interactions. While our problem... | Summary: This paper proposes TFS-NeRF, a semantic-NeRF framework leveraging no prior templates of dynamic scenes for 3D reconstruction. Guided by INN-driven LBS prediction and semantic-aware ray sampling, TFS-NeRF separately consider deformable and non-deformable parts during geometric learning but composite them to le... | Rebuttal 1:
Rebuttal: **Robustness to mask accuracy:** Please refer to the *Author Rebuttal* section.
**Robustness to pose accuracy:** We agree that for the practicability of TFS-NeRF, it is important to evaluate it from this aspect, and apologize for not presenting the same in the initial submission. We have added qu... | Rebuttal 1:
Rebuttal: We are grateful for the constructive feedback from the reviewers and are pleased that they found our paper "well-motivated, well-organized, and easy to follow" (Rbt4, KrvP, UY9B). They highlighted that our paper includes a "detailed introduction, sufficient details, and extensive evaluation on sev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image | Accept (poster) | Summary: This paper introduces a novel method to reconstruct 3D from a single image. The method is two-stage: (i) 4 images corresponding to the orthographic views are first generated along with normal maps, (ii) then, the multi-view maps are used to initialize and optimize a mesh using differentiable rendering techniqu... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and valuable comments, some of which are fundamental and in-depth suggestions that help us greatly improve our paper. To address your concerns, we present the point-to-point response as follows.
**Comment 1: Unrigorous technical presentation**
We appreciate y... | Summary: The paper focuses on single-image-to-3D. Given a single image, it first finetunes Stable Diffusion Image Variations models to generate orthogonal multi-view RGB/normal images and ControlNet-Tile models to enhance resolution. Then, it proposes the instant and consistent mesh reconstruction algorithm (ISOMER). I... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and valuable comments, some of which are fundamental and in-depth suggestions that help us greatly improve our paper. To address your concerns, we present the point-to-point response as follows.
**Comment 1: Limited Qualitative Results to Frontal View, Worse S... | Summary: This paper proposes a novel method for converting a single image to 3D. The method mainly consists of two stages: multi-view RGB and normal generation, and multi-view guided mesh optimization and texturing. The key innovation of the paper is a multi-view-normal-based 3D mesh reconstruction module. Specifically... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive and thorough comments. Your main suggestions on the experimental setting and ablation studies help us refine our paper. To address your concerns, we present the point-to-point response as follows.
**Comment 1: Narrow Evaluation Scope**
Thanks for your th... | Summary: This paper introduces Unique3D, a framework aiming to generate 3D meshes from single-view images with high quality and fidelity. Driven by the observation that 2D image pixel and normal priors with higher resolution can be crucial in generating intricate textures and complex geometries, Unique3D integrates a m... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and valuable comments, some of which are fundamental and in-depth suggestions that help us greatly improve our paper. To address your concerns, we present the point-to-point response as follows.
**Comment 1: Multi-view Consistency Analysis**
We appreciate the... | Rebuttal 1:
Rebuttal: We thank the reviewers for their patience in reviewing, and we will respond to each of them individually, with additional experiments added in the Appendix PDF.
Pdf: /pdf/336b26b1659a208b93b4965db2a48c5890eeaba3.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved learning rates in multi-unit uniform price auctions | Accept (poster) | Summary: This paper considers online learning in repeated multi-unit uniform price auctions, motivated by the strategic participation of electricity producers in the electricity day-ahead market. By introducing a new modeling of the action space to EXP3, the authors propose an algorithm that achieves $\tilde{O}(K^{3/2}... | Rebuttal 1:
Rebuttal: Thank you for your review.
W1/Q1 It is inexact to say that our setting is not the same as in [1]. While the way our allocation policy is described indeed differs from the description in [1] it results in the same allocations as allocating the $j^{th}$ items to the $j^{th}$ highest bid.
Indeed, ... | Summary: This work studies the problem of multi-unit uniform price auctions. By introducing a new modeling of the action space, the paper improves the regret of the online learning problem to $\tilde{O}(K^{4/3}T^{2/3})$ under bandit feedback, and $\Omega(T^{2/3})$ is a regret lower bound under this feedback model. Unde... | Rebuttal 1:
Rebuttal: Thank you for your review.
W1. The all-winner feedback model is indeed a particular case of partial feedback that is widely studied. However, it has never been studied in multi-unit auctions which is the focus of our study. We will clarify this in the related work section.
W2/Q1. The key idea i... | Summary: The paper studies no-regret bidding algorithms in multi-unit uniform-price auctions with adversarial competing bids. In this problem, the bidder has diminishing marginal values for units of the item, and submits one bid for each unit. The top K bids win, and the payment is uniformly the lowest winning bid fo... | Rebuttal 1:
Rebuttal: Thank you for your review. Thank you for noticing the typos and for suggesting some improvements in the presentation of our approach. We will clarify this part and make the necessary correction in the revision.
Line 41: Indeed you are right, we will reformulate according to your suggestion.
“... | Summary: The paper analyzes repeated multi-unit uniform price auctions through the lens of online learning. At each time step, a bidder submits a sequence of bids $(b_1,\dots,b_K)$ to win up to $K$ identical items for which the bidder holds known valuations that depends only on the number of won items and are the same ... | Rebuttal 1:
Rebuttal: Thank you for your review.
Q1. Indeed our upper and lower bound do not match for the parameter K. We believe the lower bound in the bandit setting is not tight with respect to its dependency in $K$, we expect the tight bound to depend on the scale of the utility $K$. Regarding upper bounds, we d... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rapid Plug-in Defenders | Accept (poster) | Summary: The paper proposes a method called CeTaD (Considering Pre-trained Transformers as Defenders) for the Rapid Plug-in Defender (RaPiD) problem, which aims to rapidly counter adversarial perturbations without altering the deployed model. The method leverages pre-trained transformer models and fine-tunes only a lim... | Rebuttal 1:
Rebuttal: Thank you for your careful review and overall positive evaluation. Here are our point-by-point responses to your concerns.
**1. l_p settings**
For the experiments in the current paper, we follow the same attack settings as in the related works. We will compare the performance of different ( l_p ... | Summary: The paper proposes CeTaD, a rapid plug-in defender (RaPiD) for deep neural networks (DNNs) against adversarial attacks. It leverages pre-trained transformer models and fine-tunes minimal parameters (e.g., layer normalization) using few-shot clean and adversarial examples. CeTaD aims to quickly counter adversar... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable suggestions. Here is our response to your concerns.
**1. effectiveness and efficiency**
This paper compares methods capable of implementing RaPiD (Rapid Plug-in Defender) (see L130-L134). As a very promising line of technique, there are indeed existing work... | Summary: This submission studies few-shot tuning-based purification for adversarial defense, especially leveraging pre-trained transformers. By only tuning the normalization layers with few training examples, the proposed defense achieves decent accuracy and robustness.
Strengths: 1. The problem setting is novel and o... | Rebuttal 1:
Rebuttal: Thank you for your careful review and valuable comments. We provide point-to-point clarifications on your concerns below and will incoporate them in the revised version.
**1. framework mechanism**
The decoder does not need to restore the input. Encoder-decoder is a general framework. Usually, a... | Summary: This paper introduces an approach for defending machine learning models against adversarial attacks using transformer-based vision models. The proposed method, Continuous Transfer and Defense (CeTaD), leverages pre-trained transformers as defenders to provide rapid and effective adversarial protection. The app... | Rebuttal 1:
Rebuttal: Thanks for your careful review and insightful comments. We provide a point-to-point response to your concerns.
**1. related methods & novelty**
As a very promising line of technique, there are indeed existing works in purification and denoising methods, which we introduce in our paper (e.g., L40... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Separation and Bias of Deep Equilibrium Models on Expressivity and Learning Dynamics | Accept (poster) | Summary: This paper offers a comparative analysis of DEQ and FNN, examining their differences in terms of structure and performance. By investigating the learning dynamics, the authors provide insights into the implicit bias of DEQ. Their theoretical findings demonstrate the advantages of DEQ over FNN in specific scena... | Rebuttal 1:
Rebuttal: Thank you for your careful review and thoughtful questions and comments. Please see our response below.
> Factors influencing DEQ's separation and bias need further discussion. For instance, is DEQ's implicit bias caused by initialization, the use of implicit differentiation in solving DEQ, or di... | Summary: - The authors study the expressive power of the ReLU-based deep equilibrium model (DEQ) and the learning dynamics (including implicit bias, convergence, and generalization) of linear DEQ.
- The first separation result shows that there exists a width $m$ ReLU-DEQ that cannot be well-approximated by a ReLU-based... | Rebuttal 1:
Rebuttal: Thank you very much for your careful review and thoughtful questions and comments. Please see our response below.
> W1. It is unclear whether the comparison between DEQ and FNN is fair. …. Therefore, the authors must provide a comparison in terms of both memory consumption and computational cost ... | Summary: This paper explores the theoretical foundations of Deep Equilibrium Models (DEQ) and their advantages over Fully Connected Neural Networks (FNNs). It demonstrates DEQs have superior expressive power compared to FNNs of similar sizes, particularly in generating linear regions and approximating steep functions; ... | Rebuttal 1:
Rebuttal: Thank you for your careful review and thoughtful questions and comments. Please see our response below.
> The assumption in Equation (9) that DEQs favor dense features might be misleading. …. It is straightforward to claim that the diagonals are dense since that is the only part of the model bein... | Summary: This paper studies the expressivity and inductive bias of deep equilibrium models. First, the authors generate a separation result for expressivity between a fully connected deep model model and a deep equilibrium model, showing that if the depth of the fully connected model scales as $m^{\alpha}$ for width $m... | Rebuttal 1:
Rebuttal: Thank you for your careful review and thoughtful questions and comments. Please see our response below.
> I was wondering whether the comparison between depth $L\leq mα$ FFNs and DEQs was a fair comparison. (See my questions below).
Please see our response for the question Q1 below.
> However, ... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to all reviewers for the valuable and constructive comments. Many of the suggestions will be incorporated in the final version of the paper.
Some reviewers have raised questions regarding the fairness of our comparison between DEQ and FFN and the validity of our... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
D-LLM: A Token Adaptive Computing Resource Allocation Strategy for Large Language Models | Accept (poster) | Summary: The paper proposes D-LLMs, a novel dynamic inference framework for large language models (LLMs) that adaptively allocates computing resources based on the importance of individual tokens. By introducing a decision module for each transformer layer, D-LLMs can decide whether to execute or skip specific layers f... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the valuable feedback. We have carefully considered the comments and suggestions and would like to address each of the concerns raised.
**Q1: Token Granularity**
We follow the tokenization released by corresponding LLMs. Today's LLMs utilize their own tokeniz... | Summary: The paper introduces D-LLMs, a dynamic inference paradigm for large language models that adaptively allocates computing resources based on token importance. D-LLMs reduce computational costs and KV-cache storage by up to 45% and 50% on various tasks.
Strengths: 1. The concept of dynamically adjusting the exec... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the valuable feedback. We have carefully considered the comments and suggestions and would like to address each of the concerns raised.
**W1: While the experimental results are impressive, the paper lacks a deep theoretical analysis of the dynamic decision mod... | Summary: This paper proposes D-LLM, a novel layer-skipping framework for LLM inference to reduce computation costs. It designs and trains (fine-tuning) the additional decision module per transformer layer and implements KV-cache eviction by adjusting self-attention masks upon the execution decision. The framework prese... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the valuable feedback. We would like to address each of concerns raised as follows.
**W1 & Q4: Additional overheads from the decision module training**
We provide an analysis of overheads taken by decision modules in global rebuttal #1.
**W2: Baselines do no... | Summary: This manuscript introduces a new dynamic inference paradigm for LLMs called D-LLMs, which adaptively allocates computing resources in token processing. With the dynamic decision module, the network unit is decided to be executed or skipped on the fly. The KV-cache eviction policy is proposed to exclude skipped... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the valuable feedback. We have carefully considered the comments and suggestions and would like to address each of the concerns raised.
**W1: Highlighting how D-LLMs differ significantly from or improve upon existing methods like AdaInfer and SkipNet would str... | Rebuttal 1:
Rebuttal: We are deeply grateful to the reviewers for their valuable feedback. We have carefully read the comments, and these insightful suggestions are crucial for enhancing our research. Given the limited length of individual rebuttals, we have chosen several key questions or concerns of interest to most ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exocentric-to-Egocentric Video Generation | Accept (poster) | Summary: This paper introduces a novel method for generating egocentric videos from exocentric videos of daily-life skilled human activities. This task is very challenging because of the significant viewpoint variations, sparsely posed exo videos, and dynamic environments. The authors propose to use a PixelNeRF-informe... | Rebuttal 1:
Rebuttal: **Thank you for recognizing our work and the valuable comments.**
# W1: Person-related design.
Thanks for your advice. We totally agree with you that person-related designs such as human hand pose prior or object states information would be very useful to improve the performance of Exo2Ego video... | Summary: This paper proposes a novel method for generating egocentric videos from multi-view exocentric videos using diffusion-based techniques. This method addresses the challenges of viewpoint variation and dynamic motion by employing a multi-view exocentric encoder and a view translation prior, along with temporal a... | Rebuttal 1:
Rebuttal: **Thanks for helpful comments.**
# W1: Differences with ReconFusion.
Our Exo2Ego prior is inspired by ReconFusion and based on PixelNeRF (see L199-L200, L204-L206 of main paper), but it significantly differs from ReconFusion in following aspects.
1) **The task of our method is significantly differ... | Summary: This paper proposes a novel diffusion-based video generation method that translates exocentric views to egocentric view. Overall, I think the idea is novel and the method performs well on multiple daily human activities.
Strengths: 1. The application of view translation using Nerf-based approach seems interes... | Rebuttal 1:
Rebuttal: **Thank you for recognizing our work and the valuable comments.**
# W1: Ablation on Exocentric CLIP features.
Thanks for your advice. We conduct additional ablation study by replacing our exocentric feature encoders with the CLIP exocentric features. As shown in the following table, **our method ... | Summary: This paper deals with the task of exocentric-to-egocentric video generation. It presents a diffusion-based framework of exo2ego-v to tackle the challenges of the significant variations between exocentric and egocentric viewpoints and high complexity of dynamic motions and real-world daily-life environments. It... | Rebuttal 1:
Rebuttal: **Thank you for recognizing our work and the valuable comments.**
# W1.1: Network architecture.
The base network architecture of the exocentric encoder and the spatial modules of the egocentric video diffusion model are extensions of the Stable Diffusion [44]. Therefore, they consist of **four dow... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and valuable comments. We also appreciate that the core contributions and the quality of our results are recognized in the review:
1. **First work** to address the **challenging** task of Exo2Ego video generation. **New** diffusion-based multi-view... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prioritize Alignment in Dataset Distillation | Reject | Summary: This paper makes the key observation that existing dataset distillation methods often introduce misaligned information during both the extraction and embedding stages, which leads to suboptimal performances. In response to this observation, the authors propose a method called Prioritize Alignment in Dataset Di... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer UeUP for valuable feedback. We make responses as follows.
**W1: PAD introduces two new sets of parameters, which add to the complexity of tuning**
Thanks for raising this concern. We would like to make the following clarifications:
- They don't need to be changed... | Summary: This paper proposes to study the information misalignment problem in dataset distillation.
It proposes two basic pruning strategies: (1) learn the synthetic data with easy real samples first, and gradually change to harder samples, and (2) only match deep layers of the network during trajectory matching. The p... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer emvM for valuable feedbacks. We make responses as follows.
**W1: Experiments to support the two strategies are not sufficient**
Thanks for the comment. We provide more results as follows (***will be included in the revision***)
- **Settings:**
Dataset: CIFAR-... | Summary: The authors claim that existing data distillation methods introduce misaligned information, so they propose Prioritize Alignment in Dataset Distillation (PAD). PAD prunes the target dataset and uses only deep layers of the agent model to perform the distillation, achieving state-of-the-art performance.
Streng... | Rebuttal 1:
Rebuttal: We thank the reviewer xUdN for the feedback. We make responses as follows.
**W1: Performance gains are limited and may have other explanations**
Thanks for the question. In Table 1, we achieve 11 SOTAs out of 12 settings. Moreover, PAD can be generalized to methods based on matching gradients (D... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable feedback, which is very important to further improve our work.
We begin by making the following responses about results of additional experiments and revision of the paper, and later to allow more space for responses to each reviewer.
### Tables... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LLM-based Skill Diffusion for Zero-shot Policy Adaptation | Accept (poster) | Summary: This paper presents a novel framework called LLM-based Skill Diffusion (LDuS), designed to enable zero-shot skill-based policy adaptation to various contexts specified in natural language. It leverages LLMs to guide a skill diffusion model, allowing for the generation of adaptable skill trajectories. The frame... | Rebuttal 1:
Rebuttal: ## [Weakness 1 & Question 1] Mathematical Justification for VAE+Diffusion
We appreciate the reviewers for addressing concerns about the mathematical justification LDuS. We provide the mathematical justification for Equation (5), where the reconstruction loss in the ELBO is replaced with the diffus... | Summary: This paper presents LDuS, a framework that adapts skill diffusion models to unseen contexts in a zero-shot manner. The proposed method first couples VAE with a diffusion planner for hierarchical skill learning. To perform zero-shot policy adaptation to the language-specified context, LDuS translates contexts t... | Rebuttal 1:
Rebuttal: ## [Weakness 1] Scope of Context
As the scope of context is limited in our experiments, we conduct additional experiments on two different types of user requirements including energy and spatial. In the energy context, the agent aims to minimize its energy consumption by reducing its acceleration ... | Summary: LDuS is a diffusion-based approach for offline skill learning that adopts several advances to improve performance in goal-driven settings. The main contribution is the adoption of LLM-based guidance, allowing to comply with contextual information/conditions, while achieving the given goal.
Strengths: The work... | Rebuttal 1:
Rebuttal: ## [Weakness 1] Problem Definition
We believe our work addresses a novel practical problem where the agent adapts to unseen language-specified contexts in a zero-shot manner, even when trained without a context-labeled dataset. Previous works on language-conditioned skill learning [1,2,3] primaril... | Summary: This paper presents an LLM-based policy adaptation framework for a language specified context. Here, context is a slight variation in how the task is performed. It has two stages, in skill-learning phase, a skill-based diffusion policy is learned with in-painting technique. Here, skill is a VAE encoding of a s... | Rebuttal 1:
Rebuttal: ## [Weakness 1] Scope of Context
We thank the reviewer for the thoughtful and constructive feedback. As the scope of context is limited in our experiments, we conduct additional experiments on two different types of user requirements including energy and spatial. In the energy context, the agent a... | Rebuttal 1:
Rebuttal: We deeply appreciate the valuable and constructive feedback from the reviewers. We addressed all identified weaknesses and questions to resolve the concerns raised. For those that require additional experiments, we have conducted further studies to provide a more comprehensive understanding. Lastl... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distribution-Aware Data Expansion with Diffusion Models | Accept (poster) | Summary: This paper proposed a training-free data augmentation method based on the diffusion models. To alleviate the poison phenomenon of the diffusion model, which is that the distribution of generated images will deviate from the natural distribution, this paper proposed a simple yet effective method based on the pr... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have dedicated to reviewing our paper. We value your feedback on our method, noting that the **prototype technology is sound and interesting** and that it is **easy to follow.** We also thank you for recognizing that our method **achieves SOTA perfor... | Summary: The authors present DistDiff, a training-free data expansion framework based on a distribution-aware diffusion model. DistDiff constructs hierarchical prototypes to approximate the real data distribution, optimizing latent data points within diffusion models through hierarchical energy guidance. The framework ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you invested in reviewing our paper.
We address the raised concerns as follows:
**Re Weakness #1:**
Thank you for your valuable feedback. We appreciate your suggestion to provide a clearer explanation of the hierarchical prototypes.
We recognize that ... | Summary: This paper focuses on data augmentation or expansion by generating synthetic data from pre-trained large-scale diffusion models. To ground the samples from these large-scale diffusion models, the paper proposes an energy-based guidance approach where the energy function depends on hierarchical prototypes. In t... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for the detailed and professional attention you have given to our work during the review process. We greatly appreciate your recognition of the **very important problem** our work addresses, as well as your acknowledgment of our **detailed ablation st... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bridging Inter-task Gap of Continual Self-supervised Learning with External Data | Reject | Summary: This paper focused on continual contrastive self-supervised learning (CCSSL), highlighting that the absence of inter-task data results in sub-optimal discrimination in continual learning. The authors then proposed a method that performed contrastive learning of external data as a bridge between continual learn... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your questions or concerns below.
##### **Q1: Novelty and technical contributions**
> **A1:** Thank you for your comments. However, we would like to clarify that our work is not a simple combination of contrastive learning and external data, but c... | Summary: This paper finds that existing methods in continual contrastive self-supervised learning (CCSSL)--a class-incremental learning scenario where the data is unlabeled--overlook contrasting data from different tasks, leading to inferior performance compared to the joint training upper bound. The authors propose to... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your questions or concerns below.
**Q1: Analysis of how BarlowTwins “contrast” during learning**
> **A1:** We treat BarlowTwins here as a generalized contrastive method. Although it is not directly contrast the anchor with negatives, [1] has appro... | Summary: The paper introduces BGE, a novel approach to address the challenge of inter-task data comparison in Continual Contrastive Self-Supervised Learning (CCSSL). BGE incorporates external data to bridge the gap between tasks, facilitating implicit comparisons and improving feature discriminability. The paper also p... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your questions or concerns below.
**Q1: The trade-off between performance improvement and time consumption**
> **A1:** The additional computational consumption of BGE comes mainly from the additional amount of data, therefore, we statistic the per... | Summary: The authors:
- argue that an optimal model for continual contrastive self-supervised learning should perform as well as a model trained with contrastive learning on the whole set of data, including negative samples taken between different temporal slices of the dataset, no just within the same temporal slice
-... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We address your questions or concerns below.
**Q1: The goal and setups of CCSSL**
> **A1:** In self-supervised learning, we expect the model could learn discriminative representation from unlabelled seen data. In Continual Self-Supervised Learning (CSSL), th... | Rebuttal 1:
Rebuttal: Thanks to all reviewers thought feedbacks. We have carefully read all the comments and summarized the recognition of our work as follows:
| Reviewer | |Comments|
|-|-|-|
| hAsz&oaUg&xWKh | Finding's Novelty| a **significant** but often **overlooked** problem in CCSSL; The finding that existing re... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models | Accept (poster) | Summary: This paper introduces RAVL, a method designed to identify and address spurious correlations in Vision-Language Models (VLMs). The authors emphasize two key areas: (a) prioritizing local image features over global image-level features, and (b) concentrating on the fine-tuning phase rather than the pre-training ... | Rebuttal 1:
Rebuttal: We thank Reviewer ONXK for reviewing our work and providing helpful feedback.
**[Q1] Assumption of region-level spurious correlations.**
We thank the reviewer for raising this point. In line with prior works in vision-only and vision-language settings [1,2,3], RaVL is specifically designed to s... | Summary: Vision-language models (VLMs) tend to exhibit poor zero-shot performance when compared to task-specific models. However, fine-tuned VLMs may capture spurious correlations in domain-specific datasets which may be small in size. The paper proposes an automated spurious correlation detection and mitigation method... | Rebuttal 1:
Rebuttal: We thank Reviewer ACX1 for reviewing our work and providing helpful feedback.
**[Q1] Computational complexity. “There is a lack of computational complexity analysis. To detect fine-grained spurious correlations, RAVL needs to segment images into multiple regions, cluster all the regions per clas... | Summary: This paper tackles spurious correlations between image features and textual attributes in fine-tuned VLMs. It proposes an approach to discover and mitigate spurious correlations using local image features (image regions rather than a whole image). Experiments are done in both controlled settings and realistic ... | Rebuttal 1:
Rebuttal: We thank Reviewer LC5I for reviewing our work and providing helpful feedback.
**[Q1] Computational complexity. “Looking into the regional features may make the approach computationally expensive. Computational complexity analysis is missing in the paper.”**
We refer the reviewer to General Resp... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful review of our manuscript. We were encouraged to see that all reviewers rated our work as a "technically solid, moderate-to-high impact paper". Reviewers also found the paper to be "well-written" and "well-structured" (Reviewers LC5I, ACX1, ONXK); the pro... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Frequency-aware Generative Models for Multivariate Time Series Imputation | Accept (poster) | Summary: This paper proposes FGTI that uses frequency-domain information with high-frequency and dominant-frequency filters for accurate imputation of residual, trend, and seasonal components.
Experimental results demonstrate FGTI's effectiveness in improving both data imputation accuracy and downstream applications.
... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and valuable suggestions. We have tried our best to incorporate the suggestions in the revised version. Below, we provide our response to the questions and concerns.
**1. (W1) The formulation and writing of this paper should be improved**
We appreciate your ... | Summary: This paper proposes a generative model, Frequency-aware Generative Models for Multivariate Time Series Imputation (FGTI), for multivariate time series imputation. The proposed model is designed to enhance the imputation performance by modeling the residual component of time series data. To this end, the paper ... | Rebuttal 1:
Rebuttal: We really appreciate your thoughtful comments and valuable suggestions. We have incorporated the suggestions in the revised paper. Below, we provide our response to the concerns.
**1. (W1) Clarify if temporal dependency modelling is sufficient**
We would really appreciate your feedback.
We apolo... | Summary: The paper proposes a new model, called FGTI, to address the issue of missing data in multivariate time series extracting frequency-domain information. The authors argue that existing methods, in general, neglect the residual term, which is the most significant contributor to imputation errors. FGTI incorporate... | Rebuttal 1:
Rebuttal: We thank the constructive comments and suggestions for further experiments. We will try our best to incorporate the suggestions in the revised version.
Below, we provide our response to the questions and concerns.
**1. (W1) Compared with the most recent baselines**
Thank you for the valuable su... | Summary: The authors present Frequency-aware Generative Models for Multivariate Time Series Imputation (FGTI), a model which addresses the challenge of missing data in multivariate time series by focusing on the often-overlooked residual term. The paper also incorporates frequency-domain information to enhance imputati... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and valuable suggestions. Below, we provide our response to the questions and concerns.
**1. (W1) Why large amplitude frequency components guide trend and seasonal term imputation**
Thank you for your comments.
We recognise that our current expression is ins... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful and constructive comments. We are greatly encouraged that they found our idea and contributions to be significant (Reviewer WWjA, qcDA, Q2t8 and SUMs), and technical sound (Reviewer WWjA, qcDA, SUMs). We are grateful that they identified ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces | Accept (poster) | Summary: The study provides a rigorous comparison of four neural decoders typically used in BCIs: Kalman Filter, KalmanNet, tcFNN, and LSTM in both offline and online conditions on a NHP performing a 2 degree of freedom dexterous finger task. Authors explore the trade-off in decoding capabilities of these decoders with... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the strengths and main contributions of the paper as well as for suggesting specific avenues for improvement. We have addressed the statistical weakness brought up by the reviewer as well as responding to their questions below.
### Weaknesses:
We thank the re... | Summary: This paper addresses the trade-off between performance and explainability in brain-machine interface (BMI) decoders. The authors introduce KalmanNet, a novel decoding algorithm that combines the traditional Kalman filter (KF) with deep learning techniques, specifically recurrent neural networks (RNNs).
Key co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their very thorough review of the paper, for recognizing its main strengths and contributions, and for suggesting specific avenues for improving our work. We have addressed the weaknesses and questions raised by the reviewer below.
### Weaknesses:
First in terms of a comp... | Summary: - This approach studies a few approaches that can be used for neural decoding. The baseline approaches are blackbox DNNs and a vanilla Kalman filter. The proposed approach, the KalmanNet, is a hybrid model, in which a DNN is used to control the gain on a Kalman filter.
- Approaches such as Kalman filtering hav... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and suggestions, as well as for recognizing the main strengths of the paper. We have addressed the paper weaknesses brought up by the reviewer in the general rebuttal. Here, we will address each of the reviewer’s questions.
### Equation 1:
We thank... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful questions, suggestions, and generally supportive comments. Our work demonstrates the tradeoffs of using KalmanNet, an explainable algorithm that combines deep learning with the Kalman filter (KF), to predict finger movements from brain data. We have address... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
3D Gaussian Splatting as Markov Chain Monte Carlo | Accept (spotlight) | Summary: This paper proposes a novel densification strategy of 3D Gaussian Splatting (3DGS) based on the Markov Chain Monte Carlo (MCMC) sampling scheme.
The authors address the ‘heuristic’ densification of standard 3DGS and adopt a distribution-aware resampling pipeline.
Consequently, they achieve higher rendering qu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions.
Here are our responses to your comments:
## Technical contribution
We respectfully disagree, as our method cannot be summarized as a mere resampling strategy. It is a completely new take on the 3D Gaussian Splatting optimizat... | Summary: The paper discusses improvements to 3D Gaussian Splatting in neural rendering. Current methods rely on complex cloning and splitting strategies for placing Gaussians, which often do not generalize well and depend heavily on good initializations. The authors propose rethinking 3D Gaussians as random samples fro... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our paper. Please let us know if you have any concerns or questions and we will try our best to respond.
---
Rebuttal Comment 1.1:
Comment: Thanks for the authors' sincere effort and detailed experiments. I keep my original score. | Summary: Current 3DGS-based methods require carefully designed strategies such as cloning and splitting to assign a 3D Gaussian at a location. Further, they also require initializing points from SFM to generate high-quality novel views. The proposed work assumes that a set of 3D Gaussians are drawn from an underlying p... | Rebuttal 1:
Rebuttal: We thank the reviewer for sharing the enthusiasm that we have for our method.
Here is our response to your comments:
## Training time between SfM points version vs Random points version
SfM has faster convergence, but to obtain the best PSNR performances both were run for about the same amount o... | Summary: The paper presents a simple and effective method to enhance the training of 3D Gaussian Splatting (3DGS). It offers two main contributions. First, it demonstrates that adding carefully designed noise to the Gaussian centers after each gradient step can boost the performance of 3DGS. This encourages more explor... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions.
## Relevance of the hybrid MCMC framework
Our work is indeed related to gradient descent methods with noise and perturbations as suggested. In our case, our motivation for opting to interpret our framework as a hybrid MCMC me... | Rebuttal 1:
Rebuttal: We are glad to see that all reviewers are positive towards our paper.
Reviewers commend the effectiveness of our method (**Cpa7**, **jFyG**, **cJQR**), especially when random initialization is used, the thoroughness of our ablations (**jFyG**, **cJQR**).
They also acknowledge the ease of use of ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Graph Learning for Cross-Domain Recommendation | Accept (poster) | Summary: This paper introduces an innovative federated CDR framework with two key modules tailored for privacy preserving and negative transfer. For privacy, it presents a solid theoretical guarantee. For negative transfer, it generates domain attentions by virtual social links and conduct a fine-tuning stage to filter... | Rebuttal 1:
Rebuttal: We would like to sincerely thank you for positive evaluations and valuable comments for improvement.
**W1:** The use of the terms "extended" and "expansion" appears to be inconsistent.
**Response:** We agree and **we will use *expand/expansion* uniformly in paper’s updated version.**
**W2:** Th... | Summary: This paper presents a novel federated framework of CDR, FedGCDR, for privacy preserving and negative transfer between domains. Its key strengths include a solid theoretical foundation analyzing the DP-based privacy preserving and a novel and dynamic attention generation method to mitigate negative transfer. By... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and suggestions. We hope our response addresses your concerns.
**W1:** Why addresses the broader-domain CDR involving more than three domains.
**Response:** Because solving the cross-domain recommendation problem with more than three participatin... | Summary: The paper proposes a novel federated graph learning framework, FedGCDR, aimed at addressing the challenges of privacy and negative transfer (NT) in Broader-Source Cross-Domain Recommendation (BS-CDR) scenarios. The framework includes two key modules: the positive knowledge transfer module and the positive know... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and suggestions. We hope our response addresses your concerns.
**W1&Q1:** Writing errors.
**Response:** Thank you. We agree and **will correct any errors you have raised and scrutinize the paper and fix any typos during the revision process.**
|... | Summary: To solve the privacy issue and negative transfer phenomenon in the cross-domain recommendation, the authors propose a novel framework named FedGCDR. Following the HVH pipeline, two key modules collaboratively transfer positive knowledge and filter the negative interference from source domains. In the experimen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive review and valuable questions that have helped us improve our work.
**W1:** The definitions provided for the intra-domain privacy and inter-domain privacy appear to be inconsistent.
**Response:** We agree and we **will give a unified and complete defini... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable comments and suggestions, which are crucial for improving our work. Here we carefully response your questions point-by-point, and more details of the responses are **in the PDF file** at the bottom of this **Author Rebuttal.**
**1:** How wel... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization | Accept (poster) | Summary: - The authors propose an optimization-based preprocessing technique called Weight Magnitude Reduction (MagR) to improve the performance of post-training quantization (PTQ) for large language models (LLMs). They motivate their method from previous work, showing that linear transformation of weights can render m... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments! We'll discuss the reference by Alizadeh et. al in the revised paper.
The primary concerns were regarding:
**Weaknesses:**
- **The use of** $\ell_\infty$-**norm**: MagR aims to reduce the range of the pre-trained weights (not the quantization noise),... | Summary: The paper proposes a novel approach called Weight Magnitude Reduction (MagR) to improve the performance of PTQ for LLM. The MagR reduces the magnitude of the weights in PTQ. The experiments demonstrate the effectiveness of MagR.
Strengths: 1. The idea is clear.
2. The paper is easy to read (except typos/error... | Rebuttal 1:
Rebuttal: Thank you for the review! We'll add ablation study and fix the typos and incorrect format as suggested, and include the details for deriving Alg. 1.
Other concerns were regarding:
**Weaknesses:**
- **Theoretical analysis**: Our new results establish that:
- The layer-wise $\ell_2$ quantizatio... | Summary: Authors propose an optimization-based preprocessing technique called MagR to enhance the performance of post-training quantization.
MagR adjusts weights by solving an l1-regularized optimization problem, reducing the maximum magnitude and smoothing out outliers.
As a nonlinear transformation, MagR eliminates t... | Rebuttal 1:
Rebuttal: Thank you for the review! The primary concerns were regarding:
**Questions:**
- **Rank deficiency of feature matrix**: Section 4.1, specifically Table 1, demonstrates that the feature matrices across all the layers of the LLaMA family models have very small singular values, less than 0.01 times t... | Summary: This paper introduces Weight Magnitude Reduction (MagR), a technique designed to smooth out outliers before LLM quantization. MagR adjusts pre-trained floating-point weights by solving an ℓ∞-regularized optimization problem. This preprocessing step reduces the maximum weight magnitudes, making the LLMs more su... | Rebuttal 1:
Rebuttal: Thank you for the review and for pointing out relevant references. We'll add the references [1,2] and discussions.
The primary concerns were regarding:
**Weaknesses:**
- **MagR vs QuIP, DecoupleQ**:
- It is possible to run additional coordinate descent iterations on top of OPTQ to further pu... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback. We address the common concerns below:
**(1) Why MagR works:**
- MagR effectively reduces the range of the pre-trained weights by employing $\ell_\infty$-minimization, as illustrated by Figure 1. Given that the quantization step (or float sc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms | Accept (poster) | Summary: The paper considers the vector-valued regression problem. Given $n $ iid samples $\{(x\_i,y\_i)\}\_{i=1}^n$ from a distribution $\mathcal{D}$ on $\mathcal{X} \times \mathcal{Y}$, the goal is to output the estimator $\hat{f}$ such that $\mathbb{E}[||\hat{f}(x)-f^{\star}(x)||\_{\mathcal{Y}}^2]$ is small. Here, $... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and helpful feedback.
**Weaknesses**
The key challenge in generalising the proof from the scalar-valued case to the general vector-valued case lies in finding the right way to harmonize the technical definition of vector-valued interpolation space, and ... | Summary: This manuscript presents the excess risk upper bound for spectral regularized algorithms whose output might belong to potential infinite-dimensional Hilbert space. Additionally, the saturation effect for a special case of vector-valued spectral algorithms, KRR, is rigorously confirmed.
Strengths: 1. The manus... | Rebuttal 1:
Rebuttal: **W1.** We thank the reviewer for their encouraging feedback and for providing the reference [Li et al. (2024)]. We were not aware of this work. We think that it may be possible to generalise the techniques in [Li et al. (2024)] to the more challenging vector-valued setting. However, the assumptio... | Summary: This paper considers the regression task of learning a mapping where both the input space and the output space can potentially be infinite dimensional. The authors formulate the problem setting by proposing a number of assumptions that can be thought of as the vector-valued counterparts of the standard assumpt... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging review of our work. We have done our best to respond to the reviewer's questions. If we have done so, we would be grateful if the reviewer might consider increasing their score.
**Weaknesses**
1. The additional difficulty with respect to the scalar-valu... | Summary: The submission explores a class of spectral learning algorithms for regression within the context of supervised learning using random design. The focus is on high-dimensional and potentially infinite-dimensional output spaces. The problem is framed as minimizing the risk associated with the least squares loss... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback and their helpful comments. We have done our best to address all the questions. If we have done so, we would be grateful if the reviewer might consider increasing their score.
**Weaknesses:**
1. We agree, we base our investigation on the typic... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their encouraging and positive feedback. They sparked very interesting discussions that we will use to improve our manuscript in the camera ready version.
As a summary here are the main points that were brought up by the reviewers:
1. As the proof for th... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This papers focuses on learning vector-valued functions in reproducing kernel Hilbert spaces (RKHS). The kernel in this case is an operator-valued function instead of a scalar-valued function. The papers considers kernel-based vector-valued regression with spectral regularization, which include ridge regressio... | Rebuttal 1:
Rebuttal: First, we thank the reviewer for their encouraging review of our work. We have done our best to respond to the reviewer's questions. If we have done so, we would be grateful if the reviewer might consider increasing their score.
First of all, the reviewer highlighted that a comparison of our resu... | null | null | null | null | null | null |
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement | Accept (poster) | Summary: A new framework is proposed for offline meta-reinforcement learning (meta-RL) that leverages transformers and world model disentanglement to enhance task generalization without the need for expert demonstrations or domain knowledge. The approach, called Meta Decision Transformer (Meta-DT), utilizes a context-a... | Rebuttal 1:
Rebuttal: **Q1. Are there any environments or types of tasks where Meta-DT's performance is notably limited? What are the challenges in extending the model to such environments, and how might these be addressed in future work?**
A1. Thank you for your insightful questions. We evaluated Meta-DT on seven env... | Summary: The paper introduces a new architecture for Meta-RL based on Decision Transformers. The new architecture uses a world model responsible for efficiently encoding task information from demonstrations. The model also introduces a prompt encoder which acts as a boosting mechanism to the context encoder to enhance ... | Rebuttal 1:
Rebuttal: **Q1. It would be nice to see more detailed evaluation in non-mujoco tasks.**
A1. Thank you for your advice. We have conducted new evaluations on Humanoid-Dir and two more Meta-World environments of Sweep and Door-Lock. The following table shows the few-shot testing performance of Meta-DT against... | Summary: This work proposes a transformer-based framework for offline meta-reinforcement learning problems. The proposed algorithm utilizes a context-aware world model for task encoding and self-guided prompting. It outperforms existing offline meta-RL baselines.
Strengths: S1. The results in the paper show the perfor... | Rebuttal 1:
Rebuttal: **Q1. The work misses the CMT baseline which is cited but not shown as a baseline.**
A1. The reason why we did not include CMT as a baseline is that **its code has not been open-sourced**. We emailed CMT's authors for requesting the source code, and received the response that the code would be ma... | Summary: This paper proposes a novel meta-RL framework called Meta Decision Transformer (Meta-DT). It leverages robust task representation learning via world model disentanglement to achieve task generalization in offline meta-RL. Firstly, it pretrains a context-ware world model to capture the task-relevant information... | Rebuttal 1:
Rebuttal: **Q1. Some intuitive explanation on the extrapolation ability are also desirable.**
A1. Thank you for your insightful comments. Meta-DT's generalization comes from the extrapolation ability of the context-aware world model $W(r,s'|s,a; z^i)$. The world model completely describes the characteristi... | Rebuttal 1:
Rebuttal: # Revision Summary
We thank the reviewers for their valuable feedback (we refer to iZYy as R1, LRM6 as R2, EjPB as R3, sbh9 as R4, and BWEr as R5). We are grateful that **most reviewers are quite affirmative to our overall contributions**, including **the novelty and motivation** ("a novel Meta-D... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a novel Meta-DT method that leverages the task representation from the world model disentanglement. Compared with the previous works, the expert demonstration is not necessary. This method could get the task representation from the trained encoder which is used as the guidance for the autor... | Rebuttal 1:
Rebuttal: **Q1. Explain more about the training hours and parameters for this method.**
A1. The following tables show the number of model parameters, the training time, and inference time for one episode. Compared to DT-based baselines, our method introduces a lightweight world model that consists of about... | null | null | null | null | null | null |
An eye for an ear: zero-shot audio description leveraging an image captioner with audio-visual token distribution matching | Accept (poster) | Summary: This paper proposes to use a well-trained visual LLM (specifically Llava-v1.5) to perform audio captioning in a zero-shot fashion. The authors propose a training framework which comprises 2 stages and 5 sub-steps. The authors also propose to use Maximum Mean Discrepancy (MMD) and optimal transport (OT) as the ... | Rebuttal 1:
Rebuttal: Thank you for your review. We’re glad to hear that you found the exploration of our **loss functions as novel directions for alignments between different modalities** and the **timely application scenarios of audio-visual LLM** valuable. Below, please find a point-by-point response to your feedbac... | Summary: This paper presents a method to align tokens created by an audio encoder with those from an image encoder to allow zero-shot description of audio and audio-visual recordings. It introduces an attention-based matching to the alignment to be able to account for objects that appear in one modality but not the oth... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We’re happy to hear that you found our paper **well written and easy to follow**, appreciated the **helpful diagrams**, and recognized the **value in our approach to addressing audio-visual correspondence and leveraging visual captioning systems**. Below we ad... | Summary: The paper proposes a method for how to adapt VLM to perform audio captioning. The pipeline is as follows: 1. perform few-shot prefix tuning on images to caption the audio. 2. Use multi-modal alignment methods, namely MMD or OT, to align token space distribution of audio and visual. 3. Distillation for audio-on... | Rebuttal 1:
Rebuttal: Thank you for your review. We’re pleased to hear that you found our paper **well-written and easy to follow** and appreciated the **novelty in our use of MMD/OT for multi-modal alignment in the audio-visual space**. In the following, we'll address your comments in detail.
>There is a lack of ins... | Summary: The paper under review looks at the problem of captioning of short audio-video clips, leveraging existing multimodal large-language-models. While many strong image captioners exist due to the abundance of image/caption pairs for training data, for clips of non-speech audio, the amount of data available are com... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We’re glad to hear that you found our paper’s **goal, contributions, and execution clear** and appreciated the **good set of experiments and visualizations,** along with the improvement in alignment demonstrated by our **application of MMD or OT learning**. We a... | Rebuttal 1:
Rebuttal: We would like to start by thanking all the reviewers for the time they spent reading carefully our work and their valuable feedback that helped us improve the quality of the submission.
We would like to underline an important contribution of our work: we add audio capability to an LVLM while **ke... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging | Accept (poster) | Summary: The paper proposes a method called MambaSCI for efficient reconstruction of quad-Bayer patterned color video snapshot compressive imaging.This method surpasses state-of-the-art algorithms with lower computational and memory costs, providing improved color accuracy and demosaicing.
Strengths: 1) The contributi... | Rebuttal 1:
Rebuttal: ### Response to Reviewer yWFS
Thanks for your valuable comments.
**Q1: Clarification of high resolution images reconstruction.**
**A:** As stated in the main manuscript (lines 233-234), we also evaluated our method on a large-scale simulated dataset. The metrics comparison and visual comparison ... | Summary: This paper investigate video snapshot compression imaging reconstruction task by Quad-Bayer CFA pattern into color video SCI. They design a Residual-Mamba-Block consisting of ST-Mamba, Edge-Detail-Reconstruction module and Channel-wise attention module to enhance reconstruction quality and edge details. Experi... | Rebuttal 1:
Rebuttal: ### Response to Reviewer xnVL
Thanks for your valuable comments.
**Q1: Clarification of the novelty.**
**A:** We have elaborated on the novelty in lines 160-167 and 289-296 of the manuscript. **Rather than merely adapting ST-Mamba, we introduced the following innovations in video SCI reconstruct... | Summary: This manuscript introduces the Mamba model and Quad-Bayer CFA pattern into color video snapshot compressive imaging (SCI) for the first time. Specifically, the proposed MambaSCI adopts a non-symmetric U-shaped encoder-decoder architecture, which includes DWConv, Residual-Mamba-Blocks, and ReConv. The Residual-... | Rebuttal 1:
Rebuttal: ### Response to Reviewer zshs
Thanks for your valuable comments.
**Q1: Differences between the proposed method and the video enhancement-based ones.**
**A:** **MambaSCI significantly differs from video enhancement technology in the nature of the task, input differences, and use of prior knowledg... | Summary: This paper presents a method for compressive video image using Mamba-Unet for quad bayer sensors.
Strengths: + The work seems to be in a less explored area of research.
Weaknesses: - The work is not making significant contribution in terms of method. Directly applying Mamba-Unet to this problem looks unnatur... | Rebuttal 1:
Rebuttal: ### **Response to Reviewer M9Ao**
Thank for your valuable comments.
**Q1. Clarification of why Mamba and not transformers or CNNs.**
**A:** We have added further discussion of this topic in lines 59-61 of main manuscript, with a more detailed analysis and experimental validation as follows:
Tra... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and insightful comments which have helped improve our paper. We are pleased that the reviewers found our introduction of Mamba into the SCI task to be very novel and well justified, and that our proposed quad-Bayer patterned SCI task is a direction worth e... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding | Accept (poster) | Summary: This work proposes a new structural encoding for graphs, which can be applied in e.g. graph transformers. The method relies on some graph partitioning / coarsening methods to generate hierarchical clustering of a graph, and the structural encoding is more informative than shortest path structural encoding. The... | Rebuttal 1:
Rebuttal: **Thank you for clearly understanding the core of our work and fully recognizing our contributions!**
**W1: Complexity**
>The graph partitioning / coarsening preprocess, including the paritioning algorithm and shortest path computation, can also be complex.
This is a fair point. We acknowledge ... | Summary: This paper leverages graph coarsening techniques to help the graph transformer capture hierarchical distance information on a graph, improving its performance on both node-level and graph-level tasks. Besides empirical validation, the paper also provides theoretical guarantee about the better expressiveness an... | Rebuttal 1:
Rebuttal: **We greatly appreciate the very detailed feedback and your recognition of our contributions! We hope our response below will further enhance your confidence in our work.**
**W1: Incompleteness of Experimental Results**
> The results for models such as ANS-GT, NAGphormer, and LINKX on Actor, Squ... | Summary: To enhance the effectiveness and scalability of graph transformers, this paper proposes a hierarchical distance structural encoding (HDSE) method to incorporate hierarchical structural information with graph transformers. Theoretical analysis of graph transformer equipped with HDSE shows the improvements of bo... | Rebuttal 1:
Rebuttal: **We greatly appreciate your comprehensive understanding of our work and recognizing our contributions! We hope our response below will further enhance your confidence in our work.**
**W1 & Q1: Impact of Coarsening Algorithms**
>It can be seen from Table 4 that the performance of the proposed me... | null | null | Rebuttal 1:
Rebuttal: **We express our gratitude to all reviewers for their invaluable time, effort, and the comprehensive, constructive feedback they have provided!**
--------
**G1 Additional Visualizations**
We attach a one-page PDF that contains additional visualization results as suggested by the Reviewer 4HNX.... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Do causal predictors generalize better to new domains? | Accept (spotlight) | Summary: This work aims to provide an empirical study of how well models trained on causal features generalize across domains compared to models trained on all features. The major result from this study is that, contrary to the existing understanding that using causal features can generalize well in different environme... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. Below, we aim to answer the reviewer’s concerns:
(*Quality in terms of writing*)
We will revise our work and adopt a more precise writing style, as well as reference in greater detail to what we are referring to.
(*Hand-selected causal features... | Summary: This paper attempts to test the hypothesis of whether models trained on causal features generalize better across domains.
The authors found that predictors using all available features both causal and non-causal, have better in-domain and out-of-domain accuracy compared to causal features-based predictors. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. To answer the reviewer’s concerns and questions:
(*Distribution Shift*)
> unclear what type of distribution shift
We are considering natural distribution shifts. For example, the distribution shift induced by switching between geographic regio... | Summary: The paper empirically investigates the hypothesis that machine learning models trained on causal features generalize better across domains. Analyzing 16 prediction tasks across various datasets, the study finds that models using all available features outperform those using only causal features in both in-doma... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and positive feedback! To answer the reviewer’s comments:
> how the domain split is done [...] can be described more clearly
Thank you for drawing our attention to that. We will state how we obtain the domain splits more clearly.
> Anti-causal... | Summary: In this paper, an extensive evaluation of Ml methods with different feature sets is performed.
Only tabular data is considered and different feature set are considered: all, arguably causal and causal.
In the experiments no advantage of using causal features is shown for the domain generalization task.
Stre... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and amazing feedback! To answer the reviewer’s question:
> add an experiment with simulated data, with varying degrees of difference in the out-of-distribution data [...] (something similar to what is done in anchor-regression literature)
Follow... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your thoughtful comments and suggestions!
(*Contribution*) We are encouraged that you found our work has "very useful, inspiring results which will probably spark new research directions and/or rebuttals" (MY3d) and "contributes valuable insights for the field" (b... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors provide a thorough benchmark and analysis of machine learning models trained on different features to generalize to unseen domains. They use tabular datasets from various fields, including health, employment, education, etc., and categorize features into groups ranging from causal to anti-causal to... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and positive feedback! We will fix the typos, thanks for pointing them out to us. To answer the reviewer’s questions:
> add the sample size to Table 1?
Yes, we will add the sample sizes to Table 1. Note that they are currently provided in Table ... | null | null | null | null | null | null |
Interfacing Foundation Models' Embeddings | Accept (poster) | Summary: The paper proposes to construct an interface to connect the embeddings and predictions from different foundation models. With the designed interface, the overall system has a promise to interleave any modality in a flexible manner. To showcase this flexibility, this paper constructs a benchmark, FIND, by lever... | Rebuttal 1:
Rebuttal: **We sincerely appreciate your thorough and comprehensive reading of our paper.** We understand your frustration with the confusing implementation details despite your diligent effort. We apologize for any confusion caused. **We kindly ask that you consider the paper from a higher-level perspectiv... | Summary: FIND is a generalized interface for aligning foundation models' embeddings using a lightweight transformer without tuning pretrained model weights. It supports various tasks like retrieval and segmentation, is adaptable to new tasks and models, and creates a shared embedding space through multi-task training. ... | Rebuttal 1:
Rebuttal: **We greatly appreciate your comprehensive comments in the weakness section. We sincerely hope that, after reading our rebuttal, you will have a new perspective.** We believe most of the confusion stems from terminology common within the small multimodal understanding community. It would be benefi... | Summary: The paper explores a unified multimodal embedding space across three image-text interleaved tasks, covering different granularity levels from image-level to pixel-level tasks.
Strengths: 1. The work investigates various multimodal tasks under image-text interleaved inputs, including grounding, retrieval, and ... | Rebuttal 1:
Rebuttal: We greatly appreciate your clear and insightful comments. We have made our best effort to address them carefully below and in [Common Question 1], and [Common Question 3]:
[Q1] Experiment on a Larger dataset.
Thank you so much for your interest in scaling up the training recipe. Unfortunately, w... | Summary: The authors propose a benchmark for evaluation of what they call 'interleave understanding', or tasks which depend on the embeddings which are aligned across both modalities and task granularity, and they call this benchmark FIND-Bench. Find-Bench includes variants of segmentation and retrieval tasks, derived ... | Rebuttal 1:
Rebuttal: Thank you very much for your comprehensive reviews. We are motivated to address your concerns in detail below. We hope this clarifies your questions and enables you to have a better impression of our paper.
[Q1] Missing related work with “Pic2word”.
Thanks for pointing out this related work, thi... | Rebuttal 1:
Rebuttal: → We thank all the reviewers for their constructive comments with the **following strengths listed**:
**[Novelty (iZKM, kGcR, kpHd, AFHH)]**: The idea of making a universal benchmark for grounding, retrieval, and segmentation at various granularities is novel. The exploration of a unified embeddi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Targeted Sequential Indirect Experiment Design | Accept (poster) | Summary: This paper designs comprehensive experiments that maximize the information gained about the query of interest within a fixed budget of experimentation, including nonlinear, multi-variate, confounded settings.
Strengths: This paper is well-motivated and relevant to the causal inference. Overall, this work is w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful comments and address the mentioned concerns/questions one by one.
## Weaknesses
1 **Practical usefulness**: Yes, we do believe our contribution is practically useful. In particular, academia (e.g., the “A-Lab” at the Berkeley lab) and industry (big pharma a... | Summary: This paper proposes a framework for designing sequential indirect experiments for estimating targeted scientific queries in complex, nonlinear environments with potential unobserved confounding when direct intervention is impractical or impossible. The authors formulate the problem as the sequential instrument... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind assessment and useful comments. We reply to the raised questions one by one.
**Causal Structure and Assumptions:** As long as the instrumental variable assumptions hold, we are agnostic to any confounding $U$ between the treatment variable $X$ and the outcome ... | Summary: The authors provide a procedure to use a sequence of encouragement designs to identify target functionals about a particular causal relationship.
Strengths: This is a really cool problem setting. It isn't obvious to me that it's particularly common, but I think the larger idea of trying to think about _which_... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind assessment and useful comments. We reply to the raised questions one by one.
**Eq. 4 giving valid bounds**: Thanks for pointing out the missing steps here. Indeed, much of the machinery to see that eq. (4) yields valid bounds is in the Bennet et al. (2023) pap... | Summary: The authors' primary goal is to design experiments that maximally inform a query of interest about the underlying causal mechanism, within a fixed experimentation budget. They address this by maintaining upper and lower bounds on the query and sequentially selecting experiments to minimize the gap between thes... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind assessment and useful comments. We reply to the raised questions one by one.
**Scalability:** Following the suggestion, we have worked on additional higher-dimensional experiments. We show all results in the main pdf of the rebuttal. In Fig. 1 and 2, we presen... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. We included the pdf showcasing various additional experiments in response to specific questions and kindly refer to the individual responses. Due to the space restrictions for the individual rebuttals, we had to heavily compress our replies to th... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ordered Momentum for Asynchronous SGD | Accept (poster) | Summary: In distributed learning environments, asynchronous SGD (ASGD) and its variants are often used to deal with computing nodes with uneven computing power. Momentum methods are beneficial for both optimization and generalization in deep model training, but directly applying momentum to ASGD may hinder its converge... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and the support of our work. Below, we respond to the raised concerns and questions point by point.
**Response to Weakness 1:**
Thank you for your valuable feedback and suggestions regarding the generalization ability of the OrMo met... | Summary: This paper proposed a new ordered momentum for asynchronous SGD based on the delayed update characteristic of asynchronous training, which weights the momentum according to the actual iteration index. The authors proved the convergence of the algorithm both theoretically and experimentally.
Strengths: * This ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and the support of our work. Below, we respond to the raised concerns and questions point by point.
**Response to Question 1 (Weakness 1):**
This is a very good question. In fact, a core contribution of this paper is exactly the theo... | Summary: The paper introduces Ordered Momentum (OrMo), a novel method enhancing the performance of ASGD by systematically incorporating momentum based on the iteration indexes of gradients. The authors provide theoretical proofs demonstrating the convergence of OrMo for non-convex problems, marking an advancement as th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments. Below, we respond to the raised concerns and questions point by point.
**Response to Weakness 1:**
In Section 3, we first propose a new reformulation of SSGDm, which serves as the inspiration for designing OrMo for ASGD. We then pre... | Summary: This paper introduces OrMo, a novel method to weight stale gradients received on the server in asynchronous SGD with momentum. The algorithm is based on the idea of organizing the sequence of gradients received on the server into "buckets" to approximate standard minibatch SGD with momentum. The method is show... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and the support of our work. Below, we respond to the raised concerns and questions point by point.
**Response to Weakness 1:**
As you pointed out, the gradients in SSGDm and OrMo are computed at different points due to the asynchron... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for the comments concerning our manuscript entitled "Ordered Momentum for Asynchronous SGD". These comments are valuable and very helpful. We have read through the comments carefully and responded to the comments point by point. Based on the comments of all the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Cover Selection for Image Steganography | Accept (poster) | Summary: This paper presents a steganographic cover optimization framework that can be used to enhance existing steganographic methods. The authors use a pre-trained DDIM to reconstruct the cover image, optimizing the latent in the process and thus reducing the message extraction error. Meanwhile, the authors deeply an... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our contributions. Our work advances steganographic cover optimization by using DDIM to adjust the cover image, reducing extraction errors and improving stego image quality. Unlike previous methods, our approach offers stronger interpretability. We show ... | Summary: This paper introduces an innovative cover selection framework that optimizes within the latent space of pretrained generative models to identify the most suitable cover images, distinguishing it from traditional exhaustive search methods. This approach offers significant advantages in both message recovery and... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our work, including (a) identifying limitations of existing cover selection methods, (b) integrating pretrained generative models with steganographic encoder-decoder pairs, and (c) demonstrating that our neural encoder hides messages within low variance ... | Summary: This paper presents a novel framework for cover selection in image steganography to enhance the message recovery performance, which optimizes the latent code. The effectiveness of this approach is validated through intensive experiments. Additionally, the paper empirically analyzes intriguing behaviors occurri... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their valuable feedback, and we will address each point in detail.
**Weaknesses**:
- **Image alteration:** We acknowledge the concern regarding the alteration of the original image content due to the optimization of latent variables. However, it is im... | null | null | Rebuttal 1:
Rebuttal: 1. We thank all the reviewers for acknowledging our contributions and providing constructive feedback. Our ***key contributions*** include (a) identifying limitations of existing cover selection methods, (b) integrating pretrained generative models with steganographic encoder-decoder pairs for co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation | Accept (poster) | Summary: This paper studied convergence rate of general adaptive stochastic approximation with biased gradient. This is an important field since only biased gradient is accessible in many practical machine learning problem. The authors establied non-asymptotic bound based on various assumption, some of which are quite ... | Rebuttal 1:
Rebuttal: Thank you for your comments.
It is true that the stochastic optimization literature is very rich and is the focus of a great deal of research activity. For instance, the work proposed in [81] focuses on the theoretical analysis of stochastic optimization problems with biased gradients.
First of ... | Summary: This paper aims to analyze SGD with biased gradients in a non-asymptotic manner, where the steps are also adaptive. In particular, under certain assumptions and conditions, it provides convergence guarantees and establishes convergence rates for a critical point of non-convex smooth functions for various adap... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful review and the opportunity to clarify our contributions.
- In our framework, the gradient is computed using a single sample, as is common in many theoretical results. However, this can easily be extended to account for batch size. In such a case, instead... | Summary: This paper proposes non-asymptotic convergence guarantuees on several gradient-based optimization methods, ranging from the SGD to AdaGrad-like methods, when the estimation of the gradients is biased. More specifically, the convergence results include a theorem with the Polyak-Lojasiewicz condition and another... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful comments.
There are three types of hypotheses discussed: those concerning only the adaptive algorithm, those concerning only the application (objective function), and those concerning both. These assumptions are discussed in the paper, but we will provid... | Summary: This paper studies the non-asymptotic convergence guarantees of SGD with adaptive step sizes and (time-dependent) biased gradient estimators for nonconvex smooth functions. Applications to AdaGrad, RMSProp and Adam are developed. Numerical experiments on bilevel and conditional stochastic optimization, as well... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful comments.
We have changed "adaptive steps" to "adaptive step sizes" in the revised paper.
In Theorems 4.1 and 4.2, since the exact form of $A_n$ is unknown (as we aim to cover all adaptive algorithms), we must assume certain properties about the precond... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for their helpful reviews and constructive feedback, which have helped us further improve our paper. Below, we provide a common response to comments made by several reviewers, followed by our point-by-point responses addressing all the specif... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Stochastic and adaptive optimization algorithms are commonly used in advanced machine learning techniques. However, the analysis of non-convex optimization with biased gradients is lacking in the literature. This work considers the general scenario of optimizing machine learning objectives with practical optim... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful review and the opportunity to clarify our contributions.
$\textcolor{blue}{\textbf{Link Between IWAE and Bilevel Optimization}}$
We agree that assuming an increasing number of samples during optimization in IWAE is unrealistic; it is used merely to illu... | Summary: The authors provide convergence guarantees for the biased adaptive stochastic approximation framework and establishes convergence to a critical point for non-convex setting of Adagrad type methods. The authors illustrate their results in the setting of IWAE.
Strengths: Stated results for the rates of converge... | Rebuttal 1:
Rebuttal: Thank you for your feedback, we give below some clarifications concerning H3. The purpose of Assumption H3 is to provide a very general framework that covers all possible applications and adaptive algorithms. Since H3 $(ii)$ is a well-known assumption [20], we understand that your question concern... | null | null | null | null |
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections | Accept (poster) | Summary: This paper proposes UniSDF, an improved NeuS architecture capable of reconstructing photorealistic reflective surfaces and robustly working in real-world scenes. The method models reflective color and base color as separate MLPs, using learned weights to blend them and obtain the final colors. Qualitative and ... | Rebuttal 1:
Rebuttal: >**Explicitly tracing the reflective ray**
In this work, similar to many recent methods that are tailored to handle reflections [12,14,22,50], we follow Ref-NeRF and parameterize part of view-dependent appearance as a function of the reflected view direction $\mathbf{\omega}\_r$. Explicitly traci... | Summary: The paper tackles the problem of 3D reconstruction in the presence of highly reflective objects. To address the problem, they propose to learn an SDF-based neural representation. Different from prior work, they use two radiance branches in their representation, one conditioned on the camera viewing direction r... | Rebuttal 1:
Rebuttal: >**Why learned weights separate specular/diffuse regions without explicit supervision**
In Ref-NeRF, the ablation study shows that when rendering reflective surfaces, using the reflected view direction as the MLP’s input is explicitly better than using the camera view direction of NeRF. In our me... | Summary: The paper proposes a method to reconstruct scenes containing both reflective surfaces and non-reflective surfaces with high fidelity. Specifically, it trains a camera view radiance field and a reflected view radiance field separately, combining them by a learnable weight. The method is evaluated on four datase... | Rebuttal 1:
Rebuttal: >**W1: Time consuming**
Our method has three fields and is based on volume rendering. Thus our rendering efficiency is not high. Recently, 3D Gaussian Splatting has become popular in view synthesis and there are some concurrent works focusing on reconstructing the surface [1*] or rendering reflec... | Summary: This paper proposes a new strategy for modelling view-dependent effects in Neural Radiance Field-based scene models. Existing approaches have used networks conditioned on camera view directions, as well as on reflected view directions using surface normals, but this work proposes and validates the idea that bo... | Rebuttal 1:
Rebuttal: >**Applying the idea in 3D Gaussian Splatting**
Thank you for your interesting suggestion. Recently, 3DGS techniques are advancing rapidly and there are some concurrent works trying to reconstruct the surface [1*] or render reflections [2*]. GaussianShader [2*] introduces shading attributes for e... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. In this global rebuttal, we address the common questions raised by the reviewers as follows:
> **Physical interpretation**
In this work, we mainly focus on robust surface reconstruction of real-world complex scenes with both reflective and non ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints | Accept (spotlight) | Summary: In this work, the authors study online convex optimization with adversarial and time-varying constraints, and aim to bound both the regret and the cumulative constraint violation (CCV). Compared with previous studies, the main contributions of this work can be summarized as: 1) a new algorithm with $O(\sqrt{T}... | Rebuttal 1:
Rebuttal: $\textbf{On the lower bound:}$
$\textbf{Comment:}$ The lower bound for convex functions only holds for the case with $d=T$ or $d>T$. However, for online problems, it may be common to consider a very large $T\gg d$, which implies that the optimality of the proposed algorithm for convex functions m... | Summary: This paper considers online convex optimization with constraint. The authors propose a new surrogate loss function, and by applying traditional online learning algorithms to the surrogate loss, $\mathcal{O}(\sqrt{T})$ regret and $\mathcal{O}(\sqrt{T \log T})$ can be obtained in the online learning setting. The... | Rebuttal 1:
Rebuttal: $\textbf{On the relevance of the OCS problem}$
$\textbf{Comment:}$ ``Compared with Section 2, ... reduce the contents in Section 3."
$\textbf{Reply:}$
The OCS problem considered in Section 3 is interesting from both technical and practical points of view for the following reasons. On the techni... | Summary: The authors study Online Convex Optimization (OCO) under adversarial constraints of the form $g_{t,i}(x) \leq 0$ where $g_{t,i}$ are arbitrary convex and Lipschitz. The authors design a simple algorithm based on Lyapunov optimization which obtains an optimal regret rate and simultaneously optimal constraint vi... | Rebuttal 1:
Rebuttal: $\textbf{Type of adversary:}$
$\textbf{Comment:}$ ``The authors do not formally define the interaction protocol in the online setting studied in this paper. Specifically, from lines 27-28 is is implied that the adversary chooses the loss function $f_t(\cdot)$ adaptively after seeing $x_t$, which ... | Summary: The major part of this paper studies adversarial OCO with unknown, time varying constraints in the soft-enforcement setup, wherein at each round, both a loss $f_t$, and constraints $g_{t,i}$ are revealed. The performance metrics are the usual regret $\mathrm{Regret}_T = \sum_t f_t(x_t) - f_t(x^*),$ and the cum... | Rebuttal 1:
Rebuttal: $\textbf{Lower Bound of Theorem 3}$
$\textbf{Comment:}$ ``My only real grouse with the paper is the lower bound of Theorem 3, which I find to be overstated. The theorem is presented as - under assumptions 1, 2, and 3, the regret and CCV are $\Omega(\sqrt{T}),$ but the proof only defines a single ... | Rebuttal 1:
Rebuttal: In the attached pdf, we report our experimental results for the credit card fraud detection problem.
Pdf: /pdf/4419fa2c7ffb73449c226c692e6154d747d290b2.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning | Accept (poster) | Summary: This paper proposes a model-based method for testing the safety properties of Deep Reinforcement Learning (DRL). The method computes a ranking of state importance across the entire state space, dividing the state space into safe and unsafe regions. The approach provides optimal test-case selection and guarante... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our idea of guiding DRL testing through an importance ranking interesting. Given the current lack of testing methodologies for DRL and the numerous potential extensions of our approach, we believe our method could significantly impact the development of testing st... | Summary: This paper proposed a novel model-based importance-driven framework for testing trained DRL policies. By focusing on testing the cases with higher importance ranks, the proposed framework improves the testing scalability. The evaluation of several case studies demonstrates that the proposed framework can more ... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our testing approach interesting. We believe our method could significantly impact the development of testing strategies for DRL and hope that it will lead to interesting follow up research. We will answer the questions and address the mentioned weaknesses in the ... | Summary: This paper presents a framework called importance-driven model based testing for RL models. It uses a model-based approach to compute estimates of safety based on the MDP explored so far using the policy and ranks the importance of states based on the impact of decisions on safety. Then it samples the poli... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed evaluation of our paper and for seeing the relevance of our proposed testing approach. We believe that our approach has the potential to significantly impact testing for DRL and hope that it will lead to additional interesting follow up research. In the follo... | Summary: This work looks into the RL testing problem. RL policies are complex and hard to understand in terms of safety and performance. This work aims to test the policies via states in which the agent’s decisions have the highest impact on the expected outcome. This paper proposes a model-based method to compute a ra... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s interest in our idea of guiding DRL testing through an importance ranking. We also agree that there is a current lack of testing methodologies for DRL. Additionally, considering the many potential extensions of our approach, we believe our method could significantly im... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable feedback. Our paper introduces model-based testing to the reinforcement learning setting. Since this is the very first paper in that direction, we agree with the reviewers that there are numerous intriguing directions yet to be explored, such as... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes an algorithm (IMT) to test a deterministic policy in finite MDPs where a model of the MDP is available. The primary contributions of the paper are algorithmic and empirical. IMT works by iteratively partitioning the state space into safe, unsafe or undetermined. Using the MDP model, IMT is a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed evaluation of our paper. We believe that our proposed testing framework has the potential to significantly impact testing for DRL. Although testing DRL is intrinsically challenging, the problem has only recently garnered attention from the testing community. ... | null | null | null | null | null | null |
Idiographic Personality Gaussian Process for Psychological Assessment | Accept (poster) | Summary: This paper presents a multi-output Gaussian process model for classification in the context of psychological studies. It's based on a linear model of co-regionalisation leveraging unit-level latent factors and RBF kernels to jointly model population and individual personality traits to tackle an old debate in ... | Rebuttal 1:
Rebuttal: Dear Reviewer A4W7,
Thank you for your valuable feedback. Please see our responses below
> In this sense, could you provide numerical evidence and comparison regarding training and prediction times for IPGP and competitors?
Sure, please see below. We’d however first like to comment that runtime... | Summary: Gives a multi-task/output GP formulation for multiple time-series (or intrinsic co-regionalisation model) for pyschological assessments. Design of the factor loadings informs the task-correlations and reflects the knowledge of individual's correlation between his responses and inter-person correlations. More o... | Rebuttal 1:
Rebuttal: Dear Reviewer mvMV,
Thank you for your valuable feedback. Please see our responses below.
> The paper is weak from the perspective of advancing state-of-the-art machine learning algorithms.
Please see the shared rebuttal regarding the scope and nature of our contributions.
> The paper is tota... | Summary: 1. This paper introduces an innovative measurement framework utilizing the Gaussian process coregionalization model to resolve the question of whether psychological attributes such as personality exhibit a universal structure among the populace or are uniquely individualized.
2. An Idiographic Personality Gau... | Rebuttal 1:
Rebuttal: Dear Reviewer YTHZ,
Thank you for your valuable feedback. Please see our responses below.
> there are many MTGPs, the author ignored comparison with them
To our knowledge, Duerichen et al. [17] is the only existing MTGP model in the behavioral literature for multivariate physiological time-ser... | Summary: UPDATE: I am updating my scores in light of the excellent authors' response.
This paper considers psychometric data composed of ordinal responses $y_{ijt}$, each of which represent how unit _i_ answered survey item _j_ during time period _t_. The key characteristic of this item-response data is that the same ... | null | Rebuttal 1:
Rebuttal: Two reviewers commented on the nature of our contributions. We post a shared comment on the scope of our contributions here.
We would like to first stress that our contributions are not merely theoretical, but that our work also represents applied machine learning for science. Both applications a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Iterative Reinforcement Learning from Human Feedback with General Preference Model | Accept (poster) | Summary: This paper explores RLHF with a general preference model. Specifically, the authors formulate a learning objective aimed at identifying a policy consistently preferred by the KL-regularized preference oracle. Furthermore, the authors propose a sample-efficient training algorithm applicable to both online and o... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and constructive suggestions! Our responses are as follows.
**Weakness 1** Include examples of the prompts used for training the preference model and responses generated by different methods.
Sure. Some example prompts are provided as below
```markdown
What is ... | Summary: This paper introduces a two-play game approach to the RLHF problems. Both offline and online algorithms are studied, including a combination of pessimism in offline learning and exploration/exploitation in the online setting. The authors conducted experiments to verify the effectiveness of their algorithm.
Th... | Rebuttal 1:
Rebuttal: Thanks for your great efforts in reviewing our paper and constructive comments!
**Question 1** Experiment details:
1. how reward models are trained? Is oracle RM trained through next token prediction and with max length = 1 such that probability of A is the score?
We will add more details to imp... | Summary: The paper considers the problem of RLHF under a general preference model, going beyond the reward-based Bradley-Terry model. In particular, they cast the problem as a KL regularized minimax game between two LLMs, and show that their framework is strictly more general than reward-based RL. They propose algorith... | Rebuttal 1:
Rebuttal: Thanks for your great efforts in reviewing our paper and thanks for recognizing our work!
**Weakness 1** The paper should also have tested using $\log(p/(1-p))$ as a target. Their current target corresponds to the IPO target.
We use $P$ directly as a target since it is more straightforward to e... | Summary: The authors develop a theoretical framework based on a reverse-KL regularized minimax game and introduce sample-efficient algorithms suitable for both offline and online learning scenarios. Empirical results validate the proposed method, demonstrating its superior performance compared to traditional reward-bas... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments! Our responses are as follows.
**Weakness 1** The empirical validation, while promising, is limited in scope. More extensive experiments, including comparisons with a broader range of state-of-the-art RLHF methods, would strengthen the paper.
Thank you for y... | Rebuttal 1:
Rebuttal: Thank all the reviewers for the constructive comments. We appreciate that all reviewers provided positive feedback during the initial review round. We would like to highlight some key points below to clarify some confusing parts.
1. Novelty — Online algorithm with general preference oracle: we pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability | Accept (poster) | Summary: This paper presents Vista, a driving world model that predict future driving video based on video diffusion model. In particular, the author introduces the idea of conditioning on prior frames and two domain-specific losses to capture dynamics and preserve structures for driving scenarios. By using LoRA, Vista... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our paper. We provide detailed explanations below to solve the questions and some potential misunderstandings.
> **W1&W2&Q1**: The improvements from GenAD seem to be incremental. The proposed dynamic priors, loss functions, and LoRA adaptation are not ... | Summary: Vista is a generalizable driving world model that excels in high fidelity and versatile controllability. By introducing novel losses to enhance the learning of dynamics and structural information, and integrating a unified conditioning interface for diverse action controls, Vista achieves high-resolution predi... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and questions. We answer each question below and will incorporate all feedback in the revision.
> **W1**: Details related to the production of Fig. 5. (1) Has SVD been retrained? Are there any other action control modules? (2) The details of long-term generat... | Summary: The paper presents a method named Vista, a novel driving world model that addresses limitations in generalization, prediction fidelity, and action controllability. Key contributions include introducing novel loss functions for high-resolution prediction, a latent replacement approach for coherent long-term rol... | Rebuttal 1:
Rebuttal: Thanks for your insightful and positive feedback. The following are our responses.
> **W1&Q3**: Surround-view generation is not supported.
We agree that supporting surround-view generation would further help driving. We are planning to extend Vista to multi-view settings like Drive-WM (Wang, et ... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers and ACs:
We express our sincere gratitude to the reviewers for their thorough and constructive comments. It is encouraging that all reviewers have acknowledged our pioneering efforts in establishing a driving world model with versatile controllability.
We have carefully taken each... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosted Conformal Prediction Intervals | Accept (poster) | Summary: This paper introduces a gradient boosting-based approach to tailor a conformal score function to better satisfy desirable properties. A more general score function "family" is introduced which adheres to a specific form dependent on parameters (e.g. $\mu, \sigma$). Instead of directly using the parameter estim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful comments. In response to each of the reviewer’s specific points:
> Is this procedure amenable to any black-box predictor and can be considered a particular gradient boosting mechanism?
- As an evaluation metric, the contrast tree algorithm applies to a... | Summary: This paper introduces a methodology for learning a conformal score function after training. Notably, the proposed method does not require model retraining, and instead learns the score function using trained model predictions using a cross-validation approach on the training data.
Strengths: **Originality:** ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time spent reviewing the manuscript. In response to each of the reviewer’s specific comments on weaknesses:
1.
> My main reservation with the proposed method is the seemingly marginal benefit it provides over CQR for conditional coverage, as shown in Table 1.
Ple... | Summary: The paper proposes to utilize the training data and gradient boosting (of the model's predictions) to optimize loss functions related to conformal prediction interval length and conditional coverage with respect to the score function. The optimized score function is then used for "plain" CP usage, via calibrat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments. In response to each of the reviewer’s specific comments on weaknesses:
- Please refer to point 1 in the global rebuttal.
- Please refer to point 5 in the global rebuttal.
Response to questions:
- Please refer to point 2 in the global rebuttal.
... | Summary: The paper resents a novel method to enhance conformal prediction intervals using gradient boosting. The proposed method focuses on improving specific properties such as conditional coverage and interval length. The key idea is to iteratively refine a predefined conformity score function via boosting, guided by... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to carefully review our manuscript. In response to each of the reviewer’s specific comments on weaknesses:
1.
> A comprehensive description of the experiment setting is necessary.
In our revision, we will more clearly describe our experimental setting. ... | Rebuttal 1:
Rebuttal: We are grateful to all the reviewers for their valuable feedback and constructive suggestions for improving our manuscript. In the rebuttals below, we have responded to each reviewer’s point individually. The attached PDF includes additional simulation results. Below, we address several common poi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy | Accept (poster) | Summary: The paper proposes a user-level differentially private mechanism utilizing Huber loss minimization for mean estimation. This approach is robust to heavy-tailed distributions and addresses data imbalance across different users.
Strengths: - The paper is well-written and easy to follow overall.
- The differenti... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments.
# Reply to weaknesses
**1. It is unclear whether the convergence results (Theorem 5) are generally applicable to generic $w_i$'s. Using $m_i\wedge m_c$ seems somewhat unclear. What if the server (in federated learning) wants to compute the mean with $w_i=m_i/\s... | Summary: The paper proposes a user-level differential private mean estimation based on minimizing weighted huber loss. The authors conduct theoretical and empirical assessments, showing that the proposed method is more robust to user-wise sample imbalance as well as heavy-tail distributions compared to the Winsorized m... | Rebuttal 1:
Rebuttal: Thanks for these valuable comments.
# Reply to weaknesses
**The definition of robustness should be clarified in the paper. Is it referring to robustness against heavy-tailed data, arbitrary outliers, or specific types of attacks? Therefore, it would be beneficial for the authors to explicitly de... | Summary: Overall, the method proposed by the authors is interesting and effectively addresses the issue of privacy protection for users with imbalanced data. Compared to existing methods, the authors' approach is more robust and is supported by mathematical proofs.
Strengths: 1. The proposed method demonstrates signif... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments.
**Q1.The authors' discussion lacks comprehensiveness. For instance, the statement "The most effective approach is the two-stage scheme, which finds a small interval first and then gets a refined estimate by clipping samples into the interval" is not fully substa... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for the reviews. We are encouraged that reviewers have positive views on the mathematical solidness, practical value (Reviewer Bmi5), novelty (Reviewer BDM5) and presentation (Reviewer zYzW) of this paper.
The detailed feedbacks of each review are provided below. We are lo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear $q^\pi$-Realizability and Concentrability | Accept (poster) | Summary: This paper proves that finite horizon offline RL under linear q^\pi realizability assumption can be solved efficiently (in terms of sample complexity) if the data are trajectories collected by a policy with bounded concentrability coefficient (that is, the density ratio between the state-action distribution in... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback.
> What is the reason that $G = \bar G$ passes the condition (14)? Is it because the concentrability assumption plus Lemma 4.2 results in a tight confidence interval for the q-value?
Yes, it is precisely as you say “because the concentrability ass... | Summary: This paper presents an important theoretical result in offline reinforcement learning (RL) with linear function approximation. The authors show that under the assumptions of linear q-realizability, concentrability, and access to full trajectory data, it is possible to efficiently learn an ε-optimal policy with... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback.
> It would be beneficial if the authors could add the concrete definition of the previous non-trajectory data to make a direct comparison with full length trajectory data (Assumtpion 2). Also, the authors could add some comments on the hardness re... | Summary: This paper considers the problem of learning the value ($Q$) function under q^{\pi} realizability and concentration assumption. The major contribution is to use trajectory data instead of independent samples to learn the target function, where negative results have been proven with independent samples.
Streng... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback.
> The presentation is a problem of this work. The notation system is not reading friendly, and there is no algorithm block. I suggest to add an algorithm to make the input and output clear.
As in the camera ready we can have an additional page, a... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mixture of neural fields for heterogeneous reconstruction in cryo-EM | Accept (poster) | Summary: This paper introduces a novel method, Hydra, for ab initio heterogeneous cryo-EM reconstruction. Different from existing approaches, Hydra separately models conformational and compositional heterogeneity by integrating K-parameterized neural fields to represent cryo-EM density maps. Furthermore, Hydra employs ... | Rebuttal 1:
Rebuttal: We thank our reviewer for their comments on the significance and the difficulty of the problem our method addresses. We hope to address their concerns in the following response.
**Synthetic Datasets**
To further validate Hydra on the tomotwin dataset, we generated a larger version of this datase... | Summary: In this paper, author(s) porpose Hydra, an *ab initio* approach to model conformational and compositional heterogeneity. They ahieve this by parameterizing structures of proteins as a mixture of *K* neural fields.
Strengths: Originality:
- Authors propose to incorporate neural network ensemble with recent *a... | Rebuttal 1:
Rebuttal: We thank our reviewer for their comments. We hope to address them in the following response.
**Distinction with Previous Works**
Hydra uses the pose search strategy and autodecoding framework from DRGN-AI [1]. It primarily differs by using several neural networks, “latent scores” (L197) and a ne... | Summary: This work describes a new method for ab initio heterogeneous reconstruction in cryo-EM using mixtures of neural fields. This generalizes previous approaches, such as CryoDRGN and DRGN-AI, which attempts to reconstruct 3D molecular densities using a single neural field representation. The resulting method is ab... | Rebuttal 1:
Rebuttal: We thank our reviewer for their constructive comments and overall positive rating of our submission.
**Validation on an Experimental Dataset Combining Compositional and Conformational Heterogeneity**
As mentioned by our reviewer, processing real cryo-EM data often comes with unforeseen and signi... | Summary: The paper presents a neural network-based methodology for modeling both compositional and conformational protein states in cryo-electron microscopy (Cryo-EM) 3D reconstruction.
In particular, the authors propose a fully *ab initio* approach, named Hydra, which enables the joint inference of poses, conformati... | Rebuttal 1:
Rebuttal: We thank our reviewer for their constructive feedback and suggestions on ways to improve the clarity of the manuscript.
**Clarification of Prior Work and Contributions**
We thank our reviewer for pointing out the lack of clarity in the presentation of prior works and apologize for the absence of... | Rebuttal 1:
Rebuttal: We thank all our reviewers for their detailed and constructive feedback. We value their appreciation of the significance of this work **[RoiP, qUkh]**, its novelty **[14wr]** and the validation of our method on experimental data **[u3f9, 14wr]**. We appreciate them highlighting the substantial imp... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This submission presents Hydra, a method for handling heterogeneous cryo-EM reconstruction. Hydra can model both conformational and compositional heterogeneity and can perform ab initio reconstruction. To achieve this, it parameterizes structures as arising from one of K neural fields. In the optimization pipe... | Rebuttal 1:
Rebuttal: We thank our reviewer for their comments. We hope to address their questions and concerns in the following response.
**Distinction with DRGN-AI**
Hydra uses the same pose search strategy and autodecoding framework as DRGN-AI. It primarily differs by using several neural networks, “latent scores”... | null | null | null | null | null | null |
FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor | Accept (poster) | Summary: This article proposes a robust algorithm for spiking neural networks. The algorithm includes a frequency-domain filter with a hard threshold and trainable neuron leakage parameters. The author's motivation in organizing the paper is based on biological interpretability, adopting an engineering approach in meth... | Rebuttal 1:
Rebuttal: ### **1. The innovation point of this work is not clear compared with StoG [11].**
We highlight the innovation of our theoretical analysis compared to StoG from two key perspectives:
1. **Theoretical focus**. For the regularizer $|\epsilon \odot \nabla_x \mathcal{L}(x)|_1$ in Eq. 5, StoG focuses ... | Summary: This paper presents a unified framework for SNN robustness, based on this framework, this paper further proposes a frequency encoding (FE) method for SNNs to decrease the input perturbations and proposes an evolutionary membrane potential leak factor (EL) to ensure that different neurons in the network learn t... | Rebuttal 1:
Rebuttal: ### **1. Please provide more evidence or analysis to support the performance improvement by FE.**
We would like to show our FE not only improves defense accuracy but also maintains clean accuracy from both **data observation** and **additional experimental validation**.
1) **Data observation**. ... | Summary: This paper aims to enhance the robustness of SNN. The authors first present a unified framework for SNN robustness. They propose a frequency encoding method that filter the noise in frequency domain. Based on that, they also propose the trainable leaky parameter to better constrain robustness. Experimental res... | Rebuttal 1:
Title: Explaination for the first question
Comment: Sorry for the ambiguous comment of my first question in Weaknesses. My question is how the authors conclude that a smaller leaky factor will increase robustness based on Eq. 6? It seems that if you directly reduce the leaky factor, the gradient term will a... | null | null | Rebuttal 1:
Rebuttal: We appreciate all the reviewers for the insightful feedback. We are encouraged that they recognize the significance and urgency of our motivation [Reviewer zYHj], the novelty [Reviewer uyY9] and effectiveness [Reviewer zYHj] of our method, and the comprehensiveness [Reviewer uyY9] and extensivene... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model | Accept (poster) | Summary: This paper presents QuadMamba, a novel Mamba architecture for visual tasks such as image classification and dense predictions. Unlike the classic Vision Mamba, which splits 2D visual data using fixed windows, the authors introduce learnable windows by using a lightweight module that predicts 2D locality more i... | Rebuttal 1:
Rebuttal: ### Q1.Details about the lightweight prediction module
Thanks for your valuable advice. We will depict the prediction module more clearly in the revised manuscript. In our design, each QuadVSS block has its specific prediction module that determines the informative regions of the current layer. T... | Summary: This paper proposes a vision mamba backbone for various vision tasks. It aims to adapt the recent popular mamba model originated from the language domain to vision tasks. The authors propose a learnable quad-tree partition strategy which can adaptively generate multi-scale visual sequences with spatial prior f... | Rebuttal 1:
Rebuttal: ### Q1.More illustrations of hyper-parameters
Thanks for your valuable advice. We will add more analysis on the hyper-parameters. The impacts of the increased number of window partition levels are explored in Table 5 of the main text. We examine the choice of partition resolutions in the bi-level ... | Summary: Authors propose a technique to adapt Mamba to vision tasks. They propose a novel Quad-tree based approach instead of flattening tokens for images in a raster scan mechanism to avoid losing local dependencies. They evaluate on three vision tasks of object recognition, objet detection and instance & semantic seg... | Rebuttal 1:
Rebuttal: ### Q1.Clear and fair model comparisons
Thanks for your valuable advice. We regret that our Li/T/S/B naming system may confuse readers compared with other methods. To clearly show our method's advantages, we rename the QuadMamba model variants (Lite -> B1, Tiny -> B2, Small -> B3, and Base -> B4)... | Summary: The paper introduces QuadMamba, an enhancement of vision State Space Model (SSM) architectures. At its core is a learnable QuadVSS network block that processes the image input patch at two different resolutions. For every 2x2 coarse window with 4 image patches, the method adaptively learns to process one of th... | Rebuttal 1:
Rebuttal: ### Q1.Significance
**Q1A1 Comparing to LocalMamba:** How to effectively preserve 2D spatial dependencies is an important challenge in adapting sequence models into the vision domain. Though it has been partially explored by previous works such as PlainMamba [1] and LocalMamba [2], it is non-triv... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank the reviewers for the positive reviews of our work and constructive comments. Here is a list of new figures and tables in the attached PDF file, and references referred to in other responses.
**Figures and tables** :
Figure 1. Plots of performance, mode size, and FLOP... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Randomized Truthful Auctions with Learning Agents | Accept (poster) | Summary: The paper considers repeated auctions with agents using no-regret learning algorithms. It first extends previous result on second price auction to all deterministic auctions that the runner-up bidder may not converge to bidding truthfully, and provides the condition how the learning rates of the bidders affec... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their detailed feedback. Please find our answers to your questions below.
> MWU is the only learning algorithm that considered for bidders. Although it is representative, it would be better see more general results for other learning algorithm, or more gene... | Summary: This work studies a setting where bidders use no-regret learning algorithms (e.g., MWU) to participate in repeated auctions. Bidders' values are assumed to be persistent.
Generalizing [Kolumbus and Nisan 2022a]'s results on second-price auctions with two equal-learning rate MWU bidders, the authors show that:... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their detailed feedback. Please find our answers to your questions below.
> Bidders having persistent ...
We view our results and the setting in which we work as orthogonal to the setting of [Cai et al 2023]. Firstly, they do not restrict themselves to tr... | Summary: The paper considers the building auction mechanism for settings where the bids supplied by agents are chosen by automated no-regret algorithms operating on their behalf. It has been shown from prior work that when bidding with asymmetric valuations, no-regret algorithms converge to bids substantially far from ... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their detailed feedback. Please find our answers to your questions below.
> The main drawback of the results is their restriction to the setting of mean-based algorithms. It would be nice if the authors could comment on whether such a restriction may be rem... | Summary: This work builds upon Kolumbus and Nisan (2022a), which studies a setting where agents use no-regret learning algorithms to bid in a repeated auction setting. The authors first focus on a deterministic setting with two bidders that use the Multiplicative Weights Update (MWU) algorithm. In this case, they show ... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their detailed feedback. Please find our answers to your questions below.
> First, as far as I understand, the results in Section 3 are limited to two bidders that use the MWU algorithm. If I am not mistaken, the results of Kolumbus and Nisan (2022a) are no... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
High-dimensional (Group) Adversarial Training in Linear Regression | Accept (poster) | Summary: This paper presents a non-asymptotic consistency analysis of prediction error for the adversarial training procedure under $l_\infty$ perturbation. It demonstrates that the convergence rate of the prediction error is up to a logarithmic factor. Additionally, the authors prove that the group adversarial trainin... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's comments on our work. We hope the following responses and clarifications can address the reviewer's concerns!
**Comment 1**
The authors aim to connect their conclusions about a linear model to adversarial training, but adversarial training is a defense strategy commonl... | Summary: The paper provides a high-dimensional analysis of Linear Regression in Adversarial training. It has two contributions:
1. it proves an improved convergence rate of prediction error of $1/n$ (previous work show $1/\sqrt{n}$).
2. It extends adversarial training for the group setting and extend the convergence... | Rebuttal 1:
Rebuttal: Great thanks for the reviewer's appreciation of our work! Regarding the numerical experimental improvement, please see our response below and the revised figures in the pdf file attached in the global response.
**Comment 1**:
The numerical experiments are the main weakness. Not so many different... | Summary: This paper provided an theoretical analysis for the optimality of adversarial training methods on linear regression, and further explored the advantages of the group adversarial training method, comparing with general adversarial training method. There are also experiments supporting their points.
Strengths: ... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's insightful comments. We hope our response and clarifications can address the reviewer's concerns!
**Comment 1:** As the paper is mainly focused on empirical errors, the contributions seem to be not enough. It is better to extend the results on test error analysis, which ... | Summary: The paper studies adversarial training in high-dimensional linear regression under $\ell_\infty$-perturbations and group adversarial training. The paper also provides non-asymptotic consistency analysis.
Strengths: The associated convergence rate of prediction error achieves a minimax rate up to a logarithmic... | Rebuttal 1:
Rebuttal: Thanks for the reviewer's careful and detailed comments on our work. We hope our responses and clarifications can address the reviewer's concerns!
**Comment 1** ...improve the convergence rate......clearly stated in the abstract...
**Response**
The reviewer is correct that the order improvement ... | Rebuttal 1:
Rebuttal: We have revised the figures for the numerical experiments as requested by Reviewer J4Ez. Please see the attached pdf file.
Pdf: /pdf/bfd9319a74c051e74866bae2cca29fc0b6f124b6.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Mixture Learning when Outliers Overwhelm Small Groups | Accept (poster) | Summary: In this paper, the authors introduce the list-decodable mixture learning problem, which can be considered as the extension of the list-decodable mean estimation problem. In such case, data is drawn from a weighted mixture of $k$ inlier distributions and an adversarial outlier distribution. A notable aspect of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the feedback which will help to improve it. Below, we address specific comments and questions.
> The two stage meta-algorithm can be time-consuming. [...] The paper lacks a presentation of the time complexity, and the experimental p... | Summary: This paper addresses the problem of estimating the means of well-separated mixtures in the presence of adversarial outliers, a scenario where traditional algorithms may fail. The authors introduce the concept of list-decodable mixture learning (LD-ML), which is particularly relevant when outliers can outnumber... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the feedback which will help to improve it. Below, we address specific comments and questions.
**Experimental evaluation**
> The paper did not include real-world datasets and additional robust learning methods for comparison. [...]... | Summary: The authors investigate the problem of mean estimation for a well-separated mixture in the presence of arbitrary outliers introduced by an adversary. They propose a meta-algorithm that leverages robust mean estimation algorithms as base learners, each with a set of prescribed properties. The authors provide an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work. | Summary: This submission considers the problem of list-decoding the means of a mixture of $k$ sub-Gaussian components, under adversarial *additive* contamination which can have size $\epsilon n$ larger than the smallest-weight component. Instead of only yielding guarantees scaling with the known component weight lower ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the feedback which will help to improve it. Below, we address specific comments and questions.
> The introduction claims results even when there is no separation between mixture components, but Theorem 3.3 makes a separation assumpt... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback and comments, which help to improve our manuscript. Below, we focus on three main concerns, which were raised by several reviewers.
**Adaptive contamination**.
We prove robustness of our algorithm in the non-adaptive contamination model, where ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper studies the problem of estimating the means of a mixture model in the presence of outliers. More specifically, each component has unknown weight $w_i$ that is lower bounded by a known quantity $w_{\mathrm{low}}$, each component is assumed to be (sub)-Gaussian, and the mixture also includes an adversa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and for the feedback which will help to improve it. Below, we address specific comments and questions.
> The contamination model of eq (2.1) requires that corruptions come i.i.d. from some distribution. Since the base learners can also hand... | null | null | null | null | null | null |
Inversion-based Latent Bayesian Optimization | Accept (poster) | Summary: This paper proposes Inversion-based Latent Bayesian Optimization (InvBO), a plug-and-play module for latent Bayesian optimization (LBO) methods. The key components of InvBO are: 1) An inversion method to find latent codes z that exactly reconstruct input samples x, addressing misalignment between the latent sp... | Rebuttal 1:
Rebuttal: - **[W1, Q1, L1] Discussion on the limitations and practical considerations of LBO.**
Good suggestion. While standard BO struggles with discrete data, LBO addresses this by mapping discrete data to continuous space. To bridge the gap between the discrete and continuous space, LBO utilizes... | Summary:
The authors propose two empirical improvements to VAE-based Bayesian optimization methodology. First, the authors propose a correction for the eponymous misalignment problem which they characterize in the paper, the idea being that the latent z corresponded to an encoded x may not correspond to the x' ... | Rebuttal 1:
Rebuttal: - **[W1] Importance of $d_{\cal X}$ bounding assumption in Proposition 1.**
For analytical convenience, we use the bounding assumption of the distance function. This is similar to the concept of image normalization, where pixel values are scaled to a specific range (often 0 to 1) to faci... | Summary: The authors propose Inversion-based Latent Bayesian Optimization (InvBO), a novel approach to improve latent space Bayesian optimization (LBO) by introducing two novel components. First, to fix the misalignment problem that typically plagues LBO methods that rely on encoder-decoder models, InvBO introduces an ... | Rebuttal 1:
Rebuttal: - **[W1] Modifying the colors of Figure 1 for red-green color blind folks.**
Thank you for the suggestion. We will modify the colors of Figure 1 for red-green color-blind folks in our camera-ready version if the paper gets accepted.
- **[W2] Additional comparison to GEGL.**
... | Summary: This paper identifies and addresses an overlooked issue in several latent space Bayesian optimization methods and proposes a new trust region anchor selection method (PAS) that incorporates the "potential" of a trust region to improve optimization. Specifically, the paper proposes an inversion method to correc... | Rebuttal 1:
Rebuttal: - **[W1] Methodological contribution of InvBO beyond LOLBO and CoBO.**
Our InvBo can be applied beyond trust region-based LBO methods (e.g., LOLBO and CoBO). We show that each component of our InvBO, such as Inversion and Potential-aware trust-region anchor selection (PAS), can be impleme... | Rebuttal 1:
Rebuttal: Thank you to the reviewers for the thorough feedback on our paper. Based on the reviews, we have organized the key strengths of our paper that reviewers identified:
### **1. Convincing motivation.**
Most of the reviewers (8vpX, wVbQ, nFyz, tYNa) provided positive feedback on our motivation. We a... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Latent Bayesian optimization has been tackled in this work. In order to solve an optimization problem on a continuous latent space, it utilizes auto-encoder-based neural networks. In particular, this work attempts to solve a misalignment problem in the latent Bayesian optimization. Some experimental results ar... | Rebuttal 1:
Rebuttal: - **[W1] Motivation of InvBO.**
Other reviewers provided positive comments regarding the motivation of our paper as follows:
> Reviewer 8vpX: This paper identifies and addresses an overlooked issue in several latent space Bayesian optimization methods.
>
> Reviewer ... | null | null | null | null | null | null |
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers | Accept (poster) | Summary: The paper proposes DiffAug, a new method for training image classifiers that are more robust. DiffAug is based on diffusion models and effective at improving classifier robustness in several ways, such as resistance to variations in the data. It also can improve the performance of classifier-guided diffusion m... | Rebuttal 1:
Rebuttal: We thank you for your insightful reviews and affirmative evaluation of our work. We thank you for your appreciation of our presentation and method’s simplicity/computational efficiency. We agree with you that it is indeed interesting to learn that classifiers can be improved without sacrificing te... | Summary: This paper applies a diffusion-based data augmentation method to enhance the robustness of classifier. First, a gaussian perturbation is applied to train examples and then a single diffusion denoising step is applied to generate the augmentations. Besides, DiffAug can also be combined with other augmentations ... | Rebuttal 1:
Rebuttal: We thank you for your detailed reviews. We thank you for appreciating the strengths of our method’s simplicity and computational efficiency and its ability to be combined with other augmentations to further improve robustness. In the following responses, we aim to address the weaknesses and answer... | Summary: The paper explores the use of diffusion as a data augmentation technique to train robust classifiers. Specifically, it investigates whether a diffusion model trained without additional data can be leveraged to enhance classifier performance. The study shows utilizing a diffusion model trained without extra dat... | Rebuttal 1:
Rebuttal: We thank you for the detailed review with suggestions to strengthen the contribution. We thank you for recognizing the strengths of our method’s novelty, simplicity and computational efficiency. We are happy to see that you found our paper clearly written with nice visualisations. In the followin... | Summary: This paper proposes a data augmentation method DiffAug for training robust classifiers. The method is very simple: first, add Gaussian noise to one training image, and then denoise the noisy images with a pre-trained diffusion model. They also propose methods for test-time augmentation using DiffAug. Experimen... | Rebuttal 1:
Rebuttal: We thank you for the insightful review with suggestions to strengthen the contribution. We thank you for your appreciation of our method’s novelty, simplicity and computational efficiency, our presentation/figures and extensive ablation experiments. In the following responses, we aim to address th... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and thoughtful reviews with suggestions for improvements. We are happy to see that the reviewers appreciated the novelty (uEtP, gb3b) and simplicity/computational efficiency (uEtP, gb3b, 3vHc, QY34) of the method and found the paper to be well-presen... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dynamic Service Fee Pricing under Strategic Behavior: Actions as Instruments and Phase Transition | Accept (poster) | Summary: The paper studies the problem of dynamic pricing of service fees in a setting where only equilibrium quantities of supply and demand curves are observable. The main contributions of the paper lies in using Instrumental Variables in an online setting, and consequently, theoretically bounding the regret of the m... | Rebuttal 1:
Rebuttal: Thank you for your comments and encouragement. The followings are our responses to your questions.
Re Weaknesses: Thank you for your suggestion. In the camera-ready version, we will include some core proofs on the additional page of the main body.
Re Questions: $P_{St}$ represents the payment re... | Summary: This paper considers a dynamic pricing problem where the buyer is strategic and provides regret analysis under this setting. In particular, they propose to use instrumental variables to estimate demand and discuss the impact of the supply randomness.
Strengths: 1. The paper is technically strong with several ... | Rebuttal 1:
Rebuttal: Thank you for your detailed remarks and questions. The followings are our responses to your questions.
Re Weakness 1 and Question 1: One application scenario could be that some online platforms, e.g., Amazon, sells a large number of products daily, and takes service fees between sellers and buyer... | Summary: The primary goal of the paper is to maximize total revenue over a given time horizon by dynamically adjusting service fees on third-party platforms, considering the strategic nature of customers and the lack of complete information on demand.
The paper presents a novel approach to dynamic pricing for third-par... | Rebuttal 1:
Rebuttal: Thank you for your kind remarks and questions. The followings are our responses to your questions.
Re Weakness 1: Thank you for your suggestion. If there is a suitable opportunity, we will consider conducting experiments on real-world data.
Re Weakness 2: We are sorry for the confusion and misun... | Summary: The paper tackles the dynamic pricing problem faced by third-party platforms specifically concerning the optimal setting of service fees in the presence of strategic and far-sighted customers. The objective is to maximize the total revenue over a given time horizon.
Strengths: + The motivation and challenge o... | Rebuttal 1:
Rebuttal: Thank you for your remarks. The followings are our responses to your questions.
Re Weakness 1: $P_{St}$ is the amount received by the seller at time $t$, and $P_{Dt}$ is the amount paid by the buyer. The gap between them is the service fee, which is $a_t$, and $Q$ is the transaction quantity. The... | Rebuttal 1:
Rebuttal: Thank you all for your meaningful reviews! We add a new experiment increasing the number of trajectories to 100 with various noise levels in the supply. Please see the added pdf for detailed results.
Pdf: /pdf/0702a14b5f76ef8e7eec7aced6014dffbc95c042.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings | Accept (poster) | Summary: This paper tries to address and analyze the challenge of task confusion (TC) in class-incremental learning (class-IL). This paper proposes the Infeasibility Theorem that demonstrates that achieving optimal class-IL through discriminative modeling is impossible due to TC, even if CF is prevented. It further pr... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful review and valuable feedback you provided on our submission. Your constructive insights are greatly appreciated, and we are committed to addressing the points you've raised.
In the revised paper, the following paragraph will be included in the introduction that clarif... | Summary: This paper presents a novel mathematical framework for class-incremental learning and prove the Infeasibility Theorem, showing optimal class-incremental learning is impossible with discriminative modeling. While generative modeling can achieve optimal class-incremental learning with the Feasibility Theorem. Th... | Rebuttal 1:
Rebuttal: We're grateful for your thoughtful review and the insightful feedback on our submission. Your constructive comments are highly valued, and we aim to respond to the issues highlighted.
In the revised manuscript, we will include the SIC.pdf file (also submitted as a global rebuttal). The formal pro... | Summary: The paper proposes a Mathematical Framework for class-incremental learning in discriminative and generative modelings, presenting a Infeasibility Theorem for discriminative models and Feasibility Theorem for generative modelings.
Strengths: The paper is easy to understand. It offers a Mathematical Framework f... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission and for providing valuable feedback. We appreciate the constructive comments and would like to address the points raised.
This response is organized as follows: first, we specifically address the reviewer's comments (Responses). Second, we pr... | null | null | Rebuttal 1:
Rebuttal: Formal proofs for Lemma 1 and Lemma 2:
(Formal proofs of Corollary 1, Corollary 2, Theorem 1, Lemma 3, and Theorem 2 are available and will be included in the revised version but not here due to lack of space.)
We'd like to provide a more formal and rigorous proof for Lemma 1, for how the loss ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Temporal Link Prediction via Temporal Walk Matrix Projection | Accept (poster) | Summary: The paper introduces a framework for analysis of relative encodings as a function of random walk matrices, and a new model for temporal link prediction. The new model offers SOTA performance on multiple link prediction datasets, and achieves this performance more efficiently than current best models. The desig... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. In response, we have clarified how ReLU can reduce estimation error and discussed the utilization of temporal information in existing methods. We have also revised the paper and code according to your suggestions. We hope this addresses your concerns and are h... | Summary: Based on the analysis of traditional methods, this paper proposes a unified framework for relative encoding and introduces a new temporal neural network, TPNet. This model not only addresses the high time complexity issues of traditional methods but also enhances relative encoding by incorporating factors such... | Rebuttal 1:
Rebuttal: Thanks for your helpful reviews. In response, we have reported the memory usage of different methods, discussed the construction of our decoding function $g(\cdot)$, and compared TPNet with a new temporal link prediction method. We hope this addresses your concerns and are happy to answer any furt... | Summary: The article investigates the application of relative encodings in the task of link prediction over temporal networks. Initially, the authors formally unify previously used relative encodings, such as DyGFormer, PINT, NAT, and CAWN, within a unique framework. Subsequently, they propose a novel model, TPNet, and... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. In response, we have discussed the temporal periodicity modeling ability of our score function and moved the motivation of the relative encoding earlier according to your suggestion. We hope this addresses your concerns and are happy to answer any further quest... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their time and valuable comments. We are pleased that the reviewers acknowledge the value of our work in providing a cohesive perspective on existing methods (Reviewers vApy, SMMz, YNEN), proposing a novel method (Reviewer vApy) with solid theoretical support (... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization | Accept (poster) | Summary: This paper studies the in-context learning (ICL) in the linear regression setting with a Gaussian prior with a non-zero mean. They first prove that linear Transformer block (LTB) will enjoy a smaller approximation error than linear self-attention (LSA) in this setting, where the non-zero mean of Gaussian emerg... | Rebuttal 1:
Rebuttal: Thank you for supporting our work! We will address your concerns as follows.
Q1: In this paper, all non-linear parts in the transformer block (softmax and ReLU) are dropped. However, similar works on the non-linear transformer block have emerged in the ICL theory. [a] considers the ReLU MLP foll... | Summary: his submission extends the earlier theoretical analyses of in-context learning with linear transformers by taking into account the data covariance and a non-zero mean on the task parameters. It is shown that a linear transformer with a linear MLP block (LTB) can implement the algorithm of one-step gradient des... | Rebuttal 1:
Rebuttal: Thank you for your comments. We will address your concerns below.
Q1: Firstly, the obvious limitations of the analysis: the problem setting is still linear, and the authors only showed adaptivity of ICL to the task mean but not the full prior (see point below). Given the additional linear block,... | Summary: In this paper, the authors investigate the in-context learning (ICL) of a linear transformer block (LTB), i.e., a single block comprised of a linear self-attention layer and a MLP layer. The task considered is ICL of linear regression with a Gaussian prior. Unlike earlier works that studied this problem, a Gau... | Rebuttal 1:
Rebuttal: We appreciate your comments. We will make sure to fix the typos in the revision. We will address your concerns below.
Q1: The paper attributes that property of learning the MLP layer, however the skip connection is also equally important. Hence it is a bit of an misleading claim to attribute thi... | Summary: This paper studies the in-context learning of linear regression with a Gaussian prior that has a finite, non-zero mean across tasks. Specifically, the authors show that a linear transformer block (linear attention with an MLP layer) can achieve near-optimal Bayes risk for this task, whereas a linear attention-... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper! We answer your questions below.
Q1: Have the authors seen experimental evidence that trained LTB models converge to GD-beta models, similar to how LSA converges to GD-0?
A1: Yes, empirically we also see that trained LTB models converge to GD-beta models. Simi... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation | Accept (poster) | Summary: This work builds on TRANSPEECH (Huang et al., 2023) by applying the diffusion method to reduce noise, thereby normalizing speech units for further generation. The authors further use classifier-free guidance to enhance non-autoregressive generation. They conduct experiments on CVSS En-Fr and En-Es datasets, co... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have dedicated to reviewing this paper. We are glad that you find our diffusion-based normalization and regularization strategy effective. Below are our responses to your comments:
> However, they merely use it to generate auxiliary training targets and still... | Summary: This paper proposes DiffNorm, a diffusion-based self-supervised method for speech data normalization, aiming to alleviate multimodal problem in non-autoregressive speech-to-speech translation (NAT). DiffNorm consists of a VAE to reconstruct the speech feature and a diffusion model to add and remove noise of la... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have dedicated to reviewing this paper. We are excited to see that you find our method effective and widely applicable! Below are our responses to your comments:
> DiffNorm seems able to reduce acoustic modalities. Unclear if DiffNorm can also do that on sema... | Summary: The authors introduce a process aiming to simplify the target distribution of speech-to-speech translation. This process uses a VAE model to map features to a latent space, followed by a diffusion model to normalize the features in the latent space. The authors use the generated dataset to train a non-autoregr... | Rebuttal 1:
Rebuttal: Thank you for the time and effort you've invested in reviewing our paper. We are grateful for your recognition of our approach's novelty and the significant improvements it offers. Below are our responses to your comments:
> Why do you think this process of adding and then removing noise can help... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain | Accept (poster) | Summary: Motivated by observation on the difference influence of amplitude and phase of adversarial examples, this paper propose a framework to generate better adversarial examples for adversarial training. Experiments verify the effectiveness of the proposed approach.
Strengths: 1. The motivation in Figure is clear a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We have provided our detailed responses below.
---
**W1: Complexity**
**R1:** Our DAT comprises **only 2** modules: the trained model and the adversarial amplitude generator (AAG). Due to the AAG's simple four-layer linear architecture, DAT's time consumpti... | Summary: The paper introduces Dual Adversarial Training (DAT). This method enhances deep neural network resilience against adversarial attacks by employing generative amplitude mix-up in the frequency domain, focusing the model on phase patterns less impacted by such perturbations, and presenting an optimized adversari... | Rebuttal 1:
Rebuttal: Thank you for your thorough and detailed reviews. Please find our responses below.
---
**W1: Additional Experiments against Feature Space Attacks**
**R1:** In **Table B.1** (more results in **Tables 1-4** of **attached PDF**), we present results assessing the defense capability against feature ... | Summary: This paper investigates a novel approach to improving adversarial training by performing data augmentation in the frequency domain. The authors propose a unique pipeline that jointly optimizes a classification network and a generator network. The generator is used to create adversarial noise, which is added to... | Rebuttal 1:
Rebuttal: Thank you for your review. Please find our responses below.
---
**W1: Natural Accuracy Compared to DAJAT [1]**
**R1:** Our DAT utilizes **only 1** augmentation per benign training sample to prioritize speed and simplicity in this paper, while **DAJAT applies **2** and **3** data augmentations ... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers and AC,
We sincerely appreciate the time and effort you have dedicated to evaluating our manuscript. The concerns and feedback raised during the initial review have significantly contributed to enhancing the quality of our paper. Below, we provide a summary of our key responses to ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.