title string | paper_decision string | review_1 string | rebuttals_1 string | review_2 string | rebuttals_2 string | review_3 string | rebuttals_3 string | review_4 string | rebuttals_4 string | global_rebuttals string | dataset_source string | conference_year int64 | review_5 string | rebuttals_5 string | review_6 string | rebuttals_6 string | review_7 string | rebuttals_7 string | review_8 string | rebuttals_8 string |
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Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms | Accept (poster) | Summary: This work deal with sample complexity of Robust MDPs. The major improvement is that, contrary to previous research that relies on generative models or pre-collected datasets, this paper focuses on RMDPs learning through interactive data collection, addressing two key challenges: distributional robustness and b... | Rebuttal 1:
Rebuttal: **Q1: It is interesting to extend to $\mathcal{S}$-rectangular case.**
**A1:** Thanks, we appreciate your suggestions! But still, we would like to emphasize that our work is the first one on robust RL with interactive data collection that proves the hardness result and provides the algorithm with... | Summary: This paper studies the learnability of the optimal policy for robust Markov decision process (RMDP) under the interactive data setting. The paper first show a fundamental hardness results which necessitates identifying a subclass of RMDPs which is actually solvable. The authors propose an algorithm whose sampl... | Rebuttal 1:
Rebuttal: **Q1: Whether our work is the first value-based interactive data collection DRRL algorithm?**
**A1:** Thanks for pointing this out! But we want to clarify that our algorithm is *model-based* since it necessitates an explicit estimation of the training environment transition kernel, denoted by $\w... | Summary: This paper studies robust RL in a finite-horizon RMDP through interactive data collection. They give both a fundamental hardness result in the general case and a sample-efficient algorithm within tractable settings.
Strengths: 1. Unlike previous work, which relies on a generative model or a pre-collected offl... | Rebuttal 1:
Rebuttal: **Q1: This paper is purely theoretical. Although the reviewer understands the focus of this paper, but still want to see some empirical results to get more insight. Moreover, since an algorithm is given in this article, some numerical studies and comparisons are required. (Weakness 1&3)**
**A1:**... | Summary: The paper addresses the challenges in distributionally robust reinforcement learning (DRRL), particularly focusing on robust Markov decision processes (RMDPs) under the framework of interactive data collection. Unlike previous work that depends on generative models or pre-collected datasets, this study emphasi... | Rebuttal 1:
Rebuttal: **Q1: About whether Assumption 4.1 reduces our problem setup to a non-robust RL problem. (Weakness 1&2)**
**A1:** Here we clarify that Assumption 4.1 does **not** reduce the problem to its non-robust counterpart.
It is a *misinterpretation* of the discussions in Appendix B.4.1 that Assumption 4... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalizability of experimental studies | Reject | Summary: The paper tries to formalize the concept of generalizability in experimental studies in machine learning research. It relies on three different types of kernels in order to quantify difference between the rankings in an experiment output. A core contribution is the development of an algorithm for estimating th... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer you for their insightful remarks and for testing our module.
**There is no discussion on the computational costs of the algorithm (except for a vague statement that it is very fast in the checklist).**
Thank you for your remark, we have addressed it by adding, in... | Summary: This paper deals with experimental studies. After providing a mathematical formalization, it focuses on the generalizability of these studies. The main contribution is a quantitative estimate of the the size of the study to obtain generalizable results. Experiments on LLMs are conducted.
Strengths: - [mathema... | Rebuttal 1:
Rebuttal: **[train / test split] A concrete problem in machine learning practical experimentation is that of train / test split, and more particularly its absence (that is, training on the test). I do not see this issue discussed in the paper. Can it be incorporated in the setting? Is it possible to clarify... | Summary: The paper tries to formalise the notion of an experimental study by considering the sampling process of acquiring a dataset. It then uses this notion to argue about generalisability.
Strengths: The problem of understanding the performance of machine learning when tested on new data is a very important proble... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, which allowed us to improve on the clarity of our paper.
**The problem of understanding the performance of machine learning when tested on new data is a very important problem. The authors use some technically sophisticate methods to tackle this problem.*... | Summary: The authors provide a formalism for the generalizability of experimental studies in ML.
Strengths: Anything pushing to get better practices in evaluation of ML is very important.
Weaknesses: I could quibble with some of the setup, which is a bit confusing to me: design factors being properties of the context... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very helpful comments.
**I could quibble with some of the setup, which is a bit confusing to me: design factors being properties of the context rather than of the alternative, for example, is kind of odd, but I don't think this is very important.**
We follow a fact... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their work.
We have noticed that in some cases there has been misunderstanding of our contribution.
Therefore, we would like to address these points here and add the corresponding sections to the manuscript.
## The experimental pipeline and the role of gener... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors propose a new mathematical framework and a corresponding new algorithm to evaluate the generalizability of published experimental studies, by adapting Montgomery's classification of experimental factors [44].
They demonstrate the efficacy of this framework in evaluating the generalizability of two ... | Rebuttal 1:
Rebuttal: Thank you for noticing these inconsistencies, we did the necessary fixes as follows:
- **1.A.a. On line 118, the symbol Rna is mentioned, but the relation of this symbol to the ranking on alternatives only becomes clear later in Definition 3.1.**
- We moved definition 3.1 to line 116.
- **1.A.b. ... | null | null | null | null | null | null |
Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making | Accept (poster) | Summary: This paper studies the impact of downstream thresholding operations on continuous (and possibly fair) prediction scores. The paper argues that inappropriate thresholding can amplify or ameliorate the disparity in predictive performance across groups defined by the protected attribute. Using a causal framework,... | Rebuttal 1:
Rebuttal: Authors: We thank the reviewer for the time spent on our submission. We would like to state clearly that the results of the paper are not at all constrained to the $t = 1/2$ setting and work for any value of $t$. We therefore hope the reviewer can reconsider the contributions in light of this.
(W... | Summary: The paper studies how much thresholding a predictor affects the disparity in the decisions according to sensitive attributes and formalizes new notions of business necessity based on the causal graph of features, outcome and prediction function.
Strengths: - Understanding the amplification of bias along the m... | Rebuttal 1:
Rebuttal: Authors: We thank the reviewer for the detailed review. We would like to draw the attention of the reviewer to some misunderstandings. The tone of the review seems much harsher than what we are used to in a venue such as NeurIPS and also how we perform our own reviews, always very respectfully. Fo... | Summary: The paper investigates how thresholding the score of a predictive model as a decision rule influences the fairness of the final decisions. In particular, the authors consider binary decisions or predictions of a true binary outcome in presence of a sensitive binary attribute. In that context, the authors show... | Rebuttal 1:
Rebuttal: Authors: We thank the reviewer for the time and effort in reviewing our paper. We are quite glad that the reviewer appreciated the ideas appearing in the paper, and considered the technical contributions strong, well motivated, and clearly presented. Below we address the main questions/concerns.
... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful reviews. We would like to mention three exciting updates to the paper that come as a result of some great questions from the reviewers. We believe these updates substantially improve the scope of the tools described in the paper:
(P1) (Reviewer 1zLn, Q1... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning 3D Equivariant Implicit Function with Patch-Level Pose-Invariant Representation | Accept (poster) | Summary: The paper addresses 3D surface reconstruction from point clouds. It proposes a patchwise rotation equivariant neural network to map query points to their 3D displacement to the surface. The local rotation equivariance allows weight-sharing between similar patches at different orientation, and displacement fie... | Rebuttal 1:
Rebuttal: Thanks for the questions and comments. Please see the following responses.
**Q1: Novelty and relevant methods discussion.**
Thanks for this question. The major novelty of our approach is to learn the equivariant implicit vector fields for 3D reconstruction. Our novelty lies in the motivation and... | Summary: This paper studies a simple task: input dense point cloud and output the implicit surface reconstruction of the geometry. To achieve this goal, the model uses an "equivariant" network to predict the displacement field. Since the input point cloud is dense, this paper crops the nearest patch on the surface poin... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments and suggestions. Please see below for the responses.
**Q1: A heuristic baseline.**
We aim to learn the equivariant implicit function that outputs the vector of each query point to its nearest point on the unknown continuous 3D surface. Since only discrete points ... | Summary: The authors introduce the 3D Patch-level Equivariant Implicit Function (PEIF), leveraging a 3D Patch-level Pose-Invariant Representation (PPIR) to address the surface reconstruction task. To overcome the limitation that existing Implicit Neural Representations (INRs) are not equivariant to 3D rotation, they de... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comment that our approach is well-motivated and novel. Please see below for responses.
**Q1: The experimental settings of "w/o rotation" and "w/ rotation" in Table 4.**
In Table 4, "w/ rotation" and "w/o rotation" represent that the testing input point clou... | Summary: In this paper, the authors address the task of surface reconstruction. They propose a patch-level pose-invariant representation of 3D objects, which is employed in the design of a patch-level equivariant implicit function. The proposed PEIF framework is composed of three modules: the spatial relation module, t... | Rebuttal 1:
Rebuttal: Thanks for these comments. We address the concerns and questions as follows.
**Q1: The visualization of the learned memory bank.**
We provided two approaches for visualizations of the learned memory bank. Please refer to Figure 1 in the attached PDF file uploaded in the top “general response”.
... | Rebuttal 1:
Rebuttal: # General Response
We appreciate the reviewers' positive comments on the novelty (especially Reviewers dfBj, 6vbf, H6Ua), motivation (especially Reviewers 6vbf, FF8y, H6Ua), and performance gain (especially Reviewers 6vbf, H6Ua, FF8y). We have responded to these questions/suggestions of reviewers,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors | Accept (poster) | Summary: This paper presents a novel method CNCA for generating customizable and natural adversarial camouflage of fooling vehicle detectors. This work is an interesting contribution in the field of adversarial attacks, especially improving the naturalness of the camouflage while maintaining high attack performance.
S... | Rebuttal 1:
Rebuttal: **W1: The clipping strategy lacks innovation; it has been used in PGD for a long time, and it is not worth spending too much space on it.**
The core contribution of our work is introducing the diffusion model to enable the customizable and natural generation of physical adversarial camouflage. Th... | Summary: The paper introduces a interesting idea and also a novel method called Customizable and Natural Camouflage Attack (CNCA) to generate adversarial camouflage against vehicle detectors, leveraging a pre-trained diffusion model. This approach allows the generation of natural-looking and user-customizable adversari... | Rebuttal 1:
Rebuttal: **W1: lines 32-35 are not clear to understand. Provide visual evidence to illustrate.**
We would like to clarify that lines 32-35 explain the two reasons why the previous camouflage methods lack naturalness. Firstly, these methods lack prior knowledge of naturalness to guide the camouflage genera... | Summary: The manuscript presents a novel approach to generating physical adversarial camouflage against vehicle detectors, leveraging a pre-trained diffusion model. The proposed method, called Customizable and Natural Camouflage Attack (CNCA), aims to produce adversarial camouflage that is both natural-looking and cust... | Rebuttal 1:
Rebuttal: **W1: Integration of Diffusion models with adversarial attack frameworks may increase computational overhead and complexity.**
We have discussed this weakness in the section on Limitations & Societal Impact. We would like to clarify that our work's novelty enables the naturalness and customizabil... | Summary: The paper introduces a novel framework, CNCA, for generating customizable and natural adversarial camouflage for vehicle detectors using a diffusion model. This work addresses critical limitations in current adversarial camouflage techniques by focusing on naturalness and customizability, which are often negle... | Rebuttal 1:
Rebuttal: **W1: The explanation of the adversarial feature generation and its integration with the diffusion model is convoluted. Quantitatively define the evaluation indicators of naturalness and attack performance or provide relevant references.**
During the normal T2I diffusion model inference process, ... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for their insightful comments on our work. Most reviewers mention the ablation studies of CNCA pipeline components and comparisons with the previous baselines in the physical world. Hence, we have extended the ablation study and physical evaluation as suggested, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing | Accept (poster) | Summary: This work proposes a method to automatically condition a (frozen) speech foundation model to a particular language and/or speaker. This method consists of 2 parts; they use an ECAPA-TDDN model to compute speaker or language embeddings from a set interval of intermediate layers of the SSL model, and a condition... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments, which have helped us improve our manuscript. Below are our clarifications:
**Speaker Decoder and Generalization Ability:**
We respectfully disagree that the speaker decoder detracts from the results. We intend to show that integrating conditioning in the SSL ... | Summary: This paper employs multi-task learning with hierarchical conditioning to adapt pre-trained speech SSL models. By utilizing lightweight task-related auxiliary decoders repeatedly at various positions, the method gradually tailors the SSL representations. A time-channel-dependent conditioner is introduced to fac... | Rebuttal 1:
Rebuttal: Thanks for the comment. Here are some clarifications.
Regarding the **generalist model concern**, we want to emphasize that CA-SSLR is considered a generalist model because it maintains the base model's integrity while improving performance on previously unseen tasks. In Table 1, CA-SSLR conditi... | Summary: This paper introduced a framework, CA-SSLR, that integrates conditioning into pre-trained Self-Supervised Learning (SSL) models by adapting only the trainable conditioner. Through a hierarchical self-conditioning mechanism, CA-SSLR can match or achieve better the performance of single-task fully fine-tuned mod... | Rebuttal 1:
Rebuttal: Thank you for the insightful comment. Here are some clarifications.
Regarding **inference cost**, the encoder parameters are shared among all three tasks, which helps to minimize expenses. The LID decoder is lightweight, with an RTF of less than 0.001, so it doesn't significantly add to the compu... | Summary: The paper introduces a method named CA-SSLR, a versatile model for various speech-processing tasks that integrates language and speaker embeddings from earlier layers to reduce reliance on input audio features while preserving the base model's integrity. More specifically, both LID and SID conditioning feature... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments. Here are some clarifications.
First, for **the generalist model without fine-tuning, the SOTA results** for pre-trained SSLR models can be found in the paper ML-SUPERB challenge [A], where the MMS-1b model performs the best with 1hr ASR WER 18.1% and LID accura... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank the reviewers for their insightful and positive feedback! We are encouraged that they appreciate various aspects of CA-SSLR, including the novelty (Reviewers Jsg6, 4eyk), the clarity and presentation of our writing (Reviewers iiaB, mDev), and the impressive experimental r... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces conditioning into self-supervised learning of speech representations. In particular, a hierarchical self-conditioning mechanism is introduced where intermediate language and speaker embeddings are used to condition upper layers. The proposed approach is used together with XSLR and mHubert... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments. We have included additional equations and figures to improve the clarity and address the writing in Sec. 3.
Regarding additional figures, we add **Figure A in the rebuttal PDF** to clarify our system as the elaboration for the original Figure 1.
Regarding add... | null | null | null | null | null | null |
Unified Covariate Adjustment for Causal Inference | Accept (poster) | Summary: The paper introduces a new framework for identifying causal estimands, referred to as Unified Covariate Adjustment (UCA).
It demonstrates that the UCA-expressible class (a class of causal estimands identifiable by UCA) is extensive, encompassing estimands identified by the (sequential) back-door adjustment, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback, and appreciate the positive assessment of our work.
---
> While the paper provides sufficient conditions for an estimand being not UCA-expressible (i.e., necessary conditions for an estimand being UCA-expressible), it lacks necessary cond... | Summary: This paper describes the estimand framework "unified covariate adjustment (UCA)" and discusses its coverage with multiple examples (Front-door, Verma's equation, Counterfactual directed effect and most importantly Tian's adjustment). Then it develops an estimator for this function class and shows that it is sc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and valuable feedback, and appreciate the positive assessment of our work.
---
> Some further experiments could be interesting even they are not necessary. For example: how does DML compare to prior scalable estimators on BD/SBD? In figure 2a,b,c how much can t... | Summary: The paper presents a class of adjustment formulas called unified covariate adjustment (UCA) which is shown to be able to express many classes of adjustments known in the existing literature. A scalable and doubly robust estimator for UCA is also presented along with some experimental results.
Strengths: The p... | Rebuttal 1:
Rebuttal: Thank you for your feedback and for the opportunity to provide further elaboration.
---
> technical notations being used without first properly defining them
We will further proofread the paper and the preliminaries.
---
> Please explain clearly why UCA avoids scalability issues.
Existing ... | Summary: The paper introduces a novel framework, unified covariate adjustment (UCA), which covers a broad class of sum-product causal estimands and additionally develops a scalable estimator (via DML-UCA) that ensures double robustness.
Strengths: * The paper presents a well-developed theoretical framework with clear ... | Rebuttal 1:
Rebuttal: Thank you for sharing your thoughts and feedback!
> UCA-class is an extension of the sequential back-door adjustment (SBD), and there are already existing studies that address similar questions
Indeed, UCA is an extension of the SBD, and we have appreciated and cited papers regarding estimating ... | Rebuttal 1:
Rebuttal: We attached a PDF to report the experimental results in respond to the following questions from Reviewer ZBfF:
1. > In figure 2a,b,c how much can the dimenion of the summed variables grow before the running time of DML reaches unreasonable values (eg. 2000)?
2. > how little can thee sample size... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks | Accept (oral) | Summary: The paper studies the emergence of the in-context ability of the GPT-style transformer model trained using autoregressive loss and arithmetic modular datasets. It analyzes the influence of the number of tasks, number of in-context examples, model capacity, etc., on the ICL capability of an appropriately traine... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and incisive questions.
## Weaknesses
**Emergent abilities / Grokking**:
The loss and accuracy curves are already presented in Figure 3 of the current version of the paper. We agree that the gradual emergence of useful representations as a funct... | Summary: * The authors propose a synthetic sequence learning problem that I would call
'in-context modular regression', an elegant generalisation of prior work
studying modular addition and in-context linear regression.
* Using carefully constructed batches the authors are able to train
transformer models to perf... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and valuable comments.
## Weaknesses
1. **Delicate training set-up**:
The structured selection of tasks (rectangular rule) and balanced batches largely serve the purpose of making the pre-training more stable. Our intuition for using the specif... | Summary: This paper studies the emergence of in context learning and skill composition in autoregressive models. They create an algorithmic dataset to probe how autoregressive models use tasks learned during training to solve new tasks. They find that more training tasks lead to a generalizing / algorithmic approach in... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
## Weaknesses
**Task Diversity**:
Our definition of task diversity follows the existing works on in-context learning with linear regression, with a key difference:
since our tasks are defined over a finite field, the total number of possible... | Summary: This paper develops novel insights into in-context learning and how it works in Transformers. To this end, the authors propose a generalization of the modular arithmetic task explored in several prior works on grokking. Unlike those works, the structure of the defined task is more rich, enabling an analysis of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback and helpful suggestions.
## Weakness
We thank the reviewer for pointing out the highly relevant references. We will add the citations and utilize the additional page allowance in the final version to discuss their relation to our work.
## Questi... | Rebuttal 1:
Rebuttal: # Global Rebuttal
We included three new figures in the attached one-page PDF. These new results address questions raised by one or more of the reviewers. Especially, the results about MLP layers are relevant to multiple reviews.
## Figure G.1
We examined how individual neurons (post-ReLU) are ac... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
4+3 Phases of Compute-Optimal Neural Scaling Laws | Accept (spotlight) | Summary: The authors consider a simple scaling model and derive scaling laws for one pass SGD in an asymptotic limit. From the scaling laws they identify several phases and subphases where certain components of the loss dominate and the compute-optimal model parameter count is affected. The loss components are related ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and suggestions which were very helpful. We address these below.
1. ***Novelty of Techniques.***
* *Analysis of learning rates via Volterra equations in concert with random matrix theory has appeared before (say \[10\]/\[11\] in the paper)... | Summary: This submission studies the Power Law Random Feature (PLRF) that depends on three parameters: data complexity, target complexity, and model parameter count. They derive a deterministic closed expression for the dynamics of SGD using a clever mapping to Volterra equations. They are able to determine the compute... | Rebuttal 1:
Rebuttal: **Responses to Weaknesses and questions:** Thank you for your comments. We will definitely add a more thorough evaluation of related works. Great catch about Footnote number 6 – it’s actually not true – we’ve removed it. We’d be very happy to hear any other comments about the content of the pa... | Summary: The paper studies a linear random feature model trained on power law data under online SGD. By using a Volterra approach and leveraging deterministic equivalence, they characterize the loss in the high dimensional limit. From this, they extract scaling laws for this model, that determine compute optimality. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and suggestions. We address below questions and concerns raised by the reviewer. Because there was a lot of depth in the questions raised, we needed additional characters to respond adequately so below is the first part of the response. *There wi... | Summary: This submission studies the generalization error dynamics of one-pass SGD in a sketched linear regression setting, where the data and target signal are distributed according to certain power laws, and SGD optimizes a linear model on top of a Gaussian random projection. Using random matrix theoretical tools, th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments which were very helpful. We address these below. Because there was a lot of depth in the questions raised, we needed additional characters to respond adequately so below is the first part of the response. *There will be an additional “Official Comment” with... | Rebuttal 1:
Rebuttal: We thank the reviewers for all the constructive comments and suggestions for comparison. All the reviewers requested additional discussion of related work.
We will do the following.
1. Expand our discussion of related work and background in the main text and add a section in the Appendix. A... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studied a solvable neural scaling law model (the power-law random features, PLRF) that involve three parameters (data complexity: $\alpha$, target complexity: $\beta$ and the number of parameters: $d$). The PLRF model here is trained by applying the stochastic gradient descent (SGD) algorithm to the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their report, for their comments on directions of improvement, and for the questions.
**Response to weakness:** Yes we agree, the comparison to existing work should be expanded. We will include a more detailed discussion of existing work and how it compares (see commen... | null | null | null | null | null | null |
Evaluating the World Model Implicit in a Generative Model | Accept (spotlight) | Summary: This paper aims to develop new metrics for assessing a model’s ability to recover a world model. The key idea is to test coherence with respect to the world, guided by the Myhill-Nerode theorem for deterministic finite automata (DFA). Specifically, if the true world model is a DFA, the learned world model shou... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We're glad you found our proposed metrics interesting and ablation studies compelling. We appreciate your comments on the clarity of our paper.
> _Inter-metric consistency problem... What is the correct conclusion when two out of three metrics suggest the pre... | Summary: This paper proposes a new metric to assess the implicit world model of generative models, such as neural LMs. Inspired by the Myhill-Nerode theorem, this metric evaluates whether a model can determine if pairs of sequences are equivalent in terms of their underlying state. The author presents two specific metr... | Rebuttal 1:
Rebuttal: Thank you for your review. Your review makes several helpful points that will improve our paper. However our rebuttal clarifies a couple of important points of your review: one involving an incorrect statement of what is in our paper (we do describe and empirically test probes), and another is a c... | Summary: This article proposes an evaluation framework for understanding whether or not transformers have learned an implicit world model. Existing metrics focus on next-token prediction and state probes, while this article proposes metrics inspired by the Myhill-Nerode theorem: Whether the network treats two action se... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful review of our paper. We appreciate your enthusiasm for the work and your remarks on the quality and significance of our paper.
> _Figure 2 is a clever illustration but it could use some more detail in the caption, and I'm not sure I fully understand it.... | Summary: This paper proposes new evaluation metrics to assess whether a learnt world model is indeed learning the underlying dynamics or logical reasoning required to fully decipher a new domain. The paper sheds light into how world models should be evaluated, compared to what is being done in the literature currently ... | Rebuttal 1:
Rebuttal: Thank you for your careful and insightful review of our paper. We're glad that you think the paper is addressing an important question and that it has the potential to be "quite significant and important to the community".
> _My only comment would be that the paper should perhaps provide more b... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful evaluation and feedback. We're glad you found our paper "brilliant" (7KXV) and offering a "novel perspective" (RjHx), with the potential to be "quite significant and important to the community" (YkfB) and to have "major impact" (7KXV). Moreover we appreciat... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI | Accept (poster) | Summary: This work proposes a novel deep learning-based framework for recovering high-quality 3D MR images from undersampled and motion-corrupted k-data. The proposed approach is well motivated and technically sound. The authors perform extensive experiments on simulated and real MR datasets, which confirm the effectiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and the positive evaluation of our work. In the following we address the weaknesses in the order as pointed out by the reviewer.
- **Weakness 1, various types of rigid motion:** We agree that in practice motion can be categorized into different types. However... | Summary: This paper proposes a motion correction MRI reconstruction algorithm for 3D brain MRI. The proposed technique consists in a deep learning-based estimation of rigid motion parameters, which allows to correct the k-space before a final reconstruction.
Estimation of motion parameters are based on a single optimiz... | Rebuttal 1:
Rebuttal: Thanks for the feedback. In the following we address the concerns and questions in the order as pointed out by the reviewer.
- **Weakness 1, experiments and alternative techniques seem to be presented throughout the result section:** Thanks for the feedback. We will make sure to describe all metho... | Summary: This paper proposes a method for estimating the motion of patients, such that accurate motion-corrected images could be reconstructed. The key idea is that a neural network trained for motion-free reconstruction has a small loss if there is no motion, thus optimizing over motion parameters passed through the r... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. In the following we address the concerns in the order as pointed out by the reviewer.
- **Weakness 1, introduction of existing modules and lack of novelty:** We would like to point out that combining a 2D reconstruction network trained on motion-free data wi... | Summary: The paper presents MotionTTT, a deep learning-based method for estimating and correcting rigid motion in 3D MRI images. The approach leverages a neural network pre-trained for 2D motion-free image reconstruction and employs test-time-training (TTT) to estimate motion parameters from motion-corrupted 3D measure... | Rebuttal 1:
Rebuttal: Thanks for the feedback and for acknowledging the novelty of our work. In the following we address the weaknesses (W), questions (Q) and limitations in the order as raised by the reviewer.
- **W 1, lack of theoretical investigation of why the optimization works:**
To get an understanding on why ... | Rebuttal 1:
Rebuttal: Thanks for the reviews!
We would like to start by emphasizing that our work is the first that enables efficient motion estimation in 3D based on a 2D neural network trained on motion-free measurements. We show that our method can reliably predict motion and that this yields significant improveme... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation | Accept (poster) | Summary: The paper proposed SureMap, a promising method for solving multi-task disaggregated evaluation problem. The key innovation of SureMap lies on transforming the problem into structured simultaneous Gaussian mean estimation and incorporating external data, e.g. from the AI system creator or from their other clien... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! We address your concerns and questions below:
1. [*Can the authors provide some analysis or insights when the data contradicts with Gaussian assumption?*]
a. Please see our discussion of this issue in the general response (Issue 1: Assumption).
2. [*Can the a... | Summary: This paper studies disaggregated evaluation, which aims to estimate the performance of models on various subpopulations. This problem is challenging due to small sample sizes in subpopulations, thus leading to inaccurate performance estimates. This issue is magnified when multiple clients use the same AI model... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! We address your concerns and questions below:
1. [*The efficiency discussion of the method needs to be expanded. It would be better to list the complexity of the proposed method with existing methods and report the actual runtime.*]
a. Please see our discussi... | Summary: The author developed a disggregated evaluation method called SureMap, which has high estimation accuracy for both multi-task and single-task disggregated evaluations. SureMap transforms the problem into structured simultaneous Gaussian mean estimatio, incorporating external data. This method further combines M... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! We address your concerns and questions below:
1. [*I would like to know if there are any rules that need to be followed in the selection of disaggregated evaluation datasets to ensure fairness, as well as the reason why the disaggregated evaluation goal is set ... | Summary: This paper introduces SureMap, a new method for disaggregated evaluation of AI systems especially for multi-task setting. The authors model the problem as Gaussian mean estimation and use a structured covariance prior that captures intersectional effects. SureMap is evaluated on several datasets, including a n... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! We address your concerns and questions below:
1. [*The Gaussian assumption may be too strong for some real-world settings [...] Have you considered non-Gaussian models? For example, a t-distribution might better handle heavy-tailed performance data that can occ... | Rebuttal 1:
Rebuttal: First we would like to thank all the reviewers for their careful and thorough reviews. We are happy to see that reviewers found the problem we tackle important (Revs. gnhf, 3rXK), the paper well-structured and theoretically well-founded (Rev. gnhf), and the experiments convincing (Revs. LQxL, 3ZUA... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents SureMap, a novel method for disaggregated evaluation, aimed at improving the estimation accuracy of performance metrics for machine learning models across different subpopulations. The proposed method is designed to address both single-task and multi-task settings, where multiple clients in... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! We address your concerns and questions below:
1. [*While the method is theoretically sound, the practical implementation of SureMap may be complex, particularly the coordinate descent algorithm used for tuning parameters. This could pose challenges for practiti... | null | null | null | null | null | null |
Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control | Accept (poster) | Summary: This study tries to enhance multiple dimensions of trustworthiness in LLM through a training-free approach. It controls the LLM's representation of intermediate hidden states so that the model achieves increased honesty or heightened safety awareness. It addresses the challenge of fulfilling multiple requireme... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking the time to review our work. We appreciate that the reviewer is optimistic about this work and provide insightful suggestions to help us further improve the paper.
**Weakness1:**
> Theoretical Foundation of GMM
**Ans for Weakness1:**
The motivation of t... | Summary: As LLMs advance, enhancing their trustworthiness and aligning them with human preferences is important. Traditional methods rely on extensive data for RLHF, but representation engineering offers a training-free alternative. This method uses semantic features to control LLMs' intermediate states, addressing nee... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing valuable comments. We appreciate your time and effort. In response to your comments, we have provided a detailed response below.
**Weakness1:**
> Applications on proprietary models
**Ans for Weakness1:**
This method cannot be directly applied to pr... | Summary: This paper proposes a new training-free algorithm for controlling specific components with LLMs to increase multiobjective criteria such as safety, factuality, and bias. They overcome the drawbacks of prior methods using the following:
- Prior methods struggle when there are multiple criteria at once to improv... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and providing valuable comments. We appreciate your time and effort. In response to your comments, we have provided a detailed response below.
**Weakness1:**
> Computational complexity vs finetuning?
**Ans for Weakness1:**
Thank you for your valuable suggestion.... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, and Program Chairs,
We sincerely thank all three reviewers for their constructive comments and insightful questions, which helped us refine our work.
*Reviewers have acknowledged the impact and superior performance of our proposed method and the comprehensive analysis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks | Accept (poster) | Summary: This paper focuses on the aggregation module in Message Passing Graph Neural Networks (MPGNNs).
It tackles the problem that sum-based aggregators, even though widely used, fail to 'mix' features belonging to distinct neighbors, preventing them from succeeding at the downstream tasks.
Accordingly, the authors... | Rebuttal 1:
Rebuttal: We are pleased that the reviewer recognized the novelty of our proposed method, its underlying mathematical foundations and the empirical performance supporting the statement of the paper. We thank the reviewer for appreciating the paper’s motivation and writing.
***
We would like to address so... | Summary: The paper analyzes the discrepancy between the theoretical guarantees of sum-based aggregators and their practical performance, which makes more complex aggregators preferred in practice. They define the notion of neighbor-mixing to explain this gap, and propose a novel aggrgeation module, named SSMA, which bu... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the theoretical analysis and the experimental section, particularly our methodological approach to ensure fair comparison.
***
We would like to address your quoted concerns:
> Can you expand on the intuition of sum-based aggregators lacking mixing abilitie... | Summary: This paper introduces Sequential Signal Mixing Aggregation (SSMA) for Message Passing Graph Neural Networks (MPGNNs), addressing the limitations of traditional sum-based aggregation methods. SSMA enhances the mixing of features from distinct neighbors by treating neighbor features as 2D discrete signals and ap... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's recognition of convolution-based aggregations as an advancement in the field, effectively addressing a major limitation of traditional sum-based aggregators. We thank the reviewer for highlighting the robust theoretical foundation, and appreciating the practical ... | Summary: This paper introduces Sequential Signal Mixing Aggregation (SSMA), a novel aggregation method for Message Passing Graph Neural Networks (MPGNNs). SSMA addresses the shortcomings of traditional sum-based aggregators, which often struggle to effectively "mix" features from distinct neighbors. By treating neighbo... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding the discussed limitations of sum-based aggregators compelling and insightful, endorsing SSMA’s innovative approach and appreciating the experimental results which demonstrate the effectiveness of SSMA.
***
We would like to address your quoted concerns:
> Other ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their valuable feedback on our paper.
We were pleased to hear that the reviewers found the explanation of the limitations of sum-based aggregators "compelling and insightful, offering a fresh perspective on the problem and effectively motivating the propo... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels | Accept (poster) | Summary: This paper present MCLIP, which aims to finetune CLIP using image and mask data without semantic labels. The goal is to make its open-vocabulary recognition ability to be adapted to position-sensitive semantic segmentation tasks. MCLIP chooses to use SAM or feature clusters of DINO to obtain masks, which are c... | Rebuttal 1:
Rebuttal: We thank reviewer 83hz for the remarks on the paper and the considerate review. We address the comments and questions from the reviewer below:
> **What role does the text encoder play in the clustering process?**
>
| | COCO | ADE-150 | PC-59 | Cityscapes | VOC |
| --- | --- | --- | --- | --- |... | Summary: The authors introduce a novel unsupervised formulation of open vocabulary semantic segmentation, adapting a pre-trained vision-language model (CLIP) to the task via distillation from vision-only segmentation models (i.e., SAM, DINO). To use the language encoder without constraining fine-tuning to the set of cl... | Rebuttal 1:
Rebuttal: We thank reviewer kepm for the remarks on the paper and the considerate review. We address the comments and questions from the reviewer below:
> **It would be helpful to quantify the costs of performing the online clustering at training time. What are the costs of performing online clustering at... | Summary: This paper proposes to learn an open-vocabulary semantic segmentation model with only unlabeled images and pretrained foundation models, such as SAM and DINO. The intuition is that CLIP model already knows what is in the image, so we only need to teach CLIP where the object is. It first uses pretrained DINO to... | Rebuttal 1:
Rebuttal: We thank reviewer rHvB for the remarks on the paper and the considerate review. We address the comments and questions from the reviewer below:
> **Compared with methods using similar VFM, such as SAM-CLIP, the proposed method performs much worse.**
>
| | COCO-Stuff | COCO-Object | ADE-150 | PC-... | Summary: This paper proposes to enhance the semantic segmentation performance of the pretrained CLIP model using unlabeled images and pseudo segmentation masks generated with vision foundation models such as SAM and DINO. Specially, the pseudo masks are acquired via a online feature clustering algorithm. Experiments on... | Rebuttal 1:
Rebuttal: We thank reviewer Dx6v for the remarks on the paper and the considerate review. We address the comments and questions from the reviewer below:
> **Comparison with MaskCLIP+**
>
| | COCO-St. | ADE-150 | PC-59 | Cityscapes | VOC |
| --- | --- | --- | --- | --- | --- |
| MaskCLIP+[61] | 18.0 | - |... | Rebuttal 1:
Rebuttal: We thank the reviewers for the remarks on the paper, as well as their considerate reviews. Especially, we appreciate the thoughtful comments on the idea and motivation for leveraging unlabeled masks (**Dx6v, kepm, 83hz**), paper being well-written and curated (**Dx6v, kepm**), the proposed solutio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improved Regret for Bandit Convex Optimization with Delayed Feedback | Accept (poster) | Summary: The paper investigates the online convex optimization problem with delayed bandit feedback.
The main contribution is introducing a new algorithm that uses a block updating mechanism with FTRL, proving the algorithm achieves a delay-dependent regret of $O(\sqrt{dT})$, which is known to be optimal when the aver... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
---
Q1: The paper could be written more clearly. I found myself understanding parts of the introduction only after going through the entire paper (e.g. line 69).
A1: Thank you for the helpful suggestion. We will improve our writing by adding necessary ex... | Summary: The paper investigates the problem of bandit convex optimization (BCO) with delayed feedback, where the value of the action is revealed after some delay. The authors proposed an algorithm D-FTBL, and proved that it enjoys a regret bound of $O\left(\sqrt{n} T^{3/4}+\sqrt{d T}\right)$, closing the gap between th... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
---
Q: The paper lacks numerical experiments ... I would be curious about how much improvement can be made in practice. In bandit setting, people often consider large time horizon, in which cases the regret is dominated by the delay-independent term.
A:... | Summary: This paper studies the problem of bandit convex optimization with delayed feedback (where the feedback for round $t$ is delayed by an arbitrary number of rounds $d_t$).
For this problem, they show $O(\sqrt{n} T^{3/4} + \sqrt{d T})$ regret in general, $O((n T)^{2/3} \log^{1/3} (T) + d \log(T))$ regret for stron... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
---
Q: The paper only makes an improvement on state-of-the-art for certain delay sequences, i.e. when $d=O((n\bar{d})^{2/3}T^{1/3})$.
A: First, we want to emphasize that both $n$ and $T$ can be very large in modern online applications, and thus the condi... | Summary: This paper studies the bandit convex optimization problem with delayed feedback, where the loss value of the selected action is revealed under an arbitrary delay.
Previous work achieves $\mathcal{O}( \sqrt{n} T^{3/4} + (n\bar{d})^{1/3} T^{2/3})$ regret bound of this problem. The authors develop a novel algor... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
---
Q: The contribution of this work is quite concerning. It would be better for the authors to emphasis the contribution (either algorithmic or analytic) on improving the delayed feedback result for BCO problem in certain conditions that the max delay $d... | Rebuttal 1:
Rebuttal: ## Common Response to the Suggestion about Experiments
We thank all the reviewers for your detailed comments. In the following, we first respond to the common suggestion about experiments, and other questions are addressed in a separate response for every reviewer. Please let us know if you have ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors consider the problem of bandit convex optimization in the adversarial setting under delays. In each round, an adversary selects a convex function $f_t$, the optimizer selects an input $x_t$ and observes $f_t(x_t)$ with a delay of $d_t$ timesteps. The goal is to minimize regret with respect to the b... | Rebuttal 1:
Rebuttal: Many thanks for the constructive reviews!
---
Q1: The practical impact of this theoretical toy seems limited, and the problem was of more interest a couple of years ago. In order to increase the reception, it would be instructive to connect the implications of these findings to other topics of r... | null | null | null | null | null | null |
On Feature Learning in Structured State Space Models | Accept (poster) | Summary: This paper studies the large width scaling behavior of a recently popular class of models known as structured state space models (SSMs). The authors demonstrate that the previous work on large width neural scaling, which prescribes a parametrization known as the Maximal Update Parametrization (muP) as the opti... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and positive assessment of our work. Your feedback has been invaluable in helping us improve the paper. We have addressed your concerns regarding improved presentation and organization of the proofs. We hope that our revisions will merit your consider... | Summary: This paper investigates the scaling behavior of state-space models (SSMs) and structured variants like Mamba as their width approaches infinity. The authors demonstrate that established scaling rules such as maximal update parameterization (μP) and spectral scaling conditions fail to yield feature learning in ... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for your thorough and insightful review of our manuscript, and useful feedback for improving our work.
**Order of limits and practical applicability.** In SSM models, $N_u$ (input dimension for SSM component) typically increases much faster than $N_x$ (laten... | Summary: Following the tensor program and maximal update parameterization, this paper studies the parameterization of initialization and learning hyperparameters in structured state space models (SSMs), e.g. S6 Mamba. The authors consider the input dimension and latent dimension of each vector in the sequences to go to... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and positive evaluation of our work. We appreciate your insights and we have carefully addressed your major concerns in the following response. We will rectify minor issues like typos in our revision. We hope that our revisions warrant your consideration fo... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely appreciate the time and effort you have invested in evaluating our work. Your insightful comments and constructive feedback have been invaluable in helping us improve the clarity and quality of our research. We have included a pdf attachment to this response which con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Understanding Hallucinations in Diffusion Models through Mode Interpolation | Accept (poster) | Summary: The paper introduce the concept of hallucinations for image diffusion models.
The key contributions includes:
1. Definition of Hallucinations: Hallucinations are defined as samples generated by diffusion models that lie completely outside the support of the real data distribution.
2. Mode Interpolation Phe... | Rebuttal 1:
Rebuttal: We are happy to see that you liked the fresh perspective on hallucinations in diffusion models and mode interpolation presented in our work, and found the paper to showcase high-quality research, and comprehensive experimental design, and have a potential impact on the development of more reliable... | Summary: This paper addresses the *hallucination* phenomenon in diffusion models, where generated samples fall outside the training distribution. The authors propose *mode interpolation* as an explanation. They analyze synthetic datasets and conclude that hallucinations occur between nearby modes due to the inability o... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed review. We are glad that you found our exploration of hallucinations in diffusion models and our explanation through mode interpolation to be novel and plausible. Your appreciation of our toy experiments validating the phenomenon is encouraging. We acknow... | Summary: This paper demonstrates and studies a particular failure mode of diffusion models termed mode interpolation. Specifically, the authors discovered that when trained on certain datasets, diffusion models (even those with 1000 denoising steps) generate samples that look like certain interpolations of some trainin... | Rebuttal 1:
Rebuttal: We appreciate your review and are glad that you found our paper well-written & easy to follow, and that you appreciated our focus on the previously overlooked failure mode of diffusion models, namely mode interpolation. We acknowledge your concerns & attempt to respond to them line by line below:
... | Summary: This paper studies the hallucination phenomenon in diffusion models, in which samples out of the support sets are generated. Specifically, the authors characterize a failure mode, termed mode interpolation, which is hypothesized to be attributed to the learned score function of the diffusion model being over-s... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad that you found our research direction intriguing (i) in exploring the hallucination problems of diffusion models, (ii) appreciated our approach of investigating regions between modes, and (iii) found our proposed metric effective for detecting hall... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback provided by all reviewers towards this submission. Across the board, all reviewers found the phenomenon of hallucination via mode interpolation as an interesting scientific inquiry and appreciated the quality of the draft that was supported with convincing, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies | Accept (spotlight) | Summary: In this paper, the authors consider the problem of uniform sampling from general convex body. The algorithm works by augmenting the state space followed by performing alternative Gibbs sampling, where one of the inner steps is implemented by rejection sampling. Non-asymptotic end-to-end bounds on the mixing ti... | Rebuttal 1:
Rebuttal: $\textbf{What practical advantage of the method?}$
The main advantage of our method is the simplicity of the algorithm and the analysis with provable guarantees in commonly used probabilistic metrics, more general than previously known. Not only is our algorithm much simpler to analyze, but it al... | Summary: The paper presents a novel random walk algorithm for uniform sampling of high-dimensional convex bodies that provides improved runtime complexity and guarantees on the result, especially with respect to Rényi divergence.
Sampling high-dimensional convex bodies, a fundamental problem in algorithm theory with n... | Rebuttal 1:
Rebuttal: $\textbf{Initialization process and parameter selection}$
- Initialization process: We assume that the initial start is $M$-warm in this work. Obtaining this warm-start is non-trivial, and requires some type of annealing algorithm in general. Warm-start generation has been studied for more than t... | Summary: The paper addresses the fundamental problem of uniformly sampling high-dimensional convex bodies. The main contribution is the proposal of the In-and-Out algorithm, analyzed within the framework of the proximal sampling scheme. Using existing analyses from the literature, the paper derives strong results. Addi... | Rebuttal 1:
Rebuttal: $\textbf{Techniques are already existing}$
As noted by Reviewer W5VJ, our paper is not “just” putting together known components. Previous work on proximal sampling works under a well-conditioned setting without hard constraints, where the number of trials for rejection sampling (for the backward ... | Summary: This work presents a new algorithm, called In-and-Out, for sampling from the uniform distribution over a convex subset $K$ of $R^d$ that comes with stronger guarantees than previous algorithms. The proposed algorithm is an instantiation of the Proximal Sampler (PS), an abstract sampling algorithm which was rec... | Rebuttal 1:
Rebuttal: $\textbf{Application}$
Our work mainly aims to provide a new algorithmic/analytic framework for the uniform sampling problem under the membership oracle model. The applications of this problem are already widespread and well-known, therefore we do not feel the need to propose any new applications... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and detailed comments. We respond to specific points below. We note at the outset that main high-level contributions are that (a) we provide the first guarantees for KL and Renyi divergences and (b) we directly relate the convergence rates to classical iso... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Efficient Recipe for Long Context Extension via Middle-Focused Positional Encoding | Accept (poster) | Summary: The paper introduces CREAM (Continuity-Relativity indExing with gAussian Middle), an efficient method for extending the context window of large language models (LLMs) to handle longer contexts without the need for extensive fine-tuning. CREAM manipulates position indices for shorter sequences within the pre-tr... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper. First and foremost, we would like to express our sincere apologies for any confusion that our work brings to you. As you may have noticed, our presentation reached an average of 3.33 / 4 among three other reviewers. Specifically, reviewe... | Summary: The paper presents CREAM (Continuity-Relativity indExing with gAussian Middle), an innovative approach to extend the context window of Large Language Models (LLMs) without the need for extensive fine-tuning at the target length. The authors address the "Lost in the Middle" problem, which plagues long-context L... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper and acknowledging the novelty and empirical superiority of our approach. Below, we provide detailed replies to your comments and hope we can resolve your major concerns.
>**W1:** **Generalizability**: While the paper shows impressive res... | Summary: The paper proposes a new method, CREAM, that better enables extrapolation to longer contexts via finetuning at the base context length. The method uses positional embedding interpolation with the embeddings divided into three areas of interest and uses a truncated Gaussian to sample the position used for the ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable suggestions and acknowledgment of our well-motivated method. Below we address your questions point-by-point and hope we can resolve your major concerns.
>**W1:** The method is fairly similar to POSE in concept and performance. While I think the idea of emphas... | Summary: The paper proposes a method for extending the context window of pretrain large language models. The approach, CREAM, relies on modifying position indices to interpolate the positional encodings. Despite the often computationally expensive nature of such work, CREAM can extend to very long context windows while... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments ``simple, but clever, effective and effcient`` and acknowledgement of our approach with ``very strong performances that encompass a broad range of tasks``. Below, we provide detailed replies to your comments and hope we can resolve your major c... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers for their hard work, and we will re-emphasize a few strengths of our work:
1. We propose a ``simple, but clever and an effective way to efficiently expose the model to a broader range of relative positional distances during training`` (Reviewer fWsk). This ``novel ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Symmetric Linear Bandits with Hidden Symmetry | Accept (poster) | Summary: The paper introduces and analyzes the problem of symmetric linear bandits with hidden symmetry. The authors study high-dimensional linear bandits where the reward function is invariant under certain unknown group actions on the set of arms. The key contributions are:
- An impossibility result showing that no ... | Rebuttal 1:
Rebuttal: # Responses to Reviewer ZVeC
We thank the reviewer for the insightful comments. Below are our responses/clarifications to your questions:
## Question 1: Practical Implications of Assumptions 5 and 16:
### Response:
**Assumption 5: sub-exponential partitions.**
Sub-exponential size naturally appe... | Summary: The authors study the problem of symmetric linear bandits with hidden symmetry (where the expected reward is a linear function of the selected arm, and is invariant under a hidden symmetry group). They show that, with no additional information, the minimax regret cannot be improved. When the partition correspo... | Rebuttal 1:
Rebuttal: # Responses to Reviewer qJvB
## Question: Practical motivation on sub-exponential partitions
### Response:
Sub-exponential size naturally appears when there is a hierarchical structure on the set $[d]$, and the partitioning needs to respect this hierarchical structure.
Particularly, let $T(d,d_0... | Summary: The paper studies high-dimensional linear bandits that are invariant w.r.t. an *unknown* subgroup of coordinate permutations. The authors first show through a lower bound that further information about the structure of the hidden subgroup is required to achieve a dimension-independent regret bound. They then p... | Rebuttal 1:
Rebuttal: # Response to Reviewer WDJx
We thank the reviewer for the insightful comments. Below are our responses/clarifications to your questions:
## Comment 1: Practical motivations
### Response:
Sub-exponential size naturally appears when there is a hierarchical structure on the set $[d]$, and the parti... | Summary: The paper explores the impact of unknown symmetry on the regret for stochastic linear bandits. Under some assumptions on the set partition induced by the unknown subgroup G, the paper develops an "Explore-then-commit" algorithm that attains optimal scaling of the regret in terms of the dimension d_0 of the low... | Rebuttal 1:
Rebuttal: # Responses to Reviewer Viv8
We thank the reviewer for the insightful comments. Below are our responses/clarifications to your questions:
## Comment 1: On the condition of $\mathcal X$ so that $\theta_\star = g\cdot \theta_\star$.
### Response:
We do not need to impose any condition on $\mathca... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive feedback. We have performed the additional experiments as requested by the reviewers and have provided the results in this PDF file. We have also added a picture in this PDF file that illustrates a practical example of how sub-exponential partitioning o... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies stochastic linear bandits with hidden symmetry, where the reward function is invariant with respect to a subgroup $\mathcal{G}$ of coordinate permutations. The paper first presents an impossibility result, showing that solely knowing a low-dimensional symmetry structure exists does not help.... | Rebuttal 1:
Rebuttal: # Responses to Reviewer qZUs
We thank the reviewer for the insightful comments. Below are our responses/clarifications to your questions:
## Question 1: What can be done if Assumption 5 does not hold?
### Response:
If the cardinality in Assumption 5 is not satisfied, one might need to find an a... | null | null | null | null | null | null |
Predicting the Performance of Foundation Models via Agreement-on-the-Line | Accept (poster) | Summary: The paper shows that agreement-on-the-line holds in finetuned foundation models across vision and language benchmarks and finds that random head initialization is critical for the phenomena. The authors also show that agreement-on-the-line holds in ensembles of different pretrained models. They demonstrate usa... | Rebuttal 1:
Rebuttal: >__The panel labels in Fig. 31 and 32 has a repetition of Random Head, which should be corrected.__
Thank you for catching this! The third panel (c) corresponds with Data Subsetting, not Random Head. We will correct this in the next version.
>__…it is important to know how robust the method is ... | Summary: The paper studies the applicability of agreement-on-the-line (AGL) to finetuned large models. Specifically, AGL is the phenomenon where the agreement in predictions of a collection of models on in-distribution (ID) data is linearly correlated with these models' agreement on out-of-distribution (OOD) data. This... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions! We address each of your concerns below in detail.
>__The paper lacks an analysis on the reason behind the different behavior observed between the vision and language models.__
The conclusions we make in this work apply to _both_ image and la... | Summary: The authors of the paper propose a method to demonstrate that foundation models can exhibit agreement-on-the-line (AGL), under certain conditions. The existence of AGL can be used to predict the OOD capabilities of models without having access to the labels for the downstream tasks.
Strengths: - While AGL has... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions! We address each of your concerns below in detail.
>__Figure 1 should be a little clearer, with the caption being a little bit more detailed. This will help a lot with understanding of the key results of the paper…__
Thank you for the suggest... | Summary: This submission studies the problem of predicting OOD performance given known in-domain performance. Building on top of recent work showing that ensembles can be used for this problem, by looking at agreements between components in the ensemble as surrogate labels to predict OOD performance, they find that a s... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions! We address each of your concerns below in detail.
>__Some critical details are missing, for instance the authors should report in the main paper how to go from Acc, Agr to OOD (line 160-164).__
We apologize for the lack of clarity. The defau... | Rebuttal 1:
Rebuttal: We thank all reviewers for their great feedback and questions about our paper! The reviewers generally found our paper interesting and praised our work for proposing a simple yet effective solution to predict the OOD performance of FMs. Here, we briefly summarize the common concerns, and new exper... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mechanism design augmented with output advice | Accept (spotlight) | Summary: This paper explores a novel setting in mechanism design where an output is provided as advice to the mechanism. The authors propose consistency, robustness, and approximation properties for strategy-proof mechanisms. They introduce four types of mechanism design problems and corresponding mechanisms, demonstra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We are sorry that this reviewer (unlike the other two) was not satisfied with the level of presentation of our results. We argue below why we find the reasons for the low presentation score to be a bit too harsh.
**Reviewer PWat stated as two weaknesses:... | Summary: The authors propose a novel paradigm for mechanism design augmented with advice. While classically learning-augmented mechanism design assumes input advice, the authors consider output/outcome advice. They use "quality of recommendation" to quantify the quality of the advice and provide approximation, consiste... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review. We address their concern below.
**Question**: *"Why do you only conduct experiments in one of the mechanism design settings?"*
**Our response**: Our work is mainly theoretical, and we provide tight results for all four problems and comprehensive c... | Summary: This paper studies the problem of mechanism design with prediction and introduces a new framework based on output predictions. Unlike most previous work, which primarily uses input predictions with varying error metrics, this paper considers output predictions and proposes a new error metric that can be applie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We address concerns about potential weaknesses below.
**Question**: *"Except for the facility location section, this paper lacks extensive comparisons to previous results with input predictions. While there are tight results with respect to the... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thorough reviews and thoughtful comments. We respond to each reviewer's questions with a separate reply to each of their reviews. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing | Accept (poster) | Summary: This paper explores methods to enhance the training of machine learning models using crowdsourced datasets and proposes a novel post-processing approach called IWBVT. The proposed IWBVT first performs instance weighting based on the entropy of the complementary label distribution. Subsequently, it employs a bi... | Rebuttal 1:
Rebuttal: **Reviewer 5XV6：**
**Q1:** According to Figure 3b, when improving model quality, why is instance weighting more effective on the Leaves dataset while the bias-variance trade-off is more effective on the Income dataset?
**Author Response:** Thanks for your valuable comments. Indeed, in Figure Fi... | Summary: This paper proposes a novel label integration method called IWBVT for crowdsourcing by using a weighting method and probabilistic loss regressions to improve the model quality. The main problem this paper solves is the model quality caused by the presence of the intractable instances. The paper is well organiz... | Rebuttal 1:
Rebuttal: **Reviewer fq5P：**
**Q1:** Compared with real-world dataset, experimental results on a few simulated datasets doesn’t improve (e.g., MV for breast-cancer/breast-w datasets). Furthermore, the number of loss for MV and IWMV is more than that for other methods in Table 1. I suggest adding some discu... | Summary: The paper studies the problem of improving quality of models trained on datasets collected through crowdsourcing. The authors propose an approach (IWBVT) that post-processes data after crowdsourcing with the goal to mitigate the impact of intractable instances by means of instance weighting. As a result, the b... | Rebuttal 1:
Rebuttal: **Reviewer Td4g：**
**Q1:** I do not understand the message behind Figure 1. How should I tract the 4 distributions on the right side of the arrow?
**Author Response:** Thanks for your valuable comments. The four distributions on the right side of the arrow in Figure 1 correspond to the four case... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transformers Can Do Arithmetic with the Right Embeddings | Accept (poster) | Summary: This paper introduces a simple yet effective encoding scheme that can be used to address the limitations of transformers at representing positional information, which is crucial in many algorithmic tasks such as those involving arithmetic operations. The authors propose an ad-hoc positional embedding, called “... | Rebuttal 1:
Rebuttal: Thank you for your valuable time, your comments have greatly improved our draft.
We answer your questions below in order:
1. While choosing k a priori could be a barrier to adoption of Abacus Embeddings in the community, we have shown that we can scale to at least one hundred more digits at test... | Summary: This paper studies a well-known problem, the length generalization issue of transformers in terms of doing arithmetic. This paper solves this problem via two natural strategies: (i) separate two operands via a newly proposed embeddings (Abacus Embeddings), and (ii) using looped Transformer architecture.
Stren... | Rebuttal 1:
Rebuttal: Thank you for your valuable time, your comments have greatly improved our draft.
We answer your question below:
We do not find that the number of recurrences is meaningfully linked to the length of the numbers in this study and we do a small visual analysis of the intermediate properties during... | Summary: The paper studies the arithmetic capabilities of transformers and the problem of length generalization, specifically the ability to solve problems larger than the ones seen during training. It introduces Abacus Embeddings, a novel positional embedding that encodes the position of each digit relative to the sta... | Rebuttal 1:
Rebuttal: Thank you for your valuable time, your comments have greatly improved our draft.
We respond to your weaknesses and answer your questions below in order:
1. We find ~100 to be roughly optimal when the training data has a maximum of 20 digits; this already allows for addition of numbers larger tha... | null | null | Rebuttal 1:
Rebuttal: Our revised manuscript will include the following new experiments and discussion, which better clarify the broad utility and flexibility of Abacus Embeddings. We thank the reviewers for their questions and suggestions that led to these new positive results!
**Varying the value of the maximal off... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz | Accept (poster) | Summary: This paper designs a novel quantum algorithm that eliminates the need for penalty terms when solving constrained problems. Instead, it constructs a subspace where the states satisfy the constraints, allowing the final solution to be constructed within this subspace. Additionally, the authors use parity check t... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback. We greatly appreciate the time and effort you have invested in evaluating our manuscript. Your insights and suggestions have been instrumental in improving the quality of our work. We are aware that the scalability of the quantum algorithm ... | Summary: Paper introduces a Hamming Weight Preserving ansatz with parity check. The proposed method is tested on quantum chemistry problems and constrained combinatorial optimization problems (eg. quadratic assignment problem). The HWP ansatz can satisfy the hard constraints in QAP. Experiments show that proposed metho... | Rebuttal 1:
Rebuttal: We greatly appreciate the time and effort you have invested in evaluating our paper, and we hope we can clarify your concerns.
> W1: A potential weakness may be less background information included in main text of paper, especially for the target audience (ML/neurips).
Ans: Thanks for your kind ... | Summary: The paper investigates combining the HW-preserving ansatz with qubit topology-aware parity checks to impose hard constraints on quantum circuits for the quantum chemistry and Quadratic Assignment Problem. It includes numerical simulations and experiments on a real quantum device.
Strengths: sufficient numeric... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback, including the comparison of previous work [1]. We would like to highlight the differences between these two papers and promise to add a remark in the related work section to distinguish them.
In [1], the authors proposed methods to analyze... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Samba: Severity-aware Recurrent Modeling for Cross-domain Medical Image Grading | Accept (poster) | Summary: Accurate disease grading in medical image analysis is challenging due to the variability within disease levels and the similarity between adjacent stages. Additionally, models must handle data from unseen target domains, where differences in feature distribution can significantly reduce performance. To address... | Rebuttal 1:
Rebuttal: **Q1**: Some parts need more detailed explanation. Although space is limited, certain sections assume a high level of pre-knowledge. For example, more explanation on Samba would be useful.
**R**: Thanks for your valuable feedback.
We will provide more explanations of the proposed Samba, such as:
... | Summary: The authors introduce a new method named Severity-aware Recurrent Modeling (Samba) for disease grading in medical imaging. Specifically, they propose encoding image patches in a recurrent manner to accurately capture decisive lesions and transmit critical information from local to global contexts. Additionally... | Rebuttal 1:
Rebuttal: **Q1**: Significance of using Mamba in cross-domain tasks; restricting Mamba module may lead to excessive specialization.
**R**: Thanks for your valuable comments, so that we could have a chance to clarify the generalization and universality of the proposed Samba. Specially,
(1) The feature dist... | Summary: The paper introduces a new method for disease grading for both within and cross-domain medical images. Three different imaging modalities have been used for experimentation including Retinal, X-ray, and H&E images. In terms of methodological novelty, the authors propose to encode image patches in a recurrent m... | Rebuttal 1:
Rebuttal: **Q1**: Is Cross-domain Breast Cancer Grading Benchmark proper? 1) not widely used. 2) It is not common to use images from two different magnifications as the "domains". 3) Evaluation on CAMELYON17, from different centers and different staining.
**R**: Thanks for your valuable comment, so that w... | Summary: The authors propose a model which they call Samba, for Severity-aware recurrent modelling, which is a method designed for cross-domain medical image grading. They introduce several challenges in medical grading, namely the difficulty models encounter in generalising to unseen domains, as well as the existence ... | Rebuttal 1:
Rebuttal: **Q1**: Unclear explanation. **R**:
1) After proceeded by BSSM, each patch embedding $\boldsymbol{f}_n$ is projected by a linear layer to get the state embedding $\boldsymbol{x}_n$. The severity base $\boldsymbol{\mu}_n$ is modeled as a mixture of Gaussian from $\boldsymbol{x}_n$.
The E-M algorit... | Rebuttal 1:
Rebuttal: General Response:
We thank the reviewers for their time and constructive suggestions, and are glad that the reviewers unanimously give appreciation in a few points:
- Technique contribution (**Wyiu**: integrating Vision Mamba layers with EM-based recalibration of image features is a nice contrib... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Enhancing Preference-based Linear Bandits via Human Response Time | Accept (oral) | Summary: The paper explores the interactive preference learning problem.
Traditional binary choice feedback is limited in conveying preference strength.
The authors propose leveraging human response time, which inversely correlates with preference strength, as additional information.
They adopt the difference-based D... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review, and for recognizing the novelty and clarity of our work.
## Weakness: algorithm analysis
In our paper draft, we provide asymptotic theoretical results to show the following intuition on why response time can improve the learning performance:
*Co... | Summary: The paper studies linear bandit preference learning, where binary preference data have been augmented with response times. A joint model for choice and response time falls from setting the linear preference model as the drift parameter in a drift-diffusion model. Experiments and theoretical analysis show that ... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive review; we appreciate your feedback and recognition of our work's novelty and usefulness.
## Weakness: real-world empirical result
We acknowledge the importance of using real-world data for evaluation. Below is the rationale behind our simulator-based ... | Summary: The submission proposes to use response times to obtain additional information from participants in preference learning settings. They apply a variant of the Drift-Diffusion Model, a popular model of human decision making from psychology, and combine it with an algorithm applicable to linear bandits. In asympt... | Rebuttal 1:
Rebuttal: Thank you for your detailed and positive review. We appreciate your constructive feedback and are glad you enjoyed the paper. We will reorganize the theoretical sections and figure presentation as suggested.
## Weakness: about EZ-DDM vs DDM and EZ-DDM's assumptions
Thank you for pointing out the ... | Summary: This paper studies whether leveraging human response times can lead to better performance in bandit learning from preference feedback. More specifically, the paper integrates the drift-diffusion model (DDM) from psychology into the best-arm identification problem in linear bandits. Given a fixed interaction ti... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review. We appreciate your recognition of the novelty of our work and positive feedback on the writing. Here are our responses to your concerns and questions:
## Weakness: non-asymptotic result
In our paper draft, we provide asymptotic theoretical result... | Rebuttal 1:
Rebuttal: We address the two major concerns raised by multiple reviewers: the limited use of real-world datasets and the lack of non-asymptotic results.
## 1. New Simulation on a Real-world dataset
We present new simulation results based on another real-world response time dataset. This dataset [1] contain... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Segment Anything without Supervision | Accept (poster) | Summary: This paper presents Unsupervised SAM (UnSAM) for interactive and automatic whole-image segmentation which does not require human annotations.
This method uses top-down clustering and bottom-up merging to obtain multi-granularity pseudo labels for supervised SAM training. This unsupervised training of SAM achie... | Rebuttal 1:
Rebuttal: Dear Reviewer ecwU, thank you for your insightful comments, and we really appreciate that you are willing to increase our score if we can answer the questions positively.
We will provide detailed responses to each of them below.
**[W1] It Has Not Been Experimentally Proven that Unsupervised Trai... | Summary: The paper presents Unsupervised Segment Anything Model (UnSAM) for image segmentation whose training does not have access to human annotations. UnSAM employs a divide-and-conquer approach to hierarchically segment the image. In the divide stage, it uses CutLER [39] to obtain masks, and in the conquer stage, it... | Rebuttal 1:
Rebuttal: Dear Reviewer wivK, we appreciate your invaluable insights and thoughtful comments. In the following sections, we address the questions you have raised:
**[W1.1] Technical Contributions**
Please check our answers in the global rebuttal. Thank you!
**[W1.2] Differences with Prior Works**
We exp... | Summary: The paper explores a new way to generate hierarchical pseudo-masks to train downstream segmentation models without human annotations. First, the image is segmented using CutLER. Then, within CutLER proposed masks (with cropping and resizing), DINO features are extracted, and patches are iteratively merged base... | Rebuttal 1:
Rebuttal: Dear Reviewer p86w, thank you for your thoughtful comments. We will provide detailed responses to your questions below:
**[W1] What is "UnSAM"?**
Nice suggestion. In the paper, UnSAM stands for **Un**supervised **S**egment **A**nything **M**odel. The pseudo-labeling strategy discussed in our pap... | Summary: This paper proposes an approach to generate masks from images in an unsupervised manner, which are then used to train segmentation models. The major distinction from previous works is the proposed divide-and-conquer strategy, which first adopts the CutLER to obtain coarse instance/segmentation masks and then u... | Rebuttal 1:
Rebuttal: Dear Reviewer S6W7, we appreciate your invaluable insights and thoughtful comments! In the following sections, we address the questions you have raised:
**[W1] Contributions and Insights**
1. *Novel and Simple Pipeline:* We introduce a simple yet effective divide-and-conquer pipeline for produc... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their valuable feedback. In this paper, we present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation that does not require human annotations. We are encouraged by the acknowledgements on:
- **Comprehensive Experiments and SOTA... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks | Accept (poster) | Summary: The paper titled "The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks" presents a novel approach for community detection in networked systems by combining the traditional map equation with modern graph neural networks (GNNs). The authors propose Neuromap, a method that adapts the map... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment of our work.
For the comments on scalability, we kindly refer to our aggregate response.
On the question about the MDL principle:
The minimum description length (MDL) principle is an information-theoretic model-selection approach that has long ... | Summary: The paper proposes an information-theoretic centered approach for clustering nodes of a given graph. The main intuition at the core of the manuscript is to rewrite the map equation in a differentiable form and to train a Graph Neural Network (GNN) with the intent of minimizing its value (thus optimizing the cl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time invested in our work and the positive assessment of our manuscript.
We agree that the detailed explanation of the symbols $q_m$ and $m_{exit}$ in section 3 can be improved. It is also correct that $Q$ and $P_m$ in Eq. (1) do not sum to 1 and are implicitly norm... | Summary: The authors formulate the well-known MAP equation for community detection as an unsupervised objective for graph clustering with GNNs. The implement this "soft" neural MAP equation in various GNN architectures, showing reasonable performance on both synthetic and real-world graph clustering tasks.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment and encouraging words. For the anwers to your questions, we kindly refer to our aggregate response to all reviewers. In this aggregate response, we also clarify the novelty of our work and highlight our contributions over the work of Tsitsulin et a... | Summary: This paper proposes a deep learning approach for graph clustering called NeuroMap, which can be seen as a neural version of InfoMap. The idea is to minimize the a relaxation of the InfoMap objective using gradient descent. The proposed approach can be combined with different neural network architectures (MLP, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and highlighting the strengths of our approach. Below, we are happy to clarify the questions:
1. Developing a relaxation of the map equation is somewhat more straightforward since no explicit regularisation term is required to avoid trivial solution. A more ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive assessment of our work. We were glad to see that reviewer 5LnL found our paper "well-written" without "any serious (or even minor) confusion in prose, notation, or figure details" and that our "experiments convincingly show that the InfoMap objective is pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Generative Model of Symmetry Transformations | Accept (poster) | Summary: The paper aims to model the data distribution along each group action orbit. The proposed two-stage method first uses a self-supervised loss to learn an invariant function that maps each sample to its prototype and then uses a normalizing flow to learn the distribution along each orbit (i.e. conditional distri... | Rebuttal 1:
Rebuttal: > My main concern is the practical applications of the method. Currently, the experiments are done on small image datasets, e.g. dSprites and MNIST. Can the authors identify some more complicated tasks where modeling the symmetry transformations could be beneficial?
Please see our general respons... | Summary: This paper proposes a Symmetry-aware Generative Model (SGM), aiming to learn (approximate) symmetry presented in a data. The model achieves this by mapping each sample onto a prototype—a unique representative on the group orbit—and learning the conditional distribution over its group orbit through maximum like... | Rebuttal 1:
Rebuttal: (1) Learning this conditional distribution is non-trivial. We show that a GAN-based method fails (see Appendix F.1). Furthermore, in sections 3.1 and 3.2 we discuss design and implementation pitfalls that make this challenging. We provide guidelines as to how to resolve them. We consider these sec... | Summary: The paper proposes a generative model that disentangles the latent space into a group-invariant part -- the latent for the prototype -- and another part which represent a group element that can be applied to the prototype to reconstruct the input. A key novelty is to simultaneously learn to predict a distribut... | Rebuttal 1:
Rebuttal: > What is the architecture for the part that predicts the distribution over the group elements?
In short, we use an MLP with hidden layers of dimension [1024, 512, 512] as a shared feature extractor. These shared features are fed into another MLP hidden layers of dimension [256, 256] that output... | Summary: The paper proposes a generative model of symmetry transformations. The work leverages recent parameterizations based on group theory to define a generative model, in which relaxed symmetry becomes a latent variable over which inference can be performed.
Strengths: The paper is very well written and proposes a... | Rebuttal 1:
Rebuttal: > Practicality It seems that the proposed SSL objective has computational benefits... Is this true, or am I reading this too positively? What would be potential disadvantage of choosing such loss?
Your understanding is correct. We provide additional discussion below. Regarding disadvantages, the ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and constructive feedback on our paper. We are pleased that the reviewers have highlighted the quality of our writing (rf86, d33o, GGon), experimental evaluation (rf86, TxHS, GGon), the novelty, elegance and interestingness of our work (rf86, TxHS, GGon). We w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization | Accept (poster) | Summary: This work proposes a novel optimization algorithm with improved iteration complexity for convex smooth simple bilevel optimization, and demonstrates its faster convergence using experiments.
Strengths: The presentation is very clear. The literature review looks comprehensive. The algorithm and complexity resu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and great questions!
**W. This work focuses on simple bilevel optimization with both levels being convex and under deterministic setting (with access to full gradients instead of stochastic gradients), so the scope is not wide. Also, as shown in Table 1, the... | Summary: The paper works on the problem of simple convex smooth bilevel optimisation, where ''simple'' means single-variable. The paper achieves the optimal rate for this problem by a combination of Nesterov's acceleration and Jiang-Abolfazli-Mokhtari-Hamedani's cutting-plane method.
Strengths: STRENGTHS.
1. The pap... | Rebuttal 1:
Rebuttal: Thank the reviewer for the insightful question!
**Q1. What limitations do the authors anticipate in extending this technique (acceleration + cutting-plane) to the non-simple case (i.e., when you have an additional variable in the upper level objective, defined as the optimizer of a parametrized l... | Summary: The paper introduces a new algorithm called AGM-BiO (Accelerated Gradient Method for Bilevel Optimization) for solving simple bilevel optimization problems where both the upper and lower level objectives are convex and smooth.
Strengths: 1. The paper is well-written and easy to follow. The assumptions are cle... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment and the positive feedback!
**W. You provide a modified algorithm (Algorithm 2) that could potentially handle this composite structure. In line 629-line 631 you mention that you can derive identical complexity results for Algorithm 2 in either the compact doma... | Summary: This work proposes an accelerated method to solve convex simple bilevel problems, with the author providing both theoretical and numerical guarantees of the algorithm's convergence.
Strengths: - This work makes a theoretical contribution to the convergence analysis, and the algorithm demonstrates an advanced ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and valuable questions!
**R.**
To address your concern in the weakness section, please refer to our answers to your first two questions. As stated in A1, the non-accelerated version of the projection-based algorithm yields unsatisfactory results. Thus, the ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Plant-and-Steal: Truthful Fair Allocations via Predictions | Accept (poster) | Summary: This paper considers the problem of fairly and truthfully allocating indivisible items where mechanisms are equipped with predictions. When the prediction is perfectly accurate, the mechanism's performance should be significantly improved (consistency); conversely, for a prediction with any accuracy, the mecha... | Rebuttal 1:
Rebuttal: "Can you further explain why studying space-efficient predictions is important and interesting?"
-- Our motivation for succinct predictions comes from the works of [5,6,7], where they show that
succinct predictions are crucial for learning the parameters from a few samples and for incorporating ... | Summary: The authors study the problem of fairly allocating a set of $m$ indivisible goods among a set of $n$ strategic agents with additive valuations in a fair manner. The goal is to obtain a truthful mechanism which guarantees a good approximation of maximin share (MMS) of each agent. It has already been shown that ... | Rebuttal 1:
Rebuttal: * "I have a more fundamental concern regarding the presented algorithm. The existing algorithms for approximate MMS (in the classic fair division setting without predictions) guarantee 𝛼-MMS allocations and currently the best known 𝛼 is marginally above 3/4. These algorithms are not truthful mec... | Summary: This submission studies the problem of approximating truthful mechanisms for the Maximin-Share allocation of individual goods whenever agents have incentives. Specifically, the authors design a learning-augmented algorithm for allocating goods to agents, given a prediction over the agents' ordinal preferences ... | Rebuttal 1:
Rebuttal: “the paper may be hard to read for someone who is not already familiar with the Maximin-Share allocation problem. For example, exactly what an agent report is is never clearly explained”
-- We will make an effort to improve readability in the camera-ready version of the paper, including a clearer... | Summary: This paper designs truthful algorithms for the fair allocation of indivisible goods in the learning-augmented framework. The algorithm is said to receive predictions about all agents' utilities for all goods (agents have additive utilities) or their ranking over the goods. The fairness notion studied is MMS (t... | Rebuttal 1:
Rebuttal: * "The predictions are either the entire valuation matrix or all the rankings, which contain a lot of information."
-- The predictions used are:
1) Rankings, which we think are much more plausible than the exact valuations – it’s easier to predict that item A is more valuable than item B than to ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful and thorough reviews. We will address all their comments and edit suggestions to improve the final manuscript. We address each reviewer’s comments/questions in the individual rebuttal sections below. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning | Accept (poster) | Summary: The paper introduces TREACLE, a reinforcement learning policy designed to optimize the selection of LLMs and prompting schemes based on a user's budget constraints in terms of cost and latency.
Strengths: 1. TREACLE enables substantial cost savings compared to existing methods by intelligently choosing among ... | Rebuttal 1:
Rebuttal: ### W1: Dynamic selection of models and re-querying could lead to increased computational costs and delays
As the reviewer points out, latency plays a crucial role in real-time settings (e.g., voice assistants). To address scenarios requiring real-time performance, we incorporated latency as a co... | Summary: The paper proposes a reinforcement learning method to select the model and prompting. It combines with monetary cost and latency constraints. The design of the features contains question text embeddings and response history. Experiments studies the cost savings.
Strengths: 1, Important problems.
2, Interestin... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s detailed and constructive feedback.
## Weakness 2: Main weaknesses of the paper are that the method overlooks the actual cost.
We acknowledge the reviewer's concern and agree that our method uses a total budget constraint which minimizes the individual query... | Summary: This paper presents a framework for managing different budgets—such as accuracy, cost, and latency—when utilizing Large Language Models (LLMs) for reasoning tasks. Recognizing that reasoning tasks can be broken down into a series of question-and-answer interactions, the authors propose a method to allocate mod... | Rebuttal 1:
Rebuttal: ## W1: The current framework is inadequate if a capable model can plan ahead by considering multiple questions or a trajectory of questions in advance, even while using various models for the answers.
An approach of considering a batch of questions at once could certainly work, whereas in our f... | Summary: This paper aims to solve the problem that LLMs can be costly, in particular using technologies such as COT. It proposes to apply RL to select the model and prompting scheme. Experimental results show that the proposed method can maintain the model performance while saving up to 85% costs.
Strengths: 1. This p... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on the manuscript. We appreciate your recognition of the reinforcement-learning based approach to achieve significant cost savings when querying LLMs. | Rebuttal 1:
Rebuttal: Thank you to the reviewers for their thoughtful reviews and constructive comments. We have provided individual responses to each of the reviewers. In addition to these responses, we have conducted additional experiments to evaluate cost-accuracy tradeoffs by including the cost constraint in the ob... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Differentially Private Subspace Estimation in a Distribution-Free Setting | Accept (poster) | Summary: The paper tackles the challenge of high costs in private data analysis due to the curse of dimensionality, despite many datasets having an underlying low-dimensional structure. It builds on prior work by introducing measures based on multiplicative singular-value gaps to quantify how "easy" a dataset is for pr... | Rebuttal 1:
Rebuttal: We thank you for the positive review.
Regarding your questions:
1. We made an effort to reduce the constants so that our algorithm can achieve high accuracy using a reasonable amount of points. But as we mentioned in Section 5, our method is only effective for instances that are very close to a... | Summary: This paper addresses the challenge of private subspace learning without assuming that the dataset follows a Gaussian distribution. They propose new measure on the hardness of subspace learning problem based on the ratio between the $k^{th}$ and the remaining eigenvalues, or between the $k^{th}$ and the $(k+1)^... | Rebuttal 1:
Rebuttal: We thank you for the positive review.
Regarding your question:
We are interested in DP algorithms that have the property that if they get "easier" inputs, they achieve "better" accuracy. The parameter $\lambda$ captures the connection between "easiness" and "accuracy" (i.e., it is a property of ... | Summary: This work studies the problem of differentially private dimension reduction without dependence on the ambient dimension d. While this is known to be impossible generally, this work gives (distinct) necessary and sufficient conditions on gaps between the kth singular value of the data matrix and its subsequent ... | Rebuttal 1:
Rebuttal: We thank you for the positive and thorough review. Your editorial comments are very helpful and we will address them in the next version. In the following, we would like to respond to specific points you make.
Regarding the weaknesses:
In the next version, we will dedicate a few more sentences to... | Summary: The paper studies private PCA under the assumption that the singular values of the data matrix shows multiplicative decay. It is a problem that was initiated by Steinke-Singhal and has not seen much improvement since that work. This works provides a more thorough study of this problem.
Strengths: PCA is one o... | Rebuttal 1:
Rebuttal: We thank you for the positive review.
Regarding your question. We currently don’t know how to improve the upper bounds, and we agree with your intuition that the lower bounds seem closer to the truth. One supporting evidence for this intuition is that our lower bound for strong estimators genera... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Bandits with Ranking Feedback | Accept (poster) | Summary: The paper studies the multi-armed bandit problem when the learner only observes the ranking of the average cumulative reward of all the arms. This is a strictly worse information environment than the standard setting. The paper proposes algorithms that still attains the instance independent and dependent regre... | Rebuttal 1:
Rebuttal: Q: _It is probably out of the scope of this paper. But the current dependence on $n$, the number of arms, is quite far from optimal in the instance dependent case. I wonder if the authors have thought about better designs to obtain linear or sublinear dependence. Some discussion would be helpful._... | Summary: The paper studies the setting where, every time an arm is pulled, a principal gets to observe a reward, but the player only gets to observe the order that emerges from the accumulated rewards so far. The authors study both the adversarial and stochastic, and both the instance-dependent and instance-independent... | Rebuttal 1:
Rebuttal: Q: _I miss some more realistic motivation for the model and some concrete applications. As I said, from a theoretical point of view, the model is very interesting, but when I tried myself, I could not come up with a clear application._
We thank the Reviewer for the question. In the following, we ... | Summary: The paper introduces a variant of the multi-armed bandit problem called "bandits with ranking feedback," where the feedback ranks the arms based on historical data without showing precise numerical differences. This approach is particularly useful in scenarios where exact measurement of values is impractical, ... | Rebuttal 1:
Rebuttal: Q: _Originality: Is the concept of "bandits with ranking feedback" truly novel?_
To the best of our knowledge, the "bandits with ranking feedback" model introduced in our paper represents a new bandit setting. Although our setting shares similarities with dueling bandits, the two settings are sub... | Summary: This paper considers a new multi-armed bandit problem where the feedbacks are ranking of the arms. In this problem, the environment gives the feedback on the ranking of the arms based on the previous pulls. The authors first consider the stochastic setting and give the lower bound of the regret for the instanc... | Rebuttal 1:
Rebuttal: Q: _"First, in the problem setting, the ranking feedback is assumed to be perfect in terms of the ranking of the averaged history rewards. (...)"_
We completely agree with the Reviewer that the introduction of imperfection/uncertainty/tolerance is of paramount importance as it would allow to capt... | Rebuttal 1:
Rebuttal: Dear Reviewers,
in the attached PDF, we provide additional experiments.
The authors.
Pdf: /pdf/a05b01cdeedd6d434c2c70e5e1268ebe6aa5f93c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hierarchical Federated Learning with Multi-Timescale Gradient Correction | Accept (poster) | Summary: This paper proposes a multi-timescale gradient correction (MTGC) methodology to deal with multi-timescale model drift. It introduces a distinct control variables to correct the client gradient towards the group gradient, and correct the group gradient towards the global gradient. Then, the stability of the pro... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the valuable comments. We also thank the reviewer for acknowledging our idea as interesting and for taking a look at our proof. Our responses are given below.
### **Practical applications of HFL**
HFL can play a key role in most of the real-world networks comprised... | Summary: This paper proposes a novel algorithm called Multi-Timescale Gradient Correction (MTGC) for Hierarchical Federated Learning (HFL). The authors address the challenge of multi-timescale model drift in HFL systems, where data heterogeneity exists both within client groups and across groups. MTGC introduces couple... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive comments and feedback. We are glad to receive your appreciation of our motivation and results. Our responses are below:
### **Discussion on CFL**
Our work focuses on HFL, employing a multi-layered structure consisting of local nodes, local aggregators, and a... | Summary: This paper presents a method to address multi-timescale model drift in hierarchical federated learning. Specifically, it introduces two control variables to correct intra-group client drift and group model drift. The paper establishes the convergence bound in a non-convex setup and demonstrates its stability a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback. We appreciate the reviewer for carefully checking our proof and are glad to receive your positive feedback on the writing of our paper. Our responses are given as below:
### **Communication cost comparison**
Compared to HFedAvg, MTGC requires i... | Summary: This paper introduces the usage of the gradient correction scheme to hierarchical federated learning. Specifically, the authors propose and analyze the multi-timescale gradient correction MTGC algorithm which is a direct generalization of SCAFFOLD to the framework where local clients aggregate their models on ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback. We are glad that the reviewer acknowledges the problem we studied is interesting.
We would like to emphasize that theoretically showing that the proposed MTGC algorithm guarantees convergence and achieves linear speedup in terms of both $H$ (the... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for providing constructive comments. In this global response, we will describe the additional experiments we have conducted, suggested by **Reviewer r1S6**, **Reviewer fhg9**, and **Reviewer zFr1**, that may be of interest to all reviewers. The figures are included in t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Mixtures of Unknown Causal Interventions | Accept (poster) | Summary: This paper explores the challenge of disentangling and identifying causal relationships in situations where interventional data is noisy and mixed with both intended and unintended effects. It focuses on applying interventions within linear SEMs with Gaussian noise without prior knowledge of the true causal gr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. Below, we answer the weaknesses and questions raised by the reviewer.
>Weakness
**Weakness 1: Experiments**
*Experiment on the real-world dataset:* We have run our algorithm on a real-world protein signaling network dataset. Our meth... | Summary: The paper proposes a method to disentangle the observational and interventional data under linear SEM with Gaussian noise.
Strengths: The algorithm proposed in this paper efficiently disentangles components of mixtures arising from unknown interventions, accommodating both soft and hard interventions. The pro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. Below, we answer the weaknesses and questions raised by the reviewer.
>Weakness
**Weakness 1: Experiment on the real-world dataset:**
We have run our algorithm on a real-world protein signaling network dataset. Our method performs e... | Summary: The paper proposes linear structural equation models with additive Gaussian noise to address the challenge of disentangling mixed interventional and observational data. The problem is highly relevant to real-world applications with mixed data.
Strengths: * The theoretical framework is robust, with clear assum... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. Below, we answer the weaknesses raised by the reviewer.
**Weakness 1**:
While we agree that the linear SEMs with unknown interventions are a relatively well-studied problem, we wish to highlight that our setting is different as we w... | Summary: This paper considers a setup where an intervention results in obtaining iid data from a mixture of multiple interventional data and observational data and the goal is to learn the mixing weights and the resulting interventional distributions. Particular focus is on the linear SEM setting with Gaussian noise wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and thoughtful comments. Below, we answer the weaknesses and questions raised by the reviewer.
> **Weakness**:
**Limited Novelty**: While we agree that our work builds on top of existing work but doesn’t follow immediately from them. To invoke these existing... | Rebuttal 1:
Rebuttal: Below, we answer some of the common weaknesses or questions raised by multiple reviewers:
> Reviewer 583w
**Automatic component selection**:
In our Algorithm-1 (Mixture-UTIGSP), we allow for the misspecification of the correct number of components in a mixture. By default, the number of comp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Geometry-aware training of factorized layers in tensor Tucker format | Accept (poster) | Summary: This paper proposes a method for neural network model parameter compression. It parameterizes weight tensors in the format of Tucker decomposition, and trains the factors instead of the origin weight tensors afterwards. The method is able to adaptively modify the tensor rank. Authors also provide detailed theo... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewer for their feedback.
**W1**: We agree that our paper can be improved by showcasing stronger empirical evaluations for larger models. We have therefore conducted several new experiments on the popular parameter-efficient fine-tuning
using low-rank ... | Summary: This paper extends the dynamic low-rank neural network training (DLRT) method to the rank-adaptive Tucker tensor format (TDLRT). The proposed reparameterization method greatly reduces the computational complexity and numerical instability of the projected gradient descent. Under certain conditions, TDLRT with ... | Rebuttal 1:
Rebuttal: First of all, we would like the thank the reviewer for the insightful feedback. Below, we provide our responses to the main points raised in the review.
**Q1+W1** Regarding "How does the one-step integration work? Does it not add significant computational overhead?". With "one-step integration''... | Summary: The authors present a novel algorithm for training neural network layers using Tucker tensor decomposition. The approach addresses common issues with layer factorisation, including the need for an initial warm-up phase and sensitivity to parameter initialisation. Th method dynamically updates the ranks during ... | Rebuttal 1:
Rebuttal: We wish to thank the reviewer for their feedback. Overall, the reviewer highlights key positive aspects of our work, such as the importance of the topic, the novelty of the approach, and the thorough theoretical analysis. The main weaknesses noted are centered on the need for additional runs and a... | Summary: The authors study the training of layer factorization models to reduce the number of parameters in deep neural networks. They propose a geometric-aware rank-adaptive training strategy to avoid requiring prior knowledge of ranks and the sensitivity to the weight initializations. Their theoretical results show c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback, below we provide our response to the raised weaknesses and questions.
**Q1**
Thank you for your feedback regarding the empirical evaluation. In response to your suggestion, we have conducted several new experiments to strengthen our empirical e... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their insightful feedback. We have considered each comment and have made several improvements as a result. Below, we address and review the key points raised by the reviewers:
1. **Empirical evaluation**:
Most reviewers commented that while our work p... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Reducing the size of neural networks is an important problem for reducing the cost, memory usage, and even inference time. Many works focus on reducing the size after the training phase and use techniques such as sparsification and quantization. This paper, on the other hand, focuses on reducing the size by re... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and appreciation of the work. Below we provide our response to the raised questions.
**Q1.** We appreciate this insightful question. Selecting the appropriate tensor structure a-priori is indeed challenging, as it depends heavily on the specific tensor st... | null | null | null | null | null | null |
Diffusion PID: Interpreting Diffusion via Partial Information Decomposition | Accept (poster) | Summary: In this paper, the authors propose a novel approach to analyze the uniqueness, redundancy, and synergy terms in text-to-image diffusion models by applying information-theoretic principles to decompose the input text into its elementary components. In particular, the proposed approach can be used to recover ge... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback and helpful comments. We address your concerns below.
> **Q1)** Clarification on Eq 2:
We thank you for mentioning this. Unfortunately, our footer note didn't make it through the submission. Eq 2 is correct, and the second equation is derived from ... | Summary: Decomposing different types of information (redundant, synergistic, unique) has long been a niche field due to the difficulty of applying these methods in realistic settings. This paper develops a way to dissect these types of fine-grained information measures in a practical way in the realistic setting of tex... | Rebuttal 1:
Rebuttal: We thank you for your comprehensive review and thoughtful comments. We also appreciate your comments for explicitly highlighting the difficulties of adapting PID to diffusion and the challenges of working on the interpretability of models.
> **Q1)** Relationship between PID maps and human intuiti... | Summary: The paper proposes a new technique called DiffusionPID to explain how diffusion models transform text cues into images through partial information decomposition (PID). This work deconstructs mutual information into redundancy, synergy, and uniqueness to analyze how individual concepts and their interactions sh... | Rebuttal 1:
Rebuttal: We thank you for your thorough review and insightful feedback. We address your suggestions below and also refer to the global response at the top in a few places.
> **Q1)** Analysis on objects and attributes
We agree that our work could be further improved by providing additional analysis on the... | Summary: Summary:
The paper adapt the concept of the Partial Information Decomposition into the diffusion model and analyze the uniqueness, redundancy and synergy terms in the diffusion model and do experiments on the Bias, Homonyms and Synonyms
Contribution: The paper adapt the concept of the Partial Information Dec... | Rebuttal 1:
Rebuttal: We thank you for your detailed review and thoughtful comments. We address your concerns below and will refer to the general response at the top for certain points.
> **Q1)** Novelty of our method and more complex use cases such as an extension to the multi-concept scenario
1. Our approach is th... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time to go through our work in detail and providing insightful reviews. We found the feedback very constructive and helpful.
We are glad that the reviewers unanimously agree that the proposed information-theoretic approach to interpret diffusion models is... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient | Accept (poster) | Summary: The authors analyze the application of Hamiltonian Monte Carlo (HMC) to Bayesian neural networks (BNNs) with ReLU activation functions.
They theoretically show that despite its piecewise linear structure, HMC still provides correct results, but that it accumulates an error due to this non-differentiability th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We have addressed your comments and questions as follows.
1. **Additional experiments:** We now added additional experiments on a larger real-world dataset, for which the results are presented in the general rebuttal and its attached PDF file above.
2. **T... | Summary: This paper addressed the inefficiency of HMC in practices applied to ReLU-based neural networks, where the local error rate of HMC will be large due to the non-differentiable activation functions in ReLU. The efficiency used here to compare is a function of the acceptance rate and the step size of HMC.
Streng... | Rebuttal 1:
Rebuttal: We are glad that you enjoy the work. We have addressed your comments and questions as follows.
1. **Additional experiments:** We now added additional experiments on a larger real-world dataset, for which the results are presented in the general rebuttal and its attached PDF file above.
2. **Lips... | Summary: The efficiency of the Hamiltonian Monte Carlo (HMC) method directly depends on acceptance rate of proposals, while it samples weights of neural network architectures. Nonetheless, the presence of ReLU activation function in the architecture might lead to high rejection rate during the sampling due to the jumps... | Rebuttal 1:
Rebuttal: We really appreciate your comments on the manuscript, and have addressed your comments and questions as follows.
1. **Additional experiments:** We have now added an additional experiment on a larger real-world dataset, for which the results are presented in the general rebuttal and its attached p... | Summary: This paper analyzes the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator on ReLU NN. This paper shows that crossing a surface of non-differentiability will cause a local error rate of $\Omega (\epsilon )$. Simulations validate the theoretical analysis.
Strengths: 1. The paper intr... | Rebuttal 1:
Rebuttal: Thank you for your comments. We have addressed your comments regarding additional experiment on a larger real-world dataset. Please see the general rebuttal and its attached PDF file for more details. The new results also confirm the findings of the manuscript. Please let us know if you have furth... | Rebuttal 1:
Rebuttal: We thank all reviewers for their helpful comments on the manuscript. We are delighted with the general positive sentiments among the reviewers about the novelty, significance, soundness, as well as representation of the work, especially on its theoretical contributions. Based on the reviewers’ sug... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper analyses the HMC algorithm with the leapfrog integrator for Bayesian neural networks with different non-linearities. In particular, the paper focuses on ReLU non-linearities and how they cause HMC to be inefficient compared to networks with smooth non-linearities. The authors derive an upper bound on... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful responses. We have addressed your comments and questions as follows.
1. **Additional experiment and reports on accuracy:** We have added an additional experiment on a larger real-world regression dataset and included reports on MSEs as requested by the reviewer (see ... | null | null | null | null | null | null |
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting | Accept (poster) | Summary: This paper introduces a deep learning weather prediction model called Stormer. Stormer is vision transformer type network that employs various techniques to improve the network's performance in weather forecasting applications, including a "weather-specific embedding" that first processes each variable separat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very detailed and constructive feedback, and for recognizing the technical contributions and good presentation of Stormer. We answer each of the reviewer's concerns below.
> PanguWeather could then also be used to implement the randomized forecast strategy. It would ... | Summary: The paper proposes a transformer-based model for weather prediction. Experiments show improvement in downstream predictions.
Strengths: - The presentation of the paper is clean, and the paper is easy to read and understand.
- Some improvements over long-term weather forecasting.
Weaknesses: - The paper has v... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and the appreciation of the good presentation and good performance of Stormer. We answer each of the reviewer's concerns below.
> The paper has very limited novelty and is incremental.
We acknowledge that some components in Stormer are similar ... | Summary: The authors introduce a vision transformer-based method, Stormer, designed for medium-range weather forecasting. Ablations identify multiple important components of the method, including a weather-specific patch embedding, "randomized" dynamics forecast, and a pressure-weighted loss. The randomized forecasting... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very detailed and constructive feedback, and for recognizing the strong results and technical contributions of Stormer. We answer each of the reviewer's concerns below.
> The paper is essentially using an ensembling technique but comparing against deterministic model... | null | null | Rebuttal 1:
Rebuttal: We thank ACs for handling our paper and reviewers for their insightful comments and constructive feedback. The suggestions by the reviewers are very helpful and have added significant insights to the paper. We have responded to each review individually, and also **submitted a PDF file containing t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Sparsity of the Strong Lottery Ticket Hypothesis | Accept (poster) | Summary: The authors propose an existence proof for Strong Lottery Tickets(SLTs) by improving over the existing method which used the Subset Sum approximation to construct source networks, which involved using subsets of variable sizes in order to approximate target parameters. Instead the authors show the existence of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback on our work.
In the following we address the reviewer’s concerns individually.
**Weakness 1**. We are sorry if the discussion in Section 1.1 in which we discuss the links between our results and \[1,2,3\] gives the impression that our work only connect... | Summary: This work focuses on the theoretical side of the Strong Lottery Ticket Hypothesis. It proposes and studies a fixed-size version of the canonically used Random Subset Sum (abbreviated RFSS) problem in previous proofs for this hypothesis. Then it applies the result of RFSS to prove that overparameterized network... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the time they invested in reviewing our paper and the appreciation they expressed for our work.
In the following we address the reviewer’s concerns individually.
**Weakness 1 and 2.** We agree with the reviewer that the paper would greatly benefit from a lo... | Summary: The authors consider the strong lottery ticket hypothesis (SLTH), which is roughly a statement that a large random neural network contains a sparse subnetwork whose performance is comparable to the entire network. By analyzing a fixed variant of the random subset sum problem, which is about the size of the sam... | Rebuttal 1:
Rebuttal: We are very grateful to the reviewer for the time they invested in reviewing our paper, which we find very valuable for improving it.
In the following we address the reviewer’s concerns individually.
**Question 1.** We agree with the reviewer that the assumption of sum-boundedness in Definiti... | Summary: This paper studies the strong lottery ticket hypothesis from the perspective of pruning sparsity. In particular, the paper noticed that all previous papers about the strong lottery ticket hypothesis fails to characterize the sparsity of the neural network after pruning, and aims at solving this question in the... | Rebuttal 1:
Rebuttal: We thank the reviewer for his precious feedback and the points they raised, which we find very valuable in improving the presentation of our result, and for the appreciation he expressed for the merits of our work.
In the following, we address their concerns.
**Weakness 1 (form of Eq. 1).** As... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to the reviewers for their thoughtful reviews and valuable feedback. We appreciate the time and effort they have invested in evaluating our work. We've carefully considered their comments and would like to summarize our response to the main points raised:
**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Navigating Chemical Space with Latent Flows | Accept (poster) | Summary: The authors built a general latent flow-based framework unifies traversal and optimization in the molecular latent space. The flow is trained by utilizing energy functions so that the vector field aligns with the gradients, with regularization imposed by an auxiliary classifier that tries to differentiate each... | Rebuttal 1:
Rebuttal: **C1: Typo: L747 "we first verify if the learned variational poster also follows a Gaussian distribution and we find that it does learn so", poster -> posterior.**
A: Thanks for pointing it out. It was a typo and we will fix it in the revised manuscript.
**C2: In Table 2 and 3, why did ChemFlow... | Summary: Designing new functional molecules within the vast chemical space is challenging, which necessitates efficient exploration and understanding of this space. The paper introduces a new framework called ChemFlow, which leverages latent space learned by molecule generative models and navigates it using flows. Chem... | Rebuttal 1:
Rebuttal: **C1: ChemFlow employs multiple approaches to learning different latent flows. However, the experiment's results show that different methods have different specialties, and the paper does not discuss the connection between flow learning and downstream tasks.**
A: Thanks for the question. This is ... | Summary: The authors propose a new method called ChemFlow, which navigates molecular distributions in chemical space through flow.
Strengths: 1. The method demonstrates high generality, applicable to various molecular optimization tasks.
2. Based on the experimental results presented, the method shows significant opti... | Rebuttal 1:
Rebuttal: **C1: The description of the experimental section lacks detail, such as which software was used to measure the docking scores?**
A: Thanks for pointing it out. We will thoroughly revise the experimental section in the revised manuscript to make sure all details are fully explained. Specifically, ... | Summary: This paper presents a novel gradient flow-based method to traverse the latent space of molecular generation models, known as ChemFlow. The authors instantiate their framework with a number of different flows inspired by dynamical systems. They also investigate the use of supervised and unsupervised guidance fo... | Rebuttal 1:
Rebuttal: **C1: The baselines for some tasks are a bit weak, particularly for the unconstrained molecular optimisation. It would be very useful to see a comparison of ChemFlow with methods like EA and RL fine-tuning.**
A: Thanks for the suggestion. As our method focuses on the latent space of deep generati... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback that helps us improve the manuscript. We appreciate reviewer’s common sentiment that our work is novel, general, applicable, and well written. We are also glad that reviewers compliment our work has extensive experiments showing the effectiven... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Images that Sound: Composing Images and Sounds on a Single Canvas | Accept (poster) | Summary: This paper explores the feasibility of synthesizing spectrograms (images that sound) that simultaneously look like natural images and sound like natural audio. This paper proposes a zero-shot approach, which leverages pre-trained text-to-image and text-to-spectrogram diffusion models that operate in a shared l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. Below are our responses.
**Technical contribution**
The reviewer appears to have taken a narrow, algorithm-centric view of what constitutes a contribution. We note that this seems to be the major complaint in their (unusually short) revi... | Summary: The paper proposes using pretrained text-to-image and text-to-speech diffusion and leveraging their compositional property to generate spectrograms that look like images and can be also be converted into meaningful sounds. The work is motivated by applications in art. The paper also curates a set of text promp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comprehensive feedback. Below are our responses.
**Importance of shared latent spaces**
This seems to be a misunderstanding: the latent spaces *must* be shared during joint sampling because if they weren't, then the latent vector would be decoded to two co... | Summary: The authors propose to leverage pre-trained text-to-image and text-to-spectrogram diffusion models, and de-noise noisy latents with both models in parallel during reverse process. They show that the proposed method can generate spectrogram aligned with audio prompt while having visual appearance of the image p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and comprehensive feedback. Below are our responses.
**Text prompt design**
We note that our paper already contains some examples with prompts that describe visual scenes, such as in Fig. 8, where we use the relatively complex text prompt of `"a p... | Summary: This paper proposes a very creative idea, synthesize spectrograms that simultaneously look like natural images and also sound like natural audio, which they call images that sound. The method is rather simple, and leverages two pre-trained diffusion models, one text-to-image and the other text-to-spectrogram. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and valuable feedback. Below are our responses.
**Simplicity**
We appreciate the reviewer's comment that this is a creative idea. We believe that its simplicity is in fact a major strength. While our method is simple, it is not obvious that noise ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough comments and appreciate the recognition of the creativity of our work, described as "a beautiful idea" (mkQc) and "an interesting topic" (RECb, quDM, w6xJ). The acknowledgment of the "thorough evaluation" (mkQc, RECb, quDM) of our method and baselines is a... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling | Accept (poster) | Summary: This paper introduces Orchid, a novel deep learning architecture that addresses the quadratic complexity of traditional attention mechanisms while still capturing long-range dependencies and enabling in-context learning. The key innovation is a data-dependent global convolution layer that dynamically adapts it... | Rebuttal 1:
Comment: Thank you for your detailed review and valuable feedback. Your recognition of the contributions of the proposed Orchid architecture and the comprehensive empirical evaluations and also your valuable feedback is appreciated. In the following, we address the points you raised in your review.
- *W1:... | Summary: This paper introduces the Orchid block, a novel sequence modeling element that employs a convolutional operator with a sample-dependent generated kernel and an $O(n \log n)$ computational complexity with $n$ being a sequence length. The kernel, matching the input sequence length, captures both long- and short-... | Rebuttal 1:
Comment: Thank you for your detailed and thoughtful review of our paper. We appreciate your recognition of the contributions and the strengths of our paper. In the following, we address your points.
- *W1:*
Although the current form of the Orchid is not compatible with causal and autoregressive models, ... | Summary: This paper presents Orchid, a novel method that conditions long convolutional layers based on the input, to obtain global context and in-context learning abilities. This is achieved through the use of a conditioning network that acts both on the spatial and frequency domain to mix close spatial and spectral to... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback on our paper. We appreciate your recognition of the novelty and soundness of our approach, as well as the compelling empirical evidence. In the following, we address the points you raised in your review.
- *Orchid is using local info for spectral and spat... | Summary: Authors introduce a method for addressing the quadratic computational complexity of the attention mechanism while retaining expressivity and model performance from transformer models. Whereas previous approaches have achieved sub-quadratic computational efficiency - e.g. hyena, ssms and CKConv - authors argue ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback. We appreciate your recognition of the contributions and the strengths of our paper. In the following, the points you raised in your review are addressed.
- *W1: Compare against 2D-convolutional long-range approaches:*
The Orchid block,... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Amortized Fourier Neural Operators | Accept (poster) | Summary: This paper tries to alleviate one of the issues in a so-called "Fourier Neural Operator (FNO)" which is a machine learning model to estimate a solution of partial differential equations (PDEs) based on the concept of "Operator learning". FNO takes into account the embedding of input field information in Fourie... | Rebuttal 1:
Rebuttal: We thank Reviewer kDgT for the detailed feedback. Below, we respond to the questions.
W1: Experimental repetition.
The results for neural operators are relatively stable. Consequently, many results for widely used baselines are taken directly from the original papers, which also do not include r... | Summary: This paper introduces Amortized Fourier Neural Operators (AM-FNOs), a novel approach to improve Fourier Neural Operators (FNOs) for solving PDEs. The key contributions to at least me are:
1. An amortized neural parameterization of the kernel function in FNOs to accommodate arbitrarily many frequency modes usi... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer okt7 for recognizing the novelty and empirical contributions of our method. Below, we address the questions raised.
W1: Inadequate baseline tuning.
We appreciate the reviewer's concern regarding baseline tuning. To ensure a fair comparison, we have adjusted all models... | Summary: Typically, FNOs require a large number of parameters when addressing high-dimensional PDEs or when a high threshold for frequency truncation is needed. To overcome this challenge, the authors introduce the Amortized Fourier Neural Operator (AM-FNO). Their method uses an amortized neural parameterization of the... | Rebuttal 1:
Rebuttal: We thank Reviewer NuVs for the valuable feedback. Below, we respond to the questions.
W1: The reason for tackling the CoD issue in the number of parameters.
In FNO, the kernel is parameterized independently for each frequency mode, resulting in complexity for the Fourier integral operator of $O(... | Summary: This paper presents the AMortized Fourier Neural Operator (AM-FNO), which utilizes an amortized neural representation of the kernel function. It allows accommodating a variable number of frequency modes while using a fixed number of parameters compared to the Vanilla Fourier Neural Network.
Strengths: S1) Amo... | Rebuttal 1:
Rebuttal: We thank Reviewer Z566 for the valuable feedback. Below, we respond to the questions. **Please note that the additional tables and references are included in the Global Rebuttal due to character limits.**
W1: More baselines.
Our method aims to enhance Fourier neural operators (FNOs), a significa... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the thoughtful reviews. We are pleased that the reviewers found our paper to be **overall well-written, with clear descriptions of the methods** (Reviewer DLAH), that our method is **original and intuitive** (Reviewer DLAH), **novel** (Reviewer okt7), our... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces the AM-FNO to address high-frequency truncation in the original FNO, which can damage the performance for PDE data with substantial high-frequency information. AM-FNO utilizes MLP or KAN to approximate the kernel function value in Fourier space for all frequency modes. For MLP based AM-FNO... | Rebuttal 1:
Rebuttal: We thank Reviewer DLAH for the acknowledgment of our method and empirical contributions. Below, we respond to the questions.
W1: The motivation for orthogonal basis functions.
Sorry for the lack of clarity. We make the following clarifications. As shown in Table 4, the performance degrades compa... | null | null | null | null | null | null |
Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization | Accept (poster) | Summary: The paper presents a novel approach for time series out-of-distribution generalization via pre-trained large language models.
The authors introduce a Tri-level learning framework that combines sample-level and group-level uncertainties accompanied by a theoretic perspective. Furthermore, a stratified localizat... | Rebuttal 1:
Rebuttal: Thanks for recognizing our work.
**(Q1)** Can this method be adapted for OOD time series regression and forecasting?
**(Reply to Q1)** Yes, our proposed tri-level learning framework is designed to learn robust representations for time series OOD generalization, which means the learned represent... | Summary: The paper explores the challenge of OOD generalization in time series data using pre-trained LLMs. The authors propose a novel framework TTSO that integrates both sample-level and group-level uncertainties. They also introduce a stratified localization algorithm tailored to this tri-level optimization problem,... | Rebuttal 1:
Rebuttal: **(W1 & W2)** The tri-level learning framework might be overly complex for practical applications, potentially limiting its adoption. The proposed method, especially the stratified localization algorithm, may incur high computational costs, which could be a barrier for large-scale applications.
*... | Summary: The paper studies the problem of OOD generalization in time series tasks, building on recent observations that use data level uncertainties and group level uncertainties. This has been a successful way to build robust, transferrable representation. The paper additionally also includes an additional maximizatio... | Rebuttal 1:
Rebuttal: **(W1)** *OOD generalization in time series*: Why is this particular method effective for time series? as far as the assumptions made in the paper go, this is a general technique that can be applied to any arbitrary dataset/modality. While it is true that time series problems do not receive as muc... | Summary: Out-of-Distribution（OOD）generalization in ML emphasizes on improving model adaptability and robustness aginst unseen and potentially adversarial data. This paper explores OOD generalization for time series data with pre-trained Large Language Models and proposes a novel tri-level learning framework to handle t... | Rebuttal 1:
Rebuttal: **(W1)** While the paper is well organized overall, the clarity of this paper can be further enhanced via providing examples to illustrate the concepts of sample-level and group-level uncertainties in time series.
**(Reply to W1)** Thank you for your insightful comments. Per your suggestion, we h... | Rebuttal 1:
Rebuttal: Figure 1, Figure 2 and Figure 3 is in the attached PDF.
Pdf: /pdf/406c8fa059148f9d04081977c1b18506429b6944.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unelicitable Backdoors via Cryptographic Transformer Circuits | Accept (poster) | Summary: This work presents a new encrypted backdoor construction technique that compiles backdoors directly into transformer architectures.
The literature research of current backdoors and give an understanding of current limitations.
The proposed method is able to overcome the limitations for NP-completeness.
The we... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review!
**W2 - “No error bars used in the experiments.”**
Thank you for this feedback! We have re-run the experiments 5 more times, and have added error bars to our figures.
**Q2 - “Performance Impact: Does the integration of cryptographic circuits into the transfor... | Summary: This paper introduces a novel approach to creating unelicitable backdoors in language models using cryptographic techniques. The authors develop two main designs: an NP-complete backdoor and an encrypted backdoor, both implemented within transformer architectures. These backdoors are designed to be extremely d... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review!
**W1 - “Lack of qualitative examples to more intuitively understand implications of what the backdoors can do and when they might be useful”**
Thank you for this feedback; this was echoed by some other reviewers. One specific example of a threat our backdoor ... | Summary: This paper introduces a novel class of unelicitable backdoors in autoregressive transformer models. These backdoors, secured by cryptographic techniques, evade detection and cannot be triggered even with full white-box access. Empirical evidence confirms their robustness against current mitigation strategies, ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their feedback and questions addressed below.
**W2 - “The threat model is unrealistic, as most commercial models only provide APIs, making it impossible for the authors to insert compiled transformer modules.”**
We mention in the abstract and introduction ... | Summary: This paper shows a construction for planting backdoors in the architecture of an autoregressive transformer model. The proposed construction is a SHA256 implemented in the transformer as a compiled module. The hashing algorithm is following a typical implementation and has been compiled to Tracr modules. The a... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful feedback!
**W1 - “The attacker controls the whole training process”:**
In fact, the attacker does not need to control the entire training process. Rather, the attacker can insert a backdoor into any pre-trained model, without requiring access to the train... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thoughtful and detailed feedback. We appreciate the reviewers’ highlighting of the strength and novelty of our construction, such as resistance to latent adversarial attacks.
## Common concerns
One common question that the reviewers (xhJJ, svZi, psD... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces a new technique of complied-weight based backdoor attack on transformer models, proposing two mechanisms, a "NP-Complete Backdoor", that is more simple but does not defeat Latent Adversarial Training, and an "Encrypted Backdoor", that defeats LAT and provides cryptographic guarantees of un... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback and recognizing the novelty and usefulness of our encrypted backdoor. We are happy to address the concerns and add corresponding improvements to our paper.
**Weakness 1** - *On “first white-box unelicitable backdoor”:*
We will be more specific and say “first... | null | null | null | null | null | null |
SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout | Accept (poster) | Summary: This paper provides a diffusion-based method for traffic simulation, called SceneDiffuser. SceneDiffuser unifies simulation, including scene initialization and scene rollout for generating initial scene layouts and simulating closed-loop agent behavior. To accelerate the time-cost diffusion process, this paper... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We address the comments below:
> I think the writing can be improved.
a. It is hard to understand Algorithm 1-3 given so many details and notations without explanation.
b. The role of Figure 4 is to illustrate the auto-regressive process, o... | Summary: The paper proposes a diffusion model data driven simulation of driving scenes. The model is able to both generate driving scenes and to perform closed loop simulation of these driving scenes. The problem is posed as an inpainting task in the scene tensor, which contains all the agents, time steps (past and fut... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We address the comments below:
> By relying on the previous solution, there seems to be a risk of getting stuck in local minima. Did you notice such situations?
Thank you for this question. Even though we utilize the previous timestep’s so... | Summary: The paper proposes a method called SceneDiffuser, to generate multi-agent scenarios for autonomous driving and rollout the scenarios. The two tasks are unified in a model by formulating them as an inpainting task for the scene tensor. The diffusion model uses an amortized diffusion technique to align the diffu... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We address the comments below:
> Figures and tables are not ordered by the reference order and are placed close to where they are referenced.
Thank you for the suggestion, we will improve the layout in the camera-ready version.
> Missing... | Summary: The paper introduces SceneDiffuser, a novel scene-level diffusion model designed to enhance traffic simulation for autonomous vehicle (AV) development. It presents a unified framework that addresses scene initialization, involving the generation of initial traffic layouts, and scene rollout, which includes the... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We address the comments below:
> While the model performs well among diffusion models, it does not exceed the current state-of-the-art performance for other autoregressive models, suggesting a need for comparison and potential integration.
... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough and thoughtful comments. We are pleased to see that all reviewers are overall positive about the work, finding our proposed amortized diffusion rollout to be a **“creative solution”** (*XARz*) that alleviates **“one of the major drawbacks (of diffusion pol... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Block Sparse Bayesian Learning: A Diversified Scheme | Accept (poster) | Summary: The paper introduces a prior named Diversified Block Sparse Prior, which can be viewed as a generalization of priors for existing sparse Bayesian learning methods. It utilizes EM algorithm and dual ascent to obtain parameter estimates and convergence of estimates to the true parameter in the $\beta$ limit has ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your feedback and constructive suggestions on our paper, which I believe will help to enrich the content of the original text. In this rebuttal, we respond to the concerns raised in the reviews.
---
### **Q1**:
Thank you for your suggestions. Since our paper primarily i... | Summary: This paper introduces block sparse bayesian learning for block sparse settings, as motivated by compressed sensing theory. The method relies on a “diversified scheme” which allows for inference that is robust to block choices by modeling intra-block covariance and inter-block correlation. The authors derive an... | Rebuttal 1:
Rebuttal: We deeply appreciate your kind words regarding the clarity of our presentation and the recognition of our theoretical and experimental work. Additionally, we value your insightful questions about initialization and the practical demonstration of the variance term. We will incorporate the content o... | Summary: The authors propose a hierarchical bayesian model for sparse inverse problems where sparsity is structured in blocks. The authors propose a diversified block sparse prior using a structured covariance taking into account both intra block and block-to-block correlations. They propose an EM algorithm to solve th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your feedback and constructive suggestions on our paper, which we believe will help enrich the content of the original text. In this rebuttal, we respond to the concerns raised in the reviews.
---
### **Q1**:
This is a very good question. Block-based methods require a pr... | Summary: In this work, the authors propose a novel prior called Diversified Block Sparse Prior towards a new framework to address the problem of recovery of block sparse signals. They provide theoretical and experimental justification as a proof of the efficacy of their work.
Strengths: The authors propose a novel di... | Rebuttal 1:
Rebuttal: Thanks for your valuable questions on our paper. In this rebuttal, we respond to the concerns raised in the reviews.
---
### **Q1 (Constant block size)**:
Thanks for your question. Since the block locations and sizes are unknown in real-world scenarios, block-based methods require a predefined ... | Rebuttal 1:
Rebuttal: # Global Rebuttal
---
## **1. Experimental Setup in Global Rebuttal PDF**
### **Fig.1:**
The test data in Fig.1 is sourced from the Audioset described in Section 5.3 of the paper. This audio data contains approximately 90 non-zero elements ($K=90$), which constitutes about 20% of the total di... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Capturing the denoising effect of PCA via compression ratio | Accept (poster) | Summary: In this paper, the authors propose a novel metric called compression ratio to capture the effect of PCA on denoising which can significantly reduce the distance of data points belonging to the same community while reducing inter-community distance relatively mildly. They try to explain this phenomenon through ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Please find our response below.
**Regarding only using single-cell data:**
We first note that single-cell is indeed a very important data-type with various applications in immunology, neuroscience, and others. The Science Journal placed it as ... | Summary: This paper introduces compression ratio, defined as the ratio of pre-and-post-PCA distances for a pair of observations, for outlier detection tasks. The authors demonstrated that this metric could capture the effect of PCA on high-dimensional data with moderate noise and proposes that points with lower varianc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Please find our response below.
**Motivation behind real-world application:**
We thank the reviewer for their input. First, we observe that such mixture model approaches are predominantly used in data with access to bulk-RNA seq data, which can... | Summary: This paper studies the denoising effect of PCA using a novel metric called "compression ratio". The metric is defined as the ratio of the pre and post-PCA between two points. The authors note that when the dataset has a community structure, outlier points tend to have a flatter distribution of compression rat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments. Please find our answers below.
**Regarding the theoretical assumptions:**
Our theoretical setting is a generalization of several popular unsupervised models, such as the Gaussian mixture models and the stochastic block model. The noise distribut... | Summary: The paper proposes a new measure called ‘compression ratio’ to determine how effectively PCA compresses data. For subspace clustered data the authors show that if the signal directions for each cluster are well-separated, the compression ratio is larger for within clusters than compared to between clusters. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. Please find our responses below.
**Comments regarding weaknesses:**
**Regarding unrealistic conditions of centers:**
The reviewer comments that the assumption of centers being nearly orthonormal is unrealistic for the case of n>>d (where n is the number of... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior | Accept (poster) | Summary: The article explores the issue of achieving fairness in Gaussian Graphical Models (GGMs), particularly in the presence of biased data. Such biases can lead to unfair behavior in the models. To address this issue, the authors propose two bias metrics aimed at achieving statistical similarity across groups with ... | Rebuttal 1:
Rebuttal: # Rebuttal for fwTq:
We greatly appreciate your positive feedback along with your valuable questions and suggestions.
We hope that our responses below maintain your positive assessment.
> **Answer to Question 1.**
Thank you for your question.
For graph-based works, the predominant choice of b... | Summary: The authors propose a method for estimating Gaussian graphical models that are fair. To do this, the methodology uses Fair GLASSO, a regularized loss for estimating the precision matrix of the GGM which is fair with respect to sensitive attributes. The authors also provide theoretical results regarding the asy... | Rebuttal 1:
Rebuttal: # Rebuttal for V8H3:
We thank you for your thorough review and detailed questions.
We are grateful for your kind words regarding the strengths of our work.
> **Answer to Weakness 1.**
Group-wise modularity is computed as in reference [19] of the paper, that is,
$$
Q(\\mathbf{\\Theta}) =
\\su... | Summary: Traditional graphical models may reinforce existing biases present in the data. This paper introduces a novel approach to ensure that the learned graphical models are fair across different groups or demographics.
Strengths: - The authors develop a penalty method that adds a fairness penalty to the GLASSO obje... | Rebuttal 1:
Rebuttal: # Rebuttal for QDSi:
We sincerely thank you for your review and your kind words.
We appreciate your insightful comments and how you link our approach to existing works.
> **Answer to Question 1.**
Thank you for your interesting question.
Your observation is key; similar to group lasso or join... | Summary: The paper introduces Fair GLASSO, a method for estimating Gaussian graphical models (GGMs) that addresses biases in data with respect to sensitive nodal attributes. The authors propose two bias metrics to promote fairness in statistical similarities across different groups, leading to the development of Fair G... | Rebuttal 1:
Rebuttal: # Rebuttal for pQPd:
We are grateful for your positive review and clear questions.
Indeed, your review helps us clarify the utility of our approach beyond conceptual discussions.
We are glad to hear that you find our work novel, thorough, and comprehensive.
> **Answer to Question 1.**
Thank yo... | Rebuttal 1:
Rebuttal: # Global
We would like to thank the reviewers for their quality comments and perceptive questions about our work.
Below we detail the main topics discussed both in the following responses and to be added to the revised paper should it be accepted.
We provide additional discussion of Fair GLASSO... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reconstruction of Manipulated Garment with Guided Deformation Prior | Accept (poster) | Summary: The paper aims to recover garments that are manipulated instead of worn. The method first generates the UV mappings from point clouds, followed by ISP to recover the complete mapping. A diffusion model is used to extract the deformation priors and guide the recovery from UV mappings to 3D mesh. Experiments sho... | Rebuttal 1:
Rebuttal: Thank you for your valuable reviews.
To provide a context, we would first like to briefly describe our pipeline. Given the point cloud, we first map each point to the UV space using the UV mapper. This yields a sparse UV map and a sparse panel mask. We then use Eq. (10) to fit the optimal laten... | Summary: The pape addresses the challenge of accurately reconstructing the 3D shape of garments that are manipulated rather than worn. The authors leverage the Implicit Sewing Patterns model and introduce a diffusion-based deformation prior to recover 3D garment shapes from incomplete 3D point clouds. The method maps t... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. We address your questions and comments as follows:
1. *Handling noisy or highly sparse point clouds.*
To evaluate performance under noisy conditions, we add per-point Gaussian noise to the input data, varying the standard deviation. As shown in Fig. 1... | Summary: This paper presents a method for reconstructing folded and crumpled garments from point cloud data. It uses the implicit sewing pattern (ISP) model to represent the 3D shape in 2D uv-maps. The proposed method converts a 3D point cloud to sparse uv-maps and corresponding masks for front and back side using an e... | Rebuttal 1:
Rebuttal: We thank you for your acknowledgement of our contribution in manipulated garment reconstruction. Below are our responses to your comments and questions.
1. *Comprehensibility.*
Thank you for pointing this out. At the end of each stage and to enhance comprehensibility, we will revise our paper an... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable suggestions and constructive comments. We have carefully considered and addressed each of the suggestions and questions raised. We will incorporate these suggestions into our revised paper.
The attached PDF file includes the following additi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models | Accept (poster) | Summary: This paper introduces a bidirectional weighted graph-based framework, to learn factorized attributes and their interrelations within complex data. The authors proposed a β-VAE based module to extract factors as the initial nodes of the graph, and leverage the multimodal large language model (MLLM) to discover ... | Rebuttal 1:
Rebuttal: *We greatly appreciate your insightful comments and commit to refining our manuscript based on your suggestions. Below, we address all your concerns.*
**W1: Why not replace $\beta$-VAE with advanced generative models**
**R1:** Thanks for your insightful comments to help us improve our work. Firs... | Summary: Researchers introduced a bidirectional weighted graph-based framework to explore factorized attributes and their interrelations within complex data. They proposed a -VAE module for extracting initial factors and utilized a multimodal large language model (MLLM) to uncover latent correlations and update weighte... | Rebuttal 1:
Rebuttal: *We greatly appreciate all of your valuable suggestions, which play a pivotal role in enhancing the quality of our paper. Below we address all your concerns.*
**W1&W3: Detailed explanations of the optimization mechanism for the entire model**
**R1:** Thanks for your feedback. We promise to enh... | Summary: To achieve fine-grained, interpretable and unsupervised disentangled representation learning (DRL), this paper proposes a new framework by integrating $\beta$-variational autoencoder ( $\beta$-VAE), multimodal large language model (MLLM) and graph learning into a single pipeline. Experimental results show that... | Rebuttal 1:
Rebuttal: *We value your insightful feedback and will refine our manuscript accordingly. Here, we address each of your concerns.*
**W1: Detailed definitions of the model**
**R1:** Thanks for your suggestions. We will detail the model's parameters, definitions, and optimization strategies in revision. He... | Summary: The paper presents a novel framework that integrates β-VAE with multimodal large language models within a graph structure, DisGraph, to enhance disentangled representation learning. This approach allows for effective handling of complex and interdependent data attributes in an unsupervised manner. The model dy... | Rebuttal 1:
Rebuttal: *We greatly appreciate your insightful comments and commit to refining our manuscript based on your suggestions. Below, we address all your concerns.*
**W1: The paper would benefit significantly from clearer writing, organization and explanations**
**R1:** Thanks for your suggestion, we promis... | Rebuttal 1:
Rebuttal: *Dear reviewers,*
*First of all, we would like to thank all reviewers for their time and efforts in reviewing this paper. These insightful comments are really helpful in guiding to improve the manuscript.*
***We have made our efforts to meticulously address each concern raised by the reviewers. ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Objective
The paper presents a novel framework for disentangled representation learning, which aims to identify and separate the underlying factors of variation in complex data. The primary goal is to enhance the interpretability and robustness of data perception and generation models.
Methodology
The propose... | Rebuttal 1:
Rebuttal: *We greatly appreciate your insightful comments and commit to refining our manuscript based on your suggestions. Below, we address all your concerns.*
**W1: Dependence on MLLM**
**R1:** Thanks for the insightful comments. First, we would like to clarify that our model leverages the commonsense... | null | null | null | null | null | null |
Improving self-training under distribution shifts via anchored confidence with theoretical guarantees | Accept (poster) | Summary: This paper presents Anchored confidence (AnCon), a novel self-training algorithm to improve test-time accuracy under distribution shifts. AnCon modifies the standard self-training algorithm by adding a temporal ensemble regularization. This regularizer is constructed by the consistency of temporal ensembles we... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer hP9c's insightful suggestions and thoughtful comments. We are pleased that Reviewer hP9c acknowledges our core technical contributions to showing the effectiveness of high-quality uncertainty estimation in the temporal ensemble for improving self-training with soli... | Summary: The paper aims to improve self-training, a common technique used for learning on unlabeled data (with iteratively generated pseudo labels), when it is applied to scenarios with distribution shifts. The proposed approach, Anchored Confidence (AnCon), essentially applies label smoothing on the pseudo labels with... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer qtUp for the insightful suggestions and constructive comments, which have significantly improved the clarity of our paper. We are glad that the reviewer enjoyed reading our paper and acknowledged our principled improvements over the existing temporal consistency methods... | Summary: the authors propose a novel approach to enhance self-training for test-time adaptation (TTA) or source-free domain adaptation (SFDA) in neural networks facing distribution shifts. The core idea revolves around a method called Anchored Confidence (AnCon), which uses temporal ensembles and label smoothing to imp... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer fUqB for the valuable suggestions and comments. We are glad that the reviewer acknowledges our rigorous theoretical analyses, efficient algorithm development, and effective performances of AnCon under different distribution shifts scenarios. We believe that addressing t... | null | null | Rebuttal 1:
Rebuttal: **G1:** On the validity of the on-average assumption
In Theorem 3.1, we made an assumption that $ \bar{p}(x; c_{0:m}) > 0.5 $, which we argue is not strong because of its dependence on the confidence thresholds $ c_{0:m} $. Specifically, $ \bar{p} $ is measured only for relatively confident predi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Parameterized Approximation Schemes for Fair-Range Clustering | Accept (poster) | Summary: The authors study the fair range clustering problem, where facilities are associated with multiple demographic labels, forming intersecting groups. They impose both lower and upper bounds on the number of cluster centers chosen from each label. For both $k$-median and $k$-means clustering objectives, they pres... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We summarize our responses in the following.
**Q1: Regarding the extension from diversity-aware clustering to fair-range clustering.**
Response: Thanks for pointing this out. Following your guidance, we found that when $k$ and $\ell$ (i.e., the ... | Summary: The paper presents fixed-parameter approximation schemes for the fair-range k-median and k-means problems in Euclidean spaces, parameterized by both the number of facilities and labels. The results improve on existing results, which could only achieve constant-ratio approximation. The main technique used is a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We summarize our responses in the following.
**Q1: Did you consider any other notions of fairness, based on other constraints (e.g., related to clients)?**
Response: Thanks for the question. For the fair clustering problem where clients are part... | Summary: - This paper studies fair range clustering, which aims to ensure that cluster centers are not dominated by specific demographic groups.
- It focuses on fair range clustering using the 1-norm and 2-norm distance metrics.
- The paper proposes an algorithm with three internal steps: (i) data reduction to low-dime... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We summarize our responses in the following.
**Q1: What is the intuitive definition of ''core set''?**
Response: Thanks for the question. A ''core set'' is a small subset of the client set. For any feasible solution, its costs on the original in... | Summary: The paper deals with the problem of fair-range clustering in Euclidean metric spaces. In fair-range clustering, one is given a set of clients and a set of possible facilities. Every facility is associated with a subset of $\ell$ many classes. The goal is to pick up to $k$ many facilities such that a given clus... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We summarize our responses in the following.
**Q1: Regarding the randomness of Algorithm 2.**
Response: Thanks for pointing this out. The probability that Algorithm 2 yields the desired $(1+\varepsilon)$-approximation solution is the same as the... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers for the in-depth reviews, which have significantly helped us in improving our work. Below, we provide detailed responses to the comments. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OPUS: Occupancy Prediction Using a Sparse Set | Accept (poster) | Summary: The paper presents OPS, a novel framework for occupancy prediction in autonomous driving. It formulates the task as a direct set prediction problem, using a transformer encoder-decoder architecture to predict occupied locations and classes simultaneously. This approach eliminates the need for explicit space mo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We have carried out experiments on Occ3D-Waymo, detailed in the global response to all reviewers. Our results underscore the generalizability of our OPS. We have also corrected the reference number for FB-Occ in Tab.3. Below are our responses to o... | Summary: This paper focuses on the sparsity property in occupancy prediction, given that most voxels are occupied. In order to reduce computation costs on empty voxels, this paper introduces a set prediction paradigm to explicitly model sparsity. OPS, the proposed framework, utilizes the encoder-decoder architecture to... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and feel honored for being approbated on our motivation, novelty and performances. Below, please find our responses to the weaknesses:
- **Error citation.** Thanks for bringing this to our attention. We have thoroughly reviewed the cited works and id... | Summary: This paper considers the problem of occupancy prediction from multi-view images for autoonomous driving. One of the main challenges in the occupancy prediction task is the high computational demand incured by discretizing the 3d space. Traditional methods typically predict the occupancy of each voxel individua... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive suggestions and feedback. We will release our full code and models once the paper is made public. Below are our responses to specific comments:
- **Writing issues.** We greatly appreciate meticulous suggestions on grammar and notations, which indeed help elev... | Summary: This paper introduces OPS, a novel framework that treats occupancy prediction as a set prediction problem. The approach leverages a transformer encoder-decoder architecture and Chamfer distance loss to align predicted and ground-truth points. The model improves performance with strategies like coarse-to-fine l... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. Please refer to the global response to all reviewers for our experiments on Occ3D-Waymo. In summary, the proposed OPS demonstrates its generality with superior mIoU results and fast inference speed. Below, we discuss the differences between Sparse... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive comments and are privileged by their praise regarding our motivation (5U1g, TqRW, Zbj3), novelty (Zbjs), writing (TqRW, iFoi), and experiments (5U1g, Zbj3). We'd like to first mention that OPS performances have further improved since our las... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learn more, but bother less: parameter efficient continual learning | Accept (poster) | Summary: The paper presents a new parameter-efficient method for continual learning in LLMs. The method focuses on two aspects of continual learning: (1) Catastrophic forgetting and (2) Forward transfer. To address catastrophic forgetting the gradient update of the current task is performed on the orthogonal space of ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback on our paper. We have thoughtfully addressed your insights and concerns, and hope our responses offer the necessary clarity on the issues raised.
> **Q1: Training stage**
Thanks for your interest. Yes, first we initialize the SVD parameters using sensitivity-... | Summary: This paper introduces LB-CL, a continual learning algorithm designed to tackle the issues of catastrophic forgetting and forward knowledge transfer. The approach integrates orthogonal low-rank SVD decomposition and sensitivity-based parameter initialization. The orthogonal subspace learning component addresses... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback on our paper. We truly appreciate the time you invested in the review. We have carefully considered your insights and addressed the highlighted concerns. We hope our responses provide clarity on the matters raised.
**W1 & Q1: About advantages and necessit... | Summary: This paper proposes the Learn More but Bother Less Continual Learning (LB-CL) algorithm for Continual Learning (CL) of Large Language Models. Unlike previous research, this paper introduces the idea of using SVD-based low-rank matrices to inject knowledge learned from previous tasks into new tasks, thereby enh... | Rebuttal 1:
Rebuttal: Thank you for your appreciation and excellent summary of our work. We also appreciate the time and effort you dedicated to reviewing our research. We have addressed your questions and concerns below:
> **W1: Line 192: It seems that Eq.5 should be corrected to Eq.7.**
Thanks for your correction. ... | Summary: This paper presents a novel approach to continual learning for large language models (LLMs). The proposed method, LB-CL, incorporates parameter-efficient tuning using low-rank subspace learning and orthogonal subspace projection to mitigate catastrophic forgetting. The study leverages incremental SVD-based low... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review as well as constructive feedback. Your comments have been extremely helpful. We have carefully addressed each of your concerns and provided detailed answers to your questions below:
> **W1: About open access to the code and datasets**
We strongly agr... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We greatly appreciate your insightful feedback and valuable suggestions. We have provided specific responses to each reviewer’s questions separately. We sincerely thank you for your contributions to improving our work. If there are any further concerns or queries, we are fully pre... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mutli-Armed Bandits with Network Interference | Accept (poster) | Summary: The paper explores regret minimization in multi-armed bandits subject to interference. At each time, the learner assigns one of A treatments to each of the N arms. The observed per-arm response is subject to interference – ie, it depends on the assignments of all the other arms. This work studies the setting o... | Rebuttal 1:
Rebuttal: **Weaknesses:**
_It took me a long time to realize that the effects were being estimated on a per-subset basis – ie, that chi^{a_i}(B(n)) is an indicator vector for the subset a_i intersected with the neighborhood of n. Originally I thought that chi^{a_i}(B(n)) =1 for all subsets of a_i, so I was... | Summary: The paper addresses the challenge of online experimentation in the presence of network interference, which is common in applications like e-commerce and clinical trials. The authors propose a multi-armed bandit (MAB) problem where a learner assigns actions (e.g., discounts) to
units (e.g., goods) over fixed an... | Rebuttal 1:
Rebuttal: **Weaknesses:**
_Although the formulation is interesting, the algorithm design and analyses seem quite standard. After transforming to the functional space, it becomes the MAB problem with effectively $A^s$ arms. The ETC algorithm and the analysis of the estimation error as well as the regret hav... | Summary: The paper investigates a multi-armed bandit problem on a set of units that affect the rewards of each other. A trivial solution will have an exponential regret in the number of units, so using sparsity assumptions on the affecting neighborhood, the authors present an algorithm with regret that is only exponent... | Rebuttal 1:
Rebuttal: **Weaknesses:**
_On the one hand, Algorithm 1 performs well only when $N$ is very large (otherwise its better to use Algorithm 3), but it also has a running time of $\Omega(N)$, so it seems bad either way. Not sure why the focus is not on Algorithm 3 instead._
We chose to focus Section 4 on Algo... | Summary: This article studies the multi-armed bandit (MAB) problem under unit interference. This unit inference problem is often considered in offline settings. This article extends it to online settings with a linear regression solution based on discrete Fourier features. Two Explore-Then-Commit algorithms are propose... | Rebuttal 1:
Rebuttal: **Weaknesses:**
_The paper lacks real examples to demonstrate the combination of online experimentation and interference. Perhaps the combination is as natural as the paper suggests. For example, In online experimentation, after every action, it may take some time for the interference to occur. I... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the time spent reviewing our work. We greatly appreciate the feedback and will use it to improve our work.
We want to clarify that we view our primary contributions to be the following.
- A framework for studying multi-armed bandits in the presence of netw... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces a Multi-Armed Bandits framework to address the challenge of online experimentation with network effects. Specifically, the authors consider a learner sequentially assigning one type of $\mathcal{A}$ actions to $N$ units over $T$ periods to minimize the regret. The reward from each unit de... | Rebuttal 1:
Rebuttal: **Weaknesses:**
_The Boolean encoding of the actions is not well-discussed._
Thank you for helping us improve the exposition of our paper! We provide a simple example of (a) binary action embeddings and (b) the corresponding Fourier embeddings below. Further, we describe how the Fourier represen... | Summary: The paper tackles a multi-armed bandit problem where there is interference that can be modeled by a network. The interference model assumes that a unit's treatment effect is affected by the treatments assigned to its neighbors in the network model. This interference model implies that in the worst case there a... | Rebuttal 1:
Rebuttal: **Weaknesses:**
_One potential weakness of the paper is that it provides limited proof sketches on results related leveraging the sparse network._
We agree that a proof sketch would help the reader better understand our algorithm and its convergence. If accepted, we will use the additional page ... | null | null | null | null |
MoEUT: Mixture-of-Experts Universal Transformers | Accept (poster) | Summary: Motivated by the superior generalization performance of Universal Transformers (UT) demonstrated in other works, this paper addresses the compute efficiency problem of this architecture. While UT decreases the parameter count drastically by sharing parameters across layers, vanilla UT underperforms dense trans... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable review and for positive comments on the methodology of the paper. Please find our responses as follows:
> .. given that MoEUT effectively interpolates between a standard transformer and vanilla UT, do the authors think that MoEUT would still ke... | Summary: This paper introduces a novel application of the mixture of experts (MoE) architecture within both the MLP and attention modules of the Universal Transformer network. The integration of MoE is further complemented by an innovative sequence-level routing regularization technique, which the authors argue enhance... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their valuable time reviewing our work. We would like to respond to the concerns raised by the reviewer as follows.
> … . Clarification on how these components synergize within the model would enhance understanding of their collective impact on performance... | Summary: This paper focuses on the problem of inefficient parameter-computation ratio in Universal Transformers (UT). UT shares parameters across layers but reduces the parameter count significantly. One naive approach is to scaling up the layer size, however, cannot be easily achieved due to the prohibitive computatio... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the valuable review and for many positive comments on the methodology of our paper. Please find our responses as follows:
> The claim of using significantly less compute and memory should be further supported by evaluation numbers. Though Table 4 have prese... | Summary: The paper suggests to use Sparse MoEs together with Universal Transformers to overcome the parameter-count limitation that the latter have when parameters are shared over consecutive layers. In particular, the work suggests to use $\sigma$-MoEs (that use sigmoid activation function in the router, rather than t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful review and for the positive comments on the clarity and methodology of the paper. Please find our responses as follows:
> The proposed method matches dense transformers on language modeling, but it’s barely better. This begs the question: w... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OneBit: Towards Extremely Low-bit Large Language Models | Accept (poster) | Summary: This paper presents OneBit, a framework for quantizing large language models (LLMs) to 1-bit weight matrices. Unlike existing methods that rely on 4-bit or 8-bit quantization to avoid severe performance degradation, OneBit introduces a novel 1-bit parameter representation and an effective parameter initializat... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have invested in reviewing our paper.
**Question 1**: "Have the authors tried using popular entropy encoders to further compress these weights?"
This maybe a great intuition! Theoretically, a model quantized to 1-bit does have the potential for further compr... | Summary: This paper explores an innovative 1-bit quantization framework for Large Language Models (LLMs) to significantly reduce their memory and computational demands. Traditional methods face severe performance drops with reduced bit-width; however, this paper introduces a novel quantization and initialization approa... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have invested in reviewing our paper.
**Weakness**: "It might lack **specialized CUDA kernel** for optimizing binary operations and the additional computational costs associated with the two FP vectors a and b may be not clear. Moreover, the **performance dec... | Summary: This paper proposes OneBits, a novel quantization-aware training methodology for 1-bit large language models (LLMs). OneBits introduces two key contributions for training 1-bit models. First, it presents a new 1-bit binary quantization linear design that separates the weight matrix into sign and value componen... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have invested in reviewing our paper.
**Weakness 1**: "It might lack a few-shot benchmark result such as MMLU."
Although few-shot evaluation is not a necessary component in most model quantization research [1,2,3], we still evaluated the **5-shot** performan... | Summary: This paper proposes OneBit, which quantizes the LLM weight matrices to 1-bit and achieves good performance and improved convergence speed by using two additional vectors with FP16 per one linear layer.
Strengths: 1. This paper is generally well-written and easy to follow.
2. The memory required for the model... | Rebuttal 1:
Rebuttal: We appreciate the time and effort you have invested in reviewing our paper.
**Weakness 1**: "It will be great if they compare with other one-bit based quantization methods such as BitNet."
Converting the **"pre-trained model"** into a low-bit representation is **the focus of almost all research ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Approximation Rate of the Transformer Architecture for Sequence Modeling | Accept (poster) | Summary: This study investigates a Jackson-type approximation rate for single-layer Transformers with one head, and compares their ability with RNNs, another nonlinear sequence-to-sequence map.
Strengths: - The literature overview is concise
- The Jackson-type approximation rate for the Transformer is derived for the ... | Rebuttal 1:
Rebuttal: 1. The main theorem (Theorem 4.2) sounds trivial because the bound is a combination of the definitions of complexities $C^\alpha$ (Sobolev smoothness) and $C^\beta$ (Barron bound).
- Firstly, we need to clarify that $ C^\alpha $ is not a Sobolev smoothness term. It is defined as the POD rank ... | Summary: This paper introduces a novel concept of complexity measures to construct approximation spaces for single-layer Transformers with one attention head, providing Jackson-type approximation rate results for target spaces that possess a representation theorem.
Strengths: - The results in this paper are presented ... | Rebuttal 1:
Rebuttal: 1. How can the results be generalized to analyze multi-layer multi-head Transformers? Will such generalization provide new insights or understanding?
- Firstly, our rate still applies to multi-layer and multi-head Transformers, serving as an upper bound. This is because our single-layer, sing... | Summary: The study explores the theoretical aspects of Transformer architectures in sequence modeling, particularly focusing on approximation rates for sequence-to-sequence relationships. A representation theorem is established, introducing novel complexity measures that analyze interactions among input tokens, culmina... | Rebuttal 1:
Rebuttal: 1. The paper deviates from the standard Transformer architecture by requiring a neural network layer before the attention mechanism to implement the Kolmogorov Representation Theorem, potentially inheriting the theorem's limitations.
- For our proposed architecture (5), the term $\hat h = \hat... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Video Diffusion Models are Training-free Motion Interpreter and Controller | Accept (poster) | Summary: This paper introduces a new Motion Feature (MOFT) that can effectively capture motion information in video diffusion models. The authors reveal that robust motion-aware features already exist in video diffusion models, allowing to encode comprehensive motion information with clear interpretability. They presen... | Rebuttal 1:
Rebuttal: We are grateful for your valuable input. Please see the detailed responses to each of your concerns listed below.
**W1: Clarification on the training process**
> The paper lacks clarity on the training process. It would be helpful to clarify which stages are trained and which are not.
Optimiza... | Summary: The paper introduces a training-free framework for understanding and controlling motion in video diffusion models. The key innovation is the MOtion FeaTure (MOFT), which is derived by removing content correlation and filtering motion channels from pre-trained diffusion model features. MOFT provides a training-... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. You will find detailed responses to each of the points you raised below.
**W1: Scalability to longer videos and higher resolutions**
> Scalability to Longer Videos: The proposed method's scalability to longer videos or higher resolutions is not adequately e... | Summary: This paper investigates the relationship between the features of video diffusion models and the motion in the generated videos. By extracting motion features and using them as guidance, training-free motion control can be achieved.
Strengths: 1. The technical aspects of this paper are clear and it is easy to ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback. I've provided detailed responses to each of your concerns below.
**W1: Clarification on the conclusion from Fig. 6**
> From Fig. 6, it is hard to draw the conclusion that "MOFT can provide more valid information than DIFT at the early design stages".
Fro... | Summary: This paper presents a training-free method for motion control in video diffusion models and explores the interpretability of features within these models. The authors demonstrate through experiments that principal components of the features, extracted using PCA, contain motion information. They propose a pipel... | Rebuttal 1:
Rebuttal: Thank you for your constructive comment. The detailed responses regarding each concern are listed below.
**W1: Explanation of content correlation information**
> In Section 3, the authors discuss the challenge of extracting motion information from diffusion features due to their encapsulation of ... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for your thorough, insightful, and constructive feedback. We are pleased that the **interpretability and clarity of MOFT** have been recognized (Reviewers P4uW, vDWU) and that the **technical soundness** of our paper has been acknowledged (Reviewers zi7R, zmG9, P4u... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching | Accept (poster) | Summary: This paper proposes a method to accelerate DiT model inference using layer caching strategy. By utilizing feature interpolation, the non-differentiable layer selection problem is transformed into a differentiable optimization problem. The routing matrix $\beta$ is learned to indicate whether the features of a ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback and constructive suggestions. Thanks so much for taking time and effort to review our paper.
> **W1: L2C requires training for different DiT models and diffusion schedulers, which limits the potential applications of this method**
Thank you for your... | Summary: This paper introduces L2C, a novel approach that dynamically caches computations in diffusion transformers, significantly reducing the computational load. L2C leverages the repetitive structure of transformer layers and the sequential nature of diffusion, optimizing caching decisions to produce a static comput... | Rebuttal 1:
Rebuttal: Thank you very much for your review of our manuscript. We appreciate your time and effort in evaluating our work.
It is encouraging to hear that you like our work and that no obvious weaknesses have been found. If you have any questions where you think further detail or explanation might be benef... | Summary: The paper presents Learning-to-Cache (L2C), a method to accelerate diffusion transformers' inference by caching redundant computations across timesteps. A learnable router dynamically determines which layers can reuse calculations from previous timesteps. L2C can eliminate up to 93.68% of computations in speci... | Rebuttal 1:
Rebuttal: We extend our gratitude for your insightful feedback and suggestions
> **W1: The paper's contribution is incremental. It would benefit from offering more innovations and deeper insights**
We greatly value your suggestion that we need to offer more profound insights in this paper. Beyond introduc... | null | null | Rebuttal 1:
Rebuttal: Dear Chairs and Reviewers,
We deeply appreciate your thoughtful comments and the time you have dedicated to reviewing our paper. Attached is a pdf containing the following:
* Visualizations of learned routers in different models
* Generated images compared with the baseline.
We look forward to ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance | Accept (poster) | Summary: This paper presents a training-free and guidance-free method for controllable image/video generation with structure and appearance control. Specifically, Ctrl-X injects structural and appearance features directly into the noised samples via cross-attention. Compared with other baselines for structural and appe... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback on our work. We address your suggestions and concerns below. **You can find the referred figures in the PDF attached with the “global” response. Tables can similarly be found in the main body of the “global” response.**
**Painting style generation results.**
O... | Summary: This paper proposes a training-free framework (Ctrl-X)to control the structure and appearance when diffusion generation without any training. The method does not need much more inference time cost or GPU resource cost. The insight is that diffusion feature maps capture rich spatial structure and high-level ap... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback on our work. We address your suggestions and concerns below. **You can find the referred figures in the PDF attached with the “global” response. Tables can similarly be found in the main body of the “global” response.**
**Performance of Ctrl-X, qualitative eva... | Summary: This paper introduces a method for controllable generation using diffusion models. The approach is designed as a training free technique for 1) structure/layout controlled generation (like e.g. controlNet) and 2) appearance transfer. The approach leverages manipulation of attention mechanisms and information t... | Rebuttal 1:
Rebuttal: Thank you very much for the in-depth reading of our paper and the helpful comments. Our responses are listed below. **You can find the referred figures in the PDF attached with the “global” response. Tables can similarly be found in the main body of the “global” response.**
**Model extension to m... | Summary: This article presents Ctrl-X, a simple method for T2I diffusion models to control structure and appearance without additional training or guidance. Specifically, it uses feature injection and spatially-aware normalization in the attention laters to align the given structure and appearance. Doing so, Ctrl-X ach... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We address your questions/concerns below. **You can find the referred figures in the PDF attached with the “global” response. Tables can similarly be found in the main body of the “global” response.**
**Model extension to multiple-subject generation.**
Great ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and extensive reading of our paper. We are grateful that reviews find our paper “clear” (tSTQ, DRHE, 6fgc), our method effective (tSTQ, YZfZ), and our experiments promising (DRHE).
Responses to individual reviewers are addressed below each review. **Any ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data | Accept (spotlight) | Summary: This paper aims to study the problem of finding adversarial examples for tabular datasets. Different from attacking image models or text models, attacking tabular models requires finding adversarial examples which are legal, which do not violate the relation between features. Moreover, it also requires to tack... | Rebuttal 1:
Rebuttal: Thank you for your review. We appreciate that you acknowledge the importance of the problem and the quality and persuasiveness of our analysis. We will address below your doubts about the originality and significance of our work.
**W1 - Existing differentiable function:**
We acknowledge that in... | Summary: This paper introduces two novel adversarial attacks, specifically designed to target the evasion of deep neural networks (DNNs) in classification tasks involving tabular data satisfying real-world constraints. The first attack, CAPGD, is a gradient-based method that enhances the constrained PGD (CPGD) attack p... | Rebuttal 1:
Rebuttal: Thank you for your support and your insightful feedback. We appreciate your comments towards improving the quality of the paper. We clarify and answer your concerns below.
**W1/Q1 - Explaining the role of the repair operator. Is CAPGD guaranteed to generate an attack that satisfies the dataset a... | Summary: The paper proposes two adversarial attack methods targeting deep learning models for tabular data. The two methods are: CAPGD (Constrained Adaptive Projected Gradient Descent) and CAA (Constrained Adaptive Attack). CAPGD is a modification based on constrained PGD with step size adjusting, repair operator, addi... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
We appreciate the opportunity to clarify and address any misunderstandings.
We will address each of your points one by one and welcome further discussion on these issues.
**W1 - Lack of novelty and contribution. CAPGD is modified based on CPGD. CAA is a combination of... | Summary: This paper considers the evaluation of the robustness of deep learning models applied to tabular data. The authors introduce two novel adversarial attack methods: Constrained Adaptive Projected Gradient Descent (CAPGD) and Constrained Adaptive Attack (CAA). These methods are designed to exploit the vulnerabili... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and your praise for the extensiveness and significance of our work in advancing tabular adversarial machine learning. We appreciate your interest and would be happy to provide further explanations to address any questions you have.
**W1 - Clarification of the ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. The reviewers agree on the importance of the problem we tackle and are satisfied with the comprehensiveness of our study and analyses.
Our work proposes the most effective and efficient attacks for Tabular Machine Learning in constrained domains. Our new... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Prospective Representation Learning for Non-Exemplar Class-Incremental Learning | Accept (poster) | Summary: This paper aims to solve the catastrophic forgetting in non-exemplar class-incremental learning. The authors propose Prospective Representation Learning (PRL) to prepare representation space for classes in later tasks in advance. Such forward compatible method first squeezes the embedding distribution of the c... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We hope the following responses can address your concerns.
---
**W1 & Q1: What are the advantages of PRL over other forward-compatible methods?**
**A1:** First, unlike previous works, PRL targets CIL in exemplar-free scenarios (NECIL). NECIL requires th... | Summary: This work introduces a Prospective Representation Learning (PRL) scheme to prepare the model for handling conflicts of the balance between the old and new classes in Non-exemplar class-incremental learning (NECIL). The author proposes to squeeze the embedding distribution of the current classes in the base pha... | Rebuttal 1:
Rebuttal: Thank you so much for the insightful questions! We will revise our manuscript accordingly and address your questions below.
---
**W1: Few analysis was conducted on the loss weight α in formula 14.**
**A1:** We set $\alpha_1=10$, $\alpha_2=10$ , and $\alpha_3=2$ by default. Due to rebuttal limita... | Summary: This paper focuses on non-exemplar class-incremental learning, specifically addressing the challenge of balancing old and new classes. The author introduces Prospective Representation Learning (PRL), which involves constructing a preemptive embedding squeezing constraint to allocate space for future classes. A... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions! We are glad that you found the paper easy to read and affirm our experiment. We hope that our response below will address your
concerns.
---
**W1: What is the meaning of IIC in equation 9? Is it a writing error?**
**A1**: We are sorry for our writing err... | Summary: The paper proposes a method to deal with incremental classification task in which no exemplars from the previously seen classes can be saved for usage during training on the newly arriving classes. The proposed method squeezes the embedding distribution of the current classes to reserve space for forward compa... | Rebuttal 1:
Rebuttal: Thank you for your positive response! We are delighted that the reviewer has found our method clear and sound. We have addressed the main points and questions below.
---
**Q1：Lack of discussion with other methods that employ feature space compression for incremental learning.**
**A1**: Thanks t... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive view of our work and valuable feedback. We responded to reviewers' comments in individual replies to each reviewer with references to weakness (**W**) and questions (**Q**).
In response to the question of Reviewer FnFn and Reviewer SYbV about the analysis... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CriticEval: Evaluating Large-scale Language Model as Critic | Accept (poster) | Summary: This work introduces a benchmark for using LLMs are critics.
The benchmark covers four settings:
1. Providing feedback
2. Correction of a response with/without feedback
3. Comparison of two responses for a given query
4. Providing meta-feedback (feedback on feedback)
Strengths: Extensive experiments across ... | Rebuttal 1:
Rebuttal: We really appreciate your valuable suggestions and insightful questions. We will address your concerns as follows.
---
**Q1: Did you consider structured generation?**
**A1:** We appreciate the reviewer’s attention to the details of our evaluation. Structured generation, such as JSON output, is ... | Summary: This paper introduces CriticEval, a benchmark designed to comprehensively and reliably evaluate the critique ability of large language models (LLMs). It assesses critique capabilities across four dimensions (feedback, comparison, correction, and meta-feedback) and nine diverse task scenarios, using both scalar... | Rebuttal 1:
Rebuttal: We really appreciate your valuable suggestions and insightful questions. We will address your concerns as follows.
---
**Q1：... relies on GPT-4 for evaluation. This could introduce a bias favoring models such as GPT-4 ...**
**A1:** Please refer to **"Global Response - Overcome Bias of GPT-4 Jud... | Summary: The paper addresses the need for a comprehensive evaluation of the critique ability of large language models (LLMs) for self-improvement and alignment with human outcomes. Current evaluation methods are critiqued for their limited scope and reliability. The authors propose CRITICEVAL, a benchmark designed to e... | Rebuttal 1:
Rebuttal: We really appreciate your valuable suggestions and insightful questions. Since some of your questions are similar to those of other reviewers, we will describe them in more detail in **Global Response**. We also provide some summary of these questions under your review comment to make our explanat... | Summary: This study constructs a comprehensive framework for LLM-based evaluation, encompassing data construction, human/machine annotation, and result analysis. It defines a single evaluation framework that covers various tasks and response types. Although previous studies have evaluated different tasks and response t... | Rebuttal 1:
Rebuttal: We really appreciate your valuable suggestions and insightful questions. Your questions are addressed as follows:
---
**Q1: One important metric for evaluation models, the ability to revise its generation with critique, is excluded ... It is necessary to evaluate each model’s ability to revise e... | Rebuttal 1:
Rebuttal: # Global Response
We thank all the reviewers for their insightful and valuable comments. Below, we will address some common questions and concerns of reviewers.
---
## **1. Overcome Bias of GPT-4 Judge (kJKF, 2SKd)**
To mitigate bias of GPT-4 as a judge, our work has made two efforts:
### **1.1 ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SpeechForensics: Audio-Visual Speech Representation Learning for Face Forgery Detection | Accept (poster) | Summary: This paper propose audio-visual face forgery detection trained using only real data. During training, audio and visual features are fused by concatenation and then processed by encoder to predict which cluster the feature for each time belong to. During test time, discrepancy between visual and audio for each ... | Rebuttal 1:
Rebuttal: >**Q1:Comparisons with state-of-the-art are mostly with detector using only vision modality which is unfair as fake audio-visual data can have fake-audio-real-visual combination.**
**A1**:Thanks for your review. What we want to emphasize is that our experiments have excluded the category of fake... | Summary: This paper works on audio-visual co-learning for face forgery detection, answering the question of how to extract semantically rich speech-related features to represent detailed lip movements. It is claimed to be the first method where unsupervised learning outperforms the supervised learning method. It is an
... | Rebuttal 1:
Rebuttal: >**Q1:What is the motivation and inspiration the authors had for designing such Siamese like framework?**
**A1**:Thanks for your insightful review! As stated in lines 39-45, we found that lip sequences and audio segments in real videos should convey the same speech contents, while fake videos do... | Summary: The main challenge in face forgery detection is the unsatisfactory generalization in previous detectors. To alleviate this issue, this paper proposes one audio-visual consistency learning framework in the unsupervised learning manner. The important local and global semantic information are learned by the propo... | Rebuttal 1:
Rebuttal: >**Q1:In lines 72-73, the authors claimed “it is the first unsupervised approach outperforming supervised baselines in this domain”. I think the authors lack of follow-up on the recent works in this domain, such as [R1] and [R2].**
**A1**:Thanks for the review. What we want to emphasize is that ... | Summary: This paper proposes SpeechForensics, a unsupervised method for detecting face forgery videos by leveraging audio-visual speech representations.
The key ideas are:
Learning semantically rich speech representations from both audio and visual modalities on real videos
Detecting forgeries by identifying discrepan... | Rebuttal 1:
Rebuttal: >**Q1:Could you elaborate on how the method handles significant asynchrony between audio and video, which is common in practical scenarios?**
**A1**:Thanks for the valuable feedback! The asynchrony between audio and video is indeed common in practical scenarios and we explore two methods,i.e., f... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers' efforts in reviewing our paper and giving insightful comments and valuable suggestions.
As suggest by the reviewers, we provide more comparisons with multimodal methods on the FakeAVCeleb dataset under the cross-dataset setting. And we would like to include... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation | Accept (poster) | Summary: This paper presents an end-to-end framework for speech-to-speech translation that preserves speaker and voice characteristics while leveraging unsupervised training. The authors also refine the speech tokenizer by distilling semantic information and enhance the sampling mechanism to support textless NAR acoust... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper and providing us with valuable, constructive feedback. The detailed responses are listed below.
**R1. About presentation (Weakness 1)**
We will reorganize the paper, particularly by dividing section 3 into two main subsections and addin... | Summary: This paper proposes a end-to-end speech translation framework that adds several improvements to the existing textless encoder decoder speech translation architectures.
Strengths: 1. There are several interesting ideas in this paper, such as the isochrony embedding and layer beam search.
2. The proposed method... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper and providing us with valuable, constructive feedback. The detailed responses are listed below.
**R1. About improving writing and clarity (Weakness 1)**
We will reorganize the paper, include ablations in the main section, revise the unc... | Summary: This paper proposes TransVIP, a speech to speech translation model with voice and isochrony preservation, i.e. pauses and segment durations are preserved between the source and the target, for example for automatic dubbing applications.
The proposed model architecture is modular, with multiple encoders for sem... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper and providing us with valuable, constructive feedback. The detailed responses are listed below.
**R1. About empirical evaluation (Weakness 1)**
We will validate the method with one more language pair in future work. Additionally, to com... | Summary: The paper introduces TransVIP, a novel speech-to-speech translation system designed to maintain both the speaker's voice characteristics and isochrony during the translation process. TransVIP simplifies the complex task of speech-to-speech translation (S2ST) by breaking it down into two sequential subtasks whi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper and providing us with valuable, constructive feedback. The detailed responses are listed below.
**R1. About not fully representative title (Weakness 1)**
Thank you for acknowledging the numerous innovations presented in our paper. We wi... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your efforts in reviewing our paper. We greatly appreciate your acknowledgment of our contributions, including multiple innovations, state-of-the-art performance, and important research work. However, we received diverse ratings, ranging from scores of 4 to 7. We not... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Models | Accept (poster) | Summary: This paper studies prompt optimization methods for finetuning language models. While previous methods mainly concern with in domain performance, this paper brings awareness of the domain generalization issue presented in existing PO methods, under the setting where the target domain is unknown. Two empirical f... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and feedback. We appreciate that our work is considered “pioneering”, “interesting”, and “neat”. We hope our response can address your concerns.
**Q1: More intuitive explanation**
We thank the reviewer for the valuable comment. Our i... | Summary: This paper focuses on improving the domain generalization of prompt tuning methods on LLMs. Specifically, this work claims that the concentration strength and concentration fluctuation of a candidate soft or hard prompt may indicate its generalization ability on new domains. By demonstrating the performance of... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and feedback. We appreciate that our work is considered “well organized”, “interesting”, and “generalizable”. We hope our response can address your concerns.
**Q1: Extension to other tasks**
We would like to thank the reviewer for th... | Summary: This paper studies the problem of prompt optimziation for domain generalization. Through a pilot experiment, they find that the domain generalization capability is tied to the attention concentration in later layers of the network. Based on this finding, the authors design a set of regularizers to improve bo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and feedback. We appreciate that our work is considered “important”, “well-written”, and “thorough”. We hope our response can address your concerns.
**Q1: Improvement and additional baselines**
**Improvement:** We will try to address... | Summary: This paper investigates the domain generalization ability of prompts for pretrained language models (PLMs). The paper finds that prompts that receive higher attention weights from deeper PLM layers and those with stable attention distributions generalize better across domains. The authors introduce a novel obj... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive comments and feedback. We appreciate that our work is considered “well-written”, “novel”, and “insightful”. We hope our response can address your concerns.
**Q1: Applicability to larger models and other tasks:**
We would like to thank the rev... | Rebuttal 1:
Rebuttal: **Global Response to All Reviewers**
---
We illustrate the Concentration Strength Distribution of prompts in the In-Context Demo format for three 7B-sized language models (Llama, Vicuna, Alpaca) across three different tasks (SA, NLI, QA). A common observation is that the concentration strength is ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Truthful High Dimensional Sparse Linear Regression | Accept (poster) | Summary: The authors present an $\varepsilon$-Bayesian incentive compatible and individually rational $k$-sparse linear regression algorithm with side payments for (almost all) privacy-oriented data providing agents. Remarkably, in the limit $d >> n >> \log d$, the accuracy and the needed budget both vanish.
Strengths... | Rebuttal 1:
Rebuttal: >**W1: Isn't the $\Pi_{\tau_{\theta}}$ the same operator as clipping (lines 2 and 3 of Algorithm 1)? It would make it much easier to read if all identical operators were written in the same way.**
We wish to thank the reviewer for pointing out this. We will add $\Pi_r$ to Line 2 of Algorithm 1 a... | Summary: The paper solves the problem of high dimensional sparse regression with subgaussian covariates. Along with doing that they also ensure differential privacy of the data providers. Finally, they also provide a payment scheme that is individually rational and which incentivizes truthfulness.
Strengths: The paper... | Rebuttal 1:
Rebuttal: >**W1: The paper seems to be a slight extension of differentially private logistic regression while also incorporating payments into it which does not seem like a major extension given the earlier works of Fallah et al. or Anjarlekar et al. have incorporated incentive-compatible payment mechanisms... | Summary: This paper focuses on mechanism design that incentivizes truthful data reporting while preserving privacy in the context of high dimensional sparse linear regression. The proposed mechanism is $(o(1),O(n^{-Omega(1)}))$-jointly differentially private, provides an estimator that is $o(1)$ accurate, is an approxi... | Rebuttal 1:
Rebuttal: We thank the reviewer for posing an interesting question. In our setting, we need to assume each user can only manipulate the response. Thus, it is unclear at this point whether our payment scheme can tackle the free-rider issue. We will leave it as a future work.
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Rebuttal Comment 1.1:
Comme... | null | null | Rebuttal 1:
Rebuttal: **Response to Reviewer BFxH's question 6 about adding experiment**
>**Q6: It would have been better to add some experimental results to highlight how the payments and model error varies with a change in the differential privacy guarantees and other parameters.**
We wish to underscore that the es... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generalizable Implicit Motion Modeling for Video Frame Interpolation | Accept (poster) | Summary: The authors propose GIMM to effectively model intermediate motions. Three core designs are: normalization over the initial bidirectional flows, motion encoding (spatiotemporal motion latent extraction from flows) and adaptive coordinate-based INR.
The framework first extracts bidirectional flows of the input ... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. Please find the following for our response.
> **Q1**:The authors use the test set ... I wonder the frame interpolation performance on Vimeo90k,...
**A1**: Due to the word limit, please refer to the **global response** and **A5-2** in our response to Revie... | Summary: This paper proposes a plug-and-play Generalizable Implicit Motion Modeling module to refine the optical flow in the task of video frame interpolation. Specifically, this module combines several core components of normalization, motion encoder, latent refiner, and coordinate-based network to achieve implicit mo... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. Please find the following for our response.
> **Q1**: The K, F, and Z symbols in Equations 6-7 should be labeled in Figure 2. In addition, the description of L150-151 for K is confusing to read, especially F is expressed as the difference between two coord... | Summary: This paper aims to solve the Video Frame Interpolation task. To improve the capability of effectively modeling spatiotemporal dynamics, the paper proposes a Generalizable Implicit Motion Modeling (GIMM) module to leverage the implicit neural fields to estimate the flow field at an arbitrary time step. GIMM tak... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. Please find the following for our response.
> **Q1**: Novelty: The idea of using time-dependent generalizable INR is not new and has been explored in human motion synthesis [1]. [1] also uses the concatenation of spatial coordinates, time step and motion l... | Summary: The paper proposed a video frame interpolation model, starting from an optical flow, encoding flows to spatial-temporal motion latent. The motion prediction model GIMM, took the encoded initial motion latent to arbitrary-timestep interpolation motions. Finally, the motions are used to predict bilateral flows w... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. Please find the following for our response.
> **Q1:** The GIMM starts from pretrained flows, which may limit the network performance. In fact, recent VFI networks still tend to estimate blurry results according to inaccurate flow estimating while facing ha... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to thank all reviewers for providing constructive feedback that helped improve the paper. Due to the word limit, we provide explanations and experiments for concerns shared by multiple reviewers in the following.
1. **Ablation on Motion Encoder (reviewer d4Ah and ZR... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach | Accept (poster) | Summary: The paper studies IRL in linear MDPs. The authors first demonstrate that the feasible reward set cannot be efficiently learned in large state and action spaces. To address this challenge, they propose a new IRL framework called reward compatibility, where the goal is to learn a classifier that determines wheth... | Rebuttal 1:
Rebuttal: We thank the Reviewer for praising our analysis as solid and novel, and for noting the significance of the proposed lower bounds. We answer the Reviewer questions and comments below.
## Weaknesses
1- We divide the answer in two parts. First, we explain why learning a reward compatibility classifie... | Summary: This paper finds that the feasible reward set cannot be efficiently learned even under linear MDPs. Therefore, the paper proposes a new notion called "reward compatibility" that generalizes the notion of "feasible set" and thereby casts IRL as a classification problem. The paper proposes an algorithm to solve ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for praising our theoretical analysis as solid, and for recognizing the novelty of the formulation of IRL as a classification problem, which we believe is an important finding of our work.
## Weaknesses
1- We agree with the Reviewer that the general terminology "*Inverse Re... | Summary: Even under the strong assumption implicit in Linear MDPs, the learning of the set of rewards that make the expert’s policy optimal doesn’t scale well. This improves somewhat, if additionally a notion of compatibility of rewards is introduced, because in this way and under these conditions the IRL problem can b... | Rebuttal 1:
Rebuttal: We are glad that the Reviewer found our paper to be impressive and our contribution to be substantial. We provide detailed replies to their questions/comments below.
## Weaknesses
> “How to” is not really addressed, “How to Scale Inverse RL to Large State Spaces?” should be “How does Inverse RL ... | Summary: This paper shows that finding the feasible reward set in IRL needs to sample $\Omega(S)$ number of samples, even when the MDP possesses a linear structure. To enable more efficient scaling with $S$, the authors propose another task in IRL called rewards compatibility: deciding whether $\pi^{E}$ is $\epsilon$-o... | Rebuttal 1:
Rebuttal: We are glad that the Reviewer appreciated the novelty and significance of the lower bound results, and the solidity of the theoretical analysis. Below, we report answers to the Reviewer's comments.
## Weaknesses
> The reward compatibility framework is not that interesting in my opinion because it... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contextual Active Model Selection | Accept (poster) | Summary: This paper focuses on the online contextual active model selection problem. Specifically, the learner receives an unlabeled data point as a context at each round, and the objective is to adaptively select the best model to predict while limiting label requests. To address this problem, the authors proposed a C... | Rebuttal 1:
Rebuttal: Thank you for the feedback on our work! Below please find our detailed responses to your questions.
---
> ***Q1:*** "In different tasks, such as image classification, and tabular data, we may have many different pre-trained models. For example, in the image classification tasks, we may choose the... | Summary: The paper introduces CAMS, an algorithm designed for online contextual active model selection. CAMS minimizes labeling costs by selecting the most appropriate pre-trained models for given contexts and strategically querying labels. The paper provides theoretical analysis of regret and query complexity in both ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our work! We greatly appreciate your recognition of the novelty of our approach, the robust and thorough theoretical foundation, and the strong empirical results of CAMS. Below, you will find our detailed responses to your questions.
---
> ***Q1:*** "The met... | Summary: This paper proposes a Contextual Active Model Selection (CAMS) method for addressing the problem in the online setting by selecting the optimal pre-trained model for given data points while minimizing labeling costs. CAMS utilizes contextual information to make informed model selection decisions and employs an... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our work! We greatly appreciate your recognition of the rigorous theoretical guarantees provided in this paper, both in terms of regret and query complexity bounds. We are also thankful for your acknowledgment of CAMS as particularly useful for real-life appli... | Summary: The paper proposes an online active model selection strategy where at each round the learner receives an unlabeled data point as a context to adaptively select the best model to predict while limiting the label requests.
Strengths: 1. The paper introduces a model selection procedure that is designed to handle... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our work! Below, you will find our detailed responses to your questions.
---
>***Q1:*** "Mainstream large-scale datasets such as ImageNet, MS COCO etc. will be ideal to validate the all-around performance of the proposed CAMS framework, especially in the quer... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their effort in assessing our work and for their helpful comments and questions.
We will respond separately to each reviewer concerning their individual questions. However, we would like to address one overarching theme from the reviews upfront: the perfor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
VMamba: Visual State Space Model | Accept (spotlight) | Summary: This paper presents VMamba, a novel vision backbone model inspired by the famous Mamba state-space sequence model. The main contribution of VMamba is its ability to achieve efficient visual representation learning with linear computational complexity. The core of VMamba is the VSS block, which incorporates the... | Rebuttal 1:
Rebuttal: # Response to Reviewer c4kg
We appreciate the reviewer’s thoughtful review and constructive comments. In our responses, we address the following concerns: a detailed comparison with SSM-based methods, the generalizability of VMamba, sensitivity to hyper-parameters, and potential for integration i... | Summary: This paper transplants the Mamba (Selective State Space Model), a linear complexity model originally designed for 1D language processing, into VMamba to process image data. It introduces the 2D selective scan and various acceleration techniques to facilitate the modeling of 2D data and enhance the speed of the... | Rebuttal 1:
Rebuttal: # Response to Reviewer ZfkW
We appreciate the reviewer’s thoughtful review and positive comments about our study. In the following sections, we address the reviewer’s primary concern regarding the lack of ablation on design choices and clarify several other issues raised.
### **More Ablation on ... | Summary: ### Summary
This paper proposes VMamba, which adopts the recently proposed selective linear state space model, Mamba, in the domain of computer vision. The paper evaluates variants of VMamba on tasks such as image classification, object detection, and semantic segmentation. To improve performance and efficien... | Rebuttal 1:
Rebuttal: # Response to Reviewer dvTH
We thank the reviewer for the constructive comments and are glad they appreciate the performance of VMamba. Below, we clarify the reviewer’s concerns regarding the detailed structure and the influence of hyper-parameters on VMamba.
### **Usage of Positional Embedding*... | null | null | Rebuttal 1:
Rebuttal: # Response to all
We thank the reviewers for their thoughtful reviews and constructive suggestions. We’re glad that the reviewers recognized the innovation and influence of the proposed 2D-Selective-Scan (SS2D) module, as well as the extensive experiments and thorough analysis supporting VMamba. ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training | Accept (poster) | Summary: This work explores the benefits of learning rate warmup in neural network training, focusing on the size of model updates via the GPT2 model. It finds that controlling update size in parameter space doesn't fully explain warmup's advantages, but quantifying updates in terms of neural representation changes sho... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback!
**W7 Update Size:** We want to begin by clarifying this as it is a very important concept for our paper. By “update size” we literally mean “the size of the update”. This can be measured in different ways, for example through the L2 norm of the update, the ... | Summary: The submission analyzes the underlying reason behind the need for a learning rate warmup in neural network training, focusing on GPT pre-training with AdamW and Lion optimizers. The authors identify three key reasons as to why the initial updates are large:
1. Momentum handling by AdamW,
2. Early updates not... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback!
**Using Adam instead of Lion:** We actually experimented with direct modification to Adam originally before moving to Lion. This worked equally well or better than the Lion modifications. However, we realized that at the start of training the gradient norms... | Summary: In this paper the authors investigate the performance benefits seen from the common practice of learning rate warmup and scheduling, attempt to understand the mechanistic underpinnings of those improvements, and engineer optimizers that mitigate the need for warmup. They conduct experiments using NanoGPT and c... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback!
**Missing Dataset Information:** Thank you for pointing this out! This was an unfortunate oversight on our behalf (and we geatly appreciate your attention to these details). The dataset we train on is the original OpenWebText dataset [1] used in the origina... | Summary: To train current deep neural network architectures, especially transformers, the learning rate of AdamW is usually first linearly increased to reach a peak before it's decreased to zero. The paper analyzes the impact of this so-called warming-up phase on GPT-2 models from the perspective of the update size. As... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback!
**Generalizability of the results:** We have added experiments with a different GPT architecture (Llama2) and dataset (SlimPajama), see global response. We find that the results are similar, suggesting some transferability within GPT-style training. Origina... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their thoughtful reviews and feedback on our manuscript. We will try to address your specific concerns and questions in our individual responses.
Here we present additional experimental results with accompanying plots in the pdf:
* Figure 1 shows what ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos | Accept (poster) | Summary: This work focuses on the VidSGG task. Specifically, it built a new UAV-based VidSGG dataset named AeroEye, and also further propose a CYCLO approach for using cyclic attention over the VidSGG task.
Strengths: 1. The paper is well-written and easy to follow.
2. From my perspective, a high quality dataset and ... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer **5Gaf** for the constructive feedback. Your suggestions on clarifying our method's UAV context, dataset comparisons, and CYCLO's scene graph relation have greatly improved this paper.
**Q1: It seems to me that, while the dataset focuses on the UAV scenario, the direct... | Summary: This paper tackles an interesting task for understanding video scenes that focuses on modeling object relationships in aerial videos. Specifically, it introduces a new dataset, the AeroEye dataset, and proposes a novel approach, CYCLO, to better model the video object relationships. Experimental results demons... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer **QFfh** for the positive feedback and constructive suggestions. We appreciate your recognition of the AeroEye dataset and CYCLO approach. We will carefully address your suggestions regarding inference cost and live video streaming applications.
**Q1: It would be great... | Summary: This paper presents a new problem: modeling multi-object relationships from a drone's perspective. To address this, the authors propose the AeroEye dataset and introduce the Cyclic Graph Transformer (CYCLO) method. This method captures both direct and long-range temporal dependencies by continuously updating t... | Rebuttal 1:
Rebuttal: We express our gratitude to Reviewer **nRfD** for your recognition of the AeroEye dataset and the CYCLO method. We will enhance our paper by improving the architecture figure, addressing Cyclic Attention issues, expanding the failure case analysis, and elaborating on the suggested limitations.
**... | Summary: This paper proposes a video scene graph generation dataset called AeroEye on aerial videos and a framework called cyclic graph transformer to tackle the problem of video scene graph generation. The authors annotated the ERA and MAVREC dataset with keyframes at 5FPS. They manually annotated the frames for bound... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer **Zf4k** for your thoughtful review. We appreciate the recognition of our novel dataset and approach. We acknowledge the need to provide clearer explanations.
**Q1: The paper is very difficult to follow. There are separate discussion sections which somewhat disrupts th... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable feedback. Reviewers **nRfD** and **QFfh** recommend acceptance, praising our CYCLO approach to multi-object relationship modeling, the AeroEye dataset, and the versatility of our approach. Reviewer **5Gaf** leans towards acceptance with a **Borde... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neurons | Accept (poster) | Summary: This work presented a framework IRCAN to locate key neurons for processing contextual cues, thereby mitigating conflicts between knowledge obtained from pre-training and knowledge within the context. Experiments on completion and multi-choice tasks showed that the IRCAN benefits the base model in knowledge con... | Rebuttal 1:
Rebuttal: We are deeply grateful for your positive assessment of our work and the recognition of the value in our work. Your feedback is highly encouraging and valuable to us. We will address each of your concerns and questions in detail:
**Re to W1:** **IRCAN’s performance on additional knowledge-related ... | Summary: The paper addresses the valuable problem of mitigating parametric and contextual knowledge conflicts in LLM generation with a novel and reasonable method. It is well-written, with a comprehensive experimental design showing significant improvements in completion and multi-choice tasks. However, the evaluation ... | Rebuttal 1:
Rebuttal: Thank you so much for your valuable feedback and insightful comments. We will address each of your concerns and questions in detail:
**Re to W1 & W3: Evaluations on datasets with long contexts and RAG tasks.**
Thank you for this valuable feedback. We understand the prevalence of long-context inp... | Summary: The paper proposed a new framework, IRCAN, to enable LLMs to pay more attention to new knowledge in context and generate context-sensitive outputs. The framework first identifies neurons that significantly contribute to context processing by utilizing a context-aware attribution score derived from integrated g... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments and valuable suggestions! We greatly appreciate you taking the time to review our work and provide constructive feedback to improve the quality of our paper. We will address each of your concerns and questions in detail:
**Re to Weaknesses #1 & Lim... | Summary: The paper introduces a novel framework, IRCAN, aimed at addressing knowledge conflicts in Large Language Models (LLMs). By identifying and enhancing neurons that are crucial for processing contextual cues using an attribution score derived from integrated gradients, the framework significantly improves the gen... | Rebuttal 1:
Rebuttal: We are deeply grateful for your positive assessment of our work and the recognition of the value in our work. Your feedback is highly encouraging and valuable to us. Below, we provide detailed answers to each of your concerns.
**Re to Weaknesses #1 & Questions: Lack of comparisons with other stee... | Rebuttal 1:
Rebuttal: Many thanks to all the reviewers for providing insightful comments and suggestions! We greatly appreciate you taking the time to review our work and provide constructive feedback to improve the quality of our paper.
The attached PDF shows all the comparison experiments we have done, including co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives | Accept (poster) | Summary: The paper proposes enhancing CLIP's compositional reasoning by generating high-quality negative image-text pairs using LLMs and text-to-image models. This approach improves beyond previous works that considered unrealistic, rule-based captions and unexplored negative images. Experiments show consistent improve... | Rebuttal 1:
Rebuttal: We are encouraged by your review! We thank you for your comprehensive evaluation of our paper. We are grateful that you found TripletData alone to be of significant value and that our experiments are comprehensive.
Please find the requested clarifications below.
> ## Response to Weaknesses
**[W... | Summary: This paper proposes a TripletCLIP, a pre-training framework aimed at enhancing compositional reasoning task. It computes the NegCLIP objective separately for each, with the image serving as the anchor in each instance. For training TripletCLIP, such triplet data is constructed as follows: Hard negative caption... | Rebuttal 1:
Rebuttal: We appreciate your review of TripletCLIP and the time you've dedicated to it. Thank you for the detailed and holistic review of our work. We are delighted that you found it valuable to the research community.
> ## Comparison with Related Works [W1]
Firstly, we want to clarify the key baselines s... | Summary: To enhance the compositional capabilities of CLIP, authors propose to generate “hard” negative captions via in-context learning and synthesizing corresponding negative images with text-to-image generators offers a solution.
Strengths: 1. Authors introduce a novel CLIP pre-training strategy that employs hard n... | Rebuttal 1:
Rebuttal: Thank you for your review. We respectfully disagree with the claims regarding our work's limited novelty and contributions. Other reviewers have unanimously recognized numerous strengths in our work despite its straightforward nature. Therefore, we would like to reiterate our research's key streng... | Summary: The paper introduces a novel pre-training strategy aimed at enhancing the compositional reasoning capabilities of CLIP models. The authors identify the limitation in current image-text datasets that restricts the compositional understanding of CLIP models and propose a solution that involves generating "hard" ... | Rebuttal 1:
Rebuttal: Thank you for your time and consideration given to our paper. We are delighted that our work is recognized as well-written, easily reproducible, and effective. We appreciate your belief in our approach and its potential to inspire similar strategies in downstream tasks.
In response to your inquir... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback provided by the reviewers. It is gratifying to observe the positive evaluations across various dimensions of our work, as highlighted by the reviewers unanimously.
- The reviewers unanimously recognize our paper as **"well-written and easy to foll... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
$\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$ | Accept (poster) | Summary: This paper proposes a dynamic method to tune the hyperparameter $\beta$ in DPO according to the data in each batch. The proposed method is tested on Pythia-410M, 1.4B, and 2.8B base models. Compared to vanilla DPO, it performs better on Anthropic HH and Reddit TL;DR summarization datasets.
Strengths: 1. The t... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for your kind review. We are glad that you found our paper meaningful and easy to follow. We provide detailed answers to your comments below.
**Q1: It is hard to evaluate the performances on SOTA (relatively large) models, e.g., 7B or 8B models.**
> A1: Thank you for raisin... | Summary: This paper studies the relation between the best $beta$ parameter of DPO and the data quality. Motivated from the observation, the authors propose a way to dynamically choose the $beta$ at the batch level. The evaluation shows the proposed method improves DPO and its variants' (IPO, KTO, SPPO) performance on A... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for your kind review. We are glad that you found our paper meaningful and easy to follow. We provide detailed answers to your comments below.
**Q1: It would be nice if the author can verify the results on another dataset with different domains.**
> A1: Thank you for raising... | Summary: This work proposes two techniques to improve the performance of the popular DPO alignment algorithm. The first technique proposes a strategy to dynamically adapt the $\beta$ coefficient of DPO, which controls the strength of the KL penalty with respect to the reference policy. The second technique proposes a f... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for your kind review. We are glad that you found our paper meaningful and easy to follow. We provide detailed answers to your comments below.
**Q1: There are very few details about the reward models used in the paper**
> A1: Thank you for pointing this out. **We directly us... | Summary: The paper presents an improvement in Direct Preference Optimization (DPO), a method for aligning and fine-tuning large language models (LLMs) based on human preferences. The authors identify two critical factors affecting DPO performance: the parameter $\beta$ and the quality of preference data. The existing l... | Rebuttal 1:
Rebuttal: Thanks for your kind review. We are glad that you found our paper meaningful and easy to follow. We provide detailed answers to your comments below.
**Q1: The experiments are limited to specific datasets and model size ranges.**
> A1: Thank you for raising this concern. To expand our approach to ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable and insightful feedback.
- We are encouraged that the reviewers found our paper meaningful (Reviewers $\color{red}{\text{fXTA}}$, $\color{green}{\text{UzDb}}$, $\color{black}{\text{4RgK}}$).
- Moreover, we are grateful that the reviewers found our propose... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors's goal is to introduce adaptive schedules for the KL regularization $\beta$ in RLHF. This is a useful quality of life improvement with high potential impact on the final performance of an RLHF algorithm (similarly to how adaptive learning rate schedules are crucial in optimization).
As a guiding p... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for your kind review. We are glad that you found our paper clear and easy to follow. We provide detailed answers to your comments below.
**Q1: How is the reward model constructed and used?**
> A1: Thank you for pointing this out. **We directly use the implicit reward model ... | null | null | null | null | null | null |
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics | Accept (poster) | Summary: The paper presents a novel approach to securing model's performance (robust and natural accuracy) against train-time data poisoning attacks by introducing a set of data purification transformations during training, specifically employing Energy-Based Models (EBM) and Denoising Diffusion Probabilistic Models (D... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and directly respond to the stated questions and weaknesses.
---
### Weaknesses
1. *The reviewer suggests that Section 3.1 could be improved by providing a more in-depth discussion on the application of EBM models within the context of the proposed method.*
... | Summary: This paper studies the generative purification methods, i.e., EBM and DDPM-based purifications, as defenses against a set of data poisoning attacks.
Strengths: 1. They suggest that a proper range of implementation steps in the EBM and DDPM-based purification methods matters for defense performance.
2. They ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and directly respond to the stated questions and weaknesses.
---
### Weaknesses
1. *The idea of employing generative methods to purify imperceptible noises has been explored before and the defense mechanism is not different from other attack paradigms such a... | Summary: This work introduces PureGen, a method to purify a potentially poisoned dataset before the dataset is used to train a classifier. The work explores using Langevin MCMC with both EBMs and diffusion models to remove potential adversarial artifacts from data, with the justification that MCMC sampling should move ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and directly respond to the stated questions and weaknesses.
---
### Weaknesses
1. *The defense requires costly Langevin iterations and has a higher computational burden compared to existing methods.*
The Langevin sampling is not as costly as it may seem. W... | Summary: This paper proposes a stochastic preprocessing defense technique, named PureGen, against train-time poisoning attacks, with EBM-Guided and Diffusion-Guided sampling processes. First, with EBM-based purification, called PureGen-EBM, the purifier first evaluates the (unnormalized) energy function of the images. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and directly respond to the stated questions and weaknesses.
---
### Weaknesses
1. *Contributions are limited and adversarial purification is a known technique.*
We respectfully disagree that our contribution is incremental and believe that conflating infer... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and the opportunity to address their concerns. Below, we provide a concise response to the main points raised by the reviewers and outline main revisions we will make to improve our paper.
---
### Responses to General Points
1. **Train-Time v... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Wasserstein convergence of Cech persistence diagrams for samplings of submanifolds | Accept (poster) | Summary: The paper provides a more fine-grained analysis of stability results for persistent homology for data sampled from manifolds, proving new theoretical guarantees for methods in topological data analysis.
Strengths: The results of the paper are in this reviewers opinion strong and interesting. Topological metho... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you very much for your interest in our work.
We address the issue of noisy data in our response to all reviewers, and we agree that similar guarantees for the Vietoris-Rips complex would be valuable; in fact, we are already working towards proving such results.
Regarding ex... | Summary: The paper describes three new theorems on the stability of persistence diagrams with respect to Bottleneck and Wasserstein distance under certain additional assumptions.
Strengths: All three results are interesting fundamental contributions to the field of topological data analysis
Weaknesses: I am not sure ... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your appreciation of the overall quality of our work.\
We discuss the soundness and importance of our noiseless data hypothesis, as well as the relevance of our work for the ML community, in our response to all reviewers.\
Regarding specifically the question *Why Neur... | Summary: The $p$-optimal transport convergence of the Cech persistence diagram of a sample of a closed embedded manifold is studied, both in a deterministic and a probabilistic setting. The paper has three main results:
1. An improvement of the classical Cech bottleneck stability result for sufficiently good samples of... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your appreciation of our work and your interesting questions.
We discuss noisy data in general in our response to all reviewers.
To answer your questions more specifically:
*Is there any hope that (at least part of) the main probabilistic result
(Corollary 4.3) still... | Summary: The authors study the behaviour of persistent homology under subsampling of compact sets. They have provided new convergence guarantees with respect to the p-Wasserstein distance and asymptotic results for their $\alpha$-persistence.
Strengths: (S1) The paper addresses a relevant problem in TDA, and proves a ... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your feedback. We address your concerns and your question in our response to all reviewers.
---
Rebuttal Comment 1.1:
Comment: I thank the authors for their response. I have read the response and would like to stick with my score. | Rebuttal 1:
Rebuttal: We thank all reviewers for their time, effort, and valuable feedback. We are grateful for the overall positive reception of our work.
The most common questions pertained 1) to the practical applications of our results, and 2) to our assumption that the data is noiseless.
We address these two point... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution | Accept (poster) | Summary: The paper proposes using stochastic amortization to speed up feature attribution and data attribution. This is applicable when the attribution technique is expensive to compute exactly (e.g. LIME), and when unbiased estimators of the attributions exist. Specifically, the paper proposes to, for a given machine ... | Rebuttal 1:
Rebuttal: Thank you very much for your review, we've responded to your points below.
> The related work section could benefit with a more direct and concrete comparison with prior work. For example, the paper says that "while there are works that accelerate data attribution with datamodels, we are not awar... | Summary: This paper presents a fast prediction explanation approach for machine unlearning models that approximates traditional explanation approaches by using an neural network. The network is trained with noisy labels (so called stochastic amortization) such that it learns to approximate the prediction explanations o... | Rebuttal 1:
Rebuttal: Thank you very much for your review, we've responded to your points below.
> It is concerning to use neural networks to explaining the predictive behaviour of another machine learning model. In fact, how do we know whether the explanation itself is trustworthy?
Thanks for raising this question—b... | Summary: In this paper, the authors introduce a framework termed stochastic amortization that can accelerate computationally expensive explainable machine learning (XML) tasks by training models with noisy but unbiased labels. They provide theoretical analysis that shows that unbiased noisy labels allow learning the co... | Rebuttal 1:
Rebuttal: Thank you very much for your review, we've responded to your points below.
> For applying stochastic amortization to a new XML task, how should practitioners determine an appropriate error level from a practical perspective?
Thanks for asking this question. Our recommendation is to create a smal... | Summary: This paper proposes a stochastic amortization framework for efficiently estimating feature attribution and data attribution values. The idea is to learn a parameterized model from noisy while unbiased samples of the value to be estimated. In comparison to naive Monte Carlo sampling, this amortized estimation i... | Rebuttal 1:
Rebuttal: Thank you very much for your review, we've responded to your points below.
> The theoretical analysis, especially Theorem 1, is not directly relevant to the main argument of this paper, i.e., stochastic amortization is more efficient than naive Monte Carlo.
Thanks for bringing up this concern. T... | Rebuttal 1:
Rebuttal: Thank you to all reviewers for your detailed feedback. We have addressed your points in individual responses below. If you find that we have resolved your concerns, we would greatly appreciate it if you would consider revising your score. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Normalization and effective learning rates in reinforcement learning | Accept (poster) | Summary: This work attempts to improve the optimization for deep reinforcement learning by inserting additional layer norms into the architecture and performing weight projection steps that constrain the magnitude of the matrix weights. The paper discusses how the implicit effective learning rate schedule resulting fro... | Rebuttal 1:
Rebuttal: We thank the reviewer for their extremely helpful comments, in particular introducing us to a number of work on optimization dynamics in scale-invariant networks of which we were not aware. We address individual concerns below.
**W1**: We thank the reviewer for the recommended citations, and will... | Summary: This paper explores the use of normalization layers in deep reinforcement learning and continual learning, as well as their impact on the effective learning rate. Although normalization layers offer a variety of benefits in stabilizing optimization and improving the loss landscape, they also introduce a signif... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful engagement with the manuscript and for their helpful comments. We will ensure to take these into account in our revisions.
**Mathematical notation.** We thank the reviewer for highlighting this. We will be sure to review the mathematical notation and improv... | Summary: Normalization layers improve various aspects of deep RL and continual learning, such as loss landscape conditioning and reducing overestimation bias. However, normalization can inadvertently decrease the effective learning rate as network parameters grow. This effect is problematic in continual learning where ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their engagement with the paper, and for their constructive comments. We address individual concerns below.
**Lack of theoretical RL analysis:** We agree with the reviewer that translating insights on the optimization and trainability of neural networks to policy optimiz... | Summary: When training a neural network with layer normalization, an increase in the norm
of the parameters can lead to a lower effective learning rate. This paper makes
the observation that, when layer normalization is used, periodic projection is
enough to overcome this vanishing step-size. The idea is verified empir... | Rebuttal 1:
Comment: We thank the reviewer for their detailed and helpful comments, and for their deep engagement with the paper. We address specific comments and questions individually as follows.
**W1:** “folk knowledge” … “interplay between layer normalization and NaP”:
We agree that further theoretical analysis ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful engagement with the work and thoughtful reviews. Reviewers generally agreed on the “wide ranging” implications of our findings [6ZG9], along with the “good theoretical and empirical work”[KJMZ], and “novel and intriguing insights”[vdAM], agreeing that while... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models | Accept (poster) | Summary: The paper tackles the problem of synthetic data generation in tabular formats and particularly focuses on data generation in multi-relational (multi-table) setups like relational databases. Whereas existing tabular diffusion models work only on single tables, the proposed ClavaDDPM extends the DDPM framework t... | Rebuttal 1:
Rebuttal: **A-W1 Metric Details:**
Thank you for your suggestion. We agree that expanding on the details about long-range dependencies in Section 4.2 and mapping them to the example hierarchy from Figure 1 would greatly enhance the clarity. We will make this change in the revised version.
$\quad$
**A-W2 ... | Summary: This paper proposes the new ClavaDDPM approach to address the scalability and long-range dependency challenges in tabular data synthesis. ClavaDDPM uses clustering labels to model inter-table relationships, particularly focusing on foreign key constraints, and employs diffusion models' robust generation capabi... | Rebuttal 1:
Rebuttal: **A1: Presentation improvements**
Thank you for your valuable feedback. We will take steps to improve the writing and overall presentation of the paper, and have fixed the table names issue, and will have a better written revised version.
To further enhance our manuscript, we would greatly appre... | Summary: This paper proposes ClavaDDPM to address two key deficiencies in multi-table data generation: scalability for larger datasets and capturing long-range dependencies, such as correlations between attributes across different tables. This approach utilizes cluster labels as intermediaries to model relationships be... | Rebuttal 1:
Rebuttal: **A1: Known relationships**
We fully agree on the importance of realistic assumptions. This work tackles a prevalent challenge in the finance industry [in collaboration with them] on tabular synthesis. Synthesizing data for multiple interconnected tables, even with known foreign keys, has been en... | Summary: This work addresses the challenges inherent in generating multi-relation tabular data and proposes a novel generation method based on hidden random variables. The approach analysis correlations between primary and foreign keys between tables by predicting hidden variables associated with these keys within sim... | Rebuttal 1:
Title: Rebuttal to the Questions
Comment: **A1: Terminologies of table**
Yes, a relation is a table. In this paper, we primarily adhere to the terminology used in relational database literature, where a “table” is formally referred to as a “relation”, and multi-relational synthesis corresponds to multi-tab... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers' efforts and constructive feedback. We humbly accept their suggestions and will make improvements accordingly. These insights will help make the paper more solid and better organized. Here, we address some general questions and misunderstandings and outline the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiGRAF: Diffeomorphic Graph-Adaptive Activation Function | Accept (poster) | Summary: Inspired by the continuous piecewise-affine based transformation, this paper argued that the activation function should not be an uniform selection for different nodes, and developed a learning activation function method, that can take the graph structure into account to activate node-specific features. The ex... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful evaluation and are happy to see that the reviewer recognized the performance increase yielded by our method. We now proceed by addressing the questions and comments, and hope you find them satisfactory to consider revising your score.
**Q1**: *Many formulas... | Summary: This paper introduces a new activation function, DIGRAF, specifically tailored for graph data in Graph Neural Networks (GNNs). The approach is based on Continuous Piecewise-Affine Based transformation (CPAB). The authors demonstrate that DIGRAF possesses the desired properties highlighted in existing literatur... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s constructive and positive feedback. We are delighted to see the reviewer has valued our experimental analysis. We proceed by answering the questions in the following.
**Q1**: *The discussion on why $\text{GNN}_\text{act}$ is effective is relatively brief. Incl... | Summary: This paper introduces DIGRAF, Diffeomorphic Graph-Adaptive Activation Function, which is a novel activation function adaptive to graph data. DIGRAF leverages Continuous Piecewise-Affine Based transformations, possesses several necessary properties of a good activation function, such as differentiability, zero-... | Rebuttal 1:
Rebuttal: We are glad to see the reviewer has particularly appreciated the soundness of our paper, while finding the theoretical analysis detailed and the experiments convincing. We proceed by answering the questions raised by the reviewer in the following. We hope that you find our responses satisfactory t... | Summary: The paper “DiGRAF: Diffeomorphic Graph Activation Functions” introduces DiGRAF, a novel graph neural network (GNN) activation function based on diffeomorphisms. DiGRAF adapts to graph structures and tasks by learning transformation parameters, enhancing performance across various GNN scenarios. The method demo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments, and appreciation of our experiment analysis. We now address them. New results and discussions were added to our paper.
**1. On Contribution**: We distinguish between CPAB's original purpose (signal alignment via diffeomorphism) and ou... | Rebuttal 1:
Rebuttal: # General Response
We would like to express our gratitude to all reviewers for their valuable feedback.
Overall, the reviewers appreciated the breadth and depth of our experimental analysis, defined *``extensive``* (**mZzm, SUte**) and *``comprehensive``* (**mZzm**). They found our experimental d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Recurrent neural network dynamical systems for biological vision | Accept (spotlight) | Summary: The paper proposes a hybrid architecture that integrates continuous-time recurrent neural networks (RNNs) with convolutional neural networks (CNNs), named CordsNet, to improve biological realism in vision models. The authors claim that CordsNet matches CNN performance on benchmarks like ImageNet while showing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful suggestions. For the sake of consistency across reviews, we would first like to clarify a few definitions used in our response:
- We think that the reviewer is referring to "convolutional RNN" as a CNN connected to an RNN (typically LSTM in liter... | Summary: Biological neural networks are continuous-time dynamical systems. However, the previous models that are carefully designed to explain biological networks are discrete-time systems or even non-dynamical ones like convolutional neural networks (CNNs). However, the model that best explains biological vision and a... | Rebuttal 1:
Rebuttal: We are heartened by the extremely positive review and high regard the reviewer has for our work. We thank the reviewer for their time and encouragement.
**Comparison of CORNet on tasks in Figure 5**
We agree with the reviewer that CORNets could potentially arrive at different solutions compared ... | Summary: The paper proposes a recurrent convolutional neural network architecture and training algorithm that simulates the biological visual system of mammals. The technical novelty is mostly in the training algorithm which is meant to mimic biological systems, with a first stage of spontaneous activities followed by ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful suggestions. In response to these points, we have performed additional analyses and made several changes to our submission, as explained below (or referred to general response).
**Important information in appendix**
We thank the reviewer for raising this im... | Summary: In this work, the authors proposed CordsNet (Convolutional RNN dynamical system), which provides incorporates a conventional neuroscientific model of RNN incorporating the convolutional layer.
Strengths: 1. The authors analyzed the proposed dynamic convolutional RNN from different aspects.
2. Derived batch ... | Rebuttal 1:
Comment: This reviewer (8dkQ) seems to not understand the field of "modelling the primate visual cortex". I suggest you to read this paper: https://www.nature.com/articles/s41593-019-0520-2 before making a new judgement. The proposed method is definitely novel because there is no dynamical model of CNN that... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort in reviewing our submission. We will address common issues here.
**Important details in Appendix**
Reviewers A7p2 and 7DkU raised the issue that important results are reported in the appendix, and recommended for their (minimally) brief inclusion ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Inspired by neuroscience, the authors propose to introduce recurrent connections in conventional convolutional neural networks (CNNs). The resulting model shows comparable performance with regular CNNs, but exhibits higher noise robustness. The authors also developed a toolkit to analyze the resulting architec... | Rebuttal 1:
Rebuttal: We truly appreciate the reviewer for the encouraging and positive review.
**Technical details in the appendix**
The reviewer has expressed concern that important technical details that are in the appendix should briefly be mentioned in the main text. We agree with this point and refer the review... | null | null | null | null | null | null |
Voila-A: Aligning Vision-Language Models with User's Gaze Attention | Accept (spotlight) | Summary: This work proposes an approach to align Vision-Language Models (VLMs) with users' gaze. To develop such a method, the project first creates a mock dataset of human gaze from image captions, known as Voila-coco. It then develops a model to integrate gaze data into VLMs. Additionally, a new dataset called Viola-... | Rebuttal 1:
Rebuttal: We sincerely appreciate your critique of our work and will take your comments into consideration for future revisions. We would like to clarify a few points that may have been misunderstood, hoping this will enable you to re-evaluate our work more accurately.
**W1 & Q1, Q2 on Motivation**
We envi... | Summary: In their paper, Voila-A: Aligning Vision-Language Models with User’s Gaze Attention, the authors introduce a dataset, a new model architecture Voila-A, which is a cognitively-enhanced VLM. After motivating the research and introducing both the datasets and model design, the authors cover a lot of experiments o... | Rebuttal 1:
Rebuttal: We sincerely thank you for your support of our work and for your attentive reading and thoughtful suggestions.
**Baseline models**
Note that except for Otter, we also have Kosmos-2 as baseline, as shown in Figure 5 and 6. For reference [43], this is a paper published in 2020 and in their experim... | Summary: This paper proposes Voila-A, an instruction-tuned VLM that integrates “gaze” information. The instruction data is constructed using tracing data from “Localized Narratives” and GPT-4 assistance, then converted to gaze-like data using the Bubbleview method. The overall approach is evaluated on newly collected d... | Rebuttal 1:
Rebuttal: Dear reviewr 3dvo, we sincerely thank you for your support of our work and appreciate your thoughtful suggestions that have helped us improve.
**W1**
Regarding the Perceiver's choices for Key (K), Query (Q), and Value (V), we can delve into a clearer explanation here. The primary function of this... | Summary: The paper aims to improve the integration of VLMs in real-world applications such as AR/VR by making the interaction seamless and user-centric. This is achieved by incorporating users’ gaze information into the VLM for a more natural conversation and a frictionless experience. First, the LN-COCO dataset is pro... | Rebuttal 1:
Rebuttal: We sincerely thank you for your support of our work, for your meticulous review, and for your thoughtful suggestions that have helped us improve.
**W1**
- Both Kosmos-2 and Otter are finetuned on the same training set of VOILA-COCO.
- During the data generation process, we generate one direct que... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Next-Generation Logic Synthesis: A Scalable Neural Circuit Generation Framework | Accept (poster) | Summary: The paper studies the application of differentiable neural architecture search (DNAS) to the problem of logic synthesis from input-output examples. The authors first analyze several challenges when directly applying existing DNAS methods to the problem. Based on those findings three modifications are proposed:... | Rebuttal 1:
Rebuttal: # Response to Reviewer qf83
We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us kno... | Summary: The manuscript describes a method to synthesize logic circuits using neural architecture search (NAS). The authors first evaluate some
short-comings of earlier approaches and develop a generation method that adds regularization of skip connections, a prior on the shape of the circuit (triangle shape), transfor... | Rebuttal 1:
Rebuttal: # Response to Reviewer Qczw
We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us kno... | Summary: Existing DNAS methods face challenges in accurately generating circuits, particularly with large-scale circuits, and exhibit high sensitivity to random initialization. To address these challenges, this paper proposes a framework named T-Net. The experiments demonstrate that T-Net can precisely generate large b... | Rebuttal 1:
Rebuttal: # Response to Reviewer PMQo
We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us kno... | Summary: The paper tackles the challenges in logic synthesis (LS) for integrated circuit design by proposing a novel neural circuit generation framework. Traditional LS methods rely on heuristics, which can be suboptimal and inefficient. The authors revisit differentiable neural architecture search (DNAS) methods and i... | Rebuttal 1:
Rebuttal: # Response to Reviewer gSL5
We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us kno... | Rebuttal 1:
Rebuttal: # Global Response
We would like to extend our sincere gratitude for your valuable feedback and constructive suggestions. For your convenience, we have prepared a summary of our responses and outlined how we have addressed the reviewers' concerns as follows. We sincerely hope that this summary will... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Latent Diffusion for Neural Spiking Data | Accept (spotlight) | Summary: In their study "Latent Diffusion for Neural Spiking Data", the authors introduce the titular LDNS model for generating realistic neural population activity, and apply it to three datasets: a synthetic dataset with true latents generated from the three-dimensional Lorenz system, and two previously published neu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed summary and evaluation and suggestions that will significantly improve our work, e.g. recommending to further assess the dynamics of the generated samples. We are also thankful for the generally positive response and describing our work as original.
**Anal... | Summary: This paper introduces the Latent Diffusion Model for Neural Spiking data (LDNS). LDNS combines the capacity of autoencoders to extract low-dimensional representations of discrete neural population activity with the capability of denoising diffusion probabilistic models to generate realistic neural spiking data... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work comprehensive and our writing and presentation clear and well-structured. We also appreciate that the reviewer finds our contributions of addressing the modeling challenges of complex neuroscientific datasets significant.
**Additional comparison with ot... | Summary: This paper proposes a new generative model for neural spiking datasets. The model consists of a deterministic, deep SSM (S4) autoencoder paired with a diffusion model of the learned autoencoder latent sequences. This enables generating accurate neural time series traces across variable length trials lasting up... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding the contributions presented in our work significant, and for noting the flexibility and performance of our model in generating unconditional and conditional neural spiking data in a wide variety of conditions.
The reviewer had questions and concerns over trainin... | Summary: The authors here propose a new autoencoder style latent variable model for neuroscience which flexibly adapts to variable time-series using S4 encoders and decoders. They then train diffusion based models with option behavioral covariates to generate realistic neural spiking data. Additionally, they use a more... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work relevant, and our approach sound. Based on your suggestions, we have now performed new baseline experiments with additional VAE-based models. We also performed latent space analyses of sampled spikes from all models (using PCA and LFADS embeddings) to sup... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive and detailed engagement with our work, resulting in many helpful comments and opportunities for clarification. We are especially grateful for several reviewers’ assessments that the work is “original” (mYEj), well written and clearly motivated (y7Q... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeoRL: Efficient Exploration for Nonepisodic RL | Accept (spotlight) | Summary: This paper proposes a model-based RL algorithm NeoRL for continuous state-action spaces in the nonepisodic setting, where the agent learns from a single trajectory without resets.
Strengths: 1. The paper provides the first regret bound for nonepisodic RL in general nonlinear systems, addressing a gap in the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are happy to see the reviewer acknowledge how our work bridges a significant gap in RL theory. In the following, we respond to the weaknesses and questions raised by the reviewer.
**W1**: Expensive computation.
**A1**: Thanks for raising this concern.... | Summary: This work introduces a novel model-based RL algorithm, named NeoRL, for nonepisodic RL problems with unknown system dynamics and known costs. A cumulative regret bound is provided for NeoRL with well-calibrated dynamic models. The proposed method achieved lower accumulative regret and average cost compared wit... | Rebuttal 1:
Rebuttal: Thank you for your feedback. In the following, we respond to the weaknesses and questions raised by the reviewer.
**W1**: Difficult to follow for people with limited knowledge of control theory and connection to other prior work on optimistic exploration.
**A1**: While control theory plays a cru... | Summary: The paper proposes NeoRL for non-episodic RL with nonlinear dynamical systems. NeoRL has a first-of-its-kind regret bound for general nonlinear systems with Gaussian process dynamics. The paper also proposes a practical implementation of NeoRL with MPC, which significantly outperforms baseline algorithms.
Str... | Rebuttal 1:
Rebuttal: We thank the reviewer for their invaluable feedback. We are happy to hear that they also appreciate the significance of our work. Below, we have our responses to the questions.
**Q1**: Is there any comment on the tightness of the regret?
**A1**: We would have loved to give a lower bound, but as... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective | Accept (poster) | Summary: The paper proposes a method for debiasing biased models through fine-tuning on a specific subset with a high portion of unbiased ("bias-conflicting") samples. The paper uses self-influence in the early training epochs to identify this specific subset, where high self-influence is an indicator of a bias-conflic... | Rebuttal 1:
Rebuttal: We thank you for your constructive comments.
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### [Q1] Self-influence is often used in mislabeled sample detection as a way to evaluate training data attribution methods. Still, SI mainly indicates that a sample is OOD (which could be a case of mislabeling). Does your approach generalize to O... | Summary: This paper tackles the problem of learning generalized models from biased data by detecting mislabeled samples. The authors use BCSI (SI estimated on a trained model with GCE) to detect bias-conflicting samples and construct a pivotal subset based on the BCSI scores of these samples for correcting the biased m... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback.
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### [Q1] The bias issue introduced in this paper is more about robustness rather than fairness since no sensitive attributes (such as gender or race) are included in the problem formulation, and no fairness evaluation (demographic parity or equalized odd... | Summary: The authors focus on detecting bias-conflicting samples to recover biased models. They propose a Bias-Conditioned Self-Influence to help identify bias-conflicting samples in the early stage of model training. Experiments on public datasets are conducted to demonstrate the effectiveness of the proposed method.
... | Rebuttal 1:
Rebuttal: We thank you for the constructive comments.
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### [Q1] The paper aims to rectify bias within a model. However, only the accuracy of models and distribution of BCSI scores are provided. Additional experiments are needed to demonstrate that the bias within a model could be reduced by the propose... | Summary: The authors propose a method to tackle spurious correlations by using influence functions. Specifically, they compute the self-influence on the training set -- the amount that a particular sample's loss changes when it is removed from the training set. Samples with the highest self-influence are then assumed t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort to review our paper.
---
### [Q1] The proposed method has no theoretical justifications, and so it is unclear under what circumstances it would fail.
Although we did not provide theoretical justification, we demonstrated the effectiveness of our metho... | Rebuttal 1:
Rebuttal: We provide Grad-CAM visualizations [1] for both ERM and ERM+Ours on BFFHQ and Waterbird in Figure 1. We also include example images from the top 100 samples, as ranked by BCSI, in Figure 2.
[1] Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Reconstructing the Image Stitching Pipeline: Integrating Fusion and Rectangling into a Unified Inpainting Model | Accept (poster) | Summary: This work proposes to integrate the fusion and rectangling of image stitching into a unified inpainting model. In particular, the weighted masks are designed to guide the reverse process in a pre-trained large-scale diffusion model, which implements this integrated inpainting task in a single inference. Extens... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful assessment of our work. Please see our responses below.
## Weaknesses
We start with weakness 3, and then discuss weaknesses 1, 2 and 4.
**W3: The reconstruction-based image fusion model has significant limitations and has fallen into the development bottleneck... | Summary: This paper proposes an image stitching algorithm that unifies fusion and rectanguling stages of a conventional pipeline with an image inpainting diffusion model applied with a progressive reverse process guided by weighted masks. Their reverse algorithm progressively inpaints seam regions by gradually increasi... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful review of our work. Please see below for the responses.
## Weaknesses
Please see the newly added PDF in Global Response. We have provided the Rebuttal Fig. 3 and Rebuttal Fig. 4 to explain your confusion.
**W1: Gradually dilating mask holes in seam regions are based... | Summary: The paper introduces SRStitcher, a novel method that integrates the fusion and rectangling stages of the image stitching pipeline into a unified inpainting model using a pre-trained large-scale diffusion model, eliminating the need for additional training. This approach addresses the issue of error propagation... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and careful reading. Please see our responses below.
## Weaknesses
We would like to clarify that SRStitcher achieves technical goals that were unattainable with the two processes handled independently, especially in image rectangling.
**1. SRStitcher impr... | Summary: This paper tried to integrate the fusion and rectangling stages in image stitching into a unified model. More concretely, a special fusion, a rectanlging step, and a mask-guided diffusion model are gathered to implement stitching-customized image inpainting, especially for the irregular boundaries. It is worth... | Rebuttal 1:
Rebuttal: We thank you for raising these issues and your comments. Please see below for the responses.
## Weaknesses
We start with weakness 2, which is most concerned by the reviewer, and then discuss weaknesses 1, 3 and 4.
**W2: DeepRectangling's experimental results of inpainting models are outdated.*... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful comments and agreement with our motivation. Here, We address some of the concerns shared by multiple reviewers and upload a PDF with rebuttal figures.
**1. We clarified key contributions**
Due to the previous manuscript's limited space, our contributi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning to compute Gröbner bases | Accept (poster) | Summary: This paper provides a machine learning algorithm to compute Gröbner bases of 0-dimensional ideals in shape position.
To address the backward Gröbner problem, the authors propose an algorithm based on 1) random generation of Gröbner bases by sampling univariate polynomials related to the shape position, then o... | Rebuttal 1:
Rebuttal: # NeurIPS 2024 Rebuttal (Reviewer q3D1)
We appreciate your thorough review and valuable feedback. Below, we answer the weaknesses and questions.
## **On Weaknesses**
**Theoretical guarantees on training.** Providing theoretical guarantees on global/local convergence, running time, and error bou... | Summary: This article investigates the use of machine learning techniques to compute Gröbner basis of polynomial systems. This problem consists in, given a term order and a finite set of polynomials $f_1, \cdots, f_m$, to compute another set of polynomials $g_1, \cdots, g_m$ that have the following desirable properties... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and strongly positive assessment of our work. We are grateful that you listed many strengths of our work.
**Reduced Gröbner basis.** The reduced Gröbner basis is defined independently from shape position. A Gröbner basis $G$ is called reduced when i) t... | Summary: The paper proposes to learn to compute Gröbner bases (as the title says). This includes two important problems. 1) Generating the dataset and 3) Finding an appropriate encoding of the problem to feed into a transformer architecture. That is finding an encoding for a system of polynomials to be solved.
The pap... | Rebuttal 1:
Rebuttal: Thank you for your strongly positive evaluation of our work!
**Benefits.**
In general, if the terms of the input polynomial system $f_1,\ldots,f_m$ are fixed and the coefficients $\{c_{i,\alpha}\}$, where $f_i = \sum_{\alpha} c_{i,\alpha} x^{\alpha}$, are considered as parameters, it is known t... | Summary: Gröbner bases are a tool of fundamental importance in the field of computational algebra. Unfortunately known algorithms for computing Gröbner bases are very ineficient, having a running time that is double-exponential on the number of variables. In this work, the authors propose a machine-learning based appro... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and insightful comments. You raise the characterization of a subclass of polynomial systems as the main concern, and our rebuttal mostly focuses on this point. We would like you to refer to the Global Response as well.
### **On Weaknesses**
**Characte... | Rebuttal 1:
Rebuttal: # Global Response
We sincerely appreciate the reviewers' time and efforts in reviewing our manuscript. We have received many insightful comments and questions, which have been carefully considered in this rebuttal and the next manuscript update.
While we will answer most of the comments and ques... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a Transformer based method to compute Gröbner basis, a known NP-hard problem. The authors focus on polynomials with 0-dimensional radical ideals and propose efficient algorithms to generate training samples of polynomial systems and their corresponding Gröbner basis. The main novelties inc... | Rebuttal 1:
Rebuttal: We appreciate your insightful and constructive comments and thorough review, as well as many pointers to related papers.
### **On Weaknesses**
**Choice of 0-dimensional ideals.**
We received several comments/questions about this assumption from several reviewers; please refer to the Global Resp... | null | null | null | null | null | null |
Learning the Expected Core of Strictly Convex Stochastic Cooperative Games | Accept (poster) | Summary: The paper tries to find an expected core under the assumption that a characteristic function is $\varsigma$-strictly convex cooperation game.
The authors provide a bandit-based sampling algorithm called Common-Points-Picking, which allows us to compute the expected core in a polynomial number of samples.
They... | Rebuttal 1:
Rebuttal: # Response to reviewer f4nV
We thank the reviewer for the insightful comments. Below are our responses/clarifications to your questions:
## Comment 1: Practical example is needed.
### Response:
Consider a facility-sharing game (a generalisation of cost-sharing games [1, 2]) where joining a coali... | Summary: This paper studies the problem of learning the expected core when only bandit feedback is available, under the assumption that the problem is strictly convex. They proposed Common-Point-Picking (CPP) algorithm that returns a point in the expected core given an oracle that provides noisy samples of the unknown ... | Rebuttal 1:
Rebuttal: # Responses to Reviewer oJVQ
We thank the reviewer for the insightful comments. Below are our responses/clarifications to your questions:
## Comment 1 - Simulation results for sample complexity
### Response:
To illustrate the sample complexity of our algorithm in practice and how it is compared ... | Summary: The paper studies the problem of finding the core for Reward allocation
when the information about reward functions is incomplete.
Specifically, previous works either study deterministic
games and assume that the reward function is known, or study stochastic games
and assume that the reward distribution is kno... | Rebuttal 1:
Rebuttal: # Response to reviewer 1Z68
We thank the reviewer for the insightful comments. Below are our responses/clarifications to your questions:
## Question 1: Are there any works that are similar to yours in terms of techniques?
### Response:
Learning the core via sampling is typically considered to be... | null | null | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive feedbacks. We have performed the additional simulations as requested by the reviewers and have provided the results in this pdf file.
Pdf: /pdf/c9b32f01b71b6c768e84cc9ae6427ef968a21a94.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
User-item fairness tradeoffs in recommendations | Accept (poster) | Summary: In this paper, the authors studied the tradeoff between user and item fairness in a recommendation setting. They proposed a constrained optimization problem that imposes user fairness as its objective and incorporates item fairness as its constraints. The authors also identified that (1) when user preferences ... | Rebuttal 1:
Comment: Dear reviewer:
I am another reviewer and an engineer who work in industrial RS. I would like to answer your question.
"Why not solving a dual-objective problem and treating user/item fairness in the same fashion? Alternatively, why not maximizing online platform's recommendation quality?"
In reco... | Summary: The paper works on the relationship between user fairness and item fairness in recommender system settings. A theoretical framework is proposed and some theoretical results and intuitions are provided based on the framework. The main results are the tradeoffs between fairness and 1) uncertainty and 2) diversit... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and suggestions; we are glad you like our theoretical framework! We agree that there are likely to be other applications of the framework, and we are currently looking at ways to further draw out and interpret the sparsity result in the writing.
### Empiric... | Summary: This paper investigates the trade-off between user fairness and item fairness in recommender systems. The authors develop a theoretical framework to characterize the user-item fairness trade-off by analyzing the recommendation strategy optimization problem. The following phenomena are found: 1. The more divers... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback; we believe addressing them makes for a stronger paper.
### Fairness definitions
We agree that it is important to study how our results extend to other definitions of fairness. In the main response, we give further justification for our choice, and show experi... | Summary: This paper develop a theoretical framework to analysis the trade-off between user fairness and item fairness. From the theoretically analysis, we understand that diverse user population benefits the recommendation, and users whose preferences are misestimated can be disadvantaged by the constraints on item fai... | Rebuttal 1:
Rebuttal: Thank you for reading our work! We agree that only recommending one item is an aspect of our current theoretical model. We note that recommendations being probabilistic (e.g., one can think of an item being sampled each time period) somewhat mitigates this aspect, though agree that more explicitly... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful feedback, and are glad that you found the paper “clear and useful” and the results to be both “rigorous” and “intuitive”. Multiple reviewers sought more justification for our **symmetry assumption** and **fairness definition**, so we discuss these issues he... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection | Accept (poster) | Summary: This paper presents Incremental Vision-Language Object Detection (IVLOD), a novel learning task designed to incrementally adapt pre-trained Vision-Language Object Detection Models (VLODMs) to various specialized domains, while simultaneously preserving their zero-shot generalization capabilities for the genera... | Rebuttal 1:
Rebuttal: ## Weekness 1
**Reviewer Concern:** The key contribution of this paper is RDB and ZiL. However, RDB has been proposed by [1]. Therefore, I would owe the novelty to the differentiated learning rate and ZiL (basically L1 norm). From this point of view, the novelty is somewhat incremental. Importantl... | Summary: In order to extend the application of VLODM in a broader domain, the authors propose to combine incremental learning and zero-shot generalization to solve the problem. However, the widespread catastrophic forgetting and maintaining zero-shot generalization capability in incremental learning are two important i... | Rebuttal 1:
Rebuttal: ### Weakness 1
**Response:** Thank you for this valuable feedback. We acknowledge the importance of clearly stating the need for this work and situating it within the context of existing research. In the revised manuscript, we will enhance the introduction and related work sections to better arti... | Summary: This paper proposes the new problem of incremental visual-language object detection (IVLOD), which aims to preserve zero-shot generalization performance of VLMs, while also adapting to new concepts over time. Authors address IVLOD by proposing the zero-interference reparameterizable adaptation (ZiRa), a light ... | Rebuttal 1:
Rebuttal: ### Rebuttal for Weaknesses
#### 1. Differences in Baseline Pre-Training
**Response:** Thank you for this important observation. We want to clarify that all compared methods, including TFA and iDETR, were indeed reimplemented using the pre-trained GroundingDINO model. This approach ensures a fai... | Summary: This paper deals with the object detection problem in a zero-shot and incremental learning setting. Specifically, Incremental Vision-Language Object Detection (IVLOD) task is proposed to incrementally adapt the pre-trained Vision-Language Object Detection Models (VLODMs) to various specialized domain.
The t... | Rebuttal 1:
Rebuttal: ### 1. The new task of IVLOD is not well described and compared with existing ones
**Response:**
Thank you for your insightful feedback. We recognize the need to clearly define and differentiate Incremental Vision-Language Object Detection (IVLOD) from existing methods. IVLOD is distinct i... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond | Accept (poster) | Summary: This paper derives novel generalization bounds for a special class of GNNs augmented with persistent homology descriptors, specifically PersLay. They empirically test the bound and use the bound to propose a regularization, which works well in practice.
Strengths: 1. The paper provides a clear background intr... | Rebuttal 1:
Rebuttal: Thanks for your feedback. We reply to your comments/questions below.
> It is hard to parse the significance of this work as the bound applies only to a restricted family of models augmented with PersLay. This issue could be mitigated by demonstrating that a model derived from this analysis is com... | Summary: The paper introduces a novel Compositional PAC-Bayes framework that addresses challenges related to the heterogeneity of Graph Neural Network (GNN) layers and persistent vectorization components. It provides data-dependent generalization bounds for PH vectorization schemes and persistence-augmented GNNs, offer... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We hope our answers below satisfactorily address your concerns. Otherwise, we will be happy to engage further.
> Clarity: Some sections of the paper may require further clarification to enhance readability and understanding for a broader audience.
Thanks for your co... | Summary: This paper introduces a novel compositional PAC-Bayes framework for analyzing the generalization of heterogeneous machine learning models, with a particular focus on graph neural networks (GNNs) augmented with persistent homology (PH) features. The work develops a general PAC-Bayes lemma for heterogeneous mode... | Rebuttal 1:
Rebuttal: Thank you for the feedback and for appreciating our work. We hope that the answers below sufficiently address your concerns. Otherwise, we would be happy to engage further.
> Empirical evaluation focuses mostly on graph classification tasks; additional experiments on node classification or link ... | Summary: This paper presents a compositional PAC learning framework for bounding the generalization gap in deep graph networks that are augmented by PersLay-vectorization of persistent homology features. Topological features can be complementary to deep features. Empirically, this combination can boost the empirical te... | Rebuttal 1:
Rebuttal: Many thanks for your thoughtful, constructive, and insightful review. We hope that the answers below sufficiently address your concerns.
> the architectures of concern in this paper are rather limited and not the ones used in practice.
Despite the generality of our framework, we focused on PH a... | Rebuttal 1:
Rebuttal: We are grateful to all the reviewers for their time and insightful comments, as well as to the (senior) area, program, and general chairs for their service to the community.
We are pleased to note that reviewers appreciate the **novelty** (zp7n, toK8, Hv3h, ynbU) and the **presentation** (zp7n, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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