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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UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior | Accept (poster) | Summary: This paper presents UniDSeg, a universal approach that enhances the adaptability and generalizability of cross-domain 3D semantic segmentation. To achieve this, we propose a learnable parameter-inspired mechanism for off-the-shelf VFMs with frozen parameters. This mechanism maximally preserves the pre-existing... | Rebuttal 1:
Rebuttal: Dear Reviewer T5Kw,
We thank you for the precious review time and valuable comments. We have provided corresponding responses and results, which we believe have covered your concerns.
We hope to further discuss with you whether your concerns have been addressed or not. If you still have any uncl... | Summary: This paper introduces the prompt-tuning concept into DG3SS and DA3SS, and proposes a learnable parameter heuristic mechanism for the off-the-shelf VFM. Modal Transitional Prompting is proposed to capture 3D-to-2D transitional prior and task-shared knowledge from the prompt space. Learnable Spatial Tunability i... | Rebuttal 1:
Rebuttal: Dear Reviewer 4rFB,
We thank you for the precious review time and valuable comments. We have provided corresponding responses and results, which we believe have covered your concerns.
We hope to further discuss with you whether your concerns have been addressed or not. If you still have any uncl... | Summary: This paper proposes a cross-domain 3D semantic segmentation model which utilizes off-the-shelf visual foundation models to boost the adaptability and generalizability. Two key designs are described to help the cross-domain task, e.g., visual prompt learning and deep query learning. Extensive experiments have b... | Rebuttal 1:
Rebuttal: Dear Reviewer KKSy,
We thank you for the precious review time and valuable comments. We have provided corresponding responses and results, which we believe have covered your concerns.
We hope to further discuss with you whether your concerns have been addressed or not. If you still have any uncl... | Summary: The manuscript proposes a universal method with the help of off-the-shelf Visual Foundation Models (VFMs) to boost the adaptability and generalizability of cross-domain 3D semantic segmentation, dubbed UniDSeg. The proposed method focus on learning visual prompt for 3D-2D transitional prior and deep query.
St... | Rebuttal 1:
Rebuttal: Dear Reviewer DeKz,
We thank you for the precious review time and valuable comments. We have provided corresponding responses and results, which we believe have covered your concerns.
We hope to further discuss with you whether your concerns have been addressed or not. If you still have any uncl... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems | Accept (poster) | Summary: UDC focuses on the divide-and-conquer-based NCO methods, proposing a novel framework that does not require heuristics and adopting an efficient training approach DCR. Compared to existing methods, UDC has significant improvements in effectiveness and applicability.
Strengths: 1. This paper conducts extensive ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find the idea of DCR quite novel, UDC exhibits effectiveness in 10 different CO problems, the article is easy to follow, and experiments give detailed explanations on the setting reason for hyperparame... | Summary: The paper introduces a novel Unified Neural Divide-and-Conquer (UDC) framework designed to address large-scale CO problems. The UDC framework leverages a Divide-Conquer-Reunion (DCR) training method that uses GNN for global instance division and a constructive neural solver for sub-problem solutions. This unif... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find the proposed UDC framework is designed with applicability to a wider range of CO problems, and its performance is shown to be generally good on certain problems.
>**Weakness 1. Novelty of UDC.**
... | Summary: The paper proposes a Divide-Concur-Reunion training approach for solving multiple "large" scale COPs.
Strengths: 1- Handling several problems under the same framework with possible different components per CO problem.
2- When compared to learning-based methods, in terms of testing time and solutions sizes,... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find the proposed UDC achieves competitive results and can handle several problems under the same framework with possible different components per CO problem.
We sincerely appreciate the effort you ha... | Summary: The paper introduces a Unified Neural Divide-and-Conquer (UDC) framework designed to tackle large-scale combinatorial optimization problems by leveraging a novel training methodology called Divide-Conquer-Reunion (DCR). This framework employs graph neural networks for the division of problems and utilizes esta... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. We are glad to know that you find the proposed UDC integrates a novel training methodology, demonstrates broad applicability across various large-scale CO problems, and outperforms several baseline methods.
We address your concer... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their constructive comments and valuable suggestions. These suggestions greatly help us to improve our manuscript. The current manuscript has received extensive positive evaluations regarding its novelty, effectiveness, applicability, and impact:
* **Reviewer E... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Probabilistic size-and-shape functional mixed models | Accept (poster) | Summary: The paper deals with estimation functional data, i.e. time-series represented as functions – under a specific observation model, that aims to separately account for a fixed effect (modeled as a mean function) and other variations (modeled as noise functions), with the added confounding variable being a norm-pr... | Rebuttal 1:
Rebuttal: **Noise Variance** The warpMix default noise variance parameter, "sigmaEpsilonTilde", which is set to $10^{-3}$ in the R implementation, is the variance of $\theta_i$, a set of parameters in the model for the phase functions (Equation (6) in Claeskens et al., Nonlinear Mixed Effects Modeling and W... | Summary: The paper considers uncertainty quantification for one-dimensional regression tasks, where the observations are noisy. A rather advanced additive model considering invariances under space-time unitary transformations is proposted. Numerical experiments demonstrate the superiority of the approach over other sta... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's concern regarding accessibility to the broader machine learning community. If the manuscript is accepted, we plan to simplify notation as much as possible and provide more intuitive descriptions of some of the mathematical concepts.
There were several factors that mo... | Summary: For the problem of the reliable recovery of a fixed effect function $\mu$, this paper focuses on sampling from and summarizing the posterior distribution of a fixed effect function $\mu$ in a functional mixed model with random object-level phase and amplitude components, without a finite-rank covariance assump... | Rebuttal 1:
Rebuttal: **Prior Models** We want to address this comment from two different perspectives. First, depending on the real data scenario, the choice of the type/number of basis functions in the model for the fixed effect function $\mu$ and the size-and-shape altering random effect $v_i$ can be different, e.g.... | Summary: The paper studies the problem of recovering a fixed effect function µ in functional mixed models, where measurement errors and object-level phase variations make the task difficult. It focuses on disentangling the size-and-shape characteristics of µ, which remain invariant under certain transformations. The au... | Rebuttal 1:
Rebuttal: **Significance, Size-and-Shape Preserving Transformations** Broadly, for *any* application involving functional data, our model has a two-fold motivation: (i) norm-preserving action $D_\gamma$ on fixed effect $\mu$ and size-and-shape preserving random effect $v_i$, expressed as linear combinations... | Rebuttal 1:
Rebuttal: We thank all reviewers for their careful consideration of our manuscript and constructive comments.
**Significance and Motivation** Employing mixed models with random effects to better model correlated input data in neural networks is fast gaining traction (e.g., Simchon et al., Integrating Rando... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: In this paper is proposed a mixed model in a functional Hilbert space for a size-and-shape (a type of geometric property) of a square-integrable fixed effect. To this end, the authors consider an isometric action of the infinite-dimensional group of phase functions. Synthetic and real experiments and compariso... | Rebuttal 1:
Rebuttal: **Notation, Figure 1, Sample Sizes** We agree that the notation is dense in certain sections. If accepted, we will try to simplify notation. We denote vectors and matrices in bold and functions and scalars in regular font. We will include dimensionality when appropriate. Figure 1 (b-c) shows the e... | null | null | null | null | null | null |
ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence | Accept (poster) | Summary: The paper introduces an extension to Neural ODEs. At it's core this is an architectural change to Neural ODEs, adding more important structure to the dynamics function through a control signal $u(t)$. The paper shows that this can lead to rich non-linear dynamics with convergence guarantees. Extensive experime... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your thorough review and valuable feedback on our paper. Your insights are crucial for improving the quality of our work. We have dealt with each of your comments or suggestions carefully.
## 1. Suggestions on the Organization of Paper
We are grateful for ... | Summary: The authors present a new class of continuous-time neural networks, ControlSynth ODEs. This new class of ODEs are able to learn the dynamics of physical systems faster and more precisely. In addition the authors provide theoretical convergence guarantees for these new models, and demonstrate their effectivenes... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your time and effort. We highly value your invaluable suggestions and sincerely apologize for the lack of clarity in certain parts of the manuscript. Please allow us to further elaborate and analyze the key issues you have raised.
**Regarding the Concept an... | Summary: The paper introduces ControlSynth Neural ODEs (CSODEs), a novel approach to modeling dynamical systems with neural ordinary differential equations (NODEs). The proposed models constraint the system to a convergent once. The CSODE framework incorporates an additional control term to ensure the existence of the ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
We sincerely appreciate your positive evaluation and valuable feedback on our work. Your insights are crucial in enhancing the quality and rigor of our research. We are particularly grateful for your observation regarding the convergence issues of Neural ODEs, which prompted us to ... | Summary: In this paper, a method called ControlSynth Neural ODEs is proposed. This method is essentially defined as a neural ordinary differential equation with a control input. In particular, the authors focus on the convergence, of which definition requires the existence of a solution and also the asymptotic stabilit... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thoughtful reviews of our work and for recognizing our study on convergence analysis. We sincerely appreciate your review comments and have carefully analyzed and responded to them, conducting a relevant experiment:
1. **Regarding the existence of special optimi... | Rebuttal 1:
Rebuttal: Dear Program Chairs and Reviewers,
We sincerely thank you for your thorough review of our paper. We have carefully considered each comment and conducted extensive supplementary experiments and analyses. Below are our responses to the main concerns raised:
## 1. CSODE as a Generalized Extension o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity | Accept (spotlight) | Summary: This paper considers the task of learning linear causal representation with data collected from general environments. Authors show that there exists a surrounded-node ambiguity (SNA) which is basically unavoidable in their setting. On the other hand, identification up to SNA is possible under mild conditions ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful feedback and suggestions. Below are our responses to the reviewer’s questions and concerns of our paper.
**Q1: What does "general environments" mean? I don't think it is a widely used concept, so perhaps describe it more explicitly and accurately.**
... | Summary: The paper is about causal representation learning (i.e, learning the latent causal graph and the unmixing function) from high-dimensional observations in the case of linear SCMs where the mixing function is also linear. The paper defines the notion of surrounded node ambiguity (SNA) and then performs studies f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and comments. In the following, we respond to the questions raised by the reviewer.
**Q1: Figure 2e looks like an undirected graph but on zooming looks like there might be directed edges. If so, this figure can be corrected.**
That was indeed the c... | Summary: This paper investigates causal representation learning from low-level observed data across multiple environments. The authors address the surrounded-node ambiguity (SNA) in linear causal models and propose the LiNGCReL algorithm, which achieves identifiability up to SNA without relying on single-node intervent... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing insightful comments and feedback. Below are our responses to the reviewer’s questions and concerns of our paper.
**Q1: Could you provide an intuitive explanation or motivations for the assumptions? What do they represent in real-world data scenarios?**
Certain... | Summary: The paper studies causal representation learning (CRL) in the linear setting, that is, linear latent SEMs and a linear mixing function. Key contributions include:
* Identifying an intrinsic surrounded-node ambiguity (SNA) that exists when the causal model and mixing function are linear. This ambiguity is unavo... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work and for giving insightful comments. Below are our responses to the questions and weaknesses mentioned by the reviewer.
**Q1: All environments share the same causal graph. My understanding of this is that soft interventions considered in this work do... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens | Accept (poster) | Summary: The paper explores the ICL capabilities of LLMs, focusing on understanding the mechanisms underlying ICL. The authors aim to investigate the ICL process using representation learning principles. They mainly use kernel methods to develop a dual model for one softmax attention layer, demonstrating that the ICL i... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the novelty of our paper. We have carefully addressed each of your concerns and considering the word limits of rebuttal, we notice that some issues might overlap, so we have organized our responses as follows.
> **Weakness 1** (The work may ignore the f... | Summary: The paper explores the in-context learning (ICL) abilities of Transformer-based models. The authors propose an interpretation of ICL through the lens of representation learning. They establish a connection between the inference process of softmax attention layers in Transformers and the gradient descent proces... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novel perspective on understanding ICL of our paper. Considering the word limits of rebuttal, we notice that some issues might overlap, so we have organized our responses as follows.
> **Weakness 1** (The paper relies on several assumptions and simplifi... | Summary: The author's present a new way of linking in-context-learning (ICL) to gradient descent.
The author's are able to demonstrate that indeed a (simplified) transformer decoder layer ICL is equivalent to "representation learning".
Using the theoretical findings the authors are also able to propose extensions to... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging the theoretical contribution of our paper. We have carefully addressed each of your concerns and considering the word limits of rebuttal, we notice that some issues might overlap, so we have organized our responses as follows.
> **Weakness 1**: Sl... | Summary: The paper investigates the in-context learning (ICL) capabilities of Transformers, explaining it through a representation learning lens. It establishes a theoretical connection between ICL and gradient descent, deriving a generalization error bound tied to demonstration tokens. The authors also suggest modific... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging feedback, especially for recognizing the theoretical contribution and insights of our paper. Our response is detailed below.
> **Weakness 1**: Generalization to Other Tasks: The paper's findings are based on specific tasks. It's unclear how well these ins... | Rebuttal 1:
Rebuttal: ### **To AC and All Reviewers**
We thank the reviewers for providing valuable suggestions that help us improve our paper.
We are particularly encouraged that the reviewers have found that (i) the fresh perspective on understanding attention mechanisms `(HnPE, cRiK, k2vC,8J5b) ` , (ii) thorough ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead | Accept (poster) | Summary: The authors consider distributed training of sparse models, by optimizing over the thresholds used to prune the models.
Strengths: I am not an expert of deep learning but the results look convincing enough.
Weaknesses: I would be interested in a discussion and comparison with other approaches that train spar... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We provide our response for each question below.
>Q1:I would be interested in a discussion and comparison with other approaches that train sparse models, such as
Meinhardt et al. "Prune at the Clients, Not the Server: Accelerated Sparse Training in Federa... | Summary: This paper introduces SpaFL, a federated learning framework that enhances communication efficiency and minimizes computational overhead by optimizing sparse model structures. They achieve this goal by defining a trainable threshold which leads to structured sparsity. Since the server and clients only exchange ... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We provide our response to each comment below.
>Q1: In Figure 8 of the FedPM paper, the accuracy achieved by FedPM is higher than what is reported in this paper. Could you clarify the reason for this discrepancy? Is it due to differences in experimental s... | Summary: This paper suggests communicating the threshold instead of the model parameters in federated learning. Through empirical validations on popular benchmarks, the proposed method, SpaFL, is shown to have lower computational overhead and achieve relatively good results.
Strengths: Communicating the threshold inst... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We provide our response to each comment below.
>Q1: I would expect to see a high-level explanation of how the threshold for each client is selected, whether they are the same for each layer, and how the selected thresholds are combined.
**A1:** In SpaFL, ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One-Shot Safety Alignment for Large Language Models via Optimal Dualization | Accept (spotlight) | Summary: The paper studies the safety alignment of language models using constrained Reinforcement Learning from Human Feedback (RLHF). The main contribution of the paper is deriving a closed-form solution of the dual function of a constrained RLHF problem. This closed-form solution reduces solving a constrained RLHF p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and the valuable feedback. We have answered all questions to the best of our ability. We are glad to address any further questions you might have.
**1. Computing resources and running time.**
Our experiments are conducted on a single 48G NVIDIA A... | Summary: This paper introduces a novel approach to aligning large language models (LLMs) with safety constraints using a dualization perspective. The key contributions are:
1) A method to reduce constrained alignment to an equivalent unconstrained problem by pre-optimizing a dual function.
2) Two practical algorithms... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive evaluation and the valuable feedback. We have answered all questions to the best of our ability, and we are glad to address any further questions you might have.
**1. Experiments are limited to a single safety constraint.**
As stated in the limitation ... | Summary: This paper aims to address the issue that Lagrangian-based primal-dual policy optimization methods are computationally expensive and often unstable in conventional RLHF. The authors improve stability by pre-optimizing a smooth and convex dual function in a closed form, eliminating the need for cumbersome prima... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive evaluation and the valuable feedback. All questions have been answered as best as we could. We are glad to address any further questions you might have.
**Additional benchmarks.**
We have conducted additional experiments on the benchmark datasets TruthfulQA... | Summary: The paper proposes a novel dualization-based method to convert a constrained alignment of a LLM to an unconstrained alignment. The proposed two-stage policy learning method, CAN, eliminates the need for cumbersome primal-dual iterations with theoretical analysis. Based on CAN, two practical algorithms, MOCAN a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive evaluation and the valuable feedback. We have answered all questions as best we could, and we are glad to address any further questions you might have.
**1. Incorporating adversarial robustness.**
Please refer to the second point in our **global respon... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful review and valuable feedback. We have addressed all questions raised by the reviewers in the separate rebuttals below and are glad to address any further concerns. Multiple reviewers have brought up several matters, so we present a global response to th... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a new constrained optimization formulation to ensure the safety of language models (LMs). Based on the proposed constrained optimization problem, the authors derive the dual formulation and provide the closed-form solution to this dual formulation. Through theoretical derivations, they show... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We have addressed all your comments/questions to the best of our ability; see our detailed response below. We hope that our response would add your openness of re-evaluating our paper. We will be happy to address any further questions you might have... | null | null | null | null | null | null |
Classification Diffusion Models: Revitalizing Density Ratio Estimation | Accept (poster) | Summary: This paper introduces a class of generative models based on neural networks which learn the conditional probability distribution of the noise level given a noisy image. It is trained by combining a maximum-likelihood (cross-entropy) objective with a denoising score matching objective. The resulting generative ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and interesting comments.
**Comparing the one step likelihood computation to an integral along the sampling trajectory**
In theory Eq. (10) should be normalized, but this is a great idea. We’ll definitely add it to the final version. We’d just like to not... | Summary: A common approach for likelihood estimation is the density ratio estimation (DRE). DRE is a method of modeling the density of a target distribution (or data distribution) in the form of a density ratio with a known reference distribution. The standard normal distribution is often chosen as the reference. To tr... | Rebuttal 1:
Rebuttal: **Likelihood estimation is the primary motivation; the paper lacks illustrations of its use**
Our primary motivation was to analyze why existing DRE methods fail to capture distributions of complex high-dimensional data, and to develop theory and a practical DRE method that doesn’t suffer from th... | Summary: This paper introduces a clever connection between Density Ratio Estimation (DRE) and Diffusion Models (DM), showing that optimal denoisers are also optimal noise classifiers. Doing so allows them to construct a new type of loss based on noise classification. This allows DRE methods to inherit the benefits of d... | Rebuttal 1:
Rebuttal: **The architecture has to be changed**
We agree with the reviewer that the architecture can't be exactly the same since DDM is a denoiser and CDM is a classifier, which may impact the comparisons a little bit. However, as the reviewer notes, the change in the architecture is minor – we replaced o... | Summary: This work develops a new generative framework called the classification diffusion models (CDMs) based on the density ratio estimation (DRE), by establishing an interesting connection between the DDPM's denoiser and noise-predictive classifier, which also helps the exact likelihood computation in a single pass.... | Rebuttal 1:
Rebuttal: **The potential of DRE-based methods to learn data distributions of larger scale datasets**
We acknowledge that the Celeb-A 64x64 and CIFAR-10 32x32 datasets we experimented with are not very large and high-dimensional by today’s standards, and we certainly agree that extending the evaluation to ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models | Accept (poster) | Summary: This paper introduces Context-Aware Testing (CAT), a novel approach that uses context as an inductive bias to guide the search for meaningful model failures. Unlike previous methods that rely solely on data to find slices where a model's predictions underperform compared to average performance, CAT addresses t... | Rebuttal 1:
Rebuttal: Dear R-ctad,
Thank you for the feedback which helped to clarify our paper. We'll address misunderstandings about contextual information and LLM necessity.
We provide grouped answers A-D & highlight paper updates.
---
# (A) Clarification on contextual information
We apologize for any confusio... | Summary: The paper introduces Context-Aware Testing (CAT), a new method for testing machine learning (ML) models. Current ML testing methods rely only on data, which often leads to high false positive and false negative rates and misses meaningful failures. CAT improves this by adding external knowledge or context to t... | Rebuttal 1:
Rebuttal: Dear R-H6db,
Thank you for your thoughtful comments to improve the paper. We provide answers (A)-(E) & highlight updates to the paper
---
# (A) Clarifying contextual information
We'd like to clarify that when we refer to "context", we mean the *relevant background knowledge of a system (e.g. a... | Summary: The paper offers a multiple hypothesis testing view of ML evaluation (in regard to test slice
finding). Authors identify problems with data-only approaches (like high amounts of false positive
and false negative model failure triggers). The paper proposes SMART, a context-aware LLM based ML
model testing metho... | Rebuttal 1:
Rebuttal: Dear reviewer bfyv,
Thank you for taking the time to carefully review our work. We provide answers below (A-F) and highlight our paper updates.
---
## (A) Method degradation with less informative context
We appreciate your interest in SMART's performance under varying context quality. We've dev... | Summary: This paper introduces context-aware testing (CAT), which is a novel tabular model testing paradigm that uses context as an inductive bias to guide the search for meaningful model failures, and build a CAT system named SMART Testing. Detailedly, SMART includes four steps: (1) use an LLM to generate hypotheses o... | Rebuttal 1:
Rebuttal: Dear R-6t3M,
Thank you for your thoughtful comments to improve the paper. We provide answers (A)-(F) & highlight updates to the paper.
---
## (A) Highlighting our systematic comparison of SMART vs data-only
We appreciate your suggestions and believe there may be a misunderstanding regarding the... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful and positive feedback!
We are encouraged that the reviewers found our work on model testing "important" (**R-6t3M, R-bfyv**) and "interesting" (**R-6t3M, R-bfyv, R-ctad**). They agreed our motivation for diverse perspective testing is "necessary for pr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models | Accept (poster) | Summary: The paper proposes a method by which large language models are utilized to generate synthetic tabular data in order to mitigate class imbalances in existing datasets. The authors provide some tips which they discovered to result in more reliable data being generated, such as enforcing a CSV format. Their metho... | Rebuttal 1:
Rebuttal: > Q1. In Table 1, outside of the Travel dataset, the other 5 datasets do not show a significant increase in F1 score or balanced accuracy between the proposed method and the next closest baseline. Given this, it is not clear how to interpret the utility of the proposed method.
A1. Please see the ... | Summary: This paper proposed an LLM-driven tabular data class balancing approach, adapting the in-context learning paradigm and trying various formats and templates to explore optimal prompts to mitigate imbalanced tabular data. Experimental results showed that the proposed method alleviated the CSV-format data imbalan... | Rebuttal 1:
Rebuttal: Due to space constraints, we denote weaknesses and questions as W and Q, respectively.
> W1. Research scope
A. Focusing on tabular data does not limit the adaptivity or impact of our method. Tabular data, composed of mixed variable types such as numerical and categorical variables, represents a ... | Summary: The paper explores the domain of tabular data generation using in-context learning with LLM, in order to improve performance of an ML classifier, especially in imbalanced classes scenarios. The paper explores different prompting techniques, with detailed results on 6 datasets as well as a visualized study on a... | Rebuttal 1:
Rebuttal: > Q1. The main discussion that I missed in the paper was regarding - how much extra data ends up being generated, and does generating more and more data using this method make any sense? I understand that the method uses actual data points, and samples from the original dataset without replacement... | Summary: The paper investigates how to use LLMs to generate synthetic tabular data for mitigating class imbalance in machine learning tasks. By exploring various prompting methods, the authors aim to identify key design elements that optimize the generation performance. The paper shows that using GPT3.5/Mistral/LLaMA t... | Rebuttal 1:
Rebuttal: > Q1. Why not use the LLM itself to perform classification: The designed method uses LLMs to generate data examples for imbalanced classes, which means the LLMs must already have a good ability in modeling the data distribution even for the imbalanced classes. If that's the case, why not use the L... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' valuable feedback and positive support, recognizing our method as
* Providing valuable guidance for researchers addressing class imbalance (zoZq, p2sf).
* Clear, well-articulated, and comprehensive in covering the method (zoZq, p2sf, 8bof, kZFF).
* Supported... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier | Accept (poster) | Summary: The submission presents a new semi-supervised learning algorithm for deep regression models: in addition to the standard supervised regression head, an auxiliary classification head is trained using a semi-supervised learning algorithm for classification that learns to classify pairs of examples. A pair is cla... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and highlighting the strengths of our approach. We appreciate your careful consideration, particularly regarding augmentation. We'd like to address your concerns and questions:
### **W1) Influence of augmentation on results**
We confirm that **weak augment... | Summary: This paper presents a simple yet effective approach that adapts existing semi-supervised classification techniques to enhance the performance of regression tasks. The perspective is novel and can effectively use existing technologies to solve regression problems.
Strengths: The article has novel ideas and sol... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on the novelty of our ideas and the strength of our experimental results. We appreciate your constructive feedback and will address your concerns as follows:
### **W1) The method description needs to be improved**
We will thoroughly revise Section 3 to enhan... | Summary: The authors present two components to improve the problem of semi-supervised regression - 1) RankUp which considers the regression problem as a ranking problem and then adapts existing semi-supervised classification methods, and 2) Regression distribution alignment (RDA) which is to refine pseudo-labels. The e... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for bringing these important points to our attention. We appreciate your feedback on the clarity and technical soundness of our manuscript. Here are our responses to your concerns and questions:
### **W1) Regarding concerns about technical novelty**
We ... | Summary: The work introduces a novel SSL-regression method called RankUp. A a pairwise ranking loss enables the SSL-method FixMatch to utilize also the unlabled split of the data to learn a regression task. The addition of the Regression Distribution Alignment (RDA) loss enables the method to also take the overall dist... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and insightful questions. We're pleased you found our approach novel and well-supported. Regarding the weakness and questions:
### **W1) Scale of the Experiments**
We acknowledge that the scale of our experiments is limited, which is indeed a common challeng... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for taking their time to review our work. We greatly appreciate the thoughtful feedback and insightful comments, which have significantly contributed to improving the quality of our paper. We have attached the revised figures in the PDF for the reference.
P... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Knowledge Graph Completion by Intermediate Variables Regularization | Accept (poster) | Summary: This paper proposes a general model for regularizing a variety of tensor-decomposition-based knowledge graph completion models (based on variants of real / complex CP decomposition, Tucker decomposition, and others). The authors first observe that all of these tensor decomposition models can be expressed as a ... | Rebuttal 1:
Rebuttal: We appreciate your careful and constructive comments. We have addressed the questions that you raised as follows. Please let us know if you have any further concerns.
$\textbf{Q1:}$ It’s not clear whether applying this rank-based theorem would be simpler than specialized proofs, or would provide ... | Summary: This paper proposes a general framework for tensor decomposition methods on knowledge graph completion. Based on the proposed framework, the authors further introduce a novel regularization method that regularizes the norms of intermediate variables in tensor decomposition. Theoretical analysis demonstrates th... | Rebuttal 1:
Rebuttal: We appreciate your careful and constructive comments. We have addressed the questions that you raised as follows. Please let us know if you have any further concerns.
$\textbf{Q1:}$ Several detail points about the proposed method are not clear enough.
$\textbf{A1:}$ We will make our statements c... | Summary: The paper addresses the challenges in Knowledge Graph Completion (KGC) using Tensor Decomposition-Based (TDB) models. The authors present a detailed overview of existing TDB models and establish a general form for these models, which is intended to serve as a foundational platform for further research and enha... | Rebuttal 1:
Rebuttal: We appreciate your careful and constructive comments. We have addressed the questions that you raised as follows. Please let us know if you have any further concerns.
$\textbf{Q1:}$ Could you provide a more detailed explanation or theoretical basis for the division of the tensor decomposition int... | Summary: The paper considers the problem of knowledge graph completion where the knowledge graph is encoded as a 3rd-order binary tensor. The authors provide an overview of existing tensor decomposition based models for KGC and propose a unifying general form that enables representing each of these models by choosing t... | Rebuttal 1:
Rebuttal: We appreciate your careful and constructive comments. We have addressed the questions that you raised as follows. Please let us know if you have any further concerns.
$\textbf{Q1:}$ Currently, the paper starts directly with the main section. I think it would be beneficial to have a small (sub-)se... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LeDex: Training LLMs to Better Self-Debug and Explain Code | Accept (poster) | Summary: The paper addresses the goal of self-debugging of generated code, while also explaining it.
The approach is to: (a) sample code outputs to natural language inputs, and keep only the wrong code outputs according to unit tests; (b) sample **refinements** ("fixes") to the wrong code outputs and test those refine... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful suggestions and questions.
## 1. Paper novelty
While there are several related works on self-debugging, our paper focuses on how to improve the model’s self-debugging capability, which is important but not yet extensively investigated.
We believe "NExT: Teac... | Summary: This work proposes a novel framework to enhance the self-debugging capabilities of smaller language models that do not benefit much from self-refine or other prompt-based debugging approaches. Sampling incorrect code samples produced by LMs, they pass the execution feedback on these to GPT-3.5/4 and prompt it ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments.
## 1. Evaluation of APPS and CodeContests
This is a good suggestion and we plan to add the results to the final version if accepted.
Below are the results on APPS and CodeContests. We test StarCoder on the full 5000 APPS test samples. However, due... | Summary: The authors teach language models to better self-debug and explain code. Particularly, they utilize code explanations in the repair process where explanations are generated before refining the programs. This is accomplished via training the models with SFT and RL on data curated from different sources and mode... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful suggestions and questions.
## 1. Explanation improvement
The reviewer might have some misunderstanding of the explanation evaluation in Table 7. In Table 7, we performed both human evaluation and LLM Judge-based evaluation to evaluate the explanation quality b... | Summary: This paper proposed a pipeline to obtain code explanation and refinement data from a stronger model (mainly GPT-3.5 with CodeLLaMA as ablation) to train weaker models (i.e., StarCoder-15B, CodeLLaMA-7B/13B) using SFT and RL methods. More specifically, it uses the weaker models to sample the incorrect solutions... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful suggestions and questions.
## 1. Data collected from the same model itself
We provide the experiment results using the synthetic data generation from the model itself for CodeLlama 7B.
We highlight some results here. Below is the CodeLlama-7B SFT/RL using th... | Rebuttal 1:
Rebuttal: # Global Response
## 1. Contribution and Novelty
While there are more and more works on self-debugging, most of the existing works focus on how to prompt existing LLMs to do self-debugging. Few works investigate the self-debugging capability of LLMs and how to improve it at the time of submission... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Authors propose a framework to perform SFT and RL training to achieve superior performance in generating code with open code LMs on the self-debugging task. They leverage the test suite present in benchmarks like APPS, CodeContests to obtain execution feedback for model refinements on CodeLM generated code. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful suggestions and questions.
## 1. APPS and CodeContests Evaluation
This is a good suggestion and we plan to add the results to the final version if accepted.
Below are the StarCoder-15B’s results on APPS and CodeContests. Results of the other two backbones can... | null | null | null | null | null | null |
Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences | Accept (spotlight) | Summary: The authors investigate the properties of self-consuming loops that arise in the training of generative models. In particular, they investigate the impact that data curation has on the iterative retraining performance of these models. The paper contains theoretical and empirical analysis of how model performan... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and are pleased that they find the question raised "highly original" and "very clearly" motivated. We further appreciate that they find it "significant" and "more realistic" than previous works. We now address the key clarification points raise... | Summary: This paper studies the impact of data curation on iterated retraining of generative models. Theoretical results are derived for the convergence state of the retraining loop when using a fraction of curated synthetic data or a mixture of real data and curated synthetic data at each step. Empirical experiments o... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and constructive comments. We appreciate that the reviewer finds the problem we tackle "important and interesting" and that our theory in connection to preference optimization “interesting and reasonable”. We now address the key points raised in the review:
#... | Summary: This paper extends Bertrand et al. 2024’s analysis of model collapse to study settings where data is filtered (based on a particular preference model) before being used for training the next iteration of generative models.
Strengths: - The paper extends prior work studying self-consuming generative models to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their nuanced review and constructive feedback. We appreciate that the reviewer has found our paper "well-written" and the integration of preference learning to the model collapse literature to be "highly sensible". We took note of the reviewer's concerns and provide clar... | Summary: This paper explores the scenario where generative models are iteratively trained on self-generated data curated by human users with some implicit reward. The key idea is that each iteration of training on the self-generated data reweights the previous distribution based on the implicit reward, which converges ... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding the problem we tackle “interesting” and to be “an accurate description of what happens when new generative models are trained nowadays”. We also appreciate that they find our experiments “interesting”. We now address below the key points raised in the review:
## ... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their detailed feedback which has allowed us to strengthen the updated manuscript. In particular, we are heartened that all reviewers (WZgs, cUrK, ej3h, cgiY, orKH) found our research question to tackle an interesting, highly-sensible, and timely proble... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Self-consuming generative models are known to have collapse or stability problems, and the curation of synthetic data is often ignored. This paper theoretically studies the impact of data curation and proves that it optimizes the expected reward.
Strengths: 1. The paper is well-written, and works on synthetic... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and review of our paper. We are glad that the reviewer finds our paper "well-written" and our connection with RLHF to be "novel" in the "extremely important" area of work on synthetic data. We address below the key points raised by the reviewer:
## Extension o... | null | null | null | null | null | null |
Can neural operators always be continuously discretized? | Accept (poster) | Summary: This paper studies the question of whether a neural operator (or a general diffeomorphism on an infinite dimensional Hilbert space) can be continuously discretized through the lens of category theory. It first proves that there does not exist a continuous approximation scheme for all diffeomorphisms. Then, it ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed comments and fair criticisms. We address all of these below:
1. "Most results presented in the paper are purely theoretical and lack a quantitative or asymptotic estimate."
The proof of Theorem 4 makes it possible to estimate the number $J$ of... | Summary: This paper focuses on the continuous discretization in operator learning. This is a very important question since it involves reducing the infinite-dimensional space to a finite-dimensional space in operator learning. The authors present cases where discretization is continuous and cases where it is not. The r... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestion to include an example based on the discretization of simple differential equations as it surely helps the readers to quickly understand the essential features of the no-go theorem of the approximation of invertible operators.
On the positive results, in Ap... | Summary: This paper investigates theoretical limitations of discretizing neural operators on infinite-dimensional Hilbert spaces. The authors first prove a "no-go theorem" (Theorems 1,2) showing that diffeomorphisms between infinite-dimensional Hilbert spaces cannot generally be continuously approximated by finite-dime... | Rebuttal 1:
Rebuttal: We appreciate the detailed suggestions, criticisms and endorsement of the reviewer. We address all of these below:
1. "On the other hand, the proofs are based on conventional analytical arguments rather than category-theoretic arguments."
The proofs are indeed based on analytical arguments. We u... | Summary: The paper addresses the problem of discretizing neural operators, maps between infinite dimensional Hilbert spaces that are trained on finite-dimensional discretizations. Using tools from category theory, the authors provide a no-go theorem showing that diffeomorfisms between Hilbert spaces may not admit cont... | Rebuttal 1:
Rebuttal: We appreciate the valuable comments and constructive feedback of the reviewer. We are pleased to address all of these below.
1. "The text and presentation require significant polishing."
We agree and sincerely regret this, and have already made many corrections to the manuscript.
2. "While the ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments and detailed questions. We will provide replies to the individual reviewers below, but first would like to make some general statements addressing a few issues raised by all the reviewers.
Common questions: Practical impact/examples of this work?... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Many-shot Jailbreaking | Accept (poster) | Summary: The jailbreaking problem, where a harmful output is desired to be obtained from aligned language models is studied in this paper. The paper addresses in-context learning jailbreaking, where examples of malicious queries and answers are given before asking the desired question. This paper extends from the previ... | Rebuttal 1:
Rebuttal: Thank you for your review!
We appreciate that you found the simplicity of MSJ to be a strength given its effectiveness, and highlighted the scaling laws as providing insight into the nature of the jailbreak, as well as mitigation attempts.
Here are our responses to some of your critique:
* **... | Summary: In this paper, the authors investigated many-shot jailbreak (MSJ), a jailbreaking method that exploits LLMs' ever-growing context window length to prefix malicous requests with a large amount of demonstrations of jailbroken dialogs. The constituents of MSJ prompts are reltively simple but MSJ manages to breach... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review!
We appreciate that you highlighted our focus on context length as a novel attack surface, which was the inspiration behind our work. We also appreciate that you found our experiments comprehensive.
We've tried to address some of your feedback — some direc... | Summary: This work presents a jailbreaking method leveraging the power of long-context attacks on Large Language Models (LLMs) called Many-shot Jailbreaking (MSJ). Various LLMs are tested and evaluated on their responses. Extensive experiments are performed with a specific LLM (called MODEL in the paper for anonymity) ... | Rebuttal 1:
Rebuttal: Thank you for your review!
We appreciate that you've found our results interesting and useful. The extent to which the scaling laws are so clean and reproducible did surprise us when we were initially getting the results.
Here's our attempt at addressing some of your critique:
* **Connection t... | Summary: This paper introduces a novel jailbreaking attack that exploits the extended context capabilities of the most advanced large language models. The authors conduct an in-depth analysis of various aspects of the attack, including its effectiveness across different models, the significance of turn formatting, its ... | Rebuttal 1:
Rebuttal: Thank you for your critique and questions.
We appreciate that you found our scaling and mitigation analysis interesting, which we perceive to be some of the central contributions of our work. We also thank you for highlighting the importance of the independent replication.
Overleaf doesn't all... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback.
We appreciate the positive feedback we’ve received on the relevance of our contributions, the significance of the results and the extensiveness of our empirical evaluation. We have already incorporated the majority of the actionable fee... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes many-shot jailbreaking (MSJ). This jailbreak technique exploits longer context windows of modern large language models by providing hundreds of demonstrations of undesirable behavior. The authors demonstrate the effectiveness of MSJ follows a power law scaling with the number of demonstrati... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review!
We appreciate that you found our empirical foray into the scaling and mitigations of MSJ thorough and valuable.
**Inference-time defenses considered:** Thank you for bringing up inference-based defenses!
We are actively following promising leads on how ... | null | null | null | null | null | null |
Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models | Accept (poster) | Summary: This work presents 3 new synthetically generated VQA evaluation benchmarks and conducts a comprehensive evaluation of the limitations of current VLMs in spatial reasoning tasks. These tasks include spatial relationships, navigation, position understanding, and counting. The authors demonstrate using their int... | Rebuttal 1:
Rebuttal: We are grateful for your support of our work and insightful comments!
> *Q1: The use of synthetic data in the evaluation, while valuable, may introduce confounding factors (e.g., (1) It is challenging to disentangle the contributions of OCR)*. (2) The synthetic data might be out-of-distribution ... | Summary: The authors make a benchmark to test the spatial reasoning of multimodal language models (MLMs) using synthetic data consisting mostly of diagrams, mazes, and charts. They benchmark a lot of existing MLMs. Their key findings are: 1) Spatial reasoning is limited in most MLMs. 2) MLMs rely heavily on textual inf... | Rebuttal 1:
Rebuttal: We sincerely appreciate your feedback and comments!
> *Q1: Some claims may be too broad; we can only state the model uses more textual cues for such data*
Thanks for the comments and the suggestion! We acknowledge the visual domain gap and have added remarks in our revised manuscript. We intent... | Summary: The paper proposes a set of synthetic tests to compare the spatial understanding in VLMs and LLMs. The tests include Spatial-Map, Maze-Nav, and Spatial-Grid, which all include an image, paired with text that describe the image, and a question. Using the synthetic data, the VLMs and LLMs are studied using diffe... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments!
> *W1: All datasets contain only 2D synthetic images, which is substantially different from real images.*
We choose synthetic data due to its controllability, scalability, and the ability to create highly specific scenarios with flex... | Summary: This paper develops a novel benchmark to understand spatial reasoning ability of LLM and VLM. Using such a benchmark, authors conduct experiments to evaluate models' performance, and reveal several results.
Strengths: 1. This paper is well-written and well-structured.
2. This paper conducts a series of exper... | Rebuttal 1:
Rebuttal: We sincerely appreciate your comments and questions, which we address in detail below.
> *Q1: Design rationales of the three benchmarks. Real world application scenarios for "Spatial-Grid"?*
Our benchmarks are designed to cover diverse aspects of spatial reasoning, such as spatial relationships,... | Rebuttal 1:
Rebuttal: **Review summary** We sincerely appreciate all reviewers for their time and effort in providing valuable feedback and suggestions on our work. We are glad that reviewers recognize our work to be _novel_ (R1, R4), _highly impactful_, and _intruguing_ (R4). Additionally, reviewers found our results ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem | Accept (poster) | Summary: The paper presents a model capable of predicting the appearance of emergent abilities, using only information on the emergence of the first ability. The model successfully predicts emergence in a 2-layer MLP solving the multitask sparse parity problem, a toy-model problem constructed with a power-law skill dis... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for taking the time to review our paper. We appreciate your comments and suggestions.
* Scope of the experiments
Additional learning of the skills, as correctly spotted, requires another order of magnitude of computation which challenges our computational budget. The pr... | Summary: I’ll write two summaries: one to state my “moral” understanding of the work and another to state the specific contributions
**“Moral” Summary:** The authors propose an analytically solvable model to study scaling laws and emergent abilities by combining the problem of Barak et al 2022 + the data distribution... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your comments on the paper and for appreciating our tables. Your moral summary represents our motivation in the paper well.
* Realistic setup and the title of the paper
We fully appreciate your comment on the scope/limitation of the paper and thank you for suggesti... | Summary: This paper provides an in-depth study of a toy dataset and model, both in terms of scaling laws and emergent skills. They study the ‘multitask sparse parity’ synthetic problem introduced by Michaud et al. (target is the parity function of a string of random bits, each task indicates a different subset of bit i... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for your kind comments and especially for dedicating the time to read through the appendix in depth.
* Material in the appendices
We appreciate your suggestion regarding the suitability of our paper for a journal format. We fully agree that a journal-style paper would ... | Summary: The paper proposes to use a certain generalization of the well-known sparse parity problem, as a theoretical framework for neural scaling laws.
The theoretical setup doesn't seem to make sense (I'm open to changing my mind). Indeed, equations (2) and (4) taken together give
\begin{equation*}
f^*(i,x) = S\... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for being open to discussing what was unclear to you in the paper. Please see our response below.
* Theoretical setup clarification
Regarding the sum of skill functions, we believe the confusion arose from interpreting the input variable $i$ – which depends on the data ... | Rebuttal 1:
Rebuttal: Dear reviewers and AC, we present a global rebuttal to address the overlapping reviews.
## Scope of the paper and its title
As most reviewers have pointed out, the primary strength of our paper is the detailed theoretical analysis of the scaling laws and emergence with a concrete model while... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning | Accept (poster) | Summary: This work proposes to perform boosting with base learner which are fitted to the Wasserstein gradient of a loss function on the space of probability distributions, which can be useful in particular to capture uncertainty of the models.
Several variants of the algorithm are discussed (by adding a diagonal Hess... | Rebuttal 1:
Rebuttal: Thank you for your strong endorsement of our work. We are delighted that you have found our methodology well-written and intriguing.
> Did you compare between the different algorithms (i.e. with and without Wasserstein Hessian preconditioner, and Kernel vs Langevin approximation) on the b... | Summary: This paper proposed a new ensemble algorithm, called Wasserstein Gradient Boosting (WGBoost), which is a novel gradient boosting framework that leverages the wasserstein gradient for probabilistic prediction. Specifically, WGBoost fits a new base learner to the Wasserstein gradient of a loss functional on the ... | Rebuttal 1:
Rebuttal: Thank you for your assessment of our work. We are afraid that there seems a misinterpretation of our method in the reviewer's concerns: (a) the proposed method seems almost identical to the stein variational gradient descent (SVGD) and (b) the paper lacks the comparison with existing work. Please ... | Summary: This paper introduces a probabilistic boosting tree algorithm called Wasserstein boosting that uses a smoothed particle gradient to provide probabilistic predictions. Experiments are performed using UCI tabular regression, and out of distribution classification.
Strengths: Originality:
- I like the applicat... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback to our work. We respond to each of your comments and concerns below. We hope that these responses will open up room for an upward revision of the score of our work. (In our response below, citation numbers such as [9] and [10] correspond to references in ou... | Summary: The paper introduces a novel gradient boosting framework called Wasserstein Gradient Boosting (WGBoost). Unlike traditional gradient boosting methods that fit base learners to the gradient of the loss function, WGBoost fits them to the Wasserstein gradient of a loss functional defined over probability distribu... | Rebuttal 1:
Rebuttal: Thank you for your strong endorsement of our work. We also believe that WGBoost is a promising direction that connects Wasserstein dynamics and gradient boosting ensemble for the first time.
> Authors did not specify details about the hyperparameter selection for Conditional Density Estim... | Rebuttal 1:
Rebuttal: We would like to express our gratitudes to all the reviewers for their efforts in assessing our work. Our full rebuttal to each reviewer has been provided in each individual rebuttal area. This global rebuttal area is used for the following contents:
- (A) Brief Summary of Each Rebuttal
- (B) Conc... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Weighted Paging with Unknown Weights | Accept (poster) | Summary: This paper considers a generalization of the classical online paging problem. In classical online paging, one needs to maintain a cache of k slots as requests for fetching pages arrive online. If the requested pages are in the cache, there is no charged cost; otherwise, w_p cost will be charged for page p. The... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful comments and encouraging review.
Regarding lower bounds, see the remark in the general rebuttal. In essence, we’ve presented lower bounds for the problem in “Our Results”, which we intend to further formalize in the camera-ready version.
Re... | Summary: This paper is the first to model and study to the online weighted paging problem with unknown weights. In this model, the weights $w_p$ of pages are initially unknown, and the eviction cost is drawn from an unknown distribution with an expectation equal to $w_p$. This study extends previous research on online ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and supportive review.
We take into account the comment regarding the presentation in Section 5. In the final version of the paper, we will edit this section to be more clear and emphasize high-level ideas.
Regarding your question about our rebal... | Summary: The paper addresses the problem of online weighted paging with eviction costs drawn from unknown page-dependent distributions. As evictions occur, the authors demonstrate how to learn an effective eviction strategy online using previous cost samples, framing this as a multi-armed bandit problem. They first app... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the encouraging review, as well as the thoughtful comments. Please see below our response regarding the weaknesses and questions mentioned.
_“… the paper is closely related to the framework of learning-augmented algorithms. I suggest that the authors mentio... | Summary: The paper studies a the online paging problem where the weights to retrieve a items are independent random variables with potentially different distributions. The paper introduces an algorithm whose expected performance differ from the optimal one by a multiplicative factor (logarithmic in the size of the cach... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thorough assessment and comments. Here is our response to the points raised.
**Suitability for NeurIPS**: we note that theory papers considering online caching/paging have appeared in ML conferences when combined with an ML-related theme. See for exam... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thorough assessments and their thoughtful remarks. Here are some clarifications regarding themes that appear in more than one review.
**Running time:**
Reviewers Yutu and qj5j made remarks regarding the running time of the algorithm, and specifically the continuo... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: Author consider caching with stochastic weights. In particular,
the cost incurred by the algorithm at eviction of a page p
is drawn independently from a fixed distribution D_p
which is different for every page and is not known to the algorithm in advance.
Authors propose an algorithm with guarantees which comb... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback, please see below our comments for the concerns raised.
**Regarding (1):** _“Hierarchical caching is already studied”_.
Assuming you refer to [1]: this paper studies generalized weighted paging, in which each page has both a weight and a size dem... | null | null | null | null | null | null |
How do Large Language Models Handle Multilingualism? | Accept (poster) | Summary: This paper delves deeper into how LLMs handle multilingualism. The authors hypothesized a three-stage multilingual workflow called MWork: understanding, task-solving, and generating and which language(s) become essential in each stage. To verify their proposed workflow, they experimentally identify language-sp... | Rebuttal 1:
Rebuttal: Dear Reviewer NLiQ,
Thank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:
> Question #1: Patterns of language-specific neurons.
We acknowledge your concerns reg... | Summary: The paper presents two key contributions. One is Parallel Language-specific Neuron Detection, a method of identifying elements of multilingual LLMs that are responsible for handling particular languages; the method only requires unlabeled text data in each language in order to detect these neurons, which makes... | Rebuttal 1:
Rebuttal: Dear Reviewer TbKY,
Thank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:
> Concern #1: Overclaim on model size and multilingual enhancement experiments.
We appr... | Summary: This paper proposes MWork, a workflow to study how large language models handle multilingual inputs. The main idea is to detect English and non-English neurons by probing their value differences and performance gaps. Based on the results, they argue that there are three steps in the workflow: understanding, ta... | Rebuttal 1:
Rebuttal: Dear Reviewer Nru9,
We appreciate the time and effort you have put into providing valuable feedback. However, we respectfully believe there is a serious misunderstanding regarding our work. We would appreciate the opportunity to clarify a few points and address your concerns as follows.
> Misu... | Summary: This paper examines how LLMs handle multilingualism. The author proposes a hypothetic workflow (MWork), which suggests that LLMs understand the multilingual query, think in English, and than generate results in the input language. A neuron detection method is proposed to detect language specific neurons. By de... | Rebuttal 1:
Rebuttal: Dear Reviwer 2b8J,
Thank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:
> Concern #1: different instances may have different splitting of layers
We appreciate y... | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews and comments. We include Figures added in rebuttal in the attached PDF.
Pdf: /pdf/683b0336466b3865700a197444fa9f179677b661.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation | Accept (poster) | Summary: This paper presents a new diffusion model that is capable of generating data following various conditional distributions in multimodal data. Rather than applying a uniform noise level to all data elements, the proposed model permits the use of differing noise levels across modalities and time dimensions. This ... | Rebuttal 1:
Rebuttal: > **[W1]**: *"The major concern is on the experiments: the majority of the empirical evaluations are conducted with the internal dataset. As there are already several publicly-available datasets (such as Landscape, AIST++, and VGGSound) that are commonly used in the literature, I strongly encourag... | Summary: The paper tackles the audio-visual cross-modality generation problem and proposes a training approach to learn arbitrary conditional distributions in the audiovisual space. At the methodological level, the authors propose to apply variable diffusion timesteps across the temporal dimension. The experiments are ... | Rebuttal 1:
Rebuttal: > **[W1]**: *"The writing and presentation of the paper can be improved. Figure 1 seems to have some issues with the first-row caption for the AIST dataset, which makes it difficult to read. The usage of math symbols is inconsistent, e.g., the $𝑥$ should be $\boldsymbol{x}$ in Line 72. The term ... | Summary: This paper introduces the Audiovisual Diffusion Transformer (AVDiT) with Mixture of Noise Levels (MoNL) for audiovisual sequence generation. The key innovation is the use of variable noise levels during the diffusion process, applied across different time segments and modalities. This approach enables the mode... | Rebuttal 1:
Rebuttal: > **[W1]**: *"While the variable noise levels concept is interesting, the overall novelty of the approach may be seen as incremental. "*
While we appreciate the reviewer's acknowledgment of the variable noise levels concept, we believe that our work offers significant advancements beyond prior ar... | Summary: The paper presents a new method for audiovisual generation where the input output condiitions may comprise of 2 modalities, namely video and audio sequence. The authors propose a new training approach to effectively learn conditional distribution in multimodal space. The main novely in the paper is a mixture o... | Rebuttal 1:
Rebuttal: > **[W1&Q1]**: *"Although the method seems to work well empirically, the paper lacks theoretical backing for the proposed method. It would be good to see some proofs that the proposed method leads to a better approximation of the variational lower bound and joint distribution."*
We appreciate the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their constructive and insightful feedback.
Their recognition of the paper's strengths has been invaluable. We are particularly grateful for their positive comments on:
- **The effectiveness of our approach**: The reviewers highlighted the innovative use of m... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction | Accept (poster) | Summary: The paper proposes a novel subgraph-based approach for predicting drug-drug interactions (DDIs) that harnesses neural architecture search (NAS) to customize subgraph selection and encoding process. The authors first introduce refined search spaces to realize fine-grained subgraph selection and expressive encod... | Rebuttal 1:
Rebuttal: Thank you for your time and efforts in reviewing our paper. Please find our responses below to your concerns.
> W1. For the supernet training phase, in addition to the algorithm process, the authors are encouraged to provide an illustration to help the reader understand the corresponding steps mo... | Summary: The article presents a novel method for predicting drug-drug interactions (DDIs) by using a customized subgraph selection and encoding process. The authors propose a framework called Customized Subgraph Selection and Encoding for Drug-Drug Interaction prediction (CSSE-DDI), which leverages neural architecture ... | Rebuttal 1:
Rebuttal: Thank you for your time and efforts in reviewing our paper. Please find our responses below to your concerns.
> W1. About task-specific customization expected for DDI.
**R1**. Please refer to the general response **GR1**.
> W2. Time efficiency and accuracy about implicit encoding sampling metho... | Summary: This work introduces CSSE-DDI, a searchable framework for DDI prediction, which refines search spaces for fine-grained subgraph selection and data-specific encoding. To improve search efficiency, CSSE-DDI employs a relaxation mechanism to continuousize the discrete subgraph selection space and use subgraph rep... | Rebuttal 1:
Rebuttal: Thank you for your time and efforts in reviewing our paper. Please find our responses below to your concerns.
> W1. Examples of symmetric semantic patterns(headache, pain in throat) are not very convincing.(sematic property)
**R1**. In the field of multi-relational graphs, the modeling of differ... | Summary: This paper addresses the challenge of predicting drug-drug interactions (DDIs), crucial for medical practice and drug development, using subgraph-based methods. It highlights the importance of customizing subgraph selection and encoding but notes the high cost of manual adjustments. Inspired by neural architec... | Rebuttal 1:
Rebuttal: Thank you for your time and efforts in reviewing our paper. Please find our responses below to your concerns.
> W1. The motivation for using NAS to search components is not clear.
**R1**. The use of NAS technology to customize components stems from our analysis and understanding of the DDI predi... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your time and comments in reviewing our paper. To summarize, all reviewers agree that **the use of NAS to customize subgraph selection and encoding for DDI prediction is a novel approach** (Qw7Z, zvSg, KE8i, 2Vrd). **The manuscript is well-structured and clearly writ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models | Accept (poster) | Summary: This paper proposes an application of the mixed precision quantization technique to the singular vectors of the delta weights, which is encountered when serving multiple aligned LLMs.
Strengths: Please see the “Questions” section.
Weaknesses: Please see the “Questions” section.
Technical Quality: 2
Clarity... | Rebuttal 1:
Rebuttal: Thanks for your questions.
# For question 2.
Representative works on mixed precision include Yao et al., Agile-Quant, and SpQR, which focus on applying mixed precision to model weights and activations, and Bablani et al. who apply mixed precision across different layers, while we adopt different ... | Summary: In the context of SVD compression of delta weight, the paper employs higher bitwidth for singular vectors corresponding to larger singular values. The available bitwidth 8, 3, 2 are empirically chosen. Once the bitwidths are assigned to the singular vectors, the vectors are group-wise quantized by GPTQ.
Stren... | Rebuttal 1:
Rebuttal: Thanks for your comment.
# For weakness
Due to limited pages, we did not include the detailed process of the decision of the number of different bit-widths in the current version. When setting $𝑟_{𝑏𝑒𝑔𝑖𝑛}$ and $𝑟_{𝑒𝑛𝑑}$, we decided based on minimizing the error between the activations of... | Summary: This paper proposes an improved way to apply delta-compression for aligned language models (compact representations of the difference in weights between the pretrained and finetuned language models), which is just as effective w.r.t compression as the most extreme existing binary quanitzation strategy (BitDelt... | Rebuttal 1:
Rebuttal: Thanks for your questions, weaknesses, and limitations which can bring much improvement to our paper.
# For question 1.
Due to limited pages, we did not include the detailed process of the decision of the number of different bit-widths in the current version. When setting $𝑟_{𝑏𝑒𝑔𝑖𝑛}$ and $�... | Summary: This paper introduces Delta-CoMe, a delta compression method for LLM. It proposes to combine low-rank compression and low-bit compression together to achieve better performance. Specifically, it applies mixed-precision quantization for different singular vectors based on their singular values. The experimental... | Rebuttal 1:
Rebuttal: Thanks for your questions
# For question 1
In delta-compression, we consider compressing $U$ and $V^{T}$. For $U$ and $V^{T}$, their inputs are crucial for adjusting weights during the quantization process, as illustrated in GPTQ. In a forward pass $Y = W X + (UΣV^{T}) X$, the input of $V^{T}$ is... | Rebuttal 1:
Rebuttal: Thank all the reviewers for the constructive suggestions. We will take into account the advice to improve the manuscript! | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Neural Krylov Iteration for Accelerating Linear System Solving | Accept (spotlight) | Summary: The authors use a neural operator approach to generate subspaces that are used for the acceleration of Krylov subspace methods for several partial differential equations setups.
Strengths: The authors are interested in a very relevant problem of computational science and engineering of solving linear systems ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the patience to read through our paper. We are pleased to re-introduce our work and respond to your comments as follows. We sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score and your c... | Summary: The manuscript introduces a novel method, referred to as NeurKItt, which combines neural network techniques with Krylov subspace methods to accelerate the solution of linear systems derived from partial differential equations (PDEs). The core innovation of NeurKItt lies in its capability to predict invariant s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to your comments 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 and your confidence. If not, please let us know your ... | Summary: This paper proposes a data-driven approach to accelerate solving linear systems. Linear Systems are widespread in scientific computing applications like solving partial differential equations (PDEs), nonlinear systems, etc. so this method has potential for major impact. The proposed method builds upon the idea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to your comments 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 and your confidence. If not, please let us know your ... | Summary: The paper develops a new method, NeurKItt, for accelerating the solution of non-symmetric linear systems. NeurKItt constructs an approximate invariant subspace of non-symmetric matrix A using the Fourier Neural Operator. This invariant subspace is then used as the initial subspace within GMRES. The paper provi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to your comments 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 know your further concerns, an... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification | Accept (poster) | Summary: The paper presents FasterDiT, a method intended to speed up the training of Diffusion Transformers (DiT) without making changes to the architecture. By utilizing insights from the Probability Density Function (PDF) of Signal-to-Noise Ratio (SNR), FasterDiT improves training strategies and the effectiveness of ... | Rebuttal 1:
Rebuttal: $\color{red}{Question1:}$ The paper would benefit from additional experiments to demonstrate the generalizability of the proposed approach. Additionally, the scalability of FasterDiT to larger datasets or more complex tasks is not sufficiently discussed, potentially limiting its applicability.
$\... | Summary: This work aims at solving the slow training convergence of Diffusion Transformer, from the perspective of Signal-to-Noise Ratio (SNR).
Different from other works, the authors formulate the probability density function (PDF) of SNR during training, and then leverage such SNR PDF to analyze the association betwe... | Rebuttal 1:
Rebuttal: $\color{red}{Question1:}$ Current Table 1 compares the CFG (classifier-free guidance) results. Please compare the Class-conditional results of Faster-DiT with DiT and SiT, under the same setting in Table 1 (or even more iterations of Faster DiT). Usually we need both CFG and Class-conditional resu... | Summary: This paper propose FasterDiT, a diffusion model training method that considers the data distribution in the definition of signal-to-noise ratio. It formulates the SNR in a new framework, estimates the PDF of the SNR, and then employs it to improve the training efficiency of DiT. Experimental results show that ... | Rebuttal 1:
Rebuttal: $\color{red}{Question1:}$ What does the $std$ means in Line 109. Does it mean the $std$ of the value of pixels in the images?
$\color{blue}{Response1:}$ The $std$ here refers to the standard deviation of the input. For traditional diffusion, it refers to the standard deviation of pixel values. F... | Summary: The paper focuses on accelerating the training process of Diffusion Transformers (DiT) without modifying their architecture. The authors identify two primary issues: inconsistent performance of certain training strategies across different datasets, and limited effectiveness of supervision at specific timesteps... | Rebuttal 1:
Rebuttal: $\color{red}{Question1:}$ The experiments were conducted on 256-resolution ImageNet only. It would be interesting to validate the proposed method on larger resolutions (such as 512, 1024, etc.). Acceleration is more critical to those scenarios.
$\color{blue}{Response1:}$ Thanks for your suggestio... | Rebuttal 1:
Rebuttal: ## Response to All Reviewers
We appreciate all of your valuable feedback. Your valuable suggestions have significantly improved our manuscript.
All reviewers have suggested that we conduct additional experiments to demonstrate the effectiveness and generalization ability of our method. As reques... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents observations on training strategies for Diffusion Transformers (DiT) using Signal-to-Noise Ratio (SNR) Probability Density Function (PDF) analysis. Through extensive experiments, the authors derive insights into training performance and robustness. Based on these findings, they propose a me... | Rebuttal 1:
Rebuttal: $\color{red}{Question1:}$ The paper lacks experiments with higher resolutions such as 512x512 or 1024x1024, which could provide insights into the method's scalability for more complex image generation tasks.
$\color{blue}{Response1:}$ Thanks for the suggestion. As requested, we have conducted exp... | null | null | null | null | null | null |
Unrolled denoising networks provably learn to perform optimal Bayesian inference | Accept (poster) | Summary: This work seeks to understand why neural network-based approaches for inverse problems can outperform methods that incorporate hand-crafted priors. In the Bayesian setting, it is known that when the true prior is available, the optimal estimator (in a mean square sense) is the conditional mean. However, in pra... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and remarks.
- **On the smoothness and product distribution assumption**: Please see the global response for the discussion about the assumptions on the prior.
- **On the notion of the complexity of denoisers**: We think that this confusion i... | Summary: This work investigates unrolling approximate message passing (AMP) for solving compressive sensing under Gaussian design and separable prior distribution. The unrolling scheme consists of parametrizing the AMP denoiser at each iteration by the layer of a neural network, which is sequentially trained using obse... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments.
- **On a relatively niche topic within the NeurIPS community**: While understanding the Bayes-AMP algorithm in itself is a relatively niche area for the NeurIPS community, our theoretical results show that the learnability of Bayes-AMP amounts to... | Summary: This paper studies the theoretical capabilities of unrolled denoising networks in the context of compressed sensing and rank-one PCA problems, with a focus on experimental validation. The authors present the first proof of learning guarantees for neural networks based on unrolling Approximate Message Passing (... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their effort in reviewing our work and address their comments below.
- **On the product distribution assumption**: Please see the global response for the discussion about the assumptions on the prior. We have also subsequently expanded experiments to the non-pr... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful reviews and constructive feedback. We address some general comments raised by multiple reviewers here and address the rest of the comments in the individual responses.
- **On the product prior assumption**: We restricted our results to product priors... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Light Unbalanced Optimal Transport | Accept (poster) | Summary: This paper proposes U-LightOT, a lightweight solver for the Unbalanced Entropic Optimal Transport (UEOT) problem. This method uses a Gaussian Mixture approximation for the potential $v_{\theta}(y)$ and measure $u_{w}(x)$. This paper proves that under this approximation, the KL divergence to the ground truth UE... | Rebuttal 1:
Rebuttal: Thank you for your thorough feedback. Please find the answers to your questions below.
**(1) The optimization objective and Gaussian Mixture approximation in Sec 4 are similar to [1].**
Our solver can be considered as the generalization of the one from [LightSB, 1] in the sense that it subsumes ... | Summary: The proposal focuses on developing a fast solver for the unbalanced entropy-regularized optimal (EOT) transport between continuous Radon measures. The authors utilize the dual formulation of unbalanced EOT and use the relationship between the optimal potentials (i.e., the dual variables) and the primal transpo... | Rebuttal 1:
Rebuttal: Thank you for your thorough feedback. Please find the answers to your questions below.
**(1) The paper does not discuss how K and L, i.e., the number of Gaussians in the mixtures, affect the results. The generalization error bound mentions that K and L will appear as constants in the error bound,... | Summary: This work focuses on the largely computationally intractable efforts in unbalanced OT dual form where neural networks are used as a proxy (used as potentials) in order to approximate Wasserstein distances. In this work, the authors set out to significantly reduce this optimization procedure by decomposing the ... | Rebuttal 1:
Rebuttal: Thank you for your thorough feedback. Please find the answers to your questions below.
**(1) Appears to be specific only to the case of having $D_{\text{KL}}$ divergence penalties for the mass constraints. [...] Do you have any intuition if one were to use other penalties beyond $D_{\text{KL}}$ t... | Summary: The paper presents a novel approach to solving the continuous Unbalanced Entropic Optimal Transport (UEOT) problem. The authors introduce a lightweight, theoretically-justified solver that addresses the challenges of sensitivity to outliers and class imbalance in traditional Entropic Optimal Transport (EOT). T... | Rebuttal 1:
Rebuttal: Thank you for your thorough feedback. Please find the answers to your questions below.
**(1) Performance of the method on the UEOT plan between Gaussian distributions, see Janati et al.,2020 [5]**
Thank you for your suggestion. Unfortunately, a comparison of our method's solutions with the analy... | Rebuttal 1:
Rebuttal: Dear reviewers,
thank you for your thorough and detailed reviews! We are highly inspired by the fact that you agree on the importance of our theoretical results (Reviewer bWBu, vYvs), clarity (Reviewer WAcu, bWBu, t9a3, nig3) of our paper and mark the efficiency of the our solver (Reviewer bWBu,... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a lightweight solver for Unbalanced Entropic Optimal Transport (UEOT) that does not rely on neural network parametrization. Instead, the authors parameterize the potential functions of UEOT using Gaussian Mixture Models (GMM). This parametrization enables the derivation of a tractable joint... | Rebuttal 1:
Rebuttal: Thank you for your thorough feedback. Please find the answers to your questions below.
**(1) The method of parameterizing the potential function using GMMs was already proposed in LightSB [1]. The only change here is the switch to a UOT objective, making the methodological contribution minimal.**... | null | null | null | null | null | null |
Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics | Accept (poster) | Summary: This paper proposes a novel approach for effectively capturing spatial and temporal correlations in multivariate time series forecasting and enhancing the interpretability of prediction models. The aim is to address the shortcomings of existing methods, which often fail to adequately reflect dynamic spatial co... | Rebuttal 1:
Rebuttal: >Q1 The proposed model may be complex to implement due to the use of structured matrix bases and singular value decomposition, potentially leading to a steep initial learning curve in practical applications. Does the appendix indicate similar results for datasets other than Electricity?
We would... | Summary: This paper presents a multivariate forecasting model underlined by a dynamic spatial structure generation function, enabled by SVD-based parameterization and theoretically-bounded output space. Experiments on six commonly benchmark datasets in comparison to several existing baselines demonstrated the overall i... | Rebuttal 1:
Rebuttal: >Q1 The obtained performance gain (Table 1) was overall marginal (up to 2-3 decimal points). Adding statistics to the results over different random seeds of experiments is important.
> Clarification on the observed margin of performance improvements and statistics/error bars to the results will be... | Summary: This paper presents a method to learn dynamic spatial structures in spatio-temporal forecasting tasks. Specifically, it proposes to parametrize the dynamic structures with a convex combination of fixed matrix bases, and the bases are further confined to be in the same coordinate system. Beyond that, it also im... | Rebuttal 1:
Rebuttal: >Q1 The forecasting horizon in the experiments is quite different from the setting in some baselines, such as PatchTST and FEDformer. While long-term forecasting is not major claim of the paper, I wonder if the method scales well with prediction length.
Thanks for your comments. Indeed, long-ter... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their precious comments and valuable suggestions. Our major points of view are summarized as follows.
- We conducted **additional experiments with a forecasting horizon of 96**.
- We provide explanations on **the choice of temporal encoding function and the p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Community Detection Guarantees using Embeddings Learned by Node2Vec | Accept (poster) | Summary: The paper presents theoretical results for the quality of embeddings learned by random walk based methods like DeepWalk and its generalization node2vec. More precisely, the main result shows that embeddings learned node2vec, and DeepWalk as a special case of node2vec, preserve the community structure of graphs... | Rebuttal 1:
Rebuttal: Thank you for your detailed reading of our work. Below are our responses to some of the weaknesses and questions about the paper.
## Weaknesses
> The considered problem is too specific...
It is true that there are other frequent applications of embeddings, which our paper does not focus on (wit... | Summary: Theoretical results showing the consistency of node2vec embedding for community detection in SBMs and DCSBMs using k-means clustering. Versions of this problem have been approached in other work, for example, node2vec rephrased as a matrix factorisation problem [39], and rank $d$ approximations for DeepWalk [... | Rebuttal 1:
Rebuttal: Thanks for your detailed reading of our paper and the constructive feedback.
## Weaknesses
> ... how this paper extends the
results in [10] and [11] ...
The comparison in the paper was limited due to space constraints, and we will expand the discussion. In summary, [10] and [11] study node2vec ... | Summary: The performance of node2vec for community detection in the degree-corrected stochastic block model is studied. It is proven that for sufficiently sparse DCSBMs, node2vec followed by $k$-means results in a clustering with only a vanishing fraction of misclassified vertices. Experiments are performed to demonstr... | Rebuttal 1:
Rebuttal: Thanks for your careful reading of our paper and pointing out some further details which can improve it. Below we address each of the mentioned weaknesses and questions.
## Weaknesses
> The presented results are interesting, but not much intuition is provided.
We aren't certain whether this is... | null | null | Rebuttal 1:
Rebuttal: We would again like to thank each of the reviewers for their time and effort in reviewing our paper, and their thoughtful comments and feedback - particularly around typos and some parts of the paper which could be made clearer - which will help us to improve our paper. We have responded to the co... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DeiSAM: Segment Anything with Deictic Prompting | Accept (poster) | Summary: This paper introduces a new reasoning-based segmentation by adding deictic logic as prompts into the SAM. Motivated by the deictic logical analysis, the authors present DeiSAM, a combination of large pre-trained neural networks with differentiable logic reasoners for deictic promptable segmentation. DeiSAM fir... | Rebuttal 1:
Rebuttal: We thank the reviewer for the fruitful feedback and for acknowledging that the paper is well-written and that combining first-order logic with prompting is interesting.
We would like to address concerns next.
> Motivation is not new. Performing reasoning segmentation via LLM is not new. There se... | Summary: This paper proposes DeiSAM to enhance the ability of current state-of-the-art referring segmentation frameworks with deictic prompting. DeiSAM is an innovative approach that combines large pre-trained neural networks with differentiable logic reasoners to perform image segmentation based on complex, deictic te... | Rebuttal 1:
Rebuttal: We thank the reviewer for the fruitful feedback and for acknowledging that the proposed framework is novel and effective and the empirical results are valid.
We would like to address the concerns raised by the reviewer.
> The proposed method should also be evaluated on other referring image segm... | Summary: The paper proposes to study Deictic references/prompts, i.e., phrases/references that describe the role/purpose/context rather than naming the object directly. Firstly, the paper constructs a new dataset with deictic prompts based on Visual Genome; Next, it is shown that existing methods do poorly on this task... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and fruitful feedback and for acknowledging that our findings are insightful, the experimental setups are meaningful, and the paper is well-written.
We address the concerns next.
> W1: many examples from the constructed dataset are unnatural by removing all... | Summary: The manuscript proposes DeiSAM, a framework that integrates large pre-trained neural networks with differentiable logic reasoners to address deictic promptable segmentation.
DeiSAM leverages large language models (LLMs) to generate first-order logic rules and performs forward reasoning on generated scene grap... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comment and for acknowledging that the paper addresses limitations in neural segmentation models and is well-structured and easy to read.
We address the concerns next.
> Over-simplified Assumptions: How would the method handle a prompt to segment the white... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and insights on the paper. Here, we would like to address concerns shared by several reviewers.
Reviewer WXVv and Reviewer 9P1c:
> What is the benefit of integrating (differentiable) logical reasoners into segmentation models?
The primary adv... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Axioms for AI Alignment from Human Feedback | Accept (spotlight) | Summary: Authors argue that for RLHF, the preferences are pairwise and we need to train a model that respects the preferences in aggregate which is in scope of social choice theory. Then they evaluate different aggregation methods on if they respect well established axioms. They showed that the popular TB model does no... | Rebuttal 1:
Rebuttal: > Could you cite the places where you obtained the axioms of social choice theory?
We consider canonical axioms from the social choice literature. See, for instance, "The Handbook of Computational Soical Choice," Chapter 2. We are happy to add further references in a revision.
> Since you did no... | Summary: Recent months have seen a flood of concurrent papers studying the relationship between RLHF, preference aggregation, and social choice theory. This paper joins these lines of work and studies how to aggregate diverse human preferences (in the context of RLHF) that are modeled as a random utility model (e..g., ... | Rebuttal 1:
Rebuttal: > The sampling strategy over voters and candidates (prompt/responses) is far from obvious and not discussed in the paper. Any additional comments about how such a sampling strategy would look like would be appreciated.
We believe you are referring to this passage in the discussion: "However, the ... | Summary: The paper presents a axiomatic social choice framework for the problem of doing RLHF on group preferences. It shows that classical RLHF (and indeed a wider class of similar methods) violates the Pareto Optimality and Pairwise Majority Consistency axioms, and shows (via explicit construction) that there are mec... | Rebuttal 1:
Rebuttal: We appreciate your discussion of limitations. You raise excellent points on the challenges here (and some relate to discussions we have had amongst ourselves!). With regard to your direct questions:
> Do you think the PO and PMC axioms (possibly along with majority consistency, winner monotonicit... | Summary: The paper proposes an axiomatic approach to study preference aggregation for AI alignment. Inspired by works in social choice theory, the authors investigate a paradigm that they call linear social choice where preferences are representable by a linear model. In this context, they notably prove that if the lin... | Rebuttal 1:
Rebuttal: > Could the authors comment on how only trying to recover part of the full ranking would impact their results?
We believe our work addresses this concern. The prompt/response datasets are derived from sampling an LLM, ensuring the corresponding feature vectors are already in a reasonable region o... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UniIF: Unified Molecule Inverse Folding | Accept (poster) | Summary: This paper proposes representing molecular systems as blocks of atoms and designs a neural network architecture that processes such representations. The method is applied for benchmark inverse folding tasks for protein/RNA. A materials-related task is also explored.
Strengths: - The proposed method seems to p... | Rebuttal 1:
Rebuttal: **W1** The protein inverse folding ablation seems to show the various introduced techniques do not influence model performance much. The insights of the models seem obscured from this perspective, especially considering block-based representations and architectures have already been explored in pr... | Summary: This paper propose a unified framework for inverse folding. Their model is applicable to small molecules, proteins and RNA.
More specifically, they propose a unified frame representation for amino acid, atom and nucleotide. They further propose a GNN-based model to learn the structure information.
Results sho... | Rebuttal 1:
Rebuttal: > **W1** Some important experimental details are missing. For instance, while the paper proposes a unified inverse folding framework, it's unclear whether the model is trained on a combined dataset from different types of molecules. Specifically, the training sets for RNA design and material desig... | Summary: Previous approaches on molecule inverse folding (IF), which is crucial for drug and material discovery, focus separately on either macro-molecules or small molecules, leading to the lack of a unified approach for different molecule types. To this end, the paper proposes UniIF to unify the IF from a data and m... | Rebuttal 1:
Rebuttal: > **W1** The ablation experiments in Table 1 (-GDP), which remove the geometric dot product features, show miner drawbacks in proteins with longer sequences. This makes the geometric interaction extractor mainly capture the interaction of the virtual inter-atoms. The idea for capturing long-range ... | Summary: This paper addresses the challenge of inverse folding, i.e. the design of novel molecules or macromolecules with specific desired 3D structure, with the goal of improving real-world drug and material design. The authors point out that this challenge has been addressed independently for different contexts, such... | Rebuttal 1:
Rebuttal: > **W1:** It is not clear that the unification is responsible for any performance improvements.
>> **Reply** The proposed unification method is responsible for performance improvements by using the block-level representation and modules, such as geometric featurizer, interaction operator, and vir... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Return of Unconditional Generation: A Self-supervised Representation Generation Method | Accept (oral) | Summary: The paper proposes a framework, coined Representation-Conditioned Generation (RCG), that aims to bring the advantages of Conditional Generation techniques to Unconditional Generation settings.
To do so, they use the output of a representation generator network instead of class labels.
This representation gener... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our method, extensive and robust experiments, and the potential to open up interesting research directions. Below we address the weaknesses (***W***) and questions (***Q***) raised by the reviewer. We hope the reviewer could consider raising the score given t... | Summary: This paper proposes generative models conditioned on representation obtained from a pre-trained self-supervised encoder to achieve high-quality diverse generation.
Strengths: 1. The writing is clear.
2. The experimental results demonstrate significant improvement over unconditional generation.
Weaknesses: 1... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our clear writing and strong experiment results. Below we address the weaknesses (***W***) and questions (***Q***) raised by the reviewer.
***Importance of the studied problem***
We respectfully disagree with the reviewer that “the studied problem has been ... | Summary: This paper proposes RCG, a novel framework to enhance unconditional image generation by leveraging self-supervised representations. The main idea is first to generate self-supervised representations unconditionally using a pre-trained encoder and then condition the image generation on these representations. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our motivation, novel idea, and significant performance improvements. Below we address the question raised by the reviewer.
***Why MoCo-v3 is the best representation for RCG***
We compare self-supervised representations from different methods in Table 7(a),... | Summary: This paper proposes a very simple unsupervised image generation framework that does not rely on human labeled annotation without compromising generation quality. This framework has two stages: i) representation generator learning and ii) image generator learning. The representation generator is trained in the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our simple but effective framework, the strong experiment results, and the great presentation of our paper. Below, we address the two questions (***Q***) raised by the reviewer.
***Training the image generator using ground-truth representations vs. generated... | Rebuttal 1:
Rebuttal: We thank all reviewers for providing lots of insightful and constructive feedback. We will definitely improve our manuscript accordingly. We are glad to see the commonly recognized strengths highlighted by the reviewers:
1. The presentation of the paper is clear and concise (zDRz, fB14, pEoh).
2... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Oja's Algorithm for Streaming Sparse PCA | Accept (poster) | Summary: This paper studies the problem of finding the top eigenvector from samples.
Given $n$ samples drawn from a distribution whose mean is $0$ and covariance matrix is $\Sigma\in\mathbb{R}^{d\times d}$, we would like to find a vector $\hat{v}$ based on these $n$ samples such that $\hat{v}$ and the top eigenvector o... | Rebuttal 1:
Rebuttal: Thank you for your kind words regarding the motivation of our problem and the simplicity of the presentation. We address your primary concerns below:
**[Re: Novelty of fundamental ideas]:** While our algorithm builds on top of Oja’s algorithm, hard thresholding of Oja’s vector for Sparse PCA has ... | Summary: The work proposes a one pass Ojas' algorithm which can achieve minimax error bound for high dimensional sparse PCA under standard technical conditions.
Strengths: The paper is extremely well written. The proposed one-pass Oja's algorithm is novel with detailed convergence analysis and convincing numerical exp... | Rebuttal 1:
Rebuttal: Thank you for your kind words regarding our presentation, the novelty of our algorithm, the motivation of our problem, and the contribution to literature. We address your primary concerns below:
**[Re: Top-k principal components]** Recent results provide a black-box way to obtain k-PCA given an a... | Summary: The paper studies the problem of streaming sparse PCA under iid data. That is, we have $x_1,...,x_n \sim \mathcal D$ iid vectors in $\mathbb R^d$ which are revealed to us in an online fashion. We want to estimate the top eigenvector of $\Sigma = \mathbb E[xx^\intercal]$. We assume that this top eigenvector $v_... | Rebuttal 1:
Rebuttal: Thank you for your kind and detailed feedback and comments about the clarity and presentation of our work, the novelty of our analysis, and the significance of our contribution to the Sparse PCA literature. We will correct all the typographical issues pointed out and will not address them here ind... | Summary: The paper studies Principal Component Analysis with O(d) space and O(nd) time, where n is the number of datapoints and d is their dimensionality.
The authors provide the first single-pass algorithm that under a general \Sigma matrix whose top principal vector is s-sparse, manages to find a close enough vector... | Rebuttal 1:
Rebuttal: Thank you for your kind words regarding the problem statement considered, the simplicity and performance of our algorithm, and the novelty of our analysis. We will correct the matrix multiplication constant to ~2.372 based on recent developments (see [a]).
References:
[a] Williams, Virginia Vas... | Rebuttal 1:
Rebuttal: We want to first thank all the reviewers for their valuable suggestions and insightful feedback. We believe we have addressed nearly all of their main technical questions. In what follows, we will address some important points each reviewer has raised. We will correct all the typographical issues ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
InversionView: A General-Purpose Method for Reading Information from Neural Activations | Accept (poster) | Summary: The paper introduces the method InversionView, which finds inputs that give rise to similar activations (the preimage). As the preimage grows exponentially with sequence length the authors train a conditional decoder to generate inputs from a target activation.
The authors show three use cases: a character cou... | Rebuttal 1:
Rebuttal: Dear reviewer fZCU,
Thanks for your feedback on our paper!
## Reply Regarding Questions
### Questions 1-3: writing and demonstrating suggestions
Thanks for your suggestions. Most importantly, we have created two figures describing the decoder and the workflow, included in the global rebuttal P... | Summary: The method proposed in the paper seeks to decipher the information encoded within neural network activations. The core idea is to examine subsets of inputs that produce similar activations, utilizing a trained decoder model conditioned on these activations. The authors perform their analysis on three different... | Rebuttal 1:
Rebuttal: Dear reviewer P3CM,
Thanks for your feedback on our paper!
## Reply Regarding Questions
### It is possible to use other distance metrics besides L2-based metrics? Why do you believe the L2 distance is effective in this context? Is there something unique about the geometric space?
Yes, we thi... | Summary: This work is mainly based on the representational geometry of activations in the activation space and chooses samples whose distances are within a defined $\epsilon$-preimage distance. A single two-layer transformer decoder model is trained using activations of each layer from the investigated model. After fin... | Rebuttal 1:
Rebuttal: Dear reviewer vTBG,
Thanks for your feedback on our paper!
## Reply Regarding Questions ##
### In line 115，what does $\mathbf{z}$ represent? ###
Sorry for not writing it clearly. The $\mathbf{z}$ is the same as $f(\mathbf{x})$ in the previous part of the paper. So it simply represents an arbit... | Summary: In this paper, the authors propose InversionView, a method to inspect the information encoded in neural activations. The proposed method is based on checking the activations difference given different inputs. The authors showcase the effectiveness of this tool on mainly three tasks: character counting, indirec... | Rebuttal 1:
Rebuttal: Dear reviewer Mv2t,
Thanks for your feedback on our paper!
## Reply Regarding Weaknesses
### The precise algorithm for the proposed method is unclear
In the global rebuttal, we provide a new figure showing the training and sampling pipelines. In its caption, we also provide detailed explanati... | Rebuttal 1:
Rebuttal: We thank all reviewers for their reviews. We are encouraged that they found our method to be effective (Reviewer Mv2t) and to provide useful and intuitive insights (Reviewer vTBG, P3CM, fZCU), our experiments solid (Reviewer vTBG), and the paper well-written and easy to follow (Reviewer Mv2t).
We... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns | Accept (spotlight) | Summary: This paper proposes a novel and effective technique to enhance long-term time series forecasting. The proposed technique, Residual Cycle Forecasting (RCF), directly models periodic cycles with learnable parameters, decomposing the learning of time series into periodic cycles and residual components. The residu... | Rebuttal 1:
Rebuttal: **Thank you for your valuable comment!**
> **W1:** The notations are not strictly consistent. The notations for instance normalization (in Section 3.2 and Algorithm 2) are independent from the whole framework and not consistent with the previous problem definition.
Thanks for pointing this out. ... | Summary: The paper introduces CycleNet, a novel time series forecasting method that enhances long-term prediction accuracy by explicitly modeling the inherent periodic patterns present in time series data. The core contribution of paper is introducing the Residual Cycle Forecasting (RCF) technique, which leverages lear... | Rebuttal 1:
Rebuttal: **Thank you for your kind and careful review!**
> **W1:** In the introduction, the author's statement establishes a close relationship between long-term prediction and periodic information. Etc.
**In fact, periodic information is indeed one of the most important factors for achieving long-term f... | Summary: This paper presents a novel technique for improving the accuracy of multivariate long-term time series forecasting. The technique, called Residual Cycle Forecasting (RCF), involves learning the cyclical patterns of time series through recurrent cycles, which can be used as a pre-processing step for any forecas... | Rebuttal 1:
Rebuttal: **Thank you for your detailed and thoughtful review!**
> **W1:** Lack of clarity of the source of results.
We will clarify this in the main text of the revised paper. Previously, we clarified this in Appendix A.4.
> **W2 & W3 & Q1: Baseline:** Add more appropriate baselines to correctly positi... | Summary: This paper proposes a learnable Seasonal-Trend Decomposition method (CycleNet) to improve the prediction performance of current long-term multivariate time series forecasting models. Specifically, it firstly model the periodic patterns of sequences through globally shared recurrent cycles and then predicts the... | Rebuttal 1:
Rebuttal: **Thank you for your valuable comment!**
> **W1:** It seems that the proposal (CycleNet) does not work well for complex datasets, e.g., Traffic. It is better to show more results on the same kind of datasets like the PEMS datasets used in the iTransformer paper.
The current CycleNet did not achi... | Rebuttal 1:
Rebuttal: **Dear AC and Reviewers,**
**Thank you very much for your time and effort in reviewing our submission.** The valuable comments provided are highly beneficial for improving the quality of our paper.
In this paper, **we propose the RCF technique**, which *utilizes learnable recurrent cycles to ex... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation | Accept (poster) | Summary: This paper tackles the problem of inverse rendering, which reconstructs geometry, material, and environmental lighting from a set of posed images with fixed lighting. It proposes two contributions: 1) representing pre-integrated illumination as a single MLP, 2) approximating self-occlusion on pre-integrated li... | Rebuttal 1:
Rebuttal: The shader uses global (indirect) illumination. We decided to evaluate using blender's PBR shader, as done in previous works, since our aim is to generate relightable meshes for use in existing rendering pipelines. Evaluating the rendering quality from one such pipeline is the most direct way of b... | Summary: This paper introduces a method for digitizing real-world objects by estimating their geometry, material properties, and environmental lighting from posed images under fixed lighting. Integrating image-based lighting techniques into Neural Radiance Field (NeRF) pipelines, the method uses a scene-specific MLP fo... | Rebuttal 1:
Rebuttal: We only adress the albedo prediction since albedo is the only material property that is shared across most commonly used BRDF models. The synthetic datasets we rely on come from 3D models which were hand-designed using a variety of complex BRDF models with properties which can't be directly transl... | Summary: The paper introduces SplitNeRF, a method that integrates the split sum approximation into Neural Radiance Field (NeRF) pipelines. This approach optimizes object geometry, material properties, and environmental lighting. The method employs a Multi-Layer Perceptron (MLP) to model scene-specific pre-integrated im... | Rebuttal 1:
Rebuttal: Estimating geometry together with materials and illumination is a very complex and unconstrained problem. Because of this, the optimization can sometimes get stuck in local minima. We believe that is happening for those scenes. For example, it is possible to model reflections via small variations ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Fractal Patterns May Illuminate the Success of Next-Token Prediction | Accept (poster) | Summary: This paper provides a detailed application of ideas from fractal geometry to natural language data, using language models to compute the relevant information-theoretic properties. They find, in particular, tell-tale evidence of self-similarity (common structure across scales) and long-range dependencies. A p... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We especially appreciate the positive assessment of the thoroughness, originality, and importance of our work. We hope that our rebuttal below addresses all of the reviewer’s questions, are happy to provide more details, ... | Summary: This paper introduce a new perspective to that language is self-similar and predictability and self-similarity together imply long-range dependency, based on empirical analysis across different scales of LMs and information theoretic views. This new perspective may enable us to understand the strong capabiliti... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We especially appreciate the positive assessment of the thoroughness, originality, and importance of our work. We hope that our rebuttal below addresses all of the reviewer’s questions, are happy to provide more details, ... | Summary: The paper draws connections between fractal patterns and language by evaluating properties such as self-similarity and long-range-dependency. Using a range of LLMs, they estimate the Holder exponent, Hurst parameter and fractal dimension for language from different domains, including web text, code, and math p... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We especially appreciate the positive assessment of the thoroughness, originality, and importance of our work. We hope that our rebuttal below addresses all of the reviewer’s questions, are happy to provide more details, ... | Summary: In this paper, the authors try to reveal the existence of fractal structures to handle language in language modeling using recent language models based on the next token prediction. For that purpose, the authors rely on several aspects of fractal structures, which are self-similarity, long-range dependence, an... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the careful and insightful feedback. We hope that our rebuttal below addresses all of the reviewer’s questions, are happy to provide more details, and look forward to the reviewer’s response to our rebuttal.
**The baseline**
The baseline we use in Figure 1... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful and constructive feedback. We appreciate the positive feedback on the thoroughness, originality, and importance of our work.
**Presentation**
We will incorporate the reviewers’ suggestions in the revised version of the paper. This includes p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection | Accept (poster) | Summary: This paper introduces a novel event-based object detection network by processing event-based data as graph data. After incorporating an SSM that acts as the selection role, graph convolution neural networks, and various attention modules, the proposed pipeline achieves good results while retaining high efficie... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We have carefully revised the manuscript based on your suggestions, particularly regarding the clarity of function definitions and the roles of specific modules. We hope these revisions meet your expectations and ask for your reconsideration during the rev... | Summary: The paper proposes a novel event-based graph spatiotemporal sensitive network (EGSST), which is the first work using Graph Transformer for object detection tasks on event cameras. This work primarily involves two key innovative modules: a spatiotemporal sensitivity module (SSM) and an adaptive temporal activat... | Rebuttal 1:
Rebuttal: **W1.** We greatly appreciate your evaluation and suggestions regarding our integration of Graph and Transformer technologies. In our integrated framework, there are two key aspects:
1) Interaction Mechanism between SSM and TAC: The Spatiotemporal Sensitivity Module (SSM) we designed processes ev... | Summary: This paper uses graph structure to model event data and realize event classification. Spatiotemporal Sensitivity Module (SSM) and an adaptive Temporal Activation Controller (TAC) mimic the response of the human eyes in dynamic environments by selectively
activating the temporal attention mechanism based on the... | Rebuttal 1:
Rebuttal: **W1.** Thank you for your attention to our choice of datasets. We acknowledge that N-CARS [7] is a significant dataset for event classification tasks. However, our research focuses on event-based object detection, which is distinct from event classification. Literature [1,2,3,4] supports our use ... | Summary: The paper introduces a novel event-based graph spatiotemporal sensitive transformer framework aimed at enhancing the efficiency of object detection in dynamic vision systems. This framework leverages the unique properties of event camera data by modelling event data through a graph structure and incorporates k... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback on our paper. We have revised the manuscript based on your suggestions and hope the changes meet your expectations. Please feel free to contact us with any questions or further information you might need during the review process.
**W1.** Regarding your sugg... | Rebuttal 1:
Rebuttal: We thank all reviewers for their very informative feedback. We have responded separately to each reviewer and attached a PDF file with figures and tables to enhance our rebuttal. Additionally, considering the page limitations of the PDF, we have briefly presented the new experimental data from Tab... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Generalization and Convergence by Enhancing Implicit Regularization | Accept (poster) | Summary: This paper proposes a new optimization scheme for deep learning. The idea is to periodically estimate the Hessian diagonal and use a larger learning rate on the smaller-curvature parameters. The paper argues that this algorithm enhances the implicit curvature regularization of the optimization algorithm whi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of our work and insightful comments. Below, we address the reviewer’s questions in detail.
- **W1.** One weakness is that the motivation comes from the stylized example in section 2, which may be unrealistic for general deep learning optimization. For ex... | Summary: This paper proposes an Implicit Regularization Enhancement (IRE) framework to accelerate the convergence of optimization algorithms towards flat minima in deep learning, thereby improving generalization and convergence. The key idea behind IRE is to decouple the dynamics along flat and sharp directions in the ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our work and helpful comments. Below, we offer detailed responses to the reviewer’s questions:
- **W\&Q1. Comparison with related works.**
**Response:** We thank the reviewer for this question and will provide a more detailed comparison with related... | Summary: The authors propose IRE to enhance the implicit regularization of base optimizers, thereby improving the generalization and convergence in deep learning. IRE decouples the dynamics of flat and sharp directions, reducing sharpness along flat directions while maintaining stability in sharp directions. The paper ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of our work and helpful comments. Below, we offer detailed responses to the reviewers questions.
- **W1.** The improvements of IRE in Table 1 and 7 are not as significant as expected. It makes one wonder about its usefulness for CNN networks and its overse... | null | null | Rebuttal 1:
Rebuttal: ### **Global Response to All Reviewers.**
- We express our sincere gratitude to all reviewers for appreciating our results, i.e,
- A novel algorithm framework (IRE), which can improve both generalization and optimization, by enhancing the implicit regularization.
- Experimentally, IRE consist... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning Macroscopic Dynamics from Partial Microscopic Observations | Accept (poster) | Summary: This paper introduces an efficient framework for learning macroscopic dynamics of complex systems. The training data consists of microscopic configurations with only partial dynamics, i.e., time derivatives of only a small subset of the microscopic variables. The paper shows how this information nevertheless a... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful feedback. Below are our responses:
**Q: Plot loss curves over the course of a training run showing the behavior of $L_x$ versus $L_z$. This will give the reader a much better intuition for how these different loss functions behave.**
A: Thanks fo... | Summary: The authors aims to use ML to compute macroscopic dynamics of a system from partially observed microscopic dynamics. The paper defines the macroscopic dynamics to be the lower dimensional latent space of an autoencoder that encodes the microscopic system. Given the autoencoder they train a macroscopic dynamics... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful feedback. Below are our responses:
**Q: What does closed mean?**
A: Let the state of a system be $z$. When we say the system is closed, we mean the dynamics of the system can be written in the following form:
$$
\dot{z} = f(z)
$$where $f$ is a fu... | Summary: The authors describe a method to efficiently obtain aggregate information about forces acting on all particles in a system, in an effort to compute dynamics of macroscopic (aggregated) quantities. The key idea described in the paper is to sub-sample the particles to a small set, and only compute the forces on ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful feedback. We have corrected all the minor issues and typos that you pointed out. We have also cited the previously missing literature you mentioned. Below are our detailed responses to the questions:
**Q: Equation 2 is not precise enough. What is... | null | null | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for providing detailed reviews and constructive feedback that have improved the paper.
Reviewer d4vx mentioned some relevant literature. We would like to compare these studies with our research to provide deeper insights into our method.
**[A]** A1 includes... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
IF-Font: Ideographic Description Sequence-Following Font Generation | Accept (poster) | Summary: This paper presents a method that generate Chinese glyphs using token prediction approach. The core contribution is to leverage the concept called Ideographic Description Sequence and develop a network architecture to generate IDS that represent the target character.
Strengths: The strengths of this paper are... | Rebuttal 1:
Rebuttal: ### Q1: what is the output of the network before putting the output together as a glyph? Is it composed of multiple tokens?
Yes, the output sequence consists of multiple tokens. Please refer to the right of Figure 1, where the small green squares around Transformer Decoder are tokens (vector quant... | Summary: This paper approaches the task of Few-shot Font Generation (FFG) for Chinese characters, proposing to model the target glyph with Ideographic Description Sequence (IDS) tokens to achieve style-content disentanglement. Reference font images and the target character’s IDS are fed into a VQGAN-based pipeline to d... | Rebuttal 1:
Rebuttal: ### Q1: The differences in most metrics seem quite small
On one hand, we attribute this to **the advantages brought by the new paradigm**. The usage of IDSs and VQ-token based decoder have already achieved good results, making our baseline strong.
On the other hand, this is due to **marginal effe... | Summary: This paper proposed IF-Font handles the task of few-shot font generation via a VQ-GAN based framework. Compared to most existing methods that encode content images, IF-Font only encodes Ideographic Description Sequence (IDS) to convey content information of target characters. Experimental results show the prop... | Rebuttal 1:
Rebuttal: ### Q1: The key difference between IF-Font and other methods that utilize component/stroke information
We believe the key difference lies in **whether style-content disentangling is performed**. Previous methods use component/stroke information but still relied on separating and combining correspo... | Summary: IF-Font introduces a novel approach to few-shot font generation by using Ideographic Description Sequence (IDS) instead of traditional source glyphs to control the semantics of generated glyphs. This method quantizes reference glyphs into tokens and models the token distribution of target glyphs using IDS and ... | Rebuttal 1:
Rebuttal: ### Q1: The advancement and necessity of our method
Font generation differs from general image generation tasks. **Our contribution lies in finding a way to describe ideographic characters as "text" and successfully applying it to font generation**, solving the problem of low-quality generation i... | Rebuttal 1:
Rebuttal: We attempted our best to address the questions as time allowed. We believe the comments & revisions have made the paper stronger and thank all the reviewers for their help. Please find individual responses to your questions below. The PDF file for the figures is attached to this general response.
... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transformer Doctor: Diagnosing and Treating Vision Transformers | Accept (poster) | Summary: This paper proposes a vision transformer diagnosing and treating framework, namely Transformer Doctor, to reveal the problems that bring about negative impact on the network performance and fix them. The paper firstly proposes the information integrating hypothesis, which argues that transformers do informatio... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on this work. We are pleased that you highlighted the core of our research, which is designing a potential method to explain why Transformers may not perform well and attempting to address these issues to improve network performance.
We acknowledge that there ... | Summary: Inspired by the information integration mechanisms and conjunction errors in the biological visual system, this paper investigates the error mechanisms within Transformers. Through a comprehensive analysis and experimental validation of the computational processes of the two core modules of Transformers, MHSA ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments. We are pleased that you find our work combining Transformers with biological vision error mechanisms to be novel and that you find our methods and findings insightful and inspiring. Below are our responses to each of your comments (each of your comments is h... | Summary: This study introduced a framework, namely Transformer Doctor, to reduce internal errors, e.g. conjunction errors, in a general vision transformer model. Building upon the information integration hypothesis, the proposed method performs several constraints, including heuristic dynamic constraints and rule-drive... | Rebuttal 1:
Rebuttal: Thank you for your diligent review and comments. We are pleased that you find this research on improving Vision Transformer both interesting and practically valuable. We are also glad that you consider our biologically inspired approach to be reasonable. Below are our responses to each of your com... | Summary: This paper presents Transformer Doctor, which diagnoses the issues with the Transformer attention mechanism and resolves them via several information integration hypotheses. The primary motive of the paper is to identify the source of incorrect information aggregation, which leads to erroneous predictions, and... | Rebuttal 1:
Rebuttal: Thank you for your diligent review and comments. We are pleased that you found the paper well-written and appreciated our step-by-step introduction of the proposed methods and hypotheses. Your recognition of our diagnostic hypotheses and the potential of the proposed information integration mechan... | Rebuttal 1:
Rebuttal: Dear Reviewers ZXFK, BM6N, 3nqh, and KQuQ,
Thank you for your diligent reviews and constructive feedback. We particularly appreciate your recognition of the novelty and insightfulness of our work and are pleased that you find our approach of integrating error mechanisms from biological vision wit... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization | Accept (poster) | Summary: This paper studies the effect of large learning rates for near-homogeneous logistic regressions. Especially, it extends previous results on linear predictors under large learning rates (an EoS phase and a stable phase for the convergence of GD; faster convergence) to nonlinear predictors with Lipschitzness and... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback. We answer your questions as follows.
---
**Q1**. The role and effect of large learning rates, especially compared to small learning rates, may need more illustration.
**A1**. Corollary 4.2 and Theorem 4.3 together show a separation between large and small ... | Summary: This work analyzes dynamics of large step-size for GD under logistic loss for non-homogenous two layer networks. They characterize two phases, first in which empirical risk oscillates and then monotonically decreases in the second phase. Additionally, they show
1) normalized margin grows nearly monotonically i... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your concerns below.
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**Q1**. However, these conditions do not hold for Relu network (assumption 1.B and 1.C breaks), which probably is most used non-linear activation out there. Infact there are a bunch of non-differentiable activation functions for w... | Summary: This paper studies GD for nearly-1-homogeneous neural networks with large stepsizes. It provides two main results.
The first one describes the late, *stable phase* of training and can be seen as an extension of Lyu and Li (2020) result to the large stepsize, nearly homogeneous setting. Yet it comes with wea... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your comments below.
---
**Q1**. Is there a specific mathematical challenge (eg wrt Lyu and Li) to prove a KKT point convergence type of result?
**A1**. Good question. The key difficulty is that KKT points with respect to an optimization problem are not... | Summary: This work studies the phase transition (from EoS phase to stable phase) of GD with large step sizes for training two-layer networks under logistic loss. Specifically, the authors proved the following:
- If the empirical risk is below a threshold depending on the step size, GD enters a stable phase where the l... | Rebuttal 1:
Rebuttal: Thank you for your support.
---
**Q1**. Despite studying two-layer neural networks, the main theorems require linear separability of datasets, except for Theorem 2.2.
**A1**. We acknowledge that linear separability is a strong assumption. We use this mainly as a sufficient condition for two-la... | Rebuttal 1:
Rebuttal: Thank you for all your feedbacks. We add two figures in the pdf.
- Figure 1 shows the test accuracy of two-layer networks for CIFAR-10 under the same setting of Figure 2(d)-(f). The
results support the intuition that large stepsizes lead to stronger implicit biases with “nicer“ features.
- Figur... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Discretely beyond $1/e$: Guided Combinatorial Algortihms for Submodular Maximization | Accept (poster) | Summary: This paper studies the fundamental problem of improving the approximation factor for non-monotone submodular maximization subject to cardinality or matroid constraints. In particular, the focus of the work is on designing combinatorial algorithms as opposed to continuous methods based on the multilinear extens... | null | Summary: In this paper, the authors develop a combinatorial algorithm for non-monotone submodular maximization under both size and matroid constraints. Their algorithm uses local search to guide RANDOM GREEDY and INTERPOLATEDGREEDY, providing both a randomized algorithm and a deterministic algorithm. For size constrain... | Rebuttal 1:
Rebuttal: > Both the results and techniques in this paper are somewhat similar to those in the recent paper (https://arxiv.org/abs/2405.13994). It might be beneficial to explain the relationship between your paper and theirs, especially the difference in the techinique ideas.
Thank you for bringing this p... | Summary: This paper studies the classical problem of maximizing a non-monotone submodular function subject to a cardinality and a matroid constraint.
A long line of work has developed approximation algorithms for these problems which achieve a 0.401approximation; however, every algorithm which achieves better than $1/e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. We respond to each point below.
> Paper Clarity: For the most part, the paper is not well organized and can be challenging to read. With the exception of Section 2.2, the writing is a bit rushed and lacking focus. Many times I had to go back and f... | Summary: The paper investigates combinatorial approximation algorithms for constrained submodular maximization problems. Observing the algorithms that pass the $1/e$ threshold are generally carries the problem to the continuous domain, they investigate the answer to the following question: "Is it possible to obtain app... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
> On lines 73-75, you mention "Unfortunately, there is no known method to derandomize continuous algorithms, as the only known way to approximate the multilinear extension relies on random sampling methods." You may want to revise this sentence ... | Rebuttal 1:
Rebuttal: We thank all reviewers for the contructive comments. We hope that we were able to answer the questions of all reviewers in our individual responses. To summarize,
in the next version,
- we will make efforts to improve the readability of the paper by minimizing the necessity of consulting the appe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Transition Matrix-Based Extended Model for Label-Noise Learning | Reject | Summary: This paper studies the problem of learning with noisy labels. To handle the instance-dependent noise, the authors propose an extended model for transition matrix-based methods. Specifically, their model combines a class-dependent transition matrix with a sparse implicit regularization term. The authors provide... | Rebuttal 1:
Comment: Since the authors did not post their responses, I maintain my initial rating. | Summary: In learning from noisy labels, existing methods generally focus on class-dependent (but instance-independent) noise that can be modeled by a transition matrix $\mathbf{T}$. Some methods have also been proposed for instance-dependent noise (modeled by $\mathbf{T}(x)$). This work belongs to the latter. In partic... | Rebuttal 1:
Title: Maintain my initial rating
Comment: Since the authors did not post their responses, I maintain my initial rating. | Summary: In noisy label learning problem, noise is often characterized by confusion matrix. In contrast to instance-independent noise, this work considers a setting where confusion matrices could be different for different samples. Under this setting, the authors proposed to use a global confusion matrix shared by all ... | Rebuttal 1:
Comment: Since the authors did not post their responses, I decide to maintain my initial rating. | Summary: This paper introduces a method that supplements the traditional estimate of a class-dependent transition matrix, which is popular in label-noise learning. Traditional transition matrix methods are less effective for instance-dependent noise. To overcome the limitation, the proposed method adds a residual term ... | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lookback Prophet Inequalities | Accept (poster) | Summary: This paper extends the standard online selection problem by enabling the decision-maker to choose previous items with some discount that is captured by decay functions. The authors analyze the competitive ratio for different observation orders by giving a reduction from general decay functions to simple decay ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our submission. Below we address the raised concerns and questions.
### Weaknesses
* **Upper bounds.** The reduction from the $D_\infty$- to the $\gamma$-prophet inequality relies on using distributions with support {$0,a,b$}, which is a limit... | Summary: **Problem Studied**
This paper introduces the problem of "lookback prophet inequalities", which is a variant of prophet inequalities. In this problem, at the stopping time, instead of always selecting the current item, the algorithm is allowed to select any item that has arrived up to now. However, items in t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our submission. Below we address the raised concerns and questions.
### Weakness
Indeed, many variants of the prophet inequality are used to model specific use cases. For example, 'Learning Online Algorithms with Distributional Advice' (ICML ... | Summary: This paper studies a variant of prophet inequalities where the agent can reuse a previous item at a decaying price. Analyzing three models, adversarial model, random order model, and iid model, this paper gives various lower and upper bounds about the competitve ratio that an algorithm may achieve in these set... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort spent on our submission. Below we address the raised concerns and questions.
### Weaknesses
* **Other models.** We studied the models where the only decision made by the algorithm is the stopping time. The only model not included in our analysis is the... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Graph Diffusion Transformers for Multi-Conditional Molecular Generation | Accept (oral) | Summary: In molecular generation tasks, there is often a desire for the generator to produce molecules that satisfy multiple properties simultaneously. Previous architectural designs did not pay particular attention to the scenario of optimizing multiple constraints in tandem. Therefore, the author has proposed a Multi... | Rebuttal 1:
Rebuttal: # Major 1: Ranking comparison in Figure 1
## Results are updated to show ~$2\times$ improvement
Thank you for your suggestion. We have created a new figure (attached in the rebuttal PDF) based on your feedback. We first identify the maximum ranking position among the three single-conditional gen... | Summary: This paper presents Graph DiT, which initially learns conditions (including categorical and numerical properties) through clustering and one-hot encodings. Subsequently, Graph DiT utilizes a Transformer architecture during the diffusion denoising phase to refine noisy molecular graphs incorporating these condi... | Rebuttal 1:
Rebuttal: # W1, W2: Contribution, limitation, and paper presentation
Thank you for your comment. We can highlight lines 41-43 and 62-64 using italics to emphasize the contributions and organize them into bullet points. Any further suggestions or discussion are highly appreciated.
We discussed limitations ... | Summary: This research introduces the Graph Diffusion Transformer (Graph DiT) for generating molecules with multiple properties, such as synthetic score and gas permeability. Unlike previous models, Graph DiT uses a new noise model and a Transformer-based denoiser to better handle molecular structures. Experiments show... | Rebuttal 1:
Rebuttal: # W1: Limited Demonstration of Multi-Condition Capability
## We evaluate models on up to four conditions, not two
Thank you for your comment. In Table 1, we evaluate all models on up to four conditions: $O_2$, $N_2$, and $CO_2$ permeability.
Based on the above points, we respectfully request a... | Summary: This work proposes Graph Diffusion Transformer (Graph DiT), which is a a molecular generation model based on multi-condition, and diffusion process. Graph DiT enables multi-conditional molecular generation, integrating multiple properties, eg. synthetic score and gas permeability.Graph DiT employs a graph-depe... | Rebuttal 1:
Rebuttal: # W1: Scalability of graph dependent noise
## Subgraph-level noise model is possible for larger graphs
Thank you for your comment. The transition matrix is manageable for molecules, as we only need to model heavy atoms, which usually number less than 50 for a molecule [1].
For larger graphs, we... | Rebuttal 1:
Rebuttal: We appreciate the time and effort of all the reviewers in evaluating our work. In response to the following comments, we have attached a PDF with three figures
1. **RRe9**: "Minor format comment: the color shades in the charts like in Figure 4 are inconsistent with their labels."
2. **hJ6K**: "C... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation | Accept (poster) | Summary: The paper proposed a novel approach, namely normalized outlier distribution adaptation (AdaptOD), to tackle this distribution shift problem, where ID classes are heavily imbalanced, i.e., the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. ... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of the novelty of our approach, empirical justification, problem setting, paper clarity, as your invaluable feedback on further enhancing the work. Please find our one-by-one responses to your concerns below:
> **W1: It would be comprehensive if the accuracy is rep... | Summary: This paper addresses the issue of true OOD samples having distribution shifts in scenarios of Long-tailed Recognition (LTR) with heavy imbalance. To cope with this, the paper proposes normalized outlier distribution adaptation (AdaptOD) with two key components: Dynamic Outlier Distribution Adaptation (DODA) an... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and appreciation of the novelty of our work and its empirical justification. We provide our response to your concerns one by one in the following.
> **W1: The overview of Figure 2 is difficult to understand. Simplifying and highlighting only the necessary ... | Summary: This paper addresses the challenge of OOD detection in LTR scenarios, where the distribution of classes is heavily imbalanced. The key issue highlighted is the absence of true OOD samples during training, which hampers the effectiveness of OOD detectors, especially the significant distribution shift between ou... | Rebuttal 1:
Rebuttal: Thank you for appreciating our contribution and for your thoughtful and detailed feedback. Please find our responses to your concerns below:
> **W1: The goal of the DNE component is to ``enforce more balanced prediction energy for imbalanced ID samples" as line 88, which is similar to BERL**
BER... | Summary: This paper introduced the normalized outlier distribution adaptation (AdaptOD) which adapts the outlier distribution to the true OOD distribution from both the training and inference stages. Such AdaptOD includes two components: one is dynamic outlier distribution adaption (DODA), which adapts the outlier dist... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty of our method and its improvement over existing methods. On the other hand, there might be some misunderstanding regarding the setting, which we will clarify in the following responses and further improve our writing.
> **W1: concern for the setting**
- **Te... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' time and invaluable feedback. We are encouraged that there is a unanimous consensus among all reviewers highlighting our method's effectiveness through extensive experiments. The reviewers also appreciate the importance of the addressed problem (KD4x, t7zA, u... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents | Accept (poster) | Summary: The paper introduces a novel approach for behavior tokenization in agent domains, utilizing a unified token transformer. The key contributions include the development of a self-supervised behavior encoder that learns a vocabulary of actions. The generated discrete tokens for actions, augments vocabularies into... | Rebuttal 1:
Rebuttal: > W1, Q1, and L2: Impact of interaction dataset and model architecture.
Thank you for your insightful comments. To clarify the individual contributions and combined impact of the new dataset and architectural methods, we conducted specific ablation studies detailed in our paper.
**Dataset Contr... | Summary: This work presents OmniJARVIS, a instruction following agent for open-world Minecraft. The agent works by learning a behavior encoder that generates behavior tokens conditioned on textual, visual and action inputs via self-supervised learning at first stage, then a multimodal interaction sequence can be packed... | Rebuttal 1:
Rebuttal: > W1: difference between GROOT and OmniJARVIS
As detailed in the **General Response**, our core contribution is to propose a novel Vision-Language-Action (VLA) model architecture termed OmniJARVIS, as shown in **Figure 1 of the supplementary PDF**, aiming at resolving the issues including inferen... | Summary: This work introduces OmniJARVIS, which jointly reasons over visual observations, instructions, self-generated text, and actions. OmniJARVIS models actions via behavior tokens, which are discrete embeddings that are separately learned on a behavior dataset. A policy decoder converts these behavior tokens to a s... | Rebuttal 1:
Rebuttal: > W1 and Q2: Details on encoder and decoder of behavior tokenizer.
Sorry for the confusion.
As shown in Fig. 2, the Behavior Tokenizer consists of three parts: Encoder, Decoder, and FSQ quantizer. We perform quantization based on the original GROOT, and the Encoder and Decoder use a design consis... | Summary: This paper presents a Vision-Language-Action (VLA) model, OmniJARVIS, for open-world instruction-following agents in Minecraft. OmniJARVIS leverages unified tokenization of multimodal interaction data to enable strong reasoning and efficient decision-making capabilities. This work introduces a behavior tokeniz... | Rebuttal 1:
Rebuttal: > W1: Novelty concerns.
Thank you for your comments. As detailed in the General response, our core contribution is to propose a novel Vision-Language-Action (VLA) model architecture termed OmniJARVIS, as shown in **Figure 1 of the supplementary PDF**, aiming at resolving the issues including infe... | Rebuttal 1:
Rebuttal: Thank all. We would like to first explain some sharing concerns.
* Comparative Framework of existing Vision-Language-Action (VLA) Models.
According to the structure of the model and the training methods, the current mainstream VLA models can be roughly divided into three categories (**Figure 1 o... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Local Differential Privacy for Mixtures of Experts | Reject | Summary: This paper introduces a novel mixture of experts model that applies local differential privacy to the gating mechanism. Their methods leverages the one-out-of-n gating mechanism and provides specific generalization bounds.
Strengths: 1. The overall insight of the paper is clear and strong.
2. Improve the tigh... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback. Here, we address the concerns raised and provide clarifications to improve the overall evaluation of our work.
1. Regarding the choice of PAC-Bayesian bounds discussed in Section 3, we would like to clarify that PAC-Bayesian bounds have been extensively stud... | Summary: This paper introduces a novel approach to regularize mixtures of experts by imposing local differential privacy (LDP) on the gating mechanism. The authors provide theoretical justifications and derive PAC-Bayesian and Rademacher bounds tailored to this approach. Experiments conducted on various datasets demons... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful feedback regarding the significance of our paper.
1. We acknowledge that there have been previous works incorporating differential privacy (DP) for regularization. Our paper, however, focuses on utilizing local differential privacy (LDP) on a part of the mo... | Summary: This paper provides generalization bounds for a particular type of mixture of experts (MoE) networks. They focus on MoE architectures where an input $x$ first goes through a gating function $g$, and then gets routed to a single (one out of n) expert $i \in [n]$ according to the $g(x) \in [0,1]^n$ distribution.... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful and detailed feedback. Your insights have highlighted several important areas for improvement and clarification in our work. We appreciate the opportunity to address the concerns raised.
1. The critique regarding the use of local differe... | Summary: The authors consider the mixtures of experts models, in particular the one-out-of-n gating mechanism for ease of theoretical analysis, and show that applying a soft-max, which is also the exponential mechanism, on the gating mechanism gives LDP and can improve generalization. The privacy techniques are largely... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback. We recognize the need to clarify our main contributions. The softmax function and LDP are well-known techniques that have been used in previous works. However, we believe that our contributions are distinct and significant since they use these... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their thorough analysis and valuable feedback on our paper. The comments have allowed us to take a step back and reflect on our work, especially regarding the presentation of our contributions. The reviews have made us realize that our title and abstract ar... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors take the first step (I though so at first) to MoE under LDP theoretically. I read again and found that the author seems to have raised the utility lower bound of existing studies. Few experiments could be found. Perhaps, I am not an expert in MoE, but it really leave a hard time.
Strengths: Imp... | Rebuttal 1:
Rebuttal: We thank the reviewer for reading our submission and providing valuable feedback. We believe that we should have emphasized more that our main contribution lies in the theoretical risk bounds presented in the article, which can be much tighter than existing bounds. In light of the review, we reali... | null | null | null | null | null | null |
DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning | Accept (poster) | Summary: This paper presents DeTeCtive, a new algorithm for detecting AI-generated text. The key insight in this paper is that instead of treating AI-generated text detection as a binary classification problem, it should be treated as a multi-class "author style" classification problem. Using this insight, the authors ... | Rebuttal 1:
Rebuttal: *We would like to thank Reviewer Tv1c for the valuable feedback. The comments and suggestions have greatly helped us in improving the quality of our work. Please see below for our responses to your comments.*
***
## Baselines seem to be quite weak/over a year old.
1. We need to clarify that the co... | Summary: This paper proposes to learn a text encoder with contrastive learning to cluster texts from different sources for fine-grained classification. A training-free incremental adaptation method is designed for detecting OOD data.
Strengths: 1: The innovation of TFIA is inspiring for OOD detection. Detection in inf... | Rebuttal 1:
Rebuttal: *We would like to thank Reviewer W7ZQ for the valuable feedback. The comments and suggestions have greatly helped us in improving the quality of our work. Please see below for our responses to your comments.*
***
## The proposed multi-level contrastive loss shares some similarities with existing w... | Summary: The paper discusses the challenges of current AI-generated text detection methods, which often suffer from performance issues and poor adaptability to new data and models. The authors introduce a new framework called DeTeCtive, which uses multi-level contrastive learning to distinguish different writing styles... | Rebuttal 1:
Rebuttal: *We would like to thank Reviewer yy53 for the valuable feedback. The comments and suggestions have greatly helped us in improving the quality of our work. Please see below for our responses to your comments.*
***
## The paper's strong performance may not generalize to other datasets or application... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their reviews and constructive feedback. Taking into account each concern and question posed by the reviewers, we have given thorough responses within our rebuttal. It is our hope that our responses will be kindly considered during the evaluation of our sub... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models | Accept (poster) | Summary: This paper introduces a systematic framework, referred to as BELM, that is designed for the specific task of exact inversion of diffusion sampling. This framework encompasses several existing intuitive exact inversion samplers as its special cases. Subsequently, the authors derive an optimal variant within thi... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work, we will answer your questions one by one regarding these weaknesses/questions.
> **Weaknesses 1:** ''There is a typographical error in the caption of Table 3.''
We apologize for the oversight. We have now corrected the error and ensured that the ca... | Summary: This paper introduces a novel method for inverting real images into a diffusion model. It presents Bidirectional Explicit Linear Multi-step (BELM) samplers aimed at minimizing the mismatch in DDIM inversion.
Strengths: The proposed Bidirectional Explicit Linear Multi-step (BELM) samplers seems reasonable, and... | Rebuttal 1:
Rebuttal: Thank you for your valuable reviews, we will answer your questions one by one regarding these weaknesses/questions.
> **Weaknesses 1:** The paper lacks discussion and comparison with several related works such as NMG, EDICT, DirectingInv, ProEdit, ReNoise, and others. These works also aim to addr... | Summary: The paper introduces the Bidirectional Explicit Linear Multi-step (BELM) sampler framework for exact inversion in diffusion models. The authors systematically investigate the Local Truncation Error (LTE) within the BELM framework and propose an optimal variant, O-BELM, which minimizes LTE for high sampling qua... | Rebuttal 1:
Rebuttal: Thank you for your valuable reviews, we will answer your questions one by one regarding these weaknesses/questions.
> **Answer to Weaknesses 1:** ''As for downstream applications of diffusion inversion, style transfer [1] should also be included. I encourage the author could apply this method to ... | Summary: This manuscript introduces a generic formulation named ``Bidirectional Explicit Linear Multi-step'' (BELM) samplers for exact inversion of diffusion sampling. In contrast to DDIM Inversion, BELM inversion establishes the relationship between $x_{i-1}$, $x_i$, $x_{i+1}$, and $\epsilon_\theta(x_i, i)$. The autho... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback, we will answer your questions one by one regarding these weaknesses/questions.
> **Answer to Weaknesses 1:** ''How about the computation cost and latency of 2-step O-BELM compared with BDIA and EDICT?''
- Theoretically, the computation cost bottleneck of di... | Rebuttal 1:
Rebuttal: ## To All Reviewers
We sincerely appreciate the time and effort you have dedicated to reviewing our paper! Your valuable feedbacks have been carefully considered, and we have provided point-to-point responses to your reviews in respective rebuttals. We remain open to any additional feedback you ma... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor | Accept (poster) | Summary: This paper focuses on the in-training backdoor defense by proactively injecting a defensive backdoor into the model during training. During inference, PDB embeds a defensive trigger in the inputs and reverses the model prediction. This operation effectively suppresses malicious backdoors and ensures the model... | Rebuttal 1:
Rebuttal: **Q1. Novelty and comparison with backdoorIndicator [2]**
**R1:** Since [2] was first accessible on May 31 on Arxiv, one week after the submission deadline of NeurIPS 2024, we did not have the opportunity to review and compare our work with it. We would like to mention that **our submission is no... | Summary: This paper investigates in-training backdoor defense by proactive defensive backdoor. They introduce a defensive poisoned dataset to train the model together with all poison data. By attach the defensive trigger onto the input sample, the potential backdoor attack is failed by such proactive defensive backdoor... | Rebuttal 1:
Rebuttal: **Q1. Training cost for DBD, NAB and V&B**
**R1:** Thanks. Here, we report both training complexities and the empirical runtime of these methods in Table 1. For simplicity, we first define the following notations:
- $C_{sl}$: Supervised training cost.
- $C_{ssl}$: Self-supervised training cost.
-... | Summary: The paper introduces a novel method called Proactive Defensive Backdoor (PDB) to counter backdoor attacks in deep neural networks. PDB differs from traditional methods, which focus on detecting and eliminating suspicious samples. Instead, PDB proactively injects a defensive backdoor into the model during the t... | Rebuttal 1:
Rebuttal: **Q1. Defend adaptive attacks that increase the poisoning ratio.**
**R1:** Thanks for this suggestion. To provide a more comprehensive evaluation of the proposed method PDB against adaptive attacks, we conduct experiments with poisoning ratios from 10% to 30%, and malicious trigger size from 4x4 ... | Summary: This paper proposes an proactive defense approach called PDB, which aims to combat malicious backdoor attacks by injecting active defense backdoors introduced by the defender. The main goal of PDB is to suppress the impact of malicious backdoors while preserving the utility of the model for its original tasks.... | Rebuttal 1:
Rebuttal: **Q1. Explanations for experimental results in Table 2**
**R1:** Thanks. Firstly, it's important to note that
* **All attack checkpoints in our experiments were sourced directly from the official BackdoorBench website**
* **All experimental results for baselines in our main manuscript align with ... | Rebuttal 1:
Rebuttal: # Common Response
**Q1. Explanation for PDB, including the design of defensive trigger and satisfaction to Principle 4**
**R1.** Thank you for your insightful comments. We would like to clarify that the proposed method, PDB, is not tied to any specific choice of defensive trigger or backdoor enh... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference | Accept (poster) | Summary: The authors analyze the encoder features of diffusion UNet and find that they share a lot of similarities across certain time steps. Based on this observation, the authors propose to reuse the encoder features and do parallel computation in sampling. This significantly fastens the generation process. Additiona... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1. Automatic selection for key time steps**
Thank you for your insightful suggestion. We f... | Summary: This paper presents an approach to accelerate diffusion model inference by capitalizing on the minimal change in encoder features across time steps. The proposed encoder propagation strategy reduces encoder computations by reusing encoder features from previous time steps. The proposed method is comparable to ... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1. Additional metrics**
Thanks for your advice. We showcase our experimental results with ... | Summary: This paper presents an extensive study of the evolution of internal activations in diffusion U-Nets and uses their findings to motivate a training-free approach for accelerating sampling from diffusion models. The method is demonstrated to successfully speed up inference in a variety of settings, including dif... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1. User study details.**
The study participants were volunteers from our college. The ques... | Summary: This method can be applied at inference time without requiring retraining to improve sampling efficiency while maintaining high image quality and can be combined with other approaches to speed up diffusion models. The approach is effective across various conditional diffusion tasks, including text-to-video gen... | Rebuttal 1:
Rebuttal: We appreciate your feedback and will incorporate the discussions mentioned below to enhance the quality of our paper. Note that we utilize the numerical references to cite sources within the main paper.
**W1\&Q2: Cooperation with few-step T2I models**
In the **Limitations** part of the main pape... | Rebuttal 1:
Rebuttal: **“global” response**
We appreciate all reviewers (**R1=wXBv**, **R2=BYFv**, **R3=wo3A**, **R4=i4aK**) for their positive feedbacks. They note that this paper is well-written (R4) and easy to understand (R4); the technique is novel (R1) and promising (R3); that the proposed algorithm is well-form... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation | Accept (poster) | Summary: The authors introduce a new framework that groups the states with a similar reward, hence reduces variance of any applied OPE estimator.
Strengths: The framework is technically sound, well discussed, the analysis is on point and addresses important questions, for example that regardless of the choice of state... | Rebuttal 1:
Rebuttal: We thank you for taking time to review our paper and appreciate you recognizing the strength of the work’s theoretical and empirical analysis. We appreciate you pointing out additional relevant references, and address related questions below.
> In [L212] you claim this is the first model-based O... | Summary: The paper introduces a new framework, STAR, for off-policy evaluation (OPE). OPE uses a chain with rewards (MRP) to conduct the evaluation. The challenge of this methodology is that the MRP estimation can introduce bias, given the shift between the behavior policy and the policy for evaluation. STAR is designe... | Rebuttal 1:
Rebuttal: We thank you for taking the time to review our paper, and appreciate your recognition of our work's contributions and analyses. Your observation regarding differing ease of model estimation across various abstractions is insightful, as detailed next. We have updated the paper to include this point... | Summary: This paper studies the problem of Off-Policy Evaluation (OPE), which consists of estimating the value of a policy $\pi_e$ from an input dataset generated from another behaviour policy $\pi_b$. One naive way of constructing the estimated return of $\pi_e$ would be to compute its associated empirical Markov Rewa... | Rebuttal 1:
Rebuttal: We thank you for your careful review of our paper and insightful comments. We appreciate your positive assessment of our contributions and the clarity of our presentation. We address your questions and comments below.
> How did you obtain the probabilities of the behaviour policy for the ICU seps... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SC3D: Self-conditioned Generative Gaussian Model with 3D-aware Feedback | Reject | Summary: This paper (SC3D) proposes a single image-to-3D reconstruction method. It combines multi-view diffusion model and a 3D reconstruction model, and uses the 3D reconstruction results as self condition to improve the multi-view generation process. The motivation of the proposed method is to improve the geometric c... | Rebuttal 1:
Rebuttal: ***1. Claim about key contributions: the 3D-feedback idea appears already in VideoMV. Since the VideoMV is already available on Arxiv in March, authors need to justify more clearly about the difference and contribution w.r.t. VideoMV.***
We will more clearly justify the difference with VideoMV in... | Summary: This paper proposes a method for 3D asset generation conditioned on a single image. The approach follows the recent trend of a two-stage feed-forward model – first generating multi-view images and then using a sparse-view reconstructor to reconstruct the 3D object (specifically, LGM in this paper). This two-st... | Rebuttal 1:
Rebuttal: ***1. Some related works lack citation and discussion: DMV3D and Carve3D.***
Thanks for your suggestion, and we will cite DMV3D and Carve3D with more discussions in our revised paper:
- DMV3D employs a 3D reconstruction model as the 2D multi-view denoiser in a multiview diffusion framework, to ac... | Summary: The paper observes that the current state-of-the-art image-to-3D generation models consist of two separate parts: generate multi-view images from a single image and run on top the 3D reconstruction. This process has no feedback loop, i.e. the reconstruction does not inform the image generation which in turn le... | Rebuttal 1:
Rebuttal: ***1. The presentation of the paper is not clear.***
- *Confusion about Fig.1 and Fig.2 in the original paper.* We slightly adjust these two figures to ensure that both outputs are 3D representations (The updated figure sees **Fig.1 in the attached PDF**.). The two decoders are the same VAE decod... | Summary: This paper proposes SC3D for the single-image-to-3D generation, which integrates the diffusion-based multi-view generation and Gaussians-based 3D reconstruction through a self-conditioning mechanism. Specifically, during each denoising step, SC3D injects the rendered image and geometric map from the reconstruc... | Rebuttal 1:
Rebuttal: ***1. Lack detailed visual comparisons with baseline methods.***
We conduct comparisons with more baseline methods, and show the visualization results in **Fig.3 and Fig.4 of the attached PDF**. We compare SC3D with the SOTA image-to-multiview and image-to-3D generation methods.
- *Image-to-multi... | Rebuttal 1:
Rebuttal: We thank all reviewers for the constructive comments and for recognizing the novelty and effectiveness of our self-conditioned image-to-3D generation method with 3D-aware feedback. We also extend our gratitude to the reviewers for identifying shortcomings in our paper's presentation and organizati... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Extracting Training Data from Molecular Pre-trained Models | Accept (poster) | Summary: This paper tackles the problem of extracting private training molecular data from pre-trained models. To address this problem, the authors propose a machine learning method based on a model-independent scoring function and a molecule extraction policy network. The privacy of the training data is an emerging is... | Rebuttal 1:
Rebuttal: ### Response to Reviewer qKJK.
We greatly appreciate reviewer qKJK’s insightful feedback and critical comments to help us refine our work.
>W1\&4: Although the authors made a scenario to clarify the problem, it is not realistic ...; The proposed method should be evaluated in the regression tasks... | Summary: This paper explores the vulnerabilities of molecular pre-trained models to data extraction attacks. The authors introduce a novel molecule generation approach and a model-independent scoring function to identify molecules potentially originating from private datasets. They also present a Molecule Extraction Po... | Rebuttal 1:
Rebuttal: ### Response to Reviewer o6iJ.
We sincerely thank reviewer o6iJ for detailed feedback and we address the reviewer’s concerns as follows.
>Q1: The experiments might not fully capture the diversity and complexity of real-world datasets, which could affect the generalizability of the findings. I r... | Summary: This paper investigates the issue of data leakage risks when using pre-trained molecular models in a shared environment and proposes a method to extract training data from such pre-trained models. Specifically, the authors employ a molecular generation method based on templates and a candidate motif bank to at... | Rebuttal 1:
Rebuttal: ### Response to Reviewer JnnP.
We greatly appreciate reviewer JnnP for the time and effort you have dedicated to reviewing our paper. We address the reviewer's concerns as follows:
>Q1: Regarding the selection of template structures, the paper ultimately chooses rings as the starting structures ... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs) | Accept (poster) | Summary: In this paper, the authors explore the statistical and computational limits of latent Diffusion Transformers (DiTs) under the assumption of a low-dimensional linear latent space. Their contributions include an approximation error bound for the DiTs score function, which is sub-linear in the latent space dimens... | Rebuttal 1:
Rebuttal: > **Reviewer's Question 1:** I have reviewed Appendix C. How does the proof for DiTs in this paper differ from that presented in [Chen et al., 2023a]?
**Response:**
Thanks for the question. Here are some clarifications.
* To prove **score approximation** (Theorem 3.1), our approach utilizes the ... | Summary: This paper explores the statistical and computational limits of latent DiTs:
1. It proves that Transformers are sufficient as universal approximators for the score function in DiTs, with their approximation capacity depending on the latent dimension.
2. Transformer-based score estimators converge to the true ... | Rebuttal 1:
Rebuttal: > **Reviewer's Comment 1:** It is meaningful to further elucidate why existing DiTs models are difficult to train.
**Response:**
Thanks for your comment. We'd like to clarify a few points.
This relates to the high-dimensional latent data representation, which increases both approximation and es... | Summary: The paper studies the statistical and computational limits of latent diffusion transformers.
Strengths: The results seem to be new and non-trivial (though I'm not an expert in the field, so I might have a wrong impression).
Weaknesses: The paper is too technical (e.g. there are many long formal definitions),... | Rebuttal 1:
Rebuttal: > **Reviewer's Comment 1:** The paper is too technical (e.g. there are many long formal definitions), and the results are hard to understand for a non-expert in the area. The formulations of the theorems contain a lot of parameters, it makes them very hard to read. I suggest that you may write dow... | Summary: This paper establishes the statistical rates and provably efficient criteria of Latent Diffusion Transformers (DiTs). Specifically, there are three main theoretical results:
* the approximation error bound for the transformer-based score estimator,
* the sample complexity bound for score estimation,
* and the ... | Rebuttal 1:
Rebuttal: > **Reviewer's Question 1:** Can the linear subspace assumption on input data be relaxed to a more general one, such as the manifold data? Since it's more natural to consider the intrinsic geometric structures of data.
**Response:**
Thanks for the question. Yes, but not trivial. Here are some cla... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Are Multiple Instance Learning Algorithms Learnable for Instances? | Accept (poster) | Summary: This paper aims to discuss the theoretical learnability of multi-instance learning. It first gives the necessary conditions for bag-level learnability and instance-level learnability. Then it discusses how the results can be used to verify the learnability of existing MIL algorithms. Finally, some empirical ex... | Rebuttal 1:
Rebuttal: We will address their feedback in the following:
**(W1)**
- **Condition 4**: The interpretation provided in Condition 4 aligns with PAC Learning theory. Specifically, $\inf R$ denotes the infimum of the risk within the given hypothesis space, representing the optimal hypothesis's risk. Thus, Eq.... | Summary: This paper mainly studies the instance-level learnability of common weakly-supervised MIL algorithms. With the PAC theoretical framework, it shows the conditions for MIL algorithms to be learnable for instances. Two general cases of instance distribution, IID instances and non-IID instances, are discussed and ... | Rebuttal 1:
Rebuttal: We appreciate your recognition and feedback on our work. We've integrated your suggestions to enhance our manuscript. For more information, see Global Response. We are addressing your specific comments as follows:
**(W1)** Reason for Condition 2 Being a Necessary Condition for Instance-Level Lear... | Summary: The paper provides theoretical considerations regarding Multiple Instance Learning.
Strengths: Theoretical consideration supported by the experimental results.
Weaknesses: There are no images depicting the intuition behind those definitions and theorems.
Paper is extremely hard to follow because it is not ... | Rebuttal 1:
Rebuttal: We appreciate your recognition and feedback on our work. We've integrated your suggestions to enhance our manuscript. For more information, see the Global Response. We are addressing your specific comments as follows:
**(W1, 2, Q1)** Improving the Presentation of the Paper
- To address the feedb... | Summary: The main contributions of the paper are theoretical:
- Proof of the fact that MIL algorithms that are not learnable for bags do not guarantee learnability for instances.
- Using PAC learning theory (under some assumptions) learnability conditions are derived. These conditions are sufficient and necessary.
- Ba... | Rebuttal 1:
Rebuttal: We appreciate your recognition and feedback on our work. We've integrated your suggestions to enhance our manuscript. For more information, see the Global Rebuttal. We are addressing your specific comments as follows:
**(W1)**
- **Importance of Instance Pooling:**
- Instance-Pooling is a fun... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful comments. We appreciate the time and effort you have invested in reviewing our work. K1fQ and W4rs recognized our focus on the theoretical foundations of our study. dSpL and 2msW acknowledged the clarity and rigor of our theoretical framework and... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper addresses a critical gap in Multiple Instance Learning research by proposing a theoretical framework to evaluate instance-level learnability of MIL algorithms. Utilizing PAC learning theory, the authors derive conditions under which Deep MIL algorithms can achieve instance-level learnability. The fra... | Rebuttal 1:
Rebuttal: We appreciate your recognition and feedback on our work. We've integrated your suggestions to enhance our manuscript. For more information, see Global Response. We are addressing your specific comments as follows:
**(W1)** Complexity of the Theoretical Framework
- To facilitate understanding of ... | null | null | null | null | null | null |
Learning Truncated Causal History Model for Video Restoration | Accept (poster) | Summary: This paper proposes a TURTLE to learn the truncated causal history model for video restoration tasks.
The proposed turtle's causal history model consists of two sub-modules: a State Align Block and a Frame History Router.
The state align block has a similarity-based retrieval mechanism that implicitly accounts... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work important and for their insightful and constructive comments.
**W1** Frame history router ablation study.
In the main paper, we conducted ablation experiments to analyze the effects of different components in CHM. We investigated three setups: No CHM, N... | Summary: This paper proposes a truncated causal history model (TURTLE) for video restoration. TURTLE is in a U-Net manner with a historyless encoder and a history-based decoder. The decoder has a causal history model (CHM), which is the core part of the TURTLE. The CHM injects history frames into a hidden state $\mathb... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments.
**Q1.1.**
We placed ablation studies in appendix given space limit, but can move them to the main paper.
**Q1.2.**
During rebuttal, we have added ablation experiments comparing softmax and topk (k=5), as shown in Table 2 of the one-pa... | Summary: This work presents a video restoration framework named TURTLE, which stands for truncated casual history model. The key innovation of TURTLE is its ability to efficiently model the transition dynamics of video frames governed by motion, a critical challenge in video restoration. Unlike traditional methods that... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and reading our work.
**W1.** The section on related work is seriously lacking in content.
Due to space constraints, we had to be selective in related work. In the rebuttal, here we would like to add a literature review on temporal modeling and c... | Summary: The paper presents a novel framework TURTLE for video restoration. TURTLE aims to improve video restoration tasks by modeling and utilizing truncated historical data of input video frames to enhance the restoration quality while maintaining computational efficiency. The proposed method demonstrates state-of-th... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our work and providing constructive comments.
**W1.** FLOPs of Turtle.
We have provided the Turtle's MACs (G) in Table 8 of the main paper and discussed its comparison to several previously published video restoration methods in Section 4.8. Additionally, duri... | Rebuttal 1:
Rebuttal: ## **Profiling Turtle**
We profile the proposed method, Turtle, in terms of per-frame inference time (in ms), MACs (G), FLOPs (G), and GPU memory usage (in MBs) on a single 32GB Nvidia V100 GPU. ShiftNet uses a context length of 50 frames and restores all 50 frames together, while VRT uses (a con... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Causal discovery with endogenous context variables | Accept (poster) | Summary: The authors tackle the challenging causal discovery task, namely, causal discovery from the pooled dataset collected under different environments, where the environment (a.k.a., the context) can be dependent on the system (endogenous) variables (i.e., the variables whose causality we are interested in). The pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing our paper and for acknowledging the importance of the problem we are working on.
We address the mentioned weaknesses as follows:
- **Section 1:** We agree that the third paragraph should be thoroughly reworked. It fails to make the p... | Summary: This paper proposes a constraint-based algorithm for context-specific causal discovery, which accommodates endogenous context variables.
Strengths: 1. This paper is well-motivated. In particular, I agree that it is important to investigate the case where the context variable is endogenous.
2. This paper prov... | Rebuttal 1:
Rebuttal: We thank the reviewer for reviewing our paper and for acknowledging the strengths of our work.
We now address the weaknesses as follows:
* **Context variable:** We agree with the reviewer that the assumptions we have made may not always reflect real-world use cases. However, as we have pointed ... | Summary: The authors consider an SCM $M$ with a labelled and observed context variable $R$ that is endogenous, i.e., causally depends on other variables in the model. In this setting it is generally not true that $P_{M}(\ldots \mid R=r) = P_{M}(\ldots \mid \mathrm{do}(R=r))$. Therefore, selecting only data for which $R... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort in reviewing our paper. We also thank the reviewer for acknowledging the strengths of our paper, and that we have made an effort to document all possible limitations of our approach. We address the weaknesses as follows:
* **Notation and typos:** We... | Summary: The authors address the problem of causal discovery in situations where causal relationships change across different contexts. They propose a modified version of the PC algorithm, which either performs a conditional independence test (CIT) on pooled data or context-specific data, depending on the scenario. The... | Rebuttal 1:
Rebuttal: We thank you for reviewing our paper, and for acknowledging the importance of the problem. Furthermore, we thank the reviewer for appreciating the value of the set of assumptions we have derived for a first step towards solving the problem of endogenous context variables.
We address the weaknesses... | Rebuttal 1:
Rebuttal: ## General Response to the Reviewers
We thank all reviewers for their valuable comments, and we are happy that they acknowledge the importance of our work and the value of our results. We agree with the reviewers that the presentation of the paper can be improved, and we summarize the main change... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distributional regression: CRPS-error bounds for model fitting, model selection and convex aggregation | Accept (poster) | Summary: The paper studies distributional regression, a statistical technique that models not just the conditional mean of the response variable given the predictors (as in traditional regression) but the entire conditional distribution. The learning task minimizes the risk function, measured by the Continuous Rank Pro... | Rebuttal 1:
Rebuttal: Thank you for your work and time for reviewing the paper. We believe indeed that distributional regression is an important technique providing a more holistic view of the data and we stress that, despite its importance in practice, CRPS minimization in distributional regression has been very littl... | Summary: This paper considers the problem of conditional distribution prediction. For covariate-response pair $(X, Y)$, the objective is to estimate the conditional distribution of $Y|X=x$ for all $x$. The paper provides concentration bounds for the empirical risk minimization (ERM) estimator with continuous rank proba... | Rebuttal 1:
Rebuttal: Thank you for overall positive appreciation of the paper! Please find below answers to your questions and concerns.
Weaknesses:
We agree that the technical development based on Hoeffding inequality is quite standard. Still, the paper provide the first detailed analysis of the CRPS risk for distri... | Summary: The paper considers the problem of distributional regression, i.e., learning the distribution of Y conditional on X. In particular, the distributional regression problem is formulated as an empirical risk minimization problem, where standard concentration techniques are applied to obtain non-asymptotic bounds ... | Rebuttal 1:
Rebuttal: Thank you for overall positive appreciation of the paper! Please find below answers to your questions and concerns.
Weaknesses:
We agree that the technical development based on Hoeffding inequality is quite standard. Still, the paper provide the first detailed analysis of the CRPS risk for distri... | Summary: The paper provides theoretical guarantees for distributional regression, which aims to estimate the conditional distribution of a target random variable Y given covariates X. These theoretical guarantees hold when the regression is learned by minimizing a particular proper scoring rule, the Continuous Rank ... | Rebuttal 1:
Rebuttal: Thank you very much for the positive assessment of the quality of the paper presentation! Please find below answers to your questions and concerns.
Weaknesses:
1) We agree that the experiment is loosely related to the theoretical results; our aim is to illustrate that the methods of interest (mod... | Rebuttal 1:
Rebuttal: We acknowledge the referees for their work and time spent on the paper. Overall, the comments are positive but two main concerns emerge from the reports that we comment below. All other (minor) suggestions have been taken into account.
1) Technical contributions --
We agree that the technical dev... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy | Accept (poster) | Summary: The paper presents a novel approach titled "AdaptiveDiffusion" for accelerating diffusion models used in high-quality image and video synthesis. The core issue addressed is the high computational cost and latency associated with existing denoising techniques in diffusion models, which are typically based on st... | Rebuttal 1:
Rebuttal: >***Q1: Method Explanation***
- **Assumption Validity:** We follow the assumptions proposed in DPM-solver $^{[1]}$, which are commonly adopted for the high-order approximation in ODE solvers.
- **Algorithm Pseudocode:** We have provided the pseudocodes of AdaptiveDiffusion and the greed search a... | Summary: ## Summary
* This paper propose a greedy approach that accelerate the Probability Flow ODE solvers for txt to image diffusion models. Empirical results on SD 1.5 and SD XL with mulitple solvers (DDIM, DPM, Euler) demonstrate the advantage of their approach over previous acceleration techniques.
Strengths: ## ... | Rebuttal 1:
Rebuttal: >***Q1: Discussion of Single-step Sampling Works***
Thank the reviewer for the valuable suggestion. We will provide a detailed discussion in the revised manuscript. Here is a brief discussion of single-step sampling works.
In addition to the acceleration paradigms mentioned in Sec 2.2., a recent... | Summary: This paper proposes a strategy to speed up image and video diffusion generative models. The speed-up is achieved by skipping denoising steps. The authors suggest implementing an adaptive skipping schedule, where the decision of which steps to skip depends on the processed image or video. Specifically, the prop... | Rebuttal 1:
Rebuttal: >***Q1: Theoretical Analysis of the Relationship between the Third-order Estimator and the Skipping Strategy.***
To explore the theoretical relationship between the third-order estimator and the skipping strategy, we need to formulate the difference between the neighboring noise predictions. Acco... | Summary: To enhance the sampling speed in diffusion models, this paper introduces the AdaptiveDiffusion framework, which utilizes a skipping strategy. Specifically, this strategy is guided by the third-order latent difference, assessing the stability between timesteps throughout the denoising process. Experiments resul... | Rebuttal 1:
Rebuttal: > **Q1: Analysis of Improvements.**
We describe the advantages of our method in two aspects.
- **Novel Method Design:** Endorsed by three other reviewers, AdaptiveDiffusion is the pioneering framework that accelerates the diffusion process adaptively for diverse prompts. Unlike the SOTA method D... | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
We are deeply appreciative of the reviewers for their valuable time and thoughtful comments. Their feedback has reinforced our confidence in the paper's **clear presentation and organization** (Reviewer XP4x, kqHE, rWvo, hdh3), **the innovative approach** of AdaptiveDiffusi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Deep Discriminative to Kernel Density Graph for In- and Out-of-distribution Calibrated Inference | Reject | Summary: This paper proposes new methods, Kernel Density Forest (KDF) and Kernel Density Network (KDN), to address issues in confidence calibration for traditional deep learning models and random forests. The motivation stems from the existing literature that deep neural networks using ReLU tend to exhibit high confide... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We are pleased that you recognize the efficacy of our proposed approach in providing an integrated solution for both ID and OOD calibration problems in traditional deep learning models and random forests. We believe we have addressed all your concerns in our... | Summary: The paper proposes a novel approach for OOD detection by learning a posterior distribution that is calibrated for both ID and OOD individuals. It models the class-wise conditional distribution of features by a gaussian kernel respectively for a set of polytopes that cover the feature space. The tail property o... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments. We are glad to see that you recognize the effectiveness of our approach in balancing ID and OOD calibration. We believe we have addressed all your concerns in our responses below. If you find these satisfactory, we would be very grateful if you cou... | Summary: The paper introduces a way to calibrate ReLU networks or random forests by breaking them down into piecewise linear functions on polytopes and replacing the linear parts with Gaussian kernels. This approximation allows to naturally calibrate the models for the ID domain, where confidence will be high due to th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the intuitive comments. We are glad that the reviewer recognized the intuition behind our approach . We believe we have addressed all your concerns in the response below. If you think these responses are satisfactory, we would be very grateful if you can update your score... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their strenuous effort and time to go through our paper and provide valuable feedback. Below, we address the common concerns:
- Reviewers were concerned about the runtime of our approach, possibly it could be an exponential function of the number of nodes. However, ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Generative Forests | Accept (poster) | Summary: This paper proposes a generative model for tabular data based on a forest of trees. It is evaluated on the quality of the generated samples (using an optimal transport based distance to a held out test set), on data imputation and density estimation.
Strengths: Generative modeling for tabular data is an impor... | Rebuttal 1:
Rebuttal: We would like to especially thank the reviewer for singling out the importance of boosting and our theoretical framework and results
## weaknesses
> Further empirical comparison could be conducted. [...]
We could be happy to do so, but kindly note that at this stage we have received criticisms ... | Summary: This paper introduces generative forests (GF), a new class of generative models designed for tabular data and based on sets of trees (forests), and a training algorithm called GF.BOOST. The algorithm is designed to be simple, making it easy to implement with minor modifications to existing CART-style decision ... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting the importance of our formal contribution !
## weaknesses
> It would be really nice to have real-world kaggle-style datasets with missing values
Actually, sigma-cabs is a Kaggle dataset with missing values. Stanford Open Policing is not Kaggle but it also has mi... | Summary: The paper proposes a generative model based on an ensemble of trees (forest) for tabular data. The proposed model enjoys the following peculiar properties:
1. Compared to generative models based on a single tree, it offers improved **expressiveness** in terms of partitioning the input space (linear for generat... | Rebuttal 1:
Rebuttal: We thank the reviewer for organising the review in a very clean way that allows for easy referencing, tagging two **significant** key strengths on which we elaborate further below, and being clearly open for discussion. We use W.X.Y to refer to the weakness section.
> W.1 "One of the major concer... | Summary: The paper presents a novel boosting algorithm based on ensembles of generative trees, addressing tasks such as data binary classification, missing data imputation, and density estimation. The proposed algorithm optimizes specific loss functions and leverages the structure of the models to efficiently estimate ... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting that our approach "[...] presents a fresh perspective that has not been extensively explored in the literature [...]" and noting that the quality of our submission is commendable.
## weaknesses
> In section 1, [...]
[cvp8-A] we assume the reviewer wants to know ... | Rebuttal 1:
Rebuttal: ## To all reviewers
[ALL] We warmly thank all reviewers for their work and for all granting our paper with general "good or excellent" contribution field. Many questions have been asked and we take it as a value found that so many contenders were proposed, added to the five (5) we already have (i... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors introduce a novel generative model (Generative Forests - GF) for sampling and density estimation, leveraging an ensemble of trees. Their method partitions the input domain by considering all possible intersections of the supports of the leaves across the ensemble, resulting in a much finer partitio... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing the soundness, novelty and versatility of our approach. We must confess we found the review a tad offensive at times [kTDz-D] [kTDz-H], accusing us of a *complete lack of seriousness* in some of our experiments, acting *not in good faith* elsewhere, finding *stra... | null | null | null | null | null | null |
Credit Attribution and Stable Compression | Accept (poster) | Summary: This paper studies credit attribution and stable compression. Credit attribution aims to assign recognition to the original creators of content generated by machine learning models. The authors propose formal definitions for credit attribution and connect it to differential privacy (DP). The proposed framework... | Rebuttal 1:
Rebuttal: Thank you so much for reading our paper, and for your comments.
With regards to the weaknesses you bring up:
> 1. The paper motivates the contributions using generative models but presents results only for PAC learnable classes. Showing how credit attribution could be applied to generative models... | Summary: The authors propose a new notion of credit attribution, which is a relaxation of differential privacy (DP). They discuss the relationship between the proposed notion, semi-differential privacy where part of the data records are public, and a DP sample compression scheme. PAC learning theory under these notions... | Rebuttal 1:
Rebuttal: Thank you very much for reviewing our manuscript. We appreciate your feedback and would like to clarify the scope and objectives of our paper to facilitate a more accurate assessment. The paper aims to explore and propose notions of credit attribution. It does not cover topics such as copyright an... | Summary: This paper addresses the challenge of credit attribution within the context of machine learning algorithms. It proposes new definitions that relax the stability of a subset of data points. The framework extends established notions of stability, such as Differential Privacy, differentially private learning with... | Rebuttal 1:
Rebuttal: Thank you so much for reading our paper, and for your comments.
With regards to addressing the weaknesses/questions:
> Examples that satisfy Definition 1 could be further discussed. Could the authors clarify examples of $(\varepsilon>0,\delta)$-counterfactual credit attributor (Definition 1) whi... | Summary: This paper studies the problem of credit attribution in machine learning tasks. Motivated by the moral and legal need to appropriately credit input data points when they significantly influence the output of a learning or generative model, the authors develop a characterization of reasonable credit attribution... | Rebuttal 1:
Rebuttal: Thank you so much for reading our paper, and for your comments. We are glad you liked our paper!
With regards to the points you bring up:
> The notion of the privacy parameter $\varepsilon$ in standard DP has some interpretability in simple contexts like that of linear queries via lower bounds on... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for taking the time to read the manuscript. We would like to reiterate that in this work, we present a first candidate notion of learning with credit attribution as well as provide a first characterization of PAC learnability under credit attribution. **This sh... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Robust Reinforcement Learning from Corrupted Human Feedback | Accept (poster) | Summary: The paper presents a robust reward learning approach by formulating it as an $\ell_1$-regularized maximum likelihood estimation problem. And it also introduces an alternating optimization algorithm, which introduces minimal computational overhead when compared to the standard RLHF approach.
Strengths: * The p... | Rebuttal 1:
Rebuttal: We would like to thank you for your constructive comments! In the following, your comments are first started and then followed by our point-by-point responses.
**W1, Q3, L1: The paper lacks an experiment for formal RLHF, such as Proximal Policy Optimization (PPO), in the context of text generatio... | Summary: The paper proposes a robust RLHF method which models the potentially corrupted preference label as sparse outliers. They prove that their method can consistently identify outliers in addition to learning the underlying reward functions, under proper conditions. The results on both robotic control and natural l... | Rebuttal 1:
Rebuttal: **W1: Error bars are expected in Table 1.**
Please refer to point 2 in the global rebuttal.
**W2: The Anthropic Helpful and Harmless dialogue preferences dataset has a high disagreement rate, so that flipping a random portion of the training labels may not increase noise as expected, which could... | Summary: The paper studies the problem of robust reinforcement learning when a small fraction of the human feedback preference data can be corrupted by adversary.
Strengths: The paper formulates the robust RLHF problem and provides a straightforward and easy-to-use $\ell_1$ regularization algorithm for learning the re... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We provide a detailed response to your questions as follows:
**Q1: In practical dataset, how do we justify the correctness of sparse perturbation assumption rather than stochastic noise in the original Bradley-Terry model?**
As discussed in Section 6 and Rem... | Summary: This paper proposes a framework for robustifing learning from human preferences. It models noise and bias in human annotation of the dataset as an offset added to the true margin between the preferred and disprefered examples. It further utilizes L1 regularization to induce sparsity in the offset. Empirically... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review and valuable feedback. We provide a detailed response to your comments as follows:
**W1: The value of the $\delta$ offset could also be interpreted as by how much is the positive completion preferred over the rejected. This offset may be orthogonal to label... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for the valuable feedback! Before we answer to each of the reviewers individually, we address common concerns and present our newly added results below:
>**1. Is it possible to run on modern preference dataset like HelpSteer2, Nectar or UltraFeedback, and ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn | Accept (poster) | Summary: This work studies a phenomenon called churn, which refers to that the outputs of a network after updates could change to unexpected values for input data not included in the training batch.
Specifically, it studies the value churn, the policy churn, and the interplay between them in deep reinforcement learning... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful review and the recognition of our experimental work. Our additional results in the one-page pdf further demonstrate the effectiveness of CHAIN in **ten more DMC tasks** and **improving the learning when larger networks are used**.
The main concerns focus on th... | Summary: The authors focus on improving the current state of deep reinforcement learning by addressing the churn effect in deep neural network training. Churn effect in deep reinforcement learning is the phenomenon where output predictions for data outside training dataset can change during training updates. This can l... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s careful review and constructive feedback, and the reviewer’s recognition of the motivation of this work and the importance of studying the churn problem in deep RL. Our additional results in the one-page pdf **further demonstrate the effectiveness of CHAIN** equipped w... | Summary: Deep RL optimisation exhibits many instabilities and performance collapses. Schaul et al. [2022] discuss a pathology termed the "policy churn", where even a single update to the value network frequently changes the optimal action for a huge fraction of all states (most of which were not present in the training... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable review and the recognition of the value of the chain effect and the method proposed in our work. Our additional results in the one-page pdf further demonstrate **the effectiveness of CHAIN in ten DMC tasks and improving the learning when larger networks are us... | Summary: This paper focues on the training instability from the non-stationary nature of DRL, whose pheononmena are unexpected shift in policy and value network outputs (policy churn and value churn). To mitigate policy and value churn, the proposed CHAIN algorithm add penalize in the policy and value updates.
Strengt... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable review and the recognition of the versatility of the proposed method. Our method is simple but versatile to improve the performance of many learning algorithms. We show that the method works across continuous and discrete control tasks, online and offline sett... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers’ careful review and valuable comments. Here **we summarize the main points of our response to each review** and **the content of our additional results** enclosed in the one-page pdf.
The summary of the responses to individual reviews:
- **[Reviewer DNMP]**... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Private Geometric Median | Accept (poster) | Summary: The paper proposes algorithms that compute the geometric median with differential privacy. Composed of two parts, the algorithm firstly finds the quantile radius that covers enough points. Secondly, it fine-tunes the geometric median. The authors suggest two methods for the second part: LocDPGD and LocDP cutti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and thorough review. Based on your suggestions, we revised the statements in Thm. 2.7, Thm. 3.1, and Thm. 3.4 to improve readability.
> **W1: dependency between parameters in Thm 3.1 and Thm 3.4.**
Note that there is **no** dependency between the parame... | Summary: This paper introduces a pair of polynomial-time DP algorithms for computing the geometric median of a dataset. The excess error guarantees of the algorithm scale with the effective radius.
The algorithm includes two parts. The first part shrinks the feasible set to a ball whose diameter is proportional to th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and thorough review! We respond to the questions below.
> **W1: Presentation of the ideas behind the algorithms**
Thanks for the suggestion. In the revised version of the paper, we expanded on these challenges and also presented the high level ideas of t... | Summary: This paper considers the differentially private geometric median problem. The goal of the geometric median problem is given n data points, find a point to minimize the sum of Euclidean distances from this point to all data points.
The previous DP-GD algorithm requires prior knowledge of the radius R of the d... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the positive comments! | Summary: This paper studies DP algorithms for geometric median. The paper starts with DP-SGD on geometric median as the baseline. The error scales linearly in R, the radius of a ball containing all data points. This can be a very loose bound since the geometric mean is known to be robust to outliers and one single outl... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thorough review and valuable feedback. Below, we address each of the points raised.
> **Q1: Robust Notion of Radius and Our Contribution**
We agree with the reviewer that proposing a new robust notion of radius is not a contribution of this work. Our... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper studies differentially private algorithms for computing geometric median (GM) of a dataset. Previous methods such as DP-SGD requires knowing in advance that all data points live in a ball of radius R (contribution bounding), and the resulting utility guarantee also has a dependency on that R, which... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and thorough review. We respond to the main points below.
> **On Our Pure-DP Algorithm**
Our pure DP algorithm is not the main focus of our paper. Our main result is a pair of polynomial time and sample-efficient algorithms for this problem using gradien... | null | null | null | null | null | null |
Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models | Accept (poster) | Summary: The paper focuses on enhancing the robustness of CLIP models against adversarial perturbations. The approach utilizes saliency maps generated by the inner products between image and text embeddings. Two regularization terms are introduced: the first aims to align the saliency maps of the original and its adver... | Rebuttal 1:
Rebuttal: **Q1: Use the terms "generalizability" and "adversarial robustness" more carefully.**
**A1**: Thank you for pointing that out! The reviewer is correct. To balance the performance between clean and adversarial samples, our goal is to maintain the generalization and enhance the adversarial robustne... | Summary: This work studies the robustness to adversarial samples for CLIP. Inspired by an observation that adversarial perturbations induce shifts in text-guided attention, the work proposes a simple yet effective approach to improve the zero-shot robostness, i.e., align the text-guided attention of clean samples and a... | Rebuttal 1:
Rebuttal: **Q1: More comparison with other types of attention.**
**A1**: We also considered this point when exploring the comprehensive effect of attention. Thus, we conducted an experiment by replacing the text-guided attention with vision-only attention, as detailed in the first part of Section 4.4 ("Dif... | Summary: This paper proposes an approach to improve the adversarial robustness of vision-language models while maintaining performance on clean images. The key idea is to aligns the attention maps of adversarial examples with clean examples. Extensive experiments demonstrate the effectiveness of the proposed method in ... | Rebuttal 1:
Rebuttal: **Q1: Applicability to other vision-language models.**
**A1**: We follow TeCoA and PMG-AFT, focusing on improving the zero-shot adversarial robustness of the CLIP model for classification tasks. To further validate the effectiveness of our method as suggested by the reviewer, we replaced the CLIP... | Summary: The paper proposes a framework, Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR), to enhance the robustness of vision-language models (VLMs) against adversarial attacks. The proposed method incorporates two modules: the Attention Refinement module and the Attention-based Model Constraint module. The At... | Rebuttal 1:
Rebuttal: **Q1: Discussion of additional computational overhead and training/inference time.**
**A1**:Thank you for your suggestion. We have evaluated our method against others in terms of memory usage, training time, and test time, and the findings are summarized below:
**Memory Usage**: Our method incre... | Rebuttal 1:
Rebuttal: **1. Summary**: We thank the reviewers for their positive and constructive comments. The reviewers agree that the topic is interesting and that the proposed method is novel, simple, and effective. All reviewers appreciate the comprehensive, solid, thorough, and detailed experiments. They also ackn... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization | Accept (spotlight) | Summary: The paper proposes a consistency regularization for improving the generalization performance of PEFT methods. Specifically, the regularization constructs two predictions perturbed by different noises and penalizes the L2 distance between them. The paper theoretically studies the benefits and shows that the reg... | Rebuttal 1:
Rebuttal: ## We thank the reviewer for insightful questions that help refine our work further.
# 1. Comparison with L2SP, DELTA & FTP.
L2SP, DELTA, and FTP aim to retain pre-trained knowledge by aligning finetuned models with pre-trained ones, reducing distance in weight space, feature space and using pro... | Summary: This paper introduces PACE, an extension to PEFT methods for ViTs that includes consistency regularization. The paper shows that consistency regularization encourages smaller gradient norms and better alignment between pre-trained and fine-tuned models, resulting in better fine-tuning performance than existing... | Rebuttal 1:
Rebuttal: ## We thank the reviewer for insightful questions that help refine our work further.
# 1. Experiment settings.
ViT/16-B and Swin-B were pre-trained on ImageNet-21K using supervised learning. Our analysis extends to self-supervised pre-trained models as well, since our goal is to retain knowledge... | Summary: This paper proposes to regularize the model consistency by optimizing the fine-tuned model to remain consistent for the same sample under different perturbations.
Strengths: 1. The paper is well-written.
2. Several experiments are conducted on four visual adaptation tasks: VTAB-1k, FGVC, few-shot learning, an... | Rebuttal 1:
Rebuttal: ## We thank the reviewer for helpful comments.
# 1. Copy the original model for consistency regularization not novel.
We believe there is misunderstanding.
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Existing "consistency" models (we will cite/compare these interesting works in paper) align features of fine-tuned model with the pretra... | Summary: The paper proposes a consistency regularization that minimizes the squared L2 distance between two outputs of a model obtained using the same parameters, but multiplying the activations by 2 different noise samples. It proves that the population loss is bounded by the gradient norm, indicating that smaller gra... | Rebuttal 1:
Rebuttal: ## We thank the reviewer for insightful questions that help refine our work further.
# 1. Theoretical analysis done for functions from $R^d\rightarrow R$.
Thank you. In practice, we use the squared L2 distance for multi-dimensional outputs for $D^{fp}$ and $D^{pace}$, which allows our one-dimen... | Rebuttal 1:
Rebuttal: # We thank all the reviewers for constructive feedback and questions shaping our revised paper.
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We have addressed all comments in individual responses to each reviewer.
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Below we just provide few highlights:
* 1. Increased computation and memory requirements: we have provided now **PACE$\... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Initializing and Retrofitting Key-Value Adaptors for Traceable Model Editing | Reject | Summary: The paper addresses the tracable sequential model editing challenge by plugging in additional model components to a transformer MLP blocks. The proposed approach adds additional model components for each edit, allowing for traceability for each edit.
Strengths: • The results indicate that it is a strong appro... | Rebuttal 1:
Rebuttal: ## Reply to reviewer T9Qb
Thank you, Reviewer T9Qb, for your valuable feedback! We appreciate your recognition of our work. This response primarily addresses the questions you raised in your review.
- Treat T-Patcher [1] as baseline (Weakness 2). The reason for missing of T-Patcher as baseline is... | Summary: This paper introduces iReVa, a novel method for model editing that explicitly initializes and retrofits key-value pairs into MLP blocks of transformer models to perform CRUD (Create, Read, Update, Delete) operations on LMs. iReVa aims to update knowledge in LMs without damaging irrelevant knowledge, offering b... | Rebuttal 1:
Rebuttal: ## Reply to reviewer QEHi
Thank you, Reviewer QEHi, for your valuable feedback! We appreciate your recognition of our work. This response primarily addresses the questions you raised in your review.
- More baselines (Weakness line 1): Indeed, the baselines you mentioned are worth comparing. We ex... | Summary: This paper introduces a novel method called iReVa for knowledge editing. iReVa initializes and retrofits key-value pairs into MLP blocks to create a new mapping of knowledge without affecting related information. Compared to existing methods, iReVa offers better interpretability and a stronger ability to make ... | Rebuttal 1:
Rebuttal: ## Reply to reviewer vMmP
Thank you, Reviewer vMmP, for your valuable feedback! We appreciate your recognition of our work. This response primarily addresses the questions you raised in your review.
- Figure issue (Weakness 1): Thanks for your reminder. We will revise the figure and adjust the im... | Summary: This paper focuses on model editing at a low cost. Evidence suggests that modules carrying knowledge in a Transformer module are primarily the MLP blocks. Therefore, the authors propose a method, namely iReVa, to initialize and retrofit key-value pairs into MLP blocks in a Transformer for explicitly inserting ... | Rebuttal 1:
Rebuttal: ## Reply to reviewer QtC8
Thank you, Reviewer QtC8, for your valuable feedback! We appreciate your recognition of our work. This response primarily addresses the questions you raised in your review.
- Increasing the number of parameters (Question 1): In Section 6.3 of our paper, we analyzed the s... | Rebuttal 1:
Rebuttal: # Global rebuttal
We appreciate all reviewers' invaluable feedbacks! This section is our global rebuttal, which addresses common questions raised by multiple reviewers. We hope all reviewers will see this. The following is the content.
## Difference between iReVa and T-Patcher
- Mistaken knowled... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation | Accept (poster) | Summary: The paper proposes the One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adaptive (PSTUDA) framework for kidney and tumor segmentation in multi-sequence MRI, addressing inefficiencies in existing one-to-one UDA methods. PSTUDA features a multi-level style dictionary and multiple cascading style fu... | Rebuttal 1:
Rebuttal: We would like to thank you for the positive consideration and the useful feedback on our work. We address all your concerns below.
**W1. About Performance Enhancement.**
The one-to-multiple UDA task presents many additional challenges compared to the one-to-one task. For example, the differences... | Summary: The authors propose a novel and efficient One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adaptive (PSTUDA) framework to address the UDA task for MRI sequences. Specifically, they developed a multi-level style dictionary that explicitly stores style information for each target domain at differen... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful comments. We address all weaknesses and questions below.
**W1. Open-source code is not explicitly provided.**
We will release the code, pre-trained models, and detailed implementation guidelines upon acceptance. To ensure reproducibility, we... | Summary: The paper presents a one-to-multiple progressive style transfer unsupervised domain adaptation framework designed for kidney and tumor segmentation.
It aims to mitigate the challenges of annotation burden and domain differences by employing a multi-level style dictionary and cascading style fusion modules.
... | Rebuttal 1:
Rebuttal: We greatly appreciate your constructive and insightful comments. We address all weaknesses below.
**W1. Practical and clinical relevance of PSTUDA.**
PSTUDA is designed as a one-to-multiple UDA framework based on multi-sequence MRI segmentation tasks, thus having strong clinical relevance. Comp... | null | null | Rebuttal 1:
Rebuttal: Dear reviewers and AC,
We sincerely appreciate your valuable time and effort spent reviewing our manuscript. We would like to thank all the reviewers for providing insightful comments and valuable suggestions. The valuable feedback from the reviewers has significantly contributed to enhancing the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stopping Bayesian Optimization with Probabilistic Regret Bounds | Accept (poster) | Summary: The paper introduces a Monte-Carlo-based stopping criterion for Bayesian optimization tasks, which has been neglected in the Bayesian optimization literatures. The authors provide a generic algorithm that can be tailored to Bayesian optimization setting and establish theoretical results and demonstrate numeric... | Rebuttal 1:
Rebuttal: Thank you for reviewing our submission. We are sorry to hear that you found our presentation of the material underwhelming. Some of your feedback was similar to that of another reviewer, so please see our global response for additional details.
**What does $f_t$ represent?**
Per the Section 2, ... | Summary: This paper develops a $(\varepsilon,\delta)$ stopping criterion for Bayesian optimization algorithms. The authors propose a probabilistic regret bound estimator, which is constructed through sampling the function and find the maximum points of these samples, to decide when to stop the algorithm. They also gi... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will respond to each of your comments below.
**Assumption A3**
In order to make high-probability statements about $f$'s global properties, we must eventually learn enough about the function. We chose to enforce this condition by assuming that $(x_t)$ is dense in... | Summary: This paper addresses the challenge of determining when to stop a Bayesian optimization process. Traditional methods rely on exhausting a predefined budget, but this work proposes an alternative approach based on probabilistic criteria as a stopping criterion.
Key Contributions:
New Stopping Criterion: The pape... | Rebuttal 1:
Rebuttal: We are pleased to hear that you enjoyed our submission.
**Is A3 practical for high-dimensional problems?**
We give a detailed reply below. The short answer is that what matters in practice is how smooth $f$ is relative to the size of $\mathcal{X}$.
For simplicity, assume $f \sim \mathcal{GP}... | Summary: This paper proposes estimators for Bayesian optimisation stopping rules backed by theoretical guarantees. The stopping criterion estimators are probably approximately correct (PAC) and derived from sample-based estimates of algorithm’s simple regret according to a Gaussian process model. Experiments complement... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We will address most of your points below.
**Missing details**
Your comment that important details about how the presented material is used in the context of BO has been heard. We will rework the text to ensure this content is clearly communicated. Please ... | Rebuttal 1:
Rebuttal: This post discusses feedback that was common to multiple reviewers. Comments raised by individual reviewers will be addressed in subsequent posts.
As a preliminary remark, we note that reviewers seem to think that our submission makes solid contributions to theory and/or practice. The primary cr... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
No-Regret Bandit Exploration based on Soft Tree Ensemble Model | Accept (poster) | Summary: This thesis presents a stochastic bandit algorithm ST-UCB based on the soft-tree ensemble model for reward estimation and regret minimization. This algorithm exploits the properties of the soft-tree model and extends the neural robber theory to a tree-based structure, proving that under appropriate assumptions... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and suggestions.
> Although the paper proves that the ST-UCB algorithm achieves regret-free performance under specific regularity conditions, these conditions may be difficult to satisfy or validate in practical applications.
> It is recommended to explo... | Summary: The paper proposes an algorithm for multi-armed bandits, where the reward function is estimated with a soft tree ensemble method. The paper present their model as an extension to NN-UCB but with sublinear regret guarantees at the cost of a reduced hypothesis space. The generalized theory of neural tangent kern... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive assessment of our paper.
We describe the answers to the question of the reviewer
> Is there a point where M is to large and the tree ensemble becomes too complex?
In theoretical perceptive, the huge $M$ improves the regret even if the complexity of ... | Summary: This paper presents the Tree Neutral Tangent Kernel (TNTK) for soft tree models and introduces the ST-UCB algorithm based on this kernel. The authors also provide theoretical guarantees for the kernel and algorithm, including a regret bound. The contribution of this study is substantial; however, many sections... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful feedback and suggestions.
To address the readability issues that the reviewer kindly pointed out, we will carefully revise our paper as follows:
- The description of TNTK:
- In Section 2, we will add the detailed introduction of TNTK, including its defin... | Summary: The paper investigates soft tree ensemble model for reward modeling in bandit algorithms.
Strengths: The work provides comprehensive formal evidence for the proposed methods.
The empirical study is also comprehensive.
Weaknesses: Not sure if there is some new insights beyond other similar works that utilize... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments.
> Could you share more insights on why soft tree ensemble model is of particular interest? In which case we should prefer tree-based reward model than ReLu-based reward model?
From a practical perspective, the performance of a bandit algorithm depends on... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Training Binary Neural Networks via Gaussian Variational Inference and Low-Rank Semidefinite Programming | Accept (poster) | Summary: The paper is concerned with variational inference in binary neural networks, e.g., neural networks that contain weights quantized to be either -1 or +1, with the aim of improving the overall performance of binary neural networks by performing Bayesian model averaging. For this, the authors review prior work on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their assessment and questions. We respond to each point inline below. We encourage the reviewer to ask any follow-up question and to comment on our responses. We hope that they will consider raising their score at the end of our exchange.
> The results look promising b... | Summary: The paper proposes a Gaussian variational inference approach to training binary neural networks. Unlike traditional VI methods, the proposed VISPA algorithm is motivated by a SDP relaxation of the binary neural network objective. In experiments, VISPA gives state-of-the-art results for training binary neural n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their assessment and questions. We respond to each point inline below. We encourage the reviewer to ask any follow-up question and to comment on our responses.
> The non-diagonal low-rank representation of the covariance adds a significant overhead. It is unclear whether... | Summary: This paper aims to propose a new method training binary neural networks.
The method, according to the authors claims, is based on variational inference and semi-definite programming.
During training, the method maintains a low-rank Gaussian distribution from which the neural network parameters are drawn.
The t... | Rebuttal 1:
Rebuttal: We thank the reviewer and regret to find that the connections to variational inference and semidefinite programming were not clear to them. We believe that, in the attempt to streamline the presentation of the mathematical component of our method, we may have omitted some steps that clarify such c... | Summary: The paper provides a theoretical framework for BNN training (a longstanding model compression and computational speed up technique) based on Gaussian variational inference. The framework succeeds in providing theoretical grounding for practical and well established techniques for BNN training - specifically St... | Rebuttal 1:
Rebuttal: We thank the reviewer for their assessment and questions. We respond to each point inline below. We encourage the reviewer to ask any follow-up question and to comment on our responses. We hope that they will consider raising their score at the end of our exchange.
> The paper can benefit from em... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time. We are grateful that most reviewers agree on the __novelty of our framework__ based on Gaussian variational approximation, the __clarity of the submission__ and the __significance of the performance improvements__ revealed by our experiments. In this rebuttal... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents an optimization framework for Binarized Neural Network (BNN) training using Gaussian variational inference, resulting in a low-rank semidefinite programming (SDP) formulation. The authors propose the Variational Inference Semidefinite Programming Algorithm (VISPA) to improve accuracy by mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for their assessment and questions. We respond to each point inline below. We encourage the reviewer to ask any follow-up question and to comment on our responses. We hope that they will consider raising their score at the end of our exchange.
> How does VISPA perform on ot... | null | null | null | null | null | null |
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning | Accept (poster) | Summary: The authors propose a new task called Open-World Video Instance Segmentation and Captioning (OW-VISCap), which involves detecting, segmenting, tracking, and describing both seen and unseen objects. To tackle this problem, they introduce two networks: an object abstractor for encoding images at the object level... | Rebuttal 1:
Rebuttal: **Despite being the first proposed method for this task, the technical contribution seems weak.**
Please see the “Technical Novelty” section in the comment addressed to all reviewers.
**1. Evaluation on Limited Benchmarks ... Other works like OV2Seg [1] and DVIS++ [2] use a variety of datasets,... | Summary: This paper propose a new task and the corresponding model: detecting, tracking, segmenting, and captioning open-vocabulary objects in a video. The authors proposed a novel online framework that contains an object detector and feature detector (Object abstractor), a Mask2former style segmentation head, and a fr... | Rebuttal 1:
Rebuttal: **From Figure 3, it is unclear how "video" is handled. ..., would the object caption in different frames be different for the same identity?**
Yes, we feed object queries in the previous frame to new frames as proposed in CAROQ. We explain this in Appendix C.
Object captions may differ in differ... | Summary: This paper introduces a task called “open-world video instance segmentation and captioning”, which combines open-world video instance segmentation (OW-VIS) and video object captioning tasks. To achieve better performance, the authors propose two key components: an object abstractor to identify new objects usin... | Rebuttal 1:
Rebuttal: **1. The introduced task ... not fundamental and novel enough.**
We think that OW-VISCap is a novel task that identifies an important gap in existing literature. We agree with reviewer 9nUt: OW-VISCap is “one of the ultimate understanding tasks for video”. Fine-grained object captioning in videos... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their helpful feedback. We are thrilled that they find our work novel (Reviewer 9nUt), thoroughly analyzed (Reviewer W8BT), and well-written (Reviewer zWxZ). Reviewers zWxZ and W8BT have raised questions about the technical novelty and experimental performance of our met... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Boosting Transferability and Discriminability for Time Series Domain Adaptation | Accept (poster) | Summary: The paper introduces Adversarial CO-learning Networks (ACON) to improve unsupervised domain adaptation (UDA) for time series classification by enhancing the transferability and discriminability of temporal and frequency features. The proposed approach incorporates multi-period frequency feature learning, tempo... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback! Due to limited space, additional results are in **PDF attached to Author Rebuttal** and we summarize the weaknesses and questions below.
### W1: Reproducibility and implementation in real
- Compared with existing works, ACON has the widest evaluation scope (8 d... | Summary: This paper studies the Unsupervised Domain Adaptation (UDA) problem in time series classification. The authors first proposed an insight that temporal features enhance transferability while frequency features enhance discriminability. Based on the insight, the authors designed a model that leverages temporal a... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback! The additional results are presented in **the PDF attached to Author Rebuttal** and we summarize the original weaknesses or questions.
### W1: The intuition behind empirical insight
Here we provide an intuition from the perspective of energy.
Acc... | Summary: The paper shows that temporal features and frequency features should not be equally treated in model training, the expression of those features are different. Frequency features show strong discriminability and temporal features show strong transferability. Then the author propose the Multi-period frequency fe... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We are grateful that the reviewer highlights our method's novelty and solid experiments. We would be happy to explain the motivations behind the different components in more detail.
### W1: Why the alignment between the temporal predictions and the frequency ... | Summary: This paper uncovers an empirical insight in time series domain adaptation - frequency features are more discriminative within a specific domain, while temporal features show better transferability across domains. Based on this insight, it develops ACON (Adversarial CO-learning Networks), which achieves clear i... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback! We are grateful that the reviewer highlights our empirical insight, technical solution and solid experiments. We would be delighted to explore the underlying reasons behind the empirical insight with the reviewer.
### W1: Why the frequency features have worse... | Rebuttal 1:
Rebuttal: Thank you to all reviewers for the thoughtful feedback.
We are pleased that all four reviewers agree with our empirical insight, method novelty and solid experiments. We are also delighted that reviewers recognized this study as a promising solution to UDA for time series.
In response to reviewe... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation | Accept (oral) | Summary: The paper proposes FunCoder, a code generation framework incorporating the divide-and-conquer strategy with functional consensus. FunCoder recursively branches off sub-functions as smaller goals during code generation, represented by a tree hierarchy. These sub-functions are then composited to attain more comp... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper! These are very helpful suggestions and we address your questions here:
> **W1: The sections detailing the approach are somewhat brief, which leaves me wanting a deeper understanding of the full method, especially since the study focuses o... | Summary: This paper presents Divide-and-Conquer Meets Consensus -- a prompting scheme for generating complex functional code. In contrast to planning ahead of time, the proposed technique performs planning in smaller steps by decomposing a coding task into smaller sub-tasks recursively and solving them when they are si... | Rebuttal 1:
Rebuttal: We thank you for your time and effort in reviewing our paper! We find your suggestions very helpful and we hereby address your questions:
> **W1: The equivalence-modulo-inputs idea requires knowing the accurate input domain and designing corresponding input samplers, which are oversimplified in t... | Summary: The paper presents FuncCoder, a novel coding framework designed to enhance code generation by incorporating a divide-and-conquer strategy with functional consensus. FuncCoder addresses the limitations of existing methods that struggle with complex programming tasks by recursively breaking down problems into sm... | Rebuttal 1:
Rebuttal: We thank you for your time and effort in reviewing our paper! These are very helpful suggestions and we address your concerns as following:
> **W1: The recursive decomposition of tasks into sub-functions inherently leads to the generation of numerous function headers, bodies, and documentations, ... | Summary: The author applies divide-and-conquer methods to code generation problems with Large Language Models (LLMs). A problem is divided into subproblems recursively; that is, the function that solves the problem is generated in a top-down manner. Given the parent function, the LLM is prompted to implement it using c... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and effort in reviewing our paper! We find your suggestions very helpful and we address your questions as follows:
> **W1: The idea of decomposing the problem into subproblems and solving them recursively with an LLM is not entirely new.**
As is discussed in ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for taking the time and effort in reviewing our paper, and we find these comments very constructive and inspiring. Hereby we address some of the most common concerns and questions, adding additional experiments and analyses as-appropriate. We hope that this information w... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Depth Anything V2 | Accept (poster) | Summary: The paper tries to answer two important questions in current monocular depth estimation: 1. whether generative-based models can generate more fine-grained depth than discriminative models. 2. The effectiveness of synthetic data and large-scale pseudo data. The results show that discriminative models can also g... | Rebuttal 1:
Rebuttal: **Q1: Quantitative comparison on depth sharpness.**
Quantitatively comparing different depth models in terms of sharpness is not trivial, mainly due to two reasons: 1) a lack of well-defined metrics for sharpness, and 2) the absence of *diverse, real, and fine-grained* benchmarks to evaluate this... | Summary: Depth Anything V2 is an advanced monocular depth estimation model that improves upon its predecessor by utilizing synthetic images, enhancing the teacher model's capacity, and leveraging large-scale pseudo-labeled real images for training. This approach results in significantly faster and more accurate depth p... | Rebuttal 1:
Rebuttal: **Q1: The performance if even more data is added.**
Thank you for raising this insightful question. Currently, we have used 62M unlabeled images from eight *highly curated* public datasets, *e.g.*, SA-1B and Open Images. As you mentioned, based on the positive scaling curve of experiments in Tabl... | Summary: The authors introduce Depth Anything V2, a powerful monocular depth estimation model. This model relies entirely on synthetic data to train a teacher depth estimation model, which is then used to generate pseudo-labeled real images. These pseudo-labeled images are subsequently fed into the training pipeline. D... | Rebuttal 1:
Rebuttal: **Q1: Our contributions and novel insights.**
We do not position the pipeline as our contribution. There are many dimensions to measuring a paper's contributions. From the very start of our paper (L1-3, L41-45), we emphasize that, *instead of proposing a new module or pipeline, this work is cente... | Summary: A system is proposed for scaling up large monodepth estimation models to achieve very strong zero-shot performance, focusing on finely detailed depth prediction. Essentially, a teacher model is trained on synthetic depth datasets with perfect ground truth, and thereafter distilled on a large in-the-wild datase... | Rebuttal 1:
Rebuttal: **Q1: Back-project the depth map into 3D as a point cloud.**
Thank you for your valuable advice. We have added some visualizations of the back-projected point clouds in the one-page PDF. Due to space constraints, we were only able to include a limited number of examples. However, we will try to i... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive and insightful feedback. We have addressed each of the raised concerns individually. Additionally, we have provided a one-page PDF below with further visualizations and qualitative comparisons. We look forward to your further feedback. Thank you ve... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
StepbaQ: Stepping backward as Correction for Quantized Diffusion Models | Accept (poster) | Summary: This paper introduces a new perspective on quantization in diffusion models. It views the quantization error as causing a "stepback" in time within the latent space during the denoising process. The paper proposes StepbaQ which adjusts the sampling steps to correct the sampling path and reduce the buildup of q... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
> This paper does not provide solid theoretical guarantees or an in-depth analysis of how the temporal shift impacts the scheduled sampling trajectory.
We would like to clarify this conc... | Summary: This paper proposes a novel perspective of quantization error in diffusion models being equivalent to a "step back" in the denoising process. The authors are show that one can effectively quantify this stepback using a small calibration dataset, and continue the diffusion process as usual after correcting for ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
> How hard is it to estimate the quantization error variance for different models/denoising methods?
Estimating the variance of quantization error is a straightforward process. We begin b... | Summary: This paper proposes the StepbaQ, a general strategy designed to enhance the performance of quantized diffusion models which employs a sampling step correction technique to realign the sampling trajectory and eliminate the accumulation of quantization error. Experiments show that the proposed StepbaQ improves p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
> It is not clear that how the definition of stepback will impact performances if the stepback consists of multiple steps.
Here we provide an example to illustrate cases where stepback c... | Summary: The paper introduces a novel method to address the issue of accumulated quantization errors in quantized diffusion models. It attributes the quantization error to a "stepback" in the denoising process, and it introduces a sampling step correction mechanism to mitigate the adverse effects of accumulated quantiz... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
> The author should provide more justification for the argument in Equation (5) that the quantization error can be modeled as a Gaussian random variable.
Please see "Author Rebuttal by Au... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their constructive comments. Common questions asked by multiple reviewers would be replied here in a unified manner.
> Justification of modeling quantization error as a Gaussian random variable
Our assumption that quantization error follows a Gaussian... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper is well organized and logically understandable. The author reconsidered the quantization error as a "stepback" problem and provided concrete theoretical illustration. The author looked into latent variables behind general quantization errors in sampling trajectories, which impressed significantly wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and constructive comments. We provide our responses as follows.
> Experimental results on sd v1.5 and sdxl-turbo only takes native PTQ and PTQD into consideration, which makes the result less competitive.
The experiments presented in Section 5.1 are ... | null | null | null | null | null | null |
Deep Implicit Optimization for Robust and Flexible Image Registration | Reject | Summary: This paper presents a novel image registration framework that aims to bridge the gap between classical and learning-based approaches. It incorporates fidelity optimization directly into the neural network as a layer. The framework employs end-to-end implicit differentiation through an iterative optimization so... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed and insightful feedback. We are excited to hear the reviewer found the paper both clear and innovative, and appreciated the paper’s technical depth, insight provided by Fig.2. We believe we have addressed all concerns, and look forward to engaging in a disc... | Summary: This paper introduced DIO, a differentiable implicit optimization layer to a registration network that aimed to bridge the gap of classical-learning-based image registration, considering the incorporation of weak supervision like anatomical landmarks into the learned features. The authors decoupled feature lea... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We are delighted to learn that the reviewer finds the paper well written and appreciates multi-scale feature optimization to be an interesting approach. We believe we have addressed all the remaining concerns and hope the reviewer increases their score and... | Summary: The authors introduce the idea of implicit optimization, and coupled feature extraction for images, to achieve robust image registration. I liked the overall idea and was eager to gain insight into how implicit optimization
Strengths: I really liked the overall ideas here.
An implicit optimization layer doe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback, and we’re glad to note that they found the paper overall very interesting. The review has been immensely helpful in improving the quality of our work. We believe we have addressed all concerns in the rebuttal, and we look forward to a discussion... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful feedback and for taking time to improve the quality of our work. We are glad that reviewers found the overall idea [UTrd, ] and multi-scale optimization idea [3syP] interesting, innovative and insightful [uXMo], clear and concise writing [UTrd, 3syP, uX... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Truthfulness of Calibration Measures | Accept (poster) | Summary: The authors evaluate a variety of existing calibration measures according to their completeness, soundness, along with truthfulness which is their main focus, by theoretically showing how the measures satisfy these aforementioned criteria. They discover that existing measures, despite being complete and sound,... | Rebuttal 1:
Rebuttal: **Related works tackling truthfulness.**
As we mention in the paragraph starting on Line 34, to the best of our knowledge, no prior work has systematically investigated or formally defined the notion of truthfulness. Instead, they observed the lack of truthfulness in certain calibration measures,... | Summary: The paper initiates the study of truthful calibration measures. It introduces a set of requirements --- truthfulness, completeness, soundness, and asymptotic calibration --- that together define a novel and fruitful set of requirements on calibration measures. It explores the classic sequential binary predicti... | Rebuttal 1:
Rebuttal: **Definition of soundness.**
At a high level, soundness mirrors the completeness and requires that intuitively bad predictions should result in a high calibration error. In this paper, we establish minimal conditions for soundness by considering two particular types of bad predictions, (1) predic... | Summary: The paper studies calibration measures in a sequential prediction setup. In addition to rewarding accurate predictions (completeness) and penalizing incorrect ones (soundness), the paper formalizes another desideratum of calibration measures — truthfulness. A calibration measure is truthful if the forecaster (... | Rebuttal 1:
Rebuttal: **Connection to "event-conditional unbiasedness".**
In our view, the event-conditional unbiasedness in [Definition 2.3, NRRX23] is a strengthening of the notion of "calibration with checking rules" from [FRST11], where each checking rule specifies a subset of time horizon $[T]$ on which the calib... | Summary: This paper proposes revisiting the calibration measure by emphasizing three main desirable properties for the metric's behavior: rewarding accurate predictions, penalizing incorrect ones, and ensuring truthfulness. The authors define truthfulness as the ability to accurately predict the conditional expectation... | Rebuttal 1:
Rebuttal: **Summary of paper.**
We believe that there is a possible typo or misunderstanding in the summary written by the reviewer---our new calibration measure, $\mathsf{SSCE}$, is obtained from the smooth calibration error ($\mathsf{smCE}$) plus subsampling, rather than from the ECE plus subsampling.
*... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods | Accept (poster) | Summary: This paper provides a tensor network (TN) view of studying convolutional networks, including forward passes, first-order and second order derivatives. Based on these notations, the authors show how to efficiently implement some algorithms such as KFAC and structured dropout. Experimental results show that thes... | Rebuttal 1:
Rebuttal: Dear Reviewer vbTD,
thanks for your support! We appreciate the time and effort you put into reviewing our work.
> The authors show faster speed of KFAC computation. Does this implementation bring better convergence, training speed or results in practice?
You have a point that we did not evaluat... | Summary: The paper presents a novel approach to simplifying and optimising TN operations for CNNs. The authors introduce an abstraction for tensor networks to efficiently handle convolutions and leverage TN simplifications to improve computational performance. The paper details the theoretical foundations, implementati... | Rebuttal 1:
Rebuttal: Dear Reviewer cYbp,
thanks for your strong support and detailed review! We will apply your suggested improvements to the text.
We would like to inform you that we conducted a new experiment with a real second-order method based on KFAC-reduce (see our [global rebuttal](https://openreview.net/for... | Summary: ### Summary of "Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods"
The paper "Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods" presents a compelling and innovative recast of convolution operations into tens... | Rebuttal 1:
Rebuttal: Dear Reviewer iQrS,
thanks a lot for your thorough review; we appreciate the work you put into it and are glad you find the paper innovative and support our idea of making the complex yet powerful tensor network toolbox accessible to the ML community.
We would like to inform you that we conducte... | null | null | Rebuttal 1:
Rebuttal: # Evaluation on a real second-order method
Dear Reviewers,
We are glad to inform you that we successfully applied our work to a real second-order method to complement the speed-ups of fundamental operations shown in the manuscript. Specifically, we took the KFAC-based SINGD optimizer [1] and ben... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Investigating Variance Definitions for Stochastic Mirror Descent with Relative Smoothness | Reject | Summary: This paper investigates a new definition for the stochastic gradient variance in mirror descent.
Most existing analyses for stochastic mirror descent require a strongly convex distance generating function to bound the gradient variance.
This limits the their applications especially when this assumption fails.
... | Rebuttal 1:
Rebuttal: Thank you for your review, we discuss your two main points below.
**Diminishing step-sizes:** One would have to characterize some kind of average variance indeed. Yet, when $\sigma^2_\eta$ converges for small $\eta$ (Proposition 2.6), one can expect to obtain bounds of the form $\sigma^2_\eta \l... | Summary: This work revisits Stochastic Mirror Descent (SMD) proofs in the (relatively-strongly-) convex and relatively-smooth setting, and introduces a new (less restrictive) definition of variance which can generally be bounded (globally) under mild regularity assumptions. Then this paper investigates this notion in ... | Rebuttal 1:
Rebuttal: Thank you for your review.
We agree with you that the minima are not the same, and this is actually a crucial point in the paper, otherwise we would directly obtain that $\sigma^2_\eta = \frac{1}{\eta}^2 \mathbb{E}D_h(x_\star, x_\star^+)$, which is not true. Yet, this holds asymptotically, as we... | Summary: The paper proposes a new analysis of SMD using a newly introduced generalized variance notion. The benefit of the new analysis is demonstrated in the application to maximum a posteriori estimation of Gaussian parameters.
Strengths: After introducing a new variance notion, the paper delves into comparison with... | Rebuttal 1:
Rebuttal: Thank you for your review and careful comments, we will make sure to fix all points you raised in the next revision.
Before we start the point-by-point answer, we kindly ask you whether you could provide references for non-asymptotic rates on $D_f(\theta_\star, \theta)$ (since they probably do n... | Summary: This paper introduces a new variance assumption for the analysis of stochastic mirror descent (SMD) to handle cases where standard bounded variance assumption does not hold. The authors show this new assumption can be shown to hold under some regularity assumptions. The authors use the new results to show some... | Rebuttal 1:
Rebuttal: Thank your for your careful review and suggestions. We will change MLE to MAP in the abstract and remove the 'strong' adjective, which refered to the fact that we obtain similar guarantees for SMD as what we have in the `usual' setting for SGD, and was not specifically aimed at the statistical set... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful evaluation and detailed feedback, which will greatly help us improve the clarity of our paper, in particular regarding the precise (and minimal) set of assumptions under which our results hold. We answer all questions point-by-point in the specific rebu... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This submission studies stochastic mirror descent (SMD) under quite mild conditions on the mirror map and objective function. More specifically, there are a variety of SMD analysis in the literature, but virtually all of them require strong conditions on the mirror map (such as strong convexity) that do not ho... | Rebuttal 1:
Rebuttal: We would like to thank you for your detailed review, understand your concerns, and clarify these points below.
**Questions 1 and 2**: From what we understand from your review, most concerns come from the `minimal assumptions on $h$' remark from lines 278-283. We realize that this remark can be c... | null | null | null | null | null | null |
ParallelEdits: Efficient Multi-Aspect Text-Driven Image Editing with Attention Grouping | Accept (poster) | Summary: This paper aims to make multiple objects or attributes editing, while preserving the quality, named ParallelEdits. The paper also introduces a new dataset PIE-Bench++. Experimental results on both PIE-Bench and PIE-Bench++ demonstrate that this method outperforms many existing editing techniques.
Strengths: S... | Rebuttal 1:
Rebuttal: **Q: Clarification of the inversion and editing method.**
**A:** We agree with the reviewer. Each baseline in Table 1 and throughout the paper includes a submethod for inversion and another for editing. The table below details these submethods for each baseline. We will update line 36 of the intr... | Summary: The paper introduces ParallelEdits, a method that manages simultaneous edits efficiently without compromising quality. This is achieved through a novel attention distribution mechanism and multi-branch design. Additionally, the authors present the PIE-Bench++ dataset, an expanded benchmark for evaluating multi... | Rebuttal 1:
Rebuttal: **Q:** The DDCM process in [1] comes to a simple conclusion that the output z equals $z_0$ after dozens of iterations.
**A:** Indeed, ParallelEdits' source branch uses DDCM, as described in [1], which uses the consistency sampling step popular in consistency model literature [6,7]. DDCM process f... | Summary: In this paper, the authors present a novel multi-aspect image editing method ParallelEdits, by incorporating the attention distribution mechanism and multi-branch editing. Besides, this paper introduces a new dataset PIE-Bench++ for evaluating multi-aspect image editing. Extensive experiments demonstrate the e... | Rebuttal 1:
Rebuttal: **Q:** The pairing process imposes an additional burden on users when performing multi-object editing.
**A:** The algorithm receives the pairing process (editing action) to determine which aspect is added, removed, swapped, or left unaltered. Meta-data of this form is not a burden but a necessity... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time, insightful suggestions, and valuable comments. We are grateful for the positive recognition of the reviewers that our idea and task are interesting (Reviewers 8ae7 and J7sk), the method is efficient for application (Reviewers J7sk), and our editing result... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Optimal Design for Human Preference Elicitation | Accept (poster) | Summary: This paper studies the problem of data collection for learning preference models. The key idea is to generalize the optimal design, a method for computing information gathering policies, to ranked lists. The authors study both absolute and relative feedback on the lists.
Strengths: 1. The considered problem i... | Rebuttal 1:
Rebuttal: We wanted to thank the reviewer for carefully reading the manuscript, and especially for pointing out how to present the algorithms better. We answer all questions below. If you have any additional concerns, please reach out to us to discuss them.
**W1: Optimal design in Section 5**
The design i... | Summary: The paper considers experiment design for collecting ranking/direct feedback, with an application to fine tuning language models.
Author formulate active exploration for fine-tuning as a ranking problem and propose an algorithm which satisfies the standard guarantees for its ranking loss. Experiments on synthe... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed feedback and positive evaluation of the paper. We answer all questions below. If you have any additional concerns, please reach out to us to discuss them.
**W1: Novelty in optimal designs**
A good introduction to optimal designs is Chapter 21 in [48]. At a high... | Summary: This manuscript deals with preference models which are at the crossroads of linear bandits, ranking models, and optimal designs. The authors consider a model where one has to rank L lists of K objects. The expected reward for object k of list i is x_{i,k}\theta^* and \theta^* \in \mathbb{R}^d is some unknown q... | Rebuttal 1:
Rebuttal: We thank the reviewer for detailed feedback. We answer all questions below. If you have any additional concerns, please reach out to us to discuss them.
**W1a: Uniform design would suffice to get a $\tilde{O}(d^2 / n)$ rate in Theorem 3**
We respectfully disagree. Consider the following example.... | Summary: This paper presents a novel approach for data collection to learn preference models from human feedback. The key innovation is generalizing optimal design, a method for computing information gathering policies, to ranked lists. The authors study both absolute and relative feedback settings, developing efficien... | Rebuttal 1:
Rebuttal: We thank the reviewer for positive feedback and acknowledging that our work is solid. We answer all questions below. If you have any additional concerns, please reach out to us to discuss them.
**Q1: Synthetic experiments beyond $L = 400$ and $K = 4$**
We vary the number of lists and items, $L \... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning | Accept (poster) | Summary: This paper studies the problem of auditing DP-SGD in the black box threat model, i.e. only black-box access to the last iterate $\theta_T$ of DP-SGD. The paper's main contribution is to show experimentally that pre-training helps to get tighter auditing. The argument behind using this idea is that pre-training... | Rebuttal 1:
Rebuttal: We thank reviewer JVDo for their helpful and constructive feedback. In the following, we address their comments and questions and clarify how we believe we can address their concerns regarding weaknesses.
> **Contributions of crafting worst-case initial model parameters**
Although the general id... | Summary: The paper considers the problem of auditing DP-SGD, i.e. deriving empirical lower bounds on the DP parameter $\epsilon$. Many previous auditing works operated in the white-box setting, i.e. are allowed to choose arbitrary gradients for the canary (sensitive example) during DP-SGD, or were in the black-box sett... | Rebuttal 1:
Rebuttal: We thank reviewer uyKT for their helpful feedback. While their questions are hopefully addressed in Point 2. of the Global Author Rebuttal comment, we address their comments regarding weaknesses here.
> **Pre-training on another distribution**
We apologize as we are not entirely sure what the re... | Summary: This paper proposes a new method for auditing DP-SGD in a black-box setting, where the auditor can only see the final parameters of the model (rather than intermediate steps). The main idea is to select worst-case initial model parameters; this seems to give a substantial advantage to the black-box analysis.
... | Rebuttal 1:
Rebuttal: We thank reviewer ncD1 for their helpful feedback. In the following, we address their comments and questions.
> **Motivation**
Thank you very much for your comments re. our motivation. Indeed, our work is primarily focused on the “New Insights” type of motivation, i.e., determining if the analys... | Summary: The paper shows that privacy auditing is tight in the threat model where the model initialization is adversarially selected. They find that the decrease in gradient norms over the course of training helps improve the "signal to noise" ratio of the auditing example.
Strengths: The paper's main finding is usefu... | Rebuttal 1:
Rebuttal: We thank reviewer qSii for their helpful feedback. In the following, we address their comments and questions.
> **When other samples’ contributions are smaller, auditing is tighter**
While the overarching intuitions are similar, our strategy is appreciably different from prior work [18, 27], as ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful feedback and suggestions. In this message, we clarify points mentioned by multiple reviewers. We also address each reviewer’s concerns separately in individual comments.
**1. Novelty of worst-case initial model parameters (qSii, JVDo)**
As also noted ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch | Accept (poster) | Summary: This paper proposes an algorithm that tackles the offline RL task (for tabular state and action space) under two key challenges (i) the mismatch between the environment dynamics used to generate the dataset and the environment used to run the learned policy (for evaluation), and (ii) the bias in the data-colle... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful feedback. In the following, we provide point-to-point responses to the weaknesses and questions.
**Question 1. Empirical performance in tabular environments** We first refer the reviewer to Section A of the appendix, where we compare our alg... | Summary: The authors propose a unified principle of pessimism using distributionally robust Markov decision processes (MDPs) to handle both data sparsity and model mismatch. They construct a robust MDP with a single uncertainty set and demonstrate that the optimal robust policy achieves a near-optimal sub-optimality ga... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful feedback. In the following, we provide point-to-point responses to the weaknesses and questions.
**Weakness 1. Experiment results on more complex and diverse benchmarks.**
We further provide two more experiments on large-scale environments in ... | Summary: This paper studies offline reinforcement learning (RL) under model mismatch. The authors propose a unified distributionally robust optimization (DRO) framework that effectively tackles both the uncertainty from limited dataset coverage and the model mismatch between the training and deployment environments.
S... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful and insightful feedback. In the following, we provide point-to-point responses to the weaknesses and questions.
**Weakness 1. Advantages compared to [5] and [35].**
In summary, our method has three major advantages compared to both baselines. Firstly, our wo... | null | null | Rebuttal 1:
Rebuttal: We thank the three reviewers for the helpful and insightful feedback. Besides the point-to-point responses, we provide some additional numerical results in the PDF, and a few responses to address some common questions.
**Compare with baselines [5], [35].**
Our method has three major advantages ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario | Accept (poster) | Summary: To enhance the scalability (mix-and-match) and robustness (details clothing texture preservation) in virtual clothing try-on tasks, the paper proposed three contributions: i) design the Hydra Encoding Block to do parallel self-attention features transmition, ii) present the Prior Model Evolution to leverage gr... | Rebuttal 1:
Rebuttal: I will respond directly to save space.
## Weaknesses
#### 1. Misuse of Citing of TryonDIffusion
**Response:** I apologize for the lack of precision in our wording here. In fact, the principles behind the injection of self-attention and cross-attention are the same, and we will address this issu... | Summary: The paper introduces AnyFit, a solution for multi-garment try-on. Specifically, they propose HydraNet to simultaneously extract features of both upper and lower garments. Then, they present Hydra Fusion to integrate these garment features into a denoising Unet. Additionally, they propose an Adaptive Mask, comb... | Rebuttal 1:
Rebuttal: Dear Reviewer v6TS,
Thank you for your detailed review and constructive feedback on our paper. We appreciate the effort you have put into assessing our work.
## Weaknesses
### 1. Major Innovations
**Response:** Thank you for your valuable feedback. We believe that identifying a problem is mor... | Summary: This paper proposes a novel approach of controllable virtual try-on for any combination of attire across any scenario. It employs a Hydra Block, a lightweight and scalable operator designed for attire combinations, facilitated by a parallel attention mechanism that enables injecting multiple garment features i... | Rebuttal 1:
Rebuttal: Dear Reviewer vsbp,
Thank you for your detailed review and constructive feedback on our paper. We appreciate the effort you have put into assessing our work. Below, we have provided detailed responses to each of your comments and concerns.
## Weaknesses
### Comparison with Baselines using SD1... | Summary: Current image-based virtual try-on methods struggle with achieving high-fidelity and robust fitting across diverse scenarios due to issues like ill-fitting garments and quality degradation. To address this, AnyFit is introduced, leveraging a lightweight, scalable Hydra Block that facilitates feature injection ... | Rebuttal 1:
Rebuttal: Dear Reviewer KSKd,
Thank you for your detailed review and constructive feedback on our paper. We appreciate the effort you have put into assessing our work. Below, we have provided detailed responses to each of your comments and concerns.
## Weaknesses
### 1. Increased Computational Requirem... | Rebuttal 1:
Rebuttal: ## Notes of the figures in the attached PDF.
**Fig.1 note: Ablation about connection from different blocks between HydraNet and MainNet.** In fact, we have questioned the connection between HydraNet and MainNet, and we aimed to understand which specific layers are contributing to this pipeline. P... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multivariate Probabilistic Time Series Forecasting with Correlated Errors | Accept (poster) | Summary: Briefly summarise the paper and its contributions. This is not the place to critique the paper; the authors should generally agree with a well-written summary.
The paper proposes an extension to multivariate forecasting model that takes into account the error auto-correlation. The issue of remaining auto-corr... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's encouraging comments and insightful suggestions.
# Questions
## Most important
> Lack of clarity whether the method will be competitive with better likelihoods.
***Response:*** Thank you for your valuable feedback. The reviewer is correct in noting that a ... | Summary: The paper proposes an approach for multivariate time series forecasting, modelling the correlation between the multivariate errors in close time steps.
The authors show that indeed multivariate errors in close time instants are correlated.
They generalise the work of Zheng et al (Aistats 2024) where the idea o... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's encouraging comments and insightful suggestions.
# Questions
> Could you elaborate if the improvement of CRPS is due to better point forecasts, better variance of the predictive distribution, or both?
***Response:*** In this paper, we use $\operatorname{CRPS... | Summary: The paper proposes a method to improve multivariate probability forecasting by accounting for potential temporal dependencies of the residuals. The paper introduces a dynamic covariance using a small number of latent temporal processes. The method is evaluated on standard dataset of multivariate time-series on... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's encouraging comments and insightful suggestions.
# Weaknesses
> The paper partly relies on the premise that incorporating time-dependency in the residuals is crucial for providing better uncertainty estimates. Demonstrating that the new residuals generated by... | Summary: This paper introduces a method for multi-variate time series forecasting where the residual errors are not assumed to be independent and modeled to be correlated using a Gaussian process model. Standard time-series approaches assume that the errors are temporally independent, however, this assumption does not ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's encouraging comments and insightful suggestions.
# Weaknesses
> The proposed method may not be a completely novel approach to time series forecasting.
***Response:*** Thank you for your valuable suggestions. Our model is not a two-stage model that first f... | Rebuttal 1:
Rebuttal: Dear AC and Reviwers,
We would like to express our gratitude for your thorough and insightful reviews. We have carefully considered each of your comments and suggestions. Below, we provide a summary of our responses to the main points raised by the reviewers.
1. Clarifications and Misunderstandi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Instruction-Guided Visual Masking | Accept (poster) | Summary: In this paper, the author introduce Instruction-guided Visual Masking (IVM), a generic and powerful visual grounding method that enhances broad multimodal instruction following tasks in a plug-and-play way. By masking out all instruction-irrelevant image regions, IVM effectively injects superior visual groundi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the positive feedback and the constructive comment on our work! Regarding the concerns from the reviewer zLHv, we provide the responses as follows:
>**W1: The routine of retraining models by preparing new datasets, as described in this paper, can be consid... | Summary: This paper introduces the Instruction-guided Visual Masking (IVM), a versatile visual grounding model designed to improve alignment between textual instructions and specific image regions. It outlines the development of a visual masking data generation pipeline and a new learning technique, Discriminator Weigh... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the positive feedback and the constructive comment on our work! Regarding the concerns from the reviewer EbuK, we provide the responses as follows:
>**W1: The IVM model architecture shown in Figure 6 is not conducive to understanding the approach.**
- Sorr... | Summary: This paper presents IVM (Instruction-guided Visual Masking). The key idea is that we could mask out the instruction-irrelevant regions in the given image. The trained model is tasked to mask out the irrelevant regions, enforcing the multimodal model to focus on the task-related visuals. Such grounding-centric ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the positive feedback and the constructive comment on our work! Regarding the concerns from the reviewer bwCu, we provide the responses as follows:
>**W1: I don't find significant problems in the paper. One possible improvement for this paper is that it wo... | Summary: For the purpose of precise instruction following performance in LLM, this paper proposes a versatile grounding model that is compatible with diverse multi-modal models. Leveraging the LLM, a visual masking data generation pipeline is built and 1 million image-instruction pairs are constructed. On top of it, an... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the positive feedback and constructive comments on our work! Regarding the concerns from the reviewer 5SdR, we provide the responses as follows:
> **W1: It seems that the comparison with visual grounding methods (VG) is missing. Will the VG task evaluation... | Rebuttal 1:
Rebuttal: ## **General Response**
We sincerely thank all the reviewers for the positive feedback and constructive comments on our work!
Here, we summarize the contents in the attached PDF.
1. For Reviewer EbuK (W1) & zLHv (Q4): We updated the Figure 6 (modified) according to the constructive comments.
2. ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity | Accept (poster) | Summary: Imitation learning in a setting where experts have contextual information not available to the imitator. Imitator sees state-action (no reward) trajectories for the expert data. The paper proposes to use the expert data as prior and then do meta-RL to learn a policy.
Strengths: - The problem setting considere... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and in-depth feedback. We are encouraged that you find the problem setting important, and the proposed method sensible with theoretical and empirical justification. We address your questions below.
> The empirical evaluation is limited to simple domains and it is not... | Summary: The paper proposes a 2-stage learning method for sequential decision making, wherein step 1 involves leaning a prior from expert demonstrations and step 2 is online RL where that learnt prior is used. The main problem being addressed is that of heterogeneity of data and/or contexts provided by the expert and t... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We are pleased that you consider the paper well-motivated, relevant, and with a conceptually easy-to-understand approach. We have addressed your questions below.
> Notation: even with the notation paragraph, I find it quite difficult to follow...?
We assum... | Summary: This paper attempt to leverage offline demonstrations to speed up online learning under unobserved heterogeneity, and unlike zero-shot meta reinforcement learning, the proposed ExPerior does not require the task labels (reward labels). ExPerior utilizes expert data to establish an informative prior distributio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We are encouraged that you find the paper to have a realistic and significant setting and sufficient theoretical and experimental results. We address your questions below.
> Just as stated at the end of the paper, the experiments conducted in the paper are... | Summary: This paper studies online transfer learning in an unknown Markov decision process. The learner has access to demonstration trajectories generated by an expert, who has access to an independent context variable. The expert can observe the values of the latent context to make a decision, but such information is ... | Rebuttal 1:
Rebuttal: Thank you so much for your helpful comments. We are pleased that you find the paper clearly written, well-organized, and with comprehensive simulations. We address your questions in the following.
> The proposed method requires the expert to follow a specific form of policy to generate the demons... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their helpful and thorough feedback. We are happy that they found our work clearly written, well-organized, and with comprehensive simulations (Reviewer ${\color{red} \text{11BA}}$), significant with a realistic setting and sufficient theoretical basis (${\color{blue... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation | Accept (poster) | Summary: This paper aims to design a universal unlearnable example generator for image segmentation tasks. Specifically, this paper aims to address three important factors for unlearnable examples in image segmentation:1) data efficiency 2) generation efficiency 3) transferable efficiency.
To design such a model, th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and valuable feedback.
**Q1: What are the benefits of using the SAM as the backbone for the unlearnable example generation model and the surrogate model.**
One straightforward benefit of using SAM as the backbone is that it renders our entire framework interacti... | Summary: This article uses the powerful SAM to fine-tune a non-learnable examples generator, achieving good protection effects on downstream datasets and models.
Strengths: This article breaks through the concept of unlearnable from classification to segmentation, and the experiment has achieved good results.
Weaknes... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable feedback.
**Q1: Main concern about the author used a relatively strong segmenter to train an unlearnable sample generator, but trained unlearnable samples on a relatively weak segmenter. Is this reasonable in the real world?**
While we understand... | Summary: Aiming to provide a solution for protecting sensitive/private images, this paper introduces UnSeg, a framework designed to generate unlearnable examples (UEs) to protect images from unauthorized usage in image segmentation models. Utilizing the Segment Anything Model (SAM) and bilevel optimization, UnSeg creat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful, constructive and encouraging reviews.
**Q1: More details on the real-world use cases and deployment of the proposed method.**
Our trained universal unlearnable noise generator is very lightweight and can be deployed similarly to SAM. Specifically, the noi... | Summary: The paper addresses the issue of privacy concerns in training large-scale image segmentation models using unauthorized private data. The authors propose a novel framework called Unlearnable Segmentation (UnSeg) to generate unlearnable noise that, when added to images, makes them unusable for model training. Th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful and valuable reviews.
**Q1: Discussion about object masks, and more experiments regarding different prompts.**
The reason why we consider only object masks as prompts, rather than boxes or points, is to **eliminate ambiguity** in the prompts. For example, ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and productive feedback.
We are grateful to read that the reviewers agreed that the motivation and the proposed method in this work is impressive and quite interesting (**2xcM, PRaH**), has been extensively evaluated and achieves excellent protecti... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation | Accept (poster) | Summary: The paper proposes DUSA, which uses diffusion models for test-time adaptation. Class codes are chosen for an image based on the results of a pretrained classifier and random sampling; these codes are used to generate noise candidates, which are combined based on the predicted probabilities for the associated c... | Rebuttal 1:
Rebuttal: **Q1: Existing art, e.g. [1], show there may be some benefit to using a few time steps. It would have been nice to see some exploration of the impact of using more than 1 step, insofar as it would likely make the approximation in Equation 9 more accurate. Was any exploration of ensembling time ste... | Summary: This work extends the Diffusion-test time adaption framework of [26] where the diffusion loss is averaged over timesteps to a simpler and theoretically justified framework, where a single timestep of the diffusion model can extract the semantic priors from the generative model. This reduces the instability of ... | Rebuttal 1:
Rebuttal: **Q1: How the joint update for the two networks is performed is unclear.**
**A1:** Thanks. Before adaptation, **a single fixed timestep is selected** and other timesteps are dismissed. Hereafter, the diffusion model is **only updated on this single timestep**, i.e., we added a **same** level of n... | Summary: This paper proposes to perform Test-Time Adaptation (TTA) with the help of diffusion models, based on the theoretical observations that effective discriminative priors are hidden within conditional diffusion losses.
The method involves a joint adaption of the task discriminative model and the generative diffu... | Rebuttal 1:
Rebuttal: **Q1: Though Eq. 10 is well-supported from theoretical perspective, the alternative Eq. 11 needs further elucidation. Insights or intuitions on why the modified objective (Eq. 11) works as well as the original objective (Eq. 10)?**
**A1:** Thanks for the valuable question. Please note that we can... | Summary: Tackling the limitations of prior research on test-time adaptation using pre-trained diffusion models, this study expands the diffusion prior for more practical scenarios. The authors extend the use of the diffusion prior to dense prediction tasks, enhancing inference speed with a refined diffusion loss functi... | Rebuttal 1:
Rebuttal: **Q1: The algorithm in Appendix should be integrated into the main paper.**
**A1:** Thanks. Following your advice, we will integrate the algorithm into the main body of the paper in the revision.
**Q2: The term "fresh" is used ambiguously in lines 66-68.**
**A2:** Good suggestion. We agree that... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their efforts in reviewing our paper and the constructive feedbacks that are quite helpful in improving the quality of the paper. We are more than encouraged that reviewers find:
+ our research topic of utilizing discriminative semantic priors within diffusion... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
How Does Message Passing Improve Collaborative Filtering? | Accept (poster) | Summary: This paper rethinks the application of message passing mechanisms in collaborative filtering methods and makes two key findings: 1) Message passing (MP) improves collaborative filtering primarily through the forward pass rather than the backward propagation process, and 2) MP is more effective for cold-start u... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We really appreciate your acknowledgment of our proposal's significance, theoretical soundness, and presentation. Please see below for our detailed response:
## W1: Clarification on Equation 4.
* The purpose of Equation 4 is comparing differences between a v... | Summary: This paper investigates the role of message passing in collaborative filtering, providing empirical analysis of its impact. Based on their findings that message passing primarily benefits through additional neighbor representations during forward passes and helps low-degree nodes more than high-degree nodes, t... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We really appreciate your acknowledgment of our proposal's efficiency and our experiment's extensiveness. Please see below for our detailed response:
## W1, Q1: Missing related works
* Thanks for pointing this out. Please see G1 (general response) for details.... | Summary: The paper investigates on an interesting topic: how message passing is playing a role in graph-based recommender systems. Upon experiments on LightGCN model, authors posit the advantages of message passing lie in the forward pass to aggregate neighborhood information while at the same time, backward propagatio... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We really appreciate your acknowledgment of our proposal's significance, insights, and efficiency. Please see below for our detailed response:
## W1, Q1, Q2: Pre-training strategies for MF models
* We agree with you that the pre-training strategies are impac... | Summary: This paper investigates the role of message passing (MP) in collaborative filtering (CF). Unlike most GNN-based CF research, which assumes that performance gains arise from improved representation learning through GNNs, this work questions that assumption. Through empirical experiments and theoretical analyses... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We really appreciate your acknowledgment of our paper’s significance, written quality, and practicality. Our detailed response to your concerns is as follows:
## W1.1: $|u_i|^2 = |i_j|^2 = 1$ does not hold in LightGCN
* We agree that $|u_i|^2 = |i_j|^2 = 1$ do... | Rebuttal 1:
Rebuttal: We thank the reviewers for their feedback and constructive suggestions. We are pleased that most reviewers appreciated **the promising efficiency and effectiveness of our proposal**, e.g.,: "the proposed TAG-CF is efficient and effective and ensures the industrial application of the proposed metho... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Efficient $\Phi$-Regret Minimization with Low-Degree Swap Deviations in Extensive-Form Games | Accept (poster) | Summary: This paper explores and advances the research direction of linear-swap regret in extensive-form games, extending it to low-degree swaps and bridging the gap to results on general swap regret. The technical contributions include the concept of k-mediator deviations, which relate to low-degree polynomials and de... | Rebuttal 1:
Rebuttal: *It seems the gap is bridged "from below", i.e., for large $k$ the results may become worse than known bounds.*
This is correct, in particular, for $k > \tilde O(1/\epsilon)$ our bounds become worse than those of Peng and Rubinstein [2024] and Dagan et al [2024].
*Allowing the learner to output... | Summary: This work aims to bridge the gap between the $N^{O(1/\varepsilon)}$ result for attaining the $\varepsilon$ swap regret and the $\operatorname{poly}(N)/\varepsilon^2$ result for attaining the $\varepsilon$ linear-swap regret for extensive-form games. To this end, the authors generalize the untimed communication... | Rebuttal 1:
Rebuttal: 1. *On "Bridging the gap"*: You are correct: while we match the $\text{poly}(N)/\epsilon^2$ result for linear-swap regret when $k=1$, we do not match the $N^{\tilde O(1/\epsilon)}$ bound for swap regret shown by Dagan et al. and Peng and Rubinstein.
2. *$k$-mediator deviations and low-degree poly... | Summary: This work seems to design some fast regret minimization algorithms. However, to be honest, I could not find the learning protocols nor understand the learning problem set-up. It is hard for me to find what information is revealed in each round after taking some action.
So, I suggest adding some simple applica... | Rebuttal 1:
Rebuttal: Q: *However, to be honest, I could not find the learning protocols nor understand the learning problem set-up.*
A: We will clarify further how the learning protocol proceeds in the revised version. Note that we operate in the standard model when it comes to learning in extensive-form games.
Q: ... | null | null | Rebuttal 1:
Rebuttal: Thanks for the reviews! We have attached to this message a pdf containing two tables which compare the results of this paper to results of past papers. We will include both of these tables in the next version.
Pdf: /pdf/be852b0e12ad6f0fe4dad928196167a0184f04e1.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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