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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Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination | Accept (spotlight) | Summary: This paper presents a novel Out-of-Vocabulary item recommendation or so-called cold-start recommendation model.
The OOV recommendation problem especially item recommendation is very important, since in short video recommendation platforms there are thousands of new post videos or new AGI videos being publishe... | Rebuttal 1:
Rebuttal: **Weakness：**
> W1. Figure 2 can be further improved. The presentation of Figure 1 is excellent, why not keep Figure 2 in the same style?
Thank you for your advice. We have revised Figure 2 in our updated PDF file. We hope this revised figure provides a clearer understanding of our work.
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> W... | Summary: The authors propose the USIM (user sequence imagination) framework to tackle the out-of-vocabulary recommendation problem. The authors point out that existing OOV recommendation models such as generative and dropout models will import significant gaps between the IV items and the OOV items, since the OOV items... | Rebuttal 1:
Rebuttal: **Weakness and Questions：**
> W1: The RecPPO, which is a crucial component of the proposed framework, is not clearly defined in Section 3.3.2. Providing more detailed information about RecPPO would help readers better understand its role and implementation within the USIM framework.
Thank you for... | Summary: The authors propose a novel User Sequence Imagination (USIM) fine-tuning framework. This framework can imagine the user sequences and then refine the generated OOV embeddings with user behavioral embeddings. Specifically, the authors frame the user sequence imagination as a reinforcement learning problem and d... | Rebuttal 1:
Rebuttal: >W1 & W4 & Q3. Performance on large-scale datasets and online environments.
Thanks for your suggestion, we conduct further experiments on other OOV recommendation datasets and on real billion-scale online recommender systems.
### Offline Experiments
We have conducted experiments on an additiona... | Summary: This submission proposes a reinforcement learning framework, termed as USIM, to deal with the issue of out-of-vocabulary items in recommendation system, which is also known as cold start issue in the community. The proposed framework considers a fine-grained user sequence imagining process. Specifically, USIM ... | Rebuttal 1:
Rebuttal: >W1. It is mentioned in the abstract that 'USIM has been deployed on a prominent e-commerce platform for months, offering recommendations for millions of OOV items and billions of users'. However, there seems no online comparison involved in the experiment. More details about online A/B tests shou... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful reviews. OOV item recommendation is increasingly important in the age of information explosion and AGI. We are honored to share our findings and engage in deeper discussions with all the AC and reviewers.
We appreciate your recognition of our ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling | Accept (poster) | Summary: This paper introduces Coral, a model that enhances cognitive diagnosis by integrating collaborative signals among learners with disentangled representation learning. Coral addresses the importance of leveraging collaborative connections among learners to better understand human learning processes. Traditional ... | Rebuttal 1:
Rebuttal: We appreciate your careful reading and detailed feedback on our paper. We address your concerns below and please let us know if there are remaining questions or unclear points.
> **Weakness 1:** Unconvincing motivation and limited methodological novelty.
Thank you for highlighting these concerns ... | Summary: This paper presents a Collaborative Cognitive Diagnosis model, named Coral, which incorporates a disentangled state encoder, collaborative graphs, and a state decoder. The goal of Coral is to model collaborative and disentangled cognitive states using representation learning techniques.
Strengths: 1. The intr... | Rebuttal 1:
Rebuttal: We appreciate your careful reading and detailed discussion of our paper. We address your concerns below and please let us know if there are remaining questions or unclear points.
For the weaknesses:
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> **Weakness 1:** Explain the solution of collaborative relation construction in detail.
Than... | Summary: This paper explores the Coral model, which combines disentangled representation learning with collaborative signals to enhance cognitive diagnosis in intelligent education. It focuses on identifying implicit collaborative links among learners to improve understanding of their cognitive states. The model integr... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive feedback! We answer your comments and questions below. Please let us know if you have additional questions.
For the weaknesses:
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> **Weakness 1:** How to mitigate bias issues in establishing learner collaboration relationships with insufficient inter... | Summary: The authors propose Coral, a Collaborative Cognitive Diagnosis model with Disentangled Representation Learning, aimed at improving our understanding of human learning by leveraging collaborative signals among learners. By disentangling cognitive states and dynamically constructing a collaborative graph, Coral ... | Rebuttal 1:
Rebuttal: Thank you for the detailed and constructive feedback! We answer your comments and questions below. Please let us know if you have additional questions.
> **Weakness 1:** Robustness under data sparsity or noise scenarios.
Thank you for highlighting the robustness of CD in data sparsity or noise s... | Rebuttal 1:
Rebuttal: **Public Response to All Reviewers**
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We would like to express our thanks to the reviewers for their thorough reading of the paper and insightful comments. We first add some common experiments as suggested by the reviewers.
> **1. Additional baselines**
We conduct experiments on the fou... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper introduces Coral, a novel model for collaborative cognitive diagnosis with disentangled representation learning, that can help in developing intelligent education systems. The key contributions are incorporating collaborative information among learners to improve cognitive state diagnosis by using d... | Rebuttal 1:
Rebuttal: Thank you for the positive remarks and insightful feedback! We address your comments and questions below. Please let us know if you have additional questions.
> **Weakness 1:** Concerns on computational efficiency.
Thank you for acknowledging our discussion of the model's efficiency limitation.
... | Summary: The paper presents a novel model called Coral, aimed at enhancing cognitive diagnosis in educational contexts by leveraging collaborative signals among learners. The authors argue that learners with similar cognitive states often exhibit comparable problem-solving performances, and thus, understanding these co... | Rebuttal 1:
Rebuttal: Thank you for the positive remarks and insightful feedback! We answer your comments and questions below. Please let us know if you have additional questions.
> **Weakness 1:** Concerns on data diversity.
Thank you for your concerns about the datasets.
We would like to emphasize that our datasets... | null | null | null | null |
RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation | Accept (poster) | Summary: This paper introduces RoboMamba, a Multimodal LLM, for robotic reasoning and low-level manipulation using the popular State-Space Model architecture. RoboMamba introduces a multi-stage joint language and vision training pipeline with robot-specific data fine-tuning. RoboMamba demonstrates strong reasoning perf... | Rebuttal 1:
Rebuttal: - **_(Weakness 1) Technical Novelty._** Thank you for your detailed comments. We would like to reiterate the technical novelties of RoboMamba in two aspects: 1) an all-round and generalist robotic MLLM framework and 2) a robotic-specific training strategy.
**1) An all-round and generalist robotic... | Summary: This paper proposes a Mamba-based framework called RoboMamba that utilizes multimodal state space model for robotic reasoning and manipulation. It addresses the limitations of existing MLLMs in complex reasoning and high computational costs. RoboMamba integrates a vision encoder with the Mamba model to align v... | Rebuttal 1:
Rebuttal: - **_Weakness 1. Can other architectures work for the proposed framework._**
Thank you for the constructive comments. We explore whether other architectures could be applied to the proposed framework from two perspectives: 1) replacing the Image Encoder and 2) replacing the LLM.
1）As you mention... | Summary: This work introduces RoboMamba to leverage SSM model’s capabilities in non-trivial sequence modeling with linear inference complexity. A simple policy head is employed for finetuning to enable RoboMamba to predict action poses. Evaluation is conducted both in simulation(SAPIEN) and real-world settings and show... | Rebuttal 1:
Rebuttal: - **_(Weakness 1). Comparison of RoboMamba-2.7B with TinyLLaVA_**
Thank you for the constructive comments. The design goal of RoboMamba is to introduce a new paradigm for adapting Mamba to multimodal robotic tasks, resulting in an innovative Robotic MLLM that integrates high-level reasoning, low-... | Summary: This paper proposes RoboMamba, which applies the Mamba state space model architecture for robotic manipulation policy learning. Prior MLLM-based robot policy learning finetunes transformer-based models and suffers from two major problems: reasoning capabilities degrade with visual input; and training computati... | Rebuttal 1:
Rebuttal: - **_(Weakness 1 & Question 1). Additional close-loop experiments_**
1）Thank you for the constructive comments. RoboMamba is not limited to performing single-step open-loop action predictions. In the submitted paper, for fair comparison, we follow the experimental settings of the latest Robotic M... | Rebuttal 1:
Rebuttal: **To all the reviewers:** First, we greatly appreciate all the reviewers' valuable comments and time. Due to character limits in the separate responses, we address some of the reviewers' questions in this global rebuttal. Please review the individual rebuttal response first and then come back to t... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes a method for robot reasoning and manipulation by developing a multimodal large language model (MLLM) based on the Mamba state space model (SSM). The joint model is able to both attack robot reasoning tasks assessed via VQA, and solve robot manipulation tasks by fine-tuning a dedicated head t... | Rebuttal 1:
Rebuttal: - **_(Weakness 1 & Question 1). Detailed inference speed comparison_**
Thank you for the constructive comments. Inference efficiency is a crucial evaluation metric in robotic manipulation and poses a major challenge for existing Multimodal Large Language Model (MLLM) based policy models. We compa... | null | null | null | null | null | null |
Semantic Routing via Autoregressive Modeling | Accept (poster) | Summary: This paper releases a large-scale routing dataset consisting of diverse multi-objective navigation queries expressed via natural languages on the richly annotated road networks of US cities. They also propose an autoregressive baseline method based on a standard transformer network and show that the baseline i... | Rebuttal 1:
Rebuttal: We appreciate your feedback and address your remaining questions below.
**Composition of query dataset.**
We appreciate your suggestion for clarifying this point in the benchmark’s construction. We agree that clarifying this point is important, and will incorporate the following answer—along wit... | Summary: The paper proposes an extended semantic routing planning task, which includes user queries expressed in natural language. The author released a large-scale dataset for this task, and proposed a method of using autoregressive models instead of graph-related methods for route planning, which has a significant pe... | Rebuttal 1:
Rebuttal: We appreciate your feedback and address your questions below.
**How are different input components identified in the autoregressive model?**
We follow the standard practice of using categorical encodings. That is, in addition to adding a position encoding to each token in the input sequence, we ... | Summary: This paper collects a large-scale benchmark for semantic routing based on road networks of US cities with rich metadata, and 1) shows that the benchmark is challenging for previous methods due to its scale and complex user preferences 2) proposes a learning-based method, where a transformer-based model is trai... | Rebuttal 1:
Rebuttal: We thank you for your feedback and address your questions below.
**Details on benchmark construction.**
We appreciate your suggestions on benchmark details that should be elaborated on. We agree these details would be clarifying, and will incorporate the below points—along with additional contex... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning | Accept (spotlight) | Summary: This paper proposes a novel contrastive self-supervised learning framework based on JEPA, namely C-JEPA. The main idea of C-JEPA is to address the limitations of the I-JEPA, especially the limited prevention of collapse with EMA, by incorporating the principles of VICReg. The authors demonstrate the effectiven... | Rebuttal 1:
Rebuttal: We thank you for the valuable comments and answer the raised questions below.
> Clarification
While C-JEPA leverages components from both I-JEPA and VICReg, its novelty lies in its theoretical grounding and practical implementation, which specifically addresses and mitigates I-JEPA's limitations... | Summary: The paper introduces C-JEPA, an enhancement to the Joint-Embedding Predictive Architecture incorporating Variance-Invariance-Covariance Regularization (VICReg) for non-contrastive self-supervised learning. This approach addresses limitations such as model collapse and inaccurate mean patch representations, enh... | Rebuttal 1:
Rebuttal: We thank you for the valuable comments and answer the raised questions below.
> Comparison with More Methods
To demonstrate the competitiveness of C-JEPA, we included our comparative analysis to include recent advancements in the field such as DINOv2, MoCo v3, and IWM. However, the comparison wi... | Summary: The paper presents C-JEPA, a novel framework integrating VICReg into the Image Joint-Embedding Predictive Architecture (I-JEPA) to address its limitations in preventing model collapse and learning mean patch representations. Empirical and theoretical evaluations demonstrate that C-JEPA enhances the stability a... | Rebuttal 1:
Rebuttal: We thank you for the valuable comments and answer the raised questions below.
> Computational Overhead
We conducted a series of performance evaluations comparing the computational costs between I-JEPA and C-JEPA. The results in the Table below detail the runtime and resource utilization, demonst... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Adaptive Layer Sparsity for Large Language Models via Activation Correlation Assessment | Accept (poster) | Summary: This paper introduces Adaptive Layer Sparsity (ALS), a novel approach aimed at optimizing large language models (LLMs) through selective pruning. The key contributions of this work include a method that estimates the correlation between intermediate layers using information orthogonality, enabling the precise ... | Rebuttal 1:
Rebuttal: Dear Reviewer 2MGm,
Thank you so much for the detailed and constructive comments, and the recognition of the novelty of the proposed method, the writing, and the experimental evaluation on image classification benchmarks. Please see our responses below to your concerns and questions one by one.
... | Summary: This paper proposes using mutual information to measure layer redundancy in LLMs and employs a linear optimization algorithm based on this measure to derive adaptive sparsity strategies, enabling dynamic sparsity configuration across different layers. The authors conducted experiments on four models, including... | Rebuttal 1:
Rebuttal: Dear Reviewer 7kYG,
Thank you for your valuable and insightful comments. We have provided detailed responses to your concerns and questions below.
### **Q1**. Pruned models' performance compared to dense models
Our response:
**(1). Uncommon and Unfair Comparison in Sparsity Research**
- Compa... | Summary: The paper presents an approach called Adaptive Layer Sparsity (ALS) for optimizing large language models (LLMs) by selectively pruning features in intermediate layers. The approach consists of two key steps: estimating the correlation matrix between intermediate layers and employing a linear optimization algor... | Rebuttal 1:
Rebuttal: Dear Reviewer ZqVF,
Thank you very much for your detailed and constructive feedback. We will address your concerns and questions as follows.
### **Q1**. Marginal improvement at low sparsity ratios
Our Response:
At lower sparsity levels (20-30%), improvements from ALS are less pronounced, as ev... | Summary: The growing size of LLMs make deployment increasingly challenging. Traditional pruning methods underperform due to uniform strategies that ignore varying feature importance across layers. The authors introduces a new Adaptive Layer Sparsity (ALS) approach that estimates inter-layer correlations using informati... | Rebuttal 1:
Rebuttal: Dear Reviewer 8iME,
Thank you so much for the detailed and constructive comments. Please see our responses below to your concerns and questions one by one.
### **Q1**. Improving the paper's presentation.
Our Response:
We appreciate the reviewer's suggestions about writing. Based on these recom... | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, and Program Chairs,
We sincerely thank all four reviewers for their feedback and constructive comments. In the initial review, 3 Accept ratings are given. Reviewers have acknowledged the **novelty**, **theoretical soundness**, **impact**, **efficiency**, **comprehensi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Few-Shot Diffusion Models Escape the Curse of Dimensionality | Accept (poster) | Summary: The paper provides a theoretical analysis of the few-shot fine-tuning problem for diffusion models. It makes the following key assumptions: (1) the pretraining and fine-tuning data distributions share a common latent distribution, and (2) a specific network architecture. Under these assumptions, the paper prov... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions. We provide our response to each question below.
**W1: The theoretical guarantee of the fully fine-tuned method.**
As shown in our real experiment and [1], when fine-tuning all parameters with a small target dataset, models tend to overfit and lo... | Summary: The paper provides a theoretical analysis showing that fine-tuning pretrained diffusion models on a few target samples achieves a small approximation error, particularly in that it requires much fewer target data than source data. The authors also show that the solution has a closed form in a special case, dem... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions on our assumptions. We discuss each assumption in detail below and will make it clearer in our next version.
**Q1: The discussion on each assumption.**
(a) The linear subspace and shared latent assumption. (Assumption 3.1).
Since the diffusion m... | Summary: This paper provides new bounds for the score function approximation and optimization in fine-tuning pretrained diffusion models (under some simplifying assumptions including linear structure of data distributions and equal latent distributions). The new approximation error bound depends only on the square root... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions. We provide our response to each question below.
**W1: The discussion on the existing fine-tuning method.**
In this part, we discuss the fully fine-tuned method and explain why we need data-efficient fine-tuning methods.
In earlier times, fully ... | Summary: This paper studies the few-shot transfer in diffusion models. Specifically, it focuses on bounding the score-matching loss for the target distribution, which can be considered the estimation error of the corresponding score function. By assuming the source and target distribution share the same linear structur... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and suggestions. We provide our response to each question below.
**W1 & Presentation.**
Thanks again for the valuable comments. Before using the approximation error to represent the score matching loss with finite datasets, we will make a clear definition in th... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Online Control with Adversarial Disturbance for Continuous-time Linear Systems | Accept (poster) | Summary: This paper considers the problem of online non-stochastic control within continuous-time linear dynamical systems. To this end, the authors proposed a two-level online algorithm, where the higher level deals with a discretized reduced problem to minimize the performance measure of regret, while the lower level... | Rebuttal 1:
Rebuttal: We extend our gratitude to the reviewer for the comments and suggestions. Below, we address the primary concern that has been raised. The minor issues and typographical errors have been corrected in our manuscript.
>Q1: Could the authors provide more detailed explanations on the technical chall... | Summary: This paper presents an algorithm for the non-stochastic control problem for continuous-time systems. In particular, the proposed algorithm discretizes the continuous-time system and balances the sampling time for discretization and update frequence of the online learning algorithm, in order to obtain a subline... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and constructive suggestions. In the following, we focus on explaining why updating policy at each step fail. We also appreciate the comments on notations and will incoporate them in the updated version.
>Q1: Why updating the DAC policy at each time ste... | Summary: This paper studies the sample complexity of online control for continuous-time linear systems. A novel two-level online algorithm was proposed. Specifically, a sublinear regret is guaranteed. Finally, the authors applied their method to the SAC algorithm and achieves improved simulation results.
Strengths: Th... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for the insightful comments. We address the reviewer's concerns belows:
>Q1: There is a gap between the theoretical results and the experimental results. More discussion is needed.
**A1:** We appreciate the reviewers for raising this issue. On a high leve... | Summary: The work tackles the challenge of applying simulated controllers to real-world scenarios to manage continuous-time linear systems that face unpredictable disturbances. It introduces a double-layer control approach that balances slow policy updates with quick feedback adjustments. This way it effectively minimi... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's valuable suggestions. We address the reviewer's questions as follows:
>Q1: The paper lacks comparison with other state-of-the-art methods.
**A1:** We appreciate the reviewer's suggestion, and we will include a discussion of some recent works in the next vers... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise | Accept (poster) | Summary: The paper presents Resfusion that leverages prior residual noise to improve restoration performance. It introduces a smooth equivalence transformation for learning residual noise and demonstrates the efficacy of Resfusion through extensive experiments and ablation studies.
Strengths: The paper is well-written... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions and comments.
> Since T' is smaller than the usually used T=1000, the sampling process of the proposed method seems to be faster than previous methods. But there are no comparisons on inference speed.
... | Summary: This paper presents a general diffusion framework for image restoration, named Resfusion. The main idea is to introduce a residual term to DDPM to directly generate clean images from degraded images. Moreover, the form of the inference process is consistent with the DDPM and allows very few sampling steps. The... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions and comments.
> How do you get/design Eq. (3)? I wonder if the term $(1-\sqrt{\alpha_t})R$ is manually designed or derived from a specific equation.
As shown in Figure 2 in the original paper,
the res... | Summary: This paper proposes to start the reverse diffusion process from the noisy degraded images for image restoration. It introduces a weighted residual noise as the prediction target and leverage a smooth equivalence transformation to find the starting noise. The experiments shows competitive performance on shalow ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions and comments.
> ResShift also shifts toward the residual term. What are the advantages and disadvantages compared with ResShift?
We provide the differences between Resfusion and Resshift:
- Similar to ... | Summary: This paper proposes a method that leverages generative diffusion for image restoration tasks. The authors suggest incorporating the residual term, defined as the difference between the corrupted and clean images, into the forward and reverse diffusion processes. The forward process is described by a Markov cha... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions and comments.
> Several notations are used without being defined. For example, the definitions of
$\alpha_t$, $\beta_t$, $\overline{\alpha}_{t}$ are missing.
For all constant hyperparameters, our def... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their valuable and encouraging comments. Below we address specific questions and comments.
## Section 1: Definition of constant hyperparameters.
For all constant hyperparameters, our definitions are completely consistent with the reference [1].
We provide a d... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
What Makes Partial-Label Learning Algorithms Effective? | Accept (poster) | Summary: The paper examines the effectiveness of partial-label learning algorithms, identifying key factors contributing to their success. It discusses techniques from other fields that can enhance partial-label learning, such as the transition from uniform to one-hot pseudo-labels and the implementation of minimal alg... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our work and the positive comments regarding its significance and writing quality. Below are our responses to the comments in Weaknesses and Questions.
> C1. The paper would benefit from providing additional details about the propo... | Summary: This paper presents an interesting survey of current popular PLL methods. The survey examines various aspects such as training techniques, optimization processes, and loss functions to identify the key factors that make PLL methods effective. Ultimately, the authors attribute the main reason for PLL's success ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our work and the positive comments regarding its significance. Below are our responses to the comments in Weaknesses and Questions.
> C1. The conclusion that "mini-batch PL purification is an important factor in making PLL methods... | Summary: The paper presents a comprehensive empirical analysis of various Partial-Label Learning (PLL) methods. The authors identify that mini-batch Partial-Label (PL) purification is a key component for achieving top performance in PLL, as it progressively transitions from uniform to one-hot pseudo-labels. The study a... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our work and the positive comments regarding its significance and writing quality. Below are our responses to the comments in Weaknesses and Questions.
> C1. The authors introduce a descriptive concept/definition called Mini-batch... | Summary: The paper offers a comprehensive empirical analysis to understand what makes partial-label learning (PLL) methods effective, focusing on the transition from uniform to one-hot pseudo-labels in mini-batch PL purification. It analyzes the complexity and diversity of SOTA PLL methods, proposing simplified algorit... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our work and the positive comments regarding its significance and writing quality. Below are our responses to the comments in Weaknesses and Questions.
> C1. The definition and implementation details of mini-batch PL purification a... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper simplifies the process of PLL by distilling critical success factors for high performance into a minimal algorithmic design through extensive empirical analysis. This work is a step forward in making PLL methods more accessible and efficient, offering substantial insights into traditional complex al... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for taking the time to review our work and the positive comments regarding its significance. Below are our responses to the comments in Weaknesses and Questions.
> C1. Q1. There are several simplified approaches achieve comparable performance, which makes the c... | null | null | null | null | null | null |
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance | Accept (poster) | Summary: In this paper, the authors propose a learning pipeline named VLG-CBM, which enhances CBM with the aid of open-domain object detectors to create more accurate concept labels, while also excluding image-irrelevant concepts (e.g,. "loud music").
Also, in order to prevent the 'information leak' in CBMs, which stan... | Rebuttal 1:
Rebuttal: Dear Reviewer i5YZ,
Thank you for your feedback. Below are responses to your comments.
**Q1:** Therefore, also comparing the performance of VLG-CBM under conventional settings in previous work would make the comparison easier.
**A1:** In this paper we do not compare the results under a dense f... | Summary: The paper uses foundational models to automate the generation of concept annotations which are then used to train a concept bottleneck model (CBM). To generate the concept set, the paper extends prior scalable CBM approaches[1] by using open-domain object detectors to weed out concepts that are not visually gr... | Rebuttal 1:
Rebuttal: Dear Reviewer MvHs,
Thank you for the positive feedback. Please see below our responses to address your comments.
**Q1:** It is not clear if NEC controls information leakage. As defined in [2], information leakage happens because of a `soft’ concept layer (that predicts probability of a concept... | Summary: This work proposed a new way to implement concept bottleneck models (CBMs), which use pretrained object detectors (Grounding-DINO) to filter and annotate the concepts. This step can make the concepts more visual and groundable, which improves the reliability of the concept predictor. This paper proposed a new ... | Rebuttal 1:
Rebuttal: Dear Reviewer QueL,
Thank you for the feedback, we believe there are some misunderstandings based on the comments. Please allow us to clarify below to address your comments.
**Q1:** This paper is an incremental work to previous LLM-guided CBMs [1, 2] by adding a step to filter concepts with an ... | Summary: The paper introduces Vision-Language-Guided Concept Bottleneck Model (VLG-CBM), an innovative approach to training Concept Bottleneck Models (CBMs) using vision-language models (VLMs). This method aims to improve the faithfulness and performance of CBMs by addressing the limitations of existing models, specifi... | Rebuttal 1:
Rebuttal: Dear Reviewer UnrN,
Thank you for the positive feedback! Please see our responses below to address your comments.
---
**Q1:** How well the grounding detector can do in discovering the concept candidate. And if the detector fails to detect a key concept, will the final recognition result be affe... | Rebuttal 1:
Rebuttal: **General response: New Experiments**
Thank you for all the thoughtful reviews. In response, we have performed many experiments during the rebuttal period as requested by the reviewers, mainly to evaluate our method on more model architectures and datasets, as well as several ablation studies to ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposed a framework that leverages existing grounding models to refine the accurate concept annotations, and therefore enables better CBM training. Also, a new metric is proposed to avoid leakage and improve interoperability. Experiments and visualizations are conducted to demonstrate the superior ... | Rebuttal 1:
Rebuttal: Dear Reviewer hsJW,
Thank you for the positive feedback! Please see our responses below to address your comments.
**Q1**. Missing comparison with recent works in CMB, for example, [1] and [2]
**A1**: Thank you for providing the reference [1, 2]. Below we discuss the differences between our met... | null | null | null | null | null | null |
The Implicit Bias of Gradient Descent on Separable Multiclass Data | Accept (poster) | Summary: The authors study the problem of multiclass SVM in the realizable (i.e., separable) case.
They show that under several assumptions on the loss function, the gradient descent converges in direction toward the (unique) solution of the hard-margin problem.
For that, they used the notion of Permutation Equivariant... | Rebuttal 1:
Rebuttal: > Line 262: Why is $u_{\pm} = 0$, while in Def. 2.2, it can be anything?
We could have worded this better, thank you for the question! All the relative margins (which comprise $\mathbf{u}$, the vector argument to the template) go to infinity so that lets us pick any finite value for $u_{\pm}$. Th... | Summary: This paper leverages the PERM (Permutation Equivariant and Relative Margin-based losses) framework proposed in [Wang and Scott, 2024], and extends the implicit bias result of binary classification to multiclass classification.
Specifically, the authors extend the exponential tail property to multiclass settin... | Rebuttal 1:
Rebuttal: Thank you for the careful reading and catching these typos! We will update our paper to reflect these changes. We also clarify line 116.
> In page 3 line 85, the meaning of $[\mathbf{v}]\sigma(j)$ doesn't seem clear to me, is it $[\mathbf{v}]_{\sigma(j)}$?
Yes. This was a typo, we will remove $[... | Summary: This paper investigates the implicit bias of gradient descent on separable multiclass data using a broad class of losses termed Permutation Equivariant and Relative Margin-based (PERM) losses, which include cross-entropy loss, multiclass exponential loss, and PairLogLoss. The main contribution is the extension... | Rebuttal 1:
Rebuttal: Thank you for your review and catching the typo. We appreciate your time and the supportive feedback. We've added numerical simulations demonstration implicit regularization towards the hard margin SVM when using the PairLogLoss, in line with our theory's prediction. It is attached to the "global ... | Summary: This paper uses the framework of permutation equivariant and relative margin-based losses of (Wang and Scott, '24) to extend the implicit bias result of (Soudry et al.,'18) to multinomial classification. Namely, the authors prove that when the loss satisfies a multiclass generalisation of the exponential tail ... | Rebuttal 1:
Rebuttal: $\newcommand{\bfx}{\mathbf{x}}$
$\newcommand{\bfW}{\mathbf{W}}$
$\newcommand{\bfw}{\mathbf{w}}$
$\newcommand{\bfD}{\mathbf{D}}$
$\newcommand{\mlc}{\boldsymbol{\Upsilon}}$
$\newcommand\pseudoindex[1]{[#1 ]}$
> From a conceptual point of view, the results of the present paper do not provide additio... | Rebuttal 1:
Rebuttal: Thank you all for the fantastic reviews! Please find attached figures to numerical simulations.
One typo that was brought to our attention was in the first equation on line 200. Here is the correct inequality, derived:
$\| \mathbf{r}(t+1) - \mathbf{r}(t)\|^2 = \|\mathbf{w}(t+1) - \mathbf{w}(t) ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Control and Rendering | Accept (poster) | Summary: They introduce a language-embedded "interactive neural radiance field" that efficiently reconstructs and controls multiple objects within scenes. Factorization decomposes the scene into more local fields that can achieve local deformation.
Strengths: My sense is that the technical novelty of this paper is hig... | Rebuttal 1:
Rebuttal: Sincerely appreciate for your kind comments and voluntary review suggestions! We have carefully reviewed and corrected the entire manuscript to improve the paper's organization and presentation.
**#Q1. Improve the presentation of the methods section**
Thanks! We have carefully revised the manusc... | Summary: The paper addresses the complex challenge of reconstructing and controlling multiple interactive objects in complex scenes from monocular videos without prior modeling of geometry and kinematics. This task is critical for advancing fields like virtual reality, animation, and robotics, where understanding and i... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the valuable feedback!
**#Q1. Enlarge texts in figures.**
Thanks. We have carefully revised the manuscript according to your comments.
**#Q2. Visualization of some latent features.**
We provide additional interaction feature visualization of x-$\bold... | Summary: This paper tackles an important problem in reconstructing interactive 3D scenes with language grounding. The authors proposed to use object-based modeling of different deformation fields over the dynamic NeRF pipeline and equip it with language embeddings for grounding interactions. The authors constructed two... | Rebuttal 1:
Rebuttal: Thank you for the constructive and voluntary review suggestions in both methodology and writing. We have carefully reviewed and corrected the entire manuscript, striving to eliminate any organizational and typos issues. Sincerely hope the proposed method and dataset in this paper will contribute t... | Summary: This paper proposed LiveScene, a NeRF-based approach to enable indoor-scale controllable scene reconstruction and novel view synthethis. By extending K-Panes with a object-aware multi-scale space factorization, scene-level 3D space with articulated objects could be modeled with motion patterns via densely coll... | Rebuttal 1:
Rebuttal: **#Q1. How to cope with time variations? Are the time dimension encoded within control variables?**
Yes. The timestep variables, 3D features, and interaction variables are hybridized and fed into the interaction probability decoder to yield the probability distribution in implementation, as shown... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer #**PajG**, #**aZ7V,** #**a4DY, and #94zG** for their thoughtful and constructive comments and suggestions. We have carefully revised our manuscript according to their comments. An **attached one-page PDF** is provided to show additional experiments and can be summarized... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PETRA: Parallel End-to-end Training with Reversible Architectures | Reject | Summary: In pipelined model training, one important issue is to reduce the bubble sizes.
One stream of work is to use the staleness, where the weight discrepancy is mitigated using stashed weights.
This work tries to reduce the overhead of storing weights with reversible architectures.
Using the non-stashed updated w... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the quality and novelty of our algorithm. We respectfully address the weaknesses reported, which imply additional engineering work beyond the scope of this paper:
1. **Wider Variety of Tasks and Architectures**: While we agree that it would be beneficial to... | Summary: This paper proposes a method that combines reversible neural networks and parallel distributed training to enable learning with minimal memory usage, while incurring only slight communication and computation overhead. In this approach, the need for storing intermediate activations in traditional backpropagatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the quality of the ideas presented in this paper. We would like to address the concerns:
1. Our paper serves as a proof of concept for a new algorithm, and our resources (engineers, clusters) currently only allow us to use a simulated environment. Several w... | Summary: In this paper, the author proposes a new alternative algorithm (Parallel End-to-End Training with Reversible Architectures) for regular backpropagation, which significantly enhance the parallelization with a limited overhead compared to regular backpropagation and other alternatives to end-to-end training.
Spe... | Rebuttal 1:
Rebuttal: First, we sincerely thank the reviewer for their positive assessment and acknowledgment of the clarity and novelty of our method. We would like to address the reported weaknesses:
1. We believe the reviewer is referring to Table 1. We would like to reformulate our discussion from the end of Secti... | Summary: The authors propose fusing delayed gradient pipeline parallelism with reversible models in order to capture the benefits of the former while mitigating the drawbacks with the latter.
Strengths: - The paper sets up a pretty compelling combination of ideas. This is a great example of a paper that clearly unders... | Rebuttal 1:
Rebuttal: First, we sincerely thank the reviewer for the very positive feedback. It's truly appreciated.
We'd like to address the weaknesses you reported:
1. We agree that developing a scalable and distributed implementation of the algorithm would be the ultimate proof of concept. However, our primary goa... | Rebuttal 1:
Rebuttal: We appreciate that every reviewer acknowledged the refreshing aspect and elegance of our method. Several reviewers noted the lack of empirical data on the efficiency of our method in a distributed environment at scale. We emphasize that this work represents the beginning of a promising line of res... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL | Accept (poster) | Summary: The authors contribute a mathematical proof showing that transferring a "exploration strategy" in sim2real scenarios as opposed to transfer the learned policy result in an exponential improvement on samples needed to learn the real task.
Strengths: - While not necessarily a fundamentally new idea (the transfe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and will work to incorporate all suggested improvements.
> I am not sure why a new basic "sim2real" formalization was used to in this paper when it already exists. The problem explored in this paper is completely equivalent to Multi-Fidelity MDPs (describ... | Summary: The authors propose a method where, instead of directly transferring a trained policy from a simulator to the real world, exploratory policies are learned in the simulator and transferred. This approach aims to enable efficient exploration in the real world, particularly in low-rank MDP settings.
Strengths: 1... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and address questions and weaknesses below.
## Weaknesses
1. > The approach relies on several assumptions (e.g., low-rank MDPs, specific access to simulators and oracles) that may not hold in all real-world scenarios.
Please see our comment to all review... | Summary: The paper shows that for transferring with a large sim-to-real gap, exploration policies have improved capabilities compared to optimally pre-trained policies, which tend to overfit the simulation up to a point where exploration is not sufficient to adapt to the changed circumstances.
Strengths: Overall, the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback.
> Providing further examples for the theoretical elaborations…
The example used to prove Prop. 1 and 2 is given in Sec. 5.2 and is a variant of the classic combination lock instance. In “real” the correct action must be taken for $H$ steps, and t... | null | null | Rebuttal 1:
Rebuttal: We thank each of the reviewers for their feedback and suggested improvements. We will work to incorporate all suggestions. We highlight several points regarding our contributions and the low-rank MDP setting, which we believe are relevant to all reviews.
### Key Contribution of Paper
We want to... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation | Accept (poster) | Summary: In this work, the authors use a selected feature space distribution as supplementary supervision signals, combining with cosine similarity as the distance metric, and implement a dual-branch weakly supervised point cloud semantic segmentation network. Experiments show that using feature space distribution as s... | Rebuttal 1:
Rebuttal: # Response to Reviewer fVFW
We sincerely appreciate your volunteered time. We will respond to your concerns and hopefully eliminate them.
* **R1 (About comparing distributions):**
As you mentioned, we compare the fitting ability of several candidate distributions in describing the feature spa... | Summary: A weakly-supervised approach has been studied to relieve the annotation burden for point cloud semantic segmentation. Unlike conventional works that mainly use priors (such as similarity or augmentation-based regularization) to overcome the lack of information in weak labels, this paper introduces a novel appr... | Rebuttal 1:
Rebuttal: # Response to Reviewer ruvS
We sincerely appreciate your insightful feedback and positive evaluation. In response to your valuable comments, we structure our response as follows:
* **R1 (About oughtness and moVMF):**
Next, we discuss "oughtness" and the superiority of the mixture of vMFs (moV... | Summary: This paper addresses the problem of weakly supervised point cloud semantic segmentation. The authors propose imparting supplementary supervision signals by regulating the feature space under weak supervision. The initial investigation identifies which distributions accurately characterize the feature space in ... | Rebuttal 1:
Rebuttal: # Response to Reviewer KHTz
We express our sincere gratitude for your valuable comments. In response to your concerns, we carefully structure our response as follows:
* **R1 (About computational complexity):**
The main computational complexity of the distribution alignment branch comes from ... | Summary: This paper outlines a study focusing on distance metric and distribution modeling, highlighting the effectiveness of combining mixture of von Mises-Fisher distributions (moVMF) with cosine similarity. A Distribution Guidance Network (DGNet) is introduced, featuring two main branches: weakly supervised learning... | Rebuttal 1:
Rebuttal: # Response to Reviewer bWaK
We appreciate your valuable comments and suggestions. We will respond to each of your concerns and hopefully eliminate them.
* **R1 (About detailed explanation):**
Due to page constraints, we analyze and interpret the main and important experimental results in the m... | Rebuttal 1:
Rebuttal: # Summary of Author Rebuttal
We respectfully appreciate the constructive comments and valuable suggestions given by each reviewer. We are confident that each reviewer has given sufficient time to scrutinize our work. To respond to the issues raised and to dispel some misconceptions, we analyze an... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding? | Accept (poster) | Summary: This paper introduces Federated Agent Cost Truthfulness (FACT), a novel mechanism that addresses the free-rider dilemma in federated learning. FACT ensures truthful reporting by agents and eliminates free-riding through a penalty system, competitive environment, and by encouraging agent participation with bett... | Rebuttal 1:
Rebuttal: Thank you, Reviewer k1Mz, for your insightful review of our paper. Below, we address all questions you raised.
## Addressing Weaknesses
---
> **Weakness 1:** Eq 10, 26, and 27 lack punctuation.
**Response to Weakness 1:** We begin by thanking you for catching the punctuation issues, and have f... | Summary: This paper introduces Federated Agent Cost Truthfulness (FACT) to tackle the issues of free riding and dishonesty in federated learning. A penalty system is proposed to eliminate federated free riding. Meanwhile, a competitive environment is proposed to incentive agents provide truthful information.
Strength... | Rebuttal 1:
Rebuttal: Thank you, Reviewer hdyq, for your insightful review of our paper. Below, we address all questions you raised.
## Addressing Weaknesses
---
> **Weakness 1:** Are there any real-world examples that illustrate the necessity of addressing free-riding and dishonesty issues in federated learning?
*... | Summary: The paper "FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?" proposes a mechanism called FACT (Federated Agent Cost Truthfulness) to address the issue of free-riding in Federated Learning (FL). The key contributions include introducing a penalization scheme that incentivizes agents to ... | Rebuttal 1:
Rebuttal: Thank you, Reviewer dpi3, for your insightful review of our paper. Below, we address all the questions you raised.
## Question Responses
---
> **Question 1:** Can you explicitly define the type of game and solution concept used in the proposed mechanism? How does it compare with alternative abs... | null | null | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their constructive feedback. We address individual reviewer questions within their respective rebuttals. Below, we detail new experiments as well as a more thorough description of our paper's limitations.
## Real World Validation of FACT via Additional Exp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mutual Information Estimation via Normalizing Flows | Accept (poster) | Summary: This paper proposes a family of mutual information (MI) estimators based on normalizing flows. The general method proposes a Monte Carlo estimator that holds for any base distribution. The authors refine this estimator for Gaussian base distributions, where the MI estimate can be calculated analytically. Fi... | Rebuttal 1:
Rebuttal: We thank Reviewer **2t4W** for the work!
We are glad to receive helpful criticism of our article.
We further provide answers to the main points raised in the review.
**Weaknesses:**
1. It is true that some parts of our work are dense with theoretical results.
As our manuscript has reached the... | Summary: This work presents an elegant and sound methodology for the estimation of mutual information (MI) in the context of high-dimensional continuous random variables (vectors).
The key intuition for the proposed methodology is that MI is an invariant measure under smooth injective mappings. Then, the authors defi... | Rebuttal 1:
Rebuttal: Dear Reviewer **m9Tu**, thank you sincerely for your profound and comprehensive review!
In the following text, we provide responses to your questions.
We hope that all your concerns are properly addressed.
**Weaknesses:** for 1 and 2 please refer to the questions section.
3. In order to assess t... | Summary: The authors aim to provide an automatic method for performing an information-theoretic analysis of DNNs
However, calculating mutual information (MI) and differential entropy of high-dimensional data are extremely hard to estimate.
The authors propose modeling a joint distribution of RVs with a Cartesian produc... | Rebuttal 1:
Rebuttal: We would like to deeply thank Reviewer **4miK** for reading the article and providing us with a profound review!
We further provide answers to the main points raised in the review.
**Weaknesses:**
1. We wanted to stress out that the biggest possible gap between the lower bound and the true value... | Summary: This paper presents a new approach for estimating mutual information (MI) using normalizing flows. The authors provide a series of theoretical results and demonstrate numerical examples.
Strengths: 1. The paper has provided comprehensive theoretical discussions for the proposed approach. The presentation is e... | Rebuttal 1:
Rebuttal: Dear Reviewer **cdSA**, thank you for reading and reviewing our article carefully!
In the following text, we provide responses to your questions.
We hope that all the concerns are properly addressed.
**Weaknesses:**
1. We agree that experiments with real datasets will further improve our work an... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their time and effort to make our work better. To address the raised concerns, we answered each reviewer in individual messages below.
Some questions require supplementary materials to be submitted, including pictures, diagrams and additional experimental ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models | Accept (poster) | Summary: This paper proposes a (training-only) data poisoning attack against vision language models (VLM). The proposed method works similarly to a clean-label targeted data poisoning attack for image classification tasks, so it is designed to be stealthy in the generated poisoned image-text pairs. In addition to the t... | Rebuttal 1:
Rebuttal: We thank Reviewer RqR6 for the detailed feedback. We are encouraged that the reviewer finds our "persuasion attack" objective novel, our paper well-written, our experiments comprehensive and strong. Below we address the reviewer's concerns in detail.
---
>Question 1: ... (a) Will the poisoned VLM... | Summary: This paper introduces Shadowcast, a data poisoning attack targeting Vision-Language Models (VLMs) to manipulate their responses. It features two attack types: Label Attack, which misidentifies images (e.g., confusing Donald Trump for Joe Biden), and Persuasion Attack, which generates misleading narratives (e.g... | Rebuttal 1:
Rebuttal: We thank Reviewer u5mc for the detailed feedback. We are encouraged that the reviewer finds our paper interesting, well-written and our experiments comprehensive. Below we address the reviewer's concerns in detail.
---
>Weakness 1: The novelty of this paper is somewhat limited. The authors manipu... | Summary: The paper introduces a subtle attack on vision-language models (VLMs). By stealthily modifying training data, the attack influences model outputs without obvious signs of tampering. Extensive experiments demonstrate the attack's effectiveness and stealthiness, revealing significant vulnerabilities in VLMs. The... | Rebuttal 1:
Rebuttal: We thank Reviewer BUID for the detailed feedback. We are encouraged that the reviewer finds our approach novel and versatile with broad application, our method well-articulated and our experiments comprehensive. Below we address the reviewer's concerns in detail.
---
>Weakness 1: The experiments ... | Summary: This paper explores the data poisoning attacks against vision language model. The Shadowcast introduces two types of attack: 1) Label Attack: the VLM generates text that misclassifies, e.g., change “Trump” to “Biden”; 2) Persuasion Attack: the VLM generates “rational” but wrong fact, e.g., illustrate the pictu... | Rebuttal 1:
Rebuttal: We thank Reviewer Tzvw for the detailed feedback. We are encouraged that the reviewer finds our paper well-written, our research interesting and timely and appreciates our human evaluation. Below we address the reviewer's concerns in detail.
---
>Weakness 1: Poisoning attacks on NLP/CV are well k... | Rebuttal 1:
Rebuttal: We thank all reviewers for their valuable feedback. We are pleased that the reviewers found our proposed approach and attack objective novel (BULD, RqR6), and recognized our paper as well-written (Tzvw, BULD, u5mc, RqR6). The reviewers also considered our work interesting and timely (Tzvw, u5mc), ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unravelling in Collaborative Learning | Accept (poster) | Summary: The paper models the collaborative learning problem using a statistical model. In this model, since each agent has its own utility with respect to the risk and the cost of sampling, they may not converge to the Nash equilibrium of optimal general risk. More precisely, the process may undergo a phenomenon known... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and answer their questions below.
> "The paper does not show empirically when the "unraveling" problem occurs or whether the mechanism in section 4.2 makes a difference. It is not clear whether the problem is common in practice."
Unravelling is a common ... | Summary: There can be strategic learners and collaborations for collaborative learning is not trivial. When data qualities are private, coalitions may undergo unravelling wherein only worst agents will be left in the coalition.
Authors propose a probabilistic verification-based mechanism to make optimal collaboratio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, although we are quite surprised by the provided summary which does not correspond to our paper. We believe that there was a mistake on their side. We however reply to their questions below.
> "Weaknesses: the authors don’t show that if the agents report t... | Summary: Adverse selection is a phenomenon studied in economics in which information asymmetry may have a negative effect in the market equilibrium. The paper considers a federated learning setting when the agents are strategic, and the authors formalize the concept of ``adverse selection'', and analyze it in several... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and are glad that they find our result interesting. We answer to their questions and remarks below.
> "Weaknesses: hard to follow all details."
We understand that our notations may appear somewhat intricate and our text dense. We tried to lighten it as ... | Summary: The paper studies adverse selection in federated learning, due to varying levels of data quality among the clients. In particular, a phenomenon of unravelling, in which the collaboration is essentially destroyed due to insufficient incentives for participation for the agents with high quality data, is identifi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, and try to address the points they raised below.
> "In the proposed frameworks, the utilities of the clients are evaluated based on upper bounds on the loss..."
We thank the reviewer for their comment. Indeed, it may seem natural to assume th... | Rebuttal 1:
Rebuttal: We deeply thank the reviewers for their evaluation of our work and their insightful feedback. We will take into account their remarks for the final version of the text. They seem to agree on the importance of adverse selection in collaborative learning and the fact that our research question is me... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives | Accept (poster) | Summary: The paper critiques the practices of privately adapting closed(-source) LLMs to private data by demonstrating that these techniques are potentially unsafe and do not yield the required quality of resulting model in terms of accuracy. The authors conclude that a focus on open LLMs should be preferable in sensit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough and thoughtful feedback, we appreciate that. We answer the concerns one by one below:
>**I would have liked to see a more detailed investigation on the effect of privacy levels beyond $\varepsilon=8$ (for most techniques excluding PATE, where there is a note... | Summary: This paper compares the performance and privacy leakage between private adaptations on closed and open-source LLMs. The authors conclude that adaptations on open-source LLMs result in better performance, lower training costs, and enhanced privacy protection
Strengths: The paper presents extensive experiments ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and we are happy that the reviewer appreciates our extensive experiments. We address individual points below one by one:
>**W1: The work lacks novelty.**
We provide a comprehensive privacy analysis and introduce differentially private prompts for... | Summary: The paper compares private adaptation between closed LLMs and open LLMs, and the authors find that adapted open LLMs always perform better than closed ones at much lower cost.
Strengths: 1. This paper presents a comprehensive overview of privacy-preserving adaptation techniques for LLMs. It thoroughly examine... | Rebuttal 1:
Rebuttal: We appreciate the positive, encouraging, and constructive feedback. We are pleased that the reviewer recognizes our thorough analysis and finds the results *"striking"*. We address the main concern below:
>**My main concern is that the claim that open LLMs are essential for achieving high-quality... | Summary: This paper compares and contrasts the privacy protections of open vs closed LLMs through conceptual threat models and experimental evaluation. The authors give experimental evidence on local gradient based adaptations performing better than their closed discrete prompt-based counterparts in the areas of privac... | Rebuttal 1:
Rebuttal: >**The paper is well-written and clearly explains what the contributions are, the background for both DP and LLMs, as well as their experimental evidence. The authors make a compelling case behind the reasons for privacy-preserving adaptations for closed LLMs not being as effective. The problem ad... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their valuable feedback and insightful comments, which greatly helped us further improve our submission. Our results were described as “striking” by Reviewer gG3r. This work contains “extensive experiments” (Reviewer k2x5), which demonstrate that adapta... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs | Accept (spotlight) | Summary: This paper deals with the question of estimating Wasserstein(-like) barycenters in the _continuous_ case based on _samples_. That is, one actually observes (iid) samples $X^k$ made of $N_k$ points from (unknown) probability distributions $\mathbb{P}_1,\dots,\mathbb{P}_K$, and the goal is to estimate $\mathbb{Q... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for spending time reviewing our paper. Your kind words that our paper well suits NeurIPS are encouraging for us. Below you can find the answers to your comments and questions.
**(W1) Difficulties with proper evaluation.**
We agree that the out-of-the-box evaluation is ty... | Summary: The authors propose a new algorithm to estimate entropic barycenters in this submission.
In particular, leveraging the c-transform based duality of the entropic OT problem, the authors reformulate the entropic barycenter problem as a constrained functional optimization problem, in which a set of dual potentia... | Rebuttal 1:
Rebuttal: Dear reviewer. Thank you for spending time reading and evaluating our work. We were greatly encouraged by your positive feedback on our theory and the readability of our manuscript. Below you can find the answers to your questions and comments.
**(W1) The role and impact of entropic regularizer... | Summary: The authors focus on the Wasserstein barycenter problem; an average between distributions given a Wasserstein cost. Specifically, they consider arbitrary cost functions to define the Wasserstein distance and they approximate the continuous barycenters. They further relate their method with energy based models,... | Rebuttal 1:
Rebuttal: Dear reviewer. Thank you for your thoughtful review. We are pleased that you noted the theoretical and practical strengths of our work. Below we answer your comments and questions.
**(W1)+(Q2). Too many details in the background section. Improving the readability by moving some secondary assumpti... | Summary: The paper proposes a methodology to approximate (regularised) barycenters of continuous-support distributions for arbitrary transport costs. To do so, the authors combine strategies and results from weak OT and transport map approximation via neural networks. The proposal is well connected with the existing (e... | Rebuttal 1:
Rebuttal: Dear reviewer. Thank you for your efforts in reviewing our paper and for your valuable comments. We are delighted that you appreciated both the theoretical and practical contributions of our paper. Below you can find the answers to your questions and comments:
**(W1) "The use of the Style GAN see... | Rebuttal 1:
Rebuttal: Dear reviewers, thank you for taking the time to review our paper! It is a great pleasure for us that you positively evaluated our paper, emphasizing the importance of the considered problem, our solid theoretical contribution and well-tailored experimental validation. Please find the answers to y... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper presents a new entropically regularized algorithm for identifying the Wasserstein barycenter (distribution on average closest to the reference distributions as measured in Wasserstein distance) among K distributions.
The proposed algorithm relies on a dual form of the entropic OT. Namely the entro... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for spending time reviewing our paper and for a positive evaluation of the work. We are happy that you found our work to be interesting and well-presented. Below we answer your questions and comments.
**(W1) Convergence bounds discussions. "[...] it is not [...] a measur... | null | null | null | null | null | null |
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model | Accept (poster) | Summary: This paper proposes a novel model-based algorithm for policy evaluation in distributional RL and shows its near optimality within the generative model framework. Additionally, they conduct an experimental study comparing their algorithm with quantile DP in a synthetic environment.
Strengths: Discretizing the... | Rebuttal 1:
Rebuttal: Thank you for your summary of our work, your feedback and your questions. We are glad to hear you found the paper well-written, the algorithm and main idea clear, and the theory sound.
We are glad also that you found the connection between categorical DP and linear mappings interesting. This is o... | Summary: The paper presents a novel algorithm for model-based distributional RL and establishes that it achieves near-minimax-optimal performance for approximating return distributions in the generative model regime. This is a significant contribution, showing that the distributional RL is sample-efficient with a gener... | Rebuttal 1:
Rebuttal: Thank you for your feedback and positive assessment of our work. We are glad to hear you believe this work provides a deeper understanding of distributional RL.
**Experiments.**
* *Comparison with QDP.* The reviewer is correct that sorting in the QDP algorithm contributes to its higher computat... | Summary: The authors propose a new algorithm for distributional reinforcement learning under the generative model setting with categorical representation. New upper bound for sample complexity is presented for the proposed algorithm. Some empirical results comparing the method with other alternatives are presented, sho... | Rebuttal 1:
Rebuttal: Thank you for your feedback and positive assessment of our work.
On the significance of the work, we believe many of the tools we have developed in the paper (such as the interpretation of the categorical operator as a particular linear map, the stochastic categorical CDF Bellman equation, the av... | Summary: This paper proposes a min-max optimal algorithm for model-based distributional RL, which is used to approximate the return distributions under the assumption of having access to generative model. New theoretical analysis is provided for categorical approaches in distributional RL, with the introduction of a ne... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback on our work. We believe we address all concerns raised in the review, and would welcome any further questions.
**Weakness 1: Categorical approximations.** These approximations are well established in distributional reinforcement learning. They have been appl... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning | Accept (poster) | Summary: The authors of this paper found that in cross-domain few-shot classification, there naturally exists a gap, which resembles the modality gap, between the prototype and image instance embeddings. Thus, this paper proposes a simple yet effective method, contrastive prototype-image adaptation (CoPA), to adapt dif... | Rebuttal 1:
Rebuttal: Thanks for your effort and time in reviewing our work. We appreciate a lot for your positive evaluation towards our work!
For your concern about the novelty of our paper, we think that our contributions can be summarized as following:
- We first noticed that prototypes play a similar role to th... | Summary: This paper claims that there exists gap between prototypes and image instance embeddings in cross-domain few-shot classification models.
URL[29], a representative work of cross-domain few-shot classification, proposes to fast fine-tune linear classifier on top of a frozen back bone with the nearest centroid c... | Rebuttal 1:
Rebuttal: We post our responses regarding your concerns in the following:
>__Weakness 1__: CoPA has two ... one linear classifier does not consider difference of two classifiers in gradients.
__Answer__: According to your concern, you also believe that a single transformation cannot consider the difference... | Summary: This paper investigates the gap between the prototype and image instance embeddings under the setting of cross-domain few-shot classification. It shows that applying the same transformation to these embeddings will shrink their gap and constrain the exploration of optimal representation distributions. Based on... | Rebuttal 1:
Rebuttal: Thanks for your effort and time in reviewing our work. We appreciate a lot for your insightful and valuable comments towards our work. Now, we post our responses regarding your concerns in the following:
> __Weakness 1__: For the empirical analysis in Section 3.2, there is an interesting phenomeno... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Large-Scale Contextual Market Equilibrium Computation through Deep Learning | Reject | Summary: This paper studies to how to use deep learning to solve large-scale contextual market equilibrium. This paper proposes MarketFCNet, a deep learning method for approximating market equilibrium. The paper propose an unbiased training loss and a metric called Nash Gap to quantify the gap between the learned alloc... | Rebuttal 1:
Rebuttal: Thank you for your mindful comments! We will clarify some misunderstandings and address the concerns you have listed.
* **Minor corrections**
> The paper propose an unbiased training loss and a metric called Nash Gap to quantify the gap between the *learned allocation* and the market equilibrium... | Summary: This paper studies the computation of market equilibrium where there are a large number of buyers and the buyers and goods are represented by their contexts. It proposes a deep-learning method, termed MarketFCNet, to approximate the market equilibrium. The method outputs the good allocation by taking in the co... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review! We appreciate that you affirmed our contributions. We will address the questions you listed.
> 1. The proof of the unbiasedness ... is $b'_i$ an independent copy of $b_i$?
We are sorry that our deductions make you confused. In our paper, we assume that ther... | Summary: The submission is not in your area and extends beyond my current expertise (from theory and applications to specific tasks and methods).
Strengths: The submission is not in your area and extends beyond my current expertise (from theory and applications to specific tasks and methods).
Weaknesses: The submissi... | null | Summary: This paper proposes a deep learning-based method called MarketFCNet to efficiently compute market equilibrium in large-scale contextual markets, where buyers and goods are represented by their contexts. The key idea is to parameterize the allocation of each good to each buyer using a neural network, and optimi... | Rebuttal 1:
Rebuttal: Thank you for your detailed questions! We will address the questions and concerns you have listed.
* **About the concerns in Weaknesses**
> Exploring ways to improve the interpretability of the learned allocation function, such as incorporating domain-specific constraints or incorporating interp... | Rebuttal 1:
Rebuttal: To reviewer 5naV: the training curve is provided in the attached PDF file.
Pdf: /pdf/1003001a17f0047909b66db165d13c9698e10a0d.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
TextGraphBART: Unifying Graph and Text with Structure Token | Reject | Summary: This paper proposes a method to integrate the processing and generation of both text and graph data using a single transformer-based model. Structure Token encodes graphs with text labels into a sequence of tokens, enabling the handling of both data types interchangeably. This approach leverages a unified repr... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We acknowledge that there are many research focuses on the textual and structural data representations. However, they usually fall into the two categories mentioned in the “Introduction” section. Our structure token approach would be the third category, and th... | Summary: This paper introduces TextGraphBART. Its a new method of encoding graph/text-input by using a structure token. This new token should preserve graph structure as opposed graph linearization or cycle training. This should also allow for the generation of graphs, with accompying text tokens, without making archit... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review. We address your main points below:
About Questions:
1. The necessity of transformer modifications is to incorporate the structural information of the graph into the models. TokenGT [1] shows how a good token design can obsolete the need of transformer modific... | Summary: The paper proposes a new unified graph-text generation framework, TextGraphBART, for the large language model. The paper tries to address both the generation and representation of text and graphs. The paper proposes a new structure token to encode text graphs into a set of tokens. The structured token can enco... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We address your main points below:
1. While the proposed framework seems to be incremental, we handle many situations that cannot be done with previous approaches. The idea of using position embeddings for structure information is well-known in graph neural netw... | Summary: - The paper highlights the limitations of two existing methods for generating text graphs: 1. The multi-stage approach does not consider multi-hop relations and cannot handle the case where two concepts have more than one relation. 2. The graph linearization approach introduces extra complexity to the Language... | Rebuttal 1:
Rebuttal: We appreciate your detailed review. We address your main points below:
1. We appreciate the mention of the aesthetic of the layout, we will adjust accordingly in the revision.
2. The error bars are not included in the experiments because our model used in each experiment is finetuned from the same... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models | Accept (poster) | Summary: This paper presents a Mamba-based traversal of rationales (Meteor) that leverages detailed image captions (multifaceted information) to provide more comprehensive image-related information to LLVMs, thereby enhancing the model's understanding and answering capabilities. The introduction of the Mamba architectu... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments! In the following rebuttals, we address all the comments you pointed out. We will definitely include these clarifications in our manuscript to enhance understanding in the potential camera-ready version.
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**Q1. The introduction of Meteor-Mamba increases th... | Summary: The paper proposes Meteor, which divides MLLM into two stages, i.e., rationale generation and question answering. Experimental results show its effectiveness agasint existing MLLMs on many benchmarks.
Strengths: 1. The proposed idea is very reasonable. The technical implementation is also novel and strongly ... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments! In the following rebuttals, we clarify the points you raised. We will incorporate this content into our manuscript in the next potential camera-ready stage.
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**Q1. How about the inference time against common MLLMs?**
**A1.** We evaluated the inference ti... | Summary: The paper "Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models" presents a new efficient large language and vision model (LLVM), Meteor, which leverages multifaceted rationale to enhance understanding and answering capabilities. The paper introduces a new concept of traversal of ra... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments! We will incorporate the following rebuttal into our manuscript to enhance overall understanding in the potential camera-ready version.
---
**Q1. Writing: All Figures (fonts, line widths, etc.) in the paper are not uniform, there is too much white space. An... | Summary: This paper introduces a novel approach to enhance the performance of large language and vision models (LLVMs). The proposed model, Meteor, leverages multifaceted rationale through the Mamba architecture to efficiently embed lengthy (latent) rationales and improve understanding and answering capabilities across... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We will incorporate the following rebuttal into our manuscript to enhance overall understanding in the potential camera-ready version.
---
**Q1. Figure 3 (Stage-One Pre-Training): More clarity and intuition behind the model design are needed. Given that the ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Foundation Model for Zero-shot Logical Query Reasoning | Accept (poster) | Summary: This paper considers the inductive setting of complex logical query over incomplete knowledge graph, in which unseen entities and relations exist. To address this challenge but the particularly important setting, this paper generalizes the foundation model of knowledge graph completion Ultra and proposes Ultra... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work highlighting the results, paper organization, and discussions. Please find our comments below:
> **W1.** novelty of the proposed methods.
In this work, we introduced a new setup – a fully-inductive, zero-shot generalization of complex query answeri... | Summary: This paper proposes a new framework for the generalization of complex logical question answering (CLQA). Specifically, the authors handle an extreme situation where the knowledge graph at the test time is completely different from the training time, which requires the model to adapt well to new entities and re... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work and would like to comment on the weaknesses:
> **W1.** I feel the main problem is the lack of a detailed description of the ULTRA method and how it is adapted to the foundation model.
In order to build a model that zero-shot generalizes to CLQA ta... | Summary: The paper presents ULTRAQUERY, a groundbreaking model for zero-shot logical query answering on knowledge graphs. It introduces a novel approach that combines inductive reasoning with non-parametric fuzzy logics to generalize to new entities and relations without additional training. The model demonstrates comp... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the constructive feedback, please find our comments below.
> **W1.** While the model's generalizability is a strength, the complexity introduced by the inductive reasoning might make it challenging to scale or adapt to very large KGs. The paper could have p... | Summary: This paper proposes ULTRA QUERY, a foundation model for zero-shot logical query reasoning on knowledge graphs (KGs). Existing complex logical query answering (CLQA) methods are either transductive or only partially inductive, requiring training on each specific graph. ULTRA QUERY overcomes this limitation by d... | Rebuttal 1:
Rebuttal: Thank you for appreciating our work and helpful comments.
> **W1.** A more in-depth analysis of the model's behavior under different conditions (e.g., varying graph sizes, query complexity) could provide further insights
We would like to point your attention to Section 5.3 in the main paper and... | Rebuttal 1:
Rebuttal: We thank the reviewers for appreciating our work and providing valuable feedback. We are delighted to see the work recognized as *“a significant breakthrough”*, *“a significant advancement in the field”* (**5vKU**), *“a groundbreaking model”* (**vHv9**), *“solid and important work”* (**rSrG**) wit... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Visual Perception by Large Language Model’s Weights | Accept (poster) | Summary: This paper presents VloRA, a paradigm for building MLLMs, which aligns visual features with the parameter space of LLMs. By representing visual information as model weights, no visual tokens is need in the input, which reduces the length of the input sequence and improves efficiency.
Strengths: (1) The motiva... | Rebuttal 1:
Rebuttal: We are deeply grateful for the reviewer's valuable comments.
**Question 1: The experimental data about the real training speed and GPU RAM requirement of VLoRA and LLaVA**
| | pre-training LLaVA | pre-training VLoRA | fine-tuning LLaVA | fine-tuning VLoRA |
| - | - | - | - | - |
|... | Summary: The paper proposes a novel way to enable visual understanding in LLMs. Instead of encoding image as visual tokens, the paper proposes converting visual input to low-rank perceptual weights which are merged with LLM weights (similar to LoRA). The paper shows that the proposed approach achieves comparable perfor... | Rebuttal 1:
Rebuttal: We are grateful for the effort the reviewer has dedicated to evaluating this work.
**Question 1: How will the model work when more than one image is used as input (such as interleaved image-text dialogue, videos, etc).**
VLoRA can naturally be extended to support multiple image inputs, here we c... | Summary: The work proposes an efficient setup for incorporating non-text modalities into pretrained LLMs for reasoning-based tasks. Instead of introducing new tokens into the LLM, they propose to dynamically generate LoRA weight matrix residuals for the linear projectors within the LLM, conditioned on the input image. ... | Rebuttal 1:
Rebuttal: We appreciate the insightful comments provided by the reviewer.
**Question 1: Look beyond QA benchmark numbers to understand what the implications of this architectural change are.**
Thank you for your suggestion. We have provided some practical examples in the PDF file, where you can see the im... | null | null | Rebuttal 1:
Rebuttal: The figure requested by reviewer cXpd has been included in the PDF
Pdf: /pdf/d5049b4d3bfc63fe41e4b09cdf2c74ee1b238b4c.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps | Accept (poster) | Summary: The paper proposes a model for spatial representation in the hippocampal formation using a residue number system (RNS) to encode positions as high-dimensional vectors. These vectors are combined into a single representation through vector binding and maintained by a modular attractor network. The model demonst... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review and accurate summary of its main points. We concur that the strengths listed: theory (proofs and experimental validation) for every part of the RNS model, in addition to testable hypotheses for experimental neuroscience, are the core results of the pa... | Summary: This paper proposes a model for spatial representations in the hippocampal formation. The model relies on a residue number system for encoding spatial positions and uses complex-valued vectors to represent individual residues. These vectors are then combined into a unified vector representing spatial position ... | Rebuttal 1:
Rebuttal: Thank you for your accurate summary of our work and fair assessment of its strengths, weaknesses, and limitations. We appreciate that you found the ideas to be interesting and comprehensive.
We agree that our modeling approach is "relatively high-level", and that such an approach comes with stren... | Summary: The paper proposes a computational model that incorporates a number of properties about encoding of space representation in the system. The mathematical framework appears to be well-justified and carry the desired properties. These properties are related to some of the observations made about the properties of... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your agreement about the rigor and suitability of the mathematical framework. Apologies if responses seem terse, we've tried to give clear explanations within the word count.
> To make a better job ... What is it that main thing that it bring to... | Summary: This paper introduces a normative model for spatial representation within the hippocampal formation, integrating optimality principles with an algebraic framework. Spatial positions are encoded using a residue number system (RNS) and represented by high-dimensional, complex-valued vectors. These vectors are co... | Rebuttal 1:
Rebuttal: Thank you for accurately summarizing our study and capturing the core strengths of the paper. We appreciate your questions, as we think they get at the fundamental context surrounding the paper. We've done our best to address each of them fully and concisely below.
>The model’s complexity might p... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for their thorough reviews and critical feedback. In addition to addressing each review individually, we'd like to discuss a few common points.
*Core contributions:* There are many fundamental yet unresolved questions about the function of the hippocampal formatio... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization | Accept (poster) | Summary: In this paper, “ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization,” the authors propose a novel post-training reparameterization method that can perform multiplication-free operations in LLMs (Large Language Models). Through a modified APoT (Adaptive Power of Tw... | Rebuttal 1:
Rebuttal: We greatly appreciate your careful review and constructive suggestions. Below are our detailed responses to your concerns.
**W1: The multi-objective optimization method is quite novel and impressive, but it needs to be tested with more cases.**
Thank you for acknowledging the novelty of our mult... | Summary: In this paper, the authors propose the ShiftAddLLM method to simplify complex matrix multiplications using simple shift and add operations. To enable efficient computation, they suggest assigning scaling factors in a column-wise and block-wise manner, where these scaling factors follow the form of powers of 2,... | Rebuttal 1:
Rebuttal: We appreciate your time and suggestions in reviewing our work. Below are our detailed responses to your concerns.
**W1 & L1: Discuss the impact of batch sizes on throughput. If only a batch size of 1 is considered, explain why this is practical?**
Following your suggestion, we have further teste... | Summary: The presented work replaces multiplications by shift and add operations as a post-training processing step of LLM neural network models. The proposed quantization improves a lot over SOTA methods using improved trade-offs and better control of the quantization error. Despite bit-level operations, the resulting... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive comments and constructive suggestions. Below are our detailed responses to your questions.
**W1: The clarity of the presentation could be improved.**
**(1) Technique applicability beyond LLMs: discussion needed.** You are correct that the idea is general and c... | null | null | Rebuttal 1:
Rebuttal: **Dear ACs and Reviewers,**
First, we deeply appreciate the time and effort you have devoted to providing reviews for our paper, particularly given the substantial scale of a conference like NeurIPS. Your efforts are truly valued.
We are immensely grateful for the positive feedback our paper has... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning | Accept (poster) | Summary: This paper studies and proves the applicability of two risk-aware objectives to Preference-Based Reinforcement Learning (PbRL), i.e., iterated and accumulated quantile risk objectives. The authors design an algorithm called Risk-Aware-PbRL (RA-PbRL), which can optimize both iterated and accumulated objectives.... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the effort in reviewing our paper and appreciate our versatility compared to prior work. The following are responses to the reviewer’s concern.
> Weakness: The proposed algorithm is very straightforward, which seems to simply combine the confidence set constru... | Summary: This paper incorporates risk-awareness into Preference-based Reinforcement Learning (PbRL). Specifically, it tackles the issue that under PbRL, the reward is episodic, meaning that it can only be computed on full trajectories. The authors adapt both iterative and accumulated quantile risk objectives to deal wi... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the positive score and for recognizing the mathematical development of our work. We summarize our responses to the concerns and revisions that we will make to this paper.
> Weakness 1 : As the manuscript already mentions, the experimental setting is very simple... | Summary: This paper focuses on the theoretical analysis of risk-aware preference-based reinforcement learning and introduces Risk-Aware-PbRL (RA-PbRL) to optimize both iterated and accumulated risk-aware objectives.
Strengths: - This paper proves that both iterated and accumulated quantile risk-aware objectives can be... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback and valuable time for evaluating our paper. We especially thank the reviewer for mentioning that “provide a theoretical foundation for future episodic RL or PbRL methods focusing on risk-related objectives.” The reviewer has raised some valid questions and p... | Summary: This paper studies preference-based RL (PbRL) where instead of the expected return, the agent optimizes a risk measure based on preference feedback. The authors study two settings called "iterated" and "accumulated" quantile risks, otherwise known as nested and static risks. They provide sublinear regret bound... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and recognizing the potential contribution of our work. Here are the responses to the issues you raised:
# For Weakness1:
We acknowledge your point. In fact, we find the terms "nested" and "static" risk measures are more widely accepted within the RS-RL communi... | Rebuttal 1:
Rebuttal: We thank all the reviewers and ACs for their time and efforts in reviewing our paper and providing insightful comments. We acknowledge reviewers[TVSN,LP4v] for recognizing our contribution of applying both iterated and accumulated risk-aware to PbRL and reviewers [QEr2, EoFm]’s appreciating for ou... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback | Accept (poster) | Summary: The paper identifies four core components in training from preference feedback (RLHF): labeled preference data, learning algorithm, reward model, and unlabeled training prompts; and conducts a study on the effect of each component separately to disentangle their contributions to performance.
The authors experi... | Rebuttal 1:
Rebuttal: Thank you for your review, and noting that our work is clearly written, with an easy to follow narrative, detailed experimental setup, and multiple interesting observations. We clarify key points of our experimental setup and choices (e.g. datasets) below, which hopefully address your key concerns... | Summary: The work summarizes the area of learning from preferences for optimizing language models. Specifically, the analyze four aspects: preference data, learning algorithm, reward model, and policy training prompts. They empirically answer questions on the downstream improvement by improvements in each of these a... | Rebuttal 1:
Rebuttal: Thank you for your review and noting that our work is well written and clear to follow, and that the insights in our work are useful for practitioners in the field. We address concerns and questions below:
**Concerns**:
1. **Novelty**
While we do not propose entirely new methods ourselves, the ... | Summary: This work concentrates on methods for LLM learning from preference feedback and conducts a lot of experiments to identify key aspects of the preference-based methods. The work gives an ordering for the importance of the core aspects: preference data quality, algorithm choice, reward model quality, and finally... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and questions. We address your concerns and questions below and hope this provides further insights into our work.
**Concerns**:
1. **Results could be improved with more ablations.**
Please see point 1 of our general response, where we additionally provide new ... | Summary: This paper disentangles core components of current learning from preference feedback algorithms in alignment, conducts comprehensive experiments on the individual effect of each component, and provides a recipe of learning from preference feedback based on experiment results.
Strengths: 1. This paper aims to ... | Rebuttal 1:
Rebuttal: Thank you very much for your review and feedback, and for noting that our results are a valuable reference for the community and enhance understanding of RLHF. We address your feedback and questions below:
**Concerns**:
1. **Testing the influence of the training dataset size on reward model & dow... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their comments and feedback. We are happy that reviewers have noted our results and findings are of interest to the community (mnCL, WxZd, wF89), and enhances understanding of RLHF (mnCL), with comprehensive experiments/datasets (P8L3, mnCL). Additionally, we are hap... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Shape analysis for time series | Accept (poster) | Summary: This paper proposes a LDDMM method for time series data (TS-LDDMM), by representing time series as deformations of a reference time series. The TS-LDDMM can handle irregularly sampled multivariate time series of variable lengths, and provide shape-based representations of temporal data. They further show the a... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments. Here are the responses to the weaknesses and questions you raised:
- **Typos.** You have pointed out our typos accurately. We acknowledge these errors and will make the necessary corrections.
- **Comparisons to other methods for the interpretability... | Summary: The paper presents and extension of the LDDMM shape analysis framework to time series data. The concepts of using deformations (diffeomorphisms) to transform data is extended to the graph of time series data. The authors define a kernel that extends to the augmented space of time+data by treating the time and ... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments. Here are the responses to the weaknesses and questions you raised:
- **Regarding the Functional Data Analysis (FDA) literature.** We have compared TS-LDMMM to Shape-FPCA [1] in Figure 1 of the attached PDF to the common rebuttal and on the classificat... | Summary: This paper extends the large deformation diffeomorphic metric mapping (LDDMM) framework to the case of univariate and multivariate functional data (time series). In particular, the focus is on understanding sample variation in the shape of irregularly sampled time series. The proposed framework leverages a gra... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments and your time, which will help us improve the quality and clarity of this paper. We appreciate your accurate understanding of the paper. We are pleased to say that we have been able to carry out the experiment you suggested during the rebuttal period.
Here are... | Summary: This paper introduces an unsupervised method based on LDDMM to highlight inter-sample shape variability in time series, with the model being able to work on irregular multivariate time series. Extensive studies are conducted to theoretically and experimentally justify the authors' choices. The interpretability... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments. Here are the responses to the weaknesses and questions you raised:
- As depicted in Table 1 of the PDF and thanks to its minimal architecture, the training time of TS-LDDMM is below that of large neural networks and beyond that of classic statistical... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our manuscript. We are grateful for your constructive criticism and the effort you put into evaluating our work. Your careful analysis and suggestions have significantly enhanced the quality of our research.
We carried out all the experiments that you reque... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Federated Learning over Connected Modes | Accept (poster) | Summary: This paper proposes a method for FL to train a simplex of linearly connected solutions with uniformly low loss. Clients are expressed in the simplex by projecting the gradient onto the simplex and similar clients are close to each other. In each communication round each client samples points in the neighborhoo... | Rebuttal 1:
Rebuttal: We are very pleased and thankful for Reviewer a16f for their thorough review. In particular we are thankful that the reviewer found our method novel and interesting, and the soundness, presentation, contribution and experimental setup of our paper good.
In the following, we will answer the reviewe... | Summary: In this study, the authors tackle challenges in federated learning by introducing FLOCO, which uses linear mode connectivity to identify a solution simplex in neural network weight space. This approach allows for personalized client model training within the simplex, while also enabling efficient updates to bo... | Rebuttal 1:
Rebuttal: We are very pleased and thankful for Reviewer 5sib for their thorough review. In particular we are thankful that the reviewer found our application of linear mode connectivity to the FL setting interesting and the soundness, presentation, contribution and experimental setup of our paper good.
In t... | Summary: A novel method is proposed to derive the mode connectivity over the simplex defined by the central server for an improved global model as well as local personalization performances in the federated settings.
Strengths: The proposed objective using Riesz s-Energy regularization along with the Euclidean project... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to Reviewer 44Bh for their detailed review. We are happy to read that the reviewer acknowledges the novelty of our method that exploits recent findings in mode connectivity to train a simplex in the FL setting, which improves local and global performance. Mor... | Summary: The authors propose federated learning over connected modes (FLOCO), where clients
are assigned local subregions in this simplex based on their gradient signals, and
together learn the shared global solution simplex.
Strengths: This paper is richer in the type of experiments.
Weaknesses: The proposed metho... | Rebuttal 1:
Rebuttal: We are deeply thankful for Reviewer bjGi for their thorough review. We are pleased to read that the reviewer appreciates the richness of our experiments, the soundness and presentation of our work. In the In the following, we answer the reviewer’s remarks and questions in more detail:
***Reviewer... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their detailed review, including remarks, questions and suggestions for improvement. As most reviewer’s pointed out, some important baselines, such as SuPerFed[1] and others are crucial to benchmark against our method. We have thus extended our experimental... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models | Accept (poster) | Summary: This paper introduces SpatialRGPT, targeting at enhancing the spatial reasoning abilities of VLMs. The authors introduce a data curation pipeline along with a benchmark that facilitates the learning and evaluation of 3D spatial knowledge. Experiments show that SpatialRGPT thrives at spatial reasoning and perfo... | Rebuttal 1:
Rebuttal: **Q:** *It'd be great if the author could show some results on how much AABB assumptions affect the overall data quality, i.e. how many objects are measured inaccurately because of the AABB assumptions of the bounding boxes.*
**A:** We conduct an ablation study to examine the effect of using axis... | Summary: This paper constructs region-aware spatial reasoning QA datasets from existing sources, resulting in the Open Spatial Dataset (OSD). Based on the OSD, they develop a model called SpatialRGPT, which integrates depth information to enable effective representation of regional information and acquisition of spatia... | Rebuttal 1:
Rebuttal: **Q:** *SpatialRGPT-Bench is constructed through the proposed data generation pipeline, sharing the same answer formats as the OSD dataset on which the SpatialRGPT is trained. This may introduce bias when directly comparing it with other models not trained on the OSD dataset.*
**A:** Please refer... | Summary: The paper introduces a novel approach for generating 3D, region-aware annotations from 2D images, transforming scene graphs into spatial QA training data for VLMs using a combination of template-based and LLM approaches. Key contributions include:
1. A novel pipeline for automatic generation of complex, metric... | Rebuttal 1:
Rebuttal: **Q:** *How close are the questions in the evaluation set to the templated questions in the data generation pipeline?*
**A:** The questions from both the evaluation set and data generation pipeline are randomly sampled from a set of templates.
---
**Q:** *What steps are taken to ensure that the... | Summary: The paper introduces SpatialRGPT, a framework designed to enhance region-level spatial reasoning in Visual Language Models (VLMs) by incorporating 3D and region-aware visual encoder architecture. The authors present a scalable data pipeline to generate region-aware spatial reasoning questions and answers from ... | Rebuttal 1:
Rebuttal: **Q:** *SpatialRGPT-Bench uses same data pipeline as the training data, so its good performance might just reflect the model learning the training data's language style.*
**A:** Please see General Response (B), we conduct additional experiments on a GPT-4 augmented SpatialRGPT-Bench. The results ... | Rebuttal 1:
Rebuttal: We thank the reviewers for recognizing the importance of our research problem (Reviewer `y3Gs`), and for acknowledging the novelty (Reviewer `ojLE`, `hHf2`), effectiveness (Reviewer `y3Gs`), and usefulness (Reviewer `v4dm`) of our approach. Below, we address the reviewers' common feedback, particu... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention | Accept (poster) | Summary: The paper presents a condition-free guidance method for diffusion models. The guidance is generated from the self-attention mechanism to perform guidance from an energy-based perspective as an alternative to classifier-free guidance. With this, the work aims to train the models for improved quality performance... | Rebuttal 1:
Rebuttal: We would first like to thank the reviewer for acknowledging the strength of our approach in its versatility. We demonstrate SEG's effectiveness in both unconditional and conditional settings, including text-conditional generation and ControlNet conditioning. This flexibility allows SEG to improve ... | Summary: The paper proposes a technique to improve unconditional sampling from diffusion models. The main idea is to translate the notion of classifier-free guidance (CFG) to the case in which there is no condition available. To this end, the paper notes that the conditional prediction is "sharp", while the uncondition... | Rebuttal 1:
Rebuttal: We sincerely appreciate your acknowledgment of our approach's strengths, particularly its elegant idea and thorough qualitative and quantitative evaluations. Thank you for your careful suggestions. We'd like to address the concerns and questions you've raised:
## Improving conditional generation
... | Summary: This paper presents a method for unconditioned image generation based on Diffusion Models, specifically using Stable Diffusion. The proposed method offers an alternative to classifier-free guidance (CFG), eliminating the need to train a classifier for adding conditions. Traditionally, the CFG denoising functio... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review of our paper. Below is our response to your review:
## When sigma approaches 0
> SEG with $\sigma\to 0$ is equivalent to the original sampling process. This doesn't necessarily mean $\\tilde{s}\_\\theta(x, t)$ goes to 0, but rather that the Gaussian kernel bec... | Summary: The manuscript introduces SEG, a novel training- and condition-free guidance method for enhancing image generation with diffusion models. The method leverages an energy-based perspective of the self-attention mechanism and introduces a technique to reduce the curvature of the energy landscape of attention, the... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful review of our paper and the recognition of SEG's strengths. We'd like to address the concerns and questions raised:
## How the model knows what to generate and why the style is different
> First, we'd like to note that our method claims an inference time boost like o... | Rebuttal 1:
Rebuttal: ## General response to reviewers
> We sincerely thank all the reviewers for their thorough evaluation and insightful feedback on our submission. We appreciate the recognition of our work's strengths and the constructive suggestions to fine-tune our manuscript. Based on the reviews, we'd like to h... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The method (SEG) discussed in the paper mainly applies an energy-based optimization on the emerging values in the self-attention to reduce the curvature of the energy landscape of attention, leading to improved image quality and less structural change from the original prediction compared to previous approache... | Rebuttal 1:
Rebuttal: We would first like to thank the reviewer for acknowledging our visualization with various conditions as insightful and our paper as very interesting. We'd like to address the concerns and questions raised:
## Additional details of quantitative evaluations
> While we guide readers more towards q... | null | null | null | null | null | null |
Hamiltonian Score Matching and Generative Flows | Accept (poster) | Summary: This paper proposes a new matching method based on Hamiltonian mechanics. The proposed method, algorithmically, is essentially a second-order ODE in which the acceleration (i.e., the drift of the velocity channel) is approximated by deep neural networks. The method is well grounded on the theory of classical H... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and for taking the time to review our work. We address your questions and comments below.
> Can the authors comment on the differences compared to AGM?
**Comparison with Acceleration Generative Model (AGM) model**: Thank you for pointing out t... | Summary: The paper proposes the Hamiltonian Score Matching framework, which is a new general framework of generative models. Inspired by the Hamiltonian dynamics in classical and statistical mechanics, the framework uses the Hamiltonian dynamics to generate data, which is also called Hamiltonian generative flow in the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and for taking the time to review our work.
> What is the motivation for designing Oscillation HGFs?
**Motivation for designing Oscillation HGFs and other force fields:** As you point out, the choice of a force field is an important design para... | Summary: The authors introduce Hamiltonian velocity predictors (HVPs) as a tool for score matching and generative models. They present two innovations constructed with HVPs: Hamiltonian Score Matching (HSM), a novel generative model that encompasses diffusion models, and flow matching as HGFs with zero force fields. Th... | Rebuttal 1:
Rebuttal: We thank you for your insightful comments and for taking the time to review our work. We are pleased to read that you consider HGFs an “interesting generative model” and address your questions and comments below.
**Explanation for EDM vs Oscillation HGFs performance:** We addressed your question ... | Summary: In this work, the authors proposed a new generative modeling approach called Hamiltonian Score Matching, which is motivated by classical Hamiltonian mechanics. This approach estimates score functions by augmenting data via Hamiltonian trajectories, and further motivates Hamiltonian Generative Flows. The author... | Rebuttal 1:
Rebuttal: We thank you for your insightful comments and for taking the time to review our work. We decided to address your questions regarding our methodology and our experimental results in the general response. We provide additional information here.
**Theory vs practice:** You correctly point out that t... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their constructive and positive feedback. We are pleased to see that our work is considered by reviewers as a “very solid theoretical contribution” introducing an “interesting generative model leveraging Hamiltonian velocity predictors”. Below, we’ve compil... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper explores the application of Hamiltonian formalism for generative modelling.
In this framework, the score function is interpreted as a force field F, thus optimizing the parameters of F yields a score-matching objective.
Authors build the relation to Flow Matching and Diffusion models as special cases... | Rebuttal 1:
Rebuttal: We thank the reviewer for insightful comments and for taking the time to review our work.
> I am curious if it would be simple to enforce equivariance as in Equivariant Hamiltonian Flows by Rezende et al.
**Enforcing equivariance:** Thank you for highlighting the work “Equivariant Hamiltonian Fl... | null | null | null | null | null | null |
PowerPM: Foundation Model for Power Systems | Accept (poster) | Summary: The paper introduces the PowerPM: Foundation Model for Power Systems, which is designed to address the challenges of learning a generic representation of electricity time series (ETS) data in power systems. The model incorporates a temporal encoder and a hierarchical encoder to effectively capture the complex ... | Rebuttal 1:
Rebuttal: Thank you for your constructive and detailed comments. Responses to specific comments are listed below.
**1. W1&Q7: Compare their framework with some state-of-art techniques**
Thank you for your advice. Since ETS is essentially a time series, we choose the time series modeling model as the basel... | Summary: A pre-trained LLM named PowerPM is proposed for modeling Electricity Time Series (ETS) data. PowerPM combines a temporal encoder for capturing temporal patterns and a hierarchical encoder for understanding hierarchical correlations. PowerPM also employs a self-supervised pre-training strategy that incorporates... | Rebuttal 1:
Rebuttal: Thank you for your constructive and detailed comments. Responses to specific comments are listed below.
**1. W1: About source of the model performance**
Thank you for pointing out that our description may confuse the reader. We have made a detailed reply in **Global Response** , please check and... | Summary: This paper proposed a foundation model, PowerPM, to model electricity time series data, providing a large-scale off-the-shelf model for power systems. PowerPM consists of a temporal encoder and a hierarchical encoder with a self-supervised pretraining framework. The authors have tested PowerPM in Demand-side M... | Rebuttal 1:
Rebuttal: We thank the reviewer for all the insightful comments. We apologize for the imprecise claims in certain parts of our manuscript and misunderstandings caused by our writing. Responses to specific comments are listed below.
**1. W1: Several places are missing space between words**
Thank you for po... | Summary: This paper learns a generic representation of electricity time series data. The proposed PowerPM model is composed of a temporal encoder and a hierarchical encoder.
Strengths: The results shown in the table exhibit good numerical results of the proposed model.
Weaknesses: It is unclear where the performance ... | Rebuttal 1:
Rebuttal: Thank you for your constructive and detailed comments. We apologize for the imprecise claims in certain parts of our manuscript. We will reconsider the claims to be more precise. We hope that the responses below could address your specific comments.
**1. W1: Clarify where the performance of the m... | Rebuttal 1:
Rebuttal: ## **Global Response to AC and all reviewers** ##
## About source of the model performance ##
Thanks to all reviewers for the careful reading and thoughtful feedback. Here we explain the effectiveness of the dataset and the source of the model performance, as a solution to similar concerns raised... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting | Accept (poster) | Summary: The paper proposed a pipeline for 3D scenes generation, which leveraging 2D prior from diffusion-based image and depth generation. The main proposed point is the hierarchical inpainting, a inpainting mask is generated from a simulated 3D environment, providing a good condition to control the 2D image generati... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4D8M
*Thank you for your insightful and constructive comments! We discuss some of your questions and concerns below.*
**1. Without the control of 3D constraints, how is the quality of the generated scenes**
To clarify, **the 3D constraint is generated by the hierarchical i... | Summary: This paper aims to generate interactive 3D scenes suitable for downstream tasks like robotics. The paper first generates an empty scene and then utilizes pre-trained 2D inpainting models to fill in the ‘foreground’ and apply visual recognition and depth estimation models to ‘lift’ the 2D objects to 3D space vi... | Rebuttal 1:
Rebuttal: # Response to Reviewer Te9g
*We appreciate your positive and insightful comments! Below, we address your concerns in detail.*
**1. Faithfulness to image generation results.**
Since our ultimate goal is to generate diverse and realistic interactive scenes, the inpainted images serve as guidance t... | Summary: This paper considers creating large 3d indoor scenes (e.g. an apartment or grocery store) by an hierarchical generation procedure. The key idea is to use diffusion models for inpainting to guide where to place the objects in the scene. This is done for both large objects (e.g. table or couch) and also small ob... | Rebuttal 1:
Rebuttal: # Response to Reviewer yxqm
*We appreciate the positive and insightful comments from you! We adress your concerns in details below.*
**1. Partially Usage of 2D Inpainting**
Generally speaking, the occlusion when inpainting large furniture is much more severe than when inpainting small objects. T... | Summary: This paper proposes a hierarchical diffusion-based 2D inpainting method for creating interactive 3D scenes. By leveraging the generative prior of 2D diffusion models, the proposed method could generate more realistic and diverse object layouts compared with Holodeck [Yang et al., 2024c] that depends on LLMs wh... | Rebuttal 1:
Rebuttal: # Response to Reviewer YmnU
*We appreciate the positive and constructive comments from you! We have modified our paper according to your comments.*
**1. Bias from LLMs**
There is always a trade-off between diversity and realism. It is true that sometimes chaotic data can be valuable in enhancing... | Rebuttal 1:
Rebuttal: # General Response to All Reviewers
*We express our gratitude to all the reviewers for their perceptive comments and helpful suggestions aimed at enhancing the quality of our work.*
**1. Our Contributions**
We are pleased that the reviewers have generally acknowledged our contributions:
* We pr... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces ARCHITECT, a generative framework designed to create complex and realistic 3D environments for Robotics and Embodied AI research. Unlike traditional methods that rely on manual design, predefined rules, or large language models (LLMs), ARCHITECT utilizes pre-trained 2D image generative mod... | Rebuttal 1:
Rebuttal: # Response to Reviewer YpUS
*Thank you for your insightful and constructive comments! We have added additional experiments and modified our paper according to your comments.*
**1. Controllability and Editing**
In short, our method combines a diffusion-based pipeline with an LLM-based method, w... | null | null | null | null | null | null |
Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization | Accept (poster) | Summary: This paper proposes a novel method, Eigen-SAM, as an improvement to Sharpness-Aware Minimization (SAM). The paper theoretically elucidates the relationship between the top eigenvalue of the Hessian matrix and generalization error and models the dynamics of SAM using a third-order stochastic differential equati... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and constructive suggestions. Below we address your comments:
Weaknesses:
* **Experiments are limited to ResNet models, raising questions about the method's effectiveness with other models. Comparative experiments with ViT and other architectures are strong... | Summary: This study establishes a theoretical connection between the top eigenvalue of the Hessian matrix and generalization error through an extended PAC-Bayes theorem. Highlighting the importance of perturbation-eigenvector alignment in mitigating sharpness, this study introduces Eigen-SAM. This method intermittently... | Rebuttal 1:
Rebuttal: Thank you for your detailed and insightful feedback. We address your concerns below:
* **The experimental findings exhibit limited strength, indicating the necessity for additional analysis and further experimentation: A. The experimental results do not sufficiently demonstrate the superiority of... | Summary: The paper focuses on improving Sharpness-Aware Minimization (SAM) by introducing a novel approach called Eigen-SAM. It establishes a theoretical connection between the top eigenvalue of the Hessian matrix and generalization error using an extended PAC-Bayes theorem. The authors derive a third-order stochastic ... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments and insightful suggestions. We address your questions one by one below:
Weaknesses:
**1. The paper claims that it's the first work establishing the relationship between the top eigenvalue and generalization error. However, some works [1,2] have discussed related ... | Summary: The authors consider sharpness aware minimization problem and derive third-order SDE governing SGD dynamics.
As a result, they obtained a bound on the generalization error of SAM, showing that SAM trajectories favor flat minimal with the smaller largest eigenvalue of the Hessian, implying that flatter minima ... | Rebuttal 1:
Rebuttal: I appreciate the time and effort you've taken to review our manuscript. Below are our responses to your concerns:
* **The full list of assumptions on the loss is not listed.** The assumptions for Theorem 4.1 can be found in Section A.2 of the appendix, including the continuity of the third deriva... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their careful and thoughtful feedback. Some concerns from the reviewers are centered on our experimental section, therefore, we would like to provide a general response here. Firstly, we would like to clarify that our paper is more theoretical in nature. Ou... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: ### Summary:
The authors first provide a theoretical connection between max e.v. of Hessian and generalization via PAC-Bayesian bounds. Then, they propose a 3rd-order SDE for SAM, which has a lower approximation error compared to existing 2nd-order SDEs in the literature. They argue that the perturbation-ei... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful feedback, which has been invaluable to the improvement of our work. Below we address your comments:
* **line 81 -- is $\nabla^3f(x)(u,v)$ a vector? Is it a multilinear function? How does it relate to the symmetric tensor $\nabla^3f(x)$?** Yes $\nabla^3f(... | null | null | null | null | null | null |
What Makes and Breaks Safety Fine-tuning? A Mechanistic Study | Accept (poster) | Summary: This work designs a synthetic data generation framework with the purpose of understanding safety fine-tuning. It investigates (1) Supervised safety fine-tuning; (2) Direct preference optimization; and (3) Unlearning.
Key observations:
(1) safety fine-tuning encourages separate cluster formations for safe and u... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We are glad that the reviewer liked our work and found our task novel with interesting observations and comprehensive analysis beneficial for reproducibility. We address specific comments below.
---
> Can you explain more about why existing real-... | Summary: This work studies the mechanism behind safety fine-tuning (and why they are failing against attacks). Particularly, the authors introduce a synthetic task that simulates model safety training, alongside proposing a data generation framework. They reveal multiple insightful findings via this framework.
Strengt... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback! We are glad that the reviewer enjoyed reading our work and found our setup and findings valuable for studying safety alignment in depth. We address specific comments below.
---
> In Fig 2, for jailbreaking attacks with mismatched generalization,... | Summary: This paper proposes a synthetic data generation framework to systematically analyze safety fine-tuning methods, including supervised safety fine-tuning, direct preference optimization, and unlearning. The empirical results indicate that safety fine-tuning encourages the formation of different clusters for safe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback! We are glad that the reviewer found the setup novel and the paper well-written, with extensive experiments and interesting observations useful for designing safety fine-tuning techniques. We address specific comments below.
---
> The authors sho... | Summary: This paper introduces a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning, jailbreaking attacks, and adversarial attacks. This paper provides comprehensive analyses on the mechanisms learned after safety fine-tuning
Strengths: Controlled way of safety f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their efforts in reviewing our work. We are glad that the reviewer found our setup novel and analysis comprehensive. We address the specific comments below.
---
> How is the quality of the synthetic dataset controlled?
We are unsure we follow the reviewer's intended mea... | Rebuttal 1:
Rebuttal: ## **Common Reply**
We thank the reviewers for their efforts in reviewing our work. We are glad that all the reviewers found our PCFG-based synthetic setup novel and our analysis comprehensive. Additionally, reviewers sLPA, SrSG and dExx found our work easy to follow and our observations valuable... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Binary Search with Distributional Predictions | Accept (poster) | Summary: The main question guiding the paradigm of algorithms with predictions is the following: if we want to solve a given instance of a classical computer science problem, but are given predictions of some form (typically by some machine learning algorithm which has seen a lot of similar problem instances as trainin... | Rebuttal 1:
Rebuttal: We thank you for the points you have brought up in your review. We will incorporate the minor comments you raised into the paper and clarify the notation. As to your main comment about the lower bound, we will improve the rigor and also hope that the following discussion clarifies the lower boun... | Summary: The paper studies the problem of binary search for an item in a sorted array in a learning-augmented setting, where the element to search for is drawn from some unknown distribution, and the algorithm has access to a prediction of this distribution. This contrasts with the majority of work in the learning-augm... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We hope we can address each of your comments below.
1. Running time versus comparisons.
We are sorry for the confusion; putting “running time” in our theorem statements was a mistake. We meant query complexity (or number of comparisons), as the reviewer real... | Summary: In this paper, the authors study the classical problem of binary searching over a sorted array in the learning-augmented setting, with distributional advice. Binary search in both the learning-augmented setting with point advice and the classical setting with known query distribution is well studied, but the a... | Rebuttal 1:
Rebuttal: We agree that it is natural to adapt the algorithms with predictions model to a distributional prediction by simply using the median as the predicted value. However, if the benchmark is the optimal solution on the true distribution, using the median as a point prediction will not give strong guar... | Summary: This paper proposes a learning-augmented algorithm for searching in a sorted array. Different from all previous learning-augmented algorithms, it takes in distributional predictions. The main result is an algorithm with query complexity $O(H(p) + \log \eta)$, where $H(p)$ is the entropy of the true distributio... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments. As the reviewer astutely observed, the bounds we claim are on query complexity, rather than running time. We apologize for conflating the two and we will make this more precise.
Query complexity is a standard metric when studying data structures. The number... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Non-convolutional graph neural networks. | Accept (spotlight) | Summary: This paper proposes a random walk-based graph neural network, where an RNN is used to combine the topological and semantic graph features along the walks. The proposed RUM model is free of convolution operators and does not suffer from the limited expressiveness, over-smoothing, and over-squashing, which are c... | Rebuttal 1:
Rebuttal: Thank you, Reviewer `jBUd`, for your thoughtful and constructive review. We also thank you for highlighting the theoretical and empirical benefits. We hope that the following points address your concerns.
## Comparison with state-of-the-art models.
> Empirically, the gains with RUM are marginal a... | Summary: The paper introduces Random Walk with Unifying Memory (RUM), a non-convolutional graph neural network (GNN) that addresses limitations like limited expressiveness, over-smoothing, and over-squashing typically associated with convolution-based GNNs. RUM leverages random walks and recurrent neural networks (RNNs... | Rebuttal 1:
Rebuttal: Many thanks, Reviewer `xhjS`, for your insightful and constructive review. We also thank you for emphasizing the clarity of our theoretical framework and its potential impact on future research. We address your questions point-by-point as follows.
## The impact of random walk lengths.
> The pape... | Summary: The paper introduced a new graph neural network that is not based on convolution but on the random walk. Based on the results of the extensive experiment, the proposed architecture is effective for heterophilic graphs and long-range graphs.
Strengths: 1. The experiment results include different tasks on diffe... | Rebuttal 1:
Rebuttal: Thank you, Reviewer `wXB7`, for your constructive and encouraging review and for highlighting that RUM is both theoretically innovative and experimentally efficient and performant. We address your question on the directed and weighted graphs as follows, which we plan to incorporate in the manuscri... | Summary: The authors of the paper proposed a non-convolution based approach to solve graph learning tasks using random walks and recurrent neural networks (RNNs), namely a random walk neural network with unifying memory (RUM). Random walks, together with the “anonymous experiment” function, allow to extract topological... | Rebuttal 1:
Rebuttal: Thank you, Reviewer `P1JD`, for your thorough and constructive review. We also thank you for recognizing our novelty in using simple building blocks to design novel methods. Based on your review, we plan to revise our manuscript to include a more comprehensive comparison with graph transformer arc... | Rebuttal 1:
Rebuttal: Thank you, all reviewers, for your constructive and detailed feedback, based on which we are further revising our manuscript for better clarity.
---
# Recap: Main contributions
With the common pitfalls of convolutional graph neural networks (GNN) identified, a new graph learning paradigm is desig... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Large language model validity via enhanced conformal prediction methods | Accept (poster) | Summary: The paper proposes a new way to filter out LLM generations such that the resulting text only contains a set number of invalid claims with a certain probability. To do so, the authors adapt a new conformal mechanism to be level adaptive i.e. conditioned on the prompts rarity for example. They also propose a boo... | Rebuttal 1:
Rebuttal: We thank for the reviewer for their constructive criticism, and we hope that this rebuttal can both address some of the limitations that they identified and certain misunderstandings.
First, we would like to briefly note that the level-adaptive CP work is novel both in and outside of the LLM sett... | Summary: The paper introduces two new conformal prediction methods aimed at enhancing the validity of large language models. The first method generalizes the conditional conformal procedure to issue weaker guarantees when necessary, thereby preserving utility. The second method improves the quality of the scoring funct... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and feedback. We address specific concerns below:
1) We agree that it is costly to obtain these tuples and it may not be feasible to do so in all cases. Nevertheless, given the large resources invested in LLM development by corporate labs, we do ... | Summary: The paper presents two new conformal inference methods for obtaining validity guarantees on large language model (LLM) outputs. The authors consider the task of filtering invalid claims from LLM responses to ensure high probability factuality guarantees on the filtered output. They discuss two limitations of e... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We will look to prepare a revision that clarifies our experimental choices and provides additional experiments addressing the reviewer's concerns. We hope the following responses address the reviewer's concerns.
Weaknesses:
1) We agree that this would be ... | Summary: The paper provides a conformal prediction framework for the generation of hallucination risk-bound. The method can involve any loss function instead of the 0-1 loss as traditional conformal classification. They also provide a conditional (group) conformal prediction guarantee. They optimize the loss function w... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and constructive feedback. In response to the reviewer's specific comments:
1) The proof of this result is given in Appendix A. Our assumption that $L(\emptyset, \cdot) = 0$ is necessary to ensure the score function, $S(\mathbf{C}_i, \mathbf{W}_i)... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments and time spent considering our manuscript.
Two of the reviewers (CDbE and tBPp) inquired about additional experiments and, in particular, ablations demonstrating the contributions of each of our methods individually. Additionally, reviewer... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SimGen: Simulator-conditioned Driving Scene Generation | Accept (poster) | Summary: This paper aims to provide a photorealistic appearance to conventional graphics engine. The basic idea is to use controlnet to convert the rendered semantic mask + depth to real images, similar to the setting of image-to-image translation and style transfer. This paper also introduces the DIVA dataset that con... | Rebuttal 1:
Rebuttal: Before diving into the details, we reclaim the benefits of our simulator-conditioned pipeline.
Simulators can reconstruct scenarios from public datasets, real-world driving logs (e.g., Waymo, nuPlan), and program-generated scenes to obtain data with diverse layouts and annotations. Also, simulato... | Summary: This paper proposes a novel framework named SimGen, which aligns the web-scaled unannotated driving footage with the simulator-generated images and annotations to obtain the appearance diversity and traffic layout diversity for generating novel driving scene images.
Strengths: The paper tackles a challenging ... | Rebuttal 1:
Rebuttal: ***W1: Content presentation suggestions for this paper.***
Thank you for the suggestion. We follow your advice to reorganize the Related Work section and move the content in Appendix D.3 to the main paper.
***Q1: The solution of the statistical shortcut.***
Integrating input conditions through ... | Summary: The paper introduces SimGen, a framework for generating diverse driving scenes to reduce annotation costs in autonomous driving. SimGen combines simulator and real-world data using a novel cascade diffusion pipeline to address sim-to-real gaps and multi-condition conflicts. It is enhanced by the DIVA dataset, ... | Rebuttal 1:
Rebuttal: ***W1: Despite achieving superior results in single image generation, the 3D consistency has not been evaluated.***
We agree with the reviewer that achieving 3D consistency will be an important future direction. However, it is not the main focus of this submission. We have preliminary attempts at... | Summary: This paper presents a simulator-conditioned scene generation framework, SimGen, which generates diverse driving scenes conditioned on simulator controls and texts. To support the model training, the authors also introduce a driving video dataset, DIVA, comprising 147.5 hours of both real-world and simulator-ba... | Rebuttal 1:
Rebuttal: ***W1: Unclear usage of DIVA-Sim dataset in training.***
As stated in L158, Tab.2, and Appendix C.3, DIVE-Sim is only used in the training of ImgDiff. ImgDIff takes RealCond from DIVA and nuScenes and ExtraCond from DIVA-Sim as inputs, without including SimCond, and outputs real images. Consequen... | Rebuttal 1:
Rebuttal: Dear reviewers and ACs,
We sincerely thank all reviewers for their detailed and constructive comments. It is encouraging that reviewers acknowledge our pioneering efforts in establishing a simulator-conditioned generative model in driving scenarios. We have taken each comment into consideration, ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper presents SimGen, a framework for generating diverse and realistic driving scenes using a cascade diffusion pipeline, aiming to generate controllable, diverse, and realistic autonomous driving images. It also introduces the DIVA dataset, including real-world and simulated data to enhance training dive... | Rebuttal 1:
Rebuttal: ***W1: DIVA Dataset quality.***
Supp Tab. 1 includes the evaluation of the annotation quality. To evaluate the VLM, we utilize the DIVA-Real and nuScenes datasets and employ the widely used ROUGE-L metric (**84.4** and **85.2**) [1] to assess the similarity between the annotated data and pseudo-l... | null | null | null | null | null | null |
Poseidon: Efficient Foundation Models for PDEs | Accept (poster) | Summary: The paper introduces a new PDE foundation model, named Poseidon. The backbone of the model is a multiscale vision transformer. A data augmentation based on the semi-group property of time-dependent PDEs is also proposed to scale up the amount of training data. After pretraining, Poseidon has shown higher accur... | Rebuttal 1:
Rebuttal: We start by thanking the reviewer for your appreciation of the merits of our paper and your welcome suggestions to improve it. We address your detailed concerns below.
[W1:] The reviewer's suggestion on evaluating the robustness of Poseidon to noisy data is excellent. We follow it up by consider... | Summary: The paper introduces a foundation model for learning PDE solution operators, with a proposed architecture, training method and training dataset consisting of fluid dynamics PDEs. The foundation model was evaluated on various downstream tasks and was shown to outperform baselines in terms of sample efficiency a... | Rebuttal 1:
Rebuttal: We start by thanking the reviewer for your appreciation of the merits of our paper and your welcome suggestions to improve it. We address your detailed concerns below.
[W1/W2:] The reviewer's concern about clarity are well-taken as are your suggestions to improve it. Given the page limit, we had... | Summary: This paper proposes a PDE foundation model based on a multiscale Swin Transfromer backbone and a flexible pretraining strategy.
Strengths: 1. The paper is well-organized and clearly written.
2. The experimental results are comprehensive and solid, which is a valuable contribution to the research.
3. The studi... | Rebuttal 1:
Rebuttal: We start by thanking the reviewer for your appreciation of the merits of our paper and your welcome suggestions to improve it. We address your detailed concerns below.
[W1:] The reviewer's suggestion to compare with DPOT is very well-taken. As we had clearly stated in our paper (see line l347-34... | null | null | Rebuttal 1:
Rebuttal: At the outset, we would like to thank all three reviewers for their thorough and patient reading of our article. Their criticism and constructive suggestions will enable us to improve the quality of our article. If our paper is accepted, we will incorporate all the changes that we outline below in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors | Accept (poster) | Summary: This paper presents a 3D human reconstruction system that takes a single RGB image and outputs 3D Gaussians, which can render the reconstructed 3D humans to any viewpoint. In contrast to existing 3DGS works, which requires per-instance optimization and cannot be generalized to unseen identities, the proposed s... | Rebuttal 1:
Rebuttal: We deeply appreciate your recognition of the insight behind our method and its model designs. Below are our clarifications for your concerns.
**Q1: How the ‘video’ diffusion model can be used for the ‘novel-view’ synthesizer? Why the authors chose this generative model for the novel-view synthe... | Summary: This paper proposes a generalizable human rendering framework from single images. The proposed method relies on different priors and achieves state-of-the-art results on multiple datasets.
Strengths: - The topic of human modeling/rendering from partial observation is important and interesting.
- Thanks to 3DG... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable comments! Below are our clarifications for your concerns.
**Q1: Why are the input views in low quality (e.g., Fig. 4)? Is this a sign of poor model capacity or inappropriate objective functions?**
**A1**:
1. (a) HumanSplat reconstructs 3DGS from a single im... | Summary: This paper proposes HumanSpat, a method that predicts 3D Gaussians from a single image of a human. The method comprises a 2D multi-view diffusion model and a latent reconstruction transformer that integrates human body prior.
Strengths: + Well-designed generalizable model that incorporates human prior. The mo... | Rebuttal 1:
Rebuttal: Thank you for your insightful and valuable feedback. It is truly inspiring to know that you appreciate the intuitive and clear design of our model, which offers competitive reconstruction times and can render novel views at real-time speeds.
**Q1: Missing ablation on human prior? The paper shoul... | Summary: - The paper, HumanSplat, focuses on photorealistic novel-view synthesis of humans from a single image.
- The key idea is to use a multi-view synthesizer based on SV3D to hallucinate the other views + latent reconstruction transformer to predict the 3DGS.
- Importantly, HumanSplat does not use optimization and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and thorough feedback. It is inspiring to hear that you find the paper well-written, organized, and easy to follow, that the method in the context of human avatar creation is novel, and that our experiments are comprehensive. Below are our clarifications fo... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their valuable, constructive, and thoughtful feedback. It is truly inspiring to hear that the majority of reviewers recognize that:
- The proposed method is meaningful (6Egy), efficient (2EjT, 6Egy), and addresses major limitations in... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices | Accept (poster) | Summary: This paper presents a novel speculation decoding method called SpecExec, designed to improve the performance of large language model (LLM) inference on consumer-level GPUs with offloading scenario. The main contribution is the application of speculative execution, a technique from CPU architecture, to the spec... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and questions. We are glad that the reviewer appreciates the practical impact of our work for LLM inference on consumer GPUs. Below we address the questions to the best of our ability:
> Although the paper targets consumer-level GPUs, the experiment results ... | Summary: In this paper, the authors propose a speculative decoding technique, SPECEXEC (Speculative Execution), that can generate up to 20 tokens per iteration for the LLaMA-2 70B model. SPECEXEC enables the LLaMA-2 70B model to run on a consumer GPU by a parallel decoding strategy. The offloading scheme can process th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback; we are glad that they appreciate our experimental results. Below, we do our best to address the concerns and answer questions.
>The paper is not well-written.
We are eager to improve our writing and respectfully ask the reviewer to suggest specific impro... | Summary: The authors present a method to improve the efficiency of speculative decoding on consumer-grade hardware. The technique addresses the inefficiencies of existing speculative decoding approaches when applied to devices with limited GPU memory, necessitating parameter offloading to RAM or SSD. SpecExec leverages... | Rebuttal 1:
Rebuttal: We thank the reviewer for the well rounded review of the paper and address the questions below:
> Some experiment results are a bit confusing. For instance, the captions in Figure 3/4 say Generation rate vs draft size for Llama 2-7B/70B models, but it's not clear where the 7B model numbers are. T... | Summary: This work introduces SpecExec, an improved speculative decoding method.
By constructing a better draft token tree and refining the process of verifying tokens, SpecExec significantly increases the number of tokens accepted in a single verification while producing exactly the same outputs as sequential samplin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and appreciate that they share our views on the algorithm performance factors. Below, we do our best to address the reviewer’s concerns and answer questions.
> It is unclear how this method performs in terms of acceleration in speculative inference systems ... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to study our paper and providing valuable feedback. We are glad to notice that all four reviewers appreciate the practical speed-ups achieved by SpecExec and its positive impact on LLM accessibility. On the reviewers' suggestions, we implemented a few add... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Stability and Generalization of Meta-Learning | Accept (poster) | Summary: The paper presents stability analysis for meta-learning. The paper first introduces a uniform meta-stability, where there is both a change of the task in the meta-sample and also a change of an example for the task at test time. For this uniform meta-stability, the paper gives high-probability bounds of the or... | Rebuttal 1:
Rebuttal: **Choice of $\lambda$.**
$\lambda=O(1/\sqrt{n})$ is just one choice of $\lambda$ that leads to non-vacuous excess risk and there are other options. For example, if choosing $\lambda=O(1/n^{\frac14})$, then under the setting where $\sqrt{n}\leq m \leq n^{3/2}$, the generalization gap from Theorem ... | Summary: This paper introduces the notion of "uniform meta-stability" to bound the generalization error of $\ell_2$-regularized meta-learning problem. Theoretical guarantees are respectively established for smooth loss functions as well as weakly convex losses that are not necessarily smooth. Variants of the algorithm ... | Rebuttal 1:
Rebuttal: **Limited Scope.** Apply l2 regularization is common in meta-learning literature [1,4,5,6,7,8] and transfer learning literature in general [9,10].
Extending our idea to MAML and S/Q learning could be interesting future direction.
**Compact radius assumption.** The compact radius assumption is onl... | Summary: This submission introduces a new bound on the transfer risk of meta-learning based on a modified form of algorithmic stability. Several examples of how the new bound can be used to analyze the transfer risk of meta-learning algorithms built on gradient-based optimisation are provided. A comparison with the exi... | Rebuttal 1:
Rebuttal: **Proof idea.** Our proof leverages the same sample-splitting approaches as described in [1,2]. The recursive structure is based on the specific telescoping sum. The design of the sequence of partitions $\mathcal{C_0},\ldots,\mathcal{C_k}$ relates to the analysis of the terms $g_{i,j}^{q,0}-g_{i,j... | Summary: The authors introduce a new notion of stability for meta-learning algorithms and they show how it is possible to bound their generalization gap by their stability property. The new definition of stability measures the sensitivity of the learning algorithm as one replaces both a task in the meta-sample as well ... | Rebuttal 1:
Rebuttal: W1 / Q5: We never claim we provide a new meta-learning algorithm. Our algorithm is indeed of the same nature as in [r2] and [A]. We now provide a detailed comparison between our result and [A].
- **Different Function Classes Considered.** The function classes considered in [A] are limited to comp... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
AttnGCG: Enhancing Adversarial Attacks on Language Models with Attention Manipulation | Reject | Summary: The main claim of this paper is that adversarial suffixes against large language models (LLMs) function by distracting the model from the original harmful goal to the suffix itself. The authors then propose a modification to GCG attack by incorporating a regularization term that increases the attention score o... | Rebuttal 1:
Rebuttal: We first thank the reviewer for the detailed comments and the appreciation of our work. We address the concerns below:
$\textbf{Q1: Are attention scores normalized?}$
Yes. The attention weights matrix is the value after softmax, and the attention weights of each target token are normalized to su... | Summary: This work proposes a new adversarial attack strategy on LLMs which improves over existing adversarial attacks. For this the authors propose a new regularizer that maximizes the weight of attention corresponding to suffix tokens, which naturally results in minimizing the weight for the other tokens present in t... | Rebuttal 1:
Rebuttal: We first thank the reviewer for the detailed comments and the appreciation of our work. We address the concerns below:
$\textbf{Q1: Concerns about transferability of adv prompts across goals}$
Thank you for your suggestion of adding transfer experiments across different goal prompts. We will con... | Summary: The authors propose a refined GCG method named AttnGCG for Large Language Model jailbreaking attacks. They focus on the attention scores of the input components, refining the loss function by adding an Attention Loss term. The attack success rates are greatly improved. Various experiments are provided to suppo... | Rebuttal 1:
Rebuttal: We first thank the reviewer for the detailed comments and the appreciation of our work. We address the concerns below:
$\textbf{Q1: Ambiguous results about solving failed 'regret' jailbreaking cases in GCG.}$
The 'regret' jailbreaking case mentioned is a subcase of failed jailbreakings, which is... | Summary: The paper proposes a new jailbreak attack method against LLMs, called AttnGCG. The method integrates a loss of maximizing the attention scores of the adversarial suffix. The paper provides experimental results to show the effectiveness of the proposed method.
Strengths: - The paper is well-written and easy to... | Rebuttal 1:
Rebuttal: We first thank the reviewer for the detailed comments and the appreciation of our work. We address the concerns below:
$\textbf{Q1: Will increasing attention scores for adversarial suffixes prioritize their content in responses?} $
No, increasing the attention scores of adversarial suffixes will... | Rebuttal 1:
Rebuttal: First, we thank all reviewers for their insightful comments. We are particularly encouraged that reviewers have appreciated:
- The novelty and impact of our central ideas: "... lead to a nice interpretability tool and/or a potential mitigation. Hence, the significance of this research question is ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Causal Temporal Representation Learning with Nonstationary Sparse Transition | Accept (poster) | Summary: Most existing causal temporal representation learning models either assume the domain variables to be observable, or have a Markov prior over them. This paper first comes up an identifiability theory for sequential data affected by non-stationary latent causal processes under unknown distributional shifts. In ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our work and contribution, and we also thank you for providing valuable questions and suggestions. Please kindly find our response below:
> W1. Parameter Settings
We appreciate the reviewer for raising this suggestion, which has improved our read... | Summary: The paper focuses on causal temporal representation learning for non-stationary time series. It adopts a sparse transition assumption, aligned with intuitive human understanding, and presents identifiability results from a theoretical perspective. Based on the theoretical result, the authors introduce a novel ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable questions and suggestions. Please kindly find our response below:
> Related Work: GCIM [1]
We appreciate the reviewer for highlighting the additional related work, GCIM. We will include this work in the related work section. Regarding the comparison, ... | Summary: The paper focuses on temporally causal representation learning, where the goal is to recover a latent causal process from nonstationary observation sequences. Existing works represent the source of the nonstationarity by either known domain variables or (autocorrelated) unknown domain variables with Markov str... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable questions and suggestions. Please kindly find our response below:
> W1. Non-Testable Assumptions in Real-World Setup
Thank you for bringing attention to this common issue in the identifiability literature. We appreciate your insight. With your permiss... | Summary: The paper introduces CtrlNS, a causal temporal representation learning framework based on a sparse transition assumption to identify distribution shifts without strong prior knowledge of the domain variables. Theoretical and experimental results show CtrlNS effectively identifies distribution shifts and latent... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for acknowledging our work and contribution, and we also thank the reviewer for providing valuable questions and suggestions. Please kindly find our response below:
> W1+Q1. Claim on Prior Knowledge
We appreciate the reviewer for their valuable suggestions to make... | Rebuttal 1:
Rebuttal: We thank all reviewers for providing valuable and insightful questions and suggestions on our work. We found that our claim of "without prior knowledge of domain variables" and the "motivation to align with human intuition" are mentioned by multiple reviewers. We give a comprehensive response here... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Transcoders find interpretable LLM feature circuits | Accept (poster) | Summary: The paper introduces the use of transcoders a tool for mechanistic interpretability as a replacement for SAE. The main difference between SAE and transcoders is that SAE uses autoencoders to take the output of an MLP and reconstruct it while transcoders (that can also be viewed as an encoder-decoder architect... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We are very glad to see that you recognize the importance of the input-invariant/input-dependent factorization, along with the power demonstrated by transcoders in reverse-engineering feature circuits in an actual model (GPT2-small). We will now ad... | Summary: The paper compares transcoders to SAEs for interpreting parts of GPT2-small, Pythia-410M and Pythia 1.4B. Transcoders are SAEs trained to replace a particular MLP in the original model instead of implementing an identity.
They find that the transcoders they train outperform the baseline SAEs they train on the... | Rebuttal 1:
Rebuttal: Thank you for taking the time to write your thoughtful review and for recognizing the importance of transcoders to the broader mechanistic interpretability research program. We are also very excited to continue working on transcoders and seeing how far they can go.
Now, we would like to respond t... | Summary: Sparse Autoencoders (SAEs) have been used for interpretability of transformer neural networks. SAEs take the output activations of MLPs within transformers and learn are trained to re-construct those outputs: $SAE(f(x)) \approx f(x)$. In this submission, the authors study using transcoders for a similar task. ... | Rebuttal 1:
Rebuttal: **TL;DR**: Thank you so much for taking the time to review our work. We were glad to see that you enjoyed our blind case studies and that you recognized the importance of the input-invariant/input-dependent decomposition of attributions. We performed some experiments in response to your questions ... | Summary: Motivated by prior claims on how MLP sublayers make interpretability challenging (arguably due to their extremely dense nature), this paper proposes "Transcoders", a protocol that is in spirit similar to Sparse Autoencoders (SAEs). Specifically, a Transcoder aims at "faithfully" matching the output of a layer ... | Rebuttal 1:
Rebuttal: **TL;DR**: Thank you for taking the time to write this detailed review. We are glad to see that you enjoyed the presentation of our work, along with our interpretability study and blind case studies. Our initial paper was insufficiently clear that the main goal of transcoders is to enable input-in... | Rebuttal 1:
Rebuttal: **Summary:** We were happy to see our reviewers recognize transcoders’ importance for mechanistic interpretability and appreciate our input-invariant circuit analysis and blind case studies. We found that we might not have adequately conveyed the main goal of our work: to use transcoders to perfor... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks | Accept (poster) | Summary: This paper introduces a backdoor attack that leverages DWT to create highly stealthy backdoor triggers, named WaveAttack. The attack employs an asymmetric frequency obfuscation technique to improve the impact and effectiveness of these triggers during both training and inference stages.
Strengths: The work’s ... | Rebuttal 1:
Rebuttal: # Response to Reviewer gVJn
## Adaption to Other Types of Wavelet Transforms
Thank you for the reviewer's insightful comments. In our wavelet transformation procedure, applying different wavelets in the Discrete Wavelet Transform (DWT) is still applicable to our method proposed in this paper. We... | Summary: This paper proposes a novel frequency-based backdoor attack method named WaveAttack, which can effectively generate the backdoor residuals for the high-frequency component based on DWT, thus ensuring the high fidelity of poisoned samples.
Strengths: 1. The paper is well-written and well-structured.
2. Extensi... | Rebuttal 1:
Rebuttal: # Response to Reviewer h6uR
We are sincerely grateful for the reviewers' insightful feedback and constructive comments. We offer comprehensive responses to all inquiries and concerns below.
## Detailed Descriptions of References
Unlike our paper based on Discrete Wavelet Transform (DWT), refere... | Summary: This paper investigated the backdoor attack, aiming at improving the fidelity of poisoned samples. A novel frequency-based backdoor attack method named WaveAttack is proposed to generate highly stealthy backdoor triggers. The experiments show that the poisoned images generated by WaveAttack can achieve high at... | Rebuttal 1:
Rebuttal: # Response to Reviewer vbLQ
We sincerely appreciate the reviewer's valuable feedback and insightful comments on our paper. We have carefully considered each issue raised and provided detailed responses to all questions and concerns below.
## Threat Model
Thank you for your feedback on the threat... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
State Chrono Representation for Enhancing Generalization in Reinforcement Learning | Accept (poster) | Summary: The paper proposes an algorithm to improve upon the recently proposed class of deep bisimulation-based methods by accounting for long-term consequences (in terms of future states) instead of relying one-step bootstrapping based on reward differences to be more robust in settings such as sparse reward cases.
S... | Rebuttal 1:
Rebuttal: Thanks for your effort and insightful comments.
> Weakness 1.& 2.
We appreciate your concern regarding the number of seeds. Due to resource constraints, we opted for a balance between the number of seeds and computational cost, thus using 5 seeds. To enhance the robustness of our results, each ... | Summary: This paper introduces SCR that extends state metric-based representations by embedding rich time-related information into the bisimulation metric learning process. SCR is designed to calculate state distances by contextually framing them in a temporal framework that considers both future dynamics and cumulativ... | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews. We address your concerns as follows:
> In Table 1, the outcomes for the default setting do not demonstrate a strong level of significance, with the possible exception of noteworthy results for Ch-Run. Sometimes, the performance appears to be worse than the c... | Summary: This paper argues that metric learning for RL from one-step reward signal faces challenges in non-informative reward / sparse reward settings. The authors thus propose SCR, attempting to incorporate multi-step information in metric learning. The key components are as follows:
1. The authors provide a MICo-like... | Rebuttal 1:
Title: Rebuttal by Authors [1/3]
Comment: Thank you for your insightful comments. We address your concerns as follows:
> 1. Eq.3 is not well-defined. What is the base case (e.g., what if $i=j$ but $i' \neq j'$, thus $i+1>j$ and the recursive step is not well defined?)
Thank you for pointing out this issue.... | Summary: The paper presents the State Chrono Representation (SCR), a novel approach to enhancing generalization in reinforcement learning (RL) with image-based inputs. SCR introduces a temporal perspective to bisimulation metric learning. The authors propose a learning framework that includes two encoders to capture in... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful feedback. We address your concerns as follows:
> 1. The discussion on limitations lacks depth. The paper could benefit from an insightful analysis of scenarios where SCR might underperform, such as the noted inferior performance on the R-Easy task compar... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning | Accept (poster) | Summary: The paper proposes a new metric for sample efficiency in online learning called Uniform Last Iterate (ULI), show that it is stronger than existing metrics, and gives algorithms that achieve near-optimal ULI.
Strengths: 1. The paper proposes a new metric for online learning that characterizes not only the cumu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Below we address your concerns.
---
**Q1:** What the authors believe could be the future works that build on this paper?
**A1:** We provide three future directions below and will include the discussion in our final version.
- It could be interes... | Summary: In this paper, the authors study algorithms with better performance metrics for both bandits and reinforcement learning. They propose a new metric, namely the uniform last-iterate guarantee, generalizing uniform-PAC, which can further ensure the last-iterate performance of the algorithm. The authors present al... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Below we address your concern.
---
**Q:** Could the authors discuss potential barriers that might prevent the phase-based algorithm (He et al., 2021) from achieving a uniform last-iterate guarantee in linear bandits?
**A:**
The main barriers for... | Summary: This paper introduces a stronger metric, uniform last-iterate (ULI) guarantee.
The authors demonstrate that a near-optimal ULI guarantee directly implies near-optimal cumulative performance across traditional metrics such as regret and PAC-bounds, but not the other way around.
The authors first provide two... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Below we address your concerns.
---
**Q1:** Necessity of the ULI itself. In your patient example in the Introduction section, does it make any major ethical difference?
**A1:** Indeed, the ULI forces the algorithm to explore more in the beginning... | Summary: This paper introduces a new form of guarantee for MAB and RL algorithms named "Uniform Last Iterate (ULI)". Just like the uniform PAC guarantee introduced in Dann Lattimore and Brunskill (2017), it unifies the traditional sublinear-regret (with optimal rates) and PAC guarantees for such algorithms. However, un... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Below we address your concern.
---
**Q:** Can you comment on where the UBEV algorithm of (Dann et al 2017) with the uniform PAC guarantee fails w.r.t the ULI guarantee (ULI even if not with near optimal rates)?
**A:** In fact, the UBEV algorithm ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DiffuBox: Refining 3D Object Detection with Point Diffusion | Accept (poster) | Summary: In this paper, the authors propose a method that make use of a point diffusion model for 3D bounding box refinement. The points around proposals are transformed into a normalized box view, and the model denoises them into accurate boxes conditioned on the points near the proposals. The model learn the distribu... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our method novel and of high quality, pointing out that we have extensive experimentation across datasets. We address detailed concerns below.
**Q1: Comparison to more recent 3D detectors**
Thank you for this suggestion, and we are actively working towards inclu... | Summary: The paper introduces a diffusion-based box refinement method aimed at enhancing the robustness of 3D object detection and localization across diverse sensor setups or geographic locations. DiffuBox utilizes a domain-agnostic diffusion model, conditioned on LiDAR points around a coarse bounding box, to refine ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback! Below, we address additional concerns:
**Q1: Request for results on additional dataset**
We are happy to provide additional experimental results on the nuScenes dataset, and report the results in the ***common questions section***. Our results ... | Summary: The article presents DiffuBox, a novel method to refine 3D object detection using point diffusion model. This approach addresses the challenges posed by domain shift, where 3D object detectors trained in one geographic region or sensor setup may not perform well in different settings. DiffuBox uses a domain-ag... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our method novel and effective, and address their concerns below:
**Q1: Additional discussion on false negatives**
Thank you for this suggestion! We include an analysis plot of Recall vs IoU threshold in Fig. 2(a) of the uploaded ***Rebuttal PDF***. DiffuBox is ... | Summary: A novel diffusion-based method for refining 3D object detection bounding boxes to address domain adaptation issues.
Domain-agnostic approach, this paper leverages the consistency of point distributions relative to bounding boxes across different domains, improving robustness
Strengths: Usage of diffusion mode... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our method interesting, robust, and pointing our the benefits of our domain agnostic method. We further address their concerns below:
**Q1: Cumulative image description of overall method**
We thank the reviewer for pointing out this possible point of confusion, ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort and are encouraged by the positive feedback. We are very excited that all reviewers are generally happy with our work, and find our method “novel” and “interesting” (spFc, DshE) and all reviewers found our work to be an “effective solution” towards ... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper proposes a diffusion model-based box refinement module to enhance detection. The experiments are conducted on several setting.
Strengths: - Using diffusion model to refine box sounds interesting.
- The proposed module has been proven effectiveness by a series of experiments.
Weaknesses: - The ide... | Rebuttal 1:
Rebuttal: Thank you so much for the insightful comments. Your valuable suggestions are very helpful for further strengthening our paper.
**Q1: The idea is simple. Refining box is not innovate although the authors use recently popular diffusion model to implement this goal.**
Thank you for your comments re... | null | null | null | null | null | null |
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators | Accept (oral) | Summary: The paper proposes a new method, STDE, to compute high-order differential operators of (high-dimensional) neural network-represented functions. Two key ingredients include a generalization of Taylor-mode AD and randomness in the algorithm. This work shows impressive performance of STDE, in terms of both speed ... | Rebuttal 1:
Rebuttal: **Weakness**
> 1. Compared with original ...
Thanks for your insightful comment. We want to first clarify that, the main takeaway we wish to show out of this comparison between FL and STDE is to highlight the fact that randomization is important for scalability in dimensionality. While FL removes... | Summary: This paper proposes a stochastic optimization approach to find the minimizer of a cost function, which involves the complicated differential operators. The problem is computationally complex, hence, the authors propose to deal with a minibatch of derivatives in each iteration which reduces computational comple... | Rebuttal 1:
Rebuttal: Thanks for your positive review of our paper. We would appreciate it if you could provide any suggestions on potential improvements to our paper, and we welcome any future questions you might have on our paper.
---
Rebuttal 2:
Title: I will keep my rating unless a major weakness is found by othe... | Summary: This work proposes a scalable method for the optimization of loss functions including higher-order derivatives. The proposed method interprets arbitrary differential operators as derivative tensor contractions which are then estimated through random contractions. These random contractions can be computed effic... | Rebuttal 1:
Rebuttal: **Weaknesses**
> 1. The presentation could be improved a tad bit. I think having 4 pages of background material is a bit unnecessary specially the general background on automatic differentiation could easily be moved to the appendix and space could be better utilized by explaining the method in mo... | Summary: The paper addresses the computational challenges of optimizing neural networks with loss functions that include high-dimensional and high-order differential operators. These challenges arise due to the scaling of the derivative tensor size with the dimension of the domain (d) and the computational graph's size... | Rebuttal 1:
Rebuttal: **Weaknesses**
> While the paper demonstrates the significant strengths and broad applicability of STDE, it is important to acknowledge some limitations and areas for future improvement. As a general method, STDE may not leverage the specific optimization possibilities that are available for parti... | Rebuttal 1:
Rebuttal: **Additional Experiments**
We have conducted further ablation studies on the randomization batch size (see the attached PDF). We ran all three equations from the Inseparable and effectively high-dimensional PDEs (Appendix I.1) with moderately high dimensions (100k), with different randomization b... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multi-model Ensemble Conformal Prediction in Dynamic Environments | Accept (poster) | Summary: This paper considers conformal prediction in the online setting with multiple fitted models, which is very interesting and in line with real applications. As it remains an active question regarding how to aggregate multiple models, the proposed approach adopts importance sampling to choose the 'best' model at ... | Rebuttal 1:
Rebuttal: Q1: The exponential update framework is theoretically justified by its ability to minimize regret, a measure of how well the algorithm performs relative to the best-fixed strategy in hindsight. The exponential update is widely used in many online setting algorithms, see [1,2,3].
Furthermore, the c... | Summary: This paper proposes an online multimodal conformal prediction method named SAMOCP, developed to address data distribution shifts in dynamic environments. Specifically, the method selects the best model from multiple candidates to create prediction sets via strongly adaptive online learning.
Strengths: 1. The ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for reading our manuscript and thoughtful comments and questions. We will address your questions as follows:
Q1: We would like to clarify that our proposed method, SAMOCP, is not simply a multimodal version of SAOCP [1]. SAMOCP includes a distinct approach for upda... | Summary: This paper investigates online conformal prediction within dynamic environments. The authors introduce a novel adaptive conformal prediction framework that leverages multiple candidate models. The proposed algorithm achieves sublinear regret, and its effectiveness is demonstrated through both real and syntheti... | Rebuttal 1:
Rebuttal: The authors would like to thank the reviewer for their valuable comments and for recognizing the novelty of our work. We will address your questions as follows:
Q1: The parameter $\epsilon$ was selected via grid search from {0.1, 0.2, …,0.9}. The one which led to the smallest prediction set size... | Summary: This paper proposes the Strongly Adaptive Multimodal Online Conformal Prediction (SAMOCP) methodology, which constructs adaptive conformal prediction sets by integrating information from multiple learning models in dynamic environments. This is accomplished by creating multiple experts at each time step, where... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our submission.
Q1: We did not conduct experiments with time series data. According to the dynamic regret analysis in Lemma 1, achieving sub-linear dynamic regret requires the variation of the loss function to be sublinear. Having continuous distrib... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their time and effort in reviewing our paper.
Additional experiments have been conducted to compare our first algorithm (MOCP) with benchmarks and SAMOCP. The results can be found in the attached file.
Pdf: /pdf/5ea9c2aea2e403942a011a14d78715f139034592... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Counterfactual Fairness by Combining Factual and Counterfactual Predictions | Accept (poster) | Summary: This work studies the problem of counterfactual fairness in ML predictions. The authors first prove the form of the best possible fair predictor in a model-agnostic manner. Then, the excess risk of optimal predictor under the CF constraint is characterized. Based on the theoretical findings, the authors propos... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the writing and theoretical contribution of our paper. We will address each of your concerns and questions below.
>**Incorporate existing counterfactual estimation procedures into the Algorithm 1**
We will follow your suggestion and modify the Algorithm 1... | Summary: This paper focuses on counterfactual fairness in ML models (regardless of the model), which means that the prediction of the model should not change if the input individual had belonged to a different sub-population. The main contribution of this paper is to provide a theoretical trade-off between the performa... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the writing and theoretical insight of our paper. We will address each of your concerns and questions below.
>**Assumption of access to ground truth and Bayes optimal predictor**
Access to ground truth counterfactuals, as discussed in Section 6, is a comm... | Summary: This paper focuses on counterfactual fairness (CF), which is a promising framework for evaluating the fairness of machine learning models, and analyzes the trade-off between accuracy and CF. The authors give some theoretical results from the perspective of the Bayes optimal classifier under the perfect CF cons... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the writing quality, the theoretical contributions of our paper, and the importance of our practical method. We will address each of your concerns and questions below.
>**W1,Q1: Experiment Settings**
Thanks for your thought. We would like to first clarify ... | null | null | Rebuttal 1:
Rebuttal: We thank all reviewers for their time reviewing our paper and providing helpful feedback. In summary, all reviewers acknowledge:
1. The theoretical contributions, especially Theorem 3.3 and Theorem 3.4, which provide an optimality solution under the constraint of Counterfactual Fairness (CF) and c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ChatCam: Empowering Camera Control through Conversational AI | Accept (poster) | Summary: This paper introduces ChatCam, a system that enables camera operation via natural language interactions. This system has two key components. CineGPT is proposed for text-conditioned trajectory generation and an Anchor Determinator for precise camera trajectory placement. Experimental results illustrate ChatCam... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback as well as thoughtful suggestions and questions. Below, we address your points individually.
**Limited Scenes & Overfitting & Trajectory Length.** The cases we test cover "a wide range of trajectories on complex scenes (indoor/outdoor, object/human-centric) wi... | Summary: This paper propose ChatCam, a pipeline that utilizes GPT to translate the natural language into professional camera trajectory, which enhances the video production process for common users.
Strengths: 1. This paper divide the task into three steps: observation, reasoning and planning. The first two steps is c... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback. While we are encouraged by your appreciation for our ideas and results, the issues you point out are crucial.
**Collision.** Collisions can indeed occur, especially when the scene is complex or the user does not provide enough anchor points. To keep ... | Summary: The paper proposes a method for generating camera trajectories for rendering a 3D scene, conditioned on natural language. This problem statement is novel and original, very useful, and the paper demonstrates convincing results. The method uses two components: a language-conditioned camera trajectory generator,... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable feedback. While we are encouraged by your appreciation of our efforts, the issues you point out are constructive. Below, we address them:
**Conversation Example & Planning Output.** Figure 2 shows an actual conversation with the LLM (with visualizations and tr... | Summary: The paper proposes a method to generate camera trajectories from user prompts. The idea is to pass on the prompt to an LLM, find a starting anchor location (searched and refined using initial images used to construct the radiance field) and then use CineGPT (a cross-model transformer) trained for the next toke... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insights. We appreciate your comments and would like to address your concerns in the following responses.
**Baselines & Comparisons**
- **Baseline Interpolation Algorithm.** We use cubic spline interpolation for translations and spherical linear interpolat... | Rebuttal 1:
Rebuttal: We want to thank all the reviewers for their time and insightful feedback regarding our first attempt at empowering LLMs beyond 1D NLP to understand 3D spatial relationships, specifically operating 3D camera trajectories in this paper. We are encouraged by the positive reception of our motivation,... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks | Accept (poster) | Summary: The paper proposes a method called "NeuralThink", which is designed to improve the same-size task generalization and different-size task extrapolation performances. The proposed algorithm composes of three components: (1) A recurrent module utilizing a LSTM network to process inputs of different scale, (2) A p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and the time spent reviewing our paper. We now address the weaknesses (W) identified by the reviewer:
+ **(W1) Differences between NeuralThink and the previous work/why NeuralThink achieves improved performance.** We would like to highlight the ... | Summary: 1. The paper introduces NeuralThink, a novel deep thinking architecture designed to efficiently and consistently extrapolate learned algorithms from smaller problems to larger ones.
2. Unlike previous deep thinking methods, NeuralThink can be applied to both same-size problems (where input and output sizes are... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewer for their comments and the effort spent on reviewing our paper. We will now tackle the weaknesses (W), questions (Q), and limitations (L) mentioned:
+ **(W1a) Ablations present in the paper:** We provide in the original paper additional ablation studies in... | Summary: The authors propose a new architecture that improves on the Deep Thinking (DT) architecture by Bansal et al, 2022. It replaces the recurrent ResNet block with a convolutional LSTM+layernorm. They also use a curriculum-based learning schema. This improves the model's extrapolation capability significantly. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and for the time spent reviewing our paper. We now address the weaknesses (W), questions (Q) and limitations (L) pointed out by the reviewer:
+ **(W1) Authors do not clearly state what is novel in their architecture in the main text.** Due to lack of space i... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank all the reviewers for the constructive and interesting questions and suggestions.
We have added a pdf with the additional hyperparameter scan requested by Reviewer **SKBz**, that highlights the robustness of our method to changes in hyperparameters.
Additionally, we r... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SSDM: Scalable Speech Dysfluency Modeling | Accept (poster) | Summary: This is an extraordinarily well-written paper that addresses speech disfluency modeling. Given a recording of a person’s speech and the reference text transcription, the proposed model named SSDM outputs a natural text explanation of the pronunciation problems in specific words and sounds in the recording. Thi... | Rebuttal 1:
Rebuttal: We really appreciate the time and effort put into giving such in-depth comments.
* Yes. Our model was trained entirely on synthetic data. We chose high-quality open-source multi-speaker TTS models like VITS and StyleTTS2 to mitigate potential overfitting, though we lack concrete evidence of overf... | Summary: This paper proposes an approach on understanding speech with disfluency.
Strengths: The proposed method is sound.
Weaknesses: This work is an application work focused on a very narrow domain in the speech area. It doesn't seem to have sufficient interest for the audience of NeurIPS. It probably fits better f... | Rebuttal 1:
Comment: Sorry for the brevity in the previous review.
My main concern on this submission is: it is an application work focused on a very specific task on a narrow domain, which doesn't seem to have sufficient interest for the general audience of NeurIPS.
According to this paper (Sec. 1 and Sec. 2.1), spe... | Summary: This paper looks at the problem of disfluency events in speech. Their core contributions are:
* Instead of learning dysfluencies that rely on high-compute SSL models, they characterize speech representations using a type of gestural modeling baed on kinematic/articulatory movements. They rely on a pre-trained ... | Rebuttal 1:
Rebuttal: Thanks reviewer for the thoughtful summarization and comments.
* Regarding the TTS question proposed in Strengths: We summarize that we performed textual dysfluency editing and simply input that text into a pretrained StyleTTS2 model, which is one of the open-sourced state-of-the-art TTS works. ... | Summary: This paper presents SSDM (Scalable Speech Dysfluency Modeling), a novel approach to modeling speech dysfluency, which is essential for applications in spoken language learning and speech therapy. The authors identify three main challenges in the current state-of-the-art: poor scalability of existing solutions,... | Rebuttal 1:
Rebuttal: Dear Reviewer:
We are pleased to address your proposed weaknesses and questions.
**For weaknesses**:
1. We acknowledge that the numerous modules and Figure 2 may be challenging to grasp. The training process involves all modules in Figure 2. For inference, we follow this path: speech -> gestura... | Rebuttal 1:
Rebuttal: Dear Reviewers:
Given the complexity of our system design, we will elaborate more on our pipeline in this general rebuttal.
While we believe the paper's writing is generally clear, we recognize there is room for improvement. Our core method involves generating gestural scores from speech, align... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation | Accept (poster) | Summary: The paper introduces Stable-Pose, a novel adapter model designed to enhance controllable T2I diffusion models by improving pose guidance, particularly in complex human pose conditions. Utilizing a coarse-to-fine attention masking strategy within a Vision Transformer , Stable-Pose effectively refines pose repre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and feedback. We have addressed each comment carefully and provided a point-by-point response below.
*__C#1__: although this method shows good results…it has performance drop on image quality and text alignment.*
We thank the reviewer for this in... | Summary: The authors introduce a novel approach for pose-guided human image generation by developing a new pose encoder block. This block incorporates a coarse-to-fine attention masking strategy within a Vision Transformer architecture, leveraging the self-attention mechanism of Vision Transformers to analyze the inter... | Rebuttal 1:
Rebuttal: We highly appreciate the Reviewer for the valuable comments and suggestions. We have carefully addressed each comment, providing a point-by-point response below.
*__C#1__: The coarse-to-fine approach does not appear promising…The improvement from constant variation is negligible.*
Thanks for thi... | Summary: The authors proposed a pose guided image generation pipeline. By introducing the PMSA ViT as pose encoder, the pipeline can generate more controllable images with corresponding 2D pose conditions.
Strengths: - According to Fig 4, the proposed method is the only one which distinguish the front and back side of... | Rebuttal 1:
Rebuttal: We appreciate the Reviewer’s comments and insightful suggestions. Below, we provide a point-by-point response to each comment.
*__C#1__: The proposed method is more like an incremental work…instead of a new innovative pipeline.*
We respectfully hold a different perspective on the novelty assessm... | Summary: To obtain more refined pose condition control, especially for challenging condition (pose & text) generation, this paper designs a coarse-to-fine Pose-Masked Self-Attention (PMSA) module with the use of pose masks, fine-tunes Stable Diffusion with pose-mask guided loss, and ultimately achieves more precise con... | Rebuttal 1:
Rebuttal: We express our sincere appreciation to the reviewer for providing valuable comments and suggestions. We have addressed the comments point-by-point, as outlined below.
*__C#1__: (No consideration of different persons & parts)*
Thanks for the insightful discussion. The pose information is encoded ... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewers for their insightful and constructive feedback. We have incorporated their suggestions and responded comprehensively to their comments. We have addressed the comments of the Reviewers individually. We have incorporated a Q&A section in the Supplementary file that c... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Randomized Sparse Matrix Compression for Large-Scale Constrained Optimization in Cancer Radiotherapy | Accept (poster) | Summary: The authors optimize the optimization problem in radiation treatment planning via sparsifying the design matrix generated from beamlines and dose delivery map.
Strengths: * The authors make an effort to formalize the problem
* Sparsifying the matrix seems to be a valid approximation
Weaknesses: * It would ha... | Rebuttal 1:
Rebuttal: Thank you for dedicating time to review our paper. Our responses to your comments are provided below.
**Weaknesses:**
> - It would have been helpful to show images / the spatial prediction
We can enhance the comparisons by including representative slices of the 3D dose distribution, as demons... | Summary: In this work, the focus is on doing matrix sparsification with its application focussed on Cancer Radiation therapy.
The authors propose a novel algorithm for matrix sparsification which is faster than most other algorithms and simultaneously has a lower feasibility gap and lower optimality gap.
An interes... | Rebuttal 1:
Rebuttal: Thank you for dedicating time to review our paper. Our responses to your comments are provided below.
**Weaknesses:**
>It will be useful to know the optimization runtime when using the original matrix A. Also, it will be useful to know what the acceptable level of relative sparsification RMR ach... | Summary: The paper titled "Randomized Minor-value Rectification: A Novel Matrix Sparsification Technique for Solving Constrained Optimizations in Cancer Radiation Therapy" presents a novel algorithm for matrix sparsification, aimed at improving computational efficiency in the optimization problems associated with cance... | Rebuttal 1:
Rebuttal: Thank you for dedicating time to review our paper. Our responses to your comments are provided below.
## Weaknesses:
### Novelty:
> While the combination of deterministic and randomized ...
### Experimental Validation:
> The experimental validation ...
Regarding data heterogeneity, we have e... | Summary: I am not an expert on the optimization method for this paper. I am selected as a reviewer due to my medical image analysis and oncology image processing research background. First of all, in my opinion (my confidence is 3 out of 5), NeurIPS is not the right conference venue for this work. The problem of dose p... | Rebuttal 1:
Title: Call for Papers of NeurIPS ‘24 includes “Optimization”…
Comment: https://neurips.cc/Conferences/2024/CallForPapers
I guess rejecting this work on the basis of not fitting NeurIPS because it is optimization work might not work out so easily.
---
Rebuttal 2:
Rebuttal: Thank you for dedicating time t... | Rebuttal 1:
Rebuttal: The attached PDF file includes two figures and one table:
Figure 1: The dose map provided in response to Reviewer 4 (hEqB).
Figure 2 and Table 1: The results for a prostate case provided in response to Reviewer 2 (oYrg).
Pdf: /pdf/80fb5c8ce2a903ae04dadcaf2299ee4ec49e72db.pdf | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Smoke and Mirrors in Causal Downstream Tasks | Accept (poster) | Summary: the paper makes a deep dive into the various types of biases that can arise in RCTs that would invalidate causal estimants
Strengths: - Interesting paper that would greatly benefit the discussions of the community
- Identifies crucial sources of bias, offers interesting potential solutions to them
- Experime... | Rebuttal 1:
Rebuttal: **Weakness 1**: *Only reviewing the last 2 years' literature*
We thank the reviewer for the feedback. We agree that we could improve on the literature review, especially with respect to classical causal inference works. We will add them to the related works. We would appreciate it if the reviewer... | Summary: This paper theoretically reveals that many common choices in the literature may lead to biased estimates. To test the practical implications of these considerations, this paper recorded the first real-world benchmark for causal inference downstream tasks on high-dimensional observations through an RCT studying... | Rebuttal 1:
Rebuttal: **Weakness 1**: *broken link*
We are sorry for the inconvenience, the anonymous hosting account was eventually disabled without further notice due to inactivity. We added a new link to review the dataset in our general answer.
**Weakness 2**: *limited description and visualization of the dataset... | Summary: This paper considers the task of causal effect estimation $P(Y|do(T))$, where the treatment is mediated through a (potentially high-dimensional) observation $X$.
Additionally, a semi-supervised setting is studied, where labels $Y$ are only available in a subset of the data.
A set of possible biases affecting t... | Rebuttal 1:
Rebuttal: **Weakness**: *doing too much (e.g., informal Thm. 3.1 difficult to follow)*
Thank you for pointing this out. Making Section 3 concise and accessible to different communities was indeed a not trivial task. We agree that Thm 3.1 is not as crisp as it could be. We suggest making the definition of T... | Summary: The paper explores the challenges associated with using machine learning, particularly deep learning, to estimate causal treatment effects from high-dimensional data, such as images, in Randomized Controlled Trials (RCTs). The authors point out that standard practices in machine learning, such as selecting mod... | Rebuttal 1:
Rebuttal: **Weakness 1**: *Why only binary outcome*
Thank you for the constructive feedback. We realize this point was overall not sufficiently remarked in the draft and we will stress it in the conclusion section in the camera-ready version. The two key points are:
* Our discussion refers to the binary ou... | Rebuttal 1:
Rebuttal: We thank the reviewers for their consideration of our paper and for their feedback. The consensus appears to be that the writing is “*superb*” [Erid] and “*well-structured*” [ZnDR, WAFK], “*addressing an important and under-explored area of causal inference*” [ZnDR] bringing “*many great contribut... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Real-time Stereo-based 3D Object Detection for Streaming Perception | Accept (poster) | Summary: This paper proposed a real-time stereo 3D object detection algorithm under the streaming preception framework setting. The proposed StreamDSGN build on the existing streaming perception work and the DSGN 3D object detection work and has three technical contributions: 1) the Feature-Flow-Fusion module predicts ... | Rebuttal 1:
Rebuttal: # Response to reviewer D5z5
### Weaknesses
---
**W1**: The setting of 3D detection from stereo is not very common in autonomous driving and the relevant dataset is limited. It would be good to extend the stereo camera setting to multi-camera setting or camera+Lidar setting and test the generaliza... | Summary: In this paper, the authors propose a real-time stereo-based 3D object detection framework for streaming perception. Specifically, the authors design feature flow fusion, motion consistency loss and a Large Kernel BEV backbone to improve the performance. The authors validate the effectiveness of the proposed me... | Rebuttal 1:
Rebuttal: # Response to reviewer Ytfb
### Weaknesses
---
**W1**: The novelty of the proposed method is not significant. The challenges in streaming perception are evident, and the technical novetly of the proposed solution is not very significant to me. According to Table2, the biggest performence improvem... | Summary: The work proposes StreamDSGN, a framework for steaming perception, evaluated on the KITTI dataset with sAP (steaming average performance metric), which takes into account the model latency and is a better fit for evaluating streaming perception than purely qulitative metrics. It is the first work to do 3D obje... | Rebuttal 1:
Rebuttal: # Response to reviewer NjQK
### Weaknesses
---
**W1**: Limitations are discussed only really shortly.
**A1**: Our discussions on limitations and future work are brief due to the page limit. We may add more detailed discussion in the appendix.
---
**W2**: Figure 2 needs a little more explanatio... | Summary: The paper presents stereo-based 3D object detection method designed for streaming perception, where the current frame (and past frames) are taken to predict the object bounding boxes in the next frame. The authors adopt a simplified DSGN++ as the backbone and add several components to enhance the perception ac... | Rebuttal 1:
Rebuttal: # Response to reviewer uBXC
### Question
---
**Q1**: Though the proposed method focuses on stereo-based 3D object detection, the new components proposed in this paper are not related to the stereo setting and seem to work generally for all BEV-based perception systems. Why not test it on more gen... | Rebuttal 1:
Rebuttal: # Summary
---
### Multi-View Setup Limitations
Some reviewers have questioned why this work did not conduct experiments on multi-view setups. One reason is the lack of high-frame-rate datasets. Additionally, existing multi-view methods typically have high latency or sparse BEV features, making it... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Dense Connector for MLLMs | Accept (poster) | Summary: The document presents a novel approach called the Dense Connector (DC), which is a simple and effective plug-and-play vision-language connector that enhances existing Multimodal Large Language Models (MLLMs) by leveraging multi-layer visual features from the frozen visual encoder.The authors propose three intu... | Rebuttal 1:
Rebuttal: **Q1.Can DC be compatible with the visual resampler architecture?**
This is a very worthwhile research question. Dense Connector (DC) is compatible with models similar to BLIP-2, which have visual resampler or Qformer structures.
Notably, models like BLIP-2 use visual resampler to obtain learnab... | Summary: This paper endeavors to delve into the visual representations in MLLMs, introducing a module, plug-and-play component named as Dense Connector (DC). This DC is designed to enhance visual representation. To this end, three instantiations are presented, including Sparse Token Integration(STI), Sparse Channel I... | Rebuttal 1:
Rebuttal: **Q1. More comparisons with COMM [1]**
[1] combines visual features from all layers by simply adding them together, which can lead to **information loss**. Additionally, ViT's low, middle, and high layers contain different information. Therefore, COMM's approach of adding them all together **lack... | Summary: This paper introduces the Dense Connector, a simple idea that aligns visual and language modalities by utilizing multi-layer visual features. The authors explore three instantiation methods and demonstrates the effectiveness of the Dense Connector across various settings, including different backbones, modalit... | Rebuttal 1:
Rebuttal: **Q1. Combining Dense Connector with LLaVA-NEXT's AnyRes technology**
Thanks! This is a very worthwhile question to explore. We extended the Dense Connector (DC) to dynamic resolution scenarios. Using dynamic resolution, DC remains effective. Compared to the baseline, DC improved performance on T... | null | null | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and insightful comments. We are glad the reviewers find that Dense Connector (DC) is a novel plug-and-play module, which is the first in the MLLM field to explore the fusion of multi-layer visual features from both token and channel dimensions. W... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning | Accept (poster) | Summary: This paper addresses the issue of spectral bias in deep neural networks (DNNs), where DNNs tend to prioritize learning lower-frequency components of a function, struggling with high-frequency features. The proposed solution involves decomposing high-frequency functions into compositions of low-frequency functi... | Rebuttal 1:
Rebuttal: We are grateful to the referee's constructive comments and insightful suggestions on this study. Below, we reply the weaknesses and questions term by term.
Response to weakness:
The aim of this study is to show that MGDL can be used to address the spectral bias issue of deep neural networks by p... | Summary: This paper tackles the spectral bias of deep neural networks by proposing a method to capture high-frequency data effectively. The authors assert that previous works have not adequately addressed spectral bias, emphasizing its significance. They propose that high-frequency functions can be decomposed into a su... | Rebuttal 1:
Rebuttal: We are grateful to the referee's constructive comments and insightful suggestions on this study. Below, we reply the weaknesses term by term.
Response to weakness item (1):
We are grateful to the referee for this suggestion. This study is to provide insights of MGDL and proof-of-concept numerica... | Summary: DNNs suffer from spectral bias, which is the tendency to prioritize learning of low-frequency functions. The authors propose a method called MGDL, which tackles the spectral bias problem by changing the network parameterization and loss function such that each layer in the network learns the residual of the fu... | Rebuttal 1:
Rebuttal: We are grateful to the referee's constructive comments and insightful suggestions on this study. Below, we reply the weaknesses and questions term by term.
Response to weaknesses (1):
The referee's criticism is well-taken. This study is to provide insights of MGDL and proof-of-concept numerical ... | null | null | Rebuttal 1:
Rebuttal: We are grateful to the referees for their constructive comments and insightful suggestions on this study. Various points made by the referees are valuable. A `global' reply to the referees is as follows.
(1) MGDL is mainly motivated from three considerations. First of all, it is inspired by the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
DMesh: A Differentiable Mesh Representation | Accept (poster) | Summary: This paper proposed a novel differentiable representation of a mesh. It takes advantage of two probabilities, $\Lambda_{wdt}$ and $\Lambda_{real}$, to generate the mesh, such that the mesh is differentiable.
Strengths: Based on weighted Delaunay triangulation and its dual power diagram, the paper proposed a n... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments and positive evaluation about our work. Please let us know if we addressed your questions correctly.
-----------------------------------------------
**Q1. Degraded efficiency of the method due to WDT**
A1. As pointed out, the main computational bottleneck in ... | Summary: This paper proposes a novel differentiable mesh representation, which focuses on both the vertex position and connectivity. To achieve this, authors come from the probability of face existence, and decompose the probability into two parts: 1) the WDT probability; 2) a probability of vertices' existence on surf... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments and positive evaluation about our work. Please let us know if we addressed your questions correctly.
------------------------------------
**Q1. Significant computational cost**
A1. Yes, in the current implementation, we can handle up to 20K points efficiently... | Summary: The paper presents a differentiable mesh representation for mesh reconstruction from input meshes, point clouds, and for multi-view reconstruction. It builds off ideas of Rakotosaona 21, and uses a weighted Delaunay triangulation framework to predict simplex probabilities. They borrow the notion of distance to... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments and positive evaluation about our work. Please let us know if we addressed your questions correctly.
------------------------------
**Q1. Non-manifoldness**
A1. Please see Global Response A2.
------------------------------
**Q2. Computational cost**
A2. Tha... | Summary: The work introduces a differentiable mesh representation where both the topology of the surface and the connectivity are differentiable. The paper builds on previous work titled 'Differentiable Surface Triangulation,' which suggests using a soft relaxation of Weighted Delaunay Triangulation (WDT) to differenti... | Rebuttal 1:
Rebuttal: We appreciate your detailed comments and positive evaluation about our work. Please let us know if we fully address your questions.
------------------------
**Q1. Description about running time of “Differentiable Surface Triangulation”**
A1. Thank you for pointing out the error. We acknowledge t... | Rebuttal 1:
Rebuttal: Thank you for all of the reviews. We can further improve this paper based on your feedback.
Before addressing the specific points, *we encourage the reviewers to refer to the index.html file in the supplementary material, which contains videos visualizing the meshes during optimization in our exp... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The authors first propose a differentiable mesh representation, DMesh, which can represent a wider variety of mesh types. They then introduce a computationally efficient approach to differentiable weighted Delaunay triangulation that can run in approximately linear time. Following this, an efficient algorithm ... | Rebuttal 1:
Rebuttal: We appreciate your comments, but we believe there may have been some major misunderstandings about our work. We have tried to address your comments as thoroughly as possible. If we have not correctly addressed your concerns, please let us know.
---------------------------
**Q1. Weakness about “d... | null | null | null | null | null | null |
LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model | Accept (poster) | Summary: This paper introduces a novel learning model for SNNs that dynamically adjusts input current and membrane potential leakage, enhancing SNN performance to match ANNs. The proposed LM-HT model can be seamlessly integrated with ANN-SNN Conversion frameworks, effectively improving the performance of converted SNNs... | Rebuttal 1:
Rebuttal: ## To Reviewer 59n9
Thanks for your valuable and constructive feedback. We are encouraged that you found our paper "significantly enhances SNN performance", "presents a seamless integration with ANN-SNN Conversion frameworks" and "has extensive experimental validation". We would like to address yo... | Summary: Traditional SNNs adopt binary spike communications, resulting in relatively poor performance compared to their ANN counterparts. In this work, the authors propose an advanced Multi-hierarchical Threshold (LM-HT) Model. In my view, LM-HT models introduce several low-bit representation precisions to model the sp... | Rebuttal 1:
Rebuttal: ## To Reviewer wxgA
Thanks for your insightful and valuable feedback. We are glad that you found our work "offers a nice perspective", "has solid theoretical analysis" and "the writing is generally good". We would like to address your concerns and questions in the following.
> 1. The authors are ... | Summary: This paper proposes a learnable multi-hierarchical threshold model for SNNs and call it LM-HT. The authors theoretically analyzes the equivalence between LM-HT, vanilla spiking models, and quantized ANNs, and demonstrate that LM-HT can achieve comparable performance comparable as quantized ANNs under a two-sta... | Rebuttal 1:
Rebuttal: ## To Reviewer JrEi
Thanks for your insightful and valuable feedback. We are glad that you found our work "provides a rigorous mathematical analysis", "offering a new perspective of SNNs" and "significantly outperforms previous SOTA methods". We would like to address your concerns and questions in... | Summary: This paper introduces the Learnable Multi-hierarchical Threshold (LM-HT) model, a novel approach to enhance the performance of SNNs to match that of ANNs. The LM-HT model dynamically adjusts the global input current and membrane potential leakage, and can be reparameterized into a standard single-threshold mod... | Rebuttal 1:
Rebuttal: ## To Reviewer zyi4
Thanks for your constructive and valuable feedback. We are encouraged that you found our proposed method "effctive", "superior" and "provides a mathematical bridge between multi-threshold SNNs and quantized ANNs". We would like to address your concerns and questions in the foll... | Rebuttal 1:
Rebuttal: ## To All Reviewers
Thanks for all your constructive and insightful feedbacks! Considering the concerns of some reviewers about detailed implementation of LM-HT SNN in the reparameterization process, we will provide an additional proof here regarding the computational equivalence before and after ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance | Accept (poster) | Summary: The paper is studying the problem of zero-shot object goal navigation. To solve the problems in traditional approaches, the authors propose a novel method, named Geometric and Affordance Maps (GAMap), which incorporates both geometric and affordance attributes as navigation guidance. A multi-scale scoring appr... | Rebuttal 1:
Rebuttal: # Response to Weakness 1
Thank you to the reviewer for the thorough review and feedback on our work. First, we would like to clarify the two key innovations of our work:
Our main contribution is the proposed **Geometric and Affordance Map (GAMap)** and the **Multi-scale Geometric and Affordance ... | Summary: The paper presents an approach for the task of zero-shot object goal navigation. The approach first generates a geometric and affordance description of the goal object. This description is then matched with the visual embedding of the RGB frame (at multiple scales) in the CLIP embedding space to compute a simi... | Rebuttal 1:
Rebuttal: # Response to Weakness 1
Thank you to the reviewer for the valuable suggestions.
While previous work has effectively utilized attributes and longer descriptions for object classification [A, B], our approach uniquely explores the role of attributes in navigation, particularly the use of afforda... | Summary: The authors propose an algorithm for the object-goal navigation/exploration problem by using a 2D navigation map containing geometric and affordance scores for the target object. Geometric and affordance features for the target object are extracted by asking an LLM to list them for the object, and these words ... | Rebuttal 1:
Rebuttal: ---
# Response to Weakness 1
We acknowledge the contributions of existing works, such as VLFM, in utilizing CLIP-like models for navigation. VLFM primarily considers semantic relevance between all objects in the scene and the target object to generate value maps, which is indeed effective.
Diffe... | Summary: The paper proposes Geometric and Affordance Maps to tackle zero-shot object-goal navigation. It focuses on how to leverage VLMs to navigate toward objects of unfamiliar categories without prior training. The authors use GPT-4 to generate multiple attributes that describe affordance and geometry, and acquire at... | Rebuttal 1:
Rebuttal: ---
# Response to Weakness 1
Thank you to the reviewer for the feedback on our paper. We understand your concerns and would like to clarify and respond to them as follows:
1. We would like to first highlight the main contributions of this paper: the Geometric and Affordance Map (GAMap) and the Mu... | Rebuttal 1:
Rebuttal: # General Response
We appreciate all reviewers for their thorough reviews and valuable suggestions on our paper. First, we address three common concerns raised by all the reviewers here, and then we provide point-by-point responses to each reviewer's specific comments.
---
## 1. Novelty of the ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Contrastive dimension reduction: when and how? | Accept (poster) | Summary: The authors consider the problem of contrastive dimension reduction/estimation. In a nutshell, this problem involves a background dataset and a foreground dataset, and it is of interest to determine if the foreground dataset contains submanifolds/subspaces that are inherently different than those in the backgr... | Rebuttal 1:
Rebuttal: Thank you for your thorough and insightful feedback on our manuscript. We appreciate the opportunity to clarify and improve our work. Below, we have summarized your questions and our responses:
**Line 81 - Assumption on $n_x$ and $n_y$ and Line 174 - Ratio $n_x : n_y$**
Thank you for the questio... | Summary: The paper deals with a contrastive dimension reduction (CDR) techniques which seeks to find a unique low-dimensional pattern that only exists in the foreground data compared to background data. Authors point out that despite of recent developments of related techniques such as contrastive principal component r... | Rebuttal 1:
Rebuttal: **Discrepancy between the p-value obtained from permutation test and the estimator happens in some cases.**
Thank you for the feedback. We would like to clarify that the hypothesis test is conservative, and therefore it is possible to observe a high $p$-value even when the estimator indicates tha... | Summary: The paper proposes to examine the existence and estimate the number of contrastive dimensions between the foreground and background groups. The authors provide a formal definition of contrastive dimensions and propose a hypothesis test method to examine the existence of contrastive dimensions. The authors also... | Rebuttal 1:
Rebuttal: **The practicality of the formulation of contrastive dimension**
Thank you for the opportunity to clarify. This example highlights the differences between our approach and methods like CPCA and CLVM.
In your example, from the CLVM perspective, $\Sigma_x = SS^\top + WW^\top + \sigma_{\epsilon} \m... | Summary: The authors address two key challenges using two datasets, categorized as foreground and background: 1) Determine whether contrastive dimensionality methods are appropriate for application to such a pair of datasets and 2) Quantify the unique information present in the foreground data.
Strengths: - The text i... | Rebuttal 1:
Rebuttal: **Hypothesis Test**
We appreciate your feedback and the opportunity to clarify our approach.
Firstly, based on Definition 1 and the notation we introduce following Model 1, the statements $d_{xy} = 0$ and $V_x \subset V_y$ are indeed equivalent. We have clarified this at the end of Section 2.
W... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback. This global response covers common questions and concerns. Further detailed responses are provided in the individual rebuttals.
**Hypothesis Test (e5ip, xiqq, Casz)**
Regarding the assumption of $V_x \subset V_y$ versus $d_{xy} = 0$ under $H_0... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
PFDiff: Training-free Acceleration of Diffusion Models through the Gradient Guidance of Past and Future | Reject | Summary: To accelerate the sampling speed in diffusion models, this paper proposes a training-free denoising method, dubbed PFDiff.
Concretely, PFDiff employs the gradient from past time steps to update intermediate states, aiming to reduce unnecessary NFEs while correcting for discretization errors.
In this manner, P... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable suggestions.
***W1: Previous gradient guided sampling and PFDiff's harm analysis.***
**A**: Let's start with a possible misunderstanding. In PFDiff, previous gradients do not assist in guiding the sampling direction; rather, it is the future gradie... | Summary: This paper proposes PFDiff, a fast training-free sampler for diffusion models. PFDiff updates the current state with both the past score network evaluation and the future score network evaluation. It can achieve good sample quality with less than 10 NFE. The authors showcase the effectiveness of PFDiff on vari... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments, they help improve our paper.
***W1: Flawed justification for future gradient.***
**A:** Thanks for pointing out that the simplified description (lines 164-167) may have led to some misunderstanding. We now provided a more comprehensive explanation of the underly... | Summary: The paper proposes PFDiff, a training-free approach for accelerating diffusion models. Motivated by the high similarity of the diffusion network outputs at adjacent timesteps on the sampling trajectory, PFDiff utilizes past and future information for sampling with time-skipping,
and decreases the number of fu... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable review of the manuscript and the recognition of our work of the work presented in the paper. Below are our responses to all questions. We kindly hope you could consider increasing the score if you are satisfied.
***W1: The highly concise writing and... | Summary: The paper proposes a new training-free acceleration method for the inference of diffusion probabilistic models. The key components of the presented time-skipping strategy are the use of past and future gradients to eliminate redundant neural function evaluations (NFE). The proposed method is shown effective co... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's recognition of our work and valuable comments. Below are our responses to all questions. We kindly hope you could consider increasing the score if you are satisfied.
***W1: The performance gap compared to training-based methods is still apparent.***
**A**: ... | Rebuttal 1:
Rebuttal: Thank you to all reviewers for their efforts and valuable comments on this paper. Here we address common concerns raised by the reviewers.
## Response to common questions
***Q1: The issue regarding the hyperparameters $k$ and $l$ needs to be adjusted for different datasets/models and the NFE.***... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work proposes a training-free time step-skipping method that can be used with existing ODE solvers for reduced NFE. The method was motivated by two observations: 1) a significant similarity in the model's outputs at time step size during the denoising process and 2) a high resemblance between the denoisin... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's efforts and insightful comments on our work.
***W1: The novelty question of PFDiff, and its distinctions from some prior important works, such as [R1] to [R5].***
**A**: We have carefully checked all the five prior works mentioned by the reviewer, and we found they ... | null | null | null | null | null | null |
Dynamic Rescaling for Training GNNs | Accept (poster) | Summary: This paper proposes the dynamics rescaling approach to improve the trainability of GAT. This paper is motivated by the rescale invariance property of GAT, i.e. the function is invariant w.r.t. some scaling terms on a neuron’s weights, and the corresponding conservation law. To balance the network throughout tr... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the importance of our work and their constructive feedback. We address their concern as follows. Note that all references to figures are in the PDF attached to the global response unless stated otherwise.
1. We gladly highlight the novelty of our work in co... | Summary: This manuscript attempts to study the rescale invariance of GNNs and suggests to dynamically rescale the network using relative gradient, i.e. g/theta, where g is gradient of theta. Several experiments are conducted to to show how the dynamic rescaling using relative gradient norms affects the learning speed a... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our work and their interest in our insights. We address their concerns as follows.
We would like to reiterate that our goal is not to propose a one-size-fits-all solution or achieve state-of-the-art performance. Rather, this work aims to cond... | Summary: This paper investigates the use of dynamic rescaling to train Graph Attention Networks (GATs), a type of Graph Neural Network (GNN). The method aims to enhance the trainability and generalization of GATs by balancing network parameters and gradients during training. GATs' rescale invariance property enables th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address their concerns as follows. Note that all references to figures are in the PDF attached to the global response unless stated otherwise.
1. Firstly, to improve the readability and comprehension of the paper, we answer the questions se... | Summary: The paper studies the various phenomena in GNN training that prevent the usage of large learning rates for faster convergence and better generalization. Based on the theoretical foundation that large learning rates can be stably used only when the rescale symmetry of the loss function is satisfied, the authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address their concerns and questions as follows.
1. Thanks for pointing out this typo. We meant training. We would like to further clarify the statement in the abstract regarding larger learning rates and robustness. Here, by robustness, we ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their encouraging comments and constructive suggestions that we have incorporated to improve the paper. We jointly answer some key common questions of the reviewers here in two parts regarding i) additional insights and ii) limitations.
**Further insights and additiona... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization | Accept (poster) | Summary: This paper aims to study the performance impact of instruction optimization (IO) and exemplar selection (ES) both isolation and combination, as the automatic prompt optimization literature mostly only focuses on either IO or ES. Its empirical results on a wide range of tasks (e.g., BBH and MMLU) show that opti... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive and insightful comments. We’d like to refer the reviewer to our point-by-point response below and also the common response to all reviewers. We hope that in light of our response, the reviewer could consider revising their rating if they feel their conc... | Summary: The paper studies the instruction optimization and exemplar selection in auto prompt optimization. It has the following contributions:
1) Showing that optimizing ES improves significantly over IO and the importance of ES
2) Showing the synergy effect between ES and IO
3) Extensive experiments are conducted to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive and positive comments about our work. Please see below for our point-by-point response, which we believe has addressed the reviewer’s concerns thoroughly. We hope that the reviewer could consider revising their score if they feel the same way. **To full... | Summary: This paper provides a comprehensive investigation of instruction optimization and exemplar selection.
Strengths: - The paper is very well motivated, and studies a very important problem, which is the relative importance of exemplar selection and instruction optimization. Therefore, the paper is likely to insp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and positive comments! Please see below for our response. We hope that in light of our response, the reviewer will consider revising their rating if they feel their concerns have been sufficiently addressed. **To fully address the reviewer's concerns, we ... | Summary: This paper profoundly discusses how the deisgn of Automatic Prompt Optimization (APO) methods influence the performance of current instruction-following pre-trained large language models (LLMs). Concretely, the authors first systematically summarize and compartmentalize current APO methods into two lines: inst... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback, which also included clear, actionable items (e.g., GPT evaluations). We’d be grateful if the reviewer could review our response below. We also hope they could consider revising their rating if they feel their concerns have been adequately addr... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and feedback! In the common response below, we’d like to provide in-depth answers to some of the questions asked by multiple reviewers.
## Additional LLMs (Reviewers 1DvC, Hw9e, dxC3)
Multiple reviewers asked for evaluations on additional LLMs. We acknowledg... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work focus on evalution and comparison study of automatic prompt optimization (APO) methods. These methods are broadly categorized into instruction optimization (IO) and exemplar selection (ES). This paper seeks to bridge the gap between these two methods by comprehensively comparing the performance of re... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback! Please see below for our response to their questions and concerns.
> More LLM choices
We thank the reviewer for their suggestions. We have included additional results on GPT and Gemini 1.5 below. With the new experiments added, we believe that o... | Summary: This work studies automatic prompt optimization, seeking to compare and to connect the literatures on instruction optimization and on example selection. They find that existing approaches for using LMs to self-generate examples and selecting between them can outperform the best existing instruction optimizers,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful and positive feedback! Please see below for our point-by-point response to the reviewer’s specific comments.
> Limitations of BIG-Bench and MMLU and more open-ended/realistic tasks.
We thank the reviewer for their suggestions! The primary reason we pick... | null | null | null | null |
DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity | Accept (poster) | Summary: This paper examines a setting in which a model is learned with an expanding dataset, such that new data is introduced after the model hits a training accuracy threshold. In particular, the new data comes from the same distribution as the existing data (or sometimes has the exact same statistics), making this a... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for providing valuable and constructive feedback on our work. We also thank you for bringing to our attention typos in Table 8. Below, we address the questions and concerns raised by the reviewer.
## **W1, Q3. Clarification on our Framework and DASH**
We acknow... | Summary: The paper examines warm-start and the loss of plasticity, identifying that noise memorization from warm-start impairs generalization in stationary data distributions. It proposes a method called DASH, which selectively forgets previously memorized noise while preserving learned features.
Strengths: - **Featur... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for providing valuable and constructive feedback on our work. Below, we address the questions and concerns raised by the reviewer.
## **W1 & W3**
Check out our global response where we address these issues. We hope our explanation and ablation study resolve al... | Summary: Warm-starting neural networks may lead to poor generalization performance or loss of plasticity, likely due to overfitting. This paper presents a framework that hypothesizes that noise memorization is the primary cause of loss of generalization in warm-starting settings. The authors then present an algorithm m... | Rebuttal 1:
Rebuttal: We would like to express our appreciation to the reviewer for your valuable and constructive comments. In the following, we address the points raised by the reviewer.
## **W1, Limitation. Clarification on our framework and link between the framework and the algorithm**
We apologize for the uncl... | Summary: This paper investigates the reasons why warm-starting a neural network by pre-training it on a subset of the full training set leads to suboptimal performance when compared to training it on the full dataset from scratch. In particular, it proposes an abstract combinatorial model of feature learning that accor... | Rebuttal 1:
Rebuttal: We would like to express our appreciation to the reviewer for your valuable and constructive comments. In the following, we address the points raised by the reviewer.
## **W1, Questions. Detailed description of DASH**
We acknowledge that our description of DASH may have caused some confusion re... | Rebuttal 1:
Rebuttal: We express our gratitude for your time and valuable comments. We would like to address the concerns and confusion raised by multiple reviewers.
## **Main intuition on our theoretical framework**
We would like to provide a clearer explanation of how our theoretical framework reflects the intuitive... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
ARC: A Generalist Graph Anomaly Detector with In-Context Learning | Accept (poster) | Summary: The paper produces a "one-for-all" generalist graph anomaly detection model. The proposed model first use feature projection and smoothness-based ranking to align the features of multiple datasets; then it employs a residual graph neural network to extract representations; finally, a cross-attention module is ... | Rebuttal 1:
Rebuttal: We appreciate Reviewer B6wn for the insightful feedback and acknowledge our contributions. The responses to the reviewer are as follows.
**Q1: Which type of feature projection method is used in the experiment should be given**
**A1:** Thanks for your valuable suggestion. In our experiments, we u... | Summary: This paper proposes a new framework for graph anomaly detection called ARC. ARC uses in-context learning to detect anomalies across various graph datasets without requiring retraining or fine-tuning. It leverages few-shot normal samples during inference to achieve superior performance in anomaly detection task... | Rebuttal 1:
Rebuttal: We appreciate Reviewer VWTk for the valuable feedback and acknowledge our technical contributions and the effectiveness of the proposed method. We address the concerns raised by the reviewer as follows.
**Q1:Why Use a shared MLP in the encoder**
**A1:** Thanks for the insightful comment! Accordi... | Summary: This paper investigates the research problem of "generalist graph anomaly detection (GAD)", targeting to address the cost and generalizability issues of the conventional GAD paradigm. This paper proposes ARC, a "one-for-all" GAD model that is pre-trained on a group of datasets and able to detect anomalies on n... | Rebuttal 1:
Rebuttal: We appreciate Reviewer bLtA for the positive review and constructive comments. We provide our responses as follows.
**Q1: If the original feature dimension is smaller than the predefined projected dimension**
**A1:** Thanks for the thoughtful comment! When the original feature dimension is small... | Summary: This paper proposed AARC, a generalist GAD approach to detect anomalies across various graph datasets on the fly. It consists of feature alignment module, residual graph encoder and in-context anomaly scoring module.
Strengths: 1. The experimental results show that the proposed method outperform most of the b... | Rebuttal 1:
Rebuttal: We are grateful to Reviewer HCAf for providing insightful feedback. The detailed responses are provided below.
**Q1: Details of feature alignment with sorting**
**A1:** Thank you for the thoughtful comment. In ARC, the smoothness-based feature sorting is performed on the projected features rathe... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable and insightful comments. We are glad that the reviewers find that the studied problem is novel and significant (Reviewer RwhU, bLtA, VWTk, and B6wn), the proposed method is novel and well-motivated (Reviewer bLtA, VWTk, and B6wn), the empiric... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper introduces an ARC method designed to detect anomalies across diverse graph datasets without the need for dataset-specific training. ARC leverages in-context learning with few-shot normal samples during inference, comprising three main components: a smoothness-based feature alignment module, an ego-ne... | Rebuttal 1:
Rebuttal: We appreciate Reviewer RwhU for the perception of our contributions and thank the reviewer for the insightful feedback. The detailed responses are provided below.
**Q1: Ambitious statement of the learning target**
**A1:** Thank you for raising this valuable comment. While we cannot guarantee tha... | null | null | null | null | null | null |
Achieving Precise Control with Slow Hardware: Model-Based Reinforcement Learning for Action Sequence Learning | Reject | Summary: This paper addresses the problem that commonly in RL, small reaction times and high action frequencies are required, which is not the case for computations in the brain. As a more accurate model, the authors propose an RL method that learns an internal model to improve performance in high-latency applications.... | Rebuttal 1:
Rebuttal: Thank you for your taking the time to read our work and for detailed review of our work.
1. We demonstrate this in the current work in Figure 3. SAC fails at human-like reaction times. We have updated the text to say that we demonstrate this in experiments.
2. We provide a control solution for t... | Summary: This paper introduces the Hindsight-Sequence-Planner (HSP), a reinforcement learning (RL) model inspired by the brain's ability to achieve precise control using slow neurons. The model aims to mimic human-like sensory and reaction times by leveraging an environmental model for sequence learning. HSP demonstrat... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and for acknowledging the novelty and the performance of our method. We address weaknesses not already addressed in the questions first:
2. The novelty of our approach is the temporal recall mechanism and the introduction of simultaneously trained model using del... | Summary: This paper proposed HSP, a bio-mimic framework for learning-based control. Motivated by human brains, HSP can deal with observation and computation in different frequencies by making the "actor" produce action sequences, similar to the functioning pattern of ganglia and the prefrontal cortex in human brains. H... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and your acknowledgement of the promising direction and applications of our work.
Weaknesses:
**Framework performance:** We understand that these concerns are based on figure 2. During training, we evaluate HSP-$J$ on action sequence length of $ASL=J$. Therefor... | null | null | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed comments. We believe that all their comments have improved the quality and clarity of our paper. Here we attach additional tables to the PDF to support our rebuttal. They are referenced in the individual rebuttals.
Pdf: /pdf/028da7432cba994d081824fe960... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Improving Alignment and Robustness with Circuit Breakers | Accept (poster) | Summary: This paper introduces a new technique called "short circuiting" that makes models more robust against outputting harmful texts without significant impacts to their helpfulness (a significant pareto improvement on the harmfulness-helpfulness tradeoff). Short circuiting works by training a model's hidden represe... | Rebuttal 1:
Rebuttal: Thank you for your careful analysis and insightful suggestions. We hope you will champion our paper. We also hope the following clarifications address your questions.
**Presentation and Details**
We have made changes based on your suggestions to enhance the presentation of our method. Here are s... | Summary: This paper presents a novel short-circuiting method to ensure LLM safety alignment and its adversarial robustness against jailbreaking attacks. Particularly, the authors propose to conduct LoRA training with their short circuiting loss in the model representation space. The short-circuited models can effective... | Rebuttal 1:
Rebuttal: Thank you for your careful analysis of our work. We hope the following response addresses your concerns.
**Details of the Method**
Short Circuiting encompasses techniques that remap model representations associated with harmful processes, redirecting them towards incoherent or refusal representa... | Summary: This paper proposes a method that manipulates the representation of potentially harmful queries to reduce the harmful generation. Extensive experiments are conducted to verify the effectiveness.
Strengths: Experiments on different scenarios are provided to illustrate the effectiveness of the proposed method.
... | Rebuttal 1:
Rebuttal: Thank you for your careful analysis of our work. We hope the following response addresses your concerns.
**Difference between Adversarial Training and Short Circuiting**
We believe the distinctions here are in fact the main contributions of our paper. Our approach represents a fundamental depart... | Summary: The authors propose and test a novel method called "short-circuiting" which aims to modify a generative model $M$ so that it is unable engage in a specified behavior $B$.
The high-level idea of short-circuiting is as follows:
1. First, transcripts demonstrating the behavior $B$ are collected. The is called t... | Rebuttal 1:
Rebuttal: Thank you for your careful analysis and insightful suggestions. We hope you will champion our paper. We also hope the following clarifications address your questions.
**Why not reduce ASR to zero?**
This is a great question. While our method significantly reduces harm under attacks by 1-2 orders... | null | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This work presented a method to short-circuit model when they attempt to output harmful content. This is conducted through representation engineering the internal representation of short-circuit model (LoRRA adaptor + forzen original model) corresponding to harmful content towards an objective orthogonal to it... | Rebuttal 1:
Rebuttal: Thank you for your careful analysis of our work. We hope the following response addresses your concerns.
**Writing Improvements**
We have included references to Figures 1 and 4 and Table 1 in the main text. For Table 1, we report the Attack Success Rate (ASR) to measure robustness and use stand... | null | null | null | null | null | null |
Probablistic Emulation of a Global Climate Model with Spherical DYffusion | Accept (spotlight) | Summary: In the paper the authors develop a generative model for global climate simulations. By combining the DYffusion framework for generative modeling with Spherical FNOs that respect the spherical earth geometry the resulting model has stable rollouts for 10-year climate simulations. Experiments show that the resul... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive reception of our work and its "important advances". We are especially glad to hear that you think we have done "a good job at making climate modeling concepts accessible to a wider ML audience." We sincerely hope that, similarly to ML-based weather forecast... | Summary: The paper presents a method for approximating a physics-based climate model with a faster data-driven model. To make the model probabilistic the method uses diffusion models (specifically DYffusion). Predictions over longer timescales are generated using autoregressive rollouts. Spherical Fourier Neural Operat... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive reception of our work, particularly noting its organization, clear relation to previous work, and the practical significance of our contribution to climate modeling and climate change.
**W1:** This is an excellent point. Our paper focuses on stan... | Summary: In this manuscript, the authors demonstrate a domain-specific, generative approach for a climate model emulator (ACE) trained to reproduce a climate model (FV3GFS) by training on FV3GFS simulation. Their approach is stable and reproduces the climate of the reference climate model with minimal biases. The addit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive reception of our work and viewing it as an “important contribution” that has “a large number of applications well outside the weather and climate field”.
**W1:** Sincere thanks for bringing this up. Firstly, we plan to tone it down by rewriting to *“(...) ... | Summary: This paper presents a new method for probabilistic emulator of climate models, based on a type of diffusion model with spherical geometry. The results for climate model emulation are generally quite impressive.
Strengths: 1. The paper establishes what appears to be a new state of the art for climate model emu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive reception of our work and “strongly” supporting its publication at NeurIPS.
**W1:** That is an excellent point. We did not try running our method for 100 years. While our current simulations cover 10-year segments due to reference data limitations, we reco... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their thoughtful, valuable, and encouraging feedback.
We are very encouraged by the consistently positive reception of our work by all reviewers, who value its organization, clear relation to previous work, and the practical significance of our contribution t... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization | Accept (poster) | Summary: The paper presents new theoretical advancements in decentralized stochastic asynchronous optimization. They introduce two new methods: Fragile SGD and Amelie SGD. Fragile SGD is designed for homogeneous setups, while Amelie SGD addresses heterogeneous setups. Both methods aim to achieve near-optimal time compl... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation of our work! Let us respond to the weaknesses:
> Although the algorithms are interpretable, they may be challenging to implement in practical systems.
We agree that our algorithms seem too lengthy, but the goal was to provide a very detailed listing to ensur... | Summary: This paper considers the decentralizd optimization problem, where clients have heterogeneous computation and communication times. The paper proved a lower bound on the optimal physical time complexity (wherein the physical computation and communication times are take into consideration). It proposed Fragile SG... | Rebuttal 1:
Rebuttal: Thank you for your review! We now respond to the weaknesses:
> 1. This is also somewhat related to the strengths part. While the paper keeps referring to and comparing against some previous works...
Before we answer this comment, note that our new methods are optimal (up to log factor) for all p... | Summary: The paper addresses the decentralized stochastic asynchronous optimization setup. The authors establish new time complexity lower bounds for both homogeneous and heterogeneous setups, assuming bounded computation and communication speeds. They introduce two methods: Fragile SGD, a nearly optimal method, and Am... | Rebuttal 1:
Rebuttal: Thank you for your time! Let us address the weaknesses:
> First, one of the two algorithms mentioned in the abstract is relegated entirely to the appendix, which is inappropriate for the paper’s structure.
We agree. However, all algorithms, theorems, and discussions that are relegated to the ap... | Summary: This paper examines the time complexity lower bounds in decentralized stochastic asynchronous optimization. It introduces two methods, Fragile SGD and Amelie SGD, which achieve near-optimal and optimal convergence, respectively. The paper also provides convergence analysis for various settings.
Strengths: The... | Rebuttal 1:
Rebuttal: Thank you for your time and review. In the following responses, we address the raised problems. **In particular, we prepared extra experiments to support our theoretical results, which the reviewer can find in the global rebuttal's PDF. We also have experiments in Section I of the paper.** Let us ... | Rebuttal 1:
Rebuttal: We thank the reviewers and the AC for their time and effort. **In the attached PDF**, we added extra experiments with logistic regression and a neural network to support our theoretical claims. These experiments, together with the experiments from Section I, provide solid evidence that our method ... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning | Accept (poster) | Summary: This manuscript proposes a differentiable pruning strategy on 3D Gaussian splatting by performing the pruning stage after full optimization and continuing the optimization of the Gaussians with additional parameters that determine when to prune the Gaussians when they splat to 2D ( 2D pruning instead of the no... | Rebuttal 1:
Rebuttal: Thank you for the issues you raised! We address your concerns below:
---
**W1: Limited novelty and scope.**
First, we humbly clarify that this work does deliver new technical insight, which is “**reducing fragment-level redundancy, in addition to commonly used primitive-level redundancy, enable... | Summary: This paper introduces a novel approach to accelerate rendering speed in 3D Gaussian Splatting (3DGS) by selectively pruning overlapping fragments. This technique serves as an orthogonal enhancement to existing pruning methods (focuses on reducing the number of primitives) by selectively pruning overlapping fra... | Rebuttal 1:
Rebuttal: Thank you for raising questions regarding our baseline choices! We provide the following clarifications:
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**W1 & Q2: No comparison with other state-of-the-art Gaussian pruning pipelines on static scenes.**
Following your suggestion, we have added the comparison with additional Gaussian primit... | Summary: This paper investeigates the relationship between primitive pruning and rendering speed. It identifies that pruning 3D primitives does not translate proportionally to higher rendering speed and shows the real measure affecting rendering speed is the number of fragments (projected splats partaking in pixel colo... | Rebuttal 1:
Rebuttal: We appreciate your recognition of our work and your suggestions to further enhance our experimental results.
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**W1: I would love to see an experiment showing rendering speed and PSNR for different fixed values of threshold vs the learnt threshold. This would effectively show that having per Ga... | Summary: The paper presents a pruning method to improve the time efficiency of 3D Gaussian Splatting. Different from existing methods that prune Gaussians, the proposed method prunes pixels per each Gaussian. Specifically, for each pixel ray, the proposed method reduces the number of Gaussians needed to compose the col... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive feedback and constructive suggestions for our work. Below, we address each of your comments in detail:
---
**W1: Title mismatch: The method changes the # of Gaussians per pixel ray.**
Thank you for the suggestion! We will revise the title accordingly and clari... | Rebuttal 1:
Rebuttal: Dear Area Chairs and Reviewers,
We would like to express our gratitude to all the reviewers for their time and effort in providing valuable feedback. Your positive and constructive comments on our paper, particularly regarding its novelty and practical applications, are greatly appreciated. It is... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Putting Gale & Shapley to Work: Guaranteeing Stability Through Learning | Accept (poster) | Summary: This paper investigates the bandit learning problem in matching markets. It introduces a critical perspective that the objective of regret minimization does not align with achieving market stability. The study explores the sample complexity required to find a stable matching. To address this issue, the authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. Please see our responses and clarifications below.
> Although some existing works consider stable regret, their theoretical guarantees also imply guarantees for the sample complexity of reaching (player-optimal/pessimal) stable matchings, as demonstrated by... | Summary: The paper studies stability in matching problems with learned preferences on one side. The two sides are agents and arms. The agents learn their preferences over the arms through sampling while the arms know their preferences over agents. Then a DA algorithm is run with either arms or agents proposing. The pap... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Please see the following responses to your questions.
> The bounds proved contain a term in |ES(m)|. This term is natural but may be quite large. It would be good to give an order of magnitude for some reasonable preferences.
Response: Please note that ... | Summary: The paper studies the sample complexity of finding a stable matching under the probably approximately (PAC) framework.
In the model, $N$ agents are to be matched with $K \ge N$ arms. Each arm has an (a priori unknown) utility to each agent, inducing the agents’ preferences; Arms also has strict preferences ov... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. We first clarify some of the comments.
> The algorithmic improvement seem marginal, as Theorem 5…
Please note that sample complexity of the AE arm-DA ($\frac{|ES(\underline{m})|}{\Delta^2}log(\alpha^{-1})$) has an improvement on that of uni... | Summary: This paper studies the unexplored question of stability in the study of two-sided market matching problems where preferences of one side are unknown and have to be progressively learned through a bandit learning mechanism. The paper makes a significant contribution in this area, which has hitherto only conside... | Rebuttal 1:
Rebuttal: We thank the reviewer for the interesting questions. Please find responses to specific questions below.
>What is benefit of the proposed methods over one that pays the cost to elicit all preferences?
Response: In the classical preference elicitation framework, each query (either it is based on i... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks | Accept (poster) | Summary: This paper studies the problem of decentralized optimization for non-smooth convex opjectives and time-varying networks. The paper introduces an algorithm to solve this problem together with matching lower bounds on the required communication and subgradient computations, thereby proving that the proposed algo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, effort, and feedback. Below, we provide our detailed response to the review.
### Weaknesses
>I believe that adding (even a small) simulation example showcasing the proposed algorithm's performance on a relevant problem would greatly improve the paper.
Thank y... | Summary: The authors introduce an algorithm which optimally bounds the complexity of algorithms for non-smooth convex decentralised optimisation over time varying networks.
Strengths: The paper introduces an algorithm for an unsolved problem setting as there have been several prior works that provide solutions and bou... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, effort, and feedback. Below, we provide our detailed response to the review.
### Weaknesses and Questions
>The paper can be quite dense and while the authors provide slight intuition about the proof sketch in the main paper, most of the actual paper lies in th... | Summary: The paper studies non-smooth decentralized optimization with time-varying communication networks. The paper presents execution time lower bounds of subgradient algorithms for strongly-convex and convex cases. Then, the paper develops algorithms achieving matching execution time.
Strengths: I am not very famil... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, effort, and feedback. Below, we provide our detailed response to the review.
### Weaknesses
>One minor weakness might be a lack of numerical experiments validating the convergence rate of the proposed algorithm. Nonetheless, I think it is not a big issue for a... | Summary: This paper derives lower bounds on the communication and computation complexities of solving non-smooth convex decentralized optimization problems over time-varying networks and designs and algorithm that achieves these lower bounds.
Strengths: The problem studied in the paper is interesting.
The results loo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time, effort, and appreciation of the theoretical results of our paper. | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, effort, and high evaluation of our work. As far as we understand, there were no major common issues raised by the reviewers. Hence, we are providing our detailed responses to each review in separate messages. | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Exploiting LLM Quantization | Accept (poster) | Summary: This paper exploits the discrepancy between the full-precision and the quantized model to initiate attacks. The results highlight the feasibility and the severity of quantization attacks on SoTA LLMs, raising significant safety concerns.
Strengths: * This paper is well-written with clear motivation and illust... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and effort spent reviewing, their insightful comments, and for their overall positive assessment of our work. Below, we address the reviewer’s questions and comments.
**Q1: Do your findings generalize also to other popular LLMs?**
Yes, as alread... | Summary: The paper points to a potential vulnerability that an attack modifies a full-precision LLM that the full-precision LLM behaves well but after quantizing the model, it can have some harmful responses. The proposed method is solid and practical which raises the public awareness of checking the security of an LLM... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their efforts spent reviewing our paper and for their highly positive assessment. We address the reviewer’s questions and comments below.
**Q1: Can you please clarify the threat model?**
Certainly. The threat model assumes an attacker that either do... | Summary: This paper studies the idea of exploiting quantization as an attack vector. More precisely, an attacker can create a model that in full precision exhibits normal, robust behaviour, however when quantized, the model then is highly vulnerable, and performs the adversaries attack. This highlights the need for car... | Rebuttal 1:
Rebuttal: First and foremost, we would like to thank the reviewer for their insightful review and their positive assessment of our paper. We especially appreciate that the reviewer shares our view about the importance and relevance of the demonstrated exploit.
In response to the reviewer’s concern about th... | Summary: The paper investigates the security vulnerabilities introduced by quantizing LLMs to lower-precision weights, a common technique used to reduce memory usage and facilitate deployment on commodity hardware. The authors reveal that current quantization methods can be exploited to create malicious LLMs that appea... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time spent reviewing our paper and for their recognition of the importance of the studied threat, and address their questions and comments below.
**Q1: Is demonstrating the exploitability of wide-spread and popular LLM quantization schemes significant and non-obvio... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their constructive, thorough, and insightful reviews of our paper. We are especially appreciative of the overwhelmingly positive reception of our work, with several reviewers highlighting its practical relevance, importance, and novelty (Reviewer ve9y: “a p... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Multimodal Large Language Models Make Text-to-Image Generative Models Align Better | Accept (poster) | Summary: This paper introduces VisionPrefer, a large-scale, high-quality, and fine-grained preference dataset for text-to-image generative alignment. VisionPrefer offers advantages in scalability, fine-grained annotations, and a comprehensive feedback format compared with existing preference datasets. The authors furt... | Rebuttal 1:
Rebuttal: Thank you for your detailed, helpful feedback. We address your feedback point by point below.
---
> **Q1**: It is better if the authors could provide some quantitative metrics.
**A1**: Thank you very much for your insightful feedback :). Firstly, in **Table 2 of the main text**, we provide a co... | Summary: The paper introduces VisionPrefer, a large-scale, fine-grained preference dataset constructed using multimodal large language models (MLLMs) as annotators. VisionPrefer aims to improve the alignment of text-to-image generative models with human preferences by providing detailed feedback on generated images. Th... | Rebuttal 1:
Rebuttal: Thank you for your detailed, helpful feedback. We address your feedback point by point below.
---
>**Q1**: Figure 3 indicates that VP-Score converges slower than others.
**A1**: In fact, **Figure 3 does not represent a comparison of the convergence speeds of VP-Score and other baselines**. Inst... | Summary: The paper presents a new AI-generated dataset aimed at enhancing text-to-image generative models by aligning them more closely with human preferences. The data is annotated by multimodal large language models (MLLMs) and captures detailed preferences across multiple dimensions like prompt-following, aesthetic,... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We address your questions point by point below:
---
>**Q1**: the overall novelty is a bit limited as it heavily relies on existing large models to generate the dataset and to train additional models.
**A1**: In fact, the main motivation for our research is to explore... | null | null | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Combining Observational Data and Language for Species Range Estimation | Accept (poster) | Summary: The paper introduces LE-SINR, a novel approach for estimating species range maps (SRMs) by combining citizen science observations with textual descriptions of species from Wikipedia. The proposed framework, an extension of previous work SINR, uses two branches one for location and one for text, for predicting ... | Rebuttal 1:
Rebuttal: **[CYPb-1] The model is given range/habitat information as input.**
It is true that the model is given this information as free-form input text. However, these range descriptions do not provide enough detail to draw a precise range map. For instance, knowing that the Gray Kingbird breeds in th... | Summary: This paper presents LE-SINR which mappes species observations and textual descriptions into the same space and enables zero-shot inference for species range mapping for unseen species. The textual description of species are encoded with an LLM and used as a species embedding that jointly trained with location ... | Rebuttal 1:
Rebuttal: **[9uUS-1] Comparison to LD-SDM.**
While related, LD-SDM (Sastry et al. arXiv 2023) uses text data in a fundamentally different way to us. Instead of using unstructured text as input, their model generates an input string for each species that encodes its full taxonomic hierarchy (e.g. class -... | Summary: The paper considers the problem of species range mapping, where the aim is to estimate, at any given location on the earth, if a particular species is present or not. The work builds on another very recent paper which developed "Spatial Implicit Neural Representations", where the aim is to estimate presence/ab... | Rebuttal 1:
Rebuttal: **[q7Tg-1] Which parts of the text were most informative for the LLM?**
This is a great suggestion. Given the space limitations we cannot provide a visualization of this in the rebuttal, but we will include this in the final revised paper. At a high level, text which more directly encodes inform... | Summary: The authors extend the SINR model for species distribution modeling (SDM) by aligning the learned, spatial, latent space with the representation of the species habitat/range provided by an LLM.
This addition allows the authors to evaluate their approach in a zero-shot setting, something that SINR or other SDM ... | Rebuttal 1:
Rebuttal: **[A4uw-1] Within scope for NeurIPS?**
We believe that NeurIPS is an appropriate venue for this work given similar work published at top ranked machine learning venues in the past. For example, NeurIPS (“Active Learning-Based Species Range Estimation” by Lange et al. and “SatBird: Bird Species ... | Rebuttal 1:
Rebuttal: Our work introduces LE-SINR, a new approach for geospatial grounding of free-form text. We apply LE-SINR to species range mapping, one of the most important problems in ecology and conservation policy. By integrating geospatial encoders with LLMs, LE-SINR achieves state-of-the-art performance on b... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: This paper considers the problem of species range maps (SRMs) estimation and proposes to combine textual descriptions of habitat or range of species from Wikipedia with geolocated citizen science species observations, building on the SINR model. The method is evaluated in the context of zero-shot and few-shot ... | Rebuttal 1:
Rebuttal: **[2RN9-1] Motivation and use case.**
The primary motivation of our work is to leverage an additional data modality, text, to improve both zero-shot and few-shot species range mapping. We observed that text data, as formatted on Wikipedia, often includes descriptions, habitat information, and r... | null | null | null | null | null | null |
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables | Accept (poster) | Summary: The paper introduces `TimeXer`, a novel approach for time series forecasting with exogenous variables. Its main contributions include a new Transformer-based model architecture designed to effectively integrate and utilize exogenous information alongside endogenous time series data. The paper demonstrates the ... | Rebuttal 1:
Rebuttal: # Response to Reviewer N2A5
We would like to sincerely thank Reviewer N2A5 for providing a detailed review and insightful suggestions.
> **W1**: There is no comparison between native models that can be used for forecasting with Exogenous Variables.
In $\underline{\text{Table 7 of the Appendix}}... | Summary: The article designs TimeXer, Empowering Transformers for Time Series Forecasting with Exogenous Variables. TimeXer effectively utilizes exogenous information to enhance the accuracy of time series forecasting. Extensive experimental results validate the effectiveness of the proposed method.
Strengths: S1. Unl... | Rebuttal 1:
Rebuttal: # Response to Reviewer 1zKu
Many thanks to Reviewer 1zKu for providing a detailed review and questions.
> **W1**: There is a potentially slight inconsistency between the description of the method and the scale of the data used in the experiments.
Thank you for your careful reading. Since most o... | Summary: This article proposes a method based on Transformer modeling to enhance the prediction accuracy of endogenous variables by incorporating exogenous variables.
Strengths: 1. The research topic is interesting and has strong practical value.
2. The paper is well-structured, making it easy for readers to understan... | Rebuttal 1:
Rebuttal: # Response to Reviewer Mh7v
Many thanks to Reviewer Mh7v for providing a detailed review and insightful questions.
> **W1**: The implementation details on how to extend TimeXer to multivariate time series prediction are not sufficiently detailed.
Sorry for the missing description. The success of ... | Summary: The paper presents TimeXer, a Transformer-based model for time series forecasting that integrates exogenous variables using innovative attention mechanisms. Unlike traditional models that either focus solely on endogenous variables or treat all variables equally, TimeXer integrates exogenous information using ... | Rebuttal 1:
Rebuttal: # Response to Reviewer r8UR
We would like to sincerely thank Reviewer r8UR for providing a detailed review and insightful suggestions.
> **W1**: Methodological Gaps in Handling Practical Situations
As the reviewer mentioned, we focus on forecasting with exogenous variables and have provided a co... | Rebuttal 1:
Rebuttal: ## Summary of Revisions and Global Response
We sincerely thank all the reviewers for their insightful reviews and valuable comments, which are instructive for us to improve our paper further.
In this paper, we dive into a practical forecasting setting in real-world applications, i.e. time series... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression | Accept (poster) | Summary: This paper conducts a comprehensive study of the learning curves of kernel ridge regression (KRR) under minimal assumptions
Strengths: The authors claimed that they provide a comprehensive analysis on the learning curves in kernel ridge regression.
Weaknesses: The learning curves of kernel ridge regression h... | Rebuttal 1:
Rebuttal: > The learning curves of kernel ridge regression have been extensively studied in recent literature...
It is really hard for me to understand the difference between the current paper with the above papers.
Thank you for your feedback. The three papers you mentioned are cited in the paper as refer... | Summary: A recent line of work has derived excess error rates for kernel ridge regression in a source and capacity setting under the assumption of Gaussian universality of the kernel features. This work investigates the validity of this assumption in this context.
The main result is that while the rates derived under ... | Rebuttal 1:
Rebuttal: > In the introduction, the authors say their work addresses three questions. While Q1 and Q2 are precisely addressed by the results, I find that the answer to Q3 falls short. First, the assumption (GF) is surely weaker, but it is still constraining.
We refer to the **Author's Rebuttal** for a de... | Summary: This paper studies learning curves of kernel ridge regression (KRR) under minimal assumptions. The authors analyze the role of key properties of the kernel, such as its spectral eigen-decay, the characteristics of the eigenfunctions, and smoothness of the kernel. They also demonstrate the validity of the Gauss... | Rebuttal 1:
Rebuttal: Thank you for your time and effort reviewing our paper. Regarding your questions,
> The presented results seem to be based on a set of different assumptions while comparing with the current bounds... it is not clear how the differences in assumptions affect the comparison.
Section A.2 is dedicat... | Summary: This paper studies the learning curve of kernel ridge regression under both eigendecay assumption and the source condition assumption. Assuming the fixed input dimension setting, this paper derives the finite sample bound for the bias and variance where features can either be generic or independent. Depending ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort reviewing our paper. Regarding your questions,
> In demosnstrating the GEP, the paper seems to only provide the upper bound for bias and variance under generic features while a matching lower bound is missing... I was wondering if the author could explain this a... | Rebuttal 1:
Rebuttal: Thank you for your time and effort on reviewing our paper. Since some of you asked for the same or similar questions concerning important points in our paper, we will refer the reviewers to here from their individual rebuttal sections.
---
## A matching lower bound for (GF)
we can summarize the... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models | Accept (poster) | Summary: This paper introduces inflationary flows, a diffusion model with deterministic denoising trajectories that allow mapping high-dimensional data to low-dimensional Normal distributions. The dimensionality reduction is controllable, and it is claimed that, as long as the score is correctly estimated, the method p... | Rebuttal 1:
Rebuttal: We appreciate the author's time and effort in reviewing our paper and apologize for some confusion.
Please see our General Rebuttal, in which we attempt to clarify several points raised by multiple reviewers, particularly
- the particular calibration and modeling task we set out to solve
- the d... | Summary: The paper proposes inflationary flows (IFs) for sampling-based density estimation. IFs belong to the growing family of diffusion-based models (DBMs) that are trained to transform distributions via a continuous process. However, the current paper notes that typical DBMs actually increase the intrinsic dimension... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their positive assessment of our work. We believe they have done a good job of articulating our contributions.
In responses the weaknesses noted by the reviewer:
- We regret our omission of injective normalizing flows (e.g., [1-4]) from the introduction and rela... | Summary: This paper presents a novel approach to Bayesian inference using diffusion-based models (DBMs). Traditional Bayesian inference methods struggle with high-dimensional integrals, and existing approximation methods either scale poorly (sampling-based) or offer limited theoretical guarantees (variational methods).... | Rebuttal 1:
Rebuttal: First, we want to thank the reviewer for their careful assessment. We believe the reviewer has correctly identified the aims and strengths of our work.
As to the weaknesses:
- The reviewer is correct that several alternative variational approaches have been able to mitigate some of the shortcomi... | Summary: This paper proposes a new version of a probability flow-based diffusion model that allows for the effective reduction in dimensionality of the data after it has been diffused. Furthermore, the proposed approach allows for proper uncertainty measures of the data by preserving local neighborhoods from latent spa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive assessment of our work. We hope that our replies to other reviewers, as well as the General Rebuttal, reinforce this assessment.
The reviewer also raises an important issue in our experiments with high-dimensional image data: the paradoxical finding that perf... | Rebuttal 1:
Rebuttal: First, we appreciate the reviewers' thoughtfulness in assessing our paper. While all reviewers agreed that the work is novel, they also noted weaknesses and requested clarification on several shared themes:
## Problem setup
**First,** we are concerned here with _unconditional generation_ in cas... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Piecewise deterministic generative models | Accept (poster) | Summary: This paper proposes a new generative model on continuous random variables similar to diffusion-based generative models, but this method uses a different family of perturbation schemes. In particular, for the forward processes, which transform the clean data to a stationary distribution, the authors propose usi... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and the time you have taken to provide valuable feedback on our paper. Below are our responses to your comments.
* *Regarding the first two points mentioned in the Weaknesses*
We agree with the remarks and we will add clearer discussions on the limitations ... | Summary: The paper explores generative models utilizing PDMPs, a type of stochastic process characterized by deterministic motion interspersed with random jumps at random times. These models offer an alternative approach to diffusion-based generative models, which have become very popular in the AI community in recent ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and for taking the time to read and appreciate the full extent of our paper. We value your detailed feedback and have addressed your concerns below.
* *Weaknesses (originality, significance)*
* Our paper focuses on establishing theoretical foundations and valida... | Summary: This interesting paper on the popular topic of generative models introduce a new family of generative models which builds on the so-called piecewise deterministic Markov process (Zig-Zag process, Bouncy Particle Sampler, Randomised Hamiltonian Monte Carlo). In contrast to many of the existing models this famil... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time invested in our paper and their feedback. We believe we addressed all the raised issues below.
* *There is a big jump in the style of writing between Section 1 and 2...*
* We agree the description of a time inhomogeneous PDMP is technical. In order to alle... | Summary: This paper considers the development of generative models based on piecewise deterministic Markov processes. The key idea proposed in the paper is to use piecewise deterministic Markov processes instead of diffusions as the "noising process" of the generative model. This relies on the fact that time reversals ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time invested in our paper and the relevant questions. Below, we believe we address all the raised concerns.
* *To me, the descriptions of approximating the process characteristics with normalizing flows are unclear. ...*
We understand that we did not provide ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive feedback, which will certainly help us improving the quality and exposition of our contribution. We have responded individually to each review to address their specific questions and remarks.
As part of our answer to some of the reviewers, we have att... | NeurIPS_2024_submissions_huggingface | 2,024 | Summary: The paper proposes using Piecewise Deterministic Markov Processes (PDMPs) for generative modelling applications, by using the property that PDMPs also admit time reversals that themselves are PDMPs. There are three major contributions in my understanding -
By characterizing certain families of PDMPs, i.e. Zi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable review. We hope the following responses will answer their main remarks.
* We will include a paragraph in the introduction motivating the PDMP approach, based both on theoretical and empirical aspects. We expect PDMPs to be successful at modelling data dist... | null | null | null | null | null | null |
Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective | Accept (poster) | Summary: This paper introduces a novel perspective by conceptualizing semantic segmentation as data compression, akin to PCA, which simplifies the role of decoders in Transformer-based models. It presents DEPICT, white-box decoders that clarify the functions of learnable class embeddings, self-attention and dot-product... | Rebuttal 1:
Rebuttal: We highly appreciate your valuable comments, especially for your attention to the details in our paper.
**Response to Weakness #1.** We conduct more experiment and provide the new experimental results on Cityscapes and Pascal Context (in Common Response). Due to time limitation, we did not provi... | Summary: This paper derives white-box decoders for Transformer-based semantic segmentation from a compression perspective, which links the process to Principal Component Analysis (PCA). The authors introduce DEPICT, which clarifies the mechanisms of black-box decoders and achieves comparable performance with significan... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. As the reviewer suggested, we have further validated our proposed interpretation and evaluated the effectiveness of our DEPICT on a set of new experiments on datasets Cityscapes and Pascal Context, including ablating the variants, testing the robustness und... | Summary: This work attempts to view Transformer-based semantic segmentation from the perspective of principal component analysis and develops interpretable Transformer decoders for segmentation based on this insight, which can achieve comparable performance to their black-box counterparts with significantly fewer param... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. It is indeed that the meta-architecture we investigated sounds not representative enough for a vast range of Transformer-based decoders. However, the insights of our work is capable to be generalized to partly interpret them.
First of all, we express our sin... | Summary: A view of compression from feature space with dimension $m$ to category $c$ is proposed for semantic segmentation. With this formulation of a PCA-like operation, a white-box encoder is designed with self-attention or cross-attention. Experiments show that the proposed encoder with ViT gets better performance t... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. As suggested, we add experiments on more datasets, including Cityscapes and Pascal Context, and find that our DEPICT performs consistently better that of its black-box counterparts, i.e., the decoder of Segmenter. And we would like to compare our DEPICT... | Rebuttal 1:
Rebuttal: We thank all reviewers for the constructive suggestions and insightful comments. We appreciate the weaknesses and limitations spotted by reviewers. To address these issues, we would like to provide more theoretical and experimental results. Briefly, we further solidify our compression-based interp... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Consistency Models for Scalable and Fast Simulation-Based Inference | Accept (poster) | Summary: The paper adopts consistency models for simulator-based inference tasks, highlighting its expressive free-form architectures and fast inference as main advantages. The new method, called consistency model posterior estimation, was shown to outperform Neural Posterior Estimation (NPE) and be competitive with Fu... | Rebuttal 1:
Rebuttal: Thank you for your assessment of our work, as well as the detailed list of questions and comments. We responded to your general points below and incorporated all items of your list of questions/edits in our manuscript. We hope that this addresses your concerns regarding the paper’s presentation an... | Summary: The paper proposes to use a conditional consistency model for amortized likelihood-free inference. Empirical evaluation shows that this approach compares favorably in terms of inference time as well as performance against competing methods.
Strengths: - The paper is the first to propose the use of consistency... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and the excellent questions you raise. We appreciate that you attest excellent scores regarding presentation as well as soundness, and we are delighted by your optimism that our paper may encourage practitioners to use CMPE in their work.
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## W1: Lack of nov... | Summary: Adopting the idea from consistency models in the generative process, the authors propose its application for posterior estimation, introducing a new type of model for simulation-based inference (SBI). The proposed method, consistency models for posterior estimation (CMPE), enjoying the benefits of the consiste... | Rebuttal 1:
Rebuttal: Thank you for your thorough assessment of our work, and for the actionable issues you pointed out. We appreciate that you found our paper well-written, easy to follow, relevant for the SBI field, and reproducible through the code we shared.
---
## W1: Data set selection
> W1: The current experime... | Summary: This paper adapts the recently introduced Consistency Models of
Song et al to the task of Simulation-based Inference. Compared to
the previous approaches based on flow matching, this technique
exhibits similar or better quality. All while being significantly
faster to sample from with fewer restrictions on the... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our work. We appreciate that you find our paper well-written, clearly organized, of very good technical quality, and significant to the field. We have addressed your concerns and are optimistic that this will substantially increase the quality of our manuscrip... | Rebuttal 1:
Rebuttal: We thank all reviewers for their assessment of our work. All reviewers agree that conditional consistency models are a promising technique for amortized simulation-based inference, that the topic is relevant to the NeurIPS community, and that our paper is technically sound.
Below, we give a detai... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
SAMPa: Sharpness-aware Minimization Parallelized | Accept (poster) | Summary: This paper studies the efficiency problem of sharpness-aware minimization (SAM) algorithms.
SAM requires two gradient calculations: one for computing the perturbation and another for computing the update direction.
Hence, SAM doubles the computation cost compared with ERM.
Furthermore, these two gradients cann... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
> Q1. Though introducing an auxiliary sequence is an interesting idea to parallelize the two gradient calculations, I have a concern about the auxiliary sequence:
Estimate the approximate error $\nabla f(x... | Summary: This paper studies a parallelized variant of sharpness aware minimization (SAM). This is achieved by introducing a sequence of auxiliary variables to break down the sequential dependence for the two gradients in every SAM iteration. The resultant approach SAMPa has convergence guarantee for convex problems whe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
> Q1. My major concern is that the runtime comparison is not fair due to the second GPU. In particular, since SAMPa uses 2 GPUs, can the authors also report data parallel for SAM with 2 GPUs as well? (or ... | Summary: This paper proposes a parallelized algorithm of Sharpness Aware Minimization(SAM) named SAMPa, which aims at making optimization more efficient by parallelizing the computation of one update in SAM. Plain SAM requires a 2x computational cost since 2 gradient computations (one for the perturbation and one for u... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
> Q1. "Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach" should be considered.
A1. We have conducted additional experiments on Sparsed SAM (SSAM) below and will include them ... | Summary: This paper proposes a modification of SAM, named SAMPa, which enables to fully parallelize the two gradient computations in SAM, in order to accelerating the training. By doubling the computation resources, parallelized SAM could approach to a twofold speedup of SAM. Theoretical analysis is provided for the co... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
> Q1. To approach to a twofold speedup of SAM, SAMPa requires double of the computation resources compare to SAM.
A1. Please note that the total computational time for SAMPa across all GPUs is comparable ... | null | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Provable Editing of Deep Neural Networks using Parametric Linear Relaxation | Accept (poster) | Summary: The authors propose HRDNN, the first efficient technique for provable editing of DNNs. HRDNN is able to provably edit the DNN to satisfy this property within 45 seconds. HRDNN is efficient because it relaxes the NP-hard provable editing problem to solving a linear program (LP). It enables HRDNN to construct t... | Rebuttal 1:
Rebuttal: > ### the first verifier-in-the-loop, and the editing stops when the verifier confirms that the DNN satisfies the property
We would like to clarify that our approach, HRDNN, is not a verifier-in-the-loop approach: as proved in Theorem 3.5, any solution to the LP problem constructed via our parame... | Summary: This paper addresses the problem of finding a minimal changes to the parameters of a DNN $f(.)$ in order to satisfy pre/post conditions defined as *convex* polytopes, i.e. $\forall x . (x \in Pre \implies f(x) \in Post ) $.
The proposed algorithm is efficient as it considers a Linear Programming (LP) relaxatio... | Rebuttal 1:
Rebuttal: > ### I think that runtime results for an editing algorithm should always be paired with its impact on predictive performance to be meaningful. I don't understand the role of the VNN Comp benchmark in this sense
We used VNNComp to construct a challenging benchmark for evaluating provable editing ... | Summary: This paper proposes a novel approach named HRDNN to provably edit a DNN to satisfy a given property. Concretely, given a DNN, its input domain P, and desired output range Q, HRDNN edits the parameters of the DNN so that for any input in P, the output of the edited DNN falls into Q. The key contribution of this... | Rebuttal 1:
Rebuttal: > ### The parametric linear relaxation is developed for specific activation functions, like ReLU, Tanh. It lacks a discussion or summary about a general paradigm to devise parametric bounds for different activation functions.
Our implementation and evaluation covers popular activation functions a... | Summary: The paper proposes a novel method for provably editing neural networks using neural network verification techniques. The proposed method is based on a new linear programming formulation of neural networks. Unlike typical neural network verification settings, in the neural network editing settings, the weights ... | Rebuttal 1:
Rebuttal: > ### Weakness 1
We will add the theoretical results and proofs related to the precision of our relaxation in the Appendix D of the uploaded PDF to the revision.
#### Regarding [1]
[1] and our approach both use linear inequalities. The main difference is that they
requires a sound input bound fo... | Rebuttal 1:
Rebuttal: > #### Scalability
Our approach does not require to encode the entire network as LP for editing, as described in Appendix B. Just like fine-tuning, our approach offers the flexibility to only edit the last few layers (Equation 8, line 849) and calls an off-the-shelf DNN verifier to compute a soun... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images | Accept (poster) | Summary: This paper focuses on 3D open-world learning on classification. To address the limitation caused by existing CAD-rendered images in open-world 3D learning, it proposes to generate depth-aligned images from point cloud projected depth maps and diffusion models.
Strengths: 1. Generating depth-aligned images to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the interesting questions.
**Q1: Would inconsistent textures in multi-view images of a single object impact performance?**
Answer: We believe that inconsistent textures in multi-view images improve model performance in 3D representation learning, rather than hinder it. ... | Summary: This paper proposes OpenDlign, a framework for depth-based 3D understanding by aligning depth-image features through training on generated depth-aligned images, which overcomes the limitations of training on CAD-rendered images. Using point cloud data from ShapeNet, a contour-aware projection method is introdu... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive questions and feedback.
**Q1: The motivation of the study is not focused on training a good encoder specifically for 3D understanding from depth maps.**
Answer: Our primary motivation is to learn robust point cloud representations for open-vocabulary pr... | Summary: The paper introduces **OpenDlign**, a novel framework for open-world 3D representation learning by leveraging depth-aligned images generated from a diffusion model. OpenDlign aims to enhance the realism and texture diversity in the 3D learning process, overcoming the limitations of CAD-rendered images. The met... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful feedback. We’re glad to hear that you found our work innovative, effective and reproducible.
**W1: Limited Dataset for Depth-Aligned Images**
Answer: Due to limited resources, our generated dataset is currently limited to ShapeNet, as discussed in the lim... | Summary: The paper introduces OpenDlign, a novel approach to 3D open-world learning. Traditional 3D learning models struggle with unseen categories and typically rely on CAD-rendered images, which lack texture diversity and realism, leading to suboptimal alignment and performance. OpenDlign leverages depth-aligned imag... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's insightful feedback and valuable recommendations.
**Weakness 1**: Unclear performance gain on ensemble dataset.
Answer: We understand the concern regarding the scalability of our methods on large datasets. Here are some explanations and additional experimenta... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed and thoughtful feedback on our submission. We are grateful that most reviewers appreciated the soundness of our model design and acknowledged its strong performance on various downstream 3D understanding tasks.
For each reviewer, we addressed their questi... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms | Accept (poster) | Summary: This paper studies a model of content creation and consumption on arbitrary online user-generated content platforms (e.g., YouTube, TikTok). It focuses on a type of Cournot competition in which creators mainly modify their creation volume. The paper provides a description of this model, a theoretical analyses ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's suggestion for additional justification of our problem setting. Below, we respond to the questions raised.
**Evidence of volume competition**
Major UGC platforms, such as YouTube, TikTok, and Instagram, primarily generate profits through ad impressions, which ... | Summary: This paper studies the problem of the tradeoff between users’ satisfaction and creators’ engagement. Authors first define the traffic competition of creators on user-generated content platforms as a Cournot Content Creation Competition (C4) and establish corresponding PNEs. Based on PNEs, this work identifies ... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out the concerns and raising the clarification questions and we respond to them below accordingly.
**Response to weakness 1: unit user traffic** First, we have to clarify that our model does not assume each user contributes only one unit of traffic. Since we do ... | Summary: The authors propose a new game-theoretical model Cournot Content Creation Competition ($C^4$), that studies the relation between the matching strategy of user-generated content (UGC) platforms and the production willingness of the platform’s content creators. Under certain assumptions, the authors show that th... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the insights of our problem setting and results. For our response to the suggested main weakness and question 2, please refer to our common response above. Below, we address the remaining questions.
**Why total content volume affects long term user engagemen... | null | null | Rebuttal 1:
Rebuttal: **Common response**
We appreciate the reviewers' overall positive evaluations about our work, especially the acknowledgement of the significance of the problem setting, novelty of our analysis and insights from our theoretical and empirical results. We are happy to integrate the reviewers’ sugges... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts | Accept (poster) | Summary: This paper provides a theoretical analysis of the convergence rate of the least squares estimator for learning MoE with sigmoid gating. Based on the results, the authors conclude that sigmoid gating enjoys a faster convergence rate and requires a smaller sample size to achieve the same error compared to softma... | Rebuttal 1:
Rebuttal: ### **Q1: The results from this paper do not fully support the main claim that “sigmoid gating is more sample efficient than softmax gating” because the comparison between sigmoid and softmax is not under the same setup.**
Thanks for your comment. Let us explain why it is reasonable to compare th... | Summary: The paper argues that the sigmoid gating function is more sample efficient than the softmax gating function in mixture of experts (MoE) modeling. It removes competition and estimates the contribution each expert independently.
Empirical studies show that sigmoid gating achieves superior performance, but the p... | Rebuttal 1:
Rebuttal: ### **Q1: The results are heavily dependent on specific assumptions, such as the distinctness of expert parameters and the boundedness of the input space. If these assumptions are violated in practical scenarios, the theoretical guarantees may not hold.**
Thanks for your comments. We will explain... | Summary: This paper presents a theoretical analysis of expert estimation in MoE models using sigmoid gating, in contrast to the more widely used softmax gating. The authors show that sigmoid gating leads to better sample efficiency compared to softmax gating for estimating expert parameters. In particular, the paper an... | Rebuttal 1:
Rebuttal: ### **Q1: The authors should provide a more concise overview of the main contributions, and remove technical details that are repeated in later sections.**
Thanks for your suggestions. We will modify the contribution paragraph as below, and consider removing repeated details in Section 2 in the r... | null | null | Rebuttal 1:
Rebuttal: # **General Response**
Dear AC and reviewers,
We would like to thank you for your value feedback and constructive comments, which have helped us improve the paper substantially. We are encouraged by the endorsement that:
- Sigmoid gating function has been **less explored** in the MoE field, and... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
Learning via Surrogate PAC-Bayes | Accept (poster) | Summary: The paper introduces a novel strategy for building iterative learning algorithms via the optimisation of a sequence of surrogate training objectives derived from PAC-Bayes generalization bounds. It also theoretical estabilishes that iteratively optimising our surrogates implies the optimisation of the original... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback. | Summary: - This paper tackles the issue that model training based on PAC-Bayes bounds can become computationally burdensome in certain applications, such as calibration tasks for biochemical models.
- To overcome this issue, the authors propose replacing the empirical risk in the standard PAC-Bayes bound with a surroga... | Rebuttal 1:
Rebuttal: - Computational burden and measure of the time
The main motivation for the SuPAC algorithm is to tackle settings when querying the empirical risk is computationally expensive. This covers cases when a risk query takes more than 10 ms. In the real-world example of Anaerobic Digestion model, each r... | Summary: This paper provides novel tools to construct surrogate minimization objectives based on PAC-Bayes bounds, where the empirical risk is replaced by a proxy. This approach has the advantage of efficiency in scenarios where querying the empirical risk is expensive. The proxy is constructed by projecting the empiri... | Rebuttal 1:
Rebuttal: Thank you for carefully examining our submission, and for your positive evaluation.
- On the difficulty of querying the empirical risk for differential equations based models
Stiff ordinary differential equations (ODE) of form $\dot X = F(X, t, parameter)$ require numerous evaluations of the fun... | Summary: The paper explores the problem of optimization where estimating the empirical loss is computationally expensive.
By optimizing the PAC-Bayes posterior distribution on an appropriate surrogate function, the paper derives an optimization strategy that minimizes the PAC-Bayes upper bound on the expected loss wi... | Rebuttal 1:
Rebuttal: Thank you for carefully examining our submission, and for stressing its highly original character.
Typos
- "In corollary 1 line 170, M(π,θ,f,γ) is undefined."
Thank you for raising this. The notation M stood for any function of π, θ, f and y outputting a matrix.
- "In section 3 line 159, "paves... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their careful reading of our work. We address all relevant points in individual rebuttals below each review. We hope our answers will clarify the concerns expressed in reviews, and hopefully encourage reviewers to revise their assessment of our submission.
We addres... | NeurIPS_2024_submissions_huggingface | 2,024 | null | null | null | null | null | null | null | null |
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