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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Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards | Accept (poster) | Summary: In this paper, the author analyzes the MED algorithm proposed by Honda \& Takemura (2011) for Bernoulli distributions in the context of general bounded distributions, and under the name KL-Maillard Sampling. This work is a follow-up of a previous work that proposed Maillard Sampling for sub-gaussian distributi... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to review our work and provide valuable feedback thoroughly.
*(1) The shared common mechanism with MED and KL-MS.*
We agree with the reviewer that under Bernoulli environments, our algorithm and MED are identical. The main reason we see our algorithm as... | Summary: This paper considers a classic bandit problem, where the algorithm should explicitly output the random distribution of the next pulling arm (as a comparason, in classic case, the algortihm only needs to generate one arm from this random distribution and outputs that arm). Existing results only work on the case... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to review our work and provide valuable feedback thoroughly.
*(1) Why do we need the exact action distribution in reality?*
Our motivation comes from the broad field of off-policy evaluation and optimization for contextual bandits and reinforcement lear... | Summary: The submission considers the vanilla setting of stochastic K-armed bandits and studies a strategy introduced by Maillard (2013), which relies on exponential weights and outputs at each round probabilities of taking each action. This is often convenient in offline policy evaluation, when estimates based on inve... | Rebuttal 1:
Rebuttal: We thank the reviewers for taking the time to review our work and provide valuable feedback thoroughly. We genuinely appreciate reviewers carefully examining our results and offering insightful comments to improve the quality of our research. We have carefully considered each of the weaknesses rai... | Summary: The paper studies the classical regret-minimization problem in the stochastic multi-armed bandit framework. In particular, the manuscript's focus is on randomized algorithms with an aim to develop one with closed-form arm-selection probabilities at each step. Data collected by such algorithms can be used for o... | Rebuttal 1:
Rebuttal: We thank the reviewer for all responses and for taking the time to review our work and provide valuable feedback thoroughly.
*(1) The plots in the appendix could be clearer.*
We appreciate the reviewer pointing out the problem. We will make necessary modifications to the plots to present them mo... | Rebuttal 1:
Rebuttal: We thank all reviewers for taking the time to review our work and provide valuable feedback thoroughly. Here we address two common points shared by reviewers.
**The comparison between algorithms in terms of regret.**
We chose the reward setting following the experimental setup of Thompson sampli... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning | Accept (poster) | Summary: This paper investigates how the transformer-based model can compositionally learn some complex functions (e.g., an MLP) by breaking them down to some atom problems (e.g., linear mapping). Such an ability is also the crux of the success of Chain-of-thought (CoT) in-context learning (ICL) methods in large langua... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and encouraging feedback. We hope that we have addressed your questions and concerns adequately below.
> **W1. Theory is too abstract:** We provide below some more detailed description of the construction that implements this filtering, which we w... | Summary: The paper proposes to study chain-of-thought (prompting) in the setting of learning MLPs. The authors build on top of recent work studying in-context learning linear regression tasks in the light of gradient descent and extent their setting to learning non-linear functions. In order to study chain-of-thought p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and encouraging feedback! We hope that we have addressed your questions and concerns adequately below.
> **Lack of clarity:** We are sorry that the reviewer feels this way. Taking the reviewer's suggestion into sincere consideration, we are working ... | Summary: This paper aims to demystify the mechanism lying in the in-context learning (ICL) and chain-of-thought (CoT). It reveals how CoT significantly reduces the sample complexity of ICL. It uses a two-layer MLP, and a backbone GPT-2 model for exploration. The experimental results reveal some interesting findings, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and for recognizing the interest and insights in our work. We hope that we have addressed your questions and concerns adequately below.
> **Lack of clarity:** We apologize to the reviewer for any confusion stemming from the unclear aspects of our wo... | Summary: In this paper, the authors explore the mechanics of Chain-of-Thought (CoT), a method that has successfully enabled language models to handle complex reasoning tasks by decomposing them into simpler steps.
The study aims to understand the underlying mechanics of CoT by investigating its impact on the ability of... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and for recognizing the novelty and value our results contribute, particularly in understanding the inner mechanisms of CoT and ICL.
>**Reasoning steps vs intermediate state:** We appreciate the reviewer's insightful comment on this matter. In the... | Rebuttal 1:
Rebuttal: We thank the reviewers for their constructive comments and insightful questions. We are gratified that many of the reviewers found our work insightful and interesting. In the following sections, we will summarize our key contributions, respond to shared concerns raised by the reviewers, and provid... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching | Accept (poster) | Summary: This research paper focuses on LVM-Med, a self-supervised learning (SSL) technique designed for medical imaging tasks. Based on a second-order graph matching strategy, LVM-Med is trained on a large-scale medical imaging dataset. The researchers found that the method significantly improves performance on a vari... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and constructive feedback!
**Question 1: Add more discussion about the challenge of applying this framework in 3D backbone to the paper.**
Thank you for the suggestions. Due to the large workload in dataset collection and experiments, we restricted this wor... | Summary: This paper proposes a self-supervised pre-training strategy for medical imaging using a graph matching approach. Each unlabeled image is transformed via a pair of data augmentations and then processed via an encoder network. The augmented pair of images become vertices in a pair of graphs, with vertex features... | Rebuttal 1:
Rebuttal: Thank you very much for your strongly positive feedback!
**Question 1: Missing related works on graph matching in computer vision Section 2 & Improving further introduction part to highlight contributions.**
We sincerely acknowledge your constructive feedback. Your suggestions are valuable t... | Summary: This paper collects a large medical imaging dataset, and it also shows that a self-supervised learning technique based on second-order graph-matching enhances performance in various downstream medical imaging tasks compared to other supervised learning methods and foundation models trained on image-text instan... | Rebuttal 1:
Rebuttal:
Thank you very much for your strongly positive feedback!
This encourages us to continue to improve and extend our research.
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Rebuttal Comment 1.1:
Comment: I have read the comments, and I keep my original score.
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Reply to Comment 1.1.1:
Title: Thank you
Comment: Dear Reviewer,
Thank ... | Summary: The paper proposes a set of networks called LVM-Med which are trained on large-scale medical datasets. The authors collected more than a million medical images from more than 50 publicly available datasets of diverse modalities and structures of interest (e.g. CT, MRI, Ultrasound...). In the work, several self... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and constructive feedback!
**Question 1: Unclear experiment settings**
**1.a: How to choose baselines?**
We employ four primary baseline types (e.g., in Table 2):
1. **2D Supervised Method**: Comparison with standard medical architectures initialized from... | Rebuttal 1:
Rebuttal:
We would like to thank all reviewers for the positive and constructive feedback, which we will leverage to improve this work.
We are very encouraged that most reviewers agree that our efforts in **creating such a large medical imaging dataset is important and needed by the community**. Reviewer... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a large scale medical imaging dataset consisting of 1.3M medical images from 55 publicly available datasets along with a new contrastive learning framework based on graph matching. Specifically, the model is firstly pre-trained on the collected dataset, and then finetuned towards different d... | Rebuttal 1:
Rebuttal:
Thank you very much for your positive and constructive feedback!
**Question 1: Provide more details regarding the usage of the datasets.**
**1.a: How are 3D data used, are they sliced into 2D data first?**
Yes, for 3D volume data, we slice them into 2D images first. We mentioned this in **S... | null | null | null | null | null | null |
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs | Accept (poster) | Summary: This paper proposes an algorithm for inverse optimal control for agents operating in a partially observable Markov decision process, using only state trajectories. Given state trajectory data, dynamics model and observation model, the algorithm estimates the parameters by three steps: first, a policy is estima... | Rebuttal 1:
Rebuttal: Thank you for the generally positive assessment of our work. Below, we expand on some of the assumptions of our method. The questions raised about these assumptions will help us improve the clarity of our paper.
Regarding the weakness point that a comparison with “more with recent IOC or IRL work... | Summary: The paper introduces a new approach to inverse optimal control which is able to deal with partially observable systems in which action signals are not known. Most existing approaches only work in fully observable systems where actions are known. The paper introduces a probabilistic formulation for inverse opti... | Rebuttal 1:
Rebuttal: Thank you for rating our paper as easy to follow, even for a reader outside this specific area of research! The raised questions, which we answer below, will help us improve the accessibility of our paper even further.
As the reviewer stated, that he has limited familiarity with the field, we woul... | Summary: The authors propose an inverse optimal control method to handle the challenging case of inferring an internal model in a non-linear partially observable system, when the action sequence is not observed. Quantitative evaluation of the proposed method was shown for some classic control problems. The authors also... | Rebuttal 1:
Rebuttal: Thank you for the positive review, and specifically for highlighting the importance of disentangling costs from uncertainty. We will use the additional page of the camera-ready version to expand on this in the introduction to make it a more prominent feature of our work. The issue of potential non... | Summary: This paper targets for solving the inverse optimal control problem for partially-observable stochastic non-linear dynamics with no observation of the action. To estimate the parameters of the cost function in a stochastic non-linear system, the author first derives a likelihood function for the model parameter... | Rebuttal 1:
Rebuttal: Thank you for the generally favorable evaluation of our paper.
Weaknesses:
Contribution: Yes, the likelihood formulation has previously been derived for linear systems in that paper. Here, we write it in a more general form, which is applicable beyond linear systems, which is why we kept it in S... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their generally positive reviews. In light of some of the questions and comments, we would like to clarify the current state of inverse reinforcement learning (IRL), inverse optimal control (IOC) when applied to human or animal behavior. Looking at the publ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a probabilistic approach to inverse optimal control for partially-observable stochastic non-linear systems with unobserved action signals. It derives an approximate likelihood function for the model parameters by linearizing the system around the observed trajectories and tracking the age... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive assessment of our work.
More comprehensive investigation into the method’s robustness: We agree that, as always, there is also in this manuscript room for even more evaluations. Figure 3 gives evaluations involving 100 datasets each for the con... | Summary: This paper is a strong contribution on the Inverse Optimal Control problem when actions cannot be observed. It is particularly interesting their modelling of the agent and the “researcher” observer. The mathematical depth is good and sound. The results may be enough for a theoretical paper. However, there are ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the strong theoretical contribution of our work. The many detailed questions, which we answer below, will help us improve the clarity of our paper.
[Noise variables] The noise variables $v_t$ and $w_t$ are both standard Gaussian random vectors. However, thi... | Summary: This paper presents the new formalization of inverse optimal control on partially-overevable Markov decision processes. The authors argue that most existing works on inverse control or inverse reinforcement learning focus on fully-observable Markov decision processes. Their approach extends iterative linear qu... | Rebuttal 1:
Rebuttal: We thank the reviewer for calling our formulation and derivations solid and well-motivated. In our answers to the specific questions below, we elaborate on the perceived lack of novelty and simplicity of our evaluations.
We are a bit unclear about what is meant by the purported weakness that "The... | Summary: In this paper, the authors propose a method to infer an agent’s internal model in a Partially Observable Markov Decision Process (POMDP) when the agent’s actions are non observable. Using local linearization, the authors show how a closed form approximation of the likelihood function for state trajectories can... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive assessment of our exposition of the problem, the experiments, and the results. However, we would like to ask the reviewer to substantiate the claim that “the contribution lacks significance for acceptance at the venue”. Looking at the publications at Neurip... |
SODA: Robust Training of Test-Time Data Adaptors | Accept (poster) | Summary: This paper proposes SODA, a test-time data adaptor with the black-box source model leveraging Zeroth-Order Optimization for the adaptor, which involves a random perturbation on the adaptor model's parameters. It also considers the scenario, namely SODA-R when the gradient information is available and online se... | Rebuttal 1:
Rebuttal: > **W1:** I would like to understand the relationship between the model perturbation and the data augmentation in the proposed framework.
**AW1:** There are **two kinds of “perturbation”** in our proposed work:
- **Parameter perturbation** used in gradient estimation of zeroth-order optimization ... | Summary: In this work, the authors aim to adapt unlabelled test data to a deployed model without access to its parameters and inner structures during the testing process. Specifically, the authors utilize a data adaptor during testing to map test data into the deployed model, which gradients are estimated via ZOO. Expe... | Rebuttal 1:
Rebuttal: > **Q1:** Experiments are not convincing. The authors only use CIFAR-10C and CIFAR-100C to verify the effectiveness of their algorithm. I suggest more datasets with various types of distribution shift should be included such as Office-31, Office-Home, PAC, etc.
**A1:** Thanks for your instructive... | Summary: This paper proposes usage of zeroth order optimization (ZOO) for test-time adaptation (TTA) to ease several practical issues regarding accessing model parameters during TTA. Since the ZOO with pseudo label, which is a standard method in TTA, might cause the unreliable gradient, the paper proposes a sample sele... | Rebuttal 1:
Rebuttal: > **Q1:** The experiment is limited to the CIFAR10-C and CIFAR100-C. As usual, I recommend adding experiments on ImageNet-C and some domain adaptation datasets.
**A1:** Thanks for your constructive suggestions. More experimental results on ImageNet-C and challenging Office-Home tasks are shown in... | Summary: To better adapt models to test distributions without changing model parameters, this paper utilizes the strategy that trains a data adaptor which can adjust the test data to fit the deployed models. To avoid the potential corruption of data features caused by the data adaptor, the proposed method treats the te... | Rebuttal 1:
Rebuttal: > **Q1**: The Pseudo-Label-Robust Data Adaptation module is the key contribution of this paper, but the design of this part is too simple. The problem here is actually noisy label learning problem and treating it as semi-supervised learning is a common strategy.
**A1**: We agree with your point t... | Rebuttal 1:
Rebuttal: We sincerely appreciate all reviewers for taking the time and effort to review our paper and provide valuable feedback. We would like to thank reviewers for their recognition of our work: 1) our problem is **realistic** (#1NX6), **well-motivated** and **interesting new** (#SVqY and #H74J); 2) our ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Do Not Marginalize Mechanisms, Rather Consolidate! | Accept (poster) | Summary: The paper presents a framework for causal reasoning that supports the simplification of large structural causal models (SCMs). One key operation that supports this simplification is consolidation of a SCM such that only a subset of endogenous variables are explicitly modelled, but in such a way that all possi... | Rebuttal 1:
Rebuttal: Thank you reviewer ZzBw for the detailed comments in your review which have led to improving our paper to its new version. In the following we go over each of the highlighted points one-by-one.
* *Regarding the set of possible interventions:*
Thank you for pointing this out, as it helped impro... | Summary: This work introduced a concept of consolidating causal mechanisms to transform large-scale Structural causal models (SCMs) while preserving consistent interventional behaviour. The author shows consolidation is powerful for simplifying SCM, disscuss the complexity and give a perspective on generalization.
St... | Rebuttal 1:
Rebuttal: Thank you reviewer tWPL for detailing some important aspects that helped improve our paper. Here are some brief comments on what we've improved and the questions you've raised.
* *Regarding discussing of compressibility*:
Compressing structural equations to a minimal representation is highly d... | Summary: An operation of consolidation on SCMs is defined and its merits laid out in numerous examples. The operation amalgamates variables while preserving aspects of the causal structure. As opposed to the similar operation of marginalization that comes from probability theory and is well-known in causal abstraction,... | Rebuttal 1:
Rebuttal: Thank you reviewer CKKq for your detailed review and appreciating our work and good examples. Your comments have helped us improve the paper. We hope to answer all points you made in the following:
* *Regarding the title and its implications of "superiority" of consolidation to marginalization:* ... | null | null | Rebuttal 1:
Rebuttal: Thanks again to all reviewers for thorough checking and commenting on our work, helping us improve it to its current form. In addition to the individual comments, please see attached the PDF with the generally requested consolidation algorithm. The algorithm summarizes the construction of causal c... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Thrust: Adaptively Propels Large Language Models with External Knowledge | Accept (poster) | Summary: LLM’s parametric memory may be inaccurate or outdated and thus retrieving additional information to LLMs can help but it’s costly and may be noisy.
So this paper proposes Thrust to measure the instance-level parametric memory and can help determine whether to use a retrieval module for enhancing LLMs, which is... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments and suggestions. We would like to address your concerns as follows.
**LLM ability (W1)**: Thanks for pointing out the discussion regarding the choice of models. We will try to add Flan-T5 in the camera-ready version to compare if instruction fine-tuning also help... | Summary: This work proposes methods IAPEK and Thrust to make instance-level decisions about when to utilize external knowledge for question answering. IAPEK is instance-level adaptive propulsion of external knowledge, essentially the use of external knowledge only when it is necessary beyond the base model. Thrust is a... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments and suggestions. For your reference, our design choice ablation and limitation discussion are provided in Appendix. We would like to address your concerns as follows.
**Design Choice (W1)**: We kindly refer you to Appendix (A.4 and Table 2) included in the suppl... | Summary: The paper addresses the limitations of large-scale pre-trained language models (PTLMs) in effectively utilizing external knowledge. It proposes the instance-level adaptive propulsion of external knowledge (IAPEK) as a solution to leverage external knowledge only when necessary. The paper introduces a novel met... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments and suggestions. We would like to address your concerns as follows. For your reference, our design choice ablation and reproducibility check are provided in Appendix and referred to in footnotes 4 and 1. Details are as follows:
**Design Choice (W1)**: We kindly ... | Summary: The authors propose a thrust score which measures if a pre-trained language (PTLM) model has the knowledge to perform the task. They then go on to use this score to choose when they should use external knowledge (when the thrust score is less). The main crux of the thrust score is knowledge representation. The... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments and suggestions. We would like to address your concerns as follows.
**Extended Usage (W1 & Q1)**: we agree that adaptive knowledge injection can be extended to other cases such as ECBD or EKP (Onoe et al., 2022, 2023). We will include this line of work in the dis... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Recasting Continual Learning as Sequence Modeling | Accept (poster) | Summary: The paper proposes to formulate continual learning as a sequence modeling problem. This new formulation views the model's learning process with continual session data as the forward pass of a sequence model, such as a Transformer, rather than relying on backpropagation. Specifically, keys and values within the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and insightful comments.
#### **Continual Learning (CL) vs. Meta-Continual Learning (MCL)**
As pointed out by the review, a large meta-training dataset is one of the fundamental assumptions of meta-learning and its variants, such as meta-continual learning.... | Summary: The paper redefines Meta-Continual Learning (MCL) as a sequence learning problem. Following this definition, the paper proposes a Transformer-based meta continual learner. The method is evaluated on several classification and regression tasks.
Strengths: Overall, I think the paper is well written and does a g... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and insightful comments.
#### **Baselines**
Since Prototypical Network (PN) and GeMCL cannot be applied to domains other than classification, we prioritized testing other baselines that can perform both regression and classification in our initial submiss... | Summary: This paper applies transformers and their efficient variants as sequence models to the problem of meta-continual learning. More specifically, instead of running gradient descent on a stream of training data, this paper trains transformers to do in-context continual learning over the data stream. It then compar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and the acknowledgment of the various strengths of our work. We believe there are some differences in viewpoints between ourselves and the reviewer concerning the relationship between CL and MCL settings. We hope our following responses help close th... | Summary: This work looks at treating the meta-continual learning problem as instead a sequence modeling problem. Instead of traditional approaches that train a model with an inner loop and then compute a meta-gradient in the outer loop, they replace the inner loop with just inference in a sequence model. The meta gradi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging and insightful comments.
#### **Meta-Overfitting and the Task Diversity**
In the context of classification benchmarks, the term “task diversity” mostly refers to the number of classes. We apologize for not using a clearer description. At the top of Table... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Lending Interaction Wings to Recommender Systems with Conversational Agents | Accept (poster) | Summary: This paper proposes a method that combines offline recommendation learning with online decision tree learning to enable recommendation in a conversational format with a few rounds of interaction. The proposed approach is evaluated on multiple datasets and in various validation settings to assess its effectiven... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. Please also see the main response above.
> CORE heavily relies on decision trees.
We want to emphasize that the core idea of our online decision tree is how to compute the certainty gain, as shown in Section 3.1. As the estimated score for each item is calculated off... | Summary: The paper proposes a novel framework called CORE that bridges conversational agents and recommender systems via an uncertainty minimization principle. The framework treats a recommender system as an offline relevance score estimator and a conversational agent as an online relevance score checker. The conversat... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. Please also see the main response above.
> The innovation and motivation of the paper.
We emphasize that the core idea of CORE is not to introduce an online component but to propose a plug-and-play framework to enable any offline RS to online query user preferences w... | Summary: The paper is about conversational recommender systems (CRS), which are systems that can interact with users through natural language and provide personalized recommendations. The paper addresses the challenge of incorporating a conversational agent into any existing recommender system in a plug-and-play fashio... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. Please also see the main responses above.
> Cite more relevant work.
We will include more relevant literature in our revision.
> More examples, figures, pseudocode, and analysis.
We provide detailed derivations of certainty gain and expected certainty gain in Appen... | Summary: In this paper, a conversational part of a recommender system is proposed. It is assumed that a recommendation model is available that assigns scores to user-item pairs (which estimate the probabilities of acceptance of the corresponding recommendations), items have a number of important attributes (numerical a... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. Please also see the main response above.
> The main questionable point is the contribution of the paper.
Please see the summary of our contributions in the main response above.
> Reference is old.
Thanks for your suggestion. We will add more recent literature in ... | Rebuttal 1:
Rebuttal: We summarize our responses and the results of the suggested experiments here. We also respond to every specific concern of each reviewer as individual comments below.
To summarize our contributions: (i) CORE is a plug-and-play method that can enable any (offline) RS to recommend (i.e., query) ite... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a learning framework called CORE that can incorporate a conversational agent into any recommendation platform, to complementarily check estimated offline relevance scores in each online user session. Experiments results on comprehensive benchmark datasets show that CORE outp... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. Please also see the main response above.
1. It is better to provide t-test results for tables 1, 2, 3, and 4.
Thanks for your suggestion. We will provide t-test results in our revision.
2. It is great to conduct CORE on online platforms.
Thanks for your advice. We ... | Summary: The authors assert that there is a gap between traditional recommender systems trained on offline (historical) preference data and conversational assistants that aim to elicit user feedback about their *current* preferences. The authors assert that conversational assistant training relies on reinforcement lear... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. Please also see the main responses above.
> Why is this certainty gain from finding an item satisfying user needs in lines 135-136?
As defined in Eq. (2), our objective is to find an item satisfying user needs, and then we assume that the session is finished because ... | null | null | null | null |
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations | Accept (poster) | Summary: The authors introduce a neural SDE model for LES flow fields. The model structure is motivated by the ideal LES approach and the model learns a data-driven closure term. The learned latent representation captures the variability and fine-scale structure of the fully-resolved DNS solution that is lost by the LE... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review. We are glad that you found our paper well-motivated and that it provides an important direction to explore, viz. stochastic modeling of turbulence using neural networks.
**Writing**
>The authors seem to use the term "closure model" more loosely t... | Summary: This paper introduces a data-driven method for approximating the closure term in Large Eddy Simulation (LES). The closure term represents the unresolved scaling effect caused by reducing the computational grid through downsampling. In comparison to the traditional physically-informed approach, the learned clos... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. Please find our response below.
**Single reconstruction loss**
We apologize for the confusion. Eq. (19) indeed contains only the reconstruction loss, which is only a fraction of the training loss we ultimately use for training (see line 218 right after Eq(18))... | Summary: This submission proposed a data-driven method to learn a.closure model to simulate the results from DNS. The key part is a latent stochastic process by Neural SDE. And finally compute the Monte-Carlo approximation.
Strengths: 1. The model treats the DNS as a stochastic process, instead of a deterministic proc... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback.
**RMSE in Fig 3**
You are correct that we treat DNS fields stochastically. However, the ideal LES field is deterministic, which is approximated by a filtered DNS instance, and is a valid approximation for short time rollouts.
Over longer time horizons, h... | Summary: This paper targets learning turbulence closure models for RANS simulations via neural networks. The paper proposes to use a neural SDE on the latent space of a transformer to predict different samples from the distribution of the next state, and then compute an average over these. This process is unrolled and ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and detailed feedback. We appreciate your encouragement and your suggestions on how to improve the quality of the manuscript.
**More scenarios**
We have included in the uploaded PDF an instance of our proposed methodology applied to cylinder wake at a high Re... | Rebuttal 1:
Rebuttal: We thank all reviewers for providing thoughtful reviews and constructive feedback. We are encouraged by the positive comments that our method is well-motivated, clearly presented and provides an important direction to explore in the area of data-driven turbulence closure modeling.
To address the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FedNAR: Federated Optimization with Normalized Annealing Regularization | Accept (poster) | Summary: This paper delves into the effect of weight decay in the realm of federated optimization. The authors conduct a series of experiments highlighting how specific elements in federated learning, such as the presence of diverse data and the execution of local updates, can amplify the influence of weight decay. The... | Rebuttal 1:
Rebuttal: ## Theoretical results
Our theoretical analysis is constructed within the broader **non-convex** framework and is underpinned by **minimal** assumptions. This accomplishment is significant given the intricacies of federated optimization coupled with non-iid data distributions. The bounds we derive... | Summary: The study discusses the role of weight decay in enhancing generalization performance in deep neural network optimization and in avoiding overfitting in Federated Learning (FL). The authors highlight the influence of weight decay value on FL algorithms' convergence. To mitigate this issue, Federated optimizatio... | Rebuttal 1:
Rebuttal: ## Additional large-scale datasets
We have integrated experiments involving CIFAR-100 and Tiny-ImageNet. Kindly refer to **Table 1** in the global PDF for a detailed presentation of the outcomes. Our FedNAR consistently upholds its superior performance with about **2%~7% improvement** across all ... | Summary: This paper first describes an important and challenge problem in Federated Learning, that the performance of FL is very sensitive to the choice of weight decay hyper-parameter for local optimization. The authors produced data to demonstrated the sensitivity of the weight decay hyper-parameter.
This paper firs... | Rebuttal 1:
Rebuttal: ## Single-epoch performance
Yes, FedNAR is also **effective** in a single-epoch setting. We perform experiments using CIFAR-10 data, employing a data heterogeneity parameter of 0.3. Our approach adheres to the identical configurations, parameters, and model specifications detailed in Section 5.1,... | Summary: The paper investigated effects of weight decay in the scenario of federated learning, especially for the stage of local updates. It was found that even subtle changes of weight decay values might lead to drastic performance drop and the observations motivated the authors to conduct convergence analysis conside... | Rebuttal 1:
Rebuttal: ## FedNAR and adaptive algorithms:
To commence, we wish to emphasize that FedNAR is also **effective** for adaptive algorithms such as FedAdam and FedAvgm. Please consult our Table 1 and Table 2 in the original submission for reference. Among the 10 settings about FedAdam and FedAvgm covered by ... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to the reviewers for their constructive and encouraging feedback. Dedicated to the continuous refinement of our work, we wish to emphasize a key contribution of our paper: **it represents the pioneering effort in systematically exploring the significance of weight ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adv3D: Generating 3D Adversarial Examples in Driving Scenarios with NeRF | Reject | Summary: This work proposes a new attack on monocular 3D object detectors utilising NERF representation to create multi-view consistent attacks utilising Lift3D [26]. The authors show successful attacks on nuScenes dataset and the effect of their design choices on the attack success. Furthermore, a mitigating strategy ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed feedback, and for appreciating that our work is innovative, well-structured and provide insightful analysis.
Below, we reply to individual questions and comments raised by the reviewer:
**(1) Mesh Comparison.**
This is a good point. We add a... | Summary: This work develops adversarial attacks against 3D object detectors by utilizing instance-level NeRFs.
They start with a representation of a vehicle, parameterized by a NeRF that predicts both geometry and texture and render the vehicle into a image, which they compose into the original image by copy-pasting.... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed feedback and for appreciating the novelty of our idea and clear writing. In the following, we reply to individual questions and comments raised by the reviewer:
**(1) Elaborate Section 4.4.**
In our adversarial training, we first infer the tr... | Summary: Deep neural networks (DNNs) have shown susceptibility to adversarial examples, which raises significant safety concerns, particularly in safety-critical applications like DNN-based autonomous driving systems and 3D object detection. While there is a wealth of research on image-level attacks, most of them focus... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed feedback and for perceiving our methods as novel and effective. In the following, we reply to individual questions and comments raised by the reviewer:
**(1,2) Real-world Experiments.**
Thank you for pointing this out. We have conducted real-... | Summary: The authors proposed new generative adversarial examples in the form of NeRFs, in the context of driving scenarios. The training objective is minimizing the 3D detection confidence from a variety of views. The parameters to optimize are the latent input to the NeRF, that encodes shape and texture info. Renderi... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed feedback and for appreciating the novelty of our idea and clear writing. In the following, we reply to individual questions and comments raised by the reviewer:
**(1) Is it necessary to use NeRF as the representation of adversarial examples?**... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their insightful reviews. Before addressing the specific questions in the individual replies, we would like to first reiterate our motivation and contribution, and then provide a detailed description of the experiments that we have added during the rebu... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes to generate 3D adversarial examples for attacking 3D object detectors in driving scenarios using NeRF. In particular, it integrates a series of techniques, including primitive-aware sampling and semantic-guided regularization, to ensure the physical realism and realizability of the generated... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the positive and detailed feedback. Below, we reply to individual comments and questions raised by the reviewer:
**(1) Real-world experiments.**
It is practicable to produce an adversarial NeRF in the real world by printing an adversarial texture. Our add... | null | null | null | null | null | null |
Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context | Accept (poster) | Summary: The work presents a multi-modal model, called CrossViViT, to perform day-ahead solar global horizontal irradiance predictions. In that, the model combines spatial information from satellites, i.e. RGB, IR and vapor channels, across Europe with time series information from six point-like stations, i.e. clear sk... | Rebuttal 1:
Rebuttal: > Normalize that forecasting values into a stated range (e.g. [0-1]) or state the value ranges otherwise an RMSE/MAE improvement by certain value cannot be put into a frame of reference
We thank the reviewer for the suggestion. We think, however, that given that all the models are compared on the... | Summary: The work presents a method to integrate information about cloud (using satellite images) with timeseries data related to Solar Irradiance to improve the solar irradiance forecasting.
Strengths: Here some interesting aspects of the paper:
- the release of a new dataset containing both timeseries and satellite ... | Rebuttal 1:
Rebuttal: > The main weakness I see is that day-ahead use case is not explicitely evaluated. I know that the sliding window is more general but an important real word use case is to have a real day-ahead prediction. Authors could test their algorithm on day-ahead use case (extracting properly the sliding wi... | Summary: This submission presents a multimodal model for next-day solar irradiance prediction. They use time series of past irradiance and satellite image to predict irradiance 24h in advance. The model consists of one transformer branch for each modality and a shared (cross-modal) transformer. Their method can be used... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and suggestions to improve the paper. We respond to the reviewer's comments below:
> Some details are missing, making it hard to understand how the method works precisely.
We are not sure we understand what the reviewer means. What type of detail... | Summary: The paper proposes a transformer-based day-ahead forecasting model for solar irradiance at a ground station.
The model ingests previous irradiances and contextual (image-sequence) information with a temporal and vision transformer.
A cross-former merges the tokens, and a temporal transformer decoder estimates ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and suggestions to improve the paper. We respond to the reviewer's comments below:
> errors/typos in domain-specific equations: GHI = DNI + DHI x cos(z) <- I believe the x cos(z) should be with the DNI (direct normal irradiance) and not the DHI to... | Rebuttal 1:
Rebuttal: We thank the reviewers for their efforts and reviews of high quality. We are happy that they appreciated our contributions to the forecasting literature, including our architecture and the “easy and hard” cases evaluation.
We carefully considered your suggestions, and believed that it ultimately ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis | Accept (poster) | Summary: **Summary**
The paper studies finite-sum minimization ($\min_{x} f(x):= \frac{1}{n} \sum_{i= 1}^n f_i(x)$) in the distributed setting with a central node and $n-1$ non-central (client) nodes. The setup and the paper's assumptions are as follows.
1. Each function $f_i$ is held in the $i$-th client node, and... | Rebuttal 1:
Rebuttal: Thank you for your advice. We reply to your comments one by one below.
1. Reply to Weaknesses in Results 1: We are sorry for the confusing argument. Indeed, SVRS is always no worse than SVRP by Khaled and Jin since
$O(n+\delta^2/\mu^2)=O(n)$ and $O(n+\sqrt{n}\delta/\mu) = O(n)$ when $\delta<\sqrt... | Summary: The authors consider distributed minimization problems under data similarity (hessian similarity). The authors consider stochastic methods that reduce communication complexity via device sampling. In particular, from the stochastic point of view, the variance reduction techniques: SVRG and Katyusha, are taken.... | Rebuttal 1:
Rebuttal: Thank you for your review and for pointing out these interesting and meaningful references. We are sorry for the incompleteness of the reference.
We will add the reference you posted in a later version because revision is not allowed in the rebuttal period this year.
In addition, we find there ar... | Summary: The paper presents a novel algorithm for distributed optimization, named Accelerated Stochastic Variance-Reduced Sliding (ASVRS). The authors focus on the problem of minimizing the average of a large number of smooth and strongly convex functions, a common scenario in machine learning and data analysis. The pr... | Rebuttal 1:
Rebuttal: Thank you for your review and advice.
The assumptions in our paper include 1) the finite-sum objective is strongly convex, 2) the finite-sum objective satisfies average second-order similarity, and 3) the proximal operator of just one part (or each part) is approximately solvable.
These assumpti... | Summary: The paper considers distributed strongly convex optimization problems in the setting where the communication between nodes is bottleneck. The authors propose new methods, SVRS and AccSVRS, that guarantee new communication complexities. Also, they proved the lower bound that ensures the optimality of the AccSVR... | Rebuttal 1:
Rebuttal: Thank you for your review. We consider there are some misleading due to the unclearness in our paper.
**Our setting is unable to be compared with the classical smooth and strongly convex setting generally.**
First of all, we reclarify the framework we studied: 1) the finite-sum objective is $\m... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work considers finite-sum (distributed) optimization problems in the strongly convex and second-order similar regime. The authors proposed SVRS and its acceleration, AccSVRS, to solve the problem and provided the corresponding communication and computation complexities, which outperform existing works in ... | Rebuttal 1:
Rebuttal: Thank you for your review and suggestions. Here we list the reply point by point.
1. Reply to Weakness 1: We are sorry for the unclearness due to the space limit of pages. We need to recognize that Steps 5 and 6 in AccSVRS are somehow tricky, so we only list some shallow understanding here.
**T... | null | null | null | null | null | null |
Online Constrained Meta-Learning: Provable Guarantees for Generalization | Accept (spotlight) | Summary: The paper studies the problem of online meta-learning with constraints. After a formalization of the problem, the paper proposes an algorithm in the case where the loss function is convex, using Follow-the-Perturbed-Leader (FTPL) to update the meta-objective.
Then the paper theoretically prove upper bounds on ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our paper. We address your concerns as follows.
>**Weakness of Clarity 1.**
**Answer:** Sorry about the missing discussion. Here are the relation of the proposed algorithm to previous works and the intuition of our algorithm design. We wi... | Summary: In this paper, a novel online constrained meta-learning framework is presented. The framework is designed to facilitate continuous learning from sequential tasks while ensuring that these tasks adhere to strict constraints. In addition to existing analyses of meta-learning, this study goes further by presentin... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. Thanks for your suggestions. We address your concerns as follows.
> **Weakness 1. The bound is scaled with $\mathcal{O}\left(\frac{1}{\sqrt{T}}\right)$. It seems to ignore the size of training datasets.**
**Answer:** The upper ... | Summary: The paper studies the theory of biased-regularization meta-learning under the sequential task setting. Despite there having been previous works in this area, this paper distinguishes itself by introducing the concept of the Online Constrained Meta-Learning problem and presenting a straightforward solution. It ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. Thanks for your suggestions and reference recommendation. We address your concerns as follows.
> **Weakness 1. Discussion of connections with conditional meta-learning.**
**Answer:** Thanks for the reference. Constrained meta-l... | Summary: The authors propose an online constrained meta-learning algorithm that is able to sequentially learn a sequence of tasks that are subject to hard (and stochastic) constraints. The authors also theoretically quantify the optimality gaps and constraint violations produced by the proposed method, by considering ... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our work. Thanks for your suggestions and reference recommendation. We address your concerns as follows.
> **Weakness 1. The statements and the notation of the paper could be simplified and made more intuitive.**
**Answer:** Thank you fo... | Rebuttal 1:
Rebuttal: We are grateful and indebted for the time and effort invested to evaluate our manuscript by all reviewers, and for all the suggestions and reference recommendations to make our manuscript a better and stronger contribution. Please find below our detailed replies to all the comments of the reviewer... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Taming Local Effects in Graph-based Spatiotemporal Forecasting | Accept (poster) | Summary: This paper proposes a method to leverage local effects in graph-based spatio-temporal forecasting. The authors claim that existing spatio-temporal graph neural networks are global models, i.e. all nodes share the same set of parameters, and thus may fail to capture some node-specific patterns. On the other han... | Rebuttal 1:
Rebuttal: Thank you for your comments and your positive opinion about our work. Please find our point-by-point answers below.
> 1. The proposed method with node specific embeddings is effective, but not new. Specifically, STID [40] proposes exactly the same technique in terms of trainable node embeddings. ... | Summary: This paper presents a methodological framework aimed at rationalizing the inclusion of trainable node embeddings in STGNNs for spatiotemporal forecasting applications. The authors examine the interplay between globality and locality in graph-based spatiotemporal forecasting and provide insights and guidelines ... | Rebuttal 1:
Rebuttal: Thanks for the review. Please find our answers below.
> The paper is not well-organized, making it difficult to understand the main points and arguments presented.
We did our best to make the structure of the paper easy to follow, an aspect that was appreciated by the other reviewers. Currently,... | Summary: This paper examines the interaction between global and local effects in graph-based spatiotemporal forecasting. It addresses the limitations of a single global model by introducing a framework that incorporates trainable node embeddings into graph-based architectures. This framework enables the learning of spe... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and useful comments. We are happy that you found our paper interesting, please find our point-by-point answers below.
> I feel it is a little too overwhelming to answer all these questions at once. [...] Sometimes I will question that, how does this claim hold fo... | Summary: In this paper, the authors explore the influence of locality and globality in graph-based spatiotemporal forecasting architectures. Existing spatiotemporal models are global trained on multiple multivariate timeseires, which can capture the strong dependency among individual nodes in a network. Standard local ... | Rebuttal 1:
Rebuttal: Thank you for the review. Before providing point-by-point answers, we’d like to remark that the main contribution of our paper is not in introducing an architecture but rather in studying a crucial aspect of graph-based forecasting, i.e., the interplay of local and global aspects of time series fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments.
We provide point-by-point answers to each reviewer and attach as supplementary results a visualization of the embedding space for different regularization strategies for load and traffic forecasting datasets (see the attached pdf for more deta... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization | Reject | Summary: This work proposes Flag Aggregator (FA) for a more robust aggregation of gradient in data-parallel training. FA formulates gradient aggregation as a Maximum Likelihood Estimation procedure using Beta densities. Theoretically, FA is analyzed using techniques from convex optimization. Empirically, FA demonstrate... | Rebuttal 1:
Rebuttal: **Q.** How about more models like RNNs and larger models like GPT2? And how about more tasks like language modeling?
**Ans.** For Tiny Imagenet experiments in the supplement, we used ResNet-50 as a larger model. In order to store these larger models in the current (or larger) scale of our distrib... | Summary: This paper tackles the problem of Byzantine robustness in distributed learning by proposing a new robust aggregation rule called Flag Aggregator. The latter is based on maximum likelihood estimation with regularization. They empirically show that using distributed gradient descent with Flag Aggregator performs... | Rebuttal 1:
Rebuttal: **Q.** In Equation 1, what are $A$, $Y$ and $C$?
**Ans.** $A$ denotes the aggregation function, $Y$ denotes the decision variable in the optimization problem, and $C$ denotes the desired constraints. The reviewer will note that later in the paper, we have explicitly defined what these are in equa... | Summary: Authors propose a gradient aggregation method for distributed optimization that is robust to Byzantine device failures in large scale distributed setups. In each round, given the set of gradients from each workers, the authors aim to find the optimal low-rank subspace that can explain the variance of a majorit... | Rebuttal 1:
Rebuttal: **Q.** Could you provide some insights into how frequent failures due to hardware/software/augmentation pipeline based issues occur in training runs.
**Ans.** Training today’s large models is a very time-consuming task that can take days or even weeks. An important problem is the fact that failur... | Summary: The paper proposes a new appraoch for aggregating gradients for distributed ML training under Byzantine failures, noise due to data augmentation, etc. The approach relies on constructing a low-dimensional subspace such that the proportion of variance of the gradient vectors contained in the subspace is maximiz... | Rebuttal 1:
Rebuttal: **Q.** Why simply considering the principal components will not work? Did we perform experiments with PCA/Robust PCA as baselines?
**Ans.** As explained in the general response, mathematically, one iteration of FA with uniform weights assigned across all workers is equivalent to PCA. The main nov... | Rebuttal 1:
Rebuttal: We thank the reviewers for spending time going through our submission in great detail, very insightful comments, and also pointing to aspects in the presentation style that can be improved. We are glad that the reviewers find our subspace based aggregation algorithm to be novel, can be derived usi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a new method to aggregate gradients in a distributed training setting. Effectively, the proposed algorithm projects gradients onto a learned lower dimensional subspace and then aggregates the projections using standard techniques like averaging. This leads to a more robust aggregation again... | Rebuttal 1:
Rebuttal: **Q.** What exactly is the IRLS procedure in Algorithm 1?
**Ans.** Answered in the general response.
**Q.** Can the authors comment on the breakdown of which parts of algorithm 1 take significantly more time, and explain any optimizations they have implemented in this context?
**Ans.** This is ... | null | null | null | null | null | null |
FAST: a Fused and Accurate Shrinkage Tree for Heterogeneous Treatment Effects Estimation | Accept (poster) | Summary: This paper proposes a novel strategy for estimating the heterogeneous treatment effect called the Fused and Accurate Shrinkage Tree (FAST). The authors confirm the consistency of the proposed tree-based estimator and demonstrate the effectiveness of their criterion in reducing prediction error through theoreti... | Rebuttal 1:
Rebuttal: Thanks for your instructive and detailed review comments. We are encouraged by the overall positive responses from the comments and suggestions. Our point-to-point responses to your comments are itemized below.
**Q1:** Thank you for highlighting the potential absence of references. This suggest... | Summary: The paper deals with the problem of estimating the heterogeneous treatment effects with multiple data sources. In particular, the paper aims to utilize the information from the observational data to better estimate the causal effects in the trial data. Inspired by the shrinkage estimation, a weighting scheme i... | Rebuttal 1:
Rebuttal: Thanks for your careful review. Our point-to-point responses are as follows and we would add the additional discussions in the revised manuscript if given a chance.
**W1&W2&W3&Q3**:
Thanks for raising the issues concerning tree-based methods. As mentioned in the abstract of the paper, one of the... | Summary: The authors propose a novel shrinkage method that fuses an unbiased estimator with a biased estimator. This method effectively reduces the MSE of the unbiased estimator. The approach offers a practical and straightforward implementation specifically tailored for estimating heterogeneous treatment effects. The ... | Rebuttal 1:
Rebuttal: Thanks for your instructive review comments. We are greatly encouraged by the overall positive responses from the comments and suggestions. Our point-to-point responses to your comments are itemized below and we would add those discussions in a revised manuscript.
**W1:** Thanks for pointing out ... | Summary: The paper introduces a Fused and Accurate Shrinkage Tree (FAST) algorithm for heterogenous treatment effect estimation given trial and observational data. The FAST algorithm introduces (i) a shrinkage based approach that combines trial and observational data for MSE reduction in treatment effect estimation, an... | Rebuttal 1:
Rebuttal: Thanks for your inspiring review comments. We are greatly encouraged by the overall positive responses from the comments. Our point-to-point responses are itemized below and the references are listed in the end. We would add the discussions and numerical experiments to the revised version if given... | Rebuttal 1:
Rebuttal: We would like to express our sincere thanks to all the reviewers for your insightful and constructive review comments and we are greatly encouraged by the overall positive responses. Based on your valuable suggestions, we have carefully done a round of revision of the manuscript. While detailed re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the problem of estimating (heterogeneous) treatment effects via both interventional and observational data. The authors proposed a new estimation, namely the Fused and Accurate Shrinkage Tree (FAST), which optimally weights the interventional and observational estimator, and combines with ... | Rebuttal 1:
Rebuttal: Thanks for your insightful and comprehensive review. We are encouraged by the overall positive responses from the comments and suggestions. Our point-to-point responses to your comments are itemized below and we would add the discussions and numerical experiments in the revised manuscript if given... | null | null | null | null | null | null |
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking | Accept (poster) | Summary: The authors suggest incremental updates of $\mathbf{x}$ for finding the minimum of strongly-convex function $f$ that guarantee decreasing $f(\mathbf{x}_{t})-f(\mathbf{x}_\ast)$ based on only 1st-order gradient information.
Their idea is in each step,
- choose a candidate matrix $\mathbf{P}_t$ based on set $\... | Rebuttal 1:
Rebuttal: Thank you for engaging with our paper during the review period!
Please see the discussion of the overhead of `CUT` (also raised by reivewer JVBS) in the overall response. For the other points:
> considering backtracking using preconditioned matrix $\mathbf{P}_t$ would be novel idea. But I'm not... | Summary: This paper provides a backtracking approach for smooth convex optimization on a per-coordinate basis with a theoretical analysis that show the gain with respect to classical backtracking line-search and that compare to the optimal per-coordinate conditioners.
Strengths: This paper is super well written and or... | Rebuttal 1:
Rebuttal: Thank you for sharing in our excitement with the paper!
We agree that one of the major drawbacks of our technique is the focus on the smooth, strongly-convex case. We do address the PL case as a relaxation of strong-convexity in the Appendix, and hope our work will lead to others exploring relax... | Summary: This paper extends backtracking to multi-dimension. The authors propose a cutting plane method to find optimal per-coordinate step-sizes (in other words, to find an optimal preconditioner) for smooth convex optimisation. Experiments on ill-conditioned logistic regression problems show that the proposed algorit... | Rebuttal 1:
Rebuttal:
Thank you for engaging with our paper during the review period!
> This paper fills a potential gap in the optimization literature by proposing multidimensional backtracking
We emphasize that, beyond the specific algorithm presented, a contribution of our work is the development of a formal def... | Summary: This paper presents a generalized backtracking line-search method, which estimates coordinate-wise stepsizes referred to as 'preconditioner' of gradient descent. Stemmed from the observation that any existing methods do not exceed the performance of backtracking line-search method, this paper designs a general... | Rebuttal 1:
Rebuttal: Thank you for sharing in our excitement with the paper!
Please see the discussion of the overhead of `CUT` (also raised by reivewer eKZX) in the overall response.
Thanks you for spotting the typo on l.121, a sentence got eaten due to a version conflict. | Rebuttal 1:
Rebuttal:
We thank all reviewers for engaging with our paper. We were very pleased to see reviewers JVBS and hZtF sharing in our excitement with the paper and appreciate the great feedback. We appreciate that reviewers 5i17 and eKZX truly engaged with our paper despite it being outside of their area of exp... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation | Accept (poster) | Summary: This paper studies the actor-critic approach for robust RL. Especially, a Double-Sampling Uncertainty Set and an Integral Probability Metric Uncertainty Set are developed to overcome the curse of problem scale. A robust natural Actor-Critic algorithm is then proposed with convergence results. A significant num... | Rebuttal 1:
Rebuttal: We are encouraged by the reviewer's comments that the paper is well-written, and that the designed uncertainty sets are novel and show advantages for large-scale problems. Below, we give a detailed response to your comments. We believe that we have addressed all your concerns, and we sincerely hop... | Summary: This paper tackles robust reinforcement learning in large state spaces, where the transition kernel is accessible only in a nominal setting. The authors demonstrate that the $f$-divergence, R-contamination, and the $l_{p}$ norm are computationally infeasible in the context of robust RL for large state spaces. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the clear summary of the paper, finding our theoretical results of substantial contribution and our empirical evaluations credible.
**Q1.** "The convergence guarantees are valid for $(s,a)$-rectangular uncertainty sets, which is rather limiting. However, it is commendab... | Summary: This paper studies the sim-to-real transfer problem. It extends the learning of a robust policy by using the framework of robust Markov decision processes (RMDPs). It extends this paradigm to large state and action spaces using two uncertainty set formulations: double sampling, and integral probability metric.... | Rebuttal 1:
Rebuttal: Thank you very much for your comments and suggestions. We are encouraged by the fact that the reviewer finds that our paper takes "practical steps forward in sim-to-real transfer with robustness approaches" and has "interesting theoretical analysis". Please see our response below with respect to t... | Summary: RL methods trained on simulators suffered from generalization problems because of the "simulation-to-reality-gap". Previous works proposed robust RL methods in a tabular setting, with limited search spaces. The paper aims to develop a computationally tractable robust RL algorithm with large search spaces. To t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and suggestions. We are encouraged by the fact that the reviewer finds that our paper "studies a critical problem" and provides "solid theoretical analysis". Please see our response below with respect to the specific comments. Please note that line numbers ar... | Rebuttal 1:
Rebuttal: ## Authors' Response to All
We wholeheartedly thank all reviewers for their time and their constructive feedback on our paper. As suggested by Reviewers Fq66 and haxo, **we have added additional MuJoCo and real-world TurtleBot experiments** in the attached new pdf.
As suggested by Reviewer haxo,... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data | Accept (poster) | Summary: This paper addresses a challenge in Federated Learning referred to as partially class-disjoint data (PCDD), where each client contributes a part of classes (instead of all classes) of samples. Without full classes, the local objective will contradict the global objective, yielding the angle collapse problem fo... | Rebuttal 1:
Rebuttal:
**We really appreciate your positive support and the constructive comments. In the following, we provide the detailed response and hope that can address your concerns. Let W, Q and A denote the shorthand of Weaknesses, Question and Answer respectively.**
> **W1:** Only 1 real PCDD federated app... | Summary: This paper mainly focuses on the partially class-disjoint data (PCDD) problem in federated learning (FL) settings, which is a common yet challenging problem in distributed data sources. Inspired by a classifier structure (simplex equiangular tight frame, ETF), the authors of the paper propose FedGELA to tackle... | Rebuttal 1:
Rebuttal: **We really appreciate your constructive comments. Regarding the questions from the reviewer, we provide detailed response as below, and hope that can address your concern. Let's use Q as a shorthand for Question.**
**Weakness and Q6:**
1)**Technical Innovation.** We would like to kindly argue t... | Summary: This paper introduces a novel Federated Learning Algorithm to address the Partially class-disjoint data (PCDD) problem. The approach is based on the simplex equiangular tight frame (ETF) phenomenon to solve the angle collapse issue and introduces a second projection to personalize an adapted structure to save ... | Rebuttal 1:
Rebuttal: **We really appreciate your positive support and the constructive comments. Regarding the weakness mentioned by the reviewer, we provide the detailed response as below, and hope that can address your concern. Let W denotes the shorthand of Weaknesses.**
**Reply to W1 and W2:**
1)We would like t... | Summary: The authors study the problem of federated learning over partially class-disjoint data and propose using equiangular tight frame (ETF) techniques that allows achieving better performance in both the global and personal learning tasks. They show that the existing federated learning approaches suffer either from... | Rebuttal 1:
Rebuttal: **Thanks for your positive support and the constructive comments. Regarding the questions and weaknesses mentioned by the reviewer, we provide the point-to-point response as below, and hope that can address your concern. Let Q, W and A denote the shorthand of Question, Weaknesses and Answer respe... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers(nvCV, UNoN, SUwJ and J5UX) for their thoughtful suggestions on our paper, and appreciate that the reviewers have multiple positive impressions of our work, including:
- **well defined problem (J5UX) and a clear motivation (SUwJ and J5UX)**
- **a novel and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adapting Fairness Interventions to Missing Values | Accept (poster) | Summary: This paper presents an information-theoretic finding that reveals the fundamental limitation of impute-then-classify approaches when considering fairness-accuracy tradeoffs. Additionally, it introduces three techniques for addressing missing features within the framework of linear fair classifiers, as well as ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful read of our paper and constructive comments!
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**W1. I believe that scenarios where sensitive attributes are missing present more practical relevance, importance, and challenges compared to scenarios where features are missing. Although the authors mentio... | Summary: This paper investigates the impact of missing values on algorithmic fairness and highlights the limitations of the commonly used "impute-then-classify" approach. The authors propose algorithms that preserve the information encoded within missing patterns, leading to improved fairness and accuracy.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review and for appreciating the merits of the work!
**W1. The proposed method is only applicable to fair classification and when the group attributes are discrete. Furthermore, the approach allows missingness only in the non-group attribute input features,... | Summary: This work investigates how different types of missing data affect algorithmic fairness, and provide algorithms that work to address this issue. Three types of missing data are considered: MCAR (missing data is independent of the observed and unobserved values), MAR (missing data depends on the observed values ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and we are glad to learn that you found our paper enjoyable to read!
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**Q1. The fairness of models returned by the algorithms is not captured in the graphs in the main body of the work. The paper touts that training classifiers from impute... | Summary: The paper works on the missing value issues in algorithmic fairness. Typical approaches tend to firstly impute the missing the data, then process for the classification task. However, the authors prove that the imputed data harms the group fairness as well as the averaged accuracy. To avoid losing missing patt... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind comments and the encouragement!
---------
**Q1. Although the overall presentation is well-done, some parts could be much better if modified accordingly (please refer to questions).**
A1. We appreciate your constructive feedback! Please find our detailed respons... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for taking the time and effort to review our paper! We are delighted to receive positive feedback for the key components of the paper; in particular, that: our information-theoretic result characterizing the limitation of impute-then-classify provides novel... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work examines the impacts of missing values in data on fairness interventions, particularly in contrast to the commonly implemented "impute-then-classify" procedure for handling missing values. The authors present the following:
- investigation of how missing values impact algorithmic fairness in the cont... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and for appreciating the novelty and value of the work!
---
**Q1. Trade-offs between the three methods for linear classifiers.**
Please refer to our response for Q2 below.
**Q2. It is unclear how one would determine which of the three linear me... | null | null | null | null | null | null |
Data Curation for Image Captioning with Text-to-Image Generative Models | Reject | Summary: This paper focuses on data curation for image captioning. This paper shows that mismatched image-caption pairs do harm to the captioning model. To address this problem, generative models are used. In detail, the BLIP model is used to generate captions based on images, and the Stable Diffusion model is used to ... | Rebuttal 1:
Rebuttal: > Evaluation of text augmentation methods
Yes there are many possible text augmentation methods, which mainly involve text augmentation which increases the total number training samples, such as [[3]](https://www.mdpi.com/2076-3417/10/17/5978). Instead, we focus on the new approach that leverages... | Summary: This paper studies data curation strategies for training image captioning models. Firstly, it identifies the “difficult samples” based on the captioning loss dynamically at the end of each epoch. Subsequently, it introduces three data curation strategies to modify the difficult samples: (1) removal of an image... | Rebuttal 1:
Rebuttal: > Effectiveness of the proposed ReplaceImg method
In Figure 5, we show that ReplaceImg generally works better for both datasets (second best for Flickr30k---0.1 CIDEr lower than REMOVE, and best for COCO). Flickr30k benefits more from removing high-loss training samples indicates the original dat... | Summary: This paper proposes a data curation model for image captioning. If the loss of a particular image caption pair is high, then either remove the image-caption pair from the training set or replace the caption with a more similar caption or they generate a new image for the difficult caption. The authors demonstr... | Rebuttal 1:
Rebuttal: > How to select a replacement caption?
This is described on L98-102. As for both Flickr30K and COCO, each image is paired with 5 caption annotations. We replace the caption by randomly selecting from the other 4 captions.
---
> Significance of our findings
We propose a model-agnostic approach ... | Summary: This paper focuses on improving image captioning by improving the quality of the existing dataset. To this end, this paper proposes three data curation methods: the removal of an image–caption sample; replacing a caption with another caption; and replacing images using a text-to-image generation model. Experim... | Rebuttal 1:
Rebuttal: > Generalization ability and cross domain evaluation
It had never occurred to us that our data curation method would reduce the gap between the training and the test set because nothing in the method knows anything about the distribution of the test data. In order to better understand how our met... | Rebuttal 1:
Rebuttal: We express our gratitude to all the reviewers for their time and helpful feedback. We are glad that all five reviewers found our work interesting and well-motivated, and `Reviewer-ndRn`, `Reviewer-Hsed` and `Reviewer-tP6L` also found our work enlightening to a broader scope of Vision-Language lear... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose an iterative training approach to improve image captioning models.
This approach _refreshes_ the training dataset every epoch with _higher quality_ image-text pairs (authors call it "data curation").
Dataset samples with very high training loss are updated -- the real image i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and recognising the contribution of our work and its potential impact to the community.
> Results do not match with the presented story: simple removal works better than ReplaceImg.
Figure 5 gives a broader context than Table 2, where it can be see... | null | null | null | null | null | null |
Subject-driven Text-to-Image Generation via Apprenticeship Learning | Accept (poster) | Summary: This paper proposes a method for subject-driven text-to-image generation, where a model is tasked to generate novel renditions of a subject given a few images of that subject. Different from previous fine-tuning approaches, this paper trains a model that conditions its generation on the given subject images. T... | Rebuttal 1:
Rebuttal: Comment #1 “The training data covers a wide range of subjects. Therefore, it is hard to tell if the apprenticeship model learns to generalize to new subjects or not. Have the authors performed de-duplication to ensure that the subjects used for evaluation do not appear in the training set of the e... | Summary: The paper presents a novel subject-driven text-to-image generator named SuTI. This model leverages in-context learning as opposed to subject-specific fine-tuning. SuTI is built upon the principles of apprenticeship learning and is capable of generating high-quality, customized, subject-specific images. Remarka... | Rebuttal 1:
Rebuttal: Comment #1 “Dataset Construction: Could you provide more details about the process of constructing the training dataset for SuTI? Specifically, how much time and resources were required to create the dataset?”
Since we can parallelize the DreamBooth training and generation, we set up 100 instance... | Summary: This paper introduces SuTI for the subject-driven text-to-image (T2I) generation method. Numerous expert models are first trained on millions of image clusters collected from the internet, each focuses on a specific visual subject. A dataset is then created, consisting of concept images, target prompts, and co... | Rebuttal 1:
Rebuttal: Comment #1 “The hyperparameters used for training the baselines, such as the number of training iterations and learning rates, should be provided”:
Thanks for the reminder. We mostly use the official colab from these papers and their default hyper-paramters for the baselines. We will add these de... | Summary: The paper proposes an in-context learning method for model customization given personalized objects. The method first collects a large-scale dataset of custom concepts ensuring all images in each custom concept cluster are similar to each other and fine-tunes the model for each concept using Dreambooth. These ... | Rebuttal 1:
Rebuttal: Thanks a lot for the highly detailed and constructive feedback!
Comment #1 “Why is there a need to train the Dreambooth expert models for training the final SuTI model? How does the performance change if the collected dataset itself is used directly to train the final model with N-1 image text... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their constructive feedback. Here we want to highlight a few things:
1. First of all, we are advocator of open research and strive to make everything publicly accessible. We plan to make the model or API publicly available to public before paper publication although... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a new model that can perform subject-driven text-to-image generation. Instead of fine tuning a leerte pretrained model on each subject, the authors use apprentice learning to first construct a virtual dataset from a large number of teacher models, each specific to a kind of s... | Rebuttal 1:
Rebuttal: Comment #1 “This paper could have been impactful if the authors plan to open source or provide a way to reproduce the results. However, from the checklist the authors indicate that they have no plan to open source the model.”
We are in full agreement that it would be far better for the community ... | null | null | null | null | null | null |
Granger Components Analysis: Unsupervised learning of latent temporal dependencies | Accept (poster) | Summary: This paper presented a novel unsupervised learning approach using Granger Causality by identifying the driving/driven components. This method was demonstrated on EEG and fMRI data, in coincide with the neurophysiological facts.
Strengths: The paper proposed an algorithm to identify the pairwise causal struc... | Rebuttal 1:
Rebuttal: ## Weaknesses
### The number of latent variables is a key parameter for this unsupervised learning, however it is pre-defined without any adaptive mechanism. similarly, no adaptive mechanism for L.
We acknowledge that we did not propose an adaptive mechanism for selecting the number of latent pa... | Summary: The paper formulates the problem of learning a pair of spatial projections that optimize a criterion based on the Granger causality between the resulting components, which is itself based on regressing the first, driving, component to the second, driven, component using a Wiener filter and the converse for the... | Rebuttal 1:
Rebuttal: ## Questions
### Is automatic differentiation an option over the numerical differentiation or the manually coded derivative?
Automatic differentiation is an option over the manually coded derivative. By "automatic" differentiation, we mean that the gradient is computing using finite differences... | Summary: This paper proposes a novel (blind) source separation method that extracts pairs of components from multivariate time series between which Granger causality (GC) is maximal. This can be a very useful tool to assess direction information flow between brain areas in an unsupervised way, without having to specify... | Rebuttal 1:
Rebuttal: ## Questions
### It would be interesting if the authors could provide more information about the extracted sources in the motor imagery real data example. Could you please plot power spectra of the sources compared to some of the relevant channels over the motor areas. Similarly, it would be inte... | Summary: The paper proposes a factorization model to extract P pairs of latent components from a multivariate time series such that in each pair one of the time series Granger causes the other. The authors apply the approach in analysing EEG and fMRI data to show meaningful conclusion.
Strengths: The paper addresses a... | Rebuttal 1:
Rebuttal: ## Questions
### Q1. How are the maps in Figure 3 and 4 generated, using w and v vectors or using A matrix?
The maps are generated using the A matrix (i.e., the forward matrix). This is motivated by the literature [1,2] which argues that the forward models, by depicting the activity that is reco... | Rebuttal 1:
Rebuttal: ## Author response to all Reviewers
We are grateful for the thorough reading and helpful feedback from all of the Reviewers.
Detailed responses to each Reviewer's feedback are provided separately. This response describes the figures that have been included in the additional PDF, and highlights ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ContinuAR: Continuous Autoregression For Infinite-Fidelity Fusion | Accept (poster) | Summary: The author proposes a general auto-regression model for multi-fidelity fusion. By simplifying the ODEs over the fidelity indicator in a linear form, close-form solutions can be derived. And the computational efficiency can be further improved using a rank-1 approximation. The experiment results also show super... | Rebuttal 1:
Rebuttal: # Reviewer 4
**Could you also provide some fidelity interpolation and extrapolation results?**
We have tested this functionality and found the interpolation results to be accurate as expected. We did not include these results due to the limitation of the space and the motivation of this work---... | Summary: Multi-fidelity models are widely used for combining training data obtained from information sources with different degrees of precision or accuracy. More specifically, this allows for the combination of greater quantities of noisier but more cheaply-obtained examples with more faithful (but limited) data. In t... | Rebuttal 1:
Rebuttal: **“main paper could be improved further to highlight the key takeaways”**
We agree with the reviewer. However, feel this to be a challenging task since our work is quite theoretical as it tried to revise the classic multi-fidelity autoregression and extend it to a tractable form for infinite fide... | Summary: This paper presents a Gaussian process (GP) based multi-fidelity model that makes use of fidelity indicators. This paper extends the autoregression two-fidelity formulation to a linear fidelity differential equation. By assuming the lowest fidelity function and all the residual functions follow GP, a joint GP ... | Rebuttal 1:
Rebuttal: **"which factor is more important to the performance difference."**
Thank you for your insightful comment! We did some investigation into the issue and we believe that the main contributing factor is joint modeling of all the fidelity data.
We find this by giving a 1st- and 2nd-order polynomial ... | Summary: The paper proposes an implementable and concrete version of Li et al's infinite dimensional fidelity DE, an method of fusing simulations at different levels of fidelity/resolution, to trade off between computational tractability and statistical accuracy.
Strengths: if i understand correctly the "infinite-fide... | Rebuttal 1:
Rebuttal: **“What even is the fusion problem”**
We appreciate the reviewer for providing such valuable feedback to us.
Please allow us to clarify the fusion problem here.
The goal for multi-fidelity fusion is to accurately predict the output of $f(\mathbf{x},T)$.
Here $f$ is the simulator, $\mathbf{x}$ is ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort of the reviewers. The valuable comments will be absorbed into our revision. Here we supply some additional graphical information to address the reviewers' concerns.
The first part is about additional experiment results where Fig 1 shows the classic sub... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery | Accept (poster) | Summary: BayesDAG proposes a hybrid SG-MCMC sampling and variational inference for drawing samples from the posterior distribution of DAGs in the context of Bayesian structure learning.
This work is closely related to a recently published "Yu et al., Dags with no curl, 2021" [NoCurl] where the space of DAGs is convert... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback for our paper. We will address your concerns in the following:
1. **Advantages compared to no-curl**: Sorry about the ambiguity. We will make it more clearer in the revised paper. There are several reasons on why the binary matrix is better than continuous on... | Summary: The authors propose a method for the posterior inference of DAG structure *and* function parameters with potential applicability to arbitrary functional relations between nodes. The authors modify a novel characterization of DAGs, and interpret this characterization in terms of a sorting operation which can be... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback for our paper. We will address your concerns in the following:
1. **Identifiability**: We would like to clarify that identifiability is a property of the SCM (the specific parametric form thereof), and not that of an estimation/inference method, which is wha... | Summary: The paper proposes a Bayesian causal discovery method based on a novel parametrization of the binary DAG space and SG-MCMC. The proposed method does not rely on DAG regularization nor restricted to linear models, overcoming the limitations of prior approaches. Experimental results demonstrate the competitive p... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback for our paper. We will address the concerns in the following:
1. **Missing MCMC baselines**: We acknowledge the importance of a comprehensive analysis. Note that all MCMC methods are only defined on linear models, usually where the parameters can be marginali... | Summary: The paper proposes a novel Bayesian causal discovery (BCD) method that infer the posterior distribution $p(G|\mathcal{D})$ by projecting the DAG $G$ into an equivalent search space. Instead of sampling $G$, the method constructs the posterior distribution by sampling a binary matrix $W$ and potential vector $p... | Rebuttal 1:
Rebuttal: Thanks for your constructive feedback for our paper. We will try to address the raised concern in the following.
1. **Major concern**: We have included a wall-clock time comparison in the supplementary PDF. From the comparison, we can see our approach, compared to Dibs and BCD, converges faster w... | Rebuttal 1:
Rebuttal: We would like to express our sincere appreciation to all reviewers for their valuable time and efforts in providing constructive feedbacks for our paper. We are delighted to hear that the reviewers generally find our work interesting (hANm, R7H9), sound and well-motivated (3g7c, zwvL), well-writte... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness | Accept (spotlight) | Summary: This paper studies the average precision issue in adversarial training. As attacking a single image may not affect the final accuracy, the average precision could be largely decreased. As a result, such a phenomenon is demonstrated to be harmful to applying adversarial training. To encourage AP robustness, a n... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments. Below we would like to address
your concerns.
**Q1:** About the motivation of this paper.
**A:** First, we'd like to clarify that average precision is important,
especially in scenarios with highly imbalanced datasets, e.g. medical
diagnosis, molecular p... | Summary: The paper focuses on adversarial training in terms of Average Precision (AP), which is guided by three design principles: trade-off between AP and robustness, robustness in terms of AP instead of accuracy, and consistency of attacks. By utilizing the techniques of stochastic compositional optimization, the pap... | Rebuttal 1:
Rebuttal: We first address what the reviewer mentioned as weaknesses and then
respond to the reviewer's questions:
**Q1:** The authors solve a non-zero-sum game to ensure consistency.
However, unlike previous work on adversarial training, the equilibrium
state of this game is unknown and requires more disc... | Summary: This paper considers the adversarial robustness of the AP metric, which is an important measure of deep learning under some imbalanced applications. To do this, the authors develop a novel formulation that combines an AP surrogate loss with a regularization term toward adversarial ranking robustness, maintaini... | Rebuttal 1:
Rebuttal: We thank the reviewer for dedicating their time to provide a
comprehensive review, and we are committed to addressing the raised
issues.
**Q1:** Adding some stronger attacks, such as PGD-based and AutoAttack.
**A:** We appreciate the reviewer's suggestion. First, we'd like to
apologize for the c... | Summary: This paper extends the discussion of adversarial robustness from accuracy to precision, and also extends TRADES solution to this new setting. The paper is fairly standard, with a new problem, a new solution, some minor theoretical studies (obviously also extended from TRADES), and some fairly good empirical re... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback on our paper. In the following,
we are committed to addressing the raised concerns and questions.
**Q1:** About the novel contributions of this work.
**A:** We agree the adversarial regularization is similarly motivated as
TRADES, which has been widely us... | Rebuttal 1:
Rebuttal: We thank the reviewers for your comments and feedback on our paper. We have included some experimental results in the PDF file, including adversarial robustness against $Auto-PGD_{CE}$ white-box attack, adversarial accuracy against white-box iterative FGSM attack on CIFAR10 dataset, and illustrati... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper investigates how to improve the robustness of a model under adversarial attacks while ensuring its Average Precision (AP) on clean data samples. This studied problem can be very important in some application scenarios but has not been extensively explored yet. By integrating the idea of existing adv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
**Q1:** To provide a more comprehensive perspective to evaluate the
robustness of trained models, stronger attack methods, such as
AutoAttack, should be included in experiments.
**A:** We appreciate the reviewer's suggestion. To provide a more... | null | null | null | null | null | null |
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models | Accept (poster) | Summary: This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis for DMs. The proposed framework covers mainstream unconditional and conditional DMs (denoising-based and score-based) and various training-free samplers for holistic evaluations.
Strength... | Rebuttal 1:
Rebuttal: Thank you for the valuable suggestions. We will reply to your comment one by one in the following.
**[Including More Details of ODE Samplers]** Many thanks for your beneficial suggestions. Our paper uses genuine ODE samplers implemented by the library "diffusers." We will also introduce the sampl... | Summary: This paper proposed a backdoor attack framework called VillanDiffusion, which extends the existing backdoor analysis capabilities for deep models (DMs). By encompassing both unconditional and conditional DMs, including denoising-based and score-based models, as well as incorporating training-free samplers, the... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work. Here is our comment on the weakness.
**[The Effective Poison Rate is Too High]** Thank you for your insightful comments. Firstly, in our threat model, the attackers would release the backdoor DMs to the public. As a result, once the utility is high eno... | Summary: This paper proposes a universal backdoor attack framework on diffusion models facing different kinds of content schedulers, different kinds of samplers, and conditional and unconditional tasks.
Strengths: 1. This paper proposes a universal backdoor attack framework on diffusion models, which are important.
2.... | Rebuttal 1:
Rebuttal: Thank you for giving me such valuable advice. We will elaborate on the following points.
**[Main Difference from BadDiffusion]** Thank you for sharing your valuable thoughts. Firstly, from a theoretical perspective, our work is not just an extension of BadDiffusion but a general framework to deal... | Summary: The paper presents VillanDiffusion, a framework for analyzing backdoor attacks on different types of diffusion models (DMs). VillanDiffusion covers various DM configurations such as unconditional and conditional DMs or training-free samplers and provides new insights into caption-based backdoor attacks.
Stre... | Rebuttal 1:
Rebuttal: Thank you for the valuable suggestions. We will reply to your questions in the following.
**[FID Score Increase]** Thank you for the constructive advice. To fully evaluate the threat of VillanDiffusion, we train the backdoor DDPM on CelebA-HQ with 20% poison rate and more training epochs (the ori... | Rebuttal 1:
Rebuttal: ## General Response
Thanks for the insightful comments. We appreciate your precious reviews. Here, we will give a general response to common suggestions.
**[Unlike standard backdoor attacks, backdoor diffusion models require modifying the diffusion process]** Based on the review comments regardi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Refutation of Shapley Values for Explainability | Reject | Summary: In this paper, the authors formally define five anomalies for an
explainability score and prove that for every n >= 4, there exist
Boolean classifiers defined over n features that exhibit one or more
of these anomalies for the SHAP score. In this way, the authors
provide evidence of the inadequacy of Shapley v... | Rebuttal 1:
Rebuttal: There is a misunderstanding in the review.
The bounds proved in the paper are *lower* bounds, and it is stated in
the paper that these are fairly loose lower bounds. The goal of these
bounds is solely to establish that the number of boolean classifiers
for which Shapley values exhibit some sort o... | Summary: This paper reviews previous work on ideas of feature importance and hi-lighted inconsistencies with Shaley values. It defines ideas of importance and irrelevance of features in a Boolean ML model. These definitions are based on the idea of a minimal set of inputs needed to freeze an model output. necessary inp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the in-depth review. We feel there is a
misunderstanding in what is being proved.
Our work builds on the definition of Shapley values for XAI studied in
recent work, namely references [7,8,21,22], but also the more recently
published paper:
[78] M. Arenas, P. Barceló, L... | Summary: The paper demonstrates / constructs functions with features whose Shapley values (i.e., attributive importance in a prediction) is misaligned with their true relevance.
Strengths: - Addresses a theoretical gap in our understanding of Shapley values.
Weaknesses: - I find the problem being investigated to be m... | Rebuttal 1:
Rebuttal: This "review" is unacceptable in any credible conference.
We fail to see how this "review" can even be accepted as a review.
There are no concrete comments on the submitted work.
The only stated strength reads: "Addresses a theoretical gap in our
understanding of Shapley values." This is not tru... | Summary: Based on definitions of feature necessity, relevancy, and irrelevancy from previous work,as well as systematic issues with Shapley values for explainability on boolean classifiers (e.g. non-zero Shapley values assigned to irrelevant features, zero Shapley values assigned to relevant features, among others) ide... | Rebuttal 1:
Rebuttal: We underscore that our paper extends significantly the experimental
results from [35]. The proofs that there exist arbitrary many boolean
classifiers for which Shapley values give misleading relative feature
importance offer a strong argument for why these results should be
presented to a wider au... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models | Accept (poster) | Summary: In this paper, the authors developed a joint model of CLIP and LLM to solve the task of Visual relation detection. In this model, images are encoded into a triplet,~\ie, object, subject and spatial branches. Then it leverages large language models (LLMs) to generate description-based prompts (or visual cues) f... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments. We are willing to address all your questions.
## Technical Novelty and Main Ideas
We appreciate your attention to prompt design for GPT-3.5. While the proposed prompts are tailored to GPT-3.5, the core idea of using compositional visual cues for VRD and utilizi... | Summary: This paper aims to address the VRD problem using LLMs. The paper decomposes the visual features into human, object, and spatial features, and designs prompts to generate visual cues that describe each of these types of visual features. The relation classification is established by calculating the distance betw... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments. We are willing to address all your questions.
## Clarification of Visual Cues
- **Examples in Figure 3 and Figure 4.**
The short prompts showcased in Figure 4 were primarily intended to demonstrate the enhanced accuracy achieved by the Guided Relation Componen... | Summary: This paper proposed a novel method for zero-shot visual relation detection by leveraging LLM (e.g. GPT) and VLM (e.g. CLIP). Specifically, the proposed approach decomposes each predicate category into subject, object and spatial component and enrich each section with the help of LLMs, which can generate the de... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments. We are willing to address all your questions.
## Comparison with More Baselines
**Table_R2**: Comparison with SOTA VRD methods on the VG dataset. Note that none of these methods can be applied in the **training-free** zero-shot setting.
| Model |... | Summary: This paper presents RECODE, a novel method for zero-shot visual relation detection (VRD), designed to address the shortcomings of models like CLIP in distinguishing subtle relation categories and spatial discriminability. RECODE leverages large language models (LLMs) to generate detailed description-based prom... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments. We are willing to address all your questions.
## Analysis of Failure Cases
We sincerely appreciate the reviewer's valuable feedback and the suggestion to include a thorough analysis of failure cases in our paper. We have conducted an in-depth examination of fai... | Rebuttal 1:
Rebuttal: We appreciate the feedback from all reviewers. First of all, we would like to clarify and highlight our **different experimental settings** and **main contributions** over the existing work. Then, we will address all mentioned misunderstandings or questions from each reviewer individually.
## Dif... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Naively utilizing CLIP with prevalent class-based prompts for zero-shot VRD has several weaknesses, e.g., it struggles to distinguish between fine-grained relation types and neglects essential spatial information of two objects. To this end, the authors propose a novel method for zero-shot VRD: RECODE, which s... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments. We are willing to address all your questions.
## Incorporated to More Recent SOTA Models
**Table_R 1**: Performance of combining with different SOTA pre-trained visual-language models on VG dataset. CLS$^\star$ denotes the model uses class-based prompts to com... | null | null | null | null | null | null |
Stein $\Pi$-Importance Sampling | Accept (spotlight) | Summary: This paper presents a novel approach for constructing an MCMC target that is specifically designed for post-processing using Stein importance sampling and Stein thinning, where the goal is to assign optimal weights to a subset of sample particles in order to construct the best possible approximations of a dist... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful report.
> A main advantage of Stein importance sampling (SIS) lies in its ability to provide unbiased estimation when the MCMC sampler used to generate the sample is biased. However, for $\Pi$ sampling, one must set up an unbiased sampler (e.g., MALA) that targets $... | Summary: The paper studies the design of the MCMC algorithm which is well suited for post-processing to obtain consistent approximation $P_n^\star$ of the target measure $P$ using Stein kernel discrepancies ($D_P( \cdot)$). The authors suggest the following novel procedure: (1) choose a measure $\Pi$ which differs from... | Rebuttal 1:
Rebuttal: Thank you for carefully considering our manuscript.
> It is well known that MALA sampler is not good for mixtures of distributions. Is it possible to replace MALA by some other MCMC sampler as HMC or adaptive MCMC?
This can of course be done in practice, but ensuring consistency of the resulti... | Summary: This paper proposes a proposal distribution $\Pi$ to generate finite points
such that a weighted version approximates the target distribution $P$ under
Stein discrepancy.
Strengths: The main strength lies in a new proposal for the sampling distribution $\Pi$ that
is more efficient for follow-up approximat... | Rebuttal 1:
Rebuttal: Thank you for your positive comments on our manuscript.
> the section on Wasserstein distance doesn't seem necessary
Thank you, we will think carefully about how to improve all aspects of our presentation, bearing in mind that Reviewers fuoL and pfRv appreciated this part of the manuscript spe... | Summary: The paper analyses which target distribution to use for MCMC in the situation where a stein-descrepency will be used to post-process it's output samples.
They propose to use a different target distribution for the MCMC to the distribution being approximated, and show this improves performance on a variety of p... | Rebuttal 1:
Rebuttal: Thank you for your kind comments on our manuscript.
> How does using the stein-discrepancy during sample generation (e.g. stein variational gradient descent) compare to using it for post-processing?
There have been some attempts to directly address this issue, in particular Stein Points (Chen ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes a design of a probability density that is more over-dispersed than the target density, so that, somewhat surprisingly, the resulting MCMC samples, after optimally reweighted, can achieve lower KSD than MCMC samples from the true target density. Consistency of the two proposed algorithms, SPi... | Rebuttal 1:
Rebuttal: Thank you for your detailed report.
> it is not clear whether the design (8) is theoretically justified, other than the heuristic argument given in Sec. 3.1
Our proposed $\Pi$ is not the only choice for which consistency can be established; consistent approximation is possible also for $\Pi =... | null | null | null | null | null | null |
Outlier-Robust Wasserstein DRO | Accept (poster) | Summary: This paper empowers WDRO with the ability to resist outliers, building upon the outlier-robust Wasserstein distance $W_p^\epsilon$. The excess risk of the solution to both the outlier-robust WDRO and its empirical version is given. An improved bound of the excess risk is derived for the setting of low-dimensio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address their concerns below:
**Comparison to WDRO with expanded radius:** We agree that this warrants further discussion. Please see **Common Response 2**.
**Tight performance bound for WDRO with expanded radius with low-dimensional featu... | Summary: It is well known that Wasserstein distances do not commute well with total variation distance - a slight perturbation in TV can change the Wasserstein distance by a lot. This means that models that are robust to corruptions in the data distribution in Wasserstein distance can still be vulnerable to outliers or... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address their concerns below:
**Contextualization w/in related work:**
Thanks for raising this fair point. We will add further discussion that compares our results to those in the literature when either $\varepsilon = 0$ or $\rho = 0$.
For... | Summary: This paper introduces an outlier-robust Wasserstein Distributionally Robust Optimization (DRO) framework that aims to capture both geometric uncertainties and non-geometric perturbations, such as adversarial outliers. By utilizing the outlier-robust Wasserstein distance, the proposed framework allows for the a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address their concerns below:
**Parameter selection:**
Thank you for this important question. Please see **Common Response 1**. We further provide a proof of our claim therein that knowledge of $\rho$ is necessary for meaningful risk bounds ... | Summary: This paper introduces a novel approach to make Wasserstein distributionally robust optimization problems robust to adversarial outliers including geometric perturbations and non-geometric contamination of the data. This goal is achieved by considering relevant Wasserstein ball which includes both type of adver... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address their concerns below:
**Relation between adversarial attacks and Wasserstein/TV perturbations:** Thank you for bringing this up. We will add the new Figure 1, along with accompanying discussion, to the introduction to clarify the nat... | Rebuttal 1:
Rebuttal: $\newcommand{\cG}{\mathcal{G}}\newcommand{\ep}{\varepsilon}\newcommand{\RWp}{\mathsf{W}_p^\ep}\newcommand{\E}{\mathbb{E}}\newcommand{\R}{\mathbb{R}}\newcommand{\sg}{\sigma}\newcommand{\mr}{\mathrm}$**Common Response:**
We thank the reviewers for their time and feedback. Below we provide a common ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewards | Accept (poster) | Summary: The paper considers a general multi-objective sequential decision-making setting where each objective may use a different discount factor. Using an axiomatic approach, the authors prove that under some axioms (vNM axioms + dynamic consistency for the relation on each objective), an aggregated preference relati... | Rebuttal 1:
Rebuttal: Thank you for your helpful commentary. Please see the General Response above re: most points.
Otherwise, the reason Section 5 is somewhat of a hybrid is because (1) the parts about intertemporal choice and stochastic preference necessarily introduce "related work" that wouldn't fit, or would be ... | Summary: The authors analyze the implications of preference aggregation within a Markov Decision Process Framework. They show that it is not possible to ensure dynamic consistency in an aggregated MDP if one also wants to be able to accommodate arbitrary preference criteria, even if the criteria are individually dynami... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and helpful commentary/questions.
> The authors attempt to motivate the contribution in the context of the current "Reward is Enough" debate in RL. This is tantalizing, but seems to me a bit forced. …
We think it is relevant for the following reason:
- Suppose... | Summary: This paper examines multi-objective reinforcement learning---the setting in which multiple distinct objectives are desired, and often combined, to form a composite objective. Concretely, the paper explores the limits of aggregating different objectives by appealing to three main pools of ideas. First, to the v... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and actionable suggestions. Please see the general response for the most important points. Otherwise:
**Primary Rec:** Regarding your suggestion to move Section 4 to the Appendix, we assume you are referring to just the “Relaxing Markov Preference” section (up t... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and detailed reviews. We find the reviewers have understood our work and have provided helpful suggestions. We are acting on several of the suggestions, as noted below, and agree the changes will improve the paper. We welcome any additional feedback and questi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms | Accept (poster) | Summary: The paper introduces a new algorithm for multi-armed bandit problems, leveraging the FTRL framework and the flexible $\beta$-Tsallis entropy family of regularizers, where $\beta \in [0,1]$. This algorithm firstly uses a new learning rate schedule to offer best-of-both-worlds guarantees for a wide range of regu... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. Please see our responses below:
***
**Q1:** There are few undefined notations used in the analysis. For instance, Equation (6) introduces the notation $D_U$
without providing a clear definition, and the same issue applies to $\phi_U(x)$
and $\phi_V(x)$
in Eq... | Summary: The authors focus on best-of-both-worlds (BOBW) algorithms based on follow-the-regularized-leader (FTRL) in multi-armed bandits.
The theoretical guarantees for most existing FTRL-based BOBW algorithms were based on the assumption that the best arm is unique in order to take advantage of self-bounding technique... | Rebuttal 1:
Rebuttal:
***
**Q1:** One weakness would be a discussion of whether removing the assumption of unique optimal arm actually improves or worsens the performance of algorithms (The reviewer expects the algorithm becomes more conservative, and the performance becomes worse.)
**A1:**
We in fact do not belie... | Summary: This paper studies the problem of designing adaptive multi-armed bandit algorithms that perform optimally in both the stochastic setting and the adversarial setting simultaneously (often known as a best-of-both-world guarantee). The authors show that the uniqueness assumption is unnecessary for FTRL with a bro... | Rebuttal 1:
Rebuttal: ***
**Q1:** This paper does not provide any experimental result. It would improve the paper if the authors could conduct empirical evaluation for their algorithms and compare to existing BOBW algorithms, to validate their theoretical results.
**A1:** We thank the reviewer for this suggestion. A... | Summary: This paper considers the problem of proving best of both worlds guarantees for algorithms based on the FTRL framework for the multi-armed bandits problem. While it has been demonstrated in (Zimmert and Seldin (2019,2021)) that Tsallis-INF (FTRL with the $1/2$-Tsallis entropy regularizer) achieves optimal regre... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. Please see our responses below:
***
**Q1:** Unlike Ito[2021], the provided bounds include an added term of order $\frac{|U|\log T}{\Delta_{\min}}$
where $U$
is the set of optimal arms and $\Delta_{\min}$
is the smallest sub-optimality gap. Thus, the bounds ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
High-dimensional Asymptotics of Denoising Autoencoders | Accept (spotlight) | Summary: This paper studies the performance of denoising autoencoders (DAEs) in high-dimensional settings. The DAEs are trained on data sampled from a Gaussian mixture with $K$ components perturbed by isotropic Gaussian noise. The DAEs are one-layer networks with arbitrary activation functions, tied weights, and a skip... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We answer their questions below:
>The authors should emphasize the results that their analytical formulas allow to obtain.
We believe a strength of our analysis lies in the fact that it allows to characterize learning metrics at the global opt... | Summary: The authors consider a two layer weight-tied denoising autoencoder with a skip connection in the regime of vanishing rate and samples proportional to the dimension. They heuristically derive an exact characterization of the optimal network parameters and the corresponding network performance in the the high-di... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments. We answer below their questions:
>It should be stated more clearly that the way the replica method is carried out only serves as a strong heuristic and not a rigorous proof.
The reviewer is entirely correct about the heuristic nature of the ... | Summary: This paper presents theoretical results of the test error of 2-layer denoising auto-encoder. In a high-dimensional limit regime where data distribution follows from Mixture of Gaussian, closed-form expressions of the error are obtained. The results are further analyzed and supported by numerical experiments on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful questions, which we answer below:
>The optimal MSE error $mse_o$ grows with the data dimension d (eq. 9), however the gap between $mse_f$ and $mse_o$ (eq 8) remains a constant, this suggests that the difference between DAE and PCA is not so significant i... | Summary: The authors set out to characterize the non-linear behavior of denoising auto-encoders (DAEs), for Gaussian mixtures, in the high dimensional limit with the number of hidden units being fixed. The authors particularly tease out the role of the skip-connection, compared to the reconstruction auto-encoder (RAE) ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work. We address below their questions:
>One would wish the long sequence of mathematical expressions could be made less opaque.
We will add further discussion beneath (13) and (14) in the revised manuscript, and provide qualitative insights in... | Rebuttal 1:
Rebuttal: We attach here a .pdf, containing additional figures which we refer to in the separate rebuttals.
Pdf: /pdf/79c55a2ca4fc0047eaeefcfab83c8995f6dcfb6a.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Provable benefits of score matching | Accept (spotlight) | Summary: The authors describe an exponential family where the sufficient statistic $T(x)$ contains all non-constant monomials of degree $\leq d$, the background density is $h(x) = \exp(-\sum_{i=1}^n x_i^{d+1})$, and the parameter $\theta$ is constrained to have infinity norm bounded by $B$. Using a reduction from $3\ma... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and for a very careful reading. We'll fix the typos -- as the reviewer notes, a few constants will have to be updated but the main results are all unchanged (up to some constant factors in the exponents). To be a bit more precise about the more mathematical typ... | Summary: This paper provides an example of fitting exponential family models for which score matching
and MLE are both statistically efficient, but MLE is computationally hard to optimize.
Strengths: The strength is in the construction of an example to showcase the benefit of score matching
over MLE.
Weaknesses: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. To address their questions:
**Q:** *``First, for the computational lower bound, the points to evaluation loss and gradient is worst case. When one has samples from such distribution, is solving MLE still computationally hard?''* Good question; un... | Summary: In this paper, the authors present a mathematical setting where Score Matching (SM) method has more statistical benefits than Maximum Likelihood (ML) technique, when estimating a parameterized probability distribution $p_\theta \in P(\mathbb{R}^n)$ known up to a normalizing constant $Z_\theta$. In particular, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. To address the reviewer's questions:
**Q:** *``Why do the authors do not keep the whole dependence in $d$ in the bound for Theorems 2 and 3 ? Does it change something between SM and ML ?''* In both cases the dependence is $O(d^3)$. We stated the ... | Summary: The paper attempts to elucidate the theoretical reasoning for the benefits seen in score matching. The author proposes a family of exponential distributions that can efficiently compute the score matching loss while having a comparable statistical efficiency to that of maximum likelihood.
Strengths: - The pa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. To address the reviewer's questions:
**Q:** *``What is the benefit of using this score matching method than taking the denoising score matching approach?''* Recall that the latter is score matching applied to an annealed version of the distributi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Collaboratively Learning Linear Models with Structured Missing Data | Accept (poster) | Summary: The paper discusses the idea of estimating least squares collaboratively when each agent has access to a different set of features of the same data. The aim is to design an effective and efficient algorithm in terms of communication cost (various agents transferring/communicating information/data). The paper i... | Rebuttal 1:
Rebuttal: Thank you for your review.
**Response to W1: limited assumptions and evaluations**: We admit that our current theory relies heavily on Gaussianity assumption.These generalizations have various technical challenges, which make giving strong theoretical guarantees (i.e., what we were able to do in ... | Summary: The authors investigate collaborative learning of least squares estimates for multiple agents with varying feature subsets. The goal is to coordinate the agents efficiently to achieve optimal estimators without exchanging labeled data. To address this, the authors propose the distributed algorithm Collab, cons... | Rebuttal 1:
Rebuttal: Thank you for your review.
**Response to justifications for our setting**: Regarding the satellite application, we agree it is a stylized example and have expanded the introduction to discuss other potential applications like sensor networks, weather stations, and hospitals. The key aspects we wi... | Summary: The paper studies statistical inference in learning a linear regression model in the cross-silo or vertical federated learning setting. The paper formulates the problem as a missing data problem and chooses single imputation methods to deal with the associated inference of the common parameter. The paper shows... | Rebuttal 1:
Rebuttal: Thank you for your review.
**Based on your comments, we believe there is a misunderstanding**. We are not doing vertical federated learning [Liu et al.]. Unlike vertical federated learning, agents in our framework are not measuring the same underlying set of users. Vertical federated learning is... | Summary: Summary
-------
The paper studies collaborative linear regression when m agents attempt to collaboratively
estimate a linear model, under communication constraints. Each agent i only observes di
of the d features. A central server designs a protocol to elicit sufficient information
from each agent and compute... | Rebuttal 1:
Rebuttal: Thank you for your review.
**Response to baseline strength**: Our method is asymptotically instance-optimal (shown by our lower bounds), meaning that no algorithm could perform statistically better on any specific problem instance. In some sense, our lower-bound is the ultimate theoretical “base... | Rebuttal 1:
Rebuttal: We want to clear up some possible confusion due to a typo we made. Our method Collab only needs the sample covariance $\hat{\Sigma}\_{i+} = X\_{i+}^\top X\_{i+}/n$ --- **not** the population covariance $\Sigma\_{i+}$ --- for our results to hold (see Algorithm 1 for the correct pseudocode). In othe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studied the problem of collaboratively learning least squares estimates with multiple agents, each of which only observes a different subset of the features. The authors proposed a distributed, semi-supervised algorithm called Collab consisting of three steps: 1) local training, 2) aggregation, and ... | Rebuttal 1:
Rebuttal: Thank you for your review.
**Response to weaknesses**: Our theory is indeed limited to Gaussian features. We did experiments on non-Gaussian data in our Folktables experiment. Though it's only a preliminary experiment, we hope that it shows our method does not overfit to the Gaussian data settin... | null | null | null | null | null | null |
Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning | Accept (poster) | Summary: The paper presents an optimal asymmetric graph structure for the label inference phase in graph-based semi-supervised learning (GSSL). The key motivation or intuition proposed by the authors is that we need to differentiate the roles of labeled and unlabeled nodes. Therefore, the authors design an efficient bl... | Rebuttal 1:
Rebuttal: Thank you for your very thoughtful review with constructive suggestions. We appreciate the recognition of our optimal graph construction approach in GSSL. We are glad to know that you find our paper novel, high-quality, rigorous with solid theoretical insights, well-organized with good writing, an... | Summary: The paper proposes a method for graph construction stage of graph based semi-supervised learning. They further evaluate their method with experimental results.
Strengths: The authors present strong theoretical results.
Weaknesses: The experimental results for the proposed method in Table 2 are only margina... | Rebuttal 1:
Rebuttal: Thanks for your recognition of our work! We highly appreciate your feedback!
**We did a statistical significance test in the experiment section**. Specifically, we perform the Friedman test with the Bonferroni-Dunn post hoc test for statistical significance analysis. Figure 2 illustrates the crit... | Summary: This paper proposes an efficient and effective method for constructing affinity graphs in Graph-based Semi-supervised Learning (GSSL), with a focus on the distinct roles of labeled and unlabeled nodes. The authors present a formulation for the GSSL problem, comprising two steps: graph construction and label in... | Rebuttal 1:
Rebuttal:
Thank you for your very thoughtful and constructive review. We appreciate the recognition of our optimal graph construction approach in GSSL. We are glad to know that you find our paper novel, well-written, rigorous, well-organized, and making a significant contribution to the field of GSSL. We w... | Summary: This paper proposes a novel methodology for graph-based semi-supervised learning by leveraging a asymetric graph construction technique. The main contribution of the paper is the design of a block-wise graph learning framework to estimate the weights of a graph.
Strengths: The main strengths of the paper are:... | Rebuttal 1:
Rebuttal:
Thanks for your detailed review. We appreciate the recognition of our derivation of the optimal affinity graph with thorough experimental evaluation. We would like to thank you for your suggestions for improvement and have addressed each of your points below. We hope these responses will address... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors proposes to solve graph-based semi-supervised learning (GSSL) problems by first finding the "optimal graph" for SSL. The optimal graph has edges only from labeled to unlabeled nodes, or between unlabeled nodes. These edge weights are computed through FISTA algorithm in the dual space, and theoretic... | Rebuttal 1:
Rebuttal: Thank you for your very thoughtful and constructive review. We appreciate the recognition of our optimal graph construction approach in GSSL. We are glad to know that you find our paper solid in all aspects - problem formulation, clever optimization algorithm, theoretical convergence analysis, and... | null | null | null | null | null | null |
Language-driven Scene Synthesis using Multi-conditional Diffusion Model | Accept (poster) | Summary: This paper approaches the task of predicting a location and orientation of furniture, conditioned upon a person’s motion sequence, existing furniture, and text. By conditioning on text, which prior work does not do, the proposed method enables users to actively specify furniture location. In addition, experime... | Rebuttal 1:
Rebuttal: Thank you for your valuable review.
**Q1: How the training works for scene synthesis? The guiding points network is trained jointly, end to end with LSDM?**
>The key reason why the training works is because of our theoretical findings. Eq. (7) shows $\tilde{S}$ explicitly contributes to the deno... | Summary: This paper focuses on language-driven scene synthesis, a new task integrating text prompts, human motions, and existing objects as multiple conditions. The proposed task is challenging as it requires a strategy for encoding the multi-modal conditions into a unified space. To solve the problem, the authors intr... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful review.
**Q1: As MIME utilizes a sequence of human motions, your method only utilizes a single-frame motion. Does your model exclusively utilize a single-frame human pose as input rather than a motion sequence?**
>Yes. We believe this distincti... | Summary: This paper targets to generate 3d scene by conditioning on text prompts and other inputs, e.g., room layouts. For this purpose, they operate on 3d point cloud representation and propose a multi-conditional diffusion model to generate guiding points to achieve 3d scene synthesis purpose. The experiments are ev... | Rebuttal 1:
Rebuttal: We greatly appreciate your valuable feedback and thoughtful review. Please see below our responses and let us know if you have any further questions.
**Q1: Could you justify more on the method choice of diffusion model in this task?**
>There are three reasons to leverage the diffusion model in ou... | Summary: This paper deals with scene synthesis with human pose, room layout, and text prompts.
The main architecture is a multi-conditional diffusion model, which performs progressive generation, where a new object is synthesized and conditioned on the existing scene point cloud and the language description.
The key co... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and valuable feedback.
**Q1: Why do you assume that $x_0$ is a uniform distribution over a domain $S$?; and Why $x_0$ be uniform is a good assumption?**
>Our assumption is based on the fact that we uniformly sample the point cloud out of each object. For your... | Rebuttal 1:
Rebuttal: **General Response**
Dear ACs and Reviewers,
Thanks for your valuable reviews and insightful comments, which have helped us improve our paper. During the initial reviews, Reviewers **uswL**, **aT28**, **c9eB** were inclined toward acceptance. We are glad that our proposed language-driven scene s... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a new task named language-driven scene synthesis. This new task takes text prompts, human motion, and existing objects to generate the next object in the scene. To handle the multiple conditions, they design a guiding points strategy to unify them. It first explicitly predict... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable feedback.
**Q1: Application of the proposed new tasks? And the "chair" user case in VR.**
>We acknowledge and agree with your example regarding the feasibility of the chair in VR settings, as users do not physically sit on the chair. However, we ... | null | null | null | null | null | null |
What can a Single Attention Layer Learn? A Study Through the Random Features Lens | Accept (poster) | Summary: This paper explores the learning capabilities of a single attention layer, assuming keys and queries to be random and frozen (as in the random features model).
Strengths: The paper is very well written, and the technical claims look formally supported.
The paper deals with an important problem, which is to t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We respond to the questions as follows.
> You consider single query token models... Has it been considered in previous theoretical work?
The family of target functions considered in the discussion doesn't contain terms that consider the interaction... | Summary: The paper considers the representational and generalization properties single-layer scalar-valued transformer models with random key and query matrices and value vectors that can depend on those random matrices. They draw a comparison to the well-studied random-feature models for two-layer neural networks. Con... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and the suggestions on our paper. We would appreciate if you could champion our paper in the discussions!
>While the bounds are interesting in their own right, the comparisons between random feature attention and MLP models focus on a few particular examples, ... | Summary: The paper examines the capabilities of a single-layer multi-head attention layer in a scenario where the Key and Query matrices are predetermined and randomly selected from a Gaussian distribution. The only modifiable component is the Value matrices, and when provided with a convex loss, the minimization probl... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We respond to the comments as follows.
### Response to questions regarding the setting and message:
>A major weakness is that instead of the common softmax function, the authors opt to use the ReLU function.
A significant portion of our results ... | Summary: This paper first studies the expressive power of a random-feature attention layer and then provides the generalization gap of the attention layer. A sample complexity bound is shown, which indicates a larger number of attention heads help the generalization. This paper also compares the results between attenti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions.
> Equation 3 is important for further derivation. From my understanding, Equation 3 indicates that the attention map is fixed for all data. I think attention layers usually have different attention maps for different data. I belie... | Rebuttal 1:
Rebuttal: **Additional Response to All Reviewers**
We thank all reviewers again for their valuable feedback on our work.
Here we would like to highlight our contributions again, which we believe were missed by some reviewers:
- The random features (RF) model is an important and widely-studied model in d... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships | Accept (poster) | Summary: This paper aims at fine-grained representation learning. The authors state that local-to-global relationships leveraged in recent fine-grained visual categorization (FGVC) works are abstract. To make such abstract relational representations more human-understandable, the authors first theoretically show the ex... | Rebuttal 1:
Rebuttal: **1. Definition of abstract** Although we briefly refer to what we mean by “abstract” in Line 23-24, we agree that it requires further elaboration and detailing. We provide an in-depth definition below, which we will also add to the final version.
Existing works that leverage relational informati... | Summary: The authors propose TRD, an algorithm that decomposes both input images and output classes into graphs over views by recovering transitive cross-view relationships for fine-grained visual categorization.
Strengths: 1. The paper is well written and easy to follow.
2. The proposed TRD is demonstrated both theor... | Rebuttal 1:
Rebuttal: **W1. Experimental settings** We agree that the number of local views is the only hyperparameter in which we differ from Relational Proxies, but the remaining settings are the same (Line 264). We note that our experiment on robustness (Figure 5) tests both TRD and Relational Proxies with the same ... | Summary: The paper presents a novel perspective on interpretable representation learning, introducing Transitivity Recovering Decompositions (TRD) as a method for identifying graphs that can learn local-to-global representations. The proposed approach achieves state-of-the-art (SOTA) performance on Fine-Grained Visual ... | Rebuttal 1:
Rebuttal: **W1. Overview** We thank the reviewer for pointing this out. We will add the following to the final version:
"After decomposing an input image into its constituent views following related literature [9, 78, 88], we initialize the relational representation by forming a graph through connecting com... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable comments and feedback. We have addressed their individual concerns in their respective rebuttal sections. Here we attach some qualitative results for addressing the comment by Reviewer mGG3 on experimenting with causal interventions.
N.B. It can be observ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective | Reject | Summary:
This paper introduces a novel approach to adversarial attacks that goes beyond traditional norm bounded attacks. Instead, the proposed method focuses on unrestricted attacks that are both effective and capable of preserving the semantic meaning of the input data.
The method utilizes Langevin Monte Carlo tech... | Rebuttal 1:
Rebuttal: Thanks for reviewing! Below is our response:
### Weaknesses
1. This attack costs more than traditional methods because we have to fit an energy-based model for each instance if we want to generate adversarial example based on this instance. In our global response, we added CIFAR10 experiment.
2.... | Summary: The adversarial examples generated by classical methods such as PGD have different semantic meaning to the original label, which means that the adversarial examples are easy to be distinguished by human. In this paper, the authors focus on the generalization of adversarial example which preserves the original ... | Rebuttal 1:
Rebuttal: Thanks! Here are our responses to each of the points you've raised in your concerns:
### Weaknesses
- Referring to Figure 1 in our global response PDF, the adversarial samples produced by PGD on CIFAR retain their semantics. However, the attacked images exhibit visual features, such as unnatural ... | Summary: This paper proposes to generate semantics-preserving adversarial examples by framing the construction of adversarial examples as a box-constrained non-convex optimization problem. More specifically, the authors propose a Langevin Monte Carlo (LMC) technique to craft adversarial examples that preserve the meani... | Rebuttal 1:
Rebuttal: Thanks for reviewing! Below is our response:
> ... the approach is only evaluated on MNIST and SVHN ... Also, studying the transferability property of their attacks would strengthen their paper, and give more confidence to the readers about the strength of their attacks.
We've incorporated a CIF... | Summary: In this work, a probabilistic view of adversarial examples based on the [projected stochastic gradient Langevin algorithm](https://proceedings.mlr.press/v134/lamperski21a.html) is introduced and used as an optimization algorithm instead of the SGD or Adam optimizer for adversarial examples. In addition, the ge... | Rebuttal 1:
Rebuttal: Thanks! Here are our responses to each of the points you've raised in your concerns:
### Weakness
- The assertion that our method 'transcends the restriction imposed by geometric distance' is based on a theoretical perspective, as outlined in lines 112-116. In this context, geometric distances l... | Rebuttal 1:
Rebuttal: We extend our gratitude to all reviewers for their insightful feedback.
Attached is a PDF containing relevant figures and tables for your reference.
During the rebuttal phase, we've incorporated four additional experiments to enrich our original manuscript:
### CIFAR10 experiment
We've introdu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Improving Diffusion-Based Image Synthesis with Context Prediction | Accept (poster) | Summary: This paper proposes to improve diffusion-based image synthesis by explicitly reinforcing each point to predict its neighborhood context during training, without extra cost at inference. To reduce computation/time complexity of context decoding the authors propose efficient large context decoding adopting Wasse... | Rebuttal 1:
Rebuttal: *We thank Reviewer 2mXr for the positive review and valuable feedback. We are glad that the reviewer found that the paper is well-written and easy to follow, the method is well-motivated with effective designs, and the method is proven effective in boosting FID scores with both continuous and disc... | Summary: This paper proposes to improve diffusion based image generative training objectives by adding context prediction loss. The motivation of predicting context comes from other non-diffusion based models like semantic segmentation and representation learning. To mitigate the complexity of predicting large per pixe... | Rebuttal 1:
Rebuttal: *We thank Reviewer ydZz for the positive review and valuable feedback. We are glad that the reviewer found that the introduction is well-written, the motivation is easy to follow, the presentation of the method including the loss derivation and the training pipeline is easy to understand, and the ... | Summary: This paper presents ConPreDiff, a method introduced to improve the performance of diffusion models by preserving the neighborhood context of predicted pixels/features. They achieve this by predicting the neighborhood context during the diffusion generation process. To simplify the modeling complexity, they pro... | Rebuttal 1:
Rebuttal: *We thank Reviewer DpwQ for your valuable feedback. We are glad that the reviewer found that our idea is intuitive and easy to understand, the proposed method is general and can be easily applied to recent diffusion models, and the performance of the proposed method is very impressive. Please see ... | Summary: This paper proposes an idea of context prediction to boost difussion-based image generation.
The core idea is that in each step of diffusion, after the denoised point is generated, neighborhood context prediction is performed.
In particular, to maintain the spatial orders of the neighborhood, a permutation inv... | Rebuttal 1:
Rebuttal: *We thank Reviewer qzoS for the positive review and valuable feedback. We are glad that the reviewer found that the idea is very interesting and sound, and performance improvement showed in experiments is promising. Please see below for our responses to your comments.*
**Q1: The proposed approach... | Rebuttal 1:
Rebuttal: ## Global Response
We sincerely thank all the reviewers for the thorough reviews and valuable feedback. We are glad to hear that the idea is interesting and well-motivated (all reviewers), the paper is well-written and easy to follow (Reviewer m5Qc, ydZz, and 2mXr), the proposed method is general ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper is proposing context-aware Diffusion Models. They make the models learn the context information by setting up auxiliary networks to estimate the neighbor distributions from the estimated denoised sample from Diffusion Models. The benefit of this approach is that additional cost from the auxiliary ne... | Rebuttal 1:
Rebuttal: *We thank Reviewer m5Qc for the positive review and valuable feedback. We are glad that the reviewer found that the motivation is agreeable, the writing is good, the method for motivation is reasonable, and experiments are done well. Please see below for our responses to your comments.*
**Q1: Int... | null | null | null | null | null | null |
Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications | Accept (spotlight) | Summary: This paper studies criticality of deep neural networks at initialization. The authors propose a new practical way to diagnose criticality by introducing the partial Jacobian of the network and analyzing the averaged partial Jacobian norm (APJN) and its recurrence relation at large depth. The authors then apply... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
## Training Dynamics and Relation to NTK
The training regime that we target enjoys feature learning and large learning rates, and hence, is far from the NTK regime. While it is possible to connect APJN with NTK and analyze linear dynamics, th... | Summary: The paper addresses the theoretical treatment of deep neural networks and introduces a novel practical approach to identify criticality within these networks. The authors work in the setting where the number of parameters per layer approaches infinity, enabling the formulation of quantitatively predictive desc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback and suggestions for improvement.
## Presentation and Clarity
We welcome the suggestions to improve the presentation of our paper:
- We will include a discussion on limitations and future work in Section 7 (Conclusion).
- We will change the Se... | Summary: The paper studies the effect of the expected value of the Jacobian norm of a particular layer with respect to a previous layer as the depth of a neural network (NNs) increases. The study is done under the assumption of infinite width NNs, and with the goal of assessing sensitivity to initialisation hyperparame... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and detailed suggestions on the presentation.
## Clarity
We believe that the logical flow of the current manuscript is similar to what the reviewer has proposed. We state the outline of our paper here -- we will add a version of this in the In... | Summary: The paper presents a theoretical framework for understanding the trainability of deep neural networks with LayerNorm and residual connections. The authors derive analytical expressions for the neural network Gaussian process (NNGP) kernel and the partial Jacobian norm (PJN) for a wide range of activation funct... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback.
## Infinite width limit
Formally, our theoretical results become crisp when $L/N \sim o(1)$ (width $N$, depth $L$). In practice, our conclusions apply whenever this ratio is small. For instance, all the phase diagrams in figures 2,4,5 are obta... | Rebuttal 1:
Rebuttal: # Global Response to All Reviewers
## Infinite width limit
For analytical results, we first take the infinite width limit and then a large depth limit. Stated differently, it means that the depth-to-width ratio $L/N \sim o(1)$. In practice, this assumption holds as long as $L/N$ is small. Moreov... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Small batch deep reinforcement learning | Accept (poster) | Summary: The paper demonstrates the advantages of employing smaller batch sizes in value-based RL algorithms. It reveals that utilizing a smaller batch size can moderately or significantly enhance performance across several value-based RL algorithms, with the exception of DQN, where there are no improvements. Nonethele... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and appreciate the positive evaluation.
Indeed, our intuition matches yours: the increase in variance from both fronts (smaller batch size and multi-step updates) seems to have a positive effect on performance. It is possible that they are adding different t... | Summary: This work studies the effect of reducing the batch size in value-based deep RL algorithms. Surprisingly, the authors find that smaller batch sizes generally improve learning performance and speed up training in terms of wall-clock time. Towards understand this "small batch effect", they empirically investigate... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review of our paper, and suggestions for improvement. We address your concerns and questions below, referencing the PDF attached to the general response above. We will also correct all the minor issues pointed out in our submission.
## The study only consider... | Summary: The work investigates the influence of replay batch size in experience replay for online reinforcement learning. The key finding is that reducing the batch size can be more beneficial, which contradicts common knowledge about regular deep learning.
Strengths: The paper expands the analysis of an underinvestig... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review of our submission, and for pointing us to the paper on experience replay in continual learning, it is indeed quite related.
Wołczyk, M., & Krutsylo, A. (2021) investigate the dynamics of experience replay in online continual learning, and focus on th... | Summary: The authors study how batch size affects RL performance, and argue that a reduced batch size might (quite surprisingly) bring better performance improvement in a number of settings, in particular for QR-DQN, a smaller batch can lead to much better performance (almost doubling the performance). Different batch ... | Rebuttal 1:
Rebuttal: We thank the reviewer for a careful read of our submission, and concrete suggestions for improving it. We address each point separately below, referencing the PDF attached to the general rebuttal at the top. We hope our responses, and the amendments we will make to our paper based on the points ra... | Rebuttal 1:
Rebuttal: Dear reviewers and (S)ACs, we are attaching a PDF with three figures that we reference in each of our reviewer-specific rebuttals. The figures are:
**Figure 1:** Gradient variance analysis (with corresponding reward curves) for five extra Atari 2600 games, that help strengthen the claim of correl... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Progressive Ensemble Distillation: Building Ensembles for Efficient Inference | Accept (poster) | Summary: The paper addresses the problem of obtaining an ensemble of small models suitable for flexible inference requirements and anytime inference, somewhat similar to cascading classifiers. A key contribution is the derivation of a weak learning condition for the distillation of a pre-trained to an ensemble of small... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and questions.
**Inference time calculations.** The inference time numbers are end-to-end numbers reported by the DeepSpeed profiler on NVIDIA 3080Tis. The No-RESCHED baseline is used as an idealized baseline, and a modified version trained using distillat... | Summary: This paper studies the problem of "progressive distillation": Given a large teacher model, the task is to decompose into smaller student model so that progressively evaluating additional models in this ensemble results into more accurate predictions.
Τhe main contributions of this paper are:
(i) A principle... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback.
Although our algorithm seems sophisticated, we note that most of the additional sophistication (on top of standard boosting) is restricted to FIND-WL sub-routine. Conceptually, the high-level algorithm has the same flavor as many boosting methods,... | Summary: This paper proposes B-DISTILL, a progressive distillation algorithm that allows for easy trade-off between accuracy and inference-time/latency at runtime. By modeling knowledge distillation as a zero-sum game problem, B-DISTILL utilizes the intermediary connection modules to train and aggregate the sub-student... | Rebuttal 1:
Rebuttal: Thank you for constructive feedback and questions, which we hope we have addressed in our response below.
**Application scope.** We kindly disagree with the reviewer that the scope and advantages of our work are quite limited. Due to resource constraints in efficient inference applications it is ... | Summary: The authors describe a new method for knowledge distillation with an ensemble of lower capacity student models, and draw connections between their method and classical boosting approaches. They provide a theoretical analysis of the risk of this method, and demonstrate the benefits of their approach in learning... | Rebuttal 1:
Rebuttal: Thank you for your many great suggestions for improving the presentation of our work, particularly the problem formulation.
**Boosting, two player games: relationship, terminology and notation.** Due to space limitations we shortened the exposition on boosting, zero-sum games and weak-to-strong l... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The main focus of this paper is to address a problem in progressive knowledge distillation, which involves approximating a single large teacher model by utilizing an ensemble of multiple smaller student models. The authors propose an algorithm called B-DISTILL to tackle this specific problem. One notable advan... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and positive assessment of our work.
**Scalability:** Although B-DISTIL takes additional inference time to make accurate predictions for ImageNet, the teacher models in this case have 100+ layers. Tasks in efficient inference that rely on smaller models for... | null | null | null | null | null | null |
Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis | Accept (poster) | Summary: The authors design robustness-aware quantization (RAQ) to speed up the noise estimation network by leveraging the robustness of early-stage diffusion models. Specifically, the authors found that the quality of generated images is less affected by the early-stage. Therefore, they reduce the bitwidth of activati... | Rebuttal 1:
Rebuttal: **Response ctiM-1: The granularity of bitwidth optimization**
In the context of the RAQ method outlined in Algorithm 1, choosing a finer granularity for Bit_act update necessitates a larger number of sampled images for the optimization process. Meanwhile, our investigation revealed that consecuti... | Summary: This paper proposes to quantize diffusion models to a different extent along the iterative process for image generation. The main motivation of the proposed approach is that diffusion model is robust to input distortion at early stages (i.e. noisy stages) of the iterative process. Therefore, the proposed appro... | Rebuttal 1:
Rebuttal: Thanks for the constructive comment.
We will now discuss 1) the bitwidth selection for different models, and 2) improvement of the sampling efficiency with RAQ.
**Response 4DBQ-1: The bitwidth selection for different models**
As you correctly highlighted, the bitwidth optimization with the prop... | Summary: This paper presents robustness-aware quantization (RAQ), a novel strategy to use mixed precisions for activations when quantizing diffusion models. The authors found that inaccurate computation during the early stages of the reverse diffusion process has minimal impact on the quality of generated images, and p... | Rebuttal 1:
Rebuttal: Thanks for the constructive comment.
We will now discuss 1) the practicality of the proposed RAQ, and 2) the granularity of Bit_act update.
**Response ZFyZ-1: The practicality of the proposed RAQ.**
As the reviewer rightly pointed out, we agree that accelerating diffusion models quantized with ... | Summary: The author initially notes that errors in the early stages of the reverse diffusion process result in minimal disturbance to the final generated image. As a solution, they suggest employing low-bit activations for the initial reverse diffusion process while preserving high-bit activations for the subsequent st... | Rebuttal 1:
Rebuttal: Thanks for the constructive comment.
We will now discuss 1) relationship between entropy and image clarity, 2) exclusion of comparison with PTQ4DM, 3) the effects of noise addition on images with varied clarity levels, 4) the performance difference between two datasets in Figure 3, 5) correction ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this submission, the authors propose a novel approach to speed up the noise estimation network by leveraging the robustness of early-stage diffusion models. Specifically, they present an algorithm to modify the quantization bit width according to the diffusion step. The proposed method shows positive result... | Rebuttal 1:
Rebuttal: **Response eumA-1: The influence of RAQ on accelerating diffusion models**
As the reviewer rightly pointed out, advanced samplers have been recently presented to reduce the sampling steps, and we agree that accelerating diffusion models quantized with irregular bit widths on a GPU poses a challen... | null | null | null | null | null | null |
A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP) | Accept (poster) | Summary: This paper performs a comprehensive study of various CLIP models on robustness to different visual factors, out-of-distribution detection, and calibrated uncertainty estimations. A total number of 53 CLIP models trained on different training sources and sizes, and different architectures, with additionally 32 ... | Rebuttal 1:
Rebuttal: >Q1: Figure 2, fine-tuning CLIPs on ImageNet (with supervised objective?) decreases shape bias … data source (ImageNet) could be the reason … fine-tune CLIP with contrastive objective to see if shape bias stays the same.
Insightful suggestion. All fine-tuned CLIP models in Fig. 2 use supervised ... | Summary: This paper studies and compares CLIP and CLIP-FT to standard models on a range of different tasks including OOD robustness, OOD detection, and model calibration. The paper constitutes a meta-analysis across different model architectures / training datasets / training algorithms or loss functions.
The authors ... | Rebuttal 1:
Rebuttal: >Q1: No clear central argument ... unifying principles
Thanks. Our central argument is the necessity of a comprehensive evaluation of CLIP's robustness. In contrast to current approaches focused on classification accuracy, we propose integrating three new safety-driven objectives: factor-level ro... | Summary: Authors closely study the robustness of vision-language models. They try to investigate their robustness in terms of common visual attributes, detecting OOD inputs, and their power in providing calibrated predictions. They consider many different CLIP models and other vision encoders with different architectur... | Rebuttal 1:
Rebuttal: >Q1: Did not see enough new ideas ... running extensive studies is definitely valuable, practical, and insightful, but is there other similar work published in NeurIPS where scaling up and running more experiments is the main contribution?
We appreciate your recognition of our extensive studies a... | Summary: This paper analyzes the CLIP model's robustness through a large number of experiments, including three main points: resilience to visual factor variations, calibrated uncertainty estimations, and the ability to detect anomalous inputs.
Strengths: 1. The experiments in this paper are very sufficient, the rese... | Rebuttal 1:
Rebuttal: >Q1. There is no deeper analysis of the reasons behind these experimental results in this paper
This work emphasizes the incorporation of three new safety-driven objectives: factor-level robustness, OOD detection, and uncertainty calibration. This enables a comprehensive assessment of critical f... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your detailed and thoughtful feedback. Inspired by your valuable suggestions, we have added more experimental analyses and included the suggested discussions.
We summarize the experiments in the uploaded PDF:
- In Fig. R-1, we evaluate the retrieval performance of ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper aims to provide a comprehensive evaluation of robustness for pretrained vision-language models. Specifically, the authors benchmark around 100 pretrained models/classifiers. Based on these empirical results, this paper also provides corresponding discussions and analysis.
Strengths: 1. The robustne... | Rebuttal 1:
Rebuttal: >Q1. Further adding image-text retrieval evaluation and analysis ... make this paper more solid
Thanks. Following this Insightful suggestion, we included retrieval tasks on MS-COCO during the rebuttal. Figure R-1 in the updated PDF reports the results. We plot retrieval performance (image-to-text... | null | null | null | null | null | null |
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability | Accept (poster) | Summary: The paper presents a method to perform calibrated simulation-based inference. To do so, the paper employs the well-known coverage and proposes a way to differentiate through this term and to use it as a regularizer during training. The authors evaluate their method on benchmark tasks and conclude that it has g... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for taking the time to review our manuscript and for your comments. Below we would like to address the questions:
- *I expect that the method is very expensive if the batch size is large because in this case, GPU will not help either. This should be clarified i... | Summary: The paper proposes a calibration term to be used directly in the training objective of NREs and NPEs. The paper shows that the introduction of this term achieves competitive or better results in terms of coverage and expected posterior density.
Strengths: * The quality of the writing and presentation is high... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for taking the time to review our manuscript and for your comments. Below we would like to address the questions:
- *Line 206: Does IS provide an improvement for NPE, even when it is not needed?*
Intuition suggests that IS should introduce noise and worsen ... | Summary: The authors suggest a new objective function for simulation-based inference that adds a penalty term to the “expected score” objective that is used by many other works. This penalty term encourages the resulting posterior approximations to be well-calibrated in the sense that the $1-\alpha$ highest probability... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for taking the time to review our manuscript and for your comments. Below we would like to address the questions:
- *Although synthetic likelihood and ABC approaches are mentioned in the introduction, the proposed method seems prohibitively costly in these scen... | Summary: The paper introduces a training algorithm for posterior distribution learning in the likelihood free setting that combats overconfident models. The authors focus on the expected coverage probability (ECP) from Hermans et al., 2022 to measure if a model posterior is conservative. Specifically, when its valu... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for taking the time to review our manuscript and for your comments. Below we would like to address the questions:
- *Why choose the one-sample Kolmogorov-Smirnov test to measure the divergence over others?*
The one-sample Kolmogorov-Smirnov test was our fir... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you very much for taking the time to review our manuscript and for your comments. Below we would like to address a question that has come up in several reviews:
*What are the advantages and disadvantages of using either the sorting-based computation or the direct computatio... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint | Accept (poster) | Summary: The paper introduces a novel approximate nearest neighbor search framework which baked in attribute constraints via a single composite index compared to many existing two stage solutions. The framework is mainly relying on a newly proposed distance function that fuses both feature vector distances and attribut... | Rebuttal 1:
Rebuttal: **W1: Experimental Details and Faiss Recall Issues**
Thank you for your suggestions. Due to space limitations, we included the environment configuration and setups for the baselines in the appendix. We apologize for the omission of important details in the main paper. We'll improve the organizati... | Summary: In this paper, authors tackle the problem of retrieving nearest neighbor items under constraints on attributes of the retrieved items. Each item is described by a feature vector and a set of discrete attributes.
Authors propose to use a distance function that uses weighted combination of feature vector based... | Rebuttal 1:
Rebuttal: **W1: Missing Baselines/Ablations**
We appreciate your suggestions and include the missing ablations to validate our edge selection and routing strategies.
* Graph construction strategy
We compare our edge selection strategy with four existing ones: NGT [9], HNSW [6], HCNNG [10], and NSG [11] o... | Summary: The paper discusses how hybrid query finds objects that are both similar to a feature vector and match some structured attributes. However, existing methods handle ANNS and attribute filtering separately, leading to inefficiency and inaccuracy. The paper proposes a new efficient and robust framework called nat... | Rebuttal 1:
Rebuttal: **W1, Q1: Storage Cost Analysis**
We appreciate your suggestions and agree that comparing the storage cost of NHQ and PQ-based methods is necessary.
* Theoretically
NHQ and PQ-based methods have the same attribute storage cost, so we only analyze their feature vector storage cost.
PQ-based met... | Summary: Attribute filtering (AF) is an important part of many scenarios using nearest neighbor search. Here, each data points has a feature vector in a geometric space and also a set of attributes (e.g., data, author) and queries must be matched to nearest vectors satisfying some attribute constraints.
While many alg... | Rebuttal 1:
Rebuttal: **W1: Dataset Issue**
We appreciate your comments. Our datasets are diverse in size, # attributes, dimensions (feature and attribute vectors), and domains (image, video, etc.). Each vector has 3-9 attribute types with various values, common in real-world scenarios [1] [2].
A data point with 3 at... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all four referees for providing us with valuable suggestions regarding the presentation and experimental studies. These suggestions have been immensely helpful in enhancing the quality of our paper. In response to the major concerns raised, we have conduct... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning a 1-layer conditional generative model in total variation | Accept (poster) | Summary: The paper investigates the sample complexity of learning conditional generative models without assumptions on the input distribution. It applies the Maximum Likelihood Estimator (MLE) to linear regression and 1-layer networks with ReLU activation. The results show that the MLE achieves small total variation er... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback, we are delighted that you find
our results to be a solid theoretical foundation and a considerable
improvement over existing literature.
Please find below our response to your questions:
**Question 1:**
"What are the practical implications of the sample
com... | Summary: This paper studies conditional distribution learning: given iid samples (x,y) where x ~ D and y ~ p(y|w*,x), the goal is to find some estimate w such that the distributions p(y|w*,x) and p(y|w,x) are close in expectation over x ~ D, or equivalently the learned distribution of (x,y) (where x ~ D) is close to th... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback, we are delighted that you find
our problem novel and the results significant.
Please find below our response to your concern regarding our claim
that the MLE is concave.
**Question 1:**
"It's claimed that the MLE is concave, however, I could
not find a proof... | Summary: In this paper, the authors consider the problem of linear regression and ReLU applied to linear regression. The goal is to recover the weight vector such that the resulting distributions are close as opposed to recovering the weight vector under certain norms such as $\ell_2$ which has been thoroughly studied.... | Rebuttal 1:
Rebuttal: We are disappointed that you've found no positives in our paper. After
reading the other reviews and this rebuttal, please can you let us
know if you have any questions that can help us improve our paper?
Please find below our responses to your concerns.
**Weakness 1:** "The proposed results wer... | Summary: The article provides complexity bounds for learning the conditional distribution y|x. One of the main novelties claimed by the authors is that the control of the TD distance between the estimated distribution and the ground truth is more meaningful. Thus, they are able to provide bounds independently of the di... | Rebuttal 1:
Rebuttal: Thank you for your detailed analysis of our paper. We will include
your suggestions in future versions.
**Question 1:**"Usually $x$ denotes features and $y$ the labels... $x
\cdot w^*$ is even weird when the label does not belong to a vector
space."
We apologize for the confusion -- we used labe... | Rebuttal 1:
Rebuttal: **General response to reviewers**
Thank you for your thoughtful reviews, we appreciate the
time and effort that you put into the review process. We are delighted that the reviewers
found our paper to have: a solid theoretical foundation [NYne], a
novel perspective [9ZPm], considerable improvement... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: 1. This paper shows how MLE can perform distribution learning in the setting of linear regression and in multi-layer ReLU networks.
2. This does not take a distribution on labels to be any specific form but rather unknows and tries to derive the sample complexity for a small total variational distance between ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback and criticism. Please find
below our response to your concerns. We wish to emphasize that we do
not make assumptions about the distribution over the labels or the
data, we only assume that the conditional distribution per-layer is
Gaussian followed by a ReLU.... | null | null | null | null | null | null |
A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization | Accept (oral) | Summary: Authors propose method of solving nonconvex-nonconcave saddle point problems with convergence rate O(eps^-2) by using gradient difference prediction and momentum acceleration to improve extragradient descent-ascent method. Proposed method is state-of-the-art in theory and leading in practice, including neural ... | Rebuttal 1:
Rebuttal: **Q**: Authors could provide more extensive empirical study, because algorithm seems to be candidate for being widely-accepted in practice and it would be good to have more justifications of its efficiency.
**A**: Thanks for your positive and valuable comments. To address your concerns, we have p... | Summary: The authors have designed a single-loop accelerated algorithm for constrained min-max optimization problems of the form $\min_{x\in X}\max_{y\in Y} f(x,y)$. The algorithm provably converges in an approximate local stationary point in three particular setting:
1. Non-convex non-concave min-max optimization, wh... | Rebuttal 1:
Rebuttal: **Q**: The recent work in [1], shows that the computation of an approximate stationary point is PPAD-complete when the action space of the two players is a joint. Notably, the findings in your paper seem to suggest a different outlook when the strategy space of the two players is a product space, ... | Summary: This work proposes a single-loop extra-gradient difference acceleration algorithm to find an \epsilon-stationary point for constrained minimax optimization, which pushes forward the best complexity bounds of NC-NC, C-NC, NC-C problems to \mathcal{O}(\epsilon^{-2}). The proposed approach can deal with more gene... | Rebuttal 1:
Rebuttal: **Q**: I suggest the authors to undertake additional analysis of the algorithm's time complexities, both theoretically and empirically. This deeper exploration would provide valuable insights, particularly for potential industrial applications.
**A**: Thanks for your positive and valuable comm... | Summary: This paper discusses a new extra-gradient difference acceleration algorithm for solving constrained nonconvex-nonconcave minimax problems. The algorithm introduces a "quasi-cocoercivity property" and momentum acceleration to significantly improve the convergence rate in the constrained NC-NC setting. The algor... | Rebuttal 1:
Rebuttal: **Q**： Can you explain more about the "quasi-cocoercivity" property and discuss how it improves the convergence rate in the constrained NC-NC setting? Is this an absolutely necessary property for the improved convergence rate to hold?
**A**: Thanks for your positive and valuable comments. To add... | Rebuttal 1:
Rebuttal: Dear Reviewers and Area Chairs:
Thank you very much for the constructive comments. More experimental results in the PDF file, and the details are as follows:
**1**. We have conducted more empirical experiments and compared the performance of all the algorithms over the running time, as shown in... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Online Corrupted User Detection and Regret Minimization | Accept (poster) | Summary: This paper presents an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors to speed up learning and identify the corrupted users in an online setting. Also, the authors propose a novel bandit algorithm RCLUB-WCU, and devise a novel online detection... | Rebuttal 1:
Rebuttal: # Responses to Reviewer Lisv
Thanks for the positive comments and valuable suggestions for further improving our work. Our responses are listed below.
## 1. About the improving the writing and contents of the introduction, abstract, and related work sections:
Thanks for giving these detailed and... | Summary: The paper considers the following bandit setup.
There are $u$ users organised into $m\ll u$ clusters. Each cluster has vector $\theta$ attached to it. On step $t$ the learner deals with a user uniformly selected from the pool and picks an arm $a$. If this is a bona fide user, the learner gets average reward $... | Rebuttal 1:
Rebuttal: # Responses to Reviewer GoSQ
We are very grateful for your strongly positive comments and appreciation. Below are our responses to your minor suggestions.
## 1. About the minor suggestions 1-2 on improving the format and readability:
Thanks for giving these detailed suggestions. We will revise th... | Summary: The authors introduce an online learning problem called LOCUD (Learning and Online Corrupted Users Detection from bandit feedback) in which the aim is to detect a small fraction of the overall users with corrupt behaviors; corrupt users occasionally perform undesirable actions, but otherwise mimic normal user ... | Rebuttal 1:
Rebuttal: # Responses to Reviewer HbaT:
Thanks for the positive comments and valuable suggestions for improving our work. We will revise the paper in the final version following your advice. Our responses are as follows.
## A. Responses to the Weakness:
### 1. About the small dataset:
Please refer to the re... | Summary: This research paper introduces an innovative method for learning and utilizing unknown user relations from disrupted behaviors to enhance the learning process and identify corrupted users in an online setting. To achieve this, a new bandit algorithm (RCLUB-WCU) is proposed, along with an online detection algor... | Rebuttal 1:
Rebuttal: # Responses to Reviewer wNgt:
We appreciate the reviewer for the positive comments and valuable advice. We will incorporate the suggestions into the final version.
## A. Responses to the Weakness:
### 1. Impact of the cluster number and sizes:
Thanks for your suggestion of adding discussions on t... | Rebuttal 1:
Rebuttal: We sincerely appreciate all the reviewers for the positive comments, the time spent on reviewing our paper, and the valuable advice for improving our work. We have done some experiments for your reference. Please refer to the global PDF.
Pdf: /pdf/b574aa0a3f9edfdfa07d9de954d6caf812521e61.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Restart Sampling for Improving Generative Processes | Accept (poster) | Summary: This paper proposes analyzes SDE and ODE-based samplers for diffusion models. Based on the analysis, this paper introduces a new solver, Restart, for sampling from diffusion models. The effectiveness of Restart is demonstrated on various unconditional and conditional generation tasks.
Strengths: - The paper i... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions.
**Q1: For Theorem 1, if we set $[t_{min},t_{max}]=[0,T]$, the terms for contracted errors TV(p_{T}^{ODE_θ},pT) and TV(p_{T}^{SDE_θ},pT) vanish because p_{T}^{ODE_θ}, p_{T}^{SDE_θ}, and p_T are all iden... | Summary: ODE-based samplers plateau in performance while SDE-based samplers deliver higher sample quality. The paper attributes this difference to discretization errors and accumulated errors. Based on these, the authors propose a sampling algorithm called Restart which alternates between the forward diffusion process ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions.
**Q1: The proposed method relies on several hyperparameters (e.g. $N_{restart,i},K_i,t_{min},i,t_{max},i$), and the hyperparameters differ in different tasks. It would be hard to effectively tune thes... | Summary: The papers propose a new sampling method for diffusion models, termed Restart Sampling. The authors first theoretically analyze the error propagation in diffusion models for stochastic and deterministic samplers under Wasserstein-1 distance and show that ODE samplers have a lower-discretization error but SDE s... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions.
**Q1: The error propagation of diffusion models has been studied before ... What are the differences in the theoretical analysis compared to prior results known for error-propagation in diffusion mode... | Summary: By analyzing of the trade-off between good sample quality and sampling time of both ODE and SDE-based generative models, a restart sampling strategy is proposed by this paper to combine the advantages of ODE and SDE sampling methods. The author proves two theorems that estimate the upper bound on the total err... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions.
**Q1: The effectiveness of Restart sampling method on high-resolution image synthesis is not confirmed. A comparison of sampling speed and accuracy on ImageNet-128/-512 should be added.**
A: Thanks fo... | Rebuttal 1:
Rebuttal: # Summary of Updates
We would like to thank all reviewers for their constructive feedback. We have revised our draft according to all the valuable comments. Below we summarize updates in the revised version. We also include all the new figures in the PDF files attached.
## 1. More experiments
I... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed a sampler that balances sampling speed and quality by adding noises and restarting the process. They provide theoretical analysis to show a better upper bound of this method compared to original ODE and SDE samplers. Experiments are done to verify their claims.
Strengths: 1. Authors identi... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and thoughtful feedback. Below we address specific questions.
**Q1: I wonder what the wall clock time looks like between Restart, SDE, and ODE.**
A: Thank you for the question. The wall clock time during sampling is approximately proportional to the NFE (number ... | null | null | null | null | null | null |
Sharp Calibrated Gaussian Processes | Accept (poster) | Summary: Motivated by the observation that the posterior variance of a Gaussian process is often poorly calibrated, the authors propose an alternative approach of attaching predictive quantiles to the posterior mean. In essence, their approach minimises the width of the predictive quantiles under an empirical calibrati... | Rebuttal 1:
Rebuttal: Thank you very much for your review and your helpful comments. Please find our answers to your questions below. If you feel that we have adequately addressed your concerns and questions, we would appreciate if you would consider updating your score.
**Assumption 4.1.** Capone et al. (2022) have s... | Summary: The authors propose a novel method for calibrating Gaussian process posterior variances using held-out data. In particular, they train a new, separate, GP using the held out data for the variance, using the GP trained on the original dataset for the mean. They do this in a way which approximately maximises the... | Rebuttal 1:
Rebuttal: Thank you for your review. We have made several improvements to the paper, particularly in the experimental section, to address your concerns about clarity. Below you can find our answers to your questions and comments. Corresponding modifications can also be seen in the PDF submitted with this re... | Summary: This paper addresses the issue that the posterior variance of Gaussian processes are often poorly calibrated, typically underestimating quantile estimation. They propose a new method to calibrated uncertainty bounds, by training a quantity related to the posterior variance with new hyper parameters. This metho... | Rebuttal 1:
Rebuttal: Thank you very much for your review. We have made changes with your comments in mind as described below. If you feel that the changes sufficiently address your comments, we would be very thankful if you would consider raising your score.
**Additional comparisons.** As you suggested, we compared o... | Summary: The paper tackles calibration of Gaussian processes in regressions. The authors argue that while maximizing the evidence is a good way to choose hyperparameters to obtain an accurate posterior mean, it generally does not produces an accurate posterior variance. For this purpose, they propose a different way to... | Rebuttal 1:
Rebuttal: Thank you very much for your review and positive remarks. Based on your and other reviews, we have made several changes to improve the paper. Below you can find those that address your questions and comments specifically.
Replacing (5) with (6) is not detrimental, as it still allows us to obtain ... | Rebuttal 1:
Rebuttal: We kindly thank all the reviewers for their helpful comments. They have helped significantly toward improving our paper. Below you will find a summary of the changes made to the paper. We have also attached a PDF with additional experiments and the revised toy problem.
**Presentation of numerical... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Improving Self-supervised Molecular Representation Learning using Persistent Homology | Accept (poster) | Summary: In this manuscript, the authors have developed an interesting self-supervised learning model, by the incorporation of persistent homology into contrastive learning module. More specifically, a special topological distance based contrastive loss is proposed. The model is novel, and the results are very promisin... | Rebuttal 1:
Rebuttal: **Thank you for carefully checking our theory part!**
We are sorry for the unnecessary confusion caused by the missing details.
We hope that the additional explanations resolve the points mentioned, esp. also Q1 and Q5, which are part of the global reply. In particular, please note that we did n... | Summary: Paper uses self-supervised learning tools for graph representation learning by facilitating topological data analysis (TDA) methods. In particular, for molecular representation learning, the authors use persistent homology outputs to improve the embeddings obtained by GNNs. They evaluated their model in molecu... | Rebuttal 1:
Rebuttal: **Thank you for the insightful comments!**
W1 and W2 are addressed in the global reply. We hope that especially our explanation there and the additional results clarify our proposal. The suggestion of comparing to SOTA also in our extra experiments (W2) was very helpful and underlines our contrib... | Summary: This paper proposes two molecular self-supervised learning methods, which consists of fingerprint autoencoder and topological distance contrastive learning. The insight behind this paper is to utilize topological fingerprint as a supervision in self-supervised learning. Thus, the authors reconstruct the topolo... | Rebuttal 1:
Rebuttal: **Thank you for pointing out this indeed very related paper!** We should not have missed such closely related work, and we hope that the below delimitation resolves some of the related issues pointed out in your review. In fact, the detailed comparison highlights the novelty of our work.
W3 and Q... | Summary: The paper proposes two approaches to leverage topological information (obtained from persistent homology) for molecular representation learning in a self-supervised setting. The first (TAE) uses an encoder-decoder architecture whose decoder aims to recover topological fingerprints. The second approach (TDL) co... | Rebuttal 1:
Rebuttal: **Thank you for the very detailed feedback!**
We reformulated the particular benefits of PH for molecular SSL below and also adapted the paper. We hope that this clarifies the initial confusion about our contribution, please let us know in case further details are needed!
W2 and W3 are addressed... | Rebuttal 1:
Rebuttal:
**We thank all reviewers for the very fair, detailed, and constructive feedback!**
We address all comments below and are happy to provide additional information if needed.
---------------------------
**G1 Summary of Additional Experiments Suggested by Reviewers**
- **Technically similar approa... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores self supervised learning in the context of molecular representation, specifically based on persistent homology. The paper proposes an autoencoder to demonstrate the general representational power of PH and a contrastive-learning-based loss that can be applied to existing SSL approaches. The... | Rebuttal 1:
Rebuttal: **Thank you for the interesting comments!**
The paper does not explicitly discuss the points mentioned in the review because these are challenging topics in themselves, but they nicely underline the future research potential and we are happy to discuss them further.
**W1 More mathematical founda... | null | null | null | null | null | null |
Ambient Diffusion: Learning Clean Distributions from Corrupted Data | Accept (poster) | Summary: This paper proposes to train diffusion models that can recover corrupted data without training on clean data. The key idea is, given a corruption matrix $A$ one can further sample a corruption matrix $\tilde{A}$ given $A$ and the model learns to predict all the existing pixels. It is empirically shown that thi... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their positive and constructive feedback. We are glad that the Reviewer appreciated many aspects of our work, including the novelty of the method, the theoretical analysis, and the implications of reducing memorization.
> More exposition is needed on some details. What ... | Summary: In summary, the authors propose a diffusion-based framework that can learn unknown distributions from highly-corrupted samples, allowing the training of generative models without relying on clean training data. Their approach introduces additional measurement distortion and successfully predicts original corru... | Rebuttal 1:
Rebuttal: We are very glad that the Reviewer appreciated the importance of the problem, the novelty, the presentation, and the theoretical and practical implications of our work!
> Can you provide the performance of the proposed method in Table 1 with more timesteps of sampling? Does the performance improv... | Summary: This paper describes a method to learn a denoising diffusion model only with corrupted data. This is an important problem in many areas of applied science where there is no access to ground truth. Another important potential benefit of this method is to overcome memorization of the training images. The main i... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their feedback!
> Equation (3), [...] is the distance between the doubly corrupted image and the clean image. The objective should be the distance from $Ax_0$. It needs to be clarified whether this is a typo or the authors actually used clean data.
The Reviewer has mis... | Summary: The paper focuses on learning clean distributions from corrupted data. In the training diffusion model, the training dataset contains only highly-corrupted examples. They propose a training algorithm of restoration model by introducing additional measurement distortion.They also provide sampling methods and th... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the constructive feedback! We are glad that the Reviewer appreciated the novelty, the presentation and the implications our work could have in various applications related to memorization.
> It would be helpful to provide an algorithm or a detailed explanation of the sam... | Rebuttal 1:
Rebuttal: We thank the Reviewers for their constructive feedback! We are very glad that our work was well-received and that the novelty, the experimental and the theoretical contributions were generally appreciated by the Reviewers.
We include separate replies to each one of the Reviewers.
We also attach... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Fast and Provable Algorithm for Sparse Phase Retrieval | Reject | Summary: The authors introduce a novel second-order method for sparse phase retrieval. Compared to previous algorithms, it exhibits faster convergence and better recovery. The method leverages sparsity to reduce the size of the linear system that needs to be solved at each iteration in order to determine the approximat... | Rebuttal 1:
Rebuttal: > 1. What are the practical implications of the sub-optimal sample complexity required for initializing the algorithm and for the refinement stage? Does it limit the applicability of the method on real-world signals?
**Reply:** Thank you for your insightful questions. The sub-optimal sample compl... | Summary: The authors propose a second-order algorithm based in Newton projection for the sparse phase retrieval algorithm. The proposed algorithm is similar to Hard Thresholding Pursuit, where the free variables (i.e. the support) is first identified by a hard thresholding step, followed by an update on the free variab... | Rebuttal 1:
Rebuttal: > 1. Please clarify on the differences in convergence results between [28] and the proposed method.
**Reply:** We appreciate your constructive suggestion. In response to your query about the differences in convergence results between our method and the one presented in [28], we provide the follo... | Summary: This paper focuses on the sparse phase retrieval problem and introduces an efficient second-order algorithm based on Newton‘s method. The algorithm aims to recover sparse signals and offers a quadratic convergence rate while maintaining the same per-iteration computational complexity as first-order methods. Ex... | Rebuttal 1:
Rebuttal: > 1. More extensive experiments would help. E.g., when designing the experiments for unknown sparsity, it would be better to try different inputs for the sparsity levels.
**Reply:** Thank you for your constructive suggestions. We have conducted an additional experiment to address your concerns r... | Summary: The work proposes a new algorithm for phase retrieval of sparse signals.
Specifically, it focuses on a faster algorithm targeting quadratic convergence with the same number of measurements that are also needed in other algorithms. A proof of a quadratic convergence rate is established and experments illustrat... | Rebuttal 1:
Rebuttal: > 1. Are there any existing second order algorithms for phase retrieval (maybe even without convergence proof)? Why were these specific existing algorithms used for comparsion?
**Reply:** Thank you for your insightful comment. You are correct that there are a few second-order algorithms for phase... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank you for dedicating your time to review our manuscript and for your insightful comments. Your feedback has significantly contributed to improving the clarity and overall quality of our paper.
In response to the concerns raised, we have conducted additional exper... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach | Accept (poster) | Summary: This paper proposes a novel algorithm for return decomposition with causal treatment. To do reward redistribution, GRD uses factored representations to model the Markovian reward function and dynamics function.
Strengths: The writing is clear and easy to follow. It is interesting to see the visualization in ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 24n7
Thank you for your positive support and constructive comments. We provide our point-wise response below.
**Weakness 1:** The technical contribution is somehow limited and the stronger experiments are expected.
> **Reply 1:** Thank you for your comments.
> As compleme... | Summary: This study introduces a novel approach, termed Generative Return Decomposition (GRD), to address a key challenge in reinforcement learning: identifying the state-action pairs that contribute to future, delayed rewards. While many methods redistribute rewards in a non-transparent manner, GRD offers a clear retu... | Rebuttal 1:
Rebuttal: # Response to Reviewer EmbG
We thank the reviewer for the comments. Below please see our responses as well as clarifications.
**Weakness 1:** The quality of the text in Figure 1 could be improved by removing the shadow around the text. The same applies to Figure 2.
> **Reply 1:** Thank you for y... | Summary: Delayed reward in reinforcement learning is the major challenge in reinforcement learning. The return distribution technique is the direct way to resolve this issue while preserving policy. The existing works redistribute the returns in an uninterpretable manner. In this regard, this paper proposes a GRD which... | Rebuttal 1:
Rebuttal: # Response to Reviewer ynzt
Thanks for your constructive feedback. We provide a point-to-point response below.
**Weakness 1:** Explanation on line 345-352 is not sufficient and hard to understand. This experiment section is very important since authors insist that GRD give the interpretable str... | Summary: The paper introduces a new algorithm called Generative Return Decomposition (GRD) for return decomposition with causal treatment. GRD addresses the problem by modeling causal relationships among variables, providing advantages over flat representations. It specifies each state and action as a combination of co... | Rebuttal 1:
Rebuttal: # Response to Reviewer 6iFi
Thank you for your positive support for our paper! Below we provide a point-wise response to your concerns.
**Weakness 1:** Writing: The paper needs a lot of work in explaining the method. Especially section 4 and section 5.1. A figure showing how the causal masks are... | Rebuttal 1:
Rebuttal: # Attached PDF
Thank you to all the reviewers for your invaluable insights and thoughtful feedback. Your expertise has greatly helped us refine and improve our work.
Here we provide five figures in the attached PDF as supplementary of our response:
- Figure 1: Evaluation with Gaussian noise.
- ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper addresses a major challenge in reinforcement learning: identifying which state-action pairs contribute to delayed future rewards. They propose a solution called "Return Decomposition" that redistributes rewards from observed sequences while maintaining policy invariance. Unlike other methods, their a... | Rebuttal 1:
Rebuttal: # Response to Reviewer Xv6U
We appreciate you for reviewing our paper! Thank you for your positive support! | null | null | null | null | null | null |
InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion | Accept (poster) | Summary: The paper proposes a contrastive chamfer distance (InfoCD) for point cloud completion. More specifically, the paper shows that minimizing InfoCD is equivalent to maximizing a lower bound of the mutual information between the underlying geometric surfaces, which plays a crucial role in generating and reconstruc... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments. Below are our responses to the questions arising in the review:
**1. Results on KITTI:** Following GRNet (*Xie et. al. "Grnet: Gridding residual network for dense point cloud completion". In ECCV, 2020.*), we take a sequence of Lidar scan... | Summary: This paper proposes a contrastive Chamfer distance loss, which introduces contrastive learning into the CD loss. Experiments are conducted on PCN, MVP, ShapeNet-55/34 and ShapeNet-Part datasets, and state-of-the-art results are achieved on these datasets.
Strengths: 1. The idea seems reasonable and the overal... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments. Below are our responses to the questions arising in the review:
**Generalization ability on different datasets:** To answer this question within limited time, we test our method on **KITTI**, a real-world dataset. Following GRNet (*Xie et... | Summary: This paper proposed a novel metric to measure the similarity between two point sets, which is based on the basic formula of InfoNCE loss and the Chamfer distances. The key idea is to implicitly estimate the MI between the two point sets, and the way to achieve such target is to treat the distance of between po... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments. Below are our responses to the questions arising in the review:
**1. Convergence:** We thank the reviewer for understanding. We will try to develop a convergence theory in our future work.
**2. Generalization ability on new tasks:** Give... | Summary: The paper introduces a novel loss function called InfoCD for point cloud completion tasks. InfoCD maximizes a lower bound of the mutual information, aiming to improve the quality of the completed point clouds. The experimental results presented in the paper demonstrate promising outcomes, indicating the effect... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for valuable comments. Below we respond to reviewer concerns.
**1. uploaded loss_utils.py code is incorrect:** Nice catch. Indeed this was not what was implemented. We maintained several versions and we apologize for accidentally uploading the incorrect version.
*... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable comments. In summary,
1. We have responded to all the reviewer comments and uploaded a PDF file to show the point correspondences in training over epochs; this is based on the comment by Reviewer Lt2U.
2. We have added results from two new... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a contrastive Chamfer distance to tackle the point cloud completion problem. The proposed CD loss maximizes the lower bound of the mutual information between two point cloud-based geometric surfaces, which leads to a more robust measurement of the similarities between two point clouds. On th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable comments. Below are our responses to the questions arising in the review:
**1.1 A hard constraint on matching for CD and InfoCD:** We think that the reviewer may misunderstand this part. Firstly, we do not claim that InfoCD does not have such a con... | null | null | null | null | null | null |
Compositional Sculpting of Iterative Generative Processes | Accept (poster) | Summary: This paper proposes an approach of Compositional Sculpting for iterative generative models, including GFlowNets and defusion models.
The model uses classifier guidance to sample from the target posterior distribution composed of pre-trained base models.
The paper also proposes a training algorithm for the clas... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper and your feedback. We’ve provided some clarification in response to your questions below.
> What is the model on line 193?
Equations (2)-(4) constitute a graphical model over the variables $x$ and $y_1, \dots, y_n$. We introduce a specialization of this mod... | Summary: The paper describes a way in which, given sequential samplers from multiple probability distributions, a combination of the samplers can be used to sample from a composition of the distributions. To be precise, the sequential samplers are either GFlowNets or diffusion models, the combination of samplers is a w... | Rebuttal 1:
Rebuttal: Thank you for the thorough review of our paper and thoughtful feedback. Please find our response to the raised questions below.
> It would be good to explain why / state as a subclaim that (8) is a policy (i.e., sums to 1 over $s’$).
Following your suggestion, in the subsequent revision of the p... | Summary: The paper studies the problem of composing independently trained generative processes of diffusion-based generative models and GFlowNets. The paper considers a setting where one has access to $m$ pre-trained samplers for $\{p_i(x)\}_{i=1}^m$, and the goal is to obtain a sampler which corresponds to a compositi... | Rebuttal 1:
Rebuttal: Thank you for your review of our paper and your insightful feedback. We have addressed the main questions and concerns you have raised below.
> Classifier guidance for GFlowNets is not too novel considering the equivalence to diffusion models.
This is a fair point. However, despite the known con... | Summary: The paper proposes a method to compose multiple iterative generative models, i.e., either multiple GFlowNets or multiple diffusion models. The idea starts out with a mixture model over the generative models. Then, one can construct a categorical distribution over the generative models that tells us which model... | Rebuttal 1:
Rebuttal: > The experiments clearly demonstrate the effectiveness and versatility of the proposed method.
Thank you for the positive feedback and insightful comments!
> The experiments are limited to toyish settings and I believe more complicated settings would greatly enhance the impact of this work... | Rebuttal 1:
Rebuttal: We thank all reviewers for the time and effort dedicated to review of our work and for the helpful and constructive feedback.
## Motivation and focus of the paper
Our work is motivated by the growing costs of general-purpose pre-training of generative models as well as the need for model reuse a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The current paper focuses on the challenge of composition generation from pretrained generative models, with a specific focus on GFlowNets and Diffusion models. In comparison to prior literature, two novel compositionality operations are introduced for generating samples that are simultaneously likely accordin... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We address specific questions below:
> I would like to gather the authors' thoughts on use of their frameworks for, say, controllable generation of language via LLMs or controllable generation of images via text-diffusion models (e.g., what would the model sh... | Summary: This paper introduces a method to combine sequential generative models, in this case GFlowNets, so as to create new distributions from base models. This is done by training classifiers that are then used to guide sampling. The method is tested on a simple grid and a molecular domain (emulating the problem of t... | Rebuttal 1:
Rebuttal: Thank you for the review and feedback on our paper.
> The appendix is incomplete
The main-text PDF included an incomplete draft of the appendix by mistake. We apologize for the accidentally caused confusion. The complete appendix is provided in the supplementary zip-archive (can be downloaded f... | null | null | null | null |
Hyper-HMM: aligning human brains and semantic features in a common latent event space | Accept (poster) | Summary: The authors propose an HMM-based model, Hyper-HMM, for characterizing variability in temporal and spatial dimensions in fMRI sequence datasets. The model is a chain-structured HMM where each discrete state (event) defines a relationship between neural activity and a stimulus embedding. Importantly, the discret... | Rebuttal 1:
Rebuttal: 1)
Yes, the stimulus embeddings were treated like a subject when fitting the model (as you’ve already noted). We include one copy of the stimulus embeddings per each subject in order to prevent the model from over-favoring the human subjects during the forward-backward step. In Algorithm 1, we wo... | Summary: This paper develops Hyper-HMM as a hybrid model that simultaneously aligns both temporal and spatial features across fMRI datasets. The proposed model learns a linearly project that maps voxels to a low dimensional latent space, in which timecourses are segmented into corresponding temporal events. The purpose... | Rebuttal 1:
Rebuttal: 1)
Our model incorporates spatial alignment within the Baum-Welch update procedure for the temporal HMM, allowing for simultaneous spatial and temporal alignment. Although the components of this model are indeed taken from previous work, the combined model and its application to fMRI data are nov... | Summary: The authors develop a method to identify and align events in the brain and external stimulus. They iteratively fit a Hidden Markov Model to find the times of events, and spatial characteristics of those events.
Strengths: Originality: This work is a minor update to past work, by accounting for time shifting a... | Rebuttal 1:
Rebuttal: 1)
Here we rely on a definition of events, and event segmentation, widely used in psychology and cognitive neuroscience. Continuous streams of information, as is the case in videos or text, can be divided into smaller and smaller chunks (e.g. a book consists of chapters, chapters are composed of ... | Summary: UPDATE:
I have raised my score and now support acceptance of this paper. I believe it will be a good contribution to the conference.
-----------------------------
The authors propose an extension for an HMM model proposed by Baldassano and colleagues to align interindividually different brain responses in bo... | Rebuttal 1:
Rebuttal: 1)
We provided an algorithm (pseudocode) in the appendix submitted as part of our supplementary materials.
2)
We used published data provided by Meshulam et al. here: https://openneuro.org/datasets/ds003233/versions/1.2.0 - please see the original dataset for details about the fMRI acquisition.... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In natural tasks that share input stimuli across participants, the cognitive states or neural responses of different participants might undergo approximately synchronous but slightly jittered dynamics. At the same time, the distribution of neural signals are not exactly consistent across participants at voxels... | Rebuttal 1:
Rebuttal: 1)
The simulations were intended only to show proof-of-concept behavior for the model, demonstrating that the architecture and fitting procedure is able to capture varying temporal onset/offset of events and topographical differences across individuals while applying increasingly high spatial noi... | null | null | null | null | null | null |
Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models | Accept (poster) | Summary: The paper studies the optimal rate for multivariate deviated models. Specifically, they consider the model $(1-\lambda) h(x) + \lambda f(x|\mu,\Sigma)$, where $h$ is known and the goal is to estimate the other parameters. The authors propose to use the notion of *distinguishability* and study the convergence r... | Rebuttal 1:
Rebuttal: **Q1: The idea of distinguishability is not wholly novel. Similar notions have been used in [1] for a different model, which in turn is derived from the notion of identifiability adopted in [2] and many other previous works.**
Thank you for raising this concern. We would like to emphasize that th... | Summary: In this paper, the authors establish the rate for estimating true parameters in the multivariate deviated model by using the MLE method. They mainly try to address two challenges encountered in deriving the rate of convergence for MLE estimators, i.e. 1) the interaction between the null hypothesis density $h_0... | Rebuttal 1:
Rebuttal: **Q1: How does this paper compare with the current literature on heterogeneous mixture detection in terms of assumptions and results?**
Thanks for your question. Different from the heterogeneous mixture detection literature where most of the results are under specific settings of $h_{0}$ and $f(x... | Summary: ## Summary
The authors study the minimax rate for parameter recovery in deviated multivariate models.
In this setting, we observe samples from a mixture (of *unknown* weight \lambda) of a "null" distribution h_0 and a distribution from a parametric family f( | \mu, \Sigma).
The goal is to recover from n sampl... | Rebuttal 1:
Rebuttal: **Q1: Are the authors the first to obtain results in this setting? If yes, please explain why studying the model is important. If not, please compare thoroughly the results with the existing ones.**
Thanks for your questions. We provide below a more thorough comparison with other existing results... | Summary: The paper studies the problem of parameter recovery in the multivariate deviated model where the data is generated according to the following distribution:
$$
(1 - \lambda) h_0 (x) + \lambda f(x | \mu, \sigma)
$$
where $f$ belongs to a mean-variance family and $h_0$ is known. One prominent example of such ... | Rebuttal 1:
Rebuttal: **Q1: Theorem 3.6 is only proved for the univariate setting (Appendix C3) while the rest of the paper focuses on the multivariate setting.**
Thanks for your comment. Although the proof of Theorem 3.6 is only proved for the univariate setting, it can be adapted to high-dimensional settings along w... | Rebuttal 1:
Rebuttal: **General Response**
Dear AC and reviewers,
We would like to express our gratitude for your constructive reviews, which help us improve our work significantly. There are two common concerns about the literature on the deviated models and the novelty of our paper. Thus, we dedicate this general ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper tackles the issue of parameter estimation in the deviated Gaussian mixture of experts problem using the Maximum Likelihood Estimation (MLE) method. The authors propose new distances and analyze the convergence of MLE under distinguishable and non-distinguishable conditions.
Strengths: This paper is... | Rebuttal 1:
Rebuttal: **Q1: The major weakness is the novelty.**
Thanks for your comment. We would like to refer you to the General Response section for our elaboration on the novelty of this paper.
**Q2: This paper basically considers a much simpler case than the paper [1]. In particular, the authors in that paper c... | null | null | null | null | null | null |
Balanced Training for Sparse GANs | Accept (poster) | Summary: This work proposes a metric named balance ratio to represent the balance between the generator and discriminator in dynamic sparse training, and furthermore proposes balanced dynamic sparse training to balance the performance and computation cost.
Strengths: (++) There are not many previous works that tried t... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's recognition of the value and motivation behind our work. We would like to address the reviewer's concerns as follows:
> **Q1. The experiments are the main problem. The baselines (SNGAN, BigGAN) seem to be out of date.**
Thank you for your valuable input. Ho... | Summary: This paper presents a method for dynamic sparse training for GANs. In particular, the authors propose the balance ratio to study the balance status between the generator and discriminator. In addition, a balanced dynamic sparse training strategy is designed by applying BR to achieve a good trade-off between pe... | Rebuttal 1:
Rebuttal:
We thank the reviewer for recognizing our work is well-motivated, well-formulated and clear. We hereby address the reviewer's questions.
> **Q1. The motivation of applying DST to GAN is not that novel.**
**We want to point out that though STU-GAN [6] is the first to apply DST on GAN, it has its... | Summary: The paper addresses the challenge of reducing the computational complexity of training GANs by leveraging dynamic sparse training (DST) techniques. The authors propose a novel metric called the balance ratio (BR) to quantify the balance between the sparse generator and discriminator during GAN training. They a... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful feedback, which recognizes our work is well-motivated, interesting, and effective. Your positive evaluation is both encouraging and valuable.
In response to the reviewer's valuable suggestion, we will follow the reviewer's suggestion to provide ad... | Summary: Motivated by the identified imbalance between the generator and discriminator during sparse GAN training, this work proposes a quantitative metric dubbed balance ratio as an indicator for the degree of balance in sparse GAN training. Leveraging this metric, this work further proposes the ADAPT framework to dyn... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our work is well-written and the proposed metric is useful. We hereby address the reviewer's concerns:
> **Q1. It is not clear why the proposed metric can outperform previous indicators or solutions. The authors are expected to provide a literature rev... | Rebuttal 1:
Rebuttal: We thank the reviewers for recognizing our work is well-written (cAMN, PnSi), useful (cAMN, 6W4E), effective (6W4E), well-motivated (6W4E, PnSi, sFon), and valuable (sFon). In response to the reviewers' requests, we have included the following additional content.
> **Literature review requested b... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Learners for Realizable Regression: PAC Learning and Online Learning | Accept (oral) | Summary: This paper studies the statistical complexity of realizable regression in the PAC learning and online learning setups. The main results are the following combinatorial conditions that characterize the PAC and online learnability:
- PAC learnability by (worst-case) ERM learner is equivalent to having a finite $... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the positive feedback on the significance and presentation of our results and the interesting questions and suggestions.
> *My only complaint is on the short conclusion and a lack of discussion on future directions (apart from the obvious one of proving Con... | Summary: This paper develops optimal learners and characterizes learnability with new combinatorial dimensions for realizable regression (where the best predictor has zero regret) in PAC and online learning, significantly depicting the landscape of learnability in PAC/online learning.
For PAC learning, they show that... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the positive feedback on the significance of our results.
> *The paper might need to discuss the limitations of the results and analysis.*
We believe that the main limitation of our work is that the OIG-based dimension we propose is more complicated than t... | Summary: This paper introduce some dimensions that characterize PAC learnability for realizable regression. The authors introduce $\gamma-$ Graph dimenion which is necessary and sufficient for PAC learnability by ERM, and $\gamma-$OIG dimension which is necessary and sufficient for PAC learnability. $\gamma-$DS dimensi... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the positive and insightful feedback and questions.
> *The various dimensions are hard to understand. It would be nice to see examples. For instance, lines 188-192 were not particularly helpful to understand Definition 5, since I am not sure what it means f... | Summary: This work analyzes the realizable regression and connects it with several notions of dimensions. They care about the online learning and the PAC learning. They first show that the $\gamma$-OIG dimension characterizes the PAC learning and that PAC learning requires finite $\gamma$-DS. Finally, they show that fo... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the positive feedback regarding the importance and the clarity of our results.
> *Not a weakness, but can the authors explain why is there a requirement for bounded labels? What happens if the labels are not bounded?*
We would like to mention that, in gen... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides combinatorial dimensions that characterize realizable regression in both batch as well as online settings. Moreover, it provides minimax optimal learner up to polylog factor in the batch setting and minimax optimal learner in the online setting.
Strengths: 1. The paper is well-written, ea... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the positive feedback and insightful questions.
> *Although the paper does provide a combinatorial characterization of realizable regression, I am not sure if the OIG-based dimension is very insightful. Theoretically, it is a useful abstraction as it has a ... | null | null | null | null | null | null |
Visual Instruction Tuning | Accept (oral) | Summary: This paper introduces the first attempt to extend instruction-tuning paradigm to multimodal domain. This work has several major contributions: (a) the curation of the first vision-language instruction-following dataset by converting public image-text pairs into appropriate format using ChatGPT, resulting in ov... | Rebuttal 1:
Rebuttal: **Q1. Why do we use a small LLaVA-Bench-COCO split with only 30 images?**
Since we divide the question into three categories, we have 90 questions for COCO and 60 questions for In-the-Wild. The amount of the questions in our test set is similar to Vicuna-Bench [1], which has 80 questions. The rea... | Summary: This presents a multi-modal instruction following model and evaluates it. The model is trained by using a frozen vision encoder whose features are used an input to a LLM, which is fine-tuned. It is trained first on simple captioning tasks using a large amount of data, and then on a multi-modal instruction foll... | Rebuttal 1:
Rebuttal: **Q1. Does the proposed pipeline work with an open-source LLM?**
In our preliminary study, we find that the capability of the teacher is crucial to the quality of the generated instruction-following data (L128-L130). Until the submission deadline, the largest Vicuna model was 13B. Just as the rev... | Summary: This paper studies instruction tuning in the multimodal domain. Instruction tuning has recently drawn a lot of attractions in the large language model (LLM) field, and hence it is interesting and important to study similar capabilities in multimodal models. This paper is a pioneer work in this direction. It co... | Rebuttal 1:
Rebuttal: **Q1. Ablations on other LLMs other than Vicuna.**
Until the paper submission deadline, Vicuna is the most adopted open-source instruction-tuned LLM. There are other great instruction-tuned LLMs coming out after that, including MPT, LLaMA-2-Chat, etc.
We present initial studies using these other... | Summary: This paper introduced LLaVA, an effective visual instruction tuning method to turn Large Language Models (LLMs) into multi-modal LLMs. LLaVA is first pre-trained on image-text pairs to connect a visual encoder (CLIP) and a LLM (Vicuna). Then the authors utilize GPT4 to generate ~150K visual instruction data fo... | Rebuttal 1:
Rebuttal: **Q1. Data Quality**
We agree that high quality instruction data is critical and have taken measures to ensure the data quality.
First, we create image descriptions directly from the well-established manually-annotated MSCOCO dataset, which contains bounding box and caption annotations (L103-L11... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their time and their thoughtful comments and questions. We are encouraged that the reviewers find that:
- Our work is a pioneer in the multimodal instruction tuning field (RuBm, MLCz, A2dU, mhaS). It will inspire a lot to the research community (RuBm) and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GAUCHE: A Library for Gaussian Processes in Chemistry | Accept (poster) | Summary: Gaussian processes are widely used for black-box optimization when data is scarce. On the other hand, effectively representing molecules, proteins, and chemical reactions is a dedicated research area in molecular machine learning.
Although separate tools exist to address these two challenges, this paper intro... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our manuscript and for providing detailed, helpful and constructive feedback. We were happy to see that you appreciate the practical usefulness of a well-designed and easy-to-use library that enables scientific experts to make use of Bayesian optimis... | Summary: The authors discuss Molecular, Reaction, and Protein Representations and provide a unified framework for these models. Python's GPyTorch library is used to train the Gaussian processes. The authors define certain kernels for Gaussian processes to fit and perform several experiments to evaluate the performance.... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our manuscript and for providing valuable and helpful feedback. We were happy to see that you appreciated the thorough treatment of the representations, kernels and applications we cover in our work.
The main concern you raised in your review is the... | Summary: This article presents a library for Gaussian process-based inference with a special focus on chemistry applications. At heart, the library contains two classes of objects: kernel, and data loaders. The article introduces Gaussian processes and chemistry-specific kernels and discusses the interfacing of the lib... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our manuscript and for providing helpful and constructive feedback. We were happy to see you emphasize how our library complements the current open-source molecular machine learning stack and acknowledge the high quality of our code and tests.
The c... | Summary: This paper presents a library for Gaussian processes on chemistry data. In this library, a number of kernels are implemented over chemical representations such as graphs, strings and bit vectors. Regression and Bayesian optimization experiments are shown using the library.
Strengths: - GP has been widely use... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our manuscript. We were happy to see you emphasize both the general usefulness of Gaussian Process models as robust molecular machine learning tools, as well as the practical impact that a well-designed and easy-to-use library can have by making them... | Rebuttal 1:
Rebuttal:
## __Overview__ ##
We would like to thank all reviewers for the time and effort put into reviewing our manuscript and for the valuable and constructive feedback they have provided.
We are delighted that all reviewers recognized the practical significance of our work, highlighting... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors present a framework called GAUCHE with comprehensive exploration of Gaussian Processes (GP) and their application to molecular machine learning. The authors thoroughly examine different ways of representing molecular structures - through hand tuned fingerprints, string notations (SMILES/SELFIES/Pro... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to review our manuscript and for providing detailed, helpful and constructive feedback. We were happy to see you emphasizing the quality of our codebase and empirical evaluation, as well as the practical importance of our work.
The main suggestions you raised... | null | null | null | null | null | null |
Adaptive whitening with fast gain modulation and slow synaptic plasticity | Accept (spotlight) | Summary: This paper proposes a normative principle for the symmetric whitening problem, i.e., a batch optimization problem which is provable to attain symmetric whitening is used to derive an online adaptive algorithm. The proposed framework unifies the previous works, and the resulting algorithm maps into single layer... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our work and for your suggestions. We regret that you found the writing lacking in some parts. We have revised our paper in accordance with your suggestions, which we believe has improved the overall clarity of the paper.
We are concerned that a central contr... | Summary: This paper gives an algorithm for learning weights of a neural network over long time scales, which allow interneurons to decorrelate the responses of excitatory neurons by modulating their gains over short time scales. On both synthetic and natural image datasets, this algorithm is shown to be effective and t... | Rebuttal 1:
Rebuttal: Thank you for your careful review and for your useful comments. We appreciate your comment that our model has broader implications for ML and applications to transfer learning.
Weaknesses: We readily acknowledge there are aspects of our model that are not biologically realistic. This is in part d... | Summary: The authors produce a mechanistic model that combines synaptic plasticity and gain modulation to adaptively whiten responses. This model is constructed from an objective for learning a whitening transformation and then considering matrix factorization. Simple factorization introduces interneurons and optimiza... | Rebuttal 1:
Rebuttal: We appreciate your positive review and thoughtful questions about the work!
Questions:
1. We can be precise in describing the set of covariance matrices that can be whitened with this decomposition. In particular, when the inverse whitening matrices lie in a $K$-dimensional linear subspace, then... | Summary: This paper proposes a neural circuit model that combines fast gain modulation and slow synaptic plasticity to adaptively white sensory inputs. It appears that this paper is a combination of the studies of ref. 11 and 18.
Strengths: ### Originality
The strength of this paper is that it combines the fast gain ... | Rebuttal 1:
Rebuttal: Thank you for your careful review and for your thoughtful questions.
Weaknesses:
Thank you for voicing this concern. Although it's not so apparent in the main text, we've resolved this issue in Appdx D.1 . Specifically, we scale the column vectors of ${\bf W}$ to have constant norm 1. This effe... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our work and for your helpful comments. We have revised our paper in accordance with your suggestions and provide individual responses below. Here we list general changes and additions to the manuscript.
1. **Adaptation with fewer interneurons than primary ne... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection | Accept (poster) | Summary: This paper uses the vision-language pre-trained model CLIP for long-tailed object detection.
The key idea is to consider not only image-level semantics but also region-level semantics, and fuse them under a soft-label scenario.
The overall idea is deployed on different object detectors with different backbones... | Rebuttal 1:
Rebuttal: ### q1: Insight for long-tail context.
Thanks for pointing out this. Exploring extra data is indeed a direct and effective approach to mitigating data scarcity. Our primary motivation arises from the fact that classification data is easy to collect and offers a more balanced distribution compared ... | Summary: - This work deals with Long-tail object detection. Authors identify two problems with using additional data, namely Semantic ambiguity and Location sensitivity.
- Authors identify that semantic ambiguity arises due to supervision with one-hot encoded labels from the image datasets and instead propose to use C... | Rebuttal 1:
Rebuttal: ### q1: Comparison with [1]
Thanks for the good suggestion. We incorporate our method into Faster-RCNN, employing R50 as the backbone for an appropriate comparison following [1].
The table shows that our method achieves a strong performance and surpasses the previous sota by more than 3 AP on rar... | Summary: To address semantic ambiguity and location sensitivity, this paper introduces a one-stage training framework that leverages additional image data to boost the detector through learning from rich semantics and coarse locations for long-tailed object detection. And their RichSem achieves consistent improvements ... | Rebuttal 1:
Rebuttal: ### q1: Can exploring extra data effectively address data scarcity?
Yes. Indeed, exploring additional data is a straightforward approach to enhancing the performance of tail categories. However, acquiring bounding box annotations for these rare categories is labor-intensive and costly.
Recogniz... | Summary: This paper adopts the CLIP model to obtain a 'soft label' supervision to train the detector under the long-tail distribution dataset and derive rich semantics from the CLIP part to enhance the tail-categories representations, which can be removed during the inference. The authors claim that the CLIP model can ... | Rebuttal 1:
Rebuttal: ### q1: L172 subscripts.
Thanks for pointing it out. We will definitely fix it in the next version.
### q2: How can semantic learning on extra classification data boost rare categories detection?
The detection head is trained not only on the LVIS dataset but also on the extra image classificatio... | Rebuttal 1:
Rebuttal: First of all, we sincerely appreciate all your valuable comments and suggestions.
We are pleased that all reviewers think our paper is well-written and easy to follow. We are encouraged that reviewers find our proposed RichSem with reasonable novelty (YRik), significant results (ndNj), and extens... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework | Accept (poster) | Summary: In this paper, the authors consider Bayesian bandit algorithms where exact posterior is not available and
only approximations are available.
The authors prove that if $\alpha_1$-divergence and $\alpha_2$-divergence between the exact posterior and approximation are small,
then a modification of Bayesian UCB (EB... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for the careful reading and valuable comments. We address the reviewer's concerns below.
Q1. "Dependency on $\epsilon$". The regret bound indeed contains $\epsilon$. The exact finite-time upper bound is provided in Step 4 in the proof of Theorem 3.7 (We only sho... | Summary: This paper studies Bayesian bandits with approximate inference errors. The authors proposed an algorithm called the Enhanced Bayesian Upper Confidence Bound (EBUCB). Under a two-bounded $\alpha$-divergence assumption, the authors show that EBUCB can achieve the optimal logarithmic regret. The authors also show... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer for the careful reading and valuable comments. We address the reviewer's concerns below.
Q1. "Bernoulli settings and the potential extension to other distributions". One of the major techniques in our analysis is Lemma C.1, which provides tight upper and lower bounds... | Summary: This paper considers the standard multi armed bandit problem with a prior on rewards, allowing the design of Bayesian algorithms such as Thompson sampling and Bayesian UCB. The problem of interest is when the exact posterior distributions are not available. Rather an approximate posterior is available. It has ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for the careful reading and valuable feedback. We address the reviewer's concerns below.
Q1. "Significance of current results for the complex settings". The key contribution conveyed in this paper is the theoretical insights and guidelines that positively suppor... | null | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning | Accept (poster) | Summary: This paper presents a significant step towards a general-purpose vision-language model with instruction-following abilities. It is built upon the existing BLIP-2 model and instruction-following dataset LLaVA-Instruct-150K [1]. It further extends the scale of instruction-tuning by automatically converting publi... | Rebuttal 1:
Rebuttal: Thank you a lot for your review and insightful comments. We have addressed your questions in the following.
----
**Q1**: Comparison to LLaVA.
**A1**: Our paper provides two comparisons between InstructBLIP and LLaVA:
1. A qualitative comparison using the same images and prompts (Appendix B).
... | Summary: The paper presents InstructBLIP, a vision-language instruction tuning framework to solve a wide range of visual-language tasks through a unified multimodal interface. The authors conduct a comprehensive study on vision-language instruction tuning, transforming 26 datasets into the instruction tuning format and... | Rebuttal 1:
Rebuttal: We appreciate your valuable review and comments. We would like to address your questions as follows:
----
**Q1**: Analysis and comparison on the open-ended out-of-domain multimodal question answering.
**A1**: We have provided qualitative comparisons with GPT-4, MiniGPT-4, and LLaVA on open-ende... | Summary: This paper presents a study of instruction finetuning for vision-language tasks. The paper follows the design of FLAN for instruction tuning and borrows ideas from Flamingo for image/text model freezing and query network. Experimental results suggest FLAN-style instruction tuning also works for vision-language... | Rebuttal 1:
Rebuttal: Thank you for reviewing and providing valuable comments. We address your questions in the following.
----
**Q1**: Novelties of InstructBLIP.
**A1**: Our paper proposes a novel vision-language instruction tuning framework that has not been previously explored. We delineate our novelties as follo... | Summary: The paper proposes InstructBLIP, a vision and language instruction tuning framework that enables general-purpose models to solve a wide range of visual language tasks through a unified natural language. It uses a diverse set of instruction data to train a multimodal LLM.
The model is initialized with a pre-tra... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and providing your insights. We value your feedback and have responded to your concerns as follows:
----
**Q1**: Human evaluation.
**A1**: Our paper evaluates the InstructBLIP models on a wide range of well-established benchmarks, which sufficie... | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for your insightful and constructive feedback. We deeply appreciate the time and effort you have dedicated to reviewing our work. We have responded to your comments and questions inside each individual review. We hope these responses provide a more comprehensive view... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes InstructBLIP, which is built on BLIP-2 and further perform instruction tuning to enable the instruction following ability of BLIP-2 models. The InstructBLIP model is trained on a set of template-based converted instruction-following data for different tasks (e.g. image captioning and VQA) a... | Rebuttal 1:
Rebuttal: Thank you a lot for your insightful review, we appreciate it a lot. Our response to your comments is as follows:
----
**Q1**: Diversity of instructions.
**A1**: Converting from existing human-annotated datasets provides high-quality instruction-following data. The data has a reasonable level of... | null | null | null | null | null | null |
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning | Accept (spotlight) | Summary: Authors propose a framework for offline-to-online tuning of offline RL algorithms tunning. The idea is to train an additional network which desides helps to keep improvement-constraint balance during finetuning.
Strengths: Approach improves all of the checked algorithms performance and can be applied to diffe... | Rebuttal 1:
Rebuttal: Your keen observations and valuable suggestions are highly appreciated, and we thank you for helping us to strengthen our paper.
> Q1: How does the modification affect the compute time required to train algorithms?
Thank you for raising this question. To examine the computational overhead introd... | Summary: This paper approaches offline-to-online RL from the intuition that at a particular state, if the dataset already contains good actions, then the subsequent online tuning should be more conservative to retain the good actions in the dataset; but if the dataset's actions is poor, then more radical policy improve... | Rebuttal 1:
Rebuttal: We want to express our thanks to you for the detailed feedback and constructive criticism that guided our revisions.
> Q1: The proposed method seems to require abundant diverse data.
Thanks for pointing this out. The confusion might stem from an unclear explanation in our manuscript, and we'll c... | Summary: The paper proposes a new algorithm to perform offline-to-online reinforcement learning. The core idea is to consider a state-adaptive balance parameter, which aims to encourage imitation of dataset behavior only if the corresponding advantages / values are high, while prior works have mostly assumed fixed bala... | Rebuttal 1:
Rebuttal: Your thoughtful comments and critiques are sincerely appreciated and have been instrumental in refining our study. **Due to the character limit, the references in this rebuttal are at the global rebuttal.** Thanks for your time and effort.
> Q1: Definition of the term data quality?
Thank you for... | Summary: The paper introduces a new method to mitigate the distribution shift problem in the offline to online RL problem. The paper states the intuition that the policy should behave differently on states with different values, that is, the policy should be more conservative on high return states and exploratory on th... | Rebuttal 1:
Rebuttal: Thank you for your careful review and constructive suggestions, which have helped us improve our manuscript.
> Q1: The exploitation vs. exploration intuition is not brand new in the offline to online setting, there is also some work with similar intuition [1]. I believe proper comparison is requi... | Rebuttal 1:
Rebuttal: ### References for All Reviewers:
**Dear reviewers, due to the rebuttals' character limit, we've placed the references for all rebuttals below. Thank you for your time and consideration.**
[1] Luo, Yicheng, et al. "Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization | Accept (spotlight) | Summary: The paper discusses reparametrizations of parameter spaces and the implied transformation rules for quantities like gradients, Hessians or probability densities.
Parameter spaces are interpreted as Riemannian manifolds $M=\mathbb{R}^d$ and the quantities of interest are coordinate independent geometric object... | Rebuttal 1:
Rebuttal: Thanks a lot for your extensive review! Your write-up on the summary and strengths of our paper is completely spot on!
Here we will address your major comments and questions. Minor comments and suggestions will be implemented directly in the text. See also our ["global" response](https://openrevi... | Summary: The paper shows that reparameterizations of neural networks can be understood uisng Riemannian geometry. They first show how a reparameterization of a neural network's parameters can be expressed via a Riemannian metric which then yields transformation rules that can be applied to any function on the parameter... | Rebuttal 1:
Rebuttal: Thanks a lot for your positive review! Please see also our ["global" response](https://openreview.net/forum?id=vtLNwa6uX0¬eId=CvzNeGzNp7) for a general discussion. Here, we address your major comments. All other comments and suggestions are implemented directly in the paper.
You are completely... | Summary: This work analyzes the invariances and non-invariances of model reparameterization in machine learning. The authors show that, if we account for Riemannian metrics in parameter spaces, then many quantities thought to be not invariant are in fact invariant to reparameterization. Thus, by properly applying trans... | Rebuttal 1:
Rebuttal: Thank you for your positive review! Please see our ["global" response](https://openreview.net/forum?id=vtLNwa6uX0¬eId=CvzNeGzNp7) for a general discussion. Here, we address your specific comments.
**Du et al.** They focus on the invariance of ReLU networks under the scaling symmetry, while we ... | Summary: Under model reparametrization, Hessian-based flatness measures, optimization trajectories, and probability densities are not invariant. Motivate by these inconsistencies, this paper studies the invariance associated with the reparametrization of neural networks. By viewing the parameter space as a Riemannian m... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback! Here we address your major comments/questions. We incorporated your suggestions into the text directly. Please see also our ["global" response](https://openreview.net/forum?id=vtLNwa6uX0¬eId=CvzNeGzNp7) for a general discussion.
**Novelty** The main contr... | Rebuttal 1:
Rebuttal: **To all reviewers:** Thank you very much for your input! To supplement the responses to your individual reviews, here we would like to address the common questions and comments.
Our work focuses on addressing _invariance under reparametrization_, i.e. under change of variable from the point of v... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Revisiting the Minimalist Approach to Offline Reinforcement Learning | Accept (poster) | Summary: This paper proposes an offline RL algorithm based on TD3+BC and BRAC, integrating popular design elements. The proposed method achieves higher scores on D4RL and V-D4RL datasets.
Strengths: The idea of exploring the popular design elements in offline RL algorithms is interesting. The authors make a great effo... | Rebuttal 1:
Rebuttal: Thank you for the review. We believe that the main concern is the limited novelty, which we address first, and then we move to more precise limitations.
----------
### On limited novelty
While we agree that the paper may seem to feature constrained technical novelty, given it doesn't propose ne... | Summary: This paper revisited recent methods in the offline RL area and discussed how different design choice impact offline RL methods' performance. In particular, the authors focus on four hyper-parameter choices (i) number of network layers, (ii) using LayerNorm, (iii) batch size and (iv) discounting factor $\gamma$... | Rebuttal 1:
Rebuttal: First of all, we would like to thank you for your time reviewing our paper and valuable comments. We believe that the main concern is the limited novelty, which we address first, and then we move to more precise limitations.
------------
### On limited novelty
We appreciate the reviewer's commen... | Summary: This paper revisits several minor design choices in recent offline RL literature and equips a standard method TD3+BC (or more generally BRAC) with these designs to attain a strong baseline for offline RL with state-of-the-art performance on both D4RL and V-D4RL benchmarks. These critical designs include deeper... | Rebuttal 1:
Rebuttal: Thank your for the review and identified weaknesses, we address them and your questions as follows:
> If I understand correctly, BRAC has adopted actor penalization and critic penalization at the same time. Indeed, BRAC-v in the original paper adds a penalty to both actor and critic learning obje... | Summary: In this work, a number of recent advancements are added to the minimalist TD3+BC baseline, and the authors found that the resulting algorithm leads to a new SOTA performance on D4RL benchmark with raw state and visual input. Extensive empirical results and ablations are provided and the authors show a dedicati... | Rebuttal 1:
Rebuttal: Thank you for the review. Regarding your questions:
> when you tune for d4rl, do you fine-tune a different set of hyperparamter for each task? Is this done for all algorithms in the comparisons?
Yes, we tune hyperparameters per-dataset for each method.
> The authors mentioned that to be fair, ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their work, hopefully, we provided a sufficient level of response to all, if not, we're open to continue our discussion. Here, we include additional results requested by the reviewers explicitly or implicitly so in order to provide comprehensive empirical r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Online PCA in Converging Self-consistent Field Equations | Accept (poster) | Summary: This paper explores online PCA methods for a certain type of non-linear eigenvalue problem.
Strengths: This is a well-written paper on an interesting problem in computational science.
Weaknesses: My main critique is that, as presented, the contribution of this paper appears not as much to machine learning or... | Rebuttal 1:
Rebuttal: Thank you for the review. Since our work lies in the applications of online PCA methods to the otherwise unknown area and the specific self-consistent Eigen problem, our work contributes to the machine learning community by expanding the reach of online PCA methods. Before our work, online PCA met... | Summary: The paper presents a new online-PCA based algorithm with some additional computational innovations to solve self-consistent systems. They add a mode-switching method and delayed calculation to improve convergence issues. The results are very good, but on a somewhat limited/niche dataset.
Strengths: The paper... | Rebuttal 1:
Rebuttal: Thank you for your constructive comment. The detailed responses regarding each concern are listed below.
For other methods, while multiple existing methods exists, most of them are some variations of the DIIS technique. An incomplete list includes energy-DIIS, augmented-DIIS, LIST, GDIIS and RMM-... | Summary: In this work, the authors approach solving the Self-consistent Field (SCF) equation from a principal component analysis (PCA) for non-stationary time series perspective. They shows that, the equilibrium state of such an online PCA corresponds to the solution of the SCF equations. By doing so, this work is able... | Rebuttal 1:
Rebuttal: Thank you for your constructive comment.
We will correct the layout error in line 181. The covered sentence is "where $\psi_0$ is the initial angle between the vector and the xy plane."
The reason to include only the first eigenvector/eigenvalue in eq (1) is for the simplicity of form, as the m... | Summary: This paper proposes a new method for solving self-consistent field equations - a form of nonlinear generalized eigenvalue problem in which the matrix being diagonalized is a function of the eigenvectors of the diagonalization. These equations are of great interest in quantum chemistry, and are typically solved... | Rebuttal 1:
Rebuttal: Thank you for your constructive comment. The detailed responses regarding each concern are listed below.
For the trade-off between convergence and efficiency, note that such a trade-off characteristic can be adjusted in our proposed method by setting different $T_{\text{cut-off}}$ in L230 of the ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TransHP: Image Classification with Hierarchical Prompting | Accept (poster) | Summary: This paper studies the problem of image classification leveraging the idea of hierarchical image classification (HIC), which exploits semantic relations across target classes to learn meaningful distinctive features.
The core idea of hierarchical classification is that if a model should classify plants and it... | Rebuttal 1:
Rebuttal: Dear reviewer, we hope our response helps clear up your initial concerns/questions. We would be happy to provide further clarifications where necessary.
**1. Although the method consistently works on the tested tasks, what are the characteristics of the tasks that let us understand that the metho... | Summary: This paper aims to improve image classification accuracy by introducing hierarchical prompts. For a dataset with L hierarchy with M prompt at each level, the author proposed to inserting M prompt at each of the L randomly selected transformer layers. Each prompt is tasked to predict the intermediate coarse lab... | Rebuttal 1:
Rebuttal: Dear reviewer, we hope our response helps clear up your initial concerns/questions. We would be happy to provide further clarifications where necessary. We hope our paper is acceptable based on the clarifications and the point-to-point responses below.
**1. The proposed method inserted M prompt i... | Summary: The paper proposes to use coarse token prompting for the task of image classification. The basic idea is to add coarse label tokens at intermediate layers of ViT and add additional coarse classification loss at intermediate level. It in some way correlates to convolutional networks based Hierarchical classific... | Rebuttal 1:
Rebuttal: Dear reviewer, we hope our response helps clear up your initial concerns/questions. We would be happy to provide further clarifications where necessary. We hope our paper is acceptable based on the clarifications and the point-to-point responses below.
**1. Related work is not well covered. For i... | Summary: This paper introduces a new approach called hierarchical prompting for hierarchical image classification (HIC). It's incorporated into a model named TransHP, which uses broader class 'prompts' to better distinguish between similar classes. The process improves image classification accuracy, data training effic... | Rebuttal 1:
Rebuttal: Dear reviewer, we hope our response helps clear up your initial concerns/questions. We would be happy to provide further clarifications where necessary.
**1. The foundation established in this paper seems to lack strength. I recommend utilizing a more robust baseline within the High Interaction C... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This works presents a novel hierarchical prompting mechanism for hierarchical image classification, named TransHP. In TransHP, a set of prompt tokens are learnt to represent coarse classes and was injected in the prompting block for coarse class prediction, and the injected prompt token can strengthen the feat... | Rebuttal 1:
Rebuttal: Dear reviewer, we hope our response helps clear up your initial concerns/questions. We would be happy to provide further clarifications where necessary. We hope our paper is acceptable based on the clarifications and the point-to-point responses below.
**1. The ImageNet performance of HiMulConE u... | null | null | null | null | null | null |
Combinatorial Optimization with Policy Adaptation using Latent Space Search | Accept (poster) | Summary: This paper presents an interesting CO agent model that conditions policy by latent vectors and finds latent vectors through CMA-ES.
In addition, considering latent vector in the learning process, this paper presents a training method that induces the agent to be specialized for various instances.
Strengths: 1... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments and positive feedback. We have updated the paper accordingly and hope our answers further clarify the aspects of the COMPASS framework and the training procedure.
> W1: Inference time is not included in Table1. Since COMPASS is a method of cont... | Summary: Building upon a pre-trained neural constructive model (such as POMO), this paper proposes COMPASS, which introduces the idea of learning a continuous latent search space to fine-tune the pre-trained POMO model parameter. The latent space allows for the sampling of a vector, which the pre-trained POMO model use... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments.
> W1: ... it would be useful to benchmark COMPASS against POMO and EAS with data augmentation. Furthermore, SGBS … is overlooked.
We are happy to provide additional benchmarking to allow comparison to published results and validate our imple... | Summary: This paper proposes COMPASS, an RL-based training framework to learn a diversified neural solver for combinatorial optimization problems. This framework trains a conditioned neural network conditioned on a prior vector sampled from a fixed distribution. During the training phase, multiple priors are sampled, a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and hope that our answers and additional experiments will clarify any concerns.
> W1: Key challenge in RL for CO is to train/generalize a model to big cases, e.g. TSP1000/10000. This framework is designed to improve upon another neural solver ... | Summary: The paper proposes a neural combinatorial optimization approach that allows for an extensive search for high-quality solutions. The approach uses reinforcement learning to train a network to construct solutions for the traveling salesman problem (TSP), capacitated vehicle routing problem (CVRP), and job shop s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and positive feedback. We will update the paper accordingly and have added additional experiments to help address their concerns.
> W1: The proposed method is very similar to CVAE-Opt [1]. The authors should make it clear in the paper and discuss the diffe... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive comments, feedback and suggestions. In particular we are pleased to see the contributions of our work; both methodological and strong empirical performance, highlighted.
We respond to each question and concern in detail for each reviewer independently. Ho... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Conditional score-based diffusion models for Bayesian inference in infinite dimensions | Accept (spotlight) | Summary: - The study proposes a method to learn the posterior distribution in infinite-dimensional Bayesian linear inverse problems using amortized conditional Score-based Diffusion Models (SDMs). This extends conditional SMDs into the infinite-dimensional function space setting, as existing conditional SDMs have previ... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their feedback.
We are happy to clarify our manuscript in response to the Reviewer's questions. We hope that this could lead to an improvement in their assessment of the paper.
1. **Discretization-based approaches and Pidstrigach's procedure.** While it wo... | Summary: This paper mathematically examines linear inverse problems in infinite dimensional vector spaces. Particularly, it is proved that the conditional denoising estimator is a consistent estimator of the conditional score in infinite dimension.
Strengths: The consistency of the conditional denoising estimator in ... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their positive feedback.
We are happy to clarify our manuscript in response to the Reviewer's remarks and questions.
1. **Practical utility against plug-and-play-type approach.** While plug-and-play methods are indeed very popular, we would like to emphasi... | Summary: This paper proposed a method to deal with inverse problems in infinite dimensions using conditional-score-based models. Specifically, they propose to directly learn the posterior distribution in infinite-dimensional Bayesian linear inverse problems using amortized conditional SDMs. Moreover, this paper also di... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their feedback.
We are happy to clarify our manuscript in response to the Reviewer's questions. We hope that this could lead to an increase in their score.
1. **Conditional SDMs.** Various approaches have been proposed in the literature for dealing with conditioning, bo... | Summary: Score-based diffusion models are successful in solving inverse problems in a finite-dimensional setting, but infinite-dimensional diffusion models needs to be constructed with care, as the definitions for Lebesgue measures and densities become less clear. The authors extends the work of Pidstrigach et al. [33]... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their positive feedback.
We are happy to clarify our manuscript in response to the Reviewer's questions.
1. **Finite-dimensional observational model.** Indeed, if the number of observations is finite, we may think that the observations
only span a finit... | Rebuttal 1:
Rebuttal: We thank the Reviewers for their valuable and constructive feedback.
Based on your comments, we have taken significant steps to enhance our Section 6. We are now including additional experiments that demonstrate the applicability of our method to large-scale problems and showcase its discretizat... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The author extends score based diffusion from finite dimensional processes to separable Hilbert space processes.
They demonstrate that on a non-linear toy data set that the method can work.
Strengths: The paper reads very well and is easy to follow. It is important to study what happens in general separable H... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for their critical feedback.
We are happy to clarify our manuscript in response to the Reviewer's questions.
1. **Numerical experiment not very convincing.** We acknowledge your remark, along with those of the other reviewers, regarding the need for improveme... | null | null | null | null | null | null |
Higher-Order Uncoupled Dynamics Do Not Lead to Nash Equilibrium - Except When They Do | Accept (poster) | Summary: The paper studies higher order dynamics, i.e., dynamics that can rely on more auxiliary states than those limited by the dimensionality of the action spaces, in network games with pairwise interactions between players. Importantly, these dynamics only depend on the sequence of payoff signals that each player r... | Rebuttal 1:
Rebuttal: Question>> Can the authors address the weaknesses mentioned above?
See below for an item-by-item discussion.
Question>> Line 22: not the most updated list of papers.
The list of papers, while admittedly brief, is representative and covers the qualitative aspects of convergence or non-converge... | Summary: This paper studies multi-agent learning dynamics and the central question is if there is an iterative learning process in a multiplayer game that leads to a Nash equilibrium. There has been substantial prior work on this question for many specific games and learning strategies—generally it is not the case that... | Rebuttal 1:
Rebuttal: Question>> I would be interested in the authors' thoughts on whether they view the class of higher-order dynamics they study as restrictive or not. It would be helpful to know, of the papers they cite, in which cases their dynamics class subsumes that of the methods proposed by that paper.
For th... | Summary: This paper studies higher order payoff based learning, and in particular higher order gradient play in this setting. The authors show that for games with isolated completely mixed NE, there are higher-order gradient play dynamics that converge to that NE. Moreover, that same NE is converged to in ‘nearby’ game... | Rebuttal 1:
Rebuttal: Question>> For the higher order dynamics, is there intuition about the bandit setting where players only observe (potentially random) realizations of their payoffs? This seems more reasonable for the cases where payoff vectors are large/there are a large number of players and complexity is a conce... | Summary:
The paper shows that for any finite game with an isolated completely mixed Nash Equilibrium, there exist a payoff based higher-order
gradient play dynamics that lead (locally) to that Nash equilibrium, both for this game and all payoff nearby games. Conversely, they show that for
any higher-order gradient pla... | Rebuttal 1:
Rebuttal: Question>> Are your dynamics more general that all the previously studied ones (such as [18, 19, 20, 23] etc)?
The above papers are specific instances of higher-order dynamics. Setting aside continuous-time/discrete-time differences, the framework of higher-order learning outlined in Section 2.3 ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper shows the lack of universality on the side of both games and learning dynamics (even for higher-order ones)! Particularly, for any game with a mixed-strategy Nash equilibrium (NE), there exists uncoupled payoff-based (possibly high-order) dynamic converging locally to the NE. However, any such dynam... | Rebuttal 1:
Rebuttal: Question>> What is the main obstacle to address instantaneous scalar payoffs rather than payoff vector setup?
We believe that the present results can be used to analyze instantaneous scalar payoffs in the case of discrete-time learning with randomized action selection.
The continuous time ordina... | null | null | null | null | null | null |
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective | Accept (poster) | Summary: The authors propose Scheduled Weight Decay (SWD), a method that mitigates the large gradient norm issue caused by constant weight decay factors. The authors demonstrate that SWD can improve the generalization performance of Adam and outperform other adaptive optimizers on CIFAR-10/100 datasets.
Strengths: 1. ... | Rebuttal 1:
Rebuttal: We highly appreciate Reviewer tfGK’s kind support and helpful comments.
The reviewer definitely realized the important value of identifying and mitigating the overlooked but serious pitfalls of weight decay. We gratefully hope the reviewer can express and insist on your opinion to avoid a possibl... | Summary: This paper studies the overlooked pitfalls of weight decay, a regularization technique used in deep neural networks (DNNs). The authors discovered that weight decay can lead to large gradient norms, particularly at the final phase of training, often indicating poor convergence and generalization. To address th... | Rebuttal 1:
Rebuttal: We highly appreciate Reviewer 3dfT’s kind support and helpful comments.
We gratefully hope the reviewer can express and insist on your opinions, which may help our community understand and employ weight decay better via our work.
We also properly respond to your concerns as follows.
Q1: The pro... | Summary: This paper studies the role of weight decay and its connection with large gradient norms in deep learning settings. In particular, the paper highlights differences in variants of weight decay and also the effect of weight decay on gradient norm in the final phase. Based on the observation that weight decay yie... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer xE1B’s hard work and helpful comments.
The comments shows that the reviewer only has concerns about theoretical evidences in our work. Our theoretical analysis is proposed not as a main contribution but to explain our interesting findings.
Our main contributions... | Summary: I would divide this paper's contributions into two parts. The first part is an algorithm (a variant of Adam) which the authors argue generalizes better than Adam/AdamW and is easier to tune. The second part is the justification for the effectiveness of that algorithm.
**The algorithm itself** The proposed a... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer e7t4 for hard work and admitting the value of our contribution.
We notice that Reviewer e7t4 currently tends to reject our work only because the reviewer believes an alternative scale-invariance mechanism of our experiments. However, we respectfully note that the... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective | Accept (poster) | Summary: This paper proposes two methods for multi-view clustering, which aims at grouping data from multiple sources or perspectives. The first part, SCMVC uses a consistent variational lower bound to learn consistent information among views. The second part, SUMVC extends the information bottleneck principle to reduc... | Rebuttal 1:
Rebuttal: Thank you for your invaluable comments and suggestions. We have addressed the points you raised as follows.
1. The proposed objective function in Eq. (4) shares similarities with that of VAE [1], and it would be beneficial if the author could provide further explanations on this matter. How does ... | Summary: The paper proposed Sufficient Multi-View Clustering , SUMVC, which is composed of two main components. The first component is a simple and reliable multi-view clustering method called SCMVC (simple consistent multi-view clustering), which utilizes variational analysis to generate consistent information. The se... | Rebuttal 1:
Rebuttal: 1. There are few comparative methods, and more comparative methods need to be selected reasonably to illustrate the effectiveness of the proposed methods.
We appreciate your suggestion. We have conducted additional comparisons with several other relevant methods, i.e., FMR
(Flexible multi-view rep... | Summary: This work introduces a new approach called sufficient multi-view clustering (SUMVC) to improve clustering performance using multiple data sources. Existing methods often focus on acquiring consistent information while neglecting the issue of redundancy across multiple views. By contrast, the proposed SUMVC pro... | Rebuttal 1:
Rebuttal: We thank the review for the constructive comments and feedback. Below we provide our responses to the key questions made by the reviewer.
1. The paper mentions that the superiority of SUMVC is demonstrated through experiments on multiple multi-view datasets. However, there is no detailed discussi... | Summary: This paper considers the problem of multi-view clustering from an information theoretic perspective. It focuses on representation learning, and optimizes said representation to improve down-stream clustering performance (with k-means). It introduces an Information Bottleneck based loss function, which consider... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the thoughtful comments and feedback. Below please find our detailed responses to the questions.
1. This paper is more about representation learning for clustering than clustering itself, right?
We appreciate your insightful observation. You're correct in noting th... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: A consistent variational lower bound is provided to explore the consistent information among views for multi-view clustering, based on which SCMVC (simple consistent multi-view clustering) is proposed. To enhance consistent information and minimize unnecessary information among views, a sufficient representati... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and feedback. Below, we provide our responses to the key questions that were raised by the reviewer.
1. It is said the proposed model does not perform well on datasets with strong heterogeneity between views, but no evidence or experiments suppor... | null | null | null | null | null | null |
Best Arm Identification for Stochastic Rising Bandits | Reject | Summary: Stochastic Rising Bandits (SRBs) model sequential decision-making problems in which the expected reward of the available options increases after every time they are selected.
While previous works addressed the regret minimization problem, this paper studied the fixed-budget Best Arm Identification (BAI) probl... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent reviewing our work and for the interesting comments. Below, we address the concerns of the Reviewer.
## Weaknesses
> In Theorem 6.1, the lower bound on the time horizon $T$ depends on $\Delta_i(T)$. The quantity, $\Delta_i(T)$, depends on the instance, ho... | Summary: The paper studies the fixed-budget best arm identification (BAI) under the stochastic rising bandit (SRB) problem. The stochastic rising bandit is to assume that the mean reward will increase as one plays the arm more. By assuming a concave increasing reward, the paper provides upper bounds for two algorithms:... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent on the review and for appreciating our work. Below, we address the concerns of the Reviewer.
## Weaknesses
> Deterministic growth function $\gamma$, meaning that the randomness of $\gamma$ does not accumulate; Not the practical case (consider SGD, former ... | Summary: The paper is about bandit best arm identification with fixed budget, in a non-stationary setting. This is a rested bandit problem: the mean reward of an arm changes each time it is pulled, but does not change when it is not pulled. The main assumption is that the mean reward is a non-decreasing, concave functi... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent on the review and for appreciating our work. Below we provide the responses to the Reviewer's concerns.
## Weaknesses
> R-UCBE depends on a parameter that needs to be tuned using unavailable information, but that theoretical weakness is directly inherited... | Summary: This study explores the stochastic rising bandits (SRB) in fixed-budget best arm identification (BAI). The authors initially formulate this novel problem setting and then introduce two types of estimators. For these estimators, they demonstrate upper bounds that match their lower bounds. Lastly, they validate ... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the time spent on the review and for appreciating our work. Below we provide the responses to the Reviewer’s concerns.
## Weaknesses
> It appears that a weakness resides in the need for a large budget. Moreover, verifying whether the condition is met could be challengin... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Structured Voronoi Sampling | Accept (poster) | Summary: The paper pushes the frontier for gradient based sampling for auto regressive models. The paper lays clear theoretical issues to apply such techniques and address two issues: 1) the major contribution of the paper consists in proposing to construct voronoi like probability space over the output token embedding... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will make sure to move Algorithm 1. to the main body of the text. The reviewer suggested shortening the theoretical part to make space for experiments. We will make sure to add the additional experiments and contextualize them with the main text in the final manuscr... | Summary: Authors have proposed a novel framework for gradient-based sampling from neural autoregressive LMs named Structured Voronoi Sampling. The core idea of the proposed approach is to map LM distribution to the embedding based version of it and then use newly proposed structured voronoi cells to perform sampling ba... | Rebuttal 1:
Rebuttal: Thank you for your careful assessment and feedback.
**Experiments**
> Authors included samples from their methods and related work they have re-implemented, and these samples look pretty bad. Their proposed method repeats the same tokens right after each other making the sample look very unrea... | Summary: The authors present Structured Voronoi Sampling, which is a gradient-based sampling approach. To be specific, the authors map the discrete distribution by a language model and defines densities; the density is then used to sample, which the process is based on hamiltonian monte carlo. The novelty of this paper... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
> i.e. Line 65-66. Not all language models share input and output embeddings. Authors should rephrase the sentence to avoid possible misunderstanding
We will rephrase this as: *_in **most** language models, the weights are shared between the language model head and ... | Summary: The paper proposes a new gradient-based sampling approach called Structured Voronoi Sampling (SVS) for controlled text generation. The key idea is to extend the discrete point distribution over word embeddings given by language models into a continuous density that spreads out probability over their correspond... | Rebuttal 1:
Rebuttal: Thank you for the careful analysis of our work and your valuable suggestions.
(1) Regarding the assumption on base measures and approximations:
thank you for your suggestions on how to strengthen the paper. Perhaps the way that is more practical to do it to approximate base measures is through i... | Rebuttal 1:
Rebuttal: We thank the reviewers for providing valuable and constructive feedback. We first provide responses to a shared concern raised by multiple reviewers. Responses to individual reviewers are provided below. We report the results of new experiments in an additional PDF.
Multiple reviewers were conce... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes Structured Voronoi Sampling: a new gradient-based algorithm to sample from a distribution (i.e. a language model). The proposed approach leads to comparably fluent text whilst being able to better follow constraints (e.g. a topic) for the desired generation.
Strengths: 1. a new sampling me... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
Regarding the suggestion on the presentation of gradient-based sampling, we will try to motivate gradient-based sampling further before going to the details in the final manuscript.
> it is not clear how the proposed approximation of the (costly!) base measure affect... | Summary: Gradient-based sampling for text generation is an important challenge, as it allows for sampling from energy-based models, such as one defined by a mixture of experts as found in classifier-guided sampling. The main challenges in gradient-based sampling for discrete distributions are encoding the discrete dist... | Rebuttal 1:
Rebuttal: Thank you for bringing up the Gibbs with gradient methods. We will ensure their discussion in the related works section of the final manuscript. However, in terms of empirical evidence, [1] doesn't feature any experiments on language generation. We believe that applying this approach to sample a t... | null | null | null | null |
Complete Neural Networks for Complete Euclidean Graphs | Reject | Summary: The authors provided theoretical analyses and proof that the 3-WL algorithm and the Euclidean version of the 2-WL algorithm can distinguish any complete Euclidean graph pairs. The authors then demonstrated that the algorithm can be approximated with GNNs and ran the proposed model on synthetic data to show tha... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. Below are our responses to the questions and concerns.
**Concern Regarding Experiments**: As stated in the Author Rebuttal and our responses to the other reviewers, this work is a theoretical work, which does not aim to devise a practical implement... | Summary: The paper analyzes neural networks for point clouds toward modeling of geometric phenomena. It considers the application of message passing networks/GNNs to Euclidean graphs, whereby a variation of the well-studied k-WL test is adapted to point clouds by using a complete graph on the point cloud and making use... | Rebuttal 1:
Rebuttal: We thank the reviewer very much for the valuable feedback. Below are our responses to the questions and concerns.
**Motivation for Separation**: Separation is a desired property for machine-learning algorithms on point clouds, which bears both practical and theoretical importance. For example, it... | Summary: This paper studies the theoretical completeness of neural networks for Euclidean/3D point clouds, from the perspective of whether they can distinguish all non-isomorphic point clouds.
Key theoretical contributions include showing that variations of the k-WL graph isomorphism test are complete for 3D point cl... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. Below are our responses to your questions/concerns.
**Concerns regarding translating ideas into practice:** Indeed the separation guarantee incurs a high computational cost. However, once one is willing to forego guaranteed separation, there exist m... | Summary: This paper seeks to theoretically demonstrate the complete determination of point clouds, up to permutation and rigid motion. The authors formulate a Euclidean variant of the 2-WL test, effectively illustrating the separation capacity of the Euclidean Graph Neural Network on highly symmetrical point clouds.
S... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Below are our responses to your questions/concerns.
**Syntax:** $(\star)$ stands for “they both have rank r, and $x_i \neq x_j$”. Then we refer to this fact in the proceeding sentence.
To improve clarity, we will replace (*) with “Due to the fact that the... | Rebuttal 1:
Rebuttal: **Response to All Reviewers**
We would like to thank the reviewers for their helpful remarks and detailed feedback, which we have read carefully. We were glad to see the reviewers recognized our novel theoretical contribution. Yet, we feel that we did not convey the significance of our results in... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Extremal Domain Translation with Neural Optimal Transport | Accept (poster) | Summary: This paper presents a novel OT problem, extremal transport (ET), in the context of domain translation. The authors propose an incomplete transport (IT) problem as a surrogate optimization problem to obtain an approximate solution to the ET problem. The theoretical convergence between IT and ET costs (plans) is... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your comments. Here are the answers to your questions.
**(1) The motivation to incorporate ET in the context of domain translation is not clear <...> in the abstract and introduction.**
The motivation to incorporate ET in the context of domain translation is implicit... | Summary: The paper proposes extremal transport (ET), a mathematical framework for achieving the best possible unpaired translation between two domains based on a given similarity function, and proving that ET maps can be learned as a limit of specific partial optimal transport (OT) problem. The contributions of the pap... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your comments. Here are the answers to your questions.
**(1) It is not clarified the motivations of defining ET problem and the relationship between specific task such as unpaired image translation and the mathematical ET problem.The process from abstraction of practi... | Summary: This paper introduces a mathematical formalization called "extremal transport" that aims to achieve optimal translation between unpaired domains based on a given similarity function. Additionally, the paper proposes a scalable algorithm that utilizes neural optimal transport to approximate extremal transport m... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your comments. Here are the answers to your questions.
**(1a) In lines 121-124, how the lower bound is defined during the process of analyzing and constructing upper and lower bounds to simplify the objective is not mentioned.**
Equation (10) states that for any $\pi... | Summary: This paper introduces the concept of extremal Optimal Transport (OT) and proposes the use of incomplete OT as a solution to the extremal OT problem. The authors present a duality method to address the incomplete OT problem and validate this approach using a toy 2D dataset and image translation tasks.
Strength... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your comments. Here are the answers to your questions.
**(1) Design distributions that have a closed-for ET solution. Compare simulation results with the ground truth.**
We conduct *Swiss2Ball* experiment in 2D, see Fig. 1, Table 1 in **the attached PDF file**. Here ... | Rebuttal 1:
Rebuttal: Dear reviewers,
thank you for your thorough and detailed feedback! We are highly inspired by the fact that you agree on the novelty of the proposed Extremal and Incomplete Transport (ET/IT) formulations (Reviewers iXGC, URfJ, Cqh5, 3fqr), find our theoretical results to be valuable (Reviewer iXG... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a novel notion of extremal transport, which relaxes the optimal transport problem by only requiring the support of the pushed-forward distribution to be a subset of the support of the target distribution. To solve this problem, the authors propose a novel approximation approach to find the ... | Rebuttal 1:
Rebuttal: Dear Reviewer, thank you for your comments. Here are the answers to your questions.
**(1) In the traditional OT problem, the OT map is in general discontinuous. This proposes difficulties in using DNN to approximate the OT map. It would be very welcomed if results on the regularity of the IT maps... | null | null | null | null | null | null |
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning | Accept (poster) | Summary: This paper considers how to best use auxiliary tasks to improve performance on target tasks. Specifically, a "ForkMerge" procedure is proposed which consists of two parallel optimization procedures, one on the target task, and one which includes auxiliary data, and the resulting weights are synchronized at reg... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer ECtn for providing insightful reviews and valuable comments. We have clarified the questions in the following response.
**Q1:** Impact of pruning strategy.
$\text{Table 4}$ illustrates the impact of the pruning strategy of ForkMerge. As the number of bra... | Summary: The paper tackles the problem of learning multiple tasks together which is known to lead to "task inteference" or "negative transfer" issues. This is usually tackled by automatically scaling the task weights or gradients based on training statistics (e.g. GradNorm or uncertainty weighing of losses). In particu... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer pM6c for providing insightful reviews and valuable comments. We have clarified the questions in the following response.
**Q1:** Concern on the training cost.
Please refer to $\text{question 2 (Q2)}$ of our global rebuttal.
**Q2:** Contribution of Findin... | Summary: The authors conduct an analysis of negative transfer in auxiliary task learning, finding that gradient conflicts are not necessarily tied to negative transfer, but that auxiliary tasks that induce large distribution shifts from the new training distribution to the test distribution tend to cause negative trans... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer uS5H for providing insightful reviews and valuable comments. We have clarified the questions in the following response.
**Q1:** Some critical details are omitted in the main text.
Thank you for the feedback. Below, we outline our intended revisions:
- W... | Summary: Auxiliary-Task-Learning (ATL) has been studied from the perspective of optimization, which aims to improve the performance of the target task by leveraging similar tasks. However, ATL can sometimes suffer from negative transfer, where the performance of the target task actually decreases when auxiliary tasks a... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer QCm1 for providing insightful reviews and valuable comments. We have clarified the questions in the following response.
**Q1:** Logical Connection Enhancement.
Please refer to $\text{question 1 (Q1)}$ of our global rebuttal.
**Q2**: Why ForkMerge is sup... | Rebuttal 1:
Rebuttal: We would like to sincerely thank all the reviewers for providing insightful reviews and valuable comments. Your reviews are of great importance to us in improving the quality of this work.
**In this global rebuttal, we aim to clarify the common questions from reviewers, and we have responded to e... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: To fully leverage the knowledge from auxiliary tasks and mitigate negative transfer issues, this paper introduces ForkMerge, which automatically searches for varying task weights for auxiliary tasks by minimizing target validation errors. ForkMerge is evaluated under various settings, including multi-task lear... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer G3Ms for providing insightful reviews and valuable comments. We have clarified the questions in the following response.
**Q1:** Clarification on the data division strategy for each branch.
Depending on the characteristics of auxiliary task learning scena... | Summary: This paper strives to mitigate negative transfer in auxiliary-task learning by optimizing the coefficients assigned to auxiliary tasks. By conducting an empirical investigation into the factors contributing to negative transfer, this paper reveals two interesting findings. Based on the findings, a new approach... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer yZ6C for providing insightful reviews and valuable comments. We have clarified the questions in the following response.
**Q1:** It is not clear how the new findings motivate the proposed ForkMerge.
Please refer to $\text{question 1 (Q1)}$ of our global r... | null | null | null | null |
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing | Accept (poster) | Summary: In non-linear compressed sensing, one would like to recover x from a
series of observations y_i = f_i(a_ix); and in generative compressed
sensing, x is drawn from a generative model. The contribution of this
paper is a framework for uniform recovery with general nonlinear
measurements and Lipschitz generative... | Rebuttal 1:
Rebuttal: Thanks for your positive assessment of this paper and the useful comments and suggestions. We respond regarding the writing quality in the general response to all reviews, and respond to the other points as follows.
(**If any one of the terms is relaxed (nonuniform, a ReLU generator, or non-dith... | Summary: This paper introduced a unified framework for uniform signal recovery in nonlinear generative compressed sensing, in particular, 1-bit generative compressed sensing (GCS) and single-index models (SIM). The authors obtain uniform recovery guarantees for 1-bit GCS, 1-bit GCS with dithering, Lipschitz-continu... | Rebuttal 1:
Rebuttal: Thanks for your useful comments and questions. Regarding the signal lying exactly in Range(G) and applying to practical generative models, please see the general response above.
(**Are the main results applicable to diffusion models?**)
In this paper, our focus is on generative models with a low... | Summary: The paper discusses a unified framework for uniform signal recovery in nonlinear generative compressed sensing. The authors utilized the use of generalized Lasso and Lipschitz approximation to allow for a lower sample size of measurements.
Strengths: In what follows the strengths of the paper are given:
1) T... | Rebuttal 1:
Rebuttal: Thanks for your recognition of this paper and the helpful comments. Regarding readability, please see the general response above.
(**How did you ensure that the assumptions that were presented in the paper hold in your experiments?**)
We would like to highlight that this work is primarily theo... | null | null | Rebuttal 1:
Rebuttal: **General responses to the three anonymous reviewers**
We are very grateful to the reviewers for their helpful feedback and suggestions. Our responses to the main concerns shared by multiple reviewers are given as follows. Other responses are given to each reviewer separately.
(**The assumption... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Faster Margin Maximization Rates for Generic Optimization Methods | Accept (spotlight) | Summary: This paper studies efficient algorithms for margin maximization with respect to a general geometry. The problem of margin maximization is interesting because it has been shown that common optimization algorithm such as gradient descent prefers such solutions through a well-known phenomenon called "implicit bia... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive and constructive feedback. We will revise the notations and enhance the overall presentation as per your advice. Your specific questions and concerns are addressed below.
>D_E is not a standard notation, why not just call it the KL-divergence?
Thank you for yo... | Summary: This work gives a unified perspective on maximal margin problems realized by a wide range of optimization algorithms. It mainly covers three cases: (i) steepest descent under a general norm, (ii) mirror descent, and (iii) momentum-based acceleration. The authors collectively refer to them as generic optimizati... | Rebuttal 1:
Rebuttal: > Difference from Wang et al 2022b seems to need more clarification. Did this previous work not address the max-margin rate of all of (i) steepest descent under a general norm, (ii) mirror descent, and (iii) momentum-based acceleration problems?
Thank you for the constructive suggestion; we will ... | Summary: The paper studies the implicit bias of generic optimization methods, which plays a key role in understanding their generalization capabilities in settings with multiple solutions.
The authors propose a new game framework to derive margin and directional error rates, which consists of transforming the optimiza... | Rebuttal 1:
Rebuttal: We deeply appreciate your constructive review and supportive comments on our work! We will refine the presentation and rectify the typos in line with your suggestions. For clarity, we will specify in the revised version that the potential in the left box of Algorithm 4 refers to the general norm s... | Summary: This paper studies the implicit bias of the generic optimization method such as mirror descent and steepest descent. By transforming the generic optimization algorithm into an online learning dynamic, this paper shows the accelerated rate and offers a new perspective.
Strengths: 1. This paper is well-written.... | Rebuttal 1:
Rebuttal: Thank you very much for the review and positive comments!
> Can the author provide some simple experiments to verify the correctness of the theoretical results? For example, using the synthetic dataset with its max-margin solution known.
Thank you for the constructive suggestion; as we discuss ... | Rebuttal 1:
Rebuttal: We are deeply grateful to all reviewers for their positive feedback and valuable suggestions. We commit to implementing your advice to refine our paper, and some of the frequently asked questions are addressed below.
>Do you believe a similar approach could be used beyond the exponential loss?
... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work introduces a novel method to derive margin maximization and directional error rates for generic optimization methods. The method consists in finding a reformulation of the regular optimization as a minmax bilinear game. Rates can then be derived using online learning techniques. The authors demonstra... | Rebuttal 1:
Rebuttal: We are profoundly grateful for your positive evaluation of our work and your detailed, constructive feedback. We address some of your specific inquiries below:
>What would be required to eliminate the term $O(\log T)$ in the $O(\frac{\log n\log T}{(q-1)T})$ for mirror descent?
We appreciate this... | null | null | null | null | null | null |
Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity | Accept (poster) | Summary: There is a continuing effort to understand the complex training dynamics and loss landscape of neural networks, and one of the most interesting discoveries is Linear Mode Connectivity (LMC). LMC is the phenomenon that when two different solutions are linearly interpolated in parameter space, the training loss ... | Rebuttal 1:
Rebuttal: **Q1: Ask for additional experiments on larger datasets such as ImageNet. “The authors only ran their experiments on MNIST and CIFAR10, which are *relatively easy* datasets. It will be important to verify its validity on larger datasets such as ImageNet.”**
**A1**: Thank you for great suggestions... | Summary: The work identifies layerwise linear feature connectivity (LLFC) and proves LLFC is sufficient for linear mode connectivity (LMC) between neural networks. Experimental evidence using two methods for finding LMC networks (spawning and permutation) finds that LMC and LLFC co-occur in a variety of models and dat... | Rebuttal 1:
Rebuttal: **Q1: “Current alignment algorithms for permuting LMC networks already directly optimize for LLFC (section 5.3), so the finding that LLFC implies LMC (Lemma 1) is formalizing a well-established phenomenon which is somewhat obvious.”**
**A1**: We'd like to emphasize that the interpretation that cu... | Summary: This paper introduces Layerwise Linear Feature Connectivity (LLFC). Compared to the better known linear mode connectivity (LMC), which states that networks trained by SGD are linearly connected modulo permutation, LLFC suggests that the feature maps of every layer is connected. As shown in the paper, LLFC is a... | Rebuttal 1:
Rebuttal: **Q1: “The analysis for the cause of LLFC is limited to ReLU activation…To demonstrate the prevalence of LLFC, it might help to include experiments on neural networks with different activations.”**
**A1**: We note that ReLU is the activation used in standard architectures including ResNet, VGG, e... | Summary: In this work, the property of Layerwise Linear Feature Connectivity (LLFC) of neural network representations is introduced, which is a stronger generalization of linear mode connectivity (LMC). They show that LLFC often occurs when LMC does. Moreover, they give a possible mechanism by which LLFC may occur (ReL... | Rebuttal 1:
Rebuttal: **Q1: Ask for additional experiments to cover the straight-through estimator method from Git Re-basin [1]. “You don't really cover the last method (straight-through estimator) from Git Re-Basin, even though it works the best in many cases. Perhaps you should note this.”**
**A1**: We greatly appre... | Rebuttal 1:
Rebuttal: **Limitations.**
1. In Appendix C, identifying a permutation that directly enforces commutativity condition involves solving a NP-hard QAP. We leave the QAP-solving problem as a future direction.
2. Our Theorem 1 predicts LLFC in an ideal case, while in practice, a scaling factor $c$ is introduce... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a special case of Linear Mode Connectivity (LMC) denoted as Layerwise Linear Feature Connectivity (LLFC). Whereas two trained neural networks present LMC if a convex combination of their parameters produce a neural network with similar training loss and accuracy, two trained neural networks... | Rebuttal 1:
Rebuttal: **Q1: Concerns about the trivial case that two Neural Networks (NNs) emerging LLFC share the similar weights. “ I cannot help but wonder about the following: Two neural networks presenting LLFC would have very similar parameters, and the fact that LLFC co-occurs with LMC implies that LMC is observ... | null | null | null | null | null | null |
Physics-informed generative model for drug-like molecule conformers | Reject | Summary: This paper proposes PIDM, a novel generative model for generating 3D molecular conformations from 2D molecular graphs. The proposed method uses an ODE based diffusion model to generate 3D molecular conformations, and designs a denoising network with multiple different modules to capture various of physical inf... | Rebuttal 1:
Rebuttal: Thank you for reviewing our submission and providing valuable feedback.
Since you have provided no explicit questions, we will attempt to address your list of weaknesses.
* The proper torsions and improper torsions introduced in line 40-41 are not formally defined and it is not easy to discrimi... | Summary: The paper presents a diffusion model to generate conformers of drug-like molecules. The score model architecture is novel.
Strengths: The authors construct a diffusion model for conformer generation whose score model architecture is inspired by the structure of classical force fields. This is an interesting a... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read our submission and provide feedback.
Let us begin with your questions.
* Line 95 ‘uses bonds as graph edges’: why not allow some message-passing between non-bonded atoms?
In chemistry, the classification of chemical groups is associated with bonded connect... | Summary: A diffusion-based conformation generation method called PIDM is proposed for molecules. It combines several geometries including bond lengths, bend angles, proper torsions, chirality and cis-trans of the noisy conformer as the input, and output the scores of the probability to iteratively generate the conforma... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission and provide a thoughtful response.
Given the extent of your comments, and the lack of explicit questions, we will attempt to address the least subjective of the weaknesses you have provided.
* The five geometries are very commonly used tha... | Summary: This paper introduces a physics-informed generative framework targeted at the task of molecular conformation generation. The method is motivated by the formulation of classical force fields and such an idea is reflected via bond, bend, and proper torsion and properly injected into the model design. The experim... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for reading our submission and providing valuable feedback.
We would like to begin by answering the given questions.
(1) Using open-sourced checkpoints
The goal of a conformer generator is to predict conformers in a manner that respects the underlying physics of a ... | Rebuttal 1:
Rebuttal: We think it is important to clarify certain aspects of molecule conformers, pertaining, in particular, to their use in drug discovery. As domain experts on this particular topic, we can speak authoritatively, and encourage the reviewers to reach out to other domain experts if they have further que... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Data Pruning via Moving-one-Sample-out | Accept (poster) | Summary: The paper proposes a new method called moving-on-sample-out (MoSo) to remove less informative samples from the training data. The criteria for removed samples is based on the change in the optimal empirical risk when the sample is removed. As exact calculation of the criterion is computationally challenging, t... | Rebuttal 1:
Rebuttal: Sincerely thanks for your appreciation of our work. Hoping our response will address your concerns.
---
**Q1: A rigorous justification of MoSo is desirable.**
A1: Thanks! Proposition 1.2 gives rigorous proof of the approximation accuracy, and we have also updated it in the support material to g... | Summary: This paper proposes a framework for data pruning that retains important samples while considering the training dynamics. While the overall methodology relies on analyzing the change in empirical risk from removing individual points, the paper introduces a first-order approximation algorithm that can be efficie... | Rebuttal 1:
Rebuttal: Dear reviewer XiFz:
Thank you also for your constructive suggestions. We will carefully address each of your concerns and revise the manuscript accordingly. If you have any new feedback please do not hesitate to let us know! We will do our best to answer your feedback!
---
**Q1: The proof f... | Summary: This paper presents MoSo, a method to identify and remove the least informative samples from a large dataset. The underlying idea is to consider the impact of each sample on the optimal empirical risk. Quantifying this exactly requires leave-one-out retraining for every point, which is intractable. So, the aut... | Rebuttal 1:
Rebuttal: Dear reviewer QDiJ:
Thanks for your time and efforts in reviewing our paper. We will address your concerns below.
---
**Weaknesses**
**Q1: It is not clear to me how the method can provide a computational speedup.**
A1: Thanks for your suggestion. We evaluated MoSo and the other baseline met... | Summary: This paper presents moving-one-sample-out (MoSo) for the purpose of coreset selection. This algorithm measures how empirical loss changes when excluding individual points during training. The paper introduces an approximation and other tricks to make this method computationally feasible. Experimentally, MoSo o... | Rebuttal 1:
Rebuttal: Dear reviewer eYrR:
Thank you for appreciating our approach. We will address your comments below.
----
**Q1: Lack of analysis of the computational cost of this method.**
A1: This is a good question! We will incorporate this experiment into the paper. We evaluated MoSo and the other baselin... | Rebuttal 1:
Rebuttal: **Q1: How close the estimator is to the true criterion?**
A1: This is a good question! We will add this to the revised paper!
We compare the approximation error comparison between ours and the well-known influence function, please refer to Figure 1 in the PDF attachment. Our method exhibits bet... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a data-pruning method. The authors argue that sample importance should not be determined by sample difficulty. Alternatively, they present the MoSo score, which quantifies the changes in empirical risk upon excluding a single data point. An efficient approximation for MoSo is proposed to ca... | Rebuttal 1:
Rebuttal: Dear reviewer xfpA:
Thank you for your comments which help us improve our work!
----
**Q1: How expensive the proposed method is compared to the baselines?**
A1: Thanks for your suggestion. We will incorporate the additional results into the paper. We evaluated MoSo and the other baseline meth... | null | null | null | null | null | null |
Imitation Learning from Imperfection: Theoretical Justifications and Algorithms | Accept (spotlight) | Summary: This paper considers the problem of offline imitation learning with supplementary data with optimality not guaranteed. The paper gives theoretical analysis on the performance gap bound between expert policy and learner's policy for behavior cloning (BC) on expert data only and naively using BC over the union o... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper, and for your constructive comments.
**Comment 1:** The proposed method, ISW-BC, might be non-robust to stochastic environment.
**Response 1:** Thank you for providing the example and initiating the discussion. However, we would like to clarify t... | Summary: The paper provides derivations of the imitation gap, the gap in performance between the trained agent and expert who provided the data, when traditional behavioural cloning (BC) is used. The specific setting assumes there is plentiful of supplementary data to train the BC agent on, but since this supplementary... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and providing us with your valuable feedback.
**Comment 1:** In "noisy expert" setting, the proposed method is clearly better than the baselines. However this setting seems rather unrealistic (proper trajectories from a policy but actions are ran... | Summary: This paper studies the problem of offline imitation learning (IL) with a supplementary dataset, which can address the scarce expert data issue in pure IL. In this setting, the challenge is that the supplementary dataset may have out-of-distribution samples. This paper considers the classical method Behavioral ... | Rebuttal 1:
Rebuttal: We appreciate your time to review and provide positive feedback for our work.
**Comment 1:** This paper is a bit dense to read. I believe that this paper would benefit from providing more intuitions and proof sketch for the theoretical results. Besides, the authors should give more analysis of th... | Summary: In the paper, the authors focus on imitation learning (IL) when working with supplementary imperfect data. They conduct a thorough theoretical analysis to understand the limitations of IL under various dataset compositions. The authors' theoretical analysis provides insights into the bounds and constraints of ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review and check our paper, and for your insightful comments.
**Comment 1:** The authors' analysis lacks consideration of alternative methods that can effectively learn from imperfect data. [1]
**Response 1:** Thank you for bringing up the work [1], which utiliz... | Rebuttal 1:
Rebuttal: We thank all reviewers for their expertise and efforts in reviewing our paper. We have responsed to each review seperately. We hope that our response can address the concerns well. Furthermore, we look forward to any additional comments or suggestions for improvement.
Please take note that we hav... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning | Accept (poster) | Summary: Cylindrical algebraic decomposition (CAD) is a technique to decompose a multi-dimensional space into a finite number of cells. CAD cells are built to respect a set of polynomial constraints such that each constraint has constant truth value in each cell. As stated by the authors, the variable ordering in the C... | Rebuttal 1:
Rebuttal: Thank you for the feedback and valuable comments.
```
Can we empirically check how much a GNN enhances the performance of GRL-SVO? To check this, the authors can for instance replace the GNN with an MLP performed on each polynomial embedding and then do a mean aggregation to have a learnable rep... | Summary: Given a curve y = x^2, it partitions the x-y plane into three sets where the sign y-x^2 is the same. On the curve y-x^2 it is zero, above the curve y = x^2, sign is positive and below the curve it is negative. Thus the polynomial has 3 cells - regions where the sign is invariant.
Given a polynomial with n-vari... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback.
```
The performance improves very quickly according to Figure 4. Is there an explanation. How stable are the runs of REINFORCE ?
```
As an example, the brown heuristic (EB & NUP in Section 2.2) only utilizes three statistical features: degree, total degree, a... | Summary: This work proposes a reinforcement learning based method for the selection of a more efficient variable order for the downstream CAD(cylindrical algebraic decomposition) task. The objective is to minimize the number of cells, a suitable metric that intuitively reflects CAD efficiency. The proposed GRL-SVP(UP/N... | Rebuttal 1:
Rebuttal: Thank you for the valuable feedback.
```
Could you clarify the role of polynomial coefficients within your proposed graph representation? Specifically, are these coefficients incorporated into the graph structure or the node embeddings? Furthermore, what is your perspective on the potential import... | Summary: This paper proposed a new method for suggesting the variables order for Cylindrical Algebraic Decomposition (CAD) problem. Their method utilized Graph Neural Networks (GNN) and Reinforcement Learning(RL). They proposed two variants: one utilizing projection(UP) and one without projection (NUP). They test their... | Rebuttal 1:
Rebuttal: Thank you for your feedback. Your suggestions are very important and helpful to improve the quality of our work. So we have arranged as many experiments as possible to further discuss our results.
```
Since you encode 14 human-crafted features in the graph embedding matrix, maybe you can show wh... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for the reviewers' valuable feedbacks, which are quite helpful to enhance the quality and clarity of our work. We have designed some experiments and attempted to complete them as many as possible during this short period. We will add the missing exper... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Fairness under Noise Perturbation: from the Perspective of Distribution Shift | Reject | Summary: The authors introduce an innovative framework that enhances the fairness guarantees of a classifier in the presence of both sensitive attribute noise and label noise, considering them independently as well as in combination. Their approach incorporates theoretical guarantees and involves training a fair encode... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment. We'll fix the typos and include the suggested reference and discussions in final paper. For **Weakness 1 (W1): Comparison with fairness under distribution shift**, **Question 1 (Q1): Connection between $P$ and $Q$**, **Q2: Non-binary setting** and **Limitatio... | Summary: The paper aims to improve the performances of fair training when the group attributes or labels in the training data have noisy information. The paper views the noisy training data problem as a kind of distribution shift, where the training data is noisy and the test data is clean. To address this issue, the p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment. For **Weakness 3: Comparison with fairness under distribution shift** and **Limitations**, please refer to the '**Comparison with fairness under distribution shift**' and '**Limitations**' parts in global rebuttal.
**[Weakness 1 (W1): Details of training ob... | Summary: The paper studies the fairness problem under noise perturbation on both label and sensitive attributes. In particular, it considers such a problem from the perspective of distribution shift and uses the normalizing flow framework to analyze the problem. Empirically, the proposed methods achieve the best utilit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment. For **Weakness 2: Discussion of limitations**, please refer to the '**Limitations**' part of global rebuttal.
**[Weakness 1 (W1): Assumption of invertible function in normalizing flow]** The invertibility is a basic formulation in normalizing flow methods [1... | Summary: This work studies noise tolerance of fairness from the perspective of subpopulation/subgroup shift, by considering the perturbation of the sensitive attributes as well as the labels _without_ the need for noise-rate estimation - by considering the noisy distribution as the source and clean distribution as the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment. We'll refine the writing and adjust the text size for plots in final paper. For **Weakness 1 (W1): Multi-valued version of loss function** and **Limitations**, please refer to the '**Non-binary setting**' and '**Limitation**' parts of global rebuttal.
**[W2:... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comment and we would like address issues that are widely concerned by reviewers in this global rebuttal.
**[Comparison with fairness under distribution shift]** We compare our method with two state-of-the-art methods [1,2] on fairness under distribution shif... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper targets the problem of ensuring fairness with noises on either sensitive attributes or labels. Specifically, this paper models the noisy data training set and clean test set as a distribution shift and proposes a regularization term to improve the fairness of classifiers.
The theoretical analysis in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comment. For **Question 3: Connection between KL-divergence**, please refer to the '**Connection between $P$ and $Q$**' part of global rebuttal.
**[Question 1 (Q1): Requirement of noise rate estimation]** We are sorry about the confusion. $\lambda_0$ and $\lambda_1$ ... | null | null | null | null | null | null |
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits | Accept (poster) | Summary: In this paper, the authors studied a variant of the parameterized bandits problem where the learner has access to auxiliary feedback that is correlated with the observed reward. The authors proposed a method that leverages the auxiliary feedback to construct a reward estimator with more accurate confidence bou... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments and suggestions. We have responded to each of the questions below.
>### *1. The description of the algorithm (OFUL-AF) could be made clearer. Currently, it appears as a rephrasing of each step without clear connections to prior derivations. It would be helpful ... | Summary: This paper leverages the method of control variates to obtain reward estimates with smaller variance for contextual bandit algorithms, since smaller variance in reward estimation means tighter confidence bound estimation and therefore smaller regret. Estimation methods for both unknown and known auxiliar feedb... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments and constructive feedback. We have responded to each of the questions below.
> ### *The means of using control variates to reduce the reward estimation variance is not a completely new idea, as the authors pointed out in the related work discussions. And the es... | Summary: This paper studies the parametrized bandit problem in which the learner observes auxiliary feedback together with the reward, and also correlated with the reward. It is motivated from the control variate approach in causal inference, the main difference is that in this paper, it extends the control variable th... | Rebuttal 1:
Rebuttal:
Thank you for the detailed comments and suggestions. We have responded to each of the questions below.
> ### *I am a little bit confused about the relationship of the variance reduction in terms of the number of auxiliary feedback being used. Under estimated $\beta$ and from Theorem 1, it seems... | Summary: This paper focus on the problem of parameterized bandits when extra auxiiliary feedback is available, which can be utilized to construct an unbiased reward estimator, which potentially shares smaller variance and thus algorithms based on such estimator can thus incur smaller regret. Experiments validates the e... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. In the following, we have responded to your questions.
> ### *The improvement is expectable, as auxiliary feedback (AF) requires more information and computation. So this can be seen as a trade-off between extra information beyond rewards, extra computatio... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and efforts in evaluating our paper and for their detailed comments and suggestions. We hope our answers to your questions will alleviate your concerns and further improve your opinion of our work. If you have additional questions, we would be happy to address... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity | Accept (poster) | Summary: This paper uses a transformer on neuroscience relevant task of spatial navigation, and shows when trained on both novel and familiar tasks simultaneously, place cell representations appear is different parts of the transformer depending on the current task – place cells in the feedforward net (post self attent... | Rebuttal 1:
Rebuttal: Thank you so much for your reviews and encouragement. The key __contributions__ of our work was to explore the resemblance between the NMDA receptor in the human hippocampus and activation functions in transformers. We are happy to see that the reviewer finds our results convincing.
Here, we wo... | Summary: This paper builds on recent work connecting the hippocampus to the transformer architecture by introducing a new hippocampus-inspired activation function for the transformer's feedforward modules. In a toy navigation task, several empirical investigations show that the choice of this activation function has a ... | Rebuttal 1:
Rebuttal: We are glad that the reviewer recognizes our __contributions__ in 1) connecting the hippocampus to the transformer architecture 2) the convincing empirical analysis which supports the transformer’s longer-term memory formation, and 3) unveiling place cell-like firing patterns in a feed-forward lay... | Summary: Many recent papers have shown a relationship between Transformers and biological structures, particularly the hippocampal formation. This work demonstrates that the types of non-linearities provided by NMDAR dynamics (which have known biological importance) can be beneficial in a Transformer architecture for a... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer finds our __contributions__ regarding 1) the connection between transformers and hippocampal formation, including the incorporation of NMDAR nonlinear dynamics, and, 2) its potential benefits for both neuroscience and machine learning communities.
Here, we would li... | Summary: This paper investigates the resemblance between the NMDA receptor (NMDAR) in the hippocampus of the human brain and the activation functions used in the transformer architecture (e.g., ReLU, GELU). Then, this paper presents a new activation function that exhibits similarities to NMDAR. It demonstrates that by ... | Rebuttal 1:
Rebuttal: Thank you so much for your reviews. The key __contribution__ of our work was to explore the resemblance between the NMDA receptor in the human hippocampus and activation functions in transformers. We appreciate that the reviewer finds our work to be a novel approach introducing a new activation fu... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their insightful comments on our study. All reviewers recognized our contribution to investigating NMDAR-like nonlinearity in the transformer's feed-forward network, which will be beneficial for both communities of ML and neuroscience. Reviewers find our work w... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization | Accept (spotlight) | Summary: This paper proposes a first-order optimization method for convex optimization that requires gradient computations and matrix-vector products. Based on recent advances on quasi-newton methods, this method converges at a rate that matches the optimal rate ($1/k^2$) when $k = \Omega(d)$ and improves upon the opti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments. We address your concerns below.
---
**Q1 It is not clear in the experiments if we are observing super-linear rates or if we see sublinear convergence. Try the log-sum exp loss, which is not strictly convex.**
**A1**
This is a very good observa... | Summary: This paper proposed a novel quasi-Newton method with faster global convergence rate. The algorithm uses the framework of MS acceleration and updates the Hessian estimator via online learning. The obtained convergence rate is impressive, it firstly show a faster global rate of quasi-Newton method which cannot b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comment. We address your concerns below.
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**Q1 Compare the detailed computation cost in a table.**
**A1**
Thanks for your suggestion. In the following table, we summarize the detailed computation cost of NAG and our method A-QNPE, and we will also include i... | Summary: This paper proposes an accelerated quasi-Newton proximal extragradient method for solving unconstrained smooth convex optimization problems. The algorithm can achieve a convergence rate of $\mathcal{O}\bigl(\min\{\frac{1}{k^2}, \frac{\sqrt{d\log k}}{k^{2.5}}\}\bigr)$, where $d$ is the problem dimension and $k... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comment. We address your concern below.
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**Q1 Comparison with NAG in terms of the total computational cost.**
**A1** Thanks for raising this point. For easier comparison, we follow the suggestion by Reviewer H1ti and summarize the computation cost of NAG and... | Summary: This paper uses the optimal and adaptive Monteiro Svaiter acceleration framework to create a quasi-Newton method that solves unconstrainted convex problems with Lipschitz gradients and Lipschitz hessians at Nesterov's accelerated rate $O(1/k^2)$ but when the number of iterations is greater enough than the dime... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. We address your concerns below.
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**Q1 In your setting the function is convex smooth + Lipchitz Hessians. It would be good to comment on how the worst-case construction also applies to your setting.**
**A1** This is an excellent point. The l... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and effort in evaluating our paper. Following the suggestions by **Reviewer H1ti** and **Reviewer N8Fk**, we have included additional plots in the attached pdf file.
- In Fig. 1, we consider the logistic regression problem $f(x)= \frac{1}{n}\sum_{j=1}^n \log... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance | Accept (poster) | Summary: This paper proposes a novel gradient-free (zeroth-order) clipped version of stochastic similar triangles method for solving non-smooth stochastic convex optimization problem under a much weaker infinite variance assumption. The derived iteration and oracle complexity bounds are optimal in both convex and stron... | Rebuttal 1:
Rebuttal: >**While the paper is a valuable theoretical contribution, the addition of experimental results would enhance the overall work by demonstrating the feasibility and effectiveness of this method in practical applications.**
Thank you for the suggestion. Please, see our general response to all revie... | Summary: In this papers, the authors build upon the work that has been done in [28] and adjust the proposed algorithms there for zero-order oracles rather than the gradient oracles. The goal is to optimize non-smooth stochastic convex optimization problems with infinite variance.
Strengths: The paper does a good job ... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed review of our work. Below, we address questions and concerns raised by the reviewer.
>**Minor typo in the abstract: ajust --> adjust.**
>**"We emphasis (--> emphasize) that this generalization requires an extension of the batching technique to (--> for) infin... | Summary: This paper proposed and analyzed a zeroth-order method for non-smooth stochastic optimization under heavy-tailed noise and adversarial noise, by combining ball-averaging-based smoothing technique (to tackle non-smoothness) and gradient clipping technique (to tackle heavy-tailed/adversarial noise). This general... | Rebuttal 1:
Rebuttal: We thank the reviewer for a positive evaluation of our work. Below, we address questions and concerns raised by the reviewer.
>**The presentation is concise, but maybe at the cost of some necessary clarity. In Eqn. (2) it's not specified what distribution should $e$ follow (should be the uniform ... | Summary: This paper presented derivative-free methods for the optimization of stochastic convex functions with a potentially infinite variance noise. Here the level of noise is defined in terms of the boundedness of modulus of a Hölder-type continuous condition. The main technique is to adopt a gradient clipping to the... | Rebuttal 1:
Rebuttal: We thank the reviewer for a positive evaluation of our work. Below, we address questions and concerns raised by the reviewer.
>**L113: it might be better to use {\xi_i}_i and {e_i}_i as the input of the function g^B.**
Thank you for the suggestion, we agree with it. We did not want to complic... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable feedback and time. In particular, we appreciate that the reviewers acknowledged the following strengths of our work: a well-motivated problem, an algorithm with tight and valid theoretical bounds, an important contribution, and a good write-up and organiza... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides high probability bounds for the convergence of gradient-free methods on convex and strongly-convex functions when the noise in the gradient oracle has infinite variance. An oracle provides $f(x,\xi)$, a noisy evaluation of the function $f$ at point $x\in \mathbb{R}^d$ by the oracle, where $... | Rebuttal 1:
Rebuttal: We thank the reviewer for a detailed summary of our main contributions. Below, we address questions and concerns raised by the reviewer.
> **Missing introduction and numerical experiments.**
We agree that the introductory part can be improved and will extend it in the final version of our work. ... | null | null | null | null | null | null |
Generalized Weighted Path Consistency for Mastering Atari Games | Accept (poster) | Summary: This paper proposed Generalized Weighted PCZero (GW-PCZero), which builds on EfficientZero and PCZero. The goal is to generalize the implementation of PCZero from board games to Atari games, which is achieved by adding the theorem “path consistency” to EfficientZero, extending the previous idea from PCZero. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive comments and suggestions, and we will carefully revise the paper accordingly. We provide a detailed response to each question below.
Q1: In Appendix 7.1, regarding the board game Hex, you mentioned that "Weighted PCZero beats the original PCZero with a scor... | Summary: This paper proposes GW-PCZero, an RL algorithm based on neural-guided Monte Carlo Tree Search (MCTS). GW-PCZero adopts the idea of Path Consistency (PC) from prior work, i.e., a regularizer that encourages evaluation function to be consistent throughout an optimal path, to improve sample efficiency. Beyond th... | Rebuttal 1:
Rebuttal: Thank you so much for your valuable suggestions. We will incorporate more discussions about algorithm’s limitations in the paper. For example, similar to many existing algorithms, the performance of the current implementation of GW-PCZero relies on the selection of hyperparameters. In the long ter... | Summary: This paper proposes a model-based RL method called GW-PCZero, which is built on EfficientZero and generalizes the path consistency (PC) constraint from board games with zero immediate rewards to environments with non-zero immediate rewards. The authors introduce a weighting average mechanism and use the mean f... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable suggestions, and in the following we address the concerns you have raised.
Q1: Can you report the median human normalized score?
A1: The median human normalized scores are 0.399 and 0.388 for GW-PCZero and EfficientZero, respectively. The results are obtai... | Summary: This paper proposes GW-PCZero, a reinforcement learning method, extending the technique of PCZero which is currently limited to board games and lacks theoretical backing. The GW-PCZero is designed for environments with non-zero immediate rewards, such as Atari games. It maintains path consistency by regularizi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable suggestions and would like to address the questions one by one as follows.
Q1: The novelty seems limited. The authors claimed a theoretical guarantee for path consistency (PC) for the first time. But the PC is the main contribution of PCZero instead of GW-PC... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper extends PCZero to more general games whre the environment emits non-zero immediate rewards and proposes Generalized Weighted PCZero (GW-PCZero). GW-PCZero is built on EfficientZero with a generalized PC constraint. Specifically, GW-PCZero add an additional value consistence loss alone the sampled pa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable suggestions and would like to take this opportunity to address the raised issues.
Q1: What's the performance of GW-PCZero if setting the updating steps to 120k?
A1: The Path Consistency (PC) constraint is able to improve the model’s learning efficiency, an... | null | null | null | null | null | null |
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