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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A Bayesian Take on Gaussian Process Networks | Accept (poster) | Summary: Gaussian Process (GP) networks are directed graphical models for continuous data, where the function mapping from parent node values to parameters of child node is a Gaussian process. Given the graphical network structure and a dataset of observations, learning the GP model for each node simply reduces to a st... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive comments and suggestions.
* *as mentioned above regarding $\lambda=0$, is my understanding correct? Can the authors comment on this?*
There is indeed a typo in equation (14), the linear terms should have been included and the index $j$ ... | Summary: This paper proposes a Bayesian structure learning of the GPNs framework that is claimed to be less computationally. To address this, the approach presented in this work utilizes Monte Carlo and importance sampling to sample from the posterior distribution of network structures. This approach compares models us... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments, and acknowledge the time they took to assess the paper.
* *The contribution of the paper is not clearly justified and seems moderate at best. It is a combination of a few approaches (Gaussian processes, Laplace approximation, and Importance ... | Summary: The paper proposes methodology to perform Bayesian inference on the (hyper) parameters of a so-called Gaussian Process Networks (GPNs), which are sets of functional equations with Gaussian Process (GP) priors on the functions relating a variable to its parents, as well as inference on the graph structure and g... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their constructive comments and interesting questions.
* *How is the Gaussian proposal for the numerator of Eq. (11) chosen ? I would have expected a Laplace - IS here too ? Perhaps clarify what is the choice made here and possibly why.*
The proposal funct... | Summary: This paper proposes an MCMC algorithm for Bayesian inference for Gaussian Process Networks where you model the distribution of a node as a function of its parents plus noise, with the function a Gaussian process.
The sampler uses a Laplace approximation to make informed moves between Networks.
The sampler is... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments, and acknowledge the time they took to assess the paper.
* *It would be helpful to see the robustness of the results of your method as you vary the prior.*
The priors we use for the hyperparameter set are standard, non-informative priors (se... | Rebuttal 1:
Rebuttal: Following the reviewers' suggestions, we attach a pdf containing additional simulation results.
Pdf: /pdf/94fe2a5c273f7c30e0602491cf601e6693324af0.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Within the broader task of causal network structure identification from observational data, i.e. finding the probabilistic graphical model which best explains (has highest likelihood) observations of a set of variables, this paper focus on a type of network known as GPN. GPNs are adapted to scenarios where all... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and suggestions, and will incorporate them into the manuscript.
* *Minor errors and typos I found [...] lines 24 and 29, citations seem quite cherry-picked and do not provide a variety of viewpoints as I would expect; I would hope there are no... | null | null | null | null | null | null |
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack | Accept (poster) | Summary: This paper proposes a model-oblivious query-efficient black-box model extraction attack. This attack is achieved by solving a proposed distributionally equivalent and max-information model extraction problem. To solve the problem, this paper develops an active sampling-based query selection algorithm, MARICH, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reviewing and encouraging comments about the novelty, presentation, and experimental results.
**Querying industrial black-box APIs.** For genuine financial constraints, we cannot run experiments by querying the industrial APIs. Instead, we trained our own ... | Summary: Given a dataset DQ and blackbox access to a model trained on a dataset DP, the authors identify a subset of the dataset DQ that can be used to train another model. They use a metric based on energy to select the samples.
The authors use with model stealing/extraction attacks as their primary related work, th... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her feedback. We answer below to his/her concerns and comments.
**Explaining the experimental design:**
**1. MI attack:** A membership inference attack shows informativeness of the extracted model, while never using the private training dataset. Our experimental res... | Summary: The authors studied black-box model extraction, which is a practical scenario in MLaaS. In order to boost attack efficiency in this extraction, they focused on distributional equivalence and max-information model extraction. As for distributional equivalence, they proposed a distributional notion of equivalenc... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer his/her valuable time spent reviewing.
**Improved presentation of Table 1:** We would like to refer the reviewer to the full paper with appendix which is provided in the supplementary materials. We apologize for the fact that we had made the rectifications in T... | Summary: The authors propose a model extraction attack which queries a model's publicly available API and chooses samples based on maximizing entropy of the target model's predictions, and maximizing agreement with between extracted model and target model predictions. These samples are then used in training a surrogate... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for acknowledging the strengths and soundness of the contribution as well as for their comments to improving the manuscript.
**Novelty of contributions:** We refer to the general comments for an in-depth discussion.
**Formulation of distributionally equivalent... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their valuable time and efforts towards improving the manuscript. In the following, we highlight the novelty of our contributions and width of our evaluation. We then address comments specific to each reviewer by responding to them directly.
**Novelty of c... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning | Reject | Summary: The paper proposes a general subgame curriculum learning framework to accelerate MARL training for zero-sum games. It adopts an adaptive initial state distribution by resetting agents to some previously visited states where they can quickly learn to improve performance. The author derives a subgame selection m... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments! We appreciate your acknowledgment of our proposed framework and recognition of the illustrative example. We hope our responses can address your concerns.
**Q1: The convergent speed is accelerated by the proposed method. How about the final performanc... | Summary: The paper proposes a novel subgame curriculum learning framework for accelerating multi-agent reinforcement learning (MARL) in zero-sum Markov games. The framework uses an adaptive initial state distribution to induce subgames of varying difficulty for agents to learn, and leverages a sampling metric that appr... | Rebuttal 1:
Rebuttal: Thank you for your appreciation of our work and thoughtful comments! We are encouraged to see your acknowledgment of our work’s novelty and the positive assessment of our experiment results. We hope our responses can address your concerns.
**Q1: The assumption that environments can be reset to an... | Summary: The paper proposes a subgame curriculum learning framework to accelerate multi-agent reinforcement learning (MARL) training for zero-sum games. The framework adopts an adaptive initial state distribution by resetting agents to some previously visited states where they can quickly learn to improve performance.... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback! We are heartened to see your recognition of our detailed analysis and strong experiment results. For your constructive questions, we hope the following response can address your concerns.
**Q1: There is a gap between the iterated RPS game and the experiments ... | Summary: This paper presents an algorithm (SACL) for accelerating MARL training in zero-sum Markov games based on the subgame curriculum learning framework. A sampling metric based on approximated squared distance to NE and a particle-based sampler are proposed to sample states for subgame generation. Experiment result... | Rebuttal 1:
Rebuttal: Thank you for your constructive comments and questions! We are encouraged to see your positive assessment of SACL’s efficiency and effectiveness. And we hope the following responses can address your concerns.
**Q1: Is the game considered in this paper perfect-information or imperfect-information?... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for taking the time to review our submission and providing constructive comments on our work. We are heartened by the consensus among reviewers about the strengths of our work, which align with our intentions and efforts:
1. **Novelty and significance:** We ap... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a framework for learning Nash equilibria in zero-sum Markov games based on subgame curriculum learning. Novel sampling metrics for subgames generation are proposed. The proposed SACL algorithm is able to achieve equal performance at lower sample complexity compared with self-play algorithm... | Rebuttal 1:
Rebuttal: Thank you for your review and feedback! We appreciate your recognition of the novelty of our work and the clarity of our presentation. In response to your questions, we provide the following explanation.
**Q1: While the baseline methods require an exponential complexity, the proposed SACL costs a... | null | null | null | null | null | null |
A case for reframing automated medical image classification as segmentation | Accept (poster) | Summary: This paper explores the benefits and drawbacks of using segmentation-based methods for classification tasks, particularly in medical imaging. This approach, known as segmentation-for-classification, has been shown to outperform traditional classification models, especially when the available dataset is small o... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and questions in your review. We were glad to hear the reviewer appreciated the many potential benefits of segmentation-for-classification Below, we answer your questions point-by-point. We would be glad for any additional discussion with or suggestions by the review... | Summary: This paper provides an intriguing and somewhat disruptive approach to medical image classification tasks. It implies that due to advancements in weakly-supervised, self-supervised, and semi-supervised segmentation techniques, the historical inclination towards image classification due to ease of training and l... | Rebuttal 1:
Rebuttal:
Thank you for your careful read of our paper and many suggestions, which strengthened our submission. Below we respond to your points and describe how we updated our manuscript in response. We would be happy for further discussion or to answer any additional questions.
*Q1: How do the authors ju... | Summary: The paper describes a set of insights obtained when using segmentation networks for a classification task. Classification was the task of choice due to issues with obtaining appropriate segmentation labels. However, with wider availability of datasets with appropriate labels, this is no longer the case. To fac... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our manuscript and for your suggestions. We were glad to hear you thought the work was thorough, addresses an important problem, and would be helpful to future readers. We were also glad to receive your comments and suggestions for improving the manuscript. Below,... | Summary: In this work, the authors explored the applications of deep learning in radiology, specifically focusing on image classification and segmentation tasks. The authors investigated the performance differences between classification and segmentation models on the same dataset and task using an information theoreti... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and questions about our paper. Below, we respond to each of your points to answer your questions, provide new results, and describe how we are updating our submission in response to your comments. We would be happy to answer any additional questions or hear more sugg... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and helpful suggestions, which helped us strengthen our submission. We were glad to hear the reviewers recognized the benefits and potential impact of segmentation-for-classification (reviewers rmFZ, RFD8, GK2b), found the paper thorough (reviewers D8HP, rmFZ,... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
One-Step Diffusion Distillation via Deep Equilibrium Models | Accept (poster) | Summary: This paper applies Deep Equilibrium Models to distillation of (conditioned) diffusion models. The key part of the architecture consists of a repeated application of weight-tied block of layers on the internal activations (theoretically until convergence to a fixed point, but in practice a few iterations). The ... | Rebuttal 1:
Rebuttal: Thank you for your extremely thoughtful feedback and suggestions. We have tried our best to answer your questions and concerns below.
> Does the iteration converge to a fixed point in six iterations?
We report the relative fixed point error $\frac{\|| f(z) - z \||} {\|| f(z) \||}$ and the resul... | Summary: The paper proposes a simple approach to distill diffusion models into generative models capable of sampling with just a single model evaluation. The method involves training a Generative Equilibrium Transformer (GET) architecture directly on noise/image pairs generated from a pre-trained diffusion model, elimi... | Rebuttal 1:
Rebuttal: We thank the reviewer for well-thought questions and valuable feedback. We have tried our best to answer your questions.
__Motivation and Advantages of DEQ__:
Our motivation to model the student network as a DEQ stems from the observation that the relatively complex process of distilling diffusi... | Summary: The submission proposes the Generative Equilibrium Transformer (GET), a lightweight refinement of vision transformer that is well-suited as an efficient single-step student model for diffusion distillation. The author empirically shows that the GET outperforms classic networks in terms of performance, model si... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback! Please find our responses to your questions and concerns below.
__Extensive Large Scale Evaluations__: It is certainly possible to scale up to larger datasets like ImageNet but this would require significantly more computing resources. For example, even tr... | Summary: This paper proposes a new model, called Generative Equilibrium Transformer (GET). GET is a deep equilibrium model, trained as an implicit model, to match noise/image pairs generated with a pretrained diffusion model, and thereby distill that pretrained (multi-step) diffusion model into a fast, single-step appr... | Rebuttal 1:
Rebuttal: We are encouraged to know that the reviewer feels that this paper is strong with thorough experiments! We have tried our best to answer your questions below.
__Variation of performance with number of samples__:
Given this is a supervised learning set up, we anticipate that GET’s generalization w... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments and suggestions. Here, we address some of the common concerns raised by the reviewers.
__What does one-step generation mean?__ We define one-step generation as the ability to generate an image directly from Gaussian noise in a _single_ forw... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new model architecture based on deep equilibrium models and applies it for distilling pretrain diffusion models, by generating samples offline and only utilizing the noise-sample pairs generated by the diffusion models. The architecture consists of embedding, injection transformer, equili... | Rebuttal 1:
Rebuttal: Thank you for your careful review and good questions. Please find our responses to your questions and concerns below.
__Motivation to use DEQ for distillation__: Our motivation to model the student network as a DEQ stems from the observation that the relatively complex process of distilling diffu... | Summary: Distillation of diffusion models into smaller models that require fewer steps for generation is an important topic in current research. The authors propose to use a deep equilibrium model as the student. In particular, the student model is a Generative Equilibrium Transformer (GET) that consists of two main mo... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback! We have tried our best to address all your questions and concerns below.
__Clarification about one-step generation__: We define one-step generation as the ability to generate an image directly from Gaussian noise in a _single_ forward pass through the netw... | null | null | null | null |
Convergence of Alternating Gradient Descent for Matrix Factorization | Accept (spotlight) | Summary: This problem considers a matrix factorization problem: it seeks to minimize a function of the form $f(X,Y) = 1/2 \Vert A - XY^T \Vert_F^2$. In general, matrix factorization problems have applications to matrix sensing, phase retrieval, and are seen as prototypical non-convex optimization problems. The special ... | null | Summary: This paper provides a new analysis for the convergence of alternating gradient descent on the low-rank matrix factorization problem $min_{X,Y} f(X,Y) = ||XY - A||_F^2$. The authors show that, by warm-starting the solution using the target matrix $A$, and appropriate step size scaling, they can achieve $\epsilo... | Rebuttal 1:
Comment: I thank the authors for their response, they have covered my questions. | Summary: The authors explore the use of alternating gradient descent (AGD) with a fixed step size for asymmetric matrix factorization. They demonstrate that a finite number of AGD iterations can achieve a high-probability -optimal factorization, even when starting from an asymmetrical random initialization. Empirical e... | null | Summary: This paper proves an improved convergence bound on alternating gradient descent for asymmetric matrix factorization problem, i.e. given $A \in R^{m \times n}$, finding $X \in R^{n \times d}$ and $Y \in R^{m \times d}$ that minimize $||XY^{\top} - A||_F^2.$ This paper establishes that if $A$ is rank $r$ for som... | null | Rebuttal 1:
Rebuttal: Thank you to all the reviewers for the helpful comments.
Response to Reviewer Zy5R
- [Typo in bound on Line 30:] Thanks! Yes, the bound should be $\frac{d}{(\sqrt{d} - \sqrt{r})^2}$, not $\frac{d}{d-r}$
Response to Reviewer BUrC
- [Generality of results] The analysis extends beyond matrix fa... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Robustness of Removal-Based Feature Attributions | Accept (poster) | Summary: This paper derives robustness results regarding a general class of feature attribution methods referred to as “removal-based feature attributions”, which includes occlusion methods, but also Shapley values and LIME explanations. The authors study Lipschitz-continuity properties of these methods with respect to... | Rebuttal 1:
Rebuttal: > “Given the formalization done in prior work, the main results are not unexpected.”
Regarding the “removal-based explanation” formalization from Covert et al., 2021, we do not intend to frame this as a contribution, hence its exposition in the Background (Section 2.2). Regarding the assumptions ... | Summary: This paper theoretically analyzes the robustness of removal-based feature attributions against input perturbation, and model perturbation with different summary techniques. Empirical experiments on synthetic datasets support their theoretical analyses such as conditional sampling is more robust to model pertur... | Rebuttal 1:
Rebuttal: > “Extending the analyses to other explanation techniques such as gradient-based explanations would make the contribution stronger. In fact, gradient-based explanations are more popularly used.”
Other works have focused on the robustness of gradient-based methods [R1, R6], but the feature attribu... | Summary: This paper studies the robustness properties of an explanation to small perturbations in the input space (i.e. like an adversarial example) and also to the model parameters. The authors use a number of Lipschitz-style bounds to then derive overall limits on how much explanations can change.
Update: As the re... | Rebuttal 1:
Rebuttal: > “The elephant in the room is that the results all need some kind of Lipschitz or Lipschitz-like bound […] there does not seem to be any evidence that the actual Lipschitz constant is at all close to being small enough to be useful.”
First, it’s worth emphasizing that our work aims to understand... | Summary: Previous research has primarily focused on the robustness of gradient-based feature attributions, but the robustness properties of removal-based attribution methods are not well understood. To fill this gap, the authors of the paragraph aim to theoretically analyze and characterize the robustness of removal-ba... | Rebuttal 1:
Rebuttal: > “The paper should be re-arranged by including experiment results in the main content and moving some theoretical results to the appendix.”
We thank the reviewer for this suggestion to improve our paper. To address this suggestion, we will move Corollary 1 to the Appendix to make room for a long... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and constructive comments, which have helped us further improve our paper. The specific issues raised by each reviewer are addressed in the individual responses below. We hope you will consider raising the scores if we have adequately addressed your ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Heavy-Tailed Algebra for Probabilistic Programming | Accept (poster) | Summary: The paper proposes a static analysis technique for probabilistic programming languages, which annotates random variables with metadata characterizing their tail behavior. In particular, generalized Gamma distributions are used for this purpose. It is shown that they are closed under a number of operations, inc... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment of our work!
> How does the system behave when the assumptions discussed above are not fully met, e.g., when there is an operation between dependent variables? Is it possible to make use of partial results for a given program?
> The discussion on posterior ... | Summary: The paper develops an algebra which acts on a three-parameter family of tail asymptotics based on the generalized Gamma distribution. The algebraic operations are closed under addition, multiplication, powers, and a full list is given in Table 1. With this algebra, tail calculation can be done automatically in... | Rebuttal 1:
Rebuttal: Thank you for reading and reviewing our paper. Since our work only focuses on univariate tails, we agree that copulas provide a promising direction which can be combined with other work applying normalizing flows to multi-variate heavy tails (ATAF paper) in order to improve multivariate heavy-tail... | Summary: During inference, we are often interested in the behavior of the tails of the distributions we are analyzing. Heavy or light tails may necessitate switching algorithms so that inference remains stable for example. This paper describes a calculus by which a probabilistic programming language may calculate the t... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment of our work! Yes, you are correct that the choice of spliced flow is non-essential. Our intention was to propose one such construction which was sufficiently flexible to capture the bulk while also respecting tail asymptotics computed by the GGA and we achieve... | Summary: The paper addresses the problem of density estimation of probabilistic models (expressed as probabilistic programs), with a focus on their tails. This is important for several Bayesian inference methods: importance sampling can exhibit infinite variance if the proposal has a lighter tail than the target, many ... | Rebuttal 1:
Rebuttal:
Thank you for taking the time to provide a detailed review and giving us the opportunity to address your concerns. We will discuss each point not addressed in our overall author response sequentially.
## Weaknesses
**Guarantees and assumptions**:
Thank you for the excellent suggestion! To impro... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable feedback. We appreciate that reviewers have recognized the novelty of our approach and its applications in ahead-of-time static analysis of probabilistic programming languages (PPL). Suggested minor changes and fixes have been incorporated into th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Co-Learning Empirical Games and World Models | Reject | Summary: The paper addresses the problem of multi-agent RL by using learned world models over multiple potential opponent policies. The technique uses a Dyna-style algorithm to train the core policy with a combination of experiences generated through a world model and experiences playing against opponents. One evaluati... | Rebuttal 1:
Rebuttal: In response to the *summary* of this paper, we wish to clarify that the primary contribution is a learning-based game-solving algorithm Dyna-PSRO (L64--66).
While we show benefits to model-based MARL we do not claim this as a major contribution (L62--63).
The Dyna-based learner uses experiences g... | Summary: This paper introduces a new approach to PSRO algorithms, where a world model of the environment is learned concurrently to the iterative PSRO strategy expansion.
Strengths: - The authors are right that the problem of having to re-learn policies from scratch is a large problem in the PSRO literature. Therefor... | Rebuttal 1:
Rebuttal: We appreciate your commendation of our analysis, as well as your useful suggestion to incorporate more extensive discussions on PSRO in our related work section.
Based on your input, we have included a paragraph on various PSRO-related algorithms.
The key additions include (and refer all suggeste... | Summary: The authors consider learning world models for deep reinforcement learning in combination with the construction of empirical games through PSRO. They first show that world models benefit from training on a diverse set of strategy profiles as can be generated through PSRO meta-game solvers. They then empiricall... | Rebuttal 1:
Rebuttal: Thank you for the kind words in your review.
The main question posed pertains to our choice of a Dyna-based method as opposed to other approaches.
The Dyna architecture is notably broad, encompassing any learner that combines learning, planning, and acting.
The reason behind our specific world mo... | Summary: This paper describes combining two things: training a world model of a game, and doing Policy Space Response Oracles (PSRO) on the game.
Doing PSRO involves getting a lot of episodes from the game (episodes are used to train the RL best-responses, and also to estimate the payoffs of the empirical game). The n... | Rebuttal 1:
Rebuttal: Thank you for engaging so extensively with our work and for the kind words on its effort and quality.
We have done our best to reply to your comments (paraphrased here) below within the word limit.
> SumRegret metric.
We have included more text defining the terms _method_ and _combined game_, ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and providing thoughtful feedback on our work.
We are delighted to hear you found the paper well written [Nexz, Lma6], the experimental analysis well designed [Lma6, WC2X], and the core of the work sound [Nexz, Lma6, WC2X].
We address reviewer-specific commen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LightSpeed: Light and Fast Neural Light Fields on Mobile Devices | Accept (poster) | Summary: This paper describes a novel representation for learning view synthesis from a set of input images with known camera poses. They parameterize a classical two-slab 4D light field using a K-Planes representation (using 6 feature planes). Feature queries are processed through many layers of 1x1 convolutions bef... | Rebuttal 1:
Rebuttal:
**We thank the reviewer for their positive comments and insightful feedback. We appreciate that the reviwer acknowledges our approach to be novel with fast and high-quality renderings. We further note the reviewer finds evaluations extensive and the paper's presentation clear and easily understan... | Summary: This paper presents the LightSpeed for real-time rendering on mobile devices. The approach involves replacing the commonly used Plücker coordinates with a light slab representation and implementing multi-level grids similar to Instant-NGP and k-planes. Additionally, It introduced a divide-and-conquer strategy ... | Rebuttal 1:
Rebuttal:
**We thank the reviewer for their positive review and feedback. We appreciate that the reviewer acknowledges the efficiency of our method and interesting design choices of using a light-slab ray paramterization with 4D grid compression via decomposition. We address the feedback provided by the re... | Summary: This paper introduces LightSpeed, which uses traditional 4D light-slab representation and merges the super-resolution network proposed by MobileR2L. LightSpeed uses the NeLF method and will be primarily implemented on mobile.
Strengths: Originality: Utilize the overlooked method of 4D light-slab representatio... | Rebuttal 1:
Rebuttal: **We thank the reviewer for their positive feedback and valuable suggestions. We appreciate that the reviewer finds our work original, explained clearly and of significance towards light field methods for mobile devices. We address concerns via more visual and on-device results and hope our respon... | Summary: Real-time novel-view image synthesis on mobile devices is challenging due to limited computational power and storage. Volumetric rendering methods are unsuitable due to their high computational cost. The authors propose using the efficient light slab representation for learning a neural light field, which achi... | Rebuttal 1:
Rebuttal:
**We thank the reviewer for their feedback and strong rating. As summarized in the review, we propose a novel real-time view-synthesis method that is based on light fields. We leverage previously overlooked 4D light-slab representation for easy discretization and grid-based representations for ne... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful comments and appreciate their findings that our novel method simplifies real-time novel view synthesis on mobile devices while performing better than exisiting works with high-quality and fast rendering even on mobile devices (bQpx, XtjZ, C... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces LightSpeed, a method aimed at simplifying real-time novel-view image synthesis on mobile devices, which typically face constraints related to computational power and storage. By adopting the traditionally underutilized 4D light-slab (two-plane) representation for learning a neural light fi... | Rebuttal 1:
Rebuttal:
**We thank ther reviewer for the valuable feedback. We appreciate that the reviewer finds our approach of integrating light fields with grid-based representations noteworty and promising. We address the concerns in the following. We hope our response can further demonstrate the strengths and rea... | null | null | null | null | null | null |
On the Universal Approximation Properties of Deep Neural Networks using MAM Neurons | Reject | Summary: This manuscript proves universal approximation results for MAM neurons. MAM neurons are essentially ReLU neurons that operate on the sum of the maximum and the minimum of the weighted inputs, plus a bias. Previous work claims these neurons are useful for reducing the memory footprint of deep neural networks. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and their valuable feedback. While we are naturally disappointed by the outcome, we value the reviewer's expertise and the insights they provided. We answer here to the reviewer's comments, hoping they could change their idea about this work.
Actually, we are ... | Summary: This paper demonstrates that the network can still maintain the universal approximation property after substituting the classical MAC hidden neurons of neural networks with the MAM neurons, which only rely on the maximum and minimum elements of the summation, allowing for more aggressive pruning. Specifically,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and their valuable feedback. While we are naturally disappointed by the outcome, we value the reviewer's expertise and the insights they provided. We answer here to the reviewer's comments, hoping they could change their idea about this work.
> While the resul... | Summary: This paper presents two universal approximation theorems for deep neural networks associated with a so-called Multiply-And-Max/min (MAM) activation function defined with the maximum and minimum of the input components and a bias constant. One is for uniform approximation and the other for approximation in the ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and valuable feedback provided by the reviewer. Below, we respond to the reviewer's comments, incorporating their insights to improve the quality of our work.
> To demonstrate some theoretical advantages of the MAM activation function. This might be done with the ... | Summary: The paper studies the universal approximation properties of ReLU networks using the Multiply-And-Max/min (MAM) neurons. Literature on the universal approximation properties of ReLU networks using the Multiply-and-ACcumulate (MAC) neurons is vast. However, the study on MAM neurons seems lacking. Hence, two theo... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and valuable feedback provided by the reviewer. Below, we respond to the reviewer's comments, incorporating their insights to improve the quality of our work.
> The requirement of the target [...] clarify why this assumption is necessary.
We can clarify the motiv... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Stable Nonconvex-Nonconcave Training via Linear Interpolation | Accept (spotlight) | Summary: This paper applies linear interpolation to make neural networks stable. Based on the analysis of instabilities, the authors propose a new optimization scheme, RAPP. RAPP achieves last-iterate convergence rates for the full range of cohypomonotone problems. Moreover, by replacing the inner optimizer in RAPP, th... | Rebuttal 1:
Rebuttal:
We thank the reviewer for their valuable feedback and address all remaining concerns below:
- **Linear convergence** when we have $\|w^{\tau+1}-w^\star\| \leq c \|w^\tau-w^\star\|$ for $c \in (0,1)$ it is standard to refer to it as linear convergence, but we will add a remark clarifying that the... | Summary: The paper gives a theoretical analysis of linear interpolation that can help stabilize neural network training. They show these instabilities in the optimization are caused by nonmonotonicity in the loss landscape. They also construct a new optimization scheme, called "relaxed approximate proximal point" (RAPP... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
- **Wallclock time** The wallclock time is essentially made to be the same across all methods in the experiments by providing each method with the same number of gradient computations (see line 263-267). A... | Summary: This paper studies the global convergence problem under cohypomonotonicity structural assumption. The authors prove the global convergence rate for the last iterate of their proposed algorithm RAPP. RAPP is the first explicit scheme to
58 have non-asymptotic guarantees for the full range of cohypomonotone prob... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback.
We agree that it is definitely interesting to see if we can extend the results further and we indeed have some preliminary positive results going beyond compositions.
---
Rebuttal Comment 1.1:
Comment: Thanks for the reply. I will keep my ratin... | Summary: This paper continues a line of work motivated by the need to design algorithms for non-convex non-concave min-max problems. Such problems arise in the training of GANs as well as reinforcement learning via self-play. Since solving general non-convex non-concave min-max problems is intractable, the main approac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
**Generality of cohypomonotonicity and simple examples** One of the simplest examples is probably Example H.2 in the appendix where we also provide the closed-form solution to $\rho$ and $L$. Another simpl... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sharp Recovery Thresholds of Tensor PCA Spectral Algorithms | Accept (poster) | Summary: This paper considers the (Gaussian, nonsymmetric, rank-1) spiked tensor model introduced in Montanari and Richard. It gives sharp thresholds for various matricization techniques (unfolding, partial traces, successive contraction). The statements about random tensors are reduced to known results about left an... | null | Summary: This paper studies recovery of planted signals from noisy tensors.
The model is the prototypical tensor PCA and the emphasis is that (i) the planted low rank tensors (in particular their dimensions) can be different along different modes; (ii) the joint scaling of different dimensions can deviate from the cla... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments and questions! We are familiar with the information-computation gap for tensor PCA and agree with your suggestion that is should be mentioned in the introduction. Here are point-by-point replies:
1. Yes, that is a typo.
2. Yes, we will add a clarification.
3.... | Summary: This paper is concerned of the tensor PCA problem (low-rank tensor recovery with Gaussian noise), in which the authors proposed three new ways to approach the problem with theoretical guarantees.
Strengths: 1. This paper uses a succinct way to introduce to readers 3 different approaches of tensor PCA, all w... | Rebuttal 1:
Rebuttal: Thank you for your review of our manuscript. Tensor unfolding and partial trace, we discover, are asymptotically equivalent in performance. Successive contraction is shown in Section 4 to achieve exact recovery above the common threshold of tensor unfolding/partial trace, and we recommend it be us... | Summary: This paper studies tensor principal component analysis (PCA) in a high-dimensional asymptotic framework, where each array dimension tends to infinity. The authors analyze matricization-based approaches, which convert tensors to matrices and then apply spectral methods. They fully analyze tensor unfolding or re... | null | Rebuttal 1:
Rebuttal: We thank the reviewers for the time and effort they have invested into the review of our manuscript, and for their helpful comments and suggestions. We would like to address the reviewers’ main criticism, that our results follow easily from PCA results of random matrix theory (RMT). The primary pu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Ability of Graph Neural Networks to Model Interactions Between Vertices | Accept (poster) | Summary: The paper studies how to characterize the ability of Graph Neural Networks (GNNs) to model the interaction given a partition of vertices. They quanlify the interaction strength with separation rank, and prove that it is governed by walk index (number of walks starting from the partition boundary). This relatio... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback. We are glad that you found our theory interesting, supported by solid experiments, and with an impressive application to edge sparsification. We address your concern below, and would greatly appreciate it if you would consider increasing your score.
> Walk ... | Summary: The paper proposes a new way to analyze the capacity of GNNs based on the complexity of interactions they can model across a partitioning of the nodes. In particular, this complexity is quantified via *separation rank*, and, for a certain kind of GNN with product aggregation, this rank is proved to scale with ... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and support!
> While the machinery of the proof appears to be quite complex, I would appreciate some description of the proof concept in the main paper, even if it is at a very high level.
Due to lack of space we deferred a high-level description of the proof ... | Summary: This paper proposes to analyze the expressivity of graph neural networks (GNNs) by their ability to model interactions. The authors consider GNNs with product aggregation scheme, and analyze their ability to model interactions for a given partition of the graph through the notion of separation rank. The author... | Rebuttal 1:
Rebuttal: Thank you for your feedback, for highlighting the interest and novelty of our theory, and for noting that the paper is well-written. We treat your comments and questions below. If our response is satisfactory, we would greatly appreciate it if you would consider raising your score.
> (a) to show t... | null | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Experiment Planning with Function Approximation | Accept (poster) | Summary: The paper studies the problem of static experiment planning under the function approximation setting, which contrasts with the adaptive setting where the reward is not observable during the static planning phase. The paper proposes two static planning strategies: (1) EluderPlanner based on Eluder Dimension, an... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their kind comments. A more in-depth discussion of the relationship between experiment planning and Reward-free RL is a great idea to improve our manuscript. We will use the extra page granted at camera ready time to achieve this. We will add the citation the revi... | Summary: The paper addresses the problem of experiment planning with function approximation in contextual bandit problems.
It is intended to solve the scenario that the datasets include a large amount of contexts while no rewards.
The authors propose two experiment planning strategies that are compatible with function... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their comments. We would like to start by pushing back on the reviewers comment regarding the comparison with existing experiment planning algorithms (see Weaknesses issue 1). The setting of experiment planning was introduced first by [37]. The only existing resul... | Summary: The authors study the problem of planning for efficient data collection. Given initial data with a lot of contexts but no reward, the question is how do you devise a policy for data collection such that when executed in the real world, it learns the reward optimally to finally learn a policy with maximum rewar... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s comments. We will make sure the final version of our manuscript contains more detailed descriptions of the experiment planning setting early on in the introduction. We would like to remind the reviewer we are not the first to come up with this problem setting. T... | Summary: The study focuses on the experiment planning problem for contextual bandits with function approximation.
The paper gives two algorithms for the problem: (1) EluderPlanning algorithm whose sample complexity depends on the elder dimension of the function class and matches the sample complexity of OLS algorithm ... | Rebuttal 1:
Rebuttal: We are extremely appreciative of the reviewer’s careful read of our manuscript. We are happy the reviewer identified the following strengths in our submission: this is the first work to explore the setting of experiment planning with function approximation and propose: a) Novel algorithms for Elud... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: We study the static experiment planning for policy learning problem in contextual bandits, with focus on the general realizable case. The paper first presents an algorithm using reward free, extending similar ideas in [37] and leveraging the eluder dimension. The paper then shares a few theoretical results, i... | Rebuttal 1:
Rebuttal: “Numerical results on the finite sample performance would be important to have” Although we wholeheartedly agree with the reviewer that an experimental evaluation of these algorithms would be of great interest, we consider the main contributions of this work to be theoretical. As such, and due to ... | null | null | null | null | null | null |
Greedy Poisson Rejection Sampling | Accept (poster) | Summary: The paper proposes a new relative entropy coding algorithm for compression without quantization. Compression without quantization is an exciting line of research that tries to eliminate training-test mismatches in learned compression by avoiding discrete representations altogether. Consequently, one can lossle... | Rebuttal 1:
Rebuttal: We thank the reviewer for their glowing review of our work; we are delighted that the reviewer shares our excitement for relative entropy coding/channel simulation!
We answer the reviewer's questions below and will gladly answer any further questions.
> Could the authors discuss in depth the comm... | Summary: The paper addresses the problem of representing a target distribution using the least possible number of bits. The authors refined the idea of encoding a sample from the target distribution as the first sample from the proposal distribution that passes a rejection sampling condition. The refined approach is sh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and feedback on our work. We would like to begin by clarifying a crucial point that, in our experience, is the most common source of confusion regarding channel simulation.
> The paper addresses the problem of representing a target distribution using the l... | Summary: This paper focuses on the channel simulation problem, which finds applications in stochastic lossy compression. In contrast to the importance sampling approach A* coding (Flamich et.al. 2022) for channel simulation with Poisson processes, this work adopts a rejection sampling method. They demonstrate that the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their nice comments; we address the reviewer's questions below.
> GPRS is thoroughly compared to A* coding both theoretically and experimentally. However, it is unclear why the baseline standard rejection sampling (Algorithm 2) is not empirically compared in Section 4 to... | Summary: This paper investigates the problem of one-shot channel simulation, which can be used as lossy compression without involving quantization.
A new rejection sampling algorithm called greedy Poisson rejection sampling (GPRS) is proposed. Then, a parallelized and a branch-and-bound variant is proposed. Those al... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and the concerns they raise, to which we respond below.
> Though this manuscript is a theoretical contribution, it would be better to discuss more about promising application scenarios and current gaps regarding both performance and efficiency, wh... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback on our paper, which will help us improve it significantly. We are delighted that all reviewers agree that our contributions are significant, that most reviewers (sb37, HQuL, 4NF6 and tLU5) found our exposition well-written and easy to follow, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new algorithm for lossy compression, using ideas from Poisson rejection sampling.
Given a sample $y \sim P_y$, Alice wants to communicate the smallest number of bits possible such that Bob can simulate $x \sim P_{x | y}$, when Alice and Bob have access to the distribution $P_{x, y}$ (and... | Rebuttal 1:
Rebuttal: We thank the reviewer for their nice comments and valuable feedback and attempt to address the concerns they raise below.
> The rejection sampler requires access to likelihoods of the conditional distribution and marginal, and hence it's not clear how much this can be generalized.
We believe the... | null | null | null | null | null | null |
Characterization of Overfitting in Robust Multiclass Classification | Accept (poster) | Summary: The main worry regarding the excessive reuse of test datasets in machine learning is its potential to cause overfitting. The objective of this paper is to characterize the relationship between the amount of robust overfitting bias and three key factors: the number of classes (m), the number of robust accuracy ... | Rebuttal 1:
Rebuttal: Thanks for reviewing our work and affirming our presentation; this means a lot to us. Below are our responses to your comments. We hope these address your concerns. Do not hesitate to reach out if you have further questions or suggestions.
**About the novelty and originality**
We feel very sorr... | Summary: This paper generalizes the framework of perfect reconstruction to the adversarial setting and studies how much a k-query algorithm can overfit the test set in the adversarial setting. Upper bounds and lower bounds are derived, which match in terms of the number of classes m and the number of queries k modulo l... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our work and your helpful suggestions.
**About the dependence of lower bound on $n$**
As we discuss in Lines 77-84, the term $\Phi _{\mathcal{D} _\mathcal{X}}(n)$ highly depends on the distribution of $\mathcal{D} _\mathcal{X}$ and is unavoidable. Its specific f... | Summary: In this paper, the authors consider the following question: Given the number of classes m, the number of robust accuracy queries k, and the number of test examples in the dataset with size n, how much can adaptive algorithms robustly overfit the test dataset? They solve this problem by giving upper and lower b... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our work.
**About the paper structure**
We focus on the question whether adaptively excessive reuse of test data lead to overfitting in robust learning. In Section 2 , we transform this problem into a problem of studying the value of $h _\mathcal{U}(k,n,m),$ an... | Summary: This paper considers the problem of learning from data while being robust to the possible transformations of these data. A common practice in machine learning is to split data into a training and a test set. The latter is a holdout to evaluate the performance of the algorithm. However, recent studies have show... | Rebuttal 1:
Rebuttal: Thank you for your for reviewing our paper. It is worth acknowledging that numerous studies have already indicated the presence of overfitting phenomena in adversarial training, e.g.,[1]. To this end, our paper is devoted to a certain **theoretical** question related to this phenomenon, that is, w... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies bounds on the maximal difference between the robust accuracy of a classifier on the test set. Here, robustness means when each instance is allowed to move within a given radius within an $L_p$ ball, and the maximal difference is obtained with respect to $1/m$, where $m$ is the number of clas... | Rebuttal 1:
Rebuttal: Thank you for reviewing our work and your considerate suggestions. Here are responses to your concerns.
**About the results.**
We apologize for our presentation. Motivated by [1] and [2], the question we focus on is: Can excessive reuse of test datasets lead to overfitting in robust learning set... | Summary: The authors present near-matching lower and upper bounds for the robust accuracy of an adaptive algorithm having a budget of k accesses to the oracle of accuracy on a test set of size n and m classes.
Strengths: The proof is really clear and pedagogical. The related work is clear.
Weaknesses: The problem is ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time you've dedicated to reviewing our work. We would like to highlight the originality of our proof techniques, which are outlined as follows.
On one hand, the upper bounds' proof technically differs from proofs in the standard case derived by [1] from the following 3... | null | null | null | null |
LEACE: Perfect linear concept erasure in closed form | Accept (poster) | Summary: The paper presents a concept erasure procedure that also takes into account the distortion of the representations during the debiasing process. To this end, the paper builds on several works on iterative nullspace projection approaches and incorporates a regularization component. First, LEACE improves upon pre... | Rebuttal 1:
Rebuttal: > 1. Is it unclear from the paper why the MSE reconstruction loss E[X-\hat{X}] is a good measure of information being retained?
We agree with the reviewer that mean squared Euclidean distance is a limited measure. Luckily, we have since proven that LEACE is highly robust to the specific distance ... | Summary: This paper introduces a novel method to guard pretrained representations of deep neural networks from linear recovery of sensitive attributes Z. The notion of linear guardedness comes from the previous literature, while this paper proposes a simple characterisation when it takes place, which allows to dramatic... | Rebuttal 1:
Rebuttal: > lack of error bars.
In Figure 1, the vertical lines crossing through each data point are 95% confidence intervals. We apologize that we did not make this clearer in the original submission and we intend to replace the error lines with a translucent error ribbon in the camera-ready version.
> F... | Summary: Suppose we are given a distribution of data points $(x,z)$, where $x \in \mathbb{R}^d$ and we have one-hot class labels $z \in \mathbb{R}^k$. This paper shows how to construct an affine transformation $\phi(x) = Px + b$ of the data so that
* $\phi(x)$ has zero covariance with $z$
* $\\|\phi(x) - x\\|$ is minim... | Rebuttal 1:
Rebuttal: > my feeling is that the paper proposes a "trivial" solution: orthogonal projection of the random vector x to the subspace of random vectors uncorrelated with z. So I find it surprising that this method was not previously known (maybe in a different field and under a different name).
We believe ... | Summary: This paper studies concept removal from features. For linear classifiers, the authors prove several equivalent characterizations of linear guardedness (reducing the accuracy of any linear classifiers to the trivial accuracy). In particular, the features that achieve linear guardedness have zero covariance with... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback on the presentation of the paper, especially the proofs. We will revise the manuscript based on their feedback for the camera ready version.
> One experiment that I am curious about is the part-of-speech tag prediction accuracy after concept scrubbing. As ... | Rebuttal 1:
Rebuttal: _Please see the attached PDF for figures cited in reviewer-specific rebuttals._
We thank all the reviewers for their helpful feedback. We would like to present two simple extensions of the theoretical results from our original submission, which we believe will make the paper even more compelling ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure | Accept (poster) | Summary: This paper studies the problem of balancing timely and accurate outcome predictions with acquisition costs. To this end, a risk averse active sensing approach (RAS) is proposed that determines when to perform feature acquisition as well as which features to acquire. The proposed approach decomposes the policy ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and efforts towards improving our manuscript. We provide responses in regard to the reviewer's concerns as follows.
## 1. Clarity
- Following the reviewer's advice, we have moved discussions related to our problem formulation and solutions to the mai... | Summary: This paper proposed an active sensing method that answers the questions of when and what diagnosis test to conduct to optimize the trade-off between the cost of acquisition and the timeliness and accuracy of the predictive model. Compared with the existing active sensing model that assumes a fixed data collect... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for helping review our paper and providing valuable comments to improve our manuscript. We address the reviewer's concerns on the clarity of our paper and the experimental evaluation of our method as follows.
## 1. Clarity
We apologize for the confusions. We have fixe... | Summary: This work studies the problem of active cost-aware feature acquisition assuming time varying feature settings via breaking-down feature selection and prediction decision making as two policies. The problem being considered is generally a difficult problem even with the non time-varying feature settings.
I hav... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's valuable comments and suggestions. We address the questions on our problem formulation and experiments as follows.
## 1. Problem formulation
### 1.1 Motivation of policy decomposition
The motivation for our sensing policy decomposition is three folds:
- Fi... | Summary: This paper investigates timely outcome prediction by proposing a novel risk-averse active sensing approach RAS. The proposed RAS decomposes the policy into acquisition scheduler and feature selector to address the composite decision problem of when to conduct the acquisition and which measurements to make. In ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments. The reviewer’s comments regarding clarity and experiment results are addressed below.
### 1. Notation table.
We thank the reviewer for the suggestion of including a notation table to improve clarity. The definition and explanation of major notat... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for taking the necessary time and effort to review our manuscript. We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
## Summary of related work mentioned by reviewer d1SC
We thank r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions | Accept (poster) | Summary: This paper proposes a model for inferring rules based on observation trajectories of (entity, time, location), with the aim of maximizing the probabilities of certain "events". The aim is to predict the next events based on past trajectories, conditioned on the latent "rules" which need to be marginalized over... | Rebuttal 1:
Rebuttal: We sincerely thank you for your recognition of our work and your insightful reviews! We hope our responses can address your questions. Our responses are listed below.
**Q1: It is not very clear how exactly the goal states are estimated (eg. in Fig 3), or how the future trajectories are generated.... | Summary: This paper presents a novel approach for human trajectory prediction, introducing a learnable rule-based framework that combines rule generation/reasoning and EM optimization. Unlike previous works in the field, this framework utilizes a neural rule generator to generate rules and treats them as latent variabl... | Rebuttal 1:
Rebuttal: We sincerely thank you for your recognition of our work and your insightful reviews! We hope our responses can address your questions. Our responses are listed below.
**Q1: The predicates are required to be manually defined and these predicates may need to be redefined when applied to new scenari... | Summary: The paper proses a method for learning spatio-temporal logic rules to explain human actions, utilizing the EM framework. The method results are more easy interpretable by humans, thanks to the logic rules. The method beats some state-of-the-art methods on two real-world motion prediction datasets.
Strengths: ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful reviews! We hope our response below addresses your concerns.
**Q1: My major concern with this work is the need for dataset-depended actions**
A1: Thanks for your suggestion. To learn specific actions would require learning recursion and predicate inven... | Summary: This study presents a logic-informed, knowledge-driven modeling framework designed to predict and understand human movements, based on the analysis of their trajectories. It takes into account that human behaviors are commonly guided by intentions, desires, and spatial relationships with surrounding objects. T... | Rebuttal 1:
Rebuttal: We sincerely thank you for your recognition of our work and your insightful reviews! We hope our responses can address your questions. Our responses are listed below.
**Q1: If additional specifics regarding the architecture could be provided, such as the input and output formats utilized by the t... | Rebuttal 1:
Rebuttal: Dear Reviewers, Area Chairs, and Program Chairs,
We are greatly thankful for the insightful comments and suggestions, which are very helpful for us to further improve this work. We are very excited that the reviewers hold positive feedback and find our work "well-articulated, offering a clear und... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Diffusion Model-Augmented Behavioral Cloning | Reject | Summary: The paper proposes a new algorithm for behavior cloning (BC) where the BC learning objective is modified with a diffusion modeling loss that models the joint state-action distribution of the expert data. The paper demonstrates the benefits of modeling both the conditional probability and joint probability of t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
**Q**: The limitations of the proposed approach can only be found in the limitations. Though I believe that is fine given space limitations, it would be nice to mention it... | Summary: The submission proposes an imitation learning method optimizing a loss that is a weighted sum of a behavioral cloning loss and a loss based on a diffusion model. The diffusion model loss penalizes the policy for generating actions that are unlikely under the diffusion model, which is pre-trained to maximize th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
**Q**: Originality
**A**: A key contribution of our method is to derive an imitation learning policy that combines the advantages of modeling the conditional and joint pr... | Summary: The paper proposes a method to augment a behavior cloning (BC) agent with additional diffusion loss. The goal is to leverage the conditional probability learned by the BC loss and the joint probability learned by the diffusion loss. The diffusion loss is calculated using the prediction error of a pre-trained d... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
**Q**: The diffusion model loss coefficient $\lambda$
**A**: As requested by the reviewer, we have conducted an additional ablation study on the diffusion model loss coef... | Summary: This paper presents a method for guiding behavior cloning via state-action joint distribution learning. They train a diffusion model to maximize the log-likelihood of state and action pairs in conjunction with an imitation learning model that learns to mimic expert actions given state observations. They combin... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
**Q**: Implicit BC
**A**: The Implicit BC paper [1] defines an implicit model as follows:
“We define an implicit model as any composition ($\arg \mathop{\min}\_{y} ◦ E_... | Rebuttal 1:
Rebuttal: This PDF file addresses **Reviewer Pa84**'s question regarding learning diffusion models with noisy expert data to address manifold overfitting.
Pdf: /pdf/f74ebc9b873ce34ee0c2f59d5970a52fe24d92e9.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a novel approach in the field of imitation learning. The authors address the challenge of learning from expert demonstrations without access to reward signals from the environment. They propose a framework called Diffusion Model-Augmented Behavioral Cloning (DBC) that combines the benefits ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough and constructive comments. Please find the response to your questions below.
**Q**: The diffusion model loss coefficient $\lambda$
**A**: In the main paper, we chose the Maze environment to ablate the diffusion model loss coefficient since it is f... | null | null | null | null | null | null |
Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement | Accept (poster) | Summary: The authors introduce an idea by which a student model can become (de) sensitive to some human-understandable concept in its decision-making process. They find CAVs in a teacher model and then transform those CAVs into the student model feature space and use orthogonal vectors to these CAVs to penalize/incenti... | Rebuttal 1:
Rebuttal: We thank the reviewer for very positive feedback. We will address the points below:
> Analysis on student model's performance
The model remains biased due to the severe bias (100%) in the training set, though we improve significantly over prior efforts. This demonstrates the effectiveness of u... | Summary: This paper proposes a methodology for training a model to sensitize or desensitize a specific concept. Particularly, it introduces a concept distillation loss that utilizes Concept Activation Vectors (CAVs) derived from a high-performing teacher classifier with abundant knowledge of the concept, aiming to redu... | Rebuttal 1:
Rebuttal: We thank the reviewer for his comments and efforts. The observation of the effectiveness of our method in 100% biased teacher is certainly an interesting point. As also explained in our response to Reviewer RoT4y, we believe this happens as we are defining the concepts using our concept sets (dif... | Summary: The paper presents the idea of concept distillation from a pretrained teacher to improve a student. They utilize the notion of concept activation vectors (CAV) as concept representations and adapt a pretrained student to sensitize or desensitize a student w.r.t a concept. They apply this idea for two applicati... | Rebuttal 1:
Rebuttal: We are glad the reviewer appreciated our core idea and its wide applicability and about it having "the hallmarks to be a useful and effective work." Our main focus is to explore the feasibility of using explainability ideas like concepts for model improvement in multiple use cases. We will improve... | Summary: This paper aims to sensitize or desensitize a (smaller) student model with respect to user-provided high-level concepts, by leveraging a (larger) teacher model. Specifically, a supervised mapping model learns a bijection between some chosen latent space in the teacher model and the student model. Then, CAVs ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for the appreciation of our idea’s potential and novelty including demonstration on real-world dataset on a reconstruction problem.
The primary goal of this work is to show that explainability ideas like concepts can be used in a loop to improve the models. We believe it is ... | Rebuttal 1:
Rebuttal: We thank reviewers for valuable suggestions and feedback. We are glad they acknowledged the potential of our work. Our primary goal is to show explainability ideas like concepts can be used in a loop to improve the models. This is an important idea with many future directions as observed by review... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LICO: Explainable Models with Language-Image COnsistency | Accept (poster) | Summary: This paper proposed LICO, which leverages the textual and semantic knowledge learned by large language models to guide latent image features. By matching relationships among images with KL-divergence globally, and distances between specific feature maps and prompt tokens with OT, the feature space of image is ... | Rebuttal 1:
Rebuttal: We appreciate your thorough summary, encouraging feedback, and constructive suggestions.
**Q1: Concerns that the model trained with LICO is not the original model**
Your understanding is correct, and we completely agree with you. The proposed LICO is not strictly an XAI method but offers a more... | Summary: This paper introduces LICO, a model that aligns visual encoder to language features. This model incorporates a frozen text encoder, a trainable image encoder, a classification loss, a manifold matching loss and an optimal transport loss. Experiments shows improvements over existing interpretation methods.
Str... | Rebuttal 1:
Rebuttal: Thank you for appreciating the motivation and comprehensive experiments.
**Q1: On comparison fairness**
We apologize for not making it clear that our comparison to baselines is fair.
- **Additional textual information**. Regarding the textual information of LLM as real-world semantic space is o... | Summary: Most visualization interpretation methods based on saliency information often generate inaccurate saliency maps due to the limited discriminative information provided by one-hot labels. The manuscript proposes a language-image consistency model (LICO) to address this challenge. LICO utilizes a large-scale visi... | Rebuttal 1:
Rebuttal: Thank you for appreciating the motivation and the good value of this work.
**Q1: Complex images evaluation & model robustness**
Great feedback. In addition to Fig. 3, we have provided more results in Figs. 1 and 2 of the Supplementary Material (SM). For example, in Fig. 1(c) of SM, LICO captures... | Summary: This paper introduces Language-Image-COnsistent (LICO) to get better interpretation for classification using the Vision-Language model. The proposed framework uses a frozen text encoder and a trainable image encoder to encode text and image information. The text is composed of several trainable prompt tokens a... | Rebuttal 1:
Rebuttal: Thank you for your insightful and valuable feedback, pushing us to rethink more comprehensive experiments.
**Q1: Training time comparison**
Thank you for pointing out this issue. We completely agree that the training time should be compared and discussed.
Per your suggestion, we reported the ... | Rebuttal 1:
Rebuttal: ## Global Responses with a PDF file
**Comments**:
Dear Reviewers,
We thank all the reviewers for their thorough summaries and valuable feedback. The reviewers appreciate that our LICO is novel and well-motivated (**rFY8**, **V7Pm**, **r2cD**) with good value of incorporating language prompts in... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow | Accept (poster) | Summary: This paper presents a generalizable framework for estimating both the NeRF model of the scene and the camera pose for video sequence simultiunusly.
The proposed method first uses the pretrained model to predict the optical flow for each video frame. Then, the optical flow is lifted up to the monodepth to scen... | Rebuttal 1:
Rebuttal: ### iMAP and robust unposed NeRF
We added iMAP methods to the related works and NeRF methods. Note that iMAP addresses a separate problem: our contribution enables end-to-end training of pixelNeRF without precomputed poses, whereas iMAP performs offline, gradient-descent based pose and NeRF optim... | Summary: This paper proposes a method to address the challenge of reconstructing 3D neural fields from images and learns them in a self-supervised manner. The main contribution of this method is the joint reconstruction of camera poses and 3D neural scene representations within a single forward pass.
Strengths: 1. Thi... | Rebuttal 1:
Rebuttal: ### The ablation study is unclear in its abbreviations and references
We agree and have updated the main paper’s ablation table with expanded ablation references as well as its corresponding caption and experiment text. Please see author response PDF Tab. 2 for expanded reference names and caption... | Summary: This paper proposes a general method for 3D neural scene reconstruction and camera pose estimation from a video sequence. The method takes a set of video frames as input and outputs the re-rendered video frames and estimated camera poses. The method is based on PixelNeRF, which is a general NeRF method that ta... | Rebuttal 1:
Rebuttal: ### Only one quantitative pose estimation is reported, and we should compare with a more recent unposed NeRF method (such as NoPe-NeRF)
Please note that we extensively benchmark with the appropriate baselines of RUST and VideoAutoencoder, including quantitative evaluations of pose estimation on e... | Summary: The paper proposes using scene flow to optimize for camera poses to produce generalizable 3D radiance field. The key contribution is the joint optimization of the camera poses and 3D neural scene representations in an single forward pass. The method has been evaluated on multiple datasets and performs well on ... | Rebuttal 1:
Rebuttal: ### Pose estimation is only compared with non-NeRF based methods, and unposed NeRF methods have been proposed which yield better results with a more accurate comparison
We add a NoPe-NeRF comparison (see author response PDF, Fig. 1b and Tab. 1b); we succeed where BARF and NoPe-NeRF fail. Also reca... | Rebuttal 1:
Rebuttal: We appreciate the time and energy the reviewers have invested in reviewing our paper and for offering insightful and constructive feedback, which will make our paper clearer and stronger. We are glad that reviewers recognized our paper’s contribution in addressing an “important problem” (kV1r) in ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Factorized Contrastive Learning: Going Beyond Multi-view Redundancy | Accept (poster) | Summary: The paper introduces FactorCL, a novel method for learning multimodal representations. The proposed method generalizes traditional MI maximization-based approaches by capturing both shared and unique information relevant to downstream tasks. Based on the information-theoretic perspective, the paper demonstrate... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful comments! We respond to some concerns below:
[Unique partition] We define shared information as $I(X_1; X_2; Y)$ and unique information as $I(X_1; Y | X_2)$ and $I(X_2; Y | X_1)$ based on information theory, and from this definition the two area... | Summary: This work addressed the problem of contrastive learning in a multimodal setting, particularly in capturing shared and modality-specific information regarding downstream tasks. Existing approaches assume that the information contained in different modalities is somewhat the same (redundant), but in the real wor... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful comments! We respond to some concerns below:
[Too many details] The details cover the exact mathematical derivation from the definitions of shared and unique task-relevant information to our final self-supervised objectives. We will add an overv... | Summary: Based on the mutual-information theory, this paper proposes a new multi-modal contrastive learning method (FactorCL) to learn both shared and unique multi-modal task-relevant information, which captures task-relevant information via maximizing MI lower bounds and removing task-irrelevant information via minimi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful comments! We respond to some concerns below:
[Automatic augmentations] We find that **augmentations that approximately satisfy the optimal multimodal augmentation defined in Eqs.17-18 are sufficient for good performance, which is simpler and str... | Summary: This paper presents FACTOR CL, a method for multimodal representation learning that captures both shared and unique task-relevant information, going beyond the common approach of focusing on shared information across different data modalities. FACTOR CL is based on three key contributions: factorizing task-rel... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful comments! We respond to some concerns below:
[Assumption] Augmentations that exactly satisfy $I(X_1; X_1') = I(X_1; Y)$ and $I(X_2; X_2'|X_1) = I(X_2; Y|X_1)$ (Eqs.17-18) are hard, so instead we relax it to $I(X_1; X_1') \approx I(X_1; Y)$ and $... | Rebuttal 1:
Rebuttal: Dear reviewers, we are extremely grateful for your valuable feedback and insightful comments. We are glad that you agree that our results are innovative (859W), original (fNpT), significant (fNpT), and applicable to a broad range of settings in contrastive learning (859W, bfCW, N8Dx, 4hLy). Your c... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The goal of the proposed work is control over the information content of representations. The most common form of contrastive learning leverages multi-view redundancy, where data points are paired across different modalities or different augmentations in the same modality, and a contrastive loss is used to e... | Rebuttal 1:
Rebuttal: [Conditional MI] We apologize for the incomplete reference in Appendix and have fixed it. This conditioning scheme is briefly stated in Lines 171-172 and elaborated in Lines 826-830 for supervised and Lines 834-837 for SSL. Conditioning is done by concatenating the encoded $X_1$ and encoded $X_1’$... | null | null | null | null | null | null |
Decompose a Task into Generalizable Subtasks in Multi-Agent Reinforcement Learning | Accept (poster) | Summary: The paper proposes a new neural network architecture for representing agent policies in multi-agent reinforcement learning. The architecture consists of two parts: (i) a subtask encoder that chooses the subtask to perform based on the subtask-observation history and (ii) a subtask semantics module that chooses... | Rebuttal 1:
Rebuttal: We greatly appreciate your advice on further enhancing this paper. We would like to discuss them one by one and would greatly appreciate any further discussion on the matter.
1. > **Weakness 1**: Experiments on another environment are needed
We conducted zero-shot experiments on the physical ... | Summary: This paper focuses on transfer learning of multi-agent reinforcement learning (MARL), by establishing generalizable sub-tasks to enable knowledge reuse. Empirical evidence underscores that the proposed algorithm demonstrates robust zero-shot generalization across a variety of tasks. Furthermore, the algorithm ... | Rebuttal 1:
Rebuttal: We appreciate your positive review, insightful feedback, and constructive comments that help improve the quality of the paper! We are glad to answer your questions and would appreciate any further response.
1. > **Weakness (a)**: The paper seems to omit the related work section, which could be pi... | Summary: This work proposes DT2GS (Decompose a Task inTO a series of Generalizable Subtasks) that addresses multi-agent reinforcement learning in the contexts of zero-shot generalization, transfer, and multi-task. DT2GS learns task-independent subtasks that are characterized by the effects of each agent on itself and o... | Rebuttal 1:
Rebuttal: Thanks a lot for your advice on further improving this paper. We would like to discuss them one by one. Any further discussion will be appreciated.
1. > **Weakness 1**: Lack of clarity
We revised our writing. Specifically :
(1) We standardized the use of subscripts in our revised paper as... | Summary: The paper introduces the DT2GS framework to improve the generality of agents in Multi-Agent Reinforcement Learning (MARL) by decomposing a task into generalizable subtasks. The authors use a scalable subtask encoder to identify appropriate subtasks based on historical entity-observation pairs, instead of actio... | Rebuttal 1:
Rebuttal: Thanks for your detailed review. We are glad to discuss your concerns one by one. Any further discussion will be appreciated.
1. > **Weakness 1**: A more detailed introduction of related work could be helpful for readers if space allows, such as the network design or methodology of ASN.
We ad... | Rebuttal 1:
Rebuttal: 1. > **Weakness 1**: Lack of a section for related work.
Due to space limitations, we only provided a brief introduction to the related work in the Introduction section. Based on the feedback received, we added a detailed section of related work to the appendix of the revised paper. And the re... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Improving Robustness with Adaptive Weight Decay | Accept (poster) | Summary: To improve the robustness, this paper proposes a method to determine the weight decay hyper-parameter during adversarial training adaptively. The key idea is to select the proper weight decay parameter $(\lambda_t )$ to keep the decay over the gradient (DoG) as a constant. The proposed method is evaluated on i... | Rebuttal 1:
Rebuttal: We thank reviewer 3gZW for their insightful comments and great editorial suggestions. We will incorporate these suggestions in the final version of the paper. Please review our response to some of the questions you asked.
> How this method will benefit pruning, as mentioned in the abstract, is n... | Summary: This paper proposes a simple but efficient way to improve model robustness: Adaptive Weight Decay, which automatically tunes the hyper-parameter for weight decay during each training iteration. Experimental results prove that this method significantly improves the robustness of the model on multiple datasets.
... | Rebuttal 1:
Rebuttal: We thank you, reviewer pbDj, for your constructive criticism and insightful feedback. Please review our response to the concerns raised.
> It would be better if more theoretical explanations about AWD could be provided.
We absolutely agree with your point. A theoretical analysis would add more ... | Summary: This paper proposed adaptive weight decay which is balancing the gradient of the loss funciton such as the coss-entropy loss and the weight decay term.
Althogh the proposed method is a simple method, it empirically improves adversarial robustness and a classification with label noise.
Strengths: The strong p... | Rebuttal 1:
Rebuttal: We thank reviewer 61Q2 for their insightful comments. Please review our response to questions asked.
> The main concern with this method is that we do not know what the algorithm is ultimately optimizing. We cannot know if the algorithm converges even in optimization problems such as convex opti... | Summary: The paper proposes adaptive weight decay (AWD) to adaptively tune the weight decay hyperparameter during training. The AWD keeps the ratio of weight decay update and cross-entropy loss update constant for the stability of training. The experiment shows that AWD improves adversarial robustness and reduces robus... | Rebuttal 1:
Rebuttal: We thank you, reviewer N3sV, for your insightful comments. Please review our following feedback.
> As a reviewer who reviewed the submission for 3 times, my major concern before is the comparison of AWD with recent baselines like MART and MAIL. I am glad that Table 2 of this version includes suc... | Rebuttal 1:
Rebuttal: Dear reviewers. Thanks for your insightful and constructive comments. Below, you may find tables mentioned in the detailed responses for each review. Due to character limitations, please find more detailed explanation of the following tables in the per-reviewer rebuttals.
|Eps|Data|Alg|Lambda/Do... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the overfitting phenomena that is known to happen during adversarial training, with focus in image classification. The main idea hinges on the fact that weight regularization can be an effective technique to prevent such overfitting. The authors propose to augment the regular cross-entropy o... | Rebuttal 1:
Rebuttal: We thank you, reviewer c9pV for your insightful comments. Please review our response to some of the questions you asked.
> While the idea is based on state-of-the-art work in the literature [Rice et al., etc.], the results are not compared with these algorithms.
Thank you for the suggestion reg... | null | null | null | null | null | null |
Learning Mixtures of Gaussians Using the DDPM Objective | Accept (poster) | Summary: The authors propose to leverage the DDPM objective to learn Mixtures of Gaussians and prove that gradient descent on the DDPM objective can efficiently recover the ground truth parameters of the mixture model under certain assumptions.
Strengths: Several interesting insights are revealed, such as those associ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and address them point-by-point. We believe that the criticisms stem from misunderstandings about the scope of what is known in this literature and its relation to our work. We have also provided numerical experiments in the rebuttal, though we also clarify... | Summary: This paper shows that the diffusion model can be used to learn mixtures of Gaussians. In particular, they show that GD on the DDPM objective can efficiently recover the ground truth parameters for both mixtures of two spherical Gaussians and mixtures of $K$ spherical Gaussians under different assumptions. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging words and for finding our paper very interesting. Here we address the questions raised, one of our main points being that while the points raised are indeed great directions for future work, we would like to underscore that prior to our work, there were no... | Summary: This paper presents a new approach to learning Gaussian mixture models using the denoising diffusion probabilistic model (DDPM) objective. The authors provide a 2-part algorithm that allows one to reconstruct the parameters of a mixture of Gaussians. The first part of the algorithm uses gradient descent with "... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and we address all the points raised by the reviewer. As the main weakness mentioned by the reviewer on the convergence of power method seems to be due to some misunderstanding and is thoroughly addressed both through theory and experiments that we performed ... | Summary: Proofs are given for showing that the true mean parameters of Gaussian mixture models (GMMs) with identity covariance matrices can be recovered when using gradient descent to optimize the DDPM objective. It is argued that it is not well understood if score-based models can provably estimate the parameters of t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful suggestions. We will make sure to include a discussion/conclusion section in the revision. Here we address their other points one-by-one. Given that it is straightforward to fix all of these concerns in a single round of cosmetic edits, and given that the on... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments, which we have responded to individually. Here we note that we also performed numerical experiments (see attached pdf) to demonstrate that 1) the **constant factors in our analysis are quite benign**, and 2) as predicted by our theory, **training with the ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Cross-Episodic Curriculum for Transformer Agents | Accept (poster) | Summary: This paper presents Cross-Episodic Curriculum (CEC) to boost the learning efficiency and generalization of Transformer agents. Specifically, CEC places the cross-episodic experiences into a Transformer’s context, which forms the basis of a curriculum. The authors also provide three concrete curriculum implemen... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We address your concerns in detail below and will update our paper accordingly.
> Compare with more recent methods
We compare against two more relevant baselines. Please refer to [global response](https://tinyurl.com/4bc2469z) for comparison and discussi... | Summary: This work proposes a new method, CEC, to boost the learning efficiency and generalization capability of the agent by structuring multiple episodes for deploying the transformer’s pattern, and sequence recognition capability. The proposed method shows improved performance compared to the baseline and shows gene... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We address your concerns in detail below and will update our paper accordingly.
> [...] It is not clear whether given prior curriculum sequence is a fair assumption.
We agree with you that accurately formulating a curriculum is challenging. In this work,... | Summary: This paper introduces a novel algorithm, referred to as Cross-Episodic Curriculum (CEC), which aims to improve the learning efficiency and generalization capabilities of Transformer agents in multi-task RL settings. The algorithm has been specifically developed to exploit the limited availability of sub-optima... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We address your concerns in detail below and will update our paper accordingly.
> Restricted comparison
Thank you for your suggestions. Since [1,2] mainly focus on improving online RL with auxiliary tasks, these are useful to improve the source agents in... | Summary: This work aims to study mechanisms of cross-episode attention to effectively learn to improve polices by training on contexts containing gradually improving trajectories.
Strengths: On the whole, this paper is well written. The topic of transformers in-context learning as an approach to planing and RL is of ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We address your concerns in detail below and will update our paper accordingly.
> Lack of comparison to existing approaches
Thank you for pointing this out. We compare against two more relevant baselines, Agentic Transformer (AT [1]) and Decision Transfo... | Rebuttal 1:
Rebuttal: # Global Response
We sincerely thank all reviewers for their thoughtful and constructive feedback. We really appreciate that all reviewers find our idea novel and important for Transformer-based agents. We attach updated versions of Figs 3 and 4 in the one-page PDF. In our response to each reviewe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
RanPAC: Random Projections and Pre-trained Models for Continual Learning | Accept (poster) | Summary: This paper proposed a frozen random projection layer with nonlinear activation to exploit pre-trained representations for continual learning. Combining PETL techniques and class prototypes, the proposed method achieves strong performance in class- and domain-incremental learning.
Strengths: 1. The paper is we... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and feedback. We address the points individually and kindly ask the reviewer to increase their score based on our response.
> “The authors claim that they borrow some ideas from PETL methods. Actually, the Phase 1 seems to (almost) inherit the pr... | Summary: The manuscript proposes a Continual Learning method called RanPAC, which belongs to the category of class prototype methods. They use a frozen pretrained model to extract feature vectors from the input images and non-linear random projections to project them to a higher dimensional space. During the training o... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and feedback. We address the points individually and kindly ask the reviewer to increase their score based on our response.
> "The paper presents the LDA formula and subsequently mentions that the authors employed the Gram matrix. Nevertheless, i... | Summary: The authors propose a method for replay-free continual learning from a pre-trained model based on random projections and prototypes. The method has a high parameter cost compared to SOTA prompting-based methods, but also is a unique method which strongly outperforms these SOTA methods. Overall, the experiments... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and feedback. We address the points individually and kindly ask the reviewer to increase their score based on our response.
> “There seems to be large number of additional trainable parameters. While for 10 tasks, this is only 10/84 of the model ... | Summary: The paper investigates the issue of continual learning using frozen pretrained vision transformers. The authors conduct a thorough analysis of potential limitations and strengths of continual learning methods that utilize pretrained models, supported by theoretical studies and derivations. Additionally, they i... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and feedback. We address the points individually and kindly ask the reviewer to increase their score based on our response.
> “...the authors have mixed background information with the method ... should consider moving the "Overview and Intuition... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Tree Variational Autoencoders | Accept (spotlight) | Summary: This paper introduces a new class of variational autoencoders that define a binary tree structure. Given a fixed tree, the generative model is a top-down hierarchical model with binary routing components and the inference network follows a top-down approach in the style of the Ladder VAE. The authors propose t... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback! We appreciate your support for this paper.
W: We will make sure to extend Section 2.6 for the camera-ready version. We intentionally kept it short, as we only use `NT-Xent` for real-world image datasets, that is CIFAR-10, CIFAR-100, and CelebA. Here, the ... | Summary: The paper presents a novel method of unsupervised hierarchical clustering by encoding structural sequential dependencies between hidden variables within the framework of variational auto-encoders. The authors adopt similar designs of top-down and bottom-up dependency structure to Ladder VAE, but imposes a bina... | Rebuttal 1:
Rebuttal: Dear reviewer NuUb,
thank you for your feedback and constructive criticism.
W1: Due to the space limitations, we have refrained from explaining the qualitative results in greater detail, however, we will make sure to include this in the camera-ready version. There are also additional figures in A... | Summary: introduces a new generative hierarchical clustering model called Tree Variational Autoencoders (TreeVAE) that uncovers hidden structure in data by adapting its architecture to discover the optimal tree for encoding dependencies between latent variables. The authors compare TreeVAE to other generative models an... | Rebuttal 1:
Rebuttal: Dear reviewer bo43,
Thank you for your comments and your thorough reading of the paper.
W1: The idea behind the claim of semantic meaningfulness is that samples of the same cluster should have a similar latent representation and also that clusters that are close to each other in the hierarchical... | Summary: This paper introduces a new deep generative model, the tree variational autoencoder, designed to discover latent hierarchical clusters in data. The generative model makes a number of latent binary choices over a pre-learned tree structure, sampling a continuous representation at each node, then finally decodin... | Rebuttal 1:
Rebuttal: Thank you for your comments and your thorough reading of the paper.
W1: We agree with this statement, as the purpose of this work is to introduce a new model class that jointly learns generation as well as hierarchical clustering. We are currently working on improving the model architecture, simi... | Rebuttal 1:
Rebuttal: Dear reviewers,
We deeply appreciate your insightful questions, constructive comments and helpful feedback! Your reviews suggest that you invested a considerable amount of time and effort into understanding our work, for which we are very grateful and thankful. We provide individualized responses... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a new architecture for variational auto encoders with a binary tree structured generative model. The approach takes the architecture of the Ladder VAE, but introduces a binary routing variable at each stochastic layer that allows generation to continue down one of two possible paths. The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments and feedback!
W1: We agree that a naive training algorithm would have a heightened computational complexity, however, due to the structure of the model we can alleviate this issue: Firstly, we can store the values of every visited node such that w... | null | null | null | null | null | null |
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation | Accept (poster) | Summary: This work introduced an online density ratio estimation method that can adaptively update the model to minimize the risk accumulated over time in the continuous covariate shift scenario. This method is able to estimate density ratios between test and training samples when the test set is varying over time. Onl... | Rebuttal 1:
Rebuttal: Many thanks for your great appreciation for our work and the helpful comments! In the following, we will address your questions. We will further improve the paper according to your suggestions.
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**Q1:** important experimental results are in the appendix which is not reasonable as the appendix ... | Summary: This paper proposes an online density ratio estimation method to adaptively train a predictor in the scenario of continuous covariate shift. The proposed method estimates the density ratio between the training and testing distributions using a small number of unlabelled samples and updates the predictor using ... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments. We will address your questions below and improve the paper according to your suggestions. To better present additional experimental results, we include them in a PDF attached with the global response.
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**Q1:** empirical study is limited to a study using sy... | Summary: This work studies the continuous covariate shift problem, where there exists an initial labelled dataset and in every subsequent round, a new unlabelled dataset is revealed. One needs to adapt the model for every round to achieve good performance. The paper uses importance-weighted ERM where the weights are es... | Rebuttal 1:
Rebuttal: Many thanks for your great appreciation and bringing the concurrent work to us! In the following, we will address your questions. We will further improve the paper according to your suggestions.
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**Q1:** Are there any possible ways to extend the current work beyond linear models.
**A1**: We ... | Summary: This paper focuses on deriving theoretical bounds for online density ratio when there exists continuous covariate shift. The formulation is based on the importance-weighted empirical risk minimization, which is a conventional one for covariate shift adaptation. The paper chooses the Bregman Divergence Density ... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments. In the following, we will first highlight the contribution of our work (Q1 and Q2) and then address your concern about the experiments (Q3). We will improve our paper according to your comments.
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**Q1：**“I am not sure how much the first part of the analysi... | Rebuttal 1:
Rebuttal:
We sincerely appreciate insightful comments and the positive feedback from all reviewers for this paper. In the rebuttal period, we conducted additional experiments to further support our claim (particularly to address the concerns from Review Ls7D), as presented in the attached PDF file. The ex... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation | Accept (poster) | Summary: This paper proposed a new framework to validate the domain-adapted model through Mixed samples. Leveraging mixed samples is not new, but application to the domain-adapted model validation is novel. The framework is very general, so it can be combined with any adaptation methods. Experiments are very extensive ... | Rebuttal 1:
Rebuttal: We are sincerely grateful for both your recognition of our contributions and your valuable and comprehensive feedback. We've taken each of your concerns into account and have provided detailed responses below.
> **Q1**: The main weakness of this paper is (as the authors also mentioned) the lack ... | Summary: Validating hyperparameters in UDA is challenging due to the unlabeled target data. For it, the paper proposes MixVal, a novel target-only approach that utilizes mixup to synthesize target samples for validation. MixVal combines inductive biases from prior approaches through intra-cluster and inter-cluster mixu... | Rebuttal 1:
Rebuttal: Thanks a lot for the constructive comments. We have addressed all of your concerns in a detailed manner as outlined below.
> **Q1**: I can't find methodological novelty. I think that the proposed method is a combination of Entropy, SND, and Mixup.
**A1**: We **respectfully disagree with this a... | Summary: In this paper propose a novel target-only method is proposed to employ mixup to synthesize in-between target samples for validation. MixVal leverages mixed target samples to directly probe the learned target structure, benefiting from an combination of inductive biases considered in prior approaches. MixVal pe... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback on our paper, particularly regarding its soundness and state-of-the-art experimental results. We have addressed your concerns as follows:
> **Q1**: The presentation need to be improved.
**A1**: Thanks for the constructive suggestion. We will revise our paper... | Summary: This paper introduces a simple validation method for unsupervised domain adaption. This method, called MixVal, proposes a new evaluation (ICE) based on MIXUP to select the most appropriate model candidates, which is obtained by training with different hyperparameters, such as loss coefficient/temperature/margi... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments. We've provided responses to each of your remaining questions below.
> **Q1**: Lack novelty. Behind the mentioned contributions, there is only one proposed technical point, the proposed evaluation ICE with MIXUP, which just takes less than half a page. It cou... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their valuable time and feedback. Particularly, we are grateful for the following recognitions:
- Our work investigates a **crucial** [Reviewer PTRC] problem which is **well-defined and well-motivated in practice.** [Reviewer zt6b].
- Our method MixVal is... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MarioGPT: Open-Ended Text2Level Generation through Large Language Models | Accept (poster) | Summary: This paper proposes MarioGPT with novelty search, a method that can generate new Super Mario Bros levels. MarioGPT is finetuned from GPT-2 to generate levels. The novelty search is used to get novel levels by randomly selecting a generated level, mutating it, and then filtering it based on a novelty criterion:... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback!
**Addressing the qs from the reviewer**
> How to control detailed level settings like the initial moving direction of the enemy?
Because of the limitations of the current dataset and its labels, we do not know the initial direction of the enemy.... | Summary: This paper proposes a novel idea of using LLMs to perform Procedural Content Generation. It addresses several challenges including the diversity of the generated environments, the existence of a feasible solution (playability) and generating with language guidance. It proposes two key algorithms.
1. MarioGPT ... | Rebuttal 1:
Rebuttal: Thanks for the great feedback!
**Addressing the questions from the reviewer**
> In section 4.4 "Guided Level Generation via Prompting", under what sampling temperature are these results measured? ... The authors mentioned that randomness helps with diversity - does this sacrifice accuracy of fol... | Summary: In this work the authors fine tune a DistilGPT2 model to produce diverse, largely playable Mario levels. They incorporate a novelty search to favor diversity in levels, and also explore conditioning level generation on natural language user prompts. Their novelty search proceeds as follows: Given an initial se... | Rebuttal 1:
Rebuttal: First off, we thank the reviewer for the great and thoughtful feedback!
**To answer the reviewer’s open questions:**
> 1. "in Table 1 I'm a little surprised that everything except MarioGPT does so poorly (LSTM does okay at least though). It'd be helpful to have a little intuition for why this i... | Summary: This submission proposes MarioGPT, which aims to generate diverse-and-playable Mario levels with LLM (GPT-2) through language prompts. The input and output of MarioGPT are level representations, not natural language. Human prompts are involved by incorporating the cross-attention layer into the LLM. A Novelty ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and useful comments!
In terms of PCG applied to other games, while Super Mario Bros. is often used as a benchmark, there are certainly a variety of other games our approach could be applied to in the future. Another common PCG-benchmark are dungeon-like g... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their insightful comments, which helped us to significantly improve our paper. The most important new additions are: (1) a comparison to an ablated version without novelty search, which shows that novelty search is indeed crucial in allowing the discove... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Anchor Data Augmentation | Accept (poster) | Summary: The authors proposed a new data augmentation algorithm for regression datasets. They borrow ideas from Anchor regression model which captures the heterogeneity of the dataset using anchor variables to create additional augmented data. They show that applying this type of augmentation improves results compared ... | Rebuttal 1:
Rebuttal: Thank you for your direct review and questions about our procedure. We will address them in the following paragraphs.
Data augmentation for regression is hard. As we mentioned in our paper, there are two papers on the topic. The results sometimes are much better than the baseline, but in most cas... | Summary: The paper proposes a new data augmentation method called Anchor Data Augmentation (ADA) that can improve the performance of machine learning classifiers, especially in over-parameterized settings.
Novelty:
The work presents the Anchor Data Augmentation (ADA) which is based on the concept of Anchor Regression... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments and discussion about scalability and intuition on hyperparameter selection.
The standard deviations of RMSE and MAPE results are in Tables 1 and 2 in Appendix B.3.
The performance of ADA is indeed dependent on the choice of the anchor matrix A. As with AR... | Summary: In this paper, the authors proposed anchor data augmentation, which borrows from the recently proposed Anchor regression method. The anchor data augmentation uses several replicas of the samples, generated according to the anchor matrix. The proposed augmentation is empirically evaluated both for linear and no... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments and discussion about scalability and intuition on hyperparameter selection.
The standard deviations of RMSE and MAPE results are in Tables 1 and 2 in Appendix B.3.
The performance of ADA is indeed dependent on the choice of the anchor matrix A. As with AR... | Summary: This paper introduces a new data augmentation method, designed for regression problems. The method is based on anchor regression, where new samples are generated via their projection to the normal subspace spanned by the anchors. The method is evaluated on several datasets and compared with ERM and C-Mixup, sh... | Rebuttal 1:
Rebuttal: Thank you for your direct review and comments about the clarity of the connection between AR and ADA and the derivation of augmentation equations 7 and 8. We will rewrite these sections to improve the fluency and notations. In particular, ADA uses a linear projection as in AR to select perturbatio... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive feedback and suggestions for improving our contribution to NeurIPS, and we hope to have a fruitful discussion in the coming days. We also want to thank the Area Chair for leading this paper review and the discussion. We address each question in our dir... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a new approach for automatic data augmentation in nonlinear regression, which requires no specific domain knowledge and the added computation cost is low. The idea is borrowed from Anchor regression and Mixups. The idea is to first run a k-means on the data, use the cluster memberships to c... | Rebuttal 1:
Rebuttal: Thank you for your positive review and questions about our paper.
ADA relies on AR to augment data samples, and it inherits the generalization properties of AR. The main objective of AR is prediction robustness concerning some directions of perturbations and interventions. Therefore, it is natura... | Summary: This paper design a new mixup data augmentation algorithm for regression problems, which is a challenging field for data augmentations. Specifically, the authors extend Anchor Regression (AR) method as Anchor Data Augmentation (ADA), which utilizes several replicas of the modified samples in AR to generate mor... | Rebuttal 1:
Rebuttal: Thank you for your balanced review and your suggestions for improvement.
The literature on Mixup methods is vast and especially interesting for image classification. There are only two valid approaches for regression (reviewed in our paper). C-Mixup is a solid paper, and it brought some of the M... | null | null | null | null |
Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests | Accept (poster) | Summary: This paper exams whether existing deep multi-instance learning algorithms are indeed "multi-instance learning". Specifically, it proposes a unit test for multi-instance learning (MIL) algorithms. The goal of this test is to examine whether an MIL algorithm satisfies the multi-instance assumption. Two widely-ac... | Rebuttal 1:
Rebuttal: We hope the below fully satisfies your concerns. Please let us know if there are any outstanding issues that we did not fully satisfy.
W2/Discussion 1: Please see our all-reviewer rebuttal, where we have added a new paragraph that we believe helps to satisfy this concern. If it does not we are h... | Summary: This work proposes a set of algorithmic unit tests to verify whether multiple instance learning (MIL) models adhere to underlying MIL assumptions. The standard assumption is that a bag of instances is positive if and only if at least one of the instances in the bag is positive, otherwise it is negative (binary... | Rebuttal 1:
Rebuttal: We hope the below answers all of your questions and resolves any concerns in supporting our manuscript. Please do let us know if there are any unresolved concerns.
Q1: With the exception of the false-frequency test, which is explicitly testing an errant bias to the frequency of items, yes the b... | Summary: This paper investigates whether multiple-instance learning (MIL) models actually respect the constraints of MIL problems.
This paper defines MIL problems as classification of bag-of-instances, such that the bag is only classified positive if any instance is classified positive (or a more complex rule based on... | Rebuttal 1:
Rebuttal: We hope the below fully addresses your concerns. Please do not hesitate to let us know if any further clarification is required.
> Why is it so important that MIL models enforce what the authors present as the natural structure of MIL problems?
Please see the paragraphs we added in the all-revi... | Summary: The paper deals with multiple instance learning (MIL), where a collection of items is considered in a bag/collection, whereby presence of certain items in the bag implies a positive label for the whole collection, and otherwise the collection has a negative label. The paper discusses prior MIL methods that do ... | Rebuttal 1:
Rebuttal: We are at response limit. Please let us know if anything was not satisfied.
Q1: $g()$ is a function, the domain is $\forall k \in [1, \ldots, K]$ that $c_k$ is an integer $\geq 0$ (though it could be relaxed to a continuous non-negative as well, this has no impact on our results, but MIL is comm... | Rebuttal 1:
Rebuttal: We are pleased most reviewers found our paper readable, sound, and technically novel in identifying a previously undocumented issue in the Multiple Instance Learning literature. One shared note of reviewers was that we could more strongly communicate the importance of this to readers outside the s... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning | Accept (poster) | Summary: This paper studies using large language models (LLMs) to assist event prediction. The authors proposed a pipeline of procedures, where 1) a traditional event sequence model is first applied to predict a set of possible events along with their time stamps, 2) then for each event candidate, an LLM is used to rea... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
> consider using a dataset that can facilitate more careful discussions other than GDELT.
We added new results on a new dataset, ICEWS. It is similar to GDELT but less dense in time so it is meaningful to predict time on this new dataset. Please see [New Dataset] fo... | Summary: This paper studies the problem of predicting future world events based on the past and proposes an approach that combines the existing event sequence models with the powerful (abductive) reasoning ability of large language models (LLMs) that results in better performance both for predicting the actual event as... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and being supportive! We added new results for you; please see [New Baselines] and [New Dataset]. Now we'll answer your remaining questions.
> The time of the first example is 2022-03-08 and so is the time of the queried effect… clarify how you selected t... | Summary: This submission proposes a large language model (LLM-) based approach to enhancing event prediction methods. Instructed by a few annotated demonstrations, a large language model is used to suggest possible causes for a proposal to be predicted. Then a search module is used to find out previous events that matc... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We have added new results for you; please see [New Baselines] and [New Dataset]. Now we'll answer your remaining questions.
> the soundness of this idea is questionable (see comments in the Soundness part). It calls for further theoretical analysis and e... | Summary: The paper investigates the use of large language models (LLMs) to improve the accuracy of event sequence models. Specifically, the authors propose an abductive reasoning framework based on an LLM. First, the event model produces some proposals of predictions. Then, the LLM suggests some causes for each proposa... | Rebuttal 1:
Rebuttal: Title: Response to Reviewer HaQW
Thank you for your constructive feedback. We have added new results for you; please see [New Baselines] and [New Dataset]. Now we'll answer your remaining questions.
> The results are not surprising, as the baselines do not rely on the same setting
Do you mean ... | Rebuttal 1:
Rebuttal:
We thank reviewers for constructive feedback! In this to-all message, we clarify our technical contributions and present new results. We will address other concerns in responses to individual reviews.
Due to 6000-char limit, we have to keep this response concise. If anyone asks for more details... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: Summary:
This paper investigates the effectiveness of large language models in reasoning and predicting event sequences. The authors propose a general framework that combines event sequence models with large language models for the task of event prediction. In this framework, an event sequence model proposes p... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and being supportive! We have added new results to resolve your concerns; please see [New Baselines] and [New Dataset]. Now we'll answer your remaining questions.
> The literature review is a bit limited… There are already works that apply LLMs to tempora... | null | null | null | null | null | null |
Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection | Accept (poster) | Summary: The paper introduces D3R, a novel anomaly detection network for unstable multivariate time series data. D3R addresses the issue of drift by dynamically decomposing the data and reconstructing it using noise diffusion. Experimental results show that D3R outperforms existing methods, with a 12% average relative ... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Experiments on computational cost.**
As three reviewers have posed a similar question, we provide a consistent response in **Q1** of the "global" response. Thanks.
---
Rebuttal Comment 1.1:
Comm... | Summary: The authors propose a new model called D3R (Decomposition with Diffusion Reconstruction) for anomaly detection in multivariate time series data. The proposed model applies a dynamic decomposition method to separate the stable components and trends in time series data. This method can effectively separate long-... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Details of dynamic decomposition.**
As two reviewers have question about why our method breaks the limitations of the local window, even though we also used the moving average to obtain the trend ... | Summary: To overcome the temporal drift issues in unstable time series data, this work proposes an anomaly detection method, $D^3R$. By considering the temporal continuity of series and relieving the constraints of information bottleneck, $D^3R$ realizes the dynamic decomposition and the noise-diffusion-based series re... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Reason for the dataset selection.**
Thanks for your suggestions. The datasets utilized in our research encompasses both server (PSM, SMD) and water treatment (SWaT) scenarios. Additionally, stable... | Summary: The paper presents a Transformer-based model called Dynamic Decomposition with Diffusion Reconstruction for Anomaly Detection in unstable multivariate time series. The authors addressed two challenges: the limitation of decomposition for long-period time series and high training cost for adjusting the informat... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Standard deviation of the experimental results.**
Due to spatial limitations, we exclusively present the mean of the results from the five runs in the paper, as it offers a more representative dep... | Rebuttal 1:
Rebuttal: **Q1: Experiments on computational cost.**
As real-world time series datasets can be large-scale and complex, we supplement the measures of training time, inference time, and model size for deep learning-based models on the SMD dataset. The experimental results are presented in the subsequent tab... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper tackles the problem that existing works omit the drift generated from non-stationary environments by focusing on stable data, which may lead to numerous false alarms.
As a solution, they propose an end-to-end anomaly detection network for real-world unstable data, named Dynamic Decomposition with D... | Rebuttal 1:
Rebuttal: Thanks for your positive comments and insightful suggestions. Please find our response below.
**Q1: Experiments on computational cost.**
As three reviewers have posed a similar question, we provide a consistent response in **Q1** of the "global" response. Thanks.
**Q2: Repetitive citation in re... | Summary: Current unsupervised methods for multivariate time series anomaly detection often overlook drift from non-stationary environments, leading to false alarms. To address this, this paper presents Dynamic Decomposition with Diffusion Reconstruction (D3R), a new anomaly detection network for unstable data. D3R deco... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We will answer the questions one by one.
**Q1: Necessity and details of dynamic decomposition.**
**Q1.1: Why not over extend the length of the sliding window?**
Below, we shall expound the reasonableness of Challenge 1 (Line 29 in the paper). Expanding the len... | null | null | null | null |
On Adversarial Training without Perturbing all Examples | Reject | Summary: The authors propose a new approach called Subset Adversarial Training (SAT), which differs from traditional adversarial training methods that generate adversarial examples on the whole training set. Instead, SAT applies adversarial training on a subset of the training data. They studied two variants of subset ... | Rebuttal 1:
Rebuttal: - **Q1: No comments on improving understanding of AT.**
While we agree that further insights into robustness transfer would be beneficial, we believe our submission remains of strong interest to the scientific community (note the reviewers comment on our suprising findings (R1, R2, R4, R5).
... | Summary: This paper demonstrates an interesting observation: when we conduct adversarial training, we can only choose to generate adversarial examples on a subset of the training data, if this subset contains the hardest examples, then adversarial training on a subset can achieve competitive performance in robustness o... | Rebuttal 1:
Rebuttal: - **Q1: Marginal contribution.** We reiterate our answer to Q4 of R3, but also add, that SAT works with 1-step FGSM-RS as well (see figure 14 in the appendix): We believe that a paper does not need to provide improved performance nor efficiency, if it can provide a set of experiments that highlig... | Summary: The authors proposed the use of Subset Adversarial Training (SAT), a technique that splits the training data into A and B and constructs AEs only for data in A. Using SAT, they demonstrate how adversarial robustness transfers between classes, examples, and tasks. The authors report several insights: 1) that th... | Rebuttal 1:
Rebuttal: - **Q1: Evaluation on L\_inf.**
We concur, that evaluation on L\_inf is an interesting addition to our submission and will do so for the final version.
For this rebuttal, we have repeated the S-ESAT experiments for ImageNet-200 $\rightarrow$ Caltech-256 and Flowers-102 using the L\_inf n... | Summary: This work considers the transferability of adversarial robustness for partially adversarially trained models. The authors examine 3 variants of subset adversarial training (SAT): Class SAT, where only samples from selected, difficult classes are adversarially perturbed in training; Example SAT, where only exam... | Rebuttal 1:
Rebuttal: - **Q1 Cost and Efficiency: SAT relies on meticulous pre-processing.**
Given a new architecture and a new dataset, the experimental process of finding good performing configurations involves training multiple models. During training, the entropy statistics for SAT can be cheaply computed wit... | Rebuttal 1:
Rebuttal: We thank all reviewers for their time and valuable feedback on our manuscript. We are very pleased to read that R1, R2, R4 and R5 found our insights interesting, surprising (R4, R5) and that it may provide insights for future works (R1).
Our experiments were commented to be clear and thorough (R3,... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper investigates the transferability of adversarial robustness among different classes and different examples. Different from previous studies, authors split the training dataset into two groups and only apply adversarial training on one group while another one using clean training. Based on experiment ... | Rebuttal 1:
Rebuttal: - **Q1: Motivation not entirely clear. How could SAT be useful in real applications?**
We emphasize, that our study is not one of improving existing methods, but of improving our understanding of adversarial training (AT) and its robustness transfer.
In that, we find our observations to b... | null | null | null | null | null | null |
A Dynamical System View of Langevin-Based Non-Convex Sampling | Accept (spotlight) | Summary: This paper presents a general framework for studying the convergence of last-iterate, noisy, and possibly biased Langevin-like discrete time approximations to the continuous Langevin flow for sampling from a distribution.
Strengths: This paper is (with one small quibble which I will explain below) very well ... | Rebuttal 1:
Rebuttal: Thank you for your input and remarks. We reply to your questions below, and we will revise our manuscript accordingly in the upcoming revision.
> My biggest complain with the paper is that perhaps the most interesting part of the framework is the 2-line proof of Theorem 2 relegated to the appen... | Summary: The authors offer theoretical guarantees on the convergence of the last iterate of a very generic class of sampling methods to the stationary distribution for a very large classe of sampling schemes in non-convex settings. This is achieved by showing that a large class of discrete sampling schemes can be mapp... | Rebuttal 1:
Rebuttal: Thank you for your input and remarks. We reply to your questions below, and we will revise our manuscript accordingly in the upcoming revision.
> The main result of this paper is showing that at very large times the sampling schemes converge to the desired distribution. It would be interesting ... | Summary: The work studies when a discretized Langevin dynamics under the Robbins-Monro-type stepsizes can converge to the Gibbs distribution. The paper obtains asymptotic results with very mild assumptions, and the framework not only includes Euler discretization, but many other sampling schemes as well, such as mirror... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer vdDX's thoughtful criticisms. After a thorough reading, we believe that these critiques primarily stem from presentational issues, which we fully acknowledge exist and will commit to improving them following your suggestions.
In light of this, we sincerely ask for... | Summary: This paper gives a unified asymptotic analysis of a broad class of stochastic algorithms that encompasses several variants of the Langevin algorithm. In particular, it can handle issues of inexact gradients, bias, noise, and problems beyond gradient-based algorithms. The key technique is the introduction of an... | Rebuttal 1:
Rebuttal: We are sincerely grateful for pointing out the missing references and remarks. We reply to your questions below, and we will revise our manuscript accordingly in the upcoming revision.
> My only major criticism is that the paper seems to overstate the novelty of the analytic methods. In particula... | Rebuttal 1:
Rebuttal: Dear AC and dear reviewers,
We wish to express our sincere gratitude for your dedicated efforts. Your insightful critiques and favorable evaluation have been acknowledged, and we have responded to all your inquiries in a detailed point-by-point manner, presented below.
After thorough considerati... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Scale-Space Hypernetworks for Efficient Biomedical Image Analysis | Accept (poster) | Summary: The authors propose a unified approach based on Hypernetworks (HN) to model the accuracy-efficiency pareto front for medical applications.
The authors claim the following contributions:
- Introducing Scale-Space HyperNetworks (SSHN) a single model that given a rescaling factor generates weights for correspondi... | Rebuttal 1:
Rebuttal:
We appreciate the constructive feedback and comments. Addressing the
raised questions:
### Weaknesses:
- **Prior Work** - We agree that the work cited in the review is
relevant to our method, and we will revise our manuscript to draw
the connections and contextualize the research.
- ... | Summary: This paper proposes to learn a spectrum for CNNs with varying internal rescaling factors and demonstrates the effectiveness of the proposed approach in several medical image analysis applications including segmentation and registration with fixed and dynamic rescaling factors. Overall, the approach is simple b... | Rebuttal 1:
Rebuttal:
We appreciate the constructive feedback and comments. Addressing the
raised questions:
- **Primary Network Architecture** - We perform an architecture
ablation experiment in Section C.1 of the supplement. For the OASIS
dataset, we find similar trends to the ones of the U-Net
archit... | Summary: CNNs, particularly those handling 3D data, can pose computational challenges due to their high expense. To tackle this, researchers frequently scale down the input data, a practice that often compromises accuracy. This paper presents SSHN, a technique designed to learn a variety of CNN models, each with unique... | Rebuttal 1:
Rebuttal:
We appreciate the constructive feedback and comments. Addressing the
raised questions:
1. **Training Cost** - We would like to clarify how the model
development process is performed with our method:
- Training - The hypernetwork is trained by generating the primary
network we... | Summary: The paper introduces a method that learns a spectrum of CNNs with different rescaling factors. The method relies on using a hyper-network to generate the parameters of the model for a given rescaling factor --- this enables the users to choose the desired accuracy-efficiency trade-off with a single architectur... | Rebuttal 1:
Rebuttal:
We appreciate the constructive feedback and comments. Addressing the
raised questions:
### Weaknesses:
- **Accuracy Improvements** - This is an important question, and we
don't have a definitive answer. However, the results of the
experiment described in Section 5.2, suggest that vary... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents Scale-Space HyperNetworks (SSHN), a method that predicts the weights for a segmentation network for range of rescaling factors. The proposed approach makes it possible to characterize the trade-off between model accuracy and inference efficiency faster, reducing the overall computational cos... | Rebuttal 1:
Rebuttal:
We appreciate the constructive feedback and comments. Addressing the
raised questions:
1. **Model Development** - We will revise the manuscript to better
explain the model development process and to differentiate between
training and inference. To further clarify, the steps we take are:... | null | null | null | null | null | null |
The emergence of clusters in self-attention dynamics | Accept (poster) | Summary: For $Q,K,V$ fixed and different structures of $V$, analyze the distribution of $x(t)$ as $t\to\infty$, where tokens are seen as particles and the self-attention mechanism is seen as particle interaction, i.e. as a McKean-Vlasov SDE. The conclusion is as $t\to\infty$, i.e. going through the layers, $x(t)$ conve... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and respond to each point individually below.
**Weaknesses.**
1. We do not believe there necessarily is a relation, as there is no easy way to predict the clustered configuration from the initial one beyond simply running the dynamics. Moreover, the numb... | Summary: This paper analyses the self-attention mechanism in (trained) Transformers under the lens of dynamical systems. The authors focus on a bare-bone self-attention architecture without the bells and whistles of standard Transformers (e.g. multi-head attention, layer norm) and assume time-independent weights, i.e. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and respond to each point individually below.
**Weaknesses.**
We have attempted to paint a more complete picture on the necessity of some assumptions made to facilitate the development of this theory through numerical experiments (Figures 1, 2, 3 of the ... | Summary: In this work, the authors develop a theoretical analysis of self-attention mechanism. In particular, the authors study the setting of a trained transformer and their goal is to characterize the output of a deep transformer with multiple layers of self-attention. For simplicity authors focus on weight sharing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and respond to each point individually below.
**Weaknesses.**
1. A result similar to Theorem 2.1 certainly holds for $d\geq 2$ and $V=I_d$, but its statement and proof would need the exclusion of numerous pathological and non-generic initial configuratio... | Summary: This paper studies the asymptotic behavior of a sequence of tokens processed by infinitely deep self-attention only Transformers, viewed as interacting particle systems (1).
The authors first study the one dimension case and show that the self-attention matrix converges to a low-rank boolean matrix.
They th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and respond to each point individually below.
**Weaknesses.**
1. We recognize that this assumption is substantial. However, it does not appear to be necessary for our conclusions; rather, it serves to direct the proof. To reinforce the broader validity o... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. We echo their concerns with regard to several assumptions we had made on the weight matrices for our analysis.
All in all, our goal was to consider the simplest setting of transformers amenable to rigorous mathematical analysis. To enhance the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow | Accept (poster) | Summary: The paper proposes a method to reconstruct 4D hand (3D hand sequence) from a short RGB sequence with two types of Fourier Query Flow (pose flow and shape flow). In the Fourier Query Flow, the 3D trajectory of each point is transformed into 3 Fourier series along the time dimension and represented with the firs... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and finding that the proposed method is “novel and interesting” [vgcD] and shows “large improvements” [rxcm, g8GH]. We also appreciate [rxcm] for finding that the “paper is clearly written and well-motivated.” In what follows, we address the con... | Summary: This paper introduces FourierHandFlow - an implicit 4d representation for learning spatio-temporal hand shape deformations.
The core idea is to introduce a coarse-to-fine implicit deformation model parameterized with a fixed Fourier basis to ensure smoothness and efficient inference. The coarse (pose / joint ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and finding that the proposed method is “novel and interesting” [vgcD] and shows “large improvements” [rxcm, g8GH]. We also appreciate [rxcm] for finding that the “paper is clearly written and well-motivated.” In what follows, we address the con... | Summary: This paper introduces an implicit spatio-temporally continuous hand representation for RGB videos. Firstly, based on LEAP [25], the occupancy function and LBS weights are pretrained as priors for query points. Then two query flow representations are introduced to model the skeleton and the shape, respectively.... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and finding that the proposed method is “novel and interesting” [vgcD] and shows “large improvements” [rxcm, g8GH]. We also appreciate [rxcm] for finding that the “paper is clearly written and well-motivated.” In what follows, we address the con... | Summary: This paper proposes FourierHandFlow, a 4d hand pose and shape representation that inherently uses Fourier series as query flow representation. Given RGB sequence, a fixed number of Fourier series are learned to represent hand pose and shape. The authors use two types of flows to decompose pose and shape: pose ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and finding that the proposed method is “novel and interesting” [vgcD] and shows “large improvements” [rxcm, g8GH]. We also appreciate [rxcm] for finding that the “paper is clearly written and well-motivated.” In what follows, we address the con... | Rebuttal 1:
Rebuttal: We provide the figures referred to in our author response in the PDF file below.
Pdf: /pdf/e32b5c99d6cea3961645cb3b75b10dfa2fd89953.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces FOURIERHANDFLOW, which is a spatio-temporal continuous representation for the human hands. It combines a continuous 3D hand occupancy field with articulation-aware query flows represented as Fourier series along the temporal axis. These query flows are parameterized by coefficients learne... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and finding that the proposed method is “novel and interesting” [vgcD] and shows “large improvements” [rxcm, g8GH]. We also appreciate [rxcm] for finding that the “paper is clearly written and well-motivated.” In what follows, we address the con... | null | null | null | null | null | null |
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model | Accept (spotlight) | Summary: ## Summary
The authors have introduced a new way to ensure LM generations provide truthful answers. They modify activations using a set of learned directions across top-K attention heads. The new method, "Inference Time Intervention," entails identifying a few attention heads with high classifier accuracy in l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and helpful feedback.
***Addressing questions***
**Only 40 samples for probing?**
Questions in TruthfulQA have an average of $7.2$ answers. Therefore, the $40$ samples provide roughly $288$ QA pairs to train and evaluate the probe. As shown in Figure ... | Summary: The authors study how to steer the text generation from different LLMs to be more truthful. They do so by finding directions in feature space (for each attention head), that correspond to truthfulness, and intervening at inference time by adding these directions to the activations of the relevant attention hea... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and helpful feedback. We will edit accordingly to perfect the reading experience of the paper.
***Addressing major concerns***
**More detailed related work**
CCS (Burns et al. 2022) is already introduced in Introduction, Related Work as well as the ex... | Summary: This paper proposed Inference time intervention, which can be used to enhance the trustfulness of LLMs. The method first uses supervised learning to identify latent vectors for factual outputs and then shift activations based on these vectors. They repeat the same intervention aggressively. The proposed method... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and helpful feedback. For the raised two questions:
**Comparison between ITI and weight editing**
ITI is an activation editing method that does not change model weights. It enjoys the benefit that the intervention strength could be tuned by a hyperpara... | Summary: This paper studies the truthfulness for LLMs through the internal representations of models, which is an important and challenging research direction. As previous works have demonstrated that LLMs can contain truthful information internally despite giving an incorrect output, this paper proposes the Inference-... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful and constructive feedback. We have clarified several elements of the paper below and will incorporate them in our updated version.
***Addressing Weakness***
**Figure 2 not a contribution**
Though (orthogonal) probe techniques have been discussed in the ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning to Modulate pre-trained Models in RL | Accept (poster) | Summary: Large-scale pretraining on a diverse dataset followed by finetuning on smaller datasets from downstream tasks has been wildly successful in domains such as computer vision and NLP. The closest analogue to this paradigm in the context of RL is arguably multi-task pretraining followed by finetuning on one or mor... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our paper and your feedback!
**Lack of clarity on the experimental setup:** Following your feedback, we revised our paper to improve clarity. In particular, we changed the following:
* In Line 181 (Experiments), we now explicitly point out that the fin... | Summary: This paper studies fine-tuning and continual learning of pre-trained decision transformers in RL. Extensive experiments are conducted to analyze naive fine-tuning, parameter-efficient fine-tuning, and prompt tuning methods on both Meta-world and DMC domains. This paper presents a new method L2M, which combines... | Rebuttal 1:
Rebuttal:
Thank you for your excellent feedback, it helped us to considerably improve our paper! We conducted additional experiments (see attached PDF). In the revised manuscript, we incorporated all your feedback and suggestions.
**Presentation of methodology:**
1. **Training in parallel:** At training ... | Summary: The authors study the problem of preventing catastrophic forgetting in DT finetuning. The proposed method leveraged a pool of LORA adaptors and only choose the relevant adaptor matrix during finetuning. The author achieve good results on continual world.
Strengths: The applicaiton of lora pools for finetuning... | Rebuttal 1:
Rebuttal: Thank you for your feedback, which helped a lot to improve our manuscript. We appreciate your positive assessment of our work: thank you. We are optimistic that our dataset will contribute to advance the RL research community.
Thank you for pointing out the lack of technical details regarding our... | Summary: The authors propose an adaptation method for pretrained DT (decision transformer) that combines two finetuning techniques, learning-to-prompt and low rank adaptation (L2P + LoRA), which have been investigated in NLP and computer vision domains. This combined method aims at exploiting the benefits of finetuning... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback. Our manuscript improved considerably by addressing and incorporating your comments.
**Analysis:** We are glad that you find the evaluation of fine-tuning techniques meaningful and helpful for readers. Regarding additional analysis on L2M, we already investigat... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you for your helpful comments, excellent feedback, and generally positive responses! We carefully read your constructive reviews and responded to all your questions and comments. Furthermore, we conducted additional experiments and report the results in the attached PDF.... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper considers the catastrophic forgetting problem in pre-training and fine-tuning RL setting. The paper proposes Learning-to-Modulate (L2M) to reduce the degradation of pretrained models by modulating the information flow of the frozen pre-trained model via a learnable modulation pool.
L2M shows state-... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and suggestions that improved our manuscript.
**Enhanced baselines:** We agree that the methods, to which the reviewer is referring, are relevant for improving the pre-training stage. However, our method aims at improving the fine-tuning phase, where it pr... | null | null | null | null | null | null |
Feature Adaptation for Sparse Linear Regression | Accept (spotlight) | Summary: This work studies the problem of sparse linear regression under the statistical model where the examples are drawn as zero-mean Gaussians with covariance matrix $\Sigma$ and each response is a t-sparse linear combination of the examples plus i.i.d. Gaussian noise. While classical results establish that the LAS... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments, and for appreciating our techniques! The question about sparsity of the estimate is indeed interesting. In general, if we use a feature adaptation approach, some feature may be a dense combination of the original covariates and so we cannot guaran... | Summary: The paper introduces an algorithm to solve sparse regression when the covariates are generated from a normal distribution with ill-conditioned covariance matrix, i.e. outlayer eigenvalues. The algorithm is based on feature augmentation where, meaning that the covariates are completed with well-chosen vectors, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. In particular thanks for the good suggestions for experimental directions to consider. It's tricky to understand which instances are the ``worst'' practical instances for the algorithm. However we can give partial answers to some of your questions... | Summary: This paper presents an innovative polynomial-time algorithm for sparse linear regression in the correlated random design setting. The algorithm adapts the Lasso technique to effectively tolerate a limited number of approximate dependencies, resulting in both computational and statistical efficiency for covaria... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. In case it was a point of confusion, we'd like to emphasize that our results do apply when $t$ is a variable, i.e. in Theorems 1.1 and 1.2, there are no factors of $t$ ``hidden'' in any constants. So for example when $t = \log \log n$ our results ... | Summary: This paper studies the correlated random design setting, where the covariates are drawn from a multivariate Gaussian, and seeks an estimator with small excess risk. This work provides a polynomial-time algorithm that, given Σ, automatically adapts the Lasso to tolerate a small number of approximate dependencie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and comments. To address their questions:
**Q:** *``compare with the related work to verify the performance of the proposed algorithm in terms of time complexity and accuracy.''* We would like to emphasize that *no* prior work has addressed the problem of spar... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions | Accept (spotlight) | Summary: This paper studies an extremely general notion of active learning in the $\ell_2$ norm. Suppose we are able to actively observe data in many different modalities. "Active Learning" means we can choose where we observe data in a domain, and "different modalities" means there are fundamentally different ways to ... | Rebuttal 1:
Rebuttal: We thank the referee for their excellent review.
## Weaknesses
### 'The paper...'
We agree. **Please see our global rebuttal for discussion and changes we will make.**
### 'There's even an....'
**We will edit and move parts of this to the main paper.** See below for details.
### 'The general frame... | Summary: This paper proposes a general framework for active learning. It claims that the proposed method can help achieve near-optimal sample complexity. In additional to the theoretical results, this paper also present 3 use cases, showing favorable results for the proposed method.
Strengths: Important topic with cla... | Rebuttal 1:
Rebuttal: We thank the referee for their informative feedback.
## Weaknesses
### 1. 'Very heavy theoretical...'
The heavy setup was also noted as a weakness by other reviewers. **As discussed in our global rebuttal, we will edit Section 2 to improve this presentation. In particular, we will add the running... | Summary: The paper proposes a framework for active learning (here meaning that the user controls the sampling strategy according to which the locations of the measurements are made) designed to handle various cases of regression (vector-valued, multimodal, and more).
To do so, it introduces the concept of generalized ... | Rebuttal 1:
Rebuttal: We thank the referee for their excellent feedback.
## Weaknesses
### Bullet 1
We agree that the framework is complicated. This was raised by other referees and we have discussed in our global rebuttal. In brief, we believe it is justified given the range of applications we address. However, we als... | Summary: The authors introduce an active learning framework for regression problems based on the concept of generalized Christoffel functions. The proposed approach is applicable to a broad range scenarios and it is evaluated on several scientific computing tasks.
Strengths: The manuscript present a comprehensive expl... | Rebuttal 1:
Rebuttal: We thank the referee for these excellent comments. We will discuss them in reverse order:
### ``Although the manuscript....''
In terms of active learning, the main contribution of this article is to extend certain active learning techniques (namely, leverage score sampling) to much broader types... | Rebuttal 1:
Rebuttal: We thank the referees heartily for their insightful comments and the time and effort they put into carefully reviewing our manuscript. Each referee has made insightful comments that will undoubtedly improve the final version of the paper. We have provided detailed responses to each review separate... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks | Accept (poster) | Summary: The present study introduces a novel model of spiking recurrent neural networks, which incorporates a spike convolutional block attention mechanism. This model is referred to as SRNN-SCBAM. The primary objective is to effectively incorporate historical information into the spatial and temporal characteristics ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful comments and insights regarding our article proposing the SRNN-SCBAM model. We would like to address the points raised and provide further clarification on certain aspects.
**W1:**"More ablations of the different gates being open or closed in Table 1 "?
*... | Summary: This study proposed the spiking recurrent neural networks model with a spiking convolutional block attention module component (SRNN-SCBAM). The proposed model invokes the historic information in spatial and temporal channels adaptively through spiking CBAM, which brings the advantages of efficient memory calli... | Rebuttal 1:
Rebuttal: **Q1:**"Table 2: What is the network structure in [23]? "
**A:**The network architecture employed in Table 2 [23] is as follows: 128C3(Encoding)-128C3-AP2-128C3-256C3-AP2-1024FC-Voting.
We supplement the network structures in Table 2 as follows:
Table 2 [19] adopts three layers of time surface pr... | Summary: The authors proposed a Recurrent spiking neural network (RSNN). The essential component of the proposed RSNN is a spiking conv block attention module (SCBAM), which contains channel and spatial attention blocks. The proposed method is validated with classification tasks on CIFAR10-DVS and DVS128 gesture datase... | Rebuttal 1:
Rebuttal: Dear Reviewer KuzU,
Thank you for the thorough review and constructive criticism. There exist some misunderstanding about our paper, we hope the following the clarification would solve the proposed problems.
**W1:**"the mismatch about the description about comparison with LIAF-NET" ?
**A:**Than... | Summary: This article proposes a spike recurrent neural network model with a spiking convolutional block attention mechanism, called SRNN-SCBAM. Its main idea is to adaptively call historical information in the spatio-temporal features of the spatio-temporal pattern, which has advantages in efficient memory calls and e... | Rebuttal 1:
Rebuttal: Dear Reviewer 9YjJ,
We really appreciate your insightful comments and feedback. We addressed your questions below. We also revised our paper accordingly.
**Q1:** "The parameter setting."
**A:**Thanks for the suggestion. All the parameter settings are listed in the following table.
Together ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their helpful feedback. We are inspired by the fact that they have found our motivation clear [Reviewer 9YjJ], reasonable [Reviewer TiEP] and our proposed approach simple but effective [Reviewer 9YjJ] and novel [Reviewer NNKj]. We appreciate that [Reviewer TiEP] express... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Unbiased Randomizers for Regression with Label Differential Privacy | Accept (poster) | Summary: This paper investigates the bias of the state-of-the-art label-differential privacy (label-DP) mechanism proposed by Ghazi et al. (2023) and proposes bias-corrected randomizers achieved through minimizing of a constrained linear programming approach. The proposed label-DP mechanisms demonstrate lower mean squa... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful comments and questions. We include below the responses to the questions.
> Computational cost and “feasibility”
Firstly, we clarify that “a linear program (LP) is feasible” (e.g. in Prop. 7) means that the feasible set, a.k.a. the solution space of the LP is ... | Summary: This paper proposes a differentially private algorithm for regression problems. The algorithm protects the privacy of the labels ("label DP"), in contrast to the entire example. The canonical application of this is digital advertising, where the label might be transaction data from a separate website. Furtherm... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful comments and questions. We include below the responses to the questions.
> key innovations beyond GKK+23
GKK+23 proposed an optimal (biased) randomizer (RR-on-Bins). While an unbiased mechanism was mentioned as a future direction in GKK+23, it was not clear h... | Summary: The paper studies the regression problems under label differential privacy (DP). It proposes a novel randomizer that generates high-quality DP labels which can be used to train a regressor. The proposed randomized mechanism is sound and the experiments on various benchmark datasets and different privacy budget... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful comments and questions. We include below the responses to the questions.
> Comparison of “optimal unbiased randomizers” to the staircase mechanism.
Indeed, the staircase mechanism was introduced as the optimal noise mechanism minimizing the _worst case error_... | Summary: This paper proposes a new family of DP label randomizers for regression models. They show that these randomizers improve the MSE of the training set at the expense of the noisy label loss, indicating an alternate bias-variance tradeoff to other similar works.
Strengths: The strength of this paper is the theo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and useful suggestions. We will add the definition of $\hat{y}$ and incorporate other suggested changes in a future revision.
> Trade-off between noisy label loss and test loss
Our main goal in the paper is to minimize the final test loss. The noi... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning | Accept (poster) | Summary: This paper presents a detailed analysis of matrix estimation problems in low-rank bandit and low-rank RL scenarios. The authors investigate the effectiveness of spectral-based matrix estimation methods and demonstrate their ability to accurately recover the singular subspaces of the matrix with minimal entry-w... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and insightful review! Please find below our responses.
*Answer to Weakness 1 and Question 1*
**A. Necessity of entry-wise guarantees in low-rank bandits and RL.** Thank you for bringing this point to our attention. Indeed, we did not explicitly motivate the... | Summary: The authors investigate matrix estimation in reinforcement learning and bandit settings with low-rank structures. They demonstrate the effectiveness of spectral-based methods in recovering matrix subspaces and minimizing entry-wise error. This enables the development of efficient RL algorithms tailored for low... | Rebuttal 1:
Rebuttal: Thank you for your insightful review, and constructive feedback! We address your comments below.
*Response to Weaknesses*
**A. About the novelty of our analysis and of our algorithms.**
**A.1.** As far as we are aware, the leave-one-out argument has been so far limited to a matrix plus noise... | Summary: This paper provides a theoretical study of low-rank matrix estimation in three contexts:
**Context 1**: in the case of standard matrix estimation with uniformly sampled entries, where the authors prove a sample complexity comparable to approximate recovery results of $\tilde{O}(m+n)$ (when the rank is $O(1)$... | Rebuttal 1:
Rebuttal: Thank you for your careful review and very positive feedback.
**A. Answer to Weakness 1 \& Question 1.** Thanks for mentioning these papers; we will cite them. We clarify below the differences between these papers and our contributions for Model I.
**A.1.** [1, 2, 5] (see the refs in your revi... | Summary: This paper studies two problems in RL involving low-rank matrix estimation. It provides entry-wise estimation error bounds for simple spectral methods in low-rank bandits and low-rank MDP under different sampling mechanisms. Based on these, it provides performance guarantees for two algorithms designed for low... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and positive feedback! Please find below our responses.
*Answer to Weakness 1.*
**A. Relaxing the assumption on the noise upper bound.** We would like to thank you for highlighting this difference between our setting and those for matrices with independent nois... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games | Accept (poster) | Summary: Authors introduce a new class of correlated equilibria called linear-deviation correlated equilibria, which can be approached efficiently if all players attain sublinear linear-swap regret.
They show LCEs are distinct from correlated equilibria and extensive-form correlated equilibria and the hardness of maxim... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and observations on our work. Below we address the questions raised and the discussed weaknesses:
(Q1) Thank you, that would indeed be interesting. As you hinted, one likely key difficulty in constructing such a representation is the fact that interesting se... | Summary: This paper studies the convergence of uncoupled strategies to a weakened notion of equilibria called "linear-deviation correlated equilibrium". This equilibrium is reached when all players minimise the no-linear-swap regret which is a specialisation of Phi-equilibria when the set of deviations Phi is the set o... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and observations on our work. Below we address the 3 questions raised:
(Q1) Thanks for the feedback. As mentioned to Reviewer vZdR, we will use the extra content page to revise our paper and include a more detailed intuition of its main proof ideas. We inclu... | Summary: The paper studies regret minimization in extensive-form games (EFG). Specifically, they study a notion of regret called linear-swap regret, which measures the regret against linear transformations of the player's sequence-form strategies. This notion is stronger than trigger regret (as trigger deviations can b... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and observations on our work. Below we address the weaknesses discussed:
(W1) The geometric contribution is the key to the algorithmic contribution of the paper: the structure provided by our characterization theorem (Thm 3.1) is what enables constructing ag... | Summary: This paper focuses on addressing the challenge of minimizing linear swap regret in extensive form games, which is considered a stronger notion compared to trigger regret in Extensive Form Correlated Equilibrium (EFCE). To achieve efficient implementation of the Phi-regret minimization problem (where Phi set co... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and observations on our work. Below we address the questions raised.
(Q1) We believe linear-deviation correlated equilibria (LCEs) are most naturally understood as the name of the equilibrium points that emerge from the higher-rationality no-regret learning ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for the detailed comments and constructive feedback. We have addressed the reviewers’ comments/questions individually below. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Large Language Model Guided Tree-of-Thought | Reject | Summary: This paper presents an approach (called Tree-of-Thought, or ToT) for boosting the problem-solving abilities of LLMs by means of backtracking in solution space. The proposed ToT architecture augments the LLM with four modules, and is broadly framed to include multiple potential implementations of those modules,... | null | Summary: This paper present tree-of-thought (ToT) as a way of using LLMs to solve problems. ToT involves search + backtracking in a tree-like structure. The work demonstrates the success of this method in simplified sudoku tasks.
Strengths: The idea is interesting, and the Sudoku tasks are a reasonable regime for eval... | null | Summary: The paper presents a novel algorithm ToT (tree-of-thought) based on:
- LLM (GPT 3.5 in this case)
- checker module (which verifies solutions and partial solutions)
- memory module
- ToT controller that guides the search (it can be a neural network or a set of rules)
- prompter agent (in this paper this is a p... | null | Summary: The paper introduces the Tree-of-Thought (ToT) framework, a novel approach to enhance the problem-solving capabilities of large language models (LLMs). The ToT technique mimics the human mind's trial-and-error thought process, allowing LLMs to explore the solution space of complex reasoning tasks and backtrack... | null | Rebuttal 1:
Rebuttal: We sincerely extend our gratitude to the reviewers for their valuable feedback, especially for the thoughtful suggestions regarding the evaluation method, ablation studies, and the discrepancy between the algorithm presented in the paper and its actual implementation for the experimental study. Yo... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a tree-of-thought (ToT) framework to improve complex reasoning and problem solving capabilities of auto-regressive language models. Specifically, motivated by how humans process thoughts with trail and error, ToT maintains a memory module, and employs a ToT controller to decide when to proc... | null | null | null | null | null | null | null |
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport | Reject | Summary: The authors propose an implementation of Vacher et. al. (2021) based on a Semi-Smooth Newton (SSN) scheme. They reformulate their optimization problem as a root finding problem (Proposition 3.1) to which they apply the SSN scheme. They provide convergence guarantees (Theorem 3.3) that gives a $O(1/\sqrt{T})$ c... | Rebuttal 1:
Rebuttal: Thank you for your time and your input. We hope that with our answer below we will convince you about the merits of our work. Below, we reply to your main questions point-by-point and have included these discussions in the revised version of our paper.
1. **The proposed method requires $O(1/\eps... | Summary: The authors focus on the problem of approximating OT numerically.
They focus on one approximated version of OT which leverages a Sum of Squares approximation to stratify both statistical guarantees and computational amenability.
While the first proposal to solve this SoS approximation relied on interior point ... | Rebuttal 1:
Rebuttal: Thank you for your time and your input. We hope that our answers below will convince you about the merits of our work. We answer your questions below one-by-one, and have included these discussions in a revised version of our paper.
1. **The rate $O(n^{-1/2d})$ should be a rate $O(n^{-2/d})$.** F... | Summary: This paper focuses on investigating kernel-based optimal transport estimation. The approach involves reformulating the problem as a nonsmooth equation model and utilizing the semismooth Newton method to solve it. The study demonstrates that the associated residual mapping exhibits **strong semismooth** propert... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments and positive evaluation! We reply to your main questions point-by-point below and have included these discussions in the revised version of our paper.
1. **The global convergence rate of the proposed algorithm is dependent on an auxiliary sequence of itera... | null | null | Rebuttal 1:
Rebuttal: **We would like to thank PCs, SACs, ACs and the reviewers for their efforts on evaluating our paper.** We appreciate that the reviewers pointed out the importance of the problem and of our algorithm given the increasing popularity of computational OT. All the comments will be addressed in the revi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Convergence of CART under Sufficient Impurity Decrease Condition | Accept (poster) | Summary: This paper improved the rate of consistency of CART based on a sufficient impurity decrease (SID) condition under regression settings. Then, the authors provided examples, which are mostly special additive models, that can satisfy the SID condition, and showed that the rate of consistency cannot be improved by... | Rebuttal 1:
Rebuttal: Thank you for your overall positive assessment of our work.
Reply to weakness 1/ question 1:
Thank you for this comment. Actually, it is possible to relax the additive model on $f^*$ such that it is ``approximately additive". More precisely, we can assume that there is an additive function $g... | Summary: The paper studies the convergence rate of CART, a greedy algorithm for building decision trees, under a regression setting. It introduces a sufficient impurity decrease (SID) condition on the underlying function that ensures the consistency and polynomial convergence of CART. It also provides examples and suff... | Rebuttal 1:
Rebuttal: Thank you for your overall positive assessment of our work.
Reply to weakness 1:
Thank you for this suggestion, we will add a discussion in the revised paper.
Reply to weakness 2 and question 2:
Thank you for this comment. Actually, it is possible to relax the additive model on $f^*$ such t... | Summary: The paper performs theoretical analysis of the well-known CART algorithm. The authors show a convergence rate of CART under the condition called 'sufficient impurity decrease' (SID), which is tighter than known ones. The authors further provide the condition for a class of functions that satisfies SID.
Streng... | Rebuttal 1:
Rebuttal: Thank you for your overall positive assessment of our work.
Reply to weakness 1:
Thank you for this suggestion. The reason that we only did the simulation for linear functions is that for other signal functions, it is hard to precisely evaluate the SID parameter $\lambda$, hence difficult to se... | Summary: The paper focuses on the analysis of the prediction error of Classification and Regression Trees (CART) for regression problems under a sufficient impurity decrease (SID) condition. The SID condition is a strong assumption on the approximation power of tree splits, which can ensure the consistency of CART. The... | Rebuttal 1:
Rebuttal: Thank you for your overall positive assessment of our work.
Reply to weakness 1:
Thanks for this great question. It depends on the specific version of classification trees under discussion and the loss used to make splits and measure the accuracy. If the prediction in each node is the majority ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the approximation of the model obtained from the decision tree learning algorithm CART is analyzed. More precisely, in Theorem 2.3, a convergence rate of the decision tree approximation compared to the true model is obtained. This analysis is performed under two assumptions of the paper (already... | Rebuttal 1:
Rebuttal: Thank you for your overall positive assessment of our work.
Reply to weakness 1:
Thank you for this comment. Actually, it is possible to relax the additive model on $f^*$ such that it is ``approximately additive". More precisely, we can assume that there is an additive function $g^*$ that appr... | null | null | null | null | null | null |
Students Parrot Their Teachers: Membership Inference on Model Distillation | Accept (oral) | Summary: Multiple previous works have proposed knowledge distillation techniques to distill the knowledge of a teacher trained on sensitive data into a student model which is supposedly protected against membership inference attacks. This paper proposes a new membership inference attack to perform membership inference ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reading our submission and writing your review!
>**Line 94:** Sorry about this! Actually, every point is a small blue plus sign! We will explain this clearly in the caption.
>**Q1:** We follow the evaluation strategy used to evaluate LiRA, where each model ... | Summary: The paper examines the effectiveness of model distillation in protecting the privacy of training data. Through the use of membership inference attacks, the authors demonstrate that distillation alone provides limited privacy across various domains. The authors also suggest several design considerations for imp... | Rebuttal 1:
Rebuttal: Thank you for your time in reading the submission and writing the review!
>**End-to-End LiRA:** We use the same student set as used to train the target student model. This is because distillation can be done on public, nonsensitive data.
>**Figure 3 & Data Processing Inequality:** The data proce... | Summary: The paper explores the privacy implications of model distillation, a technique used to transfer knowledge from a teacher model to a student model. The authors investigate membership inference attacks on both the teacher and student training sets to evaluate the privacy provided by distillation.
The authors ex... | Rebuttal 1:
Rebuttal: Thank you for your time in reading the submission and writing the review!
>**Differential privacy:** This is an interesting question. Part of the goal of our work was to consider distillation *without privacy* because there exist past works that attempt to show distillation can achieve a strong n... | Summary: The authors in this paper investigate the efficacy of membership inference attacks (MIA) in model distillation. Their novel attack(s) show that MIA is possible even when the teacher model is only queried on the most influential points in the student inputs. Finally, they also demonstrate how their attacks are ... | Rebuttal 1:
Rebuttal: Thank you for your time in reading our submission and interesting questions!
>**Feature correlation:** This is an interesting intuition, and we agree that the example in Figure 1 does seem to be due to this “red”ness (and one might draw similar conclusions about the examples in Figure 9 in the su... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Training Chain-of-Thought via Latent-Variable Inference | Accept (poster) | Summary: This paper introduces a principled and practical approach to boost the training of the "chain-of-thought" through the inference of latent variables. Instead of the typical variational inference, the authors opt for a MCMC-EM method to circumvent the issue of posterior collapse, a common occurrence in auto-regr... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions! We will try to answer them below; please let us know if anything remains unclear or if there are particular points that you would like to see emphasized in the final paper.
# 1. Acceptance probability depends on previous state
Our use of a binary (determi... | Summary: This paper explores considering Chain-of-Thoughts (CoT) as a latent variable and introduces TRICE: an MCMC-EM algorithm for optimizing CoT-latent-latent variable models using a Metropolis-Hastings algorithm (rejection sampling) coupled with a control variate.
The authors tested the model by fine-tuning a pro... | Rebuttal 1:
Rebuttal: Thank you so much for the thoughtful questions and suggestions. We hope that our response below will address your concerns.
# 1. Structural improvements
This is a great suggestion. To reduce the risk that readers will lose the main thread, in the final version we will more clearly set apart as “... | Summary: This work introduces a new method for training models for chain-of-thought prompting, aimed at maximizing the marginal probability of generating a correct answer using Markov-chain Monte Carlo, expectation maximization, and a novel control variate technique, along with prompt tuning. Much of the work is spent ... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback!
# Additional experimental results
While there is limited space in the main text, we will add an appendix with a full table of per-task BBH results.
Also, we added an experiment on GSM8K; see the top comment for details.
### Qualitative analysis of rationale... | Summary: This paper proposes treating rationales as latent-variables and considers the marginal distribution over answers, averaging over possible rationales. To do so, a MCMC procedure is proposed, in which rationales are proposed via an independence sampler. The approach is compared to STaR (Zelikman et al., 2022) as... | Rebuttal 1:
Rebuttal: Thank you so much for the thoughtful questions and suggestions. We hope that our response below will address your concerns.
# Larger datasets
We added an experiment on GSM8K; see the top comment for details.
# Sensitivity to number of MCMC steps
In Algorithm 1 (and in all of our experiments) w... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for their many helpful and thoughtful comments.
Most of our responses are in the per-review comments, but we want to highlight here two sets of new experimental results that we will add to the paper.
# BIG-Bench Hard results with a stronger base model
After our ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a prompt-tuning strategy that tries to maximize the marginal log-likelihood of generating a correct answer using CoT prompting.
Strengths: The paper presents a prompt-tuning strategy that aims to optimize the marginal log-likelihood of producing accurate answers through the utilization of ... | Rebuttal 1:
Rebuttal: Thank you for your suggestion to compare against text-davinci-003. Our model size is in the 40–75 billion parameter range, making it significantly smaller than text-davinci-003’s 175B parameters. We will add published text-davinci-003 BIG-Bench Hard (BBH) 3-shot results to Table 1 for comparison (... | null | null | null | null | null | null |
3D molecule generation by denoising voxel grids | Accept (poster) | Summary: This paper describes a new method of unconditional small molecule generation by parameterizing small molecules as 3d voxel arrays.
Strengths: - Novel characterization of the small molecule generation task and a new way to parameterize the data. This comes with benefits, chief among which is the ability to gen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions. Below we address additional concerns from the reviewer.
**Metrics:**
We agree with the reviewers that unconditional molecule generation is hard to evaluate. We followed the reviewer's suggestion and evaluated VoxMol on the MiDi metrics (Ta... | Summary: This paper proposes VoxMol, a novel method for generating 3D molecules in the form of voxel grids. The proposed method adopts neural empirical Bayes as the basic probabilistic framework to develop generative models for 3D voxel grid representations of molecules. Experiments are conducted to show that the propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions. Below we address additional concerns from the reviewer.
**Implicitly learning the number of atoms is not an advantage:**
We agree with the reviewer that VoxMol is implicitly learning the number of atoms (together with all other information... | Summary: This paper proposes a novel 3D molecule generation routine dubbed VoxMol. The highlight of it lies in its introduction of a connection between traditional molecular graph representation and 3D voxel representation. In VoxMol, the molecules are first translated into voxel representation. A denoising network is ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions. Below we address additional concerns from the reviewer.
**The method does not significantly beat EDM on these benchmarks:**
Table 1 and 2 on the attached pdf compares our method with EDM using MiDi metrics (as suggested by reviewers). Alth... | Summary: This paper proposes doing diffusion on voxel space for molecule conformation generation. Because the voxel space is discrete, an efficient sampling method is proposed.
Strengths: This paper proposes doing diffusion on voxel space for molecule conformation generation. Because the voxel space is discrete, an e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions. Below we address additional concerns from the reviewer.
**Diffusion model:**
We would like to clarify that the proposed model is _not_ a diffusion model (this is a big point of the paper). Our model is based on neural empirical Bayes and w... | Rebuttal 1:
Rebuttal: (R IDs: R1=R82i , R2=j1qM, R3=bpDM, R4= zHHZ)
We thank the reviewers for the detailed and helpful reviews. In particular, we thank the suggestion of evaluating on MiDi metrics [R1, R4]. The results on these metrics corroborate the findings of the submission: VoxMol performs slightly worse than ED... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Rehearsal Learning for Avoiding Undesired Future | Accept (poster) | Summary: The paper proposes the framework of Structural Rehearsal Models (SRMs) which is similar to Structural Causal Models but not based on causal relations but instead on rehearsation [1]. In this kind of relation, edges can be bi-directional and indicate that some values influence each other. SRMs are then used to ... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments! Below we address your questions in a point-by-point fashion.
> W1. Regarding the experiments and if causality-based methods can be used (if needed, by lumping together the variables from one clique).
Thanks for the insightful questions! We want to emphasize t... | Summary: This work presented a rehearsal learning framework to avoid undesired future. The framework was characterized by a
probabilistic graphical model called rehearsal graphs and structual equaitons, and the actionable decisions that enable the
outcome to be altered are found under a bayesian famework, and the cor... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments! Below we address your questions in a point-by-point fashion.
> W1. Regarding the difference between the Strcutural Rehearsal Model (SRM) and Structural Causal Model (SCM), and the dynamic issue in decision problems.
Thanks for raising the question! We are sorry... | Summary: The authors present a formulation called the rehearsal learning framework to study problems where reasoning about undesirable future outcomes can be leveraged to avoid those undesirable futures---a kind of forward-looking counterfactual reasoning. The authors additionally show how decisions can be made within... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments! Below we address your concerns and questions in a point-by-point fashion.
> W1. Regarding comparison with other baselines and sample-efficient methods (e.g. SAC / PPO).
Thanks for the suggestion! We conducted new experiments with SAC and PPO. The average suc... | Summary: This paper presents a new graphical architecture, called SRM, whose goal is to be in-between correlational studies and causality models that are SMC. The idea is that identifying variables influenced by decision on other variables is easier than causality learning, and sufficient for decision making. The decis... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments! Below we address your questions in a point-by-point fashion.
> W1. It is unclear why other tools cannot apply to the current problems and frameworks.
Thank you for raising this point! Indeed, other decision tools such as MDP can be applied to the AUF problem.... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper argues that in decision-making, correlation is usually not enough but causation can be excessive. It introduces the idea of “rehearsation” which is a compromise between the two. Specifically, the paper proposes a novel rehearsal learning framework which models the interactions between interrelated (b... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments! Below we address your questions in a point-by-point fashion.
> W1. Regarding comparison to methods in causal inference literature, e.g. Causal Bandits/BO.
Thanks for your suggestion! We need to clarify that causal bandits (CB) and causal Bayesian optimizati... | null | null | null | null | null | null |
Thought Cloning: Learning to Think while Acting by Imitating Human Thinking | Accept (spotlight) | Summary: This paper develops a method for "thought cloning", which involves imitating a human's thought process during task performance. The authors apply this to a partially observable 2D grid world domain. They show that thought cloning yields superior performance compared to behavior cloning.
Strengths: - The appro... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review of our paper and your acknowledgment of the strengths of our work, including the novelty and soundness of the method, the strong empirical case, and the contribution to AI Safety and Interpretability. We appreciate the depth of your feedback and are pleased ... | Summary: This paper provides Thought Cloning (TC), an imitation learning method that clones not only behaviors but also thoughts. Here, thoughts are descriptive texts for each behaviors. The basic idea is that language can help agents to better plan their actions and adapt to a new environment. More specifically, the T... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and feedback on our manuscript. We are delighted that you consider our idea simple yet effective, and recognize our contribution to the capabilities and interpretability of agents. Below we address each of your concerns and questions.
***“How can we effectivel... | Summary: The paper studies the problem of imitation learning and attempt to improve existing IL algorithms by training the agent to think like the expert that is being imitated. Authors propose Thought Cloning - an extension to behavior cloning that seeks to imitate thoughts expressed in natural language. They evaluate... | Rebuttal 1:
Rebuttal: Thank you for your review, and for noting our method is reasonable and supported by our evaluations and arguments.
We sincerely believe our paper presents a significant and beneficial contribution that would enrich the ML community upon publication. Reflecting upon your concerns, we believe they... | Summary: This paper presents an approach incorporates discrete intermediate-level descriptions and goals to train RL agents to perform higher level actions. These intermediate-level descriptions and goals are described in natural language, and the authors refer to them "thoughts" that the RL agent learns which then in... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and acknowledging the strengths of our work, including its novelty and saying it is ***“very significant”*** (provided your requested experiments confirm TC outperforms BC, which they all did!).
We have addressed each of your concerns, significantly improving th... | Rebuttal 1:
Rebuttal: We are deeply grateful to the reviewers for their comprehensive evaluations and thoughtful feedback on our work.
We are encouraged by the reviewers' positive comments including:
- “The approach is innovative, and could be **highly impactful** once it is scaled up.” (HBeY) “The idea of generating ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization | Accept (poster) | Summary: The paper proposes a new framework for rigid protein docking called BiDock. BiDock is based on an Evoformer-based model that predicts a distance matrix and a gradient-based optimization of the optimal roto-translation based on the predicted distance matrix. In order to be able to pass gradients through the opt... | Rebuttal 1:
Rebuttal: - We sincerely thank the Reviewer for all the comments, and it is a great honor for us to inspire your interest. We have addressed all your questions below and hope they have clarified all confusion you had about our work.
1. > I do not understand the strong emphasis that the authors put on “bein... | Summary: This paper studies the rigid protein-protein docking problem. The authors fuse the sequence features and structural features of proteins and unify these features into single features and pair features as done in AlphaFold2. These features are then fed into an evoformer-like cross-modal transformer to produce u... | Rebuttal 1:
Rebuttal: - We sincerely thank the Reviewer for your careful reading. We would like to address the concerns by providing responses as well as additional experimental results.
1. > Some important related works are not discussed and compared. For example, Geodock, DockGPT, xTrimoDock and Diffdock-PP.
**Answ... | Summary: This paper introduces BiDock, a novel approach that integrates sequence- and structure-modal information to improve the accuracy of rigid protein docking predictions. The proposed method uses multimodal information through bi-level optimization, enabling joint optimization of the docking score and the weights ... | Rebuttal 1:
Rebuttal: - We highly appreciate constructive comments from the Reviewer on our work.
1. > (1) The paper does not clearly explain why sequential/coevolution representations are useful specifically for rigid docking problems. (2) Ablation study between implementation with and without sequential information ... | Summary: This paper proposes BiDock, a novel rigid protein docking model that integrates sequence and structure information through bi-level optimization. It achieves promising results, outperforming baselines by up to 234% in challenging antibody-antigen docking.
Strengths: As claimed by the authors in the paper, thi... | Rebuttal 1:
Rebuttal: - We sincerely thank the Reviewer for spending time and providing valuable feedback. We appreciate all of your suggestions and we have addressed all your questions below by providing our responses.
1. > The authors seem to overlook the discussion and comparison of some protein docking methods bas... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Differentially Private Approximate Near Neighbor Counting in High Dimensions | Accept (spotlight) | Summary: This paper studies the problem of answering approximate range queries privately. The paper presents a data structure to answer queries privately that helps avoid dimension dependence in the additive error for utility but incurs a multiplicative factor. The paper also showcases how to efficiently implement the ... | Rebuttal 1:
Rebuttal: W1: The privacy guarantee is limited to (ε,δ)-DP and not pure differential privacy. Some discussion needed.
A: We could in fact obtain pure DP, although in this case the algorithm would suffer from a small probability of having large runtime. We focused on approximate DP for simplicity. To conver... | Summary:
The papers shows how to use a variant of LSH to approximately count the neighbors in an r-ball (r fixed apriori). It shows how to obtain a differentially private LSH sketch. It analyzes the algorithm’s theoretical properties.
Strengths:
The technical contributions look solid and the analyses seem correct, ... | Rebuttal 1:
Rebuttal: W1: The presentation leaves a great deal to be desired.
A: Apologies if the presentation was unclear. We will implement the reviewer’s suggestions in the final version of the paper.
W3: There doesn’t seem to be any exposition supporting the claim that the paper yields the most space efficient ... | Summary: This paper provides a new polytime algorithm for differentially private approximate near neighbor counting---that is, privately counting the number of points inside l2 balls of fixed radius r, or more precisely a relaxation that may answer any value between the number of datapoints in B(x,r) and the number of ... | Rebuttal 1:
Rebuttal: W1: It's not clear whether the multiplicative approximation factor is necessary to get a good additive dependence on both n and d. ... Relatedly, the lower bound (in addition to being for l_\infty rather than l_2) does not take into account the multiplicative approximation.
A: Indeed, we do not k... | Summary: In this work, the authors propose a differentially private data structure to approximately count the number of data points from within a dataset that lie within a certain small radius of a query point.
The preliminary data structure, intended for datasets that lie on a unit sphere, recursively splits the reg... | Rebuttal 1:
Rebuttal: W: The improved results only hold for small values of r which is fixed in advance and is not a part of the input.
A: It is true that our algorithm assumes that r is fixed. However, it does not need an assumption that r is small, see Appendix A.2.
Q1: Can you convert any data-independent LSH wi... | Rebuttal 1:
Rebuttal: We thank all reviewers for their useful comments and feedback. We will fix the typos and presentation issues in the final version of the paper. In what follows we address the issues identified by the reviewers as weaknesses and/or listed as questions. | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding | Accept (poster) | Summary: This paper studies the universal language model for generic graph representation learning. To model the complex attributes on multiple types of nodes and links with the consideration of graph structure, the authors proposed WalkLM. Specifically, to compose meaningful textual sequences, the attributed RWs and a... | Rebuttal 1:
Rebuttal: We thank the reviewers for their positive and detailed comments, which affirm the importance of the problem we studied. In response to the weaknesses, our answers are as follows:
> **W1:** It is suggested to include the deeper analysis on why the proposed framework can stay strong with a small si... | Summary: GNNs for training require sufficient training data for downstream tasks to achieve strong performance. Self supervised learning approaches are inefficient due to the presence of a variety of node attributes and complicated relations between nodes. Inspired from the success in LLMs, they convert the graphs into... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive summary and accurate description of our major contributions. As for the several weaknesses you mentioned, our responses are listed as follows:
> **W1:** The authors do not present results on the graph level classification task. Only node classificati... | Summary: The paper proposed a method for knowledge-graph-embedding (KBE) task using integration of language model and random walks. Specifically, authors first verbalized the path via random walks in KB, then, fine-tuning language model for the verbalized path, finally, using the embedding layer of language model as th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments. Our responses are listed as follows:
> **W1:** My top concern is the novelty. The proposed method simply fine-tuned a language model based on path from random walk. However, similar ideas have been widely explored by previous papers like [1]-[4].
The setti... | Summary: This paper proposes WalkLM, an unsupervised graph representation learning leveraging the power of the language model. WalkLM first samples a set of sequences of entities from attributed graphs by random walk and fine-tunes the language model on textualized walks. The learned embedding by these procedures is em... | Rebuttal 1:
Rebuttal: We thank the reviewer for the overall positive evaluations and detailed suggestions. In the following, we focus on the main issues to provide our feedback:
> **1:** The conditions of the tasks and datasets to apply, and the number of datasets used for evaluation.
The target of this work is gener... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing | Accept (poster) | Summary: The paper presents a new preprocessing method for training shallow overparametrized sparse neural networks. It significantly improves the preprocessing time yet achieves same performance on query time. They also show that their algorithm is very close to optimal.
Strengths: 1. Clearly written. Easy to underst... | Rebuttal 1:
Rebuttal: Thanks so much for your great efforts and helpful comments. Please refer to the general response for the practicality concerns.
Regarding the sparsity assumption, we note that we do not assume the activated neurons are sparse. But we use a result proved in [SYZ21] that as long as setting a unifie... | Summary: This paper analyzes a specific neural network setting: two-layer neural networks with $m$ neurons in the hidden layer and a ReLU activation. Given input data of dimension $d$ and $n$ training examples, it normally requires O(mnd) operations to compute the hidden activations. This paper follows prior work in sh... | Rebuttal 1:
Rebuttal: Thanks so much for your helpful suggestions! Please refer to our general response for your concern about the practicality of the algorithm.
Regarding the generalization to more complex settings, we think it is possible that our data structure will work for other activation functions. Roughly spe... | Summary: In the paper, the authors proposed fast optimization algorithm for over-parameterized two-layer networks. They proved that by using the sparsity firing feature from the neural network, the proposed method requires only O(nmd) time in preprocessing and still achieves o(nmd) time per iteration.
Strengths: 1. A ... | Rebuttal 1:
Rebuttal: Thanks so much for your great questions. Please refer to our general response for your concern about the experiments.
And we agree with the referee that our data structure requires $O(mn)$ space. However, we would like to highlight that the primary objective of our research is to study the neura... | Summary: This paper investigate the efficient training methods than the usual training protocol which requires the complexity $O(nmd)$ for 2-Layer ReLU networks. The authors improve the complexity in the previous study [SYZ21] by proposing the preprocessing method utilizing the tree data structure for both data and wei... | Rebuttal 1:
Rebuttal: Thanks so much for taking the time to read and understand our paper and for your helpful suggestions. Please refer to our general response for your concerns about the empirical analysis and the comparison to [SYZ21]. In the final version, we will add a remark to compare our techniques to [SYZ21].
... | Rebuttal 1:
Rebuttal: ## General response:
We thank all the referees for the valuable comments. Here, we give general responses to some common questions.
First, regarding the concerns on empirical practicality, we want to kindly emphasize that our purpose is to present a training algorithm that is both efficient and ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation | Accept (poster) | Summary: The paper tackles model-based 6D object pose estimation using SE(3) diffusion model-based point cloud registration. Point cloud registration is trained with diffusion and denoising processes on SE(3), which gradually perturb the optimal pose and learn a denoising network to refine the noisy transformation prog... | Rebuttal 1:
Rebuttal: **Q1**: The title of the paper says the method is "robust". However, it seems there is a dilemma in sample diversity and sample efficiency for the proposed method. That means a fixed set of hyper-parameters might not work well for different kinds of datasets. In other words, the method is not as r... | Summary: The authors propose an approach to address the problem of 6D pose estimation on real-world data based on a denoising diffusion process.
They introduce a novel diffusion process on the SE(3) manifold, leveraging Lie algebra se(3) to shift the process from the linear Euclidean to the nonlinear SE(3) space.
Furt... | Rebuttal 1:
Rebuttal: **Q1**: Where do the evaluation results for the related methods in Table 1 come from? Did you evaluate the methods yourself? If so, which code base did you use, or where did you find these results? Most methods do not provide them in their papers.
**A1**: Thanks for your positive score and encour... | Summary: This paper introduces a point cloud alignment method based on a diffusion model on SE(3). For this, the forward and reverse diffusion processes are performed in the lie group se(3). The method is evaluated on challenging real datasets, showing significant improvements over its baselines.
Strengths: Relevant A... | Rebuttal 1:
Rebuttal: **Q1**: 179: There are several different ways for interpolating rotations; I've often seen quaternions used for this. Is there a motivation for using the exponential map over other representations? Similarly for the perturbations.
**A1**: Thanks for your valuable and positive comments. (1) There... | Summary: This paper proposes a SE(3) diffusion model-based point cloud registration framework for robust 6D object pose estimation, which formulates point cloud registration as a denoising diffusion process and enables progressive refinement of the transformation between the source point cloud and the model point cloud... | Rebuttal 1:
Rebuttal: **Q1**: Compared methods should focus on 6D object pose estimation methods obviously, not just point cloud registration methods.
**A1**: Thanks for your diligent comments to help improve our work. Considering that the RGB(D)-based pose estimation methods would suffer from limited robustness to ch... | Rebuttal 1:
Rebuttal: To address Q2 raised by Reviewer-NS5b, we have included some qualitative comparisons of DCP and Diff-DCP on TUD-L, LINEMOD, and Occluded-LINEMOD datasets in the attached PDF file.
Pdf: /pdf/5d4797bfcf68cd7d63510a38236fbf0170ec1aee.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes and SE(3) diffusion model for pose estimation i.e. rotation R and translation t for the use case of registration two pointclouds. The paper addresses an important problem in 3D vision and which has applications in Robotics, AR/VR. The author claim that the convential diffusion models won't w... | Rebuttal 1:
Rebuttal: **Q1**: Instance-based pose estimation has been solved? Why not compare categorical benchmarks?
**A1**: Thanks for your valuable comments to help improve the quality of our paper.
(1) Although the current RGB(D)-based instance-level pose estimation methods have achieved relatively good perform... | null | null | null | null | null | null |
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series | Accept (poster) | Summary: This paper explores a very interesting direction to represent time series as plotted images and then stack vision transformers to perform representation learning. Following this idea, this paper has conducted empirical studies on some classification benchmarks of both irregularly sampled time series and regula... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for the valuable feedback and constructive comments. Please find our response addressing the concerns below.
**Response to W1: Detailed hyper-parameter tuning procedure and Robustness test**
The training/validation/test sets are randomly split. Init... | Summary: The paper investigates the use of pre-trained Vision Transformers (ViT) for both regularly and irregularly sampled time series classification. Two primary aspects are discussed: Transforming time series into images and performing time series classification using pre-trained ViT. Through a series of experiments... | Rebuttal 1:
Rebuttal: We appreciate your valuable feedback. Here is our response to address your concerns:
**Response to weakness**:
- **Layer frozen**: In our implementation, we did not freeze any of the layers. All the parameters are tunable during fine-tuning on the time series dataset.
- **Visualization strateg... | Summary: The paper focuses on learning from irregularly sampled time series data. The paper presents a simple approach that converts irregularly sampled time series into an image where different input channels are line graphs. The converted images are then modeled using a standard Transformer model. Experiments show th... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback. Below is our response addressing your concerns.
**Response to W1: adhoc way to deal with missing values and irregularity**
While our method may seem ad-hoc, it is simple and notably effective. It largely simplifies model design for irregular time s... | Summary: This paper describes a surprisingly simple and effective approach for applying computer vision Transformer models to time series classification. Multivariate time series inputs are plotted in a grid to produce images that are used to fine-tune pretrained vision Transformer models. This approach achieves state-... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's recognition of the value and strengths of our work, as well as the constructive feedback and suggestions. We aim to address your concerns in our response as follows:
**Response to W1: Robustness to seemingly arbitrary choices**
Our approach does involve sev... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: For the classification task based on irregular time series data, the authors introduced Vision Time Series Transformer (ViTST) approach where irregular time series data is displayed as line graph then fed to pretrained transformer type models. The authors tested this approach using several time series data fro... | Rebuttal 1:
Rebuttal: Thank you for valuing our work and recognizing its strengths. We aim to address your concerns with our response below.
**Response to W1: Time series data is everywhere but the authors tested only a few dataset from medical domain and human activity domain. Thus "any shape" in line 357 sounds too ... | Summary: The authors propose a method to model irregularly sampled time series. The method is based on transforming numerical time series data to line graphs and then applying pretrained vision transformers to that data. For multivariate datasets, every variable is plotted separately, and plots are aggregated in a grid... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable feedback. Below are our responses to your concerns:
**Response to W1: Concerns on “significant” improvement**
We base our claim of "significant" improvement on extensive comparisons across three datasets and their associated metrics. In all the evaluated dat... | null | null | null | null |
Optimistic Meta-Gradients | Accept (poster) | Summary: This paper studies a connection between optimization and meta-learning. For the case of a single task, it shows an equivalence between GD with momentum and GBML, and another equivalence between GD with Nesterov acceleration and the recent Bootstrapped Meta-Gradient algorithm. Theoretical analyses are done for ... | Rebuttal 1:
Rebuttal: Thank you for your review, we are glad you found the paper well presented with novel insights. We appreciate that multi-task meta-learning is a common problem setting and one that we do not consider in this paper. While multi-task meta-learning has been fairly extensively analyzed, we are not awar... | Summary: This paper shed an interesting perspective on meta-learning by studying the connection between gradient-based meta-learning and convex optimisation in the single task setting. It shows that meta-gradients contain gradient descent with momentum and Nesterov Acceleration as special cases. Furthermore, gradient-b... | Rebuttal 1:
Rebuttal: Thank you for your review; we are delighted that you liked the paper and found our results important and significant. In this paper, we focused on the theoretical aspect, but we agree that further empirical investigations is an exciting area for future research. We hope that the theoretical insigh... | Summary: This work discovers the connection between gradient-based meta-learning and convex optimization of the meta parameters. From there, the authors observe that common gradient descent and its variants with momentum are special cases. To match the conventional $O(1 / T^2)$ convergence rate, the authors propose the... | Rebuttal 1:
Rebuttal: Thank you for your review, we are glad you found the connections we made interesting. We fully agree that the restrictions on the meta-learner are limiting. Unfortunately, this is an inherent limitation we face when using convex analysis, since neural networks are not convex. Hence, a non-convex i... | Summary: The submission studies connections between recent advances in convex optimisation and heuristic meta-learning update rules. The provided framework contains standard methods such as heavy ball and Nesterov's momentum as special cases, while also containing rules that correspond to online meta-learning. Bootstra... | Rebuttal 1:
Rebuttal: Thank you for your review, we are glad you found the paper interesting and potentially helpful in your future work! We agree that studying the non-convex case empirically is an exciting area for future research. We take some initial steps in this direction with our ResNet experiment, in which the ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Collapsed Inference for Bayesian Deep Learning | Accept (poster) | Summary: The paper presents a new method for calculating Bayesian integrals such as the Bayesian model average (BMA) based on volume computation schemes. Specifically, the authors draw inspiration from a weighted volume computation (WVC) problem. Since the WVC is intractable for common neural networks they approximate ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for appreciating our work for a novel view of computing Bayesian integrals, a practical and efficient framework, and a clear presentation. In what follows, we will address your concerns, with the references put in the general response due to the character limit.... | Summary: The paper provides a closed-form approximation for the posterior predictive distribution in Bayesian deep learning, in both regression and classification. The paper is overall clear and quite well written. The theory seems reasonable, however a theoretical analysis on the approximation error, depending either ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for appreciating our work for its clear presentation, solving a meaningful problem, reasonable approximation, and thorough empirical evaluations. In what follows, we will address your concerns, with the references put in the general response due to the character... | Summary: The paper proposes to use techniques from weighted volume computation to deterministically marginalize over (a subset of) the weights in a BNN rather than sampling them when computing the predictive posterior. The experiments find the method to perform competitively with some standard baselines from the litera... | Rebuttal 1:
Rebuttal: We deeply thank the reviewer for appreciating our work for its novelty, clear presentation, and thorough empirical evaluations. We truly appreciate that you find the connection between sub-fields proposed in our work to be quite valuable. In what follows, we will address your concerns.
[runtime c... | Summary: The authors propose to tackle the intractable problem of Bayesian model averaging (BMA) in Bayesian neural networks by re-formulating it as “collapsed BMA”, where a small number of “collapsed” samples from a subset of the parameter space (e.g., the last layer) is equipped with a posterior conditioned on the re... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer for appreciating our work for its significance, originality, novelty, and structures. We will address your concerns below, with references in the general response.
[Questions on W2, W3]
[Q1] The appropriateness of piecewise polynomial approximation is justified by t... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for their insightful comments and valuable suggestions, and for deeming our work to be well written (all reviewers) with helpful intuitions (eHBs, cv4P) while presenting a novel and creative view (eHBs,cv4P,e8p8) to an important problem (eHBs,PnkR) that bui... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Unified Conditional Framework for Diffusion-based Image Restoration | Accept (poster) | Summary: The paper proposes a new framework for diffusion-based image restoration. The underlying architecture is based on that of "DvSR" [49], in which there is a deterministic predictor that does an initial restoration, used in conjunction with a probabilistic diffusion denoising network that applies the diffusion re... | Rebuttal 1:
Rebuttal: > It is suggested to conduct experiments with DvSR on the low-light denoising and JPEG restoration tasks
Thanks for the advice. We will try the DvSR [4] on low-light denoising and JPEG restoration tasks. In the Table 1 and Figure 1 of the newly attached PDF, we show the latest LPIPS and the visua... | Summary: This paper proposes a unified framework for diffusion-based framework. In the proposed framework, an initial predictor is used to produce a rough restoration of the input degraded image. The roughly restored image is then used as condition to the diffusion model using a Conditional Integration Module. Experime... | Rebuttal 1:
Rebuttal: > The technical novelty is limited (residual prediction formulation and the inter-step patch-splitting strategy.).
As described in L67-L81, we actually did not claim the residual prediction as one of our contributions. The residual formulation is a common practice and has been adopted in many pre... | Summary: This paper introduces a unified conditional framework for image restoration tasks based on diffusion models. The framework utilizes a UNet to predict initial guidance and incorporates multi-source conditional information into each block to enhance the generative model's guidance. Adaptive Kernel Guidance Block... | Rebuttal 1:
Rebuttal: > Visualization of the guidance map
We appreciate the reviewer's recognition of our efforts to enhance the perceptual quality and qualitative results of our method. In the revised version, we will include visualizations of the guidance map to provide further insights into our approach.
> It is... | Summary: This paper proposes a novel framework for supervised image restoration. The framework consists of an Initial predictor and a newly designed conditional diffusion model. The initial predictor first produces an initial restoration result, then the conditional diffusion takes the initial result as well as the deg... | Rebuttal 1:
Rebuttal: > It is suggested to compare the proposed method with the existing backbone.
To demonstrate the advantage of our designed backbone, we included an additional existing method, DvSR [3], in the ablation studies. As indicated in Table 1 of the newly attached PDF, our approach exhibits comparable com... | Rebuttal 1:
Rebuttal: We thank reviewers for their valuable and professional comments. Overall, reviewers (4fFj, 8ANp, V6gu, 37QZ) acknowledge the novelty and the performance of our paper. We have uploaded a one-page PDF containing figures and tables to help us addressing the reviewers' concerns, and then, we will resp... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a new framework for image restoration with diffusion models. They design strategies to better use the condition information and also propose a strategy for high-solution images. The results are competitive over baselines. The presentation is clear. I tend to accept this paper.
Strengths: -... | Rebuttal 1:
Rebuttal: > Provide more high-resolution image results
In our paper, the testing images of SID dataset are high-resolution (4256x2848). Due to the limited space, we only show some crops in fig.3 and provide some full-resolution images in the supplementary material. We will revise paper to make this point c... | null | null | null | null | null | null |
Leveraging the two-timescale regime to demonstrate convergence of neural networks | Accept (poster) | Summary: This paper studies the training of 2-layer neural networks and proposes a two-timescale limit/regime. In this
limit/regime, the learning rate of the first layer is much smaller than the learning rate of the second layer.
As a result, the training of the network can be viewed as training the first layer and p... | Rebuttal 1:
Rebuttal: > How general is this strategy? Is it possible to apply this strategy to problems (even toy ones) with dimension higher than 1
and more standard 2-layer networks?
We have evidence showing that **the two-timescale strategy applies to other settings (higher-dimensional problems, ReLU networks for... | Summary: This paper studied the problem of fitting a piecewise constant univariate function with a shallow neural network. Specifically, the authors consider a gradient flow with a time-scale difference between the dynamics of the first and the second layer weights. It is shown that the trained shallow network can be a... | Rebuttal 1:
Rebuttal: We agree with the reviewer that the main focus of this paper is on the study of a specific problem and that it does not readily lead to practical implications. Below, we detail why we still believe that our work contributes towards a theory of neural networks.
**The theory of the optimization of ... | Summary: In this paper, the authors considered the problem of learning piece-wise linear function in 1d using two-layer neural network. They considered gradient flow on mean-square loss with different learning rates for 2 layers (two-timescale). Specifically, the outer layer weights are moving much faster than the inne... | Rebuttal 1:
Rebuttal: > The problem considered is only in 1d and it would be interesting to see if the analysis could be generalized to multi-dimension.
This question is shared with the other reviewers and is adressed in the common rebuttal.
> In the experiments, I was wondering if one could elaborate in Figure 5 tha... | Summary: The paper studies the training dynamics of fitting a one hidden layer shallow network with heaviside activation to a piecewise ground truth function with one-dimensional input. It proves that gradient flow always recovers the ground truth in finite time with only mild over-parametrization.
Strengths: This pap... | Rebuttal 1:
Rebuttal: Your question on the generalization to higher-dimensional problems, including numerical experiments, is shared with the other reviewers. It is thus addressed in the common rebuttal. | Rebuttal 1:
Rebuttal: Dear reviewers,
We warmly thank you for your time and relevant comments, which will help us improve our work. If accepted, we will take into account your suggestions, making use of the additional page.
Since all reviewers raised the relevant question of the **applicability of our approach to mo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models | Accept (poster) | Summary: This paper presents a new attack against black-box, prompt-based PLMs. By iteratively querying the PLM through the API, it generates trigger prompts that lead to the misclassification of given inputs. Compared with existing trojan attacks against PLMs, this work focuses on the setting of discrete prompts, blac... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the manuscript and constructive comments.
**Question 1: The proposed attack seems more like a universal adversarial attack rather than a trojan attack. Typically, a trojan attack modifies the behavior of the target model and makes it sensitive to ... | Summary: The paper introduces TrojPrompt, a framework aimed at conducting real-world backdoor attacks on large-scale language models. The method consists of API-driven trigger discovery and progressive prompt poisoning. Experimental results demonstrate that TrojPrompt effectively inserts a Trojan into text prompts to a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the manuscript and constructive comments.
**Question 1: The first step of trigger discovery involving reinforcement learning-based trigger search and poisoned prompt generation entails significant computational and optimization costs, which need d... | Summary: This paper presents an approach called TrojPrompt that, given few-shot examples for an NLP task and a black-box LLM, synthesizes a poisoned prompt and an adversarial trigger. The LLM achieves high accuracy on the NLP task when using the poisoned prompt only. The attacker can add the adversarial trigger to call... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the manuscript and constructive comments.
**Question 1: Clarify the difference between RLPrompt and TrojPrompt/ What is the contribution?**
RLPrompt is designed to search for clear prompts, while TrojPrompt aims to find triggers and poisoned promp... | Summary: This paper uses automated prompt design methods to develop prompts and trojan triggers such that appending the task prompt to an input string increases clean accuracy of the LLM on the classification task, and putting the trojan trigger between an input and the task prompt results in a specific target class. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful reading of the manuscript and constructive comments.
**Question 1: Clarify contributions and proposed methods**
We rephrased the contributions list as follows:
(i) We've developed black-box backdoor attacks as an alternative to white-box backdoor. This is ... | Rebuttal 1:
Rebuttal: PDF
Pdf: /pdf/d6b6803fcc814e3cbdea7fd9776b25744f06ef17.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Local Convergence of Gradient Methods for Min-Max Games: Partial Curvature Generically Suffices | Accept (poster) | Summary: This paper studies the convergence of Gradient based methods to local Nash equilibria. The properties of the “potential” and the “interaction” parts of the game are analyzed, and conditions when this leads to convergence of gradient methods are studied.
Strengths: This paper provides insights into the converg... | Rebuttal 1:
Rebuttal: We address each listed Weakness separately.
- In Figure 1(a), the datapoints shown by circles represent the observed convergence rate $r$ of GDA with step-size $\eta$, obtained by really running GDA for many iterations for a small $\eta$.
On the other hand the lines represent the quantity $\tilde{... | Summary: This paper focuses on min-max games with partial curvature, i.e., the symmetric part S of the Jacobian is p.s.d. and nonzero, and specifies the necessary and sufficient conditions for the convergence of gradient flow. The authors show that when the interaction term dominates, the convergence rate could depend ... | Rebuttal 1:
Rebuttal: The relation between convergence rate and average of the eigenvalues of $S$ is only valid under a randomized setting.
For a fixed $S$, in the worst case, the convergence rate does depend on the minimum eigenvalues of $S$ (to be precise, on $\sigma\_{\min}(Q) + \sigma\_{\min}(R)$, by the eigenvalue... | Summary: The authors study the convergence of gradient descent-type algorithms for saddle-point problems of the form $\displaystyle\min_{x \in \mathbb{R}^n } \displaystyle\min_{x \in \mathbb{R}^m} f(x,y)$. Let $M$ denote the Hessian of $f$ at a local saddle point: convergence is governed by the minimum real part of all... | Rebuttal 1:
Rebuttal: Regarding the second paragraph of "Weaknesses":
Section 4 is not an experimental section, but an application of the previous considerations to a particular class of min-max problems which is of its own interest in game theory:
$
\min\_{\mu \in \mathcal{P}(\mathcal{X})} \max\_{\nu \in \mathcal{... | Summary: This research investigates the local convergence properties of gradient dynamics in two-player zero-sum differentiable games towards Nash equilibria. Existing knowledge suggests that such dynamics converge locally when the symmetric part of the Jacobian at equilibrium, denoted by S, is positive definite (S ≻ 0... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of the paper. We address each concern separately.
1. **Clarity on MP, EG, and Overparametrization:**
The benefit of using extrapolated gradient methods such as MP or EG for last-iterate convergence is well-known, for a general min-max optimization conte... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | Accept (spotlight) | Summary: This paper focuses on bilevel optimization in a federated learning environment. Bilevel optimization has various applications in federated learning (FL) and few recent works proposed versions of bilevel optimization schemes for FL. A challenging step in bilevel optimization is the computation of the "hypergrad... | Rebuttal 1:
Rebuttal: We thank the reviewer DZHN for the time and valuable feedback!
**Q1: Hyperparameter optimization is known to be a hard unsolved problem in FL because of the overall communication overhead. This makes it hard to see how the proposed federated bilevel framework can live up to its practical potenti... | Summary: This work consider the federated bilevel optimization problem. Compared to existing methods, the authors develop a new and simple method named SimFBO without subloops and requiring much fewer communication rounds at each iteration. In the setting with system-level heterogeneity like diverse local steps, they f... | Rebuttal 1:
Rebuttal: We thank the reviewer TKNN for the time and valuable feedback!
**Q1: Is it possible to develop fully first-order methods given the current SimFBO framework?**
**A:** Good point! One possible idea is to approximate the Hessian- and Jacobian-vector products using the finite-difference tricks, i.... | Summary: This work studies the federated bilevel optimization, where the lower- and upper-level objectives are defined over all clients. Since the lower-level solution is the minimizer of the average of all client objectives (i.e., in a global manner), the main computational challenge is to compute the global hypergrad... | Rebuttal 1:
Rebuttal: We thank the reviewer MEss for the time and valuable feedback!
**Q1: Why FedNest and AggITD perform poorly over CNNs?**
**A:** As we illustrate in Figure 1, FedNest and AggITD both contain one or more sub-loops of communication rounds in each outer iteration, which leads to a high per-iteration... | Summary: The paper addresses the bilevel optimization problem in the federated learning context. The authors propose a novel gradient-based algorithm that effectively updates the arguments in both the inner and outer optimization problems simultaneously. Additionally, the paper extends this algorithm to handle data het... | Rebuttal 1:
Rebuttal: We thank the reviewer 8pmc for the time and valuable feedback!
**Q1: Unclear notations, typos, missing details and other presentation issues.**
**A:** Sorry about the confusion and missed details. Also thanks for pointing them out for us! We will definitely follow your suggestions to improve th... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal testing using combined test statistics across independent studies | Accept (poster) | Summary: [Update1: During the rebuttal, I updated my score from 5 to 6. The reason is that I want to stronger weigh in that the paper is technically solid. My concern whether NeurIPS is a perfect fit for this paper remains, and I ask the AC to judge that part.]
The paper studies aggregation strategies for combining tes... | Rebuttal 1:
Rebuttal: We appreciate the time and effort the Reviewer has put in evaluating our work. The Reviewer also raises a few concerns to which we respond below.
*1 -- Significance of the approach:*
We agree that in case of independent trials typically the test statistics are set independently from the number... | Summary: This theory paper provides a minimax lower and upper bounds for the testing risk (sum of Type-I and Type-II errors) for different combination methods in the specific setting of many normal means model. With the testing goal is to detect the presence or absence of the signal component in this normal means model... | Rebuttal 1:
Rebuttal: We thank the Reviewer for reviewing our manuscript and the constructive feedback. We address below the raised concerns point-by-point.
*1 -- Phrasing of on the paper's contributions:* Based on your feedback, to futher improve our mansucript, we have restructured and emphasised the contributions i... | Summary: Authors study a problem of optimal combination of p-values in a meta-analysis context. Specifically, they focused on characterizing the minimax separation rate for a family of "smooth-ish" combination methods that aggregate p-values (or e-values). They show that:
* The family contains a lot of methods that ar... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort spent on evaluating our paper, the positive feedback and the insightful suggestions. Below, we address the specific suggestions raised by the Reviewer.
*Novelty of the results:* Yes, indeed, we were also surprised by the lack of theory for meta-analysi... | Summary: The paper addresses the problem of combining test statistics from multiple independent studies in the context of null-hypothesis significance testing. The authors derive a mathematical framework to quantify the cost of compressing multiple independent trials of a study into one real-valued test statistics, and... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the effort of evaluating our paper and we are happy to hear that the Reviewer
shares the opinion that the problem is important to study. The Reviewer does not specifically comment on the soundness
of the mathematical framework, but highlights potential misleading use of... | Rebuttal 1:
Rebuttal: First of all we would like to thank the Reviewers for carefully reading our paper and their interest in our work. We are happy to hear that the majority of the reviewers found our paper "well presented" (aj9v), "well-written and organised" (qxf1), written in a clear and without excessive statisti... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper lies in the context of meta-analysis of multidimensional models. Usually, meta-analyses are performed by combining p-values or e-values. In both cases, the statistical power is not well-known. The authors provide a constrained framework of many means normal model. Based on this framework, they derive... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the thorough analysis and thoughtful feedback, we appreciate the time and effort. We address below the questions and comments of the Referee point-by-point:
* Thank you for pointing this out, we have clarified in the new version of the paper that $\mathbb{E}_0$ correspon... | Summary: The paper considers methods to aggregate test statistics from different, independant, sources, in order to construct an aggregated test with hopefully more power. The key contribution of the paper is the study of the minimal treatment effect which can be detected in a standard gaussian noise setting, for which... | Rebuttal 1:
Rebuttal: We express our sincere thanks to the Reviewer for the taking the time and effort to thoroughly review, the insightful comments and constructive feedback on our paper. The Reviewer also identifies areas for improvement, which we will address point-by-point below.
*Typos:* Thank you for pointing ou... | null | null | null | null |
Large Language Models Are Semi-Parametric Reinforcement Learning Agents | Accept (poster) | Summary: The paper proposes an LLM + RL architecture for text-based domains. The key idea is to run Q-learning on the side and augment LLM's prompt with Q values for available actions. The evaluation is done one WikiHow and WebShop.
Acknowledging the rebuttal, I'm satisfied with authors responses and happy to increase... | Rebuttal 1:
Rebuttal: Thanks for your kind review.
**About Q-Learning and $Q$ function architecture**: The $Q$ function is
implemented with the experience memory as a lookup table. Q-Learning is applied
to the experience-memory-based lookup. Note that, we don't use this lookup to
predict a $Q$ value for the new observ... | Summary: The authors introduced Reinforcement Learning with Experience Memory (RLEM) to update the memory of the LLM agent, enabling it to evolve its capability without fine-tuning the parameters of the LLM. Extensive experiments were conducted on two RL task sets to evaluate the proposed framework. The experimental re... | Rebuttal 1:
Rebuttal: Thanks for your kind concern. First, we want to further clarify the necessity
of updating the $Q$ values in the experience memory. Compared to the
traditional ICL (in-context learning) methods with labeled dynamic exemplars,
there will be both good and bad experiences in the memory of Rememberer. ... | Summary: This paper proposes a framework to combine RL w/ LLM using an offline Q-learning setting. An experience memory component is proposed to store past experience for estimating Q values. Evaluation on WikiHow and WebShop demonstrate the effectiveness of the proposed method and framework.
Strengths: The paper prop... | Rebuttal 1:
Rebuttal: Thanks for your valuable review and advice.
**Generalizability**: For this question, we refer you to global reply 2.
**$\max$ in Eqn. 1**: This $\max$ is calculated from the actions already
recorded for $(g, o_{t+1})$, as we cannot traverse all the possible actions
when there are free-form langu... | Summary: This paper introduces an interesting approach that harnesses the capabilities of large language models (LMs) to tackle reinforcement learning (RL) problems. The method involves estimating Q-functions using RL algorithms and providing advice to the LMs about actions with high and low Q-values. The expectation i... | Rebuttal 1:
Rebuttal: Thanks for your valuable review and advice.
**About extensive observation space**: Extensive observation space may result
in a much larger experience memory, which may require more scalable and more
efficient approaches to store the experiences. Meanwhile, we refer you to the
experiments in Sec.... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for kind review. We collect all the opinions and
give a reply to several common concerns here.
1. **Role of Q-Learning (and what the $Q$ function is)**: We'd like to further
clarify our motivation and the role of Q-Learning in our proposed Rememberer
approach. Ou... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes REMEMBERER, a novel framework for Large Language Models (LLMs) that employs a persistent experience memory and a Reinforcement Learning with Experience Memory (RLEM) mechanism. This setup aims to enable LLMs to learn from previous interaction experiences in decision-making tasks, improving t... | Rebuttal 1:
Rebuttal: Thanks for your valuable review and advice.
**Impact of memory size**: In practice, we didn't limit the capacity of the
experience memory, hence it can accommodate as many experiences as the hardware
memory allows. To have a perspective on the impact of actual memory size, we
refer you to the exp... | null | null | null | null | null | null |
Continuous Parametric Optical Flow | Accept (poster) | Summary: The submission 6736, entitled "Continuous Parametric Optical Flow," presents a novel multi-frame optical flow strategy that expresses the flow continuously. This is in contrast to conventional flow strategies, which encode this displacement in a discretized manner. This result is made possible by regressing th... | Rebuttal 1:
Rebuttal: *1.Typos and Fluency:*
- **Response:** Thanks for the valuable suggestion. We will fix all the typos and proofread the manuscript as suggested.
***
*2.Motivation & Related Work:*
- **Response:**
- Thanks for the suggestion. In the paper, first, as agreed by all the reviewers, our novel concept of... | Summary: This paper proposed a continuous parametric optical flow estimation algorithm with B-spline temporal trajectory representation and ODE-ConvGRU based feature extraction. Experiment has been done on both synthetic and real-world dataset and the proposed continuous parametric method performs better than tradition... | Rebuttal 1:
Rebuttal: **Weakness**
***
*1.Flexibility of Parametric Curve:*
- **Comment:** It is somewhat unclear how to specify the flexibility of the trajectories, as in Table 3, different numbers of control points may affect the performance significantly, and in real scenarios, different objects in a scene may have ... | Summary: This paper suggests a new model to pixel wise compute temporally continuous optical flow by using B-splines. The input to the neural model are sequences of images, the output are N 2D control points for each pixel of the spline model. During training, the input is sampled from the dataset at varying time insta... | Rebuttal 1:
Rebuttal: **Weakness**
***
*1.Motion Claim:*
- **Response:** Thanks for the comment. We agree that all the real-world motions follow classic physical principles. Thus, it is important to incorporate physical principles based on explicit constraints rather than directly using a neural network to regress poin... | Summary: This paper presents a parametric representation of dense and continuous pixel motion over arbitrary time intervals. The ``continuous parametric flow`` concept is interesting. However, one of the core technique contributions is encoding the image with the ODE-ConvGRU, which is not closely related to the ``con... | Rebuttal 1:
Rebuttal: **Weakness:**
***
*1.Unfair Comparison:*
- **Comment:** The comparison is unfair. The proposed model is trained with $ N_{gt} = 8 $ but the PIPs is only trained with 4-frames. Can you provide the performance of an 8-frame PIPs model and the officially released PIPs model?
- **Response1:** Thank... | Rebuttal 1:
Rebuttal: **Global Response**
In the attached PDF, we provide two figures and one table. Table 1 reports the comparison with PIPs with 8-frame inputs on real-world datasets. Figure 1 shows Special Cases of extremely Small Motion & Large Motion. Figure 2 illustrated the updated pipeline of our proposed fram... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: A temporally continuous parametric optical flow method, based on B-splines, is presented. The proposed network takes as input L frames (and timestamps) and outputs a tensor of size 2NxHxW, i.e N control points for each pixel. In practice N=6. The network architecture relies on neural ODE, ConvGRU and multi-tim... | Rebuttal 1:
Rebuttal: **Weakness**
***
*1.Typos*：
- **Comment:** The paper contains many typos, e.g. l.152 feed - > fed, l.154 neural, l.251 will, etc.
- **Response:** Thanks for pointing out the typos. We will fix all the typos and further proofread the manuscript.
***
*2.Data Error*：
- **Comment:** Some results are... | null | null | null | null | null | null |
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations | Accept (poster) | Summary: The paper concerns the automatic generation of architectures that are robust to diverse perturbations. Neural architecture search (NAS) has been used for the automatic generation of such architectures, but the paper notes that most of those architectures are dedicated to the clean accuracy, which leaves the re... | Rebuttal 1:
Rebuttal: **W1. A number of paragraphs and legends are not clear.**
* We will clarify that CC refers to the Common Corruption dataset, and HRS accuracy stands for Harmonic Robust Score, which is the harmonic average of clean and robust accuracy in the paper, and change the names accordingly.
---
**W2. Prio... | Summary: The paper proposes a lightweight approach to generate new architectures with robustness formulated in the NAS process.
The paper claims that the proposed method is capable of generating architectures that can learn generalized features with higher robustness.
Strengths: Efficient algorithms for generating ro... | Rebuttal 1:
Rebuttal: **W1, 2. Experimental results are limited to FGSM and PGD.**
- First, we would like to clarify that we report experimental results on three types of perturbations, including **FGSM, PGD, and 16 types of common corruption (Table 1, 2, 3)**. However, following your suggestion, we further provide add... | Summary: This work proposes a new zero-shot proxy to find robust NN architecture at initialization. The proxy utilizes the consistency of model features and gradients for clean and perturbed input. Experiments are conducted on robust NAS benchmarks. Performance is also provided for end-to-end NAS on DARTS search space.... | Rebuttal 1:
Rebuttal: **W1. The theoretical insight is unclear and the ablation experiments on different weight initialization methods and adversarial training methods are needed.**
- The underlying theoretical insight of our proxy is premised on the notion that **a robust model should learn invariant useful features ... | Summary: This work introduces a lightweight proxy, CRoZe, designed to facilitate the development of Neural Architecture Search (NAS) based architectures that are robust across a diverse set of semantic-preserving perturbations. CRoZe operates by measuring consistency across the features, parameters, and gradients for a... | Rebuttal 1:
Rebuttal: **W1. Evaluation against recent adversarial attacks is needed (i.e., LGV, SPSA).**
* Thanks for your comment, we provide additional experimental results on recent adversarial attacks such as CW, DeepFool, SPSA, LGV, and AutoAttack by evaluating Spearman’s rank correlation on the NAS-Bench-201 sear... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We deeply appreciate the time and effort you have invested in reviewing our paper. During the initial response period, we did our best to address all the concerns you raised in the response. Moreover, **we have thoughtfully included the additional experimental results that you re... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Definition of Continual Reinforcement Learning | Accept (poster) | Summary: The paper looks at developing a foundation for continual reinforcement learning (CRL). The authors develop definitions and insights, aiming to formalize the intuitive concepts in continual learning fields. They also provide two examples of CRL to illustrate the difference between traditional RL and CRL, which ... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for reviewing our paper. We address the reviewer’s primary questions and concerns below, and will plan to update the paper in line with the reviewer’s suggestions.
> 1, Though it's not the focus and goal of this paper, the idea of agent basis contains r... | Summary: In this paper, the authors develop a simple mathematical definition of the continual RL problem. These definitions, insights, and results formalize many intuitive concepts at the heart of continual learning, and may open new research pathways surrounding continual learning agents.
Strengths: The mathematical... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for reviewing our paper. We address the reviewer’s primary questions and concerns below, and will plan to update the paper in line with the reviewer’s suggestions.
> 1.The abstract is too simple that readers cannot get enough information and key ideas fr... | Summary: The paper proposes a mathematical formulation for the problem of continual reinforcement learning in an infinite horizon setting.
Strengths: 1. The authors propose a new mathematical formalism for continual RL.
2. The paper can be of interest to mathematically inclined readers and could potentially lead to th... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their time and energy in reading and reviewing our paper. We address the reviewer’s primary questions and concerns below, and will plan to update the paper in line with the reviewer’s suggestions.
> Abstract: there already are foundations for CRL. Yo... | Summary: - This paper lays out a foundation for continual reinforcement learning (CLR) from the ground up—establishing definitions for the purpose of building towards a technical definition of CRL itself, proving various properties of CRL, and through employing simple CRL examples, demonstrating some of these propertie... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their time and energy in reading and reviewing our paper. We address the reviewer’s primary questions and concerns below, and will plan to update the paper in line with the reviewer’s suggestions.
> The abstract in this case does not provide much mor... | Rebuttal 1:
Rebuttal: **[Overall Response]**
First we would like to thank all of the reviewers for their time and energy in reading and commenting on our paper. We recognize this takes considerable effort, and we appreciate it
**Summary:** Overall, our impression of the reviews is that there is a lot of enthusiasm ar... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a new definition of continual reinforcement learning where an optimal continual learning agent will not converge to a fixed policy. This is formalized through the generate operator, which defines the "searching" between different policies; and the reach operator, which defines whether the a... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their time and energy in reading and reviewing our paper. Below, we respond to each in point detail, and we are happy to update the paper to reflect the reviewer’s suggestions, and to continue the discussion.
> It is not immediately obvious how this ... | Summary: This is an ambitious paper. The paper notes that the problem of "Continual Reinforcement Learning" lacks a rigorous definition and seeks to provide one. It mathematically defines the reinforcement learning problem and then explores the conditions in which an instance of the RL problem is a CRL problem.
The ... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their time and energy in reading and reviewing our paper. We recognize the reviewer spent a lot of time understanding and commenting on our work, and we appreciate it. Below, we respond to each in point detail, and we are happy to update the paper to ... | Summary: In the paper the authors propose a formal framework for reasoning about continual reinforcement learning problems. To this end they introduce mathematical definitions for environments and agents which serve as a basis for defining the general reinforcement learning setting as well as the continual setting. In ... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their time and energy in reading and reviewing our paper. We address the reviewer’s primary questions and concerns below, and will plan to update the paper in line with the reviewer’s suggestions.
> Definition of agent vs. behaviour…
This is a great... | null | null |
TART: A plug-and-play Transformer module for task-agnostic reasoning | Accept (poster) | Summary: This paper proposes and evaluates a method (TART) for improving the in-context learning of large, pretrained base models applied to downstream binary classification tasks. TART stacks a second transformer on top of the pretrained base transformer. The TART transformer is trained to rely on in-context examples ... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments and helpful feedback on our work. We are glad you found our approach well-motivated, novel and exciting.
We completely agree with the three main contributions that you highlighted in your review. While we addressed some of these concerns in the general response... | Summary: The authors study why in context learning doesn't perform as well as task specific fine tuning by decomposing the process of in context learning into representation and reasoning. They discover that the gap is due mostly to deficiencies in reasoning and propose a new method called TART to bridge the gap. They ... | Rebuttal 1:
Rebuttal: Thank you for the positive comments. We are glad that you found our work interesting on both the problem and the solution front, and found the paper well-written and clear.
**Demarcating examples**. This is an interesting challenge. Currently, our code takes as input a demarcating limiter (which... | Summary: The paper studies why in-content learning achieves inferior performance compared with finetuning and adaptor, then proposes an LM-based inference module that learns to perform logistic regression based on the sample and previous (sample, label) sequence, where the sample and linear cutting-plane are sampled ve... | Rebuttal 1:
Rebuttal: Thanks for the detailed comments and feedback. We are glad that you find TART interesting and see applications of our work to settings beyond ICL (i.e., meta-learning).
**Comparison with linear probing.** As we highlighted in the general response above, our main objective with this work is to und... | Summary: The paper presents an recipe for adapting an LLM to perform classification tasks in a task agnostic manner. They first try to tease apart if existing "in-context" methods which construct prompts to describe the task and then ask for the inference result are not achieving great performance because of informatio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments on our work and we are glad you found our proposed method novel. First, we address your major concern regarding the presentation of our work, and then respond to your questions.
**Quality of presentation.** While other reviewers (tMpn, CKNq) found the qualit... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments and feedback. We are glad that they found TART to be **novel** and **practical** (VGrP, CKNq, tMpn), **well-motivated** (CKNq, tMpn), and supported by **extensive experiments** (VGrP, tMpn, CKNq).
Recall that our paper shows that in-contex... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Structured Semidefinite Programming for Recovering Structured Preconditioners | Accept (poster) | Summary: This paper develops a general preconditioning framework called matrix-dictionary recovery. This framework follows the matrix multiplicative weights update method and is applied to solve two classes of problems:
(1) Two diagonal preconditioning problems: outer scaling and inner scaling
This paper gives the ... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts. We are glad that you found the paper easy to understand.
Regarding weakness (1), we note that our method computes a (constant-factor) optimal diagonal preconditioner, and typically algorithmic or statistical applications of diagonal preconditioning are inter... | Summary: This paper studies preconditioning, which is one of the most important techniques in numerical linear algebra with numerous applications in optimization and machine learning. It proposes a general framework based on the matrix-dictionary recovery problem, where are given a matrix $M$ and $M_1,\dots, M_n$, and ... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review; we are similarly optimistic of the utility of the tools we provide for future numerical linear algebra problems. Regarding Algorithm 1, indeed the eigenvalue assumption was just for simplicity in stating our error bounds. Each $\mathbf{M}_i$ can always be res... | Summary: In this paper, a framework is presented to compute approximately-optimal preconditioners in order to solve linear systems.
In the case of diagonal preconditioning, an algorithm is provided whose runtime is (up to log factors) polynomial in the desired accuracy, optimal condition number of the re-scaled matri... | Rebuttal 1:
Rebuttal: We are glad you found our comparison to the literature well-explained, and that our results were interesting. We agree with your comment on M-matrices, which will be addressed in a revision.
Like many theoretical papers that appear at NeurIPS, due to space constraints, the technical details of ou... | Summary: This is a theoretical paper that studies the problem of diagonal matrix preconditioning, where given a PSD matrix $A$, the goal is to find a (positive) diagonal scaling $W$, such that $WAW$ has a small condition number, given the promise that such scaling exists. This problem can be solved using SDP, but that ... | Rebuttal 1:
Rebuttal: Thank you for your kind review of our paper and your insightful questions.
We are currently not aware of a variant of our algorithm (and matrix dictionary recovery framework) which extends to simultaneous inner and outer scaling, though it is worth noting that prior work [QGH+22] does obtain suc... | Rebuttal 1:
Rebuttal: Reviewers HjkM and pK6D asked about practical implementations of our algorithm. We agree that experiments are an important next step towards bringing the results of our paper to practice. Our primary motivation was theoretical: existing guarantees for the problems we study are off from linear time... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Beyond Confidence: Reliable Models Should Also Consider Atypicality | Accept (poster) | Summary: This paper focuses on the question of uncertainty quantification in classification by introducing the atypicality during recalibration. This paper exhibits a highly explicit motivation, yet it suffers from certain deficiencies in definitions and errors therein, and I harbor reservations regarding the soundness... | Rebuttal 1:
Rebuttal: Dear Reviewer 7E3C,
Thank you very much for your detailed review and your kindness! We appreciate the time you took to review, and we are really excited that you find our motivation clear and supported with copious experimental evidence!
Below are the responses to your questions:
### Response ... | Summary: This paper is addressing the problem of atypicality in data, and how this impact performance and confidence.
Strengths: Good perspective on the needs to consider atypicality in data
Good presentation
Good review on the links between atypicality in data and performance with confidence
Weaknesses: N/A
Technic... | Rebuttal 1:
Rebuttal: Dear Reviewer vUKB,
Thank you for your kind remark. Let us know if you have any questions or comments. | Summary: This paper proposes a series of "atypicality" measures to be used in complement to the more popular uncertainty metrics.
The authors defined input and class atypicality for both classical classification tasks as well as NLG.
Such atypicality measures are then combined with temperature scaling to improve calibr... | Rebuttal 1:
Rebuttal: Dear Reviewer AQHi,
Thank you very much for your detailed review. We really appreciate that you find the problem important and could be helpful in practice; we share the same ideas! We are also very happy to hear that you find a lot of interesting ideas in the paper, thank you very much for your ... | Summary: The paper questions the reliance of probabilistic classifiers on the confidence score alone for measuring reliability. This is an important question that has not been asked that rigorously in the machine learning literature. In particular, there are two notions of uncertainty: aleatoric and epistemic. A widesp... | Rebuttal 1:
Rebuttal: Dear Reviewer y4KB,
Thank you very much for your detailed review! We really appreciate to hear that you find that the notions proposed in the paper can start interesting discussions in the community and our empirical evaluation to be thorough. We really appreciate your approval and kindness!
Bel... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations | Accept (poster) | Summary: The paper presents an approach for self-supervised learning of PDEs. The main approach uses Lie symmetries (similar e.g. to translational symmetries for images) in the solutions of differential equations not only for data augmentation (as has been done before), but for representation learning for downsteam tas... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking their time to review the paper and providing valuable comments and feedback. We are glad the reviewer is happy with the quality of the experiments and the completeness of our manuscript. We address the reviewer’s questions and comments below.
***
### Question 1:
... | Summary: This paper proposes to learn general-purpose representations of PDEs from heterogeneous data by implementing joint embedding methods for self-supervised learning. Learned representation outperforms baseline approaches for invariant tasks such as regressing the coefficients of a PDE and improve the time-steppi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and insightful comments. We address the reviewer’s questions below and at times, challenge their criticisms. We welcome further discussion.
***
### Novelty
We acknowledge that SSL for computer vision and the study of symmetry groups of PDEs are *separatel... | Summary: In this paper, the authors propose to use self-supervised learning for obtaining an embedding that can robustly be used for predicting some quantities of interest or for time stepping. Particularly, they use the joint-embedding framework for SSL. They use symmetry groups for training the embedding to be invari... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking their time to review the paper, praising the novelty of our work, and providing valuable comments and feedback. We address the reviewer’s questions and comments below.
***
### SSL and Multi-fidelity modeling
Part of the reviewer’s response was cut off. Could the... | Summary: This paper presents a general framework for self-supervised learning in a PDE context. In a way that is principled and natural, PDE symmetry groups are used to make the requisite augmentations from which self-supervision will learn structure; the augmentations are selected carefully so as to keep the regressed... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for the valuable comments and suggestions. We are glad that you found our high-level idea novel and our work well-presented. We acknowledge the concerns raised and would like to address them as follows.
___
### On preserving boundary condition... | Rebuttal 1:
Rebuttal: ## Summary Response
We thank the reviewers for their insightful comments and many great questions. We have responded to each reviewer’s comments separately, and are sharing a summary response covering the common threads in the reviewers’ responses. To enhance our responses, we have added experime... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations | Accept (poster) | Summary: This paper focuses on box-simplex games that are min max of degree 2 polynomials being bilinear in the inner and outer optimization variables. The inner problem is over the simplex and the outer problem is over the unit hypercube. The goal of the paper is to unify the previous framework derived in [She17] with... | Rebuttal 1:
Rebuttal: Thank you for your reviewing efforts. We are happy to hear that you found our paper well-written and interesting, and that you felt it was understandable for non-experts.
We would like to note to the reviewer that the format and scope of our paper, which addresses a fundamental theoretical probl... | Summary: The authors study first-order algorithms for box-simplex games, a special kind of two-player zero-sum bi-affine constrained games. The constraints of these games dictate that the first player selects an action represented by a vector within the n-dimensional box ($[-1,1]^n$), while the second player chooses an... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback; we appreciate that you found both the problems we study and our insights to be interesting. We agree with your suggestions on presentation, and discussed some potential directions for incorporating them in the global response. We are happy to incorporate any... | Summary: This paper looks at the bilinear min-max problem Area Convexity framework proposed by Sherman through the lens of Relative Lipschitzness condition of Cohen et al. which relates to the standard Bregman analysis in mirror-type algorithms in first-order optimization. Leveraging this connection, they provide the s... | Rebuttal 1:
Rebuttal: Thank you for your careful reviewing efforts and many helpful comments. We agree with the revisions suggested by your Questions 1, 2, 3, 4, and 5, and will incorporate them. Regarding Question 1, in the main body of our revision, we will include an “abstract” variant of Algorithm 3 in the suppleme... | Summary: In this paper, the authors consider box-simplex games, a bilinear min-max optimization problem with box and simplex constraints. The current best-known method for solving such problems is Sherman's algorithm, which is based on the concept of "area convexity". The key insight of this work is to reinterpret area... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments, and we are glad that you found our technical contributions insightful. We agree with your suggestions regarding the supplement, and outline some directions for improvement in the global response (though we are happy to incorporate any further suggestions as... | Rebuttal 1:
Rebuttal: Reviewers Wg5p, E7hD, and Vb7x asked about inconsistencies between our main submission and supplementary material which hindered readability. We thank you for raising this important concern and completely agree; we will make efforts in a revision to make the two more consistent. Specifically, afte... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores the relationship between area convexity and extragradient methods, and provides improved solvers for subproblems required by variants of the algorithm. The paper also presents a state-of-the-art first-order algorithm for solving box-simplex games and a near-linear time algorithm for a matri... | Rebuttal 1:
Rebuttal: Thank you for your careful reviewing efforts. We are glad that you found our technical contributions interesting and our paper easy to read.
Regarding prior box-simplex game solvers, our paper is most directly comparable to [She17], which it improves upon by a logarithmic factor in the runtime b... | null | null | null | null | null | null |
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval | Accept (poster) | Summary: The authors improve multi-vector retrieval to move beyond the standard retrieve-gather-score stages of ColBERT. In particular, they modify the objective function during training as well as the scoring mechanism so it doesn't require gathering all token vectors of each candidate document before the final scores... | Rebuttal 1:
Rebuttal: Thank you for your detailed review.
**A1. This comparison doesn't count the actual FLOPs used. It also doesn't measure the latency of the scoring stage or the full pipeline.**
As the reviewer summarized, our goal of the paper is to simplify the three-stage inference of ColBERT while making the s... | Summary: Authors propose a better document retrieval method. They build on top of ColBert and instead of reranking using all tokens of documents retrieved by stage 1(a query token document token retrieval), they just use retrieved document tokens from stage 1 and perform retrieval using them.
Strengths: - Results seem... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. Please read our clarification for any misunderstanding you might have had while reading the paper.
**A1. The paper overcomplicates a simple concept (Eq 4 can be further simplified from what I understand). Max over j would collapse then.**
First of all, the def... | Summary: This paper deals with the problem statement of document retrieval. First, the paper contrasts and explains the differences between single vector and multi-vector retrieval models. As multi-vectors retrieval models perform better due to their accessibility to more tokens, it involves significant inference costs... | Rebuttal 1:
Rebuttal: Thank you for your review.
**A1. Clarity: I feel this paper needs little fine-tuning in clarity.**
We will definitely add a few more sentences to make the problem statement and the experiment sections clearer. For instance, the third paragraph of the introduction has the problem statement where ... | Summary: The XTR model extends the ColBERT neural IR model by removing one of the efficiency issues: candidate documents (selected using a dense vector index, e.g. FAISS) have to be re-scored by loading as much vectors as there are tokens in the document.
The proposed approach simply reduces the number of vectors repr... | Rebuttal 1:
Rebuttal: Thank you for your review.
**A1. No experiment measuring the observed latency are reported.**
Since it is difficult to reimplement every baseline within our hardware and infrastructure, apples-to-apples latency comparisons are a bit tricky. Admitting the differences in implementations, we report... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments and feedback.
Most of our reviewers agree that XTR effectively mitigates the problem of three-stage inference of multi-vector models and provides significant improvements over strong baseline models. Some of the main concerns include latenc... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work proposes XTR, ConteXtualized Token Retriever, which is a method for multi-vector retrieval with a simple and effective objective function. Comparing to prior work on multi-vector retrieval method ColBERT where all the tokens from query and candidate document needs to be computed in order to calculate... | Rebuttal 1:
Rebuttal: Thank you for your detailed review on our work.
**A1. The XTR method is mainly built on T5 models, without exploration on other architectures.**
We mainly used T5 models (encoder-only) since it is easier to scale the architecture (from base to xxl) and has been shown to work well for the initial... | null | null | null | null | null | null |
Normalizing flow neural networks by JKO scheme | Accept (spotlight) | Summary: The paper introduces a novel approach to train continuous normalizing flows/score-based diffusion models that exploits the Wasserstein gradient flow theory and the JKO iterative scheme.
The main idea is to approximate the density evolution of the variance-preserving forward dynamics of a diffusion model model... | Rebuttal 1:
Rebuttal: Please refer to the global response for the questions on experiments of image generation and FID scores.
* **(Weakness 3rd paragraph) The exposition would benefit from a more extended section connecting the JKO approach to the standard score-matching diffusion theory. This would greatly help rea... | Summary: This paper presents an innovative method for enhancing the stability of trajectories in CNF (Continuous Normalizing Flow) models. The authors propose incorporating the JKO scheme, which introduces regularization between the current density and the base distribution. The core concept revolves around leveraging ... | Rebuttal 1:
Rebuttal: Please refer to the global response for the question on image quality and additional results of image generation using VAE latent space.
* **(Weakness 2) The variety of experiments conducted in this work may be richer. In particular, it would be interesting to see how the proposed approach works... | Summary: This paper proposes learning an invertible normalising flow using a neural ODE as a unique solution to the Fokker-Planck Equation (FPE) of the transport problem from the data distribution to the equilibrium solution. The solution of the FPE is obtained by using the JKO scheme by formulating it as a variational... | Rebuttal 1:
Rebuttal: Please refer to the global response for the questions on experiments of image generation.
* **Different kernels in MMD to evaluate generative models**
We agree with the reviewer that the MMD loss metric depends on the choice of the kernel, and a variety of kernels may be adopted for evaluating ... | Summary: The authors introduce a novel normalizing flow training algorithm that integrates continuous normalizing flows and the JKO scheme. The objective of the training algorithm is to minimize KL divergence between the current density and the equilibrium density. The proposed method is inspired by the JKO scheme to e... | Rebuttal 1:
Rebuttal: Please refer to the global response for the questions on experiments of image generation and FID scores.
* **(Weakness 2 & Question 2): The proposed scheme uses a pre-trained auto-encoder, where some of the other methods do not. How good is the proposed method without using a pre-trained auto-en... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments and feedback provided by all the reviewers. The common questions are about the experiments on image generation, which we first address here. The additional questions and comments of each reviewer are addressed in the specific responses below. R1 = Reviewer NZGt... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback | Accept (poster) | Summary: The paper studies low-rank MDPs with adversarially changing losses in the full-information feedback setting. They assume the unknown transition probability function admits a low-rank matrix decomposition. They present POLO algorithm, a policy optimization-based algorithm, and prove it has sublinear regret in t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. Our response to each question is provided in turn below.
**Q1. "The full information feedback is a restrictive and unrealistic assumption."**
We do believe that extending the analysis of our work to the bandit feedback case is an i... | Summary: This work focuses on the low-rank MDPs with adversarial losses in the full-information feedback setting. Different from the previous work which assumes known features, this work considers the combination of representation learning and regret minimization problem, and is of the first result under this specific ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. Our response to each question is provided in turn below.
**Q1. "The designed algorithm suffers from huge computation cost ...."**
Indeed, our algorithm has nearly the same computation efficiency as previous works studying policy-op... | Summary: The authors consider the problem of learning an adversarial low-rank infinite episodic MDP with unknown transition and full information feedback.
The idea behind this problem is that in many RL applications, the state and action spaces might be prohibitively large, rending results that scale with these metric... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. Our response to each question is provided in turn below.
**Q1. "Is there a lower bound for this problem, and could you elaborate on the cost of learning the representation?"**
We now provide a lower bound for the representation lea... | Summary: This work studies low-rank MDPs with unknown and fixed transition and full-information adversarial losses. The proposed algorithm generalize RepUCB from the fixed reward setting to the adversarial reward setting. The main idea of the algorithm is to replace the greedy policy in RepUCB to an incremental policy ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. Our response to each question is provided in turn below.
**Q1. "The technical novelty is a bit limited."**
As previous works studying adversarial linear mixture MDPs using policy-optimization (PO) based methods [1,2] relying upon s... | Rebuttal 1:
Rebuttal: **Proof of the Regret Lower Bound (Cont.)**
and by
$\mathbb{E}_{\left(i^*, a^*\right)} \triangleq$
$ \mathbb{E}_{\operatorname{Alg}, \mathcal{M}_{\left(i^*, a^*\right)}}$
the expectation with respect to $\mathbb{P}_{\left(i^*, a^*\right)}$.
**Step 1: Regret of $\operatorname{Alg}$ over $\math... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
PyNeRF: Pyramidal Neural Radiance Fields | Accept (poster) | Summary: This works tackles the problem of antialiasing in grid based Neural Radiance Field representations (e.g. INGP, DirectVoxGo). To this end, a very simple solution is proposed: *instead of training a single NeRF with multiscale features, train separate NeRFs at different resolutions and decide which to use based ... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the helpful comments!
**Generalization.** With regards to improvements being marginal for other backbones such as TensoRF, we admittedly focused on hashtable approaches for our original submission. As the authors of TensoRF state, it is mainly designed f... | Summary: PyNeRF replaces the implicit representation in Mip-NeRF with a voxel-based representation method, which combines the cone sampling method and explicit structural representation by interpolating on different voxels based on coordinates of different scales. This method can be easily applied to existing accelerat... | Rebuttal 1:
Rebuttal: Thank you for reading our paper and for the constructive feedback.
**Contribution.** To the best of our knowledge, our work is among the first to combine fast NeRF rendering with anti-aliasing. We agree that testing on more accelerated NeRF approaches would improve our paper. To that effect, we p... | Summary: The authors introduce a pyramidal radiance field reconstruction method, which reuses multi-scale feature grid representation and area matching algorithm for level indexing. Specifically, the method trains a pyramid of models at different scales and interpolates point features between neighboring levels determi... | Rebuttal 1:
Rebuttal: Thank you for reading our work and for the constructive feedback!
**Single vs multi-resolution.** You ask why existing methods such as iNGP perform worse on multi-resolution datasets. Mip-NeRF [1] originally points out that prior work struggles on scenes where the same scene content is viewed f... | Summary: This work presents a method for anti-aliased renderings for grid-based NeRF representations by jointly optimizing a hierarchy of coarse-to-fine grids. The idea is neat and well justified by empirical evaluations that show quantitative and qualitative improvements over baselines.
Strengths: 1. The proposed met... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper! We’re glad that you appreciate the simplicity and effectiveness of our approach.
**Nerfstudio.** Our implementation is indeed built on the library (we will release our code as a plugin) and is closest to Nerfacto. We list comparison numbers in the tables attach... | Rebuttal 1:
Rebuttal: We are glad that reviewers agree that we address "an interesting and important anti-aliasing problem," (JoFm), appreciate "simple ideas that lead to good performance improvement," (LTbu), and acknowledge that "experiments are extensive, and demonstrate the effectiveness of the proposed method." (P... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a method to address the aliasing artifacts in grid-based NeRF. It introduces a pyramid of grids of different resolutions to represent a scene. To query the color and density of a 3D point with a certain integration volume, the method finds the two pyramid levels that best describe the point... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and for the valuable feedback!
**Contribution.** Rather than seeing the simplicity of our method as a weakness, we humbly agree with Reviewer LTbu's statement that "the main strength of this paper *is* its simplicity". As you state, our simple method significant... | null | null | null | null | null | null |
Encoding Human Behavior in Information Design through Deep Learning | Accept (poster) | Summary: The study demonstrates commendable efforts in employing supervised learning techniques and utilizing Amazon Mechanical Turk to acquire data to develop a human behavior descriptor. The authors further utilize neural networks to optimize the sender's signaling scheme based on the fitted human decision-making mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. We first respond to your major concerns.
**[The assumption of Bayesian rationality as a limitation]** While Bayesian persuasion has provided an elegant framework for studying information design, it has made several assumptions that limit the pract... | Summary: Information design is the problem in which a sender would like to optimize the information sent to a receiver, such that the receiver takes actions that the sender likes. As a simple example, a company may want to optimize what information they communicate about their product, to get consumers to buy the produ... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments! We will incorporate the clarification suggestions. Below we respond to the two major comments.
**[Scalability]**
In our framework, there are two separate scalability considerations:
*Scalability of optimizing information policy*: We'd like to begin by noting... | Summary: The paper focuses on the problem of automated information design, where the sender strategically reveals information to persuade the receiver to take specific actions. The main contribution of this paper is addressing the challenge of modeling human behavior when individuals do not act as Bayesian rational age... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback and comments!
**[Scalability]** Please see our responses to Reviewer 5W54.
**[Differentiable human models]** While the standard rationality model is not differentiable, many of the other models are, e.g., discrete choice models and data-driven models. The diff... | Summary: This paper proposes a neural network based framework for automated information design that can optimise both the senders and the receivers behaviour. In situations where the receivers behaviour cannot be approximated analytically, a neural network that learns the preferences from existing data can be utilised.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review.
**[Contributions]** We first highlight our contributions. Firstly, we introduced a data-driven optimization framework for information design. Note that even in standard settings where humans are Bayesian rational, designing optimal information pol... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Depth-discriminative Metric Learning for Monocular 3D Object Detection | Accept (poster) | Summary: This work focuses on the monocular 3D object detection task. As many works indicated, depth estimation is the bottleneck of this task, and the authors propose the apply metric learning to improve the accuracy of the depth estimation sub-task. The proposed metric-learning-based loss encourages the model to extr... | Rebuttal 1:
Rebuttal: >**Q1.** In lines 68-69, the authors claim their proposed work is 'the first approach that applies metric learning to monocular 3D object detection.' In fact, there is another work that discussed how to apply metric learning in monocular 3D object detection with a focus on dimension estimation. Th... | Summary: This paper introduces a novel metric learning scheme for extracting more depth-discriminative features in the monocular 3D object detection task. A distance-preserving function is adopted to build the relation between feature space and the ground-truth object depth. The authors propose a quasi-isometric loss t... | Rebuttal 1:
Rebuttal: >**Q1.** I am curious about the efficiency of the proposed method during training. The calculations of relative distances between objects in both original and feature space would be enormous. Besides, the complexity of calculating the distance matrix grows with the square of the number of objects.... | Summary: One main challenge of monocular 3D object detection models is the lack of depth information from RGB images. The authors proposed a metric learning scheme to encourage the model to extract depth-discriminative features. Based on the presented theoretical results, the authors proposed a quasi-isometric loss and... | Rebuttal 1:
Rebuttal: >**Q1.** The high-level idea of the proposed approach resembles previous contrastive learning approaches. I could imagine adding depth-based feature contrastive losses to the baseline loss. Would that work? What would be the advantage of the proposed approach compared to contrastive losses?
**A1.... | Summary: The paper proposed a new approach to monocular 3D object detection. The critical contribution of the work is the application of metric learning to improve depth estimation and an additional head with auxiliary depth prediction. The resulting approach improves 3D object detection accuracy without increasing inf... | Rebuttal 1:
Rebuttal: >**Q1.** I don't think the second auxiliary head contains a lot of novelty compared to related works such as MonoCon. This limits the novelty of the paper to some extent.
**A1.** We acknowledge your concern regarding the novelty of the auxiliary head.
While it may not present a significant techni... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments.
For your convenience, please download and assess the **attached PDF**.
To simplify cross-referencing, figures, and tables in the main paper, supplementary materials, and rebuttal paper are denoted as `M-[Table X/Figure X]`, `S-[Table X/Figure... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a metric learning scheme to learn depth-discriminative features for object depth prediction, which helps improve the overall task of monocular 3D object detection, without negatively impacting the performance of the other sub-tasks (e.g., object class, bounding box size) wherein. Specifical... | Rebuttal 1:
Rebuttal: >**Q1.** Are those hyper-parameters $K, B,$ and $\epsilon$ easy to find in practice? Will different backbone architectures require different setups of $K, B,$ and $\epsilon$?
**A1.** Finding the *"Optimal"* hyper-parameters can be challenging across diverse backbones and datasets. However, our ex... | null | null | null | null | null | null |
PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas | Accept (poster) | Summary: They describe a novel method for panoramic view synthesis given wide-baseline panoramas as input. They first obtain depth maps for the input panoramas use a custom spherical depth estimation method guided by a pre-trained monocular depth estimator. They then extract geometry and appearance features from the ... | Rebuttal 1:
Rebuttal:
Dear Reviewer WLFo:
Thank you for the review and comments. We are glad to see your positive comments and score. We hope the following responses could solve your concerns.
* For Weakness1:
>In many ways the work is an adaptation of NeuRay to the spherical case. It could be argued that the work... | Summary: This work tackels large-baseline (up to 2 meters) multi-view stereo for panorama images. Extending NeuRay framework, the density and raidiance field are estimated based on the feature extracted from nearby views following panorama projection. To predict stereo depth as geometric feature, this work propose to u... | Rebuttal 1:
Rebuttal: Dear Reviewer 74Ux:
Thanks for the review and comments. We hope the following responses could solve your concerns.
* For Weakness 1:
>Adapting from perspective cubemap to equirectangular projection...
We illustrate the key differences between PanoGRF and NeuRay from many aspects in the ... | Summary: This paper presents a method called PanoGRF for synthesizing novel panoramas using two wide-baseline panoramas, with the incorporation of 360 scene priors into Spherical NeRF to generate new views. The method involves extracting appearance and geometry features from the input panoramas and estimating spherical... | Rebuttal 1:
Rebuttal: Thanks for your effort for reviewing our paper. We hope the following responses could solve your major concerns.
* For weakness 1:
>The level of novelty...
PanoGRF is not a simple combination of NeuRay and the 360° novel view synthesis task. For a more detailed clarification, pleas... | Summary: This paper introduces PanoGRF, a method for generalizable novel view synthesis of sparse panorama images with wide baselines. This PanoGRF is basically built upon the perspective view synthesis method, NeuRay. Previous generalizable NeRF methods are mainly designed for perspective images are may introduce extr... | Rebuttal 1:
Rebuttal: Thank you for the review and comments. We are pleased to receive positive feedback on our performance and writing. We hope the following responses will address your concerns effectively.
* For Weakness 1:
>The overall idea for generalizable view synthesis is not new and is basically built upon t... | Rebuttal 1:
Rebuttal: Dear Reviewers:
Thank you for acknowledging the strong performance of this work. As Reviewer un5g said, quantitatively PanoGRF shows a large improvement in rendering quality and this is also evident from the example images in the figures and the videos in the supplementary. We clarify the contrib... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents PanoGRF, Generalizable Spherical Radiance Fields for Wide-baseline Panoramas, which introduces mono-guided 360 ◦ depth estimation and leverages each panoramic view based on spherical projection. The experiments show that the proposed method significantly outperforms state-of-the-art general... | Rebuttal 1:
Rebuttal: Dear Reviewer zV2U:
We sincerely appreciate your review and comments. We are delighted to receive your positive feedback and score. We hope the following responses will address your concerns effectively.
1. Weakness 1:
> it seems to use false citation: S-NeRF[17].
We apologize for causin... | null | null | null | null | null | null |
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting | Accept (spotlight) | Summary: This paper constructs a Markov chain that transfers between high-resolution and low-resolution by shifting the residual between them. They achieve competitive performance to SOTA methods with only 20 sampling steps.
Strengths: 1. The paper structure is clear and the proposed method is well-motivated.
2. It o... | Rebuttal 1:
Rebuttal: > **Q1. A brief figure to introduce ResShift should be provided.**
Thanks for your suggestion. We have provided an overview of our model in Fig 3. (a) in the attached rebuttal document, and will add it to our paper in the revised version.
> **Q2. Why this method is called ResShift? The motivatio... | Summary: This paper studies diffusion-based image super-resolution (SR) with the goal of reducing the number of diffusion steps. The key intuition is to only learn the residual between an LR-HR image pair, thereby shortening the diffusion path. To this end, the paper introduces ResShift, a novel diffusion framework whe... | Rebuttal 1:
Rebuttal: > **Q1. The comparison with LDM is unfair. More advanced samplers should be considered.**
As suggest, we conduct a comparison to LDM with 20 sampling steps accelerated by more advanced samplers, including PNDM (ICLR 2022) and DPM (NeurIPS 2022). The quantitative comparison results on the testing ... | Summary: The paper presents a new image super-resolution diffusion model called "resshift", which aims to address the efficiency issue in diffusion models. Existing acceleration strategies often yield over-smooth results. To combat this, the authors propose a new diffusion model for super-resolution that can produce fa... | Rebuttal 1:
Rebuttal: > **Q1. The major issue with this paper lies in its presentation. The central idea of the paper is not clearly articulated. In the second section, although the method is described meticulously, it does not seem significantly different from existing methods. The authors do not clearly highlight wha... | Summary: The authors propose a new diffusion model, based on the principle of residual shifting, that is able to converge to a good looking image in a low number of diffusion steps, improving in terms of high resolution image inference time at least over the Latent Diffusion Model (LDM) SR variant, another well-establi... | Rebuttal 1:
Rebuttal: > **Q1. Efficiency comparison with the other methods in Table 3 and 4.**
We have offered more comprehensive comparable analysis on the efficiency as suggested. Please see Q2 of the global response.
> **Q2. The choice in presenting the results of their ablative study (Table 1) versus the against-... | Rebuttal 1:
Rebuttal: Dear AC and reviewers,
We sincerely thank all reviewers for their constructive comments. Since Reviewer vFLR, Reviewer RmTK, and Reviewer k6dY all concern the performance comparison with other related methods, Reviewer vFLR and Reviewer RmTK both require to supplement the efficiency comparison to... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a diffusion-based image super-resolution method.
The proposed method starts to generate an HR image from a given LR image directly (learns to generate the residual image between LR and HR), not start from a noise.
Therefore, the method can generate the output image faster than previous wo... | Rebuttal 1:
Rebuttal: > **Q1. Visual comparison on the synthetic dataset.**
As suggested, we have presented one visual comparison on the synthetic dataset in Fig. 2 of the associated rebuttal file. Evidently, the proposed method outperforms other competing approaches in terms of both fidelity and realism. In our revi... | null | null | null | null | null | null |
Sparse Parameterization for Epitomic Dataset Distillation | Accept (poster) | Summary: This paper introduces the insight of sparse coding into dataset distillation and proposes a sound method. This work can efficiently generate syn data by adopting the multi-head SCMs as the shared source of the syn images and using a recurrent model to generate the syn patches. It can cooperate with various pre... | Rebuttal 1:
Rebuttal: We appreciate reviewer 27jp for the insightful and constructive comments and are glad that the reviewer finds our method novel and interesting. In response to the concerns raised, we will address them as follows:
1. **More discussions about the relations between dataset distillation, sparse codin... | Summary: The paper proposes a new framework(SPEED) to perform dataset distillation.
The new framework is composed of 3 parts:
1. Spatial-Agnostic Epitomic Tokens (SAETs)
2. Sparse Coding Matrices (SCMs)
3. A Feature-Recurrent Network (FReeNet)
The paper also employees multi-head attention to ensure the diversity of ... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer 7J1t for the pertinent and valuable feedback. We are delighted to learn that the reviewer finds our method achieves good performance across multiple datasets demonstrated in Table 1 and Table 2. The concerns are fully addressed as follows.
1. **Incomplete evaluation re... | Summary: This work proposes a new memory-saving method of dataset distillation by distilling the dataset into a set of Spatial-Agnostic Epitomic Tokens which are indexed by Sparse Coding Matrices and decoded into images by a Feature-Recurrent Network. This method is plug-and-play compatible with existing distillation m... | Rebuttal 1:
Rebuttal: We appreciate reviewer yNYq for the insightful suggestions and are happy that the reviewer finds our work interesting and effective. We are glad to address the concerns and take the suggestions as follows:
1. **The presentation of tables 1 and 2. Re-parameterization should not be directly compare... | Summary: This paper proposes a new parameterization for dataset distillation. The new parameterization considers image patches, use sparse matrix and recurrent feature net to generate synthetic images. The total parameters follows storage constraint. The experimental results show improvement over previous methods.
Str... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer kT2g for the valuable comments and feedback. We deeply appreciate the reviewer's acknowledgment of the merits of our proposed method, including its effectiveness in reducing spatial redundancy, its capacity for generalization, and its superior performance compared to pr... | Rebuttal 1:
Rebuttal: We would like to appreciate all the reviewers for their time and effort in the review process. Overall, we are pleased that the reviewers recognize the novelty (reviewer 27jp, yNYq, 7J1t), impressive experimental results (reviewer kT2g, 7J1t), and clear presentation (reviewer 27jp, yNYq, 7J1t) of ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning | Accept (spotlight) | Summary: The authors describe a theoretical framework for contrastive learning of representations in an open-world setting, where both labelled data and unlabelled data of potentially new classes is available. They explicitly describe the graph encoding positive sample connections and formulate a contrastive loss that ... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the insightful questions! Below we address each of your comments in detail.
#### **- Discussion on the augmentation graphs.**
We have noted the reviewers' concerns regarding the definition of the augmentation graph from several angles, and we address these concerns as ... | Summary: This paper presents spectral open-world representation learning that aims to learn low-rank approximation of a constructed adjacency matrix. From this perspective, the authors study how the label information help (or hurt) the classification performance by analyzing the error bound. Experiments on image classi... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the insightful questions! Below we address each of your comments in detail.
#### **- Generalization of the theoretical analysis.**
Fair concern! We are happy to expand our thoughts on the generalization of our theoretical analysis within a broader context. The primary... | Summary: This paper formulate the open-world representation learning using the graph.
This paper provides theoretical analyses for the framework (Thm 3.1) as well as the framework's performance (Thm 4.1 & 4.2).
The experimental results show that the framework outperforms the exiting method in the open-world learning s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful questions! Below we address each of your comments in detail.
#### **- Experiments with different labeling ratios.**
Great suggestions! We provide the comparison by reducing the labeling ratio from 50% (default) to 25%, 10% and 0% on CIFAR-100, while keep... | Summary: The paper tackles the domain of open-world representation learning, which aims to learn representations that can correctly cluster samples in the novel class and classify samples in the known classes by utilizing knowledge from the labeled data. Notably, the motivation of this paper is to provide a theoretical... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the insightful questions! Below we address each of your comments in detail.
#### **- Results on ImageNet-100.**
Sure! As suggested, we report below results on the ImageNet-100 benchmark, which is commonly compared in the literature. For ImageNet, the training and ev... | Rebuttal 1:
Rebuttal:
We thank all the reviewers for their constructive and valuable feedback.
We are honored that the reviewers acknowledge the novelty of our graph-theoretic framework (R1, R3, R4) with excellent contribution (R1, R4) and soundness (R1).
Multiple reviewers value the theoretical nature of our pape... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graph | Reject | Summary: This paper tackles the problem of representation learning on text-attributed graphs (TAGs), which has gained significant attention in recent years. The primary focus of this research is on few-shot node classification. Existing two-stage methods have been unsuccessful in effectively capturing the complex relat... | Rebuttal 1:
Rebuttal: Thank you very much for reading our paper and affirming the motivation, summary of related work, results, and insights of our paper! Also, thank you for your valuable suggestions and questions regarding our paper! Based on your comments, we have done the following work:
**1. Paper revision:**
* F... | Summary: In this paper the authors present a new framework that combines the benefit of Graph models with the Large Language Models. The authors argue that with the current modeling paradigm, the LLMs are trained in a downstream task agnostic fashion although using prompts they could be fine-tuned for specific tasks. G... | Rebuttal 1:
Rebuttal: Thank you for your positive acknowledgment of the innovation in our method, its practical applications, and the experimental results concerning few-shot and zero-shot scenarios. We also greatly appreciate your suggestions and the questions you raised. Based on your feedback, we have made the follo... | Summary: The paper feeds manually designed prompts to LLMs to get task-specific text features, instead of BERT-based fixed features. Then a GNN is applied on top of node features for node classification. Few-shot node classification on 3 datasets are conducted for evaluation.
Strengths: 1.The motivation is clear and r... | Rebuttal 1:
Rebuttal: First of all, thank you very much for reading our paper and affirming the problem setting we proposed and the effectiveness of our method. Based on your comments:
* we added discussions about soft prompts when introducing model prompts, including how our method can combine with soft prompts, and... | Summary: The paper presents G-Prompt, a novel framework designed to model Text-attributed Graphs (TAGs) more efficiently. G-Prompt addresses the existing limitations of current methods by combining a graph adapter with task-specific prompts to extract node features, thereby integrating information from both the graph s... | Rebuttal 1:
Rebuttal: First of all, thank you very much for reviewing our paper and for your positive acknowledgment of the problem setting we presented in our paper, as well as your recognition of the effectiveness of our method. Following your suggestions, we have made the following modifications:
**1. Conduct exper... | Rebuttal 1:
Rebuttal: To all reviewers,
We sincerely appreciate your affirmation of G-Prompt and your insightful suggestions on its current limitations. Guided by your comments, we have conducted extensive additional experiments to supplement this paper, along with revisions to the content. As there are many changes, ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a new framework called G-Prompt for analyzing text-attributed graphs, which are commonly found in real-world networks. The existing methods for analyzing these graphs have limitations in improving performance when there is limited training data. G-Prompt addresses this issue by combining a... | Rebuttal 1:
Rebuttal: First of all, we sincerely appreciate your careful reading of our paper, your positive affirmation of our research questions, and your recognition of the effectiveness of our proposed methods.
The summary of our response is as follows:
1. **GraphAdapter Design (Question 2):**
- We have reorga... | null | null | null | null | null | null |
On kernel-based statistical learning theory in the mean field limit | Accept (poster) | Summary: The authors explore the mean field limit of kernels and their reproducing kernel Hilbert spaces, providing novel theoretical tools and insights for tackling large-scale problems. Furthermore, they employ these kernels in statistical learning, with a particular focus on Support Vector Machines. This new form of... | Rebuttal 1:
Rebuttal: Thank you very much for your review and insightful questions. Below we address your concerns and answer the posed questions.
**Weaknesses**
>Section 4 only provides a consistency result instead of a generalization error bound.
Indeed, we only consider convergence per se in Section 4 and do not p... | Summary: This paper studies the theory of RKHS consisting of the functions over the space of probability distributions. Complementing the related study [15], the work develops the particle-based approximation theory to RKHS and develops the support vector machines fed distributions as inputs. Moreover, the statistical ... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful review. We now address your concerns and answer the posed questions.
**Weaknesses**
>While I recognize many potential applications, it would be nice if the authors could provide concrete examples and datasets to emphasize the importance of the paper.
We ha... | Summary: This paper develops mathematically rigorous construction of the mean field limit of kernels of probability measures, which is obtained as a limit of a sequence of kernels with increasing input dimensions, and its application to SVMs.
This is motivated by the analysis of interacting particle systems, when many ... | Rebuttal 1:
Rebuttal: Thank you very much for your very detailed and careful review. Below we address your concerns and answer the posed questions.
**Weaknesses**
>Misleading title [...]
Thank you for pointing out this risk of confusion, which we weren't aware of. To avoid any possible confusion, we suggest to change... | Summary: This work derives mean-field limits of kernels and their associated Reproducing Kernel Hilbert Spaces. In particular, the authors quantify the relationship between the finite-input RKHS and the RKHS of the mean-field kernel, derive a Representer Theorem for mean-field kernels, and provide asymptotic convergenc... | Rebuttal 1:
Rebuttal: Thank you very much for your review and insightful questions.
**Weaknesses / Questions**
>1. While the results are interesting on its own, I am a little skeptical about the applicability of these results in existing ML problems. I think this work would greatly benefit from a more detailed discuss... | Rebuttal 1:
Rebuttal: We thank the reviewers for their careful, detailed reviews and their interesting and insightful questions. We have answered each reviewer separately, and hope to have addressed all of their concerns and questions.
In the general part, we would like to address an aspect that was raised by more tha... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this work, the authors considered learning problems where the input is a interacting-particles / multi-agent system and studied the mean-field limit of the kernels, RKHS functions and SVM solutions as the number of particles tends to infinity. The authors showed that, essentially, taking the infinite-parti... | Rebuttal 1:
Rebuttal: Thank you very much for your careful and detailed review. Below we address your concerns, answer the posed questions, and provide additional comments and remarks.
**Weaknesses**
>While the infinite-particle limit of the learning problem leads us to interesting theoretical investigations, its prac... | null | null | null | null | null | null |
Learning to Reason and Memorize with Self-Notes | Accept (poster) | Summary: The paper presents a general prompting approach for LLMs: instead of generating intermediate "thoughts" after processing the prompt as Chain-of-Thought (CoT), this paper proposes adding "Self-Notes" **while reading the prompt**. That is, while the initial reading of the prompt, the model can generate intermedi... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review. In particular, we appreciate the thorough and well understood list of our contributions including our empirical efforts across various benchmarks and supervision scenarios. We have addressed your comments regarding the GPT3+ and other OpenAI models in the r... | Summary: The authors introduce a method called "Self-Notes" that allows the model to think and write down its thoughts during the reasoning process. Unlike other approaches, this method enables the model to deviate from the input context, integrate previous reasoning steps, and enhance its memory with useful informatio... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We have addressed your comments regarding LLaMa and GPT-3.5 in the comment to all reviewers.
----
> It is recommended to conduct "Semi-supervised Self-Notes" experiments for all tasks shown in the paper.
Across the two tasks we conducted semi-supervised ex... | Summary: One very general and beneficial method for improving the outputs of LMs is called *chain-of-thought* (CoT) reasoning, by which an LM is trained or prompted to first output its step-by-step reasoning before outputting the answer to a problem. This paper proposes a major extension of CoT wherein the LM is traine... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and valuable review. We appreciate the nice summary and the comment highlighting the importance of improving LM reasoning with Self-Notes. We have addressed your comments regarding GPT-3.5 and GPT-4 in the response to all reviewers.
----
>All of the examples f... | Summary: This paper introduces a variation to the chain of thought and scratchpad techniques that can easily be applied to pre-trained transformers. While reading a passage, the model can insert "self notes" at any point in the input sequence. These self-notes allow the model to perform chain-of-thought reasoning, by... | Rebuttal 1:
Rebuttal: Thank you for the careful review and helpful comments. We appreciate the attention to detail, the concise description of contributions, and beneficial suggestions. Thank you also for highlighting the potential high-impact of our work on future research. We have addressed your comments regarding th... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for the invaluable feedback. We greatly appreciate the encouraging and helpful comments which have made our paper stronger. All reviewers pointed out the main contributions of our work and noted the potential impact of our work on future research. We respond to... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses | Accept (poster) | Summary: The authors present a method to improve perceptions for patients using visual prostheses using a combination of a deep learning encoding model and a patient-specific tuned set of parameters for stimulation learned via preferential Bayesian optimization. They use simulated data and patient choices to demonstrat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review and insightful analysis.
## Weaknesses
> Simulated Data and Patient Ranges
We agree that proper selection of patient parameters is crucial for realism. The process of choosing these ranges of patient parameters was complicated by the variety of dat... | Summary: In this work, the authors proposed a pipeline for optimizing the deep neural network based encoder, which is to generate visual stimulus for neuroprostheses. The pipeline considers human-in-the-loop optimization. The authors use preferential Bayesian optimization techniques to reduce the number of queries to t... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their encouraging comments and thoughtful questions.
## Weaknesses
> 1. It is not made super clear whether and how the parameters w of the encoder is being updated in the manuscript.
We apologize for any confusion, and will update the paper with improved nota... | Summary: * This paper proposes a flexible framework addressing personalized stimulus optimization predominantly seen in _visual prostheses_.
* The authors propose integrating the state-of-the-art deep learning with a preferential Bayesian Optimization (BO) strategy to learn optimal patient-specific parameters in fewer... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s attentive analysis and helpful feedback.
## Weaknesses/Limitations
> The main weakness lies in its limited novelty, as the proposed approach is merely a combination of state-of-the-art forward models and Bayesian optimization.
As the reviewer points out, our wo... | null | null | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful analysis and feedback, which has been invaluable for understanding how to improve our paper. We are pleased that reviewers in general agreed on the paper’s significance towards realistic optimization of prosthetic vision, and that it advances state of th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On Learning Latent Models with Multi-Instance Weak Supervision | Accept (poster) | Summary: The authors define and study the problem of multi-instance partial label learning (PLL), where weak supervision is given in the form of a (potentially) unknown transition function $\sigma$, which maps the ground truth labels onto some label set $S$.
Under this problem setting, the paper goes on to show multi... | Rebuttal 1:
Rebuttal: > Q: As mentioned in the paper, scalability seems to be an issue with the experimental setting. On what seems to be a not overly complex task (a weighted sum of 4 MNIST digits), performance indeed significantly drops, which calls into question the widespread potential of the PLL setting (and not t... | Summary: This paper studies the questions of learnability and generalization under an interesting form of supervision feedback: namely, when the true label of interest is not observed, but the learner instead has access to the output of a "transition function" $\sigma(y_1, \ldots, y_M)$ computed on the labels of an $M$... | Rebuttal 1:
Rebuttal: > Q: Core technical contributions / difficulties are not explained enough (e.g., Lemma 1 seems like key result for Thm 1, and the rest follows from standard tools?) What was the key technical challenge to proving the generalization results, and why is it novel / original?
Thanks for the comment. ... | Summary: This paper studies a weakly supervised learning scenario where supervision signals are given to sets of instances (instead of individual instances), while the goal is still to predict labels of unseen individuals. For example, the learner is provided with a dataset in which each training example comprises a se... | Rebuttal 1:
Rebuttal: > Q: The theoretical results presented in this submission have limited significance. The authors state that they prove learnability under distributions that concentrate mass on a single instance or label. This assumption can be invalidated in the real world. ... Consequently, positive results unde... | Summary: The paper connects latent structural learning and neuro-symbolic integration and provides the first theoretical study of multi-instance PLL with possibly an unknown transition. Under such weakly supervision scenario where the transition is deterministic, it defines the necessary and sufficient condition, the m... | Rebuttal 1:
Rebuttal: > Q: The author better move some discussion of the necessity to let the transition function be deterministic to the introduction part to prevent confusion since it seems too strict compared with the commonly used assumption.
We mainly focus on deterministic transitions, since our work was motivat... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to all the reviewers for their valuable feedback. Below, we address some commonly raised issues.
**Comments on the importance of multi-instance PLL raised by reviewers vhBR & KpA9**. Multi-instance PLL captures neuro-symbolic learning and latent structural l... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
AdANNS: A Framework for Adaptive Semantic Search | Accept (poster) | Summary: This paper proposes to use the Matryoshka Representations for approximate nearest neighbor search (ANNS). Matryoshka Representations provide the flexibility to adjust the budget for index search, index storage, and distance computation by changing the dimension of the used embedding. The paper instantiates the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback and are glad they found our work impressive and well suited for ANNS. We address the reviewer’s feedback below:
1) **Details on Matryoshka Representations**: We have briefly discussed the details of Matryoshka Representations in L154 - 163 and sha... | Summary: Matryoshka Representation representations have the advantage that the first m-bits of the d-dim vector can as-is serve as a good m-dim representation of the original d-dim vector. This paper demonstrates how Matryoshka Representations (MR) can be used together with approximate nearest neighbor search indices t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We are glad the reviewer found our work easy to follow and clever. Below, we answer the questions raised in the review:
1) **Jumps to Appendix**: Thanks for letting us know of requiring jumps to the appendix, we shall improve readability to minim... | Summary: The authors introduced AdANNS, a framework that effectively harnesses the flexibility of Matryoshka Representations. This approach is applied to two fundamental components of typical ANNS systems: (a) the search data structure that stores datapoints, and (b) the distance computation that maps a given query to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We are glad the reviewer acknowledged our work’s potential to provide superior real-world ANNS indices. Below, we answer the questions raised in the review:
1) **ANNS components in the Introduction**: Thanks for the valuable suggestion on restruc... | Summary: The author introduced an adaptive method for searching near-neighbors called AdANNS, which employs different representations of the same item at various stages of the engine. Rather than relying on traditional fixed vector representations, the authors utilized Matryoshka representations, creating a nested repr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. Below, we answer the questions raised in the review:
1) **Limited datasets**: We discuss our reasoning to experiment on only ImageNet and Natural Questions datasets in more detail in Related Works (L130-140). To summarize:
- *Existing ANNS bench... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and valuable feedback. We are happy to know that the reviewers found the paper to be very well written, easy to follow, and clever along with extensive experimentation and analysis showcasing state-of-the-art accuracy-compute tradeoff for ANNS building blocks ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Stability Guarantees for Feature Attributions with Multiplicative Smoothing | Accept (poster) | Summary: The paper is about the stability of explanations in the feature attribution setting for image classification tasks. They illustrate that in certain settings, swapping/removing a pixel of an explanation completely changes the classifiers prediction, which is undesired.
They define what a "stable" explanation i... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and comments. We will accordingly update our exposition to better clarify our methods, experiments, and contributions. Below we group some of the Reviewer's comments and questions in our response.
### Weakness 1 / Question 1: Meaning of smoothness.
Each coord... | Summary: This paper aims to make the classifier robust to feature removal and addition. The authors find that adding a patch to the mask obtained from explanation method may cause the classifier to make substantially different prediction. They introduce the notion of incremental
stability and decremental stability to m... | Rebuttal 1:
Rebuttal: We thank the Reviewer for the helpful comments, questions, and references. We will include additional exposition and discussion to better clarify our sample complexity, especially relative to other smoothing methods. Moreover, we will include discussions on the listed references, which we believe ... | Summary: In this paper the authors have introduced a framework to measure the stability of feature attribution methods. They do so by introducing two relaxed notions of stability called incremental stability and descremental stability which check for stability in a neighbourhood of the original feature set. They show t... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their time and comments. We will include additional discussion based on these comments, especially regarding how practitioners may apply and train for stability. Moreover, we remark that we have also attached a Rebuttal Supplemental document with additional examples and e... | Summary: This paper presents a technique for extracting feature attributions that are certifiably stable in the sense that the model's predictions are consistent on supersets of attributed features. As the title suggests, the approach is based on multiplicative smoothing, a novel type of Bernoulli smoothing based on ma... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their positive impression of our paper and helpful comments on how to improve readability, especially on the notion of masking equivalence and our particular definition of stability. Moreover, since our submission, we have fine-tuned our models for longer and rerun a numb... | Rebuttal 1:
Rebuttal: We thank the Reviewers for their time and constructive feedback. The Reviewers have raised many insightful comments and useful suggestions on how to improve the expositional narrative, technical presentation, and experimental evaluations. In addition, the Reviewers have also suggested a number of ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The draft addresses an important problem of feature attribution method selection and formalizes a notion of attribution stability to do this. The approach is based on modifying classifiers to make them Lipschitz wrt to feature masking. Once done, the new classifier has provable radii of stability defined as L1... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their positive reception of our paper. We have rerun some experiments under more generous time budgets to improve our experimental results and better address the highlighted weaknesses. We include some of these in the Rebuttal Supplementals, and we address each of the Rev... | Summary: This paper studies the stability of binary attributions. The attribution is defined to be stable if the prediction does not change when adding additional features. The multiplicative smoothing is proposed to achieve the Lipschitz condition, which is proved to infer the relaxation of stability. Experiments veri... | Rebuttal 1:
Rebuttal: We thank the Reviewer for their constructive comments. We will include additional exposition, discussion, and examples in our revised manuscript to better motivate our work and clarify our contributions. Furthermore, we have attached a Rebuttal Supplemental document with relevant material.
### We... | null | null | null | null |
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems | Accept (poster) | Summary: This work proposes a new condition called the aiming condition, which looks similar to quasar-convexity but provides fundamentally different convergence guarantees for SGD. Under the aiming condition, along with several other regularity conditions, SGD can achieve the same sample complexity as GD. It is then s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feeback. We address your concerns below.
**W1:** *I am expecting more discussions on the comparison of the aiming condition against existing conditions (e.g. quasar-convexity).This involves two aspects: 1. I would like to see some more examples where the aiming condi... | Summary: This work shows a regularization condition for SGD in the interpolation regime which allows it to have same fast linear convergence rate as deterministic gradient descent. Hence, the theory presented in this paper supports the practical observation that with the same (large) learning rate, mini-batch SGD has a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. We address the concerns below:
**W1:** *Looking at the theorems, I still feel the assumptions made are too strong too hold in a non-convex landscape especially Aiming. For theorem-1.2, which consdiers a non-convex loss landscape on $w$, it is uncle... | Summary: This work presents a set of conditions under which the convergence rate of SGD with large step size is similar to that of gradient descent (the deterministic setting). This is in contrast to prior work, where the convergence of over-parameterized SGD under PL condition require small step size, and converge slo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments. We address your concerns below in detail.
**W1:** *The paper states that its goal is to improve stepsize selection and convergence rate of SGD for nonconvex problems under certain conditions (see lines 133-134). However, the paper does not directly... | Summary: This paper studies the convergence of SGD with large step size. It is shown that under some regularity conditions, SGD enjoys a fast linear convergence rate, both in expectation and with high probability. These results can be applied to show fast convergence of SGD for wide enough feed-forward networks.
Stren... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments. We address the concerns as follows:
**W1:** *The main application of the results is for wide enough neural networks in the NTK regime, which seems restrictive.*
**A:** We would like to point out that the ”NTK regime“ setting is in line with mu... | Rebuttal 1:
Rebuttal: We provide additional experimental results here, requested by the reviewers. Please see the other rebuttals below, directly after each review.
**1: An estimate of aiming condition:** In principle, the aiming condition is difficult to verify, as
it involves $proj_S(w)$---the nearest point of $w$ t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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