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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Diffusion Representation for Asymmetric Kernels via Magnetic Transform | Accept (poster) | Summary: The paper introduces the concept of the magnetic transform to define diffusion representation and diffusion distance for asymmetric kernels. The key idea is to transform an asymmetric kernel into a Hermitian one, enabling the application of standard techniques such as eigen-decomposition.
By leveraging this m... | Rebuttal 1:
Rebuttal: Thanks for your careful reviewing and insightful suggestions. We address your concerns as below:
**R3.1 Contributions of (Eq.3).**
Thank you for your insightful thoughts on the difference from existing works. The contribution of our work lies in the development of a diffusion representation fram... | Summary: This paper studies the asymmetric kernel case for the diffusion map. The authors utilize the magnetic transform technique to develop a diffusion representation framework for the asymmetric kernel case. They investigate several properties of the proposed magnetic transform kernel. The challenge lies in defining... | Rebuttal 1:
Rebuttal: Thanks for appreciating the novelty of our work and for providing insightful comments. We address your concerns as below:
**R2.1 Some quantitative results would help figure out the advantages of the proposed method.**
We sincerely appreciate your insightful comments. In response, we have conduct... | Summary: The paper proposes proposes a new method called MagDM in which it connects Diffusion maps (DM) and the Magnetic transform (MT). DM is a nonlinear dimension reduction technique obtains a lower dimensional embedding by using the information of the diffusion distances that assume symmetry. However, in practice th... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and appreciation of the novelty. We address your concerns as below:
**R1.1 Quantitative experiments are needed.**
We sincerely appreciate your insightful comments. In response, we have conducted the quantitative experiments as suggested. For further details, p... | null | null | Rebuttal 1:
Rebuttal: Dear Program Chairs, Area Chairs, and Reviewers,
First of all, we would like to thank you for your time, constructive critiques, and valuable suggestions, which greatly help us improve the work. We are grateful to reviewers SsqM and sG91 for recognizing the novelty and significance of our work. T... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning threshold neurons via edge of stability | Accept (poster) | Summary: The paper is a study on the dynamics of neural network training, particularly focusing on the edge of stability. The authors explore the behavior of gradient descent in the context of a simple sparse coding model. They find that the dynamics of training exhibit a phase transition at the edge of stability, wher... | Rebuttal 1:
Rebuttal: Thank you for your review. We address some of your points below.
- Regarding freezing the biases: we acknowledge that even with frozen biases, it is possible for neural networks to succeed at learning binary CIFAR-10. The full story of generalization in deep learning is complex and we do not claim... | Summary: The authors analyze the dynamics of pairs of ReLu neurons with an input bias, no weight matrix, and a readout layer. They show that it takes large learning rate for non-zero biases to be learned, and at these large learning rates there are formal guarantees of EOS behavior as well. They use a model with random... | Rebuttal 1:
Rebuttal: Thank you for the review, and for the additional references; we will cite them. We address your points below.
- Firstly, regarding the advantage of our toy model analysis over prior works: this is a fair criticism. We argue that compared to prior models, our model and analysis is the simplest demo... | Summary: In this paper, the authors studied the problem of edge of stability (EoS) phenomena (training with large learning rate) in simple settings. Specifically, the authors first studied the problem of minimizing $\ell((xy)$ ($\ell$ is loss function) and showed that under certain condition of loss function, gradient ... | Rebuttal 1:
Rebuttal: Thank you for your review. We address some of your points below.
- We agree that extending our analysis to more general settings is of great interest, although it’s beyond the scope of this work. In fact, a recent work [1] was able to prove similar results to ours for more general settings of neur... | Summary: The paper studies two simple problems (single-neuron linear network and mean ReLU model for sparse coding with unknown basis) to investigate learning of threshold neurons. In particular, the authors discover a threshold for the learning rate below which threshold neurons are not learned and where NN training f... | Rebuttal 1:
Rebuttal: Thank you for your generous review and we are glad that you enjoyed our paper. We will follow your suggestion to move Figure 1 to page 2 and we will correct the typos that you mentioned. Below, we answer your questions:
- In Fig. 1, the bias was indeed initialized at 0. Note that Figure 1 plots th... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States | Accept (poster) | Summary: The authors present a grid-independent generative model to learn PDEs, which supports irregular grids. Amortized variational inference is employed for posterior approximation and multiple shooting, a recent method to train neural ODEs efficiently, is used and adapted to PDEs.
Results show a quite significant ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address the raised concerns below.
**Additional experiments**
Thank you for your suggestions. We agree that more experiments are required to fully demonstrate the advantages of our method. We conducted multiple additional experiments and present the results... | Summary: The authors proposed a new method in learning PDEs by combining interpolation and NODEs. The difference from NODEs is that the authors uses spatial derivatives in hidden dynamics as PDE, compute the loss between shooting gaps with KL divergence, and compute the initial conditions with a transformer. The author... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address the raised concerns below.
**Using multiple shooting in generative modeling, and gap minimization.**
Indeed, the first two paragraphs of Section 3.2 only motivate the use of multiple shooting, but the rest of the section goes into details about how ... | Summary: This work proposes a latent variable model for PDEs with an encoder-decoder architecture that evolves the latent variables in time with a neural ODE. The model achieves independence of the locations of the evaluation points by linearly interpolating the data onto points distributed in a fixed pattern around ea... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address the raised concerns below.
**Motivating the use of generative modeling and Bayesian inference**
We agree that we can improve our presentation regarding these points. Generative modeling is a standard approach in state space modeling which allows to ... | Summary: This paper introduces a new method for learning time-dependent PDE solutions with noisy, partially-observed data on irregular grids. This setting is quite challenging and is aligned with real-world data acquisition. They adopt a generative framework and combines two techniques for solving PDEs: the collocation... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address the raised concerns below.
**Paper structure**
Thank you for your suggestions regarding the structure of our manuscript. We will improve our work by incorporating them into the revision. We will move details about forecasting to the main text and wi... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their careful reading and detailed comments. We believe that the suggested revisions have improved our manuscript. We believe our answers address all review comments, but if anything remains unclear we are happy to provide further clarifications.
We provid... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a grid independent model for learning PDEs from noisy experimental data. The proposed framework is probabilistic with an encoder that handles data efficiency and makes the solution grid independent. I think better differentiating from Ayed et al. and DINO would be helpful for the novelty co... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address the raised concerns below.
**Comparison to numerical solvers**
As discussed in Section 2, we assume the dynamics are unknown, hence numerical solvers are not applicable since they require fully known system dynamics.
**Comparison to other methods**... | Summary: Modeling systems with spatiotemporal evolutions, such as the ones arise in problems governed by PDEs, is challenging. This is more pronounced when the system's underlying mechanisms are too complex or unknown. The authors propose a grid-independent generative model from noisy and partial observations on irregu... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address the raised concerns below.
**Heuristics**
We would be glad to rely on theory to guide all our choices, but unfortunately it is not always available for neural network parameterized models, and what remains is heuristics and empirical evidence.
An i... | null | null | null | null |
Iterated Deep Q-Network: Efficient Learning of Bellman Iterations for Deep Reinforcement Learning | Reject | Summary: This paper focuses on learning the projection of the empirical Bellman operator's iteration on the space of function approximator (neural model). This being done through increasing the number of gradient steps using multiple heads with a certain form of update. While retaining the same total number of gradient... | Rebuttal 1:
Rebuttal: We thank the reviewer for the extensive feedback. It seems that this work has raised questions we are happy to discuss.
**Weaknesses**
> 2. The choice of $K$ [...]
Several reviewers have raised this point. We kindly ask the review to refer to point $I$ of the general answer that addresses this ... | Summary: This paper presents Iterated Deep Q-Network (iDQN), a new DQN-based algorithm that incorporates multiple Bellman iterations into the training loss. The paper highlights the limitations of traditional RL methods that only consider a single Bellman iteration and proposes iDQN as a solution to improve learning. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
**Weaknesses**
> 1. It would be helpful if the paper included more comparisons with widely-known baselines in the field. While the paper compares iDQN to DQN and Random Ensemble Mixture, it would be valuable to see how iDQN performs against other ... | Summary: The paper considers the problem of how to get accurate approximations of optimal Q-functions. The paper introduces a new algorithm called iterated DQN (iDQN). iDQN incorporates multiple consecutive Bellman iterations into the training process, which aims to allow for better approximation of optimal action-valu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and insightful questions.
> 1. Can the authors also develop a theoretical guarantee for iterated DQN by analyzing its convergence speed besides the intuitive explanation in Section 4.1? It would be better if the claim is also demonstrated theoretically.
Sev... | Summary: This work proposes an extension to DQN aimed at improving projection steps in Q value updates. There are two main contributions of the paper:
- The paper intuitively explains the Q-value learning characteristics of DQN variants caused by a mismatch between the optimal Bellman operator and the set of represent... | Rebuttal 1:
Rebuttal: We thank the reviewer for the useful suggestions and comments.
**Weaknesses**
> 2. As discussed by the authors, iDQN doesn't outperform more recent DQN variants which employ other tricks to improve performance.
Several reviewers have raised this point. We kindly ask the review to refer to point... | Rebuttal 1:
Rebuttal: **General comment to all reviewers.**
We thank all the reviewers for their valuable feedback. For each reviewer, we address their concern with specific answers. Here, we summarize the common points, describe the content of the rebuttal PDF in the attachment, and provide answers that we will add t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, a new variant of DQN algorithm, iDQN, is proposed by replacing the classical Bellman iteration with several consecutive Bellman iterations and using multiple Q networks.
Intuitively, this new Bellman operator propogates reward sigals more efficiently thus speeds up learning, with the cost of mor... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and feedback.
> 1. It will make this work much better if a theoretical analysis of the proposed Bellman operator is provided, such as convergence speed, the affect of the number of Q networks, etc.
Several reviewers have raised this point. Theref... | null | null | null | null | null | null |
Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality | Accept (spotlight) | Summary: This paper introduces a CIL approach that specifically addresses the use of pre-trained models. It is intriguing and significant to explore the most suitable CIL method for pre-trained models.
Strengths: 1. CIL and how to do CIL in pre-trained models are very important
2. The writing is smooth and very easy... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version.
**Q1: Considering the extensive exploration of pre-trained models in NLP, it is crucial for this paper to compare... | Summary: This work provides a comprehensive analysis of state-of-the-art prompt-based approaches for continual learning with the use of pre-training. The authors empirically demonstrate a clear performance degradation of current strategies under realistic self-supervised pre-training and extensively analyze the exposed... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version.
**Q1: Based on proofs in supplementary materials, is the notation $\bar c$ in Eq.(8) equal to $y$? Please check i... | Summary: This paper studies application of prompts in pre-trained models for continual learning. Building upon the problem of [1], the authors introduce the Task-Adaptive Prediction (TAP) for the CIL problem using pre-trained networks. They demonstrate that a good TAP, WTP, and TII performances are necessary and suffic... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version.
**Q1: The method heavily relies on pre-trained models trained with ImageNet. This can be an issue, especially for... | Summary: The authors provide strong empirical analysis on existing "prompting for continual learning" papers. They propose a new hierarchical prompting method that includes several components which take advantage of unstructured data representations. The authors not only propose an interesting approach with SOTA perfor... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version. We are pleased that our implementation code is appreciated. It will be published after acceptance.
**Q1: Task-id ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their great efforts and constructive comments, which help us to further improve the manuscript. We have tried our best to address these comments with additional experiments, explanations and discussions. Please let us know if you have any further questions.
Pdf: /pdf/775... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper suggests a new prompt-based continual learning method by leveraging combinatorial objectives with *within task prediction*, *task-identity inference*, and *task-adaptive prediction*. The paper first summarizes recent prompt-based continual learning techniques and demonstrates their unstable performan... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and insightful comments. Below, we provide a point-to-point response to these comments and summarize the corresponding revisions in final version.
**Q1: The effect of the prompt ensemble in the method is not discussed.**
A1: We would respectfully point out th... | null | null | null | null | null | null |
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering | Accept (poster) | Summary: The paper proposes CAAFE, a Context aware automated feature engineering approach to support auto ML by utilizing Large Language Models (LLMs). Proposed method generates new features or transforming the feature space from tabular data by using LLMs and incorporate them to train new models. Transforming the feat... | Rebuttal 1:
Rebuttal: We deeply appreciate the time and effort taken by you to evaluate our paper. We've carefully considered each point raised and aim to address them comprehensively below.
For this year's NeurIPS revisions, it is not possible to upload a modified paper, but only a one-page rebuttal PDF. Hence, we've... | Summary: This paper presents Context-Aware Automated Feature Engineering (CAAFE) for integrating domain knowledge into the AutoML process using LLMs. CAAFE automates feature engineering for tabular datasets, generating Python code that generates semantically meaningful features based on the dataset and a textual descri... | Rebuttal 1:
Rebuttal: We deeply appreciate the time and effort taken by you to evaluate our paper. We've carefully considered each point raised and aim to address them comprehensively below. Thank you for appreciating the paper presentation!
We would like to address your questions and our changes in response to them i... | Summary: In this paper, the authors propose a feature engineering method that builds upon LLMs. Features are engineering in an iterative process of prompting the LLM to generate code for new features, evaluating the features with a ML model and generating a new prompt. Hence, the overall approach follows the common wra... | Rebuttal 1:
Rebuttal: We deeply appreciate the time and effort taken by you to evaluate our paper. We've carefully considered each point raised and aim to address them comprehensively below. Thank you for appreciating the paper presentation - to answer your question about GPT-4 use: We did indeed use GPT-4 to prepare t... | Summary: This paper presents a novel approach to automated feature engineering utilizing large language models (LLMs). Authors propose to use LLMs to generate code for feature generation. In the proposed method CAAFE, LLM is given a prompt with dataset description and a task of writing code for feature generation, the ... | Rebuttal 1:
Rebuttal: We deeply appreciate the time and effort taken by you to evaluate our paper. We've carefully considered each point raised and aim to address them comprehensively below. We are especially happy that you appreciate the interpretability of our generated features. We have in the meantime received lot... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive feedback. We believe that the reviews have helped us to improve our work significantly. For this year's NeurIPS review, it is not possible to upload a revised paper, only a one-page rebuttal PDF. Hence, we've detailed the changes we'll be making in the fin... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed Context-Aware Automated Feature Engineering (CAAFE) approach to integrate new features learned from dataset descriptions using large language models into AutoML process for tabular datasets. The proposed approach was evaluated using 14 datasets.
Strengths: The paper is well-written with cle... | Rebuttal 1:
Rebuttal: We deeply appreciate the time and effort taken by Reviewer QFgW to evaluate our paper. We've carefully considered each point raised and aim to address them comprehensively below. We would also appreciate you reading through the general reviewer feedback, which outlines the changes we made and migh... | null | null | null | null | null | null |
Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models | Accept (oral) | Summary: This paper proposes a diffusion-based method for fMRI-guided image synthesis and claims that it can identify the selectivity of various ROIs in the visual cortex. The motivation of this work is well-defined and is of vital importance to the computational neuroscience field. While the method in this work is int... | Rebuttal 1:
Rebuttal: We are strongly encouraged by your evaluation that our work is interesting for neuroscience, it is well-written, and it has good experiments. We appreciate your advice on clarification. We address specific comments below and refer to the general response for results.
> **Scope of the paper**
We ... | Summary: The paper presents a new algorithm to guide a diffusion model to decode maximally activating images for particular voxel subregions of human fMRI using an encoding model trained to predict brain activity from images. The algorithm identifies stereotypic features for defined ROIs, such as faces or food items. T... | Rebuttal 1:
Rebuttal: We are deeply grateful for your excellent suggestions! We will address specific questions below. Please see the PDF in the general response for additional figures.
> **Additional validation**
We agree with your comments on additional validation. **There is ongoing work to investigate the perfor... | Summary: This study proposes BrainDIVE, a system for synthesizing optimal stimuli for any given region of interest in the brain. The model combines a pretrained latent diffusion model for image generation with a linear “brain encoder” trained to map CLIP feature vectors onto the corresponding brain activity. At test ti... | Rebuttal 1:
Rebuttal: We appreciate your detailed and concrete suggestions! We will incorporate all of your feedback into our paper.
> **Comparisons to prior work**
Indeed, our work builds upon the foundations laid by Ratan Murty et al. (2021), Ozcelik et al. (2021), and Ozcelik et al. (2023). It was not our intenti... | Summary: This paper introduces Brain Diffusion for Visual Exploration (BrainDiVE) that aimed at exploring the fine-grained functional organization of the human visual cortex. Motivated by the limitations of previous studies that relied on researcher-crafted stimuli, BrainDiVE leverages generative deep learning models t... | Rebuttal 1:
Rebuttal: Thank you for the excellent suggestions. We address specific questions below, and will include additional details in the general response.
> **Formatting and presentation**
We will update our paper to follow other image synthesis papers like OpenAI's DALL-E 2 [1] and GigaGAN [2], they similarly s... | Rebuttal 1:
Rebuttal: We are grateful to all reviewers for their constructive suggestions, which we agree will significantly improve the communication of our work.
We are very encouraged by reviewers’ evaluation on the quality of this paper. All four reviewers find the work interesting ("methodology shows promise in c... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
LuminAIRe: Illumination-Aware Conditional Image Repainting for Lighting-Realistic Generation | Accept (poster) | Summary: This paper proposes a method for illumination-aware conditional image repainting (LumiAIRe).
Different from conventional conditional image repainting (CIE), LumiAIRe combines environmental lighting estimation, 3D normal estimation, and illumination injection for achieving harmonized lighting effects in both f... | Rebuttal 1:
Rebuttal: Thanks for your very detailed review and suggestions, and here we give responses to the mentioned concerns. We are looking forward to discussing with you during the author-reviewer discussion period.
### Concerns about data diversity
We have tried our best to collect car models with sufficient ... | Summary: This paper presents the ilLumination-Aware conditional Image Repainting (LuminAIRe) task to address the unrealistic lighting effects based on recent conditional image repainting (CIR) methods. The main contributions include : 1) introducing a new task of ilLumination-Aware conditional Image Repainting (LuminAI... | Rebuttal 1:
Rebuttal: Thanks for your very detailed review and suggestions, and here we give responses to the mentioned concerns. We are looking forward to discussing with you during the author-reviewer discussion period.
### More details on dataset creation
> How to perform the "warping" to achieve aligned envmap?
... | Summary: This paper proposes a new method called LuminAIRe to address unrealistic lighting effects in image repainting analogous to cut-and-paste object insertion. This method estimates 3D geometry and environment lighting conditions from background images and parsing masks and uses physically-based illumination render... | Rebuttal 1:
Rebuttal: Thanks for your very detailed review and suggestions, and here we give responses to the mentioned concerns. We are looking forward to discussing with you during the author-reviewer discussion period. Due to the length limit, please refer to the global response for detailed reference items.
### Mo... | Summary:
This paper tackles the task of illumination-aware conditional image repainting. Given an input image and a set of conditions, the proposed method aims to inpaint / re-generate a certain region based on the input conditions. This can be used to achieve functionalities such as object insertion and image composi... | Rebuttal 1:
Rebuttal: Thanks for your very detailed review and suggestions, and here we give responses to the mentioned concerns. We are looking forward to discussing with you during the author-reviewer discussion period.
### Comparison of different lighting representations
The parameters in **Eq. 9** share many com... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable and constructive feedback on our paper, and we are looking forward to a more comprehensive discussion during the author-reviewer discussion period.
We are glad and encouraged to see that the reviewers’ comments that our paper is “well-written and easy to ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposed a learning-based method for conditional image inpainting. The method
takes an image with a masked region and the conditioned attributes as input, and
synthesizes a new image by filling the masked region. Previous works for this task
usually fail to generate images with realistic shading e... | Rebuttal 1:
Rebuttal: Thanks for your very detailed review and suggestions, and here we give responses to the mentioned concerns. We are looking forward to discussing with you during the author-reviewer discussion period.
### Imperfect parsing masks as input
> What if a rough mask that may extend to regions outside... | null | null | null | null | null | null |
Diffused Task-Agnostic Milestone Planner | Accept (poster) | Summary: This paper proposed a novel method to solve long-horizon, sparse-reward tasks and multi-task problems, which outperforms offline RL methods on many benchmarks.
Strengths: - Provide an elegant and general idea to solve sparse-reward problem -- which is hard for RL-based methods.
- The paper is well-written and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments including the positive comment on the soundness of our paper and the suggestion of ablation study to make our manuscript more instructive.
We present the responses to the reviewer's concerns and questions below.
**Q. If the authors can provide more ... | Summary: This paper extends diffusion-based latent milestone planners to long-term planning, vision control, and multi-task settings. Specifically, an encoder is trained to project observations into the latent space. The authors employ goal-conditioned imitation learning to train both the encoder and the prior goal-con... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments including acknowledging our contribution on proposing an effective method to generate shortest path and suggestions to strengthen our paper.
We present the responses to the reviewer's concerns and questions below.
**Q. Could the authors also provid... | Summary: This paper introduces a hierarchical architecture named DTAMP to solve sequential decision-making problems. Specifically, the high-level part of the architecture is realized by a diffusion model to decompose the long-term goal into several short-term milestones. Then, the low-level part makes basic decisions a... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments including summary of our contributions and pointing out some of our explanations that was not clear enough.
We present the responses to the reviewer's concerns and questions below.
**Q. The biggest concern would be that the training process is not clear... | Summary: This paper uses sequence modeling method in applications like long-term planning, vision-based control, and multi-task decision-making. They formulate a novel method which uses diffusion-based generative sequence model to plan a series of milestones in a latent space and to have an agent to follow the mileston... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments including summary of our contributions and pointing out typos we missed.
We present the responses to the reviewer's concerns and questions below.
**Q. Language of the paper could be improved.**
A. Thank you for your valuable comment for improving t... | Rebuttal 1:
Rebuttal: Dear all the reviewers.
We thank the reviewers for acknowledging the contributions of our work and for making constructive comments to improve the submitted manuscript.
We are pleased that reviewers find out the major strengths of the proposed method, which can be summarized as follows:
* Plannin... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies the problem of latent-space planning for sequential decision-making tasks using generative diffusion models. Similarly to works such as Diffuser, they train an endpoint-conditionned generative model to generate sub-goals along a path starting at a given state and reaching a certain goal. Diff... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable reviews including the positive comment on legibility.
We present the responses to the reviewer's concerns and questions below.
**Q. Can you elaborate on the distinction of your DTAMP method vs the Diffuser algorithm?**
A. The main distinctive features of DT... | Summary: The paper proposes uses a diffusion-based generative model to plan a sequence of milestones in a latent space and have the agent follow this latent plan to accomplish a given task. The authors show results on AntMaze, and more importantly on the CALVIN Benchmark to show the effectiveness of their method for i... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments, including acknowledging the contribution of our work and pointing out unclear explanations.
We present the responses to the reviewer's concerns and questions below.
**Q. Can the authors describe how can the policy recover from failure to reach a `mile... | null | null | null | null |
SING: A Plug-and-Play DNN Learning Technique | Reject | Summary: The paper proposes SING, a simple gradient preprocessing technique that, combined with any optimizer of choice, argues for improved stability and generalization. The paper further provides a theoretical convergence analysis of the approach
Strengths: 1. The paper is clear
2. The technique is simple and easy ... | Rebuttal 1:
Rebuttal: First off, we would like to thank the reviewer for their time and feedback. We hope our answers will help clear up some misunderstandings.
1) **About the incremental aspect and the difference with adaptive techniques**
The novelty of this paper comes from a novel combination of existing ideas, t... | Summary: The paper proposed a simple and hyper-parameters-free way to improve the stabilization and generalization properties of optimizers used in deep-learning scenarios. They show that with gradient centralization and gradient normalization methods, SING can escape the local minima with large step sizes theoreticall... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for reviewing our submission.
1) **About the limited novelty**
The novelty of our work indeed comes from the combination of multiple ideas but also from the theory developed to explain the behavior of such a combination.
The relevance of the proposed co... | Summary: In this paper, authors have proposed a method (called SING) for stabilizing the optimization algorithms used in training of deep models. The proposed method is based on only a layer-wise standardization of the gradients without introducing any additional hyper-parameters. In addition, a theoretical analysis fo... | Rebuttal 1:
Rebuttal: First off, we would like to thank you for your time and comments, and we hope that our answers will help clarify the paper.
1) **Convergence to a stationary point**
As pointed out in Definition 3.1, for a saddle point $\mathcal{B}(x^*) = \lbrace x^*\rbrace$ hence $r=0$, and therefore Theorem 3.1... | Summary: The paper presents SING (StabIlized and Normalized Gradient), a new method designed to enhance the stability and generalization capabilities of the Adam(W) optimizer. SING involves a layer-wise standardization of the gradients that are input into Adam(W), and does not require the introduction of additional hyp... | Rebuttal 1:
Rebuttal: First off, we would like to thank you for your kind comments and your detailed feedback.
1) **Misalignment between theoretical analysis and practical method**
We refer the reviewer to point 4) of the global comment for more details about our theoretical analysis.
2) **Confusing definition of th... | Rebuttal 1:
Rebuttal: First, we thank all the reviewers for their time, consideration, and hard work.
1) **Reparameterization of Gamma [JvsR]**
Following the advice of reviewer JvsR, we decided to modify Equation (3) such that $\Gamma(x)\_i = \sqrt{D} \|x\_{I\_k}\|\_2$ for $k \in [\\!|1,D|\\!]$ and $i \in I\_k$. This... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes SING, a plug-and-play approach to enhance optimizers without introducing additional hyperparameters.
The idea consists of standardizing gradients in a layer-wise manner prior to the host optimizer’s execution, and is motivated by factors such as easier escaping of narrow minima and invarian... | Rebuttal 1:
Rebuttal: We would like to thank reviewer JvsR for their detailed and insightful review. The reviewer’s feedback allowed us to improve the presentation of the paper. We will make sure to thank them in the final version of the paper. We hope our modifications and answers will help the reviewer reconsider the... | null | null | null | null | null | null |
Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction | Accept (spotlight) | Summary: This paper studies the convergence of various algorithms falling under the umbrella of *Mean-Field Langevin Dynamics* (MFLD). Those algorithms encompass diverse approximations of the usual Langevin dynamics
$$ dX_t = -\nabla\frac{\delta F(\mu_t)}{\delta \mu}(X_t) + \sqrt{2\lambda}dW_t $$
on three main points:
... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments. We address the technical points below.
**Q:** *Some parts of the appendix are also a bit rushed, especially Appendix A, which only lists the necessary conditions for the examples to fit Assumptions 1 and 2 without any proof.*
**A:** Thank you ... | Summary: This work considers the analysis of mean field langevin dynamics as implemented algorithmically. i.e,
a) particle approximation b) time discretization and c) stochastic gradients. Under the assumption of certain logarithmic sobolev inequalities, prior works were mostly restrict
Strengths: Extensive and expli... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments.
**Q:** *What are the main technical insights in this work? It seems like an extension of Wibisono and Vempala's LSI analysis of LMC while accounting for the mean-field and stochastic gradients.*
**A:** First, we note that the analysis of the... | Summary: In the work authors study mean field Langevin dynamics under stochastic gradient updates and prove uniform in time propagation of chaos that takes into account discretisation, stochastic errors which allows to establish convergence rates of MFLD.
Strengths: 1. Strong theory with explicit bounds
2. Excellent o... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation.
Please find our answer to your concerns.
**Q:** Seeing some numerical evaluation could make results even stronger to support the theory.
**A:** Thank you very much for your suggestion.
Since the theoretical part already occupies the whole part of the p... | Summary: This paper studies the mean-field Langevin dynamics (MFLD) with stochastic gradient updates. In particular, the authors propose a general framework to prove a uniform-in-time propagation of chaos for MFLD. The authors establish the convergence rate guarantees to the regularized global optimal solution, simulta... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation. We address the technical comments below.
### Weaknesses:
**Q:** Purely theoretical paper. No numerical experiments are provided.
**A:** Since the theoretical part already occupies the whole part of the paper, we chose not to include the numerical exper... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper provides a set of results to analyze convergence of mean-field Langevin dynamics with a set of related algorithms. These results are in discrete time and space. Using log-Sobolev inequality techniques from optimal transport, which creates the opportunity for extensions to other settings. The propaga... | Rebuttal 1:
Rebuttal: Thank you for your supportive comments. We address the technical comments below.
### Weaknesses
**Q:** I think the differences from Nitanda and Chizat could be articulated more clearly.
**A:** Thanks for your suggestion. Please notice that the differences from Nitanda et al. (2022) and Chizat ... | null | null | null | null | null | null |
Intervention Generalization: A View from Factor Graph Models | Accept (poster) | Summary: This paper studies the problem of generalization in causal inference. In particular, it extends the factor graph to interventional factor graph (IFM). It shows when can such model be identified as well as proving practical algorithm for learning. The setting assumes knowing the factorization, this type of stru... | Rebuttal 1:
Rebuttal: Thanks for commenting that our paper is "well motivated with good theoretical results and empirical experiments"!
**Real-world applicability:** coming from a more domain-specific theory, such structures also emerge from models of equilibrium. Consider this example that can be found in [8], Sectio... | Summary: This work proposes the use of factor models as a graphical causal model to generalize from past experiments. The authors introduce factor models, describe their relative merit then describe how a factorization can be derived for a given intervention, and give approaches for estimation using deep energy based m... | Rebuttal 1:
Rebuttal: Thank you for finding our idea to be "very interesting", implemented with "elegant additions" on uncertainty quantification and a "through empirical evaluation"! In what follows, we address your questions.
**Trade-offs in the representation:** one of the main defining features of DAGs is the natu... | Summary: The authors present the interventional factor model, a more general formalization used to predict the effect of treatment on an outcome in unseen regimes. It is more general than existing formalizations since it does not assume causal graphs to be directed acyclic graphs (DAGs). The authors show in this new fo... | Rebuttal 1:
Rebuttal: Many thanks for your comments, and by finding our article "well written" and "original"!
**On identification/elicitation/learning:** we are not claiming that such a family is universally superior to DAGs, but there is no shortage of domains where energy-based formulations are more natural than d... | Summary: This paper consider how to use data from past interventions to allow it to generalize to new unseen interventions. This is an important practical problem to consider, as running additional experiments is often costly/infeasible. In order to tackle this problem, the authors consider a graphical models approach,... | Rebuttal 1:
Rebuttal: Thanks for all of your feedback, for the kind words on this being a "very important problem" and "innovative" in this solution! Moreover, thanks for asking clarification questions that will definitely improve on the presentation of the paper.
**Method clarification:** to fit a deep energy model, ... | Rebuttal 1:
Rebuttal: Thank you all for such detailed and helpful reviews! Getting the time and attention of no fewer than six experts is no small privilege.
Although there is some overlap among questions, it's not too substantive. As there are many reviewers, we think it will be most convenient for them if we make o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper maps from available observational and experimental datasets to unseen interventional distributions given the factorization of the joint distribution of the intervened system. They utilize an interventional factor model equipped with factor graphs to provide necessary and sufficient conditions for ca... | Rebuttal 1:
Rebuttal: Thank you for commenting that our paper is "quite interesting" and that it "has a nice flow in the writing and is easy to read". Much appreciated!
In the following, we address your comments and questions.
**"Some concepts... should have been defined... .":** We were struggling for space and we d... | Summary: The paper introduce Interventional Factor Models (IFM), a graphical model that encodes assumptions about a data-generating process and the interventions that can be performed on top of it. The model explicitly includes intervention variables to impose a factorization over the distributions generated by the mod... | Rebuttal 1:
Rebuttal: Thank you very much for the appreciation and excellent questions! We found the clarification questions about the generality of identifiability to be particularly useful for readers.
**On Theorem 3.2:** Indeed we don't make any claims of completeness outside of the class of PR transformations. We ... | null | null | null | null |
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation | Reject | Summary: The paper addresses the overlap violation problem in observational datasets for causal inference by presenting an interpretable balancing method for overlap violation identification and causal effect estimation for binary treatments. The method BICauseTree adapts decision tree classifiers to the stated problem... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, comments, and sharing his expertise. We respectfully disagree with their framing of our method as primarily “addresses the overlap violation problem”. Our method, first and foremost, estimates causal effects by stratification into sub-population with natural e... | Summary: This paper proposes a new method called BICauseTree for interpretable causal effect estimation. BICauseTree is a hierarchical bias-driven stratification method that identifies clusters where natural experiments occur locally. The method is designed to reduce treatment allocation bias and improve interpretabili... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review, insights, and comments, and for the time spent reviewing our work.
- Reliance on data quality and on model assumptions:
We agree that data quality is a major factor on the success of the model in estimating effects, however, this is true for all m... | Summary: This paper focuses on achieving interpretable causal effect estimation, where the goal is to ensure that each decision within the algorithm is explicit and traceable. The authors propose a decision tree-based balancing method to address this problem, which identifies clusters where local natural experiments oc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful comments and suggestions, and for time spent reading our work.
- paragraph spacing:
We thank the reviewer for noticing, we have found the source for this error and fixed the spacing, leaving the main text untouched (by slightly resizing the figures).
- ... | Summary: The paper introduces a decision tree methodology to identify regions where selection bias no longer ensures covariate balance. These regions, which have some level of interpretability, can then be removed in subsequent analysis.
Strengths: The paper presents an interesting decision tree methodology.
The pape... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, helpful comments and insights.
- Guarantee of balance:
As pointed, our method cannot guarantee covariate balance. This is true for most practical methods; Matching, for instance, guarantees balance only if done by exact matching on the covariates, which is im... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, helpful comments and for sharing their expertise.
We present in our work a model for effect estimation in observational data that is inherently interpretable, scalable, and has the useful consequence of abstaining from inference on subgroups where inference ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
OTOv3: Towards Automatic Sub-Network Search Within General Super Deep Neural Networks | Reject | Summary: OTOv3 is an automated system that trains general super-networks without pretraining or fine-tuning, constructs search spaces automatically, and produces high-performing sub-networks. Experimental results show competitive or superior performance compared to state-of-the-art methods across different benchmark da... | Rebuttal 1:
Rebuttal: Dear Reviewer bA4X,
We appreciate your constructive comments and valued suggestions. Please see our responses as follows. Look forward to further discussion.
- **Lacks novelty compared with OTOv2**.
> Thank you for the question. Please refer to our general response above along with a PDF to il... | Summary: This paper proposes OTOv3, an approach to train general supernets and discover promising subnetworks. It claims to be able to automatically generate the search space, and construct subnetworks based on hierarchical half-space projected gradient. The proposed approach has been evaluated on a number of datasets,... | Rebuttal 1:
Rebuttal: Dear Reviewer h4H8,
We appreciate your constructive comments and valued suggestions and responded in details below. Look forward to further discussion.
- **The delta between OTOv3 and OTOv2 are minor**.
> Thank for the question. Please refer to our global rebuttal response along with a PDF whi... | Summary: The authors process a training method to efficiently and automatically find optimal subnetworks without the need to configure the search space or a pre-specified supernetwork.
Strengths: - A clever way to build the search space without manual intervention using zero-invariant group partition
- Avoiding the n... | Rebuttal 1:
Rebuttal: Dear Reviewer csCP,
We appreciate your insightful comments and constructive suggestions. Please see our responses to the comments. Look forward to further discussion.
- **Can this approach be extended to transformer-based architecture search?**
> That is a great question. Certainly, OTOv3 can ... | Summary: The paper "OTOv3: Towards Automatic Sub-Network Search Within General Super Deep Neural Networks" presents a new automated system called Only-Train-Once (OTOv3) for Neural Architecture Search (NAS). Unlike existing NAS methods that often depend on pre-specified super deep neural networks with handcrafted searc... | Rebuttal 1:
Rebuttal: Dear Reviewer XuwW,
We appreciate your valued comments and favorable recommendations for our work. Please see our responses to the constructive suggestions. Look forward to further discussion.
- **The term search space and supernet are not well defined in the paper. To my understanding, superne... | Rebuttal 1:
Rebuttal: Dear reviewers and ACs,
We deeply appreciate all the insightful comments and constructive suggestions that helped us improve our manuscript. We have carefully addressed each comment and will include into the revision. Below, we present our responses to the general questions regarding the differen... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Test-time Training for Matching-based Video Object Segmentation | Accept (poster) | Summary: This paper investigates several test-time training methods for improving matching-based video object segmentation algorithms. It proposes and compares three types of test-time training methods and finds that the mask cycle loss works the best. The mask cycle loss forward/backward propagates a mask and requires... | Rebuttal 1:
Rebuttal: > __Timings for the fine-tuning stage__
We thank the reviewer for bringing attention to this. In the supplementary, we report that one iteration of tt-MCC takes ~814 ms for STCN (supp-line68). Timing estimates for a single video (on average and on DAVIS) are as follows: it takes roughly 3 second... | Summary: This paper points out that Current state-of-the-art approaches use a memory of already processed frames and estimate the segmentation masks of follow-up frames through matching. Lacking any adaptation mechanism, such methods are prone to test-time distribution shifts. To address this, this paper explores task... | Rebuttal 1:
Rebuttal: > __Lack of technical contributions [...] this seems to be a very similar technique that with minor modification (mask) from other works__
Although Cycle Consistency (CC) has been used in the past, our method is the first one to have such a loss tailored for the task of one-shot VOS, i.e. for a s... | Summary: This paper focuses on test-time training for matching-based VOS methods. Three methods are presented, including entropy loss, auto-encoder loss and cycle consistency loss. The cycle-consistency loss is tailored for VOS and utilizes the first-frame mask for supervision. Two evaluation protocols are provided, th... | Rebuttal 1:
Rebuttal: > __The proposed method focuses solely on triplets and overlooks longer temporal consistency.__
We generally followed exactly the same design that each base method uses during training and adopted the use of triplets for STCN and octuplets for XMem. We briefly explore performance using longer seq... | Summary: This paper revisits test-time training in video object segmentation (VOS) and introduces three losses (entropy loss, mask auto-encoder loss, and mask cycle consistency loss) that significantly enhance top-performing methods, particularly under extreme distribution shifts between training and testing sets. Addi... | Rebuttal 1:
Rebuttal: > __Clarify how the proposed model can be extended to handle multiple objects__
The proposed test-time training (TTT) method is a way of performing test-time adaptation over a given base method (STCN/XMem in our paper). It does not change the way the base method does inference, and for STCN and X... | Rebuttal 1:
Rebuttal: We would like to thank all five reviewers for insightful and constructive reviews.
We are pleased that the feedback is __overall positive__. Four out of five reviewers highlighted the significant performance improvements of our proposed method under test-time distribution shifts. We are happy th... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces an adaptation/test-time training algorithm for matching-based video object segmentation, specifically addressing the challenges posed by out-of-distribution videos(corruptions, stylization, and sim-to-real transfer). The proposed framework incorporates test-time training with various losse... | Rebuttal 1:
Rebuttal: > __Distinctions between this work and previous approaches__
Although Cycle Consistency (CC) has been used in the past, our method is the first on to have such a loss tailored for the task of one-shot VOS: Unlike methods like “Space-time correspondence as a contrastive random walk” [16] or the co... | null | null | null | null | null | null |
Robust Learning with Progressive Data Expansion Against Spurious Correlation | Accept (poster) | Summary: The paper describes Progressive Data Expansion (PDE), a new method for training models which are robust to spurious features. The idea of the method is to split the training into two phases: warmup and expansion. In the warmup stage, the dataset is balanced, and the model learns a classifier that ignores the s... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback and suggestions that helped us improving our work. We hope our clarifications answer your questions.
---
**Q1a**: Consider DFR^Val.
**A1a**: Thanks for pointing this out. We agree that all methods use the information from validation data for hyper-parameter... | Summary: In this paper, the author propose a learning framework for improving the performance of classifiers on the worst group in the presence of spurious features. The authors focus on a two-layer convolutional neural network. They first show that imbalanced data and easy-to-learn spurious features can lead to bias i... | Rebuttal 1:
Rebuttal: We appreciate the feedback and questions raised on the potential confusions. Please find our detailed response below, for which we hope that the reviewer considers increasing their evaluation of our work.
---
**Q1**: The theoretical analysis in the paper is focused on binary classification. The ... | Summary: The paper works on understanding and mitigating the impacts on spurious features. Specifically, the authors provide theoretical analysis on the learning process of a non-linear two-layer CNN, under spurious features. Theoretical insights reveal the need to start with balanced data, and progressively expand the... | Rebuttal 1:
Rebuttal: We're grateful for your strong support and suggestions on our work, for which we have accordingly made modifications to our manuscript. Please find our detailed response below for the questions raised in the review.
---
**Q1**: It would be better to mention/discuss the efficiency of some fine-t... | Summary: The authors propose a new method called PDE to address spurious features and improve generalization. Building on the existing literature, the authors consider an imbalanced binary classification problem where two types of features coexist: core features and spurious features. The core features can lead to good... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We believe that there are some misunderstandings of the contribution of our paper. In light of our clarifications below, we hope that the reviewer considers increasing their evaluation of our work.
---
**Q1**: This paper misses the effect of overparameteri... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for the feedback and especially the detailed suggestions that help us improve our manuscript. We appreciate that many reviewers commend our paper for its clarity, the alignment of our theoretical insights with empirical findings, the simplicity and novelty of our m... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization | Accept (poster) | Summary: The paper shows that when every agent has a known concave utility function, the Nash equilibrium exists and can be reached independently via best response updates. Moreover, when all agents have linear costs, the proposed budget mechanism (which modifies the utility function) will lead to Nash equilibrium that... | Rebuttal 1:
Rebuttal: Thanks for the comments, suggestions and questions.
**Weakness 1:** The paper makes two assumptions: a) the cost and payoff functions are common knowledge, and b) any agent utility (payoff and cost) only depends on the number of samples contributed. These assumptions limit the applicability/signi... | Summary: This paper studies a collaborative Federated Learning framework. Specifically, the authors proposed a utility model, which is the agent’s payoff function minus the data sharing cost function. Under the assumption that the payoff function is concave and the cost function is convex, the authors show the existenc... | Rebuttal 1:
Rebuttal: Thank you for the detailed comments.
**Question:** Can you provide more justification for why the agents always prefer to participate in the collaboration, even if the data-sharing cost is zero when the agent quits the collaboration?
**Response:** In a general federated learning framework an ag... | Summary: The paper proposes a simple and elegant mechanism for incentivizing data sharing in FL. The mechanism is budget balanced, and any p-mean welfare is maximized at Nash equilibrium. We show the existence of Nash equilibrium (NE) under mild assumptions on agents' payoff and costs. They also show that agents can di... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions.
**Weakness 1:** The mechanism seems to be largely based on the previous literature on the public good provision, especially (Falkinger et. al 2000). The mechanism is basically the same as the one in (Falkinger et. al 2000).
**Response:** Indeed our mech... | Summary: The authors study the Nash equilibrium in federated learning, under the assumption of concave utility and convex cost, and design welfare maximizing payment for users. Experiments are conducted to verify the mechanism.
Strengths: The topic is of important value, and the paper is well written.
Weaknesses: My ... | Rebuttal 1:
Rebuttal: Thank you for your comments and questions.
**Weakness 1:** The analysis of equilibrium and best response dynamic is standard, especially under the assumptions such as convex cost and concave utilities.
**Response:** Using fixed point theorems to prove the existence of Nash equilibrium is a wide... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time, suggestions and questions that we believe will improve the quality of the paper. Below we summarize our overall response to the reviewer’s questions and comments.
- We discuss our assumption of agent costs being common knowledge. We borrow this assumption fr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Loss Decoupling for Task-Agnostic Continual Learning | Accept (poster) | Summary: This paper investigates the stability-plasticity tradeoff problem in a task-agnostic continual learning (CL) setting by decoupling the loss of the new task (LODE). LODE introduces two separate objectives for the new task: new/old class distinction and new class distinction. The authors analyze the impact of th... | Rebuttal 1:
Rebuttal: **Q1: The experiments only employ ResNet18; it would be beneficial to include a larger model for evaluation.**
**A1**: Our experiments are built on top of the mammoth [3] continual learning repository in PyTorch like many existing works [2,3]. This repository consists of only ResNet18 and a small... | Summary: The paper titled "Loss Decoupling for Continual Learning" addresses the challenge of catastrophic forgetting in continual learning, where a model needs to learn multiple tasks sequentially. The paper focuses on the task-agnostic problem, where task identities are not available during inference. The main contri... | Rebuttal 1:
Rebuttal: **Q1: The paper focuses solely on the task-agnostic problem. ... investigate the application of loss decoupling to different problem settings.**
**A1**: Task-agnostic problem we consider in this work is a challenging problem where task identities are not available during testing. In contrast, for... | Summary: Several replay based continual learning methods decouple their loss into a loss based on samples from the replay to maintain performance on old tasks, and a loss based on data from the current task to learn the new task. This paper proposes to further decouple the latter loss into a component that helps the mo... | Rebuttal 1:
Rebuttal: **Q1: For section 4.2.3, did you do a hyperparameter search for the values of $β_1$ and $β_2$ for the methods that weren’t yours?**
**A1**: In Section 4.2.3, we did not do a hyperparameter search for the values of $β_1$ and $β_2$ for the methods that weren’t ours and kept their value consistent w... | Summary: This paper investigated class incremental continual learning (CL). By showing that two objectives, i.e., classifying among new classes and classifying between new classes and old classes, can have different impacts on the performance of CL, this paper proposed to decouple the new task loss and assign different... | Rebuttal 1:
Rebuttal: **Q1: The results in Figure 2(a) seem very counter-intuitive. The authors didn't give a convincing explanation of the underlying reason for the phenomenon.**
**A1**: The results in Figure 2 (a) of the paper are intuitively reasonable. First, since replay-based methods usually keep limited samples... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' positive comments:
1. The problem is interesting to investigate the different impacts inter-task and intra-task classification on the performance of class incremental CL. (reviewer Y5HQ)
2. The proposed method is intuitive and can improve performance in many continual ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Make You Better: Reinforcement Learning from Human Gain | Reject | Summary: The authors present a new reinforcement learning objective, dubbed Reinforcement Learning from Human Gain (RLHG), that explicitly incorporates an understanding of human performance with an intervention into the objective function. They show that training with this added component improves outcomes in a MOBA, b... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper! We greatly appreciate your feedback on our work. We provide clarification below for your questions and concerns. If you have any further questions or comments, we will be happy to discuss them further.
----
**Q1**: Clarification on Weaknesses
**A1**:... | Summary: This paper proposes a new method called Reinforcement Learning from Human Gain (RLHG) to effectively enhance human goal-achievement abilities in collaborative tasks with known human goals. The paper evaluates the RLHG agent in the widely popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings, by ... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper! We greatly appreciate your feedback on our work. We provide clarification below for your questions and concerns. If you have any further questions or comments, we will be happy to discuss them further.
----
**Q1**: Clarification on Contributions
**A1... | Summary: The authors propose Reinforcement Learning from Human Gain, an RL algorithm that explicitly optimizes for enhancing human abilities in cooperative human-AI settings. Given a predefined set of human goals, the main approach first learns a value network to estimate the primitive human performance at achieving sa... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper! We greatly appreciate your positive feedback on our work. We provide clarification below for your questions and concerns. If you have any further questions or comments, we will be happy to discuss them further.
----
**Q1**: how sensitive the method is... | Summary: This paper focuses on the fine-tuning of a pre-trained agent to assist and enhance the performance of a given human model in achieving specific goals. The authors assume access to a human model and a pre-trained agent. The authors propose a two-step approach.
1. The human model's initial performance is determ... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper! We greatly appreciate your positive feedback on our work. We provide clarifications below for your questions and concerns. If you have any further questions or comments, we will be happy to discuss them further.
----
**Q1**: Clarification on Contribu... | Rebuttal 1:
Rebuttal: Thanks to all reviewers for carefully reviewing our paper! We are grateful for your valuable feedback and suggestions, which we have addressed and incorporated into the revised manuscript. If you have any further questions or comments, we would be more than happy to discuss them.
Moreover, we hav... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks | Accept (poster) | Summary: The authors propose a novel dual-guided spatial-channel-temporal attention mechanism to audio-visual problems, which leverages pre-trained audio and visual encoders. And they show the improvement in various audio-visual tasks such as event localization, parsing, segmentation, and question answering.
Strengths... | Rebuttal 1:
Rebuttal: Dear Reviewer TYiB,
Thank you so much for giving us positive feedback and the very insightful questions. Please see our point by point responses below.
------
**Weakness 1 - The use of notations in Section 3.3 is very complicated and difficult to follow.**
Thank you for your suggestion. We wil... | Summary: This paper proposes a parameter-efficient approach, DG-SCT. DG-SCT can adapt pre-trained audio and visual models on downstream audio-visual tasks without updating pre-trained encoders (i.e., keep pre-trained encoders frozen.)
Strengths: $+$ DG-SCT can achieve state-of-the-art results on several downstream aud... | Rebuttal 1:
Rebuttal: Dear Reviewer myp2,
Thank you so much for taking the time to read our paper and providing very insightful and constructive comments and questions, please see the following for our point-by-point reply. We also conducted quite many new experiments to address your concerns.
------
**Weaknesses 1 ... | Summary: This work proposes a new mechanism to utilize audio-visual features as novel prompts to extract task-specific features from large-scale models. This work introduces an attention mechanism named Dual-Guided Spatial-Channel-Temporal (DG-SCT), which utilizes audio and visual modalities to guide the feature extrac... | Rebuttal 1:
Rebuttal: Dear Reviewer VeZU,
Thank you for the positive feedback and all these very valuable and constructive questions/suggestions! Let us respond to your questions point by point.
------
**Question 1 - The proposed mechanism more resembles with Adapters than Prompts.**
Thank you very much for you kin... | Summary: This paper mainly proposes a new attention mechanism named Dual-Guided Spatial-Channel-Temporal (DG-SCT), which utilizes audio and visual modalities to guide the feature extraction of their respective counterpart modalities across spatial, channel, and temporal dimensions. Experiments on 4 tasks shows the adva... | Rebuttal 1:
Rebuttal: Dear Reviewer BzrV,
Thank you for taking the time to consider our paper and giving us positive feedback!
------
Regarding your question, **"I am wondering if it would negatively impact the performance of joint classification tasks."**
It is a very good question. In joint classification task, ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal Transport-Guided Conditional Score-Based Diffusion Model | Accept (poster) | Summary: This paper introduces a novel approach for training conditional score-based models. The proposed approach integrates the optimal-transport-based semi-supervised learning method into the popular score-based modeling framework with SDE sampling processes. The theoretical analysis presented in Section 4 showcases... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions on our paper.
**Q1: Computational cost of our training method.**
In our experiments, considering that the number of paired data could be small in real-world applications, we set $K$ to 3 for animal images and 10 for digits. We wi... | Summary: This paper studied the conditional score-based diffusion model (SBDM), which is an important topic in current machine learning research, and has great potential for real-world application. To overcome the limits in existing methodology, i.e., requirements of paired data and lacks methodology for conditional mo... | Rebuttal 1:
Rebuttal:
We thank the reviewer for the insightful comments and suggestions on our paper.
**Q1: The justifications for OT-guided model could be enhanced. In Eq. (9) and Section 3.2, the OT-guided model is defined, where the difference between $J_{DSM}$ and $J_{CDSM}$ is the “weights” $\mathcal{C}$ and $H... | Summary: This paper propose Optimal Transport-guided Conditional Score-based diffusion model (OTCS), which is a novel approach in training unpaired / semi-paired image-to-image translation network. The proposed approach leverage unsupervised / semi-supervised optimal transport to earn coupling between images and use su... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions on our paper.
**Q1: Computational cost of the training method.**
As illustrated in Algorithm 2 in the Appendix A.2, our method consists of three processes in training：(1) *learning the potentials $u_{\omega}$ & $v_{\omega}$*, (2... | Summary: This paper proposes to use the Optimal Transport to guide the training of the conditional score-based diffusion model. The method first computes the coupling of the source and target data and then includes the coupling into the conditional score-based model objective. CelebA face super-resolution, AFHQ animal,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and suggestions on our paper.
**Q1: The OT part is computed first, and the couplings are fixed before training the diffusion model. So, the whole training pipeline is not end-to-end, which may yield sub-optimal results.**
In this paper, we aim t... | Rebuttal 1:
Rebuttal: Dear ACs and reviewers,
Thanks for handling our paper. We have responded to the comments of each reviewer individually and discussed how to revise our paper according to the comments and suggestions. Meanwhile, we attached a pdf file containing the figures as support material for the rebuttal.
... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Behavior and Convergence of Local Bayesian Optimization | Accept (spotlight) | Summary: Local Bayesian optimization has been popular high-dimensional optimization methods, which achieves the state-of-the-art empirical performance over various benchmarks. This paper investigates the behavior of local Bayesian optimization (BO) methods. Under a prototype algorithm, the authors empirically demonstr... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide our response as follows.
**Comment:** The assumptions in theoretical analysis may not hold in many real-world problems.
We definitely agree that these assumptions may not hold in real-world problems – most real world functions are not GP sample paths. The ... | Summary: Local Bayesian optimization methods are widely used to cope with the curse of dimensionality when optimizing high-dimensional black-box functions. Although these methods have very good performance empirically, virtually all theoretical results focus on the global optimization setting by proving bounds on the o... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide our response as follows.
**Comment:** The x-axis of Figure 1.
Thanks for spotting this typo! Indeed, the x-axis in Figure 1 is the indices of the dimension list {1, 5, 10, 20, 30, 50}. We will replot the x-axis of the figure.
**Comment:** Line 220.
We wi... | Summary: The paper investigates the behavior and convergence property of the local Bayesian optimization approach, in particular, the method GIBO [16]. The paper provides the convergence rates of GIBO in both the noiseless and noisy settings. The paper also performs various empirical experiments to understand the tight... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide our response as follows.
**Comment:** The paper actually focuses only on one of the local BO approaches (the GIBO method).
See the global response above.
**Comment:** Sample path assumption with known hyperparameter.
While this assumption is standard and... | Summary: This paper gives theoretical considerations on local Bayesian optimization methods for black box optimization. In particular, for gradient-based local BO, when the objective function satisfies the smoothness assumption, the rate of convergence to the stationary point is derived for the noiseless and noisy case... | Rebuttal 1:
Rebuttal: Thank you for your comments. We provide our response as follows.
**Comment:** It would be nice to have a theory that connects convergence to a stationary point and regret analysis.
We agree this is an interesting question to convert our convergence rates to global regret bounds. However, this is... | Rebuttal 1:
Rebuttal: We thank all reviewers for their encouraging comments and helpful feedback on our submission.
All reviewers share a common question of whether and how our theory might be extended beyond descent direction approaches like GIBO to trust region algorithms like TuRBO. In general we agree it would be ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs | Accept (poster) | Summary: This paper presents a theoretical analysis of curriculum learning, in which a neural network first presented with “easier” examples is able to more efficiently learn a target over more “complex” examples. The authors specifically consider the problem of learning $k$-sparse parities over a distribution which is... | Rebuttal 1:
Rebuttal: Please see the global response for the differences with the previous work [CM23].
- Q1: A minor weakness is that the algorithm is a bit restrictive and has some nonstandard modifications. In particular, during the first stage the biases are chosen to be very large Theta(d), and then resampled to... | Summary: The authors present a separation result between curriculum learning and standard learning in the number of noisy-(S)GD training steps for a 2-layer ReLU network for case of learning noiseless k-parity. While the precise training algorithm deviates from the common deep learning setup, experiments verify that le... | Rebuttal 1:
Rebuttal: - Q1. What about training only on easy samples?
A1. Note that neural networks do not know that the target function is a parity function a priori, thus, they cannot recover the parity function only from the sparse data. (We agree that if the learner knew that the target is k-parity it would have b... | Summary: This paper presents provable results showing the efficiency of curriculum learning for a specific problem setting with training data $(x,y)$, where $x\in(\pm1)^d$ is mixed distributed and $y=\Pi_{j\in\mathcal{S}} x_j\in(\pm1)$. What's more, the study utilizes a 2-layer fully connected network and the noisy-SGD... | Rebuttal 1:
Rebuttal: - Q1. This paper introduces a highly specific data setting. How can this setting be applied to real-world applications or more general settings?
A1. In our opinion, the closest curriculum method in the real world is the use of input length for NLP and reasoning tasks. Note that $+1$ is the identi... | Summary: This paper studies the impact of curriculum learning for the family of parity functions. Compared to previous work, in which their settings have been distanced from a realistic setting, e.g., considering a non-common activation function with a particular initialization, or a non bounded learning rate, this pap... | Rebuttal 1:
Rebuttal: Please see the global response for the differences with the previous work [CM23]. We address the rest of the remarks and questions in the list below.
- Q1. Does $T_1$ depend on $k$?
A1. $T_1$ depends on $\mu^k$, through the parameter $L_{\mu}$. This dependence appears in the 1-step arguments as ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their constructive comments. We address the remarks and questions in the lists below.
## Comparison to [CM23]
Our result is not only an improvement of [CM23] in terms of having more natural training settings and hyperparameters (i.e., we use SGD with bounded batch size... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization | Reject | Summary: This proposes a novel surrogate for Bayesian optimization (BO), kernelized tensor factorization (BKTF). The authors claim that BKTF is able to model more complex functions (nonstationary, nonseparable) compared to additive and product kernel Gaussian processes. For inference, they leverage Gibbs sampling to do... | Rebuttal 1:
Rebuttal: We appreciate your time and thank you for your review and your acknowledgment of the contribution of this model. Through the review, however, we believe there is some misunderstanding and misinterpretation of the paper. In the response below, we will try to explain and clarify these points as much... | Summary: Bayesian optimisation most commonly uses Gaussian Processes with the Squared exponential or Matern kernel as the surrogate model. The authors propose a new type of surrogate model, "Bayesian Kernelized Tensor Factorization" which introduces some advantages and disadvantages over Gaussian Processes. There seems... | Rebuttal 1:
Rebuttal: We appreciate a lot for your thorough and detailed review! For the comments in "Summary",
1: we are really glad that you talked about the "random discretization", since we also discussed this many times when proposing the model, we will talk more about this in the following; 2: for "implementatio... | Summary: The paper presents a new surrogate model called Bayesian Kernelized Tensor Factorization (BKTF) for Bayesian Optimization (BO).The BKTF model approximates the solid in the D-dimensional space using a fully Bayesian low-rank tensor CP decomposition. It uses Gaussian process (GP) priors on the latent basis funct... | Rebuttal 1:
Rebuttal: Thank you for your positive and detailed review! We are very glad and appreciate that you agree with the novelty of this work. We explain the weaknesses below, and hope the answers will address your concerns.
1. For the model scalability, we have discussed the computational cost of BKTF in terms o... | Summary: This paper presents a surrogate based on tensor decompositions for approximating complex functions, allowing for Bayesian-style maximization.
Numerical experiments show the (slight) superiority of this model over classical Bayesian approaches. However, a limitation of this approach is the small dimensionality ... | Rebuttal 1:
Rebuttal: Thank you for your review! We are really glad that you agree with the potential of this work. The main concern is the small dimensionality of the test functions, i.e., the scalability of the model, which relates to the computational cost and grid assumption. $\textbf{We have explained the computat... | Rebuttal 1:
Rebuttal: Dear reviewers,
Thank you for your time and the thorough and invaluable feedback. We appreciate that the contribution of our paper is well perceived and recognized, as reflected in the contribution scores (3, 3, 3, 3). We are also pleased that the concept of introducing a kernelized low-rank fact... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Spike-driven Transformer | Accept (poster) | Summary: This paper proposes a Spike-driven Transformer that incorporates the spike-driven paradigm of SNNs into the Transformer and is hardware-friendly for neuromorphic chips. The authors use SDSA to transform the multiplication operation between Query, Key, and Value into a combination of mask and sparse addition op... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We have carefully studied your comments and argue that **your concerns can be addressed**.
We incorporate the spike-driven paradigm into Transformer. In spike-driven Transformer, there are only sparse additions. This limitation is quite severe, but we stil... | Summary: This paper proposed a Spike-Driven Self-Attention (SDSA) module, which uses Hadamard product, column-wise summation, and spiking neuron layer to replace the matrix multiplication and softmax operation. Experiments on static and neuromorphic image classification demonstrate competitive performance and energy ef... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We first list your advice and questions, then give our detailed answers.
> *Weakness 1 and 2:* There is still a noticeable drop between the spike-driven transformer and the original transformer. Can the authors show some results of the model with more time ... | Summary: This paper proposes an improved spike transformer by replacing Spike-Element-Wise shortcut in an existing spike transformer (ref [20]) with Membrane Shortcut from spike ResNet (ref [26]) .
Strengths: Match SOTA ImageNet top-1 precision achieved by ResNet [26] with slightly lower estimated energy consumption.
... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. We first list your advice and questions, then give our detailed answers. **And, we really hope that you would re-consider your rating, given this work's notable contribution to the SNN field.**
> *Summary and Weakness 1*: This paper proposes an improved spi... | Summary: The authors propose a variant of transformer networks with spiking neurons based on the LIF neuron. The submission reformulates the self-attention to use sparse addition and masking, and modifies the residual connections to transmit information in the domain of membrane potentials.
They achieve a state of the ... | Rebuttal 1:
Rebuttal: Thanks for your insightful feedback and your time in reading our paper. Due to space constraints in the paper, the explanations for Eq.(15) and (16) are rough, and we put some details of energy consumption evaluation in the Supplementary Material. After careful inspection, we found that there is a... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Should We Learn Most Likely Functions or Parameters? | Accept (poster) | Summary: This paper investigates an important yet neglected question in machine learning: should we train the model in the parameter space or function space? The authors show the benefits and shortage of function space MAP estimation which could provide a guide for practitioners. Last but not least, the authors propos... | Rebuttal 1:
Rebuttal:
Thank you for your thoughtful and constructive questions and suggestions!
We were pleased that you found our manuscript to be **"well-written and easy to follow"** and that you highlighted our **"thorough analysis"** of the pros and cons of PS-MAP and FS-MAP.
We address your questions and comme... | Summary: This work investigates the task of performing inference over model functions rather than model parameters. In particular, the authors propose an objective function *function-space MAP (FS-MAP)*, which intuitively is the usual MAP objective where the prior term is taken over functions rather than parameters. De... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive questions and suggestions!
We were pleased that find that our submission is **"of interest to the community"** and that our findings **"have the potential to be impactful for future work on function-space inference"**. We were also happy to see that ... | Summary: This paper analyzes the question: should we find the parameter that maximizes $p(\theta|D)$ or $p(f_\theta|D)$, given a data distribution $D$.
The former is the classic MAP, or PS-MAP ("parameter space") in this work, and the latter is called FS-MAP ("function space").
The paper shows that neither is universa... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive questions and suggestions!
We were pleased that you highlighted the **"extensive comparison"** of limitations and applicability of PS-MAP and FS-MAP and that the paper addresses **"practical considerations"**.
We address your questions and comments ... | Summary: The paper compares the widely-used parameter estimation of machine learning against function estimation for the maximum a posterior (MAP) estimation. The authors provide detailed analysis and mathematical variation why there are significant difference in results for function vs. paramter MAP, and introduce con... | Rebuttal 1:
Rebuttal:
Thank you for your thoughtful and constructive questions and suggestions!
We were pleased that you **"enjoyed reading the paper"** and that you found it to be **"well-written"**.
We address your questions and comments below. Please let us know if you have any remaining questions.
---
> Even t... | Rebuttal 1:
Rebuttal: ## General Response to All Reviewers
We thank all reviewers for their feedback, support, and unanimous recognition that our paper is interesting, well-written, and thorough in presenting the pros and cons of the considered approaches. We start by providing a general response aimed at addressing t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores the question of learning most likely functions vs most likely parameters in the context of MAP estimation, a common setting including minimizing log likelihood with L1 or L2 regularization, as these regularizations correspond to imposing a prior. Differences in parameterization affect the M... | Rebuttal 1:
Rebuttal: We're glad you appreciated the paper and found it well-written. We address your comments below.
Please let us know if you have any remaining questions.
1. **When and why FS-MAP can outperform PS-MAP and reduce over-fitting**
Thank you for asking these important questions at the heart of this pap... | null | null | null | null | null | null |
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels | Accept (poster) | Summary: This paper proposes a deep learning paradigm, namely scale-teaching, to cope with time series noisy labels, with designing a fine-to-coarse cross-scale fusion mechanism for learning discriminative patterns by using time series at different scales to train multiple DNNs simultaneously. Additionally, each networ... | Rebuttal 1:
Rebuttal: **W1 & Q1**: The meanings of the operator || in Eq. (2), and how the operator || to operate the r_i^k, v_i^k.
**A1**: Thanks for your comment. The operator || in Equation (2) means to concatenate two vectors into a new vector. For example, vector **a** = [0,1], vector **b** = [2,3], then **a** |... | Summary: This paper a paradigm to improve the small-loss criterion for time series noisy labels, which mainly addresses the problem that external noises easily distort time series’ discriminative patterns. The motivation is clear, and experiments have shown good results. However, the reviewer is still concerned about s... | Rebuttal 1:
Rebuttal: **W1 & Q1**: The network is similar to multi-scale feature learning like FPN and the label propagation part is also not novel. Also, what is the fundamental difference between the proposed method with FPN and traditional label propagation in graph learning?
**A1**: Thanks for your comment. Bas... | Summary: The paper proposes a time series classification algorithm that works across multiple scales with a noise-robust loss function. The authors claim improvements due to the multi-scale network architecture and the objective function capable to handle label noise on a variety of datasets in UCR128 and beyond.
Stre... | Rebuttal 1:
Rebuttal: **Q1**: The work seems large incremental, given that the method (3.4) is composed of techniques used in [40], [44], and label propagation. Could you highlight the main conceptual novelty of your algorithm compared to previous approaches?
**A1**: Thanks for your question.
Literature [40] uses or... | Summary: This paper presents a "scale-teaching" framework aimed at addressing label noise in time series classification tasks. The proposed approach involves multiple encoders to extract multi-scale time series features. These features are then concatenated for the final loss calculation, enabling the selection of clea... | Rebuttal 1:
Rebuttal: **W1**: The utilization of a multi-scale framework has already been explored in time series forecasting tasks, and the application of a semi-supervised approach to enhance performance is not novel in the context of general LNL tasks [R1].
**A1**: Thanks for your comment.
In Section 2, we have d... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the estimation of persistence intensity functions and linear representations of persistence diagrams | Reject | Summary: This paper considers the problem of estimating _persistence intensity (resp. density) functions_, which are topological summaries arising when considering multiples realizations $\mu_1,\dots,\mu_n$ of persistence diagrams---which are counting measures supported on an open-half-plane $\Omega$. Namely, (Chazal a... | Rebuttal 1:
Rebuttal: **Motivation and rationale for the intensity function.**
We thank the reviewer for the constructive and expertly crafted comments. We agree with all of them! We regard the issues raised by the reviewer as features and not drawbacks of our approach. Please see our general comments for a clarifica... | Summary: The paper proves several theoretical inequalities involving the optimal transport distance, intensity, and density functions on a plane triangle with a non-standard boundary motivated by persistent homology.
Strengths: The paper rigorously proves in appendix C six theorems and three corollaries from section 3... | Rebuttal 1:
Rebuttal: **Comparison with previous literature.**
The notion of persistence intensity function is neither due to us nor new, and has been considered and used before: it has been suggested by [CWRW15], used in practical applications, e.g. by [WNv+ 21], and recently formalized and studied in great detail by... | Summary: This work develops a set of methods and theories for statistical inference for TDA based on samples of persistence diagrams:
a. The work focuses on the estimation of the persistence intensity function. The work also proposes the novel persistence density function, which is the normalized version of the persi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments.
1. **Experiments.** In the MNIST data set, we treat each image as a point cloud and construct a persistence diagram. Images representing the same number can be regarded as generated from the same distribution, while those representing different... | Summary: The paper tackles the problem of estimating the persistence intensity function, describing the distribution of rando persistence diagrams, and proposes a variant called persistence probability function that integrates to one. The paper starts with a theoretical analysis of the estimation error bound of the in... | Rebuttal 1:
Rebuttal: Our methodology can certainly be used in practice. However, our statistical guarantees only show the consistencies of our estimators. In order to carry out more sophisticated inferential tasks, e.g. hypothesis testing and confidence sets, a more refined analysis is in order. For example, in order ... | Rebuttal 1:
Rebuttal: We would like to clarify a few important points about our paper that perhaps we did not express as clearly as we intended to. We will include additional text in the introduction and throughout the manuscript to make sure there will not be confusion.
Our work is not intended to suggest an alterna... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Accelerating Motion Planning via Optimal Transport | Accept (poster) | Summary: With the aim of improving efficiency in motion planning, this paper proposes an efficient, gradient-free optimization method, MPOT. This is enabled by the introducing the Sinkhorn step, a zero-order parallelizable update rule that is guaranteed to converge under smoothness and boundedness assumptions. The auth... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments and constructive feedback.
**Regarding the comment about RRT\*/I-RRT\*:** Initially, our intention was to use RRT*/I-RRT* as an indicator of feasibility of the tested environments since they enjoy probabilistic completeness, i.e., at infinite time... | Summary: This paper proposes MPOT, a gradient-free method that optimizes a batch of smooth trajectories with nonlinear costs even for high dimensional tasks. In especial, a zero-order and highly-parallelizable update rule called Sinkhorn Step is proposed to facilitate the optimization process. MPOT outperforms the base... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation of our work.
1. **How do you process the sequences with different waypoint numbers?** Currently, for vectorizing the update of all waypoints across the batch of trajectories, we flatten the batch and horizon dimensions and apply Sinkhorn Step, t... | Summary: The paper focuses on the optimization of motion planning problem, by introducing a gradient-free method that is parallelizable and smooth. The method first probes the costs at several vertices, then decides the optimization direction by aligning the weight matrix with the cost matrix. Furthermore, to enforce t... | Rebuttal 1:
Rebuttal: We appreciate the positive feedback on our contributions.
Regarding the reviewer's comments and questions:
- **On the dimensionality of trajectory optimization problem**: We do not consider any movement primitives in our experiments. In all cases, we consider optimizing the full-state (the conca... | Summary: The paper proposes a trajectory optimization method using Sinkhorn Step which is able to perform efficient gradient-free batch optimization with non-linear objectives.
Strengths: Gradient-free motion optimization is an important area with broad potential applications.
The contribution of the paper is novel.... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our paper contributions.
Learning-based motion planning methods [1] typically utilize a dataset from previously successfully generated plans to learn generative priors for generating plans directly [2] or using rollout samples as initialization for motion pl... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in reviewing our paper, and their constructive and positive evaluations of our work. Below are the main questions and concerns from the reviewers that we have addressed. We briefly state the discussions as follows (full answers can be found in the r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Intelligent Grimm - Open-ended Visual Storytelling via Latent Diffusion Models | Reject | Summary: The authors aim to generate a series of coherent images given a series of text prompts resembling a visual storybook. To do so, the authors focus on two fronts: (1) leveraging the Stable Diffusion model to generate the series of images and (2) generating a diverse dataset used to train the model on a range of ... | Rebuttal 1:
Rebuttal: Thanks for the positive feedback on our task setting, architecture and results. Hope the response below will resolve your confusion and thus raise the score accordingly. We are always open to further discussion.
- W1&2. The name and necessity of human feedback
- Please refer to Q5 of the globa... | Summary: This work proposes the model StoryGen for the task of visual storytelling. Visual storytelling is a task to generate a sequence of consistent images given a story (several sentences). StoryGen is a diffusion model taking in both image and text as conditions, and outputs an image consistent with the conditions.... | Rebuttal 1:
Rebuttal: Thanks for your affirmation and appreciation for our writing and dataset. Hope the response below will resolve your confusion and thus raise the score accordingly. We are always open to further discussion.
- W1. Not much technical novelty
- Please refer to Q1 of the global response.
- W2. Limite... | Summary: The work focuses on the application of image generation based on a given story. Specifically, the proposed model is conditioned on the current sentence and prior generated images to ensure the story is engaging and coherent. A progressive training strategy is proposed to achieve a good model. To improve the pr... | Rebuttal 1:
Rebuttal: Thanks for your affirmation and appreciation of our writing and dataset. Hope the response below will resolve your confusion and thus raise our score accordingly. We are always open to further discussion.
- S2. Limitation of training the model without single-frame pre-training
- We have stated i... | Summary: This paper presents StoryGen, an auto-regressive image generator that leverages text and image conditioning. StoryGen incorporates a style transfer module integrated into the text-conditioning module, along with a visual context module. The authors also constructed a substantial dataset called StorySalon, comp... | Rebuttal 1:
Rebuttal: Thanks for your affirmation on our writing and dataset. Hope the response will resolve your confusion and raise our rating accordingly. We are always open to further discussion.
- W1. Inconsistent illustration
- Please check Q4 of global response.
- W2. Limited improvement of human feedback
- ... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for the valuable comments and feedback. Hope the following response can fully resolve the raised concerns. We will release all codes, datasets, and models for future research purposes.
- Q1. Novelty and Contribution(ALL)
We would like to start the rebuttal by elabora... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper propose an approach for fine-tuning diffusion models for the task of story generation, where a model must generate frames for sentences in a story. To do so, they propose adding adaptors conditioned on both images and text into a pre-trained stable diffusion UNet. The authors also introduce a scraped... | Rebuttal 1:
Rebuttal: Thanks for the positive feedback on our writing, dataset and simplicity of the proposed method. We hope the following response can fully resolve the raised concern and thus raise our score accordingly. We are always open to further discussion.
- W1. Presentation
- Please refer to Q4 of the globa... | null | null | null | null | null | null |
AmadeusGPT: a natural language interface for interactive animal behavioral analysis | Accept (poster) | Summary: This paper presented Amadeus, a natural language interface for interactive animal behavior analysis. To accommodate modern LLM (GPT3.5) for behavior analysis, the authors proposed to use an API document to constrain GPT3.5’s knowledge space. Furthermore, the authors proposed Dual Memory Mechanism to read and w... | Rebuttal 1:
Rebuttal: Firstly we greatly thank you for your rating and recognition of the novel approach and performance of AmadeusGPT. Below we address your questions the best we can, and also agree it will be very exciting to see this deployed on more behaviors.
To address your noted **weaknesses**:
- **(1)** Ther... | Summary: This paper proposes AMADEUS, a GPT3.5 powered system to perform animal behavior data analytics given a natural language user-given query and a video depicting animal behavior. The model works by using GPT-3.5 to generate python code which makes calls to a instance segmentation and animal pose model, as well as... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out the high performance, flexibility, and new additions that our work brings that related work have not addressed yet (dual-memory), and also we thank you for praising our related works- its a quickly moving and exciting time for LLMs+computer vision! We have w... | Summary: This paper proposes Amadeus, a novel natural language-based interface that leverages large language models like ChatGPT and vision-language models like SAM for animal behavior tracking and analysis. Amadeus leverages LLMs to generate code outputs, which can be executed to retrieve visual information and memory... | Rebuttal 1:
Rebuttal: We thank the reviewer for noting our novel idea and future potential to scale more broadly, we agree and are excited about this prospect. We also appreciate your noted weaknesses and do our best to address them.
To address your noted **weaknesses**:
- We will clarify Figure 4 by noting the gener... | Summary: This paper presents a novel interface and deep-learning system that enables interactive animal behavioural analysis using only natural language, on tasks that previously required extensive coding expertise and technical machine learning knowledge. The proposed framework integrates various modules based on LLMs... | Rebuttal 1:
Rebuttal: Firstly we would like to thank the reviewer for pointing out the novelty of our overall system and the novel dual-memory system. We’d like to add that we aimed to be generous in related works, we are one of the first systems to show LLM-generated executable code and error correction, and our Rephr... | Rebuttal 1:
Rebuttal: Firstly we’d like to thank the reviewers, all who noted novel (or appreciable) advances with our use of LLM for behavior and our dual-memory system for LLMs:
- “Overall I found the contribution novel, of potential immediate utility to academic neuroscience labs performing behavioral analysis, and ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This submission introduces a system, Amadeus, for performing behavioral analysis on animal videos. This system combines three elements: an API with descriptive docstrings for performing common behavioral analysis tasks, a modified version of GPT3.5 with an enhanced context window, and a prompt tuning of this G... | Rebuttal 1:
Rebuttal: Thank you for noting the novelty and potential strength of our application to life sciences, and thanks for the suggestion to consider more experimentally constrained (i.e., imaging) settings, which we can try in the future. Here, we focused on classical behavioral tests, mostly for this reason, a... | null | null | null | null | null | null |
Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion | Accept (poster) | Summary: This work adopts a conditional diffusion model to generate the abstract driving scene.
Strengths: It has great potentials to generate future scenes with diffusion model.
Weaknesses: (W1) No comparsion with existing works at all. It is significant to compare with previous methods to demonstrate the superiorty... | Rebuttal 1:
Rebuttal: Thank you for your detailed review!
* (W1) We have tried to address in the global response why we did not provide a comparison to autoregressive models, and the difficulty of a metric that highlights the need for joint inference, rather than conditional inference. Our hope is that the additional ... | Summary: This paper proposes a novel approach to use latent diffusion for driving scenario generation. The generated driving scenario is defined as bounding boxes with associated trajectories. The proposed diffusion model is comprised of an autoencoder to encode the BEV image into latent space and a conditional UNet fo... | Rebuttal 1:
Rebuttal: Thank you for your detailed review!
We have tried to address the comment about metrics in the global response. We respectfully disagree with the comment that “the paper only measures the rate of off-road trajectories”. We believe that the statistics reported in the different tables comparing the... | Summary: This paper tackles the problem of generating both the initial configuration and vehicle trajectories for driving scenarios given an input top-down map. It proposes a latent diffusion model that operates in the latent space of a learned autoencoder trained to decode vehicle boxes and trajectories. The model can... | Rebuttal 1:
Rebuttal: Thank you for your detailed review!
We tried to address the question about additional metrics in the global response, but we agree that we did not provide sufficient detail for the comparison and the advantages of the diffusion model, and will add these to the final paper if accepted. We apprecia... | Summary: This paper presents a conditional latent diffusion model (LDM) for generating oriented BEV vehicle bounding boxes, each associated with a 4-second trajectory (2s past, 2s future) at a 1s temporal resolution. The autoencoder component of this LDM uses an object detection architecture similar to CenterNet, with ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review!
We have tried to address the question about comparison to existing techniques in the global response, but we agree that we did not provide sufficient detail for the comparison and the advantages of the diffusion model, and will add these to the final paper if a... | Rebuttal 1:
Rebuttal: We appreciate the reviewers thoughtful and detailed comments, and agree with the majority of the comments and suggestions. In terms of the overall identified weaknesses, the reviewers’ concerns can be roughly grouped into:
* Absence of comparisons to previous work, especially previous autoregressi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Generalization bounds for neural ordinary differential equations and deep residual networks | Accept (poster) | Summary: 【Post-rebuttal Comments】
I thank the authors for the discussions after the authors' rebuttal. My questions are appropriately answered. So, I want to keep my score and vote for acceptance.
【Original Comments】
This paper evaluates the generalization performance of the class of functions represented as solut... | Rebuttal 1:
Rebuttal: > To the best of my knowledge, it is rare to impose smoothness between the weights of successive layers in a discretized ResNet model. In addition, the prediction accuracy of those models has yet to be confirmed to be sufficient.
We agree that imposing smoothness between successive layers is not ... | Summary: The authors present a generalization bound for a large class of ODEs in this work. They connect ODEs to residual architectures to control the generalization with the differences between weight matrices.
Strengths: The authors present a theoretical bound, which is something worth highlighting when everybody is... | Rebuttal 1:
Rebuttal: We consider in this paper a residual network model that is not adapted for time series: the input is a vector $x \in \mathbb{R}^d$ that is used on the first layer of the network. On the contrary, models for time series typically input data at each layer, corresponding to a new time step. The time ... | Summary: The paper explores the generalization ability of neural ordinary differential equations and deep residual networks through Lipschitz-based complexity arguments. The bound, specifically for the discretized version, involves the maximum magnitude of the difference between successive weight matrices, which is not... | Rebuttal 1:
Rebuttal: > The paper would benefit from additional citations, particularly where named theorems are mentioned in the main text.
We will add citations for the Picard-Lindelöf theorem (actually the citation is present in the appendix but not in the main paper) and Grönwall’s inequality.
> Have you conducte... | Summary: The paper provides a generalization bound for the large class of time-dependent and time-independent neural ODEs. In addition, by leveraging on the connection between neural ODEs and deep residual networks, the paper provides a depth-independent generalization bound for the class of deep residual networks. The... | Rebuttal 1:
Rebuttal: 1. The notations $X$, $Y$, $R_X$ and $R_Y$ are defined in Section 3.1 (lines 111-113).
2. $n$ is indeed the sample size (defined in Section 3.1, line 111), and the training sample is drawn i.i.d. from the same distribution as the test sample (as specified in Section 3.1). Does this answer your co... | 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 intend to take into account your suggestions, making use of the additional page.
We answer the specifics of questions pointed out by the reviewers in individual response... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration | Accept (poster) | Summary: This paper addresses the problem of sample-efficient exploration in sparse-rewards deep reinforcement learning. It builds on previous literature on the maximization of the state entropy as an exploration objective, which was mostly used in reward-free settings, proposing a value-conditioned state entropy objec... | Rebuttal 1:
Rebuttal:
Dear Reviewer FYPf,
We sincerely appreciate your efforts and insightful comments to improve the draft. We respond to each of your comments one-by-one in what follows.
---
**Q1. Importance of value estimation**
**A1.** In Figure 7(a) of the original draft, we reported that the performance furt... | Summary: This paper present a exploration technique that maximizes the value-conditional state entropy, which separately estimates the state entropies that are conditioned on the value estimates of each state, then maximizes their average. By only considering the visited states with similar value estimates for computin... | Rebuttal 1:
Rebuttal: Dear Reviewer pL2M,
We sincerely appreciate your efforts and insightful comments to improve the draft. We respond to each of your comments one-by-one in what follows.
---
**Q1. Motivation and Novelty**
**A1.** In this work, we aim to improve the sample-efficiency of deep RL algorithms by intro... | Summary: The paper proposes an improvement over a popular intrinsic reward based on state entropy. Instead of encouraging large state entropy uniformly over all states which may deviate the policy toward failure states, the proposed intrinsic reward motivates the agent to maximize the value-conditioned state entropy. T... | Rebuttal 1:
Rebuttal:
Dear Reviewer MwTR,
We sincerely appreciate your efforts and insightful comments to improve the draft. We respond to each of your comments one-by-one in what follows.
---
**Q1. Analysis on the effect of $k$**
**A1.** Following your suggestion, we conducted additional experiments with $k \in \... | Summary: The paper investigates an exploration technique that maximizes the entropy of visited states while taking into account the expected return of the states. The goal is to explore the part of the state space that is both less visited while avoiding too much exploration for the low-value states.
Strengths: The pa... | Rebuttal 1:
Rebuttal: Dear Reviewer CsYB,
We sincerely appreciate your efforts and insightful comments to improve the draft. We respond to each of your comments one-by-one in what follows.
--------
**Q1. Unclear how the presented algorithm actually enforce more exploration on the high expected return state as compar... | Rebuttal 1:
Rebuttal: Dear reviewers,
We sincerely appreciate your efforts and insightful comments. Following your suggestions, we provide a one-page pdf that contains (i) the analysis on the effect of $\beta$ and $k$ and (ii) additional results on a high-dimensional Quadruped environment. We will incorporate them int... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression | Accept (poster) | Summary: This paper suggests a notion of compatibility, which is an algorithm-dependent and data-dependent term to bound the excess risk of overparameterized linear regression models. Using this notion, the authors come up with three regimes on the covariance matrix, and characterize the compatible region (that is the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and meaningful comments which would greatly help us revise our manuscript.
The reviewer's major concern comes from whether we can cover the results from the relationship between early-stopping and ridge regression.
Below, we show why our results cannot be ... | Summary: This paper suggests a new framework for evaluating the generalization error called compatibility. Compatibility takes into account both the data and algorithm and says the two are compatible if there is an early stopping region, as $n \to \infty$ that has zero excess risk. The paper then considers the linear r... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewer for their valuable feedback and insightful comments.
We apologize for not making everything clear and ensure to revise our manuscript according to the reviewer's suggestions.
Below we do our best to address the reviewer's concerns.
... | Summary: This paper considers the benign overfitting phenomenon in overparameterized linear regression problems under early stopping type gradient descent.
Specifically, this paper gives a time variant bound for the excess risk during the gradient descent training.
By comparing the optimal time variant bound with the... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's supportive and insightful comments.
We ensure to add the related discussion in the revision. Below, we do our best to address the reviewer's questions adequately.
>Q1: For the examples and the numerical experiments in this paper, it would be better if the authors co... | Summary: The authors present a new mathematical framework for characterizing generalization of over-parameterized machine learning models. The new framework provides improved bounds from prior work, and improves on prior work by combining both data and algorithm information. To summarize their contributions: The paper ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude to the reviewer for the valuable feedback and supportive comments.
The reviewer's major concern comes from the linear example.
Indeed, we only derive a linear example to express the effectiveness of compatibility. However, the idea behind is general... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Generalizable One-shot 3D Neural Head Avatar | Accept (poster) | Summary: Authors proposed a method for one-shot facial animation with high level of details saved from the input image. To handle facial deformations they disentangle expression and appearance and introduces a neural point cloud renderer for that. To train a model authors used quite popular set of loss functions and da... | Rebuttal 1:
Rebuttal: **Comparison to latent 2D avatar methods.**
- Please see A1 in the Global Response above. Note that we cannot compare to Megaportraits since its code is not publicly available. For "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing", we used a public third-party implementatio... | Summary: The method aims to build a generalizable model to create an animatable 3D human head representation from a single-view portrait source image. The resulting representation can be used to reenact the source image with target images with different subjects and expressions.
The key idea is to use three tri-planar... | Rebuttal 1:
Rebuttal: **Appearance branch.**
- Please see A3 in the Global Response above. As discussed in the supplementary (Line 105 - 108), to prevent the source expression from leaking into the final animation results, the appearance branch masks out the facial region in the input source image and only captures the... | Summary: The paper proposes a novel approach for building single-shot animatable head avatars. This is achieved through three branches: the canonical reconstruction branch, the detailed appearance branch, and the expression branch, all of which are represented as tri-plane nerfs. The three tri-planes are added together... | Rebuttal 1:
Rebuttal: **Weakness of the result.**
- Please see A2 in the Global Response above, Fig.4 in the PDF and the video we sent to AC. The "jaw opening" issue is caused by the long-tail expression distribution of the training dataset rather than using the additive expression branch. With training data containing... | Summary: This paper presents a method for generating Neural Head Avatars given a single image of a subject. More specifically, the input is a source image of a person as well as a target image specifying the expression, and the goal is to generate a rendering of the person in the source image with the expression of the... | Rebuttal 1:
Rebuttal: **3DMM accuracy.**
- Since our expression branch solely relies on the 3DMM renderings to provide target expression information, it is indeed affected by the 3DMM's accuracy. When the 3DMM model fails to predict the target expression correctly from the target image, our model replicates the inaccur... | Rebuttal 1:
Rebuttal: ### A1. Comparison to 2D baselines (**ufhJ**, **k8VD**)
**Motivation.**
We discuss the benefits of 3D avatars towards talking head synthesis in Lines 35 - 37 and Lines 317 - 319 of the main paper. Other important reasons to study 3D avatars are:
- Human faces are inherently 3D, thus it is physic... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: 1. This work proposed a new framework for generalizable head animation in one-shot setting.
2. The results are better than several baselines.
Strengths: 1. The target problem is important.
2. The method is reasonable.
Weaknesses: 1. Task definition. 1) The practical usage situation need to be clarified? The ... | Rebuttal 1:
Rebuttal: **Task definition and 2D baselines.**
- Please see A1 in the Global Response above and Fig.1 in the rebuttal PDF. We emphasize that compared to the 2D baselines, our method learns a strong 3D geometric prior of human faces and hallucinates more realistic missing parts, especially when the source p... | null | null | null | null | null | null |
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power | Accept (spotlight) | Summary: The paper proposes a version of the 2-dimensional Weisfeiler-Leman algorithm that restricts the distance between nodes in the node tuples processed. A GNN based on this restriction is also proposed and investigated regarding its ability to count cycles and other substructures.
Strengths: **S1** In general, th... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions. We respond to all weaknesses and questions below.
**Reply to W1.**
Thanks for the valuable suggestion. To understand the concept behind $d$-DRFWL(2), one has to first understand FWL(2). FWL(2) works by (i) assigning each 2-tuple $(u,v)\in\m... | Summary: The paper deals with constructing expressive GNNs (i.e., more expressive than 1-WL) with low complexity. The authors focus on cycle count ability, motivated by their importance in real life chemical datasets. With this problem and motivation at hand, the authors propose a family of algorithms possessing a stri... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and thorough comments. We respond to all points below.
**Reply to W1.**
We do agree with your comment that the efficiency of our model is only guaranteed when graphs are sparse. Still, our model can handle graphs with even $n\approx 500$ nodes, as long as the averag... | Summary: The paper focuses on the task of counting or detecting substructures such as cycles or small cliques in graphs. This task is (theoretically and practically) impossible for standard GNNs. While higher-order GNNs are able to count such substructures, they are slow, using $n^2$ space and $n^3$ time which is infea... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contributions as well as providing the insightful suggestions.
**Reply to W1.**
$d$-DRFWL(2) GNNs process a graph G in two steps: (i) extract all 2-tuples $(u,v)\in\mathcal{V}_G^2$ that satisfy $d(u,v)\leqslant d$; (ii) initialize and update the embeddings of all ... | Summary: In this paper, the authors introduce the d-DRFWL(2) GNNs to count certain graph substructures, including cycles, cliques, and paths, which is essential for various graph-mining tasks. The authors prove that with d = 2, d-DRFWL(2) GNNs can already count up to 6 cycles, retaining most of the cycle counting power... | Rebuttal 1:
Rebuttal: Thanks for your insightful comments and suggestions. We respond to all the weaknesses and questions below.
**Reply to W1.**
We thank the reviewer for providing relevant works [1][2]. Although [1] claims that message passing GNNs (MP-GNNs) can be Turing complete, the assertion is made under fair... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful feedbacks and constructive suggestions. All the comments have been scrupulously considered, and we will integrate the suggestions into our revised version of the paper. To address the common concerns of the reviewers, below we restate the focus of ou... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work explores the ability of graph neural network to count certain graph substructures, especially cycles. While past works counts by collecting subgraphs, the work avoids such burdensome procedure and construct a local method. Theoretical analysis are presented to show that Folklore Weisfeiler-Lehman tes... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and thorough investigation of additional related works. We respond to all the weaknesses and questions below.
**Reply to W1.**
Thanks for introducing the related works and offering the suggestion. To compare the performance and efficiency of our model with ... | null | null | null | null | null | null |
UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition | Accept (poster) | Summary: This paper studies the recommendation unlearning problem and focuses on the exact unlearning approach. The authors rethink the exact unlearning framework from an ensemble-based perspective, and decompose the error into three components. The authors mainly modify the existing SOTA framework regarding the first ... | Rebuttal 1:
Rebuttal: **Q1**: Optimization process in stage III (model combination).
**Response**: Although different from prior work [6, 7] in terms of the model used, our method also utilizes the stochastic gradient descent algorithm for optimization. We will provide further explanations regarding these details duri... | Summary: This paper tackles the issue in recommendation unlearning algorithm, specifically focusing on RecEraser. The authors introduce UltraRE, a framework devised to optimize the RecEraser. UltraRE aims at mitigating three primary losses - redundancy, relevance, and combination. By integrating transport weights in th... | Rebuttal 1:
Rebuttal: **Q1**: Novelty of our proposed UltraRE.
**Response**: Please refer to *Response to Q1* in the rebuttal to all reviewers.
**Q2**: Relationship between clustering performance (inertia) and unlearning performance.
**Response**: Our proposed UltraRE, being an exact unlearning approach, inherently ... | Summary: This paper addresses the problem of recommendation unlearning, which arises due to privacy concerns and the right to be forgotten. The authors identify limitations in previous methods, which prioritize unlearning efficiency over preserving model utility and fail to optimize the balance between collaboration an... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and suggestions. We hope our response addresses your concerns.
**Q1**: Discussion about approximate unlearning.
**Response**: First of all, it is important to highlight that, in this paper, we focus on exact recommendation unlearning, which is or... | Summary: This paper investigates the problem of recommendation unlearning, with a specific focus on the exact unlearning approach. The authors adopt an ensemble-based perspective to redefine the exact unlearning framework and break down the framework into three components regarding prediction error. The primary modific... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable comments and suggestions. We hope our response addresses your concerns.
**Q1**: Inconsistent improvements between inertia and model utility.
**Response**: Inertia is the summation of the inner-cluster distance of all sample-centroid pairs, ranging in $[0,... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their valuable comments and suggestions, which are crucial for improving our work. We hope our response addresses your concerns.
**Q1**: Major technical contribution compared with RecEraser (SOTA).
**Response**: We summarize the contributions in the table... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Self-supervised Object-Centric Learning for Videos | Accept (poster) | Summary: This paper introduced a fully unsupervised method, SOLV, for segmenting multiple objects in real-world sequences. In this paper, the author employed the token drop strategy to reduce computation and enhance regularization. Additionally, Spatial-temporal Binding was proposed to aggregate the features of objects... | Rebuttal 1:
Rebuttal: We extend our sincere appreciation to the reviewer for their valuable insights and constructive feedback. We have responded to their questions and concerns, and we hope our explanations thoughtfully encompass the raised points.
## W1. Explanations on slot merging
Please refer to Q4 of the global ... | Summary: The paper introduces an approach to segment multiple objects for video sequence in both real and synthetic data without utilizing any additional signals besides RGB frames. It has 3 components including a visual encoder, a spatial-temporal binding model used for grouping pixels into slots across different time... | Rebuttal 1:
Rebuttal: We appreciate the feedback and valuable comments you have shared. In response, we have furnished explanations, aiming to address your concerns effectively, hoping the scores can be raised accordingly.
## W1. Single object segmentation
We did test DAVIS2017 in our paper, the results are reported i... | Summary: This paper proposes an unsupervised segmentation method in videos. The backbone network is pre-trained with the self-supervised method. Then, the model spatially binds objects to slots on each frame and then relates these slots across frames. The framework is trained to reconstruct the middle frame in a high-l... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's feedback and perceptive remarks. We have provided responses and hope these will resolve any issues you've raised.
## W1. The method is not capable of detecting the boundary of objects
We agree with the reviewer. Our method currently only segments objects at patch... | Summary: This paper proposes a new self-supervised method for multi-object segmentation in videos called SOLV (Self-supervised Object-centric Learning for Videos). It adopts axial spatial-temporal slot attention to group pixels into slots within frames and then relates these slots across frames to track objects. It use... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback and valuable comments, we have provided detailed clarification, hope these can resolve your concern, thus raise the score accordingly.
## W1. Comparisons on real data
We agree with the reviewer that performance on synthetic data is questionable, this is exactly... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their valuable comments and feedback. We hope the following response can fully resolve the raised concerns. For referred images, please see the **Figure Document (FD)** attached below.
## Q1. Contribution Summary
We would like to start the rebuttal by elaborating o... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a Self-supervised Object-centric Learning framework for unsupervised multi-object video segmentation. To achieve this, this paper proposes to derive object-centric representations in a self-supervised manner to facilitate video segmentation tasks. The proposed approach adopts axial spatial-... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thorough review and insightful comments. We have offered comprehensive clarifications and hope these will address your concerns.
## W1. Concern on novelty and significance
Please refer to Q1 of the global response for our contributions. We would like to highlight that... | null | null | null | null | null | null |
Distribution-Free Statistical Dispersion Control for Societal Applications | Accept (spotlight) | Summary: This study extends the literature on distribution-free uncertainty quantification by providing bounds for various statistical dispersion measures, particularly those commonly employed in fairness evaluations of algorithms. The authors enhance the theoretical foundation by introducing additional results that al... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and overall positive feedback. Here, we provide answers to your concerns point by point.
**C1:** I am a bit torn on the significance of this contribution...
**A1:** We agree that our main focus is to extend existing methods to address societal applications more than... | Summary: The paper demonstrates how to give distribution-free probabilistic control of certain statistical dispersion measures that are relevant in societal applications. An example is the GINI coefficient.
The paper technically builds on and slightly extends the work of Snell et al., which shows how to control quanti... | Rebuttal 1:
Rebuttal: We really appreciate your careful examination, thoughtful and overall positive feedback. We will address your comments below.
**C1:** ...I would really encourage the authors to do a major rewrite during the revision stage. The writing and presentation need work...
**A1:** Thank you for your sug... | Summary: Given a trained predictor, this paper proposes a framework for bounding various measures of dispersion of the loss distribution.
The approach taken here is similar to that of conformal prediction, where one uses a calibration set to estimate the distribution of the loss. In this paper, the authors use the emp... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions and overall positive feedback. We will revise our paper according to your helpful suggestions and will include the important citations we missed. Here, we provide answers to your other concerns point by point.
**C1:** In many applications... under the c... | Summary: The paper proposes a methodology for distribution-free confidence intervals for a wide class of dispersion measures. This is motivated by validating the performance of machine learning algorithms with respect to more complex notions of performance, such as group-based measures of loss balance or the Gini coeff... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and overall positive feedback. Here, we provide answers to your concerns and questions point by point.
**C1:**...main concern is with the technical presentation in Section 4...have a lot of technical content to cover with limited space, so right now much of it is def... | Rebuttal 1:
Rebuttal: We thank all four reviewers for their constructive and positive feedback, and for thinking our paper addresses important applications, has the potential for significant impact on social science, and addresses existing gaps in the literature on bounding the observed loss beyond means and quantile-b... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence | Accept (poster) | Summary: This paper uses findings from vision sciences research to provide an image-segmentation model that could be used in ANNs. The “grouping with temporal coherence” assumes that grouping information emerges via spiking synchrony. This is implemented in their network, called GUST. The network is composed of two mai... | Rebuttal 1:
Rebuttal: __A1. Framing of the question__.
The major problem this paper focuses on is indeed the binding / grouping problem (in ANN / neuroscience literature), which is clearly framed in Section 2 in main text. The review paper [7] in main text frames binding problem as representation, segregation and com... | Summary: The paper introduces GUST (Grouping Unsupervisely by Spike Timing network), a network architecture inspired by the human brain that aims to address the challenges of grouping sensory information in artificial neural networks (ANNs). The network incorporates biological constraints to bias the network towards a ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer to spend the valuable time for carefully reviewing the paper and providing the inspiring comments and helpful suggestions. We appologize for the confusions the paper raise to the reviewer, which will be clarified as followings.
__A1. The response to concerns on related ... | Summary: The research topic of this study is development of a model that can learn to group (segment) the pixels in an image into objects in an unsupervised manner and in a way to enable systematic (combinatorial) generalization with respect to the number of objects. Toward this goal, the authors proposed a model, name... | Rebuttal 1:
Rebuttal: __A1. Major response__ to the concern about the difference with Zheng’s work.
We will fist detail the six aspects in Section 5 and then point out more technical differences.
First of all, the two papers are __answering different questions__ of different levels, not just advanced. Intuitively, th... | Summary: This paper tackles the challenge of grouping information from individual visual elements into whole perceptual units, i.e., how compositional generalization is achieved, through proposing a GUST (Grouping Unsupervisely by Spike Timing network) that leverages spiking synchrony for grouping. This framework intro... | Rebuttal 1:
Rebuttal: __A1 Benchmarking analysis and selective bias__: See __A1, A2 in global response__.
__A2. Concerns on hierarchical case (brain organization / hierarchical grouping)__.
First, we consider combinatorial generalization of _single-level grouping_ in this paper, where varying object number seems to ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for spending their valuable time to carefully read the paper and writing reviews. The comments and suggestions are very valuable and insightful, which helps to revise the paper and motivate future directions. We take all suggestions carefully when we revise the paper.
S... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Training neural operators to preserve invariant measures of chaotic attractors | Accept (poster) | Summary: This paper mentions that MSE loss is insufficient for learning the solution operator for chaotic dynamics. Two approaches are proposed to solve the problem. One is to use sinkhorn loss, and the other is to use contrastive learning. The authors evaluate the effectiveness of the proposed methods through experime... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
**W1**:
> Although the problem setting is important and interesting, the proposed method seems to be a straightforward combination…
Our proposed approaches are not a simple reshuffling of methods to solve a variant of the originally proposed problem (e.g. general rep... | Summary: This paper proposes two methods to preserve invariant measures of chaotic systems in the multi-environment setting when training neural operators. Given some expert knowledge of the underlying dynamical systems, they propose a new optimal transport loss, which uses this knowledge to match the statistics. Witho... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
**W1**:
> These two methods are… relatively separate…
While the two proposed methods use different machine learning tools, they are very much related by a shared goal and overall approach: to train emulators to capture long-term chaotic behavior by preserving the inv... | Summary: The authors proposed a training framework to preserve preserve invariant measures of chaotic attractors. First, they identify training standard neural operators using MSE on chaotic dynamics does not work. Then they suggested to train neural operators to preserve the invariant measures of chaotic dynamics and ... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
**W1**:
> The experiment part could benefit from more substantial content. Comparison with previous methods should be addressed in the experiment part. See below for the two questions as well.
Prior methods for training emulators generally use the standard MSE loss w... | Summary: The paper uses neural operators to track the invariant statistics of chaotic systems. It novelly proposes to use the optimal transport loss and contractive learning to match the distribution, so that the learned model correctly track the attractor among the chaotic behavior. The paper uses FNO as a backbone to... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
**W1**:
> It will be very interesting to… scale to 2d Navier-Stokes…
We agree that scaling to 2D Navier–Stokes and even higher-dimensional chaotic problems would be an interesting extension to this work. We note, however, that our current results on 1D spatiotemporal... | Rebuttal 1:
Rebuttal: We would like to first thank the reviewers and ACs for their helpful comments and questions. We are happy to see that the reviewers generally appreciate the importance of the problem of training better emulators for chaotic dynamics and find the proposed methods and ideas well-motivated. In our re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work considers the problem of learning invariant statistics of chaotic dynamical systems. It is motivated by the observation that training neural operators by standard MSE loss focuses on short-term predictability and thus may miss an attractor’s properties. Two ways of augmenting the MSE loss for capturi... | Rebuttal 1:
Rebuttal: Thank you for your feedback!
**W1**:
> The experiments primarily show that on any of the evaluation measures that particular method wins…
We propose emulators with much more consistent long-term behavior and that accurately capture the dynamics of the chaotic system. We make significant gains in... | null | null | null | null | null | null |
TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation | Accept (poster) | Summary: The paper proposes TMT-VIS, combining multiple VIS datasets for a unified VIS model. Specifically, TMT-VIS introduces taxonomy embedding as prompts to make the model aware of taxonomy from different datasets. Experiments on YVIS-2019, YVIS-2021, OVIS, and UVO demonstrate the effectiveness of dataset unificatio... | Rebuttal 1:
Rebuttal: At the beginning, we want to thank you for the detailed, insightful and constructive comments.
### UNINEXT
Thanks for pointing out our wrong claim. We acknowledge that the UNINEXT is the first DETR-based method which jointly trains multiple VIS datasets. It simply utilizes BERT language encoder ... | Summary: Due to the lack of large-scale datasets in the VIS task, the authors propose a multi-dataset joint training method. Due to the heterogeneity of the category spaces of different datasets, simply stacking datasets may lead to performance degradation. For this situation, the author designs a two-stage classificat... | Rebuttal 1:
Rebuttal: At the beginning, we want to thank you for the detailed, insightful and constructive comments.
### Label space in Taxonomy Compilation Module
The specific dimensions of label space are $K \times D$, where $K$ refers to the total number of categories in datasets, and $D$ represents the hidden dime... | Summary: This paper works on multi-dataset training on video instance segmentation. The authors built on top of Mask2Former, and introduced two components to enable the model to work under different label sets, and take advantages of the given label sets. The two components are both ablated in experiments. The overall ... | Rebuttal 1:
Rebuttal: At the beginning, we want to thank you for the detailed, insightful and constructive comments.
### Reorganization of Table 3
In Table 5 of supplementary material, we have posted several experiments of zero-shot performance of our methods when compared with previous methods. In this part, we updat... | Summary: This article addresses video instance segmentation from a perspective of multi-dataset joint training. The proposed method is based on DETR, while the main contribution is to inject label taxonomy into model training based on CLIP. The model is evaluated on several standard benchmarks.
Strengths: While multi... | Rebuttal 1:
Rebuttal: At the beginning, we want to thank you for the detailed, insightful and constructive comments.
### More metrics
Thanks for your careful suggestions. Firstly, it’s true that performance will improve as data volume increases, but our method successfully alleviates the heterogeneity in category spa... | Rebuttal 1:
Rebuttal: At the beginning, we want to thank all reviewers for the detailed, insightful and constructive comments.
In the PDF file, we have attached the reorganized version of the ablation study on training with multiple VIS datasets, as well as the key statistics of these datasets. In this part, we want ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors propose a multi-dataset joint training method for the video instance segmentation task. They build on the MaskFormer-VIS model, introducing two additional modules: the Taxonomy Compilation Module and the Taxonomy Injection Module. And the proposed method shows compelling performance.... | Rebuttal 1:
Rebuttal: At the beginning, we want to thank you for the detailed, insightful and constructive comments.
### Uniqueness for "video" instance segmentation.
When injecting the taxonomic embeddings, we add a spatio-temporal adapter to generate video-specific modulated taxonomic embeddings. This approach is ... | null | null | null | null | null | null |
OpenMask3D: Open-Vocabulary 3D Instance Segmentation | Accept (poster) | Summary: This paper focuses on the task of open-vocabulary 3D instance segmentation, which involves predicting 3D object instance masks and their corresponding categories. The authors highlight the limitations of traditional closed-vocabulary approaches that operate within a predefined set of object categories, which r... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback, and aim to address the questions in the following:
### Q1: Evaluation on more datasets and settings
The reviewer suggests evaluating on other datasets to show the generalizability of OpenMask3D.
We agree and additionally run experiments on Replica... | Summary: The paper proposes to perform open-vocabulary instance segmentation by utilizing class-agnostic 3D instance segmentation masks from a 3D instance segmentation model trained on scannet200 and generating class labels for it using CLIP. The paper proposed to first obtain class-agnostic instance masks from a super... | Rebuttal 1:
Rebuttal: We thank the reviewer for the extensive feedback, we really appreciate the helpful suggestions for experimental setups!
### Open-vocabulary Evaluation & Generalization Beyond Training Categories
The reviewer correctly highlights that Mask3D is trained on closed-set segmentation masks dataset, an... | Summary: This paper presents a method for 3D open-vocabulary instance segmentation. It proposes to use a class-agnostic Mask3D to get some instance mask proposals, project the instance points to 2D views to get some 2D segments, and extract some instance features based on the 2D segments by CLIP image encoder. Then, te... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. We are happy that the reviewer found our paper well-organized and easy to understand, and appreciated the zero-shot ability of our open-vocabulary 3D instance segmentation approach. Below, we hope to answer the questions raised by the reviewer.
### W1/W2: G... | Summary: This paper addresses the problem of text-based 3D instance segmentation. To tackle this problem, the authors propose OpenMask3D, a zero-shot approach for 3D instance segmentation that utilizes class-agnostic 3D instance masks and multi-view fusion of CLIP-based image embeddings to aggregate per-mask features. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing valuable feedback, and we are really glad that the reviewer found our paper enjoyable to read! We address the questions and concerns in the following:
### Q1: Differences between OpenMask3D (instance segmentation) and OpenScene (semantic segmentation)
The revi... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their valuable feedback, we appreciate their detailed suggestions. We reply to each reviewer’s questions and concerns in the individual responses, and we have added tables and figures in the attached rebuttal PDF, which we reference and explain in the responses.
He... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes to solve open-vocabulary 3D instance segmentation. It uses a class-agnostic 3D instance segmentation model to obtain instance masks, then gather multi-scale image features from multiple frames by CLIP and SAM to do the open-vocabulary classification task.
Strengths: 1. The paper proposes to... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback.
### Q1: What is the latency and inference cost of the model during inference?
In the table below, we provide the runtime of our approach on ScanNet for generating the queryable 3D scene representation, and also for performing a query on that repre... | null | null | null | null | null | null |
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts | Accept (poster) | Summary: This paper proposes a method to detect AI-generated texts based on intrinsic dimensions of sequences generated by humans and LLMs. The authors use a method called persistence homology dimension (PHD) to estimate intrinsic dimensions of both human- and LLM-generated texts. Using a variety of datasets across mul... | Rebuttal 1:
Rebuttal: Thank you for your review!
We have considered adding more analysis of ID estimations for the texts to appendices. In fact, we have explored some types of "linguistic noise", i.e., deviations from standard language. In particular, we use data from Reddit that contains quite informal texts (with a ... | Summary: The paper describes work on detecting AI-generated text using intrinsic dimensionality (ID) estimation methods through Persistent Homology Dimension (PHD) (and MLE). The authors motivate this approach by highlighting that written texts between machine and humans observe differences in topological representati... | Rebuttal 1:
Rebuttal: Weaknesses.
1. We suppose that modem language models can mimic human-written texts very well in terms of common linguistic properties such as grammar, semantic, style etc., but there are subtle differences that can be captured via numerical analysis of the topology of text embeddings or the curva... | Summary: This paper proposes a new method for artificial text detection(ATD) with intrinsic dimension (ID) estimation. First, contextualized representation of tokens in the text is extracted by a RoBERTa model. Next, the author estimates the ID of this set of contextualized representations : (1) N tokens and their corr... | Rebuttal 1:
Rebuttal: Thank you for your review!
First, we address the “Weaknesses” section.
1. We use the notion “ID of text” for simplicity; in contrast to image processing, text embeddings are the main numerical representation of texts widely used in NLP nowadays, so we believe there is very little chance of conf... | Summary: The paper proposes using the intrinsic dimensionality of the manifold underlying the set of embeddings of a given text to detect AI-generated texts. Because the average intrinsic dimensionality of AI-generated texts is lower than that of natural language. It is found that the intrinsic dimensionality of diff... | Rebuttal 1:
Rebuttal: Thank you for your review.
In our work, we study Intrinsic Dimensionality estimation for text embeddings and show experimentally that this mathematical value can reflect some useful information about the given text, namely, helping to separate artificial and human written texts. We admit that a h... | Rebuttal 1:
Rebuttal: We are thankful to all the reviewers for their inspiring reviews! In the attached file, we provide examples and figures illustrating extreme cases of ID values. We discuss these results in the direct answers to the reviewers (SSuo and oVCJ).
Pdf: /pdf/c84d48f2166a10a70ce42642e9499a5bcad27ade.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Constant Approximation for Individual Preference Stable Clustering | Accept (spotlight) | Summary: This paper continues the study of individual preference stability (IP stability) initiated by Ahmadi et. al. (2022). Given a set of points in a metric space, a k-clustering is said to be 1-IP stable if for every point in the set, its average distance to the points in its cluster is at most its average distance... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address your concerns and questions below.
> While Algorithms 1 and 2 are described very clearly, little motivation or intuition is given.
Thanks for the suggestion. It did indeed require a good amount of work for us to figure out the details of the b... | Summary: This paper considers the problem of finding stable clusterings under a specific notion of stability - individual-preference stability which roughly requires the clustering produced to have the property where the average distance of any datapoint to points within its own cluster to be smaller than the average d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address your concerns and questions below.
> The extension to f-stable clusterings is obvious, and the results for min and max stable clusterings are also very elementary
Indeed, the results for min and max stable clusterings come from quite straightf... | Summary: This paper concerns alpha-individual preference (IP) stable clusterings, meaning clusterings such that its average distance to points in its cluster is at most alpha times greater than to that in any other cluster. Previously, only O(n)-IP solutions were known, 1-IP solutions were known to not always exist, an... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address your concerns and questions below.
> The biggest weakness is that, while the improvement in approximation is significant, I think the problem is somewhat narrow in scope. I noticed the authors only cited one past work on this area (presumably, ... | Summary: In $\alpha$-individual preference (IP) stable clustering, the average distance between every data point to other points in its cluster must be at most $\alpha$ times the average distance between it to points in any other cluster.
This paper gives the first polynomial time $O(1)$-IP stable clustering algorith... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Below is our response.
> "[...] the constants for IP approximation and k-center approximation may be too large."
The focus of this paper is to give a constant factor approximation, improving upon the only O(n)-approximation of the IP-stability clustering... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation | Accept (poster) | Summary: In this paper, the authors propose a new heterogeneous knowledge distillation approach. The core idea is to map the intermediate layer features of the network to a unified logit space to eliminate feature mismatches caused by different structures. The author conducts thorough experiments on distilling between ... | Rebuttal 1:
Rebuttal: > **Weakness 1:** The newly proposed method is very similar to deep supervision
**Response to the weakness 1:** Thanks for your suggestions in improving quality of our work. Deep supervision [1,2,3] introduces intermediate supervision during training to mitigate the gradient vanishing/exploding p... | Summary: This paper introduces a new method to distill knowledge between heterogeneous models named OFD-KD. This paper proposes to project the intermediate features into logits for distillation. A new loss function is also introduced in this paper to adaptively enhance the target information. Extensive experiments veri... | Rebuttal 1:
Rebuttal: # Response to Reviewer R7Hg part (1/2)
**Response to the weakness 1:** In Table 5, the improvement when compared to methods tailored for homogeneous architectures, such as ResNet34-ResNet18, is indeed marginal. However, when evaluating heterogeneous pairs, as shown in Table 1, our OFA-KD consiste... | Summary: This paper tackles the problem of cross-architecture distillation, that is, the teacher and the student in KD are of different model architectures.
By using centered kernel alignment, the authors observe that features learned by models of different architectures shows significant feature divergence, indicatin... | Rebuttal 1:
Rebuttal: > **Weakness 1:** In the CKA analysis, it seems that when comparing features of models of the same architecture, the authors just using features of one model in both x-axis and y-axis, as the corresponding heatmaps are symmetry. If using two models, such as ResNet18 vs. ResNet34 or two ResNet18 tr... | Summary: This paper first demonstrates that there is significant feature divergence of the learned features between heterogeneous teacher and student models, which is a scenario rarely explored in previous knowledge distillation community. And the authors point out that the hint-based methods are ineffective in this cr... | Rebuttal 1:
Rebuttal: > **Weakness 1:** The additional branches will increase the training cost.
>
> **Question 2:** Could the authors provide more details about the branches
**Response to the weakness 1 & question 2:** Thanks for your valuable comments. The inclusion of extra branches inevitably demands more computa... | Rebuttal 1:
Rebuttal: # Response to all reviewers
We thank all the reviewers for their elaborate and constructive feedback. Their valuable suggestions help improve the quality of our paper greatly.
### **Response to Reviewer R7Hg part (2/2)**
**Response to the question 1:** In our experiments, we primarily utilize t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
FLSL: Feature-level Self-supervised Learning | Accept (poster) | Summary: This paper proposes Feature-level Self-supervised Learning (FLSL) to handle dense prediction downstream tasks. Specifically, the authors employ the transformer for joint embedding and clustering and construct the objectives from the mean-shift and k-means perspectives. Experiments show that FLSL yields signifi... | Rebuttal 1:
Rebuttal: \
__Weaknesses:__
__1.__ _The relation between ADCLR and FLSL is not clear. I see both ADCLR and FLSL use cross-attention to learn patch-level information. So, what's the main difference between ADCLR and the inter-view objective?_
Thanks for raising this question. The main difference between A... | Summary: This paper tackles self-supervised representation learning and primarily forces on learning representations for dense downstream prediction tasks, such as object detection and instance segmentation. To improve upon prior work, the main idea of the paper consists of two parts: (1) the paper leverages the underl... | Rebuttal 1:
Rebuttal: \
__Weaknesses:__
__1.__ _A few comparisons or discussions with related works are missing. For instance, DeepCluster [a, b], PCL [c], and CDL [d] show similarities with the presented approach as they also leverage clustering. A comparison with CDL would be the most interesting as it relies on in... | Summary: Current self-supervised learning (SSL) methods, including SimCLR, DINO, VICReg, and MOCOv3, focus mainly on instance-level representations, limiting their use in tasks like object detection and segmentation. To overcome this, a new two-level feature clustering SSL method named Feature-Level Self-supervised Lea... | Rebuttal 1:
Rebuttal: \
__Weaknesses:__
__1.__ _The idea of considering similar pixels or patches as positive pairs is not new. Many previous works have explored this idea already, e.g. [1]._
Thanks for bringing [1] to our attention. Yes, FLSL belongs to the family of SSL methods that consider similar pixels or patch... | Summary: The authors point out the limitations of previous SSL methods on dense prediction tasks, because of tis instance-level objectives. On the other hand, recent studies focusing on dense prediction based on region, patch, and pixel to learn globally semantic representations on these sub-regions. To this end, the ... | Rebuttal 1:
Rebuttal: \
__Weaknesses:__
__1.__ _Some SSL methods that are strong in dense prediction tasks are omitted from the tables. (e.g., iBOT and RC-MAE). For example, iBOT with ViT-S/16 outperform the methods in Table 1 in terms of $AP^{bbox}$ and $AP^{mask}$. RC-MAE is also comparable to iBOT._
Thanks for poi... | Rebuttal 1:
Rebuttal: Please find the attached pdf file that includes (Figure 1) AAS visual comparison between FLSL and DINO, and (Table 1) ADE20K semantic segmentation results.
Pdf: /pdf/2eb6ba617723dd126a7f71b6e43c8bed8bbf134f.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents FLSL a self-supervised learning model designed to give good performances of the pre-trained model on downstream dense prediction tasks. The proposed method can be summarised as a two-level clustering problem to achieve this objective :
- Intra-view clustering: which aims to cluster togethe... | Rebuttal 1:
Rebuttal: \
__Weaknesses:__
__1.__ _How the performance on bbox-aligned k-NN classifier correlates with dense prediction tasks?_
Thanks for raising this question. FLSL learns dense semantic representations rather than a single instance-level representation. Therefore, we design a bbox-aligned k-NN classif... | null | null | null | null | null | null |
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells | Accept (poster) | Summary: Shows how grid modules of grid cells can emerge as solutions to a self-supervised learning framework, implemented as a recurrent neural network.
They take insights from Continuous Attractor models (velocity dependent weights), Dorrell et al (2023) representation theory (path invariance), and ideas about effici... | Rebuttal 1:
Rebuttal: We thank Reviewer 1Nr3 for their time and feedback and suggestions. Replying to each comment, suggestion and/or question in turn:
> The advance on previous work is not so clear, given that each aspect has been presented previously, with representation theory covered by Dorrell et al., nice analys... | Summary: The paper proposes a computational framework for the emergence of grid cells in the mammalian cortex through self-supervised learning. The learning objective is formulated combining requirements of path independence for location code, error-correcting coding, efficient coding. Validity of the approach is demon... | Rebuttal 1:
Rebuttal: ## Author Response
We thank Reviewer crXg for their time, positive feedback and high score. Replying to each comment, suggestion and/or question in turn:
> Authors do not discuss relation of their work to the literature arguing for grid cells role in predictive representation (e.g., Stachenfeld ... | Summary: This work reviews some of the issues with existing models of grid cells (cells in mammalian brains that fire when the animal is located at the vertices of a hexagonal grid) and suggests a new model based on recurrent neural networks (RNNs). The model is self-supervised, eliminating the worry that the structure... | Rebuttal 1:
Rebuttal: ## Author Response
We thank Reviewer dHUQ for their time and feedback. Replying to each comment, suggestion and/or question in turn:
> The authors present this work as a significant advance over methods based on supervised learning because the latter depend on specific design choices. However, th... | Summary: The paper shows that recurrent networks trained with a "self-supervised" loss leads to units of the internal representations that organize as grid cells. In particular, the paper defines a loss that promotes separations between neural representations encoding different spatial locations, encourages a represent... | Rebuttal 1:
Rebuttal: ## Author Response
We thank Reviewer keKQ for their time, feedback and high score.
Replying to each comment, suggestion and/or question in turn:
> How robust are the results to other hyperparameters, like batch-size, learning rate, etc?
> e.g having a sufficient number of examples with overlap... | Rebuttal 1:
Rebuttal: ## Global Response to Reviewers
Here we take the opportunity to better motivate our paper, and to explain why we view it as a novel and significant contribution. In short, multiple approaches have been taken to understand grid cells, but each contains at least one limitation. Our paper combines t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Sparse Deep Learning for Time Series Data: Theory and Applications | Accept (poster) | Summary: This paper introduces an expansion of sparse deep learning theory, specifically tailored for the analysis of time series data. The authors provide a detailed explanation of the theoretical foundation behind this theory, employing recurrent neural networks (RNNs), particularly long short-term memory (LSTM) netw... | Rebuttal 1:
Rebuttal: Thank you so much for your review. We provide a point-by-point response to your comments below.
$\textbf{W1: Complex notation}$
In the revision, we will simplify the notation and add more explanations to the assumptions and results, making the paper more accessible to the readers.
$\textbf{W2:... | Summary: This paper focuses on the theoretical analysis of sparse deep learning to time series data. Statistical propoerties of sparse RNNs are investigated including consistency and asymptotical behaviour. The paper presents some numerical results showing that sparse deep learning outperforms existing methods in pre... | Rebuttal 1:
Rebuttal: Thank you so much for your review. We provide a point-by-point response to your comments below.
$\textbf{W1: Readability: Theorems and Lemmas}$
Thank you very much for your thorough review of our paper. We sincerely appreciate your feedback, and in the camera-ready version, we will address the m... | Summary: For iid data, sparse DL has been shown as a way towards consistency of input-output mappings and well understood distribution of model predictions. This theory is missing for time-series data however, which the authors thus introduce here. In particular, the following results for RNNs with Gaussian mixture par... | Rebuttal 1:
Rebuttal: Thank you so much for your review. We provide a point-by-point response to your comments below.
$\textbf{W1: Comparison to other uncertainty quantification approaches}$
We apologize for the confusion. We actually included MQ-RNN [1] (multi-horizon probabilistic forecasting) and DP-RNN [2] (dropo... | Summary: This paper proposes to extend sparse deep learning in the context of time series data. This context is different from the classical one as samples are not *i.i.d.* but dependent. The paper shows theoretical results -- posterior consistency and asymptotic normality of the weights --, a computation method based ... | Rebuttal 1:
Rebuttal: Thank you so much for your review. We provide a point-by-point response to your comments below.
$\textbf{W1 and Q1: Novelty}$
The seminal work [1] established a general theoretical framework for studying the asymptotic behavior of posterior distributions and Bayesian estimators for high-dimensio... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper extends the sparse DNN theory from [1, 2] to time series data and proposes the sparsity method for RNNs. This method aims to improve uncertainty quantification on time-series tasks and provide state-of-the-art RNN compression.
Strengths: * The combination of DNN sparsification and uncertainty quanti... | Rebuttal 1:
Rebuttal: Thank you so much for your review. We provide a point-by-point response to your comments below.
$\textbf{W1: Novelty}$
The seminal work [1] has established a general theoretical framework for studying the asymptotic behavior of posterior distributions and Bayesian estimators for high-dimensional... | null | null | null | null | null | null |
Towards Stable Backdoor Purification through Feature Shift Tuning | Accept (poster) | Summary: Based on the observations that the previous FT and FP methods fail at low poisoning rate scenarios, this paper finds out the potential reasons in terms of clean and backdoor feature separation degree. It proposes FST to solve the problem by disentangling the features and validates its effectiveness with multip... | Rebuttal 1:
Rebuttal: **First of all, thank you for your recognition of our work.**
### Response to Weakness 1:
Thanks for your comment.
We will release all of our code, including the corresponding tuning parameters and training checkpoints, in our final version to ensure that the results of all our experiments are ... | Summary: This paper studies the effectiveness of finetuning in backdoor defense with a low poisoning rate and finds that the feature entanglement at low poisoning rate affects the effectiveness of finetuning. Thus, this paper proposes 3 new finetuning strategy FE-tuning, FT-init, and FST. The experiments demonstrate a ... | Rebuttal 1:
Rebuttal: **First of all, thank you for your recognition of our work.**
### Response to Weakness 1 & Question 1:
**Response to “How much data is used in FST?”:**
As mentioned in Line 238-241 of Section 5.1, we follow previous work and only reserve either 2% or 5% of training data as the tuning dataset ... | Summary: This paper observes that while fine-tuning and linear probing can act as effective defenses in the high-poisoning-rate regime, they completely fail in the low-poisoning-rate regime. The paper further shows that this is due to the fact that in the low-poisoning-rate regime, extracted features of backdoored and ... | Rebuttal 1:
Rebuttal: **First of all, thank you for your recognition of our work.**
### Response to Weakness minor 1 and 2:
Thank you for your helpful suggestion! We apologize for the confusion caused. We will add explanations about FE-tuning in Line 52 in our revised version. We will correct all the typos and careful... | Summary: This paper finds that fine-tuning is less effective in defending against backdoor attacks with a low poisoning rate, due to the strong coupling between the clean feature and the backdoor feature. Therefore, a tuning-based backdoor purification method called feature shift tuning (FST) is proposed, which is simp... | Rebuttal 1:
Rebuttal: **First of all, thank you for your recognition of our work.**
### Response to Weakness 1:
Thanks for your constructive comments.
We add evaluations of vanilla FT and LP using ResNet-50 (increased model capacity) and Dense-161 (different model architecture) on CIFAR-10. We also take evaluation o... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your recognition of our work! We sincerely appreciate all of your precious time and constructive comments.
All these comments and suggestions are very insightful and beneficial for us to improve the quality of this work. We have responded to each review separately ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a novel backdoor defense approach called Feature Shift Tuning (FST) that actively promotes feature shifts to disentangle the learned features of a deep neural network. The authors conduct a thorough assessment of widely used tuning methods, vanilla Fine-tuning (FT), and simple Linear Probin... | Rebuttal 1:
Rebuttal: **First of all, thank you for your recognition of our work.**
### Response to Weakness 1:
**Response to “The contribution of paper ...... previously researched findings.”:**
1. The authors of [1] propose that end-to-end supervised training makes the model learn backdoor features. Hence, they pr... | null | null | null | null | null | null |
Investigating how ReLU-networks encode symmetries | Accept (poster) | Summary: The paper considers networks with ReLU activations and investigates in which specific way their internal activations learn to be equivariant given an invariant data distribution.
Section 2 presents a theoretical result, applying specifically to two-layer networks with non-singular weight matrices. It is shown ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and helpful review.
> Why is section 2 considering linear instead of affine layers? Do the results not hold when biases are summed?
This is an excellent question. We wrote it this way (1) for simplicity, (2) because Elesedy & Zaidi [9] use the sa... | Summary: This work investigates the relationship between end-to-end equivariance of a network and layerwise equivariance. It theoretically investigates when we can guarantee that an equivariant network is layerwise equivariant, and also cases where layerwise equivariant is not guaranteed or is harmful. In the case of C... | Rebuttal 1:
Rebuttal: We thank the reviewer for their useful suggestions and helpful review.
> The point about layerwise equivariance is covered in some prior works that are not cited. Much of appendix D.1. in particular is discussed in depth in [1]. Limitations of linear layerwise equivariance is discussed in [Finzi ... | Summary: This paper provides an investigation on whether equivariance of a trained deep neural network with ReLU activations implies that each of its learned layers are equivariant. The authors show that this should be true in some sense, i.e., some kind of group action must be present in the intermediate feature space... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and helpful comments.
> While reading the paper, I was quite confused about the implication of formulation in Appendix D.1. It seems the results, in particular Proposition D.4, explicitly proves that equivariance of a network implies layerwise equivariance, ... | Summary: Exploring the symmetries of representations and parameters in neural networks is crucial. This paper provides several valuable contributions. First, the authors theoretically proved that for Relu-Networks equivariance implies layer-wise equivariance, but not vice versa. Second, inspired from the conjecture by ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their good comments and questions which will help us improve the paper.
> Proposition 2.3 shows that layer-wise equivariance is NOT a necessary condition of equivariance. But the experiments did show that CNN with equivariant training boils down to be layer-wise equivari... | Rebuttal 1:
Rebuttal: We thank all reviewers for their well-written and very helpful reviews.
Reviewer k5hy suggested an extra experiment which we have carried out and believe will be of interest to all reviewers.
We train VGG-11 nets on CIFAR-10 *without* horizontal flipping data augmentation. These models have lower... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper shows that CNNs will be close to G-CNNs if they are trained to be equivariant. In addition, they also provide some theoretical analysis and conjectures regarding the layerwise equivariant of ReLU-networks.
Strengths: 1. They show that equivariance implies layerwise equivariance with a scaled permut... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful review. We aim to clarify the writing in the paper in accordance with our replies to the reviewers comments below.
> In general, the paper may have limited or vague benefits for applications.
It is true that there might not be immediate benefits, but this is... | null | null | null | null | null | null |
When Does Optimizing a Proper Loss Yield Calibration? | Accept (spotlight) | Summary: This paper elucidates the relationship between proper losses and calibration by providing the minimal necessary and sufficient condition for proper losses to induce calibrated models.
The condition, local optimality, delineates the concept that no post-processing functions can improve a proper loss anymore.
Th... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback! Here is our response to the individual comments and questions in your review:
**Re. relation to Guo et al. (2017).**
Thanks for the great question. The issue is subtle. We discussed it in our paper (see Line 119) and would like to further elaborate below.
It ... | Summary: This work introduces a local optimality condition for models (with respect to proper losses) based on (additive) post-processing with a 1-Lipschitz function that is necessary and sufficient for calibration. The authors also connect their results to the idea of implicit regularization, showing that structural r... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback! Here is our response to the individual comments and questions in your review:
**Re. temperature scaling.** Typically temperature scaling is applied to the logits when we optimize the cross entropy loss. Thus temperature scaling is more closely related to Defin... | Summary: The paper considers calibration in binary classification when training was performed with proper losses. The authors showed that
the post-processing gap of a predictor, which is a maximum improvement of the loss given any 1-Lipschitz update (calibration) function,
could be both lower and upper bounded by a sim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback, and for recognizing our work on an "important and quite original problem." We are glad the reviewer rates our soundness, presentation, and contribution as "good." We are thus unsure why the overall score is a borderline reject, but we have inclu... | Summary: The paper provides a novel characterization of calibration as local optimality of the predictor w.r.t the global loss under post-processing of the prediction through a class of functions. The paper proves that any predictor satisfying such a condition is smoothly calibrated in the sense of [Kakade and Foster, ... | Rebuttal 1:
Rebuttal: Thank you for the comprehensive review! Here is our response to the individual comments and questions in your review:
**Re. technical contributions.** A main contribution of our work is formulating the right generalization of Claim 2.1 where 1) the notion of calibration is meaningful, and at the ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper seeks to explore and formalize the relationship between minimizing proper loss and calibration in machine learning models, particularly deep neural networks (DNNs). It presents a local optimality condition that is necessary and sufficient to ensure model calibration. The work discusses the implicati... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback! | null | null | null | null | null | null |
Closing the Computational-Statistical Gap in Best Arm Identification for Combinatorial Semi-bandits | Accept (poster) | Summary: The paper presents Perturbed Frank-Wolfe Sampling (P-FWS), an algorithm for the best arm identification problem in combinatorial semi-bandits in the fixed confidence setting. The algorithm achieves instance-specific minimal sample complexity in the high confidence regime and polynomial sample complexity guaran... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and positive feedback.
About experiments. In this paper, we wanted to highlight its strong theoretical contributions: for combinatorial bandits, our algorithm P-FWS is the first polynomial-time algorithm that is asymptotically optimal in the high confidence reg... | Summary:
This manuscript studies the asymptotically optimal sample complexity for the best arm identification in stochastic combinatorial semi-bandits, under the fixed confidence setting. The main contribution is the introduction of a computationally efficient algorithm which achieves the asymptotically optimal sample... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and feedback.
About the paper scope. We believe that combinatorial bandits with semi-bandit feedback constitute one of the classical problems in the bandit literature. They find numerous applications, see [11, 12, 14, 16, 23, 36, 43, 50] (references are those f... | Summary: The paper is about best arm identification with fixed confidence, in combinatorial semi-bandits. The state of the art in that setting is that we have algorithms that have optimal asymptotic sample complexity, but their computational complexity is very large. The authors provide a new algorithm which remediates... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and positive feedback.
About the comment:
> Some components of the algorithm seem to be included to make the analysis work, like the comparison of \sqrt{t} with the norm of the empirical mean vector.
We agree that naturally, some of the components of our algor... | Summary: The paper introduces a computationally efficient algorithm, P-FWS (Perturbed Frank-Wolfe Sampling), designed for best arm identification in Combinatorial semi-bandits. This algorithm operates in polynomial time and offers minimal sample complexity guarantees or polynomial sample complexity, depending on the pr... | Rebuttal 1:
Rebuttal: Many thanks for your careful review and feedback.
About the presentation and readability, thanks for your feedback! We had chosen to write a long introduction to summarize all the ideas and contributions of the paper, and to help the reader understand the paper structure. This “unconventional” pr... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Riemannian Exponential Augmented Lagrangian Method for Computing the Projection Robust Wasserstein Distance | Accept (poster) | Summary: This paper considered the problem of computing the projection robust Wasserstein distance between two discrete probability measures. By formulating this problem as an optimization problem over the product space of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints, th... | Rebuttal 1:
Rebuttal: Many thanks for your positive comments on the basic results achieved in the paper and your helpful comments and suggestions. The following are our point-to-point responses to your comments.
- Response to Weakness 1.
Thank you for your comments. We acknowledge that, due to the page limit, some ... | Summary: This paper reformulates the projection robust Wasserstein distance as an optimization problem over the product of the Stiefel manifolds and a subset of a Euclidean space. A Riemannian exponential augmented Lagrangian method (REALM) is proposed to solve this problem. The proposed method is empirically more stab... | Rebuttal 1:
Rebuttal: - Response to Question 1.
Many thanks for this insightful comment. Regarding the current overall Algorithm 1, we were not able to establish the iteration complexity due to the following two main difficulties: (i) characterizing the connection between the two complementarity measures $\\|W^k\\|\... | Summary: The authors first reformulate the computation of the PRW distance as an optimization problem over the Cartesian product of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints. And then they also propose a Riemannian exponential augmented Lagrangian method (REALM) for s... | Rebuttal 1:
Rebuttal: - Response to Question 1.
Many thanks for your suggestion. We agree with you that it is always helpful to provide a proof outline or a preview of the proof before delving into detailed theoretical analysis. We shall take your suggestion into account when we revise the paper.
- Response to Quest... | Summary: This work proposes a new method, called REALM, to compute the projection robust Wasserstein (PRW) distance.
The method REALM is an extension of the exponential augmented Lagrangian method to the Riemannian space.
The convergence of REALM is established.
To solve a subproblem during REALM, this work propo... | Rebuttal 1:
Rebuttal: Many thanks for your positive comments on the basic results achieved in this paper and your helpful comments and suggestions.
- Response to Weakness.
Thanks for your comments. We want to take this opportunity to elaborate more on the nonmonotone linesearch condition (25) to clarify this issu... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: this paper proposed a Riemannian Exponential Augmented Lagrangian Method for solving the projection robust wasserstein distance problem. the authors claimed two contributions compared with the previous works: 1) the proposed algorithm is much more stable as \eta needs to be small in previous works. 2) a Rieman... | Rebuttal 1:
Rebuttal: Many thanks for pointing out the weakness, which gives us an opportunity to clarify it.
First, there are indeed some systematic studies comparing exponential ALM and exponential penalty approaches. We wish to highlight several main results from the existing literature on this matter:
- Convex ... | null | null | null | null | null | null |
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent | Accept (poster) | Summary: This paper provides new stability bounds for SGD: they rely on perturbation theory for Markov Chains and on the ergodicity of SGD.
Strengths: - Well written, well presented paper.
- New connection between the theory for Markov Chain and generalization of learning algorithms.
- First (to my knowledge) uniform ... | Rebuttal 1:
Rebuttal: Thanks for a careful reading of our paper and finding that our paper is well-written with some new results on the connections between the theory for Markov Chain and generalization of learning algorithms.
Thanks for the excellent question. In our paper, most of the theory is derived using the too... | Summary: This paper studies the algorithmic stability of SGD in order to bound its expected generalization error when using Lipschitz losses. To do so, they consider Wasserstein stability instead of the standard uniform stability, which is defined using the dual representation of the Wasserstein distance and the repres... | Rebuttal 1:
Rebuttal: Thanks for your time invested in our paper. Below are our responses to your questions:
**Weaknesses**:
1. Weaker result with respect to other parameters: We get an optimal rate with respect to the number of samples. But dependence on other parameters might not be optimal in some results, for in... | Summary: In this paper, the authors studied an approach to obtain generalization bounds on SGD algorithm under different class of objective functions such as "quadratic strongly convex", "smooth strongly convex" and one subclass of non-necessarily convex functions described in Assumption 3.3.
Interestingly they convey... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our paper. Below are responses to your questions:
**Weaknesses**:
* As the referee pointed out, our analysis applies to non-convex losses that satisfy some conditions (a dissipativity condition and a pseudo-Lipschitz gradient growth condition). ... | Summary: This paper studies the generalization bounds of (noisy) SGD via Wasserstein stability. The paper presents a unified guideline to derive the Wasserstein stability for stochastic optimization with a constant step size, which allows to derive stability bounds with a three-step proof technique: showing the optimiz... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our paper. Below are responses to your questions:
**Weaknesses:**
* We will add an intuitive explanation behind formula (3.10) and its implications for SGD. The parameter $\hat{\eta}$ appears in the definition of $\bar{\eta}$ that appears in th... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper derives Wasserstein stability bounds for a variety of cases for a "surrogate loss" under convex/non-convex settings.
Strengths: The problem is interesting. The bounds in the non-convex case can be useful. The paper is well-written.
Weaknesses: I am not sure how novel the convex part is, also noted ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time invested in our paper.
As far as we can understand, the reviewer has two main concerns: 1) novelty of the strongly convex part and 2) the use of the surrogate loss function. Below, we clarify both of these points and we hope that the reviewer could reconsider... | null | null | null | null | null | null |
Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement | Accept (poster) | Summary: This paper introduces the Hierarchical INTerpolation Network (HINT), a hierarchical framework that leverages the residuals on observed points to guide the estimation of the target function. HINT comprises multiple lightweight interpolation blocks arranged sequentially. The first block estimates the main compon... | Rebuttal 1:
Rebuttal: ### Response to Reviewer WnAW
**Comment 1:** The paper's novelty, especially regarding its hierarchical residual refining framework, isn't clearly articulated, with insufficient distinction from previous works on hierarchical residual architectures.
**Response 1:** Thank you for your thoughtful... | Summary: The authors proposed an algorithm named Hierarchical Interpolation Network (HINT) to predict unseen point values in scattered data to replace the usage of manually designed interpolation algorithms. The HINT used the residuals on observed points to guide target function estimation and the hierarchical local co... | Rebuttal 1:
Rebuttal: ### Response to Reviewer ce6Q
We are grateful to the reviewer for the comprehensive comments. The following responses are structured to address each comment.
**Comment 1:** Limited novelty in network design..
**Response 1:** Our HINT and NIERT exhibit significant differences:
1. **On architect... | Summary: This work presents a new transformer based approach for scattered data interpolation. By extending NIERT [4] in several aspects, authors achieved improved results in the target task. The extensions include 1) hierarchical residual refinement to produce fine-grained interpolation results and 2) hierarchical loc... | Rebuttal 1:
Rebuttal: ### Response to Reviewer RpoB
Many thanks to the reviewers for the thorough feedback. We will proceed to respond to each of the comments provided.
**Comment 1:** The datasets used in experiments consists of two-dimensional data. Performance should be evaluated with sparse dataset of much higher ... | Summary: In this paper, a novel hierarchical residual refining framework called HINT (Hierarchical Residual Refining for Scattered Point Interpolation) was proposed to improve interpolation accuracy. The framework utilized residual information from observed points to guide the prediction of target points in a coarse-to... | Rebuttal 1:
Rebuttal: ### Response to Reviewer G7AZ
We appreciate the reviewer's constructive feedback. In the following sections, we address each point raised.
**Comment 1:** The assumptions made in Section 2.3 are not validated experimentally.
**Response 1:** In relation to the assumptions highlighted in Section 2... | Rebuttal 1:
Rebuttal: ### Supplementary Result to Reviewer WnAW
We evaluated the impact of varying observed point counts on interpolation accuracy using the Mathit-2D dataset, as shown in the supplementary figure. On Mathit-2D, both our HINT method and other techniques exhibit a marked reduction in interpolation error... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces the Hierarchical INTerpolation Network (HINT) as an accurate interpolation algorithm for various theoretical and engineering applications. HINT consists of lightweight interpolation blocks arranged sequentially, where the first block estimates the main component of the target function, and... | Rebuttal 1:
Rebuttal: ### Response to Reviewer 746Q
We sincerely thank Reviewer 746Q for the insightful comments and suggestions. We will address each comment in the following.
**Comment 1:** The absence of time cost comparison between HINT and other methods and comparison with traditional methods.
**Response 1:**
... | null | null | null | null | null | null |
Provably Efficient Offline Reinforcement Learning in Regular Decision Processes | Accept (poster) | Summary: The paper presents an offline RL algorithm to learn near-optimal policies in a (episodic) Regular Decision Process (RDP). The problem is to learn a policy given a data set. The algorithm is split into two parts. First, is to learn the transition function of the RDP. Then, the problem of off-line learning on RD... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and recognition of our novelty. Please find our response to your questions and concerns below.
### Response to Questions:
Input prior knowledge and assumptions are, generally, the main limiting factors to the practicality of learning algorithms. Our a... | Summary: The authors presents a novel algorithm for offline RL in episodic Regular Decision Processes (RDP), a computationally feasible subclass of Non-Markovian Decision Processes. The presented algorithm have two phases: 1) learning the underlying automata by a novel algorithm, and 2) Markov transformation of the res... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and constructive suggestions. Please find our response to your questions and concerns below:
### Response to Questions:
- We believe extension to discrete structured RDPs (with a known structure) could pose some mild challenges, in terms of algorithm d... | Summary: The paper presents RegORL, an algorithm for offline RL in episodic RDPs. The algorithm combines automata learning techniques with state-of-the-art offline RL algorithms for MDPs. The authors provide a non-asymptotic high-probability sample complexity bound for RegORL, which guarantees the learning of an $\epsi... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and constructive suggestions. Please find our response to your questions and concerns below:
### Response to Weaknesses:
1. As suggested by the reviewer, we will include a more explicit comparison with the other papers that address RL in RDPs. We had i... | Summary: This work studies the problem of offline reinforcement learning (ORL) of episodic regular decision processes (ERDP) where we wish to find an near-optimal policy for an unknown ERDP with a small dataset of trajectories (collected with a behavioral policy). The authors took a reductive approach to first find the... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading, constructive suggestions, and recognition of our novelty. Please find our response to your questions and concerns below:
### Response to Weaknesses:
As suggested, we will give further context and background about the main challenges in the introductio... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the sample complexity of offline reinforcement learning (RL) in environments with non-Markov observation, i.e., partially observable Markov decision processes (POMDPs). In specific, the paper considers the episodic regular decision process (RDP) where the spaces of state, action, and observa... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and constructive suggestions. Please find our response to your questions and concerns below:
1. In the worst case scenario, the reviewer's intuition is correct. The distinguishability parameter in $L_\infty$ distance may be exponentially small in the r... | null | null | null | null | null | null |
Convex and Non-convex Optimization Under Generalized Smoothness | Accept (spotlight) | Summary: This submission claims to relax the Lipschitz assumption on the gradient of the objective function in nonconvex optimisation, and obtains convergence bounds similar to textbook convergence results.
Strengths: The presentation of the material is clear and the narrative flows well.
Weaknesses: The result prese... | Rebuttal 1:
Rebuttal: We would like to thank the Reviewer for the comments. However, we do NOT think there is any simple way to restrict the feasibility set to the neighborhood around the initialization point based on gradient information around the initialization, as claimed by the reviewer.
In particular, we want t... | Summary: Relaxed smoothness conditions have been introduced to study the gradient clipping algorithm, and show that clipping in particular allows fixed step-size convergence without smoothness under this relaxed assumption. This paper further generalizes the relaxed smoothness notion used for clipping by bounding the H... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments! We will try to address the concerns and questions below.
1. Regarding the stepsize, we agree that using an adaptive stepsize (e.g. gradient clipping technique, which essentially uses a larger step size when gradients are small) may accelerate conve... | Summary: This paper generalizes the recently introduced (L0,L1)-smoothness which itself extends the L-smoothness which is key in analyzing rates of convergence of optimization algorithms. The authors introduce the concept of $\ell$-smoothness where $\ell$ is a function of the gradient of the function to minimize (e.g.:... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback! We will add some experimental results to support our theories in the revision. We will also consider adding more related works. Below we try to address your questions.
1. First, we do mean positive infinity in Line 106, and thank you for pointing i... | Summary: This paper introduces a new assumption generalizing classical smoothness, and named $\ell$-smoothness, motivates it by giving providing examples of non-smooth functions belonging to this class, and studies classical algorithms under this assumption.
Strengths: - The paper is clear and fairly compared to relat... | Rebuttal 1:
Rebuttal: We would to thank the reviewer for the insightful thoughts and comments! Below we will clarify the three points in the review.
**1. Regarding the first point**, the reviewer made a very interesting reasoning regarding the optimal worse-case step-size $\gamma_{\ell}$, as defined in the comment. Ho... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper generalizes the classic Lipschtiz smooth gradient condition, as well as a recent improvement. The proposed condition is essentially saying the Hessian norm is bounded by a non-decreasing function of the norm of gradient. Using such conditions, the authors proved convergence rates of gradient descent... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments! Below we will try to address the questions of the reviewer.
1. Regarding the proof of Theorem 5.1, this splitting is indeed novel and we are not aware of any previous optimization analysis using similar techniques. Let us briefly talk about the mot... | null | null | null | null | null | null |
No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning | Accept (spotlight) | Summary: This work proposes a novel active learning method for graph neural networks, extending the classic expected model change maximization method in the general active learning setting. The proposed method starts from a new Bayesian interpretation of the SGC model and unifies the training process of GNN models, inc... | Rebuttal 1:
Rebuttal: Thanks for your insightful review! We are glad to know that you found our paper novel, sound, clear, and significant. We hope these responses will address your concerns appropriately.
## 1. Background knowledge of EMCM method
**The background knowledge of EMCM method in general active learning... | Summary: This paper proposes a new active learning strategy based on EMCM principle under the task of graph node-level semi-supervised predictions. The most significant contributions of this paper are 1) extending the EMCM principle to GNNs leading to the MAP estimate correlated (interpretable) acquisition function on ... | Rebuttal 1:
Rebuttal: $$\textcolor{red}{\text{The detailed rebuttal will be given if needed. Please let us know which point needs further clarification after reading this compact response.}}$$
Thanks for such a brilliant and constructive review!
1. Model name. The new name is a great suggestion due to the implied in... | Summary: The authors present yet another active learning approach for graph neural networks, claiming it to be novel. They attempt to build upon the classic expected model change maximization method but in a general active learning setting. To do so, they introduce a Bayesian interpretation of the SGC model, which supp... | Rebuttal 1:
Rebuttal: Thanks for your constructive review! We are glad to know that you found our paper novel, well-motivated, and well-written. We hope these responses will address your concerns appropriately.
## 1. Connections between the unified Bayesian interpretations of GNNs and the subsequent proposed active ... | Summary: The authors propose an active learning method for GNNs, extending the Expected Model Change Maximization (EMCM) principle to GNNs. A Bayesian interpretation for the node embeddings generated by GNNs under the semi-supervised setting is presented. By establishing a connection with expected prediction error mini... | Rebuttal 1:
Rebuttal: Thank you for your very thoughtful and constructive review. We appreciate the recognition of our active learning approach and the Bayesian interpretation of GNN training. We are glad to know that you found our paper clear, well-motivated, and supported by sufficient experiments. We would also like... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On the Convergence of Encoder-only Shallow Transformers | Accept (poster) | Summary: This paper proves the convergence rate of GD on one-layer transformer training. In their setting, the transformer is composed by a single attention layer, followed by relu unit and a fixed fully connected layer. The linear convergence rate is achieved. The proof technique follows the standard neural network pr... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer UvAq for appreciating that this is the first theoretical work on the convergence of Transformers and the insightful feedback. We address the concerns below.
---
> **Q1:** [The first part of the proof (proposition 1) pretty much follows [3].]
**A1:** The framework... | Summary: This paper studies transformer networks consisting of one layer, with average pooling and a scalar output. The authors consider encoder type softmax attention (unmasked) and show convergence to a global minimizer of the loss.
The results hold for the cases that the temperature/scaling inside the softmax $\tau... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer sZxG for appreciating that this is the first theoretical work on the convergence of Transformers and its significance for practice.
---
> **Q1:** [No result on sequence to sequence (seq2seq) model. The formulation of outputting a scalar inconsistent with practice.... | Summary: This paper presents global convergence results of shallow transformers (one self-attention layer followed by an MLP layer). The authors consider two different scalings for the factor $\tau_0$ used in the attention matrix as a function of $d_m$, the number of rows of $W_Q$ and $W_K$. Specifically, they prove th... | Rebuttal 1:
Rebuttal: We thank the reviewer p3n5 for the insightful feedback and for appreciating the significance of the paper. We address the concerns below.
---
> **Q1:** [The biggest weakness of the paper is its lack of clarity.]
**A1:** We appreciate the reviewer pointing out some typos. According to your sugge... | Summary: The transformer network is a popular but complicated architecture, which are widely used in NLP and computer vision. The contribution of this paper is two-folded:
1. it aims to give convergence guarantee for a shallow transformer network (encoder part) under different weight initialisations. Also, the paper co... | Rebuttal 1:
Rebuttal: We thank the reviewer 9ERM for the insightful feedback and for appreciating our theoretical analysis of Transformer and its value for the theoretical community. We address the concerns below.
---
> **Q1:** [Only the shallow (encoder part of the) Transformer network is considered. ]
**A1:** We ag... | Rebuttal 1:
Rebuttal: **General Response**
Dear reviewers,
We appreciate your insightful comments. Below, we address three core topics raised by reviewers and then answer individually to the questions of each reviewer.
Concretely, below we make the following responses:
- discuss the extension to the residual Trans... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proves the global convergence of the shallow transformer under a more realistic setting. While I don't often read theoretical machine learning papers, I found some of the analysis interesting. However, the paper does feel a bit incremental compared to previous work.
Strengths: I would like to comm... | Rebuttal 1:
Rebuttal: We thank the reviewer n1Tb for the insightful feedback. We address the concerns below.
---
> **Q1:** [Most of the analysis is done on shallow Transformer. The practical implication of the analysis is not yet clear.]
**A1:** We agree with the reviewer that this setting is a bit far away from pra... | Summary: The paper presents convergence results for shallow transformer networks for commonly used Gaussian-based initialization schemes in deep learning (LeCun, He) and different scaling regimes under finite overparameterization. In addition, the authors provide limiting neural tangent kernel (NTK) analysis for its mi... | Rebuttal 1:
Rebuttal: We thank the reviewer YNcT for the insightful feedback and for appreciating our theoretical analysis of Transformer and its value for the theoretical community. We address the concerns below.
---
> **Q1:** [Suggestions on intuitive remarks for proof sketch.]
**A1:** Thanks for the suggestion, ... | null | null | null | null |
A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization | Accept (poster) | Summary: The paper proposes a unified discretization framework for the differential equation (DE) approach to convex optimization. The DE approach relates optimization methods to continuous DEs with Lyapunov functionals, providing intuitive insights and convergence rate estimates. However, there has been a lack of a ge... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and suggestions. We really appreciate it.
Below are our responses to your criticisms in Weaknesses and Questions.
>Weakness 1. Knowing the accelerated gradient flow:
We feel the reviewer misunderstood our problem setting. Let us explain our standpoin... | Summary: This paper focuses on the design of convex optimization schemes based on a general discretization framework applied to differential equations (DE). The authors heavily rely on Lyapunov based inequalities to provide convergence rates.
The authors build a systematic framework on top of the one proposed by Su, B... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and suggestions. We really appreciate it.
Below are our responses to your criticisms in Weaknesses and Questions.
>Weakness 1. The estimated rates are not always optimal as demonstrated by the authors (some sub-cases of Theorem 5.5 for strongly convex... | Summary: The paper considers unconstrained convex optimization problems, studied from the angle of their close relation with differential equations. Specifically, the authors propose a framework for translating results from continuous time methods to their discrete time counterparts. They propose Discrete Gradients (a ... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and suggestions. We really appreciate it.
Below are our responses to your criticisms in Weaknesses and Questions.
>Weakness 1. Reference [1] seems to have quite some overlap in terms of results and considered classes of functions. In this light, a more... | Summary: This paper introduces a family of oracles called wDG (weak discrete gradient) verifying (8).
This family constraint (8) has been created in order to make proof works in the discrete setting and has been inspired by the observation of what happens in the continuous setting.
As expected, authors propose results ... | Rebuttal 1:
Rebuttal: Thank you very much for your careful reading and suggestions. We really appreciate it.
Below are our responses to your criticisms in Weaknesses.
>Weakness 1. It might be because I did not know this line of work, but based on the title, I thought of a completely different result, on how, using gra... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mitigating the Effect of Incidental Correlations on Part-based Learning | Accept (poster) | Summary: This paper builds up a part-based learning algorithm for few-shot classification with components preventing incidental correlations during training. ViT is adopted as the architecture, which facilitates part interpretation by recognizing each image patch as a specific part/foreground/background. Experiments on... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> Missing important ablation studies....
We perform an ablation analysis to investigate the impact of $L_{mix}$ and $L_Q$ during the pretraining phase on the MiniImageNet datase... | Summary: The authors propose a strong part-based learning method for few-shot learning. Noticing the incidental correlations between part signals, the method proposed in the paper, named DP-ViT, learned to disentangle foreground and background parts. This is done by introducing a mixture-of-experts formulation of parts... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> It looks to me that DP-ViT is a quite general improvement to the ViT architecture. However, to support its full utility, I think an experiment on ImageNet is needed, where DP-V... | Summary: The paper addresses the impact of incidental correlations on part-learning and proposes several regularization methods to mitigate this. The first regularization separates foreground and background to guide the part-based learning towards relevant input regions. To this end, the paper proposes a mixture of par... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> The choice of sparse and spectral norms is not well explained in my opinion. I understand the need for sparsity in the image space when training part-based models, but in the p... | Summary: The paper proposes to produce a more robust and interpretable part-based learning method for image classification problems (though, probably extensible to other tasks). The main contributions include 1) the Disentanglement of foreground/background regions via a weakly supervised loss and 2) the Use of sparse a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and suggestions. We respond below to each of the concerns/suggestions.
> [W1] Some details are slightly unclear: The pretraining portion is mostly quite clear but I have some clarity issues with the finetuning (distillation) portion of the proposed ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their positive feedback: proposed method tackles an important problem in a novel way with increased interpretability in representations [gyRR, Tpfu]; is clearly motivated and is technically sound [DtGy, QGVs]; achieves state-of-the-art performance with improvements over ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Near-Optimal $k$-Clustering in the Sliding Window Model | Accept (poster) | Summary: This paper proposes the first near-optimal $(1+\varepsilon)$-approximation algorithm for $(k, z)$-clustering problem in the sliding window model. The core part is the $(1+\varepsilon)$-coreset of $(k, z)$-clustering in the sliding window model which is based on an online coreset algorithm for $k$-clustering pr... | Rebuttal 1:
Rebuttal: > Some symbol is not explained, like $[\Delta]^d$.
We will clarify that $[\Delta]^d$ means that each of the $d$ coordinates of each point must lie within $\{1,\ldots,\Delta\}$.
> The merge-and-reduce framework lacks some citations.
We will add references to previous work that use the merge-and... | Summary: This paper studies the $k$-clustering problem in sliding window model. In sliding window model, a window of size $W$ is given to capture the most recent $W$ updates in the stream, where good clustering approximation should be maintained in the window with small space complexity. In previous work, in order to a... | Rebuttal 1:
Rebuttal: > The techniques used in this paper seem to rely heavily on the current SOTA method for offline coreset construction [1] (using ring structures and independent sampling method) and the consistent approximation scheme proposed by Meyerson et al. [2].
Our main algorithm utilizes (1) a framework for... | Summary: This paper studies $k$-means and $k$-median clustering in the sliding window, and proposes an $(1+\varepsilon$)-approximation algorithm on the top of an coreset maintained through the stream. The space complexity is roughly $k/\text{poly}(\varepsilon) \cdot \text{poly}\log n$ where $n$ is the number of points,... | Rebuttal 1:
Rebuttal: > I am confused by line 388 389, it seems that the window size is too close to the stream size. Though theoretically the window size is not an important parameter, but doing this converges back to the streaming model.
We remark that the window size was set rather close to the stream size in our e... | Summary: The paper proposes an near-optimal algorithm to build (1+$\epsilon$)-online coreset for k-clustering problem, and apply it to the sliding window model. The (near) optmality is established by a matching lower bound, also proved in the paper.
A coreset is a compression of a dataset, such that any clustering cen... | Rebuttal 1:
Rebuttal: > As mentioned in the Summary, the main idea seems to be an (arguably) straightforward adaptation of [24], which somewhat limits the novelty.
Our main algorithm utilizes (1) a framework for sliding window algorithms using a new randomized online coreset construction along the lines of [8], (2) an... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments and valuable feedback. We appreciate the positive remarks, such as
- The problem studied is important and well-motivated. (Reviewer 8X17)
- It's nice to see that the result of [23] and [24] can be ported to the online setting. (Reviewer 8X17)
-... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MixFormerV2: Efficient Fully Transformer Tracking | Accept (poster) | Summary: This paper proposes an efficient pure-Transformer-based tracking framework MixFormerV2. By replacing dense prediction heads and complex updating-score mapping modules with simple four-box tokens, the tracking pipeline is streamlined with high efficiency. Besides, Dense-to-Sparse Distillation and Deep-to-Shallo... | Rebuttal 1:
Rebuttal: ***C1: The proposed method is only applied on one baseline tracker MixFormer. The generality is not well proved on other Transformer trackers.***
**R1:** Thanks for your suggestion. To demonstrate the generality in rebuttal, we conduct experiments of **dense-to-sparse distillation** (i.e. the pre... | Summary: Since main-stream tracking methods are somewhat limited in efficiency, this paper is motivated to develop efficient trackers. The proposed method (named MixFormerV2) is based on the recent tracker MixFormer. The key improvement of architecture is to use a token-based prediction head to replace the corner-based... | Rebuttal 1:
Rebuttal: ***C1: Does Tea-skip4 use PMDP? If not, does its higher performance mean that PMDP is not necessary? If not, why can it achieve similar performance to PMDP?***
**R1:** *Tea-skip4* is a **special initialization** method, which chooses the skiped four layers (layer-3/6/9/12) of the teacher (MixViT-... | Summary: This paper focuses on designing efficient transformer-based single object tracking algorithm. The basic idea is to replace the original corner-based convolution head with a more lightweight MLP head input w/ only 4 learnable tokens. Moreover, both dense-to-sparse and deep-to-shallow distillations are designed... | Rebuttal 1:
Rebuttal: ***C1: What’s the effect of w/o applying the progressive strategy for depth pruning?***
**R1:** As shown in the table, the baseline is the **most usual initialization** method, which employs the first four layers of the teacher (MixViT-B) to initialize the student backbone, denoted as *Tea-fir4*.... | Summary: This paper proposes a fully transformer tracking framework without any dense convolutional operation and complex modules. The key contribution is the design of different input tokens and the distillation-based model reduction paradigm. Mixed attentions are performed between prediction tokens and the image gene... | Rebuttal 1:
Rebuttal: ***C1: The details of some components are missing, such as the details of the Score Head.***
**R1:** We will add the detailed structure of the heads in our revision. The Score Head is a simple MLP composed of `two linear layers with the hidden dimension of 768.`
Specifically, firstly we average... | Rebuttal 1:
Rebuttal: We thank all reviewers' efforts in reviewing our paper and giving insightful comments and valuable suggestions. We have provided the visualization of intermediate results and curve between performance and pruned depth in the PDF file.
Pdf: /pdf/c62db2e9f9da2ee17b0de8ec9ad005031c6930c7.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Grand Illusion: The Myth of Software Portability and Implications for ML Progress. | Accept (poster) | Summary: The paper explores the combination between hardware and software for machine learning, and ask the question: How portable are popular ML software frameworks? The authors conduct a quantity study of the portability of mainstream ML frameworks across different hardware types. With the experiment result, they c... | Rebuttal 1:
Rebuttal: # Rebuttal (fKz5):
We thank the reviewer for their feedback, including their positive observation that this work "raise[d] a concern for lack of machine learning portable quantity, which is not payed enough attention to in the current research" and the positive view of the experimental rigor and ... | Summary: This paper studies the portability of three different ML frameworks (PyTorch, TensorFlow, and JAX) across different hardware types (GPUs and TPUs). The authors sample a variety of functions implemented in these frameworks and test to what extent these functions are interoperable across platforms, and also comp... | Rebuttal 1:
Rebuttal: # Rebuttal (Twor)
We thank reviewer Twor for their feedback and for noting the core strengths of our work, including "portability is a key practical concern and therefore the paper provides a valuable contribution for the ML community" and "evaluation of GPU vs TPU execution reveals many interest... | Summary: The authors study the portability of a core set of PyTorch, TensorFlow, and JAX functions across hardware platforms, specifically GPUs and TPUs. They find significant fractions of functions fail to run on a given platform fully, partially, or within a tolerable latency. They provide the generated dataset.
Str... | Rebuttal 1:
Rebuttal: # Rebuttal (FGUA)
We thank the reviewer for their review and constructive feedback. We appreciate your recognition of the timely importance of our analysis of the portability of ML software frameworks across distinct hardware devices. It is great that you found our paper provides “Significant and... | null | null | Rebuttal 1:
Rebuttal: # Global Response
We thank all reviewers for taking the time to evaluate our manuscript. We appreciate the recognition of the timeliness and relevance of our study (R fKz5 and FGUA), emphasizing the often overlooked issue of ML software framework portability across different hardware types (R fKz... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Shared Safety Constraints from Multi-task Demonstrations | Accept (poster) | Summary: The authors propose a novel method for constraint learning from expert demonstrations by an optimal CRL policy. The idea behind the algorithm is to frame the problem as a zero-sum game between a policy player and a constraint player, using inspiration from a two-player zero-sum game expression of IRL and CRL. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words about our work. Responding in order to the concerns raised:
W1: Because of the overly conservative constraints single-task ICL produces on some problems (e.g. a maze), our main goal with the single-task experiments was to show that this is not always the... | Summary: The paper proposes a novel inverse constraint learning (ICL) approach that leverages constrained reinforcement learning (CRL) and a game-theoretic formulation. Specifically, the paper shows that the game-theoretic view of inverse reinforcement learning (IRL) naturally extends to ICL, by forming the Lagrangian ... | Rebuttal 1:
Rebuttal: We appreciate that the reviewer found our figures / algorithm descriptions clear and that our algorithm seemed straightforward to implement – we think this bodes well for its application to real-world problems. Responding to the concerts raised:
W1: Please see our global response for the relatio... | Summary: This paper proposes to use inverse constraint learning (ICL) in multi-task scenarios. It uses game solving to describe the ICL problem, and then illustrates the learning algorithm under the single-task and multi-task problems. The authors give a theoretical analysis to explain under what conditions the learned... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our theoretically-grounded approach. Responding to the concerns raised:
W1: Please see our global response for the relationship between our work and that of Chou et al.
W2: We’re not exactly sure what a “human prior” is here, could the reviewer cla... | Summary: Broadly, the paper address the challenge of safety constraints for robots, in particular the difficulty of handcrafting these. They propose learning safety constraints from expert demonstrations, particularly in a multi-task setting where each task reward is known and the safety constraints are task-agnostic.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for an incredibly detailed summary of our work which evinces a very thorough reading of our work.
W1: Sec. 3.1 and 3.2 are standard algorithmic techniques. We would be happy to add something like “Prior Work” to the subsection titles.
W2: If we understand correctly, the re... | Rebuttal 1:
Rebuttal: We thank all reviewers for their carefully considered feedback.
**Limited Experiments:**
We would like to begin by noting that, in comparison to many of the standard benchmarks in safe RL, our considered tasks are higher dimensional (e.g. our ant-based tasks are higher dimensional than every tas... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the challenge of learning safety constraints for agents in various tasks from expert demonstrations, instead of manually specifying them. The authors extend inverse reinforcement learning (IRL) techniques to the space of constraints, aiming to learn constraints that prevent highly rewardin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough description of our work. Responding to the concerns raised:
W1: Intuitively, if we see a more diverse set of behaviors from the expert by using multi-task data, our constraint learner will spuriously forbid fewer states. Importantly, it is not the greater ... | null | null | null | null | null | null |
Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination | Accept (poster) | Summary: The authors proposed a method for semantic segmentation applied to unmanned aerial vehicle (UAV) laser scanning (ULS), namely SOUL, to discriminate leaf from wood points. It is based on PointNet++ with an additional sampling scheme and an innovative training loss function to handle the high imbalance between t... | Rebuttal 1:
Rebuttal: Thank you for your acknowledgment of our effort and the very helpful comments.
Q1: Will the dataset be release publicly? ...
We will release the labelled ULS data publicly along with the SOUL code, since this kind of data is
still extremely rare indeed. It should make a useful contribution to th... | Summary: This paper proposes a dataset and an algorithm for 3D semantic segmentation in forest scenes. From data collection to the algorithm design, this paper covers the whole pipeline that are oriented for forest segmentation. In terms of the algorithmic part, the solution itself is not fully satisfied with me, but I... | Rebuttal 1:
Rebuttal: Thank you for your very positive and supportive review. We truly appreciate your acknowledgment of our efforts.
Q1: Can you conduct an ablation study for the rebalance loss? If possible, I want to see the quantitative/qualitative result based on this loss design.
Since the class imbalance proble... | Summary: They describe an approach for automatically segmenting a LIDAR scan of a forest into wood and leaf points. They train a PointNet++ model and use resampling to address the extreme class imbalance in the data. In comparison to previous methods they achieve a much higher balanced accuracy on their dataset.
Str... | Rebuttal 1:
Rebuttal: Thank you for very helpful comments, and thank you for bringing this recent work[1] to our attention.
We would add the discussion and cite the paper in the final revision.
Q1: It was not clear to me why they needed to first cluster the tree into large segments; perhaps they
could better explain t... | Summary: This paper introduces a neural network model based on the Pointnet ++ architecture which makes use of point geometry only (excluding any spectral information). To cope with local data sparsity, it proposes a sampling scheme that aims to preserve local important geometric information. It also proposes a loss fu... | Rebuttal 1:
Rebuttal: Thank you for your comments and advice. We appreciate the opportunity to address your concerns.
Q1: Probably need to go beyond the pointnet++ methods, which is relatively outdated.
In fact, the recent work by Qian et al.[1] at NeurIPS 2022 demonstrates PointNet++ backbone’s enduring relevance. ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments and for underlining the existence of new and good
features in our work, in particular regarding new methodological contributions, reproducibility and
relevance of the experiments, while also suggesting improvement and clarification. We also note that
the i... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This submission proposes an end-to-end approach, Semantic segmentation On ULS (SOUL), for leaf-wood semantic segmentation that is based on PointNet++ [8]. By considering the imbalanced class label in the collected ULS dataset, a rebalanced loss is used. Moreover, a geodesic voxelization decomposition (GVD) met... | Rebuttal 1:
Rebuttal: Thank you for your valuable and constructive comments, we appreciate this opportunity to respond to your comments and address your concerns.
Q1: The writing and the organization of the submission need to be improved.
We will improve both in the revised version and check carefully and correct all... | null | null | null | null | null | null |
Continual Learning with Global Prototypes: Beyond the Scope of Task Supervision | Reject | Summary: This paper studies continual learning in NLP by leveraging global prototypes. The authors attribute the catastrophic forgetting to the disruptive updates caused by the misalignment between the knowledge learned from observed tasks and the knowledge required for future tasks. To tackle this problem, the authors... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for the time and effort you have dedicated to evaluating our work. We are glad that you think our perspective in the paper is important. We address your concerns and questions below.
**1\. Points of our method**
Thanks for the suggestion. We would like to illustrate the ... | Summary: This paper focuses on continual learning in NLP and introduces a regularization-based method to tackle the problem. The main contributions of this work include global alignment, highlighting general-purpose knowledge across tasks, and neighbor attention, which offers a novel parameter-efficient tuning approach... | Rebuttal 1:
Rebuttal: Dear reviewer, we sincerely thank you for thoroughly evaluating our work and raising insightful questions. We address your concerns and questions below.
**1\. Our regularization vs. others**
Compared to other regularization methods, we regularize data representations’ deviation from the space o... | Summary: The authors address the problem of catastrophic forgetting in continual learning and propose to connect observed and unknown tasks by means of task-specific data representations which can be seen as general-purpose representations useful for a wider range of tasks. To this end, they introduce the notion of glo... | Rebuttal 1:
Rebuttal: Dear reviewer, we appreciate the time and effort you have dedicated to evaluating our work. It is inspiring to see you find our method interesting. We address your concerns and questions below.
**1\. Elaboration on neighbor attention insertion**
Based on the desiderata proposed in Section 3, al... | Summary: This paper proposes a continual learning method for both task and class incremental learnings by incorporating global prototypes. These global prototypes are derived from a pre-trained masked language model and are used to make connections with task specific prototypes. By maintaining these connections, the pr... | Rebuttal 1:
Rebuttal: Dear reviewer, thank you for thoroughly evaluating our work and raising insightful questions. We address your concerns and questions below.
**1\. The connection between global knowledge and task-specific knowledge in Eq. (2)**
Sorry for the confusion. In Eq. (2), the global knowledge is provided... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their insightful comments and suggestions. We have added new experimental results in the Rebuttal PDF with **(1)** CL baselines using prompt or adapter structures and **(2)** more CL in NLP baselines. Results show that our proposed methods can still achieve adv... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the continual learning problem by leveraging a concept called global prototypes, which are invariant features that are not altered during task-specific continual learning. The training is thus augmented with an additional alignment loss of the data features to the prototypes. The paper the... | Rebuttal 1:
Rebuttal: Dear reviewer, we thank the time and effort you have dedicated to evaluating our work. We address your concerns and questions below.
**1\. Clarification on Section 3**
We would like to clarify the misunderstanding in the statement “there is a simple argument to summarize Section 3: continual lea... | null | null | null | null | null | null |
Real-World Image Super-Resolution as Multi-Task Learning | Accept (poster) | Summary: This paper revisits the real-world super-resolution task from the perspective of multi-task learning, considering each type of degradation as a separate task. However, there are countless types of degradation in the real world, which often results in severe task competition in previous methods. The authors pro... | Rebuttal 1:
Rebuttal:
Dear Reviewer o9KK,
We appreciate your thoughtful comments. Please find below our detailed response addressing your concerns.
### Q1. Detailed selection strategy for different degradation tasks in Section 3.1 and Figure 1 are recommended.
Please note that we've stated in **Line 115** that the ... | Summary: The authors rethink the real-world super-resolution problem from the perspective of multi-task learning. And point out the primary challenge: task competition problem. To address this issue, they propose a task grouping method to identify unsatisfactory tasks and introduce TGSR to handle them separately, there... | Rebuttal 1:
Rebuttal:
Dear Reviewer yhLt,
We'd like to thank you for your positive feedback and address the concerns raised in your comments.
### Q1. The authors should provide the calculation time of the performance indicator, although the performance indicator is obviously faster than directly fine-tuning a single... | Summary: This paper aims at the task conflict issue of real-world image super-resolution (SR) with multiple degradation tasks, and proposes a task grouping approach to group similar tasks together to mitigate task competition. In addition, this paper designs a real-SR network called TGSR (task grouping-based real-SR ne... | Rebuttal 1:
Rebuttal:
Dear Reviewer 4Sx7,
We appreciate your feedback and would like to address the concerns raised in your comments, which include some factual errors and misunderstandings.
## Factual Errors & Misunderstandings
### Q1. The authors compare the PSNR distance... However, they only number 1-100 for ... | Summary: This paper models a real-world image super-resolution (real-SR) from a multi-task learning perspective, that is treat real-SR as solving multiple distinct degradation tasks. To this end, the authors propose a task-grouping approach by grouping similar tasks together. Extensive experiments demonstrate the effec... | Rebuttal 1:
Rebuttal:
Dear Reviewer tGSk,
Thank you for your feedback. We'd like to address some factual inaccuracies in your comments and clarify the misunderstandings.
## Factual Errors & Misunderstandings
### Q1. The multi-task Real-SR method seems to be not smart. There are infinite number of degradations. The... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper casts real-world image SR as a multi-task learning task for complex image degradations. By comparing the performance gap between the real-SR model and the fine-tuned model for a specific degradation, the satisfactory task and unsatisfactory task are divided. And thus it proposes a task grouping meth... | Rebuttal 1:
Rebuttal:
Dear Reviewer LkK8,
Thank you for your feedback. We’d like to provide some contextual information to help you better understand our method and address your concerns.
## Task Grouping in Multi-task Learning
(1) The task grouping approach (e.g., the CVPR2018 best paper: Taskonomy [2]) widely use... | null | null | null | null | null | null |
Data-driven Optimal Filtering for Linear Systems with Unknown Noise Covariances | Accept (poster) | Summary: The paper builds on the duality between estimation and control, in order to develop an online data-driven method for MSE-optimal filtering of linear systems with linear observations. Process and observation noise covariances are considered unknown, but stochastic states are assumed to be bounded. The paper pr... | Rebuttal 1:
Rebuttal: **Q**: *... However, it is still very hard to follow the assumptions...*
**A**: Please see the Response to All for clarification on the technical results and the changes we made for the revision.
**Q**: *...Particularly, Thm.2 and it's proof are not clear ...*
**A**: We provide the following o... | Summary: This submission examines the learning of the Kalman filter gain for linear systems with unknown covariance matrices using noisy output data. Similar to learning the linear quadratic regulators for unknown linear systems, the learning problem here is posed as a stochastic policy optimization problem which minim... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments, clarifications, and reference to relevant literature that will indeed improve this manuscript.
**Q:** *The setup is not very practical as the system matrices are assumed perfectly known but only the covariance matrices are unknown...*
**A:** Please see o... | Summary: This paper focuses on learning the optimal filtering policy (Kalman gain) in a linear system with known system matrices and unknown covariance matrices of noise. The paper considers a proxy objective to avoid learning with hidden variables and characterizes a dual form of the objective, optimized subsequently ... | Rebuttal 1:
Rebuttal:
The authors would like to express their gratitude to this reviewer for
their insightful comments that helped us improve the
presentation and empirical results of this manuscript.
***Q:** The paper lacks enough empirical experiments to support their
idea. The only two figures appear in the supple... | Summary: This paper studies the learning of optimal steady-state Kalman filter gain for a linear dynamical system with known system matrices but unknown process and measurement noise covariances. In particular, the learning process involves minimizing the prediction error of the observed output using a dataset of indep... | Rebuttal 1:
Rebuttal: The authors would like express their gratitude to this reviewer for their insightful comments.
**Q:** *The results of remark 7 can be discussed earlier maybe within the informal theorem 1.*
**A:** We are going to replace Remark 7 with a formal version of Theorem 1 that includes the necessary as... | Rebuttal 1:
Rebuttal: We appreciate reviewers' feedback and suggestions that have helped improve the paper. We provide a summary of the response to the main concerns here, followed by individual responses to the reviewers.
**Presentation of the technical results:** In order to improve the presentation of the technica... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Streaming algorithms for evaluating noisy judges on unlabeled data - binary classification. | Reject | Summary: The paper considers the problem of estimating the accuracy of noisy judges/classifiers in a streaming fashion, using only unlabeled data. Specifically, the goal is to compute the accuracy of each judge while processing items and the judge predictions for each item as part of a stream, without any associated la... | Rebuttal 1:
Rebuttal: We disagree strongly that Platonious et al. have already done this work and "better". As discussed in the general rebuttal, they are using the ensemble decisions PLUS additional logical constraints. Nowhere in their paper do they flag that three independent classifiers have a closed algebraic solu... | Summary:
This paper introduces a new inferential evaluator for evaluating noisy binary classifiers on unlabeled data in a streaming manner. Specifically, compared to the evaluator based on majority votes, the new evaluator gives a more complete and reasonable modeling of the true label prevalence and each classifier’s... | Rebuttal 1:
Rebuttal: The experiments where meant to highlight what is unique about our algebraic evaluator - that it can detect when its own evaluation assumptions have failed. Since there is no other published evaluator on unlabeled data that can detect the failures of its own assumptions these experiments cannot hav... | Summary: This paper considers the problem of evaluating noisy binary classifiers on unlabeled streaming data. It aims to estimate the prevalence of the labels and the accuracy of each classifier on them, given a data sketch of label predictions by the members of an ensemble of noisy binary classifiers. The authors prop... | Rebuttal 1:
Rebuttal: 1. We agree with the reviewer that classifiers may not be independent. But as asserted in the paper, we consider the operational scenario where one engineers an evaluation ensemble that is nearly independent. What remains then, for safety reasons, is the ability to detect when the classifiers are ... | Summary:
The paper addresses making decisions based on the outputs of three binary classifiers. More precisely, it focuses on evaluating the performances of noisy classifiers. It considers majority voting on one hand, and a proposed evaluation scheme based on the classifiers' accuracies. The paper establishes several ... | Rebuttal 1:
Rebuttal: 1. We agree with the reviewer that this work has large applicability to the literature of social choice. We are also aware of the extensive work that has been done in other fields with this universal problem of principal/agent monitoring. That is precisely why we chose to frame the concerns of the... | Rebuttal 1:
Rebuttal: This section will address two criticisms shared by two or more reviewers - 1. that the paper is not novel and the problem was treated already in Platonius et al. (2014) 2. the experimental results are weak and like baseline comparisons. The purpose of the paper was to devise an algebraic evaluator... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers the problem of evaluating an ensemble of binary classifiers on unlabeled data in a streaming setting. The authors first describe a baseline which treats the majority vote as the correct label and evaluate each classifier accordingly. Then they propose an evaluator based on an assumption th... | Rebuttal 1:
Rebuttal: 1. Relation of Platonious et al paper to this work. Their work is concerned with estimating the accuracy of multi-class classifiers whenever we have their decisions AND additional logical relations that can serve as ground truth constraints. Since whatever logical relations are chosen are specific... | null | null | null | null | null | null |
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances | Accept (poster) | Summary: The authors tackle the problem of hyperparameter tuning across problem instances, as proposed by Balcan et al. (2022). They propose three novel learning guarantees regarding the sample complexity of tuning regularization parameters: i) an improved upper bound on the pseudo-dimension for elastic net; ii) a matc... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and suggestions. We really appreciate that the reviewer acknowledges the theoretical contributions of our paper and we will address the reviewer’s concerns as below. In particular, we will state the target early on in the paper for improving clarity.... | Summary: The paper analyses the complexity of hypotheses classes where the hyperparameter of logistic regression and linear regression are tuned, in the setting where multiple datasets are available.
Strengths: The paper derives tighter upperbounds for the elastic net setting and proves a matching lowerbound, which ar... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback. We really appreciate that the reviewer acknowledges the contribution of our work and also thank them for their time understanding our work. We will take the reviewer’s comments into account for improving the readability of the paper and use the extr... | Summary: The main idea of this paper is to address the challenge of tuning regularization coefficients in regression models with provable guarantees across problem instances. The authors investigate the sample complexity of tuning regularization parameters in linear and logistic regressions under l1 and l2 constraints ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing constructive feedback.
**Clarity and visual support**: We will take your comment into account for the camera-ready version. We already have a figure illustrating the computation of the GJ algorithm (Figure 1) and we are happy to use the additional page in the c... | Summary: This paper studies the sample complexity of tuning regularization parameters in linear and logistic regressions under $\ell_1$ and $\ell_2$ constraints in the data-driven setting. Theoretically, it provides a new bound for the pseudo-dimension of the validation loss function class, which significantly improves... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments, and for appreciating the theoretical contributions of our work.
**Typos in Lines 503-507**: Thank you for pointing out. We will fix the typos in the revised manuscript.
**Concerning practicality over single instance work**: In most application... | Rebuttal 1:
Rebuttal:
We thank all the reviewers for their thoughtful comments and suggestions. We are glad that all of the reviewers appreciate our theoretical contribution. The main concerns are the clarity of the settings and more visual supports, which we aim to improve by providing figures describing key concepts... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Rethinking Gauss-Newton for learning over-parameterized models | Accept (poster) | Summary: This paper studies applying the Gauss-Newton method to train the neural network to solve a regression problem in the NTK and mean-field regime. The paper establishes a linear convergence rate on Gauss-Newton in this setting. The authors further provide experiments in the teacher-student network setting to stud... | Rebuttal 1:
Rebuttal: Thank you for your review. We believe the following clarifications should address all your concerns about the strength of our contribution. If you still have concerns, we are more than happy if you could kindly share them as this would help us improve this work.
1. **Convergence:** The non-con... | Summary: This work theoretically and empirically studied learning dynamics and generalization properties of one-hidden-layer networks trained with the Gauss-Newton algorithm. The main theoretical result is that the authors provided a loss convergence guarantee, which is provably faster than the same networks trained wi... | Rebuttal 1:
Rebuttal: Thank you for your review. We believe there is a misunderstanding of the relevance of our contribution. The following clarifications should address all your concerns. If you still have other concerns, we are more than happy if you could kindly share them as this would help us improve this work.
... | Summary: This paper investigates theoretically and empirically the implicit biases of the Gauss-Newton (GN) optimization algorithm on over-parametrized one hidden layer-models (e.g. the capacity of the model is much higher than that of the ground-truth function to approximate). The main theoretical contribution of the ... | Rebuttal 1:
Rebuttal: Thank you for your extremely precise and outstanding review! We are very pleased to read that you found the paper well-written and that you took the time to carefully check the proofs. We hope our answer clarifies all the remaining points.
I. **Main points:**
1. **MNIST experiments:** We a... | Summary: The authors study the Gauss-Newton optimization algorithm in the overparameterized setting. They derive conditions for convergence of the Gauss-Newton algorithm with parameter-dependent damping in terms of the convexity of the loss, the smoothness of the loss Hessian, and the damping constant.
They then carry... | Rebuttal 1:
Rebuttal: Thank you for your review. We are glad you find our study of the generalization and implicit bias of GN to be interesting. We believe the following clarifications should address all your concerns. If you still have concerns, we are more than happy if you could kindly share them as this would help ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models | Accept (poster) | Summary: in this paper, the authors propose to improve the pretrained text-to-image diffusion model with human feedback in an online-reinforcement learning manner. The problem formulation, the differences between the online update and the supervised finetuning are clearly stated. Experiments show that the proposed meth... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. The connection between Section 4.3 and experiments:**
It is hard to make a one-to-one mapping between each claim in Section 4... | Summary: The paper introduces the idea of finetuning text-to-image diffusion models using reinforcement learning. The core idea is relatively simple: generate samples from a trained diffusion model, use the samples and a reward function to update the model's parameters, and iterate. The paper further introduces a KL di... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. Lacking experiments, training with more prompts, not scale to a larger set of training prompts**
Thanks for your feedback. Pl... | Summary: This paper proposes to use RL to finetune t2i diffusion models and use KL regularization into supervised fine-tuning of diffusion models. The paper shows that RL fine-tuning can avoid the overfitting that arises in SFT, and is generally superior to SFT with respect to both image-text alignment and image quali... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. Novelty is limited, similar ideas have already been proposed in [1]**
We respectfully disagree with the reviewer’s assessment.... | Summary: This paper studies RLHF fine-tuning of diffusion models to learn from and align with human feedback. Specifically, they introduce (1) an online RL strategy and (2) a KL regularization (inspired by a similar regularization for the RLHF via PPO of LLMs). Empirically, they show better alignment with the optimized... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail, one by one below.
**Comment 1. Ablation study to verify the effect of exploring online samples**
Response 1: This is an interesting question! However, we no... | Rebuttal 1:
Rebuttal: Overall author rebuttal:
We thank all reviewers for their thoughtful comments. We greatly appreciate all the reviewers' acknowledgment that our method is **empirically effective with solid theories**. To address common concerns about scaling up the training prompts and human evaluation, we have a... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes to adapt the popular RLHF framework used for fine-tuning LLMs to fine-tuning of diffusion generative models.
Given a reward model, the goal is to finetune the parameters of the diffusion model such that the resulting images from the
sampling process achieve high reward. Importantly, the rew... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments. We found them extremely helpful in improving our draft. We address each comment in detail below.
**Comment 1. Fine-tuning on a single or few prompts at a time**
Thanks for your suggestion! We added experiments that contain training with a large var... | null | null | null | null | null | null |
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration | Accept (poster) | Summary: The paper proposes a strategy called Training-frEE calibratioN (TEEN) for Few-Shot Class-Incremental Learning (FSCIL) scenario to enhance the discriminability of new classes by fusing the new prototypes with weighted base prototypes. TEEN demonstrates remarkable performance and consistent improvements over bas... | Rebuttal 1:
Rebuttal: **Q1** The paper demonstrates significant improvement on new classes. However, based on the experimental results, it appears that the False Negative ratio, which involves classifying base instances into incorrect classes, may increase. It would be beneficial to address how this problem is handled... | Summary: This paper tackles the problem of few-shot class incremental learning based on prototypical network. Motivated by the observation that novel classes are easily misclassified as base classes, the authors propose a prototype calibration strategy. The calibrated prototype is a weighted sum of prototype computed f... | Rebuttal 1:
Rebuttal: **Q1** The paper introduces two hyper parameters which need to be exhaustively searched for every dataset.
**A1** We thank the reviewer for the constructive question. We show the performance trend with respect to $\alpha$ and $\tau$ on each benchmark dataset in **Figure 2 in the Rebuttal PDF**.... | Summary: The authors work on the Few-Shot Class-Incremental Learning (FSCIL) scenario and propose the Training-frEE calibratioN (TEEN) strategy. This strategy enhances the discriminability of new classes by fusing the new prototypes with base prototypes. This approach is different from previous methods, which either in... | Rebuttal 1:
Rebuttal: **Q1** The issue of misclassification has already been observed and studied in previous few-shot learning works. Therefore, the poor performance of new classes is not surprising in the task of Few-Shot Class-Incremental Learning (FSCIL).
**A1** We thank the reviewer for the suggestion. **We must... | Summary: This paper presents a novel training-free calibration approach for few-shot class incremental learning. The authors make an observation that one main problem with FSCIL is that data of new classes can be easily mis-classified as base session classes. By utilizing the well-calibrated embeddings of base session ... | Rebuttal 1:
Rebuttal: **Q1** One of the reviewer's concern is in terms of the robustness of the method to hyper-parameter $\alpha$ and $\tau$. Are the optimal hyper-parameters the same for different datasets or different incrementing procedures (i.e., how many classes per incremental session)?
**A1** We are sorry for ... | Rebuttal 1:
Rebuttal: We express our profound gratitude to the reviewers for their insightful and valuable comments. We are pleased that the reviewers find the simplicity and efficiency of the proposed method TEEN (RFbX, wHh2, 3Zyg, TNew) and our work clear and easy to follow (RFbX, 3Zyg, aEVg). They also consider our... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper closely looks at problems in prototypical networks and methods in the context of few shot class incremental learning. The authors observe semantic similarity between new and base prototypes along with new classes being misclassified regularly as semantically similar base classes. Towards this they us... | Rebuttal 1:
Rebuttal: **Q1** Session 4 uses a variety of terminology. Using a consistent term for each prototype could potentially read better. (For example ill-calibrated new prototypes, new prototypes, biased prototypes)
**A1** We thank the reviewer RFbX for the kind and meaningful writing suggestions. We will fix t... | null | null | null | null | null | null |
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training | Accept (spotlight) | Summary: This paper propose to add a new component, a P-Former, to the BLIP-2 vision-language pre-training framework. The P-Former is a sentence encoder that learns to project texts into the input space of a LLM. During vision-language pre-training, an additional alignment loss is applied between BLIP-2 Q-Former's visu... | Rebuttal 1:
Rebuttal: **W1: The motivation and problem formulation is a slight misrepresentation of the actual method. The actual role of the P-Former is to serve as a surrogate of the LLM to help the Q-Former better align with the frozen LLM in both stage-1 and stage-2 pre-training.**
Re: The P-Former does indeed fun... | Summary: This paper introduces a vision-language pre-training method with the help of the proposed Prompt-Transformer. As the first step, the P-Former is optimized to learn the "optimal" soft prompt that can guide the LLM to generate the target texts. After that, the trained P-Former is frozen and used to train the vis... | Rebuttal 1:
Rebuttal: **W1: In Table 1, it is not fair to only list the numbers of image-text pairs considering that P-Former also consumes language data for training. Similarly, in the paragraph of Line 216, the overhead of training P-Former should also be taken into consideration.**
Re: We agree that it's crucial to... | Summary: This paper introduces a prompt-transformer (P-Former), which is pretrained on text corpus, that can trigger the LLM to generate better text prompts to align with the vision-and-language models with better visual features. Empirical experiments on the top of BLIP-2 shows promising results on several tasks.
St... | Rebuttal 1:
Rebuttal: **W1 and Q1: Where is the gain from? I assume the stage 1 in the Figure 3 and Line 211, is just taken from the pretrained BLIP model, right?**
Re: Our approach in stage 1 is grounded in equation (8) and comprises dual learning objectives: the first one originates from BLIP2, while the second alig... | Summary: To improve image-to-text generation, this paper proposes a proxy model P-Former to predict LLM prompts and uses it as an auxiliary loss in BLIP-2 to align selected features with LLM prompts. Results show promising results, especially in 0-shot VQA tasks.
Strengths: Nice novelty by introducing a proxy model f... | Rebuttal 1:
Rebuttal: **W1 and Q1: This paper only shows the effect of the LLM prompt prediction in an incremental way, i.e., as an auxiliary loss in the existing BLIP-2. It would be more interesting if we could show the effectiveness of P-Former in a cleaner (simpler) setup.**
Re: A potential pitfall in bypassing the... | Rebuttal 1:
Rebuttal: Dear Reviewers:
We sincerely thank you for your thoughtful reviews and constructive feedback on our paper. We are heartened to see that our proposal of adding a new component, a P-Former, to the X-language (X being any modality) pre-training framework resonated well with all of you. Your apprecia... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a novel approach for optimizing the application of large language models in resource-intensive vision-language pre-training. Unlike the traditional approaches of using visual features as prompts to guide language models, the paper focuses on identifying optimal prompts to align with visual... | Rebuttal 1:
Rebuttal: **W1: Seems to lack an intuition on why learning an ideal language prompt helps?**
Re:
- Models like BLIP2 consist of three sequential components: (1) ViT, (2) VL-connector, and (3) LLM decoder. Given that we use a LLM for generation, optimizing closer to the LLM (i.e., prompts, comparing to vis... | null | null | null | null | null | null |
3D Indoor Instance Segmentation in an Open-World | Accept (poster) | Summary: This work tackles the task of incremental object-discovery for 3D semantic instance segmentation. Unlike numerous concurrent works, it is not unsupervised but enables users (or oracle) to label objects that were identified as unknown in each iteration. The method is evaluated on ScanNet200, the paper proposes ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific queries below.
**Clarification on 3D-OWIS-PC-CT.**
3DOWIS-PC-CT is an extended Mask3D with predictive capabilities to encompass unknown classes. Unlike the original closed-set baseline Mask3D... | Summary: This paper proposes a pipeline to do 3D open-world instance segmentation. The authors provide a problem definition and introduced three different scenarios. Moreover, to overcome the possible problems that may lead to lower performance on known classes, the authors proposes different modules like probability c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific queries below.
**Explanation about PC and CT.**
3D-OWIS-PC-CT represents our method without Probability Correction (PC) and without Confidence Threshold (CT). The final model 3D-OWIS includes... | Summary: This paper introduces an open-world 3D indoor instance segmentation method, where an auto-labeling scheme is employed to produce pseudo-labels during training and induce separation to separate known and unknown category labels. Pseudo label quality are further improved subsequently.
Strengths: 1. Open world 3... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestions to improve the clarity of the paper.
**On probability correction assuming unknowns are far from knowns.**
In our open-world 3D instance segmentation framework, we use the auto-labeler to generate pseudo-labels for the unknowns. The contrastive clustering... | Summary: The paper presents a new application of open-world object detection to the setting of 3D instance segmentation. In this setup, a 3D point-cloud segmentation model is required to label each point with instance information, whether this instance is part of training or not. The paper uses ScanNet and proposes a f... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable feedback. Detailed answers to the reviewer's queries are provided below.
**3D-specific insights.**
As recommended, we present here both empirical and qualitative results to provide more insights specific to our 3D setting. We empirically observe that a direc... | Rebuttal 1:
Rebuttal: We thank all the reviewers (PXM7, CNBM, WNwG, L857, HHFW) for the positive and valuable feedback, and we appreciate the comments to improve our work. **Reviewer PXM7:** "Idea is easy to follow and the presentation is overall clear. Illustration figures are clear. The corresponding modifications ta... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the challenge of 3D instance segmentation in open-world scenarios. It starts with a formulation for this problem, including the definition and setup of known and unknown objects and different splits of categories for simulating different open-world cases. Accordingly, this work proposes a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the encouraging and insightful comments. Please find our responses to specific queries below.
**Preliminary section after Sec 3.2.**
We thank the reviewer for this suggestion. As suggested by the reviewer, we will add a new section to describe our closed-set baseline “M... | null | null | null | null | null | null |
DAC-DETR: Divide the Attention Layers and Conquer | Accept (poster) | Summary: This paper introduces DAC-DETR, a method to improve the training efficacy of DEtection Transformers (DETR) by addressing the contrary impacts of cross-attention and self-attention layers in the DETR decoder. DAC-DETR divides the cross-attention layers into an auxiliary decoder, which focuses on learning the cr... | Rebuttal 1:
Rebuttal: **VXuS-Q1: The reviewer is curious about the performance of the one-to-many branch (C-decoder).**
**[Ans]:** Thanks for this question. C-Decoder (with NMS) achieves slightly lower accuracy than the O-Decoder. Since C-Decoder has no self-attention layers and makes duplicate detection, NMS is prere... | Summary: This paper observes the problems in cross-attention and self-attention that impacts the queries, and proposes to use divide-and-conquer to improve the training accuracy.
Strengths: 1. The paper is considered novel to me. The insights in analyzing the cross-attention and self-attention and the proposed design ... | Rebuttal 1:
Rebuttal: **QUSJ-Q1: The design seems a little complex. Not sure if this design can be plugged into other DETR-like model easily.**
**[Ans]:** Thanks for this question. Plugging our method into the DETR-like models is easy. Given a DETR-like model, we only need to append a C-Decoder with two steps: **1)** ... | Summary: The authors find that the cross-attention and self-attention in the DETR decoder have opposite effects on object queries. This phenomenon reduces the training efficiency of DETR model. To resolve the contradiction, this paper proposes a Divide-And-Conquer DETR that employs an auxiliary decoder that shares para... | Rebuttal 1:
Rebuttal: **CkgD-Q1: This article is a combination of a series of existing methods and is not innovative enough.**
**[Ans]:** We respectfully disagree with this point. We would like to highlight our major contributions as below:
1) We reveal a characteristic of DETR, *i.e.*, the cross-attention and self-... | Summary: This paper proposes a simple modification to the DETR architecture that improves upon several prior implementations. The paper identifies that the single decoder approach causes the model to try and achieve opposing objectives in terms of the query coverage and deduplication, and proposes a solution to the pro... | Rebuttal 1:
Rebuttal: **LpXx-Q1: Analysis on if there is any difference in performance on large/small objects and common/rare objects.**
**[Ans]:** Thanks for this suggestion. The performance gain on large/small objects is slightly larger/smaller (as shown in Table 1 in the manuscript), and the performance gain on c... | Rebuttal 1:
Rebuttal: **General response**
We thank all the reviewers for their valuable comments. We provide point-to-point responses to each reviewer, as well as a supplementary PDF for some visualization results.
Pdf: /pdf/44629ce49151eef158dcdd1efdb59e46ac4e06f9.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper reveals the “gather ↔ disperse” effects between cross-attention and self-attention layers in DETR decoder and proposes to add a decoder as the auxiliary branch without the self-attention blocks. The proposed approach achieves competitive detection performances.
Strengths: 1. this paper leverages t... | Rebuttal 1:
Rebuttal: **9psD-Q1: How general does the "gather$\leftrightarrow$disperse" effect apply? Is there more examples beyond Fig.1 ?**
**[Ans]:** Thanks for this good question. During rebuttal, we make a statistic across 20\% randomly-sampled images in COCO-2017 and validate that the "gather$\leftrightarrow$di... | null | null | null | null | null | null |
Systematic Visual Reasoning through Object-Centric Relational Abstraction | Accept (poster) | Summary: The paper proposes to combine an object-centric representation model (Slot Attention) with a relational reasoning module to create the Object-Centric Relational Abstraction (OCRA) model. For this model, Slot Attention is first pre-trained in an unsupervised way to represent objects separately. Then, the relati... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful suggestions and comments. We provide a point-by-point reply below:
1. Positioning relative to previous work:
- We have endeavored to cite all relevant previous work in the introduction and related work sections. While it was not feasible to implement ba... | Summary: - The paper seeks to tackle various visual reasoning problems with a focus on systematic generalization.
- The paper argues that we need explicit inductive bias to extract object-object relationships and express these relationships via a low-capacity representation.
- The paper seeks to do this by first extrac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful suggestions and comments. We provide a point-by-point reply below:
1. Mechanism enforcing the relational bottleneck:
- The reviewer asks whether the dot product enforces a relational bottleneck only because it compresses inputs to a single dimension. To... | Summary: The research topic of this study is the development of a learning machine that achieves systematic generalization in reasoning over complex relations of objects in a visual input (still image). Toward this goal, the authors proposed a new neural network model (OCRA) taking inspirations from the recent results ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful suggestions and comments. We provide a point-by-point reply below:
1. Lack of source code:
- We thank the reviewer for bringing this to our attention. **We have now provided the AC with an anonymized link to the source code for our model. We will also m... | Summary: This work proposed a new method, named OCRA, that combines object-centric presentation learning (for object abstraction) and a relational bottleneck (for relational abstraction). Particularly, OCRA consists of three components: 1) a slot attention to extract object level representations, 2) a relational operat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful suggestions and comments. We provide a point-by-point reply below:
1. Synthetic vs. real-world benchmarks:
- The primary focus of the benchmarks we consider is on the more extreme **out-of-distribution generalization and low-sample regimes**. In the mo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their many insightful suggestions and comments. We have added a number of new experiments to address the concerns raised, and revised the paper to improve clarity and provide further elaboration on some issues. We believe the paper is significantly improved... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks | Accept (spotlight) | Summary: This paper focuses on the loss functions for time-based training schemes of SNNs, which propagate gradients only when the neurons fire a spike. The authors propose to map rate-based loss functions to time-based ones and explain why they also work. Besides, the authors propose the enhanced counting loss to repl... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our research direction and the innovative approach we have employed. We are truly grateful for your recognition of the solid analysis, well-structured organization, and clear exposition in our work. In response to your valuable feedback, we are fully committed to addres... | Summary: This paper explores the loss function for SNNs and proves that rate-based loss functions can be also used in the time-based training scheme. Besides, the authors propose a new loss function called enhanced counting loss, which improves the network performance (compared with previously-used mean square counting... | Rebuttal 1:
Rebuttal: Thank you for providing such an encouraging and positive response. We sincerely appreciate your recognition of the significance of our work, its alignment with an important direction, its well-written nature, and the inclusion of rigorous proofs. In light of your feedback, we are fully dedicated t... | Summary: In this work, the authors propose a framework that applies the rate-coding-based loss functions to time-based training. They show that the proposed method outperforms existing time-based ones.
Strengths: 1. The proposed method suggests a way to combine different approaches to train spiking neural networks.
... | Rebuttal 1:
Rebuttal: We express our sincere gratitude for providing constructive and insightful feedback such as the addition of the algorithm description. It is truly gratifying to know that you value the meticulous derivation and lucid explanation of our pipeline. In light of your input, we are fully dedicated to ad... | Summary: This paper focuses on the time-based training approach for spiking neural networks (SNNs). It first explains why rate-based loss functions can be used in time-based training for SNNs by establishing a link between rate-based losses and time-based ones. Then it does some analysis on the overall gradient provide... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive and insightful feedback. It is truly heartening to learn that you acknowledge the sound analysis presented in our paper and find merit in our newly proposed loss function, which contributes to the stabilization of the entire training process. We are commit... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Efficient Subgame Refinement for Extensive-form Games | Accept (poster) | Summary: The paper proposes GS2, a method to overcome the problem of large information states in subgame solving in imperfect-information games. Theoretical results and experimental evaluation on GuanDan are presented, with impressive results.
Strengths: The main idea of the paper, to use a diversity function to filte... | Rebuttal 1:
Rebuttal: Thanks for your appreciation and valuable comments/suggestions for our works. We hope these response would address the lingering concerns.
* **Regarding Question 1**.
Thank you for your insights. In GuanDan, we indeed operate under the assumption that opponents possess complete information. ... | Summary: The paper proposes a novel subgame resolving algorithm in Extensive-Form games called GS2, which will only sample a portion of subgames and dramatically reduce computation complexity.
Strengths: - The new subgame resolving algorithm GS2 has a theoretical guarantee, as all previous work did.
- The paper has su... | Rebuttal 1:
Rebuttal: We appreciate your recognition of the merits of our work. In response to your concern, we are in the process of refining our code to ensure its clarity and ease of use. Once finalized, we commit to making it publicly accessible.
---
Rebuttal Comment 1.1:
Title: Re: Rebuttal by Authors
Comment: L... | Summary: The contributions of the paper are twofold:
- On the theoretical hand, the paper introduces a bound on the total increase in exploitability when refining the strategy in a subgame that consider only some of the possible infosets of the adversary in a stochastic way.
- This bound is based on considering that ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We sincerely appreciate your valuable feedback. Here are our responses.
- **Regarding the exploitability bound.**
Thank you for pointing out this aspect. Indeed, the bound presented is specific to the infoset. The reason for this is rooted in the fact th... | Summary: The paper presents a generative subgame solving framework that can scale to games with a large amount of hidden information. One of the key ideas behind the generative framework is to prioritize exploration based on diversity. The paper evaluates on small-sized tabular games and a large poker-like game called ... | Rebuttal 1:
Rebuttal: Thank you for your valuable and insightful comments.
- **Regarding the choice of functions.**
We appreciate your insight on the importance of thoroughly ablating the choice of our diversity-based generation function. In the current study, our choice for the diversity-based generation functio... | Rebuttal 1:
Rebuttal: ### **Global response**
Dear Reviewers,
Thank you very much again for your helpful comments. We appreciate your recognization of our work and would like to engage with you in our responses to your questions/comments. If you have any questions about our work or our response, we would be happy to ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning | Accept (poster) | Summary: This paper presents a method for learning world models from in-the-wild videos. By utilizing a context encoder to capture contextual information, the proposed method explicitly models both the context and dynamics to facilitate knowledge transfer across scenes. Experiments are performed on various simulation b... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer MYvK for providing insightful comments and questions.
**Q1**: Discussion and ablation study on context frame selection
**Single vs. multiple**: We agree with the reviewer that, in general, a single context frame cannot provide perfect contextual information, and it is ch... | Summary: Learning a world-model that can generalize to different domains and tasks is difficult. The authors enhanced an existing framework for pre-training world models using in-the-wild videos, which can be fine-tuned on downstream tasks. In particular, the authors introduce a contextual encoder which helps in disent... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer wPqn for providing a detailed review and insightful questions.
**Q1**: Utilizing contexts for predicting the reward
We agree that, in general, contextual and dynamics information are both important for task-relevant predictors (reward predictor, actor, and critic). W... | Summary: This paper studies whether large-scale in-the-wild datasets can be used to pre-train world models for efficient downstream reinforcement learning. Specifically, they introduce Contextualized World Models (ContextWM), an architecture specifically designed to learn to separate context and dynamics modeling. Thei... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer ajER for providing a thorough review and valuable questions.
**Q1**: **Comparison with APV**
We **respectfully disagree with the comments that we do not compare with APV**. We apologize for not clarifying that **our 'IPV w/ vanilla WM' in $\underline{\text{Fig. 5 and 6 o... | Summary: This paper proposes a Contexturelized World Model with In-the-wild Video Pretraining, which extends recently proposed action-free pre-training from videos (APV) to the case of contextualized video-prediction models. Specifically, they propose to sample a randomly chosen frame and use it as "contextualized info... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer JHvL for providing an insightful review and valuable comments.
**Q1**: **How we incorporate contextual information**
**Clarification**: We apologize for using the ambiguous term 'context' without elaborate clarification. Videos and visual control trajectories are **spatio... | Rebuttal 1:
Rebuttal: ## Global Response to All Reviewers
We would like to thank the reviewers for their detailed comments. This paper aims to pre-train a broadly generalizable world model from in-the-wild videos to boost sample-efficient learning of downstream visual control tasks. Extensive experiments on large-scal... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This submission presents the contextualized world model (ContextWM), a framework for leveraging in-the-wild videos for pre-training of a world model to be used in model-based reinforcement learning. Following the work from Seo et al. (2022), the authors pre-train an action-free version of the recurrent state-s... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer kJR6 for providing a detailed review, valuable suggestions, and a positive evaluation of our paper.
**Q1**: Details of cross-attentions in the decoder
In $\underline{\text{Appendix C.3 in the supplementary material}}$, we have elaborated the details of how our multi-s... | null | null | null | null | null | null |
Conservative State Value Estimation for Offline Reinforcement Learning | Accept (poster) | Summary: This paper proposes Conservative State Value Estimation (CSVE) for offline reinforcement learning, which directly penalizes the V-function on out-of-distribution (OOD) states and guarantees conservative value estimation under specific state distributions. The authors develop a practical actor-critic algorithm ... | Rebuttal 1:
Rebuttal: Thank you very much for the comments. Nevertheless, we believe that there should be some misunderstanding points that require to clarify or discuss further. As commented by other reviewers, the paper does have clear technical contributions and evaluation. We explain on them as bellow.
## Q1: The ... | Summary: The paper proposes a method to tackle the overestimation of values in offline RL by focussing on state-values instead of state-action values and using in-data policy optimization techniques based on model-based RL. They propose an actor-critic variation for their approach and apply the method on various offlin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and detailed comments. We respond to specific questions and comments as bellow.
## 1. Concerns
Concern 1: Table is hard to read
In response to your suggestion, we have revised the paper and included two modified tables in the pdf attached. In these tables,... | Summary: << I have read the authors' rebuttal and had raised my score based on the discussion >>
The paper discusses challenges in Reinforcement Learning (RL), particularly in real applications where online learning from scratch is often risky and unfeasible. To address this, the authors introduce Conservative State V... | Rebuttal 1:
Rebuttal: W1: neglecting discrete action space problems and only three seeds for performance appears limited
Recently, we have conducted additional experiments with seven more seeds on HalfCheetah-medium, HalfCheetah-medium-replay, and HalfCheetah-medium-expert tasks. The updated scores can be found in the... | Summary: In this paper, the authors propose an offline RL algorithm for conservative state value estimation (CSVE). This work is different than prior works that learn conservative state-action values like CQL or COMBO, in that it penalizes OOD states rather than OOD state-actions.
The authors show that CSVE gets simi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and detailed comments. We are glad to hear that Reviewer EMsr believes that our work presents good theoretical and empirical results illustrating the potential benefits of CSVE. We respond to specific questions and comments below.
## 1. Main concerns
Con... | Rebuttal 1:
Rebuttal: We have included two updated tables in the attached PDF. In these tables, we have highlighted the scores that exceed 90% of the highest score. The average score is also provided for a more comprehensive comparison.
We have conducted additional experiments with seven more seeds on HalfCheetah-medi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling | Accept (poster) | Summary: This paper combines OPS (one posterior sampling) with bounded beta-divergence, resulting in a DP mechanism that applies to a general class of inference models. This approach bounds the sensitivity of the procedure without bounding the feature space or statistical functionals thereby improving on previous appr... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the importance of our contribution to overcoming the limitations of sensitivity bounding. We address your questions and concerns below.
### Improving the presentation of Sec. 3 and providing more intuition
The most important points for readers unfamiliar with Bayesian... | Summary: In this paper, authors propose a modified version of one posterior sample (OPS) mechanism. The OPS mechanism releases a sample from a posterior distribution in which the likelihood is tempered based on the privacy guarantee. This can be shown to be a simple instance of exponential mechanism which provides the ... | Rebuttal 1:
Rebuttal: Thank you for acknowledging our contribution to DP probabilistic inference and the potential of moving away from log-score updating. We address your questions and concerns specifically below.
### Numerical stability of using pdf's instead of the log-pdf's
The $\beta$D loss *sums* the p.d.f.s to a... | Summary: Maintaining differential privacy in a Bayesian setting, can be implemented using Gibbs posterior sampling, which can be viewed as the exponential mechanism with respect to the score function, defined by the the sum of the log prior probability and the log likelihood of the dataset multiplied by a factor govern... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the importance that moving away from log-score updating contributes to DP probabilistic inference. We address your questions and concerns specifically below.
### Clarifying Thm. 2 and Prop. 2, the first paragraph of Sec. 4 and Prop. 1
Space in the paper is limited and... | Summary: The paper introduces a privacy mechanism called \betaD-Bayes, which combines the one-posterior sampling (OPS) technique with the \beta-divergence to provide differentially private (DP) parameter estimation for a wide range of inference models. The goal is to ensure that sensitive information in the training da... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the potentially high impact that is brought by our paper’s broadening of differential privacy research within Bayesian inference. We address your questions and concerns specifically below.
### The breadth of our empirical study (incl. neural network classification)
T... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their thoughtful feedback and for appreciating the importance of our contribution. We have changed all minor comments, and addressed their suggestions in individual responses.
Alongside our rebuttal we provide a PDF demonstrating how we will alleviate some... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models | Accept (poster) | Summary: Since the introduction of Deep Equilibrium Models (DEQs), many papers have been written about the certified robustness properties of DEQ models including MonDEQ and GMonDEQ. In addition, many methods have been proposed to study the certified robustness of conventional neural works, such as AOL, SLL, and Sandwi... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and thorough evaluation. We are happy to address your concerns and questions.
**I believe this paper would benefit from a clear discussion of the application scenarios of the proposed method. For example, when does the proposed method fail to provide satisfactory cer... | Summary: The paper addresses the robustness certification of deep equilibrium models (DEQs). It proposes a novel approach that generalizes classical Lipschitz-constrained networks by presenting them as special cases of Lipschitz-bounded equilibrium networks (LBEN). The researchers' contribution is two-fold: first, they... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and thorough evaluation. We are happy to address your concerns and questions.
**The paper does an excellent job bridging different types of DEQs and 1-Lipschitz neural networks, which I find commendable. However, the experimental part is very limited and doesn't matc... | Summary: This paper demonstrates that various widely-used Lipschitz network structures, including convex potential layers (CPL), SDP-based Lipschitz layers (SLL), almost orthogonal layers (AOL), Sandwich layers, and monotone DEQs (MonDEQ), can all be reparameterized as specific cases of the Lipschitz-bounded equilibriu... | Rebuttal 1:
Rebuttal: Thank you for your thought and thorough evaluation. We respond to your comments as below.
**While the theoretical results are intriguing, I believe that additional numerical results should be conducted to further validate the theorem. The numerical results are quite preliminary and only discuss ... | Summary: The paper studies the l2-certified robustness of DEQs from the Lipschitz bounded view. They not only proved the advantages of DEQs against other models on certifiable robustness but also show the links between other popular Lipschitz layers like convex potential layers, SDP-based Lipschitz layers, almost ortho... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful evaluation and comments. Now we provide detailed responses to each of your comments.
##
**The results for SLL are much lower than their paper's report. More than 4% lower for natural accuracy and about 10% lower for 72/255 certified accuracy compared with SLL small.*... | Rebuttal 1:
Rebuttal: # General Response
First of all, we would like to thank each reviewer for their constructive feedback. We are glad to see that our theoretical connections on Lipschitz structures were generally well-received and there seems to be many interesting future directions. We would like to clarify some a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Analyzing Generalization of Neural Networks through Loss Path Kernels | Accept (poster) | Summary: Thanks to authors for their submission. Their paper tackles generalization of neural network from neural tangent perspective applicable on gradient flow. The novelty of the paper stems from the new tangent kernel $\overline{K}(w,z,z')$ proposed. It is defined as an inner product of loss gradients w.r.t. weight... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our paper and constructive comments!
---
**Q1. Definition of loss path kernel and the existence of the integral.**
A1. Indeed, this is a great question. Please allow us to address your concerns point-by-point below [this discussion will be added... | Summary: The paper proposes a new complexity measure for neural networks based on tracking the changes of the weight vector of the entire model. The work is rooted in theory by linking the proposed measure through the Neural Tangent Kernel framework to Rademacher complexity resulting in a new, meaningfully tight bound... | Rebuttal 1:
Rebuttal: We thank the reviewer for the kind comments and positive feedback!
---------
**Q1. The proposed method is computationally very expensive, and so not that easy to use in practice**
A1. Thank you for raising the question about the computational cost of our method. For small models, our method is n... | Summary: This paper introduces a new generalization bound based on dynamic NTK, called loss path kernel in the paper. The loss path kernel is a kernel based on the integration of a loss tangent kernel (NTK with loss function) so that the generalization bound can be determined by the training dataset and training trajec... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, thoughtful comments, and for appreciating the novelty and value of the work!
---
**Q1. The empirical part of writing is not clear enough. I cannot get the message between Theorem 3 and Figure 2 (c). Also, Figure 1 looks strange. The paper does not... | Summary: The submission provides data-and-architecture-dependent Rademacher complexity generalization bounds for neural networks. Unlike previous work, the approach takes the evolution of the neural tangent kernel under gradient flow into account. First gradient flow under an evolving kernel is expressed as learning wi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback, thoughtful review, and for appreciating the merits of the work!
---
**Q1. No insights about the role of depth, width, or other choices of architecture on generalization.**
A1. Thank you for the insightful question! Please refer to Appendix D.3, wh... | Rebuttal 1:
Rebuttal: ### **Global Response**
We would like to thank all the reviewers for taking the time and effort to review our paper! We are delighted to learn that our paper was positively received, and the reviewers found that:
- the background, relevant work, and the proposed method are brilliantly presented... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Label-efficient Segmentation via Affinity Propagation | Accept (poster) | Summary: This paper proposes a novel universal component for weakly-supervised segmentation by formulating it as an affinity propagation process. It simultaneously utilizes a global and a local pairwise affinity term to generate soft pseudo labels. An efficient algorithm is also developed to reduce the computational co... | Rebuttal 1:
Rebuttal: Thank you so much for the careful and thoughtful reviews. Please find what below our itemized responses.
**Q1. Whether parameters ${\zeta_s}$, ${\zeta_g}$ are consistent across different tasks and datasets.**
R: The parameters ${\zeta_s}$ and ${\zeta_g}$ control the sensitivity to variations in... | Summary: This paper proposes an affinity propagation algorithm for weakly supervised segmentation. Given image segmentation data with only sparse box/point/scribbles annotations, the proposed method propagates these annotations to other pixels as pseudo masks for training. Global affinity and local affinity are propose... | Rebuttal 1:
Rebuttal: Thank you so much for the constructive and insightful comments. Please find what below our itemized responses.
**Q1. About incorporating deep features from self-supervised, pre-trained networks (e.g. MAE).**
R: We have tested the incorporation with the deep features of the network during traini... | Summary: This paper develops a weakly-supervised segmentation framework based on affinity propagation. It overcomes the drawback of simply modeling neighboring pairwise potentials and proposes both global and local affinity terms to generate pseudo labels. The authors demonstrate the effectiveness of the proposed metho... | Rebuttal 1:
Rebuttal: Thank you so much for acknowledging the strength of our method. We have tried our best to clarify each issue. Please find what below our itemized responses.
**Q1. The details of the global one and making a graphical illustration.**
R: To facilitate comprehension, we provide a detailed graphical... | Summary: This paper proposes an affinity propagation method within local and global perspectives to improve the pseudo labels generated by the model for the parts without GT masks. Meanwhile, they then propose an efficient implementation to solve the heavy computation by graph modeling. The authors conduct experiments... | Rebuttal 1:
Rebuttal: Thank you so much for the thoughtful comments. Please find what below our itemized responses.
**Q1. Main differences with the existing approaches.**
R: Different from the existing approaches, our method considers object topology and captures fine-grained global affinity through an efficient imp... | Rebuttal 1:
Rebuttal: **General Response**
We express our gratitude to all reviewers for their insightful comments, which significantly strengthen our paper. We will revise our manuscript accordingly.
As three out of the five reviewers concern the differences between our work and some existing works[1-3], we'd like ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper utilizes local and global pairwise affinity terms to generate accurate soft pseudo labels and incorporates an efficient algorithm to reduce computational costs. Experimental results demonstrate the approach's superior performance in various segmentation tasks.
Strengths: Experimental results demons... | Rebuttal 1:
Rebuttal: Thank you so much for the constructive comments. Please find what below our itemized responses.
**Q1. The comprehensive analysis and comparisons with the existing affinity-based methods.**
R: Previous works [1-4] involve affinity modeling, and our proposed approach is largely different from the... | null | null | null | null | null | null |
Towards Automated Circuit Discovery for Mechanistic Interpretability | Accept (spotlight) | Summary: This paper first presents an overview and a useful distillation of existing mechanistic interpratility work on discovering interpretable circuits in transformer models. They say most existing work happens in 3 steps:
1. Observe a behavior (or task) that a neural network displays, then create a dataset to measu... | Rebuttal 1:
Rebuttal: Thank you for your helpful feedback on our work! We appreciate the concise distillation of our identified three steps of the mechanistic interpretability workflow and how you highlighted the diverse strengths of our work.
This response is intended to address both of the weaknesses you brought up ... | Summary: This paper introduces a method for pruning nodes in a computation graph that is meant to be used in the context of mechanistic interpretability, i.e., to find sub-graphs that explain/reproduce certain behavior of the overall graph while being much smaller.
Strengths: - The problem is well-motivated, and the ... | Rebuttal 1:
Rebuttal: Thank you very much, pJGB, for your comments on our work! We hope that this reply answers all your questions, and look forward to further discussion.
**New insights from the method should be featured more prominently.** We agree that showing the practical relevance of new methods is important evi... | Summary: This paper proposes an approach for the automatic discovery of circuits (ACDC) in artificial neural networks (applied to transformer-based LLMs), which works by recursively constructing a subgraph of "important" nodes identified through the patching of model activations on datapoints relevant to a specific tas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind words about our work. In particular, we are happy to see that they are also excited about open-sourcing ACDC and that they recognize how it has already been used by the community to accelerate mechanistic interpretability research. We were also pleased to see t... | Summary: The paper is a fresh take on mechanistic interpretability, focusing on the automation of the interpretability task, demonstrating it on attention based models. The method, ACDC, finds the pareto optimal subgraphs of the network, thus bringing down the number of connections to highlight the role each unit plays... | Rebuttal 1:
Rebuttal: Thank you very much for your well-considered review! We are very happy that you appreciate the work we put into exploring the shortcomings and advantages of each algorithm. Thank you also for the insightful questions, and please let us know if you have any more.
**Comparison to HISP.** Thank you ... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their detailed and positive feedback on our paper.
*All* reviewers recommended an acceptance and together found our contribution “well-motivated” (pJGB) and a “fresh take on mechanistic interpretability” (6BEj) that is an “important clarity/systemization to the work... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration | Accept (poster) | Summary: The submission #3482, entitled "Tame a Wild Camera: In-the-Wild Monocular Camera Calibration" presents a novel self-calibration strategy where the regression of the incidence field of the camera is predicted via a deep neural network before using a RANSAC to filter outliers and regress the intrinsic parameters... | Rebuttal 1:
Rebuttal:
**Response to R5 ioKN**
We sincerely thank R5 for the detailed comments. We deeply appreciate your feedback and aim to address your concerns thoroughly.
Before responding to the concerns, we believe that there could be a possibility of misunderstanding in our approach:
Different from [24, 26,... | Summary: This paper proposes a method for single-image camera calibration using a 3D prior the authors refer to as an incidence field. The incidence field is the collection of rays originating from some 3D point towards the camera origin, that are incidental to the image plane. The authors describe how the incidence fi... | Rebuttal 1:
Rebuttal:
**Response to R4 Ujoz**
We appreciate the reviewer's suggestions to make our paper accessible to a wider audience. We extend special gratitude to the reviewer for recognizing the technical novelty of our approach and its potential contribution to the community. We will adhere to the reviewer'... | Summary: This paper introduces incidence field, a per-pixel representation for single image camera intrinsics calibration. The incidence field is defined as a 2D vector, pointing to the principal point of the image, normalized by the focal length. It is invariant to cropping and resizing. The paper suggests using a neu... | Rebuttal 1:
Rebuttal:
**Response to R3 PGSe**
We sincerely thank for the exceptional comments and for giving our paper an "Accept." We are particularly grateful for recognizing the validity of parametrizing intrinsic as an incidence field. We next delve into your specific feedback.
**R3, Q1 Undistorted Image Assumpt... | Summary: An in-the-wild monocular camera calibration method is proposed. It allows to estimate the focal lengths $f_x$ and $f_y$ as well as the optical center $b_x$, $b_y$ without any additional information such as a checkerboard or the Manhattan world assumption. The proposed method consists in employing a neural netw... | Rebuttal 1:
Rebuttal:
**Response to R2 wjnN**
We thank the reviewer for the positive comments on the soundness and presentation of the paper. We greatly appreciate the valuable, constructive criticism. Your concern helps us to further address the missing connection between Sec. $3.2$ and other sections in the manuscr... | Rebuttal 1:
Rebuttal: **To all Reviewers:**
We value the reviewers' recognition of our method's novelty (R1, R3, R4, R5) and its strong performance (R1, R3, R4). We also thank reviewers R1 and R5 for recognizing the lucidity of our paper's explanations.
We present a method for calibrating $4$ DoF intrinsic of in-the-w... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a novel learning based approach for 4 DOF camera intrinsics calibration from a single image in the wild. They propose to use a neural network to predict the incidence rays for each pixel in an image from which the camera intrinsics can be recovered. The authors motivate the prediction of th... | Rebuttal 1:
Rebuttal:
**Response to R1 TGwc**
We sincerely appreciate the valuable feedback provided by the reviewer to enhance our manuscript quality. We also agree on the importance of properly introducing metric units in tables and providing clear references for baselines. We will address these aspects appropriate... | null | null | null | null | null | null |
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources | Accept (poster) | Summary: This paper contributes a new data-generation method to train a OOD detector. The paper proposes to first create an auxiliary task for a generator to generate in-distribution samples and OOD samples by using regions of disjoint latent space (equ 6-7). This can lead to disjoint support set by enforcing a distanc... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments and generous supports! Please find our responses below.
> Q1. The major concern is the performance of the generator (or possibly I miss that piece of information). While section 3 is convincing, in practice (section 4) how to guarantee high Mo... | Summary: The paper propose to fix the mistaken OOD generation issue in generative model based approach to out-of-distribution data detection, where the mistaken OOD generation means generated OOD data have semantics of ID data. To fix this issue, auxiliary task-based OOD learning (ATOL) is proposed, which is claimed to... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments and generous supports! Please find our responses below.
> Q1: The drawback and strength of generative model based OOD detection and its comparison with other approaches like scoring or regularized training is not fully discussed in the paper, ... | Summary: The paper tries to overcome the impact of directly applying incorrect OOD data on the OOD model through auxiliary tasks, thereby improving the performance of OOD tasks. The theoretical part is hard to follow, and the experimental part proves the effectiveness of the theory.
Strengths: 1. The paper introduces ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments and generous supports! Please find our responses below.
> Q1. The proof of C2 is not clear enough to allow me to clearly understand why achieving C2 can better utilize OOD data.
A1. Thank you for your valuable concern. We would like to answe... | Summary: One of the techniques for detecting OOD instances is to train a model on OOD data. However, that task is not easy due to difficulty inherent with collecting such OOD data. Rather than collecting such data, this paper proposes instead to generate it, and to train an auxiliary task to improve the OOD detection c... | Rebuttal 1:
Rebuttal: We sincerely thank you for your constructive comments and generous supports! Please find our responses below.
> Q1. Although this paper addresses the OOD detection problem from a data-generation perspective, I would have very much liked to see how their approach fair with other techniques like di... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows | Accept (poster) | Summary: The authors present an analytical framework to quantify the effects of numerical errors on sampling, density and ELBO estimation in variational flows. The framework leverages the shadowing theory to derive error bounds based on the shadowing window sizes and the local Lipschitz constants of the dynamical opera... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript! Please see our point-to-point respoonse as follows.
> My primary concern about this work is mainly on the usefulness of the
> presented framework in practice. All results are predicated on the basis that
> the shadowing property is already established.
Pl... | Summary: This paper investigated the impact of accumulated numerical error on the sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flows. It demonstrated that the results produced by flows are not destroyed by the serious numerical instability. To explain this phenomenon, it lever... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript! Please see our point-to-point respoonse as follows.
> What is the definition of $s_k$ in theorem 4.1.
"s" stands for "shadowing". $(s_k)_{k = 0}^N$ denotes an exact orbit starting at $s = s_0$ that "shadows" the numerical trajectory (as mentioned in line 1... | Summary: The paper "Embracing the chaos: analysis and diagnosis of numerical instability in variational flows" investigates the impact of numerical instability on the reliability of sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flows. The authors treat variational flows as dyna... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript! Please see our point-to-point respoonse as follows.
> “One potential weakness of the paper "Embracing the chaos: analysis and
diagnosis of numerical instability in variational flows" is that it focuses
primarily on theoretical analysis and does not provide a... | Summary: The paper discusses the (non)impact of numerical stability concerns when implementing variational flows, specifically, the robustness of sampling, density evaluation or ELBO computation against the chaotic behaviour of numerical implementations of variational flows.
The results are the following: while small ... | Rebuttal 1:
Rebuttal: Thank you for your review and comments! Before we address them, we would like to point out that [33] does not introduce the finite shadowing theorem—[17] does. We assume that when you refer to [33] in your review, you mean [17]. Hopefully we did not misunderstand!
[17]: Brian Coomes, Hüseyin Koça... | Rebuttal 1:
Rebuttal: # General response to the reviewers
We thank the reviewers for their valuable feedback. In this response, we address shared comments. Specific responses to each reviewer will follow separately.
## 1. Comparison to [23] (MECE, sFEg)
Our work is not an extension or competitor to the work of [23];... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper investigates the impact of numerical instability on the reliability of sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flows. It demonstrates through empirical examples that numerical instability can lead to deviations in the flow map affecting sampling, densit... | Rebuttal 1:
Rebuttal: Thank you for reviewing our manuscript! Please see our point-to-point respoonse as follows.
> “Due to rather strong assumptions, the paper's claims may not be applicable in all scenarios.”
Please see our general response 5 for a comprehensive discussion of the assumptions.
> “The experiments f... | null | null | null | null | null | null |
Geometry-Informed Neural Operator for Large-Scale 3D PDEs | Accept (poster) | Summary: This paper presents geometry-informed neural operator (GINO), solving computational fluid dynamics (CFD) problems. It combines graph neural operations (GNO) and Fourier neural operators (FNO) to adapt to irregular discretized grids. The authors have tested the model on two large-scale datasets.
Strengths:
1... | Rebuttal 1:
Rebuttal: ## Response to Reviewer zgWV
> **Q1:** The definition of the κ operator in the graph operator block is unclear. It is not specified whether it measures the distance between two points or the similarity of their features. Additionally, it would be helpful to know if the κ operator has any learnab... | Summary: This paper introduces a novel approach for applying Fourier or other Neural operators to complex geometries by prepending a “learnable projection step” via Graph Neural Operator (GNO). Unlike previous methods that morphe complex geometries into regular domains, this approach projects (learnable) sampled nodes ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 5VBN
> **Q1:** Insufficient number of datasets and comparisons:
More datasets should have been included, such as cylinders and airfoils from [1].
Comparisons with representative methods from the GNN family, such as MeshGraphNet[1], MSGNN-Grid[2], and BSMSGNN[3], should hav... | Summary: The authors address the task of learning to solve large-scale PDEs based on a geometry-informed neural operator. The combination of graph neural operators (GNO) and Fourier neural operators (FNO) allows the exploration of the benefits of being able to handle irregular grids and locality of operations to allow ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer RPer
> **Q1:** The discussion of limitations provided by the authors is rather short. Blending out the benefits of physics-informed approaches, that led to extreme speed-ups over OpenFOAM particularly for fluid simulation as well as offer generalization to novel scenes wit... | Summary: This paper proposes a framework to learn a neural operator for large-scale 3D PDEs. The framework uses a well-implemented Graph Neural Operator (GNO) to transform the irregular grid into a regular grid, so that it enables the powerful Fourier Neural Operator (FNO) to work on irregular input data, such as point... | Rebuttal 1:
Rebuttal: ## Response to Reviewer rW9d
> **Q1:** Although the experiments have demonstrated the main ability of the model, the experiments are not complete enough to support all claims and novelties.
**A1:** To further support the results, we add two more experiments that compare GINO with the solver and ... | Rebuttal 1:
Rebuttal: # General response
We are grateful to the reviewers for their insightful feedback and constructive comments.
It is encouraging to note that part of the reviewers agree with the
- scalability of the proposed model to realistic 3D aerodynamic simulations,
- acknowledge the significant speed-up offe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a geometry-informed neural operator for arbitrary geometry, to facilitate the learning on the solution operator for large-scale 3D CFD simulation.
Strengths: The proposed GINO model applies graph-kernel blocks for the encoder and decoder, for processing features in the latent uniform space... | Rebuttal 1:
Rebuttal: ## Response to Reviewer zqvK
> **Q1:** For the experiment, the authors only perform the evaluation on the car category of the ShapeNet dataset. It’ll be more persuasive to have the proposed model evaluate the other categories rather than only a single category.
**A1:** Thanks to the reviewer for... | null | null | null | null | null | null |
Nearest Neighbour with Bandit Feedback | Accept (poster) | Summary: This paper considers contextual bandits in a nearest-neighbor paradigm. In this paradigm, the contexts exist in a metric space, such that contexts that are close in the metric space are also likely to admit the same "correct" action.
In other words, the decision boundary of the optimal mapping from context to... | Rebuttal 1:
Rebuttal: Thank you for your review - we have the following comments and responses (to the phrases in quotation marks).
"In other words, the decision boundary of the optimal mapping from context to action is assumed to be small"
- We note that our comparator policy $y$ can be anything - it does not need to... | Summary: The paper under review investigates the novel application of nearest neighbor search in the context of contextual bandit problems, which I find both new and intriguing. The main contribution lies in the derived result that bounds the regret using \Phi(y), a metric that quantifies the likelihood of disparate op... | Rebuttal 1:
Rebuttal: Thank you for your review - we have the following comments and responses (to the phrases in quotation marks).
"a metric that quantifies the likelihood of disparate optimal choices among closely related contexts."
- $y$ can be anything. i.e. $y(x)$ needs not be the optimal choice for $x$ (see belo... | Summary: This paper studies the contextual multi-armed bandit problem in the adversarial setting. The authors propose an algorithm, CanProp, which utilizes an adaptive approximate nearest neighbor data structure to select the arm to pull for a given context.
Strengths: The authors provide a novel algorithm for the con... | Rebuttal 1:
Rebuttal: Thank you for your review - we have the following comments and responses (to the phrases in quotation marks).
" The algorithm relies heavily on data-structures that do not seem practically feasible"
- Could you please elaborate on the phrase "do not appear to be practically feasible"? While these... | Summary: This work studies adversarial contextual bandits. The approach to solving this problem considered in this work is to use the nearest neighbor (NN)search sub-routine algorithm, and the regret bound depends on a term that characterizes the efficiency of the NN oracle. The main advantage of using an NN-based algo... | Rebuttal 1:
Rebuttal: Thank you for your review - we have the following comments and responses (to the phrases in quotation marks).
"the per-trial computation time can be improved exponentially, compared with previous EXP-4 based algorithms."
- By Exp4 do you mean our “initial idea” of combining Exp4 and Belief propag... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time spent in reviewing our paper.
We would like to note that for our example problem (that of Theorem 3.4) we can reduce the asymptotic dependance on $T$ and $K$ to $\tilde{O}(T^{d/(d+1)}K^{1/(d+1)})$ by first quantising the contexts (a.k.a. binning) as a pre-pro... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper considers non-stochastic contextual bandit problems in which the regret is defined with an arbitrary decision policy that maps contexts to arms.
For this problem,
a framework of algorithms based on the nearest neighbor rule is developed.
The paper provides generic regret bounds for this framework an... | Rebuttal 1:
Rebuttal: Thank you for your review - we have the following comments and responses (to the phrases in quotation marks).
"Comparisons with existing studies are limited."
- As far as we are aware our work is the first to achieve any of the results given in our paper. In the case of Theorem 3.4 (and the new m... | null | null | null | null | null | null |
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP | Accept (poster) | Summary: The paper tackles the open-vocabulary panoptic segmentation with a frozen CLIP. Unlike previous two-stages works, the paper uses the same backbone from the frozen CLIP for the mask generator and classifier. The single-stage framework accelerates the training and inferring speed and achieves SOTA performance.
... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and address the concerns below.
> ***W1: Novelty, comparison to F-VLM***
Please refer to **C1: Relationship to F-VLM** and **C2: Novelty/Contribution**
> ***W2: ViT-based or CNN-based CLIP for dense prediction***
We thank the review... | Summary: This paper proposes to build a one-stage open-vocabulary detector with a frozen language encoder (CLIP) and acvhieves good performance.
Strengths: 1. Strong performance. Performance on many benchmarks is better than previous models.
2. Simple framework. One stage does look more simple to use.
Weaknesses: 1. ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and address the concerns below.
> ***W1: Novelty***
Please refer to common concerns **C2: Novelty/Contribution**
> ***W2: One-stage v.s. two-stage methods***
A one-stage framework can re-use a shared feature extractor across differe... | Summary: The authors propose an approach based on CLIP model and extend it for zero-shot semantic segmentation. The authors argue that pervious works solve the problem with a two-stage approach, which first generates mask predictions using one backbone and then extract features from another backbone using CLIP model is... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and address the concerns below.
>***W1, Q2: Comparison to OpenSeg***
In the submission, we have already carefully discussed the limitations of naive single-stage methods such as OpenSeg, and how FC-CLIP differs from them. We summarize them agai... | Summary: This work proposes a new approach to open-vocabulary panoptic segmentation that unifies the mask generator and CLIP classifier into a single-stage framework. This is achieved by sharing the feature extractor between them, which presents two challenges: disrupting the alignment between image and text features d... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and address the concerns below.
>***W1: Missing ablation study on different components/design choices***
We have **already provided the asked ablation studies** on model designs in the Table 1 and Table 4 of Supplementary, e.g., combi... | Rebuttal 1:
Rebuttal: We appreciate all reviewers for their valuable suggestions, and we address the common concerns as follows. For the remaining concerns, please see the individual post for each reviewer.
>***C1: From reviewers N4iz W1, VW2t W1, Relationship to F-VLM***
We thank the reviewers for the suggestion. We... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this submission, the authors proposed a new method for open-vocabulary panoptic segmentation. In the open-vocabulary segmentation setting, the model is trained on seen category annotations and tested on unseen categories. The frozen features of CLIP/ALIGN have been demonstrated to be effective in new catego... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and address the concerns below.
>***W1: Relationship with F-VLM***
Please refer to **C1: Relationship to F-VLM**
>***W2: Other CNN-based CLIP***
We thank the reviewer for the valuable suggestion. We provide results with different CN... | null | null | null | null | null | null |
Expanding Small-Scale Datasets with Guided Imagination | Accept (poster) | Summary: The paper proposes an image generation framework for expanding small-scale datasets. The proposed Guided Imagination Framework (GIF) leverages large language-vision models (i.e., CLIP) and generative models (i.e., DALL-E2, Stable Diffusion, and MAE) based on two criteria that help to generate informative new i... | Rebuttal 1:
Rebuttal: Thanks a lot for the highly constructive comments. We are glad to see that the task significance and the method originality are appreciated. We answer all questions point by point as follows.
---
>**Q1. Would it be possible to put more technical details in the main paper?**
We highly apprec... | Summary: This paper introduces a novel task that addresses the expansion of a limited dataset into a larger one by utilizing a Guided Imagination Framework. The proposed approach harnesses latent features to generate new data instances while placing emphasis on preserving class-specific information and enhancing sample... | Rebuttal 1:
Rebuttal: Thanks for the comprehensive and constructive comments, particularly for recognizing that our solution is promising and remarkably effective. We address all the concerns as follows.
---
>**Q1. Concern on the results in Table 1, particularly regarding Cars**
The experimental results are indeed... | Summary: This paper proposes to expand a small dataset by automatically creating new labeled samples with pre-trained generative models, such as DALL-E2 and Stable Diffusion. The proposed framework, namely Guided Imagination Framework, contains two key parts, i.e., class-maintained information boosting and sample diver... | Rebuttal 1:
Rebuttal: Thanks for the effort in reviewing our paper. We are glad that the interesting idea and the important task are appreciated. We address the concerns point by point as follows.
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>**Q1. The proposed solution to dataset expansion is too easy**
Thanks for the feedback on the simplicity of our s... | Summary: This paper describes a new task called dataset expansion, which aims at expanding the size of small datasets to boost the performance of data-driven AI models on tasks like object classification. The paper proposes a framework called Guided Imagination Framework (GIF) to achieve it by utilizing pre-trained lar... | Rebuttal 1:
Rebuttal: Thanks for the insightful feedback, particularly for recognizing the significance of the studied task and our proposed method. We next address the concerns as follows.
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>**Q1. Effectiveness in boosting CLIP fine-tuning**
Thanks for pointing this out. In fact, we have evaluated the effecti... | Rebuttal 1:
Rebuttal: **General Response**
---
We deeply appreciate all the reviewers for dedicating time and effort to reviewing our paper. Here, we first address the general question below, and subsequently, we will provide detailed responses to each reviewer's specific questions and comments.
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>**G1. Concern ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper explores new dataset expansion task that solves data scarcity of small datasets while minimizing costs.
They leveraged generative models to create an automatic data generation pipeline.
Strengths: - Dataset expansion is interesting and useful research topic for small-scale domain.
- The proposed me... | Rebuttal 1:
Rebuttal: Thanks a lot for the comments, particularly for recognizing that the task is useful and the proposed method is simple and easy to use. We address the concerns as follows.
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>**Q1. Concern about the novelty of our work**
Although expanding datasets is not a completely new concept, our work ... | null | null | null | null | null | null |
One-step differentiation of iterative algorithms | Accept (spotlight) | Summary: In bilevel optimization, or optimization problems with equilibrium constraints, computing a derivative of the upper-level problem is a well-known stumbling block as this requires "differentiating through" the solution to the lower level problem.
This paper provides a theoretical study for one approach to ove... | Rebuttal 1:
Rebuttal: We greatly thank the referee for his detailed comments and evaluation of our manuscript.
1. We will mention Inexact AD and discuss the references proposed by the referee in the related work section. In our situation, it is not really clear how to apply inexact AD. Indeed, inexact AD is often appl... | Summary: In this paper, the authors consider the problem of differentiating the fixed-point of an algorithm with recursive update, with respect to some parameter of the algorithm. They propose a one-step automatic differentiation technique where, once having approximated the fixed-point through the recursive algorithm,... | Rebuttal 1:
Rebuttal: We thank the referee for his thoughtful comments.
Indeed, super linearly convergent algorithms / second-order like methods are fast, but the price to pay is that each step is very expensive (compared to first-order methods, for example). There is no free lunch, and in that case, our analysis only... | Summary: The paper presents a method called one-step differentiation, also known as Jacobian-free backpropagation, as an alternative to automatic and implicit differentiation. The authors analyze the theoretical approximation of one-step Jacobian and provide specific examples such as Newton's method and gradient descen... | Rebuttal 1:
Rebuttal: We thank the referee for his positive feedback. Experiments and validation on highly nonconvex neural network is something that we are currently investigating. From our intuition so far (and as predicted by the theory we developed), one-step differentiation is not a panacea that will perfectly wor... | Summary: This paper develops a convergence-rate analysis for the approximation of the Jacobian under the technique of one-step differentiation. This technique is similar to that of iterative differentiation (aka unrolling, differentiating through optimization), except only the last iterate of the algorithm is used to c... | Rebuttal 1:
Rebuttal: We thank the referee for his feedback. We provide below detailed answers.
1. We agree with the referee that some algorithms may appear not directly in the form of a contraction or even not directly in the iterative form given in (1). A typical example is the heavy ball method for strongly convex ... | Rebuttal 1:
Rebuttal: We thank the referees for the feedback on our work. We are glad that the referees found our paper *well-written* (WGTU, G3kk, USU8, qFET) with a *clear message* (qFET). Both the theoretical analysis and our experiments seems to have been appreciated by the reviewers.
We propose to include remarks... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the gradient of iterative algorithms, specifically examining one-step differentiation of these algorithms. This approach replaces the complex Jacobian inverse in implicit differentiation with a simple and fast identity approximation. The work refines the theoretical approximation analysis fo... | Rebuttal 1:
Rebuttal: We greatly thank the referee for his constructive comments. Regarding the weaknesses pointed out by the reviewer, we propose the following modification which we hope will address the concerns of the reviewers so that the reviewer can update his evaluation.
- One-step differentiation has indeed c... | null | null | null | null | null | null |
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees | Accept (poster) | Summary: This paper introduces the best order score search (BOSS) algorithm and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in machine learning and causal discovery. BOSS achieves state-of-the-art performance in accuracy and execution time, making it a valuable tool for problems with highly con... | Rebuttal 1:
Rebuttal: Apologies for truncating a few of your comments, we ran out of characters for our rebuttal.
**One potential weakness is...**
Thanks for your comments and criticisms; we have found them helpful and constructive! See our responses to your points below.
**Validation on more Real-World Data: The... | Summary: It's beyond my knowledge so please disregard my scores and reviews.
Strengths: It's beyond my knowledge so please disregard my scores and reviews.
Weaknesses: It's beyond my knowledge so please disregard my scores and reviews.
Technical Quality: 2 fair
Clarity: 2 fair
Questions for Authors: It's beyond my... | Rebuttal 1:
Rebuttal: Thank you for your honesty! | Summary: The authors introduce two novel methods for learning Directed Acyclic Graphs (ADGs): best order score search or BOSS and grow-shrink
trees or GSTs. These methods have a similar performance to state-of-the-art approaches (namely GRaSP and also others: fGES, DAGMA, LiNGAM, etc), but the authors proof that are le... | Rebuttal 1:
Rebuttal: **Validation on real fMRI is (somewhat understandably) weak because the scale-free connectivity of the brain remains a theory rather than a proven fact.**
Thank you for your comment, we will add references (a few relevant references are listed below) to bolster this particular point. These refere... | Summary: The paper proposes a computationally efficient algorithm using the grow-shrink trees to iterate through some of the combinatoric number of directed acyclic graphs in a graphical model. The approach builds on early work but is faster.
Strengths: The paper is straightforward, easy to read (examples are given),... | Rebuttal 1:
Rebuttal: **The paper's focus on the approach also means little perspective is given until the final discussion.**
We will add more details to / rework the introduction to help readers understand our perspective. In order to help us do this, could you describe what perspective you gained in the final discu... | Rebuttal 1:
Rebuttal: We thank all reviewers for your comments! We noticed that none of the reviewers were especially confident in their reviews (none had confidence greater than 2); perhaps our responses can help in this regard.
The reviewers did not have many comments regarding the technicalities of the algorithm o... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories | Accept (poster) | Summary: This paper introduces Trajectory-Aware Imitation Learning from Observations (TAILO) for offline imitation learning. TAILO tackles the problem of learning from incomplete trajectories, where other state-of-the-art (SOTA) methods fail. Specifically, TAILO proposes a simple yet effective solution. It first learns... | Rebuttal 1:
Rebuttal: Thanks for valuable feedback.
### Q1. Inaccurate statements.
- Offline imitation from observations aims to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available.
This is correct. Note, we are discussing offline **imitation (Learning) fro... | Summary: This paper provides an offline imitation learning method upon a specific problem setting where task-specific expert states (observations) are restrictively available and task-agnostic non-expert state-actions are supplementarily available.
In this problem setting, the authors follow the line of works using D... | Rebuttal 1:
Rebuttal: Thanks for valuable feedback.
### Q1. The absence of ablation studies in Sec. 3.2 and 3.3.
Part of the requested ablation studies are in **Sec. F.6.** We test the remaining ablations and summarize here.
**1. The ablation of Sec. 3.2.** In Sec. F.6, we compare ours and ours-V2, where we study ... | Summary: This paper studies offline imitation from observations, assuming a small amount of task-specific expert states and task-agnostic non-expert state-action pairs are available. The method is to learn a discriminator to identify expert states in the task-agnostic dataset and then apply weighted behavior cloning to... | Rebuttal 1:
Rebuttal: Thanks for valuable feedback.
### Q1: Lack of theoretical foundation, especially for Eq. 4, 5 and 6.
**1. Eq. 4 and 5 do not lack theoretical foundations.** The objective functions in Eq. 4 and 5 come from established Positive-Unlabeled (PU) learning works [24]. Eq. 4 is a binary classification ... | Summary: The authors propose TAILO, Trajectory-Aware Imitation Learning from Observations, a method to solve MDPs from offline data in the form of task-specific expert states and task-agnostic state-action trajectories. The method addresses the instabilities of algorithms like DICE, takes the context of a trajectory in... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work and are excited about the positive feedback. Thanks a lot. | Rebuttal 1:
Rebuttal: We genuinely thank the reviewers for their valuable opinions and advice. We are delighted to see that the reviewers have carefully evaluated our work, given many valuable feedbacks, and highlight that
1) we study the DICE family of algorithms in offline LfO, pointing out that they suffer from mi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a simple weighted BC algorithm for offline RL with missing data. The authors first study that DICE family of algorithms suffer from missing data due to inaccurate value estimation or sparsity of observations which causes undesired monotonicity. The authors propose training a reward model fr... | Rebuttal 1:
Rebuttal: Thanks a lot for the valuable feedback.
### Q1. The paper studies DICE with noisy data, but the proposed method is not focusing on circumventing issues with DICE methods.
Though our method looks very different, our motivation is to solve the issues in DICE methods as simply as possible. Below is... | null | null | null | null | null | null |
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings | Accept (poster) | Summary: This paper aims to optimize the design of graph neural networks on large-scale heterophilous graphs. It proposes a new framework called LD2, which decouples the node feature embedding and topology embedding. By obtaining a low-dimensional adjacency embedding and long-distance feature embedding through pre-comp... | Rebuttal 1:
Rebuttal: ## W1
There are two concerns regarding comparison fairness. We would like to address them separately.
### Time complexity of 2-hop propagation
As elaborated in line 275 & 287, we *do not explicitly compute the $A^2$ matrix* in propagation. Instead, we perform the $A$ multiplication twice to the ... | Summary: This paper introduces a scalable decoupled model designed to address the challenges posed by large-scale heterophilous graph.
The model consists of two main components. In the first component, recognizing the effectiveness of $A^2$ in heterophilious graph, the model precomputes a matrix, denoted as $P_A$ in ... | Rebuttal 1:
Rebuttal: ## W1
We wish to highlight that our major contribution lies in proposing a GNN design that specifically targets the scalability issue under heterophily.
* Our model achieves *improved time and memory complexity*. Compared to spectral GNNs, our design escapes the iterative and full-graph propagati... | Summary: This paper studies an important problem of graph learning on large-scale heterophily graphs.
The paper presents a novel approach LD2 model, decouples the embedding process from the convolutional process, allowing for more efficient and scalable learning.
LD2 learns graphs under heterophily, which is particular... | Rebuttal 1:
Rebuttal: ## W2 & Q1
Yes. A preliminary version of the project code, example data, and reproducibility instructions has already been provided in **Section H** of the supplementary material. We have also sent the same link to the AC in a separate comment following the rebuttal policy.
## W1 & Q2
The propose... | Summary: The paper proposes a new graph neural network (GNN) model called LD2, which specifically targets learning on heterophilous graphs, where connected nodes have different labels. The authors argue that existing models for heterophilous graphs often require iterative full-graph computations, which can be computati... | Rebuttal 1:
Rebuttal: ## Q1
Yes. Our proposed model LD2 is capable of handling inductive tasks. This can be implemented by conducting precomputation respectively on the training and inference graphs. After that, the feature transformation model can be easily trained on the precomputed embeddings of the training graph ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable feedback and insightful comments from all our reviewers, and are delighted to see that our effort toward improving the scalability of heterophilous GNNs is acknowledged by the reviewers. We have exerted substantial effort to investigate and address all the issu... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
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