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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Leveraging automatic strategy discovery to teach people how to select better projects | Reject | Summary: This works addresses two problems: first, the problem of solving a specific class of information-gathering decision processes, and second, applying the resulting algorithm in the real world by creating an intelligent tutoring system. The algorithm is a straightforward greedy optimizer that gathers information ... | Rebuttal 1:
Rebuttal: We agree that the strategies discovered by MGPS are not “optimal strategies” as MGPS makes use of a greedy heuristic and will reword the manuscript accordingly.
MGPS identifies a near-optimal greedy planning heuristic by approximating the value of computation for each available planning action. I... | Summary: The problem this paper tries to tackle is how to improve human decision making in the specific problem of selecting a project between a candidate set of existing projects. The strategy to improve the human's decision making is to build an agent that can solve the project selection itself by framing as a POMDP ... | Rebuttal 1:
Rebuttal: Question 1: Our intention behind the human training experiment was to teach people how to use the decision strategy discovered by MGPS themselves, as opposed to replacing the human decision maker or providing a tool that is to be used in an online fashion. Showing the recommended action during tes... | Summary: This paper focuses on the problem of project selection (how does a human choose which, among a set of possible projects, is the best one to pursue). To address this problem, they develop an algorithm called MGPS that discovers a rational greedy strategy for solving this problem, and then they attempt to teach ... | Rebuttal 1:
Rebuttal: Question 1:
- We briefly experimented with a multistep version of MGPS when designing the algorithm. For numbers of steps larger than 1, the computational complexity increases rapidly, as it requires discretizing the belief state updates and searching through an exponentially increasing state spa... | Summary: The authors pose the problem of teaching decision-makers how to take a single action (picking a project from among a set of projects) based upon costly advice from experts across different, weighted criteria. The authors develop a reinforcement learning approach to creating a tutor that approximately solves th... | Rebuttal 1:
Rebuttal: Question 1: In this case, a multi-armed bandit formulation would be sufficient to model the object-level decision. As the multi-armed bandit problem is a special case of the more general MDP, we chose the MDP formulation to keep our environment model as general as possible. MGPS does not require a... | Rebuttal 1:
Rebuttal: Response to concerns about fit and novelty
Since multiple reviewers expressed similar concerns regarding the novelty of MGPS and the intelligent tutor, we will address these in a single response. The novelty of our work lies in (1) the development of a new strategy discovery algorithm (MGPS), (2)... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper addresses the problems of (1) sequential decision-making of information gathering through asking experts for information about the rewards for different projects (as meta-reasoning towards project selection), and (2) teaching people how to make near optimal decisions in the same problem through trai... | Rebuttal 1:
Rebuttal: Question 1:
- Our work is grounded in prior work on metareasoning and strategy discovery which has been modeled with metalevel-MDPs in the past, making it the natural candidate for our adapted problem setting (e.g. [4], [6], [10], [14]).
- We chose the POMDP framework because it was sufficient to ... | null | null | null | null | null | null |
The Target-Charging Technique for Privacy Analysis across Interactive Computations | Accept (poster) | Summary: Drawing inspiration from the Sparse Vector Technique (SVT), the authors introduce a privacy analysis framework called Target Charging Technique (TCT). TCT operates by primarily accounting for the privacy loss incurred through queries that hit pre-specified target sets. This framework relies on the concept of q... | Rebuttal 1:
Rebuttal: Thank you for your review!
Response to “weaknesses”: Please see our general response with numerical comparisons and a demonstration of the practicality of TCT and concrete improvements over prior work. We hope this will at least partially address the concern! We plan to incorporate this in the... | Summary: This paper introduces the Target Charging Technique (TCT) privacy analysis framework that focuses on providing better privacy utility trade-off on sensitive dataset with multiple differential private algorithm access. The essential idea is to only pay budget cost to the positive target and negative targets be... | Rebuttal 1:
Rebuttal: Thank you for your review!
Response to “weaknesses”:
1. [q-target] We agree that the definition is formal. We do not ensure that this holds (there is always a target with $q=1$ but this is not very interesting) but instead show how to use its existence in privacy analysis. We then demonstrate... | Summary: Introduced Target-Charging Technique (TCT) for privacy analysis over interactive private computations
Strengths: 1. Introduced Target-Charging Technique (TCT) for privacy analysis over interactive private computations
Weaknesses: 1. They could add experiment part
Technical Quality: 4 excellent
Clarity: 4 ... | Rebuttal 1:
Rebuttal: Thank you for your review!
Response to “weaknesses”: Please see our general response with numerical comparisons and a demonstration of the practicality of TCT and concrete improvements over prior work. | Summary: This paper proposes Target Charging Techniques: a method that generalizes sparse vector technique and top-k selection from private candidates. Specifically their goal is to obtain better privacy guarantees in the settings where only a small number of computations are *successful*. One of their main ideas is to... | Rebuttal 1:
Rebuttal: Thank you for your review!
Response to “weaknesses”: Please see our general response regarding numerical comparisons and a demonstration of the practicality of TCT and concrete improvements over prior work.
Response to Questions:
-- Definition 2.1 formalizes the notion of a “target” that w... | Rebuttal 1:
Rebuttal: We are grateful to the reviewers for their comments. All reviewers expressed a desire to see some numerical comparisons of the gains of TCT with respect to prior work. In this comment, we place our results in this context and plan to revise our presentation accordingly. Responses to additional c... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents a new privacy analysis framework called Target Charging Technique (TCT). Designed for interactive scenarios where sensitive datasets are frequently accessed using differentially private algorithms, TCT offers a unique perspective. Unlike traditional composition schemes where privacy assuran... | Rebuttal 1:
Rebuttal: Thank you for your review!
Response to Question: Please see our general response regarding numerical comparisons and a demonstration of the practicality of TCT and improvement over prior work. As for open-source: TCT privacy analysis is simple to add and we hope it will be incorporated in DP li... | Summary: The submission studies a privacy analysis framework for interactive computations. The main contribution is extending the conventional differential privacy analysis framework, namely the sparse vector technique, which counts the privacy cost only for the positive response. Particularly, the proposed framework, ... | Rebuttal 1:
Rebuttal: Thank you for your review!
Answers:
As for experimental results, see our general response!
Question 1: $q$-targets: We list examples of useful q-targets. For private testing, the $q$-target can be (selected one of) true/false responses. For Top-$k$ selection, the $q$-targets are mapped to be... | null | null | null | null |
A Theory of Multimodal Learning | Accept (poster) | Summary: This paper aims to provide a theoretical framework for multimodal learning, that aims to explain why multimodal learning is sometimes better than unimodal learning even when the model is only applied on unimodal tasks. To do this, the authors measure the generalization bounds of multimodal learning on the exce... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback and the thoughtful questions posed. We'll address each of your concerns below.
**More natural assumptions:** our work is devoted to providing a general theory of multi-modal learning, which inevitably comes at the cost of some loss in practicality. We feel ... | Summary: This paper introduces a theoretical framework aimed at elucidating the phenomenon wherein a multimodal neural network, trained on multimodal data, can exhibit strong performance on unimodal data. The framework incorporates Gaussian averaging and operates within a semi-supervised context. The findings demonstra... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough feedback and constructive suggestions! We will address each of your questions individually and please feel free to let us know if you have further concerns.
**Q1:** thank you for highlighting this. Your understanding is accurate, and we will revise our language... | Summary: This study proposes a new theoretical foundation for multimodal learning. In particular, regarding the phenomenon that models trained in multiple modalities perform better on single-modality tasks than fine-tuned single-modality models, this paper proposes that multimodal learning is a composition of connectio... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback and inquiries!
**Unnatural setting:** we agree that the mapping + predictor learning process we described, may not perfectly align with practical multi-modal learning scenarios. However, the goal of our work is to propose a general theoretical framework, t... | Summary: The paper establishes theoretical bounds for generalization in multimodal learning, where functions mapping between two modalities and to the label are learned. The authors demonstrate that multimodal learning achieves better generalization by decoupling the learning of hypotheses and provide insights regardin... | Rebuttal 1:
Rebuttal: We appreciate the constructive feedback and the thoughtful questions posed. We'll address each of your concerns below.
**The choice of the example:** we discussed the simpler example purely for the sake of clarity, and the harder example in Remark 2 is strictly stronger because the separations be... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors presents an interesting theoretical framework that allow them to estimate a generalisation bound for multimodal learning.
The main result consist in proving the the bound for the multimodal case is superior to the unimodal one up to a factor $O(\sqrt{n})$ that depends on the sample size of the data... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback and constructive suggestions!
**Fine-grained analysis:** the scope of this current study is primarily to establish a general theoretical framework for multi-modal learning. We will certainly delve into specific algorithms based on our framework in future wor... | null | null | null | null | null | null |
Unbiased Watermark for Large Language Models | Reject | Summary: This study explores watermarking large language models without reducing output quality. It introduces "unbiased watermarking" which avoids trade-offs in prior work. Two novel techniques - $\delta$-reweight and $\gamma$-reweight - are proposed along with an improved likelihood ratio test for detection. Risks of... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review and valuable feedback on our paper.
We appreciate your recognition of our novel introduction of "unbiased watermarking" and the importance of ensuring output quality. We're glad you've noted the improvements we've offered over previous techniques, as well ... | Summary: The paper proposes a modification of the watermark of Kirchenbauer et al. that ensures each next token prediction is marginally indistinguishable from a regular sample from the language model (whereas Kirchenbauer et al. bias some tokens over others). The main idea is to use inverse transform sampling to sampl... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for recognizing the saliency of the problem that our study addresses.
> The empirical validation of the watermarked proposed in the paper is somewhat lacking.
We've expanded the empirical validation with a new robustness experiment, and in add... | Summary: This paper discuss about the important problem of how to watermark the outputs from language models while keeping the model not impacted by watermarking. A perfect watermark scheme should be undetectable without prior information and should have no harm on the utilities of LLMs. This paper proposes some desire... | Rebuttal 1:
Rebuttal: We're deeply grateful for your acknowledgment of our work's importance and pioneering status. Your recognition is invaluable to us. The following addresses each point in your feedback.
> 1: The quality of the watermarked texts are only evaluated by automatic metrics.
> 1As I stated before, if som... | Summary: This paper introduces a novel framework for embedding watermarks into Large Language Models (LLMs) without compromising their output quality. The proposed watermark is designed to be undetectable by LLM users.
A general framework is put forth for incorporating this unbiased watermark into LLMs. This is achie... | Rebuttal 1:
Rebuttal: Thank you for recognizing the novelty and significance of our work. We appreciate the time you took to review our paper and the feedback you provided. Here's our response to address your concerns:
> This paper does not provide a comparison with any existing watermark baselines.
Actually, we did p... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
HyTrel: Hypergraph-enhanced Tabular Data Representation Learning | Accept (spotlight) | Summary: This paper proposes to transform a source table into a hypergraph. Each cell is a node in the graph, where nodes in the same row, column, and table are connected using three types of hyperedges. The authors claimed that by modeling the hypergraph of the table, the proposed method, named *HyTrel*, is able to ge... | Rebuttal 1:
Rebuttal: **Q1**: Complexity. The main concern lies in the efficiency of the proposed method. It needs to build a huge hypergraph on the tables, where the number of nodes is equal to the number of cells in the table, and the number of edges explodes when we combine nodes, columns, and rows. As we know, it i... | Summary: The paper proposes a framework for tabular representation learning by modeling the structure of the tables as a hyper-graph working on different granularities, namely, rows, columns, and the entire table.
Strengths: - The paper addresses a substantial question of how to best incorporate the structure of a tab... | Rebuttal 1:
Rebuttal: **Q1**: Missing Inferences
**A1**: Thanks for pointing out and we will include them in our paper. Here are the relevance and differences of these papers compared with ours.
[1] MATE belongs to the second group of studies we have categorized in Section 5 (Related Work). MATE explicitly restricts... | Summary: This work presents a novel tabular language model that represents tables as hypergraphs called HyTrel. HyTrel is designed to capture the structural properties of tabular data, including 1) invariance to row/column permutations, 2) structural similarity within columns, 3) high-order multilateral relations, and ... | Rebuttal 1:
Rebuttal: **Q1**: No detailed comparison of computational efficiency or runtime on the number of epochs needed for pretraining compared to prior works.
**A1**: Thanks for pointing out the pretraining comparison. We **have discussed the pretraining epochs** as compared with previous work in Section 4.3 (lin... | Summary: This paper proposes a tabular language model (HyTrel) that utilizes the hypergraphs of the data table. Specifically, the hypergraph is constructed with cell values representing each node and the row, column, table representing the hyeredges. In the proposed framwork, the nodes and edges are first fed into the ... | Rebuttal 1:
Rebuttal: **Q1**: The method only applies to a single table scenario. In real-world applications, this could be a rare case where tabular learning is involved. Another dimension that can be added to the hypergraph can be key/foreign key relationships, which may extend the model to work in multi-table settin... | Rebuttal 1:
Rebuttal: We sincerely thank all the reviewers for their thoughtful feedback. We address the reviewers' comments below individually and will incorporate all the feedback in our paper. | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In the paper, the authors aim to capture the structural properties of tabular data using hyper-graphs with four different types of hyperedges based on the co-occurrences in the table. The experimental results on four downstream tasks show the advantages of the proposed method on other competitive baselines.
S... | Rebuttal 1:
Rebuttal: **Q1**: Since hyper-graph excels majorly in handling hyper-edges of varied orders. It would be sub-optimal for hyper-graph to handle the hype-edges of fixed order in the tabular data?
**A1**: We do not fix the orders of hyperedges. The **orders of hyperedges depend on the size of the tables**- an... | null | null | null | null | null | null |
Federated Learning with Manifold Regularization and Normalized Update Reaggregation | Accept (poster) | Summary: The authors proposed a new algorithm for federated learning based on a Lorentzian regularization. The proposed algorithm achieves desired sub-linear convergence with linear speed-up. Numerical experiment shows the efficacy of the proposed algorithm over existing federated learning algorithms
Strengths: The pr... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments which helps us improve the paper. We have prepared our responses to each of your questions.
**- Q1(major):The presentation of the paper is confusing:
- I cannot link the proposed Algorithm 1 with all the argument in Section 3.2. More specifically, how ... | Summary: The authors propose a novel Federated Learning Algorithm, FedMRUR that uses hyperbolic graph diffusion to reduce the effect of data heterogeneity and thereby model inconsistencies. The authors also propose a normalized aggregation scheme to achieve faster convergence. The algorithm FedMRUR achieves state-of-th... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments which helps us improve the paper. We have prepared our responses to each of your questions.
**- Weaknesses: Finally, while convergence speed is quicker for FedMRUR, Table 3 in the Supplemental Materials actually has FedCM to have the quickest total time t... | Summary: This paper studies the problem of model inconsistency across clients in federated learning (FL). The authors propose a method called FedMRUR, which uses a hyperbolic graph manifold regularization term and a normalized update aggregation scheme to alleviate the issues introduced by model inconsistency. Compared... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments which helps us improve the paper. We have prepared our responses to each of your questions.
- Q1: It is not clear what is the “manifold structure of their representations”.
- A1: In our paper, we design the hyperbolic graph fusion scheme to mitigate the mode... | Summary: The authors found the existing vanilla distillation in FL, the model inconsistency caused by the local data heterogeneity across clients results in the near-orthogonality of client updates, which leads to the global update norm reduction and slows down the convergence. Moreover, the authors argue previous work... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments which helps us improve the paper. We have prepared our responses to each of your questions.
- Weakness
**- W1: There are some unclear points and confusing notations.**
- A1: Thank you for the careful readings. We check the whole paper and correct ... | Rebuttal 1:
Rebuttal: **Thank you for the constructive comments which helps us improve the paper. We have prepared our responses to the common questions.**
**-Q1: Problem on hyperbolic space and presentation of algorithm. (EAwW;jAXP;mqsa)**
- A1:Sorry for our unclear presentation, we describe our method more clearl... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a federated learning framework called FedMRUR to deal with the model inconsistency caused by the local data heterogeneity across clients and insufficient geometric representation ability. To do this, it adopts a hyperbolic graph manifold regularizer to ensure that the representations obtain... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments which helps us improve the paper. We have prepared our responses to each of your questions.
- Weakness:
**- W1: There are many ways to exploit the manifold structure. What's the advantage of adopting the graph fusion scheme, especially in the hyperboli... | null | null | null | null | null | null |
Discriminative Entropy Clustering and its Relation to K-means and SVM | Reject | Summary: In this paper, the authors first presented an analysis of the relationship between the regularized information maximization (RIM) clustering framework to K-means and SVM-based clustering methods, showing stronger similarities to the SVM-based clustering than K-means. Then they proposed a new loss function and ... | Rebuttal 1:
Rebuttal: **the first two pages (introduction) are confusing... and limit the space from the actual contributions...**
It would greatly help if the reviewer could point out specific confusing points and redundancies. Adding the definitions of cross-entropy and KL divergence sounds good, thanks.
**"concep... | Summary: This paper first discusses a number of general properties of entropy clustering methods, including their relation to K-means and unsupervised SVM-based techniques.
Then the aurthors find that cross-entropy is not robust to pseudo-label errors in clustering.
Finally, this paper proposes a new loss function bas... | Rebuttal 1:
Rebuttal: **"This paper is not well organized. There are too many details for the proposed method. Some of them can be moved to Appendix."**
If possible, please indicate which specific parts of Section 3 you prefer to be in the supplementary materials.
**"There can be more descriptions and examples about ... | Summary: The paper presents a very interesting analysis of discriminative entropy clustering in the literature and their use for self-labeling highlighting clear interpretation of the conditional and marginal entropy terms as decisiveness (push to have confident predictions) and fairness (to encourage desired proportio... | Rebuttal 1:
Rebuttal: **"But in Resnet-18, the inductive bias learned from pretraining is helping, then the improvement from the proposed loss might not improve very significantly with the proposed loss. Also in Table 4, the regularization on the feature extractor $w$
done or not in the loss or by weight decay?"**
W... | Summary: The authors consider discriminative entropy clustering and produce a discussions linking previous works. They have a version of the algorithm based on EM and a modified KL-divergence term. Experiments show the modified algorithm works better than competing methods with small networks.
Strengths: - The autho... | Rebuttal 1:
Rebuttal: **"The pointing out of a proof error in [1] is helpful but is not significant on its own."**
While we agree, there are many other contributions in our paper. For example, besides finding a fundamental problem in
their proof, we also show a counterexample proving that their actual claim is wrong.... | Rebuttal 1:
Rebuttal: # General points for all reviewers
## Entropy Clustering and Margin Maximization (Sec 2.2)
Reference [1] provided by aVDq not only strengthens the arguments in Sec. 2.2 but also inspires some additional analysis that improves our understanding of how the regularization parameter $\gamma$ in entro... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning | Accept (poster) | Summary: The paper proposes two data augmentation techniques, MIXUP-SELFAUG and MIXUP-DIFFUSION, for differentially private learning. The authors investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose these two techniques specifically... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and detailed comments. We will correct typographical errors and make suggested improvements to our paper. Here are our clarifications:
**1. The absence of a theoretical analysis for MIXUP-SELFAUG in the submitted work is notable.**\
Our work is largely empi... | Summary: This paper considers the privacy-utility tradeoff for ML models trained with differential privacy guarantees, and develops a technique using data augmentation on image datasets to train models with high accuracies on standard benchmarks with DP guarantees. Mixup, a commonly used augmentation technique in compu... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and detailed comments. We will correct typographical errors and make suggested improvements to our paper. Here are our clarifications:
\
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**1. The paper combines the augmentation techniques developed for DP in [10].**\
We agree with the reviewer that we exp... | Summary: The paper studies mixup data augmentation for differentially private (DP) machine learning. The traditional mixup data augmentation requires multiple samples. Therefore, applying them in DP model training is not straightforward. The paper first shows that a naive way of implementing it with micro-batches of si... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and detailed comments. We will correct typographical errors and make suggested improvements to our paper. Here are our clarifications:
\
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**1. The experimental comparison between self-aug and the proposed mixup variants in Tables 2 and 3.**\
For Self-Aug, i... | Summary: This paper proposes a data augmentation technique for differentially private deep learning using mixup regularization. Mixup is a popular augmentation technique which involves taking linear combinations of training samples to create new samples. However, such a technique cannot be directly applied to different... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback and detailed comments.
\
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**1. The proposed method isn't too different from the method proposed by De et al.**\
Our work uses the same insight as De et al. [10] --- by clipping the gradients aggregated over all the augmentations we preserve the DP guarantee... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We would like to clarify a few points.
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**1. Motivation & novelty.**\
Data augmentation has the potential to improve DP-SGD, but naive application of techniques such as mixup compromises privacy. We propose two methods, Mixup-SelfAug and Mixup-Diffusion, to use mixup... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
ExPT: Synthetic Pretraining for Few-Shot Experimental Design | Accept (poster) | Summary: The authors introduce a novel approach SynTO to address a challenging setting - few-shot black-box optimization. Specifically, SynTO adopts a pretraining-adaptation pipeline. SynTO can be pretrained using synthetic functions and then adapt to downstream tasks via an in-context learning manner. Comprehensive ex... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for the recognition of the technical contributions of SynTO and the presentation of our paper. We answer each of the reviewer's concerns below.
> SynTO assumes the access to large amounts of unlabeled data, which may cause unfair comparisons... | Summary: The paper tackles black-box-optimization from few-shot examples, by pretraining a transformer model on synthetic proxy tasks using in-context learning, and evaluating with the same procedure but with real data. The synthetic tasks are generated by using 1) real unlabeled data and 2) a synthetic generative proc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for the recognition of the simplicity, soundness, strong performance of SynTO, and thoroughness of our experiments. We answer each of the reviewer's concerns below.
> Generating tasks that are "diverse and challenging" from the unlabelled da... | Summary: The paper presents a method for tackling few-shot black-box optimization problems in which the model queries a few hundred data points from the black-box function. The proposed method utilizes synthetic pretraining, where a family of synthetic functions is employed to generate data for in-context learning of a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for the recognition of the significance of the paper and the strong performance of SynTO. We answer each of the reviewer's concerns below.
> There's no guarantee that real-world downstream functions follow a Gaussian Process with a Radial Ba... | Summary: This paper investigated the problem of few-shot black-box optimization, and presented Synthetically pre-trained Transformer for Optimization (SynTO). By combining synthetic pretraining with in-context learning to enable few-shot generalization, SynTO demonstrate its superior performance on Design-Bench.
Str... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and for the recognition of the strong performance of SynTO and the good presentation of the paper. We answer each of the reviewer's concerns below.
> Can the problem in this paper be directly solved by few shot learning methods?
While it is tru... | Rebuttal 1:
Rebuttal: We conducted additional experiments to gain more insights into the performance of SynTO. The experiments are:
- (Table 1 and 2) Pretraining SynTO on different data distributions, including different GP kernels (GP-Cosine, GP-Linear, GP-Periodic), randomly initialized 1-layer neural networks (Rando... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy | Accept (poster) | Summary: The authors propose a novel methodology for capturing long-range interactions in graph-based learning models, that is based on mult-scale graph construction and merging. Specifically, following the Select-Reduce-Connect framework [1], the authors introduce a novel pooling method called Edgepool that enables r... | Rebuttal 1:
Rebuttal: Thanks for your invaluable reviews. We provide point-by-point responses below.
>0. The authors propose some variants to alleviate the high computational complexity, however it becomes more unclear how each one of the choices impact the behavior of the model. It seems ...
We'd like to clarify tha... | Summary: In summary, the paper proposes a mechanism to learn on graph in a multiscale and hierarchical manner. This is one of the approaches that can address the long-range problem, in which the graph has a large diameter, i.e. the length of maximum shortest paths between all pairs of nodes is long, in graph learning.
... | Rebuttal 1:
Rebuttal: Thanks for your invaluable reviews. We provide point-by-point responses below.
>1. Novelty: The long-range problem has attracted increasing attention from the graph learning community. There are similar ideas / works about multiscale and hierarchical structure learning being developed in parallel ... | Summary: This paper proposes MeGraph, a GNN architecture that interleaves local and hierarchical structural information in a graph at multiple-scales, to capture long-range interactions (LRI). The authors propose S-EdgePool that generalizes EdgePool by allowing more than two nodes to be clustered in order to achieve a ... | Rebuttal 1:
Rebuttal: Thanks for your invaluable reviews. We provide point-by-point responses below.
>1. [W1] There is no discussion regarding the hierarchies discovered by MeGraph for specific datasets/tasks, which could shed some light into the way the method is working. [Q3] Does S-EdgePool tend to create groups of ... | null | null | Rebuttal 1:
Rebuttal: # Overall Response
We thank all reviewers for the consistently positive feedback and invaluable reviews. We provide point-by-point responses below by commenting on each of your reviews.
We report the following new results as suggested by the reviewers.
* As suggested by Reviewer Y5z6, we created ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity | Accept (poster) | Summary: The authors extend previous work on deep feedback control (DFC) learning to make the functional form of plasticity a more realistic reflection of what is observed biologically. In particular the authors:
1. Introduce a feedback control mechanism using targeted, neuron-specific inhibition signal that allows for... | Rebuttal 1:
Rebuttal: > (...) to me the principal weakness is that the contribution is very incremental: (...)
While our results are comparable to previous work in terms of performance, we argue that our model offers distinct computational and conceptual advantages:
First, the dis-inhibitory controller we use causes ... | Summary: The authors propose a neural plasticity mechanism with a key role for disinhibition. In particular, they apply a deep feedback control framework whereby feedback driven inhibitory neurons mediate changes in the feedforward excitatory connections. The proposed rule is argued to hold desirable properties in that... | Rebuttal 1:
Rebuttal: >I am not fully convinced of the extent of novelty within this work. (...) is the key difference that the interneurons enable the network to avoid the need to discriminate between the feedforward (ff) activity and the total activity (...)
We realize that we did not sufficiently explain the advant... | Summary: This paper uses adaptive control theory to derive plasticity rules for a fairly plasubile model of multi-layer networks in the brain, which are capable of matching the performance of backpropagation without restrictive assumptions such as mirrored or very weak feedback connections. Specifically, each excitator... | Rebuttal 1:
Rebuttal: >There are still significant constraints placed on the feedback weights
We acknowledge that there are constraints on the feedback weight matrix. Specifically, we used the transpose of the network Jacobian to obtain the feedback weights, which caused feedback weights to vary over time. To address ... | Summary: This paper adds recurrent inhibition to each layer of a DNN architecture in order to facilitate a more biologically-plausible form of credit assignment. It shows how this circuit can explain some of the features of plasticity found in vitro and that DNNs with this circuit can learn to perform simple visual tas... | Rebuttal 1:
Rebuttal: > The motivation and innovation was not entirely clear to me. (...)
We realize that we did not explain the paper's motivation clearly. What our article contributes is a plausible mechanism consistent with experiments of how neuronal circuits can translate credit signals into weight changes. We do... | Rebuttal 1:
Rebuttal: We’d like to thank all reviewers for their helpful and constructive comments on our manuscript. We have responded to each of you individually in our rebuttals below. Based on their collective feedback, we have performed additional simulations which we think strengthen the manuscript. We discuss t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Energy-Efficient Scheduling with Predictions | Accept (poster) | Summary: This paper studies an energy-efficient scheduling problem under the setting of prediction. In this problem, each job has a release time and processing time. The job arrives online and the algorithm can determine the speed of the machine. The higher speed means a higher energy cost. The total energy cost integr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and positive review.
$\bullet$ “I am one of the reviewers of the previous version of this paper. My previous major concerns are […] In this version, the author addresses these two points appropriately.”\
We are happy to hear that the previous weaknesses we... | Summary: The authors study energy-efficient scheduling with predictions. There
are already previous works on minimizing energy consumption in a setting
with deadlines. The authors provide a unified way to address both
the scheduling with deadlines as well as (weighted) flow time plus energy cost.
It was already known t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and positive review.
$\bullet$ “elaborate a bit on how does your competitive ratio behave as a dependency on prediction error”\
Such a discussion is provided in Appendix G.1. We would be happy to move that discussion to the main body of the paper. A slight... | Summary: The paper considers speed scaling scheduling with learning augmented predictions. In contrast to previous works that considered the deadline-based version of the problem, the current paper studies a more general model that allows for different quality of service objectives to be optimised alongside the energy ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful review. We believe there is a misunderstanding regarding the major weakness raised by the reviewer, which we address first below. Please let us know if this is not the case, we would be happy to also answer any follow-up questions.
$\bullet$ “This is tackle... | Summary: This paper adds to the literature on energy efficient scheduling by defining an algorithm that extends the problems of "energy minimization with deadlines" and "energy plus flow time minimization" to the case where predictions about future jobs are available. The paper assumes an existing algorithm for online ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful and overall positive review. We hope the answers below address the reviewer’s concerns. Please let us know if this is not the case, we would be happy to also answer any follow-up questions.
$\bullet$ “One place that I had trouble were understanding how spee... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity | Accept (poster) | Summary: This manuscript looks at time series forecasting in neural multi-channel electrical signals. They propose a new graph neural network based approach called AMAG that infers connectivity between channels to improve the ability to forecast over competing approaches, most of which are designed to infer latent dyn... | Rebuttal 1:
Rebuttal: **W1**. We thank the reviewer for pointing out the need for further information regarding the forecasting metric versus additional metrics. We focused on forecasting since the metric indicates the extent to which future signals, assumed unknown, could be predicted. For perfect forecasting accuracy... | Summary: This paper presents AMAG — a graph neural network for modeling and forecasting neural population dynamics. The graph neural network has mechanism to describe the additive and multiplicative interactions between neurons, and a sample-dependent matrix to adjust the additive component. Experiments are carried out... | Rebuttal 1:
Rebuttal: **W1**. We thank the reviewer for recommending including statistics in Table 2. We agree that adding multiple runs would elucidate the extent of variation and robustness of the results. Originally, we included runs as separate tables (one in the main paper and one in the Appendix). Following the ... | Summary: This paper proposes a graph neural network with additive and multiplicative message passing operations for neuron activity forecasting. The proposed model AMAG consists of a temporal encoder (TE), a spatial interaction (SI) module, and a temporal readout (TR) module. TE and TR modules are sequence models such ... | Rebuttal 1:
Rebuttal: **W1** & **Q1**. We thank the reviewer for pointing out this concern for experiments set up. As the reviewer noted, we provided additional experiments in the Appendix on a second train-test split for all four datasets. The test set of the first experiment is effectively the validation set since we... | Summary: The authors introduce a graph neural network in their study to predict neural activity. This network comprises a temporal encoding and decoding layer specific to each channel, with a spatial interaction layer positioned in between. The inclusion of the explicit spatial interaction layer aids in capturing the u... | Rebuttal 1:
Rebuttal: **W1**. We appreciate the reviewer pointing out the use of non-standard acronyms in the paper. These are names of related methods introduced by their authors. We will make sure to include the full name of these methods at their first mention along with explanations of their origin, and provide cit... | Rebuttal 1:
Rebuttal: We thank all reviewers for their insightful feedback.
We tried to address all the questions for each reviewer in the rebuttal session below with references to weakness (**W**) and questions (**Q**).
Pdf: /pdf/e8004cffab42285c7a63be8c205084e339ccb2c7.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a graph neural network-based model to forecast neural activities, which advances the DNN technology for neural understanding. It also proposes a method to leverage the causality structure of the signal into the moded design that generalizes the neural reconstruction task.
Strengths: The pap... | Rebuttal 1:
Rebuttal: **W1**. We thank the reviewer for suggesting additional analysis of the roles of Self-connection, Add and Modulator modules. These three modules are motivated by typical components in modeling neural activity, i.e. current activity, external additive input and gain modulation and according to our ... | null | null | null | null | null | null |
A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time | Accept (poster) | Summary: This paper studies the problem of constructing a spectral clustering oracle with sublinear pre-processing and query time complexity. The paper introduces a new algorithm which improves on previous methods with respect to the running time at the expense of a slightly worse approximation guarantee. In contrast w... | Rebuttal 1:
Rebuttal: Thank you very much for the careful reading and the helpful comments. We fixed the typos in the updated manuscript. We slightly summarized your questions and provided detailed answers below.
**Question 1: The statement of Theorem 1 is quite difficult to follow. What is the purpose of $\xi$ in The... | Summary: This paper studies oracles for spectral graph clustering, i.e., local algorithms that answer membership queries for single nodes in a spectral clustering of a graph. There is a line of research on testing cluster structure in degree-bounded graphs, and recently, the learning version of the problem studied in t... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to review our paper. We address your concerns in the following.
**Summary: The intention of the experiments is not clear to me, as there is no comparison with other algorithms or insights on how the theoretical algorithm needs to be modified and tuned for ... | Summary: This paper proposes a spectral clustering oracle with sublinear pre-processing time and query time. The query is in the form of $(G, x)$ where $G$ is a graph with underlying clusters and $x \in V$ is a vertex. The goal is to (1) construct the oracle efficiently, (2) report which cluster vertex $x$ belongs to e... | Rebuttal 1:
Rebuttal: Thank you very much for taking your time to review our paper. We are happy to know that you like our robustness result. We address your concerns in the following.
**The major concern is that the contribution of the main result is quite limited. Yes, polynomial on k/eps is an important factor, but... | null | null | Rebuttal 1:
Rebuttal: In response to the review comments, we have incorporated two additional experimental outcomes and included a figure outlining the parameter tuning process for the theoretical algorithm. We invite you to refer to the attached PDF file for both the experimental results and the figure explanation. Yo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects | Accept (poster) | Summary: The proposed method in Structures from Duplicates leverages multiple appearance of an identical objects in a single image to reconstruct the geometry and material properties of this object. Each instance is assigned a virtual camera, such that the shared object representation is aligned in the same space. Ther... | Rebuttal 1:
Rebuttal: **Clarification on abaltion studies**: The analysis on the number of instances were performed on a randomly selected scene. To validate our analysis holds for other scenes, we conduct the same experiments on all objects. Due to resource constraints, we only managed to fnish training on two more *r... | Summary: This paper presents a pipeline for recovering object shape and surface materials from one single of multiple identical duplicate instances. The pipeline first extracts instance masks, registers camera poses using COLMAP (with in-plane rotation augmentation) and recovers shape, materials and environment lightin... | Rebuttal 1:
Rebuttal: **The task is NOT trivial**: **We respectfully disagree with the statement that "the task is trivial."** How to make inverse graphics/3D reconstruction more robust and work under more extreme scenarios is a challenging and longstanding problem in computer vision. In this work, we take a step forwa... | Summary: This paper tackles the inverse graphics task of predicting the geometry, material and illumination from a single image containing multiple identical objects. The key insight is to leverage the multiple instances depicted in the image to frame this single-view multi-object reconstruction problem into a better c... | Rebuttal 1:
Rebuttal: **Importance of the objectives**: To better understand the contribution of the loss terms, we start from the full model and subtract each loss respectively. As shown in the table below, removing either component will degrade the performance.
- *Metallic loss*: Since the metallicness of natural ma... | Summary: The paper presents a method for reconstructing the geometry, material, and illumination of an object using as input an image containing multiple copies of the object. The method leverages the appearance of multiple instances of an object in a single image to essentially create a multi-view supervision signal. ... | Rebuttal 1:
Rebuttal: Thank you for your recognition! The authors were extremely thrilled when first coming up with the idea of formulating a duality between multiple copies of an object in a single image and multiple views of a single object, and how to leverage it for inverse graphics. We are extremely excited that t... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful comments and valuable suggestions. We are very excited that the reviewers appreciated the novelty of our approach [`Reviewer sRgJ`, `Reviewer qVSc`], found the idea particularly interesting (*e.g.*, " a nice example of thinking outside the box") [`Review... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents "Structure from Duplicates" (SfD), a novel inverse graphics framework introduced to reconstruct the 3D structure, material, and illumination of multiple identical objects from a single image. The key steps include identifying these duplicate objects in an image and estimating their 6DoF pos... | Rebuttal 1:
Rebuttal: **Dependence on object similarity**: Over the years, the community has been actively investigating how to harness multi-view information from videos or sparse, extreme-view images, and push forward the frontier of 3D reconstruction and inverse graphics. Our work can be seen as an attempt in such a... | null | null | null | null | null | null |
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs | Accept (poster) | Summary: This paper focuses on graph neural networks (GNNs) for link prediction on knowledge graphs. It introduces the concept of conditional message-passing neural networks, which compute pairwise node representations based on a source node and a query relation. The paper analyzes the expressive power of these network... | Rebuttal 1:
Rebuttal: Thank you for your review and your comments on our paper. We address each below.
>“The title and some lines in the introduction seem to be a little bit overclaimed. For instance, the title named a theory of link prediction seems to be claimed on the general link prediction task while the main dis... | Summary: The paper investigates graph neural networks (GNNs) for link prediction in knowledge graphs. The authors propose a GNN that generalizes several existing techniques based on the labeling trick and NBFNets and investigate its expressive power by relating it to the Weisfeiler-Leman method and giving a logical cha... | Rebuttal 1:
Rebuttal: Thank you for your review and your comments on our paper. We address each below.
>“The experimental comparison focuses on the design choices of the proposed architecture but does not compare to existing works. This would be desirable, in particular, since higher-order GNNs subsume some of the tec... | Summary: The authors note that while GNNs are understood well in the context of simple graphs, there is a lack of comprehensive understanding when it comes to knowledge graphs. This study aims to systematically explore the use of GNNs in knowledge graphs, specifically in relation to the task of link prediction. The res... | Rebuttal 1:
Rebuttal: Thank you for your review and your comments on our paper. We address each below.
>“In the background section, the author first mentioned $G = (V, E, R, c)$ while change it to $G = (V, E, R, \mathbf{x})$ later. I know the author says it usually should be $\mathbf{x}$ instead of $c$. But can we dir... | Summary: This paper explores the expressive power of several GNNs designed for link prediction in knowledge graphs. The authors propose a conditional message passing framework with various designs for each component, a generalization of NBFNets. They also prove that the proposed framework can match the expressive power... | Rebuttal 1:
Rebuttal: Thank you for your review and your comments on our paper. We address each below.
>“Using relational asymmetric local 2-WL (rawl$_2$) to measure the expressive power of C-MPNN is a little awkward. The definition of rawl$_2$ is quite similar to C-MPNN, leading to potential confusion.”
The color re... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. We have responded to each concern in detail in our individual responses. In addition, we include a **rebuttal.pdf** to this post containing the results of all additional experiments.
Here is a summary of the changes to be made to the paper in light of the... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation | Accept (spotlight) | Summary: This paper builds on recent advances in black-box optimization for science and engineering inverse problems to find a satisfactory state with small physical error while decreasing the query times to the physical evaluation. The authors propose an error correction method GEESE, which is composed of a hybrid sur... | Rebuttal 1:
Rebuttal: We thank you for the insightful comments. We address the comments by grouping them into two categories: questions and discussion about weaknesses.
### Replies to questions
We would like to sincerely thank the reviewer for this excellent question. Let us assume an observation vector $\mathbf{y}$, a... | Summary: This paper studies the problem of estimating states from observations (inverse problem). The problem is important since there are many applications such as engine design for aircraft. The current solution usually uses a black box simulator to simulate the observations given the estimated states from the model ... | Rebuttal 1:
Rebuttal: We thank you for the insightful comments. We address the comments by grouping them into two categories: questions and discussion about weaknesses.
### Replies to questions
**1** Thank you for the question. In practice, different types of simulators exist for the same problem. For instance, for pr... | Summary: This paper proposes a sample-efficient method for correcting failed states in surrogate inverse problems. This is achieved through error estimation of optimized surrogate states, where some computationally expensive errors are approximated with a neural network, while the simple errors are computed explicitly.... | Rebuttal 1:
Rebuttal: We thank you for the insightful comments. We address the comments by grouping them into two categories: questions and discussion about weaknesses.
### Replies to questions
**1** Thank you for starting this interesting discussion. From our perspective, the biggest difference between RL and black-bo... | Summary: The paper is very good and interesting - and on a relatively novel topic that has so far recently seen very less attention in the AI community, and is only starting to see more attention with the recent questions on fairness and trust in AI models. The proposed GEESE algorithm is interesting, the foundation of... | Rebuttal 1:
Rebuttal: Thank you very much for appreciating our work and the very useful suggestion. Indeed the title should link better with the machine learning community. The new title will be **"A physics-driven machine learning framework for correcting inverse estimation"**. | Rebuttal 1:
Rebuttal: Dear reviewers,
We are attaching a PDF file here for providing the information that we mentioned in separate rebuttals. Thank you very much.
Pdf: /pdf/f45825ea32f490649393571e1153664333fe2534.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The article presents a new method of solving inverse problems using what they call is a grey box method. The introduce key concepts of using Generative methods to reduce the number of objective function invocations in an optimization problem. The presented results are very good and show a lot of promise for th... | Rebuttal 1:
Rebuttal: We thank you for the insightful comments. We address the comments by grouping them into two categories: questions and discussion about weaknesses.
### Replies to questions
**Q1** Thank you for asking this interesting question. The purpose of the exploration generator $\mathbf{G}_R$ is to rando... | null | null | null | null | null | null |
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera | Accept (poster) | Summary: The paper titled "Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera" presents a novel approach for unsupervised optical flow estimation using spike cameras. The authors propose a method that leverages dynamic timing representation to estimate optical flow without the need... | Rebuttal 1:
Rebuttal: Thank you for your precious time and reviews.
**Q1. Lack a thorough discussion of the limitations.**\
We fix the data length of spike streams which participate in the calculation of our unsupervised loss function. In scenes with normal brightness, this fixed-length spike data can provide sufficie... | Summary: The paper proposes a method for estimating optical flow from event streams captured with a spike camera. The approach is based on unsupervised learning and achieves good results in the comparison to other works in the SotA, including conventional and event-based approaches. Authors collect their own dataset an... | Rebuttal 1:
Rebuttal: Thank you for your precious time and reviews.
**Q1. Authors do not make clear the differences between spike and event camera.**\
Both spike camera and event camera have ultra-high frequency, and they can continuously record high-speed motions. However, the working mechanism of them are different.... | Summary: This paper propose an unsupervised learning framework for spike-based optical flow estimation, which is mainly developed for spike input representation and spike loss function. In general, I think the method has merit, but the experiment is unconvincing in its lack of clarity.
Strengths: 1. Propose a lightwe... | Rebuttal 1:
Rebuttal: Thank you for your precious time and reviews.
**Q1. About the reliability of the optical flow ground-truth.**\
The SSES dataset is a verification dataset containing various corner cases in the autonomous driving field and it is generated by CARLA. CARLA is an open-source simulator for autonomous ... | Summary: This paper works on the optical flow estimation problem, especially in the unsupervised setting, for high-frequency spike camera inputs. Specifically, a dynamic timing representation module and a spike-based unsupervised loss are proposed to improve performance. A new synthetic dataset, namely SSES, is created... | Rebuttal 1:
Rebuttal: Thank you for your precious time and reviews.
**Q1. Do authors have plan to share their code and generated datasets ?**\
We will release the code, models and datasets.
**Q2. The problem about the network may choose wrong way to approximate light intensity at the starting iterations of training.*... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper introduces a method for optical flow estimaiton for spike cameras. Because of the high temporal resolution of the camera, a preprocessing step based on temporal dilated convolution and attention layers is used to automatically select the best temporal scale for a given sequence.
The authors also intr... | Rebuttal 1:
Rebuttal: Thank you for your precious time and reviews.
**Q1.The latency and computational time of the proposed method.**\
In USFlow, to estimate the optical flow from timestamp t0 to timestamp t1, a spike stream should be collected containing (100 + dt) spike frames from t0, where dt represents the number... | null | null | null | null | null | null |
Multi-Agent Learning with Heterogeneous Linear Contextual Bandits | Accept (poster) | Summary: This paper studies the heterogeneous multi-agent contextual bandit problems. They propose the H-LinUCB algorithm, where agents coordinate at the beginning stage by pooling and synchronizing the data, until a certain time determined by the dissimilarity. They show the algorithm is optimal when the tasks are hig... | Rebuttal 1:
Rebuttal: We thank you for your feedback. We address your concern about the experiments as follows:
> The experiment part is relatively weak to me. Currently, the authors consider three settings of different levels of dissimilarity to highlight the advantage of the proposed H-LINUCB in achieving good regre... | Summary: This paper considers a multi-agent linear contextual bandit (with central server coordination) setting where $M$ agents each has (possibly different) unknown but fixed $d$-dimensional bandit parameter $\theta_m$, and the $\ell_2$-norm bound on the bandit parameters $\epsilon$ (i.e., $||\theta_i - \theta_j||_2 ... | Rebuttal 1:
Rebuttal: We thank you for your suggestion. We address your concerns as follow:
> The introduction of the proposed algorithm (Section 4.1) is not easy to follow and understand, many new variables are used without explanations
In line 196, we define these variables as sufficient statistics. Specifically,... | Summary: This paper studies multi-agent linear stochastic bandit under a model of heterogeneity of the agents. Concretely, the model assumes a centralized controller who can communicate information to and from all of the M different agents in the network. At each time, every agent in the network, will play an arm from ... | Rebuttal 1:
Rebuttal: We thank you for your insightful feedback and questions. We hope to clarify our contributions compared to Ghosh et al. as follows:
Besides the stochastic assumption, Ghosh et al. also make a **strong distributional assumption** on how the arm is being generated. Essentially, they require a lower ... | Summary: This work considers a multi-agent linear contextual bandit model with heterogeneity among the agents. The authors propose a novel algorithm called H-LinUCB to minimize the group cumulative regret when agents communicate through a central server. When the level of heterogeneity is known to the agents, they show... | Rebuttal 1:
Rebuttal: We thank you for your time and effort to review our submission. We hope to address your concerns as follows.
> The proof of Theorem 4.1 in Appendix A.2 is not easy to follow, because the steps aren't explained in detail. Even though the regret analysis is extended from (He et al. (2022), Wang et... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies multi-agent linear contextual bandits problem where each agent faces different bandits model. The heterogeneity is captured by the $l_2$ distance between the environment parameters. An upper confidence bound (UCB) algorithm, termed as H-LinUCB is proposed. The regret upper bound nearly-match... | Rebuttal 1:
Rebuttal: We thank you for your suggestions. We address your concerns and questions as follows:
>.... Can author discuss whether this method works?
We appreciate your discussion and thank you for raising an insightful question. Mathematically, in the scenario where $\epsilon$ is known, your proposed rule ... | null | null | null | null | null | null |
Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills | Accept (poster) | Summary: This paper proposes an imitation learning method IMC that can model multi-modal behaviors. IMC avoids the mode-averaging issue with an objective similar to reverse-KL. To cover all modes in the dataset, IMC further introduces a mixture model with multiple components, each focusing on different data distributio... | Rebuttal 1:
Rebuttal: > 1. IMC is a well-motivated method for multi-modal density estimation and provides an elegant reverse KL-based solution.
> 2. The experiments in low-dimensional control environments are extensive. IMC is compared against major generative models (with maximum likelihood objective) and addresses t... | Summary: The authors study a method, Information Maximizing Curriculum (IMC), that performs behavioral cloning by having the model selectively choose a learned weighing of the demonstration data for which the model is best at predicting (via minimizing the reverse KL divergence). To avoid the mode-seeking behavior of t... | Rebuttal 1:
Rebuttal: > The paper is overall well written. ff.
We thank the reviewers for acknowledging our contribution and are committed to addressing your questions and concerns.
> The main weakness I see is that this work seems almost identical to Li, et al (2023), which proposes largely the same method.
We agre... | Summary: This paper proposes a curriculum based approach for imitation learning. Overall, the imitation learning problem is posed as a conditional density estimation problem. Given the multi-modal nature of underlying data, this paper proposes to learn a curriculum based mixture of expert policy. Intuitively, each expe... | Rebuttal 1:
Rebuttal: > The paper presents an interesting and grounded approach for learning from multimodal data distribution. ff.
We thank the reviewers for their feedback on our paper. We are delighted to hear that the overall idea of using a curriculum to weight data samples is well-received. We would like to addr... | Summary: The paper proposes a learning protocol that learns multiple policies ("skills"), distribution over skills, and a per-skill priority over experience buffer. This is achieved by maximizing a variational lower bound of a certain averaged regularized KL distance. Namely, the objective is a sum of two terms. The fi... | Rebuttal 1:
Rebuttal: In order to adhere to the character limit we will jointly address most of the concerns regarding the empirical part and those of the technical part.
**Regarding concerns on the empirical part:**
We agree with the reviewers that moving the definition of the entropy for the different experiments t... | Rebuttal 1:
Rebuttal: We express our gratitude to the reviewers for their valuable suggestions and constructive feedback. Here, we post additions that might be of interest for all reviewers:
We have included a proof sketch, outlining the convergence of the algorithm proposed in our work, providing a more thorough unde... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Random-Access Infinite Context Length for Transformers | Accept (poster) | Summary: The paper presents a new architecture for long-range decoder-transformers: a new "landmark token" is inserted into the input within constant strides (that is, after every $k$ tokens). Every such "landmark token" is thus the "representative" of a block of tokens.
The attention score to this "landmark token" is ... | Rebuttal 1:
Rebuttal: Dear Reviewer tGXa,
We make the following comments:
1. Our argument on infinite context length relies on the model's capabilities. While practical demonstration of infinite context length isn't feasible, our method's efficacy is shown in Appendix G at 32k context length. The original transformer... | Summary: This paper presents a novel approach that enables the Transformer language models to process much longer sequences. Specifically, the authors propose to group the input sequence into multiple blocks, each of which is represented with a landmark token. The attention scores are calculated regularly among all tok... | Rebuttal 1:
Rebuttal: Dear Reviewer g7s5,
We make the following comments to address your questions and concerns:
1. We thank you for the valuable suggestions to increase clarity (and also for pointing out the typo) and will apply them in the final revision.
2. Please note that all existing methods for handling long ... | Summary: This works proposed a hierarchical structure to organize previous context in blocks and represent them via novel landmark tokens at the end of each block. Additionally, a novel GroupedSofxmax mechanism is proposed to replace the original softmax to enable current token to attend on both local tokens and retrie... | Rebuttal 1:
Rebuttal: Dear Reviewer fLCX,
We make the following comments to address your questions and concerns:
1. We did not observe any training instability due to using landmark attention. Furthermore, as mentioned in the paper, the additional computational costs are negligible especially when combined with Flash... | Summary: The paper proposes a new attention mechanism that uses "landmark" tokens to allow access to long contexts while retaining the flexibility of standard attention. The landmark token represents each block of the input context, and the attention mechanism is trained to use landmark tokens to select relevant blocks... | Rebuttal 1:
Rebuttal: Dear Reviewer Hd8A,
We make the following comments to address your questions and concerns:
1. In Appendix G, we demonstrate how offloading the KV cache to the CPU, along with reduced retrieval flexibility, enables us to extend LLaMA's context length to 32K. As a result, we achieve 98% accuracy i... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We would like to address some of the common concerns raised by the reviewers with the following comments:
* We have successfully used offloading parts of KV cache to CPU to perform inference at 32k context lengths using LLaMA. We have described this result in Appendix G. Note tha... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors propose a new transformer architecture, the Landmark Transformer, which uses a novel approach to the self-attention mechanism to allow the model to handle longer sequences.
The Landmark Transformer introduces a new type of token called a "landmark token" which acts as a gateway to a block of token... | Rebuttal 1:
Rebuttal: Dear Reviewer DxW4,
We make the following comments to address your questions and concerns:
1. The language modeling experiments were done using 4 A100 40GB GPUs. LLaMA fine-tuning was done using 8 A100 80GB GPUs.
2. We have successfully used offloading parts of KV cache to CPU to perform infere... | null | null | null | null | null | null |
Certification of Distributional Individual Fairness | Accept (poster) | Summary: This paper considers the problem of certifying individual fairness (IF), which is of great importance to reliable machine learning algorithms. To this end, the authors propose a novel convex relation of IF constraints that greatly reduces the computational cost. In addition, the authors propose to certify dist... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments including their clarifying points about our notation and praise regarding our experimental section. We will ensure that the notational points are adjusted in the final version of the paper
### On the separation between distributional robustness and fairnes... | Summary: This paper studies formal guarantees for notions of individual fairness (IF) for predictors given by neural network models. After relaxing common definitions for IF metrics by means of $\ell_\infty$ balls (or orthotopes), they adapt methodology based on adversarial robustness to provide upper and lower bounds ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments on the presentation of our paper and appreciate the detail of their review. Below, we address each point raised by the reviewer. We clear up any minor misconceptions and provide a clear action to improve the presentation of the final version of the... | Summary: This paper studies the problem of individual fairness in supervised learning. The focus is on studying how to certify distributional individual fairness (IF) (individual fairness over a set of distributions close to the observed empirical data distribution) in neural networks. Prior work has focused largely on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments on the presentation of our paper. Below we comment on the points and questions raised by the reviewer by providing specific actions that will be taken to address these presentation points.
### On Clarity of Section 5
We thank the reviewer for this... | null | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis | Accept (poster) | Summary: In this paper the authors propose a similarity metric to compare dynamical systems – in particular neural networks. They use a set of recent methods to transform a general dynamical system into a basis in which the dynamics is approximately linear. They compare two dynamical systems by comparing their linear d... | Rebuttal 1:
Rebuttal: We are thankful for the reviewer's extensive feedback and appreciate their comments--we believe we can answer all of their questions, which correspond to valuable improvements to the paper.
> I don't understand why section 2.3 was separate from section 3...
This is a reasonable point and we agre... | Summary: Understanding how different neural populations process a particular computation is critical for the study of brain computation, the development of brain-inspired technologies like brain-computer interfaces (BCI), and AI applications. Existing methods compare the underlying representations based on the spatial ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and greatly appreciate that they think the paper is clear and technically sound. We hope that our response will clarify some of the weaknesses and questions that the reviewer mentioned:
> Applications to neural data
While we haven't applied to neural data... | Summary: The authors developed a new computational tool named Dynamical Similarity Analysis (DSA) to measure the similarity between two systems focusing on the dynamics. They constructed the method by combining and modifying Dynamical Mode Decomposition and Statistical Shape Analysis. Their method successfully identifi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and are very happy that they found the paper elegant, significant, and useful! We found their suggestions for improvements quite helpful as well, and hope that we can implement them in a manner that they agree with:
In their point that some of the figure'... | Summary: The authors propose Dynamical Similarity Analysis (DSA), a method for assessing the dynamical similarity between dynamical systems. The method combines ideas from the data-driven Koopman operator literature and a Procrustes-type distance between linear operators. By focusing on dynamics rather than geometry, t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and suggestions. We are glad that the simplicity of the method was appealing, and we hope that our responses to each of your comments will allow you to further appreciate DSA.
We appreciate the suggestion to add a spectral method of comparison, and... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bias in Evaluation Processes: An Optimization-Based Model | Accept (poster) | Summary: This paper studies the issue of biases present in evaluation processes like hiring and school admissions. The authors propose a model that estimates the distribution of utility that incorporates two main features: resource constraints for information, and risk-averseness of the decision-maker. They formulate a... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We are glad that you like our approach. There do seem to be some misinterpretations that we have clarified. We hope that you will increase your support for the paper.
**"...lack of a formal model...what...OptProg...represents"** OptProg is an abstract model of h... | Summary: The paper proposes an optimization-based framework for modeling bias in evaluations. The perspective of the paper is to provide a well-founded and interpretable model of evaluations that can replicate biases observed in real settings without invoking an intrinsic utility for producing biased evaluations. This ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We are glad that you think that our model can guide policy-makers to understand when certain interventions will be more effective than others. Thanks also for appreciating the invocation of the maximum entropy principle to generalize previous models.
**"proof of Theo... | Summary: This work presents a theoretical model to quantify bias in the task of evaluating candidates (ie minimizing loss while subject to an information constraint). It presents a formula/representation of the problem, parametrized by roughly "real-world" factors of 1) resource-information tradeoffs; and 2) risk-avers... | Rebuttal 1:
Rebuttal: Thank you for appreciating the model, the empirical work, and the presentation of the paper. We have addressed your questions and concerns below and hope that you will consider supporting our paper further.
**"I'm inclined to accept .. "** Thanks. Please take a look at the review of reviewer gz... | Summary: The paper studies how to examine the group distributional difference using loss minimization. The authors propose a loss with a max-entropy constraint.
Strengths: The paper studies an important problem of how to examine the evaluation bias in many applications such as hiring and school admissions. The author... | Rebuttal 1:
Rebuttal: Thank you for appreciating the paper. We address your questions and concerns below and hope you will strengthen your support for the paper.
**"..since it seems that the authors are trying to do a density estimation task with certain constraints on the density function. Why not minimize class... | Rebuttal 1:
Rebuttal: We thank the area chair for their time and effort in engaging with the reviewers and considering our rebuttal.
We thank all the reviewers for their excellent suggestions which will help improve the paper and for considering our rebuttal. We take the feedback of reviewers seriously and have addres... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: In this paper, the authors model evaluation processes that estimate the density of value for an individual (on a task) as a loss minimization problem subject to constraints. The authors proceed to derive various properties of the output densities of their model and evaluate it on two real world datasets.
Stre... | Rebuttal 1:
Rebuttal: Thanks for your feedback. We are glad that you find our work as a good solution to an important and difficult problem. We address your specific questions below.
**"hard to read this paper as someone not too familiar with the field"** We apologize that you found it hard to read parts of the paper... | null | null | null | null | null | null |
Generalized equivalences between subsampling and ridge regularization | Accept (poster) | Summary: The authors investigate the problem of ridge regression, and prove equivalence results between ridge regularisation and ensambling of weak learners trained on subsamples of the original dataset.
The equivalences hold under very mild assumptions, and notably there is no requirement on the data model.
Two kind o... | Rebuttal 1:
Rebuttal: Many thanks the nice summary, positive feedback, and the thought-provoking comment about plausible variations of the equivalence paths or even non-equivalences! We are glad that you found the paper interesting and appreciate the kind words.
Below we address the question and limitation raised.
-... | Summary: This paper shows an asymptotic equivalence between an ensembled+subsampled (E+S) version of ridge regression and the standard version, in the proportional asymptotic regime. The equivalence result shows that there exists a linear path in the space (ridge-parameter, aspect-ratio) along which all estimators yiel... | Rebuttal 1:
Rebuttal: Many thanks for the excellent concise summary, encouraging comments, and kind words!
We are delighted to hear that you found our paper enjoyable to read.
Paper aside, we also find the structural and risk equivalences quite neat, in understating the effects of ridge regularization, and particularl... | Summary: The authors study the relationship between ridgeless ensembles constructed from subsampled data and a ridge estimator in a setting with mild assumptions on the joint distribution $(Y,X)$. They establish equivalences for a generalized class of risk functionals, which include quantities related to coefficient ... | Rebuttal 1:
Rebuttal: Many thanks for the encouraging comments and feedback!
We are glad to hear that you liked the relaxing of distributional assumptions, the novelty of theoretical analysis, and the clarity of presentation. In the sequel, we will first comment on the weaknesses raised and then address the questions.
... | Summary: This submission establishes equivalences between ridge regression (i.e. $\ell_2$-penalized
linear regression) and ensembles of linear models trained on sub-sampled datasets.
In particular, the authors prove that for a fixed feature/sub-sample-size $d/k$, ratio, there
exists a ridge-regression model with risk ... | Rebuttal 1:
Rebuttal: Many thanks for the detailed constructive feedback! We appreciate the careful reading and questions.
Below we comment on the weaknesses within the allowed space.
- **[W1]** While it is true that the connection between ridge regression and sub-sampled ensembles has been previously established, ou... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work compares the ridgeless ensemble and the ridge estimators in the proportional limit setting (i.e., $d/n\to \phi$). Prior works [11,12,13] show that these two estimators achieve the same out-of-sample risk. The main contribution of this paper has been to (1) weaken the assumptions and (2) broaden the e... | Rebuttal 1:
Rebuttal: Sincere thanks for the positive feedback, insightful comments, and the list of typos.
We are happy that you enjoyed reading our paper and appreciated the presentation (that we paid special attention to while writing the paper).
To echo your sentiment, results in Section 5 are also our favorite!
... | null | null | null | null | null | null |
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds | Accept (poster) | Summary: This paper provides a lower bound on the mutual information between the output weights and the input training data in the context of stochastic convex optimization to examine the tightness of the mutual information-based generalization bound. It is shown in the paper that mutual information grows with the dime... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review. It seems the main weakness pointed out is an alleged contradiction between the result and existing upper bounds. There is no such contradiction and hopefully below it will be clarified, please do ask for further clarifications if this is not the case o... | Summary: This paper challenges the tightness of some information-theoretic upper bounds on the generalization error in the stochastic convex optimization (SCO) setting (i.e. convex, Lipschitz, bounded loss on a bounded domain). More precisely, it challenges the bound from Xu and Raginksy that (vaguely) states that the ... | Rebuttal 1:
Rebuttal:
Thanks for the review. Hopefully, the rebuttal will help the reviewer be convinced that there is no reason for providing such a low score. Especially given that the reviewer identifies strengths (important problem and interesting techniques) and that the weaknesses can be easily addressed and req... | Summary: This work considers the setting of stochastic convex optimization in $\mathbb{R}^d$ with learning algorithms that achieve less than $\epsilon$ expected excess risk when at least $m(\epsilon)$ examples are given. The main result of this work (Theorem 1) states that such algorithms have $\tilde{\Omega}\left(\fra... | Rebuttal 1:
Rebuttal: Thanks for your feedback and comments. The proofs are correct, but certain typos have been identified by the reviewer, and indeed, a certain assertion was neglected that could clarify things (see below).If further clarifications are in order, those can be delivered..
> In the line after equation ... | Summary: The paper shows that there exist stochastic convex optimization problems, which are easy to learn but for which the mutual information based generalization bound of [Xu-Raginsky, 2017] scales at least linearly in the dimension of the parameter space.
Strengths: The paper presents the limitation of mutual inf... | Rebuttal 1:
Rebuttal: Thank you very much! | Rebuttal 1:
Rebuttal: Thank you very much for the thoughtful reviews.
Several reviewers pointed out to the work of Bu et al. and asked it the techniques can also be applied to this individual sample bound, here I am providing a proof sketch explaining why the technique easily applies. I will only show dependence on th... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information | Accept (poster) | Summary: In this paper the authors propose (diagonal) Fisher information leakage (dFIL) as a theoretically-grounded and practical framework for assessing the privacy guarantees of instance encoding. At its core, dFIL quantifies the potential invertibility of the encoding mapping of an instance encoding scheme. The auth... | Rebuttal 1:
Rebuttal: We thank for the insightful review and feedback. We provide our answers to the questions and comments below. For the new evaluation data and their brief explanation, please check the global response.
**Relative importance of dFIL compared to the information theorist's Fisher information**. While ... | Summary: This paper discusses the privacy concerns surrounding data encoding methods employed in machine learning (ML) operations. Its primary objective is to present a theoretical framework that quantifies the extent of privacy leakage in an encoding scheme and facilitates the calculation of its invertibility. The aut... | Rebuttal 1:
Rebuttal: We thank for the insightful review and feedback. We provide our answers to the questions and comments below. For the new evaluation data and their brief explanation, please check the global response.
**Reconstruction attacks are simplistic**. We want to first highlight that the attacks we studied... | Summary: Instance encoding (and some closely related lines of research) aim at finding ways to encode data examples (in a training set) as $E=Enc(e)$, in such a way that one can train models on the encoded examples $E_1,...E_n$, while $E_i$ does not leak much about $e_i$ to an adversary who inspects it.
Previous effor... | Rebuttal 1:
Rebuttal: We thank for the insightful review and feedback. We provide our answers to the questions and comments below. For the new evaluation data and their brief explanation, please check the global response.
**dFIL against sensitive attribute inference**. The initial motivation for dFIL was to protect ag... | Summary: This paper introduces a new theoretical measure, the "diagonal Fisher Information Leakage" (dFIL), for quantifying the privacy leakage in instance encoding mechanisms in machine learning models. The authors construct a framework that balances the trade-off between data privacy and utility in instance encoding.... | Rebuttal 1:
Rebuttal: We thank for the insightful review and feedback. We provide our answers to the questions and comments below. For the new evaluation data and their brief explanation, please check the global response.
**Comparison with similar measures**. Despite the popularity, there is *very little work on theor... | Rebuttal 1:
Rebuttal: We thank the reviewers for their insightful reviews and feedback. We respond to the concerns and questions individually in a separate rebuttal for each review. Here, we upload a supplementary PDF containing additional results that were asked by the reviewers. We also give a brief explanation:
**T... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Joint processing of linguistic properties in brains and language models | Accept (poster) | Summary: While several papers have recently shown that large language model embeddings are aligned with / predictive of fMRI reading data, this paper pushes these analyses forward by asking: what properties of the representations are responsible for this alignment? In particular, they compare LLM/fMRI alignment on rea... | Rebuttal 1:
Rebuttal: *We thank the reviewer for their strong positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript.*
**1. The Pearson correlations in Figure 3 are quite low, on average less than 0.1. What do the authors make of this? The ROI-level analys... | Summary: This paper aims to investigate the correspondence between HIPSs and models based on Neural Networks such as the transformers. The key innovation of this paper is to study this correspondence by elimiating specicific linguistic properties form BERT and to observe how this intervation affects the alignment with ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable comments and suggestions which are crucial for further strengthening our manuscript.
**1. There is a major innovation of this paper that is the cornerstone of the theory. Yet, this is not listed as a major result: the removal of linguistic properties from ... | Summary: The paper investigates the relationship between specific linguistic properties and brain alignment across layers of a language model. The authors provide evidence for the importance of a linguistic property to the brain alignment. The focus of the study is to understand the degree to which various linguistic p... | Rebuttal 1:
Rebuttal: *We thank the reviewer for their positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript.*
**1. Choice of Language Model: BERT vs autoregressive models (GPT2)**
* Please check “Common responses”, and Table 1 and Fig 1 in the rebuttal ... | Summary: The authors provide an analysis of correlations between BERT representations and fMRI data when abstracting away different linguistic properties from the BERT representations. They provide an in-depth analysis of the ways in which elimination of word length, tree depth, top constituents, tense, subject number ... | Rebuttal 1:
Rebuttal: *We thank the reviewer for their strong positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript.*
**1. Overall motivation for section 5.3 (Decoding task performance vs brain alignment)**
* While the previous analyses revealed the impo... | Rebuttal 1:
Rebuttal: *We thank the reviewers for their strong positive, insightful and valuable comments and suggestions which are crucial for further strengthening our manuscript.*
**Why choose BERT over other models? (reviewers YHJW and p9mD)**
* The primary rationale behind our choice to incorporate the BERT mode... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Masked Image Residual Learning for Scaling Deeper Vision Transformers | Accept (poster) | Summary: This paper finds that the deep layers of ViT fail to benefit from MIM pre-training. The authors replace deeper layers of MAE pre-trained ViTs with random initialization and demonstrate that this modified model achieves better performances than the original MAE pre-trained model. To this end, the authors conclu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments. We answer the questions as follows.
**Q1. Unfair comparisons against other methods. MIRL incorporates an extra exponential moving average (EMA) while others do not, which appears to be an unfair comparison ... .**
**A1.** We address this concern... | Summary: In this paper, the authors propose a new mask image modeling method, which is helpful for deep ViT model pretraining.
Strengths: Please refer to Questions
Weaknesses: Please refer to Questions
Technical Quality: 3 good
Clarity: 3 good
Questions for Authors: ### strength
1. The paper is well-written and ea... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our observations interesting and method novel. In the following, we address the concerns of the reviewer.
**Q1. The experiment lacks some necessary results. For example, ViT-L is deeper than ViT-B and has 24 layers by default, it may benefit from the proposed met... | Summary: This paper delves into the degradation issue encountered in the deeper layers of Vision Transformer (ViT) and proposes a self-supervised learning framework called Masked Image Residual Learning (MIRL). MIRL reformulates the learning objective to recover the residual of the masked image and makes scaling ViT al... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work, considering the area addressed in our paper as an important under-explored area of research. In the following we address the concerns of the reviewer.
**Q1. While the proposed method has shown promising results, it still lacks an in-depth ... | Summary: This paper clarifies that MIM pretraining can induce negative optimization in deeper layers of ViT through comparison between vanilla MAE and truncated MAE. Based on this observation, this paper proposes a MAE-based framework named Masked Image Residual Learning (MIRL) for alleviating the degradation problem a... | Rebuttal 1:
Rebuttal: We thank the reviewer for considering our idea interesting. We address the reviewer's concerns as follows.
**Q1. Although this paper has cited some previous works that provide supervision to different layers of ViT (like deepMIM[1]), there is no comparison between MIRL and these previous methods ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: * This paper propose a Masked Image Modeling pretraining framework, which boost the pretraining performance in deeper deeper layers of ViT architecture.
* Deeper vision transformers are hard to train, so authors introduce a new pretraining objective for mim, which can alleviate the degradation problem in de... | Rebuttal 1:
Rebuttal: **Question1 (Q1). Since MIRL with deeper layers is different from the standard vit architecture(more deep than original vit), the fully supervised performance of the the same layers should be reported, the compare is a little in Table 3.**
**Answer 1 (A1).** We provide an additional comparison b... | null | null | null | null | null | null |
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport | Accept (poster) | Summary: The authors propose to use semi-dual formulation of unbalanced optimal transport for generative modelling. Via the experiments on image datasets, the authors illustrate the advantages of the proposed method, namely outlier robustness, stability and fast convergence, and extensively compare with other methods.
... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for acknowledging that "UOTM is indeed an efficient method for generative modeling under the presence of outliers". Also, we agree with the reviewer that, in terms of method, our model is a direct parametrization of the semi-dual form of UOT [67]. Nevertheless,... | Summary: The paper derives a semi-dual formulation for the unbalanced optimal transport problem (using the known dual reformulation) and solves it with neural networks. The most notable contribution of the paper is that this formulation is applied to generative modeling and achieved nearly SOTA results on several stand... | Rebuttal 1:
Rebuttal: We appreciate the reviewer providing valuable advice. We think answering the reviewer's comments has significantly improved our work. We hope our replies to be helpful in addressing the reviewer's concerns.
### **1. Theoretical concerns**
$ $
### **1.1 Concerns already discussed in the manuscri... | Summary: In this paper, the authors propose a novel model (UOTM) that uses (as an objective function) a semi-dual form of the unbalanced optimal transport (UOT) problem. Since UOT relaxes the hard constraint of OT on distribution matching, they also provide the theoretical upper bound of divergences between marginals. ... | Rebuttal 1:
Rebuttal: We are deeply grateful to the reviewer for reading our paper and offering thoughtful feedback.
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> **Minor comments**
**A.** Thank you for the valuable advice regarding the presentation of our work. Following the advice, we would revise our manuscript as follows:
- The order and caption... | Summary: Standard optimal transport (OT) problem aims at comparing two probability distributions by finding an optimal coupling that achieves a minimum geometrical cost. A major bottleneck of standard OT is the equality of total transported mass between the underlying distributions. This restraints based-OT data analy... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for spending time reading our manuscript carefully and providing thoughtful feedback. We hope our replies to be helpful in addressing the reviewer's concerns.
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> **Q1.** Introducing tuning parameters $\lambda$ to the divergence terms, i.e., $\lambda_{1} D_{\Ps... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
A State Representation for Diminishing Rewards | Accept (poster) | Summary: The authors study a problem of DMU in RL, e.g. if visiting the same state may result in a smaller reward. They introduce a \lambda R representation that considers a particular form of diminishing rewards and provides convergence results. They extend it to continuous domains and perform preliminary experimental... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review and helpful feedback! We are glad that you appreciated the convergence results and the extension of the $\lambda$R to continuous domains. We aim to address your concerns below:
- Assumptions: This is a thought-provoking point! We completely agree that... | Summary: This paper studies the phenomenon of diminishing marginal utility in RL, where the a state-based reward r(s) decays as the agent visits it more often. To solve this problem setting, this work introduced a novel state representation, named the λ representation (λR). The author showed that we fail to define a Be... | Rebuttal 1:
Rebuttal:
Thank you very much for your detailed review and feedback! We are glad that you appreciated the problem setting, found the writing to be clear, and valued our theoretical analysis. We aim to address your concerns below:
- Typos: Thank you for pointing these out! We’ll correct them in the update... | Summary: The paper considers the case of non-Markov rewards, where rewards at states decay over time (motivated by the diminishing marginal utility phenomenon). While successor representations (SR) have been used in standard MDPs, the decaying of rewards means that they cannot be used here, and so the paper develops a ... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed feedback, and we’re glad that you liked the paper! We hope to address your concerns below:
- Baselines: We completely agree, and identifying appropriate baselines was a challenge for us, simply because (as far as we know) DMU has not been addressed in an RL ... | Summary: The authors explore diminishing marginal utility in the context of reinforcement learning. Specifically, they study reinforcement learning when reward obtained at a state diminishes with each visit to that state (following a particular mathematical form). The authors show that, under such diminishing rewards, ... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed review and careful consideration of our paper! We are glad that you appreciated the originality of the work, found it to be of high quality, well-structured, and clearly written. We aim to address your concerns below:
- Motivation: We believe that there are t... | Rebuttal 1:
Rebuttal:
We’d like to sincerely thank all reviewers for their time and for their helpful feedback and suggestions for the paper. We believe that incorporating this feedback will make the paper stronger, and we look forward to a constructive discussion! We are glad that reviewers consistently appreciated t... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
On student-teacher deviations in distillation: does it pay to disobey? | Accept (poster) | Summary: The paper performs a careful analysis of the discrepancy between the predictions made by the student and teacher in the context of knowledge distillation. Prior works has shown that (1) distillation improves student performance, so that it can sometimes outperform the teacher, and (2) the predictions made by t... | Rebuttal 1:
Rebuttal:
Thank you for appreciating the breadth of our experiments and the clarity in our theory. Also, thanks for the very detailed review!
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> Why do you think you see more student overconfidence in language tasks compared to vision (line 140)?
Indeed, this is a curious phenomenon — thanks for payi... | Summary: This paper explores the paradox in knowledge distillation where a “student” network deviates from the “teacher” network’s probabilities but still outperforms the teacher. The authors found that the student network exaggerates the teacher’s confidence levels across various ar... | Rebuttal 1:
Rebuttal: We are pleased to hear that you have enjoyed reading the paper. Thanks in particular for examining our supplementary results and for raising many positive points about work.
> Consider an actual density plot with a heatmap or a contourplot as choice of visualization instead.
You’re right that it... | Summary: This paper aims to understand the counter-intuitive phenomenon that the
student can sometimes outperform the teacher in terms of generalization
performance even when it deviates from the teacher's soft-labels during
training. Using a linear regression model, the authors provide a
theoretical result that explai... | Rebuttal 1:
Rebuttal: Thank you for taking the time to provide your feedback on our paper. You’ve raised some interesting questions, some of which the paper addresses. We explain why below.
------
> whether the theoretical insights can be generalized to classification problems;.
We’d like to note that **we have prov... | Summary: This work delves into the understanding of knowledge distillation by studying the deviations between teacher and student models during the distillation process. It reveals two primary observations: students often underfit points that teachers find challenging, and the initial training phase is not crucial for ... | Rebuttal 1:
Rebuttal:
Thank you for appreciating the breadth of our empirical findings and giving our paper a positive rating!
------
> It would be intriguing to explore whether varying the teacher's interpolation would correspondingly impact the degree of the student's exaggeration.
Interesting question! Based on ... | Rebuttal 1:
Rebuttal: The response PDF contains Fig A2 as requested by Reviewer vVNQ (R2) and Fig A1 as requested by Reviewer zo9s (R4).
(Note: In case a link to the PDF is not visible, clicking on `Revisions` above should lead to a link.)
Pdf: /pdf/bf7de493c6d19241c3db46e2002fe20f8db756fa.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper investigates one of the surprising findings in the field of knowledge distillation, which is that often times, the student deviates from the teacher in the process of mimicking it, and that some times this results in the student performing even better than the teacher (e.g., self-distillation). The a... | Rebuttal 1:
Rebuttal: Thank you for your positive score on the paper, and for appreciating how we connect the two disjoint lines of research!
--------
> "distillation can hurt the student when the teacher does not achieve sufficient top-1 accuracy on the training data." - seems to be contrary to many of the other re... | null | null | null | null | null | null |
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning | Accept (poster) | Summary: This paper presents a simple and effective multi-task learning strategy of a joint pretraining followed by fine-tuning, where pretraining is on the high-resource task and fine-tuning is on a mixture of high and low-resource tasks. This significantly improves performance on the low-resource tasks, while perform... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We respond to the weaknesses and questions brought up below:
**W:** *Why does pretraining longer improve the loss for high-resource NMT tasks but degrade the loss for high-resource language modeling tasks (c.f., Figure 9(a) and 9(b)).*
**Respons... | Summary: The paper proposes a method called pretraining and joint-finetuning, which pretrains a model on a high-resource task than finetunes the model with joint high- and low-resource tasks, benefiting from both static sampling method and naïve transfer learning. The method is verified on multilingual translation and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Here is our response:
**W1a:** *The algorithm is sensitive to the sampling proportion, resulting a need to search proportion for every model trained.*
**Response:**
Our method is not any more sensitive to the sampling proportion than scalarization, which... | Summary: The paper presents a multi-task training approach that involves pre-training on high resource tasks, followed by joint fine-tuning on the full set of tasks. The intuition is that high-resource tasks need a larger number of steps to reach convergence, whereas low-resource tasks will risk over-fitting if trained... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and positive response. Our responses to the weaknesses and questions brought up are below:
**W1:** *I would have loved to see some more realistic MT experimental settings. Pretraining on en-fr for either en-zh or en-hi (or vice versa) feels very u... | Summary: This paper presents an analysis of multi-task training with the existence of low-resource tasks (in the context of the paper, they focused on multi languages). The paper argues that it is better to first "pre-train" on a high resource task and then "fine-tune" on a joint of high and low resource tasks. Experim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Our responses to weaknesses and questions are below:
**W:** *[…]the benefits of having a multilingual model (or in general a multi-task model) is that a single model can be used in various situations, without the need of (continue) training on different d... | Rebuttal 1:
Rebuttal: **Q:** *Reviewers wUUC and V1qx both pointed out that our method focused on the multilingual setup, and were curious whether we ran experiments on the general multi-task setup.*
**Response**: In our work, we frame multilingual learning (NMT, language modeling) as a multi-task optimization problem... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper presents an empirical finding: in the context of data imbalance in multi-task learning, pre-training on high-resource tasks then fine-tuning on a mixture of high/low-resource tasks can achieves superior results, compared to standard weighted sampling training. Authors applied the proposed training m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. Here is our response to the weaknesses and questions pointed out by the reviewer.
**W1a:** *In the main paper, authors only reported perplexity metrics in the evaluation results.*
**Response:** For the NMT experiments, we report BLEU score evaluations in... | null | null | null | null | null | null |
Tempo Adaptation in Non-stationary Reinforcement Learning | Accept (poster) | Summary: This paper studies reinforcement learning in non-stationary environments. The authors propose a proactive tempo-control model-based (PTM) framework to adjust how often the learning agent to adjust its policy to the environment tempo. Two different variants of PTM are considered: The PTM-T variant uses natural ... | Rebuttal 1:
Rebuttal: $\textbf{1) W 1,2: clairty of main contribution and meaning of tradeoff}$
Thanks for pointing out the clarity of the main theoretical results and corresponding contribution and the meaning of the “trade-off”. We would like to emphasize our work’s main contribution is interpreting the non-statio... | Summary: The paper introduces a novel framework for handling non-stationary environments in reinforcement learning (RL) called Proactive Tempo-control Model-based (PTM) framework. The authors argue that in non-stationary environments, an additional factor emerges alongside the classical exploration-exploitation trade-o... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments and the followings are our answers to weakness, question that the reviewer has raised.
$\textbf{1) W1: clarity of contribution and difference from related works}$
First, We would like to emphasize that our work’s $\textbf{main contribution}$ is interpret... | Summary: This work introduces the tempo of adaptation in a non-stationary RL problem. The authors provide Proactive Tempo control Model-based (PTM) framework, and two specific instances PTM-T and PTM-G. By adjusting the tempo of the algorithms, the proposed algorithm can match the tempo of the environment to address no... | Rebuttal 1:
Rebuttal: $\textbf{1) W1: complicated symbolic notation}$
Thanks for pointing out that the paper contains too many notations and those make readers follow up the paper hard. We will reduce notations and make theorems much more straightforward in the camera-ready version if we get accepted.
$\textbf{2) Re... | Summary: This paper presents a solution to nonstationary RL issues through a model-based framework called Tempo-Control (PTM). Specifically, the authors identify a new trade-off in nonstationary RL problems: the trade-off between learning an accurate model and learning an optimal policy. Based on this, a new framework ... | Rebuttal 1:
Rebuttal: $\textbf{[About the latent variables : assumption validity]}$
Thanks for pointing out the validity of the assumption.
First, we would like to mention the biggest reason for the observable $\mathcal{O}$ assumption is not to increase the gap between theoretical analysis (PTM-T framework) and sol... | Rebuttal 1:
Rebuttal: We appreciate constructive comments from all reviewers.
We would like to organize our $\textbf{motivation and main contribution}$ on this global response to help reviewers to better understand our paper. We also have elaborated more details on each reviewer's responses.
$\textbf{1. Motivation}... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes to look at non-stationarity in reinforcement learning (RL) from a new point of view. While previous approaches did not generally consider the practical implications of the time elapsed while learning a policy, the idea of the current paper is to consider exactly these implications.
A trad... | Rebuttal 1:
Rebuttal: Thanks for your constructive comments and the followings are our answers to the weakness, questions that the reviewer raised.
$\textbf{1-1) W1,Q1: validity of inter-episode changing MDP}$
We have defined the non-stationary as changing across the episode and keeping stationary during the episode... | null | null | null | null | null | null |
Statistical and Computational Trade-off in Multi-Agent Multi-Armed Bandits | Accept (poster) | Summary: This paper studies the Multi-Agent Multi-Armed Bandits problem with factor graph reward structure, which is motivated by the real-world antenna tilt optimization problem. It first proposes an asymptotic lower bound that involves an optimization problem with exponential number of variables and constraints. Then... | Rebuttal 1:
Rebuttal: We thank reviewer pqkD for the constructive and comprehensive review. We address questions in the order they are presented.
- The intuition here is that in the exploration phase, we sample local actions that are the farthest from satisfying $N_{t,i,a_i} \approx w_{t,i,a_i} \log(T)$, for all a... | Summary: This paper proposes the ESM algorithm for the regret minimization problem in Multi-Agent Multi-Armed Bandits, where the rewards are defined through a factor graph. The ESM algorithm attains a dedicate trade-off between statistical efficiency and computational efficiency in n Multi-Agent Multi-Armed Bandits. T... | Rebuttal 1:
Rebuttal: We thank reviewer EJNU for the comprehensive and detailed review. We address the main questions in the following.
- *"The regret lower bound looks not new and the proofs does not contribute new techniques. They look like straightforward applications of techniques from structured bandits."*
We a... | Summary: This paper focuses on an interesting problem, which is a version of multi-armed bandits in which there are multiple agents and an action needs to be selected for each one of them. This is a problem that has been studied before in the literature and the authors here provide improved bounds on the regret by appr... | Rebuttal 1:
Rebuttal: We thank reviewer 7bJY for the comprehensive and detailed review. We address the main questions in the following.
- *"The theoretical results are shown to be an improvement against [2] and [36] but the experimental part does not compare against [2] and [36]. Why do you not compare against [2] an... | Summary: This paper consider the regret minimization problem in MAMABs, which typically has an exponentially large action space in relation to the number of agents. The authors first establish a lower bound result for general MAMAB by generalizing techniques from single agent bandit literature. More precisely, the lowe... | Rebuttal 1:
Rebuttal: We thank reviewer Pi4y for the constructive and comprehensive review. We clarify the main questions in the following.
1. *"What is the trade-off when letting $\varepsilon,\gamma \to 0$ in Algorithm 1? It appears that Theorem 6.1 always encourages selecting small values for $\varepsilon,\gamma$*
... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comprehensive and detailed reviews. We address the reviewers' questions individually in the rebuttals below. As requested by the reviewers, we have run additional experiments during the rebuttal period. We present the results of these experiments in the accompanyin... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative MARL | Accept (poster) | Summary: The paper focuses on value decomposition without IGM constraints by proposing Dual self-Awareness Value dEcomposition (DAVE) framework. DAVE is inspired by dual self-awareness studied in psychology and uses an ego model, i.e., a policy for each agent for actual action selection and an alter ego model, i.e., a ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and interesting comments! We are glad that you have read our paper carefully. We hope we can address your concerns below.
**Q1**: Wouldn’t simply feeding the resulting joint action (e.g., as one-hot vector) be sufficient to compute the TD-target for Eq. 4?... | Summary:
This paper proposes a different approach for multi-agent RL algorithms that does not depend on individual global max (IGM), but instead builds a new framework, DAVE, based on dual self-awareness. The algorithm is shown to perform favorably in several testing cases.
Strengths: The paper is well organized an... | Rebuttal 1:
Rebuttal: We thank the reviewer for the sincere comments! We thank you for pointing out the many strengths of our paper. We hope we can address your concerns below.
**Q1**: How is the dual self-awareness model different from an actor-critic model?
**A1**: We apologize for misleading you into thinking that... | Summary: This paper proposes a novel MARL algorithm, Dual self-Awareness Value dEcomposition (DAVE), which avoids the IGM constrain obeyed by most of the previous researches. The algorithm introduces three different policies including Ego Policy, Alter Ego Policy and Anti-ego Policy, where the Ego Policy tries to fit t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive comments! We are glad that you have read our paper and the supplementary material carefully! We hope we can address your concerns below.
**Q1**: The Ego policy and $Q_\text{tot}^\text{alter}$ network are trained individually. How to ensure t... | Summary: This paper presents DAVE, an IGM-free value decomposition method, to enhance coordination ability in MARL. Drawing inspiration from the concept of dual self-awareness in psychology, DAVE consists of two components: an ego policy responsible for executing actions, and an alter ego value function involved in cre... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and inspiring comments! We hope we can address your concerns below.
**Q1**: Why did the authors choose to compare DAVE with FACMAC-nonmonotonic instead of FACMAC?
**A1**: FACMAC is an IGM-based method. Because in the canonical implementation of FACMAC, the ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
Thank you for your time and effort in reviewing our manuscript. We are delighted to receive your comments and suggestions, which have been valuable in improving the quality of our research.
As part of the rebuttal process, we have submitted a PDF as an additional auxiliary materi... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes a novel value decomposition framework for MARL based on the notion of dual self-awareness in psychology. The framework, called DAVE, consists of two neural network models for each agent: the alter ego value function model and the ego policy model. The former participates in the evaluation of... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and inspiring comments! We sincerely thank you for taking the time to read our paper and the supplementary material carefully! We hope we can address your concerns below.
**Q1**: Can you provide more context into what is needed to tune exploration coefficien... | null | null | null | null | null | null |
Bounding training data reconstruction in DP-SGD | Accept (poster) | Summary: The paper studies the training data reconstruction robustness of DP-SGD. The paper observes that there is a huge gap between the existing lower bound and upper bound (attacks) on reconstruction error. Motivated by this observation, the paper proposes a tighter lower bound and a stronger attack, which empirical... | Rebuttal 1:
Rebuttal: Thanks rZtg, answering your questions below.
> Eq 1: The first term is an inner product whereas the second term is l1 error. Intuitively, we want the first term to be large, and the second term to be small. It is unclear why it makes sense to "sum up" these two terms as the loss. Do we want to ma... | Summary: This paper explores reconstruction attacks within the threat model associated with the usage of DP-SGD. The study provides a tighter upper bound on the success rate of the reconstruction attacks than previous works. Additionally, the paper presents an attack that exploits a strong discrete prior and aligns clo... | Rebuttal 1:
Rebuttal: Thank you for the review
> The bound appears to be a straightforward adaptation of the general DP bound.
We respectfully disagree. Our reconstruction bound leverages the notion of blow-up/trade-off function and uses the concavity/convexity of this function in a novel way. The fact that our bound... | Summary: This paper pertains to a line of work exploring reconstruction attacks (RA) as a complement to the standard membership inference attacks (MIAs) for evaluating the privacy leakage of ML models. The idea is that a model can be made resistant to RAs for large values of the privacy budget epsilon for which it woul... | Rebuttal 1:
Rebuttal: Thank you for the review!
> Not convinced 1 out of n classification is the right approach to frame the reconstruction problem.
Reconstruction for reconstruction's sake is a naive view on the goal of privacy attacks. Successful reconstruction (in the reviewer's sense) implies privacy leakage, but... | Summary: The paper proposes a novel bound on Reconstruction Robustness from DP training using a notion of a blow-up function. In order to use the bound, one needs to estimate the quantities $\kappa$ (prior probability of reconstruction) and $\gamma$ (blow-up between neighboring noise distributions) using Monte-Carlo me... | Rebuttal 1:
Rebuttal: Thanks wroj for the in-depth review, we’ve tried to answer and clarify your points below.
> How can one evaluate the accuracy of the upper bound given inherent error in the Monte Carlo estimates? Is there a way to provide an upper confidence bound at a given level of certainty as opposed to an as... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper is about the bounds of differentially private training for protection against data reconstruction attacks. This paper provides an upper bound on the success of any reconstruction attack against DP-SGD. Also it includes experiments that match the expected bounds. The experiments also include differen... | Rebuttal 1:
Rebuttal: Thank you for the review, paNh. We’ve answered your comments and questions below.
> Why pretrain on ImageNet.
Excellent question. There are a number of reasons we chose to pretrain on ImageNet. Reporting results directly on ImageNet is technically possible but computationally challenging.
- In ... | null | null | null | null | null | null |
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning | Accept (poster) | Summary: The paper proposes a new paradigm to look at operator learning via the lens of frame theory termed as Representation Equivalent Neural Operator (ReNO). It talks about an important missing piece in the literature, the one regarding the balance of continuous nature of operators and the discrete nature of data th... | Rebuttal 1:
Rebuttal: We start by thanking the reviewer for their appreciation of the merits of our paper and their welcome suggestions to improve it. We address their detailed concerns below.
1. In a CRV, if accepted, we will ensure that all figures have captions and are referenced within the main text.
2. The revi... | Summary: Many recent studies have emerged in the field of operator learning. However, many models attempt to learn operators using the discretized values of functions rather than sending functions as operators to other functions. In this paper, the authors interpret the relationship between infinite-dimensional functio... | Rebuttal 1:
Rebuttal: We start by thanking the reviewer for their welcome suggestions to improve our paper. We address their detailed concerns below.
1. We apologize for the lack of clarity in the explanation of frame theory. To address this shortcoming and following the excellent suggestion of the reviewer, we sugges... | Summary: In this paper, the authors investigate the concept of neural operators in the context of operator learning architectures. They address the fundamental question of what defines a neural operator and propose the notion of Representation equivalent Neural Operators (ReNOs) that satisfy a systematic consistency be... | Rebuttal 1:
Rebuttal: We start by thanking the reviewer for their appreciation of the merits of our paper and their welcome suggestions to improve it. We address their detailed concerns below.
1. Regarding the reviewer's concerns about *experimental limitations, lack of SOTA architectures, benchmarks*, we start by say... | Summary: This paper investigate the aliasing error of operator learning. The work points out that many existing neural operators have aliasing error, so the error is higher on super-resolution problem. To deal with the aliasing error, the paper proposed Representation equivalent Neural Operators (ReNO), which have no a... | Rebuttal 1:
Rebuttal: We start by thanking the reviewer for their appreciation of the merits of our paper and their welcome suggestions to improve it. We address their detailed concerns below.
1. Following the reviewer's welcome suggestion, we propose to change the title to the hopefully more informative *Representati... | Rebuttal 1:
Rebuttal: At the outset, we would like to thank all five reviewers for their thorough and patient reading of our article. Their criticism and constructive suggestions will enable us to improve the quality of our article. If our paper is accepted, we will incorporate all the changes that we outline below in ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The concept of “representation equivalence” is introduced in the context of operator learning. The definition amounts to requiring zero aliasing error from the mode. It is shown whether several popular architectures satisfy the new definition.
Strengths: The paper is well written and easy to follow. The math ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and remarks, which we address below:
1. We strongly acknowledge the significance of neural operators, as defined in [14] as a concept, and this is exactly why we intend to draw inspiration from it and further develop upon it. Although the revi... | null | null | null | null | null | null |
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects | Accept (poster) | Summary: This paper presents an empirical study on out-of-distribution (OOD) generalization in two different settings: contexts that are either beneficial or irrelevant. The authors highlight an interesting finding that models performing well in one setting tend to struggle in the other. They analyze a population of mo... | Rebuttal 1:
Rebuttal: > “the authors describe the proposed data augmentation method without adequately establishing its connection to the motivation of “adaptively using context to recognize objects in new settings””
We establish the connection between our analysis and our proposed method in the introduction “In order... | Summary: This paper examines the influence of context on visual recognition capabilities. The authors distinguish two regimes, one where background context can help disambiguate objects and another one where the object information is orthogonal to the background information. The study shows that models can thrive in on... | Rebuttal 1:
Rebuttal: > “Context can also hurt performance.”
We thank the reviewer for pointing out the relevant works. We believe that the referred works are consistent with ours and will add them to our references. It is true that context can also hurt performance for both models (as observed in our experiments too)... | Summary: This work investigates how visual models leverage background and foreground information for out-of-distribution (OOD) generalization. The authors trained a large number of models and evaluate their performance in two OOD settings. They find that there is a tradeoff for the models in these two OOD settings as t... | Rebuttal 1:
Rebuttal: > “Is the tradeoff observed in this work just a result due to not enough training data? When there are more data, maybe the model can achieve the optimal performance in both benchmarks.”
The two standard settings (ColorObject and SceneObject) we test on for fair comparison with prior works are in... | Summary: This work investigates how vision models use background information in various contexts and find that models that are more invariant to backgrounds are less able to use the background to disambiguate. A new objective function and augmentations are proposed to control the balance between ignoring the background... | Rebuttal 1:
Rebuttal: > “There is some re-treading of scientific points from Xiao et al, 2020.”
Xiao et al. 2020 focus on showing that models over-rely on background. In fact, they argue that models rely on background too much, writing that models “exploit background correlations” and hoping for models to exhibit “rob... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and effort in reading our paper. We are glad to see that reviewers view the paper to be “helpful to the community” (D3vS) and “innovative” (WSbu), with a “systematic examination” (FMxu) and “detailed analysis…of factors that influence generalization performanc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Long-Term Fairness with Unknown Dynamics | Accept (poster) | Summary: This paper examines the issue of enduring fairness within a dynamically responsive population, framing it as a reinforcement learning (RL) problem with certain constraints. In this scenario, the state distribution is subject to change dynamically based on the actions of the deployed agent. To address this prob... | Rebuttal 1:
Rebuttal: > Depiction of Algorithm 1 could be clearer. Same for Section 3.1.1.
We express our gratitude for this advice. In the event of acceptance, we intend to enhance the presentation of Algorithm 1 and Section 3.1.1 in the final version. To provide greater clarity, our planned revisions include:
1. Re... | Summary: This paper considers a binary classification task with long-term fairness. The authors formulate the problem as a constrained MDP where the classifier is an action, the distribution is the state, the distribution will shift reacting to an action. The problem aims to optimize the expected long-term reward under... | Rebuttal 1:
Rebuttal: > The paper seems to directly apply LSVI-UCB and TD3 algorithms... It would be much better if the authors can discuss how the algorithm design and proof techniques are different from original works in (Jin et al. (2020)) and Fujimoto et al. (2018).
The L-UCBFair differs from LSVI-UCB (Jin et al. ... | Summary: This paper introduces a new method to ensure long-term fairness in reinforcement learning. The method is compatible with different utility measures (e.g., the accuracy of the classifier when the goal is to predict a label) and different fairness constraints (e.g., demographic parity or equal opportunity). The ... | Rebuttal 1:
Rebuttal: Thank you for your constructive review. We hope that our rebuttal satisfactorily addresses the perceived weaknesses of the submission and questions you have.
### Weaknesses
> The proposed method is an online RL method… intermediate policies could lead to unfair behavior.
Unfortunately, there is... | Summary: The paper demonstrates that reinforcement learning algorithms can be used to satisfy long term fairness constraints in dynamic environments in which a classifier and population interact while finding high-value equilibria.
The main approach is to treat the problem as a constrained optimization and optimize t... | Rebuttal 1:
Rebuttal: Thank you for your review, your constructive feedback, and your comments.
### Weaknesses
> I found the experiments to be a little bit unclear…
We will incorporate more succinct takeaways from the experiments in revision.
*What was demonstrated:* Our experiments seek to confirm our central hypo... | Rebuttal 1:
Rebuttal: We thank our reviewers for their constructive feedback and questions, as well as pointing out ways in which we could improve our submission (e.g., by highlighting the novelty of our proofs and improving our figures in subtle ways, which we have done).
With the attached PDF, we have regenerated Fi... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
AbDiffuser: full-atom generation of in-vitro functioning antibodies | Accept (spotlight) | Summary: This paper introduces AbDiffuser, a new equivariant diffusion model, designed to generate full antibody structures efficiently and with high quality. By incorporating family-specific priors and utilizing a novel neural network architecture called APMixer, AbDiffuser demonstrates improved performance in modelin... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We address your comments in turn.
> The core components of the proposed model, including the diffusion model and the mixer model, bear a strong resemblance to corresponding reference models. While there are some minor additions, such as the physics-informed r... | Summary: The paper proposes AbDiffuser, a new diffusion model for antibody structure and sequence generation. AbDiffuser proposes several ideas to improve generation: (i) Euclidean diffusion with frame averaging to achieve SE(3) equivariant diffusion, (ii) antibody sequence alignment to standardize sequence length acro... | Rebuttal 1:
Rebuttal: Thank you for your in depth questions. We urge you to carefully consider our reply as it addresses the issues you raised and used to motivate your low score.
> Biggest issue: poor presentation. There are simply too many new ideas that comprise AbDiffuser and all the details are left to the append... | Summary: The authors propose a new approach, called AbDiffuser, that improves protein diffusion by leveraging domain knowledge and physics-based constraints. They follow MLPMixer's architecture to reduce memory complexity by an order of magnitude, enabling backbone and side chain generation. They validate AbDiffuser in... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments, we answer them below.
> AbDiffuser does not directly use epitope information, which may harm generalization. For example, given a newly seen antigen, how can this method be applied to design an antibody that has a high binding affinity?
This is a pertinent... | Summary: Antibody design has an extreme importance for both fundamental and application biologic science. The submission intriduces an equivariant and physics-informed diffusion model called AbDiffuser
Strengths: Originality: There are few tools that have already addressed the same problem (for example, DiffAb, https:... | Rebuttal 1:
Rebuttal: Thank you for your review and recognising the potential of our work. We answer your comments below.
> There are few tools that have already addressed the same problem (for example, DiffAb, https://github.com/luost26/diffab). (..) Even though the paper provides comparison of AbDiffuser with other ... | Rebuttal 1:
Rebuttal: We thank all reviewers for their thoughtful comments and suggestions. In response, we performed additional experiments, adding two state-of-the-art baselines to our current comparison and showcasing how AbDiffuser significantly outperforms previous methods in SAbDab CDR inpainting.
### HER2 binde... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Directed Graphical Models with Optimal Transport | Reject | Summary: This paper uses optimal transport for learning the parameters of a DAG structure that represents a Bayesian network given samples drawn from it (data). In the proposed algorithm the data can be incomplete (i.e. some random variables may be latent).
The algorithm can be seen as a generalization of the existing... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our work. We address the reviewer's concerns as follows:
**1. This method is only suitable for learning the parameters of Bayesian network DAGs. It can not be used for learning the DAG structure.**
The primary goal of this work is to introd... | Summary: In this submission, the authors propose an optimal transport-based method to infer the parameters of probabilistic directed graphical models from partial observations.
In particular, given a DAG associated with the target model, the proposed method reparameterizes the probability of a node conditioned on its ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our work. We address the reviewer's concerns in the following.
**1. Connection with WAE:** WAE can be viewed as an application of OTP-DAG on a simple graphical model with only $2$ (sets of) nodes: the observed node $X$ and latent variables $Z... | Summary: The authors propose a method for learning parameters $\theta$ of a DAG within an optimal transport (OT) framework, minimizing the Wasserstein distance between the data distribution $P_d$ and the model distribution $P_\theta$ in $\theta$. The Kantorovich formulation of this problem is a minimization of an expec... | Rebuttal 1:
Rebuttal: We thank the reviewers for acknowledging the richness of our experiments. Our responses to the reviewer's questions are as follows:
*Part 1: Questions*
**1. Is the data distribution $P_d(X_{O})$ a mixture of point masses, or a continuous (but unknown) distribution?**
The data distribution is t... | Summary: The authors propose a framework for learning the parameters of directed graphical models based on the idea of fitting by selecting the parameter values that minimize the Wasserstein distance (WD) between the data and model distributions. They prove (Thm. 1) that these distances can be characterized as the resu... | Rebuttal 1:
Rebuttal: We thank the reviewers for acknowledging the originality of our proposed method. We respond to the reviewer's questions as follows:
**1. Limitations:**
In terms of the requirement for reparametrization, related approaches experiences similar inflexibility, i.e., advanced VI-based ones e.g., re... | Rebuttal 1:
Rebuttal:
We thank the reviewers for acknowledging the novelty of our method and richness of the experimentation. We immensely appreciate your support for acceptance of our paper. We here summarize the key points of our discussion with the reviewers.
Diverging from the existing approaches, we propose a n... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Change point detection and inference in multivariate non-parametric models under mixing conditions | Accept (poster) | Summary: This paper studies the problem of localization of multiple change points for offline multivariate time series. A non-parametric kernel-based CUSUM statistics is used together with the SBS algorithm. Moreover, a two-step estimation procedure is proposed where the initial estimate is further modified to the fina... | Rebuttal 1:
Rebuttal: We are grateful for your constructive comments. We reply to your comments below, with corresponding edits in the revision.
**Weakness**
Thank you for your suggestions. We will endeavor to improve our presentation and enhance numerical results in the revision. For this response, extra simulatio... | Summary: This paper proposes algorithm to localize the multiple change points in nonparametric, short-term dependent time series. Assumptions required on the time series are certain mixing conditions and the smoothness in terms of density. The core idea of the algorithm is based on CUSUM and seeded binary segmentation.... | Rebuttal 1:
Rebuttal: We are grateful for your constructive comments. We reply to your comments below, with corresponding edits in the revision.
**On temporal dependence**
Thank you for bringing these references to our attention. We indeed overlooked them in the literature review stage. We will modify our claim cor... | Summary: The submission studies offline multivariate non-parametric change point detection.
The submission proposes a method for this task by combining 1) CUSUM estimator/statistic with 2) seeded intervals and 3) a refining procedure. The proposed estimator has 1) an improved error bound with weaker assumptions and 2)... | Rebuttal 1:
Rebuttal: We are grateful for your constructive comments. We reply to your comments below, with corresponding edits in the revision.
**Intuition on algorithms**
Thank you for your valuable comments. We will include all in the camera ready version where an additional page is allowed. For this revision, w... | Summary: The authors consider offline change point detection for multi-variate data where there could be multiple change points (specifically changes in marginal distributions from one time step to the next). The marginal densities of the underlying generative model are assumed to be smooth (specifically Hölder contin... | Rebuttal 1:
Rebuttal: We are grateful for your constructive comments. We reply to your comments below, with corresponding edits in the revision.
**Motivation on $\alpha$-mixing sequences.**
Thank you for pointing this out. We have included the following in the revision.
The $\alpha$-mixing condition with exponential... | Rebuttal 1:
Rebuttal: We are grateful for your constructive comments. Taking advantage of the extra page submission we include the following extra simulations to help us to address each of the particular reviews.
**Independent data**
We examined Scenario 1 with $p = 3$, $n \in \{150, 300\}$, and $X_t$ as i.i.d.~$N(0_... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies non-parametric offline change-point detection with the assumption that the probability density functions are Holder continuous on some compact support, and the time series is $\alpha$-mixing. The authors propose a two-stage algorithm, first roughly divide the time series into different segme... | Rebuttal 1:
Rebuttal: We are grateful for your constructive comments. We reply to your comments below, with corresponding edits in the revision.
**$\alpha$-mixing coefficients**
We appreciate the reviewer's comments regarding the need for a more detailed discussion on $\alpha$-mixing coefficient $c$. We will include... | null | null | null | null | null | null |
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation | Accept (poster) | Summary: The authors consider the problem of high dimensional mean estimation with communication and privacy constraints. For federated learning or distributed SGD, model updates must be communicated to central server, but as models become much larger this can be a bottleneck within the computation. As a result, previo... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our manuscript and for providing positive and constructive feedback. We are glad that the reviewer acknowledged the improvements we provided over the prior order-optimal solutions and the importance of unifying the previous schemes under our exact-optima... | Summary: The paper studies the distributed mean estimation (DME) problem with communication & privacy constraints. The goal is to construct an unbiased estimate of a unit vector $v$ using $b$-bits that minimizes the mean squared error and provides $\epsilon$-LDP. It is well known that any scheme achieving the above co... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our manuscript and providing positive and constructive feedback. The reviewer appreciated going beyond the asymptotic error bounds and found our k-nearest algorithm worthy of independent interest. The reviewer also asked clarifying questions, which we tr... | Summary: This paper studies the mean estimation problem under communication and local differential privacy constraints in the non-asymptotic (exact optimal) setting, and proposed a randomization mechanism that satisfies the identified necessary property of the exact optimality.
Strengths: 1. The authors proved a neces... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our manuscript and providing positive feedback on our contributions. Specifically, we are happy to see that the reviewer recognized our proposed algorithm, Random Rotating Simplex Coding (RRSC), as the first exact-optimal scheme for distribution mean est... | Summary: This paper focuses on the problem of distributed mean estimation under local differential privacy (DP) and communication constraints, with shared randomness between users and the server. Previous works either achieve exact optimal mean squared error (MSE) using $O(d)$ bits or achieve order-optimal MSE with a l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in reading our manuscript carefully and for providing positive feedback. We are glad that the reviewer found our contributions valuable and liked the organization and writing of the manuscript. We discuss their point on shared randomness below.
### **Shared r... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for carefully reading our paper, and providing positive and constructive feedback to further improve it. All the reviewers seem to appreciate the technical contributions of the paper in studying the exact optimality of the distributed mean estimation proble... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work considers the problem of distributed mean estimation and aims to obtain "exact optimal" estimators under communication, (local) differential privacy and utility constraints. Exact optimality here means that instead of focusing on the order of complexity, the focus is also on the constants as well. Pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our manuscript and for providing constructive feedback to further improve it. Specifically, we are glad that the reviewer found the theoretical and empirical improvements over the existing order-optimal schemes impressive; and highlighted the significanc... | null | null | null | null | null | null |
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent | Accept (poster) | Summary: 1. This paper proposes a novel VQA framework that uses LLMs to select external tools in multiple stages to extract the necessary information to answer the question.
2. The authors gather human decision data to develop a decision graph and construct in-context examples to guide the LLM to perform API selection... | Rebuttal 1:
Rebuttal: **Q. Use GPT**
We change the backbone LLM as GPT4
| Model Configuration | Result (%) |
|--------------------------------------|------------|
| AVIS w/ GPT-4 | 61.9 |
| GPT-4 w/ PALI* | 13.1 |
| GPT-4 w/ PALI* + Object ... | Summary: This paper proposes a VQA system that mimics the human decision-making process and leverages LLM and web searches to perform multimodal reasoning. The system consists of three main components: a transition graph, an LLM planner, and an LLM Reasoner. The transition graph is manually designed based on the human ... | Rebuttal 1:
Rebuttal: **Q: Error Analysis**
A: We look through the error samples by AVIS and categorize them with three major types of error:
- LLM Planning Module Errors: Cases where the LLM planning component failed to discern crucial information, leading to inaccurate decision-making.
- LLM Reasoning (QA) Module... | Summary: This paper introduces an autonomous framework for visual question answering framework named AVIS. AVIS utilizes a Large Language Model (LLM) as its core component to dynamically strategize the utilization of external tools. The framework comprises three key components: the planner, reasoner, and working memory... | Rebuttal 1:
Rebuttal:
**Q: it would be advantageous to conduct experiments on the A-OKVQA[1] dataset, which incorporates the OK-VQA and Infoseek human-annotated data as in-context samples.**
A: We appreciate the emphasis on the generalizability and practicality of our model. To address this concern, we've conducted e... | Summary: This work aims to more general VQA task (often needs external knowledge) via LLM-based information seeking. First, the info-seeking system is built with three components: planner, reasoner, and memory, seeking useful information with external tools/APIs. Second, they build a dataset with human decision-action ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and remarks.
**Q: Compare AVIS with end-to-end models? Will external knowledge still be useful?**
A: The key difference between AVIS and a single end-to-end model is that AVIS separates knowledge memorization from reasoning. Within this architecture:
- The ... | Rebuttal 1:
Rebuttal: # Response to Reviewers
We thank the reviewers for their valuable comments and remarks.
In the rebuttal, we mainly add:
**1. Experimental results with GPT4 on Infoseek dataset**
| Model Configuration | Result (%) |
|--------------------------------------|------------|
| AVIS ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training | Accept (poster) | Summary: This paper presents a new method to defend against depth function preserving model extraction (DFME) attacks. The method proposed by the authors, called MeCo, adds data-dependent random perturbations to the input data, making it difficult for attackers to extract useful information from the black-box model. Th... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your constructive feedback.
**Q1**: lack of relevant experiments on large data sets led to some doubts about the extensibility of the model.
**A**: Thanks for your suggestions. As requested, we included the results obtained on the MiniImageNet... | Summary: * __Problem Statement__: The paper proposes a defense against data-free model extraction attacks (where the attacker black-box queries a victim image classifier, s.t queries are then used to train a clone model)
* __Approach__: The proposed defense "DRO" returns class probabilities as a result of the defender ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for your insightful comments.
**Q1.1**: DRO optimization setup: Based on Fig. 2 and L183-190, it appears that the requirement is to perturb images s.t the label is flipped (i.e., maximize CE loss wrt parameters )?
**A1.1**: Eq 4-6 is to **maintain... | Summary: In this paper, authors propose a novel principled defensive training framework that substantially improves the memory and computation efficiency during deployment to defend against DFME attacks and a distributionally robust optimization method to randomly perturb the inputs to defend against DFME effectively w... | Rebuttal 1:
Rebuttal: We would like to express our appreciation for your valuable suggestions.
**Q1**: In my opinion, the method proposed by the author has defects in protecting the performance of the original model, that is, it does not fully take into account the impact on the performance of the target model, which... | Summary: This paper proposes a defense against data-free model extraction attack (DFME). The basic idea is to add randomized data-dependent perturbations to the input query. It proposes a new training method that considers the perturbation generator to mitigate the risks of dropping the benign accuracy. Namely, it will... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback.
**Q 1.1**: Adding perturbation to test data decreases benign accuracy.
**A 1.1**: We explain why our method maintains benign accuracy by considering three cases: (We invite you to refer to **Figure 3 for visual depiction in the "rebuttal.pdf" wit... | Rebuttal 1:
Rebuttal: # Global Response (Theoretical Analysis)
Given the random perturbation applied to the query input, the loss function for extracting the victim model becomes noisy. Attacker has to employ the following noisy loss function to extract the target model.
$\mathcal{L}_{C}^{\Delta}(\theta_C) := KL(T(x +... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning Energy-based Model via Dual-MCMC Teaching | Accept (poster) | Summary: This paper proposes a novel method for training and inference of energy-based model (EBM). The author claim that although the use of generator as an initializer model may improve MCMC sampling, training unbiased generator is an open problem . Thus they propose a joint learning framework in which generator is l... | Rebuttal 1:
Rebuttal: Thanks for your encouraging feedback and the correction of typos. We shall follow the suggestion and fix the typos in the revision. In the **General Response**, we provide clarifications for concerns regarding the motivation of our method, connection to Divergence Triangle, and computational cost ... | Summary: This paper investigates the problem of Maximum Likelihood (ML) estimation for Energy-Based Models (EBM). Building on previous research, the authors propose a novel approach that involves learning a surrogate generative model to initialize the costly Langevin steps. The authors chose a latent-based generative m... | Rebuttal 1:
Rebuttal: Thanks for your detailed reviews, and we appreciate the time you spent on reviewing our paper. In **General Response**, we describe the motivation behind our method and discuss the computational and memory costs (see also the attached **PDF**).
---
**Q1. Density Estimation.**
Thanks for the n... | Summary: This work presents a new approach for training energy-based models (EBMs). These are commonly trained by approximating the maximum-likelihood estimate of the model parameters. This is done by minimising a certain Kullback--Leibler divergence via stochastic-gradient descent. To estimate the necessarily gradient... | Rebuttal 1:
Rebuttal: Thanks for your supportive comments and all the corrections, such as typos, formats, and captions, and we shall correct them in our final version. Please also find a brief description and model comparison in the **General Response** and the attached **PDF**.
----
**Q1. Convergence of the MCMC Ch... | Summary: This work investigates learning EBMs for image data using auxiliary generator and inference models. The model generates samples by drawing a latent normal vector, passing it through the generator, and refining the generator sample using MCMC with the EBM. An inference network, which predicts latent vectors fro... | Rebuttal 1:
Rebuttal: Thanks for your insightful feedback. We appreciate the correction of the references and shall fix them in our final manuscript.
We provide the clarification in **General Response** for
- Connection with Divergence Triangle (**G.2**)
- Motivation of our method (**G.1**)
- Computational cost (**G.... | Rebuttal 1:
Rebuttal: We thank the valuable comments from all the reviewers. The reviewers note that (1) our idea is *novel* (**Reviewer** **uj7J**, **RJbi**, **XcPg**), (2) our experiments are *clear* and *solid* (**Reviewer** **uj7J**, **iX9Y**, **XcPg**), (3) our paper is *well-written* (**Reviewer RJbi**, **XcPg**)... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Language Model Tokenizers Introduce Unfairness Between Languages | Accept (poster) | Summary: The paper studies the discrepancy in the tokenization length in different languages. It shows the unfairness of utilizing tokenization in different languages due to the cost, latency, and long-term language dependencies. The paper evaluates the unfairness between different tokenization strategies and model arc... | Rebuttal 1:
Rebuttal: > Besides the aspects mentioned by the paper, it is unclear if the choice of the tokenization strategies would impact the performance of LLM in particular languages.
Tokenization does also affect downstream performance. In the Related Works section, we mention the work by Zhang et al. (2022), whi... | Summary: Tokenization is a crucial, yet underappreciated component of language models. This paper presents a welcome investigation into the effects of tokenization choices across languages. The paper introduces the notion of premium for language A relative to B which measures the ratio of the average number of tokens f... | Rebuttal 1:
Rebuttal: >The paper doesn’t discuss how to achieve the goal of training multilingually fair tokenizers, apart from providing an overview of previous approaches to developing multilingual tokenizers. An obvious solution is to reserve more tokens for the languages with high premiums.
Please see our "On the ... | Summary: This paper investigates the disparities between languages caused by tokenization policies used in large language models (LLMs). The authors compare the numbers of tokens needed to represent the translations of the same sentence in different languages and observe that the number of tokens needed in one language... | Rebuttal 1:
Rebuttal: > The authors point out the problems caused by the disparities but do not present a concrete solution. They argue that LLMs should be trained from scratch with a multilingually fair subword tokenizer but do not provide any experimental results towards that solution.
Please refer to our "On the de... | Summary: The paper proposes the concept of tokenizer parity as a way to measure the fairness of tokenization across different languages in natural language processing. The authors argue that achieving tokenizer parity is necessary to improve the performance of multilingual models and address potential unfairness in the... | Rebuttal 1:
Rebuttal: > One major weakness is that the paper does not provide a clear roadmap for how the concept of tokenizer parity could be integrated into existing natural language processing pipelines. While the authors suggest that training language models with a multilingually fair subword tokenizer is the only ... | Rebuttal 1:
Rebuttal: # On the development of a multilingually fair tokenizer
Reviewers Fdv1, EmCj and Qimt mentioned as a shortcoming of our work that we did not develop a new multilingually fair tokenizer. We would like to highlight several reasons why we found this to be more challenging than it may seem. These are... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper is very interesting in highlighting the disparities in tokenization across different languages, leading to cost, latency, and long-distance modeling inequalities in LLMs. The authors show that this is not limited to one type of tokenizer or a single family of LLMs. They show that different types of t... | Rebuttal 1:
Rebuttal: > I found the argument around disparities in long-distance modeling a bit thin. The second paragraph of Section 5.3 discusses this briefly, but additional discussion or experiments are needed to strengthen the argument. For example, it might be helpful to include analysis with multilingual documen... | null | null | null | null | null | null |
IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval | Accept (poster) | Summary: This paper proposes to use invariant learning based on causality inference for domain adaptive retrieval. In the proposed IDEA model, a feature disentanglement module is deployed for obtaining causal and non-causal features. A generative model is designed with non-causal features intervention for reconstructin... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
>Q1. How about the exp... | Summary: This paper focuses on an unsupervised domain adaptation method for deep hashing. This paper proposes to disentangle each image into causal and non-causal features, where casual features represent label information and non-causal features represent domain information. The causal features are used to compute has... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
> Q1. While using non-... | Summary: This paper proposes a novel method called Invariance-acquired Domain Adaptive Hashing for generating high-quality and interpretable hash codes. The approach incorporates the concepts of causal and non-causal features, leveraging the information bottleneck principle and consistency learning to optimize the hash... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
>Q1. The contribution ... | Summary: - In this research, the authors investigate the problem of unsupervised domain adaptation for hashing, which aims to expedite learning on a target domain with limited label information by leveraging knowledge from a source domain with abundant labels. The IDEA model begins by decomposing each image into a caus... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper, your insightful comments and support. Your positive feedback is incredibly encouraging for us! In the following response, we would like to address your major concern and provide additional clarification.
Q1：It is suggested to ... | Rebuttal 1:
Rebuttal: Dear Reviewers,
We thank you for your careful reviews and constructive suggestions. We acknowledge the positive comments such as "The problem is interesting" (Reviewer BQ1H), “The techniques seem correct” (Reviewer BQ1H), "Clarity" (Reviewer MFjc), "Quality of analysis” (Reviewer MFjc), “Theore... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This work studies apply hashing for domain adaptive image retrieval. Specifically, the authors propose Invariance-acquired Domain Adaptive Hashing (IDEA) to consider alignment invariance, and causal effects. IDEA decomposes each image into causal and non-causal features, and introduce invariant learning to min... | Rebuttal 1:
Rebuttal: We are truly grateful for the time you have taken to review our paper and your insightful review. Here we address your comments in the following.
> Q1. The motivation of this work seems to be not stated clearly. The author claim invariance needed, however it lacks explanation in whether and why i... | null | null | null | null | null | null |
Fragment-based Pretraining and Finetuning on Molecular Graphs | Accept (poster) | Summary: In this paper, the authors proposed contrastively and predictively strategies for pretraining GNNs based on graph fragmentation. Using principle subgraph extraction, the authors pretrain two separate encoders for molecular and fragment graphs, capturing structural information at different resolutions.
Strengt... | Rebuttal 1:
Rebuttal: Thank you for your time and insightful review. We are happy to answer your concerns.
(1) **The technical contribution is limited. For example, the principle subgraph extraction module is borrowed from [19].**
The focus of the paper is on developing pretraining and finetuning strategies based on ... | Summary: This paper presents a novel approach to pretrain Graph Neural Networks (GNNs) at the fragment level for property prediction on molecular graphs. By utilizing a compact vocabulary of prevalent fragments and introducing fragment-based contrastive and predictive pretraining tasks, the authors overcome the limitat... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our paper and your thoughtful insights! We are happy to address your concerns.
(1) **Empirical performance is not strong enough. The authors are encouraged to report the average score over all tasks in molecular property prediction.**
We understand that the aver... | Summary: Based on the belief that learning with fragments can help capture structural information at multiple resolutions, this paper proposes a fragment-based strategy for pretraining and fine-tuning.
First, the paper extracts fragments by an existing heuristic algorithm called Principle Subgraph Mining algorithm to... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful questions!
(1) **The fragment extraction strategy can be described and compared with more details. ..., the authors comment MGSSL as "their multi-step fragmentation may overly decomposes molecules, losing the ability to represent high-order structural patterns"?**
Rule... | Summary: The authors propose a novel method for generating representations for molecule graphs where two GNNs are contrastively learned. Using this new represntations, the authors achieve good results compared to a variety of baseline methods.
Strengths: The paper and method are presented clearly.
The results are str... | Rebuttal 1:
Rebuttal: Thank you for your time spent reviewing our paper! We appreciate the thoughtful comments and are happy to address your questions regarding the wider applicability of the model.
(1) **I realise that many molecular benchmarks are based on similar organic compounds, but I am curious how the method ... | Rebuttal 1:
Rebuttal: We want to thank the reviewers for their time and insightful comments!
We would like to use this space to summarize and address some common concerns. Citations follow those in the paper.
**(1)** Reviewer **RaR1** and Reviewer **wcQm** raise concerns about the level of contribution of our work. R... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper proposes a contrastively and predictively strategy for pretraining GNNs based on graph fragmentation. Specifically, it leverages a frequency-based method for extracting molecule fragments, and performs fragment-based contrastive and predictive tasks to jointly pretrain two GNNs on a molecule graph a... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful suggestions! We are happy to address your concerns.
(1) **The technical novelty is limited, because it is a combination of existing methods**
We would like to argue that our work is not simply a combination of existing methods. Besides Micro-Graph [1], ours is the o... | null | null | null | null | null | null |
Hierarchical clustering with dot products recovers hidden tree structure | Accept (spotlight) | Summary: For agglomerative clustering, this paper proposes a method where the affinity of clusters at each hierarchy is naturally visualized as their height. Section 2 describes its statistical model (a nonparametric model based on first order moments) and the clustering algorithm derived from it (Algorithm 1) is prese... | Rebuttal 1:
Rebuttal: > **Weaknesses**
>> I have not been able to find any convincing weaknesses for this paper in the initial peer review. However, I have some concern about whether the statistical model assumed by the proposed method is causing model specification for data with so-called multiple clusters. I have inqu... | Summary: This paper presents new analysis and perspectives on one of the most widely used clustering algorithms, hierarchical agglomerative clustering (HAC). In particular, the authors consider the relationship between a particular generative process of data and a dot-product based linkage of HAC. Empirical and theoret... | Rebuttal 1:
Rebuttal: > **Weaknesses**
>> - W1. I think that more explicit treatment of Eq. (2) and (3) would improve the presentation; e.g., showing where/why these hold under the model in Eq. (1).
(2) and (3) are not a consequence of (1), but rather distributional assumptions we make about the ingredients of (1), we... | Summary: The authors discuss a phylogenetic reconstruction problem (I'm not 100% clear on the exact problem, though) and suggest to use the dot product as a measure of similarity (or affinity). In particular, they seem to use the UPGMA algorithm (Alg. 1) where similarity is defined by dot product (scaled by $1/p$).
Th... | Rebuttal 1:
Rebuttal: >**Weaknesses**
> - presentation should be improved so that the problem under study is clearer
See response to questions below.
> - it seems that methodologically, there is no novelty beyond proposing to use the dot product as a similarity measure in the UPGMA method
Indeed beyond the point of ... | Summary: The paper studies hierarchical agglomerative clustering under a specified generative process for the underlying data vectors. The main focus of the paper is similarity based clustering that deals with inner products between vectors rather than computing pairwise distances that has been studied before.
The goa... | Rebuttal 1:
Rebuttal: > The generative model should be better explained and compared to other previously studied models for hierarchical clustering. ... see e.g. papers below ... having a better exposition and examples for why this particular model you proposed is a natural one, would help the readers a lot.
Thanks fo... | Rebuttal 1:
Rebuttal: The authors are very grateful for the effort which the reviewers have put in to reading our manuscript and providing feedback.
We are pleased to see that, on the whole, the reviewers have recognised and engaged with the new perspective on hierarchical clustering and tree recovery which we report ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper discusses a new perspective on hierarchical clustering using the dot product as similarity measure instead of some distance. Under mild conditions on the probabilistic graphical model that generates the data, the proposed algorithm is shown to faithfully recover the underlying tree geometry. Surprisi... | Rebuttal 1:
Rebuttal: >The only concern I have is that the results claim that the performance of the method improves if the dimensionality of the data increases. This is probably caused by the mixing condition ... I would appreciate seeing results based on, e.g., lower numbers of TF-IDF features to understand how this ... | null | null | null | null | null | null |
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand | Accept (poster) | Summary: The paper studies gradient descent dynamics in duopoly competitions with reference effects and logit demand.
Convergence results are proven to show that Online Projected Gradient Ascent (OPGA) with decreasing step size converges to a stationary Nash equilibrium. This is a novel result requiring new analysis b... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. Please find our responses below. We hope they address your concerns and provide further clarity.
> W1: The analysis is ... relevant topic within NeurIPS.
A: We understand your concern and agree that deriving convergence results for general online g... | Summary: The author consider a multi-period pricing competition problem between two firms, dubbed H and L. Each of these firms sells 1 product. At each time step, the demands on each product is governed by an MNL model, which is paramaterized by not only the firms' offered prices in the current time period, but also th... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We hope the responses below address your concerns. We highly appreciate a re-evaluation of our work and a kind reconsideration of the review score.
> W1: The model assumption ... equilibrium.
A: We agree with you that the Gumbel noise is somewhat restrictiv... | Summary: The authors' objective is to develop an algorithm to aid the firms in converging to a stationary Nash equilibrium (SNE). In pursuit of this, the authors have:
* Proposed an online projected gradient ascent (OPGA) algorithm.
* Proven the global convergence of the OPGA to SNE within the given problem setting.
*... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful questions. Please find our responses below. We highly appreciate your re-evaluation of our work and a kind reconsideration of the review score.
> Q1: Discussion about online mirror descent.
A: For consistency, we assume a maximization form and use the term... | Summary: This paper investigates the problem of dynamic pricing in a competitive environment, where there are two revenue-maximizing competing firms selling substitutable products to customers and each of them has no access to the information of its competitor. In addition, customer’s utility follows a linear function ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work. We hope our responses below provide further clarity.
> S4: Comparison with the related work [21].
A: We thank the reviewer for the feedback. We'd like to clarify the distinctions between our work and [21] from two perspectives.
**Model Formulation... | Rebuttal 1:
Rebuttal: # General Response
We would like to express our sincere gratitude to the reviewers for reading our paper and providing valuable feedback. Below, we answer two common questions and provide a background for the discrete choice model. Please find our responses to other questions in the personalized r... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models | Accept (oral) | Summary: Summary: The paper closes the gap between the previously established lower bound on learning a single index model with Gaussian inputs, giving a modified learning algorithm that matches the best possible sample complexity.
Strengths: Strengths: The alteration to SGD is simple but clever, and the given argume... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed and thoughtful review. Please let us know if you have any further questions or if anything is still unclear.
> experiments are using an analytic formulation of the smoothed loss, based on the hidden knowledge that the link functions (in the exp... | Summary: This paper considers the problem of learning single index models in high dimensions, i.e., functions of a high-dimensional parameter that only depend on a one-dimensional projection of this parameter. This paper is interested in the case where the link function (the function of the one-dimensional projection) ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed and thoughtful review. We’ve corrected the typos you identified and we’ll try to clarify the higher level questions below. Please let us know if you have any further questions or if anything is still unclear.
> Running Algorithm 1 requires to b... | Summary: This paper studies the sample complexity of learning a single-index function \sigma(w*^Tx) via SGD on a smoothed correlation loss. The authors show that when k* is the first non-zero Hermite coefficient of \sigma, with optimally tuned smoothing, defined as averaging the loss over a sphere of radius \lambda cen... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed and thoughtful review. We’ve corrected the typos you identified and we’ll try to clarify the higher level questions below. Please let us know if you have any further questions or if anything is still unclear.
> Discussion of the analysis of E[|... | Summary: This paper aims to fill the gap between the sample size complexity of online SGD and CSQ lower bound for learning a single index model. Inspired by implicit regularization of minibatch SGD, the authors show that online SGD on a smoothed correlation loss only needs sample size $n=\Omega(d^{k^*/2})$ to efficient... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the detailed and thoughtful review. We’ve corrected the typos you identified and we’ll try to clarify the higher level questions below. Please let us know if you have any further questions or if anything is still unclear.
> The main concern is the CSQ lower... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their detailed and thoughtful reviews. We’ve addressed the most common questions in this rebuttal section.
## On the CSQ lower bound
The connection between the CSQ framework and gradient descent with square loss is that GD only interacts with the labels $... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL | Accept (poster) | Summary: This paper studies the divergence phenomenon in Q-value iteration methods (e.g. Q-learning), especially focusing on the offline RL scenario. They introduce a theoretical framework for studying this issue, predicting divergence and even the training step at which it is likely to happen. Such analysis, which is ... | Rebuttal 1:
Rebuttal: Thank you for your time and constructive feedback. Please see the response below.
# Q1: Clarification on EMA, Double-Q, and LayerNorm's Contributions
Your suggestion to illustrate the interplay of EMA, Double-Q, and our LayerNorm in mitigating Q-value divergence is particularly insightful. To this... | Summary: The paper theoretically investigates the problem of value function overestimation in offline RL through the lens of neural tangent kernel (NTK). Additionally, the paper presents empirical findings that validate the effectiveness of incorporating LayerNorm before each activation function in mitigating value net... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful review. Please see the response below.
# Linear NTK Value
Please refer to the global response for a detailed explanation.
# About Missing References
Thank you very much for pointing out these two papers that we missed. Since we conducted literature research a... | Summary: This paper analyzes Q-value divergence in Offline-RL by considering a
neural tangent kernel for the value function. They show that
consideration of this kernel is predictive of Q-value divergence.
This analysis further leads to the observation that using a LayerNorm
yields a kernel that behaves more like one ... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful review. On your questions, please see the response below.
# Explanation for Figure 5
Please note that the color in Figure 5 represents the normalized NTK. Indeed, in the left figure, the absolute NTK surrounding $x_0$ is positive. However, values farther from... | null | null | Rebuttal 1:
Rebuttal: **Note**: We appreciate the constructive feedback from all reviewers. We have prepared a PDF document for reviewers, containing figures about extensive experiments and results that further illustrate and support our response in the rebuttal.
# Why linear function and large NTK value?
Some reviewe... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
VPGTrans: Transfer Visual Prompt Generator across LLMs | Accept (poster) | Summary: This paper mainly focuses on the transferability of visual prompt generator in VL-LLM. The authors conduct extensive experiments among different LLM types and sizes. By combining the experiment results they propose a new VPN training pipeline that engages projector warmup stage and vanilla finetuning stage.
S... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and constructive reviews, which will definitely help consolidate our paper. We are also grateful that you acknowledge the strengths of our work. Following, we present the response to address your concerns.
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**Q1: I wonder if different structures for the pro... | Summary: The paper discusses the transfer of a visual prompt generator (VPG) across different vision-language language models (VL-LLMs) to reduce computational costs. VL-LLMs include a VPG module that bridges the gap between vision and language, encoding visual inputs into fixed-length soft prompts. To reduce the cost ... | Rebuttal 1:
Rebuttal: We are grateful that you acknowledge the strengths of our work so much. Your support definitely encourages us to improve the work further and push forward. Following we present the response to address your concerns.
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**Q1: Reliance on the existence of a well-functioned VPG model.**
**A:** Th... | Summary: * This paper presents an interesting study on how to effectively transfer the small VL model to the large VL model, which is very practical under the fact that the LLMs are very expensive to finetune.
* The empirical study shows the effectiveness of the methods.
Strengths: * The paper proposes an effective wo... | Rebuttal 1:
Rebuttal: We sincerely thank you for the valuable suggestions. Following, we present the point-to-point response to address your concerns. And if you feel our responses are effective, please kindly raise your evaluation.
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**Q1: The paper writing is very complex to understand.**
**A:** Thanks for your ... | Summary: The paper presents a technique for VPG (Visual Prompt Generator) transferability across LLMs where the transferability can be between LLMs of different sizes (eg: OPT125M -> OPT2.7B) or across LLMs of different types (eg: OPT350M-> Flan T5base). This is achieved using a two stage strategy where a projector is ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and the careful review. Your suggestions will definitely help improve our paper. Following, we present the response to address your concerns. And if you feel our responses effectively relieve your concerns, please kindly reconsider your evaluation.
---
**Q1: N... | Rebuttal 1:
Rebuttal: # General Response to All Reviewers
Dear reviewers,
Thanks for all of your time to write valuable and constructive comments. Your feedback will definitely assist us in enhancing the quality of our paper, and thus we are committed to incorporating your suggestion in our revision process.
Meanwhil... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Causes and Effects of Unanticipated Numerical Deviations in Neural Network Inference Frameworks | Accept (poster) | Summary: The authors a wide range of hardware platforms including both CPUs and GPUs and found inference result (bitwise) of ML model is not consistent across the platforms and even non-deterministic on the same platform. The authors identified the causesof these numerical deviations and attibuted them to accumulation/... | Rebuttal 1:
Rebuttal: Thank you very much for your careful analysis of our paper!
> "The numerical deviations author disclosed are well known to the industry [...] So I don't think authors found significant new sources of numerical deviations."
Clearly, the sources of numerical deviations are known. This is why we do... | Summary: This paper explores why the same code & data can result in different results from a trained neural network on multiple different, or even the same, architectures. Considering CPU, GPU, and algorithmic implementation, the paper isolates several key factors that cause variance in the calculated results, which ha... | Rebuttal 1:
Rebuttal: Thank you for pointing us to additional related work, which we unfortunately overlooked when doing our literature search.
Zhuang et al.’s work on training variance gives valuable insights on the training process as a whole, whereas we focus on pinpoint observations concerning inference. Combining... | Summary: The paper explores the causes and effects of the same model and data for inference on different platforms that result in different variations of numerical values. Furthermore, many different hardware specifications within the cloud which are mostly CPU platforms are chosen to evaluate different factors that ca... | Rebuttal 1:
Rebuttal: Thank you for your remarks regarding the organization of sections, writing issues, and description of related work. We will update the revised version of the paper to ensure the contributions of our research are well-articulated.
> "Real environments might take multiple factors all at once to gen... | Summary: The paper presents an extensive empirical study on the numerical instabilities for convolutions across different platforms. The findings of why these instabilities occur are interesting and informative. They further cluster them into equivalence classes for ease of explanation.
Strengths: Extensive evaluatio... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper.
> "The results and findings are very interesting, but the utility values of the EQCs and the errors they find is not demonstrated. Essentially, how do you use this information to make inference more robust for example?"
The concrete utility of this wo... | Rebuttal 1:
Rebuttal: We thank all four reviewers for their insightful feedback and thoughtful suggestions. We are very glad that the suggestions by different reviewers are compatible (and partly overlapping). This enables us to incorporate all of them into the final version of the paper.
Responses to specific questio... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Weitzman's Rule for Pandora's Box with Correlations | Accept (poster) | Summary: This paper considers Pandora’s Box problem with correlated values. Previous work gives a 9.22 approximation for this problem. This problem considers two variants, depending on whether the algorithm updates based on exact values or on the event that the value is large, with approximation factors of 5.828 and 4.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and typos caught; we will add a figure in the final version showing how the histogram method works, to better illustrate the proof.
To answer the question: when we initially started considering variant 2, we were not sure whether it would give a better appr... | Summary: This paper considers the Pandora's box problem with correlated values. In each step, the algorithm chooses an unopened box and observes its value generated from the known distribution. The goal is minimizing the sum of the minimum value among the opened boxes and the total opening cost. The distribution is giv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments, and we will clarify that a box can be opened many times during the run of the algorithm.
* Regarding the algorithm’s behavior: a box can be opened many times, therefore after we open it once (and make the cost 0) it could be re-opened at a later stage of t... | Summary: This paper provides an exploration of Pandora's box problem with correlations. The authors innovatively modify the computation of reservation values within Weitzman's algorithm. They further solidify their contribution by proving the approximation ratio of the proposed algorithms under various distribution upd... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments, we can add the appropriate clarifications in the final version. We answer the question below.
The (correlated) distribution is a set of vectors of size $n$ (described end of page 3), where each is drawn with some probability. When we open a box and see a v... | Summary: This paper studies the problem of Pandora's boxes with the values of the boxes correlated. The authors extend the classical algorithm Weitzman’s Rule for independent values of boxes to the correlated case, and propose new algorithms with better approximation than previous works that are learnable from samples.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments, and we will clarify and define the notions that aren’t currently clear. To answer the question: on the theoretical side, removing the strong assumption of independence generalizes the problem, and gave rise to new techniques that may be used to solve similar... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the Pandora’s box problem with correlation. The problem is as follows: a decision maker is presented with $n$ boxes to explore, and each box $b_i$ is associated with a hidden value $v_i$ and a known cost $c_i$ that needs to be paid to reveal the value. The values in the boxes are drawn from ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments, we are going to fix the citation inconsistencies, and add more intuition on the difference between the two algorithms versions. Also apologies for the abstract, the author who submitted the paper was careless with copying everything to the NeurIPS template. ... | null | null | null | null | null | null |
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference | Accept (poster) | Summary: This paper works on PU learning with a selection bias. They propose a weighted risk estimator to solve this problem, and estimate the weight via neural networks. Extensive experimental results validate the effectiveness of the proposed approach.
Strengths: - The studied problem is very important for PU learni... | Rebuttal 1:
Rebuttal: *Q1:* **The class prior is under estimated by using $n_P/n$ to replace $\pi$.**
*A1:* Thanks for the nice conern. We actually use $N_P/n$ to replace $\pi$, that is, the proportion of the total number of positive samples to the total number of samples. $n_P=n_L$ is the number of labeled samples, $... | Summary: This paper considers PU learning issue. Existing cost-sensitive-based methods often rely on strong assumptions that examples with
an observed positive label were selected entirely at random. This work relaxes the assumption and proposes a unbiased novel method PUe based on the causal theory.
Strengths: 1. The... | Rebuttal 1:
Rebuttal: *Q1:* **I am very confused on how to estimate the prior $\pi$.**
*A1:* Thanks. There is a lot of work on estimating $\pi$, such as the paper "Learning from corrupted binary labels via class-probability estimation" presented at ICML 2015, "Mixture proportion estimation via kernel embedding of dist... | Summary: This paper considers the problem that existing cost-sensitive-based methods often rely on strong assumptions that examples with an observed positive label were selected entirely at random in Positive-Unlabeled (PU) learning. The authors propose a PU learning enhancement (PUe) algorithm based on causal inferenc... | Rebuttal 1:
Rebuttal: *Q1:* **This paper contains several spelling errors.**
*A1:* Thank you for your advice. We will carefully check spelling, grammar and typographical errors, thoroughly proofread the paper, improve the overall quality and credibility of the paper, and carefully revise it in the final version. Lines... | Summary: The paper introduces an algorithm for Positive-Unlabeled (PU) learning that tackles the problem of biased labeled data. By utilizing causal inference theory, the algorithm utilizes normalized propensity scores and inverse probability weighting to reconstruct the loss function and achieve an unbiased estimate o... | Rebuttal 1:
Rebuttal: *Q1:* **Comparison with paper in AAAI 2022.**
*A1:* Thanks for the nice concern. The main content of aaai's article is introduced and explained respectively in the case of Local Certainty and Probabilistic Gap Scenario PS estimation which is s identifiable. There are four points about the novelty... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper noted that existing Positive-Unlabeled (PU) learning methods assumed that positive samples are selected entirely at random, ignoring the prevalent selection bias in real-world PU problems. To overcome this limitation, the authors proposed a PU learning enhancement (PUe) algorithm based on causal inf... | Rebuttal 1:
Rebuttal: *Q1:* **$R_{PN}$ should be $\hat{R_{PN}}$. Eq.11 looks a little weird.**
*A1:* Thank you for pointing out the issue. It is true that the expression forms here are not uniform. The empirical risk should be represented by $\hat{R_{PN}}(g|y)=\frac{1}{n}\sum_{i=1}^{n}[y_iL(g(x_i),+1)+(1-y_i)L(g(x_i),... | null | null | null | null | null | null |
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks | Accept (poster) | Summary: This paper proposes an epistemic uncertainty assessment framework which comes with statistical coverage guarantees and low computation costs for over-parametrized neural networks. This approach seeks to remove procedural uncertainty by using one auxiliary network. In addition, approaches are provided to const... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for the careful reading and valuable feedback. We address the reviewer's concerns below.
Q1. (Toy problems) To fully evaluate our approaches, we conducted additional experiments on the real-world dataset. For additional experiments, please refer to our global re... | Summary: The paper focuses on uncertainty quantification, specifically on estimating the statistical range of predictions made by a deep neural network (DNN). This is achieved through the use of a novel DNN called Procedural Noise Correcting (PNC), which is capable of estimating the variability inherent in the training... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for the careful reading and valuable feedback. We address the reviewer's concerns below.
Q1. (NTK and algorithms) Most of the essential notations are introduced in the statement of Proposition 3.1 and at the beginning of Section 3.1, while we defer the details r... | Summary: The authors focus on the task of uncertainty quantification for neural networks.
They contribute (i) a procedure to remove procedural uncertainty, an uncertainty that arises due to
randomness in the training procedure, and (ii) an approach to cheaply construct confidence intervals with asymptotic coverage gua... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for the careful reading and valuable feedback. We address the reviewer's concerns below.
Q1. (“extremely”) Thanks for pointing this out. We agree that “extremely” should be clarified. In the revised version, we will avoid using “extremely” but provide more concr... | Summary: In the paper, authors present a new approach to quantify and mitigate a specific aspect of epistemic uncertainty in model predictions. They identify and quantify "procedural variability," a type of epistemic uncertainty that arises from noise in the training process. Based on the Neural Tangent Kernel (NTK) th... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer for the careful reading and valuable feedback. We address the reviewer's concerns below.
Q1. (Experiments) For additional experiments on real-world datasets, please refer to our global response to all reviewers and our discussions there. We hope this would allev... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewers for their careful reading and valuable feedback.
In this global response, we will show some additional experiments to showcase our approach to more challenging problems than in the paper via “simulated” real-world datasets. In the following, we will first disc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Explaining Predictive Uncertainty with Information Theoretic Shapley Values | Accept (poster) | Summary: The paper aims to define Shapley values to decompose the conditional distribution of the outcome so to attribute the uncertainty of the outcome to individual variables. This aims to extend prior work, which has mainly focused on explaining the change in the conditional mean of the outcome.
Strengths: The manu... | Rebuttal 1:
Rebuttal: We thank TWY9 (henceforth R4) for their close reading and insightful feedback.
We respond to specific issues below.
W1. We thank R4 for pointing us to Williamson & Feng (2020), a reference we had not previously come across. Their SPVIM method is an elegant frequentist alternative to the Bayesian... | Summary: I have previously reviewed this paper at another conference in which, after reviewers' discussion, it was a borderline reject, below my review is enriched with the previous conference discussions.
This work proposes calculating a modified Shapley value where the coalitional game represents the entropy of the ... | Rebuttal 1:
Rebuttal: We thank reviewer yQq8 (henceforth R3) for taking the time to read and comment on our manuscript (again!). We learned a great deal during the review process for ICML, incorporating reviewer feedback to improve the manuscript in numerous ways—an improvement widely acknowledged by reviewers, who rev... | Summary: This paper introduces a method for explaining the uncertainty in predictions made by DNNs. The authors extend the Shapley values from explaining the value of DNN outputs to explaining the uncertainty of the DNN outputs. Then, the authors demonstrate the close relationship between these Shapley values and the c... | Rebuttal 1:
Rebuttal: We thank reviewer DQBN (henceforth R2) for their time and feedback. We were pleased to see that R2 found our paper “well-organized and easy to follow”, but would like to clarify several points of potential confusion that were raised in the “Weaknesses” section.
W1. R2 objects that our method “fai... | Summary: Authors aim to explain uncertainty in model outputs by adapting Shapely value framework. In specific, authors try to explain predictive distributions through the lens of entropy, cross-entropy, KL and information gain. Authors offer some theoretic interpretation of the Shapley values adapted with these metrics... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer 2QPT (henceforth R1) for their attentive comments and overall positive assessment. We were especially pleased to find that R1 considered our presentation “very thoughtful” and judged “the paper to be clear and intuition which follows propositions and theorems to be very... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their thoughtful comments and constructive feedback. Working through our replies has deepened our understanding of the material and helped us further refine our manuscript.
We reply to all individual reviewer comments below. However, we open with one general note h... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? | Accept (poster) | Summary: The paper introduces a study of pre-trained visual encoders for Embodied AI, and produces, among others, a new pre-trained ViT-L encoder (VC-1) using Masked Auto-Encoding on a dataset combining an expanded collection of egocentric video and ImageNet. VC-1, when adapted for agents solving 17 task types covering... | Rebuttal 1:
Rebuttal: Thank you for taking time to review our paper and sharing your thoughts. Please find responses to your questions/suggestions below. Please let us know if any additional clarifications are needed.
> Line 370 states "MAE adaptation does not help TriFinger performance, this is likely due to the mere... | Summary: • This paper presents the largest and most comprehensive empirical study of pre-trained visual representations for Embodied AI based on proposed CORTEXBENCH.
• This paper focuses on studying the effect of pre-training data size and diversity for PVRs, further proposing VC-1.
• This paper shows that task- or ... | Rebuttal 1:
Rebuttal: Thank you for taking time to review our paper and sharing your thoughts. Please find responses to your questions/suggestions below. Please let us know if any additional clarifications are needed.
> Add a concise introduction to multimodal-based pre-training visual representation in the related wo... | Summary: This paper focuses on the analysis of large-scale pre-trained vision models for interactive tasks. The authors curate CortexBench which includes tasks covering dexterous manipulation, navigation, and also scene-level interaction as the testing benchmark. Further experiments were made on testing existing pre-tr... | Rebuttal 1:
Rebuttal: Thank you for taking time to review our paper and sharing your thoughts. We have addressed your concerns below. Please let us know if any additional clarifications are needed.
> First, there is no task analysis for the curated tasks in CortexBench.
Because CortexBench is curated from existing b... | Summary: This paper evaluates different pre-trained visual representations w.r.t. their capability to serve as foundation models for Embodied AI. To do so, they compile a dataset (CortexBench), with 17 simulated EAI tasks. The intuition here is that a single pre-trained visual representation which would perform well ac... | Rebuttal 1:
Rebuttal: Thank you for taking time to review our paper. We believe there may be some major misunderstandings about our submission, and we would like to take this opportunity to clarify.
> No data contribution. From the introduction, it initially seemed as thought it's newly proposed benchmark in this pap... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful comments. We want to begin with a few high-level observations.
There appears to be consensus that our work presents “a large scale dataset with a diverse set of EAI tasks” (nrzP) – i.e., CortexBench. Several reviewers believe CortexBench will be “extrem... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Bayesian Optimization with Cost-varying Variable Subsets | Accept (poster) | Summary: The paper introduces a novel Bayesian Optimization algorithm for the case where we select a subset of variables to query at each iteration. Furthermore, each subset will incur a different _cost_. Examples of this include: control of soil nutrients in farming, advanced manufacturing, and targeting specific subg... | Rebuttal 1:
Rebuttal: Thank you for your time and effort spent writing this highly detailed and insightful review. Allow us to answer some your concerns:
**Weakness 1: relation to Causal BO.** You are correct in pointing out that Causal BO as formulated in Aglietti et al. (2020) is a more general case. Specifically, o... | Summary: The submission studies the problem of Bayesian optimization (BayesOpt) where we can choose to control a subset of the decision vector while the other variables are randomly selected.
Unlike previous works on contextual BayesOpt and BayesOpt with uncertain input, this paper leaves the choice of which variables ... | Rebuttal 1:
Rebuttal: Thank you for the time and effort spent writing this review. We answer your questions below:
**Questions**:
1. Certainly, as long as the joint distributions are known and induce conditional expectations that can be computed or approximated via Monte Carlo sampling. Algorithm 1 will likely work we... | Summary: This work introduces a new black-box function optimization setting where only a collection of the subsets of variables can be optimized while the values of the complement variables for each set are randomly sampled. The authors propose a new BO framework based on GP-UCB, called UCB-CVS, to solve this optimizat... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and review our paper. Let us address some of your concerns:
**Weakness 1, on application to real-world problems**: We have attempted to demonstrate the general applicability of our algorithm by using multiple cost sets. As explained in Sec. 5, while these co... | Summary: The authors study the problem of Bayesian optimization with cost-varying variable subsets (BOCVS) where in each iteration, the learner chooses a subset of query variables and specifies their values while the rest are randomly sampled. Each chosen subset has an associated cost. The authors analyze how the avail... | Rebuttal 1:
Rebuttal: Thank you for your time spent reading our paper and writing this review.
**Questions**:
1. As part of the problem specification in Sec. 4, the learner is given a collection $\mathcal I \subseteq 2^{[d]}$ of control sets indexed by 1,2, ..., $m \coloneqq |\mathcal I|$. These are the control sets... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning the Efficient Frontier | Accept (poster) | Summary: The authors propose a novel deep learning method, NeuralEF, to tackle large-scale constraint convex optimization problems by formulating them as sequence-to-sequence (SEQ2SEQ) problems. The key idea is to learn the complex relationship using an attention mechanism between financial conditions and optimal portf... | Rebuttal 1:
Rebuttal: # W(1) & Q(1):
Both EQ(3) and NeuralEF rely on known expected returns, volatilities, and correlation matrices for optimization, yet they can be used in tandem with methods for handling uncertainty, such as Monte Carlo (MC). in the global author response, we provide two practical examples illustra... | Summary: The paper presents a sequence-to-sequence (SEQ2SEQ) deep learning model, called NeuralEF, to solve the Efficient Frontier (EF) problem in portfolio optimization. The authors reformulate the EF problem as a SEQ2SEQ task and train a deep neural network (DNN) to approximate the convex optimizer. The proposed meth... | Rebuttal 1:
Rebuttal: # W(1):
[1-2] and our work explore input set optimization, each with distinct yet interconnected goals. Combinatorial optimization tackles discrete choices for optimal arrangements, while EF addresses continuous portfolio weight selection for risk-return equilibrium. PPO [2] and POMO [1], powerfu... | Summary: This paper considers the problem of solving efficient frontier (EF), a fundamental resource allocation problem where one has to find an optimal portfolio maximizing a reward at a given level of risk. Traditionally, this optimization is solved by quadratic optimization techniques.
This paper introduces NeuralE... | Rebuttal 1:
Rebuttal: # W(1-2)& Q(2):
Out-of-sample results were not discussed in detail, mostly due to the page limit. The safe use of NeuralEF could be solved by training it on a bigger domain than what was shown in Table 1 (most likely on a longer time period and with more data) if the input domain needs to be larg... | Summary: In this paper, the authors propose NeuralEF, a deep neural network (DNN) approach that approximates what is known as the ``efficient frontier'' (EF) in economics. To this end, they use a stacked transformer encoder architecture, as well as pre-processing steps (such as ordering assets) and a greedy algorithm (... | Rebuttal 1:
Rebuttal: # W(1)&Q(9):
We meant to say that the use of self-attention is to help the model understand the relationships between inputs. The relationships of EF are well understood. An expansion in the appendix can detail these relationships: e.g. assets with higher expected returns and lower risks yield hi... | Rebuttal 1:
Rebuttal: First, we would like to thank all reviewers for their appreciation of our paper and their valuable questions and comments. We answer all questions and provide new experimental results enhancing both the clarity of the paper and the experimental section. We would be happy to address all these aspec... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Mask Propagation for Efficient Video Semantic Segmentation | Accept (poster) | Summary: This paper presents a mask propagation method, MPVSS, for video semantic segmentation (VSS). MPVSS uses Mask2Former to obtain mask predictions and queries from the key frame. Then, a motion encoder extracts pixel-wise motion from the key frame and its adjacent frame. The queries from the key frame are used to ... | Rebuttal 1:
Rebuttal:
Thanks for your constructive comments and we address your questions as follows.
**Q1.** The method may not fully address the mentioned redundancy problem as the key frames are selected at fixed intervals.
**A1.** First, we acknowledge that employing fixed key-frame intervals only results in a ... | Summary: An efficient mask propagation framework for VSS, called MPVSS, is proposed in this paper.
MPVSS adopts a strong query-based image segmentation based on sparse key frames and warp prediction to non-key frames by generating a segment-aware flow from a newly designed flow estimation module.
With the proposed qu... | Rebuttal 1:
Rebuttal:
We thank you for your valuable feedback and address your questions as follows.
**Q1.** Fair comparison between DFF and Accel by unifying the key frame segmentation network.
**A1.** Thanks for your advice. We integrate DFF[78] and Accel[20] with Mask2Former to conduct fair comparisons on Citys... | Summary: This paper presents an approach for video semantic segmentation (VSS) by focusing on the aspect of efficiency. The authors propose a novel mask propagation framework that is built upon a computationally intensive query-based image segmentor called Mask2Former[1]. Instead of processing every frame, the framewor... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and constructive questions.
**Q1**. It would be inappropriate to claim that the proposed method (MPVSS) "achieves SOTA performance".
**A1**. Thanks for your advice. We will demonstrate our contribution as "achieve SOTA accuracy and efficiency trade-offs".
**... | Summary: The paper is to develop an approach to video semantic segmentation via propagating the segmented mask in key frames to non-key frames. Experiments were conducted on several databases with various comparisons.
Strengths: Focusing on improving the computational efficiency for video semantic segmentation;
The ... | Rebuttal 1:
Rebuttal:
Thanks for taking the time to review our paper and we address your questions as follows.
**Q1.** Questions about the novelty of the proposed method.
**A1.** As recognized by Reviewers oxr6, 41Ay, and cyWY, the primary innovation of this research lies in a **novel and efficient mask propagation... | Rebuttal 1:
Rebuttal: We sincerely thank all reviewers for their valuable comments.
### Novelty
Most reviewers recognize the novelty of our method.
*"The combination of segmentation and optical flow in a single model is a novel approach, particularly considering the use of query-based flow maps."* (Reviewer oxr6)
... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper investigates an important and fundamental task: video semantic segmentation (VSS). While image semantic segmentation has received significant research attention, VSS has been relatively overlooked due to limited datasets and computational resources. This paper makes a valuable contribution to the fi... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments. We address all the questions below:
**Q1.** Training details for the proposed flow module, e.g., loss function used to train query-based flow, initialization of the flow network (encoder/decoder), utilization of pretrained weights. More visual examples of... | null | null | null | null | null | null |
Language Model Alignment with Elastic Reset | Accept (poster) | Summary: This paper proposes a new approach for fine-tuning language models (LMs) with RL in order to achieve a good trade-off between maximizing reward and minimizing drift from the initial model, which is typically undesirable since it results in losing some capabilities while acquiring others. The approach consists ... | Rebuttal 1:
Rebuttal: Thank you for the review, we are glad you find the approach simple and effective, the writing clear, and the problem of high importance to the community. We agree that drift is a major limitation for RLHF methods and wish to address it robustly.
**Testing with another Reward Model**
We agree tha... | Summary: This paper aims to address the problem of language drift issue during RLHF (reinforcement learning with human feedback), which is also known as alignment tax and reward hacking. The problem is that during RLHF process, the model can "overfit" to the given suboptimal rewards while forgetting some important skil... | Rebuttal 1:
Rebuttal: We are glad the reviewer finds our method simple and effective. We hope the following clarifies the method and importance of our work.
**Novelty**
We are not aware of any method that resets to an EMA to counter overfitting, and we can’t find any method that also resets the EMA model as we do wit... | Summary: This paper proposes Elastic Reset, a simple technique for countering language drift and reward model overfitting when optimizing a language model policy against some communicative reward via reinforcement learning, as is done in RLHF. The idea of elastic reset is to periodically reset the trained model to an e... | Rebuttal 1:
Rebuttal: Thank you for the detailed review! We are glad you find our method simple but effective and tested against sensible baselines across a breadth of experiments. We also believe pareto curves are the right way to think about language drift and hope that our work increases their adoption as a standard... | Summary: Finetuning language models with reinforcement learning (RL) using human feedback, i.e. RLHF, has emerged as a promising paradigm for aligning large language models (LLM) to human preferences. Though RLHF has shown promising results when training models such as ChatGPT, RL has some inheritance drawbacks. Merely... | Rebuttal 1:
Rebuttal: Thank you for your review! We’re glad you found our problem compelling and clearly defined, our method easy to implement, and our experiments thorough in demonstrating the benefit of our method.
**On-policy Elastic Reset vs prior work off-policy Reset**
A major difference between our work and pr... | Rebuttal 1:
Rebuttal: We've run some extra experiments in response to reviewer comments, please find three figures attached:
1. Plotting each reset of Elastic Reset separately
2. PPO and Elastic Reset with best KL vs without KL (coefficient 0)
3. Elastic Reset over a range of reset frequencies
Pdf: /pdf/a2cb2b8e120cfc... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Towards A Richer 2D Understanding of Hands at Scale | Accept (poster) | Summary: This paper builds a large-scale hand-object interaction dataset named Hands23 that contains 257K images, 401K hands, 288K objects, and 19K second 33 objects and combines different image sources (Visor, Epic-kitchen, COCO, Internet articulation and novel videos). Compared to the previous hand-object interaction... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback which we believe will improve the paper.
**Q1: Could you provide more motivation for second-objects?**
**A1:** We will include a more explicit and centrally organized discussion of second objects if accepted. Since humans use tools to accomplish ... | Summary: This paper introduces a hand object interaction-related dataset, Hands23, that providing rich labels for hand images (segemantion and boundingbox of left hand, right hand, object and second object, types of contact, grasp and touch). It labels the existing three datasets (COCO, EPIC-KITCHENS VISOR, and Interne... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and answer the questions below.
**Q1: What are the benefits of multi-task training? Does it hurt the performance on segmentation?**
**A1:** The benefits of the multiple tasks is primarily the richer predictions, which we envision will enable var... | Summary: The paper presents a method and dataset for hand-object understanding. Specifically, the dataset provides bounding boxes and segmentation annotations for (1) hands, (2) objects that are in contact with hands, and (3) second objects touched by tools. The annotation also includes contact and grasp type for secon... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review of the paper and respond to their three questions and weaknesses below.
**Q1: Does the method model the correlation between three classes?**
**A1:** Thank you for the great question. It is true that our model treats the three classes independent... | Summary: This work presents a framework that outputs rich interaction information for hands that are in an interactive state on a daily basis, and a large dataset that supports the model. The framework is based on the standard RCNN object detection mechanism and has a simple structure that can be easily extended to mee... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We respectfully disagree on a number of key points that we will describe below.
We believe that the reviewer is suggesting that 3D hand-object reconstruction is sufficient to solve the task we tackle, and so we will respond to this first before responding to... | Rebuttal 1:
Rebuttal: We put all figures mentioned for each response in this PDF.
Pdf: /pdf/1bea5d9cd691af03ceb3507de0463b45baa52dfc.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning | Accept (poster) | Summary: The paper proposes a set of methods for modeling chemical reactions that involve radicals during the reaction process. The authors first introduce the current landscape of chemical reaction datasets based primarily on the USPTO and discusses the USPTO shortcomings in terms of reaction interpretability and its ... | Rebuttal 1:
Rebuttal: We appreciate the constructive comments from the reviewer. We have addressed most of their comments in the revised version of the manuscript.
**Comments on weaknesses**
* We agree that the description of the pathway search experiment is too succinct, especially as we believe that the ability to d... | Summary: The authors provide a reaction predictor system that provides an accurate and interpretable prediction of radication reactions. Due to the lack of training data, there is a dearth of reaction predictors for radication reactions. The authors present 3 deep-learning-based approaches. The first approach is a two-... | Rebuttal 1:
Rebuttal: We appreciate the comments from the reviewer. We have addressed most of their comments in the revised version of our paper.
**Comments on the weaknesses**
* We agree that it is not obvious which model was used for RMechRP. The model used is the best combination of the two-step prediction method (... | Summary: - a new model is described for prediction of radical chemical reactions
- the model is trained on a dedicated database of radical reactions for atmospheric chemistry, an important application
- several, reasonable baselines are evaluated
Strengths: - reasonable, state of the art ML modelling (contrastive lear... | Rebuttal 1:
Rebuttal: We appreciate the fair comments from the reviewer and the fact that they acknowledged the importance of this work. We agree with all the suggested changes. We have revised our manuscript by implementing all the comments from this reviewer. Specifically, within the revised draft, we have removed th... | Summary: Authors present two models that predicts radical chemistry reactions. The first model 'OrbChain' is comprised of two components 1) one GNN model for predicting pairs of reacting atoms/groups, 2) a model which ranks the plausibility of these pairs. The second model is a a fine-tuned Rxn-Hypergraph model, adapte... | Rebuttal 1:
Rebuttal: We appreciate the comments from the reviewer. We have addressed most of their comments in the revised version of the manuscript.
**Comments on the weaknesses**
* We agree that references [20, 21] followed a similar approach toward reaction prediction. Nonetheless, our reaction predictor marks a s... | Rebuttal 1:
Rebuttal: This one-page pdf file includes three figures that are generated to improve the clarity of the paper and also to provide a better response to the reviewers' comments.
Pdf: /pdf/367681b88861369410d1d9ebce9685fa2dd26598.pdf | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Adversarially Robust Distributed Count Tracking via Partial Differential Privacy | Accept (poster) | Summary: This paper studies adversarial robustness for the problem of counting on distributed streams, where the input may be adaptive based on previous outputs by a protocol across $k$ sites and the objective is for a central server to output an additive $\alpha m$ approximation to the stream of length $m$. When the i... | Rebuttal 1:
Rebuttal:
1. The communication cost is actually $O(k+ \sqrt{k}(\log N)/\alpha)$ rather than $O(\sqrt{k}(\log N)/\alpha)$, so the regime of improvement is for $k > 1/\alpha^2$, though it should be noted that this same weakness is also present in previous work [13] on oblivious distributed streams. Nevert... | Summary: The paper provides an algorithm or the distributed count tracking that both enjoys the communication advantage from randomization and is robust to adaptive adversaries. Another contribution of the paper is that it introduces a new *partial differential privacy* definition and completes related generalization t... | Rebuttal 1:
Rebuttal: 1. No experiments.
Our primary focus is on the theoretical aspects of the problem. Since the robustness of an algorithm against all possible adversaries, which is a desirable property, can only be established by theoretical proofs, we think a thorough theoretical study is of higher priority. H... | Summary: This work studies adversarially robust count tracking in a distributed computation setting. In particular the work considers a setting with an adaptive adversary that can choose future inputs based on the outputs produced by the server so far. This is in contrast to prior work on distributed count tracking tha... | Rebuttal 1:
Rebuttal: 1. This paper doesn’t provide a discussion of the privacy consequences of using their partial DP notion to provide privacy in distributed settings. Since this work defines a new notion that they call privacy, I believe it is essential to provide at least some discussion of: the limitations of this... | Summary: The paper considers a distributed count tracking problem introduced at PODS 2012: k parties receive (possibly adversarial) updates to a common counter, and need to communicate with a server to maintain an approximation of the total number of updates received. The goal is to minimize communication from the part... | Rebuttal 1:
Rebuttal: 1. The paper does not really try to argue for relevance to machine learning. Can you elaborate on why the results of the paper are of interest for a machine learning audience?
* First, as we have repeatedly emphasized in our paper, a key contribution of this paper is introducing a new notion ... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Synthetic Experience Replay | Accept (poster) | Summary: Authors propose to use diffusion models for generating new data based on online interactions or offline dataset. Generating new samples allows online and offline algorithms to perform better. Method can be useed with any offline method and any online method which utilize replay buffers.
Strengths: Useful ide... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time to read our paper and provide useful comments. We now address their individual concerns.
**Antmaze experiments**
We thank the reviewer for this comment. We would like to point out that the larger dataset offline experiments are more for validation (e.g., ensu... | Summary: The paper presents Synthetic Experience Replay, a reinforcement learning algorithm employing a generative model based on diffusion to enrich the training dataset of a learning agent. The method is adapted to offline and online RL, and compared to traditional augmentation strategies (e.g., the addition of rando... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time to read our paper and provide useful comments. We now address their individual concerns.
**More seeds**
Thank you for pointing out this oversight on our behalf. We have now increased the seeds used for all the experiments in our paper to at least 6 and show a... | Summary: The paper proposes Synthetic Experience Replay (SYNTHER), a novel diffusion-based approach to upsample an agent's collected experience in deep reinforcement learning (RL). The authors demonstrate that SYNTHER is effective for training RL agents in both offline and online settings, and can improve performance f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time to read our paper and provide useful comments. We now address their individual concerns.
**The role of the offline experiments/small networks**
We thank the reviewer for this point; we note that the D4RL datasets are by construction relatively large and gener... | Summary: Building on the recent success of generative models, the authors propose Synthetic Experience Replay to improve how experience or data is used within reinforcement learning algorithms. While data is normally collected through interaction with an environment, the authors suggest that the experience replay can b... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time to read our paper and provide useful comments. We now address their individual concerns.
**Novelty**
Thank you for the question - we appreciate the reviewer’s concerns. While we agree that other methods have been proposed to generate synthetic data, the stren... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their helpful and valuable feedback. We were pleased that the reviewers found our paper well-written, believed that our idea was widely applicable, and agreed that our experiments were thorough and of a high standard.
We noticed that each reviewer had fair... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
The Exact Sample Complexity Gain from Invariances for Kernel Regression | Accept (spotlight) | Summary: This paper analyzes the impact on sample complexity of encoding invariances into kernel functions in the context of kernel ridge regression. A kernel function is invariant to the actions of a group if its output does not change when its inputs are acted on by members of the group. The paper shows that for fini... | Rebuttal 1:
Rebuttal:
We thank the respected reviewer for their helpful comments. Here we provide a response to the questions/weaknesses mentioned by the reviewer.
- Question: "It would ... generalization bound."
- Answer: We thank the reviewer for their comment. While we devoted a full section to examples and... | Summary: The paper analyzes the generalization error of kernel ridge regression on manifolds with a kernel invariant to a group action. Compared with previous work, the results hold more generally for groups of positive dimension.
Strengths: The paper tackles an important issue, is well-written, with well-chosen and ... | Rebuttal 1:
Rebuttal:
We thank the respected reviewer for their helpful comments. Here we provide a response to the questions/weaknesses mentioned by the reviewer.
- Question: "Assumptions on f*"
- Answer: We thank the reviewer for mentioning this important comment. Indeed, the KRR algorithm does not need any ... | Summary: In this work the authors theoretically study how encoding invariances into models improves sample complexity. They approached the problem from differential geometric viewpoint, rather than common strategy of using invariant polynomials. Since the problem is algorithm and model dependent, the authors considered... | Rebuttal 1:
Rebuttal: We thank the respected reviewer for their positive review and appreciation of the theoretical results. For the next version, we will pass the paper and make it more readable to address the issue mentioned in the limitations section. Indeed, we are planning to add figures to the paper to make the ... | Summary: This article investigates the sample complexity gained from encoding invariances into learning models. The article focuses on the study of kernel ridge regression on compact manifolds for functions that are invariants to a group action on the manifold. The main result of this article (Theorem 3.1) gives an upp... | Rebuttal 1:
Rebuttal: We thank the respected reviewer for their helpful comments. Here we provide a response to the questions/weaknesses mentioned by the reviewer.
- Question: "To be honest ... can be ensured."
- Answer: We appreciate the reviewer for mentioning this important comment. While we cannot put th... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks | Accept (poster) | Summary: This paper describes a formalization and experimental verification of the thesis that a quantum state $\vert\phi\rangle$ is the local minimum in the process of the QNN training. The paper is well written and provides all necessary support documents for its understanding. This work is an extension and is comple... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and recognition of our work as interesting and technically sound. A detailed response to the reviewer's comments and questions is provided below in a point-by-point manner.
>$\textbf{Comment 1:}$ ``The main weakness is the significance of the result. While the... | Summary: The authors present a no-go theorem that reveals the limitations of learning unknown quantum states using QNNs, even with high-quality initial states. They prove that the probability of avoiding local minima decreases exponentially with the number of qubits but grows polynomially with circuit depth. The curvat... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of our work as a technically solid paper with good impact. Below is out point-by-point response to the reviewer's questions.
>$\textbf{Comment 1:}$ "In this work, it focus on learning pure states through QNN, and whether the statement is also true ... | Summary: The paper studies parameterized quantum circuits (aka QNNs). These architectures face trainability issues (e.g. barren plateaus) as the number of qubit grows, and several approaches have been explored to mitigate these. The paper analyzes these strategies using the task of training a circuit to transform |0> i... | Rebuttal 1:
Rebuttal: We are very grateful for the reviewer's recognition of our work as a novel and technically solid paper with good impact. A detailed response to the reviewer's comments and questions is provided below in a point-by-point manner.
>$\textbf{Comment 1:}$ ``The paper is focused on a specific loss, spe... | Summary: This paper investigates the learnability of the QNN in the task of quantum state learning from a statistical perspective. The paper develops a no-go theorem that proves that when the loss function value is lower than a critical threshold, the probability of avoiding local minima decreases exponentially with th... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive assessment on the correctness, novelty and value of our work. We also thank the reviewer for the helpful feedback. Below is our point-by-point response to the comments and questions.
> $\textbf{Comment 1:}$ ``The theoretical analysis is only applicable to pur... | Rebuttal 1:
Rebuttal: Dear PC,
We want to express our sincere gratitude to the Program Committee for their hard work in shaping the conference's scientific program. We would also like to thank all the reviewers for recognizing our work as a novel and technically solid paper and recommending acceptance of our submissio... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a new statistical analysis for training variational quantum circuits (e.g., in terms of quantum neural networks), where the alternating-layered ansatz (Nakaji et al.; ALT), also referred to as the entanglement circuit for quantum ML in Chen et al. 2019 [4], has been characterized as a gen... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition of our work as technically solid, well written, and valuable to the community. We are also grateful to the reviewer for drawing our attention to the literature that we had overlooked. We will ensure to include these references in the revised version... | null | null | null | null | null | null |
When is Agnostic Reinforcement Learning Statistically Tractable? | Accept (poster) | Summary: The authors study the problem of sample complexity in finite horizon MDPs from an agnostic perspective. More specifically, the term agnostic refers to the following setting: given a (finite) policy class $\Pi$, they aim at understanding the number of samples that are required to output a policy that is $\epsil... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time. Below we respond to your points and questions.
**A Metric that Characterizes Both Scenarios.** To the best of our knowledge, there is no metric that captures the complexity of agnostic PAC RL in both generative and online RL settings. We introduc... | Summary: This paper studies the minimax sample complexity of learning the best policy within a given policy class $\Pi$, i.e., the best sample complexity of learning $\argmax_{\pi\in \Pi}V^\pi$ in the worst-case MDPs. As a motivation, Proposition 1 shows that without future assumptions on the structure of $\Pi$, the be... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time. We respond to your questions and points below.
**Sunflower Structure Lacks Motivation.** We address this concern, along with other questions/points on the sunflower property, in a joint response to all the reviewers.
**Computational Complexity.... | Summary: This paper studies conditions on which agnostic reinforcement learning is statistically tractable. The paper introduces a new concept of complexity measure called spanning capacity, which sorely depends on the policy class. The authors studies in what cases the sample complexity of agnostic RL can be polynomia... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time. Below we respond to your questions and points.
**The Sunflower Property is Overly Strong.**
We address this concern, along with other questions/points on the sunflower property, in a joint response to all the reviewers.
**Regarding Definition 2... | Summary: This work studies agnostic learning in RL, and seeks to characterize when, given some policy class $\Pi$, it is possible to learn an $\epsilon$-optimal policy in $\Pi$ regardless of the MDP. They propose a novel complexity measure—the spanning capacity—which depends only on the policy class $\Pi$ (i.e. is inde... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and time. Below we respond to your questions and points.
**Spanning Capacity is Worst-Case.**
We agree with the reviewer that spanning capacity is a worst-case notion, much like classic learning theoretic quantities like VC dimension and Littlestone dimens... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and valuable feedback. Since concerns about the sunflower property were raised by all the reviewers, please find a shared response below. If you have any further concerns, please let us know, and we would love to discuss further.
**Necessity of the Sunflo... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Optimal and Fair Encouragement Policy Evaluation and Learning | Accept (poster) | Summary: There exist (many) healthcare programs where it is impossible to compel individuals to take treatment; rather, the problem of solving for an optimal policy takes the form of providing beneficial services and recommendations to patients. This paper formalizes a causal framework for these cases and develops stat... | Rebuttal 1:
Comment: Thanks for the review! The quote block is our own emphasis. | Summary: The authors study the problem of providing treatment recommendations in systems where those receiving recommendations posses the ability to dissent or deviant from the suggested course of action. Within these types of problems the authors develop a method for providing treatment recommendations which can both ... | Rebuttal 1:
Rebuttal: Thanks for the summary and assessment of strengths!
We respond to questions/limitations/weaknesses.
1. Baselines:
Good question. Note that we actually plot the full curve of objective value and constraint reduction in Fig. 2. To your question about what happens if we wrongly assume a 100% c... | Summary: The paper addresses consequential decision making situation where we have a decision-making policy that outputs decisions (referred to as recommendatinos) R, individuals who may adhere to the decisions and realize a treatment T, which then leads to an outcome Y. The problem arises, when individuals do not adh... | Rebuttal 1:
Rebuttal: We respond point-by-point below. Note many of these points are *already* in the paper.
1. Adding section numbers to our narrative description is straightforward and we will do so.
2. No, our background is split between the related work and problem setup.
We introduce policy value functions in l... | Summary: This paper focuses on fair optimal decision rules, enhancing statistical estimators, and robustness checks for algorithmic recommendations with randomized decisions. It introduces a two-stage procedure with a complexity bound for optimizing within a constrained policy class, ensuring less conservative out-of-s... | Rebuttal 1:
Rebuttal: Thanks for your review! Below we make some clarifications, which we hope will help clear up that these are not weaknesses of the paper. We will add these explanations to clarify.
Weaknesses
- 1: See lines 40-46 which discuss scenarios where the fairness constraint should be on the expectation o... | Rebuttal 1:
Rebuttal: Thanks to all the reviewers for feedback and suggestions. We are encouraged that the reviewers find that we provide comprehensive theoretical/empirical results in tackling an important problem!
We respond point-by-point below and remark on some common points here. We believe these very minor fe... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The authors characterize optimal and resource fairness-constrained optimal decision rules, and develop a doubly-robust estimator for the optimal decision rules.
Strengths: I'm not very familiar with the areas of doubly-robust policy learning and am unable to assess the paper adequately.
Weaknesses: The autho... | Rebuttal 1:
Rebuttal: Thanks for the review! See global rebuttal where we propose to reiterate the limitations we discuss throughout the paper (e.g. our discussion right after assumptions) in a new concluding paragraph at the end.
> “In summary, we provide theoretical characterization of fair encouragement designs w... | null | null | null | null | null | null |
DNDesign: Denoising is All You Need for Protein Inverse Folding | Reject | Summary: In this work, the authors present DNDesign, a denoising training module atop inverse folding networks (IFNN). The folding physics learning plug-in module (FPLM) is trained following score-matching with noise added to the protein backbone. It also contains five operations, including summation, cross-attention, ... | null | Summary: This work proposes DNDesign, a denoising-enhanced protein fixed backbone design method that effectively captures the protein energy landscape. By integrating denoising training and a plug-in module, DNDesign demonstrates its ability to generate promising protein sequences based on pre-designed structures.
In ... | null | Summary: This paper proposed to use denoising diffusion probabilistic model and score-based model to solve the protein inverse folding problem, e.g., generate amino acid sequence given a protein backbone structure. Specifically, this paper used denoising diffusion model to generate unstable protein structure with highe... | null | Summary: This paper combines the denoising pretraining technique with protein inverse folding models, achieving competitive results to baselines. The denoising pretraining has been proven to be effective in molecule and protein representation learning. Therefore, the rediscovery of the phenomenon in protein design is t... | null | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Conformalized matrix completion | Accept (poster) | Summary: This paper addresses the problem of uncertainty quantification in matrix completion by developing a distribution-free method for predictive inference. The authors propose a novel approach based on the conformal prediction framework, aiming to overcome the limitations imposed by stringent model assumptions suc... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback! Our replies below address the individual points raised in your review.
- In response to the 1st Weakness (“Firstly, the authors frequently emphasize that their proposed method is "distribution-free" “),
Thanks for the suggestions. We will add clar... | Summary: This paper utilizes conformal inference techniques to address uncertainty quantification in the matrix completion problem. The authors present a novel method for constructing prediction sets for the missing entries estimated by any given matrix completion algorithm, employing a simple data hold-out strategy. T... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback! Our replies address the individual points raised in your review.
- [Weakness 1] Thank you for the question. The main novelty of our work is in reformulating the matrix completion problem as an instance of weighted exchangeability, for which weighte... | Summary: This paper presents a distribution-free method for constructing prediction intervals in the matrix completion problem, where randomness only arises from the sampling of observed entries. The approach utilizes weighted conformal prediction and establishes a lower bound on the probability of each unobserved entr... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback! Our replies below address the individual points raised in your review.
- In response to the Weakness and to the 1st bullet point under Questions (“Are there any experimental results when the sampling probabilities are completely misspecified…”),
I... | Summary: This paper proposes to use conformal prediction for uncertainty quantification of matrix completion. The proposed conformalized matrix completion offers provable predictive coverage regardless of the accuracy of the low-rank model. Empirical results on simulated and real data demonstrate that cmc is robust to ... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback! Our replies below address the individual points raised in your review.
- In response to Weakness 1 and Weakness 2,
Thank you for these points. These questions are inherent to the conformal prediction framework, and are not specific to our specifi... | Rebuttal 1:
Rebuttal: We are grateful to all the reviewers for their helpful feedback, comments, and suggestions on our manuscript. In the comments below, we have replied to each reviewer’s points individually.
The attached pdf contains additional simulation results and details of each setting are stated in the rebutta... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
An Efficient End-to-End Training Approach for Zero-Shot Human-AI Coordination | Accept (poster) | Summary: The authors propose an Efficient End-to-End training (E3T) approach for zero-shot human-AI coordination. E3T uses a mixture of an ego policy and a random policy as a simple way of training a diverse policy that is still capable of coordination. Unlike prior population based approaches, E3T does not require tra... | Rebuttal 1:
Rebuttal: We thank the time and effort reviewer DWEk has invested in reviewing our paper, and we appreciate that you concur with the main advantages of our method: (1) simplicity of the method (2) the reasonableness and effectiveness of introducing the partner modeling module (2) thorough experiments and su... | Summary: This paper proposes E3T, an method to train agent for zero-shot coordination with humans. The main contribution in E3T is that, in contrast to population-based approaches, it can be trained in a single stage, significantly reducing training time. E3T also includes a model to predict the next action of the part... | Rebuttal 1:
Rebuttal: We thank the time and effort reviewer dKj6 has invested in reviewing our paper, and we appreciate that you concur with the main advantages of our method: (1) simplicity and high training efficiency (2) thorough experiments and superior performance over existing methods (3) clarity of motivation an... | Summary: This paper proposes a simple end-to-end training mechanism for zero-shot coordination. Existing works in the ZSC literature often make use of population-based training, where a large pool of policies is trained to play well against itself while maintaining a certain form of diversity to prepare for an unknown ... | Rebuttal 1:
Rebuttal: We thank the time and effort reviewer kGQD has invested in reviewing our paper, and we appreciate that you concur with the main advantages of our method: (1) simplicity and high training efficiency (2) experiments are performed against various agents. We have provided detailed explanations and cla... | Summary: This paper introduces a one-stage training framework for human-AI coordination. The key insight is that partner policies should exhibit both coordination skills and diversity. However, the traditional approach of constructing a competent and diverse partner population in the first stage and training an ego pol... | Rebuttal 1:
Rebuttal: We thank the time and effort reviewer dkSb has invested in reviewing our paper, and we appreciate that you concur with the main advantages of our method: (1) it is well-motivated and easy to follow (2) the substantial experiments (3) sound theoretical analysis of the proposed method. We have provi... | Rebuttal 1:
Rebuttal: We thank the time and effort reviewers have invested in reviewing our paper. We have provided detailed explanations and clarifications to resolve your concerns regarding experiments and insights. If you have further concerns, please feel free to respond to us and we would like to discuss them with... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release | Accept (poster) | Summary: This paper deals with a problem of gradually releasing a resource to a population modeled as a risk-of-ruin constrained experiment. Namely, at every stage $t$ we have an arriving population $\mathcal{N}_t$, and we need it into a control and treatment groups $\mathcal{C}_t$ and $\mathcal{T}_t$ respectively (the... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reading our paper and encouraging remarks. We appreciate reviewer's feedback on the points that might be confusing to readers and will incorporate the clarification remarks to these points in future versions of this manuscript. We would also refer the revie... | Summary: This paper considers the problem of phased releases and formulates the problem into a batched bandit.
Strengths: This paper is very well-written and the problem very motivated. It is great to see bandit algorithm to solve a real-world application rather than stay in the theory world. The algorithm is fairly... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for taking the time to review our paper and for providing your encouraging remarks. We will respond to the following remark given that it is listed as weakness of the paper.
>"The algorithm is fairly simple and the theory is routine. Well for an application... | Summary: The paper presents an algorithm for conducting automated phased release strategies that balance risk and reward by controlling the risk of ruin while maximizing ramp-up speed. The authors propose a framework that models the problem as a constrained batched bandit problem and uses an adaptive Bayesian approach.... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's careful reading of our paper. We've addressed their feedback in the responses below. We kindly request the reviewer to reconsider their acceptance opinion, taking into account of the suggested revision and the attached one-page PDF simulations that address their concer... | Summary: The authors address the problem of finding a risk-sensitive strategy for phased releases. A model that involves a risk budget is proposed and an algorithm based on Bayesian updates is presented to find a solution. The proposed algorithm is empirically tested on a range of problem setups and is shown to outperf... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time spent reading our paper and encouraging remarks. We would like to take this opportunity to respond to the questions raised.
>"Although this is arguably a rather narrow and specific problem, the authors do contribute some useful ideas in applying the Bayesian ap... | Rebuttal 1:
Rebuttal: ***We provide a one-page PDF of simulation requested by reviewer m8cf in the attachment.**
We extend our gratitude to all the reviewers for their diligent review of our paper, and we highly value the insights they have provided through their feedback. We intend to consolidate our responses into a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Offline Imitation Learning with Variational Counterfactual Reasoning | Accept (poster) | Summary: This paper proposes OILCA, which addresses the scarcity of expert data in offline imitation learning by generating counterfactual samples, to imagine what the expert will do under a unobserved state. OILCA takes the perspective of Structual Causal Model (SCM). The algorithm consists of four steps:
1) A heuri... | Rebuttal 1:
Rebuttal: **Dear reviewer srX3:**
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
--------
**Weaknesses:**
**W1.The related work section could be expanded to include more related work.**
**A**: Thanks for your suggestions. Firs... | Summary: This paper focuses on the problem of offline Imitation Learning, where an agent aims to learn an optimal expert behavior policy without additional interactions with the online environment. This setting widely exists in the real world as it usually consumes a lot of human effort and cost to collect expert data.... | Rebuttal 1:
Rebuttal: Dear reviewer Txp8:
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-------
**Weaknesses**
**W1.No clear connection between the sub-optimal dataset and spurious feature is mentioned.**
**A**: The motivation of our me... | Summary: The paper propose a novel learning framework OILCA for offline IL, which generate counterfactual data to augment the scarce expert data. They analyze the disentanglement identifiability of the constructed exogenous variable and the counterfactual identifiability of the augmented counterfactual expert data. The... | Rebuttal 1:
Rebuttal: Dear reviewer XaVg:
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-------
**Writing and structure:**
**1.In this paper, it's not clear what the spurious relations under the MDP structure are.**
**A**: We follow a... | Summary: This paper introduces OILCA, a causality-regularized data augmentation method for offline imitation learning tasks. Overall, the idea is novel to me. The empirical results shown in the experiment section seem very promising. However, to support the claims made in the paper, more experiments are needed. Please ... | Rebuttal 1:
Rebuttal: Dear reviewer asgz:
Thanks for your review of our paper. Here's our response to the weaknesses, questions, and limitations you have highlighted:
-------
**Weaknesses:**
**W1. Missing references.**
**A**: Thank you for the valuable suggestion. We will include and discuss the raised related wor... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Globally injective and bijective neural operators | Accept (poster) | Summary: This paper extends several known results on ReLU networks from finite dimensional domains to the more challenging infinite dimensional domains. Namely:
(a) Conditions for injectiviity of infinite dimensional ReLU networks are provided
(b) Universality of infinite dimensional ReLU networks was proven
(c) Extens... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for your comments and careful eye.
_I think this paper is not appropriate for this venue: it is not clear to me why the ML world should care that much about infinite dimensional ReLU operators, and the authors do not make an effort to explain this. There is cer... | Summary: The paper considers the question of when neural operators, which have infinite-dimensional inputs and outputs, are injective and bijective. This question is answered in quite some generality in different settings and under different assumptions.
Strengths: --- A careful and at times, deep, analysis for the qu... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for your comments, careful eye, and fair criticisms.
_Relevance and Scope_
We should have motivated the application of injectivity and invertibility more. We will, please see the global comment.
_Why introduce...begin with._
You make a good point. The for th... | Summary: The authors present theoretical results in the field of operator learning, specifically dealing with operators are injective and surjective. They build on existing finite-dimensional work and consider the infinite-dimensional case of learning mappings between infinite-rank Sobolev spaces. The paper contributes... | Rebuttal 1:
Rebuttal: Thank you for your careful review, suggestions, and strong endorsement. Please find answers to your questions and feedback below.
_Just a note on writing, too many proofs and examples are black-boxed and put in the appendix. At least a description of various proof techniques in the main text woul... | Summary: This paper provides a theoretical analysis of the injectivity and surjectivity of neural operators.
Section 2 discusses the injectivity of a single layer of neural operators. For the ReLU activation, the injectivity of the layer is characterized by the directed spanning set. On the other hand, if the activati... | Rebuttal 1:
Rebuttal: Thank you for your careful review, suggestions, and endorsement. We have address your feedback below
_The paper contains a noticeable number of grammatical and typographical errors. It is recommended that an up-to-date automatic checker is used to revise the paper and correct these issues._
We a... | Rebuttal 1:
Rebuttal: We appreciate all the reviewers' valuable comments, and close attention.
We have a few things that we would like to say globally, in response to points brought up by multiple reviewers. We have also replies to individual reviewers below.
Several reviewers remarked that the question of injectivity... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This study investigates the injectivity and bijectivity of neural operators (NOs) in the infinite-rank setting which is less investigated than their finite-rank counter, such as invertible flow networks. This study is based on the finite-rank analysis by Puthawala et al. (2022a). As a previous work, Alberti et... | Rebuttal 1:
Rebuttal: We appreciate your valuable comments, constructive feedback, and endorsement. We have addressed your feedback below
_I believe that the NOs in consideration cover a wide range of practical examples, but it would broaden their potential readers if the authors could explicitly showcase such exampl... | null | null | null | null | null | null |
Strategic Classification under Unknown Personalized Manipulation | Accept (poster) | Summary: - This paper studies strategic classification with unknown and personalized manipulation in an online/distributional setting, i.e. different agents can manipulate their features to a different degree, and the learner does not know the extent to which agents can manipulate their features.
- The paper assumes r... | Rebuttal 1:
Rebuttal: We thank Reviewer 6Wy9 for their valuable comments. In response to their questions, we provide the following clarifications and explanations.
> The motivation for the easier cases.
For the easiest model (x is observed first):
- Consider a teacher giving students a writing assignment or take-hom... | Summary: The paper studies strategic classification in the setting where agents generally have different power of manipulation, which is unknown to the classifier. The authors consider both the online model and the PAC model, and investigate 4 increasingly difficult settings in which the classifier observes different ... | Rebuttal 1:
Rebuttal: We thank Reviewer Hsdn for their valuable comments. In response to their questions, we provide the following clarifications and explanations.
> Extension to general hypothesis class and the agnostic case.
We agree with the reviewer that the general hypothesis class and the agnostic case are inte... | Summary: The paper studies strategic classification where a sequence of agents, given information about decision rule, may manipulate their features strategically to receive favorable decisions. The goal of the learner is to find a hypothesis that minimizes the number of mistakes through sequential interaction with age... | Rebuttal 1:
Rebuttal: We thank Reviewer V2P4 for their valuable comments. In response to their questions, we provide the following clarifications and explanations.
> One prime difference between the present paper and the prior works is that this paper considers personalized manipulation with non-ball manipulations.
... | Summary: The paper explores a setting of strategic classification - where agents respond to deployed predictive policies by potentially manipulating their feature vector so as to achieve positive predictions. The authors explore this setting while allowing agent manipulations to be personalized (no assumptions regardin... | Rebuttal 1:
Rebuttal: We thank Reviewer XhgR for their valuable comments. We are particularly pleased that the reviewer finds merit in our modeling of personalized manipulation sets, the setting of unknown manipulation, and the algorithmic ideas involving bisecting the version space. In light of their questions and con... | null | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies an online learning problem where the learner interacts with a strategic agent in the following way: the learner chooses a hypothesis from a hypothesis class; the agent, after observing the chosen hypothesis, can manipulate its feature vector to be within some ball of unknown and personalized... | Rebuttal 1:
Rebuttal: We thank Reviewer Ro1X for their valuable comments. In response to their questions, we provide the following clarifications and explanations.
> Extension to general hypothesis class and the agnostic case.
We agree with the reviewer that the general hypothesis class and the agnostic case are inte... | null | null | null | null | null | null |
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception | Accept (poster) | Summary: This paper proposes a Human structure-Aware Pre-training (HAP) method that incorporates human structure priors into the masked image modeling (MIM) training strategy [26] for tasks related to human-centric perception. The authors have demonstrated the advantages of the proposed method on 5 human-centric percep... | Rebuttal 1:
Rebuttal: **Q1: The accuracy of pose estimation methods seems to significantly impact the performance of the HAP proposed by the author. However, it appears that the author hasn't conducted sufficient analysis, such as how OpenPose or AlphaPose specifically affect the performance of HAP.**
R1: Thanks for p... | Summary: The authors introduce masked image modeling as a pre-training method specifically designed for human-centric perception tasks. To this end, the authors incorporate human structure priors (high scaling ratio, mediate masking ratio, block-wise masking), human part prior (2D keypoints as guidance for mask samplin... | Rebuttal 1:
Rebuttal: **Q1: The technical novelty is limited. The proposed method is essentially a combination of many previous techniques used in masked image modeling. For example, the block-wise masking has been proposed in BEiT [2]. Semantic-guided masking has been proposed in prior works [5, 25, 33, 36, 41, 60, 74... | Summary: The work studies masked image modeling (MIM) in human-centric perception. It first revisits the vanilla MIM and finds that human structure prior (2D pose) helps the downstream human-related tasks. This encourages the authors to incorporate this prior into the classical MAE. Based on this human-centric masking ... | Rebuttal 1:
Rebuttal: **Q1: The performance on multiple benchmarks is overclaimed. For example, the results of Attribute recognition are marginal when comparing HAP with other human-centric pre-training methods. With bells and whistles, HAP (multi-dataset training and larger image size) outperforms other methods signif... | Summary: This paper propose a pertaining strategy for human-centric vision tasks. They extend the mask image modelling approach by incorporating a prior on the human body parts to guide the mask sampling strategy. In short, they mask parts of the image which contains body part. Authors also propose an alignement loss t... | Rebuttal 1:
Rebuttal: **Q1: Lack of a simple baseline: pre-training on LUPerson by training to regress the 2D keypoints extracted by ViTPose.**
A1: Thanks for your constructive suggestion. We experiment this simple baseline and achieve 50.3\% on MSMT17 and 89.8\% on MPII with 100-epoch pre-training. This baseline is s... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their insightful and constructive comments. We are encouraged that reviewers generally recognize the strengths of our paper in:
- Method: positive impact [QQMw], simple and effective [BM1x], make sense [QQMw], well motivated [ueQY], intuitive and appropriate [rHqg]... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DELTA: Diverse Client Sampling for Fasting Federated Learning | Accept (poster) | Summary: The paper proposed an unbiased client sampling method in Federated Learning. The proposed method is motivated by considering both variance and gradient diversity when doing client sampling. The authors theoretically proved that the proposed sampling method can achieve better convergence rate for non-convex obj... | Rebuttal 1:
Rebuttal: Dear reviewer 9Bi1, thank you for providing constructive feedback. We have fully revised our manuscript and have addressed all of the comments, as well as added new experiments to further strengthen our work. Please find our responses to your raised questions below:
> Assumption 4 is a very stron... | Summary: The authors introduce DELTA (Diverse Client Sampling) which is an unbiased method for client selection in Federated Learning (FL). DELTA is heavily inspired by Importance Sampling (IS) and cluster-based IS, resulting in sampling diverse clients with significant gradients however without the clustering. They p... | Rebuttal 1:
Rebuttal: Dear reviewer yPdX, thank you very much for your appreciation of our work. Please find our responses to your raised questions below:
> Minor: "developing an efficient and effective practical algorithm for gradient-based sampling methods" in future work section (Section 6) could confuse the reader... | Summary: This paper proposes a client sampling scheme in federated learning for faster convergence. The authors argue that the previous client sampling method based on importance sampling ignores gradient diversity. Convergence analysis is provided, as well as experiments on four image datasets.
Strengths: The proble... | Rebuttal 1:
Rebuttal: Dear reviewer stNZ, thanks for your time in reviewing our paper, please find our responses to your raised questions:
> Both theoretical and main method seems to rely on gradient information. However, gradient information is usually unavailable in a federated learning setting. It is unclear to me ... | Summary: The paper proposes a novel unbiased client sampling scheme called DELTA for Federated Learning (FL). The authors address the issue of unrepresentative client subsets in FL, which can lead to significant variance in model updates and slow convergence. They show that existing unbiased sampling methods have limi... | Rebuttal 1:
Rebuttal: Dear Reviewer 1Yft, we sincerely appreciate your thorough review of our paper. We have diligently incorporated your feedback to enhance the quality of our work. Kindly find our comprehensive responses to your questions outlined below:
> In Corollary 4.2, the convergence rate of the practical alg... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their time and efforts in reviewing our paper.
We are encouraged they found our motivation and idea to be interesting(stNZ, yPdX), novel (1Yft, yPdX, 9Bi1), and promising (1Yft, yPdX). We have carefully considered their suggestions and incorporated them into our re... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper studies the sampling schemes in federated learning. In particular, the authors first develop a new analysis method for important sampling (IS) strategy, which achieves a better convergence rate and then they propose a new sampling approach called DELTA, which can outperform the IS scheme. In additio... | Rebuttal 1:
Rebuttal: Dear reviewer 71Y7 , we would like to thank you for your time spent reviewing our paper and for providing constructive comments. Please kindly find our responses to your raised questions below:
> The novelty of the analysis is unclear. Please clearly state the main differences between the propose... | null | null | null | null | null | null |
Adversarial Model for Offline Reinforcement Learning | Accept (poster) | Summary: A fundamental principle in offline RL is pessimism, which however is not free from the performance degradation with respect to the baseline reference policies, i.e., the currently running policies in the system.
In viewing this issue, this paper proposes Adversarial Model for Offline Reinforcement Learning (AR... | Rebuttal 1:
Rebuttal:
Thank you for the detailed questions. We hope that our answers below would address your concerns and clarify the importance of the contribution we are making.
**Weakness 1**
Thank you for bringing it up. We would like to emphasize that realizability is a standard assumption in the literature... | Summary: This paper introduces ARMOR (Adversarial Model for Offline Reinforcement Learning), a novel model-based framework for offline reinforcement learning (RL) that addresses the challenge of performance degradation. Offline RL allows learning decision-making policies from logged data without requiring new data coll... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing the paper and providing valuable feedback. We hope our answers below can address your concerns.
**Weakness 1**: “[the paper] fails to provide a comprehensive discussion of the existing limitations and challenges that hinder [offline RL’s] practical adoption”
As ... | Summary: The paper propose a new model-based offline RL framework, called ARMOR, which can robustly learn policies that improve upon an reference policy by adversarially training a Markov decision process (MDP) model. ARMOR aims to optimize for the worst-case relative performance over uncertainty. In experiment, ARMOR... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback on improving the readability and constructive comments. We hope that our responses below would help resolve the remaining concerns you might have.
**W 1**: We thank the reviewer for pointing this out.
[How Eq.(1) → L228]:
We have a detailed discussion regardi... | Summary: The paper introduces the Adversarial Model for Offline Reinforcement Learning (ARMOR), a model-based offline RL framework. ARMOR uses adversarial training to robustly learn and improve policies over any given reference policy, regardless of data quality. The framework utilizes the concept of 'relative pessimis... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We will incorporate them in the revision. We have addressed your comments below.
**Weakness 1**
We would like to clarify that ARMOR (conceptually) maintains a set of models (i.e., the version space), not just a single one. We also assume that the model c... | Rebuttal 1:
Rebuttal: We would like to thank all the reviewers for taking the time to review our work and providing their constructive feedback. Here we provide responses to some of the common concerns
**RPI Experiments**
Reviewers “suos”, “pF8E” and “sNsB” had questions about the RPI experiments, specifically regar... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Preference-grounded Token-level Guidance for Language Model Fine-tuning | Accept (poster) | Summary: This paper aims to tackle the misalignment between sequence-level preferences and token-level language model in NLG. The authors design an iterative training framework that integrates the sequence-level preference into token-level training guidance, mitigating the granularity mismatch. Experiments are conducte... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for your thoughtful review. We would like to draw your attention to our **General Response** on human evaluation results on CNN/DM summarization and a discussion on why our summarization results are below the SOTA.
Below, we address your remaining concerns in deta... | Summary: To fine-tune an LM, the paper proposes “token-level guidance” by leveraging sequence-level preference. The algorithm alternates between two stages: (1) learning token-level “guidance” (aka reward function) and (2) fine-tuning LM using the “guidance”/reward.
To aggregate token-level rewards, the authors propos... | Rebuttal 1:
Rebuttal: Thank you for your careful reading of our paper and insightful comments.
We appreciate it if you can also consider our additional results and clarifications in the **General Response**.
Below are our detailed responses to your questions.
> **Q1.** The reward-retraining scheme seems expensive.
**... | Summary: The paper proposes to solve the issue of granularity match in preference-based tuning of LMs (RLHF for e.g.,), that is task-based preference is defined at the sequence level (via pairwise preference learning) while the reward model training and policy optimization is done at the token level. The paper proposes... | Rebuttal 1:
Rebuttal: Thank you for your time and careful review. We first want to bring your attention to our **General Response** for our human evaluation results on CNN/DM summarization.
Below are our detailed responses to your other concerns.
> **Q1.** Evaluation is only on two tasks. It's not clear why these t... | Summary: This paper proposed break the pairwise sequence preference into token-level guidance signal by iterating between learning a token-level reward from sequence level preference and improving LM with the learned token level guidance. Experiments are conducted on two language generation tasks and competitive result... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising several important questions. Please would you first check our **General Response**, which should address your concern on testing against the RLHF summarization paper. We answer your remaining questions in details below.
> **Q1.** The clarity of our paper.
**A.*... | Rebuttal 1:
Rebuttal: ## General Response
We thank all reviewers for the valuable comments. Below are our additional results and responses to common concerns.
> **Q1.** Human evaluation results on the CNN/DM summarization.
**A.** We conduct human evaluation on the quality of the generated summaries on the CNN/DM da... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper presents a new approach to training language models that address the mismatch between coarse-grained sequence-level preferences and fine-grained token-level rewards. With more fine-grained rewards, the proposed framework reduces the reliance on supervised data. Specifically, given the preference of a... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer for the careful comments.
We would like to draw your attention to **General Response** for additional results and common responses. The other questions are answered in detail below.
> **Q1.** About the meaningfulness of discrete-prompt generation task. May test o... | null | null | null | null | null | null |
Robust Lipschitz Bandits to Adversarial Corruptions | Accept (poster) | Summary: This work studies Lipschitz bandits robust to adversarial corruption, where both strong and weak adversaries are considered. Robust Zooming along with its cumulative regret upper bound is proposed for strong adversary with known corruption budget. For unknown corruption budget, this work proposes RMEL for weak... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments on our work. We are happy to know you think our contributions are solid in theory. Please see our response to your concerns as follows:
**Weakness 1: There is a gap between upper and lower bounds for both weak and strong adversaries:**
We also listed this p... | Summary: The article focuses on the problem of Lipschitz bandits in the presence of adversarial corruptions, where an adaptive adversary corrupts the stochastic rewards up to a total budget $C$. Both weak and strong adversaries are considered, where the weak adversary is unaware of the current action before the attack,... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work. We are pleased to know you think our work is solid and our presentation is clear and intuitive. Please see our response to your concerns as follows:
**Weakness 1: $c_t(x) > 1$?**
The condition $|c_t(x)| < 1$ is used for the proof of Lemma. A.7 i... | Summary: The paper considers a model of Lipschitz bandits with adversarial corruptions. Two types of adversaries are analysed hand-in-hand: weak adversary which may not have knowledge of the current action of the learner before injecting its corruption into the reward that is actually observed by the learner, and stron... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments on our work. We are happy to know you think our contributions are commendable and our presentation is clear. Please see our response to your concerns as follows:
**Weakness 1-3: Typos and notations:**
We are grateful for your meticulous review. We will corre... | Summary: This paper studies Lipschitz bandits with adversarial corruptions, where the reward of the pulled arm can be maliciously corrupted by an adversary. The authors consider both weak adversary and strong adversary and present robust algorithms for each setting. Under the weak adversary setting, the paper proposes ... | Rebuttal 1:
Rebuttal: Thank you for your valuable questions, and please see our responses to your concerns below:
**Weakness 1: For weak adversaries, the regret bound of the proposed algorithm has a multiplicative dependence on C, an additive dependence is desired.**
We also state this point as a limitation of our pa... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you dedicated to evaluating our manuscript, and we value the insights provided to enhance the quality of our work. We are happy to know that you find our work studies an essential and timely topic, with solid theory analysis and promising performance in ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studied robust Lipschitz bandits under two types of corruptions - a weak adversary who perturbs the reward function before observing the selected action; and a strong adversary who perturbs the instantiated reward of the selected action. When the attack budget is known, the paper proposed to use enla... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments on our work. We are happy to know you find our work pushes the frontier on a timely topic and is relatively complete. Please see our response to your question as follows:
**Can the authors provide some discussions on whether it's possible to derive regret lo... | null | null | null | null | null | null |
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation | Accept (poster) | Summary: Authors propose a speech-text pretrained model for spoken language tasks which leverages already existing pre-trained speech and language models. Modality mapping/alignment is based on a concatenation of paired speech-text (no need of word level alignment). The model is evaluated on a speech-to-text translatio... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. We have provided our responses right after each question.
1. In what aspect does your method really differ from SpeechT5, SLAM, etc ?
Answers:
Our methods differ from those used in SpeechT5, SLAM, etc. in the following ways:
A) The cross-modality learn... | Summary: This paper proposes a composite speech-language model for speech-to-text (ComSL) translation. ComSL first leverages existing pre-trained models for initialization, including Whisper speech recognition model and mBART machine translation model. And then proposed several modality alignment methods that do not re... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. We have provided our responses right after each question.
1. The training process may be unstable. As shown in the appendix, multiple losses are distributed by various weights from 0.1 to 0.8. The authors do not explain how these hyperparameters are determ... | Summary: This work presents a speech-language model built from both speech-only and language-only pretrained models. By compositing pre-trained models from 2 modalities, the authors show that a data-efficiency for spoken language tasks can be achieved. In particular, the authors proposed a few cross-modality loss funct... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. We have provided our responses right after each question.
1. After the finetuning on CoVoST2, I think the model can still perform ASR ? But it would be great that the authors to clearly state that (or explain why it cannot).
Answers: You are correct. The ... | Summary: With the goal to improve speech translation task, this work, leverages a multi-task training approach optimizing weighted sum of ASR, ST, MT and cross-modality learning (CML) objectives. Using mBART encoder and decoder Transformer blocks, the CML objective is purposed to better align speech and text modalities... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. We have provided our responses right after each question.
1. What was the motivation for not evaluating other tasks, as this model is trained in a multi-task setting, at least the ASR evaluation makes sense.
Answers:
Due to space limitations in the full ... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for taking time and effort to review our paper. We appreciate all your valuable comments and suggestions. To the common concerns and questions, our responses are listed here.
1. ASR evaluation
Due to space constraints in the full paper, we have included the A... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation. | Accept (poster) | Summary: This paper discusses a method of quantizing weights based on Bayesian neural networks (BNN) by clustering them as closely as possible to a set of powers-of-two values. Unlike the weight fix network (WFN) that quantizes by clustering the fixed weights of the network around the nearest centroid, the proposed Pro... | Rebuttal 1:
Rebuttal: We appreciate the comprehensive feedback provided on our paper. Herein, we address your concerns systematically:
**Q1) The Determination of 0.05 in Equation 4**
The value 0.05 was empirically determined through experiments on Cifar-10 using ResNet18. While this parameter demonstrated consistent ... | Summary: PWFN (Probabilistic Weight-Shared Fusion Network) is a technique that combines weight-sharing quantization with Bayesian neural networks to achieve highly compressed and quantized neural networks. It models each weight as a draw from a distribution, allowing for the quantification of uncertainty in weight valu... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and constructive feedback on our submission. We appreciate the time and effort you've taken to review our paper.
**Complexity in Training Process**: We understand your concern about the additional complexity introduced by PWFN during the training process and... | Summary: This paper discusses the Weight-sharing quantization technique to reduce DRAM read costs. To address this issue, an iterative weight fixing scheme was employed, which involved alternating between network training and weight clustering. This paper proposes a BNN-based framework that utilizes a new initializatio... | Rebuttal 1:
Rebuttal: We greatly appreciate the feedback and constructive criticisms provided by the reviewer. Your insights are invaluable to improving our manuscript. Here, we address the concerns raised:
**Concern 1: Weight Position Handling**
The weight position is indeed utilised in our methodology when determin... | Summary: This paper presents a novel quantization scheme based on iterative training and clustering of the weights into a very limited and finite set of choices. The paper follows a Bayesian approach to assign weights to clusters. Experiments are demonstrated on ImageNet for ResNet and Transformer architectures.
Stre... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and valuable insights on our paper.
**Training Costs**: We appreciate the concern raised about the training costs. Given its recurrent mention in the reviews, we've expanded on this aspect, providing a comparative analysis between PWFN and other prevalent quan... | Rebuttal 1:
Rebuttal: ### General Comments
Thank you to all the reviewers for their effort in reviewing our paper, kind comments, and suggestions for improvement. We have taken the comments and suggestions into consideration.
As a general confirmation, it is true that our work is the first to train Bayesian Neural Ne... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper introduces a novel approach to weight-sharing quantization, a technique aimed at reducing energy costs associated with inference in deep neural networks. The authors propose a method that takes into account the context of weights in the network, arguing that this strategy can better preserve the net... | Rebuttal 1:
Rebuttal: First and foremost, we genuinely appreciate your constructive feedback on our paper. It is our aim to present our research in the clearest possible manner, and your insights are invaluable in this pursuit.
**Bayesian Neural Networks (BNNs) for Model Quantization**: You're right in pointing out th... | null | null | null | null | null | null |
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery | Accept (poster) | Summary: The work presents TempME, an explanation methodology for temporal graph neural networks that incoporates the idea of temporal motifs. By incoporating temporal motifs, the method demonstrates better cohesiveness, since motifs are constructed to be cohesive, i.e., connected and localized, as well as better expla... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback, which significantly improves the quality of this work. We would like to address the following potential concerns you raise.
>The goal and setting of the evaluation are not clear. Clarification to “AUC value of the proportion of generated explanations that have t... | Summary: This paper provides a method to extract temporal motifs in order to capture correlated building blocks of networks.
Strengths: The method of extracting temporal motifs is interesting; it is based on information theoretic concepts.
Weaknesses: In my view there are two main weaknesses of this paper.
The fir... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and positive comments on the theoretical contribution. We address below the questions in order.
> The assumption that ... may not hold when there are seasonal effects.
We also believe that considering these seasonal effects is crucial. However, we think develop... | Summary: TempME is an inductive explainer for temporal graph neural networks (TGNNs) over link prediction tasks. It explains TGNNs using temporal motifs, guaranteeing temporally proximate and spatially adjacent explanations, hence more human interpretable. TempME’s pipeline can be broken down into three parts: sampling... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback, which significantly improves the quality of this work. We also address below the potential concerns.
>Lack of Experiments on synthetic datasets
Thanks for your constructive comments. The synthetic dataset is a point process where the arrival of an event can af... | Summary: This paper proposes an approach, Temporal Motifs Explainer (TempME), to find out key sub-graph structures from temporal graph neural networks (TGNNs) that mostly influence the prediction for a better ability of explanation. It employs an generative approach including the steps, motif extraction and sampling, t... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable suggestions and positive comments on the motivation, and organization in this work. However, there are some misunderstandings in the novelty and experimental setting of this work. We would like to clarify as follows.
>The novelty seems not sufficient.
Our mai... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers for their valuable time and effort in reviewing this manuscript. We have extended our experiments, which we detail below. We appreciate your feedback on this, and we agree that additional empirical verification would better support our proposed framework and t... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The Explainability of TGNN is essential for human understanding of model prediction results. This paper proposed a framework, TempME, to construct meaningful, cohesive explanations by utilizing temporal motifs. The framework consists of temporal motif extraction, motif encoder, information-bottleneck-based imp... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback and positive comments on the novelty, experimental results, theoretical analysis, and paper writing in this work. We address below the potential concerns.
>The performance of TempME might be susceptible to temporal motif extraction in the first stage. Discussions... | null | null | null | null | null | null |
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees | Accept (poster) | Summary: The authors present a decision aware actor-critic algorithm and then try to analyse the same. Subsequently they also provide some numerical results.
Strengths: The authors try and provide an analysis of an actor-critic algorithm. However, there are a number of questions that are unclear to me that I write in... | Rebuttal 1:
Rebuttal: **Part 2 of the rebuttal (please refer to the global rebuttal for Part 1)**
[4] *...don't see any conditions written on the step sizes...*
Both step-sizes $\alpha_c$ and $\alpha_a$ can be set according to the smoothness of the critic ($L_t(\omega)$) and actor ($\ell_t(\theta)$) objectives. Using... | Summary: The paper addresses the issue of objective mismatch in Actor-Critic methods by designing a joint objective that enables training the actor and critic in a decision-aware manner. The proposed algorithm ensures monotonic policy improvement, irrespective of the chosen policy and critic parameterization.
Strength... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and address their questions below.
[1] *Can the algorithm be applied to more challenging environments, such as the standard benchmark tasks in MuJoCo? If so, how well does it perform in those environments? If there are limitations or challenges i... | Summary: The authors develop a generic decision-award AC algorithm where both the actor and the critic take steps iteratively to optimize some “policy improvement lower bound” under the FMAPG framework. In essence, each step takes the gradient estimation error as the critic error, and characterizes the policy improveme... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and address their questions below.
[1] *How many samples are used for each GD step in your experiment?*
We varied the number of samples -- $\{1000, 5000\}$ for Cliff World and $\{1000, 10000\}$ for Frozen Lake in order to estimate the $Q^\pi, A^... | Summary: This research addresses the mismatched objectives in actor-critic (AC) methods used in reinforcement learning (RL). By introducing a joint objective for training the actor and critic in a decision-aware fashion, a generic AC algorithm is developed. The algorithm ensures monotonic policy improvement regardless ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and address their questions below.
[1] *Using linear representations as general function approximation is a bit weak. I presume this is the reason why only simple RL problems have been demonstrated in this work*
We emphasize that the main contri... | Rebuttal 1:
Rebuttal: **We respond to the major comments in the review of Rev. uLAb here. We believe that it would be helpful for all the reviewers to go through this response as it highlights the paper's key contributions, addressing possible misunderstandings**
We thank the Rev. uLAb for their feedback. However, we ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Polyhedron Attention Module: Learning Adaptive-order Interactions | Accept (poster) | Summary: This paper presents a feature interaction learning module called the polyhedral attention module (PAM).
The authors show that for any fully ReLU activated DNN, any input x is transformed with respect to a polyhedron defined as the intersection of the halfspace of each layer. Because the input is divided into... | Rebuttal 1:
Rebuttal: We greatly appreciate your positive review of our work and encouragement and we are open to any additional insights you may have.
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Rebuttal Comment 1.1:
Comment: Thank you for your response. After reading the other reviews and the authors' responses, I remain confident in my assessment. | Summary: The paper proposes a Polyhedron Attention Module (PAM) to create piecewise polynomial models to learn feature interactions in multivariate predictive modeling. Specifically, the input space is split into polyhedrons which define the different pieces and on each piece the hyperplanes that define the polyhedron ... | Rebuttal 1:
Rebuttal: Thank you for your follow-up questions and comments. Below we address each question and minor questions in a point by point fashion.
Weaknesses:
Response: The mathematical derivation can develop the approach rigorously and helps demonstrate the solid foundation of PAM, but we agree that it makes... | Summary: The ReLU-activated DNN will split the input space into pieces such as polyhedrons. The author proposes a polyhedron attention module (PAM) to capture the interaction between different pieces of input spaces. And they propose an approximation theorem for PAM. The polyhedrons are generated via an oblique tree, a... | Rebuttal 1:
Rebuttal: Thank you for your follow-up questions and comments. Below we address each question and minor questions in a point by point fashion.
Weaknesses:
Response to 1: Reviewer asked about the baseline methods in Section 6.3 and whether these methods can find the same effects and interactions. As far as... | Summary: The paper introduces a more general nonlinearity, PAM, to better capture the data features' interactions. Theoretical justification and interoperability are performed along with empirical results.
Strengths: The method seems to be inspired from sharp mathematical observations. It has mathematical interpretati... | Rebuttal 1:
Rebuttal: Thank you for your follow-up questions and comments. Below we address each question and minor questions in a point by point fashion.
Weakness:
Response: We further explain the rational of the presentation of our paper here and will revise our paper to explain better with examples. Feature intera... | Rebuttal 1:
Rebuttal: We appreciate all four reviewers for their positive and encouraging summary about our paper's strength. All have noted that our paper provides a sound, novel approach with theoretical justification and empirical verification (e.g., from reviewer 61Ki, "sharp mathematical observations", "mathematic... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Learning World Models with Identifiable Factorization | Accept (poster) | Summary: Learning efficient world models requires architectural priors to learn better representations that can capture different aspects of the environment. However, existing methods like Dreamer do not focus on learning disentangled representations and lack the ability to separate noise from the reward-relevant infor... | Rebuttal 1:
Rebuttal: **Dear Reviewer,**
Thank you for your valuable feedback. Below, we address your concerns in a point-by-point manner and have added various experiments, following your suggestions.
**Weaknesses:**
1. Lack recent works in world models such as [4,5,6] as baselines
*Response:* Thank you ... | Summary: - The paper introduces a framework called IFactor for modeling latent state variables in reinforcement learning (RL) systems. The framework categorizes these variables into four distinct types based on their interactions with actions and rewards. The paper further establishes block-wise identifiability of thes... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback. Please see our responses to your questions point-by-point below.
1. M**issing related work on separate modeling**:
Thank you for bringing our attention to the related works, including InfoPower [1] and Iso-Dream [2]. We will make sure... | Summary: The authors propose an alternative way to create world models in an RL system by separating them into blocks dependant on their casual effects on future observations, states and rewards. They theoretically show that under some assumptions it is possible to identify such classes of variables, even if specific i... | Rebuttal 1:
Rebuttal: **Dear Reviewer,**
Thank you for your detailed feedback on our paper. Your helpful comments have helped us further improve the paper. We've worked hard to deal with all the things you pointed out, and here, we explain the changes we made based on your advice.
**Q1: Disentanglement scores for com... | null | null | Rebuttal 1:
Rebuttal: Dear Reviewers,
We sincerely thank the reviewers for their effort and helpful comments regarding our paper. We have carefully revised the manuscript according to your comments. A summary of the primary changes we've made is outlined below:
1. **Identifiability Scores**: We've incorporated Ident... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Hierarchical Randomized Smoothing | Accept (poster) | Summary: The paper proposes a new threat model for the adversarial robustness - intersection of $\ell_0$ and $\ell_2$ ones where the $\ell_0$ is measured as the number of modified rows of a matrix. To certify robustness to this threat models, the authors propose a variant of randomized smoothing - hierarchical smoothin... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please note that we cannot update the paper during the rebuttal period according to the rebuttal policy, and we therefore carefully describe all changes directly here in the rebuttal.
### Concerning the technical contribution (Comment 1)
Please consider that our certi... | Summary: A randomized smoothing based robust certification approach is presented for machine learning under test-time/inference attacks, when only a subset of data is under attack e.g. a subset of nodes in graphical data. Robustness certificates are derived for discrete and continuous domains, and empirically certified... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please note that we cannot update the paper during the rebuttal period according to the rebuttal policy, and we therefore carefully describe all changes directly here in the rebuttal.
### Concerning the overall approach (Comment 1)
So far there are no randomized smoothi... | Summary: The paper introduces a new variant of the randomized smoothing algorithm for certified robustness. The paper considers the setting where the input data is split into multiple parts or sites (e.g., a graph) and an adversary can perturb at most $r$ sites at the same time.
To obtain robustness certificates for th... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please note that we cannot update the paper during the rebuttal period according to the rebuttal policy, and we therefore carefully describe all changes directly here in the rebuttal.
### Concerning a visualization of Propositions 1 and 2 (Comment 1 and 2)
Thank you fo... | Summary: This paper proposes hierarchical randomized smoothing, a variant of randomized smoothing that not only randomly perturbs input data at test time but also randomly selects which rows of the input to perturb. This is motivated by a threat model in which an adversary only selects a subset of rows of matrix-valued... | Rebuttal 1:
Rebuttal: Thank you for your review!
Please note that we cannot update the paper during the rebuttal period according to the rebuttal policy, and we therefore carefully describe all changes directly here in the rebuttal.
### Concerning the paper structure (Question 1)
We followed your suggestion and moved... | Rebuttal 1:
Rebuttal: We would like to thank all reviewers for their valuable feedback.
Please note that we cannot update the paper during the rebuttal period according to the rebuttal policy, and we therefore carefully describe all revisions directly in the rebuttals. In the rebuttal PDF we further include a Figure a... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity | Accept (poster) | Summary: This paper proposes using hessian-based updates to solve the unlearning problem for strongly-convex-strongly-concave minimax learners. They also provide theoretical analyses of the error of such an unlearning algorithm as well as its sensitivity to deleted data.
Strengths: 1. This paper is well-written and s... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her careful reading of our manuscript and numerous constructive remarks and questions.
**Notational mistake, size of $U$, and miswrote Assumption 2 in Lemma 2:**
Thank you for your careful reading and pointing out these issues. We will fix them and thoroughly proofr... | Summary: Machine unlearning is a privacy-inspired area to remove certain training samples of users’ data from well-trained model making those samples uninfluential while the unlearning approach does not cause the model to be retrained from scratch (not cause comprehensive computational cost) to achieve the baseline rel... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her careful reading of our manuscript and numerous constructive remarks and questions.
**Regarding 1) providing some preliminary results on some datasets and 2) discussing scenarios or applications:**
Thank you for the valuable suggestions. For 1), we will consider ... | Summary: The papers proposes a Newton-based differentially private algorithm for stochastic minimax problems and analyse the generalization rate and deletion capacity for the algorithms.
They analyse the generalization bound for weak primal-dual risk in SC-SC, C-SC and SC-C cases. Also the deletion capacity they derive... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her careful reading of our manuscript and numerous constructive remarks and questions.
**Regarding strong primal-dial risk vs weak primal-dual risk:**
Thank you for the valuable suggestion. We have derived new population performance results in terms of the strong pr... | Summary: This paper studies the problem of machine learning for the minimax model. By using Newton's step update with the Hessian information of the leftover data and Gaussian Mechanism, the proposed method improves the deletion capacity from $O(n/d^{1/2})$ to $O(n/d^{1/4})$.
Strengths: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her careful reading of our manuscript and numerous constructive remarks and questions.
**Some of the technical details are used before being defined properly:**
Thank you for your valuable suggestion. We will move Assumptions 1 and 2 from Sec 4 to Sec 3.3.
Addition... | Rebuttal 1:
Rebuttal: In this general response, we would like to first thank all reviewers for their careful review and valuable comments. Next, we provide the responses to two common comments that are shared by at least two reviewers. We provide the remaining point-to-point responses to each reviewer in our individual... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper studies approximate unlearning for minimax problems. They design learning and unlearning procedures and provide bounds on deletion capacity in terms of generalization performance (weak gap). Akin to minimization (statistical learning), the deletion capacity for strongly convex-strongly concave settin... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her careful reading of our manuscript and numerous constructive remarks and questions.
**Regarding additional challenges due to the minimax structure and comparison to prior works:**
Thank you for your valuable suggestion and constructive comments. Due to the charac... | null | null | null | null | null | null |
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective | Accept (poster) | Summary: In recent years, there has been a surge in papers suggesting Specialized Multi-Task Optimizers (SMTOs). These papers show the empirical advantage of using SMTOs compared to linear scalarization (LS). However, recently, there have been several papers that suggested that LS with proper tunning can match SMTOs pe... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer aGcP for their constructive feedback. We are grateful that the reviewer appreciates the significance of the problem we tackled as well as the theoretical contributions. We hope to address all points in the review below, following the order they were made.
**Why sc... | Summary: This paper is related to multi-objective optimization area.
They post a research question: if linearly weighting multiple objectives can fully explore the Pareto front?
Through theoretical analysis and a simple experiment, the answer is negative.
Hence, this might prove that multiple gradient descent algorithm... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer CFe6 for taking the time to review our paper. We appreciate that they found the problem of our study important. Below we attempt to address the reviewer’s concerns, following the order they were made.
**Weaknesses.** Please refer to point 2 of the general response... | Summary: This paper revisits linear scalarization from a theoretical perspective. The authors study multi-task learning with a two-layer linear network and reveal a multi-surface structure of the feasible region. They show the necessary and sufficient conditions for full exploration in the under-parameterized regime. T... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer 2hHW for taking the time to review our paper. We appreciate that they found our work interesting and novel. Below we attempt to answer the reviewer’s questions, following the order they were made.
**Are $q=1$ and $q=k-1$ representative?** A rigorous study for the g... | Summary: This paper studies the linear scalarization approach in multi-task learning (MTL). It shows theoretically that the linear scalarization is not able to fully capture the Pareto optimal (PO) solutions. It also identifies necessary and sufficient conditions of full exploration of the PO solutions for under-parame... | Rebuttal 1:
Rebuttal: We express our sincere gratitude to Reviewer c3Pi for taking the time to review our paper, providing valuable and constructive feedback, and pointing out useful references. We hope to address all comments in the review below, following the order they were made.
**Comparison with prior works (Wea... | Rebuttal 1:
Rebuttal: Here we address some common concerns raised by multiple reviewers.
**1. The under-parameterized regime.** (Reviewer J52d, c3Pi, 2hHW, aGcP)
We acknowledge that the over-parameterized regime is more practical, and the conclusions in this paper may not directly generalize. As a matter of fact, eve... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: This paper revisits linear scalarization in multi-task learning from a theoretical perspective.
The authors reveal that scalarization is, in general, incapable of tracing out the Pareto front.
Specifically, when the model is under-parametrized, a multi-surface structure of the feasible region is revealed by th... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer J52d for taking the time to review our paper. We are grateful that the reviewer appreciates our theoretical contributions. The concerns raised by the reviewer are addressed in the general response. Specifically,
**The under-parameterized regime.** Please refer to p... | Summary: In this paper, the authors introduce a novel mathematical framework to answer the problem “Under what conditions can (or cannot) linear scalarization recover the Pareto front of a given multi-objective optimization problem?”. To this end, the paper considers the multi-task learning setting using a simple line... | Rebuttal 1:
Rebuttal: We thank Reviewer nBcV for their insightful comments and constructive feedback. We address concerns and answer questions posed by the reviewer below, following the order they were made.
**Why scalarization cannot explore intersection points (Weakness 1 and Question 1).** Please refer to point 3 o... | null | null | null | null |
LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion | Accept (spotlight) | Summary: In this paper, the authors formulated a new linker design task where the fragment poses are unknown. The authors proposed a 3D equivariant diffusion model which enables the co-design of fragment poses and the linker structure in a unified framework.
Strengths: 1. The paper is well-written and easy to follow.
... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions. Please see below for our responses to the comments.
**Q1: The authors do not show the application of LinkerNet for molecule generation conditioned on the target protein.**
A1: Thank you for pointing this out! This is indeed one limitation ... | Summary: The article discusses the problem of designing linkers to connect different molecular fragments in order to form stable drug-candidate molecules, specifically in targeted protein degradation techniques such as PROteolysis TArgeting Chimeras (PROTACs). One significant challenge in these techniques is that exist... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions. Please see below for our responses to the comments.
**Q1: How is the training/sampling complexity compared with existing methods, especially with DiffLinker?**
A1: For the training complexity, DiffLinker converges within 300 epochs and take... | Summary: The paper presents a diffusion model for molecular linker design. Given the 3D structures of two molecular fragments, the model generates a pose for each of the fragments, as well as types and positions of atoms that can be added to link the two fragments in a single molecule.
Strengths: The method and evalua... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and suggestions. Please see below for our responses to the comments.
**Q1: “In section 3.3 I found the description of the fragment pose prediction module hard to understand...”**
A1: Equation (12) describes a way to update poses by predicting ... | Summary: The authors describe a novel method for the computational design of linkers using equivariant diffusion models. This is coupled with a fragment pose prediction step that allows the design of linkers without first knowing the relative orientation of the fragments.
Strengths: - Comprehensive related works secti... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and suggestions. Please see below for our responses to the comments.
**Q1: “The authors need to better introduce the problem for a more general machine learning conference… The writing needs to be improved … ”**
A1: We thank the reviewer for the suggestions... | Rebuttal 1:
Rebuttal: We thank all reviewers for their efforts and time in evaluating our submission and providing valuable suggestions and feedback. In the general response pdf, we
* Add one example to show that the fragment poses are **not** fixed in the PROTAC design (Figure 1). Both PROTACs bind with the same pr... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field | Accept (poster) | Summary: The paper presents DDF-HO, a method for handheld object reconstruction based on Directed Distance Fields. Given a single RGB image containing a hand grasping an arbitrary object, DDF-HO reconstructs the object without requiring a template or depth priors. Previous methods addressing this problem have relied on... | Rebuttal 1:
Rebuttal: Thank you for the constructive review. Below we try our best to address your concerns and questions. We have already revised the paper according to the reviews.
## Q1. Efficiency and Model Complexity of DDF-HO
IHOI is a typical SDF-based hand-held object reconstruction method. We list detailed co... | Summary: This work proposes a novel pipeline that uses DDF as the shape representation for hand-held object reconstruction from a single image. DDFs provide benefits over SDFs, eg. they are directed, provide intersection with object information, and can capture symmetry. Extensive experiments on ObMan, HO3D, and MOW da... | Rebuttal 1:
Rebuttal: Thank you very much for taking the time to thoroughly review our paper. Your insights are highly valuable and provide us with guidance to enhance the paper. Below we try our best to address your concerns and questions. We have revised the paper according to your suggestions.
## Q1. Different Spl... | Summary: This paper proposes a directed distance field-based method for hand-held object reconstruction from a single RGB image. The paper aggregates 2D ray-based features to capture ray-object intersection and 3D geometric features of ray-hand intersection. In particular, it extracts local to global cues via the above... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable and constructive comments. Our detailed responses are listed below and we revised the manuscript accordingly.
## Q1. Unclear Descriptions of Symmetry Loss
Following other baseline methods, we exclude the symmetry loss except for ablation studies (L. 243 in th... | Summary: The authors introduce a novel approach called DDF-HO, which uses Directed Distance Field (DDF) for 3D hand-held object reconstruction. Unlike SDF, DDF includes origins and directions of the views in the 3D space. They show that the ray-based feature aggregation scheme and 3D intersection-aware hand pose embedd... | Rebuttal 1:
Rebuttal: We sincerely appreciate you for the precious review time and valuable comments. We revised the manuscript according to the review. Below we try our best to address your concerns and questions.
## Q1. Efficiency
IHOI is a typical SDF-based hand-held object reconstruction method. We list detailed c... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable work by all ACs and reviewers. We are delighted that DDF-HO is considered to show "improvements over baselines on nearly all metrics" [9guR, e2Ui, dmTA, hGDA], "stronger modelling capability" [7sso], "novelty in hand-held object reconstruction" [1CC4]. We revis... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The paper proposes an algorithm for reconstructing hand-held object from a single RGB image. Instead of using the traditional Signed Distance Fields (SDF), this paper proposes to leverage Directed Distance Field (DDF) as the shape representation. Experiment shows that the proposed algorithm outperforms SOTA.
... | Rebuttal 1:
Rebuttal: Thank you for the constructive review. We revised the manuscript accordingly. Below we try our best to address your concerns and questions.
## Q1. Running Speed and Model Complexity
IHOI is a typical SDF-based hand-held object reconstruction method. We list detailed comparisons with IHOI in the f... | Summary: This paper presents a system for joint hand and hand-held object 3D reconstruction. The authors propose a pipeline to (1) predict the hand (MANO hand model) and camera poses with an off-the-shelf pose estimator; (2) extract image features with an off-the-shelf ResNet; (3) sample "3D ray representations", proje... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable and constructive comments. We are delighted that you found our method “predicts more accurate shape” than competitors. Our detailed responses are listed below and we revise the manuscript accordingly.
## Q1. About Ray Sampling from (R-A) to (R-B)
We define DD... | null | null | null | null |
In-Context Learning Unlocked for Diffusion Models | Accept (spotlight) | Summary: The authors propose an image generation framework. Given the textual description of a task and an input-output example pair, Prompt Diffusion can inpaint the missing output in a consistent way like in the input-output example and the textual guidance instruction. Prompt Diffusion is trained (in a supervised ma... | Rebuttal 1:
Rebuttal: We thank Reviewer Gb8x for acknowledging the novelty of our proposed framework, appreciating our presentation, and proposing constructive feedback. Below, we address the concerns raised in your review point by point.
> Claiming in-context learning and generalization to new tasks is very broad.
... | Summary: The paper proposes a novel prompt design and model called Prompt Diffusion, for in-context learning in vision-language tasks. The vision-language prompt is designed by replacing text examples with paired image examples and the text query with an image query. The paper conducts extensive experiments to demonstr... | Rebuttal 1:
Rebuttal: We thank Reviewer vt4T for acknowledging the innovativeness of our approach to the problem and appreciating the results of our experiments. Below, we address your questions and concerns:
- We acknowledge the potential benefits of utilizing a larger and more diverse in-context learning dataset to ... | Summary: Prompt Diffusion is a novel framework that enables in-context learning in diffusion-based generative models. It consists of a vision-language prompt and a diffusion backbone, which is trained jointly on six different tasks using their respective prompts. The resulting Prompt Diffusion model becomes the first d... | Rebuttal 1:
Rebuttal: We thank Reviewer 7sFz for evaluating the significance of our work and providing encouraging feedback. We provide more clarifications below.
- We commit to exercising greater prudence in claiming the ability of in-context learning in our revised manuscript.
- Appreciate your insight. Indeed, in... | Summary: This work introduces Prompt Diffusion. It is a framework designed to facilitate in-context learning within diffusion-based generative models.
By providing a pair of task-specific example images, such as depth from/to image and scribble from/to image, along with textual guidance, the designed solution can aut... | Rebuttal 1:
Rebuttal: We thank Reviewer foSi for providing positive feedback. We hereby provide a more detailed comparison between our work and Painter.
- **Task.** Painter follows [1] and focuses on solving high-level discriminative and low-level processing tasks, such as semantic segmentation, instance segmentation... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Causal Fairness for Outcome Control | Accept (poster) | Summary: The authors analyze a new dimension of decision making: outcome control and benefit from decisions. Specifically, this work uses causal analysis to introduce new definitions of fairness on benefits and outcome control. The authors also propose new algorithms to study fairness and design fair algorithms for out... | Rebuttal 1:
Rebuttal: We thank the reviewer for the provided comments, which were encouraging. We provide further clarifications regarding the points raised below.
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[W1: Limited Evaluation] Please see global comment [G1].
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[W2: Computing Counterfactuals] Please see global comment [G2]. Also, we have added th... | Summary: The paper proposes a new fairness notion called benefit fairness that considers fairness in the outcomes of the decisions. In order to avoid the unidentifiability issue of the principle fairness, this paper proposes to condition on the conditional average treatment effect (CATE). The paper provides an algorith... | Rebuttal 1:
Rebuttal: We thank the reviewer for the provided comments, it was nice to see that the main strengths were appreciated! We respond point-by-point in the sequel.
[W1: Connection with traditional notions] Great question, the manuscript indeed does not touch on this. We make the following observations and add... | Summary: This paper studies the problem where a decision maker must allocate a treatment $D$ to optimize an outcome variable $Y$ while ensuring that the decision is fair, formulating fairness as the protected attribute $X$ not having an influence on $D$. It uses a clinical decision-making process (a running example on ... | Rebuttal 1:
Rebuttal: In the weaknesses section, the questions were good and we believe to have addressed them in a factual and robust way. Please let us know if there are any further issues!
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[W1a: PO Limitations] Thanks for pointing this out. See global comment [G1] for a detailed response.
[W1b: Biased $\Delt... | Summary: The paper focuses on outcome control from a causal perspective of the decision-maker. The authors introduce benefit fairness taking the perspective of the decision maker and provide theoretical guarantee that the algorithmic result is optimal and satisfies benefit fairness. To support the decision maker in ind... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort in reviewing the paper. We are encouraged by the fact that the reviewer appreciated the main strengths of the paper. We provide further clarifications to the questions raised in the sequel.
[W1: Historical biases] We appreciate this insightful and fu... | Rebuttal 1:
Rebuttal: The authors would like to sincerely thank all the reviewers for this paper. The main strengths and novelty were clearly appreciated, and the questions raised were quite useful for us to revise and improve the paper.
In our response, we index all weaknesses with W, questions Q, and limitations L. ... | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games | Accept (poster) | Summary: The paper presents an improved quantum online learning algorithm for approximating the Nash equilibrium of a zero-sum game. Notably, this is the first algorithm to achieve $\tilde{O}(1)$ regret with quantum speedup. The presented algorithm can then be applied to linear programming problems using the primal-dua... | Rebuttal 1:
Rebuttal: We appreciate the reviewer taking the time to thoroughly review our paper and provide helpful feedback.
Regarding the use of QRAM, we would like to clarify that prior works (Refs. [50] and [7]) also have exactly the same requirement of QRAM (though under slightly different names). Specifically, R... | Summary: The paper studies the online learning quantum algorithms for zero-sum games. It proposes the online quantum algorithm for zero-sum games with near-optimal regret, which makes progress to online learning algorithms in the quantum setting. The algorithm quantizes classical algorithms based on the MMW method and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments.
For the contributions of quantum computing in achieving the speedup, we would like to explain as follows: to obtain the speedups over prior work, we proposed Sample-based Optimistic Multiplicative Weight Update (SOMWU for short) as the framework ... | Summary: This paper considers the problem of developing quantum, low-regret, algorithms for solving zero-sum games. This paper provides a quantum algorithm which matches the state-of-the-art for solving zero-sum games while improving upon the regret of the associated algorithm. The paper achieves this result by providi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback highlighting opportunities to better articulate the novelty and situate our techniques relative to prior work.
Specifically, we will include a detailed comparison of our Gibbs sampling approach to existing methods from quantum zero-sum game literature. Our ... | Summary: In this paper the authors consider the task of computing the eps-approximate Nash equilibrium of zero-sum games. In particular, for a matrix A in R^{m x n}, the goal is to find the minimum distributions x,y s.t., max_y x^t A y - min_x x^t A y is at most epsilon, given quantum query access to A. In this paper ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer identifying potential areas for improvement. However, we believe the core techniques demonstrate non-trivial novelty for the following two points.
First, our framework of Sample-based Optimistic Multiplicative Weight Update is considered for the first time in both class... | null | NeurIPS_2023_submissions_huggingface | 2,023 | null | null | null | null | null | null | null | null |
Counterfactual Memorization in Neural Language Models | Accept (spotlight) | Summary: This paper defines counterfactual memorization as the difference between a model's expected performance on a sample when it is included in the training data and that when it is not. The performance is measured by per-token prediction accuracy. The paper studies common patterns across counterfactually memorized... | Rebuttal 1:
Rebuttal: Thank the reviewer for the helpful suggestions and detailed questions. It seems there is some confusion regarding the definition and analysis of counterfactual memorization. Due to the 6000-character limit, we focus on the main questions below. We hope the reviewer could increase the rating if the... | Summary: This paper introduces a concept "counterfactual memorization," defined as the anticipated change in a model's output when a specific training example is omitted. Experimental analysis was conducted on three widely employed text corpora in the domain of language modeling, and the phenomenon of memorization was... | Rebuttal 1:
Rebuttal: Thank the reviewer for the positive review and helpful comments. Please see below for detailed clarifications.
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**Q1**: lacks a section on related work
The “related work” section is put in Appendix A due to space constraint. We will rearrange the contents in the updated manuscript to make it... | Summary: This paper proposes a metric for whether a sequence is memorized based on the degree to which exposure during training increases the probability of producing the sample.
Strengths: The proposal here is strong, and remedies a real problem in the memorization literature: a failure to consider the *inherent* pre... | Rebuttal 1:
Rebuttal: Thank the reviewer for the strong support of our paper and helpful suggestions!
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**Q1**: This paper doesn't distinguish between "simplicity" due to duplication and simplicity due to inherently predictable sequences, like repetitions of a single character.
Thanks for the comments. We agree th... | Summary: This work studies memorization in neural language models. The major scientific question in this work is how to filter out common memorization in language models. To this end, this work first formulates a notion of counterfactual memorization which
characterizes how a model’s predictions change if a particular ... | Rebuttal 1:
Rebuttal: Thank the reviewer for the positive review and useful feedback!
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**Q1**: computation cost may become a major concern for its application in large language models and with larger scale training sets
We acknowledge this limitation of our work. Our main focus for future work along this line is ... | Rebuttal 1:
Rebuttal: Thank all the reviewers for their useful comments and suggestions. We appreciate the reviewers found that our paper is clearly written, provides “strong proposal”, “sound analysis”, “useful metrics”, “novel perspective and tools”, “insightful findings” to the study of language model memorization. ... | NeurIPS_2023_submissions_huggingface | 2,023 | Summary: The study proposes two novel metrics: “counterfactual memorization” and “counterfactual influence”, the first one can measure how a model’s predictions change if a sample is omitted in training, and the later one measure how a sample in the training set influences the prediction of a validation sample. The aut... | Rebuttal 1:
Rebuttal: Thank the reviewer for the positive review and useful suggestions!
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**Q1**: pointers to classical causality studies
Thanks for the pointer! We will revise the manuscript to add missing references to the classical causality studies and discuss the connections. Our formulation does not involve... | Summary: This paper formulates a notion of counterfactual memorization to measure the "one-point" generalization performance of the model (they do not convey this definition as this). Equipped with this definition, the authors conduct plenty of experiments to explore the components that are related to such counterfactu... | Rebuttal 1:
Rebuttal: Thank the reviewer for the comments and suggestions! It seems there are some misunderstandings of how our metrics relate to existing notions of generalization and how the 0-1 prediction loss is chosen. We answer those questions below and hope the reviewer could raise the rating if the answers clar... | Summary: The paper studies an important problem of the "memorizing" effect in neural language models, particularly focusing on rare or isolated pieces of information in the training data that get memorized, as opposed to widely duplicated or common information.
It formulates a notion of "counterfactual memorization" t... | Rebuttal 1:
Rebuttal: Thank the reviewer for the strong support of our paper and useful suggestions!
> **Q1**: One potential weakness of this paper is the application of such analysis remains elusive. I can imagine many interesting questions could be answered with the provided concept, e.g., what kind of stereotypes/... | null | null |
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