Split (1)

train · 15k rows

paper_id stringlengths 19 21	paper_title stringlengths 8 170	paper_abstract stringlengths 8 5.01k	paper_acceptance stringclasses 18 values	meta_review stringlengths 29 10k	label stringclasses 3 values	review_ids list	review_writers list	review_contents list	review_ratings list	review_confidences list	review_reply_tos list
nips_2022_sFQJ0IOkHF	DivBO: Diversity-aware CASH for Ensemble Learning	The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success of ensemble learning, recent AutoML systems build post-hoc ensembles to output the final predictions instead of using the best single learn...	Accept	After a thorough discussion with the authors, all reviewers agree that the paper should be accepted at NeurIPS. The reviewers appreciated the idea of incorporating diversity in the combined algorithm selection and hyper-parameter optimization (CASH) framework and the subsequent use of the diverse models in an ensemble ...	train	[ "4fYX79ntpM", "l04QgmfZ4X3", "3b50w2ZTmPQ", "M5NO6EV9AsM", "gKKJGLUaEbF", "E_e0H-wh8lu", "SvCAlHrvPik", "vCL0p_5NGJw", "s1A-mARzfO8", "cK5D5rKp9ZH", "N_d3oCuQXkC", "re3QDKRZGU", "An7NtAp-KSi", "KRMaMlyr-m0", "3FXOy6Al1b", "Dg_A3p0_NDx", "xGsbS6XKhBl", "RcP-nahbnPj", "NdCLAPKMxW5"...	[ "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_re...	[ " Thank you for the explanations in AQ2 and AQ4 and the new results in AQ3 and AQ5. AQ5 definitely makes sense. \n\nFollow up on AQ3. It is somewhat surprising to see that we need $\\beta$ to be so small otherwise diversity hurts more than it helps. It is somewhat disappointing that the crux of this paper is that d...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 5, 4 ]	[ "l04QgmfZ4X3", "3b50w2ZTmPQ", "M5NO6EV9AsM", "re3QDKRZGU", "3FXOy6Al1b", "vCL0p_5NGJw", "cK5D5rKp9ZH", "s1A-mARzfO8", "NdCLAPKMxW5", "N_d3oCuQXkC", "RcP-nahbnPj", "An7NtAp-KSi", "KRMaMlyr-m0", "xGsbS6XKhBl", "Dg_A3p0_NDx", "nips_2022_sFQJ0IOkHF", "nips_2022_sFQJ0IOkHF", "nips_2022_...
nips_2022_FNzLe2-ppRO	TREC: Transient Redundancy Elimination-based Convolution	The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation ...	Accept	This work proposes a redundancy pruning mechanism for convolutional neural networks to optimize their performance for extremely resource-constrained edge devices, such as microcontrollers. The work is based on a novel gradient-optimized locality sensitive hashing approach that removes a lot of the indeterminacy of pre...	train	[ "jFr1JvQh4K", "K7QpD9LNERo", "Zmn-3rlzKo", "Tvco_5o5H2X", "SkPpnLxxwox", "oah_EQztILA", "Zfu5kgabGW", "F2oQgjf50EW" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " # NeurIPS 2022 author response\n\nWe thank the reviewers for insightful comments. We focus on answering the following concerns from reviewers:\n## Motivation for TREC design\nAs mentioned in the Introduction Section, in addition to designing a differentiable implementation of LSH clustering, TREC's contribution l...	[ -1, -1, -1, -1, 5, 6, 5, 5 ]	[ -1, -1, -1, -1, 3, 4, 3, 3 ]	[ "F2oQgjf50EW", "Zfu5kgabGW", "oah_EQztILA", "SkPpnLxxwox", "nips_2022_FNzLe2-ppRO", "nips_2022_FNzLe2-ppRO", "nips_2022_FNzLe2-ppRO", "nips_2022_FNzLe2-ppRO" ]
nips_2022__j8yVIyp27Q	Bidirectional Learning for Offline Infinite-width Model-based Optimization	In offline model-based optimization, we strive to maximize a black-box objective function by only leveraging a static dataset of designs and their scores. This problem setting arises in numerous fields including the design of materials, robots, DNAs, proteins, etc. Recent approaches train a deep neural network (DNN) mo...	Accept	This paper studies Offline Model-Based Optimization. This paper proposes a gradient-based method for solving Offline MBO problems using infinite-width Deep learning models. The key novelty of the paper is in proposed use of a distillation objective to constrain the optimized design-score pairs. All three reviewers ide...	train	[ "4vaLL23PTEa", "3eoHuS5QRM-", "WIOQV0gZiUn", "j8dIBmdFIiO", "JO4prL8inPn", "6eG9P-Z-oH", "C5UbIvoNAdF", "muJrF6zHqN", "Q-O_ye6F23X", "r4WTCDYQRif", "0PilWkDcAX", "ogzn2WT31Ml", "ZebZ8nM1VI", "xaGD_SS9OmF", "qqfMFrJZBkp", "bFd5Cx2gSS4", "2KuycUBCl-m", "_M9owUpMXf", "bWVQzEOkk_e" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your great questions, which make our paper stronger.\n\nWe will certainly include the main points of our discussion in an improved version.", " Thanks for responding to my questions at length, each of my major concerns has been resolved. In my current evaluation of the paper, I intend to increase ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 2 ]	[ "3eoHuS5QRM-", "6eG9P-Z-oH", "j8dIBmdFIiO", "JO4prL8inPn", "Q-O_ye6F23X", "ogzn2WT31Ml", "muJrF6zHqN", "0PilWkDcAX", "r4WTCDYQRif", "bWVQzEOkk_e", "_M9owUpMXf", "ZebZ8nM1VI", "xaGD_SS9OmF", "qqfMFrJZBkp", "bFd5Cx2gSS4", "2KuycUBCl-m", "nips_2022__j8yVIyp27Q", "nips_2022__j8yVIyp27Q...
nips_2022_iuW96ssPQX	A Transformer-Based Object Detector with Coarse-Fine Crossing Representations	Transformer-based object detectors have shown competitive performance recently. Compared with convolutional neural networks limited by the relatively small receptive fields, the advantage of transformer for visual tasks is the capacity to perceive long-range dependencies among all image patches, while the deficiency i...	Accept	This work proposes a new object detector architector that is based on a CNN stem, combined with a mostly transformer-based architecture, with the addition of a cross-fusion module that allows for reconciling coarse and high-grained features for more precise object detection. Thee paper is well-written, novel and prese...	val	[ "ww3o8sL5X02", "6XktyXmW3hV", "He2xMGP_qP", "PvC4WhKLAs_", "XkyZuSLzHwS", "mOmpCDCIphm", "JKWuGzF8bO", "YWu3XhsudX", "dBvZnX3qZTJ", "-myShTq_alh", "fkE1G6HwBb", "f0bEHpM1fB", "8WHAD28ddE2", "Nroi5oia6bB", "4UeUdflDamq", "hLxWk4NBmnQ", "8h4wlFcerMG" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for your recognition of our manuscript.", " Thanks for your reply, which solves my question. I keep my original score.\n\n", " Thank you for the support. We will add the new results and analysis in the paper.", " I appreciate the authors' response. Now, it's clear why the proposed archit...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 5 ]	[ "6XktyXmW3hV", "-myShTq_alh", "PvC4WhKLAs_", "JKWuGzF8bO", "8h4wlFcerMG", "8h4wlFcerMG", "8h4wlFcerMG", "hLxWk4NBmnQ", "hLxWk4NBmnQ", "hLxWk4NBmnQ", "4UeUdflDamq", "4UeUdflDamq", "4UeUdflDamq", "4UeUdflDamq", "nips_2022_iuW96ssPQX", "nips_2022_iuW96ssPQX", "nips_2022_iuW96ssPQX" ]
nips_2022_0zlLhfG6rxI	Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres	We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an $\mathrm{SO}^{+}(2,1)$-equivariant neural network. This model takes advantage of the of the azimuthal correlations known to exist in fibre speckle patterns and naturally ...	Accept	This paper proposes a new learning-based technique for imaging through multimodal fibers (MMF). A key idea of this work is to exploit the property that the transmission matrices associated with MMFs are approximately diagonalizable by Bessel basis. This idea then allows one to significantly reduce the number of paramet...	train	[ "DYPHYwRMLX0", "0-QgR8yA1Wt", "qTxaO861ByR", "euccr3gIuJ8", "msIn9z1u8BK", "OC0aM2YVtXe", "Ki9s_sWyLW", "ADTEPW7XCam", "4Jem9hOzTJu", "P1KOGy_Iy8J", "HzDeNvC4t_h" ]	[ "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your response! We are happy those sections addressed your most serious concerns.\n\nAlso, thank you for raising the point on cells. We were hoping to give an application where requiring a smaller training dataset would be a benefit, if a future system were to be re-calibrated in situ (with a specifi...	[ -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4 ]	[ "qTxaO861ByR", "Ki9s_sWyLW", "OC0aM2YVtXe", "msIn9z1u8BK", "OC0aM2YVtXe", "P1KOGy_Iy8J", "HzDeNvC4t_h", "4Jem9hOzTJu", "nips_2022_0zlLhfG6rxI", "nips_2022_0zlLhfG6rxI", "nips_2022_0zlLhfG6rxI" ]
nips_2022_WE92fqi-N_g	VICE: Variational Interpretable Concept Embeddings	A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a t...	Accept	This paper proposes a method to learn meaningful representations of data by incorporating a pick-odd-one-out task on triplets of images to learn embeddings through variational inference using a spike-and-slab Gaussian prior. The reviewers agreed that the paper was well written, had a clear narrative, that the results...	train	[ "06FlhTKaVaU", "9F0umk-7Pom", "pHLspvFF4Gu", "Aa5hWEgH5N", "9M_TEVtg21", "6-TR1nReXrM", "G_kN0Pqkzeb", "3XHnpwMkIgv", "VjRUZq88Vtp", "AcrSijBMF5E" ]	[ "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " It's unclear for the neural network settings and how to get the embedding vectors.\n\nVICE has access to the human responses rather than to the image representations of the objects. From these responses, VICE learns an embedding representation for each object. Although there is a softmax function involved to ...	[ -1, -1, -1, -1, -1, -1, 6, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, 1, 3, 3, 3 ]	[ "AcrSijBMF5E", "VjRUZq88Vtp", "Aa5hWEgH5N", "3XHnpwMkIgv", "G_kN0Pqkzeb", "nips_2022_WE92fqi-N_g", "nips_2022_WE92fqi-N_g", "nips_2022_WE92fqi-N_g", "nips_2022_WE92fqi-N_g", "nips_2022_WE92fqi-N_g" ]
nips_2022_vRwCvlvd8eA	Chefs' Random Tables: Non-Trigonometric Random Features	We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax kernels. CRTs are an alternative to standard random kitchen sink (RKS) methods, which inherently rely on the trigonometric maps. We present variants of CRTs where RFs are positive, a key ...	Accept	The primary motivation of the paper is the scalable training of transformers, particularly their efficient softmax-attention approximation (3). As a classical approach relying on trigonometric random Fourier features (RFF) does not guarantee positivity (which makes the training of transformers unstable), the authors co...	train	[ "PjmyUMJb8b9", "Gn9xKjDqCnt", "Xhx06X2jKkc", "i6yoGhWMi7t", "QiYI1jcrRJN", "mFXVGs7Dkh", "4YXrXARiVX0", "8Vx3n8YDEeD", "tyBA_IGN1hv", "QHPpS1KMXo", "FxNEp3oiRdN", "zMG-0xCex9S" ]	[ "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank you again for your time and helpful comments. We hope we have addressed your concerns and you might consider raising your score. If you have any further concerns, we'd be glad to address them.", " We would like to sincerely thank the Reviewer for all the comments.\n\n*It seems that the discretely-induc...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 2, 3, 3 ]	[ "nips_2022_vRwCvlvd8eA", "zMG-0xCex9S", "i6yoGhWMi7t", "FxNEp3oiRdN", "QHPpS1KMXo", "4YXrXARiVX0", "tyBA_IGN1hv", "nips_2022_vRwCvlvd8eA", "nips_2022_vRwCvlvd8eA", "nips_2022_vRwCvlvd8eA", "nips_2022_vRwCvlvd8eA", "nips_2022_vRwCvlvd8eA" ]
nips_2022_WSxarC8t-T	SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems	Gradient Boosted Decision Tree (GBDT) is a widely-used machine learning algorithm that has been shown to achieve state-of-the-art results on many standard data science problems. We are interested in its application to multioutput problems when the output is highly multidimensional. Although there are highly effective G...	Accept	After rebuttal, the reviewers unanimously agree that the submission should be accepted for publication at NeurIPS. Reviewers were excited about the achieved speed-up.	train	[ "n7ur2OZWO0", "GeH7OAmVbvd", "-9kidQ81EDY", "EvjXWu_19-y", "wR1OAzYnTua", "WLlA9x4-XIs", "tDuwNcvcELP", "nC2IQ6xMspU", "Vs0WlmZybuj0", "bQbR5vN_HLK", "gCLree-cVM", "6CphUQHtrmQ" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your feedback and suggestions for improving our work, especially the experiment section! \n", " Thank you for your feedback and for the idea to compare SketchBoost with TabNet!", " Dear Reviewer psGW,\n\nThe reviewer-author discussion period will end on August 9. Have you had a chance to look at...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "EvjXWu_19-y", "wR1OAzYnTua", "tDuwNcvcELP", "Vs0WlmZybuj0", "nC2IQ6xMspU", "nips_2022_WSxarC8t-T", "bQbR5vN_HLK", "gCLree-cVM", "6CphUQHtrmQ", "nips_2022_WSxarC8t-T", "nips_2022_WSxarC8t-T", "nips_2022_WSxarC8t-T" ]
nips_2022_wOI0AUAq9BR	SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks	In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification. While, from the theoretical viewpoint, most GNNs can operate on graphs of any size, it is empirically observed that their classification performance degrades when they are applied on graphs with sizes ...	Accept	This work proposes a regularization approach (based on graph coarsening and alignment) to allowing graph neural networks to generalize across different graph sizes. The approach proposed here is simple, yet shown to be effective. While the reviewers had some concerns regarding this paper and the results in it, these we...	train	[ "z5W_RxyKJfa", "RM8LhCQTT1u", "DmhjjI9YsOK", "DiKcuaqQYA4", "386X_MbhS9d", "BwRiFZfXoTN", "AGnyxYgVy6c", "LlWK3k-Yon5", "uGeSC5yxh3W", "9Bxcs1YmGip", "T6LZ8EJaoxP", "bHTn3dS_qV-" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We sincerely thank the reviewer for taking the time to answer our rebuttal. We will try to address the standing concerns below.\n\n- Q1. The unattributed datasets in [6] were designed to test the theoretical assumptions behind the model proposed in [6], and in fact there is a big discrepancy (in the ranking of th...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "RM8LhCQTT1u", "LlWK3k-Yon5", "BwRiFZfXoTN", "LlWK3k-Yon5", "nips_2022_wOI0AUAq9BR", "AGnyxYgVy6c", "bHTn3dS_qV-", "T6LZ8EJaoxP", "9Bxcs1YmGip", "nips_2022_wOI0AUAq9BR", "nips_2022_wOI0AUAq9BR", "nips_2022_wOI0AUAq9BR" ]
nips_2022_GkDbQb6qu_r	CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers	Development of transformer-based text-to-image models is impeded by its slow generation and complexity, for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel autoregressive generation. We pretrain a 6B-parameter transformer with a simple and flexible...	Accept	This paper describes a less auto-regressive approach for text-to-image generation. Reviewers were somewhat split, with one reject and one strong accept. Overall, the results the authors get are perhaps less compelling given the progress the field has made over the past few months since submission time, but I think the ...	train	[ "ZbNFfm1nN5", "V4Scasj2jIj", "Q0zUciAAAh", "Iv_AWJPjeo8", "au7ym-X3icx", "A5uUHVKPAcJ", "O6pz7RUfDag", "IS-KPcyimMM", "SR9o2rOH40", "ag5GOZXc_5P", "A_tdGDBj_0u" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the clarification and additional information!", " I appreciate authors for providing the extra visualizations. Most of my questions are resolved, but still I am not very convinced but the masking strategy. I raise the score to weak accept based on overall contributions. ", " \nThank you very much f...	[ -1, -1, -1, -1, -1, -1, -1, 3, 6, 8, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 5, 3, 3, 4 ]	[ "Iv_AWJPjeo8", "A_tdGDBj_0u", "A_tdGDBj_0u", "ag5GOZXc_5P", "SR9o2rOH40", "IS-KPcyimMM", "IS-KPcyimMM", "nips_2022_GkDbQb6qu_r", "nips_2022_GkDbQb6qu_r", "nips_2022_GkDbQb6qu_r", "nips_2022_GkDbQb6qu_r" ]
nips_2022_nxw9_ny7_H	Deep invariant networks with differentiable augmentation layers	Designing learning systems which are invariant to certain data transformations is critical in machine learning. Practitioners can typically enforce a desired invariance on the trained model through the choice of a network architecture, e.g. using convolutions for translations, or using data augmentation. Yet, enforcing...	Accept	The decision for this paper was a hard one. I pondered the scores with respect to the engagement of the different reviewers. I believe the initial scores were due to a misunderstanding of the limitations of the baseline model Augerino, and how the proposed method solves some of the failures and limitations of Augerino...	val	[ "f7ThyXEWYm", "6Ptp6AnIuM1", "1zXiE_CvF9Y", "7-o57NZ5Qo_", "MLqDP2DFj4", "YR_41m6nH-z", "7dFMEnqkUQF", "9ntBV2MYmi", "zMDfPT6MCG", "ed7pyM0zZ1i", "NqFRBjvhwTM", "madgNlDgfv", "fUCM7hc1zRAy", "h_iOuUbGCaW", "TVL0YmPskKA", "plJubC1tAQ5", "FTL99AJUi5v", "B85nwj8yqQW" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for their new comments and are glad that our new experiments and revised manuscript helped to convince of the relevance of our contribution. Please note however that your rating has not been improved yet.\n\nConcerning the caption of Figure 3, we now better understand what was the proble...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 5, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "6Ptp6AnIuM1", "YR_41m6nH-z", "7-o57NZ5Qo_", "9ntBV2MYmi", "nips_2022_nxw9_ny7_H", "7dFMEnqkUQF", "B85nwj8yqQW", "zMDfPT6MCG", "FTL99AJUi5v", "NqFRBjvhwTM", "plJubC1tAQ5", "fUCM7hc1zRAy", "h_iOuUbGCaW", "TVL0YmPskKA", "nips_2022_nxw9_ny7_H", "nips_2022_nxw9_ny7_H", "nips_2022_nxw9_ny...
nips_2022_7HTEHRMlxYH	FNeVR: Neural Volume Rendering for Face Animation	Face animation, one of the hottest topics in computer vision, has achieved a promising performance with the help of generative models. However, it remains a critical challenge to generate identity preserving and photo-realistic images due to the sophisticated motion deformation and complex facial detail modeling. To ad...	Accept	After rebuttal all reviewers recommend acceptance. The authors are encouraged to follow the reviewer suggestions on improving the final paper.	train	[ "_Axbg_KoyY", "A7eID1fsZrm", "Ww2bkBrP-2G", "WnBGLJ01vED", "dYgWHvNcc2f", "A8TCWiFLueP", "U8DpX55lAJt", "VoqUk3p7olg", "JxgB5ywhRGi", "7YWNkfM5yx", "1LVLhWb5sy2" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We will definitely do it. Thank you again for your supportive comment.", " Thank the authors for providing such a comprehensive reponse to all my questions. The provided video is much better than the one before. Please try involving the provided details and changes to the revision and supplementary accordingly....	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 4 ]	[ "A7eID1fsZrm", "dYgWHvNcc2f", "7YWNkfM5yx", "7YWNkfM5yx", "7YWNkfM5yx", "nips_2022_7HTEHRMlxYH", "JxgB5ywhRGi", "1LVLhWb5sy2", "nips_2022_7HTEHRMlxYH", "nips_2022_7HTEHRMlxYH", "nips_2022_7HTEHRMlxYH" ]
nips_2022_UPnJuDKqOfX	HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details	Neural rendering can be used to reconstruct implicit representations of shapes without 3D supervision. However, current neural surface reconstruction methods have difficulty learning high-frequency geometry details, so the reconstructed shapes are often over-smoothed. We develop HF-NeuS, a novel method to improve the q...	Accept	This paper presents a new way to build centered weights for volume rendering, utilize displacement maps, adaptive scale, as well as other techniques to provide better high frequency details in neural SDF representations. The reviewers also acknowledged the rebuttal and the revision and the authors addressing their m...	train	[ "7WY8w2NiW9H", "Lk3rOcxdX3", "3IAFHvvsZ8u", "4G1_xUDrGYR", "3bkQ2VsVdw", "HX-AZ2xaYZ2", "DvcW6UsNYp4", "GiMjjGfQ6BD", "ZqNqB67GLa", "ZHbzhrIb6EgE", "fGlxP_9PUj1Q", "KTpqZ1wPyPc", "63nZlEv0Lou", "E_g0DJY0ys", "KWpgoXFWOFn", "T38rDcJnxG9", "RyCn8cKqI6c", "EyKHQu24ZXs", "m1R7Bb9W26c...	[ "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_re...	[ " Thank you for the response. My questions have been sufficiently addressed and some of my feedback has been integrated into the revised paper.\n\nI have also read the other reviews and the authors' response to those papers, and have no further questions.\n\nI stand with my original rating and recommend accepting t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4, 5 ]	[ "63nZlEv0Lou", "EyKHQu24ZXs", "HX-AZ2xaYZ2", "3bkQ2VsVdw", "RyCn8cKqI6c", "KTpqZ1wPyPc", "m1R7Bb9W26c", "m1R7Bb9W26c", "fGlxP_9PUj1Q", "RyCn8cKqI6c", "EyKHQu24ZXs", "T38rDcJnxG9", "KWpgoXFWOFn", "nips_2022_UPnJuDKqOfX", "nips_2022_UPnJuDKqOfX", "nips_2022_UPnJuDKqOfX", "nips_2022_UPn...
nips_2022_jzd2bE5MxW	TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels	State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even when centralized SGD easily finds a solution that is simultaneously performant for all clients, current federated optimization methods fail to ...	Accept	This paper introduced a novel two-stage scheme to combine the feature learning capacity of neural network and for efficient optimization linear model. It makes interesting empirical observations that are relevant to NeurIPS and may inspire future work. In particular, the observation that FedAvg learns useful features e...	train	[ "a3eXfmUSBa", "FuTOBxt25KI", "P7wEXchyNyl", "krFURGWt-7T", "_aSB9T3vNIN", "ec-BBoYgU6o", "W4jZcYLgJ91", "w4DMfezoljM", "xl_VJUaO0HF", "_DgChOC4msz", "pP1YH-nxZ-", "pIJkqnVIevA", "13fPyaoYajH", "_vC1nFDhB8d", "CoYcAC3Ml3y", "YRDX79Rnge" ]	[ "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Authors response and new experiments have resolved my concerns. Thus I will raise my score. ", " I would like to thank the authors for their extensive response that cleared a lot of my concerns. I would also encourage the authors to provide the discussion around the sources of non-iidness in the main text as we...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "P7wEXchyNyl", "pP1YH-nxZ-", "krFURGWt-7T", "pIJkqnVIevA", "nips_2022_jzd2bE5MxW", "xl_VJUaO0HF", "nips_2022_jzd2bE5MxW", "YRDX79Rnge", "YRDX79Rnge", "CoYcAC3Ml3y", "CoYcAC3Ml3y", "_vC1nFDhB8d", "_vC1nFDhB8d", "nips_2022_jzd2bE5MxW", "nips_2022_jzd2bE5MxW", "nips_2022_jzd2bE5MxW" ]
nips_2022_OxfI-3i5M8g	Scalable Neural Video Representations with Learnable Positional Features	Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos. Here, the main challenge is how to (a) alleviate a compute-inefficiency in training CNRs to (b) achieve high-quality video enco...	Accept	The paper proposes a coordinate-based architecture for representing a video in a parameter-efficient and computationally efficient manner. Such architecture can be used for video compression, inpainting and frame interpolation. The initial concerns were addressed during the rebuttal period and all the reviewers had a ...	train	[ "di0GkRzvpw5", "WFcfavFTmUH", "prMEV5l3wXY", "yi9yye93t_4", "L4RGdu7PK1v", "5zUxMvUcxwX", "q6cLtZVtENG", "eMBadi3LXgA", "jWgxQGbbMK", "AssIQQ89b_G", "iVWeYWNYNwr", "xRB1tU4RzXI", "WKcX34hI4SB", "Xc81zRU3pKT", "GIHYK7Vhstz", "8jae65LSbxF", "jAhoqJ3fvR3", "DAFO2_y9QS", "sNf6xpmXSPy...	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your response! We are happy to find that our rebuttal successfully addressed all of your concerns. Although our primary focus is not video compression, we also think that providing the comparison results with deep video codecs under the fair evaluation condition would further strengthen our paper an...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "WFcfavFTmUH", "jAhoqJ3fvR3", "AssIQQ89b_G", "L4RGdu7PK1v", "jWgxQGbbMK", "xRB1tU4RzXI", "Xc81zRU3pKT", "jAhoqJ3fvR3", "nips_2022_OxfI-3i5M8g", "nips_2022_OxfI-3i5M8g", "DAFO2_y9QS", "DAFO2_y9QS", "sNf6xpmXSPy", "sNf6xpmXSPy", "dqgAN-6_b_", "dqgAN-6_b_", "dqgAN-6_b_", "nips_2022_Ox...
nips_2022_UPZCt9perOn	Metric-Projected Accelerated Riemannian Optimization: Handling Constraints to Bound Geometric Penalties	We propose an accelerated first-order method for the optimization of smooth and (strongly or not) geodesically-convex functions over a compact and geodesically-convex set in Hadamard manifolds, that we access to via a metric-projection oracle. It enjoys the same rates of convergence as Nesterov's accelerated gradient d...	Reject	The paper deals with accelerated methods on Riemannian manifolds. A particular challenge that the paper tries to address, which the AC believes is important, is related to the bounding of the iterates. The paper starts with an explicit bounding constraint on the manifold (and relaxes to the ball constraint for certain ...	test	[ "OpWEagbPtn1", "U8CCrJb8pH", "DAc2IU8pUJ6", "6lmR8RIwmq", "6fEkijqVXLz", "SLmydIBk_wa", "WfW0aTSeJQM", "LsL9guTP5E2", "H4NNasFDUmk", "Ohfu_ZO6OAE", "PWJn03a-2nS", "hphTALmqLSk", "VXtpvx1vKG9U", "wqlFQYdwCmV", "APk1U6e1Nwb", "DNcVRKHtra7" ]	[ "author", "author", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " To all reviewers, please note we updated the supplementary material to include a new section in the Appendix (section C) including the new subroutines and their proofs of linear convergence", " Could you clarify why after the response your impression of the paper is the same? The main concerns, namely the imple...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4 ]	[ "LsL9guTP5E2", "WfW0aTSeJQM", "6lmR8RIwmq", "SLmydIBk_wa", "DNcVRKHtra7", "wqlFQYdwCmV", "PWJn03a-2nS", "H4NNasFDUmk", "nips_2022_UPZCt9perOn", "DNcVRKHtra7", "APk1U6e1Nwb", "wqlFQYdwCmV", "wqlFQYdwCmV", "nips_2022_UPZCt9perOn", "nips_2022_UPZCt9perOn", "nips_2022_UPZCt9perOn" ]
nips_2022_bi1BTcXa8Q	Cross-modal Learning for Image-Guided Point Cloud Shape Completion	In this paper we explore the recent topic of point cloud completion, guided by an auxiliary image. We show how it is possible to effectively combine the information from the two modalities in a localized latent space, thus avoiding the need for complex point cloud reconstruction methods from single views used by the st...	Accept	The paper proposes a point cloud completion method that can take an auxiliary image as guidance. All the reviewers rate the paper slightly above the bar. They like the reported strong performance over the prior baseline and also the capability of using the auxiliary input. Although several reviewers raise concerns abou...	test	[ "tGYNy0IK5Pe", "zJuaIU7F0CQ", "cC-oO9sLMu", "-vkIb8PYi7m", "Z4ZDeBaRRN", "ATc87KYJQz4", "NkP8w3Jt_Ij", "InZkK0-wInM", "YyfHHX8C2sy", "rIZHcz7ztY3" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The authors addressed most of my concerns. I will change the final score to borderline accept.", " The rebuttal addressed my concerns. Therefore, I change my ratings to borderline accept.", " We thank the reviewer for the constructive feedback and for all the suggestions to improve the paper. \n\n> *The main...	[ -1, -1, -1, -1, -1, -1, 5, 5, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, 4, 2, 5, 4 ]	[ "ATc87KYJQz4", "-vkIb8PYi7m", "rIZHcz7ztY3", "YyfHHX8C2sy", "InZkK0-wInM", "NkP8w3Jt_Ij", "nips_2022_bi1BTcXa8Q", "nips_2022_bi1BTcXa8Q", "nips_2022_bi1BTcXa8Q", "nips_2022_bi1BTcXa8Q" ]
nips_2022_dp0zWsdOV1h	Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions	The "Patient Instruction" (PI), which contains critical instructional information provided both to carers and to the patient at the time of discharge, is essential for the patient to manage their condition outside hospital. An accurate and easy-to-follow PI can improve the self-management of patients which can in turn ...	Accept	The authors propose and evaluate a method to automatically generate "patient instruction" drafts. There was a consensus amongst reviewers that this is an interesting application. While the technical innovation here may be modest, the empirical results firmly establish the benefits of the proposed "Re3Writer" approach...	train	[ "A_w7N1M0F4j", "XHt8lGjFzBh", "YhwUOr-oXT", "OAnf_CHn_A", "wQJFyglIT9m", "ieD96g-LOlF", "k40K1LyJS8", "so62kMf_v2f", "0DTHNxlQzlr", "VEChKQ5masm", "h9brTxqaQDY", "cMxZ9vWjEqo" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for acknowledging the response. We are genuinely happy that our response properly addresses fellow reviewers' concerns. We thank the reviewer again for the constructive feedback which have helped us improve our paper!\n", " We thank the reviewer for acknowledging the response. We are genui...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4 ]	[ "OAnf_CHn_A", "wQJFyglIT9m", "VEChKQ5masm", "ieD96g-LOlF", "k40K1LyJS8", "cMxZ9vWjEqo", "h9brTxqaQDY", "0DTHNxlQzlr", "VEChKQ5masm", "nips_2022_dp0zWsdOV1h", "nips_2022_dp0zWsdOV1h", "nips_2022_dp0zWsdOV1h" ]
nips_2022_Z9ldMhplBrT	Rethinking the compositionality of point clouds through regularization in the hyperbolic space	Point clouds of 3D objects exhibit an inherent compositional nature where simple parts can be assembled into progressively more complex shapes to form whole objects. Explicitly capturing such part-whole hierarchy is a long-sought objective in order to build effective models, but its tree-like nature has made the task e...	Accept	The paper presents a regularization for point cloud representation learning aiming to promote a part-whole hierarchy through a hyperbolic space. Most of the reviewers agree the idea of using the hyperbolic space is new and interesting. The experiment results seem to be sufficient. There was some confusion on how part i...	train	[ "nC_4hYkyS0o", "UezFn4AyY5f", "6OFaqrpmbPK", "Ku5kQ9N2dUt", "EeMGid93iX", "x69Vha6lIGO", "0vULY7Etcx7", "k6-EMUCcz0n", "DLoXOa_WA1M", "yxzgwQTuUeA", "wQUhnW2inRY", "7mG9TLBJjW", "3GbteWpoZr73", "fRm6ELeIzMj", "fY9DiJPFCI", "B_58HVlxahL", "NcdjnjUsW68", "LTPK8g1zaWK", "PP4emTAJXDC...	[ "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_re...	[ " We report the results of using partial shape augmentation to reinforce the DGCNN backbone. The training generated partial shapes with a random number of points to supplement the full shapes. As mentioned above, the improvement over the non-augmented baseline is not significant. However, we do observe improvements...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 4, 7, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3, 4, 4 ]	[ "UezFn4AyY5f", "6OFaqrpmbPK", "Ku5kQ9N2dUt", "k6-EMUCcz0n", "wQUhnW2inRY", "0vULY7Etcx7", "7mG9TLBJjW", "DLoXOa_WA1M", "PP4emTAJXDC", "LTPK8g1zaWK", "NcdjnjUsW68", "3GbteWpoZr73", "B_58HVlxahL", "fY9DiJPFCI", "nips_2022_Z9ldMhplBrT", "nips_2022_Z9ldMhplBrT", "nips_2022_Z9ldMhplBrT", ...
nips_2022_ZuSiW0EixjX	Redistribution of Weights and Activations for AdderNet Quantization	Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions (i.e., L1-norm). To achieve higher hardware efficiency, it is necessary to further study the low-bit quantization of AdderNet. Due to the li...	Accept	The reviewers were mostly positive about this paper [8,6,6,4], while the negative reviewer did not update the review or respond after the author's response. I do not see any major issues remaining. The suggested method seems interesting, novel, and achieves good empirical results.	train	[ "EHI2TjL368", "dDYeqb5esCg", "gJJ35xuiukx", "aI3sMQJgxrY", "xn2v6JCgIqi", "4blwLIxn_gO", "uCm49e8MC6O", "mhvJdh4d-_e", "16elRJ5NSKq", "56aLZhvEFfB", "Fw55hZ1lwor" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The authors' response has solved all of my concern, I will keep my rating about this work.", " We would like to sincerely thank the reviewer for providing a constructive review and detailed comments.\n\nQ1: Better to compare the accuracy drops in quantized CNNs as well as the currently presented accuracy dr...	[ -1, -1, -1, -1, -1, -1, -1, 6, 4, 6, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "uCm49e8MC6O", "mhvJdh4d-_e", "mhvJdh4d-_e", "16elRJ5NSKq", "16elRJ5NSKq", "56aLZhvEFfB", "Fw55hZ1lwor", "nips_2022_ZuSiW0EixjX", "nips_2022_ZuSiW0EixjX", "nips_2022_ZuSiW0EixjX", "nips_2022_ZuSiW0EixjX" ]
nips_2022_wjClgX-muzB	Rethinking Variational Inference for Probabilistic Programs with Stochastic Support	We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support. Existing approaches to this problem rely on designing a single global variational guide on a variable-by-variable basis, while maintaining the stochastic control...	Accept	The reviewers have reached consensus after processing the authors' feedback. They all agree that this manuscript presents an interesting approach to applying variational inference in a setting of probabilistic programming that is of interest to the community. The reviewers raise tangible points that the authors have in...	train	[ "8MezfFjm8bt", "nQ0By83IMUD", "86sQmhMJLzm", "H53vUFd2Swq", "FXeiCrS63gp", "OM17mcSahrK", "0FhZ0LvnW_Z", "LGkISGLHysv", "4F8cfP7WDTr", "PgtWuAF-YT", "JU9ggTsMaFt", "Ed6I8MqQ5h", "8QgoRWkQNaF", "eS9LdC6xNCW", "oGETq0ZD_P0", "gaJxoZPu_0P", "ypDiqxlDQWX", "m3SttBGt3EB", "odOLPGMUpK2...	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_...	[ " We agree that there are already more complex, custom variational families that were proposed for specific models with stochastic support and that the section you highlight could be more clear about this. We will emphasize more clearly that we are focusing on automated guide construction methods and we are going t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 5 ]	[ "86sQmhMJLzm", "H53vUFd2Swq", "Ed6I8MqQ5h", "Ed6I8MqQ5h", "0FhZ0LvnW_Z", "LGkISGLHysv", "oGETq0ZD_P0", "m3SttBGt3EB", "PgtWuAF-YT", "ypDiqxlDQWX", "nips_2022_wjClgX-muzB", "8QgoRWkQNaF", "eS9LdC6xNCW", "ZgkiugRDayY", "Ip8iKEAWb66", "ypDiqxlDQWX", "AlhZVqR5mIv", "YJok-jP1WDH", "ni...
nips_2022_rwdpFgfVpvN	Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond	This paper considers online convex optimization with hard constraints and analyzes achievable regret and cumulative hard constraint violation (violation for short). The problem distinguishes itself from online convex optimization with soft constraints, where a violation at one round can be compensated/cancelled by a co...	Accept	This paper provides an algorithm for online convex optimization with varying unknown constraints. Reviewers agree that the methods involved appear novel and interesting. However, the authors are strongly encouraged to add a discussion of the computational complexity of the method, which may provide the missing tradeoff...	train	[ "bB7c_FifyoH", "2iOlBxmu4-e", "DIWbuvLbCTc", "RTstfRaiRIf", "EPb8jV-nCVe", "PITGJQ8hvsm", "X_KdTnquHbS", "ahj-9Xtgd29", "rYTFUuGaJ-", "K8lM4H8dAu", "7aSayy5W-I6", "0xu4iq-fTeY", "Iah9IOLI-9" ]	[ "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewers,\n\nWe appreciate your detailed comments again. If you have any further questions, please let us know so we can address them before the rebuttal phase ends. Thank you very much for your time!", " Dear Reviewer DX8X: \n\nSince it has been a few days that the author-reviewer discussion period start...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 2, 3, 5 ]	[ "nips_2022_rwdpFgfVpvN", "K8lM4H8dAu", "RTstfRaiRIf", "Iah9IOLI-9", "PITGJQ8hvsm", "0xu4iq-fTeY", "ahj-9Xtgd29", "K8lM4H8dAu", "7aSayy5W-I6", "nips_2022_rwdpFgfVpvN", "nips_2022_rwdpFgfVpvN", "nips_2022_rwdpFgfVpvN", "nips_2022_rwdpFgfVpvN" ]
nips_2022_sRKNkpUMQNr	Information-Theoretic GAN Compression with Variational Energy-based Model	We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model. Because the direct computation of the mutual infor...	Accept	This work concerns the compression of generative adversarial networks and other image generation networks, such as dense prediction/image to image networks. Where existing approaches to compress these models rely on matching pairs of outputs, this work optimizes the Barber-Agakov lower bound on the differential mutual ...	train	[ "kKYJqg529L", "vmATO-0ThZi", "WrWPbIRiw_", "-7e6HyzqrqB", "lQ2MRDmUMj", "OA1l1sSSxAc", "ao_nSHKTOOp", "rARv2VY3v4", "jYbekigQdQ_", "beQPeAoXx4V", "GtrgH7agn_7", "tZUEx2i0H3B", "J4a6pietHs", "7Sb95teitJ0" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We sincerely thank all reviewers for their constructive and positive comments and we present the summary of our responses to each reviewer as below. \n\nQ1. Visual quality compared with the previous methods \n\nA. Upon request by Reviewer tn1T, we present more qualitative results of VEM, OMGD, and CAGC in Figure ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 4, 5 ]	[ "nips_2022_sRKNkpUMQNr", "-7e6HyzqrqB", "beQPeAoXx4V", "lQ2MRDmUMj", "jYbekigQdQ_", "ao_nSHKTOOp", "rARv2VY3v4", "GtrgH7agn_7", "7Sb95teitJ0", "J4a6pietHs", "tZUEx2i0H3B", "nips_2022_sRKNkpUMQNr", "nips_2022_sRKNkpUMQNr", "nips_2022_sRKNkpUMQNr" ]
nips_2022_7vmyjUHgm9_	Less-forgetting Multi-lingual Fine-tuning	Multi-lingual fine-tuning (MLF), which fine-tunes a multi-lingual language model (MLLM) with multiple source languages, aims to gain good zero-shot performance on target languages. In MLF, the fine-tuned model tends to fit the source languages while forgetting its cross-lingual knowledge obtained from the pre-training ...	Accept	The paper proposes a method for finetuning multi-lingual pre-trained language in multiple languages simultaneously. The task is formalized as a constrained optimization problem and the upper bound of the forgetting is given in theory. A method is developed for multi-lingual fine-tuning to minimize the upper bound. ...	val	[ "vP8693mzth", "tdmq57icrm", "Kf6cnxj3XIO", "5ozI0HlUMq", "MpLL2QKOiCZ", "41F7TfCRLiE", "3p2FBlfOyc-q", "OVkTHmUrfTl", "2P4UaMjJ7n5", "vaJc0v6Nga_" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We highly appreciate your time for reading our revised paper. Your constructive and professional comments help a lot for improving this work. As to the limitations, we have discussed from two perspectives: (1) the assumption of being close to the original pretraining benefits the cross lingual generalization; (2)...	[ -1, -1, -1, -1, -1, -1, -1, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 3, 3 ]	[ "5ozI0HlUMq", "Kf6cnxj3XIO", "3p2FBlfOyc-q", "41F7TfCRLiE", "vaJc0v6Nga_", "2P4UaMjJ7n5", "OVkTHmUrfTl", "nips_2022_7vmyjUHgm9_", "nips_2022_7vmyjUHgm9_", "nips_2022_7vmyjUHgm9_" ]
nips_2022_c3HrNgQE7d	Exploring Figure-Ground Assignment Mechanism in Perceptual Organization	Perceptual organization is a challenging visual task that aims to perceive and group the individual visual element so that it is easy to understand the meaning of the scene as a whole. Most recent methods building upon advanced Convolutional Neural Network (CNN) come from learning discriminative representation and mode...	Accept	Overall, the reviewers commend the motivation of the approach, the core ideas presented in the paper, and the extensive experiments conducted for four different applications including camouflaged and salient object detection, infection, and polyp segmentation. In response to Reviewer fHq8, the authors have mentioned ...	train	[ "5Tpm7HjYrF", "BhFkAzAi2lh", "lTvRmJFVJlQ", "2x2dLIW3aFH", "LcqzRA4sm6g", "HnduKggnUOL", "w3BkHgY400s", "MC39_CEdxtl", "V995MCUhRRB", "kEsKXMXJbS4", "eH2nPk8WntV", "shp0GNjPmD", "hyNGpKKinTr", "IgdyH_89gVq", "7hfkIet67f" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your new comments, we clarify your concern below.\n\n\nQ#: Regarding the selection of two cues, I understand that the selected cues are of importance. However, how about other cues mentioned in the related work section since they are also \"factors that affect Figure-Ground assignment\".\n\nA#:...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 5, 4 ]	[ "BhFkAzAi2lh", "2x2dLIW3aFH", "IgdyH_89gVq", "LcqzRA4sm6g", "HnduKggnUOL", "IgdyH_89gVq", "MC39_CEdxtl", "7hfkIet67f", "hyNGpKKinTr", "shp0GNjPmD", "nips_2022_c3HrNgQE7d", "nips_2022_c3HrNgQE7d", "nips_2022_c3HrNgQE7d", "nips_2022_c3HrNgQE7d", "nips_2022_c3HrNgQE7d" ]
nips_2022_8li9SYYY3eQ	Language Conditioned Spatial Relation Reasoning for 3D Object Grounding	Localizing objects in 3D scenes based on natural language requires understanding and reasoning about spatial relations. In particular, it is often crucial to distinguish similar objects referred by the text, such as "the left most chair" and "a chair next to the window". In this work we propose a language-conditioned t...	Accept	Reviewers where in agreement that the method and manuscript are strong and provide a valuable connection between 3D perception and language. The evaluation suffers somewhat from the fact that there are no good datasets targeted at this problem. Authors mitigate this by performing a thorough evaluation with numerous ab...	train	[ "5eKBg8K3OPL", "FXQPRhynjO", "V_bDbGLeap", "Lg_JbI12kiv", "ipNSHgKXbHd", "7J_h_iFInSw", "__TrIvMY7XR", "pSOYWwp_N4", "02Vdl4VPUS" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the detailed response. The discussions about baselines and the language encoders are helpful. Since there is a huge performance gap between different language encoders, I would suggest the authors add this discussion to the main paper. Overall, I am satisfied with the author's responses and would ke...	[ -1, -1, -1, -1, -1, 7, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, 3, 4, 3, 2 ]	[ "Lg_JbI12kiv", "02Vdl4VPUS", "pSOYWwp_N4", "__TrIvMY7XR", "7J_h_iFInSw", "nips_2022_8li9SYYY3eQ", "nips_2022_8li9SYYY3eQ", "nips_2022_8li9SYYY3eQ", "nips_2022_8li9SYYY3eQ" ]
nips_2022_dz79MhQXWvg	Weakly supervised causal representation learning	Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is however identifiable in a weakly supervised setting. This involves a dataset with paired sample...	Accept	The reviewers were split about this paper: on one hand they would have liked to see better experimental results, particularly for larger graphs, on the other they appreciated the identifiability results and the ILCM algorithm. After going through the paper and discussion I have voted to accept for the following reason:...	train	[ "8xU24BWibI", "pUgHe97mVR", "IOQTTar-go", "JqsQLMPimE3", "Pg84bQjpf81", "6jTtkWZLmmC", "zJ1R7agw4Pc", "dAypEhMKfu1", "yZh-u7swWBE", "B3ZyVz90pgl", "cIDYGYq4aMw", "T_W7mNLNzG", "8iSQB5Cpo9", "xMhNmUv4C4v", "Sk_Erlibqp", "G3svjvZ3WVj", "wevYiyJVQ0L" ]	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the reply, we are glad to hear that we were able to clarify our approach.\n\nYour questions and the toy example were very helpful. In addition to improving our description of ILCMs along these lines, in the final version of our paper we will also discuss which of our assumptions are required for red...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 4, 4, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 4 ]	[ "pUgHe97mVR", "JqsQLMPimE3", "JqsQLMPimE3", "Pg84bQjpf81", "T_W7mNLNzG", "dAypEhMKfu1", "nips_2022_dz79MhQXWvg", "nips_2022_dz79MhQXWvg", "wevYiyJVQ0L", "G3svjvZ3WVj", "Sk_Erlibqp", "8iSQB5Cpo9", "xMhNmUv4C4v", "nips_2022_dz79MhQXWvg", "nips_2022_dz79MhQXWvg", "nips_2022_dz79MhQXWvg", ...
nips_2022_tro0_OqIVde	HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions	Recent progress in vision Transformers exhibits great success in various tasks driven by the new spatial modeling mechanism based on dot-product self-attention. In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also...	Accept	This paper introduces a new operation gnConv and a computer vision network architecture HorNet. Motivated by the success philosophy of vision Transformers, the key idea of gnConv is to build a recursive form of gated convolution. It make the module input-adaptive and with long-range and high-order spatial int...	val	[ "7xxFoo3ZAV1", "3anNWkWmFeDh", "j9YaO4udujL", "9nq_zcPB3U", "amll9CVcER-", "X_wvB6sOvGa", "MMb6cU_t0Xu", "MzZTpdYb24", "wpEacKUrgqH", "V_EoWH4iJXu", "IfJzWYf_Uuw", "-IuYWTa3tPR", "K0XufsrXDBo", "IX5190b0vH_" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for addressing my concerns. I have increased the rating to 5.", " Thanks a lot for checking our response and providing valuable feedback.\n\nHigh-order spatial interaction is the key concept introduced in our paper. Previous work on Transformer-like architectures usually explores the long-term and input-...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 5 ]	[ "3anNWkWmFeDh", "j9YaO4udujL", "X_wvB6sOvGa", "MzZTpdYb24", "wpEacKUrgqH", "IfJzWYf_Uuw", "nips_2022_tro0_OqIVde", "IX5190b0vH_", "V_EoWH4iJXu", "K0XufsrXDBo", "-IuYWTa3tPR", "nips_2022_tro0_OqIVde", "nips_2022_tro0_OqIVde", "nips_2022_tro0_OqIVde" ]
nips_2022_I-ggHgon-Az	What You See is What You Classify: Black Box Attributions	An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nature of such networks. Most existing approaches find such attributions eithe...	Accept	The paper proposes an attribution prediction approach to enhance the interpretability of DNN models. For this purpose, a second “explainer” model is used which can generality class-specific masks for the classification of relevant regions. The reviewers have overall commended the novelty of the approach, clear writin...	train	[ "CqHSdWhm_I6", "zqAoNCB8_D", "paT6J9_42Fc", "UOP3tdQTuGX", "dwezajD8q7c", "8F_O1R4Iot5", "wW3xd2xZG_Q", "3IMhr9PBwa7", "lC5gFzWPAJy", "h7XO2jPIp0X" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for the detailed response. \n\nThe authors have clarified the problem setting context. I would argue that this is not a post-hoc explainability model in the classical sense. Using ground truth class labels for training, the masking network adds elements that are not faithful to the base classi...	[ -1, -1, -1, -1, -1, -1, -1, 8, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "dwezajD8q7c", "h7XO2jPIp0X", "nips_2022_I-ggHgon-Az", "h7XO2jPIp0X", "lC5gFzWPAJy", "lC5gFzWPAJy", "3IMhr9PBwa7", "nips_2022_I-ggHgon-Az", "nips_2022_I-ggHgon-Az", "nips_2022_I-ggHgon-Az" ]
nips_2022_p9_Z4m2Vyvr	Amortized Mixing Coupling Processes for Clustering	Considering the ever-increasing scale of data, which may contain tens of thousands of data points or complicated latent structures, the issue of scalability and algorithmic efficiency becomes of vital importance for clustering. In this paper, we propose cluster-wise amortized mixing coupling processes (AMCP), which is ...	Accept	In this paper. the authors propose a novel amortized clustering method in which intra-cluster mixing and inter-cluster coupling are introduced. The optimal transport is used to learn the relationship between samles and reference distribution with intra-cluster mixing. The inter-cluster coupling assign samples to cluste...	train	[ "HRDKNcHVjfB", "K2l6rKehry", "i1P1BD9Rfkf", "D8HLg-Z8fX", "nSC6r_5ZmqJ", "rfBJT-GtWE-z", "4A8a34DaAy", "_nLxQ5rwhB0", "h7VoeCOZSh7", "_zZnrz7wEas", "YXw6FaDc6EJ" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for your positive feedback. Our paper won't be better without your nice suggestions. Thanks again.", " Thank you very much for increasing score! Our paper won't be better without your valuable suggestions. Thanks again.", " I would like to appreciate the prompt and rewarding responses give...	[ -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "i1P1BD9Rfkf", "D8HLg-Z8fX", "nSC6r_5ZmqJ", "rfBJT-GtWE-z", "YXw6FaDc6EJ", "_zZnrz7wEas", "h7VoeCOZSh7", "nips_2022_p9_Z4m2Vyvr", "nips_2022_p9_Z4m2Vyvr", "nips_2022_p9_Z4m2Vyvr", "nips_2022_p9_Z4m2Vyvr" ]
nips_2022_INzRLBAA4JX	Revisiting Sparse Convolutional Model for Visual Recognition	Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sparse convolutional models, which assume that a signal can be expressed by a linear combination of a few elements from a convolutional dictio...	Accept	In this paper, the authors introduce a convolutional sparse coding layer, which is intended as a replacement for a convolutional layer that is has greater interpretability and stability. Experiments show that a ResNet modified with this CSC-layer can achieve comparable performance on standard datasets as convolutional...	train	[ "90W88iuDif", "ZJqNiWIp0q0", "vapRlkFEwmp", "I1RilkEObZ", "XxDQii6l0Ju", "mY3EYbkobI0", "WeMOMstw1TUf", "fy5BdC95bwe", "96rTFKvFlxz", "7_7MvUX6HN", "e_vbTgs8Jp1", "7CxjdvC29S", "JlPDGbyfO0j" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear reviewer EVjq,\n\nFor Q1, it might be caused by the number of FISTA iterations. We will conduct more ablation studies and visualization in the future version. \nAlso, thanks for the suggestion about the visualization, we will add a border between different filters to make better visualization.", " Thanks ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "WeMOMstw1TUf", "vapRlkFEwmp", "I1RilkEObZ", "7_7MvUX6HN", "e_vbTgs8Jp1", "JlPDGbyfO0j", "96rTFKvFlxz", "JlPDGbyfO0j", "7CxjdvC29S", "e_vbTgs8Jp1", "nips_2022_INzRLBAA4JX", "nips_2022_INzRLBAA4JX", "nips_2022_INzRLBAA4JX" ]
nips_2022_vt516zga8m	Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever	Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss. For efficiently training recommender retrievers on modern hardwares, inbatch sampling, where the items in the mini-batch a...	Accept	The idea of more representative mini-batches sounds like a natural extension of the work done on stratified sampling. The reviewers were convinced the idea is both new and effective on real data. In particular, the discussion with mWFy clarified that this work is alternative way to explore the capacity of ranking optim...	train	[ "L-7S3CTvD-i", "AOZguDUkzaH", "BN9OYbkKnTd", "-JAcLZIoz1q", "dciMvLyYiu", "k4u3tCZNLCU", "Hot3AqG2LFDu", "qL6ggx0CkVr", "Lzg_294QaTy", "2trKDGSHCXa", "D8Op6AL27UU", "rExhbl85t1j", "ndWegZgBPA", "Ji-39VpOiau" ]	[ "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the rebuttal and appreciate the efforts. I think most of my comments/questions are addressed. Other reviews and related discussions also resolve my concern on the level of contributed. Changing the rating to 6.", " Dear Authors:\n\nThank you so much for further feedback to clarify the concerns about ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 6, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 3, 5 ]	[ "ndWegZgBPA", "-JAcLZIoz1q", "dciMvLyYiu", "dciMvLyYiu", "Hot3AqG2LFDu", "rExhbl85t1j", "rExhbl85t1j", "ndWegZgBPA", "Ji-39VpOiau", "D8Op6AL27UU", "nips_2022_vt516zga8m", "nips_2022_vt516zga8m", "nips_2022_vt516zga8m", "nips_2022_vt516zga8m" ]
nips_2022_SZDqCOv6vTB	Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems	Belief Propagation (BP) is an important message-passing algorithm for various reasoning tasks over graphical models, including solving the Constraint Optimization Problems (COPs). It has been shown that BP can achieve state-of-the-art performance on various benchmarks by mixing old and new messages before sending the n...	Accept	4 knowledgable reviewers reviewed the paper, 3 of them recommending weak acceptance, 1 borderline rejection. The reviewers engaged with the authors and a discussion among the reviewers took place. The reviewers appreciate the considered problem, the novelty of the proposed approach and the reported performance improvem...	test	[ "iQNd38p_4CK", "krlgNvO2WR", "kwb75orZXpg", "EXWHe1EzI-", "iZeJF64KA9C", "h8hq0MRGFwV", "051ThR0SfJU", "Ttm6q2-1-ZM", "SlA8ZsfbHl", "nJJ4X0k-_Zi", "hEtc543WXkg", "bKU3YrpIsCM", "2I5yS8kyQUN" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the very detailed and helpful response. I don't have any more questions now.", " Thank you the authors for answering my comments and for making clear the changes on the paper.\nI've modified original rating accordingly\n", " Thanks for the further clarifications. I appreciate the efforts in prov...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 3 ]	[ "Ttm6q2-1-ZM", "h8hq0MRGFwV", "EXWHe1EzI-", "iZeJF64KA9C", "051ThR0SfJU", "2I5yS8kyQUN", "bKU3YrpIsCM", "hEtc543WXkg", "nJJ4X0k-_Zi", "nips_2022_SZDqCOv6vTB", "nips_2022_SZDqCOv6vTB", "nips_2022_SZDqCOv6vTB", "nips_2022_SZDqCOv6vTB" ]
nips_2022_FgDzS8_Fz7c	Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset	6D object pose estimation is one of the fundamental problems in computer vision and robotics research. While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environm...	Accept	This paper received 4 reviews with the following scores: SR - BR - WA - A. The reviewers acknowledged importance of the addressed problem, the dataset contribution, clear presentation, and a meaningful approach with solid empirical performance. Main disagreements were around comparisons with existing methods (some publ...	train	[ "bj01odG7H6l", "W_xxLCPORkZ", "ITprRje415Y", "K1fsGZZzOee", "AzESPHo3zM", "BsEGND-BSwK", "eTcf7uY8MV7", "fap1Js30L8l", "w3aDelysmNm", "Z4YMP-01Rvj", "P-ACXnBn5HT" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for responding to and addressing my questions and comments.", " Dear ACs and Reviewers, \n\nThank you so much again for the detailed feedback. We have reached half way of the author-reviewer discussion period. However, there are no responses yet to our replies.\n\nPlease do not hesitate to let us know...	[ -1, -1, -1, -1, -1, -1, -1, 2, 7, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 5, 4, 3, 5 ]	[ "ITprRje415Y", "nips_2022_FgDzS8_Fz7c", "P-ACXnBn5HT", "Z4YMP-01Rvj", "Z4YMP-01Rvj", "w3aDelysmNm", "fap1Js30L8l", "nips_2022_FgDzS8_Fz7c", "nips_2022_FgDzS8_Fz7c", "nips_2022_FgDzS8_Fz7c", "nips_2022_FgDzS8_Fz7c" ]
nips_2022_IfFZr1gl0b	Uni-Mol: A Universal 3D Molecular Representation Learning Framework	Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for ...	Reject	This paper proposes a new framework for molecular representation learning (MRL) using both 2D and 3D molecular data. This framework is general and applied to various problems (e.g., protein-ligand binding pose prediction and molecular conformation prediction). I believe this paper is potentially quite impactful and abl...	train	[ "s4ZAaJZY5gy", "6PV7yHcXFPp", "pGMAjn_byTL", "vr-lLKZiOhH", "-jeGmGfwQQM", "Zhd-TZoWHQ0", "ev4A1EXxID", "s9TqG3LkgIwy", "wZ0UXiJP5P", "J4YP9qvto9p", "VlIgJJsDQp6", "7wQjSwTgTau", "PnpE-7HLJh_", "Uv-HpgJzDqK", "Y2Ghep82GlE", "jZWLM_S3Kq1", "3djj7SBxF8H", "BJHsVZ0g2ot", "pD2kvQRCZ8...	[ "author", "author", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear reviewers, \n\nThank you so much for the comprehensive and insightful reviews. Based on the review comments, we made a significant effort to finish additional 5 ablation studies from scratch in the tight discussion period. And we have updated the Appendix with these new results, and more discussions accordin...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 5, 4 ]	[ "nips_2022_IfFZr1gl0b", "nips_2022_IfFZr1gl0b", "J4YP9qvto9p", "-jeGmGfwQQM", "Zhd-TZoWHQ0", "PnpE-7HLJh_", "s9TqG3LkgIwy", "7wQjSwTgTau", "VlIgJJsDQp6", "nips_2022_IfFZr1gl0b", "pD2kvQRCZ8O", "BJHsVZ0g2ot", "Uv-HpgJzDqK", "3djj7SBxF8H", "jZWLM_S3Kq1", "nips_2022_IfFZr1gl0b", "nips_2...
nips_2022_k6WzeLZjxuP	Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits	While there has been extensive work on learning from offline data for contextual multi-armed bandit settings, existing methods typically assume there is no environment shift: that the learned policy will operate in the same environmental process as that of data collection. However, this assumption may limit the use of ...	Accept	This paper studies off-policy learning with an environment shift, where the distributions of both contexts and rewards can change. The authors address both challenges in a factored form, and derive error bounds for both off-policy evaluation and optimization. The proposed approach is evaluated on real-world datasets. T...	train	[ "NnMnTwiIesQ", "ufWkYWJ3G_M", "HRRwU43kR17", "7c_XPY4MN5f", "b5BQ9Uycu4X", "xE2uyRVReonm", "mZMd7DHewZC", "F4FPeMp4a5Y", "igXFAJr2UK", "pi1taCsTLy", "UujdRhgOhyO", "qcjuBHyOtyX", "ZzDEQ-KIaNH", "QQ2tH3Wa6Uo", "eUnDPEPDJpq" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the clarification. I do not have further questions. ", " The response addressed my primary concerns, and filled all the technical \"holes\" I could point out in the review. I do think much of the technical content overlaps quite a bit with the work by Si et al. (2020), but the authors have demonstrat...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 3, 3 ]	[ "xE2uyRVReonm", "igXFAJr2UK", "F4FPeMp4a5Y", "nips_2022_k6WzeLZjxuP", "QQ2tH3Wa6Uo", "ZzDEQ-KIaNH", "qcjuBHyOtyX", "eUnDPEPDJpq", "UujdRhgOhyO", "eUnDPEPDJpq", "nips_2022_k6WzeLZjxuP", "nips_2022_k6WzeLZjxuP", "nips_2022_k6WzeLZjxuP", "nips_2022_k6WzeLZjxuP", "nips_2022_k6WzeLZjxuP" ]
nips_2022_25XIE30VHZE	SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning	The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning. In this paper, we investigate the problem of applying the YJ transformation in a cross-silo Federated Learning setting under privacy constraints. For the first...	Accept	This paper studies data preprocesing in the federated learning setting. It proposes a simple and elegant algorithm for performing a Yeo-Johnson (YJ) power transform on univariate numerical data. This is a nonlinear transform intended to make the data more like a Gaussian. The paper shows that the likelihood objective ...	train	[ "Z9NZorGKg15", "AEERKlWjuC", "ERUN2zvYqI", "SU7O2joWkku", "KNjX92_lCKk", "ms8zqPx-92h", "_7YB2GYO2np", "RJAgRPulBsX", "aWJtiUnYaYT", "yu9wtI_fnGd", "7lSIWWj2H-M", "Ex7JhV9lkqX", "MuZjO20dQ9C", "NTZ0iVfGk4", "e4ZsMUrZsF", "XO1-_jvcNhA", "wvfxxiPZL57", "GWEx3nQzeWj", "Gh6_OpSmxnR",...	[ "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", ...	[ " Dear reviewer W31E,\n\nThe discussion deadline is approaching, and we would like to know whether our detailed answer successfully adresses your remarks and questions. If it is the case, we would appreciate if you could reconsider your evaluation. Otherwise, we would be happy to discuss further with you any remain...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 4, 3, 4 ]	[ "Gh6_OpSmxnR", "KNjX92_lCKk", "ms8zqPx-92h", "wvfxxiPZL57", "Ex7JhV9lkqX", "_7YB2GYO2np", "RJAgRPulBsX", "nips_2022_25XIE30VHZE", "NTZ0iVfGk4", "7lSIWWj2H-M", "nips_2022_25XIE30VHZE", "MuZjO20dQ9C", "jN0HJno7gCx", "MdU9DtLjATH", "XO1-_jvcNhA", "Gh6_OpSmxnR", "GWEx3nQzeWj", "nips_20...
nips_2022_Fytzfxj3Bq7	Fixed-Distance Hamiltonian Monte Carlo	We propose a variation of the Hamiltonian Monte Carlo sampling (HMC) where the equations of motion are simulated for a fixed traversed distance rather than the conventional fixed simulation time. This new mechanism tends to generate proposals that have higher target probability values. The momentum distribution that is...	Accept	This paper proposes a new variant of Hamiltonian Monte Carlo. Rather than using a fixed number of iterations (as in the original HMC) or choosing the step-size adaptively (as in NUTS) the paper simulated the dynamics until a fixed distance has been traversed. The paper gives some arguments why this might be a good id...	train	[ "H_84q325puW", "O0Wzu8N-I8u", "wBjJi9rPVKj", "YCmkDBA-Slf", "oJZnkobyfuk", "YSvVnPfeSTE", "5C995JeVCh", "vvdxHanOFhO", "GJw-o8ItF6", "agMZgsxcW86" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Once again, thanks for your suggestions and time spent on reviewing our paper.\nWhile we can still communicate, in case you have a particular numerical experiment in mind or could refer us to a related theoretical study that would potentially add to the value of the paper, we would appreciate if you could share i...	[ -1, -1, -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "O0Wzu8N-I8u", "vvdxHanOFhO", "YCmkDBA-Slf", "oJZnkobyfuk", "agMZgsxcW86", "GJw-o8ItF6", "vvdxHanOFhO", "nips_2022_Fytzfxj3Bq7", "nips_2022_Fytzfxj3Bq7", "nips_2022_Fytzfxj3Bq7" ]
nips_2022_BbUxkmrstyk	An Investigation into Whitening Loss for Self-supervised Learning	A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of positive pairs under the conditioning that the embeddings from different views are whitened. In this paper, we propose a framework with an ...	Accept	This paper studies the impact of whitening losses used in recent Self-supervised learning (SSL) methods. It shows that the symmetric whitening loss can be decomposed into two asymmetric losses, explaining important behaviour experimentally observed (e.g. why some whitening transformations -e.g. PCA- are not always ...	train	[ "ZOd4wpYBh_0", "Zk7WxqeJ4c", "wgP6FqqPi6H", "F_1GL0xSTNT", "hQRmBL1L1Q_", "lu3oGRgA9Fl", "x_GHY4_Czu-", "TxJEg1BTej", "VVaP_YTVg4j", "9qN8891QCin", "rSugjUExorR" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the rebuttal, actually I don't have a lot of hesitation to accept this paper with well writing and sufficient experiments. If only 4 GPUs is the truth, I hope in the future with released code, there can be future job to make it finished. I still hold my rating as 7. Accept.", " Thank you for the rebu...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "TxJEg1BTej", "hQRmBL1L1Q_", "x_GHY4_Czu-", "nips_2022_BbUxkmrstyk", "lu3oGRgA9Fl", "VVaP_YTVg4j", "rSugjUExorR", "9qN8891QCin", "nips_2022_BbUxkmrstyk", "nips_2022_BbUxkmrstyk", "nips_2022_BbUxkmrstyk" ]
nips_2022_q16HXpXtjJn	Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits	In the infinite-armed bandit problem, each arm's average reward is sampled from an unknown distribution, and each arm can be sampled further to obtain noisy estimates of the average reward of that arm. Prior work focuses on the best arm, i.e. estimating the maximum of the average reward distribution. We consider a gene...	Accept	This paper studies offline and online statistical estimation in the infinite-armed bandit setting and gives a set of almost tight upper and lower bounds on the sample complexity. Initially, some reviewers raised concerns about the motivation of general functional estimation and the comparison with existing statistical...	train	[ "Ogzh9cfnOHs", "86XqYg89zpF", "lLb9Q-m1GlN", "GTYfpTwAMH8-", "KKaP9Sl7USj", "DV6Km_RYIWH", "byEiXHS38DR", "4IxRoxDD8KdM", "mG0cG704W3", "F3TAi0UoVC", "3nH9STRkPrS", "DWH1ydaTvTn", "uZ-fNyiPED" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Many thanks for your detailed comments and modification of your draft.\nI understand your use of the Doubling trick and its relevance to the related research. Although I still believe the work shouldn't be reviewed solely from the bandit's perspective, I acknowledge the contribution. I raised the score to 5.\n\nB...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 4 ]	[ "lLb9Q-m1GlN", "byEiXHS38DR", "DV6Km_RYIWH", "uZ-fNyiPED", "DWH1ydaTvTn", "3nH9STRkPrS", "F3TAi0UoVC", "mG0cG704W3", "nips_2022_q16HXpXtjJn", "nips_2022_q16HXpXtjJn", "nips_2022_q16HXpXtjJn", "nips_2022_q16HXpXtjJn", "nips_2022_q16HXpXtjJn" ]
nips_2022_4-bV1bi74M	🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation	Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable similar success stories in Embodied AI. We propose ProcTHOR, a framework for procedural generation of Embodied AI environments. ProcTHOR enables u...	Accept	Summary The paper presents ProcThor, a framework to generate interactive 3D environments from an underlying distribution of room and object layouts. In the current work, 10000 3D environments of varying sizes, # rooms, and object distributions are sampled and enable simulation for object search and manipulation task...	train	[ "C5tBcHGGlq4", "oU8JAC-4E8w", "MmIkCK1y5Ib", "0lqBe2AFFTZ", "VmKms6fvNIwO", "Uq2dKEhlCyi", "FVA96BnGFoj", "HYArW8bFU1WE", "O_lQ55QpsQk", "Vsi1qwnDNKb", "iRB6vYvIGNGt", "lYPczfQCw1D", "iG-XnnsV3kB", "quTNP5JCuUh", "XgNJZM4m4y", "hPkFpUvkv-M", "arB0LJ1_MrB", "JXpjVKrsxCj", "g8VaCGU...	[ "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", ...	[ " We thank the reviewer for their thoughtful feedback and support of the work.\n\n> I'd recommend adding the response to W.5 into the paper to make it slightly stronger.\n\nThank you for the suggestion. We agree and have added the results to Section H of the Appendix.", " We thank the reviewer for their detailed ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 8, 9 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "HYArW8bFU1WE", "Vsi1qwnDNKb", "VmKms6fvNIwO", "XgNJZM4m4y", "iRB6vYvIGNGt", "Vsi1qwnDNKb", "quTNP5JCuUh", "iG-XnnsV3kB", "lYPczfQCw1D", "JXpjVKrsxCj", "nips_2022_4-bV1bi74M", "XgNJZM4m4y", "XgNJZM4m4y", "XgNJZM4m4y", "NihVzPgZSSY", "g8VaCGU4CH8", "g8VaCGU4CH8", "g8VaCGU4CH8", "0...
nips_2022_2GsQ8dyfe45	M$^4$I: Multi-modal Models Membership Inference	With the development of machine learning techniques, the attention of research has been moved from single-modal learning to multi-modal learning, as real-world data exist in the form of different modalities. However, multi-modal models often carry more information than single-modal models and they are usually applied i...	Accept	This work studies membership inference attacks for multimodal models. It proposes a few different attacks under different assumptions on the attack model, and evaluates them empirically. The reviewers found the problem interesting and the paper well-written. The paper is a welcome addition to the literature on membersh...	train	[ "g7vgPMYGERb", "6dTs5a-7czX", "sak57AaABQ0", "_d9nRmTYGIb", "XQ-VTs3YMMu", "MSIN-Yr3rF3", "oRG6jjzrVC", "MZs-PsYHmMS" ]	[ "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your comment. As we responded previously, we considered METEOR [r2], a metric based on precision and recall scores, as an additional metric, but there is not much difference in terms of performance in comparison to what the paper used. Due to little difference in terms of performance and space limitati...	[ -1, -1, -1, -1, -1, 6, 4, 9 ]	[ -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "6dTs5a-7czX", "XQ-VTs3YMMu", "MZs-PsYHmMS", "oRG6jjzrVC", "MSIN-Yr3rF3", "nips_2022_2GsQ8dyfe45", "nips_2022_2GsQ8dyfe45", "nips_2022_2GsQ8dyfe45" ]
nips_2022_qtQ9thon9fV	FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction	The advent of deep learning has led to significant progress in monocular human reconstruction. However, existing representations, such as parametric models, voxel grids, meshes and implicit neural representations, have difficulties achieving high-quality results and real-time speed at the same time. In this paper, we p...	Accept	This paper received 3 positive reviews: 2xBA + A. All reviewers acknowledged that this work introduces meaningful and non-trivial contributions, it is well presented, and the claims are supported by strong empirical performance. The remaining questions and concerns were addressed in the authors' responses, which seemed...	val	[ "M6rqQb7-hlx", "9eN2CqumnDV", "W9AraIXswtX", "UMGP2SxeYW", "bLeFpXIu81w", "5PlE-6bsopa", "gvHTgxYFTWh", "dHBS8kLaRHP", "b76tx2saz3" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank authors' time and effort to answer my questions. I think the newly-added results are informative and demonstrate FOF's robustness. I maintain my score for this work.", " Dear Reviewers:\n\nThank you very much for your time and effort in reviewing our paper. It is less than 15 hours before the end of...	[ -1, -1, -1, -1, -1, -1, 5, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, 5, 4, 4 ]	[ "W9AraIXswtX", "nips_2022_qtQ9thon9fV", "b76tx2saz3", "dHBS8kLaRHP", "dHBS8kLaRHP", "gvHTgxYFTWh", "nips_2022_qtQ9thon9fV", "nips_2022_qtQ9thon9fV", "nips_2022_qtQ9thon9fV" ]
nips_2022_pfEIGgDstz0	Non-rigid Point Cloud Registration with Neural Deformation Pyramid	Non-rigid point cloud registration is a key component in many computer vision and computer graphics applications. The high complexity of the unknown non-rigid motion make this task a challenging problem. In this paper, we break down this problem via hierarchical motion decomposition. Our method called Neural Deformatio...	Accept	All reviewers agree this work is a creative approach to nonrigid registration, which is particularly hard in the mesh-free point cloud setting. The discussion between the authors and reviewers was extremely productive and addressed most of the major concerns about this work. In preparing the camera ready, the authors...	train	[ "MQybYHIBaoK", "FM4SicqE5j0", "ZpWBxyYEaEm", "4A-yYQcXMzar", "LJ5AqphgpjB", "HUvm3_PYhH8", "oG-dtzjSYOp", "gsKgJsUNiO8Z", "Zyhj4IcUCl_", "vXqyWKZso6A", "Vuutvcw8Xmp", "tw3X98-zvZ" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The author feedback addresses most of my concerns. Taking into account the comments from other reviewers and the author feedback, I am inclined to accept this paper, but rejecting would it not be that bad.\n", " I thank the authors for their effort to address my concerns.\n\nI have carefully read the rebuttal a...	[ -1, -1, -1, -1, -1, -1, -1, -1, 7, 4, 4, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3, 4 ]	[ "HUvm3_PYhH8", "gsKgJsUNiO8Z", "nips_2022_pfEIGgDstz0", "vXqyWKZso6A", "Zyhj4IcUCl_", "Vuutvcw8Xmp", "Vuutvcw8Xmp", "tw3X98-zvZ", "nips_2022_pfEIGgDstz0", "nips_2022_pfEIGgDstz0", "nips_2022_pfEIGgDstz0", "nips_2022_pfEIGgDstz0" ]
nips_2022_YgK1wNnoCWy	Green Hierarchical Vision Transformer for Masked Image Modeling	We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones. Our approach consists of three key designs. First, for window attention, we propose a Group Window Attention schem...	Accept	After rebuttal and discussion all reviewers recommend acceptance. The AC sees no reason to overturn this recommendation.	test	[ "9knBlOMv5vo", "qUIjtjDPra", "fepmyKQ5536", "gpzBZbAFkZjT", "FYcMqSoLXOP", "SXh5eukv0f9", "XghQFxBTqo", "ykX2LO-Ow2P", "4_wUAaZAYds", "U_R7fPslxvb", "wqbox9KFCB", "Nw8YQrjs_Y", "xgcy7J31I-" ]	[ "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I do not have further questions. ", " Dear reviewer, thanks again for the careful reviews and constructive suggestions. We have provided additional explanations and experiments to address the concerns accordingly. Since the deadline of the author-reviewer discussion period is approaching soon, we would like to ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 5 ]	[ "fepmyKQ5536", "xgcy7J31I-", "wqbox9KFCB", "FYcMqSoLXOP", "SXh5eukv0f9", "4_wUAaZAYds", "xgcy7J31I-", "xgcy7J31I-", "Nw8YQrjs_Y", "wqbox9KFCB", "nips_2022_YgK1wNnoCWy", "nips_2022_YgK1wNnoCWy", "nips_2022_YgK1wNnoCWy" ]
nips_2022_L8ESR8IQ7Gb	Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models	Transfer learning aims to leverage knowledge from pre-trained models to benefit the target task. Prior transfer learning work mainly transfers from a single model. However, with the emergence of deep models pre-trained from different resources, model hubs consisting of diverse models with various architectures, pre-tra...	Accept	The submission introduces an approach called Hub-Pathway to leverage a diverse collection of pre-trained models for transfer learning. Hub-Pathway trains a pathway generator network to route examples to various models in a data-dependent manner and aggregates the outputs to produce task-specific predictions. Noise is a...	train	[ "CxH3ftHiiby", "CrCDkj8cjGL", "L7GpOjjRwrj", "IfLd_E8SpNo", "LLYwpOcUXL9", "B7P8nHXMP2G", "hBeNdgGU0Jj", "zfPPXbcvjjC", "c-d617Lvv9N", "WU568XNnBgc", "QX_gju7Utx8", "g3h89MCecwz", "szBTdYCgwSq", "k00HPMBPmOL", "ha85E6HloL", "UZhDxIUX-sZ", "3Ww_gLjpWmt", "AlBVBHHhzld", "NOCCB3QKli...	[ "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", ...	[ " Thank you for providing inspiring comments and timely responses. ", " Dear Authors,\n\nThank you for responding to the questions. This work is really interesting, and I, therefore, stand by my (accepting) recommendation.", " Many thanks for your efforts in reviewing our paper and responses, and your comments ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3, 5 ]	[ "CrCDkj8cjGL", "gwr53nFE0St", "IfLd_E8SpNo", "sXXkI7jCY-o", "B7P8nHXMP2G", "hBeNdgGU0Jj", "zfPPXbcvjjC", "WU568XNnBgc", "QX_gju7Utx8", "QX_gju7Utx8", "AlBVBHHhzld", "szBTdYCgwSq", "k00HPMBPmOL", "ha85E6HloL", "UZhDxIUX-sZ", "X0GuEKBzoOC", "fbXyWBj6rt9", "fbXyWBj6rt9", "sXXkI7jCY-...
nips_2022_rcrY85WLAKU	Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs	This work discusses tensor network embeddings, which are random matrices ($S$) with tensor network structure. These embeddings have been used to perform dimensionality reduction of tensor network structured inputs $x$ and accelerate applications such as tensor decomposition and kernel regression. Existing works have de...	Accept	Summary: The major strength is that the sketch size is polynomial in the number of modes for tensor train. This was not known in previous work, for example in the paper by Rakhshan and Rabusseau https://arxiv.org/abs/2003.05101 which is reference 38 and gets a sketch size which is exponential in the number of modes. ...	train	[ "WBcvgLfrJgY", "r9pR8EC_jno", "yBqrqWHqkBK", "0_1e2f6DIEO", "-sqntrDjLOo", "F1h__Iu71M5", "kl_qUJOM6Kd" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We would like to thank the reviewer for the constructive feedback and great questions! Our comments to your suggestions are as follows:\n\nQ: The intro of the alg details (sec. 4) is hard to follow, and the meaning of U in (4.2) is not clear:\n\nA: Thanks! $(U_i,V_i)$ in (4.2) represents the contraction of two in...	[ -1, -1, -1, -1, 6, 5, 6 ]	[ -1, -1, -1, -1, 2, 2, 4 ]	[ "kl_qUJOM6Kd", "F1h__Iu71M5", "-sqntrDjLOo", "nips_2022_rcrY85WLAKU", "nips_2022_rcrY85WLAKU", "nips_2022_rcrY85WLAKU", "nips_2022_rcrY85WLAKU" ]
nips_2022_jPx7vYUNUCt	Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators	The spectacular successes of recurrent neural network models where key parameters are adjusted via backpropagation-based gradient descent have inspired much thought as to how biological neuronal networks might solve the corresponding synaptic credit assignment problem [1, 2, 3]. There is so far little agreement, howeve...	Accept	Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators The authors propose a biologically plausible method for temporal credit assignment called ModProp. They apply their framework on rate-based recurrent neural networks (RNNs), and show that it outperforms previous approaches. ...	train	[ "U4CTdYXjnb8", "2Cc9uGi9FV", "jvJ31NsbWS", "zF3RSqy9Iqu", "S8walkrW75l", "EUugZmzV58s", "6i9x-8ohuvl", "xWC-8XpDtKt", "xY7zSa_zSSC", "EdvAz5nCGsV", "uoQLvw_FJtK", "Ohv9vEkCu7Z", "GIUXNPORPEm", "TriNcsolIl1L", "nHsR12K3SJU", "EzgIWeV72Rp", "avMofTyYkyN", "elzW55GFmgJ", "0-56s89er3...	[ "author", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", ...	[ " We have updated the submission to remove all blue coloring of texts on August 9th. ", " We are very grateful to this reviewer for reading our response and taking that into consideration to revise their score. We thank the reviewer again for all their constructive feedback that led to the improvement of this pap...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 2, 4 ]	[ "EzgIWeV72Rp", "jvJ31NsbWS", "wiUydbJt9xE", "EUugZmzV58s", "6i9x-8ohuvl", "EdvAz5nCGsV", "seZcY92TfoF", "xY7zSa_zSSC", "Ohv9vEkCu7Z", "uoQLvw_FJtK", "elzW55GFmgJ", "nHsR12K3SJU", "nHsR12K3SJU", "4MszX1b-X6L", "4MszX1b-X6L", "nips_2022_jPx7vYUNUCt", "Z8EbupXOFME", "Z8EbupXOFME", "...
nips_2022_OlGu-BXgJ-	Wasserstein $K$-means for clustering probability distributions	Clustering is an important exploratory data analysis technique to group objects based on their similarity. The widely used $K$-means clustering method relies on some notion of distance to partition data into a fewer number of groups. In the Euclidean space, centroid-based and distance-based formulations of the $K$-mean...	Accept	This paper provides a Wasserstein-based k-means formulation for clustering probability distributions. Though the overall reception was mildly positive but two reviewers raised their scores following the author feedback. There remains some doubts that there is enough evidence to support all claims in the paper--most rel...	train	[ "ASvdpUWTyVy", "rys1kUGLNs_", "xXHb2SSYNKQ", "R4oABfb2SDD1", "xqqr05hDDuh", "2O-AUBm9g8q", "g6I-7EGHfg9", "vWKvv5lf1hy" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " (__On scalability of our approaches__) The scalability issue is one of our concerns. As we pointed out in Appendix B, we may consider several methods to bring down the time cost e.g. subsampling-based method for K-means and SDP. In the revision, we can observe the time complexity issue for Wasserstein $K$-means m...	[ -1, -1, -1, -1, 4, 6, 7, 5 ]	[ -1, -1, -1, -1, 2, 3, 4, 3 ]	[ "vWKvv5lf1hy", "g6I-7EGHfg9", "2O-AUBm9g8q", "xqqr05hDDuh", "nips_2022_OlGu-BXgJ-", "nips_2022_OlGu-BXgJ-", "nips_2022_OlGu-BXgJ-", "nips_2022_OlGu-BXgJ-" ]
nips_2022__4xg5moXVg	Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules	To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond task accuracy, it is unclear if such learning rules converge to solutions that exhibit different levels of generalization than their non-bio...	Accept	This paper applied ideas about generalization in the ML literature to biologically plausible architectures and learning rules. Especially, it explored links between curvature and generalization in biologically plausible learning. There was active discussion about this paper, and three reviewers raised their scores dur...	train	[ "G51mBY7UFxo", "qF9Ik0Ie5KE", "Ll-g0lkzSBq", "oTFL7nwrVaQ", "ZjrJcehtem", "5BGtUiXWCj7", "5Y4DoFNE5uv", "yzVlqhvVZBr", "fwcneIw6H0m", "KC3llYU3e9j", "3_MC2D5coJM", "9dlmhrRwzoD", "ZdVQz3Wo-71", "w7sSzyXANf1", "trE5VAqyXlv", "eYCLY_vttPW", "K0MgBpEuYJt", "f4bafnugqmH", "bRwfxsjhdJ...	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer",...	[ " We would like to take this final moment to thank the reviewer once more for a stimulating exchange and encouraging feedback. We hope the reviewer will be satisfied with the changes made to the paper and appendix, as they suggested. We would be grateful if this would enable a score increase.\n\nbest regards,\nthe ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 5, 4 ]	[ "5BGtUiXWCj7", "5BGtUiXWCj7", "5BGtUiXWCj7", "ZjrJcehtem", "5Y4DoFNE5uv", "KC3llYU3e9j", "nRmWsix2wj", "2yeyDZ2bdw3", "0Qn0mtD-iXX", "3_MC2D5coJM", "eYCLY_vttPW", "nips_2022__4xg5moXVg", "nRmWsix2wj", "nRmWsix2wj", "1OK9HSJhi9", "1OK9HSJhi9", "2yeyDZ2bdw3", "2yeyDZ2bdw3", "0Qn0mt...
nips_2022_oDWyVsHBzNT	Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief	Model-based offline reinforcement learning (RL) aims to find highly rewarding policy, by leveraging a previously collected static dataset and a dynamics model. While the dynamics model learned through reuse of the static dataset, its generalization ability hopefully promotes policy learning if properly utilized. To tha...	Accept	I went through the manuscript, reviews and authors' responses. I think this paper is qualified for NeurIPS publication.	train	[ "ER0gTgELF_F", "wpIRTIHinzK", "EFQivrnq3I3", "-RtVCxVl0r", "69jVhiNORQI", "Rt01aVd8VxZ", "vTW7hmCTPCv", "-al2f-beuz-", "AW6qrRJqf7Z", "vNS1lRbBcAc", "xKMYXqoWRKh", "Sd63hkSIfz", "a8qT8X8NAjw", "Uuwsmb6R8fj", "bzEaOVOtqHL", "HMvhWqJy8rL", "Vid6s74DSYt" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks very much for your time to further evaluate the value of this work!", " I have carefully read your new comments. Considering the potential impacts to RL community, I believe this work deserves a higher score. I've raised my score. Congrats to the authors for the remarkable work!", " We thank all review...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 8, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 3, 4 ]	[ "wpIRTIHinzK", "EFQivrnq3I3", "nips_2022_oDWyVsHBzNT", "AW6qrRJqf7Z", "Rt01aVd8VxZ", "xKMYXqoWRKh", "-al2f-beuz-", "Sd63hkSIfz", "Vid6s74DSYt", "HMvhWqJy8rL", "HMvhWqJy8rL", "bzEaOVOtqHL", "Uuwsmb6R8fj", "nips_2022_oDWyVsHBzNT", "nips_2022_oDWyVsHBzNT", "nips_2022_oDWyVsHBzNT", "nips...
nips_2022_mNtFhoNRr4i	Hierarchical classification at multiple operating points	Many classification problems consider classes that form a hierarchy. Classifiers that are aware of this hierarchy may be able to make confident predictions at a coarse level despite being uncertain at the fine-grained level. While it is generally possible to vary the granularity of predictions using a threshold at infe...	Accept	The submission benchmarks several hierarchical classification techniques showing that flat softmax on the leaf nodes dominates most methods. This is quite a negative result for previous works, indicating that efforts on hierarchical classification losses are often not leading to better results. The authors introduce ...	train	[ "An_1g8accfa", "SmVPvBM0v81", "WCO_3hvuALs", "Wn3jgW7C9Wx", "xSWlXLgU0D8", "DxAvIICF8kG", "P3H-oJDlYsr", "DhV-tDMmUT6", "__g2hw7B1YG", "-jBTV0Z59ma", "lUOunTrhFiE" ]	[ "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " This helps with the understanding", " We have uploaded a revised version of the paper to address the main requests. Here is a summary of the changes.\n\n* Change paragraph 2 of Introduction to make motivation of curves more explicit\n* Add bullet point contributions at end of introduction\n* Related work: Remov...	[ -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3 ]	[ "DhV-tDMmUT6", "nips_2022_mNtFhoNRr4i", "DxAvIICF8kG", "xSWlXLgU0D8", "P3H-oJDlYsr", "-jBTV0Z59ma", "__g2hw7B1YG", "lUOunTrhFiE", "nips_2022_mNtFhoNRr4i", "nips_2022_mNtFhoNRr4i", "nips_2022_mNtFhoNRr4i" ]
nips_2022_YR-s5leIvh	CLEAR: Generative Counterfactual Explanations on Graphs	Counterfactual explanations promote explainability in machine learning models by answering the question “how should the input instance be altered to obtain a desired predicted label?". The comparison of this instance before and after perturbation can enhance human interpretation. Most existing studies on counterfactual...	Accept	This paper proposes a new method for producing counterfactuals on graphs. This is performed using a VAE on graphs with auxiliary variables to identify independent components and promote causality. While this work is mainly a combination of existing ideas, the resulting method is not trivial. The engaged discussion cl...	train	[ "lS9S5Z5czXZ", "pi9Eqq0mu_P", "yYgzkNeMVI", "yYxCvdKA818", "7FaANxkZaE", "sgE_KaI38py", "X5hmKSqXymj", "bgIHgLnEop", "IxYeXt1WUS", "iu-mTMB-3le", "7O4K674rpVp", "m5TJkBfDPu", "3KmyohAtGW", "arsTcYUymKh", "ez_cHc3Cu_K", "qBdQWczCBfl", "qe2t1c6rW1", "p3baE5u-zGw", "LTOqVZE7qJD" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Yes. The graph structure of the counterfactuals is different. Thank you for this suggestion, we will add this clarification in the paper.", " When you say \"not the same\" you mean that the discrete graph structure is different? If so, that is encouraging, you might want to clarify this in the paper.", " Than...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 5 ]	[ "pi9Eqq0mu_P", "yYgzkNeMVI", "yYxCvdKA818", "IxYeXt1WUS", "bgIHgLnEop", "X5hmKSqXymj", "7O4K674rpVp", "iu-mTMB-3le", "qBdQWczCBfl", "m5TJkBfDPu", "arsTcYUymKh", "3KmyohAtGW", "p3baE5u-zGw", "ez_cHc3Cu_K", "qe2t1c6rW1", "LTOqVZE7qJD", "nips_2022_YR-s5leIvh", "nips_2022_YR-s5leIvh", ...
nips_2022_MHjxpvMzf2x	Symmetry Teleportation for Accelerated Optimization	Existing gradient-based optimization methods update parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows parameters to travel a large distance on the loss level set, in order to improve the convergence speed in subsequent steps. Teleport...	Accept	This paper proposes a novel, symmetry teleportation approach to optimize the parameters of ML models. The proposed approach allows iterates to move along the loss level set and improves the convergence speed. The teleportations also exploit the symmetries that are present in the optimization problem. The paper also in...	test	[ "NOxhvLXzKiz", "qM8-KTuGbNq", "-sA4iLaouQ", "bYGhlDXfMGa", "0Y2KKVt4TI", "yWmXYE3PrE2", "XohTGspg1LGp", "1jYqH_cuwiF", "oHu4Y0LHYxW", "Iwvam7EWo8X", "vP0Rbsb_NrV", "Im0m0q_n0UP", "E2xLfNU9zx", "GoHhYeMyvHL", "uDdwTYuLwwq", "QrmutSIE2EB", "YQQkHBaa9Bz", "tRMSpyjeJzS", "RbJ68Cxazp-...	[ "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I would like to thank the authors for the clarification. Now, it makes sense to me and I have raised the score. ", " Thanks for reading our responses and proofs! We really appreciate it.\n\nWe have updated the paper, where we added a new section Appendix C.5. We have formalized the improvement brought by telepo...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 3, 2 ]	[ "qM8-KTuGbNq", "-sA4iLaouQ", "oHu4Y0LHYxW", "nips_2022_MHjxpvMzf2x", "XohTGspg1LGp", "nips_2022_MHjxpvMzf2x", "RbJ68Cxazp-", "tRMSpyjeJzS", "tRMSpyjeJzS", "YQQkHBaa9Bz", "YQQkHBaa9Bz", "QrmutSIE2EB", "uDdwTYuLwwq", "uDdwTYuLwwq", "nips_2022_MHjxpvMzf2x", "nips_2022_MHjxpvMzf2x", "nip...
nips_2022_y8FN4dHdxOE	Fused Orthogonal Alternating Least Squares for Tensor Clustering	We introduce a multi-modes tensor clustering method that implements a fused version of the alternating least squares algorithm (Fused-Orth-ALS) for simultaneous tensor factorization and clustering. The statistical convergence rates of recovery and clustering are established when the data are a noise contaminated tenso...	Accept	The paper proposes a multi-mode tensor clustering method using an alternating least square algorithm. The reviewers unanimously like the paper and the content. Some reviewers have sought a few clarifications including on the proofs. Hope the authors would address them in the revised version.	train	[ "vjcLA6_r0xv", "lVs6Tp9I0K", "7-xe_M4_DkV", "bI7XrHAU9Iz", "rPDxU5i67DES", "lYaxkDJD_Rxp", "1qtseNrM0Yx", "PyIh0UWp0Xd", "W2xFDUDWsok", "e_sOn_wSflx", "fI4lyJFdDQq", "I2VW2HAMJ3K", "JaR9eozPdWx", "OnYlF6nvpIt" ]	[ "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Authors, thank you for the detailed response.\nI am satisfied with the explanation provided for my questions. I stick to my original rating. ", " On the sixth issue For the derivation of Line 100, the whole error bound can be split into 2 parts, the first part is in the order our final result of Theorem 1, ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 2 ]	[ "e_sOn_wSflx", "7-xe_M4_DkV", "bI7XrHAU9Iz", "rPDxU5i67DES", "lYaxkDJD_Rxp", "W2xFDUDWsok", "nips_2022_y8FN4dHdxOE", "JaR9eozPdWx", "JaR9eozPdWx", "I2VW2HAMJ3K", "OnYlF6nvpIt", "nips_2022_y8FN4dHdxOE", "nips_2022_y8FN4dHdxOE", "nips_2022_y8FN4dHdxOE" ]
nips_2022_GjWDguPZRmr	Improving Variational Autoencoders with Density Gap-based Regularization	Variational autoencoders (VAEs) are one of the most powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optimization goal of VAEs is to maximize the Evidence Lower Bound (ELBo), which consists of a conditional likelihood for generation and a ne...	Accept	The paper addresses the KL collapse of VAE models by proposing a new regularization. Reviewers generally acknowledge the novelty of the work and have the tendency of recommending acceptance.	train	[ "jJ_2Sx3V7uj", "szl37BZVkRl", "44mMCswbSm4", "nOYQlXDKHP", "jbsntyREtGe", "GVwwHw6OLs", "WFhw_INFmyx", "3So4PCaGdfU", "Tx1jdhD5yUH", "Pe6Ro9En2Co", "FBUX9rf-Ta", "e6gW1OGY6-" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for the encouraging comment! We are glad that you found our reply helpful.", " Thanks to the authors for their response to my review (and apologies for my late response to the authors). I found the response helpful -- specifically, the authors' comments on runtime have given me a better idea...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 8, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "szl37BZVkRl", "3So4PCaGdfU", "nOYQlXDKHP", "Tx1jdhD5yUH", "GVwwHw6OLs", "WFhw_INFmyx", "e6gW1OGY6-", "Pe6Ro9En2Co", "FBUX9rf-Ta", "nips_2022_GjWDguPZRmr", "nips_2022_GjWDguPZRmr", "nips_2022_GjWDguPZRmr" ]
nips_2022_DSEP9rCvZln	Inherently Explainable Reinforcement Learning in Natural Language	We focus on the task of creating a reinforcement learning agent that is inherently explainable---with the ability to produce immediate local explanations by thinking out loud while performing a task and analyzing entire trajectories post-hoc to produce temporally extended explanations. This Hierarchically Explainable R...	Accept	The paper proposes a hierarchical approach to explainable RL which combines different modules, including a knowledge graph, to generate natural language explanations. There has been a debate between the reviewers about this approach being novel or not which was the main concern left after the rebuttal phase. Other co...	train	[ "92v42qZWl20", "H1NX7NE-tMl", "3fHKHZnMJ4I", "gF3Q_JaiKPD", "PUc7WCN7hZD", "HH1PeBEQGZJ", "Fgm2Z4-0I6", "yC4IL5u9urM", "PhV7sC0lFB", "dIqUIDj5-x", "6K5l-Pi7ejF", "xnyJuKV5UI", "Zcw2eb0S_Cn" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for responding to our changes and for the time you have spend in writing a thorough review with points for improvement. We are encouraged to hear that you think the paper is ready for acceptance and hope that a fruitful discussion continues into the post-rebuttal period with the other reviewers.", " M...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 7, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "H1NX7NE-tMl", "yC4IL5u9urM", "Zcw2eb0S_Cn", "xnyJuKV5UI", "6K5l-Pi7ejF", "nips_2022_DSEP9rCvZln", "Zcw2eb0S_Cn", "xnyJuKV5UI", "6K5l-Pi7ejF", "nips_2022_DSEP9rCvZln", "nips_2022_DSEP9rCvZln", "nips_2022_DSEP9rCvZln", "nips_2022_DSEP9rCvZln" ]
nips_2022_hciwLGxCt6S	It's DONE: Direct ONE-shot learning with Hebbian weight imprinting	Learning a new concept from one example is a superior function of the human brain and it is drawing attention in the field of machine learning as a one-shot learning task. In this paper, we propose one of the simplest methods for this task with a nonparametric weight imprinting, named Direct ONE-shot learning (DONE). D...	Reject	## Summary Humans can learn a new task just from a couple of examples whereas often supervised deep learning models require lots of labeled samples to learn a task. This paper proposes a one-shot learning method inspired by Hebbian learning by adding a new class to the output layer of the network with quantile normali...	train	[ "7pRRxMxTsRC", "7zL5SCJpkCS", "6Si4SPtKvJ", "bBJgBScgIKR", "KjmVUTTngY6", "SG-1sdvVdq_", "QZ4SI9K9Nyk", "HHflK7UMjYZ", "4Q4tPc_1yiy0", "WMPDJrLh4Ns", "VZ0ny8D9quU", "ENWmtUtFNKg", "G46I_DmE8NYP", "8F-C0hW1ue", "vQdbXCjuDLd", "VVq0XJG-PtS" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for not only your precious comments and discussion to improve our paper, but also for raising the score.\n\nWe have learned a lot from the discussion with you. Thanks to you, as with our papers, our understanding also progresses, which will be encouraging for future research as well.\n\nWe aga...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "7zL5SCJpkCS", "vQdbXCjuDLd", "bBJgBScgIKR", "KjmVUTTngY6", "SG-1sdvVdq_", "QZ4SI9K9Nyk", "HHflK7UMjYZ", "ENWmtUtFNKg", "WMPDJrLh4Ns", "VZ0ny8D9quU", "VVq0XJG-PtS", "vQdbXCjuDLd", "8F-C0hW1ue", "nips_2022_hciwLGxCt6S", "nips_2022_hciwLGxCt6S", "nips_2022_hciwLGxCt6S" ]
nips_2022_ZidkM5b92G	BagFlip: A Certified Defense Against Data Poisoning	Machine learning models are vulnerable to data-poisoning attacks, in which an attacker maliciously modifies the training set to change the prediction of a learned model. In a trigger-less attack, the attacker can modify the training set but not the test inputs, while in a backdoor attack the attacker can also modify te...	Accept	The paper proposes a new method for certified defense against data poisoning, in both trigger-less and backdoor scenarios. The method augments previous work (Bagging) with random flipping of labels. The latter enables computation of probabilistic certificates, although this results in a huge computational overhead. Var...	train	[ "8rHyuP7pKgg", "nu7HcRR38x", "3iKpOmww-vY", "1LbGgagkRbT", "g_lsCN6Gvxy", "EfKNTQ_86q0", "RHNPmV5Ydx1", "_MO-8-hcQGO", "-NpHci-nT5", "Otam2dF5op3" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " 1. The authors clarify that the novelty of the method is a novel smoothing distribution combining the distributions of Bagging and FeatFlip. I realized the proposed method's difference through the authors' rebuttals, and I would like to reconsider the contribution (2 fair) of the original review.\n\n2. Thanks to ...	[ -1, -1, -1, -1, -1, -1, -1, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 2 ]	[ "nu7HcRR38x", "g_lsCN6Gvxy", "RHNPmV5Ydx1", "EfKNTQ_86q0", "Otam2dF5op3", "-NpHci-nT5", "_MO-8-hcQGO", "nips_2022_ZidkM5b92G", "nips_2022_ZidkM5b92G", "nips_2022_ZidkM5b92G" ]
nips_2022_JvIFpZOjLF4	Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction	Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack explicit multi-view geometry constraints, hence usually fail to generate geometry-consistent surface reconstruction. To address this challenge, w...	Accept	This paper introduces new and useful losses, presents a good experimental setup, supply analysis on the bias, and is clearly written. I encourage the authors to discuss similarities and differences to NeuraWarp and pointcloud->SDF methods in their revision.	train	[ "alZYd-1Ivn", "LUXzpNG4YEB", "HOny6jzdia", "ifv8WuRcqie", "r_OIQsqXBOM", "DNTWxA8ionRM", "C36-1vsy-tD", "yJ5RMieDgxJ", "TxBxAKaOubw", "i0pMhJoZHkZ", "Uak6pSh69P", "GidTo6MYlec" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for the careful review and constructive discussions. We will revise our paper following the comments made by all the reviewers.", " I would like to thank the authors for carefully addressing my concerns, especially w.r.t point2surf and feature generation from pointclouds. I have updated my r...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 4, 4 ]	[ "LUXzpNG4YEB", "r_OIQsqXBOM", "ifv8WuRcqie", "yJ5RMieDgxJ", "TxBxAKaOubw", "i0pMhJoZHkZ", "Uak6pSh69P", "GidTo6MYlec", "nips_2022_JvIFpZOjLF4", "nips_2022_JvIFpZOjLF4", "nips_2022_JvIFpZOjLF4", "nips_2022_JvIFpZOjLF4" ]
nips_2022_JSBgIaxAXk9	Differentially Private Linear Regression via Medians	Linear regression is one of the simplest machine learning tasks. Despite much work, differentially private linear regression still lacks effective algorithms. We propose a new approach based on a multivariate extension of the Theil-Sen estimator. The theoretical advantage of our approach is that we do not directly rely...	Reject	Though the reviewers appreciate the contribution overall, and the application of median methods to regression for the purpose of avoiding/circumventing clipping is novel, the strength of the contributions remains limited in light of other existing work that achieves more favorable bounds and/or uses fewer assumptions. ...	train	[ "LUJTNEPnKh", "9w8SiwzWUkK", "7gI41sbdrDa", "dAHa_8uzUgs", "RgrAqzP2A2l", "vvGKmnCnA5i", "Vp_toF_XdMS", "l3Qw_2MxkKx", "yA_d2z92QIm", "khIXIC4UfA3", "vw8fvwkWYW" ]	[ "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " > 1. If the covariance is unknown, does the proposed algorithm have any error bounds guarantees?\n\nThanks for the great question! \n\nWe can modify Lemma A.6 (in a black-box manner) to work for a non-spherical design matrix. If the data comes from $N(0,\\Sigma)$ instead of $N(0,I)$, this is equivalent to replaci...	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3 ]	[ "9w8SiwzWUkK", "dAHa_8uzUgs", "Vp_toF_XdMS", "RgrAqzP2A2l", "vvGKmnCnA5i", "vw8fvwkWYW", "khIXIC4UfA3", "yA_d2z92QIm", "nips_2022_JSBgIaxAXk9", "nips_2022_JSBgIaxAXk9", "nips_2022_JSBgIaxAXk9" ]
nips_2022_P6uZ7agiyCT	Sparse2Dense: Learning to Densify 3D Features to Boost 3D Object Detection	LiDAR-produced point clouds are the major source for most state-of-the-art 3D object detectors. Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect. We present Sparse2Dense, a new framework to efficiently boost 3D detection performance by learning to densify point clouds in la...	Accept	This paper proposes to utilize point cloud completion tools to densify sparse point clouds which could subsequently improve the performance of point cloud detection methods. After rebuttal, reviewers agree on the novelty of the method and its effectiveness on the Waymo open dataset. AC recommends this paper for accepta...	train	[ "Sac6WpJQB_m", "gPzDqnpQamA", "hvybdtcz-aH", "ngp7Dsidgn", "aTQySFUbBZt", "nuito-YBZ4H", "lYsG8ZFgAwy", "58w803SWyX", "k2nVnSah5Rs", "cEebHkVF-n1" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for the detailed reply. I think the paper is now above the acceptance bar and I increase my rating to weak accept.", " Dear Reviewers: Thank you for your effort and time in reviewing our paper. We are encouraged to see positive ratings from all reviewers and your recognition of the good & no...	[ -1, -1, -1, -1, -1, -1, 6, 6, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, 4, 5, 3, 5 ]	[ "aTQySFUbBZt", "nips_2022_P6uZ7agiyCT", "cEebHkVF-n1", "k2nVnSah5Rs", "58w803SWyX", "lYsG8ZFgAwy", "nips_2022_P6uZ7agiyCT", "nips_2022_P6uZ7agiyCT", "nips_2022_P6uZ7agiyCT", "nips_2022_P6uZ7agiyCT" ]
nips_2022_ByYFpTwgLGO	Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies	Estimating the effect of intervention from observational data while accounting for confounding variables is a key task in causal inference. Oftentimes, the confounders are unobserved, but we have access to large amounts of additional unstructured data (images, text) that contain valuable proxy signal about the missing ...	Accept	Reviewers agreed the paper presents a novel method addressing an important problem, building on and expanding prior work in the field. Specifically, it strongly relates to the CEVAE model, adding to it the ability to deal with multiple proxies each with a different structure, as well as introducing a new inference appr...	train	[ "pUrtoRbeNJE", "BH_U5BCxGV", "6f5QEsBv5LU", "OLCHt440rpm", "MuWTRETeX9s", "ooU9jAc0iOp", "iHEIrlMOhOx", "ooFjL4M_tki", "Teu1hbtQgLi", "pNdRmuEXwh0", "_BvvogV-EC-", "JNHchPwGhH", "M2obpj9-UwF", "SLcdPwRjJaW" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Experiments with Modified Causal Graph Structures\n\nIn our previous response, we argued that our core method handles modified causal graph structures with possibly small modifications. Here, we perform a simulation study to confirm these results empirically. In our experiments, we find that DMSE __recovers t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "6f5QEsBv5LU", "pNdRmuEXwh0", "OLCHt440rpm", "MuWTRETeX9s", "iHEIrlMOhOx", "Teu1hbtQgLi", "ooFjL4M_tki", "SLcdPwRjJaW", "M2obpj9-UwF", "_BvvogV-EC-", "JNHchPwGhH", "nips_2022_ByYFpTwgLGO", "nips_2022_ByYFpTwgLGO", "nips_2022_ByYFpTwgLGO" ]
nips_2022_E9HNxrCFZPV	NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation	Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation. Previous TTA schemes assume that the test samples are independent an...	Accept	The paper proposed two test-time adaptation methods a) instance-aware batch normalization and b) prediction-balanced reservoir sampling and used these to show that the proposed method is better in the non-iid setting. The reviewers found this to be an important problem the experiments generally convincing. Reviewers o...	val	[ "BnZuReLSjyx", "i-JEjbkjjgR", "v4PIY92_5eA", "1SGzegtgiOu", "OHT5H4Zab62", "eD_gavTXdQ4", "izOEmksbpDL", "0fIbHJ_26lf1", "OygYPlYaWop", "ji80Gi3T9A1", "8j06jEc9wB", "sz1muyMVHE-", "Zj6EtZy_A3qa", "NWqRST6C9oT", "OuyoyEm3Ecz", "qWPYZ3e6xxy", "MVcWsiacSev", "pLhU1eCRXO", "-dw4WhZ8F...	[ "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_...	[ " Thank you for your response to our rebuttal! We agree that the concerns you pointed out are the current limitations of our study and could be further improved in the future. Still, we believe our contributions make a meaningful step towards the practical applications of the test-time adaptation paradigm, as you a...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 4, 4 ]	[ "v4PIY92_5eA", "1SGzegtgiOu", "sz1muyMVHE-", "qWPYZ3e6xxy", "eD_gavTXdQ4", "OygYPlYaWop", "nips_2022_E9HNxrCFZPV", "nips_2022_E9HNxrCFZPV", "cvGIFr7ETV-", "cvGIFr7ETV-", "xl8by_jGVvt", "xl8by_jGVvt", "-dw4WhZ8FFi", "-dw4WhZ8FFi", "-dw4WhZ8FFi", "-dw4WhZ8FFi", "pLhU1eCRXO", "nips_20...
nips_2022_tZUOiVGO6jN	A Deep Learning Dataloader with Shared Data Preparation	Executing a family of Deep Neural Networks (DNNs) training jobs on the same or similar datasets in parallel is typical in current deep learning scenarios. It is time-consuming and resource-intensive because each job repetitively prepares (i.e., loads and preprocesses) the data independently, causing redundant consumpti...	Accept	The paper proposes a new data loader called Joader for parallel DNN training on overlapped datasets that allows tasks to share memory and computational resources for data preprocessing. Joader implements a new sampling mechanism and cache policy to reduce cache misses due to data access from multiple tasks and a new da...	train	[ "IOE6P2K4cuC", "OGJagwA6x23", "i7p79qnkypz", "YYcP_DZSHy", "A718UuM2IBy", "ZWz6paCV0M", "wRxUIjqlKPI", "UYpm88oAhc" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I totally understand it is hard to prove the optimality in the N-job case in such a practical scenario. The evaluation in the paper and your comments convinced me that the contributions of the DSA's current version are enough for the community. Measuring the degree of breaking the strict correlation among jobs ma...	[ -1, -1, -1, -1, -1, 5, 8, 5 ]	[ -1, -1, -1, -1, -1, 5, 5, 3 ]	[ "YYcP_DZSHy", "nips_2022_tZUOiVGO6jN", "UYpm88oAhc", "wRxUIjqlKPI", "ZWz6paCV0M", "nips_2022_tZUOiVGO6jN", "nips_2022_tZUOiVGO6jN", "nips_2022_tZUOiVGO6jN" ]
nips_2022_gE_vt-w4LhL	Squeezeformer: An Efficient Transformer for Automatic Speech Recognition	The recently proposed Conformer model has become the de facto backbone model for various downstream speech tasks based on its hybrid attention-convolution architecture that captures both local and global features. However, through a series of systematic studies, we find that the Conformer architecture’s design choices ...	Accept	The paper conducts thorough analysis of the Conformer architecture and brings insights and techniques from other fields to simplify and improve the model structure, which is also demonstrated to show nice gains. Though as pointed by reviewers the novelty is limited, the study is very useful to the field.	val	[ "MU30AZIGMOX", "4YHdvtFvBTG", "UQZEbgD8lQ_", "JZYRysqiHr6", "pRWdbijega", "8iQjAsRLOOV", "NErDY0uGrY", "FmnBs-qWysX", "SnVFeNMHsIv", "cbOnvcF0N9G", "FOkHfvj5juH", "FpJmwrxLAdb", "Af5A0CL5wPo", "nxY2UXJXGnC" ]	[ "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the final round of edits; I believe the presentation (once Figure 1 is also iterated on) is now fair and very clear. I also appreciate the authors' extensive insights and experiments: here, showing the improved blank/non-blank token repetitions of Squeezeformer-CTC over Conformer-CTC, and to the other ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 5 ]	[ "UQZEbgD8lQ_", "pRWdbijega", "pRWdbijega", "NErDY0uGrY", "FmnBs-qWysX", "nips_2022_gE_vt-w4LhL", "nxY2UXJXGnC", "nxY2UXJXGnC", "Af5A0CL5wPo", "Af5A0CL5wPo", "FpJmwrxLAdb", "nips_2022_gE_vt-w4LhL", "nips_2022_gE_vt-w4LhL", "nips_2022_gE_vt-w4LhL" ]
nips_2022_fpfDusqKZF	Neural Basis Models for Interpretability	Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations. Generalized Additive Models (GAMs) are an...	Accept	The paper proposes an approach, Neural Basis Model (NBM), that can be seen as a new subfamily of Generalized Additive Models for interpretability. The proposed model is compared to alternatives, showing competitive performance while being computationally more efficient. The authors successfully addressed questions rais...	val	[ "sglLX1DpYjn", "jLYYgW6MeD", "oWNyu5RrGqy", "hz2Lxvg6wIH", "4VvDiLXCCXR", "eKn-SCJVzT", "zjS5knxuU8l", "v6NniSz4o8K", "EpfbwNuwRIU", "YDkxWSBGXFa", "nPpQWqST3eP", "RNTBuHyYZO", "CBh_j0TVmlE" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your time and input. \n\nWe will make sure to include following in the camera ready: results on all 16 datasets (9 new from rebuttal) with appropriate baselines and updated SOTA, additional visualizations (some are already added in the updated supplementary, see Appendix Section A.4 and Figure A.2),...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 5, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 3, 3 ]	[ "jLYYgW6MeD", "eKn-SCJVzT", "hz2Lxvg6wIH", "zjS5knxuU8l", "CBh_j0TVmlE", "RNTBuHyYZO", "v6NniSz4o8K", "nPpQWqST3eP", "YDkxWSBGXFa", "nips_2022_fpfDusqKZF", "nips_2022_fpfDusqKZF", "nips_2022_fpfDusqKZF", "nips_2022_fpfDusqKZF" ]
nips_2022_TwuColwZAVj	Scalable Interpretability via Polynomials	Generalized Additive Models (GAMs) have quickly become the leading choice for interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they lack expressive power and easy scalability, and are hence not a feasible alternative for real-world tasks. We present a new class of GAMs that use tens...	Accept	The paper notes that polynomial functions are inherently interpretable models, and takes algorithmic advantage of the connection between polynomials and tensors by learning the coefficients of the polynomials using a low-rank tensor factorization. The resulting algorithm is shown to outperform prior SOTA interpretable ...	train	[ "_qnXKCvLof9", "it65fSGlEyc", "k-XMKRS9aBB", "XSG3tN0OuLl", "19uV1HlJoVd", "2mFqRLodPhT", "ta-_KtdABil", "9sFHjci-PcJ", "67_Nt8JxPUs", "E7d2EfVz4gx" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The author-response phase closes today. Please acknowledge the author rebuttal and state if your position has changed. Thanks!", " The author-rebuttal phase closes today. Please acknowledge the author rebuttal and state if your position has changed. Thanks!", " I do not have futher concerns if the above will ...	[ -1, -1, -1, -1, -1, -1, -1, 7, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "E7d2EfVz4gx", "67_Nt8JxPUs", "2mFqRLodPhT", "E7d2EfVz4gx", "67_Nt8JxPUs", "9sFHjci-PcJ", "nips_2022_TwuColwZAVj", "nips_2022_TwuColwZAVj", "nips_2022_TwuColwZAVj", "nips_2022_TwuColwZAVj" ]
nips_2022_kHeotl7q9dU	NS3: Neuro-symbolic Semantic Code Search	Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional sentences, and multi-step...	Accept	The paper studies the problem of retrieving code snippets given textual queries (NS3, Neuro-Symbolic Semantic Code Search). The work is motivated by language models’ limitations on encoding longer and not providing a faithful explanation of their reasoning on compositional queries. NS3 supplements the query sentence ...	train	[ "XFTppm2CTqW", "ZhdkB4D_90", "ZsM7v9gRmsq", "L8fcn8paQNp", "HQ9QwGGzPJz", "cosyre0Pld5", "VWfeOZlB_O", "6LNiC6sG3jV", "BNLkhGqoghr", "RELTsPKGqXP", "a9LqXGV2RdC", "OGmzPbl2eWl", "65_eFSsW54o", "haZvCLBEQ-uH", "cBleEJpIKT", "HR3y86wZ_Pa", "6z8_cqWb714", "3jYg0KpyF6", "ynjqfwA6Cl",...	[ "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "officia...	[ " Thank you for addressing my concerns regarding parsing rules and providing additional experiments about the parser success rate on other datasets. However, given that the parser success rate on other CodeSearchNet datasets is still low (<=41%), a user still needs to add NL parsing rules when they use the proposed...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 4 ]	[ "ynjqfwA6Cl", "HR3y86wZ_Pa", "ndfG3I7-W_l", "ZNF0I8gzSWU", "VWfeOZlB_O", "ZNF0I8gzSWU", "REjXBoVvCM", "ndfG3I7-W_l", "haZvCLBEQ-uH", "ndfG3I7-W_l", "REjXBoVvCM", "ZNF0I8gzSWU", "bzEuFDADzfa", "bzEuFDADzfa", "ZNF0I8gzSWU", "ZNF0I8gzSWU", "REjXBoVvCM", "REjXBoVvCM", "ndfG3I7-W_l", ...
nips_2022_Ijq1_a6DESm	On the consistent estimation of optimal Receiver Operating Characteristic (ROC) curve	Under a standard binary classification setting with possible model misspecification, we study the problem of estimating general Receiver Operating Characteristic (ROC) curve, which is an arbitrary set of false positive rate (FPR) and true positive rate (TPR) pairs. We formally introduce the notion of \textit{optimal RO...	Accept	I have read all the reviews and discussions carefully. The reviewers all praised the novelty and the significance of the work. The major complaint is that the proofs are too long to be carefully checked. The authors have appropriately addressed some comments from the reviewers. Given the unanimous support, I have deci...	train	[ "monGsw5zwH1", "lQPEcSgSlEG", "jOdcwCNZhsr", "59-wYhhAtLw", "p2dmuSFXNB5", "vWtpCRRHaFZ", "BfE14_ijNA1", "R6M8LBB-N8h", "lYKkzR2BdIY", "qMhKp1UYNXy", "OluPV9r9SBF", "eAT3qJXwUG" ]	[ "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your further suggestions. \nWe have made some additional edits to the article\nbased on your suggestions. In particular, \n- We have rewritten the statement on line 86. \n- We will expand the real data examples in the main file if our paper is accepted. ", " I thank the authors for their response....	[ -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 2, 3, 4 ]	[ "jOdcwCNZhsr", "vWtpCRRHaFZ", "R6M8LBB-N8h", "nips_2022_Ijq1_a6DESm", "eAT3qJXwUG", "OluPV9r9SBF", "qMhKp1UYNXy", "lYKkzR2BdIY", "nips_2022_Ijq1_a6DESm", "nips_2022_Ijq1_a6DESm", "nips_2022_Ijq1_a6DESm", "nips_2022_Ijq1_a6DESm" ]
nips_2022_-o0kPsyzErW	Parallel Tempering With a Variational Reference	Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating between the posterior distribution and a fixed reference distribution...	Accept	The idea of this paper is to tune the reference distribution for parallel tempering to improve efficiency. The key idea is simple: Assume the reference distribution is in the exponential family and use sufficient statistics. Experimental results show that this typically helps in terms of metrics like effective sample s...	train	[ "ALRz7TDoAHJ", "ky8jfzWR5Sa", "ay2Oaq11ud7", "GqH9CIBdaBDq", "8dPtgTLkhKaR", "_G3SsmlMJj5", "eZ8VjT890fK", "w-jp_h5SVD", "KVVZ4s_ZoOD", "4hFBLAot6t", "_bWKWY_SuI7", "3XQ7q2490rd", "cLUju83TiI8", "xl8c3IDRA2l" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the feedback! \n\n> Suppose a particle is stuck in a very deep local mode before it explores the whole domain, the reference distribution is optimized mainly based on samples from this local mode, and hence the reference distribution (obtained from moment matching or similar) at this moment actually...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 4, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 2 ]	[ "ky8jfzWR5Sa", "ay2Oaq11ud7", "GqH9CIBdaBDq", "8dPtgTLkhKaR", "_G3SsmlMJj5", "KVVZ4s_ZoOD", "w-jp_h5SVD", "xl8c3IDRA2l", "cLUju83TiI8", "_bWKWY_SuI7", "3XQ7q2490rd", "nips_2022_-o0kPsyzErW", "nips_2022_-o0kPsyzErW", "nips_2022_-o0kPsyzErW" ]
nips_2022_pqCT3L-BU9T	Periodic Graph Transformers for Crystal Material Property Prediction	We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a regular lattice in 3D space. How to effectively encode these periodic structures poses unique challenges not present in regular graph rep...	Accept	This paper had borderline reviews. While the reviewers felt that the method was novel and the presentation very good, they also cited weaknesses such as limited performance gains over baselines and limited novelty. The authors responded in detail to many of the concerns, including with additional experiments, causing s...	train	[ "hUJOBrIqvU", "U57m-Ghf6S", "X5M2CLzT7wf", "B0_NN-7E4E", "4LOZ-a2H6Y", "K_-LLgDqFz", "BMzWmTE4LXH", "DtJPJjPy8GZ", "k46hHj8Buj", "3tKGGq9-tj", "3qO8_-p7BPk4", "iVrQ65jjN2k", "B0LwuUcUIFfa", "q00toC9DCzB", "Xx6Xa4b4gOcR", "dWFyVh4j7sP", "2JA-ADcvjSC0", "SJXzvF1v-kv", "ZjL5_XIKE06"...	[ "author", "author", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author"...	[ " Dear Reviewer ifKM,\n\nThanks again for your valuable comments and suggestions in your initial review, which helps improve our work a lot. Regarding your main concerns on comparison with Graphormer, clarifications in writing, and performances when no periodicity or repetitive patterns in the graph, we...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 3 ]	[ "tJ4luCl2umg", "K_-LLgDqFz", "B0LwuUcUIFfa", "DtJPJjPy8GZ", "DtJPJjPy8GZ", "covwb7ZpzUSH", "tJ4luCl2umg", "3qO8_-p7BPk4", "iVrQ65jjN2k", "iVrQ65jjN2k", "iVrQ65jjN2k", "g9BjlI_U2wz", "qWTUs78AOO0", "qWTUs78AOO0", "qWTUs78AOO0", "qWTUs78AOO0", "4_dMLdjd7SO", "4_dMLdjd7SO", "4_dMLdj...
nips_2022_2EBn01PJh17	Adaptive Cholesky Gaussian Processes	We present a method to fit exact Gaussian process models to large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computational overhead. From an empirical observation that the log-marginal likelihood often...	Reject	This paper proposes some nice ideas on speeding up Gaussian process inference based on approximating the marginal using subsamples. However, several reviewers noted gaps and potentially flaws in the technical details. The reviews as well as detailed replies during the rebuttal period will help the authors prepare a str...	val	[ "y_SE3AFLsJx", "EcccFK9X9Ua", "i6KDnDEaiw", "ersnQf7N9Vo", "XxkndtIIUzD", "zi3vHFaops", "FF5IFMI6KwG", "nyVK0s2jFOg", "l186Q1BbZ7m", "-xrhce6ozAp", "L11pV5T9H1v" ]	[ "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear reviewers,\n\nThank you for your feedback on our paper. Having rerun our experiments, we have now uploaded a revised manuscript. In particular, we would like to highlight the changes to Figure 2, where we have corrected a mistake in the timing of the exact GP. The figure now clearly shows that the overhead o...	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3 ]	[ "nips_2022_2EBn01PJh17", "i6KDnDEaiw", "ersnQf7N9Vo", "XxkndtIIUzD", "nyVK0s2jFOg", "L11pV5T9H1v", "-xrhce6ozAp", "l186Q1BbZ7m", "nips_2022_2EBn01PJh17", "nips_2022_2EBn01PJh17", "nips_2022_2EBn01PJh17" ]
nips_2022_xijYyYFlRIf	GAUDI: A Neural Architect for Immersive 3D Scene Generation	We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we first optimize a latent representation that disentangles radiance fiel...	Accept	This paper proposes a framework to learn disentangled latent representation of radiance field and camera pose from trajectories of 3D scenes. The denoising diffusion probabilistic model can be further trained on the extracted latent representation for a conditioned or unconditioned generation. Experiments are conducted...	train	[ "manQuoaDDLq", "H03vtaX5VMx", "OOQXCuWAhjO", "zILC_x1xKkd", "WclHdFc2RN", "aaKIyoe62G", "B2sqmXoQy2Z", "WFK36BoFvAJ", "EOYsFy9IDb", "xQ3yNd-IEg", "S4ZmkyGPpVC", "hoYDVrFy0Vm", "K3qL-Gj05QT", "jSHGkHSBoxs", "Hq8i3-o_gtw", "wS5C2Ee7Q13" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We would like to thank the reviewer for engaging in discussions with us and for their comments. We address them in the following:\n\n* “I am a little confused by the response the training set of trajectories are scene agnostic. If my understanding is correct, during training, each scene has a different set of cam...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3, 3 ]	[ "WclHdFc2RN", "zILC_x1xKkd", "aaKIyoe62G", "xQ3yNd-IEg", "EOYsFy9IDb", "hoYDVrFy0Vm", "EOYsFy9IDb", "nips_2022_xijYyYFlRIf", "K3qL-Gj05QT", "jSHGkHSBoxs", "Hq8i3-o_gtw", "wS5C2Ee7Q13", "nips_2022_xijYyYFlRIf", "nips_2022_xijYyYFlRIf", "nips_2022_xijYyYFlRIf", "nips_2022_xijYyYFlRIf" ]
nips_2022_dRgHxaOJsiV	3DB: A Framework for Debugging Computer Vision Models	We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation. We demonstrate, through a wide range of use cases, that 3DB allows users to discover vulnerabilities in computer vision systems and gain insights into how models make decisions. 3DB captures and ...	Accept	Reviewers found the presented work to be a usefulf framework, the paper to contain adequate experiments and intereting demonstrations of the framework's capabilities, and be overall well written. They appreciated tha significatn prior results can be replicated easily with the the proposed framework. On the flip side, t...	train	[ "80p9YWRfVO8", "6AqWEq6Qbgm", "KlvyrU8VDf1c", "PcFKW_Bg3m", "qPgGU6p-k4", "tANkNpVulu-", "t78ms-7dvyB", "r1wNvgJoKEU", "_aOJp1HsmVO", "mXmaupg0Z0Y", "_jL100uWT0w", "hn1-akFIWzG", "j23-92toEsN" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " \nThank you for the detailed rebuttal. A key reference for probabilistic programming include - Kulkarni et al, 'Picture: A probabilistic programming language for scene perception', CVPR 2015. There are several frameworks that incorporate 3D graphics and rendering (e.g. Pyro: Deep probabilistic programming fram...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3, 4 ]	[ "_aOJp1HsmVO", "PcFKW_Bg3m", "t78ms-7dvyB", "tANkNpVulu-", "tANkNpVulu-", "j23-92toEsN", "hn1-akFIWzG", "_jL100uWT0w", "mXmaupg0Z0Y", "nips_2022_dRgHxaOJsiV", "nips_2022_dRgHxaOJsiV", "nips_2022_dRgHxaOJsiV", "nips_2022_dRgHxaOJsiV" ]
nips_2022_Xg-yZos9qJQ	Exploration via Elliptical Episodic Bonuses	In recent years, a number of reinforcement learning (RL) methods have been pro- posed to explore complex environments which differ across episodes. In this work, we show that the effectiveness of these methods critically relies on a count-based episodic term in their exploration bonus. As a result, despite their succes...	Accept	The reviewers appreciated the fundamental questions the paper was asking, clear writing and argumentation of the paper and convincing empirical experiments. While there were concerns about the theoretical rationale of resetting covariance matrix, the empirical results show it is indeed important. For these reasons, I r...	train	[ "wDJMlk2pbdz", "SZV6S-r5yvL", "BktZXS1jEGv", "KLVreeyFmPx", "1M1wkPib_EH", "7zQHwAhkdAL", "xH0yBCZsiSH", "Q-Yrd7MO4F-", "rA3uKN4IFn8", "bgqs7tohK5", "V-juRSWtWA", "zzBncHmSULf", "tvMCHyAsTr", "iy3rlnYOSV", "9Hzh34aJrNh", "Cmjqk5R2P6U" ]	[ "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for reading our response. Although we still respectfully disagree with the current rating, we do appreciate you raising your score. We address the concerns you bring up below:\n\n$~$\n\n1. Although you say “naively using count-based bonus obviously does not make sense”, we would like to point out that t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3 ]	[ "SZV6S-r5yvL", "KLVreeyFmPx", "1M1wkPib_EH", "1M1wkPib_EH", "tvMCHyAsTr", "V-juRSWtWA", "rA3uKN4IFn8", "nips_2022_Xg-yZos9qJQ", "Cmjqk5R2P6U", "9Hzh34aJrNh", "9Hzh34aJrNh", "iy3rlnYOSV", "iy3rlnYOSV", "nips_2022_Xg-yZos9qJQ", "nips_2022_Xg-yZos9qJQ", "nips_2022_Xg-yZos9qJQ" ]
nips_2022_h4kN_apci_R	Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data	Many real-world datasets contain missing entries and mixed data types including categorical and ordered (e.g. continuous and ordinal) variables. Imputing the missing entries is necessary, since many data analysis pipelines require complete data, but challenging especially for mixed data. This paper proposes a probabili...	Accept	The authors propose a single and multiple missing value imputation method for mixed data under the MCAR (missing completely at random) assumption. The method is based on using a latent Gaussian distribution in the form of an ordinary Gaussian copula model for ordered data (ordinal and continuous) and an extended Gaussi...	train	[ "ub25V7QjwgK", "QtBcIZC-bu", "NEii1FtBrQc", "SufR8LO7F8", "ndecbGw-Fk-", "y-sryl9C3HH", "jpEiFlIZJtw", "Gm_Cubo-GLu", "AhGUlieRiir", "QEz_M8UcDAx" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for raising your score! We actually selected the value of M and beta on randomly generated category probabilities before we conducted any experiment reported in this paper. Thus the marginal estimation performance on our selected M and beta are already test data performance! \n\nYou could also refer to ou...	[ -1, -1, -1, -1, -1, -1, -1, 7, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 2, 4 ]	[ "QtBcIZC-bu", "ndecbGw-Fk-", "SufR8LO7F8", "jpEiFlIZJtw", "QEz_M8UcDAx", "AhGUlieRiir", "Gm_Cubo-GLu", "nips_2022_h4kN_apci_R", "nips_2022_h4kN_apci_R", "nips_2022_h4kN_apci_R" ]
nips_2022__L7f0ySKMWY	Near-Optimal Multi-Agent Learning for Safe Coverage Control	In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density. In practice, the density is rarely known $\textit{a priori}$, further complicating the original NP-hard problem. Moreover, in many applications, agents cannot visit arbitrary locati...	Accept	This paper presents a novel method for multi-agent coverage control over an unknown density and safety constraints. There is some concern about the level of significance of the approach but it is interesting and sound. There were also concerns about scalability and the use of GPs for density modeling but the authors ha...	train	[ "LEef8Omnr4f", "D-uIlgUL9jh", "fHWmzhqwVZ", "-dxL5SZNZu-", "SK2RmD-d_l", "GK_WK4g_bI", "AETSVcVbsNK", "-S01qHdgp_t", "Elm2AwFgU_U", "ppd4VfPRoPI", "N8mHxBnyqgz", "Gtj1-QZAA5", "bWFSOUxjnLe", "ifyqmMr8mFh", "pzo92J0BzW" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " > As in the original submission there appears to be a region where the proposed approach is most useful (for low to medium number of samples, PassiveMac is far better), for high number of samples, Two-Stage is about equal or not much worse. \n\nFor the reviewer comment above, we would like to add that,\n\nPassive...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 3, 4, 5 ]	[ "D-uIlgUL9jh", "-dxL5SZNZu-", "SK2RmD-d_l", "AETSVcVbsNK", "Elm2AwFgU_U", "nips_2022__L7f0ySKMWY", "nips_2022__L7f0ySKMWY", "Gtj1-QZAA5", "bWFSOUxjnLe", "ifyqmMr8mFh", "pzo92J0BzW", "nips_2022__L7f0ySKMWY", "nips_2022__L7f0ySKMWY", "nips_2022__L7f0ySKMWY", "nips_2022__L7f0ySKMWY" ]
nips_2022_AhbTKBlM7X	Learning State-Aware Visual Representations from Audible Interactions	We propose a self-supervised algorithm to learn representations from egocentric video data. Recently, significant efforts have been made to capture humans interacting with their own environments as they go about their daily activities. In result, several large egocentric datasets of interaction-rich multi-modal data ha...	Accept	The paper presents self-supervised representation learning from egocentric video data. The reviewers unanimously support the paper. Although WY3G has not updated the rating, the reviewer commented that s/he is upgrading the rating to Weak Accept, making the paper get three unanimous Weak Accept ratings. All three revie...	train	[ "exX0UhsTzwB", "ywrlXmcFs9h", "QJ7JhZAfTRP", "pw36liRg6Kz", "1gpPLgnfR-E", "5hqdfYMZOVe", "p3UGij_9XJQ", "WS5AoYHOJWx", "IF0sazx3LWg", "YIp7RRNzNuTW", "fPRbj-_oF2r", "CVgnH9x-MSv", "yt1PbmmLAWx", "hn18lMTENm", "_cGP3J-mMxi", "2povrMJwVGS", "znwbZoJKoWr", "faC_Uiks94G", "O6KIDaZsu...	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the response. \n\nI agree with the other two reviewers that the comparison with other state-of-the-art methods is not sufficient in the original submission and the additional comparisons provided should be added into the paper. I agree with Reviewer coBj that some examples should also be provided in...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "IF0sazx3LWg", "QJ7JhZAfTRP", "hn18lMTENm", "O6KIDaZsuRb", "faC_Uiks94G", "znwbZoJKoWr", "nips_2022_AhbTKBlM7X", "YIp7RRNzNuTW", "O6KIDaZsuRb", "fPRbj-_oF2r", "CVgnH9x-MSv", "yt1PbmmLAWx", "faC_Uiks94G", "_cGP3J-mMxi", "znwbZoJKoWr", "nips_2022_AhbTKBlM7X", "nips_2022_AhbTKBlM7X", ...
nips_2022_agNTJU1QNw	Geometric Order Learning for Rank Estimation	A novel approach to rank estimation, called geometric order learning (GOL), is proposed in this paper. First, we construct an embedding space, in which the direction and distance between objects represent order and metric relations between their ranks, by enforcing two geometric constraints: the order constraint compel...	Accept	This paper proposes a new approach named geometric order learning (GOL) for rank estimation. Reviewers found that the idea is novel and the paper is well written. The authors have also clearly addressed most questions from reviewers in their responses. Thus, I recommend the acceptance of this paper.	train	[ "HRU5DR_BdcZ", "JSUasE3CrRM", "c9QKOVAl_zD", "tqRlxOeeu-x", "EDFS14vald7", "s4cLQWDL0eN", "ks-RMcveOBe", "qko_CUZV8Z2", "1OYc7F3esg2", "Db3X9rhM7_a", "n769zbUCOJ_", "pCWKa3P8qh9", "99DWMlgx03q", "3fcvCxF6E9_" ]	[ "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you again for your constructive and insightful review on our paper. We do appreciate it. ", " Thanks for the clarifications. I don't have any other questions. ", " Thank you for your feedback. We appreciate it greatly. \n*\nAsymmetric $v_\\mathrm{f}$ and $v_\\mathrm{b}$:** \n\n> What we meant by 'st...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "JSUasE3CrRM", "c9QKOVAl_zD", "tqRlxOeeu-x", "s4cLQWDL0eN", "nips_2022_agNTJU1QNw", "3fcvCxF6E9_", "3fcvCxF6E9_", "99DWMlgx03q", "pCWKa3P8qh9", "nips_2022_agNTJU1QNw", "nips_2022_agNTJU1QNw", "nips_2022_agNTJU1QNw", "nips_2022_agNTJU1QNw", "nips_2022_agNTJU1QNw" ]
nips_2022_pBpwRkEIjR3	Enhanced Bilevel Optimization via Bregman Distance	Bilevel optimization has been recently used in many machine learning problems such as hyperparameter optimization, policy optimization, and meta learning. Although many bilevel optimization methods have been proposed, they still suffer from the high computational complexities and do not consider the more general bileve...	Accept	The paper studies bilevel optimization problems, provides three algorithms for different settings, and improves the convergence analysis in terms of the condition number. In addition, numerical experiments are conducted that provide illustration of the effectiveness of the algorithms. Three reviewers all agree that the...	train	[ "iIODO0Y2XsU", "p8Rphr5lIxH", "M3gwXQ53dc", "6fSohfQfZRG", "9p0W9d6mWbI", "jhLtNORQNhsG", "eOgVesah4x3", "NfOFi13e2Ws", "smplBqKfVVX" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the response. You resolve my questions, but I want to remain my score.", " Thanks for your response. Questions have been solved.", " I really appreciate the author's response. All my questions are answered.", " Thanks so much for your comments.\n\nQ1: Weaknesses: 1) This paper only mention...	[ -1, -1, -1, -1, -1, -1, 7, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "jhLtNORQNhsG", "9p0W9d6mWbI", "6fSohfQfZRG", "smplBqKfVVX", "NfOFi13e2Ws", "eOgVesah4x3", "nips_2022_pBpwRkEIjR3", "nips_2022_pBpwRkEIjR3", "nips_2022_pBpwRkEIjR3" ]
nips_2022_Vg_02McCRnY	Optimal Comparator Adaptive Online Learning with Switching Cost	Practical online learning tasks are often naturally defined on unconstrained domains, where optimal algorithms for general convex losses are characterized by the notion of comparator adaptivity. In this paper, we design such algorithms in the presence of switching cost - the latter penalizes the typical optimism in ada...	Accept	This is a technical, but interesting paper on online linear optimization. The nice contribution is a control of the switching cost (moving from one action to another) which makes the problem highly non-trivial. The contribution is to consider a "smaller" set of assumptions (hence a weaker asymptotic result) than in th...	train	[ "lvu6W2CKFPE", "h2091y7giwk", "12W-a6qSun", "MsrhMKvdx7j", "SLFrIITZ3yY", "qZDhzxZGR0Z", "6UAlOV0asS", "6JbO2KjduAq", "SAI4Xa3y7L", "3algeuBdqST", "kXxikA6Sogq", "VuQRq1NJWBd" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I think the authors have properly addressed my questions. It would be very nice if the authors could incorporate those discussions in the revised version. So I will increase my score to acceptance.", " Thank you for the detailed response. \n\nAlthough I still worry that the techniques used in this paper is some...	[ -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 1, 4, 3, 4, 1 ]	[ "qZDhzxZGR0Z", "MsrhMKvdx7j", "VuQRq1NJWBd", "kXxikA6Sogq", "3algeuBdqST", "SAI4Xa3y7L", "6JbO2KjduAq", "nips_2022_Vg_02McCRnY", "nips_2022_Vg_02McCRnY", "nips_2022_Vg_02McCRnY", "nips_2022_Vg_02McCRnY", "nips_2022_Vg_02McCRnY" ]
nips_2022_3uj_8G7fxgs	Multi-objective Deep Data Generation with Correlated Property Control	Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advance of deep generative models is limited by the challenges to generate objects that possess multiple desired properties ...	Accept	All three reviewers argue to accept (albeit one borderline). Extremely substantial response from the authors addressing individual reviewer comments, which led to reviewers raising their scores, and to a much revised paper with new experiments. This effectively led to a second round of review, with engaged reviewers w...	train	[ "v-1OuWhUlq", "gmI6fhZ_9Au", "9l60dqpdrI-", "vUIthqPDPTj", "JfblpkrDxk", "i4BDPd-kdgP", "MV4Wb8WcfezO", "jmu7w7SxDQc", "sTiNmqqj8UP", "4crt81Gj3iO", "Ii9mQgzUXkyT", "2TzjVAsWa-w", "E64hFLLC5eP9", "ZS6cvsv2QMi", "veiF-WnHrWP", "3gnEy8lYbDS", "m3Mgv9w3Xtt", "smBe3JwDx2b", "GU0VImi3...	[ "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " My comments have been properly addressed.", " We sincerely thank the reviewer for approving our clarification and we are glad to further discuss the evaluation of generated data.\n\nEvaluation metrics include novelty, uniqueness, diversity, validity and similarity to original distribution such as Fréchet Incept...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 2 ]	[ "MV4Wb8WcfezO", "vUIthqPDPTj", "ZS6cvsv2QMi", "JfblpkrDxk", "i4BDPd-kdgP", "E64hFLLC5eP9", "m3Mgv9w3Xtt", "GU0VImi3IEs", "smBe3JwDx2b", "smBe3JwDx2b", "smBe3JwDx2b", "smBe3JwDx2b", "smBe3JwDx2b", "veiF-WnHrWP", "GU0VImi3IEs", "nips_2022_3uj_8G7fxgs", "nips_2022_3uj_8G7fxgs", "nips_...
nips_2022_lgNGDjWRTo-	Deep Generative Model for Periodic Graphs	Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and polygon mesh. Their generative modeling has great potential in real-world applications such as material design and graphics synthesis. Classical models either rely on domain-specific predefined generation principles (e.g., in...	Accept	This paper proposed an interesting model for generating periodic graphs. The hierarchical representation of periodic graphs decomposes into local structure and global structure, and greatly reduces the size of the structure to be modeled, which is an interesting contribution. All reviewers liked the idea of this repr...	train	[ "jjaMyjWQ6dr", "WE3ycH9mL5", "yYujJgMMkbX", "X-fjVX-ucqA", "fOzYVbU0WV", "NUJKYywLdl", "YsfKj3yNxLw", "uh7UPxbEDWy", "n__G6ZvxzQB", "exo0JpbvG_5", "F3N1QrX4p9S", "iFo3zb3y7dR", "anbOKfRDUbi", "keqGZfvVTeX", "EtEJwQ9xCNM", "M1lt_ADs2UVP", "tSRw8S7UH8b", "KRQFV49EHnO", "SnyJmnu75DC...	[ "official_reviewer", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", ...	[ " My comments have been properly addressed.", " We sincerely appreciate for reviewers' comments and feedbacks that made our paper further improved when we were addressing their concerns. We are also glad that reviewers approved our clarifications and are satisfied with how we addressed their comments. The followi...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 8, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 3, 4 ]	[ "exo0JpbvG_5", "nips_2022_lgNGDjWRTo-", "keqGZfvVTeX", "tSRw8S7UH8b", "NUJKYywLdl", "YsfKj3yNxLw", "q5Fs1EMrkF", "n__G6ZvxzQB", "q5Fs1EMrkF", "KRQFV49EHnO", "q5Fs1EMrkF", "q5Fs1EMrkF", "q5Fs1EMrkF", "EtEJwQ9xCNM", "SnyJmnu75DC", "tSRw8S7UH8b", "nips_2022_lgNGDjWRTo-", "nips_2022_lg...
nips_2022_fp33Nsh0O5	Deep Generalized Schrödinger Bridge	Mean-Field Game (MFG) serves as a crucial mathematical framework in modeling the collective behavior of individual agents interacting stochastically with a large population. In this work, we aim at solving a challenging class of MFGs in which the differentiability of these interacting preferences may not be available t...	Accept	This paper proposes a novel, simple but effective algorithm to solve Mean Field Games. Reviewers found the paper well written, presenting an exact and flexible method. Despite its simplicity, the method solves a wide class of MFGs. Authors were also able to demonstrate the computational advantage of their method, provi...	train	[ "gAWjrjza-hM", "gBTXQUzSeS1", "Bx3B5odoKgC", "geMijoTY4Rh", "pq9PwlJYopu", "uhEmeAozQbH", "ioWyiu1yePz", "fDp1CYCWlf", "iv6LpODV-h", "236QopZ6zHm", "QqluvKeGJfDt", "3yGUYZeDsQ9", "5IS0Q1Td2qi", "gIJudO1oveOM", "3iHrvuSBbV7y", "7xqRbkq4hE", "IvGjllPnU4", "kZzHQhayjgCB", "xPs93ck2P...	[ "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", ...	[ " We thank the reviewer for the reply. We are pleased that the reviewer appreciated our clarifications, and we greatly appreciate the reviewer's willingness to raise the score. To ease the reviewer's burden, we provide the following list of content, linking each of our responses to the [_section, line, page_] in th...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 4 ]	[ "gBTXQUzSeS1", "gIJudO1oveOM", "7xqRbkq4hE", "pq9PwlJYopu", "uhEmeAozQbH", "ioWyiu1yePz", "iv6LpODV-h", "236QopZ6zHm", "236QopZ6zHm", "QqluvKeGJfDt", "xNPEYkus7s5", "5IS0Q1Td2qi", "hWkBAkB7eew", "3iHrvuSBbV7y", "fdCEu3U3JMQ", "IvGjllPnU4", "xPs93ck2PKY", "nips_2022_fp33Nsh0O5", "...
nips_2022_espX_4CLr46	Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources	Extraction of latent sources of complex stimuli is critical for making sense of the world. While the brain solves this blind source separation (BSS) problem continuously, its algorithms remain unknown. Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistica...	Accept	This paper presents a method for blind separation of correlated sources, which is a challenging task. Applying the weight similarity matching approach to the Det-Max optimization, the authors develop a biologically-plausible two-layered neural network that can separate correlated sources from their linear mixture. All ...	train	[ "wYmH7uFjksk", "7V8_f7Sq2mQ", "zFYJ_t85Pl", "yD_v2X7B8RW", "Rkt2LG-MDY", "61N0b2AT2X", "RCE7SR3dbr8", "-QyJls9z8bx", "TNrUMwo_pfZ", "NdNNupLuiWD", "x-a9saNKa8", "NCT8otgP_OZ", "JU57hLkDIAO", "yEnR1YJEAYV", "eIxESI6cv5t", "rE-G4rASliQ", "S6kPhx3_UB", "YDjWuJaG8vz", "6Nq9meKaBg", ...	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", ...	[ " We would like to thank you for your useful feedback and suggestions.", " We would like to thank you for your useful feedback and suggestions.", " We would like to thank you for your useful feedback and suggestions.", " We would like to thank you for the useful feedback and suggestions. In the final version...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 8, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 5, 1 ]	[ "-QyJls9z8bx", "Rkt2LG-MDY", "TNrUMwo_pfZ", "61N0b2AT2X", "j-Wrs7f8E6", "S6kPhx3_UB", "rE-G4rASliQ", "YDjWuJaG8vz", "eIxESI6cv5t", "j-Wrs7f8E6", "j-Wrs7f8E6", "j-Wrs7f8E6", "8azOK4QpBcA", "8azOK4QpBcA", "8azOK4QpBcA", "sJ6r3hLnie9", "sJ6r3hLnie9", "6Nq9meKaBg", "nips_2022_espX_4C...
nips_2022_QNjyrDBx6tz	Practical Adversarial Multivalid Conformal Prediction	We give a simple, generic conformal prediction method for sequential prediction that achieves target empirical coverage guarantees on adversarial data. It is computationally lightweight --- comparable to split conformal prediction --- but does not require having a held-out validation set, and so all data can be used fo...	Accept	This paper proposes a conformal prediction based method for sequential prediction, relaxing the exchangeability assumption. It is robust to distribution shift, and achieves group-conditional coverage guarantees. The method is efficient, novel, and outperforms existing methods. All the reviewers, including myself, fi...	val	[ "JnRYZu6KNfK", "oyz1Rmm_Qsv", "_wcu_bMmSkmd", "y67dsvG4IEy", "o8J9it7g9Xpv", "XNedzv0CP3", "EshtFYE56e", "0R_FGzdN52p", "sY2oxEv1w_Y", "NAmLlkFpdJd", "Wfr1IhR1vSG", "ISHdf4AHB_" ]	[ "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you --- and thanks once again for the time you spent reviewing!", " Thank you for the clarifications! My concerns are resolved. I recommend acceptance.", " I appreciate the detailed rebuttal and clarifications.", " Thank you!", " Thank you for the clarifications. While most of my concerns are resolv...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "oyz1Rmm_Qsv", "sY2oxEv1w_Y", "0R_FGzdN52p", "o8J9it7g9Xpv", "XNedzv0CP3", "ISHdf4AHB_", "Wfr1IhR1vSG", "Wfr1IhR1vSG", "NAmLlkFpdJd", "nips_2022_QNjyrDBx6tz", "nips_2022_QNjyrDBx6tz", "nips_2022_QNjyrDBx6tz" ]
nips_2022_KxVSnZVuZZ	Constrained Langevin Algorithms with L-mixing External Random Variables	Langevin algorithms are gradient descent methods augmented with additive noise, and are widely used in Markov Chain Monte Carlo (MCMC) sampling, optimization, and machine learning. In recent years, the non-asymptotic analysis of Langevin algorithms for non-convex learning has been extensively explored. For constrained ...	Accept	After going through all the reviews, rebuttals, and discussions in detail I am recommending a borderline acceptance for the paper. More precisely, the technical contribution of the paper is significant, even though there have been some concerns raised regarding the motivation/applicability of the setup. However, I do b...	train	[ "K6LmPBMtohrr", "fEVm8__a9zq4", "x_9DpKnkbGx", "QcpBf0Zb559", "VSgLg21mLfd", "y-PbbUf7NVCh", "sFp5nKQbQeR", "3fVWpGPMLPY", "LWRuIv1_Zaa", "RV0n2SvFb4n", "z_B--AlSmpx" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the response. I stand by my original evaluation.", " (5) Response to Q2:\n\n- Sampling from Gibbs distribution, i.e. Langevin algorithms is used for high-dimensional and large-scale sampling applications. Gibbs distribution is an invariant distribution of the Langevin dynamics. And Langevin dynami...	[ -1, -1, -1, -1, -1, -1, -1, 5, 4, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 4 ]	[ "sFp5nKQbQeR", "LWRuIv1_Zaa", "LWRuIv1_Zaa", "3fVWpGPMLPY", "RV0n2SvFb4n", "RV0n2SvFb4n", "z_B--AlSmpx", "nips_2022_KxVSnZVuZZ", "nips_2022_KxVSnZVuZZ", "nips_2022_KxVSnZVuZZ", "nips_2022_KxVSnZVuZZ" ]
nips_2022_Jupoos_K4xt	Equivariant Networks for Zero-Shot Coordination	Successful coordination in Dec-POMDPs requires agents to adopt robust strategies and interpretable styles of play for their partner. A common failure mode is symmetry breaking, when agents arbitrarily converge on one out of many equivalent but mutually incompatible policies. Commonly these examples include partial obse...	Accept	This paper proposes a test-time algorithmic modification to address a multi-agent coordination problem where agents choose incompatible strategies due to symmetries in the environment, showing that it is applicable to ZSC tasks like Hanabi. The proposed symmetrizer is applied to LSTM recurrent policies. Reviews were m...	train	[ "edgRxKdnWiQ", "dfzwnXwC_eY", "8H1dFmMA4dl", "7tiQtRAYOCq", "ClXUejf9Z3t", "1A_HzTdnQryg", "i9D_rdgrazr", "etU0VRGpuQz", "6QUHGwtjUt", "Ui0iFgAOFWC", "bH_s3PLaQ7r", "Ohn3gFpFFLA" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your comment and consideration.\n\nThe paragraph in Section 3 promising an empirical test was regarding testing whether the averaging scheme helped in improving ZSC (which we did find as EQC improves ZSC), not to comparing it to using $e \\in G$. We agree that a formal ablation comparing the two app...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "dfzwnXwC_eY", "i9D_rdgrazr", "7tiQtRAYOCq", "ClXUejf9Z3t", "1A_HzTdnQryg", "etU0VRGpuQz", "Ohn3gFpFFLA", "bH_s3PLaQ7r", "Ui0iFgAOFWC", "nips_2022_Jupoos_K4xt", "nips_2022_Jupoos_K4xt", "nips_2022_Jupoos_K4xt" ]
nips_2022_ylila4AYSpV	Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions	Internet ad auctions have evolved from a few lines of text to richer informational layouts that include images, sitelinks, videos, etc. Ads in these new formats occupy varying amounts of space, and an advertiser can provide multiple formats, only one of which can be shown. The seller is now faced with a multi-parameter...	Accept	Reviewers agreed that rich ad auction is significant and are excited about theoretical bounds on the positive result (achieved by a simple mechanism) and the negative result. Overall, this is a solid theoretical paper on an important and classical problem in industry.	train	[ "R9BnwKNK-TR", "t889ymFq-Q0", "70rhPMl1A4ZO", "-jeO7TpSOzM", "hRg6V2Kpde6N", "l3hPfNeQb8", "fsWYuTDoeFD", "hdpGfIO31zz", "HxvBaw6MWf5", "8qQcmtpSwt", "xSptrXWA4Ld" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The reviewer rnSK is indeed correct. The time reported in Table 1 is the mean time in seconds. We will correct it to be milli-seconds in the final version.", " I think Figure 4 in the appendix is more consistent with what I would have expected. It has the median time for VCG between 10 and 20 msec. But Table ...	[ -1, -1, -1, -1, -1, -1, -1, 4, 7, 8, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 2, 5, 4, 1 ]	[ "t889ymFq-Q0", "hRg6V2Kpde6N", "xSptrXWA4Ld", "8qQcmtpSwt", "HxvBaw6MWf5", "hdpGfIO31zz", "nips_2022_ylila4AYSpV", "nips_2022_ylila4AYSpV", "nips_2022_ylila4AYSpV", "nips_2022_ylila4AYSpV", "nips_2022_ylila4AYSpV" ]
nips_2022_Yb3dRKY170h	One-shot Neural Backdoor Erasing via Adversarial Weight Masking	Recent studies show that despite achieving high accuracy on a number of real-world applications, deep neural networks (DNNs) can be backdoored: by injecting triggered data samples into the training dataset, the adversary can mislead the trained model into classifying any test data to the target class as long as the tri...	Accept	The paper presents a method for defending deep neural networks against backdoor attacks, i.e., attacks that inject “triggered” samples into the training set. The method can be seen as an improvement on Adversarial Neuron Pruning (ANP) that uses (i) soft weight masking (SWM), (ii) adversarial trigger recovery (ATR) and ...	val	[ "DEUtoVWNJ-W", "IWtMfuQD1H", "wVU3zTsZSwy", "1YTTLZLjr8", "cz9mF2Kk2nB", "Yx-2LrJE01", "HnHkHlzSSpk", "7f93mML3WAp", "9eYgULnge3A", "RUGmjYqQcLA", "fR5gcQLmpIX3", "XxYEKfzDGim", "HKDd_k3hzl", "oSRjdWiZdcp", "akJw83dFs2h" ]	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your reply and raising your score. Yet we would like to further clarify some points in your last comment and hope to address your concerns on the contribution and hyperparameter search.\n\nIn terms of our contributions, we respectively disagree with you. Our method is NOT “a heuristic variant of ANP”...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "IWtMfuQD1H", "1YTTLZLjr8", "oSRjdWiZdcp", "cz9mF2Kk2nB", "Yx-2LrJE01", "HnHkHlzSSpk", "RUGmjYqQcLA", "9eYgULnge3A", "XxYEKfzDGim", "akJw83dFs2h", "oSRjdWiZdcp", "HKDd_k3hzl", "nips_2022_Yb3dRKY170h", "nips_2022_Yb3dRKY170h", "nips_2022_Yb3dRKY170h" ]
nips_2022_sr0289wAUa	ASPiRe: Adaptive Skill Priors for Reinforcement Learning	We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) f...	Accept	This paper proposes a method to adaptively combine and use skill priors for reinforcement learning. The setting is very practical and the method proposed is novel and effective empirically. The main concerns from the reviewers were around (1) the experimental setup being too narrow and simple, and (2) the clarity of th...	train	[ "esV2X2yd-e", "86eJhUNUK7S", "2hTUm6goM6x", "mUW_V_4cW28", "R_73ECsZbqq", "MOm8x22BtbC", "Y65-82lR-BZ", "YXfzg__qMT", "1wqqX33Cgxi", "qQz12S29VB", "2hnY116bYeo", "N1VDdVwgmbv", "bdp2GEZgkXZ", "idQrqcfDRSI" ]	[ "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " 1. Result from robotic manipulation task: Thanks for adding this experiment! It definitely adds to the overall soundness of the paper. I strongly encourage the authors to finish some baseline evaluations and add its corresponding plot to Figure 2. \n\n2. Thanks for the clarification! This makes sense.\n\n3. I app...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 4 ]	[ "1wqqX33Cgxi", "MOm8x22BtbC", "1wqqX33Cgxi", "R_73ECsZbqq", "YXfzg__qMT", "idQrqcfDRSI", "bdp2GEZgkXZ", "N1VDdVwgmbv", "2hnY116bYeo", "nips_2022_sr0289wAUa", "nips_2022_sr0289wAUa", "nips_2022_sr0289wAUa", "nips_2022_sr0289wAUa", "nips_2022_sr0289wAUa" ]
nips_2022_o4neHaKMlse	Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone	Vision-language (VL) pre-training has recently received considerable attention. However, most existing end-to-end pre-training approaches either only aim to tackle VL tasks such as image-text retrieval, visual question answering (VQA) and image captioning that test high-level understanding of images, or only target reg...	Accept	This paper proposes a two-stage pretrain visual language model which can deal with both high level and region level downstream tasks. Experiments show significant improvement SotA models. Main concerns from reviews are some missing references while the author gave detailed comparisons in the responses. Although revi...	val	[ "DSHKpcdZzU0", "svixthLYKba", "OYFDJ0wzhv0", "6g7jSSHM2W", "LJIghAo46je", "qXPCZaZQNhR", "_AwM-Kupuzou", "E_5UhJ4xgxE", "vgV7oaO9c_Q", "hHDPO1mHSF4", "R_b_5hiKp_T", "E0VDYjRQvhn", "RBYK-8SFR-7R", "Dn1zKeOJn7h", "pg8IJqh7y_2", "B3VJUXG_iic", "f5KkA3n0a1", "e0lQlR-XgN0", "NiFg9_DGD...	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the update!\n\nWe would like to kindly point out that we have evaluation results on ~30 datasets in the paper. Many of these tasks (e.g. VQAv2, COCO object detection, RefCOCO+) are highly competitive, yet we can obtain consistent performance improvements over strong baselines, often outperforming me...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 5 ]	[ "svixthLYKba", "OYFDJ0wzhv0", "XRtCzJDthe5", "LJIghAo46je", "Dn1zKeOJn7h", "e0lQlR-XgN0", "NiFg9_DGDzO", "XRtCzJDthe5", "hHDPO1mHSF4", "R_b_5hiKp_T", "E0VDYjRQvhn", "RBYK-8SFR-7R", "XRtCzJDthe5", "NiFg9_DGDzO", "B3VJUXG_iic", "e0lQlR-XgN0", "nips_2022_o4neHaKMlse", "nips_2022_o4neH...
nips_2022_AcHUIG2wA8-	Non-Gaussian Tensor Programs	The Tensor Programs framework has produced a series of powerful results by 1) expressing any deep learning computation of concern as a principled composition of element-wise nonlinearities and matrix multiplication, and 2) inductively reasoning about the program behavior as the sizes of the matrices in the program tend...	Accept	This submission is borderline. Reviewers were generally in consensus --- all felt that the theoretical contribution is sound and non-trivial, but delivers a relatively minor addition to the Tensor Programs framework. I fully agree with this perspective, and similarly to all reviewers, recommend that the paper be acce...	train	[ "EzvqbrvFvwr", "YEkW6alfT1n", "5L1pMv6N3J6", "f9XWxu3-1h7", "m2JrcpRD8CB6", "H2qPuFyzBvs", "3dhIdK91_tR", "cTRSILK1in", "5f4YWKe24tt", "5Aes-X7Rm5N" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I want to thank the authors for their response and clearing up some of my misunderstandings about their work. The theoretical contribution is much stronger than I initially understood and I'll update my scores/review appropriately.", " I appreciate the authors cleaning up the notation and thank you for addressi...	[ -1, -1, -1, -1, -1, -1, 6, 6, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, 2, 2, 1, 3 ]	[ "H2qPuFyzBvs", "5L1pMv6N3J6", "cTRSILK1in", "3dhIdK91_tR", "5f4YWKe24tt", "5Aes-X7Rm5N", "nips_2022_AcHUIG2wA8-", "nips_2022_AcHUIG2wA8-", "nips_2022_AcHUIG2wA8-", "nips_2022_AcHUIG2wA8-" ]
nips_2022__bqtjfpj8h	Ask4Help: Learning to Leverage an Expert for Embodied Tasks	Embodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be deployed in real, user-facing, applications. In this paper, we ask: can we bridge this gap by enabling agents to ask for assis...	Accept	I thank the authors for their submission and active participation in the discussions. This paper introduces a method for learning a policy that can ask an expert for help, i.e., to obtain the expert action. On the positive side, reviewers found the method to be general [uya8], original and significant [gw2r], intruigin...	train	[ "HmoDcek1dX", "tMcswrMk0Gb", "lsQwtRvLnSh", "XwTs8qhnP1", "fbrUKeh9aNI", "Xe7McnXJP5m", "pkHnXdaX6_C", "CmTCm7EeCk", "mjdB5LyCNaE", "8aVwUwqqNxL", "sL-0zxzb5tT", "wdfdDPaECxzq", "s_nn2Lz6219", "FbsxKgWxL8", "ojIdQu2Vz-1", "tIaCYCU5AtD", "_Ro-f-VRKCe", "xcBWyJZdKcT", "IuolXUqfaKp"...	[ "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_r...	[ " We are happy to hear that our rebuttal has addressed some of your concerns and thank you for increasing your rating of our work.\n\nTherefore, I'm increasing my score to a borderline, hoping that the authors would clarify more on the potential of the current method as a building block for future studies.\n\nW...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 8, 4, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4, 4 ]	[ "tMcswrMk0Gb", "sL-0zxzb5tT", "8aVwUwqqNxL", "CmTCm7EeCk", "mjdB5LyCNaE", "pkHnXdaX6_C", "ojIdQu2Vz-1", "wdfdDPaECxzq", "FbsxKgWxL8", "tIaCYCU5AtD", "ShwB63hMO8L", "kUQGOPIQkV9", "kUQGOPIQkV9", "tzew69pMfGP", "IuolXUqfaKp", "xcBWyJZdKcT", "nips_2022__bqtjfpj8h", "nips_2022__bqtjfpj...
nips_2022_WV1ZXTH0OIn	Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization	Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications. Bayesian optimization (BO) is a popular, sample-efficient method that leverages a probabilistic surrogate model and an acquisition function...	Accept	This paper studies a Bayesian optimization method where some of the variables are discrete and some are continuous and proposes using probabilistic reparameterization. Reviewers unanimously agreed that the paper is well-written, solves an important real-world problem, and most reviewers found the experimental results t...	train	[ "qzP9cvrE37S", "NktDD8BTpS", "cCsmcnMgdNK", "3uQvEtkpX7_", "nOyZcnxTT-", "VtSr60zwPoo", "2prvGHR6JxM", "kUAYcaC_th", "7rz0tyJR2mZ", "zQJOB5qENOr", "WY4U_RqEApY", "ipIQNMYZPE", "-0qma4BT1q3", "RPsAHsgpRLz", "uHmDFpjEzFD", "KUS7mc8l4cr", "vyEpCV7amUF", "eWy3IUAUs9U", "zr_wTevD75", ...	[ "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_r...	[ " - Thank you. I understand better the flaw I thought is not present. I will raise my score.\n\n- I also checked your experiments in F.1 and there is some evaluation to the number of MC samples. So indeed you performed this check, which is nice. This makes it well-rounded practical evaluation. \n\n- I have to say I...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 5, 5, 9 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 5, 4 ]	[ "3uQvEtkpX7_", "vyEpCV7amUF", "aed2z6ofCU", "nOyZcnxTT-", "uHmDFpjEzFD", "kUAYcaC_th", "ipIQNMYZPE", "7rz0tyJR2mZ", "zQJOB5qENOr", "uHmDFpjEzFD", "eWy3IUAUs9U", "PyZBBTkyhDF", "RPsAHsgpRLz", "uHmDFpjEzFD", "KeM7YvVl1gs", "vyEpCV7amUF", "aed2z6ofCU", "s8C0wPVZzb8", "nips_2022_WV1Z...
nips_2022_9a1oV7UunyP	When to Update Your Model: Constrained Model-based Reinforcement Learning	Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to...	Accept	This paper studies the relation between model shift and the performance of model-based reinforcement learning. The paper proposes a new algorithm that leads to empirical improvement over certain data sets. All the reviewers agree that the paper provides useful theoretical insights into model-based reinforcement learnin...	test	[ "d9iFUQp_SGd", "9rhqQ9HN2CB", "WjKE4fAi48U", "9p2kgfusP0R", "VZVWseLjx5", "LX4D7us5oNf", "vUxm3lzapf", "-kX-Fc2HiaQ", "_tRLzDnDxZ8", "ytLLQBNsrPW", "ByB5gJlmnZ", "EpGEfLOCk6r", "v22RSWkON3p", "ARIV52KLKC6", "qrfp7qoLFZK", "XLRFOT2Rk_U", "5bxM80s2Ip5", "4Tzy8Q7gZxCs", "CBCavhzBf0"...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", ...	[ " Dear reviewer, \n\nWe thank the reviewer for your insightful and constructive comments and suggestions, which provide much helpful guidance to improve the quality of our paper! We really enjoy communicating with you and appreciate your efforts! \n\nBest wishes!\n\nThe authors.", " Thanks for the comments. Yes,...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 4, 5 ]	[ "yJSAXbpbIFZ", "v22RSWkON3p", "9p2kgfusP0R", "0JpPFh_kT6U", "-kX-Fc2HiaQ", "vUxm3lzapf", "ByB5gJlmnZ", "XLRFOT2Rk_U", "2oNSoPueuVvE", "2oNSoPueuVvE", "2oNSoPueuVvE", "5bxM80s2Ip5", "CBCavhzBf0", "qrfp7qoLFZK", "k54_3pS64ui", "4Tzy8Q7gZxCs", "cUL37YAAdzw", "0JpPFh_kT6U", "yJSAXbpb...
nips_2022_J0nhRuMkdGf	Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees	Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transport and robust optimization. With increasing data and problem sizes necessary to train high performing models across various applications, w...	Accept	Dear Authors, We had a long discussion about this paper. Overall, the reviews are positive. Several reviewers raised their scores after the rebuttal phase, and they found the response by the authors satisfactory. However, there were some concerns about the novelty of the paper that I summarize here: This paper comb...	val	[ "z067atOXxop", "R8M4qH8bfk0", "1d-4xGTMqBU", "sDw1E5xEF02", "EThTETUDzds", "tOrktJC477n", "9uKJabbbqdQI", "CtFXk5J9F8", "we7XXlFhwA", "RG0VVnlZwI6", "1CKiisvjocm", "lpm6CTA9bXs", "feurdYz8Ri8", "LcajO32Xy0H", "CiovS66XRP4", "o6KR5q9_xfz", "Zq3zWYlcn5_", "6CfERbe3clX", "QSGYF4U2pp...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", ...	[ " We are grateful for raising the score! Thanks again for the review, response, important comments, and positive final feedback!", " Thanks again for your thorough response. I think all my questions are resolved, and I decide to raise my score to 7.", " We thank Review 1QcY for the response. See answers be...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 6, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 3, 3, 3, 3 ]	[ "R8M4qH8bfk0", "1d-4xGTMqBU", "sDw1E5xEF02", "1CKiisvjocm", "tOrktJC477n", "lpm6CTA9bXs", "nips_2022_J0nhRuMkdGf", "we7XXlFhwA", "Zq3zWYlcn5_", "IIQ5LeAi990", "QSGYF4U2ppB", "6CfERbe3clX", "Zq3zWYlcn5_", "o6KR5q9_xfz", "nips_2022_J0nhRuMkdGf", "nips_2022_J0nhRuMkdGf", "nips_2022_J0nh...
nips_2022_yfNSUQ3yRo	Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling	Machine learning has been highly successful in data-driven applications but is often hampered when the data contains noise, especially label noise. When trained on noisy labels, deep neural networks tend to fit all noisy labels, resulting in poor generalization. To handle this problem, a common idea is to force the mod...	Accept	The work proposes a simple method for training an 'attention layer' that can give weights for different input samples. These weights are learned during the training process. This method appears to the theoretically justified, the method relatively simple and the empirical results seem reasonable. One concern that I sha...	train	[ "KVZ5sb035HX", "eg5pomG1Bci", "8kNH9WD9C3t", "hz0fw4dUzM", "2HafN27-d5z", "V4oDBR6Eg8S", "0ssD9X9bx-", "SXUFbYjdGAU", "cJM2yeFYHFB" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the response. We have updated the Appendix for more experiments on hard samples. In Appendix F, we provide the movement of $\\tau$ distribution in the early learning stage to empirically explain how hard samples can be learned by using the proposed method.", " I appreciate the authors' response t...	[ -1, -1, -1, -1, -1, -1, 6, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, 5, 4, 4 ]	[ "eg5pomG1Bci", "V4oDBR6Eg8S", "nips_2022_yfNSUQ3yRo", "SXUFbYjdGAU", "cJM2yeFYHFB", "0ssD9X9bx-", "nips_2022_yfNSUQ3yRo", "nips_2022_yfNSUQ3yRo", "nips_2022_yfNSUQ3yRo" ]
nips_2022_G1uywu6vNZe	Exponential Family Model-Based Reinforcement Learning via Score Matching	We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family distributions with $d$ parameters and the reward is bounded and known. SMRL uses score matching, an unnormalized density estimation technique ...	Accept	This is a clear and carefully written paper with a solid mathematical contribution. The reviewers are unanimous in supporting acceptance.	train	[ "YSxYwhAQAY", "eEtwWFFlon", "60cy9tXD_tI", "LlnZuqanrDg", "_Jdkh9zxYaz", "qfXhBg79DY", "bH5hau_iiPs", "HI7NAiRyDLT", "AX_qmGtfhhl" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the detailed response. It'll be interesting to see how this idea works out in practice. I'm satisfied with the response and have updated my score.", " After reading the other reviewers' comments, I will keep my score unchanged.", " Thanks for your detailed response.\nGiven that my concerns have ...	[ -1, -1, -1, -1, -1, -1, 7, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, 3, 3, 4 ]	[ "qfXhBg79DY", "_Jdkh9zxYaz", "LlnZuqanrDg", "HI7NAiRyDLT", "AX_qmGtfhhl", "bH5hau_iiPs", "nips_2022_G1uywu6vNZe", "nips_2022_G1uywu6vNZe", "nips_2022_G1uywu6vNZe" ]

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.