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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_fhO6vCGuuag	On the inability of Gaussian process regression to optimally learn compositional functions	We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information-theoretic lower bounds for posterior contraction rates for Gaussian process regression in a continuous regression model. We show that if th...	Accept	The reviewers unanimously agree that the theory here exhibiting a particular case where Gaussian process priors are inferior to deep Gaussian processes is interesting, and furthermore that the proof techniques themselves are novel. Indeed, reviewers had minimal or no substantial concerns about the paper, and most of th...	train	[ "518ec8iJz2O", "MsxoNJqWU2Q", "IzIWEARD0c3", "t5RY0cjPPLi", "l4v46ytwvtg", "m2fdwXc9h7o" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the positive assessment of our work and kind words.", " Thank you for the constructive suggestions and helpful comments. In reply to your comments:\n\n1. The symbol $L$ is overloaded to imply both Lipschitz constant and the $L^2$ space.\n\nThe Lipschitz constant has been changed to $\\Lambda.$\n\n...	[ -1, -1, -1, 7, 7, 7 ]	[ -1, -1, -1, 5, 4, 3 ]	[ "m2fdwXc9h7o", "l4v46ytwvtg", "t5RY0cjPPLi", "nips_2022_fhO6vCGuuag", "nips_2022_fhO6vCGuuag", "nips_2022_fhO6vCGuuag" ]
nips_2022_jHIn0U9U6RO	Understanding the Eluder Dimension	We provide new insights on eluder dimension, a complexity measure that has been extensively used to bound the regret of algorithms for online bandits and reinforcement learning with function approximation. First, we study the relationship between the eluder dimension for a function class and a generalized notion of \em...	Accept	All reviewers and AC believe this paper is valuable contribution to the theoretical understanding of reinforcement learning.	train	[ "1Y73ARmCdji", "_7SiqlpdJ07", "W2MvIQHDJf5", "5z9oNa2oGSV5", "YMDxWPn6jG3", "uDC8rPWmAO", "uTnscUU_nxt", "vX_lCYP21AC" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Makes sense! Thanks for your explanation.", " Answering the reviewers questions:\n1. Does comparing $\\sigma$-rank and eluder dimension help us understand when eluder dimension is bounded? What is the consequence of eluder being exponentially smaller than $\\sigma$-rank? Yes, understanding the connection be...	[ -1, -1, -1, -1, -1, 7, 7, 5 ]	[ -1, -1, -1, -1, -1, 3, 3, 3 ]	[ "5z9oNa2oGSV5", "W2MvIQHDJf5", "vX_lCYP21AC", "uTnscUU_nxt", "uDC8rPWmAO", "nips_2022_jHIn0U9U6RO", "nips_2022_jHIn0U9U6RO", "nips_2022_jHIn0U9U6RO" ]
nips_2022_jcIIVkbCaHO	Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets	We present a family $\{\widehat{\pi}_p\}_{p\ge 1}$ of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell_p$ norms, where $\widehat{\pi}_2$ corresponds to Bellman-consistent pessimism (BCP), while $\widehat{\pi}_\infty$ is a novel gene...	Accept	The reviewers are in agreement that this paper provides a minimax optimal solution to the problem of offline linear contextual bandits. This new family of learning rules beat state of the art approaches and provide a unified view on existing approaches, such as Lower Confidence Bound and Bellman-Consistent Pessimism. T...	train	[ "BISWZ0opbjt", "AGMInIajTZH", "nMaRL4loyv", "I2pKmfnpCgYh", "M21UENsOQW", "qauVJt3sU2s", "6prQjRVcK7E", "cG7mVIKAtzA", "lCMhu4FgAAO", "Qwvf2RbLiq_" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for the clarification and my question is well-addressed. I believe this is a good work and I'll thus keep my score.", " I thank the authors for their response. They address my concern about the theoretical contribution of the work. But I still doubt its real-world applicability since real-wo...	[ -1, -1, -1, -1, -1, -1, 8, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, 2, 4, 2, 3 ]	[ "I2pKmfnpCgYh", "nMaRL4loyv", "Qwvf2RbLiq_", "lCMhu4FgAAO", "cG7mVIKAtzA", "6prQjRVcK7E", "nips_2022_jcIIVkbCaHO", "nips_2022_jcIIVkbCaHO", "nips_2022_jcIIVkbCaHO", "nips_2022_jcIIVkbCaHO" ]
nips_2022_sc7bBHAmcN	Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries	Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN architectures as well as their basic theoretical properties are still largely unexplored. In this paper, we study the most prominent form of subgr...	Accept	This paper studies the recent hot topic in GNN, namely subgraph-based GNNs which apply GNN to each node-centered subgraph copy of the original graph instead of directly applying GNN to the full graph. These GNNs were shown to be more expressive than 1-WL but were unknown in terms of their upper bound of expressive powe...	train	[ "Mx071i5o4lS", "uC7UdHJd_3T", "YYoaflKIENe", "F7ymMFHJtZn", "pLygWmGGkY", "nF95jDan9Q7", "9-zmAvBbu47", "NPzrQvkmQv", "WI3vsIIQPsI", "sznMeQVAz5Z", "vOBDwssPglD" ]	[ "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We kindly bring the attention of the Reviewers to a new manuscript revision we have just uploaded. The revision implements the additions discussed in the previous general comment and in specific responses to Reviewers.\n\nChanges are visually signalled in _blue_; they include:\n- A more thorough and detailed intr...	[ -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 2, 4, 4, 4 ]	[ "nips_2022_sc7bBHAmcN", "nips_2022_sc7bBHAmcN", "vOBDwssPglD", "pLygWmGGkY", "sznMeQVAz5Z", "WI3vsIIQPsI", "NPzrQvkmQv", "nips_2022_sc7bBHAmcN", "nips_2022_sc7bBHAmcN", "nips_2022_sc7bBHAmcN", "nips_2022_sc7bBHAmcN" ]
nips_2022_e3qH65r_eZS	Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization	Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation, i.e., regions belonging to the same class may exhibit a very different appearance ...	Accept	This paper proposes a teacher-student scheme for semi-supervised semantic segmentation. A consistency regularization is setup between a prototypical classifier and a linear classifier and different augmentation degrees (weak vs. strong) are applied to the teacher and student networks. On the positive side, the reviewer...	test	[ "O_dJihF5AF-", "GZo_FzF6ZNa", "QyyJDE5S613", "NpQz5rlNWvq", "TGM5e45yytT", "WnSiDnsCW3R", "o8632XKKleq", "xZkgjt-8iDL", "DrZWi_TXtlQ", "H6gEh6mToeB", "CtJgMj2aW2-", "2jf6M6YbL0O", "lxX42O3_RvD", "KXM6dSoNRTq", "dK-Td-AfT4-", "uCkPpy5gtjd" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I have read the author responses to questions raised by other reviewers, and decided to further increase the rating. I am highly impressed by the simplicity of this approach (à la x-Match style works in SSL), and very surprised that this simple method can achieve the results it does. This line of thought deserves...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 5, 4 ]	[ "GZo_FzF6ZNa", "WnSiDnsCW3R", "lxX42O3_RvD", "KXM6dSoNRTq", "dK-Td-AfT4-", "xZkgjt-8iDL", "nips_2022_e3qH65r_eZS", "uCkPpy5gtjd", "uCkPpy5gtjd", "dK-Td-AfT4-", "KXM6dSoNRTq", "lxX42O3_RvD", "nips_2022_e3qH65r_eZS", "nips_2022_e3qH65r_eZS", "nips_2022_e3qH65r_eZS", "nips_2022_e3qH65r_eZ...
nips_2022_5L-wxm0YLcZ	CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation	Referring image segmentation aims at localizing all pixels of the visual objects described by a natural language sentence. Previous works learn to straightforwardly align the sentence embedding and pixel-level embedding for highlighting the referred objects, but ignore the semantic consistency of pixels within the same...	Accept	The paper was reviewed by four reviewers and received all positive scores at the end: 2 x Borderline Accepts and 2 x Weak Accepts. Most initial concerns with the paper were with exposition and experimental validation. These concerns, however, were addressed convincingly during the rebuttal period with additional experi...	train	[ "Rg8LYtmTkIC", "SOaYDpPKvL", "zT0RE0-WyH", "Jzcf1vWZT_h", "avTqqobEXZv", "UMuFrU0gPv5", "yB6W1dO7OiM", "4micdEBIcDm", "-wLYglob9v3", "SbHbNz-BrcS", "tHmkXJ1TFfF", "Kxu5HXiEMr-", "eTZ3yEH8DS", "voy10ZiwcrY", "stUL42Asjk-", "tDT8k4vmxaV", "PoGEGBimkU", "FmnonUmP3hh", "Qmz6SSE-XkK",...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_r...	[ " Dear reviewer,\n\nThank you very much for your support!\n", " Thanks for solving my concerns with new experiments and detailed explanation. And I am glad to raise my rating up for accepting the paper.", " Dear reviewer,\n\nThank you very much for your support!", " Thanks for the new experiments on comparing...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 5 ]	[ "SOaYDpPKvL", "4micdEBIcDm", "Jzcf1vWZT_h", "UMuFrU0gPv5", "yB6W1dO7OiM", "-wLYglob9v3", "stUL42Asjk-", "SbHbNz-BrcS", "voy10ZiwcrY", "Kxu5HXiEMr-", "nips_2022_5L-wxm0YLcZ", "eTZ3yEH8DS", "SufrqIMEU-", "Qmz6SSE-XkK", "FmnonUmP3hh", "PoGEGBimkU", "nips_2022_5L-wxm0YLcZ", "nips_2022_...
nips_2022_noyKGZYvHH	coVariance Neural Networks	Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the projection of data on the eigenspace of the covariance matrix and draws similarities with the graph convolutional filters in GNNs. Motivated ...	Accept	This paper proposes coVariance neural networks (VNN), which is a new architecture of graph neural network that is more robust to perturbations in covariance matrix. Most reviewers liked the new architecture as the intuition is clearly presented and the experiment results are interesting (in particular the results demon...	train	[ "pR2roJ-6v1K", "ciM0a9HDR9S", "7lefPejZhnKx", "uX5nJ0eSz65s", "uPQ7XFRhCnk6", "ha_L3DK_9_g1", "O5GeMELKAA", "Byfa96xBZVs", "F1acbaHS4EW" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for considering our previous response. We address further concerns raised by the reviewer as follows.\n\n>The analogy of VNNs to GNNs with CNN vs GNN does not quite hold since graph convolutions are SIGNIFICANTLY different from image convolutions.\n\nPlease note that there exists a rich literature in ...	[ -1, -1, -1, -1, -1, -1, 7, 7, 3 ]	[ -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "ciM0a9HDR9S", "uPQ7XFRhCnk6", "O5GeMELKAA", "Byfa96xBZVs", "F1acbaHS4EW", "F1acbaHS4EW", "nips_2022_noyKGZYvHH", "nips_2022_noyKGZYvHH", "nips_2022_noyKGZYvHH" ]
nips_2022_kB9jrZDenff	Unsupervised Cross-Task Generalization via Retrieval Augmentation	Humans can perform unseen tasks by recalling relevant skills acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve this kind of cross-task generalization ability of massive multi-task language models, such as T0 and FLAN, in an unsup...	Accept	This paper presents an approach called ReCross that improves zero-shot task performance by retrieving and fine-tuning on examples of similar supervised tasks. This method is shown to help multi-task finetuned models when evaluated zero-shot on novel tasks. The interesting finding of the paper is that fine-tuning on re...	train	[ "APpb-aofGO", "U7Cvnnei0JH", "veRU2Qckoax", "BXB1OEJt5To", "i2qNx3irkA", "13LUkU81gd", "C81PrmuHV91", "cyrJSSD0zTp", "e7TLPAfvo55Y", "IdzEMFLyPYx", "si2TefgGDf", "8zHjw6dxseu", "J6y02YWJdA", "a1AesqcUEA", "n6vt8rRpASL", "pV4bG3CeeiWR" ]	[ "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for your detailed reply and raised score! \n\nWe will revise the final version accordingly based on these valuable suggestions and comments. Specifically, we will reframe the introduction of the reranker such that we have more space to add our analysis to the main paper. We will also rephrase ...	[ -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, 3 ]	[ "veRU2Qckoax", "i2qNx3irkA", "8zHjw6dxseu", "J6y02YWJdA", "si2TefgGDf", "e7TLPAfvo55Y", "cyrJSSD0zTp", "a1AesqcUEA", "n6vt8rRpASL", "nips_2022_kB9jrZDenff", "pV4bG3CeeiWR", "n6vt8rRpASL", "a1AesqcUEA", "nips_2022_kB9jrZDenff", "nips_2022_kB9jrZDenff", "nips_2022_kB9jrZDenff" ]
nips_2022_WESmKHEH5nJ	Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization	We consider the problem of minimizing the sum of two convex functions. One of those functions has Lipschitz-continuous gradients, and can be accessed via stochastic oracles, whereas the other is ``simple''. We provide a Bregman-type algorithm with accelerated convergence in function values to a ball containing the mini...	Accept	The authors design an algorithm for composite stochastic optimization that leverages both smoothness and strong convexity with respect to the same (general) norm, using a stochastic counterpart to recent work by Diakonikolas and Guzman. They then show how to leverage this algorithm and randomized smoothing in order to ...	train	[ "iWkpb1DImCP", "ErysE6CfxxJ", "qDx_aOY0mAl", "1ZWIsjeMc9i", "7PE69WHbwvD", "rSF_B8AvVdxN", "Z7gBYHbavy", "QGjH3nVq5G", "nEDRpQkusGe", "Ixuan0zTW6S" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear AC,\n\nThank you for your question. We are replying now, as soon as possible after your question, because you asked this directly to us, expecting an answer.\n\nThe work that you reference does not put in question our novelty claims, as explained in the following points. Overall, there are indeed many papers...	[ -1, -1, -1, -1, -1, -1, -1, 3, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 3, 3 ]	[ "ErysE6CfxxJ", "nips_2022_WESmKHEH5nJ", "nips_2022_WESmKHEH5nJ", "rSF_B8AvVdxN", "Ixuan0zTW6S", "nEDRpQkusGe", "QGjH3nVq5G", "nips_2022_WESmKHEH5nJ", "nips_2022_WESmKHEH5nJ", "nips_2022_WESmKHEH5nJ" ]
nips_2022_CTqjKUAyRBt	Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization	We analyze the convergence rates of stochastic gradient algorithms for smooth finite-sum minimax optimization and show that, for many such algorithms, sampling the data points \emph{without replacement} leads to faster convergence compared to sampling with replacement. For the smooth and strongly convex-strongly concav...	Accept	All reviewers acknowledge that the paper fills a gap in the literature, with good results for a wide variety of settings.	train	[ "EeKYai0kES3", "qVoUpUUgZSt", "l8ZEKtymr34", "_St1Vl_8gxQ", "0bWG4Exgqfkg", "gkdWiXksUH", "L-LWu-5bkoc", "AkX-rBHeFJg", "dC3jjldRQ2XJ", "4bDVik7dbsk", "4iZ8x1lGYT", "RWSHDIxuoMA", "EKoV0tupZ-N", "hfFZuMurO_j", "v6Eaewj9L15" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for answering my questions. I will keep my score.", " Thanks for the authors' response!\n\nI am not fully convinced about the technical novelty here. As Reviewer R12f pointed out, \"the main difficulty (or let's say the main difference to the existing analysis of RR) lies in rewriting the G...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 4, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 3 ]	[ "dC3jjldRQ2XJ", "gkdWiXksUH", "4iZ8x1lGYT", "L-LWu-5bkoc", "EKoV0tupZ-N", "EKoV0tupZ-N", "v6Eaewj9L15", "v6Eaewj9L15", "hfFZuMurO_j", "RWSHDIxuoMA", "RWSHDIxuoMA", "nips_2022_CTqjKUAyRBt", "nips_2022_CTqjKUAyRBt", "nips_2022_CTqjKUAyRBt", "nips_2022_CTqjKUAyRBt" ]
nips_2022_AQgmyyEWg8	Beyond spectral gap: the role of the topology in decentralized learning	In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. We consider the setting in which all workers sample from the same dataset, and communicate over a sparse graph (dece...	Accept	The paper studies decentralized optimization and considers all machines work on the data that follow the same distribution. Most of the reviewers think the paper is interesting. I recommend an acceptance.	val	[ "zOaPDkl66iZ", "NNhaZrn0RR", "k2QUbbDHgcy", "dzHnMoIFXGP", "vd1W4bueNg", "UBcAFuXEM9", "vUrouopdKk", "isgveAfLAA6", "58ZUzTsND67" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your detailed responses, which have answered most of my questions.", " Thank you for your quick reply.\nWe agree with all your concrete suggestions on clarity and typos, and are very grateful for your in-depth review and contributions to the quality of the paper.\n\nFor the initial rebuttal, we had a...	[ -1, -1, -1, -1, -1, -1, 6, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, 4, 5, 4 ]	[ "vd1W4bueNg", "k2QUbbDHgcy", "dzHnMoIFXGP", "58ZUzTsND67", "isgveAfLAA6", "vUrouopdKk", "nips_2022_AQgmyyEWg8", "nips_2022_AQgmyyEWg8", "nips_2022_AQgmyyEWg8" ]
nips_2022_F02H1zNl213	Are GANs overkill for NLP?	This work offers a novel theoretical perspective on why, despite numerous attempts, adversarial approaches to generative modeling (e.g., GANs) have not been as successful for certain generation tasks, particularly sequential tasks such as Natural Language Generation, as they have in others, such as Computer Vision. In ...	Accept	In the context of text generation, the paper gives a theoretical argument that GAN objectives are equivalent to maximum-likelihood training when the generator and discriminator families are 'paired'. Reviewers generally felt that the perspective was interesting (broM, jtPN, UUT1) and the theory was insightful (jtPN, UU...	train	[ "Mq3kQWRq1d", "sOzTGMS-38c", "SlAYeZml-I", "uxAtqMWEfrf", "p04cmF4tDf", "TGFcbjg0l35", "hZTy743skt", "bF2_esz-SCQ", "MHUL-cGysRU", "53f5njtiCFn", "SweKBcLxj-2", "GAMh_ylN_b", "GsjarHqw5NU", "JRddaGGnsW", "SlWLBI9qdbG", "A6XcqAAFTc", "c3oaoTwpKEh", "vIAu3eGC2A6", "7qjsqB2K95k", ...	[ "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your response! Our purpose is to attract greater attention of the community to an important area of research, and we believe the conceptual and mathematical contributions of this paper would be broadly useful in the context of generative models. We've edited the paper at several places including t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 3, 3, 3 ]	[ "sOzTGMS-38c", "vIAu3eGC2A6", "MHUL-cGysRU", "hZTy743skt", "MHUL-cGysRU", "SlWLBI9qdbG", "GsjarHqw5NU", "nips_2022_F02H1zNl213", "53f5njtiCFn", "A6XcqAAFTc", "GAMh_ylN_b", "GsjarHqw5NU", "9wCI82UNEFb", "7qjsqB2K95k", "c3oaoTwpKEh", "vIAu3eGC2A6", "nips_2022_F02H1zNl213", "nips_2022...
nips_2022_xONqm0NUJc	Relational Proxies: Emergent Relationships as Fine-Grained Discriminators	Fine-grained categories that largely share the same set of parts cannot be discriminated based on part information alone, as they mostly differ in the way the local parts relate to the overall global structure of the object. We propose Relational Proxies, a novel approach that leverages the relational information betwe...	Accept	This paper proposes a novel approach for fine-grained image recognition, which utilizes the relational information between the global and local views of an object. It is a reasonable and important finding that not only representing local parts but relating them are critical to establishing superior performance. The a...	val	[ "XGowPicg2qA", "dOOs3Y1PdHA", "GLX8eBgMP7", "fawBt7P2c1U", "k-eFNMoL5-7Z", "58AkdzIhFt", "9B8iXtGlqyb", "XA6qg4Vuqzt", "Fg0b_2gsZOf", "Eu_v14hcmz", "NPzyzl5v71w", "0JHyaaknHpQ", "PpSx7RDZdX-", "0y1yic1l98q", "zXWKCL1bPNp", "l4iGnf1QNRo", "Rvj9lGjmM88" ]	[ "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for taking the time to go through the rebuttal, appreciating our intuitive explanations and additional experiments, and increasing their score.\n", " I thank the reviewers for their response, and appreciate their efforts in providing additional clarifications and revising the paper. The an...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 7, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3, 3, 3 ]	[ "dOOs3Y1PdHA", "Fg0b_2gsZOf", "fawBt7P2c1U", "XA6qg4Vuqzt", "nips_2022_xONqm0NUJc", "0JHyaaknHpQ", "zXWKCL1bPNp", "Rvj9lGjmM88", "zXWKCL1bPNp", "l4iGnf1QNRo", "0y1yic1l98q", "PpSx7RDZdX-", "nips_2022_xONqm0NUJc", "nips_2022_xONqm0NUJc", "nips_2022_xONqm0NUJc", "nips_2022_xONqm0NUJc", ...
nips_2022_vMQ1V_z0TxU	Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE	Unsupervised out-of-distribution (OOD) detection is essential for the reliability of machine learning. In the literature, existing work has shown that higher-level semantics captured by hierarchical VAEs can be used to detect OOD instances. However, we empirically show that, the inherent issue of hierarchical VAEs, i.e...	Accept	This paper studies unsupervised out-of-distribution detection based on hierarchical VAE models. In particular, it (1) investigates the posterior collapse issue, (2) proposes a training procedure by increasing the mutual information between the input and latent representations, and (3) proposes an adaptive likelihood ra...	train	[ "5zJ1dOyPq06", "P1K4uv4il5w", "Z1abMj_3vb7", "hbakHmSQpMK", "2RuxaQTF0AY", "g0Wl7jzp8Hm", "9qC4mhcINEX", "SxTRoxbxUvr", "UABDFtlrYar", "sWLkfM5boXK", "NCw9AhX13eT", "eSX-_A9LfPl", "6jnyZBhiIXk", "n76THBZ-Xso", "G9jZPscqAiT", "2nos73R0EW8", "zg3XRhFCb27", "duXswuDelz", "NfOju3OS4S...	[ "author", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", ...	[ " Dear Reviewer 4FiY:\n\nThanks again for your effort in reviewing our paper and give us a great chance to improve the quality of this paper . \n\nConsidering that the discussion period is coming to an end, we would like to know if you have any other questions about our paper, and we are still glad to have a discus...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 3, 4 ]	[ "dtG_FJqqbpr", "Z1abMj_3vb7", "hbakHmSQpMK", "2RuxaQTF0AY", "g0Wl7jzp8Hm", "9qC4mhcINEX", "UABDFtlrYar", "Flv0ymK2q-q", "LMbD-6Fe4ft", "dtG_FJqqbpr", "n76THBZ-Xso", "6jnyZBhiIXk", "LMbD-6Fe4ft", "a_p5KW5sIR7", "2nos73R0EW8", "zg3XRhFCb27", "Flv0ymK2q-q", "NfOju3OS4So", "dtG_FJqqb...
nips_2022_o762mMj4XK	Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation	Modern approaches for simulation-based inference build upon deep learning surrogates to enable approximate Bayesian inference with computer simulators. In practice, the estimated posteriors' computational faithfulness is, however, rarely guaranteed. For example, Hermans et al., 2021 have shown that current simulation-b...	Accept	The paper proposes a modification to the neural ratio estimation algorithm in the context of SBI (simulation-based inference) that tends to avoid overconfident posteriors. This is important for applications (for example in scientific discovery) where excluding plausible inferences can be more detrimental than including...	train	[ "WrgBz6ddS2m", "U3ky3JJojRV", "AsEt7fj5s0b", "-ZEXJTjzPw3", "hq7dhKU8Rqq", "3aJJ6UeOpc", "KVt5VMOmqQ", "1MvK46KhP0l", "pzyto-H0izC", "_IP_aiPyt99", "c-vPJs_G0in", "NLbqLe_qwHM", "JBtRJeZvjloL", "glaLONgO91j", "ytKjy_lDwGE", "G4pnS9EGS8", "d71VQgqr_yi", "SydkzNUhyZ7", "PFshcbP6ec9...	[ "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_r...	[ " We have now updated the limitations in Section 6 to reflect this:\n\n> Third, the benefits of BNRE remain to be assessed in high-dimensional parameter spaces. In particular, the posterior density must be evaluated on a discretized grid over the parameter space to compute credibility regions, which currently prohi...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "U3ky3JJojRV", "AsEt7fj5s0b", "3aJJ6UeOpc", "hq7dhKU8Rqq", "KVt5VMOmqQ", "c-vPJs_G0in", "1MvK46KhP0l", "_IP_aiPyt99", "_IP_aiPyt99", "2oJEZOpRDYy", "NLbqLe_qwHM", "JBtRJeZvjloL", "PFshcbP6ec9", "SydkzNUhyZ7", "d71VQgqr_yi", "nips_2022_o762mMj4XK", "nips_2022_o762mMj4XK", "nips_2022...
nips_2022_d229wqASHOT	Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition	Deep learning models have shown their vulnerability when dealing with adversarial attacks. Existing attacks almost perform on low-level instances, such as pixels and super-pixels, and rarely exploit semantic clues. For face recognition attacks, existing methods typically generate the l_p-norm perturbations on pixels, h...	Accept	This paper studies adversarial attacks on facial recognition systems. The key contribution is that, instead of directly manipulating pixel space, this paper proposed to perturb the facial attributes for generating inconspicuous and transferable adversarial examples. The initial concerns are mostly about requiring 1) mo...	train	[ "UTMz7UK2Yot", "Q8454TVbR6", "8eqafMy2z0e", "OJuAq2DDn4D", "9vNAHpn-WdV", "O918jL5ubQK", "LC7b7wvpi3I", "Vko4bl1F1e1", "UURI7QzRcRF", "TU2Cya-A8ie", "NNw94j-0lJY", "AvOTKbOB2yW" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Many thanks for your reply and we will further address your concerns as follows.\n\n[Q3: Semantic inconsistency.] In the revised supplementary material, we provide more qualitative results from the FFHQ and CelebA-HQ datasets. Figure E and Figure F compare the original source faces, the edited faces by origi...	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "8eqafMy2z0e", "OJuAq2DDn4D", "9vNAHpn-WdV", "O918jL5ubQK", "UURI7QzRcRF", "TU2Cya-A8ie", "AvOTKbOB2yW", "NNw94j-0lJY", "nips_2022_d229wqASHOT", "nips_2022_d229wqASHOT", "nips_2022_d229wqASHOT", "nips_2022_d229wqASHOT" ]
nips_2022_nX-gReQ0OT	Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?	Finding accurate solutions to the Schrödinger equation is the key unsolved challenge of computational chemistry. Given its importance for the development of new chemical compounds, decades of research have been dedicated to this problem, but due to the large dimensionality even the best available methods do not yet rea...	Accept	There is a clear consensus among the reviewers that this is a quality paper and worthy of acceptance (in fact, this may be the first time I've ever seen 4 reviewers give the exact same score), so I recommend accept. I do however have one additional comment. I find the current title somewhat unwieldy and wonder if it w...	train	[ "oOfGUR5Yjj", "CHOWTb1CXD_", "yjG4yiywau", "4Lj66G-Z8E", "dtch0ur1HUt", "9loB3M4kwBrR", "BCKGbpUnQ3W", "vATYRI-UftQ", "XGiFILiqHrh", "TqeKdUiehpK", "bsNygndN_LAr", "-pIxqKcoQTp", "AsVt6zn4K3M", "9EgffXp72yK", "jbUaHN_lp07", "VtjKOqcQOFf", "c1MbEXRO0Qm" ]	[ "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the detailed and clarifying response.", " Thank you once more, for reviewing our paper and helping us to improve it!", " Thank you for reviewing our paper and your constructive feedback!\n\nYes, for the systems such as 4th row atoms (K, Fe), and large molecules (e.g. Glycine), there are no publi...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 2, 4, 3 ]	[ "bsNygndN_LAr", "9loB3M4kwBrR", "BCKGbpUnQ3W", "dtch0ur1HUt", "-pIxqKcoQTp", "XGiFILiqHrh", "TqeKdUiehpK", "-pIxqKcoQTp", "c1MbEXRO0Qm", "VtjKOqcQOFf", "jbUaHN_lp07", "9EgffXp72yK", "nips_2022_nX-gReQ0OT", "nips_2022_nX-gReQ0OT", "nips_2022_nX-gReQ0OT", "nips_2022_nX-gReQ0OT", "nips_...
nips_2022_7hhH95QKKDX	Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks	The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores. Nonetheless, we note that if the loss trend of the outputs is slightly perturbed, SQAs could be easily misled and thereby become much ...	Accept	This paper proposes a defense against score-based black-box attacks by post-processing the output probabilities to misguide the attacker. The method enjoys several advantages such as not reducing test-time accuracy or increasing the train-/test-time cost, improving calibration for the model, and superior performance un...	train	[ "NQqbKli24Z", "aLkLjiRw_yd", "KtBKLo7HGQ_", "hqq5u6eS0R8", "cacoukOUvw7", "itSr8-BUvd", "-NwL_TnNb8QC", "jiKSENTHhmX", "1bFRe2KvfAc", "S5dn0Dj7jZ", "zn1sb-CWWQS", "yVnwY3jIY5v", "0Z1wxB5KGXr", "H9oqtwVcw_", "2TwoWG0-FK" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Program Chairs, Area Chairs, and Reviewers,\n\nThanks for the constructive comments and helpful discussions, we have carefully modified the manuscript according to the reviewers’ suggestions.\n\n- Descriptions on AAA-sine for adaptive attacks (Line 53-56, 62-64, 162-183, 216-219, 312-332)\n- Discussions ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 5 ]	[ "nips_2022_7hhH95QKKDX", "S5dn0Dj7jZ", "itSr8-BUvd", "cacoukOUvw7", "1bFRe2KvfAc", "-NwL_TnNb8QC", "H9oqtwVcw_", "2TwoWG0-FK", "0Z1wxB5KGXr", "yVnwY3jIY5v", "nips_2022_7hhH95QKKDX", "nips_2022_7hhH95QKKDX", "nips_2022_7hhH95QKKDX", "nips_2022_7hhH95QKKDX", "nips_2022_7hhH95QKKDX" ]
nips_2022_yW5zeRSFdZ	Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models	Transformer architecture has become the fundamental element of the widespread natural language processing~(NLP) models. With the trends of large NLP models, the increasing memory and computation costs hinder their efficient deployment on resource-limited devices. Therefore, transformer quantization attracts wide resear...	Accept	This paper proposes an outlier suppression method to improve transformer quantization. The method is derived based on careful analysis and thorough experiments demonstrate the efficacy of it. All reviewers agreed that this is a good paper. I recommend acceptance.	train	[ "WzfMlzneog9", "u_Giy2f3mp", "KPi4xVKsfiS", "2gVh7U0pv3o", "EsKMXbbybee", "fMqljYmE1eE", "BFDKWp_VhYA", "I4JtIPGyT2C", "ovm4S1Cg9uY", "IAKuEfK8poF", "0bjfZMrd0Vx", "G3b7IZuEjOk", "tGK7Qdoxc87" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I appreciate the detailed responses and the additional experiments conducted to answer my questions. I have increased the soundness score for the paper.", " Thanks for the responses to my questions. The explanations and the experiments added answered my questions well, which makes this manuscript more solid. In...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "IAKuEfK8poF", "fMqljYmE1eE", "0bjfZMrd0Vx", "0bjfZMrd0Vx", "tGK7Qdoxc87", "tGK7Qdoxc87", "G3b7IZuEjOk", "G3b7IZuEjOk", "0bjfZMrd0Vx", "0bjfZMrd0Vx", "nips_2022_yW5zeRSFdZ", "nips_2022_yW5zeRSFdZ", "nips_2022_yW5zeRSFdZ" ]
nips_2022_aAs8KTbZvc9	Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms	The support/query episodic training strategy has been widely applied in modern meta learning algorithms. Supposing the $n$ training episodes and the test episodes are sampled independently from the same environment, previous work has derived a generalization bound of $O(1/\sqrt{n})$ for smooth non-convex functions via ...	Accept	The reviewers and AC are in agreement that this paper is a solid work, and its contributions are significant. The theoretical results of this paper advance the theory of meta-learning, and, in particular, the provided generalization guarantees are strong. All reviewers were satisfied with the responses provided by the ...	train	[ "gLCPi-RYfIC", "6KZolUIjzOI", "zj2gOGM9G8s", "ylMyQv6WXC", "LPUqh5HhMd", "dy83VBaw--", "bjW8UcaZCcX", "T5VgsijGoeGJ", "_nUS3yq5na", "wK6Lj2Iw-Ft", "i9nLvL8RJq5", "e0sJlLK-dcP", "Yfe3WioyGf4", "I5zwatDfJyJ", "goohdZaO6J", "OTyjbmapI9C", "fnGyC1A2e3P", "3pPcuy1_i6Z" ]	[ "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We appreciate your support very much!", " I updated the review and increased the score as promised.", " We appreciate your support very much!", " **Q1. The authors did not mention that the fast generalization bound for PL functions is \"deformed\" neither in the abstract nor in the contribution section. I w...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 2 ]	[ "6KZolUIjzOI", "ylMyQv6WXC", "LPUqh5HhMd", "dy83VBaw--", "Yfe3WioyGf4", "_nUS3yq5na", "T5VgsijGoeGJ", "fnGyC1A2e3P", "3pPcuy1_i6Z", "fnGyC1A2e3P", "fnGyC1A2e3P", "OTyjbmapI9C", "goohdZaO6J", "nips_2022_aAs8KTbZvc9", "nips_2022_aAs8KTbZvc9", "nips_2022_aAs8KTbZvc9", "nips_2022_aAs8KTb...
nips_2022_xaWO6bAY0xM	Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective	Designing neural networks with bounded Lipschitz constant is a promising way to obtain certifiably robust classifiers against adversarial examples. However, the relevant progress for the important $\ell_\infty$ perturbation setting is rather limited, and a principled understanding of how to design expressive $\ell_\inf...	Accept	The paper presents novel theoretical results and a novel architecture for designing Lipschitz constrained neural networks (with respect to the infinity norm). The authors have addressed all the concerns from the reviewers properly. All the reviewers agreed that the paper contains significant contributions and should be...	train	[ "xYGx06fRjL9", "-tybpQJZRTy", "5tNunerdaeD", "5D3-Y1f7oD0", "VcUuDpaLntkm", "noObnzZGIym", "-wfZBo2O3ym", "prM3p7aVdRF", "SbNhFcz0IxZ", "uTK-droDpzf", "1GbWfjtlNw", "xRwBlZjTNW9", "CkaS5pPHcDr", "hi34jv2_VtB" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the detailed discussion of the points that I raised.\n\nThe new theoretical result is exciting and helps to complete the previously presented theoretical work. The additional results, that provide the error bars, are also a great addition.\n\nThe discussion here on other $\\ell_p$ norms is interesti...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 8, 8, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 4 ]	[ "SbNhFcz0IxZ", "uTK-droDpzf", "nips_2022_xaWO6bAY0xM", "hi34jv2_VtB", "CkaS5pPHcDr", "CkaS5pPHcDr", "xRwBlZjTNW9", "xRwBlZjTNW9", "xRwBlZjTNW9", "1GbWfjtlNw", "nips_2022_xaWO6bAY0xM", "nips_2022_xaWO6bAY0xM", "nips_2022_xaWO6bAY0xM", "nips_2022_xaWO6bAY0xM" ]
nips_2022_mMdRZipvld2	Deconfounded Representation Similarity for Comparison of Neural Networks	Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to understand neural networks by comparing their layer-wise representations. However, these metrics are confounded by the population structure of data items in the input space, leading to inconsisten...	Accept	The paper makes the observation that neural network similarity indexes can be misleading when compared across domains with different examples. The paper presents a fix via covariate adjustment, which improves quality of similarity indexes across neural networks across domains. The approach is simple, and the reviewers ...	train	[ "gkGGAo_r4uA", "aA06dFgBtVRw", "zHmJSMD4EYe", "hZNkvikEBZN", "vRkFaW95ewl", "rnw-Fk6AJXI", "sUVBcSyq78J", "lZXnVHxcZPj", "FtpbQqyvlzi", "kPCJz0u1h9Z" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for clarifications.", " Thank you for the good comments again!\n\n> Q2. Thanks for the clarification. Actually, I didn't understand that the authors \"averaged the evaluation metrics of layer-wise similarities\" when I first saw Figure 2, which is now very clear. I feel that this point (how to measure...	[ -1, -1, -1, -1, -1, -1, -1, 7, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "sUVBcSyq78J", "zHmJSMD4EYe", "vRkFaW95ewl", "nips_2022_mMdRZipvld2", "kPCJz0u1h9Z", "FtpbQqyvlzi", "lZXnVHxcZPj", "nips_2022_mMdRZipvld2", "nips_2022_mMdRZipvld2", "nips_2022_mMdRZipvld2" ]
nips_2022_kCU2pUrmMih	Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM	Many problems in machine learning can be formulated as optimizing a convex functional over a vector space of measures. This paper studies the convergence of the mirror descent algorithm in this infinite-dimensional setting. Defining Bregman divergences through directional derivatives, we derive the convergence of the s...	Accept	All reviewers recommend the paper. The authors should think about ways to make the paper more accessible to a machine learning audience, but I recommend accepting. When preparing the camera-ready version, please take into account the reviewers comments and please also specifically address these two points raised in the...	train	[ "qvNTOCB3IsD", "VyMfOyUUHQm", "8_1zrfFrkEp", "HsZ0hPB2vvs", "q_rVwSsRD-M", "6YVLWYGeQAm", "WGEwDh0LyR", "7FWH3qvgih", "Hmb8I3LSIE8", "r4dSIzj-A5X" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the authors for answering our questions. It would be nice if the authors could include this discussion about the rates of convergence in the paper or in the supplementary material. We keep our rating unchanged.", " We thank the reviewer his positive comments and interest.\n\nQuestion 1.: As written in...	[ -1, -1, -1, -1, -1, -1, 6, 9, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, 3, 4, 4, 2 ]	[ "HsZ0hPB2vvs", "r4dSIzj-A5X", "Hmb8I3LSIE8", "7FWH3qvgih", "WGEwDh0LyR", "nips_2022_kCU2pUrmMih", "nips_2022_kCU2pUrmMih", "nips_2022_kCU2pUrmMih", "nips_2022_kCU2pUrmMih", "nips_2022_kCU2pUrmMih" ]
nips_2022_CmD5z_2DVuM	Learning Energy Networks with Generalized Fenchel-Young Losses	Energy-based models, a.k.a. energy networks, perform inference by optimizing an energy function, typically parametrized by a neural network. This allows one to capture potentially complex relationships between inputs and outputs. To learn the parameters of the energy function, the solution to that optimization proble...	Accept	This paper introduces a new notion of regularized energy function using generalized Fenchel conjugates. Reviewers were leaning towards accept, the least convinced reviewer discussed at length with the authors the contribution of the paper and the comparison of the proposed method to prior work, and leaned also towards...	train	[ "h-1r2zIdMh", "KaYiyvqWIs", "lF71vlTyTpl", "Fe-5NDgJet", "Yck7SSI2X9A", "GCMdkezCfkYH", "Ogrfa51rSisl", "3Cq1AVJSScm", "vF9xjiQrZ7", "hBBeano50Rr", "hfd6j3Xlzac4", "U2kZSpPghLk", "U2JYUW2mcvw", "yedKwILEYkh" ]	[ "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for making these amendments. I have now raised my score to 5 to account for this", " Thank you very much for the constructive comments. We hope that your concerns are now addressed satisfactorily. \n\n> I think the references should be discussed earlier in the intro\n\nThis is now addressed in the rev...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "KaYiyvqWIs", "lF71vlTyTpl", "Fe-5NDgJet", "Yck7SSI2X9A", "hBBeano50Rr", "U2JYUW2mcvw", "vF9xjiQrZ7", "nips_2022_CmD5z_2DVuM", "U2kZSpPghLk", "U2JYUW2mcvw", "yedKwILEYkh", "nips_2022_CmD5z_2DVuM", "nips_2022_CmD5z_2DVuM", "nips_2022_CmD5z_2DVuM" ]
nips_2022_wcBXsXIf-n9	Reaching Nirvana: Maximizing the Margin in Both Euclidean and Angular Spaces for Deep Neural Network Classification	The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vectors are used during classification for the methods maximizing the margin in Euclidean spaces whereas t...	Reject	This paper proposed to use least-squares loss functions in training deep neural networks. The main idea is to encode class means, whose mutual distances are equivalent. The method is simple but efficient. However, the similar idea has been widely used in multi-class classification (SVM and Fisher discriminant analysis)...	val	[ "fm6zlb8uua2", "cE2g4BAvUQ2", "K6tCupmTs3u", "sT30yYcve5T", "OUkyecs1Aw", "tUpt67GoRh4", "RJQQj8jhBNX", "YHIPBIj_v6", "P18NlXgHQ2s0", "KFdIL9E9_gu", "dvZ5zJaXgjZ", "q5Me_f0m9Mmi", "rja5nFr-Ol7", "NohUECiqEous", "SMG52nWZOrj", "LsSQQVxKxxE", "i7iN_0Svvc", "zgf4E5Q8Uw6", "Lm2GrdZbs...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_r...	[ " 1) We have written a Motivation subsection to explain our motivation. There are theoretical proofs showing that the data samples lie on the vertices of a regular simplex (equivalently on the boundary of a hypersphere) in high-dimensional spaces. Therefore, it makes perfect sense to map the class-specific data sam...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, -1, -1, -1, -1, -1, 5, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "cE2g4BAvUQ2", "NohUECiqEous", "sT30yYcve5T", "OUkyecs1Aw", "YHIPBIj_v6", "RJQQj8jhBNX", "SMG52nWZOrj", "P18NlXgHQ2s0", "KFdIL9E9_gu", "i7iN_0Svvc", "rja5nFr-Ol7", "rja5nFr-Ol7", "nips_2022_wcBXsXIf-n9", "LsSQQVxKxxE", "Lm2GrdZbsZl", "6dmycsk2Of2", "_PIdOxESwc2", "Lm2GrdZbsZl", "...
nips_2022_bBgNsEKUxmJ	Universally Expressive Communication in Multi-Agent Reinforcement Learning	Allowing agents to share information through communication is crucial for solving complex tasks in multi-agent reinforcement learning. In this work, we consider the question of whether a given communication protocol can express an arbitrary policy. By observing that many existing protocols can be viewed as instances of...	Accept	Reviewers found the paper's connections between MARL and GNNs interesting and well-written, and the experiments convincing. Given the unanimous support, I recommend acceptance. That said, I encourage the authors to integrate reviewer feedback, including trying to move some of the details and plots requested to the main...	test	[ "JDPuNP7Rgc3", "8YkyOlHW_6_", "VRMD6kucME", "8mO8WHlr7v8", "pYBDaOY7Mc4", "Fq3c9mnSWwr", "H5XMYr-mRn", "Pln4yoSCYB", "YoOhHXtN1Rm", "oslt6Bl9tXy", "fIX7FyNJ4v", "ru_MwuRprS", "8ZO0JO9dJ_q" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " 1. If the purpose of including epochs was to illustrate the convergence rates of different experiment setups, I still believe that training curve figures is better than adding additional rows indicating the best performing epoch. Best performing epoch information can be rather deceptive in case evaluations in pre...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 4 ]	[ "8YkyOlHW_6_", "8mO8WHlr7v8", "ru_MwuRprS", "Pln4yoSCYB", "nips_2022_bBgNsEKUxmJ", "8ZO0JO9dJ_q", "ru_MwuRprS", "fIX7FyNJ4v", "oslt6Bl9tXy", "nips_2022_bBgNsEKUxmJ", "nips_2022_bBgNsEKUxmJ", "nips_2022_bBgNsEKUxmJ", "nips_2022_bBgNsEKUxmJ" ]
nips_2022_Q-HOv_zn6G	Efficient and Modular Implicit Differentiation	Automatic differentiation (autodiff) has revolutionized machine learning. It allows to express complex computations by composing elementary ones in creative ways and removes the burden of computing their derivatives by hand. More recently, differentiation of optimization problem solutions has attracted widespread atte...	Accept	The reviewers have discussed the paper at length and have reached a consensus after the authors have clarified the applicability and limitations of their proposed method. I recommend that the authors continue to polish their manuscript with the points they raised in their summary to the Area Chairs and congratulate the...	train	[ "DmpwX8N11Qo", "pcOCXCWHCN", "QXlvhjTeuoE", "soN-FgXiK-Y", "gr2cBxZPV5", "Slsogjob9mS", "WVFuaMLbkdV", "gTQmnHprbDi", "g5mXEZ20yjH", "VUl0iP3eZSH", "F7mteGJtY50", "JOFhKKzifSJ" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " >While we agree that the hypothesis of the smooth implicit function theorem may be challenging to check for general nonsmooth optimization problems, we would like to clarify that they hold at least for lasso regression, under mild hypothesis over the design matrix. To support this claim, we added Appendix E with ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 9, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 4 ]	[ "pcOCXCWHCN", "QXlvhjTeuoE", "gr2cBxZPV5", "gTQmnHprbDi", "Slsogjob9mS", "WVFuaMLbkdV", "VUl0iP3eZSH", "JOFhKKzifSJ", "F7mteGJtY50", "nips_2022_Q-HOv_zn6G", "nips_2022_Q-HOv_zn6G", "nips_2022_Q-HOv_zn6G" ]
nips_2022_Z4kZxAjg8Y	Autoregressive Search Engines: Generating Substrings as Document Identifiers	Knowledge-intensive language tasks require NLP systems to both provide the correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive language models are emerging as the de-facto standard for generating answers, with newer and more powerful systems emerging at an astonishing pace. In this ...	Accept	This paper proposes a method (SEAL) for document retrieval where a language model (LM) conditioned on a question generates n-grams as document identifiers. This is done by training BART on question and n-gram pairs, where the n-grams are sampled from the gold passages, and at test time constraining generation to output...	test	[ "oT-LIxjPQ5U", "Wuh8VjJIiv", "FGF1NrzazG-X", "OaZ2xPVTPRm", "hrj27-HBV2J", "2cc3kcs5xUx", "iXxGqu3a6x", "X9X0xDxuSH", "p6Zj8JJ2YRp", "qhApTs72KJP" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the score increase and for all the suggestions on how to strengthen the paper! We will revise the paper accordingly.", " Thanks for providing the response!\nBased on the response to my review and the author's responses to other reviews, I am happy to increase my score to 6.\n\nSome followup commen...	[ -1, -1, -1, -1, -1, -1, 7, 6, 8, 6 ]	[ -1, -1, -1, -1, -1, -1, 5, 5, 4, 4 ]	[ "Wuh8VjJIiv", "OaZ2xPVTPRm", "qhApTs72KJP", "X9X0xDxuSH", "iXxGqu3a6x", "p6Zj8JJ2YRp", "nips_2022_Z4kZxAjg8Y", "nips_2022_Z4kZxAjg8Y", "nips_2022_Z4kZxAjg8Y", "nips_2022_Z4kZxAjg8Y" ]
nips_2022_7-bMGPCQCm7	Heatmap Distribution Matching for Human Pose Estimation	For tackling the task of 2D human pose estimation, the great majority of the recent methods regard this task as a heatmap estimation problem, and optimize the heatmap prediction using the Gaussian-smoothed heatmap as the optimization objective and using the pixel-wise loss (e.g. MSE) as the loss function. In this paper...	Accept	This paper proposes to use earth mover distance to measure the loss function between a predicted heatmap and ground truth heatmap. It initially received mixed reviews. After rebuttal and discussion, all reviewers converged to acceptance of the paper. Reviewers believe this paper is novel and achieved significant practi...	test	[ "3SZk8PGMYlH", "FcHuQxkWmez", "aYyHJXk_9eQ", "1sxt_fL1rTe", "F1q4BaiFYPu", "wayHXr2hHfl", "XbXZYU3OSjd", "IREZ5n0MYv6", "GoNJU3j8VKZ", "STyjzKKg6yq", "K4x5AO3vbe", "_ZbnwJ1Hgno", "USbh0kO0TaX", "swkU4QS2u1F", "VDl_iC17e32", "kKYD44TMxJJ", "bCcXNkALcem" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Your replies generally answered my concerns and thus I change my rating. The suggestion of clarifying the core idea and supplementing the missing ablation study in the revised version, as mentioned in Weakness, still holds.", " We thank the reviewer for the additional thoughtful discussions. In the following,...	[ -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, 5, 5, 3, 5 ]	[ "FcHuQxkWmez", "aYyHJXk_9eQ", "XbXZYU3OSjd", "nips_2022_7-bMGPCQCm7", "wayHXr2hHfl", "swkU4QS2u1F", "IREZ5n0MYv6", "GoNJU3j8VKZ", "VDl_iC17e32", "K4x5AO3vbe", "kKYD44TMxJJ", "USbh0kO0TaX", "bCcXNkALcem", "nips_2022_7-bMGPCQCm7", "nips_2022_7-bMGPCQCm7", "nips_2022_7-bMGPCQCm7", "nips...
nips_2022_q-FRENiEP_d	SageMix: Saliency-Guided Mixup for Point Clouds	Data augmentation is key to improving the generalization ability of deep learning models. Mixup is a simple and widely-used data augmentation technique that has proven effective in alleviating the problems of overfitting and data scarcity. Also, recent studies of saliency-aware Mixup in the image domain show that prese...	Accept	This paper studies the point cloud data mixup with the saliency guidance. The proposed SageMix focus on the mixup over the local regions to preserve salient structures which are more informative for downstream tasks. The whole paper is well organized with clear logic to follow. The proposed method is simple but effect...	train	[ "jIJ3IURrn1U", "0bdOVAo127g", "c4LiKLigG9m", "Exdbx6DAxTE", "5Ep2H6EFOtc", "pFVlkVYQB3s", "EpNXkB2hRsf", "t8jOQt-9ges", "7-5xyNQO2Ie", "t0G2R-HiXqL" ]	[ "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer yU1N, we appreciate the reviewer for constructive feedback and comments.\n\nThe end of the Author-Reviewer Discussion is close. Through rebuttal, we have addressed all your concerns, and we believe that our responses have answered your suggestions and questions. So, would it be possible to check our...	[ -1, -1, -1, -1, -1, -1, -1, 5, 4, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "Exdbx6DAxTE", "pFVlkVYQB3s", "EpNXkB2hRsf", "t0G2R-HiXqL", "7-5xyNQO2Ie", "7-5xyNQO2Ie", "t8jOQt-9ges", "nips_2022_q-FRENiEP_d", "nips_2022_q-FRENiEP_d", "nips_2022_q-FRENiEP_d" ]
nips_2022_yQDC5ZcqX6l	Efficient and Effective Optimal Transport-Based Biclustering	Bipartite graphs can be used to model a wide variety of dyadic information such as user-rating, document-term, and gene-disorder pairs. Biclustering is an extension of clustering to the underlying bipartite graph induced from this kind of data. In this paper, we leverage optimal transport (OT) which has gained momentum...	Accept	The reviewers discussed strengths and weaknesses of the paper. One potential issue (to which the author's answer was rather unhelpful) was resolved by a reviewer running the experiments with higher precision output. Reviewers were mostly convinced by the strong empirical improvements.	train	[ "4_PfQVKqzQ", "yT6X_0u9nv", "aQakfo1kWnj", "ye6tQI0cFC7", "hA796uTdIIf", "FXEMdwmuz82", "Bk5mX_8U7VO", "n4CVQsoDHSg", "Wa8it9NLGTe" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for their comments. Please revise the mentioned part in the manuscript and probably add some more details about the computational complexity (answer 9) in the manuscript. The sd =0 still look suspicious and need more clarifications.", " We thank you for your response and the interest you sho...	[ -1, -1, -1, -1, -1, -1, 4, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "hA796uTdIIf", "aQakfo1kWnj", "FXEMdwmuz82", "Wa8it9NLGTe", "n4CVQsoDHSg", "Bk5mX_8U7VO", "nips_2022_yQDC5ZcqX6l", "nips_2022_yQDC5ZcqX6l", "nips_2022_yQDC5ZcqX6l" ]
nips_2022_B_LdLljS842	Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions	One of the most important AI research questions is to trade off computation versus performance since ``perfect rationality" exists in theory but is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable attention due to the significant performance improvement in various c...	Accept	I found this to be an interesting paper. As the reviewers indicated, it could be improved in terms of clarity, and I strongly encourage the authors to consider those comments carefully, as ultimately this could only make their paper more impactful. In particular, the authors could consider how to be clearer about the...	train	[ "V-imCg8efHS", "XxQkxowhVk_", "fhhiFcmiNSV", "cTME-veTe4", "I1i26J6KzK8", "YdW1cVlFfj", "WpMGchitMkh", "a58NooVzSEX", "QxGg0TO-cyG" ]	[ "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear reviewer oRW8,\n\nWe kindly remind you that the final stage of discussion is ending soon, and so please kindly let us know if our response has addressed your concerns.\n\nHere is a summary of the revisions:\n\n- We further clarified the main distinctions between our work and the Time Management algorithm...	[ -1, -1, -1, -1, -1, 3, 5, 7, 5 ]	[ -1, -1, -1, -1, -1, 4, 3, 5, 4 ]	[ "YdW1cVlFfj", "QxGg0TO-cyG", "a58NooVzSEX", "WpMGchitMkh", "YdW1cVlFfj", "nips_2022_B_LdLljS842", "nips_2022_B_LdLljS842", "nips_2022_B_LdLljS842", "nips_2022_B_LdLljS842" ]
nips_2022_4MT-e8mn3X	Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity	We consider optimization problems in which the goal is find a $k$-dimensional subspace of $\mathbb{R}^n$, $k<<n$, which minimizes a convex and smooth loss. Such problems generalize the fundamental task of principal component analysis (PCA) to include robust and sparse counterparts, and logistic PCA for binary data, amo...	Accept	The submitted work presents a local linear convergence guarantee for a projected gradient descent (PGD) algorithm on an explicit parameterization of the Stiefel manifold. Such a guarantee is easy to make if the convex objective f is assumed to be strongly convex. Instead, this work considers allowing f to be non-strong...	train	[ "j4SBb1N04F", "ks3ywFaQJgc", "-lhAtq0gXrC", "bkzgyKl3IFm", "sdj2Vgd8dSJ", "YNgGeltn-1b", "MG_eAR5fLZq", "7SQ-cJ1a0w3", "1mAKTyh9cf-", "GPnUg5qciqk", "ieEqzPAVUH-", "sLucwT_3DJ", "dm8i53cfXko", "ueK5JRbsiQV", "GAE9tE7Dixe" ]	[ "author", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer Lsrb,\n\nHave we answered your main concerns? If so, would you consider raising your score? Otherwise, we will be very happy to try and answer additional concerns.", " Dear Reviewer FuJo,\n\nHave we answered your main concerns? If so, would you consider raising your score? Otherwise, we will be ve...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "GPnUg5qciqk", "7SQ-cJ1a0w3", "ieEqzPAVUH-", "sdj2Vgd8dSJ", "YNgGeltn-1b", "1mAKTyh9cf-", "nips_2022_4MT-e8mn3X", "GAE9tE7Dixe", "ueK5JRbsiQV", "dm8i53cfXko", "sLucwT_3DJ", "nips_2022_4MT-e8mn3X", "nips_2022_4MT-e8mn3X", "nips_2022_4MT-e8mn3X", "nips_2022_4MT-e8mn3X" ]
nips_2022_mSiPuHIP7t8	GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs	Despite the remarkable success of graph neural networks (GNNs) for graph representation learning, they are generally built on the (unreliable) i.i.d. assumption across training and testing data. However, real-world graph data are universally comprised of outliers in training set and out-of-distribution (OOD) testing sa...	Accept	The authors propose a mixture modeling approach to train GNNs so that out-of-distribution data can be properly down-weighted during training and detected during testing. The reviews were mixed, with some reviewers criticizing the technical novelty and experimental comparison. Indeed, the authors could have explained th...	test	[ "XPrF61b5cA-", "9tdYv_8jUnX", "C6vE-PFuP4f", "-yfl6nx_Ql", "r4xrwpqF5e", "GHTNPM0vzl", "cf-w9sUvkcY", "2L7lYLEup4h", "_XfRnkd6uGh", "ZVfvPR3CKfB", "on24RX4vAb8", "QnTZHKgx3p6", "HU809loT765", "KW7z_mVFZQY", "uc8-nsBTKCI", "RQFb2L7zjaT", "7ba4NhJdNki", "nYg0Y4pGPnS", "85-LwVG-3T-"...	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer",...	[ " While the other reviewers have acknowledged our rebuttal and raised their rating accordingly, we are wondering whether our responses have addressed your concerns properly. Your feedback will definitely help reach a more reasonable decision on our submission. Thank you!", " While the other reviewers have acknowl...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 3, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 4, 3 ]	[ "Orf7LqzZmH1", "CtB6b_wgtPm", "r4xrwpqF5e", "GHTNPM0vzl", "ZSSxDGr3FGT", "2L7lYLEup4h", "nips_2022_mSiPuHIP7t8", "_XfRnkd6uGh", "28OVnYATqxs", "on24RX4vAb8", "QnTZHKgx3p6", "Orf7LqzZmH1", "KW7z_mVFZQY", "uc8-nsBTKCI", "CtB6b_wgtPm", "7ba4NhJdNki", "ZSSxDGr3FGT", "Dg7_l9TMqUf", "n...
nips_2022_AREqvTvv6gG	Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator	Tyler's M-estimator is a well known procedure for robust and heavy-tailed covariance estimation. Tyler himself suggested an iterative fixed-point algorithm for computing his estimator however, it requires super-linear (in the size of the data) runtime per iteration, which maybe prohibitive in large scale. In this work...	Accept	The scores on this paper were quite spread (and the reviews at times a little imprecise), however looking more closely at the discussion as well as reading the paper myself, I believe this paper should be accepted.	train	[ "PZZ3PU3Xve0", "FmriOBVrOrG", "lsNbTcBGjyJ", "YskgiReUctZ", "Ytlha5sVuA", "qN89lI-IFG3", "pH6TSn5CW9j", "vUbt4KarAQN", "VjHpnB7CqgP0", "vl31Srr8Bik", "N8RGNY3hRq", "gLafsu2NcLon", "RvXgN7o7jfF", "FKqKa8pbqP", "OhEEOjgn0nA", "Jhlspx2RWll", "g-x495XQdV7", "wU-jttT-F00" ]	[ "author", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for reconsidering your score! We really appreciate it. \n\nRegarding projected gradient: this will have complexity the same as fixed point iterations, since projecting onto the feasible set and inverting the matrix iterates will require O(p^3) time, and computing the gradient will take O(np^2) time - sa...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 4, 3 ]	[ "lsNbTcBGjyJ", "RvXgN7o7jfF", "YskgiReUctZ", "qN89lI-IFG3", "vl31Srr8Bik", "FKqKa8pbqP", "vUbt4KarAQN", "gLafsu2NcLon", "nips_2022_AREqvTvv6gG", "N8RGNY3hRq", "g-x495XQdV7", "wU-jttT-F00", "Jhlspx2RWll", "OhEEOjgn0nA", "nips_2022_AREqvTvv6gG", "nips_2022_AREqvTvv6gG", "nips_2022_AREq...
nips_2022_9Qjn_3gWLDc	Object-Category Aware Reinforcement Learning	Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL. Recent works that try to solve OORL tasks without additional feature engineering mainly focus on learning the object representations and then solving tasks via reasoning based ...	Accept	This paper received three positive reviews and one borderline reject. In the rebuttal, the negative reviewer did not propose a response, but the authors have given detailed responses to the problems. And the other reviewers did not propose further concerns. Thus, taking the comments of the reviewers into account, the A...	test	[ "X-CtcKUxJuC", "Vi-nEQpfrjW", "JxfzaVuMrEFK", "QlzrN4gAUAl", "OIZPjHHaqEO", "_q2kpbnY5wt", "-soaAakCA1", "bM0B-CGoEfR", "ETYSwlqH4kn", "ReODMDfYiU", "T3J_RgpQSjse", "hC15WMEZm8P", "XtBfK4oQ3o2", "LxMBHufC0-j", "LHTZMomMW0K", "V3wlNUiUyq4" ]	[ "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your advice. We have uploaded a new revision that includes this explanation.", " Thanks the author for the rebuttal, my concerns are resolved", " > Yes, in the current paper, we are more interested in the generalization to unseen object combinations. Generalization to novel object instances does ma...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 7, 4, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 5, 3 ]	[ "JxfzaVuMrEFK", "T3J_RgpQSjse", "ETYSwlqH4kn", "nips_2022_9Qjn_3gWLDc", "V3wlNUiUyq4", "-soaAakCA1", "LHTZMomMW0K", "ETYSwlqH4kn", "LxMBHufC0-j", "XtBfK4oQ3o2", "hC15WMEZm8P", "nips_2022_9Qjn_3gWLDc", "nips_2022_9Qjn_3gWLDc", "nips_2022_9Qjn_3gWLDc", "nips_2022_9Qjn_3gWLDc", "nips_2022...
nips_2022_LdAxczs3m0	Efficient Risk-Averse Reinforcement Learning	In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns. A risk measure often focuses on the worst returns out of the agent's experience. As a result, standard methods for risk-averse RL often ignore high-return strategies. We prove that under certain conditions this inevita...	Accept	Overall, the reviewers were satisfied with the author response and overall recommend acceptance. However, there were many discussion points and nuanced details that arose during post-rebuttal author-reviewer discussion. Reviewers would like to see these discussion points, clarifications, and requests for revision add...	train	[ "njPD9UUtIJ", "_M-rH6ty4V", "IZxh4IVxHhv", "vwjpwaJC9WY", "2nZhD-HV9sw", "15ibH1DMU7", "LAHA0oHhgT", "nI6ZiF2DCST", "AAMnsS1Jfcm", "KB8iNx48_xu", "CFwk8w96Ipj", "zX3nR_qchmP" ]	[ "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks again for your response, and apologies about the late reply. \n\nIn terms of algorithm choice, I was only suggesting to extend the comparison from Guarded maze to traffic and server control domains, since I suspect (though could be wrong) that DRL should be less brittle on those problems than it would on t...	[ -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4, 2 ]	[ "IZxh4IVxHhv", "2nZhD-HV9sw", "vwjpwaJC9WY", "nI6ZiF2DCST", "zX3nR_qchmP", "CFwk8w96Ipj", "KB8iNx48_xu", "AAMnsS1Jfcm", "nips_2022_LdAxczs3m0", "nips_2022_LdAxczs3m0", "nips_2022_LdAxczs3m0", "nips_2022_LdAxczs3m0" ]
nips_2022_qtZac7A3-F	Enhance the Visual Representation via Discrete Adversarial Training	Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thus has limited usefulness on industrial-scale production and applications. Surprisingly, this phenomenon is totally opposite in Natural Langu...	Accept	This paper proposes a discrete adversarial training scheme for improving the robustness of vision models. Reviewers find the paper is well written, the proposed idea seems to be novel/interesting, and the approach leads to improved empirical performance. This work may also inspire new approaches for improving both robu...	train	[ "peS_HFyyRE9", "e2EpqV79LYG", "SrtGTL8rOU6", "CuEabBdxPA", "MNaDTdG2UXg", "gXeYEd7rA55", "bPvciiSlgz5", "hLc9NFqHrGG", "JKF_iztQzR7", "h6XcIEiKUHC", "r7DYUmhUdGP", "6iwIrEWuzy4O", "Juif_1WK9iNS", "V996WVz7bpR", "oxv-Q87Mmu", "cOA0WnGQMdn", "dC1nWx-ayqR", "reuDqe3USKT", "CUe1oHD9W...	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", ...	[ " Dear reviewer wZbH,\n\nWe are appreciate for getting an affirmation from you about our response. Many thanks again for your precious review time and valuable comments to help us improve the paper. \n\nBest, \n\nAuthors of Paper 2664", " Authors response has convincingly addressed my concerns and I am willing to...	[ -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, 7, 6, 5, 6 ]	[ -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, 4, 3, 4, 5 ]	[ "e2EpqV79LYG", "CUe1oHD9WwA", "V996WVz7bpR", "MNaDTdG2UXg", "h6XcIEiKUHC", "O5Wm2NX7rJa", "hLc9NFqHrGG", "JKF_iztQzR7", "h6XcIEiKUHC", "r7DYUmhUdGP", "Juif_1WK9iNS", "V996WVz7bpR", "oxv-Q87Mmu", "GLwQt-bz9eH", "cOA0WnGQMdn", "dC1nWx-ayqR", "O5Wm2NX7rJa", "nips_2022_qtZac7A3-F", "...
nips_2022_8AB7AXaLIX5	Concept Activation Regions: A Generalized Framework For Concept-Based Explanations	Concept-based explanations permit to understand the predictions of a deep neural network (DNN) through the lens of concepts specified by users. Existing methods assume that the examples illustrating a concept are mapped in a fixed direction of the DNN's latent space. When this holds true, the concept can be represented...	Accept	All reviewers have found the paper as a solid contribution on a highly important topic, addressing the major shortcomings of the notable work, CAV in concept-based explainability area. On such shortcoming is that CAV assumes that examples corresponding to a concept are all mapped in a fixed direction in the DNNs latent...	train	[ "XbjrLkV6s2x", "V3acIj-TF84", "VR1w_6n_-j", "EZk7ggxpWW8", "i3mzRkyWEP0", "qQILpanzQWX", "CIWceAjIOsG", "XlPO4pEmRzF", "M0kQoVrLlEH", "3-hn0-auiDo", "WaGm0j74oR3", "PcgZJkSKQd", "jeFKFsFAsJT", "_2ibg3z6imj", "1xwZWXvoM4P", "ZW-GK-V7W2u", "Y45Kj6l2rv", "dR7XgsdFH7f" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for the extensive rebuttal. \n\nAfter reading the other reviews and the authors answers, I find that the authors addressed most of the concerns. I therefore raise my score accordingly. ", " Dear reviewer,\n\nas requested, we have performed an analysis of TCAR by using the CAR sensitivity\n\n...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "CIWceAjIOsG", "VR1w_6n_-j", "PcgZJkSKQd", "i3mzRkyWEP0", "dR7XgsdFH7f", "Y45Kj6l2rv", "ZW-GK-V7W2u", "dR7XgsdFH7f", "dR7XgsdFH7f", "dR7XgsdFH7f", "Y45Kj6l2rv", "Y45Kj6l2rv", "Y45Kj6l2rv", "ZW-GK-V7W2u", "ZW-GK-V7W2u", "nips_2022_8AB7AXaLIX5", "nips_2022_8AB7AXaLIX5", "nips_2022_8A...
nips_2022_pUPFRSxfACD	ZIN: When and How to Learn Invariance Without Environment Partition?	It is commonplace to encounter heterogeneous data, of which some aspects of the data distribution may vary but the underlying causal mechanisms remain constant. When data are divided into distinct environments according to the heterogeneity, recent invariant learning methods have proposed to learn robust and invarian...	Accept	This paper has been well received by the reviewers - all reviewers are positive including significant revisions upwards after rebuttal. Notable strengths are clarifying when you can/cannot identify environments for invariant learning and proposing sufficient and necessary conditions for the same. Further some reviewers...	train	[ "6yR8MQqJosg", "KvTDupQjSKy", "l3e478f4PfI", "xQDBdjdA2I", "hL_xAI3_O7f", "Ch8o837tuqV", "KL7bGF6PuWH", "DNRB-mo-6Q", "q6fW52PRhUw", "-wUvmA28Qij", "yjja9FTUBR", "YQc6V0k_ff", "-y-YuaiNEPJ", "RzrgE6Wm5Nu" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your clarifications! Then I have no further questions. ", " Thanks for the responses and congratulations on a nice paper.", " Thanks for clarifying the questions and taking the suggestions into account!", " I thank the authors for providing their feedback and addressing all my concerns. \n\nI ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 8, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3, 4 ]	[ "DNRB-mo-6Q", "hL_xAI3_O7f", "q6fW52PRhUw", "KL7bGF6PuWH", "RzrgE6Wm5Nu", "-y-YuaiNEPJ", "-y-YuaiNEPJ", "YQc6V0k_ff", "yjja9FTUBR", "nips_2022_pUPFRSxfACD", "nips_2022_pUPFRSxfACD", "nips_2022_pUPFRSxfACD", "nips_2022_pUPFRSxfACD", "nips_2022_pUPFRSxfACD" ]
nips_2022_NXHXoYMLIG	EfficientFormer: Vision Transformers at MobileNet Speed	Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally times slower than lightweight convolutional networks. Therefore, ...	Accept	This work proposes a purely transformer-based vision model for mobile vision purposes. This proposition is somewhat surprising, since transformers did not excel at low-latency inference on resource-constrained hardware, especially compared to convolutional networks. This is achieved by using a clever design that allo...	train	[ "HeQ0Ex8TgNS", "p0d3toOs0D7", "MLgu9DeoG6e", "3L6_A7_V_pf", "ELPORpDi5B", "NY-mmtZWnLk", "XXJgNO9NHO", "FNpadAD9sgK7", "PYJTD_al9fcF", "-FFzfBJtr7v", "DbrtZSU5mRZ", "UqYItUwc-OI", "CpBcoV_LZFZ", "MWwxIl5vHOG", "wgz-vCdFQCG" ]	[ "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer mbmg,\n\nThanks again for your time and reviewing efforts to help improve our work! We appreciate your positive rating and insightful comments. \n\nAs a kind reminder, we provide suggested results and comparisons in the authors' response, including the demonstration of the advantageous performance o...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 3 ]	[ "wgz-vCdFQCG", "CpBcoV_LZFZ", "3L6_A7_V_pf", "UqYItUwc-OI", "MWwxIl5vHOG", "wgz-vCdFQCG", "wgz-vCdFQCG", "wgz-vCdFQCG", "wgz-vCdFQCG", "CpBcoV_LZFZ", "CpBcoV_LZFZ", "MWwxIl5vHOG", "nips_2022_NXHXoYMLIG", "nips_2022_NXHXoYMLIG", "nips_2022_NXHXoYMLIG" ]
nips_2022_MIhgxhsJMtY	A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP	As an important framework for safe Reinforcement Learning, the Constrained Markov Decision Process (CMDP) has been extensively studied in the recent literature. However, despite the rich results under various on-policy learning settings, there still lacks some essential understanding of the offline CMDP problems, in te...	Accept	This paper considers offline reinforcement learning in the constrained MDP framework. It proposes an algorithm that provably obtains a near-optimal policy (under a single-policy concentrability assumption) and proves an upper bound (and a corresponding lower-bound) on the resulting sample complexity. The reviewers fo...	train	[ "Scg4nLj0n6k", "Zbk41ikwWm2", "FvBYhX5_WN0", "m3WjAckPtfP", "W6d4iwZfGAk", "fbYQjkLJrp1", "bDJ2zuJgfj3", "djNyoYt-S2H" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for her/his time and thoughtful feedback. We address the comments in detail as follows.\n\n$\\mathbf{Weakness1.}$ I would question the relevance of the manuscript as the assumptions needed to conclude the sample complexity are heavy. Despite one of them is necessary (Slater), it is not clear...	[ -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, 2, 3, 4, 3 ]	[ "fbYQjkLJrp1", "djNyoYt-S2H", "bDJ2zuJgfj3", "W6d4iwZfGAk", "nips_2022_MIhgxhsJMtY", "nips_2022_MIhgxhsJMtY", "nips_2022_MIhgxhsJMtY", "nips_2022_MIhgxhsJMtY" ]
nips_2022_4qR780g2Mg	Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning	Multi-agent reinforcement learning has drawn increasing attention in practice, e.g., robotics and automatic driving, as it can explore optimal policies using samples generated by interacting with the environment. However, high reward uncertainty still remains a problem when we want to train a satisfactory model, becaus...	Accept	The reviewers carefully analyzed this work and agreed that the topics investigated in this paper are important and relevant to the field. They believe that the NeurIPS community could benefit from the ideas and techniques presented in this work. They argued, e.g., that the paper is novel and interesting, technically so...	train	[ "7JD0P7J6V0r", "am62vuVWnT0", "rS44RXpN_cq", "4p5iuiMAd3c", "X6OJ9jg4rZI", "3P-F1jVXfA", "C2hK-kKk2h", "6kxkjUYD9te", "yim4V_Rvaq9", "nHT7i7-vkAf", "T3u684HmJkG", "HUcbgXFTXv", "Nc1HN_DDxwB", "PQMIKacOzAJ", "F_Ct61PngUK", "nGRdOL16rc", "fnoll44TjEG", "9qwY83DsjpH", "VIxb9nPV9Uj",...	[ "author", "author", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer JEsw,\n\nWe appreciate the reviewer's positive feedback and worthy suggestions for our paper. Furthermore, the recommendations of ablation studies and the clarification of our framework help us improve the quality of our paper further. As the end of the discussion is approaching, we are wondering if...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 6, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 2, 3, 4, 2 ]	[ "fnoll44TjEG", "nGRdOL16rc", "4p5iuiMAd3c", "yim4V_Rvaq9", "3P-F1jVXfA", "C2hK-kKk2h", "T3u684HmJkG", "VsQQMAAwNOm", "nHT7i7-vkAf", "VIxb9nPV9Uj", "9qwY83DsjpH", "Nc1HN_DDxwB", "fnoll44TjEG", "F_Ct61PngUK", "nGRdOL16rc", "nips_2022_4qR780g2Mg", "nips_2022_4qR780g2Mg", "nips_2022_4q...
nips_2022_9-SZkJLkCcB	KSD Aggregated Goodness-of-fit Test	We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAgg, which aggregates multiple tests with different kernels. KSDAgg avoids splitting the data to perform kernel selection (which leads to a loss in test power), and rath...	Accept	The paper proposes a novel method of statistical tests with Kernel Stein Discrepancy, aggregating multiple tests with different kernels. The method can avoid data splitting, which is commonly used to choose a kernel aiming at better power but may not be effective with a smaller sample size. The paper gives theoretica...	train	[ "9vq-EgGFg6L", "uW15Vgw2Cd", "04heoo8p-MS", "5Xq5-8ofPaC", "nSU-N0P4-6", "o_Voi5JcJnQ", "Z6FrpioBpzZ", "7c56NiCrdzg", "Oskp49RIfXY", "UEDkRUpzPP", "X7MURglWkUa", "fpUlJ3r_RYB", "LXuucxdJHyO", "R1y1pUORQ3a" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank reviewer ZqFj for their reply, and for increasing their score! \n\nWe will follow their suggestion and include a discussion of the advantages of the multiple testing strategy used against the classical Bonferroni correction.\n\nYes, KSDAgg selects the bandwidth 0.002 and split extra selects 2437. Split e...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "Z6FrpioBpzZ", "nSU-N0P4-6", "o_Voi5JcJnQ", "nips_2022_9-SZkJLkCcB", "Oskp49RIfXY", "UEDkRUpzPP", "7c56NiCrdzg", "R1y1pUORQ3a", "LXuucxdJHyO", "fpUlJ3r_RYB", "nips_2022_9-SZkJLkCcB", "nips_2022_9-SZkJLkCcB", "nips_2022_9-SZkJLkCcB", "nips_2022_9-SZkJLkCcB" ]
nips_2022_pkzwYftNcqY	Efficient Aggregated Kernel Tests using Incomplete $U$-statistics	We propose a series of computationally efficient, nonparametric tests for the two-sample, independence and goodness-of-fit problems, using the Maximum Mean Discrepancy (MMD), Hilbert Schmidt Independence Criterion (HSIC), and Kernel Stein Discrepancy (KSD), respectively. Our test statistics are incomplete $U$-statisti...	Accept	The paper discusses fast computation methods for kernel-based statistical tests: MMD, HSIC, and KSD. The paper uses incomplete U statistics in constructing the methods, shows decent theoretical results including the rate analysis, and confirms favorable numerical results. The paper has significant theoretical contrib...	train	[ "MFP7RFfr3g", "1Y0-j2aBWlR", "uWrGLVtlpLo", "J9LyXat879h", "JQvx2LfdwvN", "mAGzNXC22_W", "lSRQFBin4jP", "3VCv2cSAKS", "5rrgvO5RLZvK", "AkGV_fj41m", "UI8GewXzJB", "Xu8TZgu9auu", "jxbEgKOM46u", "xcw-bmiX8fN" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We warmly thank reviewer aMao for increasing their score! \n\nWe will make sure to clarify the following points in the final version:\n\n(i) The tests we propose have a computational cost which can be specified by the user (the size of the design between $1$ and $N^2$), there is a tradeoff between test power and ...	[ -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, 3, 4, 4 ]	[ "mAGzNXC22_W", "JQvx2LfdwvN", "J9LyXat879h", "AkGV_fj41m", "lSRQFBin4jP", "3VCv2cSAKS", "5rrgvO5RLZvK", "xcw-bmiX8fN", "jxbEgKOM46u", "Xu8TZgu9auu", "nips_2022_pkzwYftNcqY", "nips_2022_pkzwYftNcqY", "nips_2022_pkzwYftNcqY", "nips_2022_pkzwYftNcqY" ]
nips_2022_YPoRoad6gzY	OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training	State-of-the-art deepfake detectors perform well in identifying forgeries when they are evaluated on a test set similar to the training set, but struggle to maintain good performance when the test forgeries exhibit different characteristics from the training images e.g., forgeries are created by unseen deepfake methods...	Accept	The reviewers unanimously accept the paper, so is the final proposal.	train	[ "BGRUCGoFEHc", "S1BJWeoX-HH", "W4lyeGyn008", "fdaQ6KSuWCy", "Z_--U2tVhRwL", "vdB7i1DvrQ", "IWV_eMbXdqY", "IhfKwQIfBO8", "q9zCcqcWrd", "Jc1wN4tYzek", "V2GdwTKxlCI", "g5GL1lzOp7", "3EuZnIvBhj6", "GRCL4wlwYsP" ]	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Many thanks for the valuable suggestions. These works will be included and discussed in our future version.", " Thank you authors for the great effort on the rebuttal. Authors have addressed my concerns to some extent. \n\n**In the revised version, please consider including a short description to compare agains...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 5, 3 ]	[ "S1BJWeoX-HH", "fdaQ6KSuWCy", "3EuZnIvBhj6", "g5GL1lzOp7", "vdB7i1DvrQ", "GRCL4wlwYsP", "3EuZnIvBhj6", "g5GL1lzOp7", "V2GdwTKxlCI", "nips_2022_YPoRoad6gzY", "nips_2022_YPoRoad6gzY", "nips_2022_YPoRoad6gzY", "nips_2022_YPoRoad6gzY", "nips_2022_YPoRoad6gzY" ]
nips_2022_adFLKRqRu1h	Fuzzy Learning Machine	Classification is one of the most important problems in machine learning and the nature of it is concept cognition. So far, dozens of different classifiers have been designed. Although their working mechanisms vary widely, few of them fully consider concept cognition. In this paper, a new learning machine, fuzzy learni...	Accept	The paper proposes an approach for the design of neural networks for classification based on fuzzy theory, and a specific implementation is presented and experimentally assessed. Arguments from cognition to justify the proposed approach are also used, although at the level of inspiration. The lack of reference to fuzzy...	train	[ "Uz9LQIpywCb", "7n1Ppzt0JoK", "IPhgQp9QIfP", "fPVfcBd8eUh", "1wTs6e6tqdY", "d_L_zfF5iFs", "f1er7W5Bj-B", "qHCfxKL5JHt", "VAAaNCS6n65" ]	[ "author", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your advice. You are right.\n\nFrom a biological point of view, the concepts of “cat” and “dog” can be defined according to their DAN features. At this time, the concepts are crisp.\n\nIn the field of ML, for example, in most image classification task, the goal is to learn the concepts from the images ...	[ -1, -1, -1, -1, -1, -1, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "7n1Ppzt0JoK", "1wTs6e6tqdY", "fPVfcBd8eUh", "VAAaNCS6n65", "qHCfxKL5JHt", "f1er7W5Bj-B", "nips_2022_adFLKRqRu1h", "nips_2022_adFLKRqRu1h", "nips_2022_adFLKRqRu1h" ]
nips_2022_tYAS1Rpys5	Simulation-guided Beam Search for Neural Combinatorial Optimization	Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches capable of high-quality solutions in a single shot are emerging, state-of-the-art approaches are often unable to take full advantage of the so...	Accept	The paper follows in the footsteps of alpha go and presents two methods for neural-network guided search, targeting in particular beam search. The paper was deemed a bit incremental, but the method is simple, is easier to parallelize than MCTS and obtains good results on problems under-explored in machine learning. Ple...	train	[ "cBab4SKUbJ", "OJ8pFRi2Lk", "rKC2Xzp1gM", "yBkwUQ-P2dv", "Fh3Ucuoi2x", "2iUNEbOKTx1", "5soqdw4_PsJ", "M3FcX_wcUD", "Aiqc7oLxe7", "Wenf_JGEvuo", "o-L2Tgth0b4", "LKx2UlXg2FI", "HTrnFtLESRt", "2RTDTDSyaAf", "7XqyiWvaZ_J" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Sorry for confusing you with our ambiguous use of the term 'policy likelihood'.\nYour description and understanding of the method is accurate, indeed. \n\nWe value your opinion and we thank you again for your hard work and time for reviewing our work.", " I don't understand the authors when they mention that SG...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 2, 4 ]	[ "OJ8pFRi2Lk", "5soqdw4_PsJ", "yBkwUQ-P2dv", "2iUNEbOKTx1", "7XqyiWvaZ_J", "7XqyiWvaZ_J", "HTrnFtLESRt", "HTrnFtLESRt", "HTrnFtLESRt", "2RTDTDSyaAf", "LKx2UlXg2FI", "nips_2022_tYAS1Rpys5", "nips_2022_tYAS1Rpys5", "nips_2022_tYAS1Rpys5", "nips_2022_tYAS1Rpys5" ]
nips_2022_3r0yLLCo4fF	Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?	Recent developments in monocular multi-object tracking have been very successful in tracking visible objects and bridging short occlusion gaps, mainly relying on data-driven appearance models. While we have significantly advanced short-term tracking performance, bridging longer occlusion gaps remains elusive: state-of...	Accept	The paper initially had mixed reviews 4567. The main concerns of the reviewers were: 1. can better show the improvement on long-term occlusions (cbmW) 2. lack of results on autonomous driving datasets w/ camera parameters. (cbmW) 3. Questions about the evaluation metrics used (yuJE, Tgjz) 4. In Tab 1, most of the HOTA ...	train	[ "o43vkBpKHFy", "4NuEMlFeP7P", "m5cGaXglJ6C", "_aZ2fFZaSuu", "pCQt-GvZwBY", "MF8D486mQi", "4H3BqEuPF-A", "5qFN2s6VsZj", "hMxvisqGIih", "5tjqUxnV3e7", "xlMOXJjrJ50", "r-jJL5Ca4vd", "r-nQzm78JhF", "3Mxa-OCb_Xt", "vbEJoxftums" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the rebuttal！Most of my concerns are adequately addressed. I keep my postive rating.", " I have read the responses from the reviewers, and they addressed my concerns. I will increase my rating after the Reviewer-Meta Reviewer Discussion phase. \n\nI recommend the authors highlight these performance a...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 4, 4 ]	[ "5tjqUxnV3e7", "4H3BqEuPF-A", "3Mxa-OCb_Xt", "r-nQzm78JhF", "r-jJL5Ca4vd", "xlMOXJjrJ50", "r-jJL5Ca4vd", "r-nQzm78JhF", "r-nQzm78JhF", "3Mxa-OCb_Xt", "vbEJoxftums", "nips_2022_3r0yLLCo4fF", "nips_2022_3r0yLLCo4fF", "nips_2022_3r0yLLCo4fF", "nips_2022_3r0yLLCo4fF" ]
nips_2022_cYeYzaP-5AF	Meta-Reinforcement Learning with Self-Modifying Networks	Deep Reinforcement Learning has demonstrated the potential of neural networks tuned with gradient descent for solving complex tasks in well-delimited environments. However, these neural systems are slow learners producing specialized agents with no mechanism to continue learning beyond their training curriculum. On the...	Accept	This is exciting work that demonstrates the ability of self-modifying networks to solve meta-reinforcement learning problems. The reviewers all agree that this is strong work, and the authors have convincingly addressed most of the concerns the reviewers brought up during the reviewing phase. There are a few lingering ...	test	[ "WDS5R5cT7h7", "UQZxk0qbWgf", "71YuwtONQnj", "nZqGD4lrn0Q", "QbhWR-qNREIl", "Ed9J8gzNQFS", "1tqcFQrkQjE", "0JtnoR4Ukz5", "F7XY0KWVDoB", "GjVHFlvQgv_", "ZWgkiaQcwQ", "-c2alWfalf", "3KVlDNW2XWv", "J-yjRplRa8G" ]	[ "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank reviewers for their diligence in evaluating our modifications and willingness to increase their score. We are enthusiastic about our work forming a stronger contribution thanks to their feedback! \n\nRegarding last comments from reviewers, we are currently working on delivering an additional analytical c...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 2 ]	[ "nips_2022_cYeYzaP-5AF", "ZWgkiaQcwQ", "GjVHFlvQgv_", "Ed9J8gzNQFS", "nips_2022_cYeYzaP-5AF", "1tqcFQrkQjE", "0JtnoR4Ukz5", "F7XY0KWVDoB", "3KVlDNW2XWv", "J-yjRplRa8G", "-c2alWfalf", "nips_2022_cYeYzaP-5AF", "nips_2022_cYeYzaP-5AF", "nips_2022_cYeYzaP-5AF" ]
nips_2022_5K3uopkizS	Robust Models are less Over-Confident	Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental challenges. One of these open problems is the inherent lack of robustness, unveiled by the striking effectiveness of adversarial attacks. Cur...	Accept	This paper empirically demonstrates that adversarially trained models are better calibrated than naturally trained counterparts. The reviewer found this paper interesting, and initial concerns are mainly about 1) missing discussions of prior works, and 2) requiring more ablations. The rebuttal well addresses most conc...	train	[ "VeZeWmwHqOC", "SMO3-gOlQ7", "dIR5wPW5SJ0", "BxRybqJEJI", "KHF63cv0ej3", "i5aysvd7LR", "VTIiNodMGN5", "WNmQ1Rt1jXT", "ros6J3vHhHb", "kKx5aGs48SA", "m7K7mEp-cQe", "te85nc0R5M4", "We6q89kvsWt", "vfti1vnaRVx", "PYahQ2KkK_Y", "UQQ-egjPTIt", "olr7zP9Lv2FQ", "hjlbEh-KU6", "p79-zaN84oN"...	[ "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", ...	[ " We would also like to point out our following comments regarding related work:\n\nhttps://openreview.net/forum?id=5K3uopkizS&noteId=nq_2pyo_YgA\n\nhttps://openreview.net/forum?id=5K3uopkizS&noteId=We6q89kvsWt\n\nhttps://openreview.net/forum?id=5K3uopkizS&noteId=p79-zaN84oN\n\nPlease also pay attention that the ot...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "dIR5wPW5SJ0", "dIR5wPW5SJ0", "KHF63cv0ej3", "WNmQ1Rt1jXT", "N9YTfafZdb", "ros6J3vHhHb", "hjlbEh-KU6", "ros6J3vHhHb", "kKx5aGs48SA", "p79-zaN84oN", "te85nc0R5M4", "We6q89kvsWt", "vfti1vnaRVx", "nq_2pyo_YgA", "r-tumqwhndq", "70SIueqjUL", "nips_2022_5K3uopkizS", "ousUEyFjbDM", "70S...
nips_2022_tmUGnBjchSC	Generalizing Bayesian Optimization with Decision-theoretic Entropies	Bayesian optimization (BO) is a popular method for efficiently inferring optima of an expensive black-box function via a sequence of queries. Existing information-theoretic BO procedures aim to make queries that most reduce the uncertainty about optima, where the uncertainty is captured by Shannon entropy. However, an ...	Accept	The paper proposed a novel acquisition function for BO, based on a generalization of Shannon entropy that enables one to incorporate problem-specific loss functions corresponding to a downstream task. The authors show that the proposed acquisition criterion generalizes a number of well-known BO acquisition functions, i...	train	[ "-qm1397_Ir", "N967pn6zYC", "htPUt2TbtE", "khI9ukrSZ-f", "4huYRc6HHSv", "H4bFFn1fhLa", "QpIUsZst4ad", "uWIYyuuDO81" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The authors have adequately addressed my concern and questions.\nI am glad they have also shown \"Probability of Improvement\" to be a special case of their approach in response to another reviewer.\nMy rating remains unchanged after considering the discussion between authors and reviewers thus far.\n", " Thank...	[ -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "htPUt2TbtE", "uWIYyuuDO81", "QpIUsZst4ad", "4huYRc6HHSv", "H4bFFn1fhLa", "nips_2022_tmUGnBjchSC", "nips_2022_tmUGnBjchSC", "nips_2022_tmUGnBjchSC" ]
nips_2022_wZk69kjy9_d	Deep Hierarchical Planning from Pixels	Intelligent agents need to select long sequences of actions to solve complex tasks. While humans easily break down tasks into subgoals and reach them through millions of muscle commands, current artificial intelligence is limited to tasks with horizons of a few hundred decisions, despite large compute budgets. Research...	Accept	This paper studies an interesting problem, and overall the reviewers agreed the exposition and validation are sufficient. We encourage the authors to consider the issues raised by the reviewers and further improve the work in the final version.	train	[ "Zja5XQBsMe", "fKk69TDTwy3p", "E8P69PAE1b4", "-Za9xX4q_Od", "V2C9VbtOHKl", "eVLn1BG1Yrj", "piP4zTVwwB", "oFZKKNeCET", "PZc4mYYIE_Z", "bdyObEErLWZ", "rxjnLkCVb38" ]	[ "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer q27r,\n\nThe discussion period is coming to an end soon and we haven't received a response from you yet. Could we please ask you to confirm whether our response has resolved your concerns or whether you see any remaining issues that motivate your current rating? If there are remaining issues, we wou...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "rxjnLkCVb38", "bdyObEErLWZ", "PZc4mYYIE_Z", "PZc4mYYIE_Z", "PZc4mYYIE_Z", "rxjnLkCVb38", "rxjnLkCVb38", "bdyObEErLWZ", "nips_2022_wZk69kjy9_d", "nips_2022_wZk69kjy9_d", "nips_2022_wZk69kjy9_d" ]
nips_2022_HFm7AxNa9Wo	Multi-Scale Adaptive Network for Single Image Denoising	Multi-scale architectures have shown effectiveness in a variety of tasks thanks to appealing cross-scale complementarity. However, existing architectures treat different scale features equally without considering the scale-specific characteristics, \textit{i.e.}, the within-scale characteristics are ignored in the arch...	Accept	All reviewers are positive about this paper. Although this paper does not achieve the best performance, it reveals some insights about some insights about scale characteristics of features, which is model-agnostic and potential to design more powerful networks. Also, the proposed method can reduce FlOPs obviously.	train	[ "LOoD0pQTXa", "6NmhGfcIDOC", "tY8czY6bCeH", "b5UDX1HkJaR", "A8LBDrs5V6", "t7Y6OZxmt", "0EiuKiyC2r6", "kYnPY4QH2F", "5kl4ErYU1Kv", "nuaqdwgEDqZp", "U_P2om0ZPGg", "tRFHErSv_Lx", "4-cP4E9coJW", "ZTM_3cFG08Y", "mi7p-eA6DGo", "JG2Bld8q-90", "JVAdFYnzNF", "AKxLioyYjrt", "AmJAp4s1Use", ...	[ "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", ...	[ " Thanks for your positive comments and suggestions. We would improve our manuscript for a clearer presentation in the next version.", " Thanks for your positive comments and suggestions. We would accordingly revise the problems and include some discussions about the concerns in the next version for a clearer pre...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 4, 4, 4 ]	[ "b5UDX1HkJaR", "t7Y6OZxmt", "A8LBDrs5V6", "rPrT6V62Qq4", "U_P2om0ZPGg", "kYnPY4QH2F", "rPrT6V62Qq4", "_PbaoqR0LGz", "uoEYQWc2jOY", "64WafatBSlw", "AmJAp4s1Use", "rPrT6V62Qq4", "_PbaoqR0LGz", "mi7p-eA6DGo", "uoEYQWc2jOY", "64WafatBSlw", "AKxLioyYjrt", "AmJAp4s1Use", "nips_2022_HFm...
nips_2022_NyAJzgHLAr	Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation	Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective category information from the support to match with the corresponding objects in query. However, they all ignored the category information g...	Accept	All reviewers lean to accept this paper and this is a clear acceptance.	train	[ "l9iNyM_akA-", "xQ27gqnj75P", "LoeViNXYP06", "L2C4ZmUIlOT", "5-XAIYd1OBnD", "NjTjDg5bXza", "AhIwdHidZC3", "oeJxNPIPYq" ]	[ "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you so much for your continued interest and positive responses to our work. Here are further responses to your concerns.\n\nQ1. Threshold of diverse samples\n\nIn the previous rebuttal, we set 1.5 as a threshold to define diverse support and 8.2\\% samples are categorized as \"diverse support\". To furt...	[ -1, -1, -1, -1, -1, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "xQ27gqnj75P", "LoeViNXYP06", "oeJxNPIPYq", "NjTjDg5bXza", "AhIwdHidZC3", "nips_2022_NyAJzgHLAr", "nips_2022_NyAJzgHLAr", "nips_2022_NyAJzgHLAr" ]
nips_2022_0-uBrFiOVf	DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection	The Mean-Teacher (MT) scheme is widely adopted in semi-supervised object detection (SSOD). In MT, sparse pseudo labels, offered by the final predictions of the teacher (e.g., after Non Maximum Suppression (NMS) post-processing), are adopted for the dense supervision for the student via hand-crafted label assignment. Ho...	Accept	This paper proposes a dense-to-dense semi-supervised object detection method, where the teacher's NMS is used to guide the clustering and ranking of bounding box candidates from the student. This is motivated from potential noise resulting from sparse-to-dense pseudo-label supervision in existing methods. Results are...	train	[ "wZ-yVDZfSfk", "AAo1PgoWLei", "uSQ8DlWWC9", "0Rg928RWGbG", "HmlaW42_ci", "dSSYeuMIiXY", "65-L5TJ-EqZ", "qXjso-9vRnh", "wFv7yx_u1Qa", "V3LbA5wBpJ9", "NM5TGhtbkfF" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your rebuttal. The author provides detailed experiments to prove this. It has addressed my concerns in the rebuttal. So I think it is a good paper.", " Thanks for the rebuttal. The authors have properly addressed the reviewer's questions in the rebuttal. Thus, the reviewer decided to keep the origina...	[ -1, -1, -1, -1, -1, -1, -1, 6, 6, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 4, 3, 4 ]	[ "uSQ8DlWWC9", "V3LbA5wBpJ9", "NM5TGhtbkfF", "HmlaW42_ci", "V3LbA5wBpJ9", "wFv7yx_u1Qa", "qXjso-9vRnh", "nips_2022_0-uBrFiOVf", "nips_2022_0-uBrFiOVf", "nips_2022_0-uBrFiOVf", "nips_2022_0-uBrFiOVf" ]
nips_2022_LIKlL1Br9AT	Contact-aware Human Motion Forecasting	In this paper, we tackle the task of scene-aware 3D human motion forecasting, which consists of predicting future human poses given a 3D scene and a past human motion. A key challenge of this task is to ensure consistency between the human and the scene, accounting for human-scene interactions. Previous attempts to do ...	Accept	Three expert reviewers have recommended accepting the paper after the discussion period. Reviewers like the overall idea and framework. The AC agrees and recommends acceptance. Please carefully revise the paper based on the reviews.	train	[ "EcVXc_zUNCk", "afWvOKiqRMk", "0Tqd9BeHwIc", "YQIgRUeX0vQ", "YQxWRBYU5er", "guh8pBoYUeG", "htr-86LXGpE", "SdPix4XY7Np", "_507hdQuFVu", "ms6dAWB_Cg4" ]	[ "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the suggestion.\n\nWe have added additional results to the supplemental material. As also mentioned in the Checklist, we will release our source code upon the acceptance of this paper which also includes the code to visualize our results. ", " Thanks for the detailed response. \n\nMost of my concerns...	[ -1, -1, -1, -1, -1, -1, -1, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "afWvOKiqRMk", "htr-86LXGpE", "YQIgRUeX0vQ", "ms6dAWB_Cg4", "guh8pBoYUeG", "_507hdQuFVu", "SdPix4XY7Np", "nips_2022_LIKlL1Br9AT", "nips_2022_LIKlL1Br9AT", "nips_2022_LIKlL1Br9AT" ]
nips_2022_7CONgGdxsV	Understanding Programmatic Weak Supervision via Source-aware Influence Function	Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model. With its increasing popularity, it is critical to have some tool for users to understand the influence of each component (\eg, the source...	Accept	This paper proposes source-aware Influence Function (IF) to study the “influence” of individual data, source, and class tuples on the performance of different label functions in the programmatic weak supervision paradigm. The proposed method has the capability to work with diverse data domains (tabular, image, textual)...	test	[ "QU1Q-MNYVv", "iOlUfX0XO4G", "pXFDuGrnq5i", "LZnP-mvyLCx", "DdtfKqcMB96", "K6R1BevR9Hl", "Pw9lhotw6qy", "OKA67Zxes23" ]	[ "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " \n\| \| \| MV \| \| \| \| \| \| DS \| \| \| \| \| Snorkel \| \| \| \|\n\| ------------ \| :--: \| :-------: \| :----: \| :-------: \| :-------: \| :-------: \| :--: \| :-------: \| :-------: \| :-------...	[ -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, 4, 2, 3 ]	[ "iOlUfX0XO4G", "Pw9lhotw6qy", "K6R1BevR9Hl", "OKA67Zxes23", "nips_2022_7CONgGdxsV", "nips_2022_7CONgGdxsV", "nips_2022_7CONgGdxsV", "nips_2022_7CONgGdxsV" ]
nips_2022_k7FuTOWMOc7	Elucidating the Design Space of Diffusion-Based Generative Models	We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets us identify several changes to both the sampling and training processes, as well a...	Accept	Ratings: 8/9/8/7. Confidence: 4/4/4/5. Discussion among reviewers: No. Summary: This is an excellent paper analyzing the design space of diffusion models. The paper clarifies the design space by disentangling the effects of (1) parameterization, (2) sampling, and (3) training separately. The researchers uniformily agr...	train	[ "b1M7dY_e9C", "2jNQZ5NMJK4", "JK2tbKgI6h_v", "VsMxG6fNCi1", "Q8k7apk1UdC", "AZvQgzXI_SN", "AqtWGbdOLDr", "kmfBuNSIYOm", "EHQP3cnRbLU", "TwVg5ExbRhs", "XyqsKy2paNd" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for clarifying the motivation of your tailored stochastic sampler. My rating about this paper stays unchanged.", " Thanks for your response. I'll stick to my original rating recommending a Strong Accept.", " Thank you for the response. I am looking forward to see Fig. 5(b) for ImageNet in the camera...	[ -1, -1, -1, -1, -1, -1, -1, 8, 9, 8, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "AZvQgzXI_SN", "AqtWGbdOLDr", "VsMxG6fNCi1", "XyqsKy2paNd", "TwVg5ExbRhs", "EHQP3cnRbLU", "kmfBuNSIYOm", "nips_2022_k7FuTOWMOc7", "nips_2022_k7FuTOWMOc7", "nips_2022_k7FuTOWMOc7", "nips_2022_k7FuTOWMOc7" ]
nips_2022_HEcYYV5MPxa	Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech	Polyphone disambiguation aims to capture accurate pronunciation knowledge from natural text sequences for reliable Text-to-speech (TTS) systems. However, previous approaches require substantial annotated training data and additional efforts from language experts, making it difficult to extend high-quality neural TTS sy...	Accept	The reviewers generally liked the proposed approach in this paper, agreed that it is novel, and that the experiments showed good improvements over reasonable baselines. There was broad concern about the ablation study in the original paper (one shared by the AC), but the authors revised that section during the discussi...	test	[ "uIyZ_t27FL9", "UvZqzAiuG0T", "MzLeN_NcKRI", "2iJFV05vZU5", "eHP8JmamRHT", "iYXCU_MwylK", "lZ-4oGxISlb", "vBQbgBgoA1", "V0Kmi153VJo", "kR70CKfVFkH", "GI1tqKa4zy_", "QmMjs-LLz8k", "OlLILDjgYuw" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks to the authors for revising the paper. The response answers my questions. I am updating the score accordingly.", " Thanks again for your great efforts and valuable comments. \n\nWe have carefully addressed the main concerns and provided detailed responses to each reviewer. We hope you might find the resp...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 4, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 5, 5 ]	[ "2iJFV05vZU5", "nips_2022_HEcYYV5MPxa", "nips_2022_HEcYYV5MPxa", "OlLILDjgYuw", "iYXCU_MwylK", "QmMjs-LLz8k", "vBQbgBgoA1", "GI1tqKa4zy_", "kR70CKfVFkH", "nips_2022_HEcYYV5MPxa", "nips_2022_HEcYYV5MPxa", "nips_2022_HEcYYV5MPxa", "nips_2022_HEcYYV5MPxa" ]
nips_2022_vhKaBdOOobB	GhostNetV2: Enhance Cheap Operation with Long-Range Attention	Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only capture local information in a window region, which prevents performance from being further improved. Introducing self-attention into convolution...	Accept	This paper aims to augment efficient CNNs with self-attention. However, since the naive approach to self-attention is computationally expensive and would contradict the point of efficient CNNs, the authors introduce a new attention mechanism which captures long-range information without substantially added computation ...	val	[ "VS4hQobanBh", "EPtLi4LZTaE", "B6AmHzyy0Sv", "swqgwmTN6Lq", "edvNiHYiDEx", "vWNJ4wxaFZ", "RTwOorslz7G", "kNGapMAj-8", "k3JWlEJAjD1", "Hya34Za4oB9", "8C1AgMTFes", "oby17P7M50", "3NlUgry_7Dt", "oURXecLNmwd", "L8OIDMusXmS", "SmKnmcr_lAL", "u6iftgwjhb" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear area chair and anonymous reviewers,\n\nThanks for your constructive comments and valuable suggestions to improve this paper. We have revised the manuscript and supplemental materials by improving the presentation and including more experiments, discussions, and explanations. If you have any questions, we are...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 6, 8, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 5 ]	[ "nips_2022_vhKaBdOOobB", "edvNiHYiDEx", "swqgwmTN6Lq", "8C1AgMTFes", "Hya34Za4oB9", "kNGapMAj-8", "oURXecLNmwd", "k3JWlEJAjD1", "u6iftgwjhb", "SmKnmcr_lAL", "L8OIDMusXmS", "oURXecLNmwd", "oURXecLNmwd", "nips_2022_vhKaBdOOobB", "nips_2022_vhKaBdOOobB", "nips_2022_vhKaBdOOobB", "nips_2...
nips_2022_-zYfrOl2I6O	CASA: Category-agnostic Skeletal Animal Reconstruction	Recovering a skeletal shape from a monocular video is a longstanding challenge. Prevailing nonrigid animal reconstruction methods often adopt a control-point driven animation model and optimize bone transforms individually without considering skeletal topology, yielding unsatisfactory shape and articulation. In contras...	Accept	The paper shows how to combine 3d model retrieval with an inverse graphics framework to recover 3D models of a diverse range of animals from video. The paper also introduces a new dataset of 3D animals that is projected to be of value in future works. While one reviewer considers the technical problem to be "an engi...	train	[ "VF7oPoJf3B3", "1tNX2xG4mm", "pXL96kKHOZ6", "4hNKy6CUspJ", "f4g18gNqH6T", "jWeQ3RGE2YW", "NlI6IS-NT7t", "VFydtWFE-cJ", "VnZTuK6QUN", "6XzKgobF_FU" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Most of my questions are answered adequately. I'd like to raise score to 7. The interesting part of the paper is the 3D skeletal model retrieval given a large database, which provides reasonable constraints when the target object falls roughly within the database. The remaining concern is that the method does not...	[ -1, -1, -1, -1, -1, -1, -1, 4, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 5 ]	[ "4hNKy6CUspJ", "jWeQ3RGE2YW", "nips_2022_-zYfrOl2I6O", "f4g18gNqH6T", "6XzKgobF_FU", "VnZTuK6QUN", "VFydtWFE-cJ", "nips_2022_-zYfrOl2I6O", "nips_2022_-zYfrOl2I6O", "nips_2022_-zYfrOl2I6O" ]
nips_2022_6H00JM-DZjU	Fair and Efficient Allocations Without Obvious Manipulations	We consider the fundamental problem of allocating a set of indivisible goods among strategic agents with additive valuation functions. It is well known that, in the absence of monetary transfers, Pareto efficient and truthful rules are dictatorial, while there is no deterministic truthful mechanism that allocates all i...	Accept	Reviewers agreed that this paper explored a natural and interesting strategic aspect of fair division (non-obvious manipulability). This helped escape classical impossibility results in fair division. Minor concerns were raised about the practical significance of NOM, but overall the sentiment was quite positive.	train	[ "jIwMat57jNj", "CMjz54AGT0v", "Rwc40XjGwbr", "CMuiwB9X2Pn", "wHBnJSBp5p", "mnwgYpaTI7J3", "RROxOv3mdU9O", "SUQHYw3zJ9p", "yV3xyunBi8O", "t6QsAxE3RCK", "zubo2tQnpqG", "u9xZs5YDhA", "jzTRXWlKSS" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We will certainly incorporate this discussion in the final version. We hope you will reconsider your score in light of the response. Please let us know if you have any further questions.", " Thanks for the additional discussion of these issues. I think bringing discussion along these lines into appropriate pla...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 8, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 4 ]	[ "CMjz54AGT0v", "Rwc40XjGwbr", "CMuiwB9X2Pn", "wHBnJSBp5p", "jzTRXWlKSS", "u9xZs5YDhA", "zubo2tQnpqG", "t6QsAxE3RCK", "nips_2022_6H00JM-DZjU", "nips_2022_6H00JM-DZjU", "nips_2022_6H00JM-DZjU", "nips_2022_6H00JM-DZjU", "nips_2022_6H00JM-DZjU" ]
nips_2022_xwBdjfKt7_W	SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training	Spiking neural networks (SNNs) are promising to be widely deployed in real-time and safety-critical applications with the advance of neuromorphic computing. Recent work has demonstrated the insensitivity of SNNs to small random perturbations due to the discrete internal information representation. The variety of traini...	Accept	This paper proposes an adversarial training method for Spike neural networks. One challenge is that spike networks are non-differentiable and the paper develops various gradient approximation methods and builds on previous attack methods like FGSM and PGD with approximate gradients. An additional innovation is the deve...	test	[ "E4WEPpQBR_g", "uATX9PK9nUA", "qgQ_zEQsbT", "jS1cuqYKab", "EPPiP_R9kI2", "IXvET_EefiO", "G4-S-vbfvl5", "WuUMJ_r7tN", "H1-yxyxMkO", "KsxmZdJmar", "7yF83O_4Dd8", "2Cgc6SU9_LQ", "n2MpYT8yEc1", "6VA-Tk5zCtX", "mmnFAs8SgdL", "W1xNu1uOujz", "WvVi_GeRRc", "u_fE3pJrGa9", "enAt10tm_jR", ...	[ "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_...	[ " After reading the rebuttal, most concerns are addressed. I will increase my score to borderline accept. The combination of adversarial training with SNN seems promising but the current version lacks theoretical contribution.", " Tabel R4: Layerwise Matrix Norm of Batch Normalization\n\| Performance \| RA...	[ -1, -1, -1, -1, -1, -1, -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, -1, -1, -1, -1, -1, -1, 5, 3, 4 ]	[ "6VA-Tk5zCtX", "IXvET_EefiO", "EPPiP_R9kI2", "H3Z0zMz1LuH", "R9UTY7uy4Av", "H1-yxyxMkO", "H3Z0zMz1LuH", "R9UTY7uy4Av", "KsxmZdJmar", "2Cgc6SU9_LQ", "nips_2022_xwBdjfKt7_W", "mmnFAs8SgdL", "R9UTY7uy4Av", "H3Z0zMz1LuH", "W1xNu1uOujz", "WvVi_GeRRc", "u_fE3pJrGa9", "2Muk4sH2d7", "nip...
nips_2022_cFOhdl1cyU-	M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design	Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often lets those tasks learn better jointly. Multi-tasking models have become successful and often essential for many sophisticated systems such as autonomous driving and indoor robots. However, when deploying MTL onto those real-world ...	Accept	This paper presents a model-accelerator co-design framework to enable on-device Multi-task Learning (MTL). At the model level, customized mixture-of-expert (MOE) layers are introduced for MTL, which alleviate gradient conflict at training time and improve the efficiency at inference time via sparse activation. At the a...	train	[ "QxGckJISqnY", "Qn2UA6sG_XW", "9U1tEAVCiSK", "R0Uf74TLDrS", "Gpq8ORTV1kl", "ertzBwxe6A-H", "y9Jk4RbmnON0", "-W-Yh2m_RSD", "JIrol0AaZyV", "1yVI4Vzi6b3", "f7gb3Up7mPl", "UzA_XxIpOS", "XgORurZT_Ud", "r9cUm6ih1ZV", "G_NAXruH3I2", "L8Al9lhUbm5" ]	[ "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your response. The rebuttal has well addressed my questions. I support this paper for its novelty and solid experiments, and I will keep my original score.", " Dear Reviewer V1Gi:\n\nSince the author-reviewer discussion period will end by tomorrow, we will appreciate if you could check our response t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 4, 4, 2 ]	[ "-W-Yh2m_RSD", "Gpq8ORTV1kl", "R0Uf74TLDrS", "Gpq8ORTV1kl", "1yVI4Vzi6b3", "r9cUm6ih1ZV", "L8Al9lhUbm5", "G_NAXruH3I2", "1yVI4Vzi6b3", "r9cUm6ih1ZV", "XgORurZT_Ud", "nips_2022_cFOhdl1cyU-", "nips_2022_cFOhdl1cyU-", "nips_2022_cFOhdl1cyU-", "nips_2022_cFOhdl1cyU-", "nips_2022_cFOhdl1cyU...
nips_2022_NaZwgxp-mT_	Training Uncertainty-Aware Classifiers with Conformalized Deep Learning	Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities. In particular, they tend to be overconfident. We begin to address this problem in the context of multi-class classific...	Accept	Decision: Accept This paper extends conformal prediction techniques to multi-class classification using deep neural networks and make the training of the neural network to be aware of the conformal inference processing. The main technical contribution is a differentiable objective to approximate a CDF-based test on th...	train	[ "MWQ40uZ86p", "ky-KOEzDU0_", "luTqRfaHUX", "TOk5gBkQyGJ", "9tDeUTly8zI", "zoWfSQH0yFK", "U9iDarhnHrg", "yRVBlTBy6Mm", "pdCp6KmoHo4", "bPKi8Thg6HV", "5D1vE2-isEj", "lHiEfbN0KY_", "Vi-6KuqupI_", "O6goads0k1d", "8y9Qx6AyrM3", "5-Mf2nPzZC", "AhPtj6ROenb", "QPD2xxvu1MZ", "AYJjsj0wUDa"...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "official_rev...	[ " Thank you for the stimulating discussion! We will incorporate these ideas in the paper, and the extra analyses in the appendix. You have also successfully convinced us to look at data augmentation more closely in the near future.", " In any event, thanks for engaging. Given the amount of discussion that has ari...	[ -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, 7, 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, 4, 4, 3, 2 ]	[ "ky-KOEzDU0_", "luTqRfaHUX", "TOk5gBkQyGJ", "9tDeUTly8zI", "zoWfSQH0yFK", "U9iDarhnHrg", "yRVBlTBy6Mm", "bPKi8Thg6HV", "5-Mf2nPzZC", "5D1vE2-isEj", "DrrdSoQVjb6", "ZAbPk_ZEsu", "iAgcSKyV1GN", "8y9Qx6AyrM3", "AhPtj6ROenb", "pPEsGlpA-wZ", "QPD2xxvu1MZ", "f3XAPkRJAO_", "nips_2022_Na...
nips_2022_aPXMGv7aeOn	Compressible-composable NeRF via Rank-residual Decomposition	Neural Radiance Field (NeRF) has emerged as a compelling method to represent 3D objects and scenes for photo-realistic rendering. However, its implicit representation causes difficulty in manipulating the models like the explicit mesh representation. Several recent advances in NeRF manipulation are usually restricted ...	Accept	This paper presents a new NeRF method based on tensor decomposition. The method supports both compression and composability, while achieving similar results compared to standard NeRF models. The method does not use a neural network. Several reviewers found the paper easy to follow, the method novel & sound, and the com...	val	[ "Ik_iKtjrzE9", "lQTGB3hyhxK", "llF5ba0ciFh", "Ihwt1rGrYqd", "T19cyBMnQNc", "BkGV6zM11d", "-VABUxny1dt", "Xzn4-PdvaEV", "w2B90SL8YN", "9HEzTid0moy", "jGRqqJWY30", "aXbDFvjTpW", "pJ-TqZ6mTJH", "OC89pgR7GFs", "a3wgB6d1UB0", "Ljk2nMqWNM5", "7640BB9fdA6", "xf4TatTslIS", "DLds5Sz5WMH" ...	[ "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "...	[ " Thanks for the answer. The authors have addressed my concerns and I will keep my score the same.", " Dear reviewers, \n\nThank you all for providing valuable comments. The authors have provided detailed responses to your comments. Has the response addressed your concerns?\n\nIf you haven't, I would appreciate i...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 4, 4, 4 ]	[ "pJ-TqZ6mTJH", "nips_2022_aPXMGv7aeOn", "Ihwt1rGrYqd", "T19cyBMnQNc", "BkGV6zM11d", "-VABUxny1dt", "Xzn4-PdvaEV", "aXbDFvjTpW", "9HEzTid0moy", "OC89pgR7GFs", "nips_2022_aPXMGv7aeOn", "DLds5Sz5WMH", "xf4TatTslIS", "7640BB9fdA6", "Ljk2nMqWNM5", "nips_2022_aPXMGv7aeOn", "nips_2022_aPXMG...
nips_2022_uxc8hDSs_xh	Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?	We propose a geometric scattering-based graph neural network (GNN) for approximating solutions of the NP-hard maximum clique (MC) problem. We construct a loss function with two terms, one which encourages the network to find highly connected nodes and the other which acts as a surrogate for the constraint that the node...	Accept	All reviewers agree that the proposed approach to use the geometric scattering transform is simple and effective both computationally and in terms of the ability of the method to identify larger cliques for the max-clique problem (except perhaps for one reviewer on the last point). The work would have more impact if i...	train	[ "2TcnilNRcL8", "9C6teTzY1z", "gR4cjDRb8GB", "dEdJ7-3fqsi", "utzLmsWXyCp", "ht2Y2JgYrM1", "VkDiWor7Fru", "7JBZDph2Cyp", "M2pVf4F5PKs", "e6s2l5qH6tU", "rnQbSpk5M-6", "8Uv5539t2GC", "K8B_5_k28ves" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We are glad that some of your concerns have been addressed. \nAnd we are happy that the reviewer agrees with us on the following points:\n1. Our model is lightweight (~0.1 % parameters count) and performs well compared to previous work.\n2. We get a more noticeable benefit on the hardness dataset.\n3. Our struct...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 3 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "9C6teTzY1z", "VkDiWor7Fru", "dEdJ7-3fqsi", "utzLmsWXyCp", "ht2Y2JgYrM1", "VkDiWor7Fru", "K8B_5_k28ves", "8Uv5539t2GC", "rnQbSpk5M-6", "nips_2022_uxc8hDSs_xh", "nips_2022_uxc8hDSs_xh", "nips_2022_uxc8hDSs_xh", "nips_2022_uxc8hDSs_xh" ]
nips_2022_R5KjUket6w	CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations	Although reinforcement learning has found widespread use in dense reward settings, training autonomous agents with sparse rewards remains challenging. To address this difficulty, prior work has shown promising results when using not only task-specific demonstrations but also task-agnostic albeit somewhat related demons...	Accept	All three reviewers have elected to accept the paper, with accept ratings of 5,6,7. The reviews were thorough and demonstrated an understanding of the paper, and the authors have addressed many of the suggested edits. I like that the paper tackles the combination of parametric vs. non-parametric learning. One weaknes...	train	[ "VTzSNrtfgq", "HdPgfwAvW4v", "Y4E7Tc-9go", "c5VY3K6UApI", "CDGlNBwuBaD", "0BN5xEK3BRz", "FwB1MYBdHhf", "7xuy38Di2V", "duz0mc4-AN1", "ezoNxEp7D3_", "dGAZ7EfH08g", "-ymyYOznsIW", "8M9qlkCbnNw", "B0L9oRjZ_FG", "0zlYybwuPBm", "VJ-TiRi8O6Y" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the clarification. I will keep my rating as a result of the author-reviewer discussion.", " Thanks for the response. I have updated my rating accordingly. ", " We thank all reviewers for their valuable and insightful comments. We have updated the pdf which integrates all advice and all new exper...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "c5VY3K6UApI", "CDGlNBwuBaD", "nips_2022_R5KjUket6w", "FwB1MYBdHhf", "0BN5xEK3BRz", "B0L9oRjZ_FG", "duz0mc4-AN1", "VJ-TiRi8O6Y", "VJ-TiRi8O6Y", "0zlYybwuPBm", "0zlYybwuPBm", "B0L9oRjZ_FG", "B0L9oRjZ_FG", "nips_2022_R5KjUket6w", "nips_2022_R5KjUket6w", "nips_2022_R5KjUket6w" ]
nips_2022_lMMaNf6oxKM	Recipe for a General, Powerful, Scalable Graph Transformer	We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack ...	Accept	This paper presents a powerful, general, scalable, and linearly complex graph Transformer. Positional encodings and structural encodings are redefined with local, global, and relative categories, and an attempt has been made to include local and global focus attentions in a graph Transformer. All of the reviewers ackn...	train	[ "Z7ikPmJiNuB", "WORCGPyI-8Z", "G9ppKtmRqw3", "jd0mwxwDK9i", "fDxtu-BLC8U", "F1mTGUo1aiw", "pL0DPsUC8Vr", "WnutjwaE7yO", "gxzpCWCPgjI", "Cvvx24DrLMw", "Y7t6COO-OLv" ]	[ "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer 719v,\n\nThank you again for your review and comments! We have tried our best to address your questions and accordingly we revised the paper.\n\nAs we are near the end of the discussion period, we sincerely hope that you could provide us with a feedback on our revision and whether it has addressed y...	[ -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "pL0DPsUC8Vr", "F1mTGUo1aiw", "jd0mwxwDK9i", "Y7t6COO-OLv", "Y7t6COO-OLv", "Cvvx24DrLMw", "gxzpCWCPgjI", "nips_2022_lMMaNf6oxKM", "nips_2022_lMMaNf6oxKM", "nips_2022_lMMaNf6oxKM", "nips_2022_lMMaNf6oxKM" ]
nips_2022_NjeEfP7e3KZ	Revisiting Heterophily For Graph Neural Networks	Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their performance compared...	Accept	In this submission, the authors revisit the existing homophily metrics and point out the limitations of existing metrics in analyzing the performance of GNN. Then the authors propose a novel homophily metric that specifics harmful heterophily, and further propose Adaptive Channel Mixing (ACM) framework to handle the ha...	val	[ "MZkSJe3OQ0S", "TR2PXzxoVAy", "ZYDvj584PDZ", "NENn38_ftnP", "0UTujAXuPo", "tPxNZ1zrlF", "OD-BvmeR1Yg", "eFqy0OjZfUmS", "eoAPjWeJ3PK", "0T3p2SJtJ6X", "ohrJRr5JTs", "EwRKKMIkumM", "YrSSrsZH2vk", "GggAs0KKnv2", "vWFdN9AHFED", "4XVcI25MMH", "DbZnBG5YmPX", "6zOWqKyMDIE" ]	[ "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer 9hm2,\n\nThanks for spending your time evaluating our paper. Since you have negative rating on our paper, we would like to know if you still have any question left to discuss. If your concerns are addressed, we respectfully request a raise of your rating. We will appreciate that.\n\nAuthors", " \n...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 3, 4, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 3, 5 ]	[ "0T3p2SJtJ6X", "ZYDvj584PDZ", "eFqy0OjZfUmS", "6zOWqKyMDIE", "tPxNZ1zrlF", "OD-BvmeR1Yg", "DbZnBG5YmPX", "eoAPjWeJ3PK", "4XVcI25MMH", "ohrJRr5JTs", "vWFdN9AHFED", "YrSSrsZH2vk", "GggAs0KKnv2", "nips_2022_NjeEfP7e3KZ", "nips_2022_NjeEfP7e3KZ", "nips_2022_NjeEfP7e3KZ", "nips_2022_NjeEf...
nips_2022_2vYmjZVT29T	Hamiltonian Latent Operators for content and motion disentanglement in image sequences	We introduce \textit{HALO} -- a deep generative model utilising HAmiltonian Latent Operators to reliably disentangle content and motion information in image sequences. The \textit{content} represents summary statistics of a sequence, and \textit{motion} is a dynamic process that determines how information is expressed ...	Accept	This paper proposes a novel type of variational auto encoder, referred to as HELO. The latent space is decomposed into a content space and a motion space, and the main contribution is the proposal to model the motion space using Hamiltonian dynamics. All reviewers agree that the idea of using Hamiltonian dynamics is in...	train	[ "VvDIC94tiV4", "zEsy_ewT0F", "TEThRlTKZqA", "PmzjpO3D_Dy", "lFN4oq4ycv", "jg-48zG-hvU", "vYmmj1SQHsM", "4Hkyzaumbu", "522HjYJyrpx", "AZ6bD7BOLBa", "SB_BBFqS0Jn", "Xy5b3_kPEjz" ]	[ "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We are very thankful for your detailed feedback on the paper and for responding to the rebuttal. We appreciate it a lot. We replied to your reviews to the best of our effort and promise to incorporate feedback in the final version. Due to limited time and computational issues pointed out in our rebuttal, we consi...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3 ]	[ "PmzjpO3D_Dy", "Xy5b3_kPEjz", "AZ6bD7BOLBa", "lFN4oq4ycv", "4Hkyzaumbu", "SB_BBFqS0Jn", "Xy5b3_kPEjz", "SB_BBFqS0Jn", "AZ6bD7BOLBa", "nips_2022_2vYmjZVT29T", "nips_2022_2vYmjZVT29T", "nips_2022_2vYmjZVT29T" ]
nips_2022_upuYKQiyxa_	Optimizing Relevance Maps of Vision Transformers Improves Robustness	It has been observed that visual classification models often rely mostly on spurious cues such as the image background, which hurts their robustness to distribution changes. To alleviate this shortcoming, we propose to monitor the model's relevancy signal and direct the model to base its prediction on the foreground ...	Accept	Initially, this paper received positive reviews. The rebuttal addresses the remaining concerns. All reviewers feel that the contributions of this work are sufficient to merit its acceptance. The area chair agrees with the reviewers and recommends it be acecpted at this conference.	train	[ "GQjSo_9NJJ8", "Ebst6DGCMB3", "NrdACp8KqaY", "-hjJkg92TTE", "U4SEw6We6U9", "MFmddy_DvHa", "OQR48A2JnZ", "Xs2vHRAosS-", "Kx1M1YXIIzL", "FsFA3lL8ei1", "8WqioCggj9t", "NKfGWmTJ0jD", "U4C0GC2XR5w", "-1brURHx6r0", "sXv9iTSb2PM", "U0e_CX4xBQ", "l4I5IgaCUxp", "ZTjsOyDf4fa", "t2r6oVx4fI"...	[ "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank the authors for an extended discussion. \n\n1. Whether supervising GAE faithfully changes the inner mechanisms of the Transformer\n\nAfter going through the references [8] on GAE and the ICML 2022 paper [35] evaluating multiple explanation methods on attention-based models, I'm convinced that GAE is indeed ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3, 4 ]	[ "Ebst6DGCMB3", "NrdACp8KqaY", "OQR48A2JnZ", "U4SEw6We6U9", "Xs2vHRAosS-", "U4C0GC2XR5w", "Kx1M1YXIIzL", "ZTjsOyDf4fa", "FsFA3lL8ei1", "t2r6oVx4fI", "NKfGWmTJ0jD", "ZTjsOyDf4fa", "l4I5IgaCUxp", "sXv9iTSb2PM", "U0e_CX4xBQ", "nips_2022_upuYKQiyxa_", "nips_2022_upuYKQiyxa_", "nips_2022...
nips_2022_eN2lQxjWL05	Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses	Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better \textit{on that specific task}. The main technical challenge associated with DFL is that it requires being able to differentiate through the optimization ...	Accept	This paper considers the problem of making decision-focused learning (DFL) more usable for both researchers and practitioners. It proposes a novel approach referred to as locally-optimized decision losses (LODL) which learns the parameters of surrogate intermediate losses to match the decision loss. Experimental result...	train	[ "2owQcx51Kvh", "apTD57W2vRm", "I1tcvBOC8rQ", "Uk-KbbW9RB-", "eizXYGZ1aaG", "l8owqhJhodu", "eLMqnbR6OR1", "d10zsF4V6E", "RBxoXKL1mJn", "ef8CHnc-IyK", "7XbTNM8splV", "udvM9gpWgi", "tZZHvz4aU3J", "h87cvf_HOts", "HPeR1pLd-kp", "Xz2GmevwoTH", "fXQUc2hpLJk", "fTYNTAYphf7", "h3kg09N_nYk...	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_re...	[ " Thank you for your response! I appreciate that you decide to include scalability results in the camera-ready and promise to clarify the issues I mentioned. I suggest the authors can also discuss more scalability in the camera-ready. Incorporating information in the common response above will be helpful. ", " I ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 6, 5, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 3, 4 ]	[ "HPeR1pLd-kp", "I1tcvBOC8rQ", "Uk-KbbW9RB-", "eizXYGZ1aaG", "l8owqhJhodu", "eLMqnbR6OR1", "d10zsF4V6E", "RBxoXKL1mJn", "ef8CHnc-IyK", "7XbTNM8splV", "udvM9gpWgi", "tZZHvz4aU3J", "Xz2GmevwoTH", "h3kg09N_nYk", "pxgQ3TLdWR", "rcOMSCpWM-", "5CFbirRWnup", "nips_2022_eN2lQxjWL05", "nip...
nips_2022_RuNhbvX9o9S	Learning General World Models in a Handful of Reward-Free Deployments	Building generally capable agents is a grand challenge for deep reinforcement learning (RL). To approach this challenge practically, we outline two key desiderata: 1) to facilitate generalization, exploration should be task agnostic; 2) to facilitate scalability, exploration policies should collect large quantities of ...	Accept	This paper proposes a method to learn world models without rewards, using a collection of agents that explore an environment. The key idea is to maximize diversity between the trajectories collected by the agents to obtain a good world model, with an emphasis on being as efficient as possible. The authors present some ...	train	[ "ue6iCgBkEU", "WCJsWhlmMPi", "swLWDA0UhV", "UA1K40tRCL", "ITmuKmq8RQI", "45CkpvW9Qsn", "BpbH-UHuNwu", "x_qN4mPlRvN", "RP74IK81ykJ", "M-TanDVG7f", "ZAVxBnnyv0G", "blF009OGmuQS", "nBarta-SX0K", "AW4K0gjkyRi", "Y-ChXaRtMl0h", "5D4Mm1Z8SDH", "_wgHiTE2T9", "o9FsPC3xnh", "qVLmXF8jjjo",...	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author",...	[ " Hi Reviewer zCLK,\n\nThank you for coming back, and for increasing your score to a \"weak accept\". It seems your only remaining concern is regarding the use of rewarding episodes as a metric for evaluating exploration. We want to reiterate that it is just being used as a proxy for depth of exploration, which we ...	[ -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, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4, 4 ]	[ "WCJsWhlmMPi", "45CkpvW9Qsn", "RP74IK81ykJ", "ITmuKmq8RQI", "blF009OGmuQS", "jJTTnPmtkYE", "nips_2022_RuNhbvX9o9S", "ZAVxBnnyv0G", "5D4Mm1Z8SDH", "ZAVxBnnyv0G", "o9FsPC3xnh", "nBarta-SX0K", "ASx3SgtsbG", "Y-ChXaRtMl0h", "nips_2022_RuNhbvX9o9S", "_wgHiTE2T9", "96JD7H-SwU1", "qVLmXF8...
nips_2022_3-3XMModtrx	Is a Modular Architecture Enough?	Inspired from human cognition, machine learning systems are gradually revealing advantages of sparser and more modular architectures. Recent work demonstrates that not only do some modular architectures generalize well, but they also lead to better out of distribution generalization, scaling properties, learning speed,...	Accept	This study investigates modular architectures, their properties, and their effectiveness in a class of synthetic yet informative scenarios. The reviewers unanimously recommend this paper for acceptance, some of them with high praise, and I enjoyed it as well: I suspect it will be read widely and have a lasting impact o...	train	[ "GPoKbSvWDz", "rVwLFfr0H8W", "TN4WSe8uXl8", "OuWRrLu_nJS", "bKGw0-hMRe8", "3Jt3tGS5QSG", "6jJGQigVlXj", "Eq_-dEyaJv6", "j5ax1FNU6Ce", "eyhEivyrbR", "dGF0yxJV5q-", "RGh9wPKxDg2", "WXsy9qgG71J", "byFbhK9cF12", "IEF5IW6-XNk", "I5_kIOTV4rr", "PMssHUCyC5B", "5K7GuKyQmws", "lGSjptQ_p7"...	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_...	[ " Thanks so much for your rebuttal! I understand your points, and believe this paper to have merits - I think it should be accepted, and my score currently reflects that! \n\nHoping that the other reviewers can similarly see the merits of this work!", " We thank the reviewer for their time and response and are gr...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 7, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "bKGw0-hMRe8", "TN4WSe8uXl8", "j5ax1FNU6Ce", "WXsy9qgG71J", "RGh9wPKxDg2", "j5ax1FNU6Ce", "Eq_-dEyaJv6", "dGF0yxJV5q-", "eyhEivyrbR", "mvCLvBUsdHz", "ALqfTKrmN2D", "lGSjptQ_p7", "5K7GuKyQmws", "IEF5IW6-XNk", "nips_2022_3-3XMModtrx", "PMssHUCyC5B", "nips_2022_3-3XMModtrx", "nips_202...
nips_2022_68EuccCtO5i	Differentially Private Model Compression	Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models for many downstream Natural Language Processing (NLP) tasks while simultaneously guaranteeing differential privacy. The inference cost of t...	Accept	This work proposes and empirically evaluates algorithms for compressing and fine-tuning a large model for a downstream task, while satisfying DP for the downstream task training data. The set up is the following: we have a large pre-trained language model such as BERT. We would like fine-tune it for a task using a data...	train	[ "jJfhpv_pf0X", "JHTD5yV_iHg", "yMNwKVRfgq", "ZWGZAEa9xcF", "76WUyEhFgZC", "AUJlrJ2HuP", "4IsA7kinovg", "yF8J6SFbPTl", "nJAkJ_m5l9w", "wEf-N-k5xC6", "JIXOX-cBW_is", "uGejnq1vRox", "4S815jBCJjY", "W3sSdPAmGJ", "e3fUoSTkCwL", "4J6vRMl4iV", "L7u1VHSTEqO", "SaGpF500-N8", "H1vv3AagDt",...	[ "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", ...	[ " We thank you for participating in the discussions. We will include experiments with epsilon = 1 in the future revisions of the paper. We will add more discussions on pros/cons of our approach DistillBERT (or any pre-trained compressed models) and elaborate on where our work is applicable. We appreciate your tim...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 5, 4 ]	[ "76WUyEhFgZC", "ZWGZAEa9xcF", "4IsA7kinovg", "W3sSdPAmGJ", "AUJlrJ2HuP", "yF8J6SFbPTl", "nJAkJ_m5l9w", "H1vv3AagDt", "e3fUoSTkCwL", "JIXOX-cBW_is", "L7u1VHSTEqO", "nips_2022_68EuccCtO5i", "nips_2022_68EuccCtO5i", "jUYHYJk5N9L", "4J6vRMl4iV", "QePr2azemVc", "SaGpF500-N8", "30wS-tc1I...
nips_2022_wwyiEyK-G5D	REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering	This paper revisits visual representation in knowledge-based visual question answering (VQA) and demonstrates that using regional information in a better way can significantly improve the performance. While visual representation is extensively studied in traditional VQA, it is under-explored in knowledge-based VQA eve...	Accept	The paper incorporates regional features to better retrieve relevant knowledge and makes direct use of the visual signal in answer prediction whereas the previous SOTA methods simply rely on the retrieved knowledge for the final prediction. The proposed method outperforms SOTA on OK-VQA by a large margin effectively sh...	train	[ "fg6uLQgwIc", "rTwYoamIfPv", "COosSKOeia8", "lhbnKiYqRp", "SCiG3BBEKWz", "QrBXFdWHl0l", "w7wPOxtyf1K", "6Tth2lzVXmk", "n922P0utmR_", "9TpWi19Sg8h", "3gtcjzqkPSt", "ZzZfeN79eul", "duA_wKEoHWU", "84ibeUA0RBb", "HfmTnvDhPdST", "8IHy-eXRQ39", "H7lKXbcBSh_", "TScHQ1ueEom", "D3bIQfmZAj...	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer 5EMT, thanks for your effort again! We are happy that our rebuttal well addressed your concerns!\n\n", " Dear reviewer uNM2, thanks for your effort again! We are happy that our rebuttal well addressed your concerns!", " Thank you for the detailed author response. After reading all the reviews an...	[ -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, 4, 4, 5 ]	[ "COosSKOeia8", "lhbnKiYqRp", "QrBXFdWHl0l", "duA_wKEoHWU", "6Tth2lzVXmk", "84ibeUA0RBb", "9TpWi19Sg8h", "n922P0utmR_", "8IHy-eXRQ39", "duA_wKEoHWU", "H7lKXbcBSh_", "nips_2022_wwyiEyK-G5D", "H7lKXbcBSh_", "TScHQ1ueEom", "D3bIQfmZAjK", "D3bIQfmZAjK", "nips_2022_wwyiEyK-G5D", "nips_20...
nips_2022__keb_XuP5oI	Generative Neural Articulated Radiance Fields	Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, however, have not been demonstrated for human bodies and the generated radiance fields of existing frameworks are not directly editable, limi...	Accept	The reviewers all recognize the quality of the work, particularly technical soundness and quality of the experimental setting and there is a clear consensus for acceptance. I ask the authors to address the reviewers concerns, particularly clear up any confusion in the manuscript and better analysis of the synthesis res...	train	[ "bsC3t18VNcF", "Jbrbh3nbrXH", "lQQqgVkbJx6", "COSGoRk_ohr", "2H-r48VbKV_", "-e5OmiEMLHf" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank Reviewer UgRe for their time spent reviewing and commenting on our work. We appreciate the note that integrating advanced radiance field implementation architectures, generative models, and articulation is not trivial and is an important contribution for future applications.\n\n**Lack of technical contri...	[ -1, -1, -1, 6, 7, 7 ]	[ -1, -1, -1, 5, 5, 4 ]	[ "-e5OmiEMLHf", "2H-r48VbKV_", "COSGoRk_ohr", "nips_2022__keb_XuP5oI", "nips_2022__keb_XuP5oI", "nips_2022__keb_XuP5oI" ]
nips_2022_nYrFghNHzz	Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion	Learning an optimal Individualized Treatment Rule (ITR) is a very important problem in precision medicine. This paper is concerned with the challenge when the number of treatment arms is large, and some groups of treatments in the large treatment space may work similarly for the patients. Motivated by the recent develo...	Accept	This paper proposes a method for learning the optimal individualized treatment rule (ITR). The proposed approach uses a fusion penalty term that encourages clustering between treatments. A dendrogram of the treatments is generated by running the proposed algorithm using different tuning parameters as a solution path. T...	train	[ "gcCAE29yv7U", "3K07sqDpaP", "3FsS3ZCMUk", "JAbYHmVFiIj", "pjtNrbvRTFX", "MTfm1qNn6IL", "4cv__hfF139", "Avcd5ZRyI4J", "h0tDYJjkh6V", "vGgzTaa1pZi", "P09UwO0zaG9", "6PRfNw-4rRG" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We appreciate your response and acknowledgement of our clarifications for the paper. Thanks for your further suggestions about the group lasso step. As you suggested, to better clarify the group lasso step, we will add some results in the supplements.", " Thank you for your comments clarifying the PDX data and ...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3, 4 ]	[ "3K07sqDpaP", "4cv__hfF139", "JAbYHmVFiIj", "MTfm1qNn6IL", "6PRfNw-4rRG", "P09UwO0zaG9", "vGgzTaa1pZi", "h0tDYJjkh6V", "nips_2022_nYrFghNHzz", "nips_2022_nYrFghNHzz", "nips_2022_nYrFghNHzz", "nips_2022_nYrFghNHzz" ]
nips_2022_sipwrPCrIS	Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks	We analyze feature learning in infinite-width neural networks trained with gradient flow through a self-consistent dynamical field theory. We construct a collection of deterministic dynamical order parameters which are inner-product kernels for hidden unit activations and gradients in each layer at pairs of time points...	Accept	This paper analyzes a dynamical mean field theory that describes feature learning via gradient flow for certain infinite-width neural networks. Self-consistent equations for the order parameters characterizing the dynamics are presented and methods for approximate numerical evaluation are discussed. Overall, this is a ...	train	[ "CqgRDH3II06", "ZytGDh-hIIQ", "nmoQp2HdAf3", "-tJyXXATcHW", "4F7SlhCmcAV", "fN2B_AKwdnQ8", "9C89tFueSO6l", "SyKL5dbsmT9D", "eZzX_uTZ2n2", "hfK6alENLU7i", "VOnvByZFV5T", "kt3ed3BDw8C", "ge5iRWS860y", "PFDy_5LUVp", "NFRhnntgIlH" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your detailed responses. I appreciate that the authors make an effect to further address my concerns. Thus I decide to keep my score and recommend acceptance.", " I'm grateful to the authors for their detailed response to all my questions. I still feel confident that the paper should be accepted a...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "eZzX_uTZ2n2", "9C89tFueSO6l", "NFRhnntgIlH", "4F7SlhCmcAV", "fN2B_AKwdnQ8", "NFRhnntgIlH", "SyKL5dbsmT9D", "VOnvByZFV5T", "hfK6alENLU7i", "PFDy_5LUVp", "ge5iRWS860y", "nips_2022_sipwrPCrIS", "nips_2022_sipwrPCrIS", "nips_2022_sipwrPCrIS", "nips_2022_sipwrPCrIS" ]
nips_2022_GFgjnk2Q-ju	Parametrically Retargetable Decision-Makers Tend To Seek Power	If capable AI agents are generally incentivized to seek power in service of the objectives we specify for them, then these systems will pose enormous risks, in addition to enormous benefits. In fully observable environments, most reward functions have an optimal policy which seeks power by keeping options open and stay...	Accept	The paper studies an alignment problem - that of agent seeking powers, and extends previous work (Turner, 2021 - which showed that optimal policies seek power, to demonstrate more generally that parametrically retargetable policies (policies whose 'target' can be changed by simple change of hyperparameters of the agent...	val	[ "G05hans_6VP", "slJUEDpWMy", "mdC2TNY4jTb", "90k3DpvIN6t", "77SNZSYqtom", "DFCtkICsWWY", "N9QKPkIEPhX", "snEWbolGK4T", "eu86zhKrGkw", "uRhBaDdOQaf" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for gathering some of the feedback on clarity. Here are some details concerning our current plan:\n> \"Overall, the writing is a bit light on “scaffolding”\" \n\nIn the beginning of each section, we will add signposting and scaffolding. For example, at the beginning of section 3, we will write: \n\n\"Se...	[ -1, -1, -1, -1, -1, -1, 6, 4, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "slJUEDpWMy", "nips_2022_GFgjnk2Q-ju", "uRhBaDdOQaf", "snEWbolGK4T", "eu86zhKrGkw", "N9QKPkIEPhX", "nips_2022_GFgjnk2Q-ju", "nips_2022_GFgjnk2Q-ju", "nips_2022_GFgjnk2Q-ju", "nips_2022_GFgjnk2Q-ju" ]
nips_2022_Z6BFQqzwuS4	Bayesian Persuasion for Algorithmic Recourse	When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a favorable decision. In many practical situations, the underlying assessment rule is deliberately kept secret to avoid gaming and maintain comp...	Accept	The paper formulates the problem of algorithmic recourse under partial transparency as a Bayesian persuasion game. It is shown that the decision-maker can design an incentive-compatible action signaling strategy with guarantees that both the decision-maker and decision-subjects are not worse off in terms of expected ut...	train	[ "bdaSvZFxHGz", "E6upms1jdM_", "ccG87ebyiv", "eWL4r6_s-nt", "sCZLODtXMUD", "MWV8K2FV7wz", "sTxT9JKevRp", "G_z9yxFcHhz", "T0tHVWXIOT0", "aQd1HOVxSgW", "EYagEYKJFUw", "dk4HwsacppA", "o76UwI-aDky5", "5Sz35CQfFMk", "uRPVNdAD9lu", "jJ7nPtX0LPO", "mbqQE1zg0_F", "YSNUauR2XXy", "nMuA8WRQ1...	[ "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_re...	[ " I think there is a distinction. In your model, the sender chooses to not disclose full information not because they are not allowed to but because they are better off not doing that. This are no restrictions on how much information the decision-maker can disclose in your model, and the case with such restrictions...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3, 4 ]	[ "ccG87ebyiv", "dk4HwsacppA", "sCZLODtXMUD", "MWV8K2FV7wz", "5Sz35CQfFMk", "uRPVNdAD9lu", "mbqQE1zg0_F", "T0tHVWXIOT0", "aQd1HOVxSgW", "EYagEYKJFUw", "nMuA8WRQ1R_", "o76UwI-aDky5", "YSNUauR2XXy", "mbqQE1zg0_F", "jJ7nPtX0LPO", "nips_2022_Z6BFQqzwuS4", "nips_2022_Z6BFQqzwuS4", "nips_2...
nips_2022_0IywQ8uxJx	Graph Neural Networks as Gradient Flows	Dynamical systems minimizing an energy are ubiquitous in geometry and physics. We propose a gradient flow framework for GNNs where the equations follow the direction of steepest descent of a learnable energy. This approach allows to analyse the GNN evolution from a multi-particle perspective as learning attractive and ...	Reject	The authors present a graph neural network for heterophilic data using gradient flows. The proposed architecture is quite simple...large sections of the architecture are fully linear dynamical systems rather than neural networks, and still achieve roughly SotA results on standard graph learning benchmarks. There was a ...	train	[ "lBW95jdWgB", "3is4MgxD7Nj", "tSTeYcDuGRJ", "-jq6jCQJF06", "qoUYZ4bc3rTm", "kku2Q_X6hov", "O675c48meXv", "a71FXRX1YOJ", "J7aG-6fvpaU", "49yUH8Eq2A0", "i1txvnK97-p", "uJnCGKuejv", "7aZKQESKNqX-", "mXjLJIdiNTQ", "t6WbmVZAMSO", "l0_mNZ4v0yI", "K9JmBtEsalG", "HgeILay1TX9", "lgkAtxhil...	[ "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer",...	[ " Thank you for the response and finding our contribution significant. Some final points:\n\n- The analysis of non-linear activations in Proposition 3.2 and the whole Section E in the SM is quite novel in the GNN literature and in fact as you acknowledged it is much more common to have theoretical analysis restrict...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 4, 4 ]	[ "tSTeYcDuGRJ", "-jq6jCQJF06", "a71FXRX1YOJ", "7aZKQESKNqX-", "kku2Q_X6hov", "uJnCGKuejv", "HgeILay1TX9", "J7aG-6fvpaU", "49yUH8Eq2A0", "F-Oh_ruEYzA", "R2n3t2ffnkS", "zkVQHhU_NoS", "mXjLJIdiNTQ", "t6WbmVZAMSO", "l0_mNZ4v0yI", "K9JmBtEsalG", "jxDn0fiTC3U", "lgkAtxhil1N", "6ayQowmlD...
nips_2022_kRgOlgFW9aP	Thompson Sampling Efficiently Learns to Control Diffusion Processes	Diffusion processes that evolve according to linear stochastic differential equations are an important family of continuous-time dynamic decision-making models. Optimal policies are well-studied for them, under full certainty about the drift matrices. However, little is known about data-driven control of diffusion proc...	Accept	This paper proposes and analyzes a Thompson-Sampling based method to learn to control continuous-time linear systems when the costs are quadratic. The authors first propose an algorithm that guarantees stabilization of the diffusion process and then give a second, Thompson-Sampling-based method with regret bounds and e...	train	[ "Amahqzs7Yb_", "FMp4LRUhR3", "1HTqNnGBbqF", "nf_hXTfFNp9", "8AwzIScQmq", "RFL5JD63BZ", "kUfI8F-O0j", "4mrxfdFoWO" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the feedback. The authors will be happy to provide point-by-point explanations to all questions of the reviewer.\n", " Thanks for the deep conceptual and technical comments the reviewer correctly provided. The authors appreciate the comprehensive review and the constructive comments, are grateful tha...	[ -1, -1, -1, -1, 6, 6, 5, 7 ]	[ -1, -1, -1, -1, 1, 4, 4, 4 ]	[ "8AwzIScQmq", "4mrxfdFoWO", "kUfI8F-O0j", "RFL5JD63BZ", "nips_2022_kRgOlgFW9aP", "nips_2022_kRgOlgFW9aP", "nips_2022_kRgOlgFW9aP", "nips_2022_kRgOlgFW9aP" ]
nips_2022_lJHkZbX6Ic1	Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations	There have been multiple works that try to ascertain explanations for decisions of black box models on particular inputs by perturbing the input or by sampling around it, creating a neighborhood and then fitting a sparse (linear) model (e.g. LIME). Many of these methods are unstable and so more recent work tries to fin...	Accept	The paper attacks the problem of how to define "local" when generating local linear explanations (e.g. LIME). Forming the linear approximation using multiple points, the proposed method attempts to balance robustness of the explanation vs its specificity. The approach of using multidimensional piecewise linear segmen...	train	[ "H-0buaAJa26", "7IUw3PI2fSl", "uHmNV6lxChO", "_GB8sS_eMVc", "4Mvj2eHdn2C", "kqBSHt8kjt", "8hOFzXRmqoH", "6VnykH2hGeI", "wVZtQBqzpHnI", "NPYMtJUPLxW", "dv-84d3z5PC", "8SHaeoRw0D3", "dmuUikECKPg" ]	[ "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Since the response period ends tomorrow. Please let us know if you have any further questions/concerns. Thank you.", " We are glad that most of your concerns have been addressed. Yes, most other methods sample in input space. Even manifold methods such as MeLime which sample in the latent space end up decoding ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 3, 2, 3 ]	[ "8hOFzXRmqoH", "uHmNV6lxChO", "4Mvj2eHdn2C", "6VnykH2hGeI", "dmuUikECKPg", "8SHaeoRw0D3", "dv-84d3z5PC", "NPYMtJUPLxW", "nips_2022_lJHkZbX6Ic1", "nips_2022_lJHkZbX6Ic1", "nips_2022_lJHkZbX6Ic1", "nips_2022_lJHkZbX6Ic1", "nips_2022_lJHkZbX6Ic1" ]
nips_2022_ZQcpYaE1z1r	A Quantitative Geometric Approach to Neural-Network Smoothness	Fast and precise Lipschitz constant estimation of neural networks is an important task for deep learning. Researchers have recently found an intrinsic trade-off between the accuracy and smoothness of neural networks, so training a network with a loose Lipschitz constant estimation imposes a strong regularization, and c...	Accept	All the reviewers agree that the paper is novel and interesting and it should be accepted. Please take into account the reviewers' comments while preparing the camera-ready version, particularly the ones on the clarity of the paper.	train	[ "hEjhb-6ix8W", "df7EmIJvtbp", "YK2UDZW9qeH", "QJZyQAD96m6", "lT7Is6FsHFN", "tU_-GkdbeB", "EEG_ckDPNLN", "akn03o95q-H", "yypPzzRiG6q", "keIXIrqAIOM", "Q-tJAJ0gag", "I38kHNA-GAI", "omxrrEh-xwN", "VXZkdm7Vrzb", "GRw98_X-XwB", "T_L6oMlPaQ", "UEd1w89AlHM", "3qDyjcn06tx", "oZ9eIBt2EKX"...	[ "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_re...	[ " We appreciate your continuing engagement in the discussion. \n- Changing the underlying geometry: yes.\n\n- Evaluation in the context of adversarial robustness: Thanks for the clarification. Given the limited time remaining for the discussion, we would not be able to provide additional experimental results. For e...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 5, 8, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 2, 2, 3 ]	[ "QJZyQAD96m6", "VXZkdm7Vrzb", "tU_-GkdbeB", "Q-tJAJ0gag", "akn03o95q-H", "omxrrEh-xwN", "yypPzzRiG6q", "omxrrEh-xwN", "I38kHNA-GAI", "nips_2022_ZQcpYaE1z1r", "3qDyjcn06tx", "oZ9eIBt2EKX", "1RefYRy8i2g", "UEd1w89AlHM", "T_L6oMlPaQ", "nips_2022_ZQcpYaE1z1r", "nips_2022_ZQcpYaE1z1r", ...
nips_2022_foNVYPnQbhk	SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration	Next Best View computation (NBV) is a long-standing problem in robotics, and consists in identifying the next most informative sensor position(s) for reconstructing a 3D object or scene efficiently and accurately. Like most current methods, we consider NBV prediction from a depth sensor like Lidar systems. Learning-bas...	Accept	The paper describes an approach to next-best-view (NBV) planning for the reconstruction of large-scale 3D scenes using depth sensors. The proposed framework models the scene using a probabilistic occupancy map and chooses the next-best-view as the free camera pose that maximizes the gain in surface coverage. Integral t...	train	[ "7u4h6Svx6R", "7B51TekbLmU", "cLepXPm7f_", "KVoyXjAEfES", "4UZ-O36ErK3", "sRyYvef_vn", "swIq_bomfa", "V40GNEqt3r1", "yuBntlB-rcc", "VrvROZ7Mt7c" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks to the authors for the rebuttle. Many of the doubts are clear. Please include the monte carlo and neural aggregator discussion briefly in the paper.", " In this third comment, we would like to answer the last questions asked by the reviewer.\n\n**Q7: L292: ‘Model suffers ... to compute coverage gain’ - d...	[ -1, -1, -1, -1, -1, -1, -1, 8, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 4, 3, 3 ]	[ "4UZ-O36ErK3", "VrvROZ7Mt7c", "VrvROZ7Mt7c", "VrvROZ7Mt7c", "yuBntlB-rcc", "yuBntlB-rcc", "V40GNEqt3r1", "nips_2022_foNVYPnQbhk", "nips_2022_foNVYPnQbhk", "nips_2022_foNVYPnQbhk" ]
nips_2022_lxdWr1jN8-h	Integrating Symmetry into Differentiable Planning	We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms, specifically on 2D robotic path planning problems: navigation and manipulation. We first formalize the idea from Value Iteration Networks (VINs) on using convolutional networks for path planni...	Reject	The paper addresses path planning with RGB inputs by leveraging the workspace symmetry. To that end, the authors propose a end-to-end differentiable planner that builds on top of VINs and evaluate the method of several 2D-grid planning tasks. The reviewers recognized that the method presents a performance improvement...	train	[ "0VVFxi3E5p7", "-py5oH4mgs6", "k786IkHKBhC", "fqtMB7jIJgt", "mpILW2Ar6bq", "zQgRMQ00pdC", "aT_jLsUgJH", "9N3ySU9xux", "u7thZdG07nV", "EWe-2DSsXUo", "phH-_8k8DKu", "FCQZ4JEdkg0", "ggbLYM9CKNO", "PoRrJJt9VjR", "BkMT4Vef6nz", "s-zxjhwEIAI" ]	[ "author", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " As a kind reminder, additional to the first revision on adding pseudocode and a new experiment, we just add a new intuitive version of the technical sections (method + framework) in appendix Section D, which is written from scratch and contains minimal terminology for equivariant networks / steerable CNNs. We hop...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 1, 2 ]	[ "BkMT4Vef6nz", "PoRrJJt9VjR", "zQgRMQ00pdC", "aT_jLsUgJH", "zQgRMQ00pdC", "EWe-2DSsXUo", "nips_2022_lxdWr1jN8-h", "BkMT4Vef6nz", "s-zxjhwEIAI", "s-zxjhwEIAI", "PoRrJJt9VjR", "PoRrJJt9VjR", "BkMT4Vef6nz", "nips_2022_lxdWr1jN8-h", "nips_2022_lxdWr1jN8-h", "nips_2022_lxdWr1jN8-h" ]
nips_2022_jQR9YF2-Jhg	Respecting Transfer Gap in Knowledge Distillation	Knowledge distillation (KD) is essentially a process of transferring a teacher model's behavior, e.g., network response, to a student model. The network response serves as additional supervision to formulate the machine domain, which uses the data collected from the human domain as a transfer set. Traditional KD method...	Accept	This paper analyzes the way in which most previous knowledge distillation methods violate IID assumptions and it aims to address the drop in performance on student models through this analysis. The paper proposes an Inverse Probability Weighting Distillation (IPWD) technique, derived in part through a causal analysis o...	train	[ "LI5cWy6Bi0x", "MZi-KlZqo9i", "yMbQ1tnXxA", "pVcIn0Z7GS", "JT9Yjei1eDd", "AhklVs9q4xe", "6Jrp6ou7CTU", "-c8Bbfr-sN", "hjqzjrnpXUe", "UjsqdMlWrw", "lLuoU59rfLJ", "sJYLD78tLv", "YldNQ3NtwQ", "vgTzGNqb8mz", "toqKWM5xUyy", "clergAPJ-04", "USK2rArPAaD", "z-bNO_kiBJJ" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the acknowledgement of our responses and for upgrading the rating! For your remaining concerns, we would like to summarize the motivation, contributions (especially the technical contribution of propensity score estimation), and empirical performance on ImageNet in the following:\n\n* **Motivati...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 3 ]	[ "MZi-KlZqo9i", "YldNQ3NtwQ", "pVcIn0Z7GS", "toqKWM5xUyy", "-c8Bbfr-sN", "6Jrp6ou7CTU", "vgTzGNqb8mz", "z-bNO_kiBJJ", "USK2rArPAaD", "clergAPJ-04", "nips_2022_jQR9YF2-Jhg", "z-bNO_kiBJJ", "z-bNO_kiBJJ", "USK2rArPAaD", "clergAPJ-04", "nips_2022_jQR9YF2-Jhg", "nips_2022_jQR9YF2-Jhg", ...
nips_2022_mfxq7BrMfga	Generalized One-shot Domain Adaptation of Generative Adversarial Networks	The adaptation of a Generative Adversarial Network (GAN) aims to transfer a pre-trained GAN to a target domain with limited training data. In this paper, we focus on the one-shot case, which is more challenging and rarely explored in previous works. We consider that the adaptation from a source domain to a target domai...	Accept	This paper focuses on the one-shot domain adaption of GAN model. The idea of disentangling style and entity transfer is simple and effective. The meta-reviewer recommends acceptance of the paper, and the authors are encouraged to take the reviews into consideration when preparing a final version of the paper.	train	[ "hsiD26gpvdC", "cAE_szxsbrJ", "QeQy-Up1NEF", "wj81F2gCTUN", "Q407KHuMRTm", "jqrXa4XzR6u", "u_zgrZoJ53P", "0jMWltjrp7e", "HL6Z2D41woH", "GNfMuIxERD", "uzAn-WduQh6", "E03tlvtrlRW", "gQ3-gX4tK3m", "ACwlD9gVT3C" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The author addressed most of my concerns. Thus, I tend to raise my score.", " Thank you for answers and extra experiments. Most of my concerns were addressed and I am raising the score accordingly. ", " __Q4. Where can see other methods produce artifacts when the entities are big?__\n\nA4. Please see the last...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 3 ]	[ "u_zgrZoJ53P", "QeQy-Up1NEF", "wj81F2gCTUN", "ACwlD9gVT3C", "gQ3-gX4tK3m", "E03tlvtrlRW", "0jMWltjrp7e", "HL6Z2D41woH", "uzAn-WduQh6", "nips_2022_mfxq7BrMfga", "nips_2022_mfxq7BrMfga", "nips_2022_mfxq7BrMfga", "nips_2022_mfxq7BrMfga", "nips_2022_mfxq7BrMfga" ]
nips_2022_htUvh7xPoa	Random Sharpness-Aware Minimization	Currently, Sharpness-Aware Minimization (SAM) is proposed to seek the parameters that lie in a flat region to improve the generalization when training neural networks. In particular, a minimax optimization objective is defined to find the maximum loss value centered on the weight, out of the purpose of simultaneously m...	Accept	All reviewers except one agreed that this paper should be accepted because of the strong author response during the rebuttal phase. Specifically the reviewers appreciated the significance of the problem being addressed, the clarity of the paper, the simplicity of the method, and the analysis. Authors: please carefully ...	train	[ "z1LZuatxMOD", "EPc2L6vvxbh", "poNL1Gg5BX8G", "Jzga_pj59p6", "51hzizhZq_sv", "vDGFc9xkDmCM", "msihoDDRAD", "GXFt8247X_D", "vl1GRud_nmJ", "hLhcnFC65sR", "zpsvtTxl37", "2E2zVUc1Yi2", "K-5dAOXnViD", "dIGDYo8l07Z", "SMdKl8qeio", "BJxQxxDvN9j", "RACcRH0dqV7" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewers! Thank you so much for your time on this paper so far.\n\nThe authors have written a detailed response to your concerns. How does this change your review?\n\nPlease engage with the authors in the way that you would like reviewers to engage your submitted papers: critically and open to changing your...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 8, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 4, 3 ]	[ "nips_2022_htUvh7xPoa", "GXFt8247X_D", "Jzga_pj59p6", "51hzizhZq_sv", "vDGFc9xkDmCM", "msihoDDRAD", "RACcRH0dqV7", "vl1GRud_nmJ", "BJxQxxDvN9j", "SMdKl8qeio", "2E2zVUc1Yi2", "K-5dAOXnViD", "dIGDYo8l07Z", "nips_2022_htUvh7xPoa", "nips_2022_htUvh7xPoa", "nips_2022_htUvh7xPoa", "nips_20...
nips_2022_9ND8fMUzOAr	Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning	Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens. Many advanced approaches have been developed to reduce the total number of tokens in the large-scale vision transformers, especially for imag...	Accept	This paper presents a method to reduce the computational cost of a trained vision transformer for dense prediction. According to the authors' presented experiments, the method can accelerate the transformers effectively without retraining. Although some experiments are not throughout (as discussed below), I see potenti...	train	[ "YOtJMWU1KGK", "_ljYXctMSFO", "UqD4w1icGP1", "MA7Wcmg6wP8", "8CrxXqj5nQ0", "EYopl8wEyee", "G-nrv1FAnmo", "qktSXEYvwN-", "tJ1XOCsdZn9", "2NEEQdq38c9", "QFf9S0S2A8K", "2-KNyTq5j_", "s-kzfNBDej7", "r6AT4zJCIEq" ]	[ "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for the previous careful reviews and valuable suggestions.\n\nWe have learned a lot through the suggested comparisons with TokenPooling[47]/DynamicViT[52]/TokenLearner[55]. We also hope to learn more from your further valuable suggestions.", " We thank the reviewer for the previous careful...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 4, 4, 4 ]	[ "s-kzfNBDej7", "2-KNyTq5j_", "QFf9S0S2A8K", "8CrxXqj5nQ0", "EYopl8wEyee", "r6AT4zJCIEq", "s-kzfNBDej7", "2-KNyTq5j_", "QFf9S0S2A8K", "nips_2022_9ND8fMUzOAr", "nips_2022_9ND8fMUzOAr", "nips_2022_9ND8fMUzOAr", "nips_2022_9ND8fMUzOAr", "nips_2022_9ND8fMUzOAr" ]
nips_2022_TjVU5Lipt8F	When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits	We study the problem of multi-armed bandits with ε-global Differential Privacy (DP). First, we prove the minimax and problem-dependent regret lower bounds for stochastic and linear bandits that quantify the hardness of bandits with ε-global DP. These bounds suggest the existence of two hardness regimes depending on the...	Accept	This paper studies the problem of multi-armed bandits under differential privacy. The reviewers are all positive about the results and presentation of the paper.	test	[ "rFMQT2jwMvh", "asXUKzGs6e", "jDPR1mgoGIA", "eBkhpBMyuj", "2t_Q2uVrWl3", "NBnkMPY1Ge", "Sc5-Mz_xSii", "iBEILsLK1b", "VFaZDiLN0V", "T7kwfq9PnTq", "pgw0RISwMgJB", "qBkLCsnGULi", "CWZ5OY39dN1", "DdaT3b8IMvk", "2xNRAmsJdKV", "yOj4MnL4_mf", "29fdSce3mVr", "IYQwvWwpjt", "fikrpuDE2u6", ...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_re...	[ " We are glad that all of your concerns have been addressed and thank you for raising your score. We will add a paragraph explaining the details discussed here and the comparaison to [1, 20, R1, 3] after Theorem 2.", " Thanks for the update.\n\nI hope the authors can add one paragraph to carefully discuss the cur...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 8, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 5, 3 ]	[ "asXUKzGs6e", "jDPR1mgoGIA", "eBkhpBMyuj", "2t_Q2uVrWl3", "NBnkMPY1Ge", "iBEILsLK1b", "qBkLCsnGULi", "VFaZDiLN0V", "CWZ5OY39dN1", "pgw0RISwMgJB", "DdaT3b8IMvk", "_jD7zvtP1Nq", "fikrpuDE2u6", "IYQwvWwpjt", "29fdSce3mVr", "nips_2022_TjVU5Lipt8F", "nips_2022_TjVU5Lipt8F", "nips_2022_T...
nips_2022_uOQNvEfjpaC	What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs	Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world paradigm, in which the objects in the input image may not have been encountered during the training of the localization mechanism. Moreover, ...	Accept	The paper presents a new approach, using two pre-trained models (CLIP and BLIP) as supervision to enable three tasks, including the newly proposed task WWbL, which is a joint open vocabulary description and grounding/localization task trained only with weak supervision. I recommend acceptance based on the revised pape...	test	[ "_FvDhxCL0J5", "LDCgPpP_SJW", "j199srD8iX", "SKa4GTws8Q9", "MYUeVg9na_r", "R0mDqVPUlKh", "HJaGo6pkvHf", "nnMUQq_2mhJ", "Ss7tGKqWVTU", "8AxZZuTxAtE", "it6u6y0VauV", "5zQ7FJl4cl", "xZ6v_UdSN_n", "_FTsFCbyMI", "tutiBrOkaO", "SdyZyw8-Xj" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear reviewer aWBt,\n\nplease look at the author response to your review, and comment on the corresponding author response, and if this changes your ratings / understanding / resolves your concerns / creates new concerns/questions.\n\nThank you, your AC\n\nPS: Don't respond to this message but directly to the aut...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 8, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 3 ]	[ "tutiBrOkaO", "j199srD8iX", "Ss7tGKqWVTU", "Ss7tGKqWVTU", "R0mDqVPUlKh", "5zQ7FJl4cl", "nips_2022_uOQNvEfjpaC", "nips_2022_uOQNvEfjpaC", "SdyZyw8-Xj", "tutiBrOkaO", "_FTsFCbyMI", "xZ6v_UdSN_n", "nips_2022_uOQNvEfjpaC", "nips_2022_uOQNvEfjpaC", "nips_2022_uOQNvEfjpaC", "nips_2022_uOQNvE...
nips_2022_hFni381edL	SAPA: Similarity-Aware Point Affiliation for Feature Upsampling	We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels ...	Accept	The paper focuses on the task of feature upsampling, specifically in decoder layers for dense prediction problems. The proposed point affiliation module can be used in upsampling kernels to produce semantically smooth and boundary preserving upsampled sets. The paper received four detailed reviewers from experts. There...	train	[ "BFhhkJlE6hj", "vJjKQThIYya", "JQ5Nt4rZav8", "fzydfh-2XdV", "9DdWooU8D17", "XOsTNBcLwe", "3gPGSInzJGX", "NKdgQ2bON_1" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for positive comments and consider our approach useful. We answer the questions as follows.\n\nActual runtime comparison.\n\nWe test the runtime on a single NVIDIA GeForce RTX 1080Ti GPU with Intel Xeon CPU E5-1620 v4 @ 3.50GHz CPU. \nBy upsampling a random feature map of size 256\\*120\...	[ -1, -1, -1, -1, 5, 5, 5, 6 ]	[ -1, -1, -1, -1, 3, 3, 4, 4 ]	[ "NKdgQ2bON_1", "3gPGSInzJGX", "XOsTNBcLwe", "9DdWooU8D17", "nips_2022_hFni381edL", "nips_2022_hFni381edL", "nips_2022_hFni381edL", "nips_2022_hFni381edL" ]
nips_2022_iQpaHC7cPfR	SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections	Inverse rendering of an object under entirely unknown capture conditions is a fundamental challenge in computer vision and graphics. Neural approaches such as NeRF have achieved photorealistic results on novel view synthesis, but they require known camera poses. Solving this problem with unknown camera poses is highly ...	Accept	This paper had notable consistent reviews. All reviews were thoughtful, and there was a consensus that this paper tackles an important problem in a way that has not been explored. While there were some weaknesses highlighted in the review process, discussion and the author rebuttal ameliorated all major concerns. Th...	train	[ "qGJwhSgFoLt", "kDFqs5jNAkhb", "gDp5CoxUFaa", "vxG70-LEQSV", "F4odPg3o94B", "z2k7n3cn2ra", "tQ8LCZ-VhFN", "5h2e2G_girj" ]	[ "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewers, Thanks for your constructive feedback. We hope to have clarified most of the reviewer questions in our response. As we are nearing the end of the author-reviewer discussion period, we would like to give a gentle reminder in case you have any more questions or concerns.", " **Comparison with nois...	[ -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "F4odPg3o94B", "5h2e2G_girj", "tQ8LCZ-VhFN", "z2k7n3cn2ra", "nips_2022_iQpaHC7cPfR", "nips_2022_iQpaHC7cPfR", "nips_2022_iQpaHC7cPfR", "nips_2022_iQpaHC7cPfR" ]
nips_2022_HjicdpP-Nth	Generalized Laplacian Eigenmaps	Graph contrastive learning attracts/disperses node representations for similar/dissimilar node pairs under some notion of similarity. It may be combined with a low-dimensional embedding of nodes to preserve intrinsic and structural properties of a graph. COLES, a recent graph contrastive method combines traditional gra...	Accept	The Authors provided a nice rebuttal, and address major issues in the last round. Therefore, I recommend to accept this paper.	train	[ "m6Rf6iLflKV", "FwuxOkxYaGi", "W2nLudS5zlT", "P94g2PP1S6x", "-e2A0L0wzJP", "7Vx19nn6VW4", "u0ISM-i_nZx", "BbPgi_Y85k9", "Iimyw9XR2O1", "0LKFYg6ifJc", "427K1wlrPCx" ]	[ "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewers and the AC for their work.\n\nAs the reviewer-author discussion period is finishing in the next few hours, we just wanted to say that we are here to help should you have any additional questions.\n\nBest regards,\nAuthors.\n\n", " # Response to Rev. 3 (3VJW)\n\n*We thank the reviewer ...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "nips_2022_HjicdpP-Nth", "427K1wlrPCx", "0LKFYg6ifJc", "0LKFYg6ifJc", "Iimyw9XR2O1", "Iimyw9XR2O1", "Iimyw9XR2O1", "Iimyw9XR2O1", "nips_2022_HjicdpP-Nth", "nips_2022_HjicdpP-Nth", "nips_2022_HjicdpP-Nth" ]
nips_2022_g_bqn4ewVG	PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories	While 3D shape representations enable powerful reasoning in many visual and perception applications, learning 3D shape priors tends to be constrained to the specific categories trained on, leading to an inefficient learning process, particularly for general applications with unseen categories. Thus, we propose PatchCo...	Accept	This is an interesting paper on class-independent 3d shape completion. Reviewers agree that the paper has good quality and is moderately original. There were initially some questions about the level of generalization to new classes, but after a strong rebuttal all reviewers find the results compelling and all of them s...	train	[ "iP0I2My5C_r", "982PpeRSyyO", "ZZMg34Zp-mQ", "rXs_MuyRofA", "JIYEIKdMl-5", "GjpeeSdtSWg", "siWbwWdOdHg", "sEC7eyVnfSj" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your valuable review; we are glad that our method was found to be 'novel' and to enable 'good generalization to unseen categories', with 'experimental evaluation [that] is done well'.\n\nApplications. Our method focuses on the problem of shape completion on objects from unseen categories.\nWe be...	[ -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, 3, 4, 4, 3 ]	[ "sEC7eyVnfSj", "siWbwWdOdHg", "GjpeeSdtSWg", "JIYEIKdMl-5", "nips_2022_g_bqn4ewVG", "nips_2022_g_bqn4ewVG", "nips_2022_g_bqn4ewVG", "nips_2022_g_bqn4ewVG" ]
nips_2022_pkfpkWU536D	Neural Shape Deformation Priors	We present Neural Shape Deformation Priors, a novel method for shape manipulation that predicts mesh deformations of non-rigid objects from user-provided handle movements. State-of-the-art methods cast this problem as an optimization task, where the input source mesh is iteratively deformed to minimize an objective fun...	Accept	While some of the scores on this paper are mixed, even the negative reviews highlight the quality and interest of the work and have specific (and somewhat debatable) technical concerns. Overall, the AE recommends accept, especially in light of the detailed and thoughtful responses during the rebuttal phase. In the ca...	val	[ "VYdPXnwycA_", "kKOpZtOo27l", "U9nGvfEWajV", "5ZPxzh8nvxg", "Q1BXyYI5BII", "uXeMer-cIQ8", "q1oGYs1Qfcu", "JmGjq-PKZ2s", "ywcU4zezp5", "TI180jT6d6K", "JbqUdqQFOVN", "rne54RJ8NFk" ]	[ "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the positive feedback! \n\nOur model learns deformation priors from a dataset containing realistic non-rigid motions. When it is directly evaluated on non-realistic or non-physical-aware handles, it will try to find the most similar realistic deformation that can best explain the given handles.\nHoweve...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 4 ]	[ "kKOpZtOo27l", "Q1BXyYI5BII", "uXeMer-cIQ8", "nips_2022_pkfpkWU536D", "rne54RJ8NFk", "JbqUdqQFOVN", "TI180jT6d6K", "ywcU4zezp5", "nips_2022_pkfpkWU536D", "nips_2022_pkfpkWU536D", "nips_2022_pkfpkWU536D", "nips_2022_pkfpkWU536D" ]

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