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作者：Gerhardus, Andreas
作者单位：Helmholtz Association; German Aerospace Centre (DLR)
摘要：In this paper, we introduce a novel class of graphical models for representing time-lag specific causal relationships and independencies of multivariate time series with unobserved confounders. We completely characterize these graphs and show that they constitute proper subsets of the currently employed model classes. As we show, from the novel graphs one can thus draw stronger causal inferences-without additional assumptions. We further introduce a graphical representation of Markov equivalen...
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作者：Li, Gen; Shi, Laixi; Chen, Yuxin; Chi, Yuejie; Wei, Yuting
作者单位：Chinese University of Hong Kong; California Institute of Technology; University of Pennsylvania; Carnegie Mellon University
摘要：This paper is concerned with offline reinforcement learning (RL), which learns using precollected data without further exploration. Effective offline RL would be able to accommodate distribution shift and limited data coverage. However, prior results either suffer from suboptimal sample complexities or incur high burn-in cost to reach sample optimality, thus posing an impediment to efficient offline RL in sample-starved applications. We demonstrate that the model-based (or plug-in) approach ac...
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作者：Wang, Wen; Wu, Shihao; Zhu, Ziwei; Zhou, Ling; Song, Peter X. -K.
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Southwestern University of Finance & Economics - China
摘要：Fusing regression coefficients into homogeneous groups can unveil those coefficients that share a common value within each group. Such groupwise homogeneity reduces the intrinsic dimension of the parameter space and unleashes sharper statistical accuracy. We propose and investigate a new combinatorial grouping approach called L-0-Fusion that is amenable to mixed integer optimization (MIO). On the statistical aspect, we identify a fundamental quantity called MSE grouping sensitivity that underp...
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作者：Zhou, Yeqing; Xu, Kai; Zhu, Liping; Li, Runze
作者单位：Tongji University; Tongji University; Anhui Normal University; Renmin University of China; Zhejiang Gongshang University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：To test independence between two high-dimensional random vectors, we propose three tests based on the rank-based indices derived from Hoeffding's D, Blum-Kiefer-Rosenblatt's R and Bergsma-Dassios-Yanagimoto's tau(& lowast;). Under the null hypothesis of independence, we show that the distributions of the proposed test statistics converge to normal ones if the dimensions diverge arbitrarily with the sample size. We further derive an explicit rate of convergence. Thanks to the monotone transform...
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作者：Buecher, Axel; Pakzad, Cambyse
作者单位：Ruhr University Bochum; Inria; Centre National de la Recherche Scientifique (CNRS); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA)
摘要：Testing for pairwise independence for the case where the number of variables may be of the same size or even larger than the sample size has received increasing attention in the recent years. We contribute to this branch of the literature by considering tests that allow to detect higher-order dependencies. The proposed methods are based on connecting the problem to copulas and making use of the Moebius transformation of the empirical copula process; an approach that is related to Lancaster int...
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作者：Chetelat, Didier
作者单位：Universite de Montreal; Polytechnique Montreal
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作者：Marandon, Ariane; Lei, Lihua; Mary, David; Roquain, Etienne
作者单位：Sorbonne Universite; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); Stanford University; Universite Cote d'Azur; Observatoire de la Cote d'Azur
摘要：This paper studies the semisupervised novelty detection problem where a set of typical measurements is available to the researcher. Motivated by recent advances in multiple testing and conformal inference, we propose AdaDetect, a flexible method that is able to wrap around any probabilistic classification algorithm and control the false discovery rate (FDR) on detected novelties in finite samples without any distributional assumption other than exchangeability. In contrast to classical FDR-con...
