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作者：Chen, Yi; Wang, Yining; Fang, Ethan X.; Wang, Zhaoran; Li, Runze
作者单位：Hong Kong University of Science & Technology; University of Texas System; University of Texas Dallas; Duke University; Northwestern University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a randomly generated contextual covariate. This setting finds essential applications such as personalized recommendations, online advertisements, and personalized medicine. However, it is very challenging to balance the exploration and exploitation tradeoff. We modify the LinUCB algorithm in doubly growi...
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作者：Jiang, Yunlu; Wang, Xueqin; Wen, Canhong; Jiang, Yukang; Zhang, Heping
作者单位：Jinan University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Sun Yat Sen University; Yale University
摘要：Testing the equality of the means in two samples is a fundamental statistical inferential problem. Most of the existing methods are based on the sum-of-squares or supremum statistics. They are possibly powerful in some situations, but not in others, and they do not work in a unified way. Using random integration of the difference, we develop a framework that includes and extends many existing methods, especially in high-dimensional settings, without restricting the same covariance matrices or ...
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作者：Rahnavard, Ali; Wilson, Jeffrey R.; Chen, Ding-Geng; Peace, Karl E.
作者单位：George Washington University
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作者：Ke, Zheng Tracy; Wang, Minzhe
作者单位：Harvard University
摘要：The probabilistic topic model imposes a low-rank structure on the expectation of the corpus matrix. Therefore, singular value decomposition (SVD) is a natural tool of dimension reduction. We propose an SVD-based method for estimating a topic model. Our method constructs an estimate of the topic matrix from only a few leading singular vectors of the data matrix, and has a great advantage in memory use and computational cost for large-scale corpora. The core ideas behind our method include a pre...
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作者：Shi, Chengchun; Zhu, Jin; Ye, Shen; Luo, Shikai; Zhu, Hongtu; Song, Rui
作者单位：University of London; London School Economics & Political Science; Sun Yat Sen University; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill
摘要：This article is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured variables exist that confound the observed actions. This assumption, however, is likely to be violated in real applications such as healthcare and technological industries. In this article, we show that with some auxiliary variables that mediate the effect of actions on the...
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作者：Camerlenghi, Federico; Favaro, Stefano; Masoero, Lorenzo; Broderick, Tamara
作者单位：University of Milano-Bicocca; Collegio Carlo Alberto; Bocconi University; University of Turin; Massachusetts Institute of Technology (MIT)
摘要：There is a growing interest in the estimation of the number of unseen features, mostly driven by biological applications. A recent work brought out a peculiar property of the popular completely random measures (CRMs) as prior models in Bayesian nonparametric (BNP) inference for the unseen-features problem: for fixed prior's parameters, they all lead to a Poisson posterior distribution for the number of unseen features, which depends on the sampling information only through the sample size. CRM...
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作者：Ding, Yi; Li, Yingying; Song, Rui
作者单位：University of Macau; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology; North Carolina State University; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
摘要：We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect of continuous actions and allow the discretization frequency to be large and diverge with the number of observations. The value function of continuous-action is estimated using penalized regression with our proposed generalized pena...
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作者：Wu, Ben; Guo, Ying; Kang, Jian
作者单位：Renmin University of China; Emory University; University of Michigan System; University of Michigan
摘要：Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high-dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images a...
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作者：Quan, Mingxue; Lin, Zhenhua
作者单位：Renmin University of China; National University of Singapore
摘要：For nonparametric regression in the streaming setting, where data constantly flow in and require real-time analysis, a main challenge is that data are cleared from the computer system once processed due to limited computer memory and storage. We tackle the challenge by proposing a novel one-pass estimator based on penalized orthogonal basis expansions and developing a general framework to study the interplay between statistical efficiency and memory consumption of estimators. We show that, the...
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作者：Barigozzi, Matteo; Cavaliere, Giuseppe; Trapani, Lorenzo
作者单位：University of Bologna; University of Nottingham
摘要：We study inference on the common stochastic trends in a nonstationary, N-variate time series y(t), in the possible presence of heavy tails. We propose a novel methodology which does not require any knowledge or estimation of the tail index, or even knowledge as to whether certain moments (such as the variance) exist or not, and develop an estimator of the number of stochastic trends m based on the eigenvalues of the sample second moment matrix of y(t). We study the rates of such eigenvalues, s...
