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- Tony Cai, Wen-Xin Zhou
- Journal of Machine Learning Research
- 2013

We consider in this paper the problem of noisy 1-bit matrix completion under a general non-uniform sampling distribution using the max-norm as a convex relaxation for the rank. A max-norm constrained maximum likelihood estimate is introduced and studied. The rate of convergence for the estimate is obtained. Information-theoretical methods are used to… (More)

- Jinyuan Chang, Wen Zhou, Wen-Xin Zhou, Lan Wang
- Biometrics
- 2017

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large… (More)

- Jinyuan Chang, Chao Zheng, Wen-Xin Zhou, Wen Zhou
- Biometrics
- 2017

In this article, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-type statistics and the parametric bootstrap techniques to compute the critical values. Different from the existing tests that heavily rely on the structural conditions on the… (More)

This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new estimator incorporating both max-norm and nuclear-norm regularization, based on which we can conduct efficient low-rank matrix recovery using a random subset of entries observed with additive noise under general non-uniform and unknown sampling distributions.… (More)

- WEN-XIN ZHOU, CHAO ZHENG, ZHEN ZHANG
- 2017

WEN-XIN ZHOU1,2,* , CHAO ZHENG2,** and ZHEN ZHANG3 1Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA. E-mail: *wenxinz@princeton.edu 2School of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia. E-mail: **zhengc1@student.unimelb.edu.au 3Department of Statistics,… (More)

- Jianqing Fan, Wen-Xin Zhou
- Journal of Machine Learning Research
- 2016

Many data-mining and statistical machine learning algorithms have been developed to select a subset of covariates to associate with a response variable. Spurious discoveries can easily arise in high-dimensional data analysis due to enormous possibilities of such selections. How can we know statistically our discoveries better than those by chance? In this… (More)

- Fang Han, Sheng Xu, Wen-Xin Zhou
- 2016

Recently, Chernozhukov, Chetverikov, and Kato [Ann. Statist. 42 (2014) 1564–1597] developed a new Gaussian comparison inequality for approximating the suprema of empirical processes. This paper exploits this technique to devise sharp inference on spectra of large random matrices. In particular, we show that two long-standing problems in random matrix theory… (More)

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