Search Results for author: Wen-Xin Zhou

Found 9 papers, 3 papers with code

Retire: Robust Expectile Regression in High Dimensions

no code implementations11 Dec 2022 Rebeka Man, Kean Ming Tan, Zian Wang, Wen-Xin Zhou

In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted $\ell_1$-penalization which reduces the estimation bias from $\ell_1$-penalization and leads to oracle properties.

regression Vocal Bursts Intensity Prediction

How do noise tails impact on deep ReLU networks?

no code implementations20 Mar 2022 Jianqing Fan, Yihong Gu, Wen-Xin Zhou

This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment.

regression

Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees

no code implementations25 Oct 2021 Kean Ming Tan, Heather Battey, Wen-Xin Zhou

We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions.

regression

Smoothed Quantile Regression with Large-Scale Inference

1 code implementation9 Dec 2020 Xuming He, Xiaoou Pan, Kean Ming Tan, Wen-Xin Zhou

Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression.

Statistics Theory Methodology Statistics Theory

Iteratively Reweighted $\ell_1$-Penalized Robust Regression

2 code implementations9 Jul 2019 Xiaoou Pan, Qiang Sun, Wen-Xin Zhou

This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonconvex regularization.

regression Variable Selection

User-Friendly Covariance Estimation for Heavy-Tailed Distributions

1 code implementation5 Nov 2018 Yuan Ke, Stanislav Minsker, Zhao Ren, Qiang Sun, Wen-Xin Zhou

We offer a survey of recent results on covariance estimation for heavy-tailed distributions.

Methodology Statistics Theory Statistics Theory

Max-Norm Optimization for Robust Matrix Recovery

no code implementations24 Sep 2016 Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou

This paper studies the matrix completion problem under arbitrary sampling schemes.

Matrix Completion

Matrix Completion via Max-Norm Constrained Optimization

no code implementations2 Mar 2013 T. Tony Cai, Wen-Xin Zhou

Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting.

Matrix Completion

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