Anru Zhang | DeepAI

DeepAI

Featured Co-authors

Mengdi Wang
71 publications
Guang Cheng
66 publications
Yihong Wu
54 publications
T. Tony Cai
46 publications
Rebecca Willett
44 publications
Tianxi Cai
42 publications
Garvesh Raskutti
25 publications
Dong Xia
25 publications
Ming Yuan
23 publications
Ziwei Zhu
23 publications
Botao Hao
19 publications

∙ 12/16/2022

Estimating Higher-Order Mixed Memberships via the ℓ_2,∞ Tensor Perturbation Bound

Higher-order multiway data is ubiquitous in machine learning and statist...

Joshua Agterberg, et al. ∙

∙ 05/03/2021

Learning Good State and Action Representations via Tensor Decomposition

The transition kernel of a continuous-state-action Markov decision proce...

Chengzhuo Ni, et al. ∙

∙ 02/26/2020

An Optimal Statistical and Computational Framework for Generalized Tensor Estimation

This paper describes a flexible framework for generalized low-rank tenso...

Rungang Han, et al. ∙

∙ 11/09/2019

ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching

In this paper, we develop a novel procedure for low-rank tensor regressi...

Anru Zhang, et al. ∙

∙ 09/21/2019

Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference

In this paper, we study sparse group Lasso for high-dimensional double s...

T. Tony Cai, et al. ∙

∙ 06/28/2019

Learning Markov models via low-rank optimization

Modeling unknown systems from data is a precursor of system optimization...

Ziwei Zhu, et al. ∙

∙ 11/28/2018

High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis

In microbiome and genomic study, the regression of compositional data ha...

Pixu Shi, et al. ∙

∙ 10/21/2018

On the Non-asymptotic and Sharp Lower Tail Bounds of Random Variables

The non-asymptotic tail bounds of random variables play crucial roles in...

Anru Zhang, et al. ∙

∙ 10/21/2018

A Non-asymptotic, Sharp, and User-friendly Reverse Chernoff-Cramèr Bound

The Chernoff-Cramèr bound is a widely used technique to analyze the uppe...

Anru Zhang, et al. ∙

∙ 10/19/2018

Heteroskedastic PCA: Algorithm, Optimality, and Applications

Principal component analysis (PCA) and singular value decomposition (SVD...

Anru Zhang, et al. ∙

∙ 09/06/2018

Optimal Sparse Singular Value Decomposition for High-dimensional High-order Data

In this article, we consider the sparse tensor singular value decomposit...

Anru Zhang, et al. ∙

∙ 04/03/2018

Estimation of Markov Chain via Rank-constrained Likelihood

This paper studies the recovery and state compression of low-rank Markov...

Xudong Li, et al. ∙

∙ 02/08/2018

State Compression of Markov Processes via Empirical Low-Rank Estimation

Model reduction is a central problem in analyzing complex systems and hi...

Anru Zhang, et al. ∙

∙ 01/29/2018

Sparse and Low-rank Tensor Estimation via Cubic Sketchings

In this paper, we propose a general framework for sparse and low-rank te...

Botao Hao, et al. ∙

∙ 10/31/2017

Functional data approaches for mixed longitudinal studies, with applications in midlife women's health

Motivated by applications of mixed longitudinal studies, where a group o...

Anru Zhang, et al. ∙

∙ 03/08/2017

Tensor SVD: Statistical and Computational Limits

In this paper, we propose a general framework for tensor singular value ...

Anru Zhang, et al. ∙

∙ 11/03/2016

Cross: Efficient Low-rank Tensor Completion

The completion of tensors, or high-order arrays, attracts significant at...

Anru Zhang, et al. ∙

∙ 06/23/2016

Semi-supervised Inference: General Theory and Estimation of Means

We propose a general semi-supervised inference framework focused on the ...

Anru Zhang, et al. ∙

∙ 04/08/2015

Structured Matrix Completion with Applications to Genomic Data Integration

Matrix completion has attracted significant recent attention in many fie...

Tianxi Cai, et al. ∙

∙ 06/05/2013

Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-rank Matrices

This paper considers compressed sensing and affine rank minimization in ...

T. Tony Cai, et al. ∙