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Yuxin Chen

55 papers hereh-index 365.9k citations82 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author24
  • middle author17
  • last author12

Across the 53 of 55 papers where every author was matched, so the position is known.

fields
  • cs.LG21
  • cs.IT15
  • stat.ML13
  • math.ST6
same name
  • Yuxin Chen — 37 papers, h 19
  • Yuxin Chen — 20 papers, h 8
  • Yuxin Chen — 14 papers, h 6
  • Yuxin Chen — 14 papers, h 8
  • Yuxin Chen — 10 papers, h 3
  • Yuxin Chen — 9 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112026
most citedNonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

378 citations · 1.8k across the 48 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

math.ST2018★ 10 cited

Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices

Yuxin Chen, Chen Cheng, Jianqing Fan

This paper is concerned with the interplay between statistical asymmetry and spectral methods. Suppose we are interested in estimating a rank-1 and symmetric matrix $\mathbf{M}^{\s…

cs.LG2018★ 378 cited

Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

Yuejie Chi, Yue M. Lu, Yuxin Chen

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventiona…

stat.ML2018★ 121 cited

Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval

Yuxin Chen, Yuejie Chi, Jianqing Fan +1

This paper considers the problem of solving systems of quadratic equations, namely, recovering an object of interest x♮∈Rn from m quadratic equ…

cs.IT2018

Nonconvex Matrix Factorization from Rank-One Measurements

Yuanxin Li, Cong Ma, Yuxin Chen +1

We consider the problem of recovering low-rank matrices from random rank-one measurements, which spans numerous applications including covariance sketching, phase retrieval, quantu…

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