378 citations · 2.2k across the 69 of their papers we have counts for
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Analytical Convergence Regions of Accelerated Gradient Descent in Nonconvex Optimization under Regularity Condition
Huaqing Xiong, Yuejie Chi, Bin Hu +1
There is a growing interest in using robust control theory to analyze and design optimization and machine learning algorithms. This paper studies a class of nonconvex optimization…
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…
Streaming PCA and Subspace Tracking: The Missing Data Case
Laura Balzano, Yuejie Chi, Yue M. Lu
For many modern applications in science and engineering, data are collected in a streaming fashion carrying time-varying information, and practitioners need to process them with a…
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 from quadratic equ…
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation
Yudong Chen, Yuejie Chi
Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative filtering, video surveillance, medical imaging, to di…
Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy
Haoyu Fu, Yuejie Chi, Yingbin Liang
We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, also known as a single-lay…
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…
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion
Kaiyi Ji, Jian Tan, Jinfeng Xu +1
Low-rank matrix completion has achieved great success in many real-world data applications. A matrix factorization model that learns latent features is usually employed and, to imp…