64 citations · 329 across the 26 of their papers we have counts for
7 papers · 1 filter
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
HanQin Cai, Jialin Liu, Wotao Yin
Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction. In this paper, we pr…
Curvature-Aware Derivative-Free Optimization
Bumsu Kim, HanQin Cai, Daniel McKenzie +1
The paper discusses derivative-free optimization (DFO), which involves minimizing a function without access to gradients or directional derivatives, only function evaluations. Clas…
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
HanQin Cai, Zehan Chao, Longxiu Huang +1
We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their…
Estimation of high-dimensional change-points under a group sparsity structure
Hanqing Cai, Tengyao Wang
Change-points are a routine feature of 'big data' observed in the form of high-dimensional data streams. In many such data streams, the component series possess group structures an…
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
HanQin Cai, Keaton Hamm, Longxiu Huang +1
Low rank tensor approximation is a fundamental tool in modern machine learning and data science. In this paper, we study the characterization, perturbation analysis, and an efficie…
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
HanQin Cai, Yuchen Lou, Daniel McKenzie +1
We consider the zeroth-order optimization problem in the huge-scale setting, where the dimension of the problem is so large that performing even basic vector operations on the deci…
Robust CUR Decomposition: Theory and Imaging Applications
HanQin Cai, Keaton Hamm, Longxiu Huang +1
This paper considers the use of Robust PCA in a CUR decomposition framework and applications thereof. Our main algorithms produce a robust version of column-row factorizations of m…