Contributed Papers

Structure Identification and Dimension Reduction Methods

Thu, Aug 7, 10:30 AM - 12:20 PM Room CC-207C Music City Center

Section on Statistical Learning and Data Science

7 Presentations

Chain-linked multiple matrix integration via embedding alignment

10:35 AM - 10:50 AM

Co-authors: Runbing Zheng (Johns Hopkins University), Minh Tang (North Carolina State University), Runbing Zheng (Johns Hopkins University)

2→∞ norm normal approximations matrix completion data integration

Dimension reduction in semi-supervised multiple quantile regression

10:50 AM - 11:05 AM

Co-authors: Youngwoo Kwon, Youngwoo Kwon, Aaron Molstad (University of Minnesota), Ben Sherwood (University of Kansas)

Alternating direction method of multipliers Constrained optimization Quantile regression Dimension reduction

Distributed Tensor Principal Component Analysis with Data Heterogeneity

11:05 AM - 11:20 AM

Co-authors: Elynn Chen, Xi Chen (New York University), Wenbo Jing, Wenbo Jing, Yichen Zhang (Purdue University)

Tensor Principal Component Analysis Distributed Inference Data Heterogeneity Communication Efficiency Tucker Decomposition

Extreme value theory for singular subspace estimation in the matrix denoising model

11:20 AM - 11:35 AM

Co-authors: Junhyung Chang (University of Wisconsin-Madison), Joshua Cape (University of Wisconsin-Madison), Junhyung Chang (University of Wisconsin-Madison)

Singular subspace inference Two-to-infinity norm Gumbel convergence Saddle point approximation Singular value shrinkage

Layered Models can "Automatically" Discover Low-Dimensional Structures via Feature Learning

11:35 AM - 11:50 AM

Co-authors: Yunlu Chen (Northwestern University), Yang Li (Massachusetts Institute of Technology), Yunlu Chen (Northwestern University), Keli Liu, Feng Ruan (Northwestern University)

layered models regularization feature learning central mean subspace reproducing kernel Hilbert space ridge regression

Random-projection ensemble dimension reduction

11:50 AM - 12:05 PM

Co-authors: Wenxing Zhou, Timothy Cannings (University of Edinburgh), Wenxing Zhou

High-dimensional mean central subspace random projection singular value decomposition sufficient dimension reduction

Sparse Convex Biclustering

12:05 PM - 12:20 PM

Co-authors: Jiakun Jiang (Beijing Normal University at Zhuhai), Binhuan Wang (AbbVie), Chenliang Gu (Center for Statistics and Data Science, Beijing Normal University), Binhuan Wang (AbbVie)

Convex biclustering Sparsity ADMM High-dimensional data