Event Date
Speaker: Cong Ma (Assistant Professor, Department of Statistics, University of Chicago)
Title: "Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices"
Abstract: Integrative data analysis often requires separating shared from individual variations across multiple datasets, typically using the Joint and Individual Variation Explained (JIVE) model. Despite its popularity, theoretical insights into JIVE methods remain limited, particularly in the context of multiple matrices and varying degrees of subspace misalignment.
In this talk, I will present new theoretical results on the Angle-based JIVE (AJIVE) method—a two-stage spectral algorithm. Specifically, we establish that AJIVE achieves decreasing estimation error with an increasing number of matrices in high signal-to-noise ratio (SNR) regimes. In contrast, AJIVE faces inherent limitations in low-SNR conditions, where estimation error remains persistently high. Complementary minimax lower bounds confirm AJIVE’s optimal performance at high SNR, while analysis of an oracle estimator highlights fundamental limitations of spectral methods at low SNR.
Bio: Cong Ma is an assistant professor in the Department of Statistics at the University of Chicago. Previously, he was a postdoctoral researcher at UC Berkeley, advised by Professor Martin Wainwright. He obtained his Ph.D. at Princeton University in 2020, advised by Professor Yuxin Chen and Professor Jianqing Fan. He is broadly interested in mathematics of data science, focusing currently on transfer learning, reinforcement learning, and multi-modality learning.
Website (external link): https://congma1028.github.io/