양은호(Eunho Yang), KAIST
Structurally-Constrained Estimations: Applications and Theories
High-dimensional inference deals with models in which the number of parameters p is substantially larger than the number of training data n. In this talk, we will discuss recent advances in the analysis of M-estimators for high-dimensional data. Specifically, we review a unified framework for establishing consistency and convergence rates for regularized estimators. After that, we will introduce a unified framework for the high-dimensional analysis of "superposition-structured" or "dirty" models, and its examples such as robust PCA.
Eunho Yang is an assistant professor at the School of Computing, KAIST. Before joining KAIST, he spent two years at IBM T.J. Watson Research Center as a Research Staff Member. He obtained his Ph.D. in 2014 from the university of Texas at Austin, and did M.S. and B.S from the Seoul National University, Korea in 2006 and 2004, respectively. His research interests are in statistical machine learning in general with the special focuses on high-dimensional statistics. He is currently developing new theories and algorithms for graphical models and deep learning, with the applications of computer vision, computational biology and medicine, etc.