Abstract: We investigate optimal algorithms for estimating a general high dimensional smooth and sparse function from the perspective of information based complexity. Our algorithms and analyses reveal several interesting characteristics for these tasks. In particular, our results illustrate the potential value of experiment design for high dimensional problems.

Bio: Ming Yuan is a Professor of Statistics at Columbia University. He was previously a Professor at University of Wisconsin-Madison, and Coca-Cola Junior Professor in the H. Milton School of Industrial and Systems Engineering at Georgia Institute of Technology. He received his Ph.D. in Statistics and M.S. in Computer Science from University of Wisconsin-Madison. His main research interests lie in theory, methods and applications of data mining and statistical learning. Dr. Yuan has been serving on editorial boards of various top journals including Journal of the American Statistical Association, and Journal of the Royal Statistical Society Series B, and is currently the Editor-in-Chief of The Annals of Statistics. Dr. Yuan was awarded the John van Ryzin Award in 2004 by ENAR, CAREER Award in 2009 by NSF, Guy Medal in Bronze from the Royal Statistical Society in 2014, and Leo Breiman Junior Award. He was also named a Fellow of IMS in 2015, and a Medallion Lecturer of IMS in 2018.