Optimal Statistical Estimation under Nonstatistical Constraints

Abstract

In the conventional statistical framework, a major goal is to develop optimal statistical procedures based on the sample size and statistical model. However, in many contemporary applications, non-statistical concerns such as privacy and communication constraints associated with the statistical procedures become crucial. This raises a fundamental question in data science: how can we make optimal statistical inference under these non-statistical constraints? In this talk, the speaker will explore recent advances in differentially private learning and distributed learning under communication constraints in a few specific settings. The results demonstrate novel and interesting phenomena and suggest directions for further investigation.

About the Speaker

Prof. Tony Cai received his PhD from Cornell University in 1996. He joined the University of Pennsylvania in 2006 and is currently the Daniel H. Silberberg Professor in Applied Mathematics and Statistics. He was also named the Medallion Lecturer at the Institute of Mathematical Statistics in 2009.

Prof. Cai’s research interests include high-dimensional inference, large-scale multiple testing, nonparametric function estimation, functional data analysis, inference for discrete distributions, and statistical decision theory, with applications to compressed sensing, chemical identification, medical imaging and microarray data analysis. He currently serves on the editorial board of the Frontiers of Statistics (book series). He was also the editor of The Annals of Statistics in 2010-2012.

Prof. Cai received the Committee of Presidents of Statistical Societies (COPSS) Presidents' Award in 2008. He was elected as a Fellow of the Institute of Mathematical Statistics in 2006 and the President of International Chinese Statistical Association in 2016.

For Attendees' Attention

• Seating is on a first come, first served basis.