Fall 2018

Robust and High-Dimensional Statistics

Oct. 29Nov. 2, 2018

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Andrea Montanari (Stanford University; chair), Emmanuel Candès (Stanford University), Ilias Diakonikolas (University of Southern California), Santosh Vempala (Georgia Institute of Technology)

This workshop will focus on recent developments in high-dimensional statistics, with an emphasis on different notions of robustness, the extent to which recent developments in theoretical computer science can lead to improvements with respect to traditional statistical metrics, challenges arising when the number of data points and the number of features diverge at similar rates, etc.  Other potential topics are inference and causality, as well as inference after selection, i.e., data snooping and problems of multiple inference. 

Further details about this workshop will be posted in due course.

Invited Participants: 

Ery Arias-Castro (UC San Diego), Laura Balzano (University of Michigan), Jelena Bradic (UC San Diego), Peter Bühlmann (ETH Zürich), Moses Charikar (Stanford University), Ken Clarkson (IBM Almaden), John Duchi (Stanford University), Zhou Fan (Stanford University), Vitaly Feldman (Google), Chao Gao (University of Chicago), Rong Ge (Duke University), Adel Javanmard (USC), Daniel Kane (UC San Diego), Ravi Kannan (Microsoft Research India), Adam Klivans (University of Texas at Austin), Jerry Li (Massachusetts Institute of Technology), Po-Ling Loh (University of Wisconsin, Madison), Lester Mackey (Stanford University), Yury Makarychev (Toyota Technological Institute at Chicago), Sayan Mukherjee (Duke University), Boaz Nadler (Weizmann Institute of Science), Anup B. Rao (Adobe), Philippe Rigollet (MIT), Chiara Sabatti (Stanford University), Andrew Saxe (Oxford University, UK), Weijie Su (University of Pennsylvania), Pragya Sur (Stanford University), Aravindan Vijayaraghavan (Northwestern University)