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.
Robust and High-Dimensional Statistics
Dates: October 29 - November 2, 2018
Location: Calvin Hall, Simons Institute, UC Berkeley
This training workshop is part of the Simons Institute's Foundations of Data Science Program being offered during the Fall 2018 semester. BIDS Senior Fellow Michael Mahoney is co-organizing and presenting.
Michael Mahoney works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning including randomized matrix algorithms and randomized numerical linear algebra; geometric network analysis tools for structure extraction in large informatics graphs; scalable implicit regularization methods; and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis.