Information theory and high-dimensional statistical inference

Friday, March 23, 2018 - 3:30pm

Galen Reeves, Duke University


How does one quantify the fundamental and computational limits of high-dimensional inference problems? Much of the theoretical work in statistics has focused on scaling regimes in which the uncertainty about the unknown parameters converges to zero as the amount of data increases. In this talk, I will describe a different approach that instead focuses on settings where the number of observations is commensurate with the number of unknowns. Building upon ideas from information theory and statistical physics, the objectives are (1) obtaining succinct formulas for the performance of optimal methods; and (2) delineating between problem regimes in which this performance can or cannot be obtained using computationally efficient methods. The primary focus will be on the standard linear model with random design matrices. I will also discuss some recent progress on generalized linear models and multilayer networks.

Seminars generally take place in 116 Old Chemistry Building on Fridays from 3:30 - 4:30 pm. For additional information contact: or phone 919-684-8029. Sorry, but we do not have reprints available. Please feel free to contact the authors by email for follow-up information, articles, etc. Reception following seminar in 211 Old Chemistry

Old Chemistry 116

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