Sébastien Bubeck; New Results at the Crossroads of Convexity, Learning and Information Theory

CORE Series
Tuesday, Apr 19, 2016, 4pm

LOW 102
Sébastien Bubeck, Microsoft Research

ABSTRACT: I will present three new results: (i) the Cramer transform of
the uniform measure on a convex body is a universal self-concordant
barrier; (ii) projected gradient descent with Gaussian noise allows to
sample from a log-concave measure in polynomial time; and (iii) Thompson
sampling combined with a multi-scale exploration solves the Bayesian
convex bandit problem. The unifying theme in these results is the
interplay between concepts from convex geometry, learning and
information theory.

bubeckBIO: Sébastien Bubeck is a Researcher at Microsoft Research (Theory Group) in Redmond, WA. Prior to Microsoft Research, he was an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University. He received his MS in Mathematics from the Ecole Normale Supérieure de Chachan and his PhD from the University of Lille 1, France, where his PhD thesis won several prizes including the Jacques Neveu prize for the best French PhD thesis in Probability and Statistics. He was awarded the Sloan Research Fellowship in Computer Science in 2015, and a Best Paper Award at COLT (Conference on Learning Theory) in 2009. He was the general co-chair for COLT 2013 and 2014, and serves on the steering committee for COLT. He is also the author of the recent monograph, Convex Optimization: Algorithms and Complexity, published in 2015 as a part of Foundations and Trends in Machine Learning.