IC Colloquium : New Results at the Crossroads of Convexity, Learning and Information Theory
Event details
| Date | 19.12.2016 |
| Hour | 16:15 › 17:30 |
| Location | |
| Category | Conferences - Seminars |
By : Sébastien Bubeck - Microsoft Research
Video of his talk
Abstract :
I will present three new results (no background in optimization will be assumed, all concepts will be defined and motivated): (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.
Joint work with Ronen Eldan.
Bio :
Sébastien Bubeck is a Researcher in the Theory Group at Microsoft Research Redmond. Prior to MSR, 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 Cachan 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, the Best Student Paper Award at COLT (Conference on Learning Theory) 2009 and the Best Paper Award at COLT 2016. He is also the author of two monographs published in Foundations and Trends in Machine Learning, “Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems”, and “Convex Optimization: Algorithms and Complexity”.
More information
Video of his talk
Abstract :
I will present three new results (no background in optimization will be assumed, all concepts will be defined and motivated): (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.
Joint work with Ronen Eldan.
Bio :
Sébastien Bubeck is a Researcher in the Theory Group at Microsoft Research Redmond. Prior to MSR, 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 Cachan 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, the Best Student Paper Award at COLT (Conference on Learning Theory) 2009 and the Best Paper Award at COLT 2016. He is also the author of two monographs published in Foundations and Trends in Machine Learning, “Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems”, and “Convex Optimization: Algorithms and Complexity”.
More information
Practical information
- General public
- Free
- This event is internal
Contact
- Host : O. Svensson