Events
Fall 2015

EconCS Whiteboard Talk Series

Sep 25, 2015 2:00 pm – 5:00 pm 

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Parent Program: 
Speaker: 

Vasilis Syrgkanis (Microsoft Research)

Location: 

Calvin Lab Room 116

Fast Convergence of Regularized Learning in Games

We show that natural classes of regularized learning algorithms with a form of recency bias achieve faster convergence rates to approximate efficiency and to correlated equilibria in multiplayer normal form games. When each player in a game uses an algorithm from our class, their individual regret decays at $O(T^{-3/4})$, while the sum of utilities converges to an approximate optimum at $O(T^{-1})$--an improvement upon the worst case $O(T^{-1/2})$ rates. We show a black-box reduction for any algorithm in the class to achieve $O(T^{-1/2})$ rates against an adversary, while maintaining the faster rates against algorithms in the class. Our results extend those of Rakhlin and Shridharan~\cite{Rakhlin2013} and Daskalakis et al.~\cite{Daskalakis2014}, who only analyzed two-player zero-sum games for specific algorithms.

Joint with Alekh Agarwal, Haipeng Luo and Rob Schapire.