A unified stochastic approximation framework for learning in games

8 June 2022

Papers citing "A unified stochastic approximation framework for learning in games"

8 / 8 papers shown

Title
The limits of min-max optimization algorithms: convergence to spurious non-critical sets Ya-Ping Hsieh P. Mertikopoulos Volkan Cevher 70 83 0 16 Jun 2020
On the convergence of single-call stochastic extra-gradient methods Yu-Guan Hsieh F. Iutzeler J. Malick P. Mertikopoulos 50 169 0 22 Aug 2019
Bandit learning in concave $N$ -person games Mario Bravo David S. Leslie P. Mertikopoulos 40 122 0 03 Oct 2018
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile P. Mertikopoulos Bruno Lecouat Houssam Zenati Chuan-Sheng Foo V. Chandrasekhar Georgios Piliouras 130 295 0 07 Jul 2018
Training GANs with Optimism C. Daskalakis Andrew Ilyas Vasilis Syrgkanis Haoyang Zeng 173 519 0 31 Oct 2017
Learning in games with continuous action sets and unknown payoff functions P. Mertikopoulos Zhengyuan Zhou 59 265 0 25 Aug 2016
Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis Alekh Agarwal Haipeng Luo Robert Schapire 60 254 0 02 Jul 2015
Optimization, Learning, and Games with Predictable Sequences Alexander Rakhlin Karthik Sridharan 95 379 0 08 Nov 2013