Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback

In this talk, I will introduce the problem of learning in zero-sum game, and especially for the problem of "last-iterate" convergence, unlike the traditional literature that looks at the average convergence (we argue it makes more sense). The interesting property is that the optimal rate is T^{-1/4} which is quite unusual (and unexpected) in this literature. 

References:
https://proceedings.mlr.press/v267/fiegel25a.html


Short bio: Vianney Perchet is a professor at the Centre de recherche en économie et statistique (CREST) at the ENSAE since october 2019. Mainly focusing on the interplay between machine learning and game theory, his research themes are at the intersection of mathematics, computer science, and economics. The spectrum of his interest ranges from pure theory (say, optimal rates of convergence of algorithms) to pure applications (modeling user behavior, optimization of recommender systems, etc.) He is also part-time distinguished researcher in the Criteo AI Lab, in Paris, working on efficient exploration in recommender systems.

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