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20082026
most citedOptimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

114 citations · 664 across the 92 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

cs.LG2026

Constant regret in general games via higher-order optimism

Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos

We introduce an uncoupled learning algorithm which, when employed by all players of an arbitrary -player normal form game with up to actions per player, guarantees $O(N^3\lo…

cs.GT2026

What preferences can - and cannot - predict in multi-agent online learning

Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos

We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinator…

cs.GT2026

Regret, equilibrium, and learning in games: A guided tour

Panayotis Mertikopoulos

This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learn…

cs.LG2026

Leveraging Similarities in Multi-Armed Bandits

Khaled Eldowa, Thibaud Rahier, Augustin Cablant +2

In many online learning and bandit problems, the actions we consider possess inherent similarities--for instance because they share latent traits, tags, or hierarchical structure.…

cs.GT2026

An empirical study of Fictitious Play for estimating Nash equilibria in first-price auctions with correlated values

Benjamin Heymann, Panayotis Mertikopoulos

This study concerns the computation of the Nash equilibria of first-price auctions with correlated values. Although some equilibrium computation methods exist for auctions with ind…

math.OC2026

Bregman meets Lévy: Stochastic mirror descent with heavy-tailed noise in continuous and discrete time

Pierre-Louis Cauvin, Panayotis Mertikopoulos

We study the robustness of stochastic mirror descent (SMD) under heavy-tailed noise, focusing on whether the method retains its convergence guarantees when run with infinite-varian…