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Junsoo Ha

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Preprint Sep 2026

A Logarithmic Regret Bound for Optimistic Hedge in General-Sum Games

Can simple no-regret dynamics attain smaller regret in self-play than against arbitrary adversaries? In $n$-player general-sum games, Daskalakis et al. 2021 proved an $O(n\log d_i\log^4 T)$ individual regret bound for Optimistic Hedge, which improves upon the classical $O(\sqrt T)$ adversarial regret bound. In this wor...

Junsoo Ha · 1 citation

#machine learning Preprint Sep 2026

A Horizon-Independent Regret Bound for Optimistic Hedge in General-Sum Games

Can simple learning rules keep their regret bounded in self-play? Recent work achieves constant regret bounds through modified regularization and higher-order prediction. Yet for Optimistic Hedge, arguably the most canonical method in games, the best known individual regret bound remains logarithmic. In this work, we p...

Junsoo Ha · 0 citations