Preprint
Aug 2026
Optimal Alternating Regret for Online Learning and Games
The minimax-optimal alternating regret is settled, a regret notion motivated by alternating learning dynamics in games, for both online linear optimization (OLO) and online convex optimization (OCO) and a matching lower bound is proved of the $\Omega(\log T)$ factor.
Yixin Tao, Weiqiang Zheng
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