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Adaptive opponents can significantly alter the landscape of regret minimization, and our new RP-Regret metric reveals how to achieve better cooperative outcomes in repeated games.
Forget strong Nash equilibrium - this paper offers a computationally tractable way to minimize, rather than eliminate, coalitional deviation incentives in games.
Learning from ranked preferences alone can be surprisingly difficult: even with access to the full ranking of actions, standard online learning guarantees break down unless the environment is sufficiently stable.
