Gabriele Farina

Constant Individual Regret in General Games

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

Computing Equilibrium beyond Unilateral Deviation

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to $Φ$-Regret Minimization

Gabriele Farina, Juan Carlos Perdomo

Online Learning and Equilibrium Computation with Ranking Feedback

Mingyang Liu, Yongshan Chen, Zhiyuan Fan, et al.

This paper studies online learning when the learner only receives ranking feedback (like "action A is better than B") instead of numeric scores, which is more practical for human feedback and privacy-sensitive applications. The authors show that learning with instantaneous rankings is fundamentally impossible, but develop algorithms that achieve good performance when utilities change slowly or when using time-averaged rankings. Their approach enables multiple players in games to reach approximate equilibrium through repeated play.