collaborators

7 papers

cs.GT2026

Equilibrium with Internal Transfers

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

Nash equilibrium (NE) arises from selfish utility maximization, yet its social welfare can be arbitrarily far from optimal. Moreover, computing an NE is intractable in general. We…

cs.LG2026

Regret Minimization with Adaptive Opponents in Repeated Games

Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu +1

In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play. The standard metric of \emph{external regre…

cs.GT2026

Computing Equilibrium beyond Unilateral Deviation

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating unilaterally. They offer no…

cs.LG2026

Online Learning and Equilibrium Computation with Ranking Feedback

Mingyang Liu, Yongshan Chen, Zhiyuan Fan +3

Online learning in arbitrary, and possibly adversarial, environments has been extensively studied in sequential decision-making, and it is closely connected to equilibrium computat…

cs.GT2026

Differentially Private Equilibrium Finding in Polymatrix Games

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

We study equilibrium finding in polymatrix games under differential privacy constraints. Prior work in this area fails to achieve both high-accuracy equilibria and a low privacy bu…

cs.LG2025

UFT: Unifying Supervised and Reinforcement Fine-Tuning

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

Post-training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post-training methods can be categorized into sup…