2 citations · 2 across the 9 of their papers we have counts for
10 papers
Constant Individual Regret in General Games
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon. We remove…
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…
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…
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…
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…
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…
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…
A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
Policy gradient methods have become a staple of any single-agent reinforcement learning toolbox, due to their combination of desirable properties: iterate convergence, efficient us…
LiteEFG: An Efficient Python Library for Solving Extensive-form Games
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
LiteEFG is an efficient library with easy-to-use Python bindings, which can solve multiplayer extensive-form games (EFGs). LiteEFG enables the user to express computation graphs in…
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
Chanwoo Park, Mingyang Liu, Dingwen Kong +2
Reinforcement learning from human feedback (RLHF) has been an effective technique for aligning AI systems with human values, with remarkable successes in fine-tuning large-language…