4 papers · 1 filter
The Representation-Rationalizability Tradeoff in Reward Learning
Jing Dong, Yaoliang Yu, Pascal Pourpart
In RLHF, each training example contains a prompt and two candidate responses , and annotators provide pairwise preferences between these responses. The learning problem i…
Last-iterate Convergence in Regularized Graphon Mean Field Game
Jing Dong, Baoxiang Wang, Yaoliang Yu
To model complex real-world systems, such as traders in stock markets, or the dissemination of contagious diseases, graphon mean-field games (GMFG) have been proposed to model many…
Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback
Jing Dong, Baoxiang Wang, Yaoliang Yu
We study the problem of no-regret learning algorithms for general monotone and smooth games and their last-iterate convergence properties. Specifically, we investigate the problem…
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
Jing Dong, Baoxiang Wang, Yaoliang Yu
In this work, we study potential games and Markov potential games under stochastic cost and bandit feedback. We propose a variant of the Frank-Wolfe algorithm with sufficient explo…