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Found 5 papers, 3 papers with code
Learning Fair Division from Bandit Feedback
This work addresses learning online fair division under uncertainty, where a central planner sequentially allocates items without precise knowledge of agents' values or utilities.
Slingshot Perturbation to Learning in Monotone Games
This paper addresses the problem of learning Nash equilibria in {\it monotone games} where the gradient of the payoff functions is monotone in the strategy profile space, potentially containing additive noise.
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games
This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last-iterate convergence property in both full and noisy feedback settings.
Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games
In this study, we consider a variant of the Follow the Regularized Leader (FTRL) dynamics in two-player zero-sum games.
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
Constructing a good search tree representation significantly boosts the performance of the proposed method.
