With opponent-action feedback in zero-sum games, an efficient algorithm achieves near-optimal t^{-1/2} last-iterate convergence in duality gap with high probability.
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Distributionally robust games are defined via coherent risk measures, with proofs of equilibrium existence for ambiguity sets, utility loss bounds, PPAD-completeness results, multilinear complementarity formulations, and numerical validation of out-of-sample performance.
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Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions
With opponent-action feedback in zero-sum games, an efficient algorithm achieves near-optimal t^{-1/2} last-iterate convergence in duality gap with high probability.
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Distributionally Robust Games via Coherent Risk Measures
Distributionally robust games are defined via coherent risk measures, with proofs of equilibrium existence for ambiguity sets, utility loss bounds, PPAD-completeness results, multilinear complementarity formulations, and numerical validation of out-of-sample performance.