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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A robust variant of binary search achieves regret O(C + log T) for dynamic pricing with known corruption C and O(C + log² T) when unknown.
An algorithm for online resource allocation with budget and general constraints achieves O(sqrt(T)) regret in stochastic and alpha-regret in adversarial regimes with bounded constraint violations.
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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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Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time
A robust variant of binary search achieves regret O(C + log T) for dynamic pricing with known corruption C and O(C + log² T) when unknown.
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An algorithm for online resource allocation with budget and general constraints achieves O(sqrt(T)) regret in stochastic and alpha-regret in adversarial regimes with bounded constraint violations.