Introduces structured Stackelberg games and the Stackelberg-Littlestone dimension to characterize the leader's optimal regret and sample complexity when context predicts follower type.
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2025 2verdicts
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Algorithms achieve O(T^{1/2}) regret in contextual Stackelberg games via reduction to linear contextual bandits, improving on prior O(T^{2/3}) rates.
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Learning in Structured Stackelberg Games
Introduces structured Stackelberg games and the Stackelberg-Littlestone dimension to characterize the leader's optimal regret and sample complexity when context predicts follower type.
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Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Algorithms achieve O(T^{1/2}) regret in contextual Stackelberg games via reduction to linear contextual bandits, improving on prior O(T^{2/3}) rates.