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• W3004180802 abstract "We consider an adversarial variant of the classic $K$-armed linear contextual bandit problem where the sequence of loss functions associated with each arm are allowed to change without restriction over time. Under the assumption that the $d$-dimensional contexts are generated i.i.d.~at random from a known distributions, we develop computationally efficient algorithms based on the classic Exp3 algorithm. Our first algorithm, RealLinExp3, is shown to achieve a regret guarantee of $widetilde{O}(sqrt{KdT})$ over $T$ rounds, which matches the best available bound for this problem. Our second algorithm, RobustLinExp3, is shown to be robust to misspecification, in that it achieves a regret bound of $widetilde{O}((Kd)^{1/3}T^{2/3}) + varepsilon sqrt{d} T$ if the true reward function is linear up to an additive nonlinear error uniformly bounded in absolute value by $varepsilon$. To our knowledge, our performance guarantees constitute the very first results on this problem setting." @default.
• W3004180802 created "2020-02-07" @default.
• W3004180802 creator A5004416155 @default.
• W3004180802 creator A5077167635 @default.
• W3004180802 date "2020-02-01" @default.
• W3004180802 modified "2023-09-29" @default.
• W3004180802 title "Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits" @default.
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• W3004180802 hasPublicationYear "2020" @default.
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