# Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits Source: [Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:13:46.432Z Authors: Gergely Neu, Julia Olkhovskaya Published: 2020-02-01T22:49:46.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Rewrite Selection on 16 QA Datasets Aggregate](): Rewrite Selection; 16 QA Datasets Aggregate; Adjusted Metric Value: 781.05. - [Coupon Recommendation on commercial product](): Coupon Recommendation; commercial product; CVR: 48.46. ## Original sources and implementation [Original paper]() [PDF]()