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We study the linear contextual bandit problem in the presence of adversarial corruption, where the reward at each round is corrupted by an adversary, and the corruption level (i.e., the sum of corruption magnitudes over the horizon) is $C\geq 0$.
Stochastic linear optimization with adversarial corruption
Li, Y · 1909
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Lattimore, T · 1911
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Abbasi-Yadkori, Y · 2011
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Zimmert, J · 2019
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Foster, D. J · 2020
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Stochastic linear bandits robust to adversarial attacks
Bogunovic, I · 2021
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Robust stochastic linear contextual bandits under adversarial attacks
Ding, Q · 2021
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Adapting to misspecification in contextual bandits with offline regression oracles
Krishnamurthy, S. K · 2021
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Lattimore, T · 2018
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Gupta, A
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Lee, C.-W · 2021
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Zhao, H · 2021
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Zhou, D · 2021
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A model selection approach for corruption robust reinforcement learning
Wei, C.-Y · 2022
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