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We study the linear contextual bandit problem in the presence of adversarial corruption, where the interaction between the player and a possibly infinite decision set is contaminated by an adversary that can corrupt the reward up to a corruption level $C$ measured by the sum of the largest alteration on rewards in each round.
Model selection for contextual bandits
Foster, D. J · 1906
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Stochastic linear optimization with adversarial corruption
Li, Y · 1909
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Using confidence bounds for exploitation-exploration trade-offs
Auer, P · 2002
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Auer, P · 2002
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Adversarial attacks on linear contextual bandits
Garcelon, E · 2002
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Reinforcement learning with immediate rewards and linear hypotheses
Abe, N · 2003
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Model selection in contextual stochastic bandit problems
Pacchiano, A · 2003
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Stochastic linear optimization under bandit feedback
Dani, V · 2008
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A contextual-bandit approach to personalized news article recommendation
Li, L · 2010
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Linearly parameterized bandits
Rusmevichientong, P · 2010
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Improved algorithms for linear stochastic bandits
Abbasi-Yadkori, Y · 2011
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Contextual bandits with linear payoff functions
Chu, W · 2011
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Adaptive bandits: Towards the best history-dependent strategy
Odalric, M · 2011
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Bubeck, S · 2012
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Deshpande, Y · 2012
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Zhou, D · 2012
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One practical algorithm for both stochastic and adversarial bandits
Seldin, Y · 2014
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Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges
Adaptivity to smoothness in x-armed bandits
Locatelli, A · 2018
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Stochastic bandits robust to adversarial corruptions
Lykouris, T · 2018
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Learning to optimize under non-stationarity
Cheung, W. C · 2019
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Better algorithms for stochastic bandits with adversarial corruptions
Gupta, A · 2019
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Corruption-tolerant bandit learning
Kapoor, S · 2019
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Data poisoning attacks on stochastic bandits
Liu, F · 2019
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An optimal algorithm for stochastic and adversarial bandits
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Villar, S. S · 2015
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An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
Auer, P · 2016
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A linear regression approach to multi-criteria recommender system
Jhalani, T · 2016
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Corralling a band of bandit algorithms
Agarwal, A · 2017
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An improved parametrization and analysis of the exp3++ algorithm for stochastic and adversarial bandits
Seldin, Y · 2017
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Jun, K.-S · 2018
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A smoothed analysis of the greedy algorithm for the linear contextual bandit problem
Kannan, S · 2018
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Zimmert, J · 2019
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Neu, G · 2020
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Stochastic linear bandits robust to adversarial attacks
Bogunovic, I · 2021
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Ding, Q · 2021
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Adapting to misspecification in contextual bandits
Foster, D. J · 2021
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Lee, C.-W · 2021
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