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Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search.
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Using confidence bounds for exploitation-exploration trade-offs
Auer, Peter · 2002
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Stochastic linear optimization under bandit feedback
Dani, Varsha, Hayes, Thomas P, and Kakade, Sham M · 2008
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Online models for content optimization
Agarwal, Deepak, Chen, Bee-Chung, Elango, Pradheep, Motgi, Nitin, Park, Seung-Taek, Ramakrishnan, Raghu, Roy, Scott, and Zachariah, Joe · 2009
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Simultaneous analysis of Lasso and Dantzig selector
Bickel, Peter J, Ritov, Ya’acov, and Tsybakov, Alexandre B · 2009
Improved algorithms for linear stochastic bandits
Abbasi-Yadkori, Yasin, Pál, Dávid, and Szepesvári, Csaba · 2011
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Contextual bandits with linear payoff functions
Chu, Wei, Li, Lihong, Reyzin, Lev, and Schapire, Robert E · 2011
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Bubeck, Sébastien and Cesa-Bianchi, Nicolo · 2012
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An empirical evaluation of Thompson sampling
Chapelle, Olivier and Li, Lihong · 2012
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An unbiased offline evaluation of contextual bandit algorithms with generalized linear models
Li, Lihong, Chu, Wei, Langford, John, Moon, Taesup, and Wang, Xuanhui · 2012
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Contextual bandit algorithms with supervised learning guarantees
Beygelzimer, Alina, Langford, John, Li, Lihong, Reyzin, Lev, and Schapire, Robert E · 2010
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Parametric bandits: The generalized linear case
Filippi, Sarah, Cappe, Olivier, Garivier, Aurélien, and Szepesvári, Csaba · 2010
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A contextual-bandit approach to personalized news article recommendation
Li, Lihong, Chu, Wei, Langford, John, and Schapire, Robert E · 2010
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Linearly parameterized bandits
Rusmevichientong, Paat and Tsitsiklis, John N · 2010
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Finite-time analysis of the multiarmed bandit problem
Auer, Peter, Cesa-Bianchi, Nicolo, and Fischer, Paul
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The nonstochastic multiarmed bandit problem
Auer, Peter, Cesa-Bianchi, Nicolo, Freund, Yoav, and Schapire, Robert E
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Taming the monster: A fast and simple algorithm for contextual bandits
Agarwal, Alekh, Hsu, Daniel, Kale, Satyen, Langford, John, Li, Lihong, and Schapire, Robert E · 2014
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Learning to optimize via posterior sampling
Russo, Daniel and Van Roy, Benjamin · 2014
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Spectral bandits for smooth graph functions
Valko, Michal, Munos, Rémi, Kveton, Branislav, and Kocák, Tomáš · 2014
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