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A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
Thompson, William R · 1933
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Convex analysis
Rockafellar, Ralph Tyrell · 1970
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The nonstochastic multiarmed bandit problem
Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E · 2002
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The epoch-greedy algorithm for multi-armed bandits with side information
Langford, J. and Zhang, T · 2008
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The offset tree for learning with partial labels
Beygelzimer, Alina and Langford, John · 2009
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Parametric bandits: The generalized linear case
Filippi, Sarah, Cappe, Olivier, Garivier, Aurélien, and Szepesvári, Csaba · 2010
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Mslr: Microsoft learning to rank dataset
Qin, Tao and Liu, Tie-Yan · 2010
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Restricted eigenvalue properties for correlated gaussian designs
Raskutti, Garvesh, Wainwright, Martin J, and Yu, Bin · 2010
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Improved algorithms for linear stochastic bandits
Abbasi-Yadkori, Yasin, Pál, Dávid, and Szepesvári, Csaba · 2011
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Yahoo! learning to rank challenge overview
Chapelle, Olivier and Chang, Yi · 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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Doubly robust policy evaluation and learning
Dudík, Miroslav, Langford, John, and Li, Lihong · 2011
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Scikit-learn: Machine learning in python
Pedregosa, Fabian, Varoquaux, Gaël, Gramfort, Alexandre, Michel, Vincent, Thirion, Bertrand, Grisel, Olivier, Blondel, Mathieu, Prettenhofer, Peter, Weiss, Ron, Dubourg, Vincent, et al · 2011
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Online-to-confidence-set conversions and application to sparse stochastic bandits
Abbasi-Yadkori, Yasin, Pal, David, and Szepesvari, Csaba · 2012
Cited alongside, same era.
Contextual bandit learning with predictable rewards
Agarwal, Alekh, Dudík, Miroslav, Kale, Satyen, Langford, John, and Schapire, Robert E · 2012
Theory of disagreement-based active learning
Hanneke, Steve et al · 2014
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Online decision-making with high-dimensional covariates
Bastani, Hamsa and Bayati, Mohsen · 2015
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Contextual semibandits via supervised learning oracles
Krishnamurthy, Akshay, Agarwal, Alekh, and Dudik, Miro · 2016
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Exploiting the natural exploration in contextual bandits
Bastani, Hamsa, Bayati, Mohsen, and Khosravi, Khashayar · 2017
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Estimation considerations in contextual bandits
Dimakopoulou, Maria, Athey, Susan, and Imbens, Guido · 2017
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UCI machine learning repository, 2013
Lichman, M · 2013
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Eluder dimension and the sample complexity of optimistic exploration
Russo, Dan and Van Roy, Benjamin · 2013
Cited alongside, same era.
Taming the monster: A fast and simple algorithm for contextual bandits
Agarwal, Alekh, Hsu, Daniel, Kale, Satyen, Langford, John, Li, Lihong, and Schapire, Robert · 2014
Cited alongside, same era.
Krishnamurthy, Akshay, Agarwal, Alekh, Huang, Tzu-Kuo, Daume III, Hal, and Langford, John · 2017
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Provable optimal algorithms for generalized linear contextual bandits
Li, Lihong, Lu, Yu, and Zhou, Dengyong · 2017
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Ensemble sampling
Lu, Xiuyuan and Van Roy, Benjamin · 2017
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A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Kannan, S., Morgenstern, J., Roth, A., Waggoner, B., and Wu, Z. S · 2018
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