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This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box oracles.
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
Halbert White · 1980
Earlier work this paper cites.
An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani · 1994
Earlier work this paper cites.
Gambling in a rigged casino: The adversarial multi-armed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 1995
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Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
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Online bagging and boosting
Nikunji C Oza · 2005
Earlier work this paper cites.
Multi-armed bandit algorithms and empirical evaluation
Joannes Vermorel and Mehryar Mohri · 2005
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The challenge problem for automated detection of 101 semantic concepts in multimedia
Cees GM Snoek, Marcel Worring, Jan C Van Gemert, Jan-Mark Geusebroek, and Arnold WM Smeulders · 2006
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Anytime many-armed bandits
Olivier Teytaud, Sylvain Gelly, and Michele Sebag · 2007
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Multilabel text classification for automated tag suggestion
Ioannis Katakis, Grigorios Tsoumakas, and Ioannis Vlahavas · 2008
Earlier work this paper cites.
The epoch-greedy algorithm for multi-armed bandits with side information
John Langford and Tong Zhang · 2008
Earlier work this paper cites.
Efficient pairwise multilabel classification for large-scale problems in the legal domain
Eneldo Loza Mencia and Johannes Fürnkranz · 2008
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Effective and efficient multilabel classification in domains with large number of labels
Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas · 2008
Cited alongside, same era.
The offset tree for learning with partial labels
Alina Beygelzimer and John Langford · 2009
Cited alongside, same era.
Mortal multi-armed bandits
Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, and Eli Upfal · 2009
Cited alongside, same era.
Algorithms for infinitely many-armed bandits
Yizao Wang, Jean-Yves Audibert, and Rémi Munos · 2009
Cited alongside, same era.
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
Cited alongside, same era.
An empirical evaluation of thompson sampling
Olivier Chapelle and Lihong Li · 2011
Cited alongside, same era.
The multi-armed bandit problem with covariates
Vianney Perchet and Philippe Rigollet · 2013
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Taming the monster: A fast and simple algorithm for contextual bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire · 2014
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Doubly robust policy evaluation and optimization
Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li, et al · 2014
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Doubly robust policy evaluation and optimization
Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li, et al · 2014
Later among the works it cites.
Thompson sampling with the online bootstrap
Dean Eckles and Maurits Kaptein · 2014
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
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Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
Cited alongside, same era.
Efficient optimal learning for contextual bandits
Miroslav Dudik, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, and Tong Zhang · 2011
Cited alongside, same era.
Active learning
Burr Settles · 2012
Cited alongside, same era.
A gang of bandits
Nicolo Cesa-Bianchi, Claudio Gentile, and Giovanni Zappella · 2013
Cited alongside, same era.
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
Cited alongside, same era.
Applied predictive modeling
Max Kuhn and Kjell Johnson · 2013
Cited alongside, same era.
Matthew D Hoffman and Andrew Gelman · 2014
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Contextual bandits with similarity information
Aleksandrs Slivkins · 2014
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A survey of online experiment design with the stochastic multi-armed bandit
Giuseppe Burtini, Jason Loeppky, and Ramon Lawrence · 2015
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Contextual bandits in a collaborative environment
Qingyun Wu, Huazheng Wang, Quanquan Gu, and Hongning Wang · 2016
Later among the works it cites.
A contextual bandit bake-off
Alberto Bietti, Alekh Agarwal, and John Langford · 2018
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Practical contextual bandits with regression oracles
Dylan J Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, and Robert E Schapire · 2018
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