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Multi-armed bandit problems provide a framework to identify the optimal intervention over a sequence of repeated experiments.
A modern introduction to online learning, 2019
F. Orabona · 1912
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A short note on learning discrete distributions, 2020
C. L. Canonne · 2002
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B. Bilodeau, J. Negrea, and D. M. Roy · 2007
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Pure exploration in multi-armed bandits problems
S. Bubeck, R. Munos, and G. Stoltz · 2009
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Causality
J. Pearl · 2009
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The best of both worlds: Stochastic and adversarial bandits
S. Bubeck and A. Slivkins · 2012
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Experiment selection for causal discovery
A. Hyttinen, F. Eberhardt, and P. O. Hoyer · 2013
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The Pareto regret frontier
W. M. Koolen · 2013
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One practical algorithm for both stochastic and adversarial bandits
Y. Seldin and A. Slivkins · 2014
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Bandits with unobserved confounders: A causal approach
E. Bareinboim, A. Forney, and J. Pearl · 2015
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The Pareto regret frontier for bandits
T. Lattimore · 2015
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Using gene expression data to identify causal pathways between genotype and phenotype in a complex disease: Application to genetic analysis workshop 19
H. F. Ainsworth and H. J. Cordell · 2016
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Causal bandits: Learning good interventions via causal inference
F. Lattimore, T. Lattimore, and M. D. Reid · 2016
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Causal Inference in Statistics: A Primer
J. Pearl, M. Glymour, and N. P. Jewell · 2016
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Corralling a band of bandit algorithms
A. Agarwal, H. Luo, B. Neyshabur, and R. E. Schapire · 2017
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Cost-optimal learning of causal graphs
M. Kocaoglu, A. G. Dimakis, and S. Vishwanath · 2017
Cited alongside, same era.
Causal bandits with propogating inference
A. Yabe, D. Hatano, H. Sumita, S. Ito, N. Kakimura, T. Fukunaga, and K.-i. Kawarabayashi · 2018
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Structural causal bandits with non-manipulable variables
S. Lee and E. Bareinboim · 2019
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Bandit Algorithms
T. Lattimore and C. Szepesvári · 2020
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Regret analysis of bandit problems with causal background knowledge
Y. Lu, A. Meisami, A. Tewari, and Z. Yan · 2020
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Corralling stochastic bandit algorithms
R. Arora, T. V. Marinov, and M. Mohri · 2021
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Causal bandits without prior knowledge using separating sets
A. A. de Kroon, D. Belgrave, and J. M. Mooij · 2021
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Best of both worlds: Stochastic and adversarial best-arm identification
Y. Abbasi-Yadkori, P. L. Bartlett, V. Gabillon, A. Malek, and M. Valko · 2018
Cited alongside, same era.
Experimental design for cost-aware learning of causal graphs
E. M. Lindgren, M. Kocaoglu, A. G. Dimakis, and S. Vishwanath · 2018
Cited alongside, same era.
Stochastic bandits robust to adversarial corruptions
T. Lykouris, V. Mirrokni, and R. Paes Leme · 2018
Cited alongside, same era.
Adaptation to easy data in prediction with limited advice
T. S. Thune and Y. Seldin · 2018
Cited alongside, same era.
Finite-time analysis of the multiarmed bandit problem
P. Auer, N. Cesa-Bianchi, and P. Fischer
Cited in the paper.
The nonstochastic multiarmed bandit problem
P. Auer, N. Cesa-Bianchi, and R. E. Schapire
Cited in the paper.
Sequential causal imitation learning with unobserved confounders
D. Kumor, J. Zhang, and E. Bareinboim · 2021
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Causal bandits with unknown graph structure
Y. Lu, A. Meisami, and A. Tewari · 2021
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Budgeted and non-budgeted causal bandits
V. Nair, V. Patil, and G. Sinha · 2021
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