X-Armed Bandits
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Note: View the original document on HAL open archive server: https://hal.science/hal-00450235v2
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References listed on IDEAS
- Sébastien Bubeck & Rémi Munos & Gilles Stoltz, 2010. "Pure Exploration for Multi-Armed Bandit Problems," Working Papers hal-00257454, HAL.
- Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2008. "Online Optimization in X-Armed Bandits," Post-Print inria-00329797, HAL.
Citations
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Cited by:
- Yuqing Zhang & Neil Walton, 2019. "Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches," Papers 1907.05381, arXiv.org.
- Daniel Russo & Benjamin Van Roy, 2018. "Learning to Optimize via Information-Directed Sampling," Operations Research, INFORMS, vol. 66(1), pages 230-252, January.
- Pooriya Beyhaghi & Ryan Alimo & Thomas Bewley, 2020. "A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging," Computational Optimization and Applications, Springer, vol. 76(1), pages 1-31, May.
- Daniel Russo & Benjamin Van Roy, 2014. "Learning to Optimize via Posterior Sampling," Mathematics of Operations Research, INFORMS, vol. 39(4), pages 1221-1243, November.
- Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
- Ningyuan Chen & Guillermo Gallego, 2018. "A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint," Papers 1812.09234, arXiv.org, revised Oct 2021.
- Saeid Delshad & Amin Khademi, 2020. "Information theory for ranking and selection," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 239-253, June.
- Nicolas Della Penna & Mark D. Reid, 2011. "Bandit Market Makers," Papers 1112.0076, arXiv.org, revised Aug 2013.
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Keywords
bandits with infinitely many arms; optimistic online optimization; regret bounds; minimax rates;All these keywords.
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