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A modern Bayesian look at the multi‐armed bandit

Citations

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Cited by:

  1. T. Law & J. Shawe-Taylor, 2017. "Practical Bayesian support vector regression for financial time series prediction and market condition change detection," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1403-1416, September.
  2. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  3. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
  4. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
  5. Chao Qin & Daniel Russo, 2024. "Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification," Papers 2402.10592, arXiv.org, revised Jul 2024.
  6. Dean Eckles & Maurits Kaptein, 2019. "Bootstrap Thompson Sampling and Sequential Decision Problems in the Behavioral Sciences," SAGE Open, , vol. 9(2), pages 21582440198, June.
  7. Zhishuai Liu & Jesse Clifton & Eric B. Laber & John Drake & Ethan X. Fang, 2023. "Deep Spatial Q-Learning for Infectious Disease Control," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(4), pages 749-773, December.
  8. Sareh Nabi & Houssam Nassif & Joseph Hong & Hamed Mamani & Guido Imbens, 2022. "Bayesian Meta-Prior Learning Using Empirical Bayes," Management Science, INFORMS, vol. 68(3), pages 1737-1755, March.
  9. Daniel Russo & Benjamin Van Roy, 2022. "Satisficing in Time-Sensitive Bandit Learning," Mathematics of Operations Research, INFORMS, vol. 47(4), pages 2815-2839, November.
  10. Vivek F. Farias & Eli Gutin, 2022. "Optimistic Gittins Indices," Operations Research, INFORMS, vol. 70(6), pages 3432-3456, November.
  11. Daniel Russo & Benjamin Van Roy, 2018. "Learning to Optimize via Information-Directed Sampling," Operations Research, INFORMS, vol. 66(1), pages 230-252, January.
  12. Yuhang Wu & Zeyu Zheng & Guangyu Zhang & Zuohua Zhang & Chu Wang, 2025. "Nonstationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference," Management Science, INFORMS, vol. 71(6), pages 4707-4727, June.
  13. Kohei Kawaguchi, 2021. "When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business," Management Science, INFORMS, vol. 67(3), pages 1670-1695, March.
  14. Mingyu Joo & Michael L. Thompson & Greg M. Allenby6, 2019. "Optimal Product Design by Sequential Experiments in High Dimensions," Management Science, INFORMS, vol. 65(7), pages 3235-3254, July.
  15. Kemper, Jan & Rostam-Afschar, Davud, 2026. "Earning While Learning: How to Run Batched Bandit Experiments," GLO Discussion Paper Series 1717, Global Labor Organization (GLO).
  16. Ben Vinod & Richard Ratliff & Vikram Jayaram, 2018. "An approach to offer management: maximizing sales with fare products and ancillaries," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(2), pages 91-101, April.
  17. Duflo, Esther & Banerjee, Abhijit & Keniston, Daniel, 2019. "The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India," CEPR Discussion Papers 13981, Centre for Economic Policy Research.
  18. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
  19. Alina Ferecatu & Arnaud De Bruyn, 2022. "Understanding Managers’ Trade-Offs Between Exploration and Exploitation," Marketing Science, INFORMS, vol. 41(1), pages 139-165, January.
  20. Maria Dimakopoulou & Zhimei Ren & Zhengyuan Zhou, 2021. "Online Multi-Armed Bandits with Adaptive Inference," Papers 2102.13202, arXiv.org, revised Jun 2021.
  21. Manini Madireddy & Ramasubramanian Sundararajan & Goda Doreswamy & Meisam Hejazi Nia & Amod Mital, 2017. "Constructing bundled offers for airline customers," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(6), pages 532-552, December.
  22. Mauersberger, Felix, 2021. "Monetary policy rules in a non-rational world: A macroeconomic experiment," Journal of Economic Theory, Elsevier, vol. 197(C).
  23. A Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Osman Shami & Alexander Teytelboym, 2024. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," Journal of the European Economic Association, European Economic Association, vol. 22(2), pages 781-836.
  24. Elea McDonnell Feit & Ron Berman, 2019. "Test & Roll: Profit-Maximizing A/B Tests," Marketing Science, INFORMS, vol. 38(6), pages 1038-1058, November.
  25. Po-Yi Liu & Chi-Hua Wang & Henghsiu Tsai, 2022. "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing," Papers 2208.09372, arXiv.org, revised Sep 2022.
  26. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
  27. Yixin Tang & Yicong Lin & Navdeep S. Sahni, 2023. "Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash," Papers 2311.14698, arXiv.org, revised Nov 2023.
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