Author:
Agrawal Shipra,Avadhanula Vashist,Goyal Vineet,Zeevi Assaf
Publisher
Springer International Publishing
Reference31 articles.
1. Agrawal, S., Avadhanula, V., Goyal, V., & Zeevi, A. (2016). A near-optimal exploration-exploitation approach for assortment selection. In Proceedings of the 2016 ACM conference on economics and computation (pp. 599–600).
2. Agrawal, S., Avadhanula, V., Goyal, V., & Zeevi, A. (2017). Thompson sampling for the MNL-bandit. In Conference on learning theory (pp. 76–78). PMLR.
3. Agrawal, S., Avadhanula, V., Goyal, V., & Zeevi, A. (2019). MNL-Bandit: A dynamic learning approach to assortment selection. Operations Research, 67(5), 1453–1485.
4. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 235–256.
5. Avadhanula, V. (2019). The MNL-Bandit problem: Theory and applications. New York: Columbia University.
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