Author:
Kano Hideaki,Honda Junya,Sakamaki Kentaro,Matsuura Kentaro,Nakamura Atsuyoshi,Sugiyama Masashi
Funder
Japan Society for the Promotion of Science
Core Research for Evolutional Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Reference22 articles.
1. Agrawal, S., & Goyal, N. (2012). Analysis of thompson sampling for the multi-armed bandit problem. In Proceedings of the 25th annual conference on learning theory (vol. 23, pp. 39.1–39.26).
2. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 235–256.
3. Cappé, O., Garivier, A., Maillard, O. A., Munos, R., & Stoltz, G. (2012). Kullback-leibler upper confidence bounds for optimal sequential allocation. The Annals of Statistics, 41(3), 1516–1541.
4. Choy, E., Bendit, M., McAleer, D., Liu, F., Feeney, M., Brett, S., et al. (2013). Safety, tolerability, pharmacokinetics and pharmacodynamics of an anti- oncostatin m monoclonal antibody in rheumatoid arthritis: Results from phase ii randomized, placebo-controlled trials. Arthritis Research & Therapy, 15(5), R132.
5. Curtis, J., Yang, S., Chen, L., Pope, J., Keystone, E., Haraoui, B., et al. (2015). Determining the minimally important difference in the clinical disease activity index for improvement and worsening in early rheumatoid arthritis patients. Arthritis Care & Research, 67(10), 1345–1353.
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