Author:
Villar Sofia S.,Jacko Peter
Publisher
Springer International Publishing
Reference35 articles.
1. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2–3), 235–256.
2. Bowden, J., & Trippa, L. (2017). Unbiased estimation for response adaptive clinical trials. Statistical Methods in Medical Research, 26(5), 2376–2388.
3. Bubeck, S., & Cesa-Bianchi, N. (2012). Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning, 5(1), 1–122.
4. Burnett, T., Mozgunov, P., Pallmann, P., Villar, S. S., Wheeler, G. M., & Jaki, T. (2020). Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Medicine, 18(1), 1–21.
5. Cserna, B., Petrik, M., Russel, R. H., & Ruml, W. (2017). Value directed exploration in multi-armed bandits with structured priors. In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence.