Multi-armed bandits with dependent arms

Author:

Singh RahulORCID,Liu Fang,Sun Yin,Shroff Ness

Funder

National Science Foundation

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference48 articles.

1. Abbasi-Yadkori, Y., Pál, D., & Szepesvári, C. (2011). Improved algorithms for linear stochastic bandits. In: Advances in Neural Information Processing Systems, (pp. 2312–2320)

2. Akshay D Kamath, S.G. (2016). Cs 395t: Sublinear algorithms, lecture notes. https://www.cs.utexas.edu/~ecprice/courses/sublinear/notes/lec12.pdf

3. Atan, O., Tekin, C., & Schaar, M. (2015). Global multi-armed bandits with Hölder continuity. In: Artificial Intelligence and Statistics, (pp. 28–36)

4. Auer, P. (2002). Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 3, 397–422.

5. Awerbuch, B., & Kleinberg, R. (2008). Online linear optimization and adaptive routing. Journal of Computer and System Sciences, 74(1), 97–114.

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