The multi-fidelity multi-armed bandit

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 1785-1793

  Kandasamy, Kirthevasan ^a   Dasarathy, Gautam ^b   Schneider, Jeff ^a   Póczos, Barnabás ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b RICE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

STOCHASTIC SYSTEMS;

BIASED ESTIMATES; LOWER BOUNDS; MULTI ARMED BANDIT; MULTI FIDELITIES; ONLINE ADVERTISING; TIME STEP; TIME-PERIODS; UPPER CONFIDENCE BOUND;

MARKETING;

EID: 85018901939     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Rajeev Agrawal. Sample Mean Based Index Policies with O(log n) Regret for the Multi-Armed Bandit Problem. Advances in Applied Probability, 1995.
2. Jean-Yves Audibert, Rémi Munos, and Csaba Szepesvári. Exploration-exploitation Tradeoff Using Variance Estimates in Multi-armed Bandits. Theor. Comput. Sci., 2009.
3. Peter Auer. Using Confidence Bounds for Exploitation-exploration Trade-offs. J. Mach. Learn. Res., 2003.
4. Yoram Baram, Ran El-Yaniv, and Kobi Luz. Online choice of active learning algorithms. The Journal of Machine Learning Research, 5:255-291, 2004.
5. Sébastien Bubeck and Nicolò Cesa-Bianchi. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends in Machine Learning, 2012.
6. Mark Cutler, Thomas J. Walsh, and Jonathan P. How. Reinforcement Learning with Multi-Fidelity Simulators. In IEEE International Conference on Robotics and Automation (ICRA), 2014.
7. D. Huang, T.T. Allen, W.I. Notz, and R.A. Miller. Sequential kriging optimization using multiple-fidelity evaluations. Structural and Multidisciplinary Optimization, 2006.
8. Kirthevasan Kandasamy, Gautam Dasarathy, Junier Oliva, Jeff Schenider, and Barnabás Póczos. Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. In Advances in Neural Information Processing Systems, 2016.
9. T. L. Lai and Herbert Robbins. Asymptotically Efficient Adaptive Allocation Rules. Advances in Applied Mathematics, 1985.
10. Dev Rajnarayan, Alex Haas, and Ilan Kroo. A multifidelity gradient-free optimization method and application to aerodynamic design. In AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, Etats-Unis, 2008.
11. Herbert Robbins. Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 1952.
12. W. R. Thompson. On the Likelihood that one Unknown Probability Exceeds Another in View of the Evidence of Two Samples. Biometrika, 1933.
13. Long Tran-Thanh, Lampros C. Stavrogiannis, Victor Naroditskiy, Valentin Robu, Nicholas R. Jennings, and Peter Key. Efficient Regret Bounds for Online Bid Optimisation in Budget-Limited Sponsored Search Auctions. In UAI, 2014.
14. Larry Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer Publishing Company, Incorporated, 2010.
15. Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu. Thompson Sampling for Budgeted Multi-Armed Bandits. In IJCAI, 2015.
16. Chicheng Zhang and Kamalika Chaudhuri. Active Learning from Weak and Strong Labelers. In Advances in Neural Information Processing Systems, 2015.