Gaussian process bandit optimisation with multi-fidelity evaluations

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 1000-1008

  Kandasamy, Kirthevasan ^a   Dasarathy, Gautam ^b   Oliva, Junier ^a   Schneider, Jeff ^a   Póczos, Barnabs ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b RICE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

BANDIT PROBLEMS; BLACK-BOX FUNCTIONS; FUNCTION VALUES; GAUSSIAN PROCESSES; MULTI FIDELITIES; SCIENTIFIC AND ENGINEERING APPLICATIONS; TARGET FUNCTIONS; UPPER CONFIDENCE BOUND;

FUNCTION EVALUATION;

EID: 85018876167     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Alekh Agarwal, John C Duchi, Peter L Bartlett, and Clement Levrard. Oracle inequalities for computationally budgeted model selection. In COLT, 2011.
2. Peter Auer. Using Confidence Bounds for Exploitation-exploration Trade-offs. J. Mach. Learn. Res., 2003.
3. E. Brochu, V. M. Cora, and N. de Freitas. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical RL. CoRR, 2010.
4. Sébastien Bubeck and Nicolò Cesa-Bianchi. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends in Machine Learning, 2012.
5. Mark Cutler, Thomas J. Walsh, and Jonathan P. How. Reinforcement Learning with Multi-Fidelity Simulators. In ICRA, 2014.
6. V. Dani, T. P. P. Hayes, and S. M Kakade. Stochastic Linear Optimization under Bandit Feedback. In COLT, 2008.
7. T. M. Davis et al. Scrutinizing Exotic Cosmological Models Using ESSENCE Supernova Data Combined with Other Cosmological Probes. Astrophysical Journal, 2007.
8. J Djolonga, A Krause, and V Cevher. High-Dimensional Gaussian Process Bandits. In NIPS, 2013.
9. Alexander I. J. Forrester, András Sóbester, and Andy J. Keane. Multi-fidelity optimization via surrogate modelling. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, 2007.
10. Subhashis Ghosal and Anindya Roy. Posterior consistency of Gaussian process prior for nonparametric binary regression". Annals of Statistics, 2006.
11. D. Huang, T.T. Allen, W.I. Notz, and R.A. Miller. Sequential kriging optimization using multiple-fidelity evaluations. Structural and Multidisciplinary Optimization, 2006.
12. D. R. Jones, C. D. Perttunen, and B. E. Stuckman. Lipschitzian Optimization Without the Lipschitz Constant. J. Optim. Theory Appl., 1993.
13. Donald R. Jones, Matthias Schonlau, and William J. Welch. Efficient global optimization of expensive black-box functions. J. of Global Optimization, 1998.
14. Kirthevasan Kandasamy and Yaoliang Yu. Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. In ICML, 2016.
15. Kirthevasan Kandasamy, Jeff Schenider, and Barnabás Póczos. High Dimensional Bayesian Optimisation and Bandits via Additive Models. In International Conference on Machine Learning, 2015.
16. Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, and Barnabas Poczos. The Multi-fidelity Multi-armed Bandit. In NIPS, 2016.
17. K. Kawaguchi, L. P. Kaelbling, and T. Lozano-Pérez. Bayesian Optimization with Exponential Convergence. In NIPS, 2015.
18. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. SCIENCE, 1983.
19. A. Klein, S. Bartels, S. Falkner, P. Hennig, and F. Hutter. Towards efficient Bayesian Optimization for Big Data. In BayesOpt, 2015.
20. R. Martinez-Cantin, N. de Freitas, A. Doucet, and J. Castellanos. Active Policy Learning for Robot Planning and Exploration under Uncertainty. In Proceedings of Robotics: Science and Systems, 2007.
21. Jonas Mockus. Application of Bayesian approach to numerical methods of global and stochastic optimization. Journal of Global Optimization, 1994.
22. R. Munos. Optimistic Optimization of Deterministic Functions without the Knowledge of its Smoothness. In NIPS, 2011.
23. D. Parkinson, P. Mukherjee, and A. R Liddle. A Bayesian model selection analysis of WMAP3. Physical Review, 2006.
24. C.E. Rasmussen and C.K.I. Williams. Gaussian Processes for Machine Learning. UPG Ltd, 2006.
25. Herbert Robbins. Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 1952.
26. A Sabharwal, H Samulowitz, and G Tesauro. Selecting near-optimal learners via incremental data allocation. In AAAI, 2015.
27. J. Snoek, H. Larochelle, and R. P Adams. Practical Bayesian Optimization of Machine Learning Algorithms. In NIPS, 2012.
28. Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In ICML, 2010.
29. Kevin Swersky, Jasper Snoek, and Ryan P Adams. Multi-task bayesian optimization. In NIPS, 2013.
30. W. R. Thompson. On the Likelihood that one Unknown Probability Exceeds Another in View of the Evidence of Two Samples. Biometrika, 1933.
31. Paul A. Viola and Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. In Computer Vision and Pattern Recognition, 2001.
32. Shifeng Xiong, Peter Z. G. Qian, and C. F. Jeff Wu. Sequential design and analysis of high-accuracy and low-accuracy computer codes. Technometrics, 2013.
33. C. Zhang and K. Chaudhuri. Active Learning from Weak and Strong Labelers. In NIPS, 2015.