Bandits attack function optimization
暂无分享,去创建一个
[1] H. Robbins. Some aspects of the sequential design of experiments , 1952 .
[2] Ponnuthurai Nagaratnam Suganthan,et al. Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .
[3] W. R. Thompson. ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES , 1933 .
[4] Rémi Munos,et al. Optimistic Optimization of Deterministic Functions , 2011, NIPS 2011.
[5] T. Rowan. Functional stability analysis of numerical algorithms , 1990 .
[6] Rémi Munos,et al. Stochastic Simultaneous Optimistic Optimization , 2013, ICML.
[7] András György,et al. Efficient Multi-start Strategies for Local Search Algorithms , 2009, ECML/PKDD.
[8] András György,et al. Efficient Multi-Start Strategies for Local Search Algorithms , 2009, J. Artif. Intell. Res..
[9] M. Powell. The BOBYQA algorithm for bound constrained optimization without derivatives , 2009 .
[10] C. D. Perttunen,et al. Lipschitzian optimization without the Lipschitz constant , 1993 .
[11] Peter Auer,et al. Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.
[12] Rémi Munos,et al. From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning , 2014, Found. Trends Mach. Learn..
[13] R. Munos,et al. Best Arm Identification in Multi-Armed Bandits , 2010, COLT.
[14] Rémi Munos,et al. Pure Exploration in Multi-armed Bandits Problems , 2009, ALT.
[15] T. L. Lai Andherbertrobbins. Asymptotically Efficient Adaptive Allocation Rules , 2022 .