2014
JMLR
JMLR 2014
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Abstract
BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave. [abs] [ pdf ][ bib ] [ code ] © JMLR 2014. (edit, beta)
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Trend Setter
— Global Optimization
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Keyword Pioneer
— sequential experimental design
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Hot Topic Early Bird
— sample efficiency
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio