方便好用的(Python)貝葉斯優化庫Spearmint
方便好用的(Python)貝葉斯優化庫Spearmint。
Spearmint is a software package to perform Bayesian optimization. The Software is designed to automatically run experiments (thus the code name spearmint) in a manner that iteratively adjusts a number of parameters so as to minimize some objective in as few runs as possible.
IMPORTANT: Spearmint is under an Academic and Non-Commercial Research Use License. Before using spearmint please be aware of the license. If you do not qualify to use spearmint you can ask to obtain a license as detailed in the license or you can use the older open source code version (which is somewhat outdated) at https://github.com/JasperSnoek/spearmint.
Relevant Publications
Spearmint implements a combination of the algorithms detailed in the following publications:
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle and Ryan Prescott Adams Advances in Neural Information Processing Systems, 2012 Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek and Ryan Prescott Adams Advances in Neural Information Processing Systems, 2013 Input Warping for Bayesian Optimization of Non-stationary Functions Jasper Snoek, Kevin Swersky, Richard Zemel and Ryan Prescott Adams International Conference on Machine Learning, 2014 Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology Jasper Snoek, PhD Thesis, University of Toronto, 2013 Bayesian Optimization with Unknown Constraints Michael Gelbart, Jasper Snoek and Ryan Prescott Adams Uncertainty in Artificial Intelligence, 2014