Spearmint is a package to perform Bayesian optimization according to
the algorithms outlined in the paper:
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle and Ryan P. Adams
Advances in Neural Information Processing Systems
This code 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 function in as few runs as
The README file contains an explanation of the two packages and
outlines how they can be used.
If you would like to contribute to spearmint, this package is also available as an open source project here.