This is a Matlab implementation of Additive Gaussian Process Optimisation and Bandits. For more details please read our paper (below).
- Just add all relevant paths to your Matlab workspace
- The demos directory has two examples on how to use this library.
- demo.m: Easy set up and uses all default configurations.
- demoCustomise.m: This sets all parameters individually. We also compare multiple instantiations of Add-GP-UCB with different group sizes along with GP-Expected Improvement and DiRect (Dividing Rectangles).
If you use this library in your academic work please cite our ICML paper: "High Dimensional Bayesian Optimisation and Bandits using Additive Models", Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos. International Conference on Machine Learning, 2015. The paper is available at: http://www.cs.cmu.edu/~kkandasa/pubs/kandasamyICML15addGPUCB.pdf.
We use DiRect to optimise the acquisition function. The implementation was taken from Dan Finkel (2004).
This software is released under GNU GPL v3(>=) License. Please read LICENSE.txt for more information.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
"Copyright 2015 Kirthevasan Kandasamy"
- For questions/ bug reports please email kandasamy@cs.cmu.edu