A lightweight framework for Bayesian and model-based optimisation of black-box functions (C++11).
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@@ -27,7 +27,7 @@ Main references
-**General introduction:** Brochu, E., Cora, V. M., & De Freitas, N. (2010). A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. *arXiv preprint arXiv:1012.2599*.
-**Gaussian Processes (GP)**: Rasmussen, C. A, Williams C. K. I. (2006). /Gaussian Processes for Machine Learning./ MIT Press.
-**Gaussian Processes (GP)**: Rasmussen, C. A, Williams C. K. I. (2006). /Gaussian Processes for Machine Learning./ MIT Press.
-**Optimizing hyperparameters:** Blum, M., & Riedmiller, M. (2013). Optimization of Gaussian Process Hyperparameters using Rprop. In *European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning*.
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@@ -45,4 +45,3 @@ Limbo is a framework for our research that is voluntarily kept small. It is desi
If you need a more full-featured library, check:
- BayesOpt: http://rmcantin.bitbucket.org/html/
- libGP (no optimization): https://github.com/mblum/libgp
