NeurIPS 2012

Practical Bayesian Optimization of Machine Learning Algorithms

NeurIPS 2012 HIPS/Spearmint

In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP).

HYPERPARAMETER OPTIMIZATION

Factoring nonnegative matrices with linear programs

NeurIPS 2012 martinResearch/PySparseLP

The constraints are chosen to ensure that the matrix C selects features; these features can then be used to find a low-rank NMF of X.