Practical Transfer Learning for Bayesian Optimization [article]

Matthias Feurer, Benjamin Letham, Frank Hutter, Eytan Bakshy
2022 arXiv   pre-print
When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extensions to Bayesian optimization and establish a worst-case bound compared to vanilla Bayesian optimization. Using a large collection of hyperparameter optimization benchmark
more » ... , we demonstrate that our contributions substantially reduce optimization time compared to standard Gaussian process-based Bayesian optimization and improve over the current state-of-the-art for transfer hyperparameter optimization.
arXiv:1802.02219v4 fatcat:gqvmfemisjb6ng7rozteoyoley