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Practical Transfer Learning for Bayesian Optimization
[article]
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
arXiv:1802.02219v4
fatcat:gqvmfemisjb6ng7rozteoyoley