9.4k citations · 16.5k across the 107 of their papers we have counts for
3 papers · 2 filters
Maximizing acquisition functions for Bayesian optimization
James T. Wilson, Frank Hutter, Marc Peter Deisenroth
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search proc…
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achiev…
Practical Transfer Learning for Bayesian Optimization
Matthias Feurer, Benjamin Letham, Frank Hutter +1
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…