36 citations · 81 across the 7 of their papers we have counts for
4 papers · 1 filter
Constrained Bayesian Optimization with Max-Value Entropy Search
Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton +2
Bayesian optimization (BO) is a model-based approach to sequentially optimize expensive black-box functions, such as the validation error of a deep neural network with respect to i…
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias Seeger +2
Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in…
A Quantile-based Approach for Hyperparameter Transfer Learning
David Salinas, Huibin Shen, Valerio Perrone
Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Traditionally, BO focuses on a single task at a time and is not de…
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Valerio Perrone, Rodolphe Jenatton, Matthias Seeger +1
Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization. Typically, BO is powered by a Gaussian process (GP), whose algorithmic compl…