Fast Bayesian hyperparameter optimization on large datasets
type: Item
instance of: scholarly article
title: Fast Bayesian hyperparameter optimization on large datasets
zbMATH Open document ID: 1421.62027
DOI: 10.1214/17-EJS1335SI
author: Peng Zhang
published in: Electronic Journal of Statistics
publication date: 2018-01-12T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.ejs/1513306864
review text: A Bayesian optimization procedure is proposed in this paper. It treats the size of a randomly subsampled dataset as an additional input to the loss function. At each evaluation, the optimization procedure allows the optimizer to actively choose such to yield most information about the loss-minimizing configuration on the full dataset per unit time spent. An automatically trade off high information gain about the global optimum against computational cost is established. The results of experiments with support vector machines and neural networks to evaluate the speed of the procedure in identifying good hyperparameter settings are presented.
reviewed by: Annibal Parracho Sant'Anna
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62M45
Mathematics Subject Classification ID: 90-08
zbMATH DE Number: 6825037
zbMATH Keywords: hyperparameter optimization
zbMATH Keywords: entropy search
zbMATH Keywords: Gaussian process
zbMATH Keywords: acquisition function
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 6825037