Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
type: Item
instance of: scholarly article
title: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
zbMATH Open document ID: 1457.68242
author: Martin Wistuba
author: Nicolas Schilling
author: Lars Schmidt-Thieme
published in: Machine Learning
publication date: 2018-04-03T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62G08
zbMATH DE Number: 6855212
zbMATH Keywords: hyperparameter optimization
zbMATH Keywords: Gaussian processes
zbMATH Keywords: sequential model-based optimization
zbMATH Keywords: meta-learning
describes a project that uses: SATzilla
describes a project that uses: Auto-WEKA
describes a project that uses: Spearmint
describes a project that uses: SMAC
describes a project that uses: WEKA
describes a project that uses: EGO
describes a project that uses: ODEXPERT
describes a project that uses: auto-sklearn
MaRDI profile type: Publication
Wikidata QID: Q59866136
full work available at URL: s10994-017-5684-y
OpenAlex ID: W2780562800
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DOI: 10.1007/S10994-017-5684-Y
DBLP publication ID: journals/ml/WistubaSS18
description: scientific article; zbMATH DE number 6855212