Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
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Publication:1707465
DOI10.1007/S10994-017-5684-YzbMath1457.68242OpenAlexW2780562800WikidataQ59866136 ScholiaQ59866136MaRDI QIDQ1707465
Lars Schmidt-Thieme, Martin Wistuba, Nicolas Schilling
Publication date: 3 April 2018
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-017-5684-y
Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (5)
Large scale multi-output multi-class classification using Gaussian processes ⋮ AutonoML: Towards an Integrated Framework for Autonomous Machine Learning ⋮ Scalable Gaussian process-based transfer surrogates for hyperparameter optimization ⋮ Dataset2Vec: learning dataset meta-features ⋮ Benchmark and Survey of Automated Machine Learning Frameworks
Uses Software
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