Title
Transfer Learning with Gaussian Processes for Bayesian Optimization
Abstract
Bayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data. Its data efficiency can be further improved by transfer learning from related tasks. While recent transfer models meta-learn a prior based on large amount of data, in the low-data regime methods that exploit the closed-form posterior of Gaussian processes (GPs) have an advantage. In this setting, several analytically tractable transfer-model posteriors have been proposed, but the relative advantages of these methods are not well understood. In this paper, we provide a unified view on hierarchical GP models for transfer learning, which allows us to analyze the relationship between methods. As part of the analysis, we develop a novel closed-form boosted GP transfer model that fits between existing approaches in terms of complexity. We evaluate the performance of the different approaches in large-scale experiments and highlight strengths and weaknesses of the different transfer-learning methods.
Year
Venue
DocType
2022
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151
Conference
Volume
ISSN
Citations 
151
2640-3498
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Petru Tighineanu100.34
Kathrin Skubch200.34
Paul Baireuther300.34
Attila Reiss400.34
Felix Berkenkamp500.34
Julia Vinogradska600.34