Transfer Learning with Gaussian Processes for Bayesian Optimization

2021/11/22 by Petru Tighineanu, Tighineanu, Petru, Kathrin Skubch +9 · 5 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.2111.11223

openalex publication_date 2021/11/22 · arxiv created 2022/03/15 · arxiv updated 2022/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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