Safe Active Learning for Multi-Output Gaussian Processes

2022/03/28 by Cen-You Li, Li, Cen-You, Barbara Rakitsch +3 · 2 citations
Computer Science · Engineering · #Advanced Control Systems Optimization #FOS: Computer and information sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2203.14849

openalex publication_date 2022/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Multi-output regression problems are commonly encountered in science and engineering. In particular, multi-output Gaussian processes have been emerged as a promising tool for modeling these complex systems since they can exploit the inherent correlations and provide reliable uncertainty estimates. In many applications, however, acquiring the data is expensive and safety concerns might arise (e.g. robotics, engineering). We propose a safe active learning approach for multi-output Gaussian process regression. This approach queries the most informative data or output taking the relatedness between the regressors and safety constraints into account. We prove the effectiveness of our approach by providing theoretical analysis and by demonstrating empirical results on simulated datasets and on a real-world engineering dataset. On all datasets, our approach shows improved convergence compared to its competitors.

Cited by

• Multi-Output Robust and Conjugate Gaussian Processes
• Computationally Efficient Estimation of Localized Treatment Effects for Multi-Level, Multi-Component Interventions to Address the Opioid Crisis

Related

• Near-Optimal Active Learning of Multi-Output Gaussian Processes
• Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
• Distributionally Robust Active Learning for Gaussian Process Regression
• Safe Active Learning for Time-Series Modeling with Gaussian Processes
• The Gaussian Process Autoregressive Regression Model (GPAR)