Safe Active Learning for Multi-Output Gaussian Processes
This work addresses safety concerns in data acquisition for multi-output regression, particularly in domains like robotics and engineering, but it is incremental as it builds on existing multi-output Gaussian process methods.
The paper tackles the problem of expensive and safety-critical data acquisition in multi-output regression by proposing a safe active learning approach for multi-output Gaussian processes, which shows improved convergence on simulated and real-world datasets compared to competitors.
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.