Safe Active Learning for Multi-Output Gaussian Processes

Cen-You Li, Barbara Rakitsch, Christoph Zimmer
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:4512-4551, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-li22d, title = { Safe Active Learning for Multi-Output Gaussian Processes }, author = {Li, Cen-You and Rakitsch, Barbara and Zimmer, Christoph}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {4512--4551}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/li22d/li22d.pdf}, url = {https://proceedings.mlr.press/v151/li22d.html}, 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. } }
Endnote
%0 Conference Paper %T Safe Active Learning for Multi-Output Gaussian Processes %A Cen-You Li %A Barbara Rakitsch %A Christoph Zimmer %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-li22d %I PMLR %P 4512--4551 %U https://proceedings.mlr.press/v151/li22d.html %V 151 %X 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.
APA
Li, C., Rakitsch, B. & Zimmer, C.. (2022). Safe Active Learning for Multi-Output Gaussian Processes . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:4512-4551 Available from https://proceedings.mlr.press/v151/li22d.html.

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