Scalable Meta-Learning with Gaussian Processes

Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1981-1989, 2024.

Abstract

Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) together with Bayesian optimization have achieved high performance. However, these methods are either computationally expensive or introduce assumptions that hinder a principled propagation of uncertainty between task models. This may disrupt the balance between exploration and exploitation during optimization. In this paper, we develop ScaML-GP, a modular GP model for meta-learning that is scalable in the number of tasks. Our core contribution is carefully designed multi-task kernel that enables hierarchical training and task scalability. Conditioning ScaML-GP on the meta-data exposes its modular nature yielding a test-task prior that combines the posteriors of meta-task GPs. In synthetic and real-world meta-learning experiments, we demonstrate that ScaML-GP can learn efficiently both with few and many meta-tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-tighineanu24a, title = { Scalable Meta-Learning with {G}aussian Processes }, author = {Tighineanu, Petru and Grossberger, Lukas and Baireuther, Paul and Skubch, Kathrin and Falkner, Stefan and Vinogradska, Julia and Berkenkamp, Felix}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {1981--1989}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/tighineanu24a/tighineanu24a.pdf}, url = {https://proceedings.mlr.press/v238/tighineanu24a.html}, abstract = { Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) together with Bayesian optimization have achieved high performance. However, these methods are either computationally expensive or introduce assumptions that hinder a principled propagation of uncertainty between task models. This may disrupt the balance between exploration and exploitation during optimization. In this paper, we develop ScaML-GP, a modular GP model for meta-learning that is scalable in the number of tasks. Our core contribution is carefully designed multi-task kernel that enables hierarchical training and task scalability. Conditioning ScaML-GP on the meta-data exposes its modular nature yielding a test-task prior that combines the posteriors of meta-task GPs. In synthetic and real-world meta-learning experiments, we demonstrate that ScaML-GP can learn efficiently both with few and many meta-tasks. } }
Endnote
%0 Conference Paper %T Scalable Meta-Learning with Gaussian Processes %A Petru Tighineanu %A Lukas Grossberger %A Paul Baireuther %A Kathrin Skubch %A Stefan Falkner %A Julia Vinogradska %A Felix Berkenkamp %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-tighineanu24a %I PMLR %P 1981--1989 %U https://proceedings.mlr.press/v238/tighineanu24a.html %V 238 %X Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) together with Bayesian optimization have achieved high performance. However, these methods are either computationally expensive or introduce assumptions that hinder a principled propagation of uncertainty between task models. This may disrupt the balance between exploration and exploitation during optimization. In this paper, we develop ScaML-GP, a modular GP model for meta-learning that is scalable in the number of tasks. Our core contribution is carefully designed multi-task kernel that enables hierarchical training and task scalability. Conditioning ScaML-GP on the meta-data exposes its modular nature yielding a test-task prior that combines the posteriors of meta-task GPs. In synthetic and real-world meta-learning experiments, we demonstrate that ScaML-GP can learn efficiently both with few and many meta-tasks.
APA
Tighineanu, P., Grossberger, L., Baireuther, P., Skubch, K., Falkner, S., Vinogradska, J. & Berkenkamp, F.. (2024). Scalable Meta-Learning with Gaussian Processes . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:1981-1989 Available from https://proceedings.mlr.press/v238/tighineanu24a.html.

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