Meta-Learning Reliable Priors in the Function SpaceDownload PDF

30 Sept 2021 (modified: 05 May 2023)NeurIPS 2021 Workshop MetaLearn ContributedTalkReaders: Everyone
Keywords: Transfer, Meta-Learning, Life-Long Learning, Bayesian Optimization, AutoML, Hyper-Parameter Tuning
TL;DR: Meta-learning reliable priors for Bayesian optimization via meta-regularization in the function space.
Abstract: When data are scarce meta-learning can improve a learner's accuracy by harnessing previous experience from related learning tasks. However, existing methods have unreliable uncertainty estimates which are often overconfident. Addressing these shortcomings, we introduce a novel meta-learning framework, called F-PACOH, that treats meta-learned priors as stochastic processes and performs meta-level regularization directly in the function space. This allows us to directly steer the probabilistic predictions of the meta-learner towards high epistemic uncertainty in regions of insufficient meta-training data and, thus, obtain well-calibrated uncertainty estimates. Finally, we showcase how our approach can be integrated with sequential decision making, where reliable uncertainty quantification is imperative. In our benchmark study on meta-learning for Bayesian Optimization (BO), F-PACOH significantly outperforms all other meta-learners and standard baselines.
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