Kaddour, J; Sæmundsson, S; Deisenroth, MP; (2020) Probabilistic Active Meta-Learning. In: Larochelle, H and Ranzato, M and Hadsell, R and Balcan, M-F and Lin, H-T, (eds.) Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020). NeurIPS Proceedings: Virtual conference. (In press).

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Abstract

Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear. To address this problem, meta-learning algorithms use prior experience about tasks to learn new, related tasks efficiently. Typically, a set of training tasks is assumed given or randomly chosen. However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner? In this work, we introduce task selection based on prior experience into a meta-learning algorithm by conceptualizing the learner and the active meta-learning setting using a probabilistic latent variable model. We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments.

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