Probabilistic Active Meta-Learning

• URL: http://arxiv.org/abs/2007.08949v2
• Date: Thu, 22 Oct 2020 23:17:28 GMT
• Title: Probabilistic Active Meta-Learning
• Authors: Jean Kaddour, Steind\'or S{\ae}mundsson, Marc Peter Deisenroth
• Abstract summary: We introduce task selection based on prior experience into a meta-learning algorithm. We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments.
• Score: 15.432006404678981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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