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.
| Type: | Proceedings paper |
|---|---|
| Title: | Probabilistic Active Meta-Learning |
| Event: | Neural Information Processing Systems 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.neurips.cc/paper/2020 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10120832 |
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