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Variational Task Encoders for Model-Agnostic Meta-Learning
Joaquin Vanschoren
Event URL: https://openreview.net/forum?id=dfYhf5IuMPE »

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on novel tasks. A critical challenge lies in the inherent uncertainty about whether new tasks can be considered similar to those observed before, and robust meta-learning methods would ideally reason about this to produce corresponding uncertainty estimates. We extend model-agnostic meta-learning with variational inference: we model the identity of new tasks as a latent random variable, which modulates the fine-tuning of meta-learned neural networks. Our approach requires little additional computation and doesn't make strong assumptions about the distribution of the neural network weights, and allows the algorithm to generalize to more divergent task distributions, resulting in better-calibrated uncertainty measures while maintaining accurate predictions.

Author Information

Joaquin Vanschoren (Eindhoven University of Technology)
Joaquin Vanschoren

Joaquin Vanschoren is an Assistant Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on meta-learning and understanding and automating machine learning. He founded and leads OpenML.org, a popular open science platform that facilitates the sharing and reuse of reproducible empirical machine learning data. He obtained several demo and application awards and has been invited speaker at ECDA, StatComp, IDA, AutoML@ICML, CiML@NIPS, AutoML@PRICAI, MLOSS@NIPS, and many other occasions, as well as tutorial speaker at NIPS and ECMLPKDD. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and co-organizes the AutoML and meta-learning workshop series at NIPS 2018, ICML 2016-2018, ECMLPKDD 2012-2015, and ECAI 2012-2014. He is also editor and contributor to the book 'Automatic Machine Learning: Methods, Systems, Challenges'.

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