The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning

• URL: http://arxiv.org/abs/2008.10857v1
• Date: Tue, 25 Aug 2020 07:32:16 GMT
• Title: The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
• Authors: Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
• Abstract summary: Biased regularization and fine-tuning are two recent meta-learning approaches. We propose conditional meta-learning, inferring a conditioning function mapping task's side information into a meta- parameter vector. We then propose a convex meta-algorithm providing a comparable advantage also in practice.
• Score: 50.21341246243422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biased regularization and fine-tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks' target vectors are all close to a common meta-parameter vector. However, these methods may perform poorly on heterogeneous environments of tasks, where the complexity of the tasks' distribution cannot be captured by a single meta-parameter vector. We address this limitation by conditional meta-learning, inferring a conditioning function mapping task's side information into a meta-parameter vector that is appropriate for that task at hand. We characterize properties of the environment under which the conditional approach brings a substantial advantage over standard meta-learning and we highlight examples of environments, such as those with multiple clusters, satisfying these properties. We then propose a convex meta-algorithm providing a comparable advantage also in practice. Numerical experiments confirm our theoretical findings.

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