Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory

ICML 2018 Ron AmitRon Meir

In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks. Under the assumption that future tasks are 'related' to previous tasks, the accumulated knowledge should be learned in a way which captures the common structure across learned tasks, while allowing the learner sufficient flexibility to adapt to novel aspects of new tasks... (read more)

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