Title: Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory,
Abstract: 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. We present a framework for meta-learning that is based on
generalization error bounds, allowing us to extend various PAC-Bayes bounds to
meta-learning. Learning takes place through the construction of a distribution
over hypotheses based on the observed tasks, and its utilization for learning a
new task. Thus, prior knowledge is incorporated through setting an
experience-dependent prior for novel tasks. We develop a gradient-based
algorithm which minimizes an objective function derived from the bounds and
demonstrate its effectiveness numerically with deep neural networks. In
addition to establishing the improved performance available through
meta-learning, we demonstrate the intuitive way by which prior information is
manifested at different levels of the network.