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Meta Learning Tutorial
Abstract
Meta-learning allows machines to learn to learn new algorithms. It is an emerging and fast developing research area within machine learning with implications for all AI research. Recent successes include automatic model discovery, few-shot learning, multi-task learning, meta-reinforcement learning, as well as teaching machines to read, learn and reason. Just as humans do not learn new tasks from scratch, but rather draw on what they learn before, meta-learning is key to efficient and robust learning. This tutorial will cover important mathematical foundations of the field and its applications, including key methods underlying current state of the art in this fast-paced field that is increasingly relevant for a broad range of AAAI attendees.