Introduction
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.
Iddo Drori is Lecturer at MIT EECS and adjunct Associate Professor at Columbia University. He holds a Ph.D in Computer Science and did his postdoc at Stanford in Statistics. He was Associate Professor at Colman; Lecturer at Tel Aviv University; research scientist and professor at NYU; Previously, he was a visiting associate professor at Cornell, won multiple conference competitions and received multiple best paper awards.
Joaquin Vanschoren is Assistant Professor at the Eindhoven University of Technology, researching AutoML and metalearning. He founded the OpenML project, co-organizes the AutoML and metalearning workshops at ICML and NeurIPS, and co-presented the NeurIPS2018 AutoML tutorial. He’s a founding member of ELLIS and CLAIRE, and action editor at JMLR.