AI Seminar sponsored by Fortive

Emma Brunskill -- Learning from Limited Samples to Robustly Make Good Decisions

Abstract: There is increasing excitement and impressive empirical successes using reinforcement learning-- where agents can learn through experience to make decisions. Yet people are remarkably able to do so much faster and for much more complicated objectives. Creating algorithms that can mimic such performance is an essential part of achieving artificial intelligence. Equally importantly, it will help us to use reinforcement learning to assist people in the numerous societal challenges, including education and healthcare, where humans plus AI may be able to do far better than either alone. In this talk, I will discuss our progress on some of the technical challenges that arise in this pursuit, including sample efficiency, counterfactual reasoning, robustness, and applications to health and education.

Bio: Emma Brunskill is an assistant professor in the Computer Science Department at Stanford University where she leads the AI for Human Impact group. She was previously an assistant professor at Carnegie Mellon University in the Computer Science department. She is the recipient of multiple early faculty career awards (National Science Foundation, Office of Naval Research, Microsoft Research) and her group has received several best research paper nominations (CHI, EDMx3) and awards (UAI, RLDM).