While we have seen substantial progress in machine learning, a critical shortcoming of current methods lies in handling distribution shift between training and deployment. Distribution shift is pervasive in real-world problems ranging from natural variation in the distribution over locations or domains, to shifts in the distribution arising from different decision making policies, to shifts over time as the world changes. In this talk, I'll discuss three general principles for tackling these forms of distribution shift: pessimism, adaptation, and anticipation. I'll present the most general form of each principle before providing concrete instantiations of using each in practice. This will include a simple method for substantially improving robustness to spurious correlations, a framework for quickly adapting a model to a new user or domain with only unlabeled data, and an algorithm that enables robots to anticipate and adapt to shifts caused by other agents.

Bio: Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, self-supervised methods for learning a breadth of vision-based control tasks, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the Microsoft Research Faculty Fellowship, the ACM doctoral dissertation award, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg.