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Principles for Tackling Distribution Shift: Pessimism, Adaptation, and Anticipation
Abstract
While we have seen immense 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 shift 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, inverse reinforcement methods for scalable acquisition of nonlinear reward functions, 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 ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, 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. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.