Chelsea Finn - 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 Profe