Some Results on Label Shift and Label Noise | Zachary Chase Lipton

Title: Some Results on Label Shift and Label Noise

Abstract: In this talk I will discuss distribution shift, both as an obstacle to be overcome to achieve generalization, and as a device for obtaining generalization guarantees. In the first part, I will discuss the problem of label shift, where the proportion among the labels can shift but the class conditional distributions do not change, including connections to some practical problems and some theoretical results. Then I will discuss a new work in which we deliberately alter the distribution of training data in order to obtain a generalization guarantee.

Bio
Zachary Chase Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research spans core machine learning methods, applications to clinical medicine and natural language processing, and the impact of automation on social systems. Current focuses include robustness under distribution shift, decision-making, applications of causal thinking to practical high-dimensional settings that resist stylized causal models, and AI ethics. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab’s website (acmilab.org).