Yisong Yue, Caltech – “New Frontiers in Imitation Learning”

April 19, 2018 @ 10:45 am – 11:45 am


Hackerman Hall, B-17


The ongoing explosion of spatiotemporal tracking data has now made it possible to analyze and model fine-grained behaviors in a wide range of domains. For instance, tracking data is now being collected for every NBA basketball game with players, referees, and the ball tracked at 25 Hz, along with annotated game events such as passes, shots, and fouls. Other settings include laboratory animals, people in public spaces, professionals in settings such as operating rooms, actors speaking and performing, digital avatars in virtual environments,
and even the behavior of other computational systems.

Motivated by these applications, I will describe recent and ongoing work in developing principled structured imitation learning approaches that can exploit interdependencies in the state/action space, and achieve orders-of-magnitude improvements compared to conventional approaches in learning rate or accuracy, or both. These approaches are showcased on a wide range of (often commercially deployed) applications, including modeling professional sports, laboratory animals, speech animation, and expensive computational oracles.


Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon
University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign.

Yisong’s research interests lie primarily in the theory and application of
statistical machine learning. His research is largely centered around
developing integrated learning-based approaches that can characterize complex structured and adaptive decision-making settings. Current focus areas include developing novel methods for spatiotemporal reasoning, structured prediction, interactive learning systems, and learning with humans in the loop. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive routing & allocation problems.


Jason Eisner


Watch seminar video.

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