[Colloquium] 11/13: Yisong Yue (Caltech) - New Frontiers in Imitation Learning

[Rob Mitchum]

*Yisong Yue, Assistant Professor, Computing and Mathematical Sciences
Department, California Institute of Technology*

Time: Nov 13, 1:30pm
Location: JCL 390
Host: Yuxin Chen


*New Frontiers in Imitation Learning*
*Abstract*
The ongoing explosion of spatiotemporal tracking & sensing 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, phenomena in nature
such as aerodynamics, and even the behavior of other computational systems.

In this talk, I will describe ongoing research in developing structured
imitation learning approaches to develop predictive models of fine-grained
behavior. Imitation learning is branch of machine learning that deals with
learning to imitate dynamic demonstrated behavior. Structured imitation
learning pertains imposing mathematically rigorous domain knowledge that
can (sometimes provably) accelerate learning, and can also provide side
benefits (such as Lyapunov stability or interpretability of policy
behavior).  I will provide a high level overview of the basic problem
setting, as well as specific projects in modeling laboratory animals,
professional sports, speech animation, and expensive computational oracles.

*Bio*
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. He is particularly interested in developing
novel methods for interactive machine learning and structured machine
learning. In the past, his research has been applied to information
retrieval, recommender systems, text classification, learning from rich
user interfaces, analyzing implicit human feedback, clinical therapy,
tutoring systems, data-driven animation, behavior analysis, sports
analytics, experiment design for science, learning to optimize, policy
learning in robotics, and adaptive planning & allocation problems.

====================================

-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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