[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Emma Brunskill Wed Nov 19, 4:30pm

Barbara Engelhardt bee at CS.Princeton.EDU
Fri Nov 14 11:02:26 EST 2014


Colloquium Speaker
Emma Brunskill , Carnegie Mellon University
Wednesday, November 19, 2014 - 4:30pm
Computer Science 105

Interactive ML for People: The Small Data Problem

Consider an intelligent tutoring system or an autonomous decision support
tool for a doctor. Though such systems may in aggregate have a huge amount
of data, the data collected for a single individual is typically very
small, and the policy space (of what to next teach a student or how to help
treat a patient) is enormous.

I will describe two machine learning efforts to tackle these small data
challenges: learning across multiple tasks, and better use of previously
collected task data, where tasks in both cases involve sequential
stochastic decision processes (reinforcement learning and bandits). I will
also present results of how one of these techniques allowed us to
substantially increase engagement in an educational game to teach
fractions.

Emma Brunskill is an assistant professor in the computer science department
at Carnegie Mellon University. She is also affiliated with the machine
learning department at CMU. She works on reinforcement learning, focusing
on applications that involve artificial agents interacting with people,
such as intelligent tutoring systems. She is a Rhodes Scholar, Microsoft
Faculty Fellow and NSF CAREER award recipient, and her work has received
best paper nominations in Education Data Mining (2012, 2013) and CHI
(2014).


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