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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).