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Speaker: Brian Ziebart, Assistant Professor
Department of Computer Science, University of Illinois at Chicago
Title:
Beyond Conditionals: Structured Prediction for Interacting Processes
Abstract:
The principle of maximum entropy provides a powerful framework for estimating joint, conditional, and marginal probability distributions. Markov random fields and conditional random fields can be viewed as the maximum entropy approach in action. However, beyond joint and conditional distributions, there are many other important distributions with elements of interaction and feedback where its applicability has not been established. In this talk, I will present the principle of maximum causal entropy—an approach based on directed information theory for estimating an unknown process based on its interactions with a known process. I will discuss applications of this approach to assistive technologies and human-robot interaction.
Bio:
Brian Ziebart is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago. He received his PhD in Machine Learning from Carnegie Mellon University in 2010, where he was also a postdoctoral fellow. His research in expanding the theory of structured prediction and applying to to problems in assistive technologies and human-computer interaction has been recognized with best paper awards, runner-ups, and nominations at ICML (2010, 2011), ECCV (2012), and IUI (2012).
Host:
Prof. Polo Chau
