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Christian Shelton @ eHarmony

12:00 lunch served
12:30 talk starts
13:30 Q/A ends






Title: Continuous-Time Models: Why and How



Discrete-time models are abundant in artificial intelligence: hidden Markov models, dynamic Bayesian networks, Markov decision processes, and (most) auto-regressive models assume time passes in discrete jumps. Yet, most processes modeled actually evolve in continuous time. This talk explores the problems inherent in this dichotomy, focusing on Markovian models.

First, I will discuss the theoretic and experimental difficulties when
modeling in discrete time. In doing so, I will present continuous-time Markov processes, drawing analogies to their discrete-time counterparts. Second, I will present the continuous-time analog of a dynamic Bayesian network: a continuous-time Bayesian network (CTBN). The talk will include an overview of the learning and inference literatures for CTBNs, showing how continuous-time aids in the development of efficient inference techniques. Finally, I will show some application results employing CTBNs on real data sets.


Christian R. Shelton is an Associate Professor of Computer Science at the University of California at Riverside. He has spent time as a visiting researcher at Intel Research and Children's Hospital Los Angeles. He was the Managing Editor of the Journal of Machine Learning Research and on the editorial board of the Editorial Board of the Journal of Artificial Intelligence Research.

Dr. Shelton received his B.S. in Computer Science from Stanford University and his Ph.D. from MIT. His research interest is in statistical approaches to artificial intelligence, mainly in the areas of machine learning and dynamic processes. He also works at the intersection of learning and topics as varied as computer vision, sociology, game theory, medical informatics, and robotics.


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  • Vaclav P.

    Here are Christian's slides:

    Video will follow

    September 18, 2013

  • Les G.

    excellent. I'm newbie to this. But got a lot of what he was talking about and learned a lot by Googling the things I didn't understand.

    September 5, 2013

  • Daniel G.

    Once again, an excellent graduate-level seminar. Much appreciated!

    September 5, 2013

  • Tim Triche, J.

    Same here, I hope that slides will be posted because I am very sad to miss this, not least since the reason I'm missing it is that I'm putting together some slides on dynamic model averaging! *grumble*

    Hope someone else can take my place.

    September 5, 2013

  • John Konrad G.

    I had to drop my RSVP. I hope someone can fill my spot. Sorry for the late notice. I would love to see slides or video of the talk!

    September 5, 2013

  • Joseph J.

    Is this being recorded or are slides to be distributed?

    September 5, 2013

  • Scott P.

    I've heard him talk twice on related topics... learned something new each time, and would gladly show up again to hear him a third time... but I won't be in town. But definitely show up if you can.

    August 28, 2013

  • Leela Krishna K


    July 28, 2013

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