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  • AI Seminar

    Fri, Jun 28, 2013 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Christian Shelton, CS Professor, UC Riverside

    Talk Title: Continuous-Time Models: Why and How

    Abstract: 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.

    Biography: 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.

    Host: David Chiang

    More Info: TBA

    Location: Information Science Institute (ISI) -

    Audiences: Everyone Is Invited

    Posted By: Kary LAU

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