Events Calendar

AI Seminar

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

Contact: Kary LAU

Event Link: TBA