Waterloo.ai Seminar: Qiang Liu on Recent Applications of Stein's Method in Machine LearningExport this event to calendar

Friday, December 6, 2019 — 11:30 AM EST

Please join us for the next institute seminar  Friday, December 6 at 11:30am in DC 1302.

We are excited to have Prof. Qiang Liu from the department of  Computer Science in UT Austin to present at our AI institute seminar series! Dr. Liu will give his perspective on the AI field and discuss some intriguing projects from his group, see more details below.


Title: Recent Applications of Stein's Method in Machine Learning

Abstract: As a fundamental technique for approximating and bounding distances between probability measures, Stein's method has caught the attention in the machine learning community recently; some of the key ideas in Stein's method have been leveraged and extended for developing practical and efficient computational methods for learning and using large scale, intractable probabilistic models.  We will give an overview of Stein's method in machine learning, focusing on a computable, kernel-based Stein discrepancy for approximating and evaluating (via goodness-of-fit test) distributions with intractable normalization constants, and a Stein variational gradient descent (SVGD) for finding particle-based approximation to intractable distributions that combines the advantages of Markov chain Monte Carlo (MCMC), variational inference and numerical quadrature methods. 


Speaker:

Prof. Qiang Liu
Department of Computer Science
University of Texas at Austin

Speaker Bio:

Qiang Liu is an assistant professor of computer science at UT Austin. His research interests are in machine learning, approximate inference, reinforcement learning, and deep learning. He is an action editor of Journal of Machine Learning Research (JMLR), and received awards such as NSF career.


Date and Time:

Friday, December 6, 2019
​​​​​​​11:30 AM - 12:30 PM

Location: DC 1302
Light refreshments will be available.

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