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[Seminar] Probabilistic Machine Learning: Exploring Uncertainties in the Spatial, Temporal, and Deep Architectures’ Perspectives

Friday, March 4, 2022

11:00 am - 12:00 pm

Speaker

Xuhui Fan
Postdoctoral Fellow, School of Mathematics and Statistics
University of New South Wales, Sydney

Location

Hybrid: PGH 232 and Zoom*
*Microsoft 365 @cougarnet.uh.edu authentication required to join via Zoom

Abstract

Machine learning has obtained tremendous successes in areas such as medicine, autonomous vehicles, high frequency trading. However, these applications could be disastrous if we rely on single valued predictions, without access to their uncertainties as “safe guard”.

In this talk, I will introduce my probabilistic machine learning research outputs in the following perspectives: (1), spatial uncertainties, which refer to nonparametric Bayesian space partition methods, with tasks of exploring various ways to generate partitions in a pre-defined space; (2), temporal uncertainties, which use stochastic point process methods to study continuous time social networks data, with the focus of exploring time-varying background effects; (3), deep architectures’ uncertainties, which provide interpretable latent units in one variant of the deep generative models however actually gain limited benefits from its deep structure.

This talk will also cover topics of model complexities and efficient inference, and will be concluded with brief discussions on the current challenges and future directions.

About the Speaker

Xuhui Fan received a bachelor’s degree in mathematical statistics from the University of Science and Technology of China, Hefei, China, in 2010, and a Ph.D. in computer science from the University of Technology Sydney, Australia, in 2015. He then worked as a project engineer at Data61 (previously NICTA), CSIRO from 2015 to 2017. He is currently a Postdoc Fellow in the School of Mathematics and Statistics at the University of New South Wales, Sydney. He focuses on the topics of deep probabilistic models, Gaussian Processes, nonparametric Bayesian space partitioning methods, social network analysis, and publishes related research work in NeurIPS, ICML, AISTATS, JMLR, T-PAMI, etc. He serves as senior PC members in IJCAI and AAAI and PC members in ICML, NeurIPS, AISTATS, ICLR.

March 4 seminar