SMS scnews item created by Lamiae Azizi at Tue 14 Jul 2020 1714
Type: Seminar
Distribution: World
Expiry: 26 Jul 2020
Calendar1: 23 Jul 2020 1600-1700
CalLoc1: Zoom
CalTitle1: An Information Geometric Approach to Increase Representational Power in Unsupervised Learning
Auth: lamiae@plamiae.pc (assumed)

# DARE Seminar: Simon Luo -- An Information Geometric Approach to Increase Representational Power in Unsupervised Learning

Machine learning models increase their representational power by increasing the number
of parameters in the model.  The number of parameters in the model can be increased by
introducing hidden nodes, higher-order interaction effects or by introducing new
features into the model.  In this talk, we will introduce the concepts in incidence
algebra and information geometry to develop novel machine learning models to include
higher-order interactions effects into the model.  Incidence algebra provides a natural
formulation for combinatorics by expressing it as a generative function and information
geometry provides many theoretical guarantees in the model by projecting the problem
onto a dually flat Riemannian structure for optimization.  Combining the two techniques
together formulates the information geometric formulation of the binary log-linear
model.  We first discuss how to apply these techniques to formulate the higher-order
Boltzmann machine (HBM) to compare the different behaviours when using hidden nodes and
higher-order feature interactions to increase the representational power of the model.
We the present techniques to include higher-order interaction terms in Blind Source
Separation (BSS) and to design efficient approach to estimate higher order intensity
functions in Poisson process.

Simon Luo is a Postdoctoral Research Fellow in the School of Mathematics and Statistics
at The University of Sydney.  He received a Bachelors of Engineering (Aeronautical) and
Bachelors of Science (Computer Science) in 2015 at The University of Sydney.  Simon has
recently submitted his PhD at The University of Sydney where he has made contributions
in transfer learning, Bayesian non-parametric models, probabilistic graphical models and
information geometry.  His work has been published several top-tier venues such as AAAI
and PAKDD and have been awarded the "Reviewers’ Choice Award" at INTERACT’17 and
"The Brian Shackel Award" which is the most prestigious award in the field (awarded
once every 2 years) for "the most outstanding contribution with international impact
in the field of human interaction with computers and information technology".

Simon was supervised by Lamiae and Prof F. Ramos (Faculty of Engineering and NVIDIA).