MaPSS: Maths Postgraduate Seminar Series: Haruki Osaka -- Introduction to Singular Learning Theory

Hello all,

The next MaPSS talk of this semester will be at 17:00 on Mon 13th August in Carslaw 535.
It’s a great opportunity to meet fellow postgrads, listen to an interesting talk, and of
course get free food!

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Speaker: Haruki Osaka

Title: Introduction to Singular Learning Theory

Abstract: Many statistical models used in data science have hierarchical structures 
and hidden variables, for example, mixture models, hidden Markov models, neural 
networks and so on. Although such models are widely used in practice, no sound 
theoretical foundation for the large sample behaviour of these models has been 
established. The main reasons for this difficulty is that of non-identifiability and a 
degenerate Fisher Information metric of these models, which are basic regularity 
conditions required for Fisher’s asymptotic normal theory. Such statistical models 
are called singular. Statistical inference for singular models are different to that of 
regular models. In addition, popular information criteria used in model selection 
such as AIC and BIC are not valid for singular models.

Singular Learning Theory is a new area of mathematics which attempts to use 
algebraic techniques to study singular models. Using these results, Drton and 
Plummer (2017) recently proposed the singular Bayesian information criterion (sBIC) 
that is valid for singular models. In this talk, I will give an overview of some typical 
problems that may occur in singular models and how singular learning theory 
attempts to resolve them.

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See you there!

Details can also be found on the school’s new Postgraduate Society website: 
http://www.maths.usyd.edu.au/u/MaPS/mapss.html 
 
Cheers,
Eric