A Tutorial on Discrete Non-Parametric Methods

Lecturer : 
Wray Buntine, Monash University
Event type: 
Guest lecture
Event time: 
2015-01-19 14:15 to 17:00
Aalto University, TuAS Building, lecture hall AS1

Hierarchies of probability vectors can be modelled, and learning/inference done using the Dirichlet Process and the Pitman-Yor Process. Discrete feature vectors (Booleans, counts, etc.) can be modelled using non-parametric versions of vectors of Bernoulli-Betas, or Poisson-Gammas, etc. (including the well published Indian Buffet process).

This tutorial will present these. The first part will be done informally on the board from notes and will give a machine learning perspective on the statisticians' models for discrete feature vectors. This will present Lancelot James' theory (http://arxiv.org/pdf/1411.2936) for easier consumption. The second part will present some of our theory of learning on hierarchies of probability variables from the longer tutorial


Many authors are now proposing the former (non-parametric models of discrete feature vectors)
be used as a component for matrix models like topic models.

About the speaker:


Last updated on 12 Jan 2015 by Antti Ukkonen - Page created on 12 Jan 2015 by Antti Ukkonen