Candidate Talk: Levy Processes and Applications to Machine Learning

  • Romain Thibaux | University Of California at Berkeley

Levy processes are random measures that give independent mass to independent increments. I will show how they can be used to model various types of data such as binary vectors or vectors of counts, with applications to text and images. These techniques fall in the category of nonparametric Bayesian methods, and are related to the better known Dirichlet process.

Speaker Details

Romain Thibaux is a graduate student in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and is advised by Michael Jordan. His work on the Dirichlet and beta processes has established new theoretical links between Statistics techniques and machine learning problems, which have led to several new algorithms. He earned his Masters in 2003 from Stanford University.

Watch Next