Copulas in Machine Learning

• Activity Number: 584
• Type: Invited
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Yaniv Tenzer*+ and Gal Elidan
• Companies: Hebrew University and Hebrew University
• Keywords: Copulas ; Machine learning ; Graphical models ; Dependence ; TP2 density

Abstract

Despite overlapping goals of multivariate modelling and dependence identification, until recently the fields of machine learning in general and probabilistic graphical models in particular have been ignorant of the framework of copulas. At the same time, complementing strengths of the two fields suggest the great fruitfulness of a synergy. In this talk we will give a taste of the of benefits that can arise from a symbiosis of the two fields. In particular, we will introduce a high-dimensional copula-based graphical model and then present a lightning-speed approach for automatically learning its structure from data. The approach is based on novel theory relating Spearman's Rho to the copula entropy, or expected likelihood of the model. No background in graphical models will be assumed.