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Talk
A Birth-Death Process for Feature Allocation
Konstantina Palla · David Knowles · Zoubin Ghahramani

Mon Aug 07 01:27 AM -- 01:45 AM (PDT) @ C4.9& C4.10
in Bayesian Nonparametrics »

We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth-death feature allocation process (BDFP). The BDFP models the evolution of the feature allocation of a set of N objects across a covariate (e.g.~time) by creating and deleting features. A BDFP is exchangeable, projective, stationary and reversible, and its equilibrium distribution is given by the Indian buffet process (IBP). We show that the Beta process on an extended space is the de Finetti mixing distribution underlying the BDFP. Finally, we present the finite approximation of the BDFP, the Beta Event Process (BEP), that permits simplified inference. The utility of the BDFP as a prior is demonstrated on real world dynamic genomics and social network data.

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Author Information

Konstantina Palla (Oxford University)

Konstantina Palla is a Machine Learning Researcher in the Healthcare AI Division at Microsoft Research Cambridge. Her research is focusing on the construction and application of Bayesian probabilistic models for discovering latent structure in data. Recently, she has been particularly interested in the application of probabilistic modelling in the Healthcare domain as a means to understand disease subtypes and patients’ subgroups. In her PhD, she developed nonparametric models for relational data with a focus on time evolving settings.

David Knowles (Stanford)
Zoubin Ghahramani (University of Cambridge & Uber)

Zoubin Ghahramani is a Professor at the University of Cambridge, and Chief Scientist at Uber. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence, was a founding Director of the Alan Turing Institute and co-founder of Geometric Intelligence (now Uber AI Labs). His research focuses on probabilistic approaches to machine learning and AI. In 2015 he was elected a Fellow of the Royal Society.

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