Ecole d'ingénieur et centre de recherche en Sciences du numérique

Random feature expansions for deep Gaussian processes

Filippone, Maurizio

Keynote Speech at UNQW02, 6 February 2018, Cambridge, UK

Drawing meaningful conclusions on the way complex real life phenomena work and being able to predict the behavior of systems of interest require developing accurate and highly interpretable mathematical models whose parameters need to be estimated from observations. In modern applications, however, we are often challenged with the lack of such models, and even when these are available they are too computational demanding to be suitable for standard parameter optimization/inference methods. While probabilistic models based on Deep Gaussian Processes (DGPs) offer attractive tools to tackle these challenges in a principled way and to allow for a sound quantification of uncertainty, carrying out inference for these models poses huge computational challenges that arguably hinder their wide adoption. In this talk, I will present our contribution to the development of practical and scalable inference for DGPs, which can exploit distributed and GPU computing. In particular, I will introduce a formulation of DGPs based on random features that we infer using stochastic variational inference. Through a series of experiments, I will illustrate how our proposal enables scalable deep probabilistic nonparametric modeling and significantly advances the state-of-the-art on inference methods for DGPs.

Bibtex

Titre:Random feature expansions for deep Gaussian processes
Type:Talk
Langue:English
Ville:Cambridge
Pays:ROYAUME-UNI
Date:
Département:Data Science
Eurecom ref:5438
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Keynote Speech at UNQW02, 6 February 2018, Cambridge, UK and is available at :
Bibtex: @talk{EURECOM+5438, year = {2018}, title = {{R}andom feature expansions for deep {G}aussian processes}, author = {{F}ilippone, {M}aurizio}, number = {EURECOM+5438}, month = {02}, institution = {Eurecom} address = {{C}ambridge, {ROYAUME}-{UNI}}, url = {http://www.eurecom.fr/publication/5438} }
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