Title	Transport based kernels for Gaussian Process Modeling
Creator	Loubes, Jean-Michel
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-05-02T16:32
Description	Monge-Kantorovich distances, otherwise known as Wasserstein distances, have received a growing attention in statistics and machine learning as a powerful discrepancy measure for probability distributions. In this paper, we focus on forecasting a Gaussian process indexed by probability distributions. For this, we provide a family of positive definite kernels built using transportation based distances. We provide a probabilistic understanding of these kernels and characterize the corresponding stochastic processes. Then we consider the asymptotic properties of the forecast process.
Extent	32.0
Subject	Mathematics; Calculus of variations and optimal control; optimization; Statistics; Scientific computing
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Université de Toulouse
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2019-03-11
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0376740
URI	http://hdl.handle.net/2429/68593
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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