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Lookup NU author(s): Professor Chris Oates
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2018 by the Authors. All rights reserved. An important task in computational statistics and machine learning is to approximate a posterior distribution p(x) with an empirical measure supported on a set of representative points {xi} n i=1 . This paper focuses on methods where the selection of points is essentially deterministic, with an emphasis on achieving accurate approximation when n is small. To this end, we present Stein Points. The idea is to exploit either a greedy or a conditional gradient method to iteratively minimise a kernel Stein discrepancy between the empirical measure and p(x). Our empirical results demonstrate that Stein Points enable accurate approximation of the posterior at modest computational cost. In addition, theoretical results are provided to establish convergence of the method.
Author(s): Chen WY, Mackey L, Gorham J, Briol F-X, Oates CJ
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Proceedings of the 35th International Conference on Machine Learning
Year of Conference: 2018
Pages: 843-852
Online publication date: 15/07/2018
Acceptance date: 02/04/2018
Date deposited: 02/01/2020
ISSN: 1533-7928
Publisher: Proceedings of Machine Learning Research
URL: https://icml.cc/Conferences/2018/Schedule?type=Poster
Library holdings: Search Newcastle University Library for this item
Series Title: Proceedings of Machine Learning Research
ISBN: 9781510867963
