Chen, WY;
Barp, A;
Briol, F-X;
Gorham, J;
Girolami, M;
Mackey, L;
Oates, CJ;
(2019)
Stein Point Markov Chain Monte Carlo.
In: Chaudhuri, Kamalika and Salakhutdinov, Ruslan, (eds.)
Proceedings of the 36th International Conference on Machine Learning.
(pp. pp. 1011-1021).
Proceedings of Machine Learning Research: Long Beach, California, USA.
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Abstract
An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which proceed by sequentially minimising a Stein discrepancy between the empirical measure and the target and, hence, require the solution of a non-convex optimisation problem to obtain each new point. This paper removes the need to solve this optimisation problem by, instead, selecting each new point based on a Markov chain sample path. This significantly reduces the computational cost of Stein Points and leads to a suite of algorithms that are straightforward to implement. The new algorithms are illustrated on a set of challenging Bayesian inference problems, and rigorous theoretical guarantees of consistency are established.
Type: | Proceedings paper |
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Title: | Stein Point Markov Chain Monte Carlo |
Event: | 36th International Conference on Machine Learning |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | http://proceedings.mlr.press/v97/chen19b/chen19b.p... |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10079222 |
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