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Geometrically coupled monte carlo sampling
Authors
,
F Chalus
+6 more
K Choromanski
A Pacchiano
M Rowland
T Sarlós
RE Turner
A Weller
Publication date
5 September 2018
Publisher
Abstract
© 2018 Curran Associates Inc..All rights reserved. Monte Carlo sampling in high-dimensional, low-sample settings is important in many machine learning tasks. We improve current methods for sampling in Euclidean spaces by avoiding independence, and instead consider ways to couple samples. We show fundamental connections to optimal transport theory, leading to novel sampling algorithms, and providing new theoretical grounding for existing strategies. We compare our new strategies against prior methods for improving sample efficiency, including quasi-Monte Carlo, by studying discrepancy. We explore our findings empirically, and observe benefits of our sampling schemes for reinforcement learning and generative modelling
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CUED - Cambridge University Engineering Department
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Last time updated on 15/07/2020