Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy

Onur Teymur · Jackson Gorham · Marina Riabiz · Chris Oates

Keywords: [ Models and Methods ] [ Kernel Methods ]

[ Abstract ]
Wed 14 Apr 6 a.m. PDT — 8 a.m. PDT


Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as a method to quantise probability measures, i.e., to approximate a distribution by a representative point set. We consider sequential algorithms that greedily minimise MMD over a discrete candidate set. We propose a novel non-myopic algorithm and, in order to both improve statistical efficiency and reduce computational cost, we investigate a variant that applies this technique to a mini-batch of the candidate set at each iteration. When the candidate points are sampled from the target, the consistency of these new algorithms—and their mini-batch variants—is established. We demonstrate the algorithms on a range of important computational problems, including optimisation of nodes in Bayesian cubature and the thinning of Markov chain output.

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