5 citations · 10 across the 3 of their papers we have counts for
7 papers · 1 filter
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
Pierre Glaser, Michael Arbel, Arthur Gretton
We study the gradient flow for a relaxed approximation to the Kullback-Leibler (KL) divergence between a moving source and a fixed target distribution. This approximation, termed t…
Annealed Flow Transport Monte Carlo
Michael Arbel, Alexander G. D. G. Matthews, Arnaud Doucet
Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC) extensions are state-of-the-art methods for estimating normalizing constants of probability distributions. W…
Estimating Barycenters of Measures in High Dimensions
Samuel Cohen, Michael Arbel, Marc Peter Deisenroth
Barycentric averaging is a principled way of summarizing populations of measures. Existing algorithms for estimating barycenters typically parametrize them as weighted sums of Dira…
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Anna Korba, Adil Salim, Michael Arbel +2
We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution on $\mathbb{…
Kernelized Wasserstein Natural Gradient
Michael Arbel, Arthur Gretton, Wuchen Li +1
Many machine learning problems can be expressed as the optimization of some cost functional over a parametric family of probability distributions. It is often beneficial to solve s…
Maximum Mean Discrepancy Gradient Flow
Michael Arbel, Anna Korba, Adil Salim +1
We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties. The MMD is an integral probability metric defined for a reprodu…