171 citations · 357 across the 21 of their papers we have counts for
5 papers · 1 filter
Efficient Wasserstein Natural Gradients for Reinforcement Learning
Ted Moskovitz, Michael Arbel, Ferenc Huszar +1
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally…
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{…
Synchronizing Probability Measures on Rotations via Optimal Transport
Tolga Birdal, Michael Arbel, Umut Şimşekli +1
We introduce a new paradigm, , for synchronizing graphs with measure-valued edges. We formulate this problem as maximization of the cycle-consiste…
Generalized Energy Based Models
Michael Arbel, Liang Zhou, Arthur Gretton
We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), whi…