17 citations · 28 across the 5 of their papers we have counts for
9 papers
Affine Transport for Sim-to-Real Domain Adaptation
Anton Mallasto, Karol Arndt, Markus Heinonen +2
Sample-efficient domain adaptation is an open problem in robotics. In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between st…
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals
Anton Mallasto
\emph{Optimal Transport} (OT) has emerged as an important computational tool in machine learning and computer vision, providing a geometrical framework for studying probability mea…
Bayesian Inference for Optimal Transport with Stochastic Cost
Anton Mallasto, Markus Heinonen, Samuel Kaski
In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic…
Entropy-Regularized -Wasserstein Distance between Gaussian Measures
Anton Mallasto, Augusto Gerolin, Hà Quang Minh
Gaussian distributions are plentiful in applications dealing in uncertainty quantification and diffusivity. They furthermore stand as important special cases for frameworks providi…
How Well Do WGANs Estimate the Wasserstein Metric?
Anton Mallasto, Guido Montúfar, Augusto Gerolin
Generative modelling is often cast as minimizing a similarity measure between a data distribution and a model distribution. Recently, a popular choice for the similarity measure ha…
Simulation of Conditioned Diffusions on the Flat Torus
Mathias Højgaard Jensen, Anton Mallasto, Stefan Sommer
Diffusion processes are fundamental in modelling stochastic dynamics in natural sciences. Recently, simulating such processes on complicated geometries has found applications for e…