17 citations · 34 across the 10 of their papers we have counts for
12 papers
Beyond Laplace: Closed-form wrapped Gaussian posterior approximations on statistical manifolds
Marcelo Hartmann, Luu Hoang Phuc Hau, Anton Mallasto +5
In Bayesian statistics, the Laplace approximation provides a computationally efficient approximation to posterior distributions. However, its Gaussian form restricts it to elliptic…
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
Quentin Bouniot, Ievgen Redko, Anton Mallasto +6
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explain…
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Oliver Struckmeier, Ievgen Redko, Anton Mallasto +3
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle. Despite its widespread use in machine learn…
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…
A Formalization of The Natural Gradient Method for General Similarity Measures
Anton Mallasto, Tom Dela Haije, Aasa Feragen
In optimization, the natural gradient method is well-known for likelihood maximization. The method uses the Kullback-Leibler divergence, corresponding infinitesimally to the Fisher…
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
Anton Mallasto, Jes Frellsen, Wouter Boomsma +1
Generative Adversial Networks (GANs) have made a major impact in computer vision and machine learning as generative models. Wasserstein GANs (WGANs) brought Optimal Transport (OT)…
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Anton Mallasto, Søren Hauberg, Aasa Feragen
Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space. In a number of applications, a priori known spatial constrain…