Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes. - Anton Mallasto; Aasa Feragen (NIPS, 2017)

Wrapped Gaussian Process Regression on Riemannian Manifolds. - Anton Mallasto; Aasa Feragen (CVPR, 2018)

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. - Anton Mallasto; Søren Hauberg; Aasa Feragen (AISTATS, 2019)

Simulation of Conditioned Diffusions on the Flat Torus. - Mathias Højgaard Jensen; Anton Mallasto; Stefan Sommer (GSI, 2019)

A Formalization of the Natural Gradient Method for General Similarity Measures. - Anton Mallasto; Tom C. J. Dela Haije; Aasa Feragen (GSI, 2019)

(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs. - Anton Mallasto; Jes Frellsen; Wouter Boomsma; Aasa Feragen (CoRR, 2019)

How Well Do WGANs Estimate the Wasserstein Metric? - Anton Mallasto; Guido Montúfar; Augusto Gerolin (CoRR, 2019)

Entropy-Regularized 2-Wasserstein Distance between Gaussian Measures. - Anton Mallasto; Augusto Gerolin; Hà Quang Minh (CoRR, 2020)

Bayesian Inference for Optimal Transport with Stochastic Cost. - Anton Mallasto; Markus Heinonen; Samuel Kaski (ACML, 2021)

Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals. - Anton Mallasto (CoRR, 2021)

Affine Transport for Sim-to-Real Domain Adaptation. - Anton Mallasto; Karol Arndt; Markus Heinonen; Samuel Kaski; Ville Kyrki (CoRR, 2021)

Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. - Oliver Struckmeier; Ievgen Redko; Anton Mallasto; Karol Arndt; Markus Heinonen; Ville Kyrki (CoRR, 2023)

Understanding deep neural networks through the lens of their non-linearity. - Quentin Bouniot; Ievgen Redko; Anton Mallasto; Charlotte Laclau; Karol Arndt; Oliver Struckmeier; Markus Heinonen; Ville Kyrki; Samuel Kaski (CoRR, 2023)

Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. - Oliver Struckmeier; Ievgen Redko; Anton Mallasto; Karol Arndt; Markus Heinonen; Ville Kyrki (Trans. Mach. Learn. Res., 2023)

From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport. - Quentin Bouniot; Ievgen Redko; Anton Mallasto; Charlotte Laclau; Oliver Struckmeier; Karol Arndt; Markus Heinonen; Ville Kyrki; Samuel Kaski (CVPR, 2025)