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2020 – today
- 2023
[j1]Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki:
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. Trans. Mach. Learn. Res. 2023 (2023)- [i10]Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki:
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. CoRR abs/2305.07500 (2023) - [i9]Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski:
Understanding deep neural networks through the lens of their non-linearity. CoRR abs/2310.11439 (2023) - 2021
- [c6]Anton Mallasto, Markus Heinonen, Samuel Kaski:
Bayesian Inference for Optimal Transport with Stochastic Cost. ACML 2021: 1601-1616 - [i8]Anton Mallasto:
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals. CoRR abs/2102.03267 (2021) - [i7]Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki:
Affine Transport for Sim-to-Real Domain Adaptation. CoRR abs/2105.11739 (2021) - 2020
- [i6]Anton Mallasto, Augusto Gerolin, Hà Quang Minh:
Entropy-Regularized 2-Wasserstein Distance between Gaussian Measures. CoRR abs/2006.03416 (2020) - [i5]Anton Mallasto, Markus Heinonen, Samuel Kaski:
Bayesian Inference for Optimal Transport with Stochastic Cost. CoRR abs/2010.09327 (2020)
2010 – 2019
- 2019
- [c5]Anton Mallasto, Søren Hauberg, Aasa Feragen
:
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. AISTATS 2019: 2368-2377 - [c4]Anton Mallasto, Tom C. J. Dela Haije, Aasa Feragen:
A Formalization of the Natural Gradient Method for General Similarity Measures. GSI 2019: 599-607 - [c3]Mathias Højgaard Jensen, Anton Mallasto, Stefan Sommer:
Simulation of Conditioned Diffusions on the Flat Torus. GSI 2019: 685-694 - [i4]Anton Mallasto, Jes Frellsen, Wouter Boomsma, Aasa Feragen:
(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs. CoRR abs/1902.03642 (2019) - [i3]Anton Mallasto, Tom C. J. Dela Haije, Aasa Feragen:
A Formalization of The Natural Gradient Method for General Similarity Measures. CoRR abs/1902.08959 (2019) - [i2]Anton Mallasto, Guido Montúfar, Augusto Gerolin:
How Well Do WGANs Estimate the Wasserstein Metric? CoRR abs/1910.03875 (2019) - 2018
- [c2]Anton Mallasto, Aasa Feragen:
Wrapped Gaussian Process Regression on Riemannian Manifolds. CVPR 2018: 5580-5588 - [i1]Anton Mallasto, Søren Hauberg, Aasa Feragen:
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. CoRR abs/1805.09122 (2018) - 2017
- [c1]Anton Mallasto, Aasa Feragen:
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes. NIPS 2017: 5660-5670
Coauthor Index
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