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Anton Mallasto
Anton Mallasto
Data Scientist, Smartly.io
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Title
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Cited by
Year
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
A Mallasto, A Feragen
Advances in Neural Information Processing Systems 30, 2017
852017
Wrapped Gaussian process regression on Riemannian manifolds
A Mallasto, A Feragen
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
372018
Entropy-regularized 2-Wasserstein distance between Gaussian measures
A Mallasto, A Gerolin, HQ Minh
Information Geometry 5 (1), 289-323, 2022
342022
How well do WGANs estimate the Wasserstein metric?
A Mallasto, G Montúfar, A Gerolin
arXiv preprint arXiv:1910.03875, 2019
252019
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
A Mallasto, S Hauberg, A Feragen
arXiv preprint arXiv:1805.09122, 2018
152018
(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
A Mallasto, J Frellsen, W Boomsma, A Feragen
arXiv preprint arXiv:1902.03642, 2019
122019
Entropy-Regularized -Wasserstein Distance between Gaussian Measures
A Mallasto, A Gerolin, HQ Minh
arXiv preprint arXiv:2006.03416, 2020
92020
Simulation of conditioned diffusions on the flat torus
MH Jensen, A Mallasto, S Sommer
Geometric Science of Information: 4th International Conference, GSI 2019 …, 2019
52019
A formalization of the natural gradient method for general similarity measures
A Mallasto, TD Haije, A Feragen
Geometric Science of Information: 4th International Conference, GSI 2019 …, 2019
32019
Affine transport for sim-to-real domain adaptation
A Mallasto, K Arndt, M Heinonen, S Kaski, V Kyrki
arXiv preprint arXiv:2105.11739, 2021
22021
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
O Struckmeier, I Redko, A Mallasto, K Arndt, M Heinonen, V Kyrki
Transactions on Machine Learning Research, 2023
12023
Estimating 2-Sinkhorn divergence between Gaussian processes from finite-dimensional marginals
A Mallasto
arXiv preprint arXiv:2102.03267, 2021
12021
Understanding deep neural networks through the lens of their non-linearity
Q Bouniot, I Redko, A Mallasto, C Laclau, K Arndt, O Struckmeier, ...
arXiv preprint arXiv:2310.11439, 2023
2023
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation
O Struckmeier, I Redko, A Mallasto, K Arndt, M Heinonen, V Kyrki
arXiv preprint arXiv:2305.07500, 2023
2023
Bayesian Inference for Optimal Transport with Stochastic Cost
A Mallasto, M Heinonen, S Kaski
Asian Conference on Machine Learning, 1601-1616, 2021
2021
3.5 Geometry in uncertainty quantification
A Feragen, A Mallasto, S Hauberg
Visualization and Processing of Anisotropy in Imaging, Geometry, and …, 0
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Articles 1–16