| POT: Python Optimal Transport R Flamary, N Courty, A Gramfort, MZ Alaya, A Boisbunon, S Chambon, ... Journal of Machine Learning Research 22 (78), 1-8, 2021 | 1111 | 2021 |
| Improving and generalizing flow-based generative models with minibatch optimal transport A Tong*, K Fatras*, N Malkin*, G Huguet, Y Zhang, J Rector-Brooks, ... TMLR, 2023 | 256* | 2023 |
| Unbalanced minibatch optimal transport; applications to domain adaptation K Fatras, T Séjourné, N Courty, R Flamary International Conference on Machine Learning, 3186-3197, 2021 | 174 | 2021 |
| Learning with minibatch Wasserstein: asymptotic and gradient properties K Fatras, Y Zine, R Flamary, R Gribonval, N Courty the 23nd International Conference on Artificial Intelligence and Statistics 108, 2020 | 99 | 2020 |
| SE(3)-Stochastic Flow Matching for Protein Backbone Generation AJ Bose, T Akhound-Sadegh, K Fatras, G Huguet, J Rector-Brooks, ... ICLR (spotlight), 2024 | 65 | 2024 |
| Minibatch optimal transport distances; analysis and applications K Fatras, Y Zine, S Majewski, R Flamary, R Gribonval, N Courty arXiv preprint arXiv:2101.01792, 2021 | 53 | 2021 |
| Simulation-free Schrödinger bridges via score and flow matching A Tong*, N Malkin*, K Fatras*, L Atanackovic, Y Zhang, G Huguet, G Wolf, ... AISTATS, 2024 | 48 | 2024 |
| Wasserstein adversarial regularization for learning with label noise K Fatras*, BB Damodaran*, S Lobry, R Flamary, D Tuia, N Courty IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 7296 …, 2021 | 41* | 2021 |
| Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees A Jolicoeur-Martineau, K Fatras, T Kachman AISTATS, 2024 | 26 | 2024 |
| Sequence-augmented se (3)-flow matching for conditional protein backbone generation G Huguet, J Vuckovic, K Fatras, E Thibodeau-Laufer, P Lemos, R Islam, ... arXiv preprint arXiv:2405.20313, 2024 | 23 | 2024 |
| Generating natural adversarial Remote Sensing Images JC Burnel, K Fatras, R Flamary, N Courty IEEE Transactions on Geoscience and Remote Sensing 60, 1-14, 2021 | 19* | 2021 |
| Population parameter averaging (papa) A Jolicoeur-Martineau, E Gervais, K Fatras, Y Zhang, S Lacoste-Julien TMLR, 2023 | 17 | 2023 |
| Proximal Splitting Meets Variance Reduction F Pedregosa, K Fatras, M Casotto The 22nd International Conference on Artificial Intelligence and Statistics …, 2018 | 14 | 2018 |
| Slicing Unbalanced Optimal Transport C Bonet, K Nadjahi, T Sejourne, K Fatras, N Courty Transactions on Machine Learning Research, 2024 | 13* | 2024 |
| No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths C Guille-Escuret, H Naganuma, K Fatras, I Mitliagkas ICML, 2024 | 6 | 2024 |
| Optimal transport meets noisy label robust loss and mixup regularization for domain adaptation K Fatras, H Naganuma, I Mitliagkas Conference on Lifelong Learning Agents, 966-981, 2022 | 6 | 2022 |
| A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods T Salvador*, K Fatras*, I Mitliagkas, A Oberman TMLR, 2023 | 4 | 2023 |
| Diffusion models with location-scale noise A Jolicoeur-Martineau, K Fatras, K Li, T Kachman arXiv preprint arXiv:2304.05907, 2023 | 3 | 2023 |
| Deep learning and optimal transport: learning from one another K Fatras Université de Bretagne Sud, 2021 | 1 | 2021 |
| Github GitHub-PythonOT/POT: POT: Python Optimal Transport S Wasserstein, R Flamary, N Courty, A Gramfort, MZ Alaya, A Boisbunon, ... Journal of Machine Learning Research 22 (78), 1-8, 2021 | | 2021 |
Nicolas CourtyFull Professor, Université Bretagne Sud, laboratoire IRISABestätigte E-Mail-Adresse bei univ-ubs.fr