| 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 | 869 | 2021 |
| Optimal Transport for structured data with application on graphs V Titouan, N Courty, R Tavenard, C Laetitia, R Flamary International Conference on Machine Learning (ICML), 6275-6284, 2019 | 304* | 2019 |
| Fused Gromov-Wasserstein distance for structured objects T Vayer, L Chapel, R Flamary, R Tavenard, N Courty Algorithms 13 (9), 212, 2020 | 125 | 2020 |
| Sliced gromov-wasserstein V Titouan, R Flamary, N Courty, R Tavenard Neural Information Processing Systems (NeurIPS) 32, 2019 | 115* | 2019 |
| CO-Optimal Transport V Titouan, I Redko, R Flamary, N Courty Neural Information Processing Systems (NeurIPS) 33, 2020 | 78* | 2020 |
| Online Graph Dictionary Learning C Vincent-Cuaz, T Vayer, R Flamary, M Corneli, N Courty International Conference on Machine Learning (ICML), 10564-10574, 2021 | 60 | 2021 |
| Semi-relaxed Gromov Wasserstein divergence with applications on graphs C Vincent-Cuaz, R Flamary, M Corneli, T Vayer, N Courty International Conference on Learning Representations (ICLR), 2022 | 44 | 2022 |
| Template based Graph Neural Network with Optimal Transport Distances C Vincent-Cuaz, R Flamary, M Corneli, T Vayer, N Courty Neural Information Processing Systems (NeurIPS), 2022 | 29 | 2022 |
| A contribution to Optimal Transport on incomparable spaces T Vayer Université Bretagne Sud, 2020 | 27 | 2020 |
| Time series alignment with global invariances T Vayer, R Tavenard, L Chapel, N Courty, R Flamary, Y Soullard Transactions on Machine Learning Research, 2022 | 13 | 2022 |
| Controlling Wasserstein distances by Kernel norms with application to Compressive Statistical Learning T Vayer, R Gribonval Journal of Machine Learning Research 24 (149), 1--51, 2023 | 8 | 2023 |
| Snekhorn: Dimension reduction with symmetric entropic affinities H Van Assel, T Vayer, R Flamary, N Courty Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein H Van Assel, C Vincent-Cuaz, T Vayer, R Flamary, N Courty arXiv preprint arXiv:2310.03398, 2023 | 5 | 2023 |
| Learning graphical factor models with riemannian optimization A Hippert-Ferrer, F Bouchard, A Mian, T Vayer, A Breloy Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 5 | 2023 |
| Entropic Wasserstein component analysis A Collas, T Vayer, R Flamary, A Breloy 2023 IEEE 33rd International Workshop on Machine Learning for Signal …, 2023 | 5 | 2023 |
| Fast multiscale diffusion on graphs S Marcotte, A Barbe, R Gribonval, T Vayer, M Sebban, P Borgnat, ... ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 5* | 2022 |
| Subspace detours meet gromov–wasserstein C Bonet, T Vayer, N Courty, F Septier, L Drumetz Algorithms 14 (12), 366, 2021 | 5 | 2021 |
| Optimization of the diffusion time in graph diffused-wasserstein distances: Application to domain adaptation A Barbe, P Gonçalves, M Sebban, P Borgnat, R Gribonval, T Vayer 2021 IEEE 33rd International Conference on Tools with Artificial …, 2021 | 3 | 2021 |
| Implicit differentiation for hyperparameter tuning the weighted Graphical Lasso C Pouliquen, P Gonçalves, M Massias, T Vayer arXiv preprint arXiv:2307.02130, 2023 | 2 | 2023 |
| Semi-relaxed Gromov-Wasserstein divergence for graphs classification C Vincent-Cuaz, R Flamary, M Corneli, T Vayer, N Courty Colloque GRETSI 2022-XXVIIIème Colloque Francophone de Traitement du Signal …, 2022 | 2 | 2022 |
Nicolas CourtyFull Professor, Université Bretagne Sud, laboratoire IRISAVerified email at univ-ubs.fr
