| Scikit-learn: Machine learning in Python F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... the Journal of machine Learning research 12, 2825-2830, 2011 | 74446 | 2011 |
| API design for machine learning software: experiences from the scikit-learn project L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... arXiv preprint arXiv:1309.0238, 2013 | 2643 | 2013 |
| Soft-DTW: a differentiable loss function for time-series M Cuturi, M Blondel Proceedings of the 34th International Conference on Machine Learning, 894--903, 2017 | 508 | 2017 |
| Higher-order factorization machines M Blondel, A Fujino, N Ueda, M Ishihata Advances in Neural Information Processing Systems 29, 2016 | 205 | 2016 |
| Large-scale optimal transport and mapping estimation V Seguy, BB Damodaran, R Flamary, N Courty, A Rolet, M Blondel International Conference on Learning Representations, 2018 | 194 | 2018 |
| Smooth and sparse optimal transport M Blondel, V Seguy, A Rolet Proceedings of the Twenty-First International Conference on Artificial …, 2018 | 146 | 2018 |
| Learning with differentiable pertubed optimizers Q Berthet, M Blondel, O Teboul, M Cuturi, JP Vert, F Bach Advances in neural information processing systems 33, 9508-9519, 2020 | 132 | 2020 |
| Differentiable dynamic programming for structured prediction and attention A Mensch, M Blondel Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018 | 132 | 2018 |
| Fast differentiable sorting and ranking M Blondel, O Teboul, Q Berthet, J Djolonga International Conference on Machine Learning, 950-959, 2020 | 128 | 2020 |
| SparseMAP: Differentiable sparse structured inference V Niculae, AFT Martins, M Blondel, C Cardie Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018 | 117 | 2018 |
| A regularized framework for sparse and structured neural attention V Niculae, M Blondel Advances in neural information processing systems 30, 2017 | 106 | 2017 |
| Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms M Blondel, M Ishihata, A Fujino, N Ueda Proceedings of the 33rd International Conference on Machine Learning, 850–858, 2016 | 96 | 2016 |
| Efficient and modular implicit differentiation M Blondel, Q Berthet, M Cuturi, R Frostig, S Hoyer, F Llinares-López, ... Advances in neural information processing systems, 2021 | 93 | 2021 |
| Learning with Fenchel-Young losses M Blondel, AFT Martins, V Niculae arXiv preprint arXiv:1901.02324, 2019 | 84 | 2019 |
| Block coordinate descent algorithms for large-scale sparse multiclass classification M Blondel, K Seki, K Uehara Machine Learning 93 (1), 31-52, 2013 | 71 | 2013 |
| A ranking approach to genomic selection M Blondel, A Onogi, H Iwata, N Ueda PloS one 10 (6), e0128570, 2015 | 53 | 2015 |
| Implicit differentiation of lasso-type models for hyperparameter optimization Q Bertrand, Q Klopfenstein, M Blondel, S Vaiter, A Gramfort, J Salmon International Conference on Machine Learning, 810-821, 2020 | 49 | 2020 |
| Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study Y Nohara, E Kai, P Pratim, R Islam, A Ahmed, M Kuroda, S Inoue, ... Journal of Medical Internet Research 17 (1), 2015 | 47 | 2015 |
| Convex factorization machines M Blondel, A Fujino, N Ueda Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 43 | 2015 |
| Momentum residual neural networks ME Sander, P Ablin, M Blondel, G Peyré International Conference on Machine Learning, 9276-9287, 2021 | 37 | 2021 |
Alexandre GramfortMetaVerified email at meta.com
