| 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 | 32321 | 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 | 945 | 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 | 133 | 2017 |
| Higher-order factorization machines M Blondel, A Fujino, N Ueda, M Ishihata Advances in Neural Information Processing Systems, 3351-3359, 2016 | 89 | 2016 |
| 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 | 68 | 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 | 66 | 2018 |
| Scikit-learn: machine learning in Python. 2011 F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... Mach. Learn. Res 12, 2019 | 64 | 2019 |
| Block coordinate descent algorithms for large-scale sparse multiclass classification M Blondel, K Seki, K Uehara Machine Learning 93 (1), 31-52, 2013 | 60 | 2013 |
| SparseMAP: Differentiable sparse structured inference V Niculae, AFT Martins, M Blondel, C Cardie Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018 | 55 | 2018 |
| A regularized framework for sparse and structured neural attention V Niculae, M Blondel Advances in neural information processing systems, 3338-3348, 2017 | 54 | 2017 |
| Smooth and sparse optimal transport M Blondel, V Seguy, A Rolet Proceedings of the Twenty-First International Conference on Artificial …, 2018 | 53 | 2018 |
| Differentiable dynamic programming for structured prediction and attention A Mensch, M Blondel Proceedings of the 35th International Conference on Machine Learning (ICML …, 2018 | 48 | 2018 |
| A ranking approach to genomic selection M Blondel, A Onogi, H Iwata, N Ueda PloS one 10 (6), e0128570, 2015 | 33 | 2015 |
| Convex factorization machines M Blondel, A Fujino, N Ueda Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015 | 31 | 2015 |
| Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion M Blondel, Y Kubo, N Ueda Proceedings of the Seventeenth International Conference on Artificial …, 2014 | 25 | 2014 |
| 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 | 24 | 2015 |
| Learning with Fenchel-Young losses M Blondel, AFT Martins, V Niculae arXiv preprint arXiv:1901.02324, 2019 | 18 | 2019 |
| Learning classifiers with Fenchel-Young losses: Generalized entropies, margins, and algorithms M Blondel, AFT Martins, V Niculae Proceedings of the Twenty-Second International Conference on Artificial …, 2018 | 18 | 2018 |
| Blind Source Separation with Optimal Transport Non-negative Matrix Factorization A Rolet, V Seguy, M Blondel, H Sawada EURASIP Journal on Advances in Signal Processing, 2018 | 11 | 2018 |
| Learning with differentiable perturbed optimizers Q Berthet, M Blondel, O Teboul, M Cuturi, JP Vert, F Bach arXiv preprint arXiv:2002.08676, 2020 | 9 | 2020 |
Alexandre GramfortINRIA, Univ. Paris-SaclayVerified email at inria.fr
