| Logistic regression G Gasso INSA Rouen-ASI Departement Laboratory: Saint-Etienne-du-Rouvray, France, 1-30, 2019 | 632 | 2019 |
| Recovering sparse signals with a certain family of nonconvex penalties and DC programming G Gasso, A Rakotomamonjy, S Canu IEEE Transactions on Signal Processing 57 (12), 4686-4698, 2009 | 390 | 2009 |
| Histogram of gradients of time–frequency representations for audio scene classification A Rakotomamonjy, G Gasso IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (1), 142-153, 2014 | 277 | 2014 |
| Partial optimal tranport with applications on positive-unlabeled learning L Chapel, MZ Alaya, G Gasso Advances in Neural Information Processing Systems 33, 2903-2913, 2020 | 128 | 2020 |
| Identification des systèmes dynamiques non-linéaires: approche multi-modèle K Gasso Institut National Polytechnique de Lorraine, 2000 | 101 | 2000 |
| Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning A Rakotomamonjy, R Flamary, G Gasso, S Canu IEEE Transactions on Neural Networks 22 (8), 1307-1320, 2011 | 97 | 2011 |
| Structure identification in multiple model representation: elimination and merging of local models K Gasso, G Mourot, J Ragot Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No …, 2001 | 82 | 2001 |
| A multiple kernel framework for inductive semi-supervised SVM learning X Tian, G Gasso, S Canu Neurocomputing 90, 46-58, 2012 | 72 | 2012 |
| Palmprint recognition with an efficient data driven ensemble classifier I Rida, R Herault, GL Marcialis, G Gasso Pattern Recognition Letters 126, 21-30, 2019 | 66 | 2019 |
| Gradient-based deterministic inversion of geophysical data with generative adversarial networks: is it feasible? E Laloy, N Linde, C Ruffino, R Hérault, G Gasso, D Jacques Computers & Geosciences 133, 104333, 2019 | 63 | 2019 |
| Identification of switching systems using change detection technique in the subspace framework KM Pekpe, G Mourot, K Gasso, J Ragot 2004 43rd IEEE Conference on Decision and Control (CDC)(IEEE Cat. No …, 2004 | 55 | 2004 |
| Screening sinkhorn algorithm for regularized optimal transport MZ Alaya, M Berar, G Gasso, A Rakotomamonjy Advances in Neural Information Processing Systems 32, 2019 | 53 | 2019 |
| Adaptive canonical correlation analysis based on matrix manifolds F Yger, M Berar, G Gasso, A Rakotomamonjy arXiv preprint arXiv:1206.6453, 2012 | 52 | 2012 |
| DC proximal Newton for nonconvex optimization problems A Rakotomamonjy, R Flamary, G Gasso IEEE transactions on neural networks and learning systems 27 (3), 636-647, 2015 | 49 | 2015 |
| Batch and online learning algorithms for nonconvex Neyman-Pearson classification G Gasso, A Pappaioannou, M Spivak, L Bottou ACM transactions on intelligent systems and technology (TIST) 2 (3), 1-19, 2011 | 43 | 2011 |
| Unbalanced optimal transport through non-negative penalized linear regression L Chapel, R Flamary, H Wu, C Févotte, G Gasso Advances in Neural Information Processing Systems 34, 23270-23282, 2021 | 40 | 2021 |
| Fast object detection in compressed jpeg images B Deguerre, C Chatelain, G Gasso 2019 ieee intelligent transportation systems conference (itsc), 333-338, 2019 | 33 | 2019 |
| Optimal transport for conditional domain matching and label shift A Rakotomamonjy, R Flamary, G Gasso, ME Alaya, M Berar, N Courty Machine Learning, 1-20, 2022 | 31 | 2022 |
| Learning constrained task similarities in graphregularized multi-task learning R Flamary, A Rakotomamonjy, G Gasso Regularization, Optimization, Kernels, and Support Vector Machines 103, 1, 2014 | 31 | 2014 |
| Screening rules for lasso with non-convex sparse regularizers A Rakotomamonjy, G Gasso, J Salmon International Conference on Machine Learning, 5341-5350, 2019 | 26 | 2019 |
Alain RakotomamonjyCriteo AI LabVerified email at insa-rouen.fr
