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2020 – today
- 2024
[j38]Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola:
Personalised Federated Learning On Heterogeneous Feature Spaces. Trans. Mach. Learn. Res. 2024 (2024)- [c62]Alain Rakotomamonjy, Kimia Nadjahi, Liva Ralaivola:
Federated Wasserstein Distance. ICLR 2024 - [c61]Matilde Tullii, Solenne Gaucher, Hugo Richard, Eustache Diemert, Vianney Perchet, Alain Rakotomamonjy, Clément Calauzènes, Maxime Vono:
Open Research Challenges for Private Advertising Systems Under Local Differential Privacy. WISE (5) 2024: 107-122 - [i45]Mokhtar Z. Alaya, Alain Rakotomamonjy, Maxime Berar, Gilles Gasso:
Gaussian-Smoothed Sliced Probability Divergences. CoRR abs/2404.03273 (2024) - [i44]Thibaut Issenhuth, Ludovic Dos Santos, Jean-Yves Franceschi, Alain Rakotomamonjy:
Improving Consistency Models with Generator-Induced Coupling. CoRR abs/2406.09570 (2024) - 2023
- [j37]Hugo Lerogeron, Romain Picot-Clémente, Alain Rakotomamonjy, Laurent Heutte:
Approximating dynamic time warping with a convolutional neural network on EEG data. Pattern Recognit. Lett. 171: 162-169 (2023) - [c60]Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari:
Continuous PDE Dynamics Forecasting with Implicit Neural Representations. ICLR 2023 - [c59]Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. ICML 2023: 2777-2805 - [c58]Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola:
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. ICML 2023: 26451-26473 - [c57]Hugo Lerogeron, Romain Picot-Clémente, Laurent Heutte, Alain Rakotomamonjy:
Learning an autoencoder to compress EEG signals via a neural network based approximation of DTW. INNS DLIA@IJCNN 2023: 448-457 - [c56]Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy:
Unifying GANs and Score-Based Diffusion as Generative Particle Models. NeurIPS 2023 - [c55]Skander Karkar, Patrick Gallinari, Alain Rakotomamonjy:
Adversarial Sample Detection Through Neural Network Transport Dynamics. ECML/PKDD (1) 2023: 164-181 - [i43]Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola:
Personalised Federated Learning On Heterogeneous Feature Spaces. CoRR abs/2301.11447 (2023) - [i42]Hugo Lerogeron, Romain Picot-Clémente, Alain Rakotomamonjy, Laurent Heutte:
Approximating DTW with a convolutional neural network on EEG data. CoRR abs/2301.12873 (2023) - [i41]Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. CoRR abs/2303.05798 (2023) - [i40]Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy:
Unifying GANs and Score-Based Diffusion as Generative Particle Models. CoRR abs/2305.16150 (2023) - [i39]Skander Karkar, Patrick Gallinari, Alain Rakotomamonjy:
Adversarial Sample Detection Through Neural Network Transport Dynamics. CoRR abs/2306.04252 (2023) - [i38]Alain Rakotomamonjy, Kimia Nadjahi, Liva Ralaivola:
Federated Wasserstein Distance. CoRR abs/2310.01973 (2023) - [i37]Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen:
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance for Non-Parametric Generative Modeling. CoRR abs/2312.08227 (2023) - 2022
- [j36]Mokhtar Z. Alaya
, Maxime Bérar, Gilles Gasso, Alain Rakotomamonjy:
Theoretical guarantees for bridging metric measure embedding and optimal transport. Neurocomputing 468: 416-430 (2022) - [j35]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, M. El Alaya, Maxime Berar, Nicolas Courty:
Optimal transport for conditional domain matching and label shift. Mach. Learn. 111(5): 1651-1670 (2022) - [c54]Alain Rakotomamonjy, Rémi Flamary, Joseph Salmon, Gilles Gasso:
Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers. AISTATS 2022: 5196-5211 - [c53]Mohammad Abdollahi, Romain Serizel, Alain Rakotomamonjy, Gilles Gasso:
Integrating Isolated Examples with Weakly-Supervised Sound Event Detection: A Direct Approach. DCASE 2022 - [c52]Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari:
Mapping conditional distributions for domain adaptation under generalized target shift. ICLR 2022 - [c51]Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari:
Generalizing to New Physical Systems via Context-Informed Dynamics Model. ICML 2022: 11283-11301 - [c50]Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter:
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. NeurIPS 2022 - [c49]Alexandre Ramé, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, Matthieu Cord:
Diverse Weight Averaging for Out-of-Distribution Generalization. NeurIPS 2022 - [c48]Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil:
Multi-source domain adaptation via weighted joint distributions optimal transport. UAI 2022: 1970-1980 - [i36]Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari:
Generalizing to New Physical Systems via Context-Informed Dynamics Model. CoRR abs/2202.01889 (2022) - [i35]Alexandre Ramé, Matthieu Kirchmeyer, Thibaud Rahier, Alain Rakotomamonjy, Patrick Gallinari, Matthieu Cord:
Diverse Weight Averaging for Out-of-Distribution Generalization. CoRR abs/2205.09739 (2022) - [i34]Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola:
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. CoRR abs/2206.03230 (2022) - [i33]Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter:
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. CoRR abs/2206.13424 (2022) - [i32]Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari:
Continuous PDE Dynamics Forecasting with Implicit Neural Representations. CoRR abs/2209.14855 (2022) - 2021
- [j34]Matthieu Kirchmeyer, Patrick Gallinari, Alain Rakotomamonjy, Amin Mantrach:
Unsupervised domain adaptation with non-stochastic missing data. Data Min. Knowl. Discov. 35(6): 2714-2755 (2021) - [j33]Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T. H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer:
POT: Python Optimal Transport. J. Mach. Learn. Res. 22: 78:1-78:8 (2021) - [c47]Alain Rakotomamonjy, Liva Ralaivola:
Differentially Private Sliced Wasserstein Distance. ICML 2021: 8810-8820 - [c46]Ruben Ohana, Hamlet Jesse Medina Ruiz, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy:
Photonic Differential Privacy with Direct Feedback Alignment. NeurIPS 2021: 22010-22020 - [i31]Mokhtar Z. Alaya, Gilles Gasso, Maxime Berar, Alain Rakotomamonjy:
Distributional Sliced Embedding Discrepancy for Incomparable Distributions. CoRR abs/2106.02542 (2021) - [i30]Ruben Ohana, Hamlet Jesse Medina Ruiz, Julien Launay, Alessandro Cappelli, Iacopo Poli, Liva Ralaivola, Alain Rakotomamonjy:
Photonic Differential Privacy with Direct Feedback Alignment. CoRR abs/2106.03645 (2021) - [i29]Alain Rakotomamonjy, Liva Ralaivola:
Differentially Private Sliced Wasserstein Distance. CoRR abs/2107.01848 (2021) - [i28]Matthieu Kirchmeyer, Patrick Gallinari, Alain Rakotomamonjy, Amin Mantrach:
Unsupervised domain adaptation with non-stochastic missing data. CoRR abs/2109.09505 (2021) - [i27]Alain Rakotomamonjy, Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso:
Statistical and Topological Properties of Gaussian Smoothed Sliced Probability Divergences. CoRR abs/2110.10524 (2021) - [i26]Matthieu Kirchmeyer, Alain Rakotomamonjy, Emmanuel de Bézenac, Patrick Gallinari:
Mapping conditional distributions for domain adaptation under generalized target shift. CoRR abs/2110.15057 (2021) - 2020
- [c45]Hachem Kadri, Stéphane Ayache, Riikka Huusari, Alain Rakotomamonjy, Liva Ralaivola:
Partial Trace Regression and Low-Rank Kraus Decomposition. ICML 2020: 5031-5041 - [i25]Mokhtar Z. Alaya, Maxime Bérar, Gilles Gasso, Alain Rakotomamonjy:
Non-Aligned Distribution Distance using Metric Measure Embedding and Optimal Transport. CoRR abs/2002.08314 (2020) - [i24]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Mokhtar Z. Alaya, Maxime Berar, Nicolas Courty:
Match and Reweight Strategy for Generalized Target Shift. CoRR abs/2006.08161 (2020) - [i23]Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil:
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport. CoRR abs/2006.12938 (2020) - [i22]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Joseph Salmon:
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression. CoRR abs/2006.13533 (2020) - [i21]Hachem Kadri, Stéphane Ayache, Riikka Huusari, Alain Rakotomamonjy, Liva Ralaivola:
Partial Trace Regression and Low-Rank Kraus Decomposition. CoRR abs/2007.00935 (2020) - [i20]Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy, Ugo Tanielian, Clément Calauzènes:
Wasserstein Learning of Determinantal Point Processes. CoRR abs/2011.09712 (2020)
2010 – 2019
- 2019
- [j32]Abraham Traoré, Maxime Berar, Alain Rakotomamonjy:
Online multimodal dictionary learning. Neurocomputing 368: 163-179 (2019) - [c44]Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon:
Screening rules for Lasso with non-convex Sparse Regularizers. ICML 2019: 5341-5350 - [c43]Abraham Traoré, Maxime Berar, Alain Rakotomamonjy:
Singleshot : a scalable Tucker tensor decomposition. NeurIPS 2019: 6301-6312 - [c42]Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy:
Screening Sinkhorn Algorithm for Regularized Optimal Transport. NeurIPS 2019: 12169-12179 - [i19]Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon:
Screening Rules for Lasso with Non-Convex Sparse Regularizers. CoRR abs/1902.06125 (2019) - [i18]Mokhtar Z. Alaya, Maxime Bérar, Gilles Gasso, Alain Rakotomamonjy:
Screening Sinkhorn Algorithm for Regularized Optimal Transport. CoRR abs/1906.08540 (2019) - 2018
- [j31]Rémi Flamary, Marco Cuturi, Nicolas Courty, Alain Rakotomamonjy:
Wasserstein discriminant analysis. Mach. Learn. 107(12): 1923-1945 (2018) - [c41]Abraham Traoré, Maxime Berar, Alain Rakotomamonjy:
Non-Negative Tensor Dictionary Learning. ESANN 2018 - [c40]Rafael Will M. de Araujo, Roberto Hirata, Alain Rakotomamonjy:
Concave Losses for Robust Dictionary Learning. ICASSP 2018: 2176-2180 - [i17]Alain Rakotomamonjy, Abraham Traoré, Maxime Berar, Rémi Flamary, Nicolas Courty:
Wasserstein Distance Measure Machines. CoRR abs/1803.00250 (2018) - 2017
- [j30]Nicolas Courty, Rémi Flamary, Devis Tuia, Alain Rakotomamonjy:
Optimal Transport for Domain Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 39(9): 1853-1865 (2017) - [j29]Alain Rakotomamonjy:
Supervised Representation Learning for Audio Scene Classification. IEEE ACM Trans. Audio Speech Lang. Process. 25(6): 1253-1265 (2017) - [j28]Haibo He, Robert Haas, Jun Fu, Barbara Hammer, Daniel W. C. Ho, Fakhri Karray, Dhireesha Kudithipudi, José Antonio Lozano, Teresa Bernarda Ludermir, Jacek Mandziuk, Stefano Melacci, Antonio Paiva, Hong Qiao, Alain Rakotomamonjy, Shiliang Sun, Johan A. K. Suykens, Meng Wang:
Editorial: A Successful Year and Looking Forward to 2017 and Beyond. IEEE Trans. Neural Networks Learn. Syst. 28(1): 2-7 (2017) - [j27]Alain Rakotomamonjy, Sokol Koço, Liva Ralaivola:
Greedy Methods, Randomization Approaches, and Multiarm Bandit Algorithms for Efficient Sparsity-Constrained Optimization. IEEE Trans. Neural Networks Learn. Syst. 28(11): 2789-2802 (2017) - [c39]Nathalie T. H. Gayraud, Alain Rakotomamonjy, Maureen Clerc:
Optimal transport Applied to Transfer Learning for P300 Detection. GBCIC 2017 - [c38]Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy:
Joint distribution optimal transportation for domain adaptation. NIPS 2017: 3730-3739 - [i16]Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy:
Joint Distribution Optimal Transportation for Domain Adaptation. CoRR abs/1705.08848 (2017) - [i15]Rafael Will M. de Araujo, Roberto Hirata, Alain Rakotomamonjy:
Concave losses for robust dictionary learning. CoRR abs/1711.00659 (2017) - 2016
- [j26]Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren:
Operator-valued Kernels for Learning from Functional Response Data. J. Mach. Learn. Res. 17: 20:1-20:54 (2016) - [j25]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso:
DC Proximal Newton for Nonconvex Optimization Problems. IEEE Trans. Neural Networks Learn. Syst. 27(3): 636-647 (2016) - [c37]Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy:
Early and Reliable Event Detection Using Proximity Space Representation. ICML 2016: 2310-2319 - [i14]Rémi Flamary, Alain Rakotomamonjy, Gilles Gasso:
Importance sampling strategy for non-convex randomized block-coordinate descent. CoRR abs/1606.07286 (2016) - [i13]Rémi Flamary, Marco Cuturi, Nicolas Courty, Alain Rakotomamonjy:
Wasserstein Discriminant Analysis. CoRR abs/1608.08063 (2016) - 2015
- [j24]Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy:
Filter bank learning for signal classification. Signal Process. 113: 124-137 (2015) - [j23]Alain Rakotomamonjy, Gilles Gasso:
Histogram of Gradients of Time-Frequency Representations for Audio Scene Classification. IEEE ACM Trans. Audio Speech Lang. Process. 23(1): 142-153 (2015) - [c36]Rémi Flamary, Alain Rakotomamonjy, Gilles Gasso:
Importance sampling strategy for non-convex randomized block-coordinate descent. CAMSAP 2015: 301-304 - [c35]Alain Rakotomamonjy, Sokol Koço, Liva Ralaivola:
More efficient sparsity-inducing algorithms using inexact gradient. EUSIPCO 2015: 709-713 - [c34]Devis Tuia, Rémi Flamary, Alain Rakotomamonjy, Nicolas Courty:
Multitemporal classification without new labels: A solution with optimal transport. MultiTemp 2015: 1-4 - [c33]Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy:
Early frame-based detection of acoustic scenes. WASPAA 2015: 1-5 - [i12]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso:
DC Proximal Newton for Non-Convex Optimization Problems. CoRR abs/1507.00438 (2015) - [i11]Nicolas Courty, Rémi Flamary, Devis Tuia, Alain Rakotomamonjy:
Optimal Transport for Domain Adaptation. CoRR abs/1507.00504 (2015) - [i10]Alain Rakotomamonjy, Gilles Gasso:
Histogram of gradients of Time-Frequency Representations for Audio scene detection. CoRR abs/1508.04909 (2015) - [i9]Alain Rakotomamonjy, Sokol Koço, Liva Ralaivola:
Greedy methods, randomization approaches and multi-arm bandit algorithms for efficient sparsity-constrained optimization. CoRR abs/1508.06477 (2015) - [i8]Alain Rakotomamonjy, Rémi Flamary, Nicolas Courty:
Generalized conditional gradient: analysis of convergence and applications. CoRR abs/1510.06567 (2015) - [i7]Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren:
Operator-valued Kernels for Learning from Functional Response Data. CoRR abs/1510.08231 (2015) - 2014
- [j22]Alain Rakotomamonjy, Caroline Petitjean, Mathieu Salaün, Luc Thiberville:
Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images. Artif. Intell. Medicine 61(2): 105-118 (2014) - [j21]Rémi Flamary, Nisrine Jrad, Ronald Phlypo, Marco Congedo, Alain Rakotomamonjy:
Mixed-Norm Regularization for Brain Decoding. Comput. Math. Methods Medicine 2014: 317056:1-317056:13 (2014) - [j20]Alain Rakotomamonjy, Sukalpa Chanda:
ℓp-norm multiple kernel learning with low-rank kernels. Neurocomputing 143: 68-79 (2014) - [j19]Devis Tuia, Michele Volpi, Mauro Dalla Mura, Alain Rakotomamonjy, Rémi Flamary:
Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM. IEEE Trans. Geosci. Remote. Sens. 52(10): 6062-6074 (2014) - [c32]Aurelie Boisbunon, Rémi Flamary, Alain Rakotomamonjy:
Active set strategy for high-dimensional non-convex sparse optimization problems. ICASSP 2014: 1517-1521 - [c31]Emilie Niaf, Rémi Flamary, Alain Rakotomamonjy, Olivier Rouvière, Carole Lartizien:
SVM with feature selection and smooth prediction in images: Application to CAD of prostate cancer. ICIP 2014: 2246-2250 - [c30]Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy:
Kernel learning as minimization of the single validation estimate. MLSP 2014: 1-6 - [i6]Rémi Flamary, Nisrine Jrad, Ronald Phlypo, Marco Congedo, Alain Rakotomamonjy:
Mixed-norm Regularization for Brain Decoding. CoRR abs/1403.3628 (2014) - 2013
- [j18]Alain Rakotomamonjy:
Applying alternating direction method of multipliers for constrained dictionary learning. Neurocomputing 106: 126-136 (2013) - [j17]Alain Rakotomamonjy, Rémi Flamary, Florian Yger:
Learning with infinitely many features. Mach. Learn. 91(1): 43-66 (2013) - [j16]Alain Rakotomamonjy:
Direct Optimization of the Dictionary Learning Problem. IEEE Trans. Signal Process. 61(22): 5495-5506 (2013) - [c29]Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy:
Filter bank Kernel Learning for nonstationary signal classification. ICASSP 2013: 3183-3187 - [c28]Devis Tuia, Michele Volpi, Mauro Dalla Mura, Alain Rakotomamonjy, Rémi Flamary:
Create the relevant spatial filterbank in the hyperspectral jungle. IGARSS 2013: 2172-2175 - [i5]Hachem Kadri, Asma Rabaoui, Philippe Preux, Emmanuel Duflos, Alain Rakotomamonjy:
Functional Regularized Least Squares Classi cation with Operator-valued Kernels. CoRR abs/1301.2655 (2013) - 2012
- [j15]Rémi Flamary, Devis Tuia, Benjamin Labbé, Gustavo Camps-Valls, Alain Rakotomamonjy:
Large Margin Filtering. IEEE Trans. Signal Process. 60(2): 648-659 (2012) - [c27]David Picard, Nicolas Thome, Matthieu Cord, Alain Rakotomamonjy:
Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm. ESANN 2012 - [c26]Florian Yger, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy:
Oblique principal subspace tracking on manifold. ICASSP 2012: 2429-2432 - [c25]Alain Rakotomamonjy:
Sparse Support Vector Infinite Push. ICML 2012 - [c24]Florian Yger, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy:
Adaptive Canonical Correlation Analysis Based On Matrix Manifolds. ICML 2012 - [c23]Devis Tuia, Mauro Dalla Mura, Michele Volpi, Rémi Flamary, Alain Rakotomamonjy:
Discovering relevant spatial filterbanks for VHR image classification. ICPR 2012: 3212-3215 - [c22]Hachem Kadri, Alain Rakotomamonjy, Francis R. Bach, Philippe Preux:
Multiple Operator-valued Kernel Learning. NIPS 2012: 2438-2446 - [i4]Hachem Kadri, Alain Rakotomamonjy, Francis R. Bach, Philippe Preux:
Multiple Operator-valued Kernel Learning. CoRR abs/1203.1596 (2012) - 2011
- [j14]Florian Yger, Alain Rakotomamonjy:
Wavelet kernel learning. Pattern Recognit. 44(10-11): 2614-2629 (2011) - [j13]Florian Yger, Alain Rakotomamonjy:
Apprentissage de dictionnaires d'ondelettes vaste marge pour la classification de signaux et de textures. Rev. d'Intelligence Artif. 25(3): 369-392 (2011) - [j12]Alain Rakotomamonjy:
Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms. Signal Process. 91(7): 1505-1526 (2011) - [j11]Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Stéphane Canu:
ellp-ellq Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning. IEEE Trans. Neural Networks 22(8): 1307-1320 (2011) - [c21]Rémi Flamary, Florian Yger, Alain Rakotomamonjy:
Selecting from an infinite set of features in SVM. ESANN 2011 - [c20]Florian Yger, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy:
A supervised strategy for deep kernel machine. ESANN 2011 - [c19]Hachem Kadri, Asma Rabaoui, Philippe Preux, Emmanuel Duflos, Alain Rakotomamonjy:
Functional Regularized Least Squares Classication with Operator-valued Kernels. ICML 2011: 993-1000 - [i3]Rémi Flamary, Alain Rakotomamonjy:
Decoding finger movements from ECoG signals using switching linear models. CoRR abs/1106.3395 (2011) - [i2]Rémi Flamary, Benjamin Labbé, Alain Rakotomamonjy:
Large margin filtering for signal sequence labeling. CoRR abs/1106.3396 (2011) - 2010
- [j10]Marie Szafranski, Yves Grandvalet, Alain Rakotomamonjy:
Composite kernel learning. Mach. Learn. 79(1-2): 73-103 (2010) - [c18]Rémi Flamary, Benjamin Labbé, Alain Rakotomamonjy:
Large margin filtering for Signal Sequence Labeling. ICASSP 2010: 1974-1977 - [c17]Florian Yger, Alain Rakotomamonjy:
Large marginwavelet-based dictionary for signal classification. ICASSP 2010: 2182-2185 - [i1]Rémi Flamary, Benjamin Labbé, Alain Rakotomamonjy:
Filtrage vaste marge pour l'étiquetage séquentiel à noyaux de signaux. CoRR abs/1007.0824 (2010)
2000 – 2009
- 2009
- [j9]Gilles Gasso, Alain Rakotomamonjy, Stéphane Canu:
Recovering sparse signals with a certain family of nonconvex penalties and DC programming. IEEE Trans. Signal Process. 57(12): 4686-4698 (2009) - 2008
- [j8]Alain Rakotomamonjy, Vincent Guigue:
BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller. IEEE Trans. Biomed. Eng. 55(3): 1147-1154 (2008) - [c16]Marie Szafranski, Yves Grandvalet, Alain Rakotomamonjy:
Composite kernel learning. ICML 2008: 1040-1047 - [c15]Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshet, Stéphane Canu:
Support Vector Machines with a Reject Option. NIPS 2008: 537-544 - 2007
- [j7]Alain Rakotomamonjy:
Analysis of SVM regression bounds for variable ranking. Neurocomputing 70(7-9): 1489-1501 (2007) - [c14]Alain Rakotomamonjy, Manuel Davy:
One-class SVM regularization path and comparison with alpha seeding. ESANN 2007: 271-276 - [c13]Frédéric Suard, Alain Rakotomamonjy, Abdelaziz Bensrhair:
Kernel on Bag of Paths For Measuring Similarity of Shapes. ESANN 2007: 355-360 - [c12]Alain Rakotomamonjy, Francis R. Bach, Stéphane Canu, Yves Grandvalet:
More efficiency in multiple kernel learning. ICML 2007: 775-782 - [c11]Massimo Bertozzi, Alberto Broggi, Michael S. Del Rose, Mirko Felisa, Alain Rakotomamonjy, Frédéric Suard:
A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier. ITSC 2007: 143-148 - 2006
- [j6]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Translation-invariant classification of non-stationary signals. Neurocomputing 69(7-9): 743-753 (2006) - [j5]Gaëlle Loosli, Sans-Goog Lee, Alain Rakotomamonjy:
Perception d'états affectifs et apprentissage. Rev. d'Intelligence Artif. 20(4-5): 553-582 (2006) - [j4]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Kernel Basis Pursuit. Rev. d'Intelligence Artif. 20(6): 757-774 (2006) - [c10]Frédéric Suard, Alain Rakotomamonjy, Abdelaziz Bensrhair:
Object Categorization Using Kernels Combining Graphs and Histograms of Gradients. ICIAR (2) 2006: 23-34 - 2005
- [j3]Alain Rakotomamonjy, Stéphane Canu:
Frames, Reproducing Kernels, Regularization and Learning. J. Mach. Learn. Res. 6: 1485-1515 (2005) - [c9]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Kernel Basis Pursuit. CAP 2005: 93-106 - [c8]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Kernel Basis Pursuit. ECML 2005: 146-157 - [c7]Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
Translation invariant classification of non-stationary signals. ESANN 2005: 473-478 - [c6]Cécile Delgorge, Christophe Rosenberger, Alain Rakotomamonjy, Gérard Poisson, Pierre Vieyres:
Evaluation of the quality of ultrasound image compression by fusion of criteria with a support vector machine. EUSIPCO 2005: 1-4 - [c5]Alain Rakotomamonjy, Vincent Guigue, Grégory Mallet, Victor Alvarado:
Ensemble of SVMs for Improving Brain Computer Interface P300 Speller Performances. ICANN (1) 2005: 45-50 - [c4]Sébastien Chabrier, Christophe Rosenberger, Hélène Laurent, Alain Rakotomamonjy:
Segmentation Evaluation Using a Support Vector Machine. ICAPR (1) 2005: 426-435 - 2004
- [c3]Christophe Rosenberger, Alain Rakotomamonjy, Bruno Emile:
Generic target recognition. EUSIPCO 2004: 1613-1616 - [c2]Alain Rakotomamonjy:
Optimizing Area Under Roc Curve with SVMs. ROCAI 2004: 71-80 - 2003
- [j2]Alain Rakotomamonjy:
Variable Selection Using SVM-based Criteria. J. Mach. Learn. Res. 3: 1357-1370 (2003) - 2002
- [j1]Alain Rakotomamonjy, Rodolphe Le Riche, David Gualandris, Stéphane Canu:
Comparaison de stratégies de discrimination de masses de véhicules automobiles. Rev. d'Intelligence Artif. 16(6): 753-784 (2002) - [c1]Alain Rakotomamonjy, Stéphane Canu:
Frame Kernels for Learning. ICANN 2002: 707-712
Coauthor Index
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