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Alain Rakotomamonjy

Alain Rakotomamonjy

D-Index & Metrics

Computer Science

D-Index
37
Citations
9814
World Ranking
10495
National Ranking
257

Overview

Alain Rakotomamonjy is affiliated with Criteo in France and has a significant body of research contributions primarily in the field of Computer Science. Their work spans various subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Signal Processing, and Computational Mechanics.

Their main research topics cover diverse aspects of machine learning and data science, including:

  • Domain Adaptation and Few-Shot Learning
  • Generative Adversarial Networks and Image Synthesis
  • EEG and Brain-Computer Interfaces
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Privacy-Preserving Technologies in Data
  • Stochastic Gradient Optimization Techniques

Rakotomamonjy has collaborated frequently with several co-authors, including Gilles Gasso, Mokhtar Z. Alaya, Maxime Bérar, Matthieu Kirchmeyer, and Rémi Flamary. These collaborations often lead to publications in well-recognized venues.

The scientist's publication record shows a preference for journals and repositories such as:

  • arXiv (Cornell University)
  • HAL (Le Centre pour la Communication Scientifique Directe)
  • Pattern Recognition Letters
  • Procedia Computer Science
  • Machine Learning

Highlighted recent papers by or connected to their research interests include:

  • POT Python Optimal Transport, 2025, HAL (Le Centre pour la Communication Scientifique Directe)
  • Optimal Transport Applied To Transfer Learning For P300 Detection, 2020, HAL (Le Centre pour la Communication Scientifique Directe)
  • Continuous PDE Dynamics Forecasting with Implicit Neural Representations, 2022, arXiv (Cornell University)
  • Approximating dynamic time warping with a convolutional neural network on EEG data, 2023, Pattern Recognition Letters
  • Diverse Weight Averaging for Out-of-Distribution Generalization, 2022, arXiv (Cornell University)

These publications reflect a focus on topics such as optimal transport methods, transfer learning applied to EEG data, neural network approaches for signal processing, and strategies for improving generalization in machine learning models.

Best Publications

  • A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update

    Fabien Lotte;Laurent Bougrain;Andrzej Cichocki;Andrzej Cichocki;Maureen Clerc

  • Optimal Transport for Domain Adaptation

    Nicolas Courty;Remi Flamary;Devis Tuia;Alain Rakotomamonjy

  • Variable selection using svm based criteria

    Alain Rakotomamonjy

  • BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller

    A. Rakotomamonjy;V. Guigue

  • Joint Distribution Optimal Transportation for Domain Adaptation

    Nicolas Courty;Rémi Flamary;Amaury Habrard;Alain Rakotomamonjy

  • Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

    F. Suard;A. Rakotomamonjy;A. Bensrhair;A. Broggi

  • More efficiency in multiple kernel learning

    Alain Rakotomamonjy;Francis Bach;Stéphane Canu;Yves Grandvalet

  • Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming

    G. Gasso;A. Rakotomamonjy;S. Canu

  • Composite kernel learning

    Marie Szafranski;Yves Grandvalet;Alain Rakotomamonjy

  • Histogram of gradients of time-frequency representations for audio scene classification

    Alain Rakotomamonjy;Gilles Gasso

  • Review: Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms

    A. Rakotomamonjy

  • A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier

    M. Bertozzi;A. Broggi;M. Del Rose;M. Felisa

  • Optimizing Area Under Roc Curve with SVMs

    Alain Rakotomamonjy

  • Ensemble of SVMs for improving brain computer interface p300 speller performances

    A. Rakotomamonjy;V. Guigue;G. Mallet;V. Alvarado

  • Support Vector Machines with a Reject Option

    Yves Grandvalet;Alain Rakotomamonjy;Joseph Keshet;Stéphane Canu

  • Frames, Reproducing Kernels, Regularization and Learning

    Alain Rakotomamonjy;Stéphane Canu

  • Wasserstein Discriminant Analysis

    Rémi Flamary;Marco Cuturi;Nicolas Courty;Alain Rakotomamonjy

  • Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM

    Devis Tuia;Michele Volpi;Mauro Dalla Mura;Alain Rakotomamonjy

  • Operator-valued kernels for learning from functional response data

    Hachem Kadri;Emmanuel Duflos;Philippe Preux;Stéphane Canu

  • $ll_{p}-ll_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning

    A. Rakotomamonjy;R. Flamary;G. Gasso;S. Canu

  • Pedestrian Detection usingInfraredimages and Histograms of Oriented Gradients

    F. Suard;A. Rakotomamonjy;A. Bensrhair;A. Broggi

Frequent Co-Authors

Stéphane Canu
Stéphane Canu Institut National des Sciences Appliquées de Rouen
Devis Tuia
Devis Tuia École Polytechnique Fédérale de Lausanne
Marco Congedo
Marco Congedo Centre national de la recherche scientifique, CNRS
Patrick Gallinari
Patrick Gallinari Sorbonne University
Christophe Rosenberger
Christophe Rosenberger Université de Caen Normandie
Alberto Broggi
Alberto Broggi University of Parma
Francis Bach
Francis Bach École Normale Supérieure
Mauro Dalla Mura
Mauro Dalla Mura Grenoble Alpes University
Fabien Lotte
Fabien Lotte French Institute for Research in Computer Science and Automation - INRIA
Andrzej Cichocki
Andrzej Cichocki Systems Research Institute

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