Publications

  1. Multi-View Data Generation Without View Supervision
    ICLR (Long Beach, USA), 2018.
  2. Multi-view Metric Learning in Vector-valued Kernel
    Spaces.
    Riikka Huusari ; Hachem Kadri ; Cécile Capponi.
    AISTATS, (Lanzarote, Spain) 2018
    (paper: HuusariAistats2018, code: MultiViewMetricLearning)
  3. General Framework for Multi-View Metric Learning
    Riikka Huusari, Hachem Kadri, Cécile Capponi.
    Chapter of Springer book « Linking and Mining Heterogeneous and Multi-view Data », Springer, Deepak P, A. Jurek Eds., to appear, 2018.
  4. Learning from imbalanced datasets with cross-view cooperation-based ensemble methods.
    Cécile Capponi and Sokol Koço
    Chapter of Springer book « Linking and Mining Heterogeneous and Multi-view Data », Springer, Deepak P, A. Jurek Eds., to appear, 2018.
  5. Fast and Provably Effective Multi-view Classification with Landmark-based SVM.
    Valentina Zantedeschi, Rémi Emonet, Marc Sebban
    Proc of European Conference in Machine Learning (ECML), 2018.
  6. Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
    Anil Goyal ; Emilie Morvant ; Massih-Reza Amini
    International Symposium on Intelligent Data Analysis (IDA), 2018.
  7. Apprentissage d’un vote de majorité hiérarchique pour l’apprentissage multivues
    Anil Goyal ; Emilie Morvant ; Massih-Reza Amini
    CAp’2018.
  8. Multi-View Data Generation Without View Supervision
    Mickael Chen, Ludovic Denoyer and Thierry Artières
    CAp’2018.
  9. Multi-view Generative Adversarial Networks
    ECML/PKDD 2017, Skopje, Macedonia
  10. Risk upper bounds for general ensemble methods with an application to multiclass classification. François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy. Neurocomputing, 219:15-25, 2017,
  11. PAC-Bayesian Analysis for a two-step Hierarchical Mutliview Learning Approach. Anil Goyal ; Emilie Morvant ; Pascal Germain ; Massih-Reza Amini. Proc of European Conference on Machine Learning & Principles and Pratice of Knowledge Discovery in Databases (ECML-PKDD), 2017, Skopje, Macedonia
  12. Theoretical Analysis of Domain Adaptation with Optimal Transport. Ievgen Redko, Amaury Habrard, Marc Sebban. Proc of European Conference on Machine Learning & Principles and Pratice of Knowledge Discovery in Databases (ECML-PKDD), 2017. (related Research report in 2016. pdf)
  13. beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. Valentina Zantedeschi, Rémi Emonet, Marc Sebban. Proc. of International Conference on Neural Information Processing Systems (NIPS), 2016. pdf
  14. Mapping Estimation for Discrete Optimal Transport. Mickaël Perrot; Nicolas Courty; Rémy Flamary; Amaury Habrard. Proc. of International Conference on Neural Information Processing Systems (NIPS), 2016. pdf
  15. Metric Learning as Convex Combinations of Local Models with Generalization Guarantees. Valentina Zantedeschi, Rémi Emonet, Marc Sebban.  Proc of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. pdf
  16. A New PAC-Bayesian Perspective on Domain Adaptation. Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant. Proc. of International Conference on Machine Learning (ICML), 2016, New York, USA. pdf
  17. Théorèmes PAC-Bayésiens pour l’apprentissage multi-vues. Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza AminiFrench Conference on Machine Learning (CAp 2016), 2016, Marseille, France. 

Cross-view Learning: theory, algorithms and benchmarks