Publications

  • Multi-view Metric Learning in Vector-valued Kernel
    Spaces.
    Riikka Huusari ; Hachem Kadri ; Cécile Capponi.
    Submitted to NIPS, 2017
    . pdf
  • Multi-view Generative Adversarial Networks
    Submitted at ICLR 2017
  • 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
  • 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)
  • 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
  • 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
  • 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
  • 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
  • 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