Gauthier Gidel

Ph.D. Candidate
Université de Montréal - DIRO
Montréal Institute for Learning Algorithms


I am a Ph.D candidate supervised by Simon Lacoste-Julien, I graduated from ENS Ulm and Université Paris-Saclay. I am currently a visiting PhD student at Sierra. I also worked for 6 months as a freelance Data Scientist for Monsieur Drive and Les Suggestions de Nelly. My work focuses on convex optimization. More details can be found in my resume.


Research interests


I am interested in any nice theoretical problems coming from any fields. For now, I worked on optimization and machine learning, in particular:
  • Convex optimization.
  • Saddle point optimization.
  • Non-nonvex optimization.
  • Sequential learning.
  • Inference on graphical models.

Papers


Machine learning and optimization

Frank-Wolfe Algorithms for Saddle Point Problems
Gauthier Gidel, Tony Jebara and Simon Lacoste-Julien.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

Article bibtex arXiv
                            @InProceedings{gidel2017saddle,
                              author      = {Gidel, Gauthier and Jebara, Tony and Lacoste-Julien, Simon},
                              title       = {{F}rank-{W}olfe Algorithms for Saddle Point Problems},
                              journal   = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)},
                              year        = {2017} 
                            }
            
HAL Code Project Poster

Generative Modeling

Adversarial Divergences are Good Task Losses for Generative Modeling
Gabriel Huang, Gauthier Gidel, Hugo Berard, Ahmed Touati, Simon Lacoste-Julien.
ArXiv 2017.

Article bibtex arXiv
                          @InProceedings{huang2017adversarial,
                            author    = {Huang, Gabriel and Gidel, Gauthier and Berard, Hugo and Touati Ahmed and Lacoste-Julien, Simon},
                            title     = {Adversarial Divergences are Good Task Losses for Generative Modeling},
                            journal   = {arXiv:1708.02511},
                            year      = {2017} 
                          }
            


Talks


Machine learning and optimization

Frank-Wolfe Algorithms for Saddle Point Problems
Gauthier Gidel, Tony Jebara and Simon Lacoste-Julien.


Extensions de l’algorithme de Frank-Wolfe pour la recherche de points selles. (In French)
Gauthier Gidel.
Introduction au domaine de recherche ENS 2016


Review of Improving branch-and-cut performance by random sampling. (In French)
Gauthier Gidel.
Paper review in MIP course of Bernard Gendron, 2017



Workshops


Machine learning and optimization

Frank-Wolfe Algorithms for Saddle Point Problems
Gauthier Gidel, Tony Jebara and Simon Lacoste-Julien.
NIPS OPT workshop (ORAL top 10% accepted submissions), 2016

Extended Abstract bibtex
                              @InProceedings{gidel2016saddleabstract,
                                author      = {Gidel, Gauthier and Jebara, Tony and Lacoste-Julien, Simon},
                                title       = {{F}rank-{W}olfe Algorithms for Saddle Point Problems},
                                booktitle   = {NIPS OPT workshop},
                                year        = {2016} 
                              }
              
Slides Poster

Generative Modeling

Adversarial Divergences are Good Task Losses for Generative Modeling
Gabriel Huang, Gauthier Gidel, Hugo Berard, Ahmed Touati, Simon Lacoste-Julien.
Principled Approaches in Deep Learning, 2017.



Contact


Gauthier Gidel
Pavillon André-Aisenstadt
2920 Chemin de la Tour, office 3331
Montreal, QC
H3T 1J4 CANADA