Latest News:
2019-02-01
2018-12-18
2018-12-07
2018-03-23
2017-10-24
2017-09-06
2017-03-02
2016-08-15
2016-04-06
2016-03-05
2016-01-14
I was awarded with the "Prix Excéllence Doctorat" from the Université Jean Monnet
2018-12-18
I defended my PhD thesis, entitled "A unified view of local learning: theory and algorithms for enhancing linear models"
2018-12-07
"Communication-Efficient Decentralized Boosting while Discovering the Collaboration Graph" presented at MLPCD 2 NeurIPS Workshop
2018-03-23
Adversarial Robustness Toolbox is now available on github
2017-10-24
2017-09-06
our work on adversarial examples will be presented at AISec2017
2017-03-02
the paper L3SVMs, co-written with R.Emonet and M.Sebban, is now available on arxiv
2016-08-15
2016-04-06
the paper Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms, co-written with R.Emonet and M.Sebban, is now available on arxiv
2016-03-05
2016-01-14
talk on c2lm at Lives ANR project kick-off at Univ. Marseille
View my profileSoftware Development
- I am the creator and one of the main contributors of Adversarial Robustness Toolbox.
Talks
- 2018-09-11 Oral Presentation at ECML2018, Dublin
Fast and Provably Effective Multi-view Classification with Landmark-based SVM
slides - 2018-06-20 Oral Presentation at Cap2018, Rouen
Decentralized Frank-Wolfe Boosting for Collaborative Learning of Personalized Models
slides - 2017-11-17 Oral Presentation at Grehack, Grenoble
Efficient Defenses Against Adversarial Attacks
slides, talk - 2017-04-10 Seminar at Magnet team, INRIA Lille
a New Surrogate Risk for Learning from Weakly Labeled Data
slides - 2016-06-02 PhD Seminar at Lab. H.Curien, Saint-Etienne
Surrogate Loss Functions as Bregman Divergences
slides - 2016-01-14 Lives ANR project kick-off meeting at Univ. Marseille
Metric Learning as Convex Combination of Local Models with Generalization Guarantees
slides - 2014-09-03 Master Seminar at Lab. H.Curien, Saint-Etienne
Statistical Classification with Fisher Kernel
slides
Teaching Assistance
- 2017-2018
- 1M: Design and Analysis of Algorithms (18h)
- 2016-2017
- 1B: Introduction to Computer Science (32h)
- 2B: Imperative Programming in Python (20h)
- 1M: Design and Analysis of Algorithms (18h)
- 1M: Data Analysis (18h)
- 1M: Introduction to Machine Learning (5h)
- 2015-2016
- 1B: Introduction to Computer Science (32h), Introduction to Latex, Excel, Bash (36h)
- 1M: Machine Learning-Ensemble Methods (2h)
Organizations
- 2018-2019 organizer of the reading groups of the Data Intelligence group at Lab. H.Curien, Saint-Etienne
- 2018 member of the program committee of Nemesis 2018 workshop
- 2018 co-organizer of the closing seminar of SOLSTICE project
- 2015-2016 co-organizer of the PhD Seminars of Data Intelligence group at Lab. H.Curien, Saint-Etienne