Rodolphe Jenatton's homepage

Rodolphe Jenatton

Software engineer/Data scientist at Criteo.

E-mail: jenatton [at] cmap [dot] polytechnique [dot] fr / rodolphe [dot] jenatton [at] inria [dot] fr

In November 2011, I finished my Ph.D which I conducted within the Sierra Team, which belongs to the LIENS and the Département d'Informatique of École Normale Supérieure. It was funded by INRIA. I had the chance to be co-supervised by Francis Bach and Jean-Yves Audibert. I was then a postdoctoral researcher with Alexandre d'Aspremont within the CMAP located at Ecole Polytechnique. Since January 2013, I have joined Criteo.

Research interests:

My research interests revolve around machine learning, statistics, (convex) optimization, (structured) sparsity and their applications to image and text processing. I am also interested in dictionary learning and various unsupervised models based on latent factor representations.

Publications and Preprints

Journal:

(2012) R. Jenatton, A. Gramfort, V. Michel, G. Obozinski, E. Eger, F. Bach and B. Thirion. Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity. SIAM Journal on Imaging Sciences, 5(3):835-856, 2012. [pdf]
(2011) R. Jenatton^*, J. Mairal^*, G. Obozinski, F. Bach (^*Contributed equally). Proximal Methods for Hierarchical Sparse Coding. Journal of Machine Learning Research, 12(Jul):2297-2334. [pdf]
(2011) J. Mairal^*, R. Jenatton^*, G. Obozinski, F. Bach (^*Contributed equally). Convex and Network Flow Optimization for Structured Sparsity. Journal of Machine Learning Research, 12(Sep):2681-2720. [pdf]
(2011) R. Jenatton, J.-Y. Audibert and F. Bach. Structured Variable Selection with Sparsity-Inducing Norms. Journal of Machine Learning Research, 12(Oct):2777-2824. [pdf] [code]

Thesis:

(2011) Structured Sparsity-Inducing Norms: Statistical and Algorithmic Properties with Applications to Neuroimaging. Ph.D thesis. Ecole Normale Supérieure de Cachan. 2011. [pdf] [slides of the defense]

Book chapter:

(2012) F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Structured sparsity through convex optimization. Technical report, HAL 00621245-v2, to appear in Statistical Science, 2012. [pdf]
(2012) F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Optimization with Sparsity-Inducing Penalties. Foundations and Trends in Machine Learning, 4(1):1-106, 2012. [pdf]
(2011) F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Convex Optimization with Sparsity-Inducing Norms. In S. Sra, S. Nowozin, S. J. Wright., editors, Optimization for Machine Learning, MIT Press, 2011. [pdf]

Conference:

(2012) R. Jenatton^*, N. Le Roux^*, A. Bordes, G. Obozinski (^*Contributed equally). A latent factor model for highly multi-relational data. Advances in Neural Information Processing Systems (NIPS). [pdf] [code]
(2010) J. Mairal^*, R. Jenatton^*, G. Obozinski, F. Bach (^*Contributed equally). Network Flow Algorithms for Structured Sparsity. Advances in Neural Information Processing Systems (NIPS). [pdf] [pdf (Arxiv technical report)] [code]
(2010) R. Jenatton^*, J. Mairal^*, G. Obozinski, F. Bach (^*Contributed equally). Proximal Methods for Sparse Hierarchical Dictionary Learning. International Conference on Machine Learning (ICML). [pdf] [appendix] [code] [bib]
(2010) R. Jenatton, G. Obozinski, F. Bach. Structured Sparse Principal Component Analysis. International Conference on Artificial Intelligence and Statistics (AISTATS). [pdf] [code] [bib]

Preprints and technical reports:

(2012) R. Jenatton, R. Gribonval and F. Bach. Local stability and robustness of sparse dictionary learning in the presence of noise. Technical report. [pdf (HAL technical report)]

Workshop:

(2011) R. Jenatton, R. Gribonval, and F. Bach. Local Analysis of Sparse Coding in the Presence of Noise. NIPS Workshop on Sparse Representation and Low-rank Approximation. [video]
(2011) R. Jenatton, A. Gramfort, V. Michel, G. Obozinski, F. Bach and B. Thirion. Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity. International Workshop on Pattern Recognition in Neuroimaging (PRNI).
(2010) G. Varoquaux, R. Jenatton, A. Gramfort, G. Obozinski, B. Thirion and F. Bach. Sparse Structured Dictionary Learning for Brain Resting-State Activity Modeling. NIPS Workshop on Practical Applications of Sparse Modeling: Open Issues and New Directions.
(2009) R. Jenatton, J.-Y. Audibert and F. Bach. Active Set Algorithm for Structured Sparsity-Inducing Norms. OPT 2009: 2nd NIPS Workshop on Optimization for Machine Learning. [pdf] [slide] [video]

Last modification : March 2013.