Machine learning scientist at Amazon.
In 2011, I finished my Ph.D which I conducted within the Sierra Team of the Département d'Informatique of École Normale Supérieure. I had the chance to be co-supervised by Francis Bach and Jean-Yves Audibert. I then spent a great year as a postdoctoral researcher with Alexandre d'Aspremont at Ecole Polytechnique. From January 2013 until May 2014, I worked for Criteo where I was in charge of improving the statistical and optimization aspects of the ad prediction engine. I am now a machine learning scientist at Amazon, Berlin.
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.
(2015) R. Gribonval, R. Jenatton, F. Bach, M. Kleinsteuber and M. Seibert. Sample complexity of dictionary learning and other matrix factorizations. IEEE Transactions on Information Theory, 61(6):3469-3486. [ieee][pdf on arXiv]
(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]
(2015) A. Freno, M. Saveski, R. Jenatton, C. Archambeau. One-Pass Ranking Models for Low-Latency Product Recommendations. SIGKDD Conference on Knowledge Discovery and Data Mining. [to appear]
(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) R. Jenatton*, J. Mairal*, G. Obozinski, F. Bach (*Contributed equally). Proximal Methods for Sparse Hierarchical Dictionary Learning. International Conference on Machine Learning (ICML). [pdf][code]
(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]
(2014) M. Seibert, M. Kleinsteuber, R. Gribonval, R. Jenatton and F. Bach. On The Sample Complexity of Sparse Dictionary Learning. Technical report, arXiv:1403.5112, 2014. [pdf]
(2012) R. Jenatton, R. Gribonval and F. Bach. Local stability and robustness of sparse dictionary learning in the presence of noise. Technical report, HAL 00737152. [pdf]
(2015) Sparse and spurious: dictionary learning with noise and outliers. Optimization and Big Data 2015, Edinburgh. [slides]
(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) J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Learning Hierarchical and Topographic Dictionaries with Structured Sparsity. In proceeding of the SPIE conference on wavelets and sparsity XIV, 2011. [pdf]
(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). [ieee pdf]
(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.
(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]