## Rodolphe JenattonSenior research scientist at Google Brain, Berlin. |

**Contact:** `rjenatton X google Y com` *(with X=@ and Y=.)*

Short bio:

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. Until April 2019, I worked as a senior machine learning scientist at Amazon, Berlin, focusing on online learning, Bayesian optimization and auto ML. I am now a senior research scientist in the Berlin Google Brain team.

Research interests:

My research interests revolve around machine learning, statistics, (convex and Bayesian) optimization, (structured) sparsity, auto-ML and the uncertainty modelling in neural networks.

Publications:

**Journal:**

(2015) F. Fogel, R. Jenatton, F. Bach, A. d'Aspremont. Convex Relaxations for Permutation Problems.

*SIAM Journal on Matrix Analysis and Application, 36(4):1465-1488, 2015*. [pdf]

(2015) R. Gribonval, R. Jenatton, F. Bach. Sparse and spurious: dictionary learning with noise and outliers.

*IEEE Transactions on Information Theory, 61(11):6298-6319*. [ieee][pdf on arXiv]

(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]

(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]

**Conference:**

(2021) M. Collier, B. Mustafa, E. Kokiopoulou, R. Jenatton, J. Berent. Correlated Input-Dependent Label Noise in Large-Scale Image Classification.

*Conference on Computer Vision and Pattern Recognition (CVPR)*. [pdf]

(2021) M. Havasi, R. Jenatton, S. Fort, J.Z. Liu, J. Snoek, B. Lakshminarayanan, A.M. Dai, D. Tran. Training independent subnetworks for robust prediction.

*International Conference on Learning Representations (ICLR)*. [pdf]

(2021) V. Perrone, H. Shen, A. Zolic, I. Shcherbatyi, A. Ahmed, T. Bansal, M. Donini, F. Winkelmolen, R. Jenatton, J.B. Faddoul, B. Pogorzelska, M. Miladinovic, K. Kenthapadi, M. Seeger, C. Archambeau. Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization.

*SIGKDD Conference on Knowledge Discovery and Data Mining*[pdf]

(2020) F. Wenzel, J. Snoek, D. Tran, R. Jenatton. Hyperparameter Ensembles for Robustness and Uncertainty Quantification.

*Advances in Neural Information Processing Systems (NeurIPS)*. [pdf] [code]

(2020) F. Wenzel, K. Roth, B. S. Veeling, J. Świątkowski, L. Tran, J. Snoek, S. Mandt, T. Salimans, R. Jenatton, S. Nowozin. How Good is the Bayes Posterior in Deep Neural Networks Really?

*International Conference on Machine Learning (ICML)*. [pdf] [code]

(2020) J. Świątkowski, K. Roth, B. S. Veeling, L. Tran, J. V. Dillon, J. Snoek, S. Mandt, T. Salimans, R. Jenatton, S. Nowozin. The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks.

*International Conference on Machine Learning (ICML)*. [pdf]

(2019) V. Perrone, H. Shen, M. Seeger, C. Archambeau, R. Jenatton. Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning.

*Advances in Neural Information Processing Systems (NeurIPS)*. [arxiv]

(2018) V. Perrone, R. Jenatton, M. Seeger, C. Archambeau. Scalable Hyperparameter Transfer learning.

*Advances in Neural Information Processing Systems (NeurIPS)*. [pdf] [supp]

(2017) R. Jenatton, C. Archambeau, J. Gonzalez, M. Seeger. Bayesian Optimization with Tree-structured Dependencies.

*International Conference on Machine Learning (ICML)*. [pdf] [supp]

(2016) J. Huang, R. Jenatton, C. Archambeau. Online dual decomposition for performance and delivery-based distributed ad allocation.

*SIGKDD Conference on Knowledge Discovery and Data Mining*. [pdf]

(2016) R. Jenatton, J. Huang, C. Archambeau. Adaptive Algorithms for Online Convex Optimization with Long-term Constraints.

*International Conference on Machine Learning (ICML)*. [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*. [pdf]

(2013) F. Fogel, R. Jenatton, F. Bach, A. d'Aspremont. Convex Relaxations for Permutation Problems.

*Advances in Neural Information Processing Systems (NIPS)*. [pdf]

(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] [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]

(2010) R. Jenatton, G. Obozinski, F. Bach. Structured Sparse Principal Component Analysis.

*International Conference on Artificial Intelligence and Statistics (AISTATS)*. [pdf] [code]

**Book chapters:**

(2012) F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Structured sparsity through convex optimization.

*Statistical Science Volume 27, Number 4 (2012), 450-468*. [Statistical Science] [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. [FOT][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]

**Technical reports:**

(2020) P. Das, V. Perrone, N. Ivkin, T. Bansal, Z. Karnin, H. Shen, I. Shcherbatyi, Y. Elor, W. Wu, A. Zolic, T. Lienart, A. Tang, A. Ahmed, J.B. Faddoul, R. Jenatton, F. Winkelmolen, P. Gautier, L. Dirac, A. Perunicic, M. Miladinovic, G. Zappella, C. Archambeau, M. Seeger, B. Dutt, L. Rouesnel. Amazon SageMaker Autopilot: a white box AutoML solution at scale.

*Technical report, arXiv:2012.08483*. [pdf]

(2021) M. Collier, B. Mustafa, E. Kokiopoulou, R. Jenatton, J. Berent. A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise.

*Technical report, arXiv:2003.06778*. [pdf]

(2020) L. Carratino, M. Cissé, R. Jenatton, J.-P. Vert . On Mixup Regularization.

*Technical report, arXiv:2006.06049*. [pdf]

(2020) L. Tran, B. S. Veeling, K. Roth, J. Swiatkowski, J. V. Dillon, J. Snoek, S. Mandt, T. Salimans, S. Nowozin, R. Jenatton. Hydra: Preserving Ensemble Diversity for Model Distillation.

*Technical report, arXiv:2001.04694*. [pdf]

(2019) V. Perrone, I. Shcherbatyi, R. Jenatton, C. Archambeau, M. Seeger. Constrained Bayesian Optimization with Max-Value Entropy Search.

*Technical report, arXiv:1910.07003*. [pdf]

(2016) R. Jenatton, J. Huang, C. Archambeau. Online optimization and regret guarantees for non-additive long-term constraints.

*Technical report, arXiv:1602.05394*. [pdf]

(2015) R. Jenatton, J. Huang, C. Archambeau. Adaptive Algorithms for Online Convex Optimization with Long-term Constraints.

*Technical report, arXiv:1512.07422*. [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]

**Selected workshops/talks:**

(2020) F. Wenzel, J. Snoek, D. Tran, R. Jenatton. Hyperparameter Ensembles for Robustness and Uncertainty Quantification.

*auto-ml seminars.*[website] [slides]

(2019) J. Świątkowski, K. Roth, B. S. Veeling, L. Tran, J. V. Dillon, J. Snoek, S. Mandt, T. Salimans, R. Jenatton, S. Nowozin. The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. 2nd Symposium on Advances in Approximate Bayesian Inference, 2019 (best student award) [pdf]

(2018) L. Valkov, R. Jenatton, F. Winkelmolen, C. Archambeau. A simple transfer-learning extension of Hyperband.

*NeurIPS Workshop on Meta-Learning (MetaLearn 2018)*. [pdf] [supp]

(2017) V. Perrone, R. Jenatton, M. Seeger, C. Archambeau. Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start.

*NIPS Workshop on Meta-Learning (MetaLearn 2017)*. [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*.

(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]

**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]

*Awards:*Winner of the best 2012 Applied Mathematics PhD thesis prize, Fondation Hadamard 2012 [more details]

Runner-up for the best 2012 Machine Learning PhD thesis, Association Française pour l’Intelligence Artiﬁcielle 2012 [more details]