
Online optimization and regret guarantees for nonadditive longterm constraints
We consider online optimization in the 1lookahead setting, where the ob...
02/17/2016 ∙ by Rodolphe Jenatton, et al. ∙ 0 ∙ shareread it

Adaptive Algorithms for Online Convex Optimization with Longterm Constraints
We present an adaptive online gradient descent algorithm to solve online...
12/23/2015 ∙ by Rodolphe Jenatton, et al. ∙ 0 ∙ shareread it

Sparse and spurious: dictionary learning with noise and outliers
A popular approach within the signal processing and machine learning com...
07/19/2014 ∙ by Rémi Gribonval, et al. ∙ 0 ∙ shareread it

On The Sample Complexity of Sparse Dictionary Learning
In the synthesis model signals are represented as a sparse combinations ...
03/20/2014 ∙ by Matthias Seibert, et al. ∙ 0 ∙ shareread it

Sample Complexity of Dictionary Learning and other Matrix Factorizations
Many modern tools in machine learning and signal processing, such as spa...
12/13/2013 ∙ by Rémi Gribonval, et al. ∙ 0 ∙ shareread it

Local stability and robustness of sparse dictionary learning in the presence of noise
A popular approach within the signal processing and machine learning com...
10/02/2012 ∙ by Rodolphe Jenatton, et al. ∙ 0 ∙ shareread it

Structured sparsity through convex optimization
Sparse estimation methods are aimed at using or obtaining parsimonious r...
09/12/2011 ∙ by Francis Bach, et al. ∙ 0 ∙ shareread it

Optimization with SparsityInducing Penalties
Sparse estimation methods are aimed at using or obtaining parsimonious r...
08/03/2011 ∙ by Francis Bach, et al. ∙ 0 ∙ shareread it

Multiscale Mining of fMRI data with Hierarchical Structured Sparsity
Inverse inference, or "brain reading", is a recent paradigm for analyzin...
05/02/2011 ∙ by Rodolphe Jenatton, et al. ∙ 0 ∙ shareread it

Convex and Network Flow Optimization for Structured Sparsity
We consider a class of learning problems regularized by a structured spa...
04/11/2011 ∙ by Julien Mairal, et al. ∙ 0 ∙ shareread it

Proximal Methods for Hierarchical Sparse Coding
Sparse coding consists in representing signals as sparse linear combinat...
09/11/2010 ∙ by Rodolphe Jenatton, et al. ∙ 0 ∙ shareread it

Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured spars...
08/31/2010 ∙ by Julien Mairal, et al. ∙ 0 ∙ shareread it

Structured Sparse Principal Component Analysis
We present an extension of sparse PCA, or sparse dictionary learning, wh...
09/08/2009 ∙ by Rodolphe Jenatton, et al. ∙ 0 ∙ shareread it

Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Bayesian optimization (BO) is a modelbased approach for gradientfree b...
12/08/2017 ∙ by Valerio Perrone, et al. ∙ 0 ∙ shareread it

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Bayesian optimization (BO) is a successful methodology to optimize black...
09/27/2019 ∙ by Valerio Perrone, et al. ∙ 0 ∙ shareread it

Constrained Bayesian Optimization with MaxValue Entropy Search
Bayesian optimization (BO) is a modelbased approach to sequentially opt...
10/15/2019 ∙ by Valerio Perrone, et al. ∙ 0 ∙ shareread it
Rodolphe Jenatton
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Senior Machine Learning Scientist at Amazon.com, Inc. since 2016, Machine Learning Scientist at Amazon since 2014, Software engineer / Data scientist at Criteo 20132014, Postdoctoral fellow at Ecole Polytechnique 20112012, PhD student at INRIA  Ecole Normale Supérieure from 20082011, Software engineer / Data scientist at Dynamic capital management 20072008