
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
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On Mixup Regularization
Mixup is a data augmentation technique that creates new examples as conv...
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The ktied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
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Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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Constrained Bayesian Optimization with MaxValue Entropy Search
Bayesian optimization (BO) is a modelbased approach to sequentially opt...
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Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Bayesian optimization (BO) is a successful methodology to optimize black...
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Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Bayesian optimization (BO) is a modelbased approach for gradientfree b...
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Online optimization and regret guarantees for nonadditive longterm constraints
We consider online optimization in the 1lookahead setting, where the ob...
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Adaptive Algorithms for Online Convex Optimization with Longterm Constraints
We present an adaptive online gradient descent algorithm to solve online...
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Sparse and spurious: dictionary learning with noise and outliers
A popular approach within the signal processing and machine learning com...
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On The Sample Complexity of Sparse Dictionary Learning
In the synthesis model signals are represented as a sparse combinations ...
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Sample Complexity of Dictionary Learning and other Matrix Factorizations
Many modern tools in machine learning and signal processing, such as spa...
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Local stability and robustness of sparse dictionary learning in the presence of noise
A popular approach within the signal processing and machine learning com...
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Structured sparsity through convex optimization
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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Optimization with SparsityInducing Penalties
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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Multiscale Mining of fMRI data with Hierarchical Structured Sparsity
Inverse inference, or "brain reading", is a recent paradigm for analyzin...
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Convex and Network Flow Optimization for Structured Sparsity
We consider a class of learning problems regularized by a structured spa...
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Proximal Methods for Hierarchical Sparse Coding
Sparse coding consists in representing signals as sparse linear combinat...
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Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured spars...
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Structured Sparse Principal Component Analysis
We present an extension of sparse PCA, or sparse dictionary learning, wh...
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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