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03/31/2023
A two-head loss function for deep Average-K classification
Average-K classification is an alternative to top-K classification in wh...
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09/30/2022
Improve learning combining crowdsourced labels by weighting Areas Under the Margin
In supervised learning – for instance in image classification – modern m...
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07/04/2022
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
In this paper, we study differentially private empirical risk minimizati...
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02/04/2022
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
In modern classification tasks, the number of labels is getting larger a...
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12/15/2021
Supervised learning of analysis-sparsity priors with automatic differentiation
Sparsity priors are commonly used in denoising and image reconstruction....
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11/04/2021
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Even though Weighted Lasso regression has appealing statistical guarante...
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10/22/2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
Machine learning models can leak information about the data used to trai...
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06/27/2021
Score-Based Change Detection for Gradient-Based Learning Machines
The widespread use of machine learning algorithms calls for automatic ch...
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06/04/2021
Spatially relaxed inference on high-dimensional linear models
We consider the inference problem for high-dimensional linear models, wh...
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05/04/2021
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Finding the optimal hyperparameters of a model can be cast as a bilevel ...
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10/22/2020
Model identification and local linear convergence of coordinate descent
For composite nonsmooth optimization problems, Forward-Backward algorith...
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09/29/2020
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
Detecting where and when brain regions activate in a cognitive task or i...
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09/06/2020
Screening Rules and its Complexity for Active Set Identification
Screening rules were recently introduced as a technique for explicitly i...
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06/24/2020
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
Owing to their statistical properties, non-convex sparse regularizers ha...
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02/20/2020
Implicit differentiation of Lasso-type models for hyperparameter optimization
Setting regularization parameters for Lasso-type estimators is notorious...
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01/15/2020
Support recovery and sup-norm convergence rates for sparse pivotal estimation
In high dimensional sparse regression, pivotal estimators are estimators...
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07/12/2019
Dual Extrapolation for Sparse Generalized Linear Models
Generalized Linear Models (GLM) form a wide class of regression and clas...
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02/16/2019
Screening Rules for Lasso with Non-Convex Sparse Regularizers
Leveraging on the convexity of the Lasso problem , screening rules help ...
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02/07/2019
Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise
Sparsity promoting norms are frequently used in high dimensional regress...
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01/31/2019
Optimal mini-batch and step sizes for SAGA
Recently it has been shown that the step sizes of a family of variance r...
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10/12/2018
Safe Grid Search with Optimal Complexity
Popular machine learning estimators involve regularization parameters th...
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06/15/2018
Statistical Inference with Ensemble of Clustered Desparsified Lasso
Medical imaging involves high-dimensional data, yet their acquisition is...
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10/24/2017
A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging
Majorization-minimization (MM) is a standard iterative optimization tech...
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05/27/2017
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
In high dimension, it is customary to consider Lasso-type estimators to ...
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03/21/2017
From safe screening rules to working sets for faster Lasso-type solvers
Convex sparsity-promoting regularizations are ubiquitous in modern stati...
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03/14/2017
On the benefits of output sparsity for multi-label classification
The multi-label classification framework, where each observation can be ...
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11/17/2016
Gap Safe screening rules for sparsity enforcing penalties
In high dimensional regression settings, sparsity enforcing penalties ha...
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06/08/2016
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
In high dimensional settings, sparse structures are crucial for efficien...
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06/08/2016
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
In decentralized networks (of sensors, connected objects, etc.), there i...
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02/19/2016
GAP Safe Screening Rules for Sparse-Group-Lasso
In high dimensional settings, sparse structures are crucial for efficien...
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11/17/2015
Extending Gossip Algorithms to Distributed Estimation of U-Statistics
Efficient and robust algorithms for decentralized estimation in networks...
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06/11/2015
GAP Safe screening rules for sparse multi-task and multi-class models
High dimensional regression benefits from sparsity promoting regularizat...
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05/13/2015
Mind the duality gap: safer rules for the Lasso
Screening rules allow to early discard irrelevant variables from the opt...
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04/16/2013
Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Popular sparse estimation methods based on ℓ_1-relaxation, such as the L...
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08/31/2012
A two-stage denoising filter: the preprocessed Yaroslavsky filter
This paper describes a simple image noise removal method which combines ...
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06/02/2012
Poisson noise reduction with non-local PCA
Photon-limited imaging arises when the number of photons collected by a ...
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12/19/2011
