59 citations · 443 across the 46 of their papers we have counts for
8 papers · 1 filter
A two-head loss function for deep Average-K classification
Camille Garcin, Maximilien Servajean, Alexis Joly +1
Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over al…
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin
Tanguy Lefort, Benjamin Charlier, Alexis Joly +1
In supervised learning - for instance in image classification - modern massive datasets are commonly labeled by a crowd of workers. The obtained labels in this crowdsourcing settin…
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
Paul Mangold, Aurélien Bellet, Joseph Salmon +1
In this paper, we study differentially private empirical risk minimization (DP-ERM). It has been shown that the worst-case utility of DP-ERM reduces polynomially as the dimension i…
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Kenan Šehić, Alexandre Gramfort, Joseph Salmon +1
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is…
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
Paul Mangold, Aurélien Bellet, Joseph Salmon +1
Machine learning models can leak information about the data used to train them. To mitigate this issue, Differentially Private (DP) variants of optimization algorithms like Stochas…
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso +1
Owing to their statistical properties, non-convex sparse regularizers have attracted much interest for estimating a sparse linear model from high dimensional data. Given that the s…
Screening Rules for Lasso with Non-Convex Sparse Regularizers
Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon
Leveraging on the convexity of the Lasso problem , screening rules help in accelerating solvers by discarding irrelevant variables, during the optimization process. However, becaus…