353 citations · 1.8k across the 69 of their papers we have counts for
12 papers · 1 filter
Private Statistical Estimation of Many Quantiles
Clément Lalanne, Aurélien Garivier, Rémi Gribonval
This work studies the estimation of many statistical quantiles under differential privacy. More precisely, given a distribution and access to i.i.d. samples from it, we study the e…
Private Quantiles Estimation in the Presence of Atoms
Clément Sébastien Lalanne, Clément Gastaud, Nicolas Grislain +2
We consider the differentially private estimation of multiple quantiles (MQ) of a distribution from a dataset, a key building block in modern data analysis. We apply the recent non…
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning
Titouan Vayer, Rémi Gribonval
Comparing probability distributions is at the crux of many machine learning algorithms. Maximum Mean Discrepancies (MMD) and Wasserstein distances are two classes of distances betw…
Minibatch optimal transport distances; analysis and applications
Kilian Fatras, Younes Zine, Szymon Majewski +3
Optimal transport distances have become a classic tool to compare probability distributions and have found many applications in machine learning. Yet, despite recent algorithmic de…
Sketching Datasets for Large-Scale Learning (long version)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven +3
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification…
Learning with minibatch Wasserstein : asymptotic and gradient properties
Kilian Fatras, Younes Zine, Rémi Flamary +2
Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning. Yet their algorithmic complexity prevents…
Compressive Statistical Learning with Random Feature Moments
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven +1
We describe a general framework -- compressive statistical learning -- for resource-efficient large-scale learning: the training collection is compressed in one pass into a low-dim…
Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso
Antoine Bonnefoy, Valentin Emiya, Liva Ralaivola +1
Recent computational strategies based on screening tests have been proposed to accelerate algorithms addressing penalized sparse regression problems such as the Lasso. Such approac…
On The Sample Complexity of Sparse Dictionary Learning
Matthias Seibert, Martin Kleinsteuber, Rémi Gribonval +2
In the synthesis model signals are represented as a sparse combinations of atoms from a dictionary. Dictionary learning describes the acquisition process of the underlying dictiona…
Sample Complexity of Dictionary Learning and other Matrix Factorizations
Rémi Gribonval, Rodolphe Jenatton, Francis Bach +2
Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), -me…
Local stability and robustness of sparse dictionary learning in the presence of noise
Rodolphe Jenatton, Rémi Gribonval, Francis Bach
A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictio…
Under-determined reverberant audio source separation using a full-rank spatial covariance model
Ngoc Duong, Emmanuel Vincent, Remi Gribonval
This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each…