output
20172025
most citedSelf-supervised learning with rotation-invariant kernels

3 citations

researchers with a paper here
collaborating institutions

14 papers

math.OC2025

A flexible block-coordinate forward-backward algorithm for non-smooth and non-convex optimization

Luis Briceño-Arias, Paulo Gonçalves, Guillaume Lauga +2

Block coordinate descent (BCD) methods are prevalent in large scale optimization problems due to the low memory and computational costs per iteration, the predisposition to paralle…

cs.CR2023

Can sparsity improve the privacy of neural networks?

Antoine Gonon, Léon Zheng, Clément Lalanne +3

Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This articl…

stat.ML2023

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…

math.OC2022

Multilevel fista for image restoration

Guillaume Lauga, Elisa Riccietti, Nelly Pustelnik +1

This paper presents a multilevel FISTA algorithm, based on the use of the Moreau envelope to build the correction brought by the coarse models, which is easy to compute when the ex…

cs.LG2022★ 1 cited

On the Statistical Complexity of Estimation and Testing under Privacy Constraints

Clément Lalanne, Aurélien Garivier, Rémi Gribonval

The challenge of producing accurate statistics while respecting the privacy of the individuals in a sample is an important area of research. We study minimax lower bounds for class…

cs.CV2022★ 3 cited

Self-supervised learning with rotation-invariant kernels

Léon Zheng, Gilles Puy, Elisa Riccietti +2

We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learni…