Exponential Convergence of Testing Error for Stochastic Gradient Methods. - Loucas Pillaud-Vivien; Alessandro Rudi; Francis R. Bach (COLT, 2018)

Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. - Loucas Pillaud-Vivien; Alessandro Rudi; Francis R. Bach (NeurIPS, 2018)

Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions. - Loucas Pillaud-Vivien; Francis R. Bach; Tony Lelièvre; Alessandro Rudi; Gabriel Stoltz (AISTATS, 2020)

Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. - Vivien Cabannes; Loucas Pillaud-Vivien; Francis R. Bach; Alessandro Rudi (NeurIPS, 2021)

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. - Scott Pesme; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2021)

Last iterate convergence of SGD for Least-Squares in the Interpolation regime. - Aditya Vardhan Varre; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2021)

Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. - Loucas Pillaud-Vivien; Julien Reygner; Nicolas Flammarion (COLT, 2022)

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. - Etienne Boursier; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2022)

Kernelized Diffusion Maps. - Loucas Pillaud-Vivien; Francis R. Bach (COLT, 2023)

SGD with Large Step Sizes Learns Sparse Features. - Maksym Andriushchenko; Aditya Vardhan Varre; Loucas Pillaud-Vivien; Nicolas Flammarion (ICML, 2023)

On the spectral bias of two-layer linear networks. - Aditya Vardhan Varre; Maria-Luiza Vladarean; Loucas Pillaud-Vivien; Nicolas Flammarion (NeurIPS, 2023)

On Single-Index Models beyond Gaussian Data. - Aaron Zweig; Loucas Pillaud-Vivien; Joan Bruna (NeurIPS, 2023)

On Learning Gaussian Multi-index Models with Gradient Flow. - Alberto Bietti; Joan Bruna; Loucas Pillaud-Vivien (CoRR, 2023)

Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). - Alex Damian; Loucas Pillaud-Vivien; Jason D. Lee; Joan Bruna (COLT, 2024)

Batch and match: black-box variational inference with a score-based divergence. - Diana Cai; Chirag Modi 002; Loucas Pillaud-Vivien; Charles Margossian; Robert M. Gower; David M. Blei; Lawrence K. Saul (ICML, 2024)

Computational-Statistical Gaps in Gaussian Single-Index Models. - Alex Damian; Loucas Pillaud-Vivien; Jason D. Lee; Joan Bruna (CoRR, 2024)

An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations. - Charles C. Margossian; Loucas Pillaud-Vivien; Lawrence K. Saul (CoRR, 2024)

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent. - Adrien Schertzer; Loucas Pillaud-Vivien (CoRR, 2024)

Convergence of Shallow ReLU Networks on Weakly Interacting Data. - Léo Dana; Francis R. Bach; Loucas Pillaud-Vivien (CoRR, 2025)

Joint Learning in the Gaussian Single Index Model. - Loucas Pillaud-Vivien; Adrien Schertzer (CoRR, 2025)