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ORCIDLoucas Pillaud-Vivien
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2020 – today
- 2024
[c14]Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna:
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). COLT 2024: 1262- [c13]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. ICML 2024 - [i14]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. CoRR abs/2402.14758 (2024) - [i13]Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna:
Computational-Statistical Gaps in Gaussian Single-Index Models. CoRR abs/2403.05529 (2024) - [i12]Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul:
An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations. CoRR abs/2403.13748 (2024) - [i11]Adrien Schertzer, Loucas Pillaud-Vivien:
Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent. CoRR abs/2407.02322 (2024) - 2023
- [c12]Loucas Pillaud-Vivien, Francis R. Bach:
Kernelized Diffusion Maps. COLT 2023: 5236-5259 - [c11]Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with Large Step Sizes Learns Sparse Features. ICML 2023: 903-925 - [c10]Aditya Vardhan Varre, Maria-Luiza Vladarean, Loucas Pillaud-Vivien, Nicolas Flammarion:
On the spectral bias of two-layer linear networks. NeurIPS 2023 - [c9]Aaron Zweig, Loucas Pillaud-Vivien, Joan Bruna:
On Single-Index Models beyond Gaussian Data. NeurIPS 2023 - [i10]Loucas Pillaud-Vivien, Francis R. Bach:
Kernelized Diffusion maps. CoRR abs/2302.06757 (2023) - [i9]Joan Bruna, Loucas Pillaud-Vivien, Aaron Zweig:
On Single Index Models beyond Gaussian Data. CoRR abs/2307.15804 (2023) - [i8]Alberto Bietti, Joan Bruna, Loucas Pillaud-Vivien:
On Learning Gaussian Multi-index Models with Gradient Flow. CoRR abs/2310.19793 (2023) - 2022
- [c8]Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion:
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. COLT 2022: 2127-2159 - [c7]Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion:
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. NeurIPS 2022 - [i7]Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion:
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs. CoRR abs/2206.00939 (2022) - [i6]Loucas Pillaud-Vivien, Julien Reygner
, Nicolas Flammarion:
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. CoRR abs/2206.09841 (2022) - [i5]Maksym Andriushchenko, Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
SGD with large step sizes learns sparse features. CoRR abs/2210.05337 (2022) - 2021
- [c6]Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. NeurIPS 2021: 21581-21591 - [c5]Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion:
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. NeurIPS 2021: 29218-29230 - [c4]Vivien Cabannes, Loucas Pillaud-Vivien, Francis R. Bach, Alessandro Rudi:
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. NeurIPS 2021: 30439-30451 - [i4]Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion:
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. CoRR abs/2102.03183 (2021) - [i3]Scott Pesme, Loucas Pillaud-Vivien, Nicolas Flammarion:
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity. CoRR abs/2106.09524 (2021) - 2020
- [c3]Loucas Pillaud-Vivien, Francis R. Bach, Tony Lelièvre, Alessandro Rudi, Gabriel Stoltz:
Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions. AISTATS 2020: 2753-2763
2010 – 2019
- 2018
- [c2]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Exponential Convergence of Testing Error for Stochastic Gradient Methods. COLT 2018: 250-296 - [c1]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. NeurIPS 2018: 8125-8135 - [i2]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. CoRR abs/1805.10074 (2018) - 2017
- [i1]Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach:
Exponential convergence of testing error for stochastic gradient methods. CoRR abs/1712.04755 (2017)
Coauthor Index
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