Loucas Pillaud-Vivien
14 papers · 2018–2025 · 5 conferences · across top CS/AI conferences
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Conferences
NIPS (7)
COLT (3)
ICML (2)
AISTATS (1)
JMLR (1)
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Keywords
stochastic gradient descent
(6)
kernel methods
(3)
gradient flow
(3)
neural network
(2)
high-dimensional regression
(2)
implicit bia
(2)
single-index model
(2)
implicit regularization
(2)
variational inference
(1)
neural network theory
(1)
spectral clustering
(1)
high-dimensional statistics
(1)
statistical query
(1)
importance sampling
(1)
semi-supervised learning
(1)
sample complexity
(1)
markov chain
(1)
uncertainty quantification
(1)
binary classification
(1)
gaussian approximation
(1)
Papers
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
JMLR 2025
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract)
COLT 2024
Batch and match: black-box variational inference with a score-based divergence
ICML 2024
On Single-Index Models beyond Gaussian Data
NIPS 2023
SGD with Large Step Sizes Learns Sparse Features
ICML 2023
On the spectral bias of two-layer linear networks
NIPS 2023
Kernelized Diffusion Maps
COLT 2023
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
NIPS 2022
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
NIPS 2021
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity
NIPS 2021
Last iterate convergence of SGD for Least-Squares in the Interpolation regime.
NIPS 2021
Statistical Estimation of the PoincarΓ© constant and Application to Sampling Multimodal Distributions
AISTATS 2020
Exponential Convergence of Testing Error for Stochastic Gradient Methods
COLT 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
NIPS 2018