Loucas Pillaud-Vivien
15 papers · 2018–2026 · 5 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Bronze 🗺️ Taxonomy Completionist (22)
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Century Club · Bronze
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Unstoppable · Bronze
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Keyword Collector
(78)
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Research topics
Keywords
stochastic gradient descent
(7)
gradient flow
(3)
kernel methods
(3)
high-dimensional regression
(2)
neural network
(2)
variational inference
(2)
implicit bia
(2)
reproducing kernel hilbert space
(2)
single-index model
(2)
sample complexity
(2)
implicit regularization
(2)
statistical query
(1)
high-dimensional statistics
(1)
importance sampling
(1)
neural network theory
(1)
spectral clustering
(1)
semi-supervised learning
(1)
markov chain
(1)
uncertainty quantification
(1)
binary classification
(1)
Papers
Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
JMLR 2026
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
JMLR 2025
Batch and match: black-box variational inference with a score-based divergence
ICML 2024
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract)
COLT 2024
Kernelized Diffusion Maps
COLT 2023
On Single-Index Models beyond Gaussian Data
NeurIPS 2023
On the spectral bias of two-layer linear networks
NeurIPS 2023
SGD with Large Step Sizes Learns Sparse Features
ICML 2023
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
NeurIPS 2022
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity
NeurIPS 2021
Last iterate convergence of SGD for Least-Squares in the Interpolation regime.
NeurIPS 2021
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
NeurIPS 2021
Statistical Estimation of the Poincaré 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
NeurIPS 2018