Aymeric Dieuleveut
24 papers · 2017–2025 · 4 conferences · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (14) π§ Keyword Pioneer π£ Hot Topic Early Bird π Conference Polyglot (4)
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Hot Topic Early Bird
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Interdisciplinary Bridge
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Conference Polyglot
(4)
π
Keyword Champion
(3)
π₯
Mega-Team
(24)
ποΈ
Keyword Collector
(87)
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Century Club
(24)
π₯
Unstoppable
(7)
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Trend Setter
β‘
Prolific Year
(5)
Conferences
ICML (9)
AISTATS (7)
NIPS (6)
JMLR (2)
Top co-authors
Research topics
Keywords
distributed learning
(6)
stochastic gradient descent
(6)
federated learning
(5)
convergence rate
(3)
missing value
(3)
communication compression
(2)
differential privacy
(2)
prediction interval
(2)
conformal prediction
(2)
unsupervised learning
(2)
variance reduction
(2)
least squares regression
(2)
stochastic gradient
(2)
uncertainty quantification
(2)
convergence analysis
(2)
optimal transport
(2)
wasserstein distance
(2)
representation learning
(2)
gradient descent
(2)
linear regression
(2)
Papers
Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation
AISTATS 2025
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
ICML 2025
Unified Breakdown Analysis for Byzantine Robust Gossip
ICML 2025
Compressed and distributed least-squares regression: convergence rates with applications to federated learning
JMLR 2024
Compression with Exact Error Distribution for Federated Learning
AISTATS 2024
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
AISTATS 2024
Random features models: a way to study the success of naive imputation
ICML 2024
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates
ICML 2024
Conformal Prediction with Missing Values
ICML 2023
Naive imputation implicitly regularizes high-dimensional linear models
ICML 2023
Super-Acceleration with Cyclical Step-sizes
AISTATS 2022
Differentially Private Federated Learning on Heterogeneous Data
AISTATS 2022
Near-optimal rate of consistency for linear models with missing values
ICML 2022
Adaptive Conformal Predictions for Time Series
ICML 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
NIPS 2022
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning
AISTATS 2022
Federated-EM with heterogeneity mitigation and variance reduction
NIPS 2021
Preserved central model for faster bidirectional compression in distributed settings
NIPS 2021
Context Moverβs Distance & Barycenters: Optimal Transport of Contexts for Building Representations
AISTATS 2020
Debiasing Averaged Stochastic Gradient Descent to handle missing values
NIPS 2020
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
ICML 2020
Unsupervised Scalable Representation Learning for Multivariate Time Series
NIPS 2019
Communication trade-offs for Local-SGD with large step size
NIPS 2019
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
JMLR 2017