Samuel Vaiter
16 papers · 2020–2026 · 4 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (26) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Keyword Champion
(2)
👥
Mega-Team · Squad
🌈
Renaissance Researcher
(5)
🔥
Unstoppable · Bronze
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The Questioner
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Century Club · Bronze
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Trend Setter
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Keyword Collector
(72)
Venues
Top co-authors
Research topics
Machine Learning
(14)
Optimization & Theory · Machine Learning
(13)
Deep Learning
(6)
Mathematics & Optimization
(5)
Optimization
(4)
Architectures
(3)
Core Methods
(3)
Artificial Intelligence
(2)
Learning Types
(2)
Optimization & Theory · Deep Learning
(2)
Interdisciplinary
(1)
Natural Language Processing
(1)
Reinforcement Learning
(1)
Applications · Natural Language Processing
(1)
Applications · Reinforcement Learning
(1)
Keywords
random graph
(4)
bilevel optimization
(4)
automatic differentiation
(3)
implicit differentiation
(3)
graph neural network
(3)
hyperparameter optimization
(3)
empirical risk minimization
(2)
hawkes process
(2)
proximal gradient descent
(2)
stochastic optimization
(2)
nonsmooth optimization
(2)
local learning rule
(2)
message passing
(1)
stability analysis
(1)
logistic regression
(1)
stochastic gradient descent
(1)
neural network optimization
(1)
concentration inequalities
(1)
sample complexity
(1)
natural language processing
(1)
Papers
CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration
JMLR 2026
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
AISTATS 2024
Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs
JMLR 2024
Derivatives of Stochastic Gradient Descent in parametric optimization
NeurIPS 2024
Provable local learning rule by expert aggregation for a Hawkes network
AISTATS 2024
One-step differentiation of iterative algorithms
NeurIPS 2023
On the Robustness of Text Vectorizers
ICML 2023
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
NeurIPS 2023
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
NeurIPS 2022
Automatic differentiation of nonsmooth iterative algorithms
NeurIPS 2022
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
NeurIPS 2022
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning
JMLR 2022
On the Universality of Graph Neural Networks on Large Random Graphs
NeurIPS 2021
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
NeurIPS 2020
Dual Extrapolation for Sparse GLMs
JMLR 2020
Implicit differentiation of Lasso-type models for hyperparameter optimization
ICML 2020