Samuel Vaiter
15 papers · 2020–2024 · 4 conferences · across top CS/AI conferences
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Conferences
NIPS (8)
JMLR (3)
AISTATS (2)
ICML (2)
Top co-authors
Keywords
random graph
(4)
graph neural network
(3)
automatic differentiation
(3)
hyperparameter optimization
(3)
implicit differentiation
(3)
bilevel optimization
(3)
proximal gradient descent
(2)
empirical risk minimization
(1)
logistic regression
(1)
stochastic gradient descent
(1)
neural network optimization
(1)
stability analysis
(1)
sample complexity
(1)
message passing
(1)
natural language processing
(1)
concentration inequalities
(1)
synaptic learning
(1)
convergence analysis
(1)
weisfeiler-lehman test
(1)
coordinate descent
(1)
Papers
Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs
JMLR 2024
Derivatives of Stochastic Gradient Descent in parametric optimization
NIPS 2024
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
AISTATS 2024
Provable local learning rule by expert aggregation for a Hawkes network
AISTATS 2024
On the Robustness of Text Vectorizers
ICML 2023
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
NIPS 2023
One-step differentiation of iterative algorithms
NIPS 2023
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
NIPS 2022
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning
JMLR 2022
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
NIPS 2022
Automatic differentiation of nonsmooth iterative algorithms
NIPS 2022
On the Universality of Graph Neural Networks on Large Random Graphs
NIPS 2021
Dual Extrapolation for Sparse GLMs
JMLR 2020
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
NIPS 2020
Implicit differentiation of Lasso-type models for hyperparameter optimization
ICML 2020