Edouard Pauwels
12 papers · 2016–2024 · 3 venues · across top CS/AI venues
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Machine Learning
(11)
Optimization & Theory · Machine Learning
(10)
Deep Learning
(8)
Optimization & Theory · Deep Learning
(6)
Mathematics & Optimization
(5)
Optimization
(5)
Architectures
(4)
Artificial Intelligence
(2)
Core AI
(2)
Core Methods
(2)
Computer Science
(1)
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Keywords
nonsmooth optimization
(3)
automatic differentiation
(3)
algorithmic differentiation
(3)
stochastic approximation
(2)
implicit differentiation
(2)
semidefinite programming
(2)
lipschitz constant
(2)
christoffel function
(2)
robustness certification
(2)
kernel methods
(2)
deep learning
(1)
strong convexity
(1)
complexity analysis
(1)
convergence analysis
(1)
gradient descent
(1)
outlier detection
(1)
hyperparameter optimization
(1)
forward-backward algorithm
(1)
parametric optimization
(1)
hyperparameter tuning
(1)
Papers
Derivatives of Stochastic Gradient Descent in parametric optimization
NeurIPS 2024
One-step differentiation of iterative algorithms
NeurIPS 2023
On the complexity of nonsmooth automatic differentiation
ICLR 2023
Automatic differentiation of nonsmooth iterative algorithms
NeurIPS 2022
An Inertial Newton Algorithm for Deep Learning
JMLR 2021
Nonsmooth Implicit Differentiation for Machine-Learning and Optimization
NeurIPS 2021
Numerical influence of ReLU’(0) on backpropagation
NeurIPS 2021
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification
NeurIPS 2021
A mathematical model for automatic differentiation in machine learning
NeurIPS 2020
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
NeurIPS 2020
Relating Leverage Scores and Density using Regularized Christoffel Functions
NeurIPS 2018
Sorting out typicality with the inverse moment matrix SOS polynomial
NeurIPS 2016