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