Predicting drug side-effect profiles: a chemical fragment-based approach. - Edouard Pauwels; Véronique Stoven; Yoshihiro Yamanishi (BMC Bioinform., 2011)

Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions. - Yoshihiro Yamanishi; Edouard Pauwels; Hiroto Saigo; Véronique Stoven (J. Chem. Inf. Model., 2011)

Relating drug-protein interaction network with drug side effects. - Sayaka Mizutani; Edouard Pauwels; Véronique Stoven; Susumu Goto; Yoshihiro Yamanishi (Bioinform., 2012)

Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. - Yasuo Tabei; Edouard Pauwels; Véronique Stoven; Kazuhiro Takemoto; Yoshihiro Yamanishi (Bioinform., 2012)

Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces. - Yoshihiro Yamanishi; Edouard Pauwels; Masaaki Kotera (J. Chem. Inf. Model., 2012)

Inverse optimal control with polynomial optimization. - Edouard Pauwels; Didier Henrion; Jean-Bernard Lasserre (CDC, 2014)

A Bayesian active learning strategy for sequential experimental design in systems biology. - Edouard Pauwels; Christian Lajaunie; Jean-Philippe Vert (BMC Syst. Biol., 2014)

The Cyclic Block Conditional Gradient Method for Convex Optimization Problems. - Amir Beck; Edouard Pauwels; Shoham Sabach (SIAM J. Optim., 2015)

Sorting out typicality with the inverse moment matrix SOS polynomial. - Edouard Pauwels; Jean B. Lasserre (NIPS, 2016)

Majorization-Minimization Procedures and Convergence of SQP Methods for Semi-Algebraic and Tame Programs. - Jérôme Bolte; Edouard Pauwels (Math. Oper. Res., 2016)

The value function approach to convergence analysis in composite optimization. - Edouard Pauwels (Oper. Res. Lett., 2016)

Linear Conic Optimization for Inverse Optimal Control. - Edouard Pauwels; Didier Henrion; Jean-Bernard Lasserre (SIAM J. Control. Optim., 2016)

The empirical Christoffel function in Statistics and Machine Learning. - Jean-Bernard Lasserre; Edouard Pauwels (CoRR, 2017)

On Fienup Methods for Regularized Phase Retrieval. - Edouard Pauwels; Amir Beck; Yonina C. Eldar; Shoham Sabach (CoRR, 2017)

Positivity Certificates in Optimal Control. - Edouard Pauwels; Didier Henrion; Jean-Bernard Lasserre (Geometric and Numerical Foundations of Movements, 2017)

Relating Leverage Scores and Density using Regularized Christoffel Functions. - Edouard Pauwels; Francis R. Bach; Jean-Philippe Vert (NeurIPS, 2018)

Extragradient Method in Optimization: Convergence and Complexity. - Trong Phong Nguyen; Edouard Pauwels; Emile Richard; Bruce W. Suter (J. Optim. Theory Appl., 2018)

Primal and dual predicted decrease approximation methods. - Amir Beck; Edouard Pauwels; Shoham Sabach (Math. Program., 2018)

Qualification Conditions in Semialgebraic Programming. - Jérôme Bolte; Antoine Hochart; Edouard Pauwels (SIAM J. Optim., 2018)

On Fienup Methods for Sparse Phase Retrieval. - Edouard Pauwels; Amir Beck; Yonina C. Eldar; Shoham Sabach (IEEE Trans. Signal Process., 2018)

The empirical Christoffel function with applications in data analysis. - Jean B. Lasserre; Edouard Pauwels (Adv. Comput. Math., 2019)

Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning. - Jérôme Bolte; Edouard Pauwels (CoRR, 2019)

Rate of convergence for geometric inference based on the empirical Christoffel function. - Mai Trang Vu; François Bachoc; Edouard Pauwels (CoRR, 2019)

Semialgebraic Optimization for Lipschitz Constants of ReLU Networks. - Tong Chen 002; Jean B. Lasserre; Victor Magron; Edouard Pauwels (NeurIPS, 2020)

A mathematical model for automatic differentiation in machine learning. - Jérôme Bolte; Edouard Pauwels (NeurIPS, 2020)

Polynomial Optimization for Bounding Lipschitz Constants of Deep Networks. - Tong Chen 002; Jean-Bernard Lasserre; Victor Magron; Edouard Pauwels (CoRR, 2020)

Long term dynamics of the subgradient method for Lipschitz path differentiable functions. - Jérôme Bolte; Edouard Pauwels; Rodolfo Rios-Zertuche (CoRR, 2020)

A Hölderian backtracking method for min-max and min-min problems. - Jérôme Bolte; Lilian Glaudin; Edouard Pauwels; Mathieu Serrurier (CoRR, 2020)

The multiproximal linearization method for convex composite problems. - Jérôme Bolte; Zheng Chen 009; Edouard Pauwels (Math. Program., 2020)

Numerical influence of ReLU'(0) on backpropagation. - David Bertoin; Jérôme Bolte; Sébastien Gerchinovitz; Edouard Pauwels (NeurIPS, 2021)

Nonsmooth Implicit Differentiation for Machine-Learning and Optimization. - Jérôme Bolte; Tam Le; Edouard Pauwels; Antonio Silveti-Falls (NeurIPS, 2021)

Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification. - Tong Chen 002; Jean B. Lasserre; Victor Magron; Edouard Pauwels (NeurIPS, 2021)

Data Analysis from Empirical Moments and the Christoffel Function. - Edouard Pauwels; Mihai Putinar; Jean-Bernard Lasserre (Found. Comput. Math., 2021)

An Inertial Newton Algorithm for Deep Learning. - Camille Castera; Jérôme Bolte; Cédric Févotte; Edouard Pauwels (J. Mach. Learn. Res., 2021)

Incremental Without Replacement Sampling in Nonconvex Optimization. - Edouard Pauwels (J. Optim. Theory Appl., 2021)

Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning. - Jérôme Bolte; Edouard Pauwels (Math. Program., 2021)

Sequential convergence of AdaGrad algorithm for smooth convex optimization. - Cheik Traoré; Edouard Pauwels (Oper. Res. Lett., 2021)

Automatic differentiation of nonsmooth iterative algorithms. - Jérôme Bolte; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2022)

A sublevel moment-SOS hierarchy for polynomial optimization. - Tong Chen 002; Jean-Bernard Lasserre; Victor Magron; Edouard Pauwels (Comput. Optim. Appl., 2022)

Path differentiability of ODE flows. - Swann Marx; Edouard Pauwels (CoRR, 2022)

Nonsmooth automatic differentiation: a cheap gradient principle and other complexity results. - Jérôme Bolte; Ryan Boustany; Edouard Pauwels; Béatrice Pesquet-Popescu (CoRR, 2022)

Curiosities and counterexamples in smooth convex optimization. - Jérôme Bolte; Edouard Pauwels (Math. Program., 2022)

Second-Order Step-Size Tuning of SGD for Non-Convex Optimization. - Camille Castera; Jérôme Bolte; Cédric Févotte; Edouard Pauwels (Neural Process. Lett., 2022)

On the complexity of nonsmooth automatic differentiation. - Jérôme Bolte; Ryan Boustany; Edouard Pauwels; Béatrice Pesquet-Popescu (ICLR, 2023)

One-step differentiation of iterative algorithms. - Jérôme Bolte; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2023)

The backtrack Hölder gradient method with application to min-max and min-min problems. - Jérôme Bolte; Lilian Glaudin; Edouard Pauwels; Mathieu Serrurier (Open J. Math. Optim., 2023)

The Derivatives of Sinkhorn-Knopp Converge. - Edouard Pauwels; Samuel Vaiter (SIAM J. Optim., 2023)

Subgradient Sampling for Nonsmooth Nonconvex Minimization. - Jérôme Bolte; Tam Le; Edouard Pauwels (SIAM J. Optim., 2023)

Derivatives of Stochastic Gradient Descent in parametric optimization. - Franck Iutzeler; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2024)

Derivatives of Stochastic Gradient Descent. - Franck Iutzeler; Edouard Pauwels; Samuel Vaiter (CoRR, 2024)

Geometric and computational hardness of bilevel programming. - Jérôme Bolte; Quoc-Tung Le; Edouard Pauwels; Samuel Vaiter (CoRR, 2024)

A second-order-like optimizer with adaptive gradient scaling for deep learning. - Jérôme Bolte; Ryan Boustany; Edouard Pauwels; Andrei I. Purica (CoRR, 2024)

The Iterates of the Frank-Wolfe Algorithm May Not Converge. - Jérôme Bolte; Cyrille W. Combettes; Edouard Pauwels (Math. Oper. Res., 2024)

On the nature of Bregman functions. - Edouard Pauwels (Oper. Res. Lett., 2024)

Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems. - Jérôme Bolte; Edouard Pauwels; Antonio Silveti-Falls (SIAM J. Optim., 2024)

Learning Theory for Kernel Bilevel Optimization. - Fares El Khoury; Edouard Pauwels; Samuel Vaiter; Michael Arbel (CoRR, 2025)

How Diffusion Prior Landscapes Shape the Posterior in Blind Deconvolution. - Minh-Hai Nguyen; Edouard Pauwels; Pierre Weiss (CoRR, 2025)