Neural network training via an affine scaling quadratic optimization algorithm. - Theodore B. Trafalis; Nicolas P. Couellan (Neural Networks, 1996)

Training of supervised neural networks via a nonlinear primal-dual interior-point method. - Theodore B. Trafalis; Nicolas P. Couellan; Sébastien C. Bertrand (ICNN, 1997)

An incremental primal-dual method for nonlinear programming with special structure. - Nicolas P. Couellan; Theodore B. Trafalis (Optim. Lett., 2013)

On-line SVM learning via an incremental primal-dual technique. - Nicolas P. Couellan; Theodore B. Trafalis (Optim. Methods Softw., 2013)

Incremental accelerated gradient methods for SVM classification: study of the constrained approach. - Nicolas P. Couellan; Sophie Jan (Comput. Manag. Sci., 2014)

Self-adaptive Support Vector Machine: A multi-agent optimization perspective. - Nicolas P. Couellan; Sophie Jan; Tom Jorquera; Jean-Pierre Georgé (Expert Syst. Appl., 2015)

Bi-level stochastic gradient for large scale support vector machine. - Nicolas P. Couellan; Wenjuan Wang (Neurocomputing, 2015)

Feature uncertainty bounding schemes for large robust nonlinear SVM classifiers. - Nicolas P. Couellan; Sophie Jan (CoRR, 2017)

Uncertainty-safe large scale support vector machines. - Nicolas P. Couellan; Wenjuan Wang (Comput. Stat. Data Anal., 2017)

A note on supervised classification and Nash-equilibrium problems. - Nicolas P. Couellan (RAIRO Oper. Res., 2017)

The coupling effect of Lipschitz regularization in deep neural networks. - Nicolas P. Couellan (CoRR, 2019)

Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers. - Laurent Risser; Quentin Vincenot; Nicolas P. Couellan; Jean-Michel Loubes (CoRR, 2019)

Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers. - Nicolas P. Couellan; Sophie Jan (Ann. Math. Artif. Intell., 2020)

Probabilistic robustness estimates for feed-forward neural networks. - Nicolas P. Couellan (Neural Networks, 2021)

The Coupling Effect of Lipschitz Regularization in Neural Networks. - Nicolas P. Couellan (SN Comput. Sci., 2021)

A novel image representation of GNSS correlation for deep learning multipath detection. - Antoine Blais; Nicolas Couellan; Evgenii Munin (Array, 2022)

Robust SVM Optimization in Banach spaces. - Mohammed Sbihi; Nicolas Couellan (CoRR, 2022)

Canonical foliations of neural networks: application to robustness. - Eliot Tron; Nicolas Couellan; Stéphane Puechmorel (CoRR, 2022)

Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation. - Thomas Gonzalez; Antoine Blais; Nicolas Couëllan; Christian Ruiz (CoRR, 2022)

LRP-GUS: A Visual Based Data Reduction Algorithm for Neural Networks. - Arnaud Guibert; Christophe Hurter; Nicolas Couellan (ICANN (5), 2023)

Cartan moving frames and the data manifolds. - Eliot Tron; Rita Fioresi; Nicolas Couellan; Stéphane Puechmorel (CoRR, 2024)

Distributional loss for convolutional neural network regression and application to parameter estimation in satellite navigation signals. - Thomas Gonzalez; Antoine Blais; Nicolas Couëllan; Christian Ruiz (Expert Syst. Appl., 2024)

Foreword. - Giancarlo Bigi; Sonia Cafieri; Nicolas Couëllan; Sophie Jan; Frédéric Messine (Optim. Methods Softw., 2024)

A Time-Dependent Subgraph-Capacity Model for Multiple Shortest Paths and Application to CO 2/Contrail-Safe Aircraft Trajectories. - Céline Demouge; Marcel Mongeau; Nicolas Couellan; Daniel Delahaye (Oper. Res. Forum, 2024)

Theoretical aspects of robust SVM optimization in Banach spaces and Nash equilibrium interpretation. - Mohammed Sbihi; Nicolas Couellan (Ann. Math. Artif. Intell., 2024)

Adversarial attacks on neural networks through canonical Riemannian foliations. - Eliot Tron; Nicolas Couëllan; Stéphane Puechmorel (Mach. Learn., 2024)