A Dual Approach to Semidefinite Least-Squares Problems. - Jérôme Malick (SIAM J. Matrix Anal. Appl., 2004)

Newton methods for nonsmooth convex minimization: connections among U-Lagrangian, Riemannian Newton and SQP methods. - Scott A. Miller; Jérôme Malick (Math. Program., 2005)

The spherical constraint in Boolean quadratic programs. - Jérôme Malick (J. Glob. Optim., 2007)

Alternating Projections on Manifolds. - Adrian S. Lewis; Jérôme Malick (Math. Oper. Res., 2008)

Local Linear Convergence for Alternating and Averaged Nonconvex Projections. - Adrian S. Lewis; D. Russell Luke; Jérôme Malick (Found. Comput. Math., 2009)

Regularization Methods for Semidefinite Programming. - Jérôme Malick; Janez Povh; Franz Rendl; Angelika Wiegele (SIAM J. Optim., 2009)

Numerical Study of Semidefinite Bounds for the k-cluster Problem. - Jérôme Malick; Frédéric Roupin (Electron. Notes Discret. Math., 2010)

Projection methods for conic feasibility problems: applications to polynomial sum-of-squares decompositions. - Didier Henrion; Jérôme Malick (Optim. Methods Softw., 2011)

Large-scale image classification with trace-norm regularization. - Zaïd Harchaoui; Matthijs Douze; Mattis Paulin; Miroslav Dudík; Jérôme Malick (CVPR, 2012)

Descentwise inexact proximal algorithms for smooth optimization. - Marc Fuentes; Jérôme Malick; Claude Lemaréchal (Comput. Optim. Appl., 2012)

Lifted coordinate descent for learning with trace-norm regularization. - Miroslav Dudík; Zaïd Harchaoui; Jérôme Malick (AISTATS, 2012)

A Fresh Variational-Analysis Look at the Positive Semidefinite Matrices World. - Jean-Baptiste Hiriart-Urruty; Jérôme Malick (J. Optim. Theory Appl., 2012)

Solving k-cluster problems to optimality with semidefinite programming. - Jérôme Malick; Frédéric Roupin (Math. Program., 2012)

Projection-like Retractions on Matrix Manifolds. - Pierre-Antoine Absil; Jérôme Malick (SIAM J. Optim., 2012)

Cut-Generating Functions. - Michele Conforti; Gérard Cornuéjols; Aris Daniilidis; Claude Lemaréchal; Jérôme Malick (IPCO, 2013)

Prices stabilization for inexact unit-commitment problems. - Sofia Zaourar; Jérôme Malick (Math. Methods Oper. Res., 2013)

On the bridge between combinatorial optimization and nonlinear optimization: a family of semidefinite bounds for 0-1 quadratic problems leading to quasi-Newton methods. - Jérôme Malick; Frédéric Roupin (Math. Program., 2013)

Improved semidefinite bounding procedure for solving Max-Cut problems to optimality. - Nathan Krislock; Jérôme Malick; Frédéric Roupin (Math. Program., 2014)

Cut-Generating Functions and S-Free Sets. - Michele Conforti; Gérard Cornuéjols; Aris Daniilidis; Claude Lemaréchal; Jérôme Malick (Math. Oper. Res., 2015)

Approximate joint diagonalization within the Riemannian geometry framework. - Florent Bouchard; Louis Korczowski; Jérôme Malick; Marco Congedo (EUSIPCO, 2016)

Decomposition algorithm for large-scale two-stage unit-commitment. - Wim van Ackooij; Jérôme Malick (Ann. Oper. Res., 2016)

Computational results of a semidefinite branch-and-bound algorithm for k-cluster. - Nathan Krislock; Jérôme Malick; Frédéric Roupin (Comput. Oper. Res., 2016)

Variational-analysis look at combinatorial optimization, and other selected topics in optimization. - Jérôme Malick (2017)

Approximate Joint Diagonalization According to the Natural Riemannian Distance. - Florent Bouchard; Jérôme Malick; Marco Congedo (LVA/ICA, 2017)

Regularized decomposition of large scale block-structured robust optimization problems. - Wim van Ackooij; Nicolas Lebbe; Jérôme Malick (Comput. Manag. Sci., 2017)

Uncontrolled inexact information within bundle methods. - Jérôme Malick; Welington de Oliveira 001; Sofia Zaourar (EURO J. Comput. Optim., 2017)

Second-order differentiability of probability functions. - Wim van Ackooij; Jérôme Malick (Optim. Lett., 2017)

BiqCrunch: A Semidefinite Branch-and-Bound Method for Solving Binary Quadratic Problems. - Nathan Krislock; Jérôme Malick; Frédéric Roupin (ACM Trans. Math. Softw., 2017)

A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning. - Konstantin Mishchenko; Franck Iutzeler; Jérôme Malick; Massih-Reza Amini (ICML, 2018)

Asynchronous Distributed Learning with Sparse Communications and Identification. - Dmitry Grishchenko; Franck Iutzeler; Jérôme Malick; Massih-Reza Amini (CoRR, 2018)

On the Proximal Gradient Algorithm with Alternated Inertia. - Franck Iutzeler; Jérôme Malick (J. Optim. Theory Appl., 2018)

Sensitivity Analysis for Mirror-Stratifiable Convex Functions. - Jalal Fadili; Jérôme Malick; Gabriel Peyré (SIAM J. Optim., 2018)

Riemannian Optimization and Approximate Joint Diagonalization for Blind Source Separation. - Florent Bouchard; Jérôme Malick; Marco Congedo (IEEE Trans. Signal Process., 2018)

Model Consistency for Learning with Mirror-Stratifiable Regularizers. - Jalal Fadili; Guillaume Garrigos; Jérôme Malick; Gabriel Peyré (AISTATS, 2019)

On the convergence of single-call stochastic extra-gradient methods. - Yu-Guan Hsieh; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (NeurIPS, 2019)

Eventual convexity of probability constraints with elliptical distributions. - Wim van Ackooij; Jérôme Malick (Math. Program., 2019)

First-Order Optimization for Superquantile-Based Supervised Learning. - Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (MLSP, 2020)

Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling. - Yu-Guan Hsieh; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (NeurIPS, 2020)

Randomized Progressive Hedging methods for multi-stage stochastic programming. - Gilles Bareilles; Yassine Laguel; Dmitry Grishchenko; Franck Iutzeler; Jérôme Malick (Ann. Oper. Res., 2020)

Device Heterogeneity in Federated Learning: A Superquantile Approach. - Yassine Laguel; Krishna Pillutla; Jérôme Malick; Zaïd Harchaoui (CoRR, 2020)

Asynchronous level bundle methods. - Franck Iutzeler; Jérôme Malick; Welington de Oliveira 001 (Math. Program., 2020)

A Distributed Flexible Delay-Tolerant Proximal Gradient Algorithm. - Konstantin Mishchenko; Franck Iutzeler; Jérôme Malick (SIAM J. Optim., 2020)

Approximate Joint Diagonalization with Riemannian Optimization on the General Linear Group. - Florent Bouchard; Bijan Afsari; Jérôme Malick; Marco Congedo (SIAM J. Matrix Anal. Appl., 2020)

Optimization in Open Networks via Dual Averaging. - Yu-Guan Hsieh; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (CDC, 2021)

A Superquantile Approach to Federated Learning with Heterogeneous Devices. - Yassine Laguel; Krishna Pillutla; Jérôme Malick; Zaïd Harchaoui (CISS, 2021)

The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (COLT, 2021)

Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach. - Krishna Pillutla; Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (CoRR, 2021)

Proximal Gradient Methods with Adaptive Subspace Sampling. - Dmitry Grishchenko; Franck Iutzeler; Jérôme Malick (Math. Oper. Res., 2021)

Distributed Learning with Sparse Communications by Identification. - Dmitry Grishchenko; Franck Iutzeler; Jérôme Malick; Massih-Reza Amini (SIAM J. Math. Data Sci., 2021)

On the rate of convergence of Bregman proximal methods in constrained variational inequalities. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (CoRR, 2022)

Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism. - Yu-Guan Hsieh; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (J. Mach. Learn. Res., 2022)

Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization. - Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (J. Signal Process. Syst., 2022)

Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models. - Waïss Azizian; Franck Iutzeler; Jérôme Malick (NeurIPS, 2023)

Newton acceleration on manifolds identified by proximal gradient methods. - Gilles Bareilles; Franck Iutzeler; Jérôme Malick (Math. Program., 2023)

Harnessing Structure in Composite Nonsmooth Minimization. - Gilles Bareilles; Franck Iutzeler; Jérôme Malick (SIAM J. Optim., 2023)

Push-Pull With Device Sampling. - Yu-Guan Hsieh; Yassine Laguel; Franck Iutzeler; Jérôme Malick (IEEE Trans. Autom. Control., 2023)

What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (ICML, 2024)

skwdro: a library for Wasserstein distributionally robust machine learning. - Florian Vincent; Waïss Azizian; Franck Iutzeler; Jérôme Malick (CoRR, 2024)

The Rate of Convergence of Bregman Proximal Methods: Local Geometry Versus Regularity Versus Sharpness. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (SIAM J. Optim., 2024)

Federated learning with superquantile aggregation for heterogeneous data. - Krishna Pillutla; Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (Mach. Learn., 2024)

Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective. - Yassine Laguel; Jérôme Malick; Wim van Ackooij (Comput. Optim. Appl., 2024)

Universal generalization guarantees for Wasserstein distributionally robust models. - Tam Le; Jérôme Malick (ICLR, 2025)

The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations. - Waïss Azizian; Franck Iutzeler; Jérôme Malick; Panayotis Mertikopoulos (CoRR, 2025)