Jérôme Malick

76 papers A* 9A 2B 1Misc 1Journal 58Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	skwdro: a library for Wasserstein distributionally robust machine learning. J. Mach. Learn. Res. Florian Vincent, Waïss Azizian, Franck Iutzeler, Jérôme Malick
2025	J	`jnl`	Delay-tolerant distributed Bregman proximal algorithms. Optim. Methods Softw. S. Chraibi, Franck Iutzeler, Jérôme Malick, A. Rogozin
2025	A*	`conf`	The Global Convergence Time of Stochastic Gradient Descent in Non-Convex Landscapes: Sharp Estimates via Large Deviations. ICML Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2025	J	`jnl`	The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations. CoRR Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2025	A*	`conf`	Universal generalization guarantees for Wasserstein distributionally robust models. ICLR Tam Le, Jérôme Malick
2024	J	`jnl`	Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective. Comput. Optim. Appl. Yassine Laguel, Jérôme Malick, Wim van Ackooij
2024	J	`jnl`	Federated learning with superquantile aggregation for heterogeneous data. Mach. Learn. Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui
2024	J	`jnl`	The Rate of Convergence of Bregman Proximal Methods: Local Geometry Versus Regularity Versus Sharpness. SIAM J. Optim. Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2024	A*	`conf`	What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis. ICML Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2024	J	`jnl`	What is the long-run distribution of stochastic gradient descent? A large deviations analysis. CoRR Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2023	A*	`conf`	Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models. NeurIPS Waïss Azizian, Franck Iutzeler, Jérôme Malick
2023	J	`jnl`	Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models. CoRR Waïss Azizian, Franck Iutzeler, Jérôme Malick
2023	J	`jnl`	Harnessing Structure in Composite Nonsmooth Minimization. SIAM J. Optim. Gilles Bareilles, Franck Iutzeler, Jérôme Malick
2023	J	`jnl`	Newton acceleration on manifolds identified by proximal gradient methods. Math. Program. Gilles Bareilles, Franck Iutzeler, Jérôme Malick
2023	J	`jnl`	Push-Pull With Device Sampling. IEEE Trans. Autom. Control. Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler, Jérôme Malick
2022	J	`jnl`	Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism. J. Mach. Learn. Res. Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2022	J	`jnl`	On the rate of convergence of Bregman proximal methods in constrained variational inequalities. CoRR Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2022	J	`jnl`	Push-Pull with Device Sampling. CoRR Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler, Jérôme Malick
2022	J	`jnl`	Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization. J. Signal Process. Syst. Yassine Laguel, Jérôme Malick, Zaïd Harchaoui
2021	Misc	`conf`	A Superquantile Approach to Federated Learning with Heterogeneous Devices. CISS Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui
2021	J	`jnl`	Distributed Learning with Sparse Communications by Identification. SIAM J. Math. Data Sci. Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini
2021	J	`jnl`	Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach. CoRR Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui
2021	—	`conf`	Optimization in Open Networks via Dual Averaging. CDC Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2021	J	`jnl`	Optimization in Open Networks via Dual Averaging. CoRR Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2021	J	`jnl`	Proximal Gradient Methods with Adaptive Subspace Sampling. Math. Oper. Res. Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick
2021	A*	`conf`	The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities. COLT Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2021	J	`jnl`	The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities. CoRR Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2020	J	`jnl`	A Distributed Flexible Delay-Tolerant Proximal Gradient Algorithm. SIAM J. Optim. Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick
2020	J	`jnl`	Approximate Joint Diagonalization with Riemannian Optimization on the General Linear Group. SIAM J. Matrix Anal. Appl. Florent Bouchard, Bijan Afsari, Jérôme Malick, Marco Congedo
2020	J	`jnl`	Asynchronous level bundle methods. Math. Program. Franck Iutzeler, Jérôme Malick, Welington de Oliveira
2020	J	`jnl`	Device Heterogeneity in Federated Learning: A Superquantile Approach. CoRR Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui
2020	A*	`conf`	Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling. NeurIPS Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2020	J	`jnl`	Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling. CoRR Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2020	—	`conf`	First-Order Optimization for Superquantile-Based Supervised Learning. MLSP Yassine Laguel, Jérôme Malick, Zaïd Harchaoui
2020	J	`jnl`	First-order Optimization for Superquantile-based Supervised Learning. CoRR Yassine Laguel, Jérôme Malick, Zaïd Harchaoui
2020	J	`jnl`	Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism. CoRR Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2020	J	`jnl`	Randomized Progressive Hedging methods for Multi-stage Stochastic Programming. CoRR Gilles Bareilles, Yassine Laguel, Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick
2020	J	`jnl`	Randomized Progressive Hedging methods for multi-stage stochastic programming. Ann. Oper. Res. Gilles Bareilles, Yassine Laguel, Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick
2019	J	`jnl`	Eventual convexity of probability constraints with elliptical distributions. Math. Program. Wim van Ackooij, Jérôme Malick
2019	A	`conf`	Model Consistency for Learning with Mirror-Stratifiable Regularizers. AISTATS Jalal Fadili, Guillaume Garrigos, Jérôme Malick, Gabriel Peyré
2019	A*	`conf`	On the convergence of single-call stochastic extra-gradient methods. NeurIPS Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2019	J	`jnl`	On the convergence of single-call stochastic extra-gradient methods. CoRR Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2018	A*	`conf`	A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning. ICML Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini
2018	J	`jnl`	Asynchronous Distributed Learning with Sparse Communications and Identification. CoRR Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini
2018	J	`jnl`	On the Proximal Gradient Algorithm with Alternated Inertia. J. Optim. Theory Appl. Franck Iutzeler, Jérôme Malick
2018	J	`jnl`	Riemannian Optimization and Approximate Joint Diagonalization for Blind Source Separation. IEEE Trans. Signal Process. Florent Bouchard, Jérôme Malick, Marco Congedo
2018	J	`jnl`	Sensitivity Analysis for Mirror-Stratifiable Convex Functions. SIAM J. Optim. Jalal Fadili, Jérôme Malick, Gabriel Peyré
2017	—	`conf`	Approximate Joint Diagonalization According to the Natural Riemannian Distance. LVA/ICA Florent Bouchard, Jérôme Malick, Marco Congedo
2017	J	`jnl`	BiqCrunch: A Semidefinite Branch-and-Bound Method for Solving Binary Quadratic Problems. ACM Trans. Math. Softw. Nathan Krislock, Jérôme Malick, Frédéric Roupin
2017	J	`jnl`	Regularized decomposition of large scale block-structured robust optimization problems. Comput. Manag. Sci. Wim van Ackooij, Nicolas Lebbe, Jérôme Malick
2017	J	`jnl`	Second-order differentiability of probability functions. Optim. Lett. Wim van Ackooij, Jérôme Malick
2017	J	`jnl`	Sensitivity Analysis for Mirror-Stratifiable Convex Functions. CoRR Jalal Fadili, Jérôme Malick, Gabriel Peyré
2017	J	`jnl`	Uncontrolled inexact information within bundle methods. EURO J. Comput. Optim. Jérôme Malick, Welington de Oliveira, Sofia Zaourar
2017	—	`book`	Variational-analysis look at combinatorial optimization, and other selected topics in optimization. Jérôme Malick
2016	—	`conf`	Approximate joint diagonalization within the Riemannian geometry framework. EUSIPCO Florent Bouchard, Louis Korczowski, Jérôme Malick, Marco Congedo
2016	J	`jnl`	Computational results of a semidefinite branch-and-bound algorithm for k-cluster. Comput. Oper. Res. Nathan Krislock, Jérôme Malick, Frédéric Roupin
2016	J	`jnl`	Decomposition algorithm for large-scale two-stage unit-commitment. Ann. Oper. Res. Wim van Ackooij, Jérôme Malick
2015	J	`jnl`	Cut-Generating Functions and Math. Oper. Res. Michele Conforti, Gérard Cornuéjols, Aris Daniilidis, Claude Lemaréchal, Jérôme Malick
2014	J	`jnl`	Improved semidefinite bounding procedure for solving Max-Cut problems to optimality. Math. Program. Nathan Krislock, Jérôme Malick, Frédéric Roupin
2013	B	`conf`	Cut-Generating Functions. IPCO Michele Conforti, Gérard Cornuéjols, Aris Daniilidis, Claude Lemaréchal, Jérôme Malick
2013	J	`jnl`	On the bridge between combinatorial optimization and nonlinear optimization: a family of semidefinite bounds for 0-1 quadratic problems leading to quasi-Newton methods. Math. Program. Jérôme Malick, Frédéric Roupin
2013	J	`jnl`	Prices stabilization for inexact unit-commitment problems. Math. Methods Oper. Res. Sofia Zaourar, Jérôme Malick
2012	J	`jnl`	A Fresh Variational-Analysis Look at the Positive Semidefinite Matrices World. J. Optim. Theory Appl. Jean-Baptiste Hiriart-Urruty, Jérôme Malick
2012	J	`jnl`	Descentwise inexact proximal algorithms for smooth optimization. Comput. Optim. Appl. Marc Fuentes, Jérôme Malick, Claude Lemaréchal
2012	A*	`conf`	Large-scale image classification with trace-norm regularization. CVPR Zaïd Harchaoui, Matthijs Douze, Mattis Paulin, Miroslav Dudík, Jérôme Malick
2012	A	`conf`	Lifted coordinate descent for learning with trace-norm regularization. AISTATS Miroslav Dudík, Zaïd Harchaoui, Jérôme Malick
2012	J	`jnl`	Projection-like Retractions on Matrix Manifolds. SIAM J. Optim. Pierre-Antoine Absil, Jérôme Malick
2012	J	`jnl`	Solving k-cluster problems to optimality with semidefinite programming. Math. Program. Jérôme Malick, Frédéric Roupin
2011	J	`jnl`	Projection methods for conic feasibility problems: applications to polynomial sum-of-squares decompositions. Optim. Methods Softw. Didier Henrion, Jérôme Malick
2010	J	`jnl`	Numerical Study of Semidefinite Bounds for the k-cluster Problem. Electron. Notes Discret. Math. Jérôme Malick, Frédéric Roupin
2009	J	`jnl`	Local Linear Convergence for Alternating and Averaged Nonconvex Projections. Found. Comput. Math. Adrian S. Lewis, D. Russell Luke, Jérôme Malick
2009	J	`jnl`	Regularization Methods for Semidefinite Programming. SIAM J. Optim. Jérôme Malick, Janez Povh, Franz Rendl, Angelika Wiegele
2008	J	`jnl`	Alternating Projections on Manifolds. Math. Oper. Res. Adrian S. Lewis, Jérôme Malick
2007	J	`jnl`	The spherical constraint in Boolean quadratic programs. J. Glob. Optim. Jérôme Malick
2005	J	`jnl`	Newton methods for nonsmooth convex minimization: connections among U-Lagrangian, Riemannian Newton and SQP methods. Math. Program. Scott A. Miller, Jérôme Malick
2004	J	`jnl`	A Dual Approach to Semidefinite Least-Squares Problems. SIAM J. Matrix Anal. Appl. Jérôme Malick