Distributed estimation of the maximum value over a Wireless Sensor Network. - Franck Iutzeler; Jérémie Jakubowicz; Walid Hachem; Philippe Ciblat (ACSCC, 2011)

New broadcast based distributed averaging algorithm over wireless sensor networks. - Franck Iutzeler; Philippe Ciblat; Walid Hachem; Jérémie Jakubowicz (ICASSP, 2012)

Analysis of Max-Consensus Algorithms in Wireless Channels. - Franck Iutzeler; Philippe Ciblat; Jérémie Jakubowicz (IEEE Trans. Signal Process., 2012)

Asynchronous distributed optimization using a randomized alternating direction method of multipliers. - Franck Iutzeler; Pascal Bianchi; Philippe Ciblat; Walid Hachem (CDC, 2013)

Fully distributed signal detection: Application to cognitive radio. - Franck Iutzeler; Philippe Ciblat (EUSIPCO, 2013)

Analysis of Sum-Weight-Like Algorithms for Averaging in Wireless Sensor Networks. - Franck Iutzeler; Philippe Ciblat; Walid Hachem (IEEE Trans. Signal Process., 2013)

Distributed Estimation and Optimization for Asynchronous Networks. (Estimation et Optimisation Distribuée pour les Réseaux Asynchrones). - Franck Iutzeler (2013)

Linear convergence rate for distributed optimization with the alternating direction method of multipliers. - Franck Iutzeler; Pascal Bianchi; Philippe Ciblat; Walid Hachem (CDC, 2014)

A stochastic primal-dual algorithm for distributed asynchronous composite optimization. - Pascal Bianchi; Walid Hachem; Franck Iutzeler (GlobalSIP, 2014)

A stochastic coordinate descent primal-dual algorithm and applications. - Pascal Bianchi; Walid Hachem; Franck Iutzeler (MLSP, 2014)

A Stochastic Coordinate Descent Primal-Dual Algorithm and Applications to Large-Scale Composite Optimization. - Pascal Bianchi; Walid Hachem; Franck Iutzeler (CoRR, 2014)

Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization. - Bikash Joshi; Franck Iutzeler; Massih-Reza Amini (RecSys, 2016)

A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization. - Pascal Bianchi; Walid Hachem; Franck Iutzeler (IEEE Trans. Autom. Control., 2016)

Explicit Convergence Rate of a Distributed Alternating Direction Method of Multipliers. - Franck Iutzeler; Pascal Bianchi; Philippe Ciblat; Walid Hachem (IEEE Trans. Autom. Control., 2016)

Distributed Production-Sharing Optimization and Application to Power Grid Networks. - Azary Abboud; Franck Iutzeler; Romain Couillet; Mérouane Debbah; Houria Siguerdidjane (IEEE Trans. Signal Inf. Process. over Networks, 2016)

Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification. - Bikash Joshi; Massih-Reza Amini; Ioannis Partalas; Franck Iutzeler; Yury Maximov (NIPS, 2017)

Distributed Computation of Quantiles via ADMM. - Franck Iutzeler (IEEE Signal Process. Lett., 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)

Large-scale asynchronous distributed learning based on parameter exchanges. - Bikash Joshi; Franck Iutzeler; Massih-Reza Amini (Int. J. Data Sci. Anal., 2018)

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

Distributed Projection on the Simplex and ℓ1 Ball via ADMM and Gossip. - Franck Iutzeler; Laurent Condat (IEEE Signal Process. Lett., 2018)

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

A generic online acceleration scheme for optimization algorithms via relaxation and inertia. - Franck Iutzeler; Julien M. Hendrickx (Optim. Methods Softw., 2019)

Sparse Asynchronous Distributed Learning. - Dmitry Grischenko; Franck Iutzeler; Massih-Reza Amini (ICONIP (5), 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)

On the interplay between acceleration and identification for the proximal gradient algorithm. - Gilles Bareilles; Franck Iutzeler (Comput. Optim. Appl., 2020)

Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy. - Charlotte Laclau; Franck Iutzeler; Ievgen Redko (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)

Harnessing the Structure of some Optimization Problems. (Autour de l'utilisation de la structure dans certains problèmes d'optimisation). - Franck Iutzeler (2021)

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

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

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation. - Aleksandra Burashnikova; Yury Maximov; Marianne Clausel; Charlotte Laclau; Franck Iutzeler; Massih-Reza Amini (J. Artif. Intell. Res., 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)

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract). - Aleksandra Burashnikova; Yury Maximov; Marianne Clausel; Charlotte Laclau; Franck Iutzeler; Massih-Reza Amini (CoRR, 2022)

Entropy-regularized Wasserstein distributionally robust shape and topology optimization. - Charles Dapogny; Franck Iutzeler; Andrea Meda; Boris Thibert (CoRR, 2022)

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)

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)

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)

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)

Risk-controlling Prediction with Distributionally Robust Optimization. - Franck Iutzeler; Adrien Mazoyer (Trans. Mach. Learn. Res., 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)