34- F. Iutzeler, E. Pauwels, S. Vaiter : Derivatives of Stochastic Gradient Descent in parametric optimization, NeurIPS, 2024.
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33- W. Azizian, F. Iutzeler, J. Malick, P. Mertikopoulos : What is the Long-Run Distribution of SGD? A Large Deviations Analysis, International Conference on Machine Learning (ICML), 2024.
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32- W. Azizian, F. Iutzeler, J. Malick, P. Mertikopoulos : On the rate of convergence of Bregman proximal methods in constrained variational inequalities, to appear in SIAM Journal on Optimization, 2024.
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31- S. Chraibi, F. Iutzeler, J. Malick, A. Rogozin : Delay-tolerant Distributed Bregman Proximal Algorithms, to appear in Optimization Methods and Softwares, Dec. 2023.
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30- W. Azizian, F. Iutzeler, J. Malick : Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models, NeurIPS, Dec. 2023.
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29- W. Azizian, F. Iutzeler, J. Malick : Regularization for Wasserstein Distributionally Robust Optimization, to appear in ESAIM: Control, Optimisation, and Calculus of Variations, 2023.
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28- G. Bareilles, F. Iutzeler, J. Malick : Harnessing structure in composite nonsmooth minimization, to appear in SIAM Journal on Optimization, 2023.
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27 Y.-G. Hsieh, Y. Laguel, F. Iutzeler, J. Malick : Push–Pull with Device Sampling, to appear in IEEE Transactions on Automatic Control, 2023
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26- C. Dapogny, F. Iutzeler, A. Meda, B. Thibert : Entropy-regularized Wasserstein distributionally robust shape and topology optimization, Structural and Multidisciplinary Optimization, vol. 66, art. 42, 2023.
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25- G. Bareilles, F. Iutzeler, J. Malick : Newton acceleration on manifolds identified by proximal-gradient methods, to appear in Mathematical Programming, 2022.
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24- Y.-G. Hsieh, F. Iutzeler, J. Malick, P. Mertikopoulos : Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism , Journal of Machine Learning Research, vol. 23, no. 78, pp. 1-49, 2022.
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23- W. Azizian, F. Iutzeler, J. Malick, and P. Mertikopoulos: The last-iterate convergence rate of optimistic mirror descent in stochastic variational inequalities , 34th Annual Conference on Learning Theory (COLT), 2021.
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22- F. Iutzeler, J. Malick: Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications, Set-Valued and Variational Analysis, vol. 28, no. 4, pp. 661-678, 2020.
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21- D. Grishchenko, F. Iutzeler, J. Malick, M.-R. Amini: Distributed Learning with Sparse Communications by Identification , SIAM Journal on Mathematics of Data Science, vol. 3, no. 2, pp. 715-735, 2021.
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20- A. Burashnikova, Y. Maximov, M. Clausel, C. Laclau, F. Iutzeler, M.-R. Amini: Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation, Journal of Artificial Intelligence Research, vol. 71, pp. 121-142, 2021.

19- Y.-G. Hsieh, F. Iutzeler, J. Malick, P. Mertikopoulos : Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling , Advances in Neural Information Processing Systems 34 (NeurIPS) spotlight, Dec. 2020.
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18- G. Bareilles, Y. Laguel, D. Grishchenko, F. Iutzeler, J. Malick: Randomized Progressive Hedging methods for Multi-stage Stochastic Programming , Annals of Operations Research, vol. 295, no. 2, pp. 535-560, 2020.
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17- G. Bareilles, F. Iutzeler : On the Interplay between Acceleration and Identification for the Proximal Gradient algorithm, Computational Optimization and Applications, vol. 77, no. 2, pp. 351–378, 2020.
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16- D. Grishchenko, F. Iutzeler, and J. Malick : Proximal Gradient Methods with Adaptive Subspace Sampling , Mathematics of Operations Research, vol. 46, no. 4, pp. 1303-1323, 2021.
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15- K. Mishchenko, F. Iutzeler, and J. Malick : A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm , SIAM Journal on Optimization, vol. 30, no. 1, pp. 933-959, 2020.
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14- Y.-G. Hsieh, F. Iutzeler, J. Malick, and P. Mertikopoulos : On the convergence of single-call stochastic extra-gradient methods , Advances in Neural Information Processing Systems 33 (NeurIPS) , Dec. 2019.
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13- F. Iutzeler, J. Malick, and W. de Oliveira : Asynchronous level bundle methods, Mathematical Programming, vol. 184, pp. 319-348, 2020.
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12- F. Iutzeler and L. Condat : Distributed Projection on the Simplex and l1 Ball via ADMM and Gossip, IEEE Signal Processing Letters, vol. 25, no. 11, pp. 1650-1654, Nov. 2018.
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11- K. Mishchenko, F. Iutzeler, J. Malick, M.-R. Amini: A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning, 35-th International Conference on Machine Learning (ICML), PMLR 80:3584-3592, Stockholm (Sweden), July 2018.
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10- F. Iutzeler and J. Malick : On the Proximal Gradient Algorithm with Alternated Inertia , Journal of Optimization Theory and Applications, vol. 176, no. 3, pp. 688-710, March 2018.
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9- B. Joshi, F. Iutzeler and M.-R. Amini : Large-scale asynchronous distributed learning based on parameter exchanges , International Journal of Data Science and Analytics, vol. 5, no. 4, pp. 223-232, June 2018.

8- F. Iutzeler and J. M. Hendrickx : A Generic online acceleration scheme for Optimization algorithms via Relaxation and Inertia , Optimization Methods and Software, vol. 34, no. 2, 2019.
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7- B. Joshi, M.-R. Amini, I. Partalas, F. Iutzeler, Yu. Maximov: Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification, Advances in Neural Information Processing Systems 30 (NIPS) , Dec. 2017.
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6- F. Iutzeler : Distributed Computation of Quantiles via ADMM, IEEE Signal Processing Letters, vol. 24, no. 5, pp. 619-623, May 2017.
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5- P. Bianchi, W. Hachem, and F. Iutzeler : A Stochastic Coordinate Descent Primal-Dual Algorithm and Applications to Distributed Optimization , IEEE Transactions on Automatic Control, vol. 61, no. 10, pp. 2947-2957, Oct. 2016.
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4- F. Iutzeler, P. Bianchi, P. Ciblat, and W. Hachem : Explicit Convergence Rate of a Distributed Alternating Direction Method of Multipliers, IEEE Transactions on Automatic Control, vol. 61, no. 4, pp. 892-904, Apr. 2016.
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3- A. Abboud, F. Iutzeler, R. Couillet, M. Debbah, and H. Siguerdidjane: Distributed Production-Sharing Optimization and Application to Power Grid Networks , IEEE Transactions on Signal and Information Processing over Networks, vol. 2, no. 11, pp. 16-28, March 2016.
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2- F. Iutzeler, P. Ciblat, and W. Hachem : Analysis of Sum-Weight-like algorithms for averaging in Wireless Sensor Networks, IEEE Transactions on Signal Processing, vol. 61, no. 11, pp. 2802-2814, June 2013.
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1- F. Iutzeler, P. Ciblat, and J. Jakubowicz : Analysis of max-consensus algorithms in wireless channels, IEEE Transactions on Signal Processing, vol. 60, no. 11, pp. 6103-6107, November 2012.
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12- Y.-G. Hsieh, F. Iutzeler, J. Malick, P. Mertikopoulos: Optimization in Open Networks via Dual Averaging, 60th IEEE Annual Conference on Decision and Control (CDC), 2021.
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11- M. Chastan, A. Lam, F. Iutzeler: Unsupervised density based machine learning for abnormal leveling signatures detection, SPIE Advanced Lithography, 2021.

10- D. Grishchenko, F. Iutzeler, M.-R. Amini: Sparse Asynchronous Distributed Learning, 27-th International Conference on Neural Information Processing (ICONIP), Online, November 2020.
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9- D. Grishchenko, F.. Iutzeler, J. Malick: Distributed First-order Optimization with Tamed Communications, Signal Processing with Adaptive Sparse Structured Representations (SPARS workshop), Toulouse (France), July 2019.
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8- B. Joshi, F. Iutzeler, M.-R. Amini: Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization, 10-th ACM Conference on Recommender Systems (RecSys), Boston (USA), Sept. 2016.

7- F. Iutzeler, P. Bianchi, P. Ciblat and W. Hachem: Linear Convergence Rate for Distributed Optimization with the Alternating Direction Method of Multipliers, 53-rd IEEE Conference on Decision and Control (CDC), Los Angeles (USA), December 2014.
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6- P. Bianchi, W. Hachem and F. Iutzeler: A Stochastic Primal-Dual algorithm for Distributed Asynchronous Composite Optimization, 2-nd IEEE Global Conference on Signal and Information Processing (GlobalSip), Atlanta (USA), December 2014.
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5- P. Bianchi, W. Hachem, and F. Iutzeler : A Stochastic Coordinate Descent Primal-Dual Algorithm And Applications , 24-th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Reims (France), September 2014.
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4- F. Iutzeler , P. Bianchi, P. Ciblat and W. Hachem: Asynchronous Distributed Optimization using a Randomized Alternating Direction Method of Multipliers, 52-nd IEEE Conference on Decision and Control (CDC), Florence (Italy), December 2013.
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3- F. Iutzeler and P. Ciblat: Fully-distributed spectrum sensing: application to cognitive radio, 21-st European Signal Processing Conference (EUSIPCO), Marrakech (Morocco), September 2013.
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2- F. Iutzeler, P. Ciblat, W. Hachem, and J. Jakubowicz : A new broadcast based averaging algorithm over wireless sensor networks, 37-th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto (Japan), March 2012.
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1- F. Iutzeler, J. Jakubowicz, W. Hachem and P. Ciblat : Distributed estimation of the maximum value over a wireless sensor network, 45-th Asilomar Conference on Signals, Systems, and Computer, Pacific Grove (USA), November 2011.
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– F. Iutzeler: Harnessing the Structure of some Optimization Problems , Habilitation Thesis, Univ. Grenoble Alpes, defended December 15th, 2021.
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– F. Iutzeler: Distributed Estimation and Optimization in Asynchronous Networks , Ph.D. Thesis, Telecom ParisTech, defended December 6th, 2013.
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– J.-F. Boulanger, F. Corset, F. Iutzeler, J. Lelong : Classifying and explaining defects with small data for the semiconductor industry , exposition of an industrial collaboration, to appear in MathS in Action, 2022.
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– C. Laclau, F. Iutzeler, I. Redko : Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy , unpublished note, 2020.
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