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Stochastic Extragradient: General Analysis and Improved Rates


Nov 16, 2021
Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou

* 50 pages, 3 figures, 2 tables 

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Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize


Nov 01, 2021
Ryan D'Orazio, Nicolas Loizou, Issam Laradji, Ioannis Mitliagkas


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Extragradient Method: $O(1/K)$ Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity


Oct 08, 2021
Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel

* 48 pages 

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Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity


Jun 30, 2021
Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien

* 35 pages, 3 figures, 1 table 

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On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging


Jun 30, 2021
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan


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AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods


Feb 19, 2021
Zheng Shi, Nicolas Loizou, Peter Richt├írik, Martin Tak├í─Ź


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Stochastic Hamiltonian Gradient Methods for Smooth Games


Jul 08, 2020
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

* ICML 2020 - Proceedings of the 37th International Conference on Machine Learning 

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Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization


Jun 20, 2020
Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik


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SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation


Jun 19, 2020
Robert M. Gower, Othmane Sebbouh, Nicolas Loizou

* 32 pages 

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A Unified Theory of Decentralized SGD with Changing Topology and Local Updates


Mar 23, 2020
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich


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Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence


Feb 24, 2020
Nicolas Loizou, Sharan Vaswani, Issam Laradji, Simon Lacoste-Julien

* 27 pages, 5 figures 

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Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip


Sep 26, 2019
Nicolas Loizou

* PhD Thesis, University of Edinburgh, 2019 

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Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols


Jun 03, 2019
Nicolas Loizou, Peter Richtárik

* 44 pages, 12 figures 

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Convergence Analysis of Inexact Randomized Iterative Methods


Mar 19, 2019
Nicolas Loizou, Peter Richtárik

* 29 pages, 4 figures, 4 tables 

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SGD: General Analysis and Improved Rates


Jan 27, 2019
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtarik

* 22 pages 

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A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion


Jan 27, 2019
Filip Hanzely, Jakub Kone─Źn├Ż, Nicolas Loizou, Peter Richt├írik, Dmitry Grishchenko

* NeurIPS 2018, Privacy Preserving Machine Learning Workshop (camera ready version). The full-length paper, which includes a number of additional algorithms and results (including proofs of statements and experiments), is available in arXiv:1706.07636 

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Stochastic Gradient Push for Distributed Deep Learning


Nov 27, 2018
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael Rabbat


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Provably Accelerated Randomized Gossip Algorithms


Oct 31, 2018
Nicolas Loizou, Michael Rabbat, Peter Richtárik


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Accelerated Gossip via Stochastic Heavy Ball Method


Sep 23, 2018
Nicolas Loizou, Peter Richtárik

* 8 pages, 5 Figures, 56th Annual Allerton Conference on Communication, Control, and Computing, 2018 

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Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods


Mar 28, 2018
Nicolas Loizou, Peter Richtárik

* 47 pages, 7 figures, 7 tables 

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Linearly convergent stochastic heavy ball method for minimizing generalization error


Dec 23, 2017
Nicolas Loizou, Peter Richtárik

* NIPS 2017, Workshop on Optimization for Machine Learning (camera ready version) 

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