Nicolas Loizou

According to our database, Nicolas Loizou authored at least 37 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-Max Optimization.

Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada

IEEE Control. Syst. Lett., 2024

Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance.

Dimitris Oikonomou, Nicolas Loizou

CoRR, 2024

Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad.

Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov

CoRR, 2024

Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates.

Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities.

Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

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

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

J. Optim. Theory Appl., November, 2023

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods.

Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác

Trans. Mach. Learn. Res., 2023

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

Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas

Trans. Mach. Learn. Res., 2023

Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes.

Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich

CoRR, 2023

Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions.

Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games.

Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods.

Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution.

Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Stochastic Extragradient: General Analysis and Improved Rates.

Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

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

Nicolas Loizou, Peter Richtárik

IEEE Trans. Inf. Theory, 2021

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

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

CoRR, 2021

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods.

Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác

CoRR, 2021

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

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

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

Robert M. Gower, Othmane Sebbouh, Nicolas Loizou

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Convergence Analysis of Inexact Randomized Iterative Methods.

Nicolas Loizou, Peter Richtárik

SIAM J. Sci. Comput., 2020

Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods.

Nicolas Loizou, Peter Richtárik

Comput. Optim. Appl., 2020

Stochastic Hamiltonian Gradient Methods for Smooth Games.

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

Proceedings of the 37th International Conference on Machine Learning, 2020

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

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

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

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

Nicolas Loizou

CoRR, 2019

SGD: General Analysis and Improved Rates.

Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik

CoRR, 2019

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion.

Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko

CoRR, 2019

SGD with Arbitrary Sampling: General Analysis and Improved Rates.

Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin

Proceedings of the 36th International Conference on Machine Learning, 2019

Stochastic Gradient Push for Distributed Deep Learning.

Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat

Proceedings of the 36th International Conference on Machine Learning, 2019

Provably Accelerated Randomized Gossip Algorithms.

Nicolas Loizou, Michael G. Rabbat, Peter Richtárik

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Accelerated Gossip via Stochastic Heavy Ball Method.

Nicolas Loizou, Peter Richtárik

Proceedings of the 56th Annual Allerton Conference on Communication, 2018

2017

Linearly convergent stochastic heavy ball method for minimizing generalization error.

Nicolas Loizou, Peter Richtárik

CoRR, 2017

2016

Distributionally Robust Games with Risk-averse Players.

Nicolas Loizou

Proceedings of 5th the International Conference on Operations Research and Enterprise Systems (ICORES 2016), 2016

A new perspective on randomized gossip algorithms.

Nicolas Loizou, Peter Richtárik

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

2015

Distributionally Robust Game Theory.

Nicolas Loizou

CoRR, 2015