Nicolas Loizou

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 59

Top Topics

Privacy Preserving

Stochastic Gradient

Variational Inequalities

Convergence Analysis

Top Venues

Publications

Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. AISTATS (2024)
Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. CoRR (2024)
Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. CoRR (2024)
Dimitris Oikonomou, Nicolas Loizou
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance. CoRR (2024)
Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada
Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization. CoRR (2024)
Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada
Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-Max Optimization. IEEE Control. Syst. Lett. 8 (2024)
Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. ICLR (2024)
Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions. NeurIPS (2023)
Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. Trans. Mach. Learn. Res. 2023 (2023)
Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. J. Optim. Theory Appl. 199 (2) (2023)
Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. Trans. Mach. Learn. Res. 2023 (2023)
Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. AISTATS (2023)
Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions. CoRR (2023)
Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich
Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes. CoRR (2023)
Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. CoRR (2023)
Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. ICLR (2023)
Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. AISTATS (2022)
Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. CoRR (2022)
Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. CoRR (2022)
Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou
Stochastic Extragradient: General Analysis and Improved Rates. AISTATS (2022)
Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. NeurIPS (2022)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS (2022)
Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. CoRR (2021)
Nicolas Loizou, Peter Richtárik
Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. IEEE Trans. Inf. Theory 67 (12) (2021)
Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. CoRR (2021)
Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. NeurIPS (2021)
Robert M. Gower, Othmane Sebbouh, Nicolas Loizou
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. AISTATS (2021)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR (2021)
Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. CoRR (2021)
Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou
Stochastic Extragradient: General Analysis and Improved Rates. CoRR (2021)
Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. AISTATS (2021)
Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR (2021)
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
Stochastic Hamiltonian Gradient Methods for Smooth Games. ICML (2020)
Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. CoRR (2020)
Nicolas Loizou, Peter Richtárik
Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods. Comput. Optim. Appl. 77 (3) (2020)
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. ICML (2020)
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR (2020)
Nicolas Loizou, Sharan Vaswani, Issam H. Laradji, Simon Lacoste-Julien
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. CoRR (2020)
Robert M. Gower, Othmane Sebbouh, Nicolas Loizou
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. CoRR (2020)
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
Stochastic Hamiltonian Gradient Methods for Smooth Games. CoRR (2020)
Nicolas Loizou, Peter Richtárik
Convergence Analysis of Inexact Randomized Iterative Methods. SIAM J. Sci. Comput. 42 (6) (2020)
Nicolas Loizou
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip. CoRR (2019)
Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
Provably Accelerated Randomized Gossip Algorithms. ICASSP (2019)
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat
Stochastic Gradient Push for Distributed Deep Learning. ICML (2019)
Nicolas Loizou, Peter Richtárik
Convergence Analysis of Inexact Randomized Iterative Methods. CoRR (2019)
Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. CoRR (2019)
Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin
SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML (2019)
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik
SGD: General Analysis and Improved Rates. CoRR (2019)
Nicolas Loizou, Peter Richtárik
Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. CoRR (2019)
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat
Stochastic Gradient Push for Distributed Deep Learning. CoRR (2018)
Nicolas Loizou, Peter Richtárik
Accelerated Gossip via Stochastic Heavy Ball Method. CoRR (2018)
Nicolas Loizou, Peter Richtárik
Accelerated Gossip via Stochastic Heavy Ball Method. Allerton (2018)
Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
Provably Accelerated Randomized Gossip Algorithms. CoRR (2018)
Nicolas Loizou, Peter Richtárik
Linearly convergent stochastic heavy ball method for minimizing generalization error. CoRR (2017)
Nicolas Loizou, Peter Richtárik
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods. CoRR (2017)
Nicolas Loizou
Distributionally Robust Games with Risk-averse Players. ICORES (2016)
Nicolas Loizou, Peter Richtárik
A new perspective on randomized gossip algorithms. GlobalSIP (2016)
Nicolas Loizou, Peter Richtárik
A New Perspective on Randomized Gossip Algorithms. CoRR (2016)
Nicolas Loizou
Distributionally Robust Games with Risk-averse Players. CoRR (2016)
Nicolas Loizou
Distributionally Robust Game Theory. CoRR (2015)