Nicolas Loizou

70 papers A* 13A 7C 1Misc 1Journal 46Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation. CoRR Robert M. Gower, Guillaume Garrigos, Nicolas Loizou, Dimitris Oikonomou, Konstantin Mishchenko, Fabian Schaipp
2025	J	`jnl`	Extragradient Method for (L CoRR Sayantan Choudhury, Nicolas Loizou
2025	J	`jnl`	Multiplayer Federated Learning: Reaching Equilibrium with Less Communication. CoRR Taeho Yoon, Sayantan Choudhury, Nicolas Loizou
2025	J	`jnl`	Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients. CoRR Dimitris Oikonomou, Nicolas Loizou
2025	A*	`conf`	Sharpness-Aware Minimization: General Analysis and Improved Rates. ICLR Dimitris Oikonomou, Nicolas Loizou
2025	J	`jnl`	Sharpness-Aware Minimization: General Analysis and Improved Rates. CoRR Dimitris Oikonomou, Nicolas Loizou
2025	A*	`conf`	Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance. ICLR Dimitris Oikonomou, Nicolas Loizou
2024	A*	`conf`	Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. ICLR Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou
2024	J	`jnl`	Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-Max Optimization. IEEE Control. Syst. Lett. Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada
2024	J	`jnl`	Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization. CoRR Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada
2024	A*	`conf`	Enhancing vision-language models for medical imaging: bridging the 3D gap with innovative slice selection. NeurIPS Yuli Wang, Peng Jian, Yuwei Dai, Craig K. Jones, Haris I. Sair, Jinglai Shen, Nicolas Loizou, Jing Wu, Wen-Chi Hsu, Maliha Imami, Zhicheng Jiao, Paul Zhang, Harrison X. Bai
2024	J	`jnl`	Locally Adaptive Federated Learning. Trans. Mach. Learn. Res. Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich
2024	A*	`conf`	Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. NeurIPS Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov
2024	J	`jnl`	Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. CoRR Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov
2024	A	`conf`	Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. AISTATS Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou
2024	J	`jnl`	Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. CoRR Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou
2024	J	`jnl`	Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance. CoRR Dimitris Oikonomou, Nicolas Loizou
2023	A*	`conf`	A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. ICLR Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
2023	J	`jnl`	AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. Trans. Mach. Learn. Res. Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác
2023	J	`jnl`	Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. CoRR Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou
2023	J	`jnl`	Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes. CoRR Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich
2023	A*	`conf`	Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions. NeurIPS Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou
2023	J	`jnl`	Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions. CoRR Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou
2023	A	`conf`	Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. AISTATS Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou
2023	J	`jnl`	Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. Trans. Mach. Learn. Res. Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas
2023	J	`jnl`	Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. J. Optim. Theory Appl. Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
2022	J	`jnl`	A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. CoRR Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
2022	A*	`conf`	Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. NeurIPS Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou
2022	A	`conf`	Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. AISTATS Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel
2022	A	`conf`	On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2022	A	`conf`	Stochastic Extragradient: General Analysis and Improved Rates. AISTATS Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou
2022	J	`jnl`	Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. CoRR Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou
2021	J	`jnl`	AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác
2021	J	`jnl`	Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. CoRR Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel
2021	J	`jnl`	On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2021	J	`jnl`	Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. IEEE Trans. Inf. Theory Nicolas Loizou, Peter Richtárik
2021	A	`conf`	SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. AISTATS Robert M. Gower, Othmane Sebbouh, Nicolas Loizou
2021	J	`jnl`	Stochastic Extragradient: General Analysis and Improved Rates. CoRR Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou
2021	A*	`conf`	Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. NeurIPS Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien
2021	J	`jnl`	Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity. CoRR Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas, Simon Lacoste-Julien
2021	J	`jnl`	Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize. CoRR Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas
2021	A	`conf`	Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. AISTATS Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien
2020	A*	`conf`	A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. ICML Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
2020	J	`jnl`	A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
2020	J	`jnl`	Convergence Analysis of Inexact Randomized Iterative Methods. SIAM J. Sci. Comput. Nicolas Loizou, Peter Richtárik
2020	J	`jnl`	Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods. Comput. Optim. Appl. Nicolas Loizou, Peter Richtárik
2020	J	`jnl`	SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. CoRR Robert M. Gower, Othmane Sebbouh, Nicolas Loizou
2020	A*	`conf`	Stochastic Hamiltonian Gradient Methods for Smooth Games. ICML Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
2020	J	`jnl`	Stochastic Hamiltonian Gradient Methods for Smooth Games. CoRR Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas
2020	J	`jnl`	Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. CoRR Nicolas Loizou, Sharan Vaswani, Issam H. Laradji, Simon Lacoste-Julien
2020	J	`jnl`	Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. CoRR Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
2019	J	`jnl`	A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. CoRR Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko
2019	J	`jnl`	Convergence Analysis of Inexact Randomized Iterative Methods. CoRR Nicolas Loizou, Peter Richtárik
2019	Misc	`conf`	Provably Accelerated Randomized Gossip Algorithms. ICASSP Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
2019	J	`jnl`	Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip. CoRR Nicolas Loizou
2019	J	`jnl`	Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. CoRR Nicolas Loizou, Peter Richtárik
2019	A*	`conf`	SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin
2019	J	`jnl`	SGD: General Analysis and Improved Rates. CoRR Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik
2019	A*	`conf`	Stochastic Gradient Push for Distributed Deep Learning. ICML Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat
2018	—	`conf`	Accelerated Gossip via Stochastic Heavy Ball Method. Allerton Nicolas Loizou, Peter Richtárik
2018	J	`jnl`	Accelerated Gossip via Stochastic Heavy Ball Method. CoRR Nicolas Loizou, Peter Richtárik
2018	J	`jnl`	Provably Accelerated Randomized Gossip Algorithms. CoRR Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
2018	J	`jnl`	Stochastic Gradient Push for Distributed Deep Learning. CoRR Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat
2017	J	`jnl`	Linearly convergent stochastic heavy ball method for minimizing generalization error. CoRR Nicolas Loizou, Peter Richtárik
2017	J	`jnl`	Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods. CoRR Nicolas Loizou, Peter Richtárik
2016	J	`jnl`	A New Perspective on Randomized Gossip Algorithms. CoRR Nicolas Loizou, Peter Richtárik
2016	—	`conf`	A new perspective on randomized gossip algorithms. GlobalSIP Nicolas Loizou, Peter Richtárik
2016	C	`conf`	Distributionally Robust Games with Risk-averse Players. ICORES Nicolas Loizou
2016	J	`jnl`	Distributionally Robust Games with Risk-averse Players. CoRR Nicolas Loizou
2015	J	`jnl`	Distributionally Robust Game Theory. CoRR Nicolas Loizou