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Nicolas Loizou
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2020 – today
- 2024
- [j7]Tianqi Zheng
, Nicolas Loizou, Pengcheng You
Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-Max Optimization. IEEE Control. Syst. Lett. 8: 2009-2014 (2024) - [c20]Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou:
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. AISTATS 2024: 3682-3690 - [c19]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 - [i33]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 abs/2403.02648 (2024) - [i32]Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou:
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. CoRR abs/2403.07148 (2024) - [i31]Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada:
Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization. CoRR abs/2403.09090 (2024) - [i30]Dimitris Oikonomou, Nicolas Loizou:
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance. CoRR abs/2406.04142 (2024) - 2023
- [j6]Ahmed Khaled
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. J. Optim. Theory Appl. 199(2): 499-540 (2023) - [j5]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) - [j4]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) - [c18]Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou:
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. AISTATS 2023: 172-235 - [c17]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 - [c16]Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou:
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions. NeurIPS 2023 - [i29]Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou:
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions. CoRR abs/2302.14043 (2023) - [i28]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 abs/2306.05100 (2023) - [i27]Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich:
Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes. CoRR abs/2307.06306 (2023) - 2022
- [c15]Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel:
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. AISTATS 2022: 366-402 - [c14]Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou:
Stochastic Extragradient: General Analysis and Improved Rates. AISTATS 2022: 7865-7901 - [c13]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: 9793-9826 - [c12]Antonio Orvieto, Simon Lacoste-Julien, Nicolas Loizou:
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution. NeurIPS 2022 - [i26]Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou:
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. CoRR abs/2202.07262 (2022) - [i25]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 abs/2206.05825 (2022) - 2021
- [j3]Nicolas Loizou
Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. IEEE Trans. Inf. Theory 67(12): 8300-8324 (2021) - [c11]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: 1306-1314 - [c10]Robert M. Gower, Othmane Sebbouh, Nicolas Loizou:
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. AISTATS 2021: 1315-1323 - [c9]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: 19095-19108 - [i24]Zheng Shi, Nicolas Loizou, Peter Richtárik
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR abs/2102.09700 (2021) - [i23]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 abs/2107.00052 (2021) - [i22]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 abs/2107.00464 (2021) - [i21]Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel:
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity. CoRR abs/2110.04261 (2021) - [i20]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 abs/2110.15412 (2021) - [i19]Eduard Gorbunov, Hugo Berard, Gauthier Gidel, Nicolas Loizou:
Stochastic Extragradient: General Analysis and Improved Rates. CoRR abs/2111.08611 (2021) - 2020
- [j2]Nicolas Loizou
Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods. Comput. Optim. Appl. 77(3): 653-710 (2020) - [j1]Nicolas Loizou, Peter Richtárik
Convergence Analysis of Inexact Randomized Iterative Methods. SIAM J. Sci. Comput. 42(6): A3979-A4016 (2020) - [c8]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: 5381-5393 - [c7]Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas:
Stochastic Hamiltonian Gradient Methods for Smooth Games. ICML 2020: 6370-6381 - [i18]Nicolas Loizou, Sharan Vaswani, Issam H. Laradji, Simon Lacoste-Julien:
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. CoRR abs/2002.10542 (2020) - [i17]Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR abs/2003.10422 (2020) - [i16]Robert M. Gower, Othmane Sebbouh, Nicolas Loizou:
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. CoRR abs/2006.10311 (2020) - [i15]Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. CoRR abs/2006.11573 (2020) - [i14]Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas:
Stochastic Hamiltonian Gradient Methods for Smooth Games. CoRR abs/2007.04202 (2020)
2010 – 2019
- 2019
- [c6]Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
Provably Accelerated Randomized Gossip Algorithms. ICASSP 2019: 7505-7509 - [c5]Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat:
Stochastic Gradient Push for Distributed Deep Learning. ICML 2019: 344-353 - [c4]Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin:
SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML 2019: 5200-5209 - [i13]Filip Hanzely
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. CoRR abs/1901.09367 (2019) - [i12]Robert Mansel Gower, Nicolas Loizou, Xun Qian
SGD: General Analysis and Improved Rates. CoRR abs/1901.09401 (2019) - [i11]Nicolas Loizou, Peter Richtárik
Convergence Analysis of Inexact Randomized Iterative Methods. CoRR abs/1903.07971 (2019) - [i10]Nicolas Loizou, Peter Richtárik
Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. CoRR abs/1905.08645 (2019) - [i9]Nicolas Loizou:
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip. CoRR abs/1909.12176 (2019) - 2018
- [c3]Nicolas Loizou, Peter Richtárik
Accelerated Gossip via Stochastic Heavy Ball Method. Allerton 2018: 927-934 - [i8]Nicolas Loizou, Peter Richtárik
Accelerated Gossip via Stochastic Heavy Ball Method. CoRR abs/1809.08657 (2018) - [i7]Nicolas Loizou, Michael G. Rabbat, Peter Richtárik
Provably Accelerated Randomized Gossip Algorithms. CoRR abs/1810.13084 (2018) - [i6]Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat:
Stochastic Gradient Push for Distributed Deep Learning. CoRR abs/1811.10792 (2018) - 2017
- [i5]Nicolas Loizou, Peter Richtárik
Linearly convergent stochastic heavy ball method for minimizing generalization error. CoRR abs/1710.10737 (2017) - [i4]Nicolas Loizou, Peter Richtárik
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods. CoRR abs/1712.09677 (2017) - 2016
- [c2]Nicolas Loizou, Peter Richtárik:
A new perspective on randomized gossip algorithms. GlobalSIP 2016: 440-444 - [c1]Nicolas Loizou:
Distributionally Robust Games with Risk-averse Players. ICORES 2016: 186-196 - [i3]Nicolas Loizou:
Distributionally Robust Games with Risk-averse Players. CoRR abs/1610.00651 (2016) - [i2]Nicolas Loizou, Peter Richtárik:
A New Perspective on Randomized Gossip Algorithms. CoRR abs/1610.04714 (2016) - 2015
- [i1]Nicolas Loizou:
Distributionally Robust Game Theory. CoRR abs/1512.03253 (2015)
Coauthor Index
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last updated on 2024-08-23 18:26 CEST by the dblp team
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