Ahmed Khaled

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 22

Top Topics

Heterogeneous Data

Computationally Costly

Stochastic Gradient

Top Venues

J. Optim. Theory Appl.

Publications

Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower
Directional Smoothness and Gradient Methods: Convergence and Adaptivity. CoRR (2024)
Aaron Defazio, Xingyu Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, Ashok Cutkosky
The Road Less Scheduled. CoRR (2024)
Ahmed Khaled, Peter Richtárik
Better Theory for SGD in the Nonconvex World. 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)
Ahmed Khaled, Konstantin Mishchenko, Chi Jin
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. NeurIPS (2023)
Ahmed Khaled, Chi Jin
Faster federated optimization under second-order similarity. ICLR (2023)
Ahmed Khaled, Konstantin Mishchenko, Chi Jin
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. CoRR (2023)
Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Proximal and Federated Random Reshuffling. ICML (2022)
Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. AISTATS (2022)
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. CoRR (2022)
Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. CoRR (2021)
Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Proximal and Federated Random Reshuffling. CoRR (2021)
Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
Tighter Theory for Local SGD on Identical and Heterogeneous Data. AISTATS (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)
Ahmed Khaled, Amir F. Atiya, Ahmed H. Abdel-Gawad
Applying fast matrix multiplication to neural networks. SAC (2020)
Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Random Reshuffling: Simple Analysis with Vast Improvements. NeurIPS (2020)
Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Random Reshuffling: Simple Analysis with Vast Improvements. CoRR (2020)
Ahmed Khaled, Peter Richtárik
Better Theory for SGD in the Nonconvex World. CoRR (2020)
Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
First Analysis of Local GD on Heterogeneous Data. CoRR (2019)
Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
Better Communication Complexity for Local SGD. CoRR (2019)
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác
Distributed Fixed Point Methods with Compressed Iterates. CoRR (2019)
Ahmed Khaled, Peter Richtárik
Gradient Descent with Compressed Iterates. CoRR (2019)