The Complexity of Finding Stationary Points with Stochastic Gradient Descent

4 October 2019

Papers citing "The Complexity of Finding Stationary Points with Stochastic Gradient Descent"

11 / 11 papers shown

Title
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling Daniil Medyakov Gleb Molodtsov S. Chezhegov Alexey Rebrikov Aleksandr Beznosikov 109 0 0 21 Feb 2025
Generalizing Reward Modeling for Out-of-Distribution Preference Learning Chen Jia 44 2 0 22 Feb 2024
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations Yao Shu Jiongfeng Fang Y. He Fei Richard Yu 35 0 0 18 Feb 2024
Accelerating Value Iteration with Anchoring Jongmin Lee Ernest K. Ryu 24 7 0 26 May 2023
How to escape sharp minima with random perturbations Kwangjun Ahn Ali Jadbabaie S. Sra ODL 34 6 0 25 May 2023
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods Junchi Yang Xiang Li Ilyas Fatkhullin Niao He 47 15 0 21 May 2023
A simplified convergence theory for Byzantine resilient stochastic gradient descent Lindon Roberts E. Smyth 31 3 0 25 Aug 2022
Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization Xia Jiang Xianlin Zeng Jian Sun Jie Chen Lihua Xie 18 6 0 06 Nov 2021
Random Reshuffling: Simple Analysis with Vast Improvements Konstantin Mishchenko Ahmed Khaled Peter Richtárik 37 131 0 10 Jun 2020
Learning Halfspaces with Massart Noise Under Structured Distributions Ilias Diakonikolas Vasilis Kontonis Christos Tzamos Nikos Zarifis 32 59 0 13 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 13 180 0 09 Feb 2020