Tight Analyses for Non-Smooth Stochastic Gradient Descent

Tight Analyses for Non-Smooth Stochastic Gradient Descent

13 December 2018

Nicholas J. A. Harvey

Christopher Liaw

Sikander Randhawa

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Papers citing "Tight Analyses for Non-Smooth Stochastic Gradient Descent"

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Title
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis Zhijie Chen Qiaobo Li A. Banerjee FedML 103 0 0 11 Nov 2024
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees Aleksandar Armacki Shuhua Yu Pranay Sharma Gauri Joshi Dragana Bajović D. Jakovetić S. Kar 105 2 0 17 Oct 2024
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better En-hao Liu Junyi Zhu Zinan Lin Xuefei Ning Shuaiqi Wang ... Sergey Yekhanin Guohao Dai Huazhong Yang Yu Wang Yu Wang MoMe 165 4 0 02 Apr 2024
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality Ziyang Wei Wanrong Zhu Wei Biao Wu 125 5 0 13 Jul 2023
Introduction to Online Convex Optimization Elad Hazan OffRL 198 1,940 0 07 Sep 2019
Privacy Amplification by Iteration Vitaly Feldman Ilya Mironov Kunal Talwar Abhradeep Thakurta FedML 86 177 0 20 Aug 2018
Potential-Function Proofs for First-Order Methods N. Bansal Anupam Gupta 50 29 0 13 Dec 2017
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 331 1,250 0 10 Sep 2013
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark Schmidt Francis R. Bach 190 261 0 10 Dec 2012
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 164 576 0 08 Dec 2012
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin Ohad Shamir Karthik Sridharan 188 769 0 26 Sep 2011