On Almost Sure Convergence Rates of Stochastic Gradient Methods

On Almost Sure Convergence Rates of Stochastic Gradient Methods

9 February 2022

Jun Liu

Papers citing "On Almost Sure Convergence Rates of Stochastic Gradient Methods"

19 / 19 papers shown

Title
Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization Corrado Coppola Lorenzo Papa Irene Amerini L. Palagi ODL 76 0 0 24 Nov 2024
A quantitative Robbins-Siegmund theorem Morenikeji Neri Thomas Powell 21 2 0 21 Oct 2024
On the SAGA algorithm with decreasing step Luis Fredes Bernard Bercu Eméric Gbaguidi 21 1 0 02 Oct 2024
Almost sure convergence rates of stochastic gradient methods under gradient domination Simon Weissmann Sara Klein Waïss Azizian Leif Döring 34 3 0 22 May 2024
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications R. Karandikar M. Vidyasagar 25 8 0 05 Dec 2023
From Optimization to Control: Quasi Policy Iteration Mohammad Amin Sharifi Kolarijani Peyman Mohajerin Esfahani 19 2 0 18 Nov 2023
The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning Nikhil Ghosh Spencer Frei Wooseok Ha Ting Yu MLT 32 3 0 06 Aug 2023
Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case Meixuan He Yuqing Liang Jinlan Liu Dongpo Xu 15 8 0 20 Jul 2023
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays Adrian Redder Arunselvan Ramaswamy Holger Karl 13 1 0 11 May 2023
High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance Krishnakumar Balasubramanian Promit Ghosal Ye He 28 5 0 03 Apr 2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim Kaiwen Wu Jisu Oh J. Gardner BDL 15 7 0 18 Mar 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression Bhavya Agrawalla Krishnakumar Balasubramanian Promit Ghosal 23 2 0 20 Feb 2023
Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption Jun Liu Ye Yuan ODL 9 1 0 15 Feb 2023
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity Youssef Allouah Sadegh Farhadkhani R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 27 49 0 03 Feb 2023
Convergence of Batch Updating Methods with Approximate Gradients and/or Noisy Measurements: Theory and Computational Results Tadipatri Uday M. Vidyasagar 11 0 0 12 Sep 2022
Emergent specialization from participation dynamics and multi-learner retraining Sarah Dean Mihaela Curmei Lillian J. Ratliff Jamie Morgenstern Maryam Fazel 16 5 0 06 Jun 2022
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning R. Karandikar M. Vidyasagar 16 0 0 08 Sep 2021
New Convergence Aspects of Stochastic Gradient Algorithms Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik K. Scheinberg Martin Takáč Marten van Dijk 23 65 0 10 Nov 2018
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 99 570 0 08 Dec 2012