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Online Covariance Matrix Estimation in Stochastic Gradient Descent

10 February 2020

Papers citing "Online Covariance Matrix Estimation in Stochastic Gradient Descent"

33 / 33 papers shown

Title
Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA Syamantak Kumar Shourya Pandey Purnamrita Sarkar 17 0 0 14 Jun 2025
Statistical Inference for Online Algorithms Selina Carter Arun K Kuchibhotla 60 1 0 22 May 2025
Smoothed SGD for quantiles: Bahadur representation and Gaussian approximation Likai Chen Georg Keilbar Wei Biao Wu 45 0 0 19 May 2025
Sharp Gaussian approximations for Decentralized Federated Learning Soham Bonnerjee Sayar Karmakar Wei Biao Wu FedML 83 0 0 12 May 2025
Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent Ziyang Wei Jiaqi Li Likai Chen Wei Biao Wu 114 0 0 04 Mar 2025
Online Covariance Estimation in Nonsmooth Stochastic Approximation L. Jiang Abhishek Roy Krishna Balasubramanian Damek Davis Dmitriy Drusvyatskiy Sen Na 119 1 0 07 Feb 2025
Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation Chuhan Xie Kaicheng Jin Jiadong Liang Zhihua Zhang 47 0 0 19 Oct 2024
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize Tong Zhang Chris Junchi Li 76 0 0 15 Jul 2024
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data Xuxing Chen Abhishek Roy Yifan Hu Krishnakumar Balasubramanian 90 1 0 29 May 2024
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning S. Samsonov Eric Moulines Qi-Man Shao Zhuo-Song Zhang Alexey Naumov 97 5 0 26 May 2024
Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent Holger Dette Carina Graw 55 0 0 21 May 2024
A Full Adagrad algorithm with O(Nd) operations Antoine Godichon-Baggioni Wei Lu Bruno Portier ODL 136 0 0 03 May 2024
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques Xuetong Li Yuan Gao Hong Chang Danyang Huang Yingying Ma ... Ke Xu Jing Zhou Xuening Zhu Yingqiu Zhu Hansheng Wang 68 9 0 17 Mar 2024
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization Wanrong Zhu Zhipeng Lou Ziyang Wei Wei Biao Wu 85 3 0 17 Jan 2024
Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference D. Huo Yudong Chen Qiaomin Xie 64 4 0 18 Dec 2023
SGMM: Stochastic Approximation to Generalized Method of Moments Xiaohong Chen S. Lee Yuan Liao M. Seo Youngki Shin Myunghyun Song 35 6 0 25 Aug 2023
Online covariance estimation for stochastic gradient descent under Markovian sampling Abhishek Roy Krishnakumar Balasubramanian 97 6 0 03 Aug 2023
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality Ziyang Wei Wanrong Zhu Wei Biao Wu 130 5 0 13 Jul 2023
A Central Limit Theorem for Algorithmic Estimator of Saddle Point Abhishek Roy Yian Ma 68 1 0 09 Jun 2023
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality Kejie Tang Weidong Liu Yichen Zhang Xi Chen 50 2 0 28 May 2023
Fairness Uncertainty Quantification: How certain are you that the model is fair? Abhishek Roy P. Mohapatra 59 5 0 27 Apr 2023
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent Rahul Singh A. Shukla Dootika Vats 56 0 0 14 Mar 2023
Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression Bhavya Agrawalla Krishnakumar Balasubramanian Promit Ghosal 107 2 0 20 Feb 2023
Covariance Estimators for the ROOT-SGD Algorithm in Online Learning Yiling Luo X. Huo Y. Mei 62 3 0 02 Dec 2022
Fast Inference for Quantile Regression with Tens of Millions of Observations S. Lee Yuan Liao M. Seo Youngki Shin 107 6 0 29 Sep 2022
Parameter-Parallel Distributed Variational Quantum Algorithm Yun-Fei Niu Shuo Zhang Chen Ding Wansu Bao Heliang Huang 52 4 0 31 Jul 2022
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming Sen Na Michael W. Mahoney 106 9 0 27 May 2022
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning Xiang Li Wenhao Yang Jiadong Liang Zhihua Zhang Michael I. Jordan 135 17 0 29 Dec 2021
An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models Yuan Gao Xuening Zhu Haobo Qi Guodong Li Riquan Zhang Hansheng Wang 71 3 0 02 Nov 2021
Statistical Estimation and Inference via Local SGD in Federated Learning Xiang Li Jiadong Liang Xiangyu Chang Zhihua Zhang FedML 58 4 0 03 Sep 2021
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling S. Lee Yuan Liao M. Seo Youngki Shin 80 32 0 06 Jun 2021
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods Xi Chen Zehua Lai He Li Yichen Zhang 102 16 0 05 Feb 2021
Asymptotic Analysis of Conditioned Stochastic Gradient Descent Rémi Leluc Franccois Portier 75 4 0 04 Jun 2020