Non-asymptotic Analysis of Biased Stochastic Approximation Scheme

2 February 2019

Papers citing "Non-asymptotic Analysis of Biased Stochastic Approximation Scheme"

31 / 31 papers shown

Title
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation Sobihan Surendran Antoine Godichon-Baggioni Adeline Fermanian Sylvain Le Corff 45 1 0 05 Feb 2024
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications Jie Hu Vishwaraj Doshi Do Young Eun 40 4 0 17 Jan 2024
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems Céline Comte Matthieu Jonckheere J. Sanders Albert Senen-Cerda 30 0 0 05 Dec 2023
Zero-Regret Performative Prediction Under Inequality Constraints Wenjing Yan Xuanyu Cao 28 7 0 22 Sep 2023
Towards frugal unsupervised detection of subtle abnormalities in medical imaging Geoffroy Oudoumanessah Carole Lartizien M. Dojat Florence Forbes MedIm 25 2 0 04 Sep 2023
State and parameter learning with PaRIS particle Gibbs Gabriel Victorino Cardoso Yazid Janati Sylvain Le Corff Eric Moulines Jimmy Olsson 36 6 0 02 Jan 2023
When Do Curricula Work in Federated Learning? Saeed Vahidian Sreevatsank Kadaveru Woo-Ram Baek Weijia Wang Vyacheslav Kungurtsev Chong Chen M. Shah Bill Lin FedML 42 11 0 24 Dec 2022
A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences Han Shen Tianyi Chen 57 15 0 21 Jun 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh Jacob R. Gardner Adji Bousso Dieng Hongseok Kim BDL 37 8 0 13 Jun 2022
Learning from time-dependent streaming data with online stochastic algorithms Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 42 3 0 25 May 2022
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 34 6 0 13 Dec 2021
Online Estimation and Optimization of Utility-Based Shortfall Risk Vishwajit Hegde Arvind S. Menon L. A. Prashanth Krishna Jagannathan 24 2 0 16 Nov 2021
Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo Ziang Niu J. Meier F. Briol 42 12 0 22 Jun 2021
Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise Thinh T. Doan 24 15 0 04 Apr 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions Arnulf Jentzen Adrian Riekert MLT 40 13 0 01 Apr 2021
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes Steffen Dereich Sebastian Kassing 34 27 0 16 Feb 2021
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm G. Fort Eric Moulines Hoi-To Wai TPM 27 7 0 30 Nov 2020
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance Thinh T. Doan 14 45 0 03 Nov 2020
Non-convergence of stochastic gradient descent in the training of deep neural networks Patrick Cheridito Arnulf Jentzen Florian Rossmannek 19 37 0 12 Jun 2020
Non-asymptotic Convergence Analysis of Two Time-scale (Natural) Actor-Critic Algorithms Tengyu Xu Zhe Wang Yingbin Liang 26 57 0 07 May 2020
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms Tengyu Xu Zhe Wang Yingbin Liang 27 25 0 27 Apr 2020
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration Wenlong Mou C. J. Li Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 33 75 0 09 Apr 2020
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation Arnulf Jentzen Timo Welti 27 15 0 03 Mar 2020
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning Yifan Hu Siqi Zhang Xin Chen Niao He ODL 41 55 0 25 Feb 2020
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation Shuhang Chen Adithya M. Devraj Ana Bušić Sean P. Meyn 21 31 0 07 Feb 2020
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation Thinh T. Doan 21 36 0 23 Dec 2019
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments A. F. Vidal Valentin De Bortoli Marcelo Pereyra Alain Durmus 26 7 0 26 Nov 2019
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods Belhal Karimi Hoi-To Wai Eric Moulines M. Lavielle 32 27 0 28 Oct 2019
Full error analysis for the training of deep neural networks C. Beck Arnulf Jentzen Benno Kuckuck 22 47 0 30 Sep 2019
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation Gang Wang Bingcong Li G. Giannakis 33 28 0 10 Sep 2019
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 79 317 0 18 Feb 2014