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Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications
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Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications

Rajeeva Laxman Karandikar
M. Vidyasagar
ArXiv (abs)PDFHTML

Papers citing "Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications"

7 / 7 papers shown
Title
Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters
Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters
Mathukumalli Vidyasagar
68
0
0
13 Jun 2025
Statistical Inference for Online Algorithms
Statistical Inference for Online Algorithms
Selina Carter
Arun K Kuchibhotla
60
1
0
22 May 2025
Revisiting Stochastic Approximation and Stochastic Gradient Descent
Revisiting Stochastic Approximation and Stochastic Gradient Descent
Rajeeva Laxman Karandikar
Bhamidi Visweswara Rao
Mathukumalli Vidyasagar
74
0
0
16 May 2025
Towards Weaker Variance Assumptions for Stochastic Optimization
Towards Weaker Variance Assumptions for Stochastic Optimization
Ahmet Alacaoglu
Yura Malitsky
Stephen J. Wright
82
1
0
14 Apr 2025
A quantitative Robbins-Siegmund theorem
A quantitative Robbins-Siegmund theorem
Morenikeji Neri
Thomas Powell
31
3
0
21 Oct 2024
Stochastic approximation in infinite dimensions
Stochastic approximation in infinite dimensions
Rajeeva Laxman Karandikar
Bhamidi Visweswara Rao
44
0
0
27 Feb 2024
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning
Rajeeva Laxman Karandikar
M. Vidyasagar
73
0
0
08 Sep 2021