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What is Stochastic Gradient Methods for SIP about?

This paper explores the use of Stochastic Gradient Descent (SGD) algorithms for solving Statistical Inverse Problems (SIP) in a linear setting, providing theoretical guarantees for excess risk performance. The authors propose a novel method that combines SGD with machine learning techniques to enhance empirical performance, particularly in Functional Linear Regression models. The paper includes theoretical results, algorithmic developments, and numerical examples to demonstrate the effectiveness of the prop

Author

vutheanh2001bk

Language

EN