Stopping Rules for SGD: Improving Confidence and Efficiency
Published:Dec 15, 2025 09:26
•1 min read
•ArXiv
Analysis
This ArXiv paper introduces stopping rules for Stochastic Gradient Descent (SGD) using Anytime-Valid Confidence Sequences. The research aims to improve the efficiency and reliability of SGD optimization, which is crucial for many machine learning applications.
Key Takeaways
- •Proposes novel stopping rules for SGD.
- •Utilizes Anytime-Valid Confidence Sequences for enhanced performance.
- •Potentially improves the efficiency and reliability of model training.
Reference
“The paper leverages Anytime-Valid Confidence Sequences.”