Finite-Sample Guarantees for Forward-Backward Operator Methods in AI

This ArXiv paper explores theoretical guarantees for data-driven optimization methods. The focus on finite-sample guarantees is crucial for practical applications where data is limited.

• •Investigates the theoretical properties of forward-backward operator methods.
• •Provides finite-sample guarantees, relevant for real-world scenarios.
• •Published on ArXiv, indicating early-stage research or pre-print.