Variance Tolerance Factor

Variance tolerance factors (VTFs) quantify the permissible variation in input features while maintaining acceptable model output, addressing the need for robust and interpretable models, particularly in complex systems. Current research focuses on applying VTFs to improve the performance and trustworthiness of various systems, including telemanipulation robots and neural networks, often leveraging techniques like influence functions and mixed-integer linear programming to analyze and optimize model behavior within defined tolerance bounds. This work is significant for enhancing the reliability and explainability of machine learning models across diverse applications, from biometrics to autonomous driving, by explicitly incorporating uncertainty and variability into model design and evaluation.