Consistent Range Approximation

Consistent range approximation (CRA) focuses on developing methods to reliably estimate a range of possible solutions or predictions, rather than a single point estimate, particularly when dealing with uncertainty, bias, or incomplete data. Current research explores CRA within diverse applications, employing techniques like deep learning for generating probability distributions of distances (e.g., in radio frequency localization) and influence functions for approximating full conformal prediction in machine learning. This work is significant because it improves the robustness and reliability of models by explicitly accounting for uncertainty and bias, leading to more trustworthy predictions and fairer decision-making across various fields.