Interval Arithmetic

Interval arithmetic is a computational approach that represents numbers as intervals, encompassing a range of possible values rather than a single point, thereby providing a means for uncertainty quantification and robust computation. Current research focuses on applying interval arithmetic to diverse fields, including improving the efficiency and accuracy of neural network verification, enhancing the robustness of machine learning explanations, and developing more efficient algorithms for temporal and spatial reasoning problems. This methodology's significance lies in its ability to provide guaranteed bounds and verifiable results, addressing critical challenges in areas such as autonomous systems, AI safety, and reliable machine learning.