Significant Dissimilarity

Significant dissimilarity, the degree of difference between data points or models, is a crucial concept across diverse fields, driving research aimed at quantifying and leveraging this difference effectively. Current research focuses on developing robust dissimilarity measures for various data types (e.g., graphs, time series, text, images), often employing techniques like kernel density estimation, adversarial validation, and specialized metrics such as GOSPA for graphs. These advancements are impacting diverse applications, from improving the accuracy and reliability of machine learning models by identifying out-of-domain predictions to enhancing personalized federated learning and mitigating privacy risks in large language models.