New Measure

Research on new measures focuses on developing quantitative metrics to assess diverse aspects of data and models, addressing limitations of existing methods. Current efforts concentrate on improving evaluation of machine learning models (e.g., for robustness, fairness, and hallucination), analyzing complex data structures (e.g., time series, graphs, and measures), and enhancing the interpretability and efficiency of algorithms. These advancements are crucial for improving the reliability, trustworthiness, and applicability of machine learning across various domains, from healthcare and autonomous systems to natural language processing and communication networks.