Non Additive

Non-additive measures, encompassing concepts beyond simple sums or averages, are increasingly crucial in diverse fields like machine learning and decision-making. Current research focuses on developing methods for handling and analyzing these measures, including novel metrics for evaluating model performance (e.g., non-monotonic metrics for event camera denoising), robust model architectures (e.g., Vision Transformers with non-additive randomness against adversarial attacks), and algorithms for optimizing non-decomposable objectives in semi-supervised learning (e.g., cost-sensitive self-training). This work is significant because non-additive measures offer more flexible and realistic modeling capabilities than traditional additive approaches, leading to improved accuracy and robustness in various applications.