Non Negative Monotone

Non-negative monotone functions and their generalizations are a focus of current research across diverse fields, aiming to develop efficient algorithms for optimization and modeling tasks involving such functions. This includes exploring novel probabilistic circuit architectures, invertible neural networks with certified monotonicity properties, and adaptive gradient methods for online learning in strongly monotone games. These advancements are improving the efficiency and accuracy of solutions in areas like machine learning (e.g., submodular maximization, imputation of missing data), and offer new approaches to problems in answer set programming and probabilistic modeling.