Monotonicity Constraint

Monotonicity constraints in machine learning focus on ensuring that model outputs change predictably (e.g., increase or decrease) with changes in specific input features, enhancing interpretability and trustworthiness. Current research explores incorporating these constraints into various architectures, including neural networks (e.g., using modified activation functions or specialized training algorithms like LipVor) and Gaussian processes, often addressing challenges related to certification, optimization, and scalability in high-dimensional spaces. This work is significant because it improves model explainability and reliability, particularly in sensitive applications like healthcare and finance where predictable relationships between inputs and outputs are crucial.