Constraint Compliance

Constraint compliance focuses on developing methods for systems to operate within predefined limitations, whether these are rules, physical laws, or data constraints. Current research explores diverse applications, from improving machine learning model accuracy by incorporating prior knowledge (e.g., using sum constraints in Gaussian processes) to mitigating errors in natural language processing by enforcing API specifications and addressing constraint violations in semi-supervised learning through techniques like pseudo-constraining. This field is crucial for building robust and reliable AI systems and for improving the accuracy and efficiency of simulations in various domains, including textile modeling and business process monitoring.