Exact Enforcement

Exact enforcement in machine learning and related fields focuses on rigorously imposing constraints or desired properties onto models and algorithms, aiming for improved accuracy, fairness, and efficiency. Current research explores diverse approaches, including optimization frameworks for enforcing consistency in generative AI and 3D modeling, novel algorithms to guarantee fairness constraints in machine learning, and techniques for imposing hard constraints on neural network outputs. This work is significant because it addresses critical limitations of existing methods, leading to more reliable, robust, and ethically sound models with applications ranging from regulatory compliance to scientific computing.