Tight Approximation

Tight approximation in machine learning focuses on developing methods to create accurate yet computationally efficient representations of complex models, primarily neural networks. Current research emphasizes improving the precision of over-approximations for verification tasks, particularly concerning robustness and safety, often employing techniques like piecewise linear approximations, tropical geometry, and dual-approximation strategies that leverage both over- and under-approximations. These advancements are crucial for deploying machine learning models in safety-critical applications, enabling formal guarantees on their behavior and enhancing trust in their predictions.