Robustness Bound

Robustness bounds quantify the resilience of machine learning models, particularly deep neural networks (DNNs) and related architectures like graph convolutional networks (GCNs) and recurrent neural networks (RNNs), to various perturbations such as adversarial attacks, noisy inputs, or data poisoning. Current research focuses on developing tighter and more computationally efficient methods for calculating these bounds, often employing techniques like linear approximation, abstract interpretation, and stochastic simulation. Improved robustness bounds are crucial for deploying machine learning models in safety-critical applications, enabling verifiable guarantees of performance and reliability in the face of uncertainty.