David Vävinggren

Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization

Adversarial training has emerged as a key technique to enhance model robustness against adversarial input perturbations. Many of the existing methods rely on computationally expensive min-max problems that limit their application in practice. We propose a novel formulation of adversarial training in…