  There has been great interest in enhancing the robustness of neural network
classifiers to defend against adversarial perturbations through adversarial
training, while balancing the trade-off between robust accuracy and standard
accuracy. We propose a novel adversarial training framework that learns to
reweight the loss associated with individual training samples based on a notion
of class-conditioned margin, with the goal of improving robust generalization.
We formulate weighted adversarial training as a bilevel optimization problem
with the upper-level problem corresponding to learning a robust classifier, and
the lower-level problem corresponding to learning a parametric function that
maps from a sample's \textit{multi-class margin} to an importance weight.
Extensive experiments demonstrate that our approach consistently improves both
clean and robust accuracy compared to related methods and state-of-the-art
baselines.
