Tag
This paper introduces TrajRS, an extension of Randomized Smoothing that provides certified robust radii for pedestrian trajectory predictors, offering verifiable safety guarantees against adversarial perturbations.
This paper introduces a meta-learning framework for anytime-valid certified robustness that uses sequential E-processes to adaptively allocate compute, achieving a 20-fold reduction in sample complexity compared to traditional randomized smoothing while maintaining rigorous statistical guarantees.
RRISE introduces a learned surrogate estimator that reduces the Monte Carlo sampling cost of randomized smoothing for certified robustness to a single forward pass, maintaining accuracy within 0.84 percentage points while replacing up to 10^4 evaluations per query.
This paper introduces a framework that connects randomized smoothing to differential privacy through privacy profiles, enabling tight provable robustness guarantees against backdoor attacks that jointly affect training and inference. The approach is instantiated for DP-SGD and Deep Partition Aggregation with experiments on MNIST and CIFAR-10.