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Found 3 papers, 1 papers with code
Provably Robust Transfer
Our experiments show how adversarial training on the source domain affects robustness on source and target domain, and we propose the first provably robust transfer learning models.
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-aware models.
Reachable Sets of Classifiers and Regression Models: (Non-)Robustness Analysis and Robust Training
This principle is highly versatile, as we show.
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