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Systematic Testing of the Data-Poisoning Robustness of KNN
In other words, they cannot falsify the truly-non-robust cases.
Certifying the Fairness of KNN in the Presence of Dataset Bias
We propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption that the training data may have historical bias caused by systematic mislabeling of samples from a protected minority group.
Single Reference Image based Scene Relighting via Material Guided Filtering
We propose a novel outdoor scene relighting method, which needs only a single reference image and is based on material constrained layer decomposition.
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