Accuracy
Fair Classification with Adversarial Perturbations
In these cases, protected attributes can be "imputed" [
44e207aecc63505eb828d442de03f2e9-Paper.pdf
Our main contribution is an optimization framework to learn fair classifiers in this adversarial setting that comes with provable guarantees on accuracy and fairness.
Typicalness-Aware Learning for Failure Detection
Typicalness-A ware Learning (T AL) to address this issue and improve failure detection performance.
39717429762da92201a750dd03386920-Supplemental-Conference.pdf
Previous structural inference methods, such as NRI, fNRI and ACD, are good at eliminatingAU intheinference results. However,asshowninFigure 3,thesemethodsmayfalsely reconstruct the structure with indirect connections. Itisinteresting that the indirect connections resulted from the transmission of signals between nodes. However,this does not conform tothe future state prediction. Yet node1 can only affect node3 through node2, which results in a superposition of functions: f(f()). B.1 ImplementationdetailsofiSIDG We summarize the described architecture of iSIDG and present the pipeline of training iSIDG in Algorithm1.
CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning
Motivated by this success, we introduce CADet (Contrastive Anomaly Detection), a novel method for OOD detection of single samples. CADet draws inspiration from MMD, but leverages the similarity between contrastive transformations of a same sample.