Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize underlying NER model. Both of these traditional objective for problem generally produce adequate performance when data distribution is balanced and there are sufficient annotated training examples. But since inherently an imbalanced tagging...

Stochastic Gradient Descent has been widely studied with classification accuracy as a performance measure. However, these stochastic algorithms are not applicable when non-decomposable pairwise measures used, such Area under the ROC curve (AUC), standard metric used classes imbalanced. Several have proposed for optimizing AUC metric, one of recent being Proximal Algorithm (SPAM). downside gradi...

Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train machine model by multiple participants. The prior work on FL mostly studied how protect label privacy during training. However, evaluation in might also lead the potential leakage of private information. In this work, we propose an algorithm that can accurately compute widely used AUC ...

The use of machine learning models in consequential decision making often exacerbates societal inequity, particular yielding disparate impact on members marginalized groups defined by race and gender. area under the ROC curve (AUC) is widely used to evaluate performance a scoring function learning, but studied algorithmic fairness less than other metrics. Due pairwise nature AUC, defining an AU...