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Algorithmic Bias in Machine Learning Based Delirium Prediction

Although prediction models for delirium, a commonly occurring condition during general hospitalization or post-surgery, have not gained …

Sandhya Tripathi, Bradley A Fritz, Michael S Avidan, Yixin Chen, Christopher R King

Interpretable feature subset selection: A Shapley value based approach

While performing Feature Subset Selection (FSS) to identify important features, a weight is assigned to each feature that is not …

Sandhya Tripathi, N Hemachandra, Prashant Trivedi

Attribute Noise Robust Binary Classification (Student Abstract)

We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, …

Aditya Petety, Sandhya Tripathi, N Hemachandra

Cost Sensitive Learning in the Presence of Symmetric Label Noise

In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric …

Sandhya Tripathi, N Hemachandra

Scalable linear classifiers based on exponential loss function

We first propose an empirical risk minimization based binary classi- fication algorithm, ExpERM, with exponential function as surrogate …

Sandhya Tripathi, N Hemachandra