4 citations · 13 across the 8 of their papers we have counts for
8 papers
Algorithmic Bias in Machine Learning Based Delirium Prediction
Sandhya Tripathi, Bradley A Fritz, Michael S Avidan +2
Although prediction models for delirium, a commonly occurring condition during general hospitalization or post-surgery, have not gained huge popularity, their algorithmic bias eval…
Deep Learning to Jointly Schema Match, Impute, and Transform Databases
Sandhya Tripathi, Bradley A. Fritz, Mohamed Abdelhack +3
An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prere…
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues
Mohamed Abdelhack, Jiaming Zhang, Sandhya Tripathi +5
Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare. Developers often train machine learning models on…
(Un)fairness in Post-operative Complication Prediction Models
Sandhya Tripathi, Bradley A. Fritz, Mohamed Abdelhack +3
With the current ongoing debate about fairness, explainability and transparency of machine learning models, their application in high-impact clinical decision-making systems must b…
GANs for learning from very high class conditional noisy labels
Sandhya Tripathi, N Hemachandra
We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification. It first generates a set of correctly labelle…
Interpretable feature subset selection: A Shapley value based approach
Sandhya Tripathi, N. Hemachandra, Prashant Trivedi
For feature selection and related problems, we introduce the notion of classification game, a cooperative game, with features as players and hinge loss based characteristic functio…
Attribute noise robust binary classification
Aditya Petety, Sandhya Tripathi, N Hemachandra
We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown pr…
Cost Sensitive Learning in the Presence of Symmetric Label Noise
Sandhya Tripathi, N. Hemachandra
In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric label noise. We first observe that - Bay…