Publications

Multi-View Representation Learning to Schema Match Databases

An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and …

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

A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues

Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare. …

Mohamed Abdelhack, Jiaming Zhang, Sandhya Tripathi, Bradley Fritz, Daniel Felsky, Michael Avidan, Yixin Chen, Christopher Ryan King

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

Deep Learning to Jointly Schema Match, Impute, and Transform Databases

An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and …

Sandhya Tripathi, Bradley A Fritz, Mohamed Abdelhack, 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

Equilibrium points and equilibrium sets of some GI/M/1 queues

Queues can be seen as a service facility where quality of service (QoS) is an important measure for the performance of the system. In …

N Hemachandra, Kishor Patil, Sandhya Tripathi

(Un) fairness in Post-operative Complication Prediction Models

With the current ongoing debate about fairness, explainability and transparency of machine learning models, their application in …

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

GANs for learning from very high class conditional noisy labels

We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification. …

Sandhya Tripathi, N Hemachandra

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