no code implementations • 8 Nov 2022 • Sandhya Tripathi, Bradley A Fritz, Michael S Avidan, Yixin Chen, Christopher R King
Although prediction models for delirium, a commonly occurring condition during general hospitalization or post-surgery, have not gained huge popularity, their algorithmic bias evaluation is crucial due to the existing association between social determinants of health and delirium risk.
1 code implementation • 22 Jun 2022 • Sandhya Tripathi, Bradley A. Fritz, Mohamed Abdelhack, Michael S. Avidan, Yixin Chen, Christopher R. King
Second, we demonstrate a deep learning algorithm for translation between databases.
1 code implementation • 19 Jul 2021 • Mohamed Abdelhack, Jiaming Zhang, Sandhya Tripathi, Bradley A Fritz, Daniel Felsky, Michael S Avidan, Yixin Chen, Christopher R King
Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare.
no code implementations • 3 Nov 2020 • Sandhya Tripathi, Bradley A. Fritz, Mohamed Abdelhack, Michael S. Avidan, Yixin Chen, Christopher R. King
With the current ongoing debate about fairness, explainability and transparency of machine learning models, their application in high-impact clinical decision-making systems must be scrutinized.
no code implementations • 19 Oct 2020 • Sandhya Tripathi, N Hemachandra
We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification.
no code implementations • 12 Jan 2020 • 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 function and relate a feature's contribution to Shapley value based error apportioning (SVEA) of total training error.
no code implementations • 18 Nov 2019 • Aditya Petety, Sandhya Tripathi, N. Hemachandra
In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that $0$-$1$ loss ($l_{0-1}$) need not be robust but a popular surrogate, squared loss ($l_{sq}$) is.
no code implementations • 8 Jan 2019 • Sandhya Tripathi, N. Hemachandra
Our computational experiments on some UCI datasets with class imbalance show that classifiers of our two schemes are on par with the existing methods and in fact better in some cases w. r. t.