11 citations · 35 across the 10 of their papers we have counts for
8 papers · 1 filter
Strategic Feature Selection
Jivat Neet Kaur, Pratik Patil, Divya Shanmugam +6
When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The ty…
Test-time augmentation improves efficiency in conformal prediction
Divya Shanmugam, Helen Lu, Swami Sankaranarayanan +1
A conformal classifier produces a set of predicted classes and provides a probabilistic guarantee that the set includes the true class. Unfortunately, it is often the case that con…
Evaluating multiple models using labeled and unlabeled data
Divya Shanmugam, Shuvom Sadhuka, Manish Raghavan +3
It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain,…
Generative AI in Medicine
Divya Shanmugam, Monica Agrawal, Rajiv Movva +4
The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinic…
Learning Disease Progression Models That Capture Health Disparities
Erica Chiang, Divya Shanmugam, Ashley N. Beecy +4
Disease progression models are widely used to inform the diagnosis and treatment of many progressive diseases. However, a significant limitation of existing models is that they do…
Machine Learning for Health symposium 2023 -- Findings track
Stefan Hegselmann, Antonio Parziale, Divya Shanmugam +5
A collection of the accepted Findings papers that were presented at the 3rd Machine Learning for Health symposium (ML4H 2023), which was held on December 10, 2023, in New Orleans,…