11 citations · 48 across the 15 of their papers we have counts for
17 papers
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,…
Using large language models to promote health equity
Emma Pierson, Divya Shanmugam, Rajiv Movva +12
Advances in large language models (LLMs) have driven an explosion of interest about their societal impacts. Much of the discourse around how they will impact social equity has been…
Coarse race data conceals disparities in clinical risk score performance
Rajiv Movva, Divya Shanmugam, Kaihua Hou +4
Healthcare data in the United States often records only a patient's coarse race group: for example, both Indian and Chinese patients are typically coded as "Asian." It is unknown,…
At the Intersection of Deep Learning and Conceptual Art: The End of Signature
Divya Shanmugam, Katie Lewis, Jose Javier Gonzalez-Ortiz +2
MIT wanted to commission a large scale artwork that would serve to 'illuminate a new campus gateway, inaugurate a space of exchange between MIT and Cambridge, and inspire our stude…
Improved Text Classification via Test-Time Augmentation
Helen Lu, Divya Shanmugam, Harini Suresh +1
Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classificatio…
Data Augmentation for Electrocardiograms
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev +2
Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to…
Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting
Divya Shanmugam, Kaihua Hou, Emma Pierson
Estimating the prevalence of a medical condition, or the proportion of the population in which it occurs, is a fundamental problem in healthcare and public health. Accurate estimat…
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
Divya Shanmugam, Samira Shabanian, Fernando Diaz +2
Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practice…
Better Aggregation in Test-Time Augmentation
Divya Shanmugam, Davis Blalock, Guha Balakrishnan +1
Test-time augmentation -- the aggregation of predictions across transformed versions of a test input -- is a common practice in image classification. Traditionally, predictions are…
Unsupervised Domain Adaptation in the Absence of Source Data
Roshni Sahoo, Divya Shanmugam, John Guttag
Current unsupervised domain adaptation methods can address many types of distribution shift, but they assume data from the source domain is freely available. As the use of pre-trai…
Image segmentation of liver stage malaria infection with spatial uncertainty sampling
Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz +4
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease. The gold standard in drug development remai…
Multiple Instance Learning for ECG Risk Stratification
Divya Shanmugam, Davis Blalock, John Guttag
Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of…