Controllability of nonlinear implicit fractional integrodifferential systems. - Krishnan Balachandran; Divya Shanmugam (Int. J. Appl. Math. Comput. Sci., 2014)

Multiple Instance Learning for ECG Risk Stratification. - Divya Shanmugam; Davis W. Blalock; Jen J. Gong; John V. Guttag (CoRR, 2018)

Multiple Instance Learning for ECG Risk Stratification. - Divya Shanmugam; Davis W. Blalock; John V. Guttag (MLHC, 2019)

Image segmentation of liver stage malaria infection with spatial uncertainty sampling. - Ava P. Soleimany; Harini Suresh; Jose Javier Gonzalez Ortiz; Divya Shanmugam; Nil Gural; John V. Guttag; Sangeeta N. Bhatia (CoRR, 2019)

Unsupervised Domain Adaptation in the Absence of Source Data. - Roshni Sahoo; Divya Shanmugam; John V. Guttag (CoRR, 2020)

When and Why Test-Time Augmentation Works. - Divya Shanmugam; Davis W. Blalock; Guha Balakrishnan; John V. Guttag (CoRR, 2020)

Better Aggregation in Test-Time Augmentation. - Divya Shanmugam; Davis W. Blalock; Guha Balakrishnan; John V. Guttag (ICCV, 2021)

Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice. - Divya Shanmugam; Samira Shabanian; Fernando Diaz 001; Michèle Finck; Asia Biega (CoRR, 2021)

Quantifying Inequality in Underreported Medical Conditions. - Divya Shanmugam; Emma Pierson (CoRR, 2021)

Data Augmentation for Electrocardiograms. - Aniruddh Raghu; Divya Shanmugam; Eugene Pomerantsev; John V. Guttag; Collin M. Stultz (CHIL, 2022)

Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization. - Divya Shanmugam; Fernando Diaz 001; Samira Shabanian; Michèle Finck; Asia Biega (FAccT, 2022)

Improved Text Classification via Test-Time Augmentation. - Helen Lu; Divya Shanmugam; Harini Suresh; John V. Guttag (CoRR, 2022)

At the Intersection of Deep Learning and Conceptual Art: The End of Signature. - Divya Shanmugam; Katie Lewis; Jose Javier Gonzalez Ortiz; Agnieszka Kurant; John V. Guttag (CoRR, 2022)

Kaleidoscope: Semantically-grounded, context-specific ML model evaluation. - Harini Suresh; Divya Shanmugam; Tiffany Chen; Annie G. Bryan; Alexander D'Amour; John V. Guttag; Arvind Satyanarayan (CHI, 2023)

Machine Learning for Health (ML4H) 2023. - Stefan Hegselmann; Antonio Parziale; Divya Shanmugam; Shengpu Tang; Kristen Severson 001; Mercy Nyamewaa Asiedu; Serina Chang; Bonaventure F. P. Dossou; Qian Huang; Fahad Kamran; Haoran Zhang 003; Sujay Nagaraj; et al. (ML4H@NeurIPS, 2023)

Coarse race data conceals disparities in clinical risk score performance. - Rajiv Movva; Divya Shanmugam; Kaihua Hou; Priya Pathak; John V. Guttag; Nikhil Garg 001; Emma Pierson (MLHC, 2023)

Machine Learning for Health symposium 2023 - Findings track. - Stefan Hegselmann; Antonio Parziale; Divya Shanmugam; Shengpu Tang; Mercy Nyamewaa Asiedu; Serina Chang; Thomas Hartvigsen; Harvineet Singh (CoRR, 2023)

Use large language models to promote equity. - Emma Pierson; Divya Shanmugam; Rajiv Movva; Jon M. Kleinberg; Monica Agrawal; Mark Dredze; Kadija Ferryman; Judy Wawira Gichoya; Dan Jurafsky; Pang Wei Koh; Karen Levy; Sendhil Mullainathan; et al. (CoRR, 2023)

Generative AI in Medicine. - Divya Shanmugam; Monica Agrawal; Rajiv Movva; Irene Y. Chen; Marzyeh Ghassemi; Maia Jacobs; Emma Pierson (CoRR, 2024)

Learning Disease Progression Models That Capture Health Disparities. - Erica Chiang; Divya Shanmugam; Ashley N. Beecy; Gabriel Sayer; Nir Uriel; Deborah Estrin; Nikhil Garg 001; Emma Pierson (CoRR, 2024)

Learning Disease Progression Models That Capture Health Disparities. - Erica Chiang; Divya Shanmugam; Ashley N. Beecy; Gabriel Sayer; Deborah Estrin; Nikhil Garg 001; Emma Pierson (CHIL, 2025)

Test-time Augmentation Improves Efficiency in Conformal Prediction. - Divya Shanmugam; Helen Lu; Swami Sankaranarayanan; John V. Guttag (CVPR, 2025)

Evaluating multiple models using labeled and unlabeled data. - Divya Shanmugam; Shuvom Sadhuka; Manish Raghavan; John V. Guttag; Bonnie Berger; Emma Pierson (CoRR, 2025)