1 code implementation • 9 Apr 2022 • Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John Guttag, Collin M. Stultz
In experiments, considering three datasets and eight predictive tasks, we find that TaskAug is competitive with or improves on prior work, and the learned policies shed light on what transformations are most effective for different tasks.
1 code implementation • 8 Oct 2021 • 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.
1 code implementation • 18 Apr 2023 • Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John Guttag, Nikhil Garg, Emma Pierson
Across outcomes and metrics, we show that the risk scores exhibit significant granular performance disparities within coarse race groups.
no code implementations • 2 Dec 2018 • Divya Shanmugam, Davis Blalock, John Guttag
We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal.
no code implementations • ICLR 2018 • Divya Shanmugam, Davis Blalock, John Guttag
Computing distances between examples is at the core of many learning algorithms for time series.
no code implementations • 30 Nov 2019 • Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz, Divya Shanmugam, Nil Gural, John Guttag, Sangeeta N. Bhatia
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease.
Cultural Vocal Bursts Intensity Prediction Image Segmentation +2
no code implementations • 20 Jul 2020 • 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.
no code implementations • ICCV 2021 • Divya Shanmugam, Davis Blalock, Guha Balakrishnan, John Guttag
In this paper, we present 1) experimental analyses that shed light on cases in which the simple average is suboptimal and 2) a method to address these shortcomings.
no code implementations • 16 Jul 2021 • Divya Shanmugam, Samira Shabanian, Fernando Diaz, Michèle Finck, Asia Biega
FIDO learns to limit data collection based on an interpretation of data minimization tied to system performance.
no code implementations • 27 Sep 2018 • Jaspreet Sahota, Divya Shanmugam, Janahan Ramanan, Sepehr Eghbali, Marcus Brubaker
We propose an energy-based framework for correcting mislabelled training examples in the context of binary classification.
no code implementations • 27 Jun 2022 • Helen Lu, Divya Shanmugam, Harini Suresh, John Guttag
Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classification models.
no code implementations • 1 Dec 2023 • Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Thomas Hartvigsen, Harvineet Singh
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, Louisiana, USA.