| Better aggregation in test-time augmentation D Shanmugam, D Blalock, G Balakrishnan, J Guttag Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 120 | 2021 |
| When and why test-time augmentation works D Shanmugam, D Blalock, G Balakrishnan, J Guttag arXiv preprint arXiv:2011.11156 1 (3), 4, 2020 | 62 | 2020 |
| Unsupervised domain adaptation in the absence of source data R Sahoo, D Shanmugam, J Guttag arXiv preprint arXiv:2007.10233, 2020 | 22 | 2020 |
| Learning to limit data collection via scaling laws: A computational interpretation for the legal principle of data minimization D Shanmugam, F Diaz, S Shabanian, M Finck, A Biega Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 16* | 2022 |
| Data augmentation for electrocardiograms A Raghu, D Shanmugam, E Pomerantsev, J Guttag, CM Stultz Conference on Health, Inference, and Learning, 282-310, 2022 | 14 | 2022 |
| Multiple instance learning for ECG risk stratification D Shanmugam, D Blalock, J Guttag Machine Learning for Healthcare Conference, 124-139, 2019 | 14 | 2019 |
| Improved text classification via test-time augmentation H Lu, D Shanmugam, H Suresh, J Guttag arXiv preprint arXiv:2206.13607, 2022 | 9 | 2022 |
| Coarse race data conceals disparities in clinical risk score model performance R Movva, D Shanmugam, K Hou, P Pathak, J Guttag, N Garg, E Pierson | 6* | |
| Kaleidoscope: Semantically-grounded, context-specific ML model evaluation H Suresh, D Shanmugam, T Chen, AG Bryan, A D'Amour, J Guttag, ... Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems …, 2023 | 4 | 2023 |
| Quantifying disparities in underreported health conditions: An application to intimate partner violence D Shanmugam, K Hou, E Pierson | 4* | |
| Image segmentation of liver stage malaria infection with spatial uncertainty sampling AP Soleimany, H Suresh, JJG Ortiz, D Shanmugam, N Gural, J Guttag, ... arXiv preprint arXiv:1912.00262, 2019 | 3 | 2019 |
| Jiffy: A convolutional approach to learning time series similarity D Shanmugam, D Blalock, J Guttag | 2 | 2018 |
| A tale of two time series methods: representation learning for improved distance and risk metrics D Shanmugam Massachusetts Institute of Technology, 2018 | 2 | 2018 |
| At the Intersection of Conceptual Art and Deep Learning: The End of Signature KM Lewis, DM Shanmugam, JJG Ortiz, A Kurant, J Guttag Workshop on Broadening Research Collaborations 2022, 2022 | 1* | 2022 |
| An Energy-Based Framework for Arbitrary Label Noise Correction J Sahota, D Shanmugam, J Ramanan, S Eghbali, M Brubaker | 1 | 2018 |
| Longitudinal changes in sexual desire and attraction among women who started using the Natural Cycles app J Gassen, S Mengelkoch, D Shanmugam, JT Pearson, ... Hormones and Behavior 162, 105546, 2024 | | 2024 |
| Use large language models to promote equity E Pierson, D Shanmugam, R Movva, J Kleinberg, M Agrawal, M Dredze, ... arXiv preprint arXiv:2312.14804, 2023 | | 2023 |
| Machine Learning for Health (ML4H) 2023 S Hegselmann, A Parziale, D Shanmugam, S Tang, K Severson, ... Machine Learning for Health (ML4H), 1-12, 2023 | | 2023 |
| Machine Learning for Health symposium 2023--Findings track S Hegselmann, A Parziale, D Shanmugam, S Tang, MN Asiedu, S Chang, ... arXiv preprint arXiv:2312.00655, 2023 | | 2023 |
| Improving the efficiency of conformal predictors via test-time augmentation DM Shanmugam, H Lu, S Sankaranarayanan, J Guttag | | 2023 |
John GuttagAdresse e-mail validée de mit.edu
