| The clinician and dataset shift in artificial intelligence SG Finlayson, A Subbaswamy, K Singh, J Bowers, A Kupke, J Zittrain, ... The New England Journal of Medicine, 283-286, 2021 | 494 | 2021 |
| Non-intrusive occupancy monitoring using smart meters D Chen, S Barker, A Subbaswamy, D Irwin, P Shenoy Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient …, 2013 | 247 | 2013 |
| From development to deployment: dataset shift, causality, and shift-stable models in health AI A Subbaswamy, S Saria Biostatistics 21 (2), 345-352, 2020 | 246 | 2020 |
| Preventing failures due to dataset shift: Learning predictive models that transport A Subbaswamy, P Schulam, S Saria The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 180 | 2019 |
| Evaluating model robustness and stability to dataset shift A Subbaswamy, R Adams, S Saria International conference on artificial intelligence and statistics, 2611-2619, 2021 | 109 | 2021 |
| Counterfactual normalization: Proactively addressing dataset shift and improving reliability using causal mechanisms A Subbaswamy, S Saria arXiv preprint arXiv:1808.03253, 2018 | 86* | 2018 |
| Tutorial: safe and reliable machine learning S Saria, A Subbaswamy arXiv preprint arXiv:1904.07204, 2019 | 84 | 2019 |
| Treatment-response models for counterfactual reasoning with continuous-time, continuous-valued interventions H Soleimani, A Subbaswamy, S Saria Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, 2017 | 69 | 2017 |
| A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models HE Wang, M Landers, R Adams, A Subbaswamy, H Kharrazi, DJ Gaskin, ... Journal of the American Medical Informatics Association 29 (8), 1323-1333, 2022 | 45 | 2022 |
| A unifying causal framework for analyzing dataset shift-stable learning algorithms A Subbaswamy, B Chen, S Saria Journal of Causal Inference 10 (1), 64-89, 2022 | 36* | 2022 |
| I-spec: An end-to-end framework for learning transportable, shift-stable models A Subbaswamy, S Saria arXiv preprint arXiv:2002.08948, 2020 | 14 | 2020 |
| Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study J Feng, A Subbaswamy, A Gossmann, H Singh, B Sahiner, MO Kim, ... arXiv preprint arXiv:2311.11463, 2023 | 5 | 2023 |
| Machine learning for health (ml4h) 2022 A Parziale, M Agrawal, S Tang, K Severson, L Oala, A Subbaswamy, ... Machine Learning for Health, 1-11, 2022 | 3 | 2022 |
| Machine Learning for Health symposium 2022--Extended Abstract track A Parziale, M Agrawal, S Joshi, IY Chen, S Tang, L Oala, A Subbaswamy arXiv preprint arXiv:2211.15564, 2022 | 2 | 2022 |
| Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens J Feng, A Subbaswamy, A Gossmann, H Singh, B Sahiner, MO Kim, ... Causal Learning and Reasoning, 587-608, 2024 | 1 | 2024 |
| A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform A Subbaswamy, B Sahiner, N Petrick, V Pai, R Adams, MC Diamond, ... npj Digital Medicine 7 (1), 334, 2024 | | 2024 |
| Scorecards for Synthetic Medical Data Evaluation and Reporting G Zamzmi, A Subbaswamy, E Sizikova, E Margerrison, J Delfino, ... arXiv preprint arXiv:2406.11143, 2024 | | 2024 |
| A hierarchical decomposition for explaining ML performance discrepancies J Feng, H Singh, F Xia, A Subbaswamy, A Gossmann arXiv preprint arXiv:2402.14254, 2024 | | 2024 |
| Causal Modeling for Training and Evaluating Dataset Shift-Stable Machine Learning Models in Healthcare A Subbaswamy Johns Hopkins University, 2023 | | 2023 |
Dr. Suchi SariaJohn C. Malone Endowed Chair; Computer Science, Statistics, Medicine, Health Policy, Johns HopkinsVerified email at cs.jhu.edu
