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 | 398 | 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 | 242 | 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 | 210 | 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 | 171 | 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 | 91 | 2021 |
Counterfactual normalization: Proactively addressing dataset shift and improving reliability using causal mechanisms A Subbaswamy, S Saria arXiv preprint arXiv:1808.03253, 2018 | 78* | 2018 |
Tutorial: safe and reliable machine learning S Saria, A Subbaswamy arXiv preprint arXiv:1904.07204, 2019 | 77 | 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 | 62 | 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 | 32 | 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 | 32* | 2022 |
I-spec: An end-to-end framework for learning transportable, shift-stable models A Subbaswamy, S Saria arXiv preprint arXiv:2002.08948, 2020 | 13 | 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 | 3 | 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 | 1 | 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 | | 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 |
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 | | 2022 |
A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models (May, 10.1093/jamia/ocac065, 2022) H Wang, M Landers, R Adams, A Subbaswamy, H Kharrazi, DJ Gaskin, ... JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 29 (9), 1656-1656, 2022 | | 2022 |
Proceedings of the 2nd Machine Learning for Health symposium, ML4H 2022 A Parziale, M Agrawal, S Joshi, IY Chen, S Tang, L Oala, A Subbaswamy PROCEEDINGS OF MACHINE LEARNING RESEARCH 193, 1-577, 2022 | | 2022 |