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Adarsh Subbaswamy

The Clinician and Dataset Shift in Artificial Intelligence
2021 · Samuel G. Finlayson, Adarsh Subbaswamy, et al. · New England Journal of Medicine · 961
From development to deployment: dataset shift, causality, and shift-stable models in health AI
2019 · Adarsh Subbaswamy, Suchi Saria · Biostatistics · 321
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Non-Intrusive Occupancy Monitoring using Smart Meters
2013 · Dong Chen, Sean Barker, et al. · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · 192
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
2018 · Subbaswamy, Adarsh, Schulam, Peter, et al. · arXiv (Cornell University) · 93
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Tutorial: Safe and Reliable Machine Learning
2019 · Saria, Suchi, Subbaswamy, Adarsh · arXiv (Cornell University) · 89
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A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models
2022 · H Echo Wang, Matthew Landers, et al. · Journal of the American Medical Informatics Association · 65
Evaluating Model Robustness and Stability to Dataset Shift
2020 · Subbaswamy, Adarsh, Adams, Roy, et al. · arXiv (Cornell University) · 54
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Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions
2017 · Soleimani, Hossein, Subbaswamy, Adarsh, et al. · arXiv (Cornell University) · 43
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A unifying causal framework for analyzing dataset shift-stable learning algorithms
2022 · Adarsh Subbaswamy, Bryant Chen, et al. · Journal of Causal Inference · 34
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Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms
2018 · Subbaswamy, Adarsh, Saria, Suchi · arXiv (Cornell University) · 27
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Scorecard for synthetic medical data evaluation
2025 · Ghada Zamzmi, Adarsh Subbaswamy, et al. · Communications Engineering · 26
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A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform
2024 · Adarsh Subbaswamy, Berkman Sahiner, et al. · npj Digital Medicine · 24
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Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach
2026 · Nathan Drenkow, Mitchell Pavlak, et al. · npj Digital Medicine · 10
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I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models
2020 · Subbaswamy, Adarsh, Saria, Suchi · arXiv (Cornell University) · 10
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Evaluating Model Robustness to Dataset Shift.
2020 · Adarsh Subbaswamy, Roy J. Adams, et al. · arXiv (Cornell University) · 9
A hierarchical decomposition for explaining ML performance discrepancies
2024 · Harvineet Singh, Fan Xia, et al. · neural information processing systems · 8
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Learning Predictive Models That Transport.
2018 · Adarsh Subbaswamy, Peter Schulam, et al. · arXiv (Cornell University) · 8
Scaling medical device regulatory science using large language models
2026 · Hanyang Li, Xiao He, et al. · npj Digital Medicine · 7
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Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
2023 · Feng, Jean, Subbaswamy, Adarsh, et al. · arXiv (Cornell University) · 5
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HistoART: Histopathology artifact detection based on large foundation model
2026 · Seyed Kahaki, Alexander Webber, et al. · Neural Computing and Applications · 4
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Should I Include this Edge in my Prediction? Analyzing the Stability-Performance Tradeoff
2019 · Adarsh Subbaswamy, Bryant Chen, et al. · arXiv (Cornell University) · 1
Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting
2026 · Zhou, Siyu, Ouyang, Jialin, et al. · arXiv (Cornell University)
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Knowledge-based anomaly detection for identifying network-induced shape artifacts
2026 · Rucha Deshpande, Tahsin Rahman, et al. · The Journal of Machine Learning for Biomedical Imaging
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The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
2026 · LaRocco, Eileanor, Tan, Sarah, et al. · arXiv (Cornell University)
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From Precision to Personalized: Catalyzing AI ‐Enabled Innovation in Drug Development
2026 · Adarsh Subbaswamy, Allison Dunn, et al. · Clinical and Translational Science
Works count: 29
Citation count: 1,991
H-index: 12
I10-index: 14
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