Non-Intrusive Occupancy Monitoring using Smart Meters. - Dong Chen 010; Sean Kenneth Barker; Adarsh Subbaswamy; David E. Irwin 001; Prashant J. Shenoy (BuildSys@SenSys, 2013)

Learning Treatment-Response Models from Multivariate Longitudinal Data. - Hossein Soleimani; Adarsh Subbaswamy; Suchi Saria (UAI, 2017)

Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions. - Hossein Soleimani; Adarsh Subbaswamy; Suchi Saria (CoRR, 2017)

Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms. - Adarsh Subbaswamy; Suchi Saria (UAI, 2018)

Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms. - Adarsh Subbaswamy; Suchi Saria (CoRR, 2018)

Learning Predictive Models That Transport. - Adarsh Subbaswamy; Peter Schulam; Suchi Saria (CoRR, 2018)

Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. - Adarsh Subbaswamy; Peter Schulam; Suchi Saria (AISTATS, 2019)

Tutorial: Safe and Reliable Machine Learning. - Suchi Saria; Adarsh Subbaswamy (CoRR, 2019)

The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance. - Adarsh Subbaswamy; Bryant Chen; Suchi Saria (CoRR, 2019)

I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models. - Adarsh Subbaswamy; Suchi Saria (CoRR, 2020)

Evaluating Model Robustness to Dataset Shift. - Adarsh Subbaswamy; Roy Adams; Suchi Saria (CoRR, 2020)

Evaluating Model Robustness and Stability to Dataset Shift. - Adarsh Subbaswamy; Roy Adams; Suchi Saria (AISTATS, 2021)

Machine Learning for Health (ML4H) 2022. - Antonio Parziale; Monica Agrawal; Shengpu Tang; Kristen Severson 001; Luis Oala; Adarsh Subbaswamy; Sayantan Kumar; Elora D. M. Schörverth; Stefan Hegselmann; Helen Zhou; Ghada Zamzmi; Purity Mugambi; et al. (ML4H@NeurIPS, 2022)

Machine Learning for Health symposium 2022 - Extended Abstract track. - Antonio Parziale; Monica Agrawal; Shalmali Joshi; Irene Y. Chen; Shengpu Tang; Luis Oala; Adarsh Subbaswamy (CoRR, 2022)

A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models. - H. Echo Wang; Matthew Landers; Roy Adams; Adarsh Subbaswamy; Hadi Kharrazi; Darrell J. Gaskin; Suchi Saria (J. Am. Medical Informatics Assoc., 2022)

Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CoRR, 2023)

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Anthony Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CLeaR, 2024)

A hierarchical decomposition for explaining ML performance discrepancies. - Harvineet Singh; Fan Xia; Adarsh Subbaswamy; Alexej Gossmann; Jean Feng (NeurIPS, 2024)

Scorecards for Synthetic Medical Data Evaluation and Reporting. - Ghada Zamzmi; Adarsh Subbaswamy; Elena Sizikova; Ed Margerrison; Jana G. Delfino; Aldo Badano (CoRR, 2024)

A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform. - Adarsh Subbaswamy; Berkman Sahiner; Nicholas Petrick; Vinay Pai; Roy Adams; Matthew C. Diamond; Suchi Saria (npj Digit. Medicine, 2024)

Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework. - Nathan Drenkow; Mitchell Pavlak; Keith Harrigian; Ayah Zirikly; Adarsh Subbaswamy; Mathias Unberath (CoRR, 2025)

HistoART: Histopathology Artifact Detection and Reporting Tool. - Seyed Kahaki; Alexander R. Webber; Ghada Zamzmi; Adarsh Subbaswamy; Rucha Deshpande; Aldo Badano (CoRR, 2025)