Jacobs M, He J, Pradier FM, Lam B, Ahn A, McCoy T, Perlis R, Doshi-Velez F, Gajos K. Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. proceedings at the Conference on Human Factors in Computing Systems (CHI). 2021;2 :1-14. Paper

Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F. Power Constrained Bandit. proceedings at the Machine Learning for Healthcare Conference. 2021;4 :1-50. Publisher's Version Paper

Futoma J, Simons M, Doshi-Velez F, Kamaleswaran R. Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables. Critical Care Explorations. 2021;1 :1-11. Paper

Narayanan S, Sharma A, Zeng C, Doshi-Velez F. Prediction-focused Mixture Models . proceeding at the International Conference on Machine Learning: Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ICML), 2021. 2021;1 :1-9. Paper

Coker B, Parbhoo S, Doshi-Velez F. Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data. proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML). 2021;1 :1-15. Paper

Coker B, Parbhoo S, Doshi-Velez F. Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data. proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML). 2021;1 :1-15. Paper

Mahinpei A, Clark J, Lage I, Doshi-Velez F, WeiWei P. Promises and Pitfalls of Black-Box Concept Learning Models. proceeding at the International Conference on Machine Learning: Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI,. 2021;1 :1-13. Paper

Wu M, Parbhoo S, Hughes M, Roth V, Doshi-Velez F. Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. Journal of AI Research (JAIR). 2021;1 :1-37. Paper

Parbhoo S, Shalmali S, Doshi-Velez F. Pre-emptive Learning to Defer for Sequential Medical Decision-Making Under Uncertainty. proceeding at the International Conference on Machine Learning: Workshop on Neglected Assumptions in Causal Inference (ICML). 2021;1 :1-13. Paper

Parbhoo S, Shalmali J, Doshi-Velez F. On formalizing causal off-policy evaluation for sequential decision-making. proceeding at the International Conference on Machine Learning: Workshop on Neglected Assumptions in Causal Inference (ICML). 2021.

Johnson N, Parbhoo S, Ross A, Doshi-Velez F. Learning Predictive and Interpretable Timeseries Summaries from ICU Data. proceeding at the Conference on American Medical Informatics Association (AMIA). 2021;1 :1-10. Paper

Johnson N, Parbhoo S, Ross A, Doshi-Velez F. Learning Predictive and Interpretable Timeseries Summaries from ICU Data. proceeding at the Conference on American Medical Informatics Association (AMIA). 2021;1 :1-10. Paper

Jacobs M, Pradier M, McCoy T, Roy P, Doshi-Velez F, Krzysztof G. How machine learning recommendations influence clinician treatment selections: example of antidepressant selection. Translational Psychiatry. 2021;1 :1-9. Paper

Ross A, Chen N, Hang E, Glassman E, Doshi-Velez F. Evaluating the Interpretability of Generative Models by Interactive Reconstruction, in proceeding at the Conference on Human Factors in Computing Systems (CHI), 2021. Vol 1. ; 2021 :1-20. Paper

Jacobs M, He J, Pradier M, Lam B, Ahn A, McCoy T, Perlis R, Doshi-Velez F, Gajos K. Designing AI for Trust in Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens, in proceeding at the Conference on Human Factors in Computing Systems (CHI). Vol 1. ; 2021 :1-14. Paper

Zhang K, Wang H, Du J, Chu B, Kindle R, Celi L, Doshi-Velez F. Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment, in NeurIPS Workshop on Machine Learning for Health. Vol 1. ; 2021 :1-9. Paper

Pradier M, Zazo J, Parbhoo S, Perlis R, Zazzi M, Doshi-Velez F. Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as Much as Possible, in American Medical Informatics Association (AMIA). Vol 1. ; 2021 :1-13. Paper

Futoma J, Simons M, Panch T, Doshi-Velez F, Celi L. The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health. 2020;3 :1-19. Paper

Gottesman O, Futoma J, Liu Y, Parbhoo S, Celi L, Brunskill E, Doshi-Velez F. Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions . presented at the International Conference on Machine Learning. 2020;3 :1-19. Paper

Lage I, Doshi-Velez F. Learning Interpretable Concept-Based Models with Human Feedback. presented at the International Conference on Machine Learning: Workshop on Human Interpretability in Machine Learnin. 2020;1 :1-11. Paper