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

Zhang K, Gottesman O, Doshi-Velez F. A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes, in proceeding at the Conference on Neural Information Processing Systems (NeurIPS): Workshop on Real World Reinforcement Learning. ; 2020 :1-12. Paper

Zhang K, Gottesman O, Doshi-Velez F. A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes, in proceedings at the Conference on Neural Information Processing Systems (NeurIPS): Workshop on Real World Reinforcement Learning. ; 2020 :1-12. Paper

Bertino E, Doshi-Velez F, Gini M, Lopresti D, Parkes D. Artificial Intelligence and Cooperation. 1st ed.; 2020 pp. 1-4. Publisher's Version Paper

Hughes M, Pradier M, Ross A, McCoy M, Perlis R, Doshi-Velez F. Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models . JAMA Network Open. 2020 :1-14. Paper

Yang W, Lorch L, Graule M, Lakkaraju H, Doshi-Velez F. Incorporating Interpretable Output Constraints in Bayesian Neural Networks. proceeding at the Conference on Neural Information Processing Systems (NeurIPS). 2020;2 :1-17. Paper

Pradier M, Hughes M, McCoy T, Barroilhet S, Doshi-Velez F, Perlis R. Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation. Neuropsychopharmacology. 2020;1 :1-7. Paper

Parbhoo S, Gottesman O, Doshi-Velez F. Shaping Control Variates for Off-Policy Evaluation, in NeurIPS Workshop on Offline Reinforcement Learning. ; 2020 :1-9. Paper

Futoma J, Simons M, Panch T, Doshi-Velez F, Celi L. The Myth of Generalizability in Clinical Research and Machine Learning in Healthcare. Lancet Digital Health [Internet]. 2020;2 :1-5. Publisher's Version Paper

Parbhoo S, Wieser M, Roth V, Doshi-Velez F. Transfer Learning from Well-Curated to Less-Resourced Populations with HIV, in proceeding at the Machine Learning for Healthcare Conference. ; 2020 :1-20. Paper

Du J, Futoma J, Doshi-Velez F. Model-Based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs., in Neural Information Processing Systems Conference (NeurIPS) 2020. Vol 2. ; 2020 :1-21. Paper

Lage I, Doshi-Velez F. Human-in-the-Loop Learning of Interpretable and Intuitive Representations. ICML Workshop on Human Interpretability in Machine Learning, . 2020;1 :1-10. Paper

Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F. Power-Constrained Bandits. ICML Workshop on Theoretical Foundations of Reinforcement Learning. 2020;2 :1-30. Paper

Coker B, Fernandez-Pradier M, Doshi-Velez F. PoRB-Nets: Poisson Process Radial Basis Function Networks. UAI. 2020 :1-59. Paper

Nair Y, Doshi-Velez F. PAC Imitation and Model-based Batch Learning of Contextual MDPs. ICML Workshop on Theoretical Foundations of Reinforcement Learning. 2020;2 :1-21. Paper

Nair Y, Doshi-Velez F. PAC Imitation and Model-based Batch Learning of Contextual MDPs. ICML Workshop on Inductive Biases, Invariances and Generalization in RL. 2020;2 :1-21. Paper