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

Bertino E, Doshi-Velez F, Gini M, Lopresti D, Parkes D. Artificial Intelligence & Cooperation. Preprint. 2020;1 :1-4. Paper

Pradier M, Hughes C, 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

McMahon A, Cooper W, Brown J, Carleton B, Doshi-Velez F, Kohane I, Goldman J, Hoffman M, Kamaleswaran R, Sakiyama M, et al. Big Data in the Assessment of Pediatric Medication Safety. Pediatrics. 2020;1 :1-11. Publisher's Version Paper

McMahon A, Cooper W, Brown J, Carleton B, Doshi-Velez F, Kohane I, Goldman J, Hoffman M, Kamaleswaran R, Sakiyama M, et al. Big Data in the Assessment of Pediatric Medication Safety. Pediatrics. 2020;1 :1-11. Publisher's Version 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. 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