J. Antoran, Yao J, Pan W, Doshi-Velez F, Hernandez-Lobato J. Amortised Variational Inference for Hierarchical Mixture Models. ICML Workshop on Uncertainty in Deep Learning. 2020 :1-11. Paper

HC. Ou, Wang K, Doshi-Velez F, Tambe M. Active Screening on Recurrent Diseases Contact Networks with Uncertainty: a Reinforcement Learning Approach. AAMAS Workshop on Multi-Agent Based Simulation (AAMAS). 2020 :1-12. Paper

Yacoby Y, Pan W, Doshi-Velez F. Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. Advances in Approximate Bayesian Inference. 2020;1 :1-17. Paper

Prasad N, Engelhardt B, Doshi-Velez F. Defining Admissible Rewards for High-Confidence Policy Evaluation in Batch Reinforcement Learning. ACM Conference on Health, Inference and Learning. 2020;2 :1-9. Paper

Ren J, Kunes R, Doshi-Velez F. Prediction Focused Topic Models via Feature Selection. AISTATS. 2020;2 :1-19. Paper

Futoma J, Hughes M, Doshi-Velez F. POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. AISTATS. 2020;2 :1-18. Paper

Wu M, Parbhoo S, Hughes M, Kindle R, Celi L, Zazzi M, Volker R, Doshi-Velez F. Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI. 2020;3 :1-9. Paper

Wu M, Parbhoo S, Hughes M, Kindle R, Celi L, Zazzi M, Volker R, Doshi-Velez F. Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI. 2020;3 :1-9. Paper

Ross A, Pan W, Celi L, Doshi-Velez F. Ensembles of Locally Independent Prediction Models. AAAI. 2020;3 :1-11. Paper

Futoma F, Masgood M, Doshi-Velez F. Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning. AMIA CRI. 2020;1 :1-24. Paper

Srinivasan S, Doshi-Velez F. Interpretable Batch IRL to extract clinician goals in ICU HypotensionManagement. AMIA CRI. 2020 :636-645. 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;145 (2) :1-11. Paper

Doshi-Velez F, Perlis R. Evaluating Machine Learning Articles. JAMA. 2020;322 (18) :1777-1779. Paper

Pradier M, McCoy T, Hughes M, Perlis R, Doshi-Velez F. Predicting treatment dropout after antidepressant initiation. Translational Psychiatry. 2020;10 (1) :1-8. Paper

Coker B, Pradier M, Doshi-Velez F. Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks, in proceedings at The Conference on Uncertainty in Artificial Intelligence (UAI). Vol 1. ; 2019 :1-37. Paper

Masood M, Doshi-Velez F. A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization. Journal of Machine Learning Research. 2019;20 (90) :1-56. Paper

Ghosh S, Yao J, Doshi-Velez F. Model Selection in Bayesian Neural Networks via Horseshoe Priors. Journal of Machine Learning Research. 2019;20 (182) :1-46. Paper

Prasad N, Engelhardt B, Doshi-Velez F. Defining Admissible Rewards for High Confidence Policy Evaluation. NeurIPS Workshop on Safety and Robustness in Decision-Making, . 2019;1 :1-12. Paper

Ross A, Du J, Sharvit Y, Doshi-Velez F. Controlled Direct Effect Priors for Bayesian Neural Networks. NeurIPS Workshop on Bayesian Deep Learning. 2019;1 :1-8. Paper

Jacobs M, Perlis R, Pradier M, Doshi-Velez F, Mynatt E, Gajos K. Integrating AI Recommendations into The Pharmacologic Management of Major Depressive Disorder. CSCW Workshop: Identifying Challenges and Opportunities in Human–AI Collaboration in Healthcare. 2019;1 :1-5. Paper