Ren J, Russell R, Doshi-Velez F. Prediction Focused Topic Models Via Vocab Filtering. NeurIPS Workshop on Human-Centric ML. 2019;1 :1-12. Paper

Pradier M, Hughes M, Doshi-Velez F. Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences. Advances in Approximate Bayesian Inference. 2019;1 :1-11. Paper

Pradier M, Pan W, Yao J, Ghosh S, Doshi-Velez F. Projected BNNs: Avoiding Weight-space Pathologies by Learning Latent Representations of Neural Network Weights. ACML Workshop on Weakly Supervised Learning Workshop. 2019;3 :1-15. Paper

Ren J, Kunes R, Doshi-Velez F. Prediction Focused Topic Models for Electronic Health Records. NeurIPS Workshop on Machine Learning for Health. 2019;1 :1-13. Paper

Wiens J, Saria S, Sendak M, Ghassemi M, Liu V, Doshi-Velez F, Jung K, Heller K, Kale D, Saeed M, et al. Do no harm: A roadmap for responsible machine learning for healthcare. Nature Medicine. 2019;25 (10) :1337-1340. Paper Amendments

Amir O, Doshi-Velez F, Sarne D. Summarizing Agent Strategies. Journal of Autonomous Agents and Multi-Agent Systems (AAMAS). 2019;33 :628-644. Paper

Vaughan D, Pan W, Yacoby Y, Seidler E, Leung A, Doshi-Velez F, Sakkas D. The Application of Machine Learning Methods to Evaluate Predictors for Live Birth in Programmed Thaw Cycles, in in proceedings at the American Society for Reproductive Medicine Scientific Congress & Expo (ASRM). ; 2019. Paper

Yang W, Lorch L, Graule M, Srinivasan S, Suresh A, Yao J, Pradier M, Doshi-Velez F. Output-Constrained Bayesian Neural Network, in proceedings at the International Conference on Machine Learning: Workshop on Understanding and Improving Generalization in Deep Learning(ICML). ; 2019. Paper

Yang W, Lorch L, Graule M, Srinivasan S, Suresh A, Yao J, Pradier M, Doshi-Velez F. Output-Constrained Bayesian Neural Networks, in proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML). ; 2019. Paper

Yacoby Y, Pan W, Doshi-Velez F. Mitigating Model Non-Identifiability in BNN with Latent Variables, in proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML). ; 2019. Paper

Yao J, Pan W, Ghosh S, Doshi-Velez F. Quality of Uncertainty Quantification for Bayesian Neural Network Inference, in proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML). ; 2019. Paper

Coker B, Pradier M, Doshi-Velez F. Poisson Process Bayesian Neural Networks, in proceedings at the International Conference on Bayesian Nonparametrics (BNP). ; 2019. Paper

Lage I, Lifschitz D, Doshi-Velez F, Amir O. Toward Robust Policy Summarization, in proceedings at the International Joint Conference on Artificial Intelligence (IJCAI). ; 2019. Paper

Lage I, Lifschitz D, Doshi-Velez F, Amir O. Toward Robust Summarization of Agent Policies, in proceedings at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). ; 2019. Paper

Lage I, Chen E, He J, Narayanan M, Kim B, Gershman S, Doshi-Velez F. Human Evaluation of Models Built for Interpretability, in proceedings at the 7th AAAI Conference on Human Computation and Crowdsourcing (HCOMP). ; 2019. Paper

Srinivasan S, Lee D, Doshi-Velez F. Truly Batch Apprenticeship Learning with Deep Successor Features, in proceedings at the International Joint Conference on Artificial Intelligence (IJCAI). ; 2019. Paper

Lage I, Lifschitz D, Doshi-Velez F, Amir O. Exploring Computational User Models for Agent Policy Summarization, in proceedings at the International Joint Conference on Artificial Intelligence: Workshop on Explainable Artificial Intelligence (IJCAI), . ; 2019. Paper

Juozapaitis Z, Koul A, Fern A, Erwig M, Doshi-Velez F. Explainable Reinforcement Learning via Reward Decomposition, in in proceedings at the International Joint Conference on Artificial Intelligence. A Workshop on Explainable Artificial Intelligence. ; 2019. Paper

Masood M, Doshi Velez F. Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies, in proceedings at the International Joint Conference on Artificial Intelligence (IJCAI). ; 2019. Paper

Gottesman O, Liu Y, Susser E, Brunskill E, Doshi-Velez F. Combining Parametric and Nonparametric Models for off-policy evaluation, in International Conference on Machine Learning (IMCL). ; 2019. Paper