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

Fan A, Doshi-Velez F, Miratrix L. Assessing topic model relevance: Evaluation and informative priors. Statistical Analysis and Data Mining. 2019;12 :210-222. Paper

Gottesman O, Johansson F, Komorowski M, Faisal A, Sontag D, Doshi-Velez F, Celi L. Guidelines for reinforcement learning in healthcare. Nature Medicine. 2019;25 :16-18. Paper

Lage I, Chen E, He J, Narayanan M, Gershman S, Kim B, Doshi-Velez F. An Evaluation of the Human-Interpretability of Explanation. Conference on Neural Information Processing Systems (NeurIPS) Workshop on Correcting and Critiquing Trends in Machine Learning. 2018. Paper

Pradier MF, Pan W, Yao J, Ghosh S, Doshi-Velez F. Projected BNNs: Avoiding weight-space pathologies by projecting neural network weights. Conference on Neural Information Processing Systems (NeurIPS) Workshop on Bayesian Deep Learning . 2018. Paper

Fernandez-Pradier M, Pan W, Yao M, Singh R, Doshi-Velez F. Hierarchical Stick-breaking Feature Paintbox. Conference on Neural Information Processing Systems (NeurIPS) Workshop on All of Bayesian Nonparametrics. 2018. Paper

Futoma J, Hughes MC, Doshi-Velez F. Prediction-Constrained POMDPs. Conference on Neural Information Processing Systems (NeurIPS) Workshop on Reinforcement Learning under Partial Observability . 2018. Paper

Parbhoo S, Gottesman O, Ross AS, Komorowski M, Faisal A, Bon I, Roth V, Doshi-Velez F. Improving counterfactual reasoning with kernelised dynamic mixing models. PLoS ONE . 2018;13 (11). Paper

Lage I, Ross A, Kim B, Gershman S, Doshi-Velez F. Human-in-the-Loop Interpretability Prior. Conference on Neural Information Processing Systems (NeurIPS). 2018. Paper

Wu M, Hughes M, Parbhoo S, Zazzi M, Roth V, Doshi-Velez F. Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. Association for the Advancement of Artificial Intelligence (AAAI). 2018. Paper

Masood MA, Doshi-Velez F. Diversity-Inducing Policy Gradient: Using MMD to find a set of policies that are diverse in terms of stete-visitation. International Conference on Machine Learning (ICML) Exploration in Reinforcement Learning Workshop. 2018. Paper

Peng X, Ding Y, Wihl D, Gottesman O, Komorowski M, Lehman L-wei H, Ross A, Faisal A, Doshi-Velez F. Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. American Medical Informatics Association (AMIA) Annual Symposium. 2018. Paper