Publications

2019
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
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
2018
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

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