Depeweg S, Hernandez-Lobato JM, Doshi-Velez F, Udluft S. Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning, in Proceedings of the 35th International Conference on Machine Learning (ICML). Vol 80. Stockholm, Sweden ; 2018. Paper

Gottesman O, Pan W, Doshi-Velez F. Weighted Tensor Decomposition for Learning Latent Variables with Partial Data, in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018. Vol 84. Lanzarote, Spain ; 2018. Paper

Hughes MC, Hope G, Weiner L, Thomas H. McCoy J, Perlis RH, Sudderth E, Doshi-Velez F. Semi-Supervised Prediction-Constrained Topic Models, in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018. Vol 84. Lanzarote, Spain ; 2018. Paper

Amir O, Doshi-Velez F, Sarne D. Agent Strategy Summarization. Autonomous Agents and Multiagent Systems, Blue Sky Ideas Track. 2018. Paper

Doshi-Velez F, Kortz M, Budish R, Bavitz C, Gershman S, O'Brien D, Shieber S, Waldo J, Weinberger D, Wood A. Accountability of AI Under the Law: The Role of Explanation. Privacy Law Scholars Conference. 2018. Paper

Jin L, Doshi-Velez F, Miller T, Schuler W, Schwartz L. Unsupervised Grammar Induction with Depth-bounded PCFG. Association for Computational Linguistics. 2018. Paper

Ross AS, Doshi-Velez F. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients. Association for the Advancement of Artificial Intelligence (AAAI). 2018. Paper

Yao J, Killian T, Konidaris G, Doshi-Velez F. Direct Policy Transfer via Hidden Parameter Markov Decision Processes. International Conference on Machine Learning (ICML) Workshop on Lifelong Learning,. 2018. Paper

Raghu A, Gottesman O, Liu Y, Komorowski M, Faisal A, Doshi-Velez F, Brunskill E. Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. International Conference on Machine Learning (ICML) Workshop on CausalML. 2018. Paper

Sussex S, Gottesman O, Liu Y, Murphy S, Brunskill E, Doshi-Velez F. Stitched Trajectories for Off-Policy Learning. International Conference on Machine Learning (ICML) Workshop on CausalML,. 2018. Paper

Liu Y, Gottesman O, Raghu A, Komorowski M, Faisal A, Doshi-Velez F, Brunskill E. Representation Balancing MDPs for Off-Policy Policy Evaluation. International Conference on Machine Learning (ICML) Workshop on CausalML. 2018. Paper

Gottesman O, Doshi-Velez F. Regularizing Tensor Decomposition Methods by Optimizing Pseudo-Data. International Conference on Machine Learning (ICML) Exploration in Reinforcement Learning Workshop,. 2018. Paper

Ross AS, Pan W, Doshi-Velez F. Learning Qualitatively Diverse and Interpretable Rules for Classification. International Conference on Machine Learning (ICML) Workshop on Human Interpretability in Machine Learning,. 2018. Paper

Jin L, Doshi-Velez F, Miller T, Schuler W, Schwartz L. Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction. Conference on Empirical Methods in Natural Language Processing (EMNLP) . 2018. Paper

Doshi-Velez F, Kim B. Considerations for Evaluation and Generalization in Interpretable Machine Learning. In: Escalante H, Escalera S, Guyon I, Baró X, Güçlütürk Y, Güçlü U, van Gerven MAJ Explainable and Interpretable Models in Computer Vision and Machine Learning. 1st ed. Springer International Publishing ; 2018. Chapter

Glueck M, Naeini MP, Doshi-Velez F, Chevalier F, Khan A, Wigdor D, Brudno M. PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models. IEEE Transactions on Visualization and Computer Graphics. 2018;24 (1) :371-381. Paper

Ghassemi M, Wu M, Hughes MC, Szolovits P, Doshi-Velez F. Predicting intervention onset in the ICU with switching state space models. American Medical Informatics Association (AMIA),. 2017. Paper

Glueck M, Naeini MP, Doshi-Velez F, Chevalier F, Khan A, Wigdor D, Brudno M. PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models. Conference on Visual Analytics Science and Technology (VAST),. 2017. Paper

Ross AS, Lage I, Doshi-Velez F. The Neural LASSO: Local Linear Sparsity for Interpretable Explanations. Neural Information Processing Systems (NIPS) Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. 2017. Paper

Wu M, Hughes M, Parbhoo S, Zazzi M, Roth V, Doshi-Velez F. Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. Neural Information Processing Systems (NIPS) Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments. 2017. Paper