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 Ghosh S, Yao J, Doshi-Velez F.
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors, in
Proceedings of the 35th International Conference on Machine Learning (ICML). Vol 80. Stockholm, Sweden ; 2018.
Paper 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
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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 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.
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