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

Parbhoo S, Roth V, Doshi-Velez F. Counterfactual Reasoning with Dynamic Switching Models for HIV Therapy Selection. Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Healthcare. 2017. Paper

Hughes MC, Hope G, Weiner L, McCoy TH, Perlis RH, Sudderth EB, Doshi-Velez F. Prediction-Constrained Topic Models for Antidepressant Recommendation. Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Healthcare. 2017. Paper

Ghosh S, Doshi-Velez F. Model Selection in Bayesian Neural Networks via Horseshoe Priors. Neural Information Processing Systems (NIPS) Workshop on Bayesian Deep Learning. 2017. Paper

Singh R, Ling J, Doshi-Velez F. Structured Variational Autoencoders for the Beta-Bernoulli Process. Neural Information Processing Systems (NIPS) Workshop on Advances in Approximate Bayesian Inference. 2017. Paper

Tan S, Doshi-Velez F, Quiroz J, Glassman E. Clustering LaTeX Solutions to Machine Learning Assignments for Rapid Assessment. Advancing Education with Data Knowledge Discovery and Data Mining (KDD) Workshop. 2017. Paper

Depeweg S, Hernandez-Lobato JM, Doshi-Velez F, Udluft S. Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables. International Conference on Machine Learning (ICML) Workshop. 2017. Paper

Parbhoo S, Bogojeska J, Zazzi M, Roth V, Doshi-Velez F. Combining Kernel and Model Based Learning for HIV Therapy Selection, in AMIA Summits on Translational Science Proceedings . Vol 2017. ; 2017 :239. Paper

Fan A, Doshi-Velez F, Miratrix L. Prior Matters: Simple and General Methods for Evaluating and Improving Topic Quality in Topic Modeling. Text as Data. 2017. Paper

Killian T, Daulton S, Konidaris G, Doshi-Velez F. Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. Neural Information Processing Systems (NIPS). 2017. Paper

Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P. A Bayesian Framework for Learning Rule Sets for Interpretable Classification. Journal of Machine Learning. 2017;18 (70) :1-37. Paper

Ross AS, Hughes MC, Doshi-Velez F. Right for the Right Reasons: Training Differentiable Models by Constraining their Explananations. International Joint Conference on Artificial Intelligence (IJCAI). 2017. Paper

Doshi-Velez F, Williamson S. Restricted Indian Buffet Processes. Statistics and Computing. 2017;27 (5) :1205-1223. Paper