Publications
224 results
224 results
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.
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.
2017
Depewag S, Hernández-Lobato JM, Doshi-Velez F, Udluft S. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. ICLR. 2017.
Depewag S, Hernández-Lobato JM, Doshi-Velez F, Udluft S. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. ICLR. 2017.
Wu M, Ghassemi M, Fend M, Celi LA, Szolovits P, Doshi-Velez F. Understanding Vasopressor Intervention and Weaning: Risk Prediction in a Public Heterogeneous Clinical Time Series Database. Journal of the American Medical Informatics Association. 2017;24(3):488–495.
Wu M, Ghassemi M, Fend M, Celi LA, Szolovits P, Doshi-Velez F. Understanding Vasopressor Intervention and Weaning: Risk Prediction in a Public Heterogeneous Clinical Time Series Database. Journal of the American Medical Informatics Association. 2017;24(3):488–495.
Doshi-Velez F, Williamson S. Restricted Indian Buffet Processes. Statistics and Computing. 2017;27(5):1205–1223.
Doshi-Velez F, Williamson S. Restricted Indian Buffet Processes. Statistics and Computing. 2017;27(5):1205–1223.
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.
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.
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.
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.
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.
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.
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.
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.
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 . Vols. 2017. 2017. p. 239.
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 . Vols. 2017. 2017. p. 239.
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.
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.
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.
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.
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.
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.