Publications

2020
Doshi-Velez F, Perlis R. Evaluating Machine Learning Articles. JAMA. 2020;322 (18) :1777-1779. Paper
Pradier M, McCoy T, Hughes M, Perlis R, Doshi-Velez F. Predicting treatment dropout after antidepressant initiation. Translational Psychiatry. 2020;10 (1) :1-8. Paper
2019
Coker B, Pradier M, Doshi-Velez F. Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks, in proceedings at The Conference on Uncertainty in Artificial Intelligence (UAI). Vol 1. ; 2019 :1-37. Paper
Masood M, Doshi-Velez F. A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization. Journal of Machine Learning Research. 2019;20 (90) :1-56. Paper
Ghosh S, Yao J, Doshi-Velez F. Model Selection in Bayesian Neural Networks via Horseshoe Priors. Journal of Machine Learning Research. 2019;20 (182) :1-46. Paper
Prasad N, Engelhardt B, Doshi-Velez F. Defining Admissible Rewards for High Confidence Policy Evaluation. NeurIPS Workshop on Safety and Robustness in Decision-Making, . 2019;1 :1-12. Paper
Ross A, Du J, Sharvit Y, Doshi-Velez F. Controlled Direct Effect Priors for Bayesian Neural Networks. NeurIPS Workshop on Bayesian Deep Learning. 2019;1 :1-8. Paper
Jacobs M, Perlis R, Pradier M, Doshi-Velez F, Mynatt E, Gajos K. Integrating AI Recommendations into The Pharmacologic Management of Major Depressive Disorder. CSCW Workshop: Identifying Challenges and Opportunities in Human–AI Collaboration in Healthcare. 2019;1 :1-5. Paper
Ren J, Russell R, Doshi-Velez F. Prediction Focused Topic Models Via Vocab Filtering. NeurIPS Workshop on Human-Centric ML. 2019;1 :1-12. Paper
Pradier M, Hughes M, Doshi-Velez F. Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences. Advances in Approximate Bayesian Inference. 2019;1 :1-11. Paper
Pradier M, Pan W, Yao J, Ghosh S, Doshi-Velez F. Projected BNNs: Avoiding Weight-space Pathologies by Learning Latent Representations of Neural Network Weights. ACML Workshop on Weakly Supervised Learning Workshop. 2019;3 :1-15. Paper
Ren J, Kunes R, Doshi-Velez F. Prediction Focused Topic Models for Electronic Health Records. NeurIPS Workshop on Machine Learning for Health. 2019;1 :1-13. Paper
Wiens J, Saria S, Sendak M, Ghassemi M, Liu V, Doshi-Velez F, Jung K, Heller K, Kale D, Saeed M, et al. Do no harm: A roadmap for responsible machine learning for healthcare. Nature Medicine. 2019;25 (10) :1337-1340. Paper Amendments
Amir O, Doshi-Velez F, Sarne D. Summarizing Agent Strategies. Journal of Autonomous Agents and Multi-Agent Systems (AAMAS). 2019;33 :628-644. Paper
Vaughan D, Pan W, Yacoby Y, Seidler E, Leung A, Doshi-Velez F, Sakkas D. The Application of Machine Learning Methods to Evaluate Predictors for Live Birth in Programmed Thaw Cycles, in in proceedings at the American Society for Reproductive Medicine Scientific Congress & Expo (ASRM). ; 2019. Paper
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

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