Publications

2020
Yacoby Y, Pan W, Doshi-Velez F. Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. Advances in Approximate Bayesian Inference. 2020;1 :1-17. Paper
Prasad N, Engelhardt B, Doshi-Velez F. Defining Admissible Rewards for High-Confidence Policy Evaluation in Batch Reinforcement Learning. ACM Conference on Health, Inference and Learning. 2020;2 :1-9. Paper
Ren J, Kunes R, Doshi-Velez F. Prediction Focused Topic Models via Feature Selection. AISTATS. 2020;2 :1-19. Paper
Futoma J, Hughes M, Doshi-Velez F. POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. AISTATS. 2020;2 :1-18. Paper
Wu M, Parbhoo S, Hughes M, Kindle R, Celi L, Zazzi M, Volker R, Doshi-Velez F. Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI. 2020;3 :1-9. Paper
Wu M, Parbhoo S, Hughes M, Kindle R, Celi L, Zazzi M, Volker R, Doshi-Velez F. Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI. 2020;3 :1-9. Paper
Ross A, Pan W, Celi L, Doshi-Velez F. Ensembles of Locally Independent Prediction Models. AAAI. 2020;3 :1-11. Paper
Futoma F, Masgood M, Doshi-Velez F. Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning. AMIA CRI. 2020;1 :1-24. Paper
Srinivasan S, Doshi-Velez F. Interpretable Batch IRL to extract clinician goals in ICU HypotensionManagement. AMIA CRI. 2020 :636-645. Paper
McMahon A, Cooper W, Brown J, Carleton B, Doshi-Velez F, Kohane I, Goldman J, Hoffman M, Kamaleswaran R, Sakiyama M, et al. Big Data in the Assessment of Pediatric Medication Safety. Pediatrics. 2020;145 (2) :1-11. Paper
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
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

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