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Finale Doshi-Velez

Online structural kernel selection for mobile health


Jul 21, 2021
Eura Shin, Pedja Klasnja, Susan Murphy, Finale Doshi-Velez

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* Workshop paper in ICML IMLH 2021 
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Promises and Pitfalls of Black-Box Concept Learning Models


Jun 24, 2021
Anita Mahinpei, Justin Clark, Isaac Lage, Finale Doshi-Velez, Weiwei Pan

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Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning


Jun 14, 2021
Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe

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Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data


Jun 13, 2021
Beau Coker, Weiwei Pan, Finale Doshi-Velez

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Learning Under Adversarial and Interventional Shifts


Mar 29, 2021
Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju

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* 19 pages including 5 pages appendix, 6 figures, 2 tables. Preliminary version presented at Causal Discovery & Causality-Inspired Machine Learning Workshop 2020 
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Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement


Feb 09, 2021
Andrew Slavin Ross, Finale Doshi-Velez

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Evaluating the Interpretability of Generative Models by Interactive Reconstruction


Feb 02, 2021
Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez

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* CHI 2021 accepted paper 
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Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible


Jan 13, 2021
Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez

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* 10 pages, 5 figures, 4 tables, AMIA 2021 Virtual Informatics Summit 
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Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment


Jan 09, 2021
Kristine Zhang, Yuanheng Wang, Jianzhun Du, Brian Chu, Leo Anthony Celi, Ryan Kindle, Finale Doshi-Velez

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* NeurIPS 2020 Machine Learning for Health (ML4H) Workshop 
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Artificial Intelligence & Cooperation


Dec 10, 2020
Elisa Bertino, Finale Doshi-Velez, Maria Gini, Daniel Lopresti, David Parkes

* A Computing Community Consortium (CCC) white paper, 4 pages 
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Learning Interpretable Concept-Based Models with Human Feedback


Dec 04, 2020
Isaac Lage, Finale Doshi-Velez

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Incorporating Interpretable Output Constraints in Bayesian Neural Networks


Oct 21, 2020
Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez

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* 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. Code available at: https://github.com/dtak/ocbnn-public 
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Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks


Jul 14, 2020
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

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* Accepted at the International Conference on Machine Learning (ICML) Workshop on Uncertainty and Robustness in Deep Learning (UDL) 2020 
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BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty


Jul 12, 2020
Théo Guénais, Dimitris Vamvourellis, Yaniv Yacoby, Finale Doshi-Velez, Weiwei Pan

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* ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning 
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Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks


Jul 08, 2020
Sujay Thakur, Cooper Lorsung, Yaniv Yacoby, Finale Doshi-Velez, Weiwei Pan

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* ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning 
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Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs


Jun 29, 2020
Jianzhun Du, Joseph Futoma, Finale Doshi-Velez

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* 20 pages, 7 figures 
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PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes


Jun 11, 2020
Yash Nair, Finale Doshi-Velez


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Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment


May 08, 2020
MingYu Lu, Zachary Shahn, Daby Sow, Finale Doshi-Velez, Li-wei H. Lehman

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* 10 pages, 9 figures 
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Power-Constrained Bandits


Apr 13, 2020
Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan Murphy, Finale Doshi-Velez

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Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders


Mar 17, 2020
Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

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* PMLR 118:1-17, 2020 
 * Accepted at the Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference 2019 
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Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions


Feb 14, 2020
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez

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* Change: Correction of typo in meta-data author names 
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POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning


Jan 13, 2020
Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez

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* Accepted, to appear at AISTATS 2020, Palermo. Note that this version is not the final camera-ready; that will appear in a few weeks 
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Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning


Jan 09, 2020
Joseph Futoma, Muhammad A. Masood, Finale Doshi-Velez

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* Accepted for publication at the AMIA 2020 Informatics Summit. This version contains an updated appendix with additional figures not found in the page-constrained AMIA version, so treat this version as the most up-to-date 
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Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks


Dec 12, 2019
Beau Coker, Melanie F. Pradier, Finale Doshi-Velez

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Ensembles of Locally Independent Prediction Models


Nov 27, 2019
Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi, Finale Doshi-Velez

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* This is an expansion of arXiv:1806.08716 with different applications and focus, accepted to AAAI 2020 
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Prediction Focused Topic Models for Electronic Health Records


Nov 15, 2019
Jason Ren, Russell Kunes, Finale Doshi-Velez

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* Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract. arXiv admin note: substantial text overlap with arXiv:1910.05495 
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