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Michael C. Hughes

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Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints

Dec 12, 2020
Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Michael C. Hughes, Erik B. Sudderth

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On Matched Filtering for Statistical Change Point Detection

Jun 09, 2020
Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller

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Hierarchical Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples

Feb 18, 2020
Gian Marco Visani, Michael C. Hughes, Soha Hassoun

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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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Optimal Transport Based Change Point Detection and Time Series Segment Clustering

Nov 04, 2019
Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller

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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization

Aug 14, 2019
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

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Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

Aug 02, 2019
Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

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MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III

Jul 19, 2019
Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi

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Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation

Nov 30, 2018
Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

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Prediction-Constrained Topic Models for Antidepressant Recommendation

Dec 01, 2017
Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy, Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

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