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Modeling Graph Node Correlations with Neighbor Mixture Models


Apr 18, 2021
Linfeng Liu, Michael C. Hughes, Li-Ping Liu


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Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach


Apr 14, 2021
Alexandra Hope Lee, Panagiotis Lymperopoulos, Joshua T. Cohen, John B. Wong, Michael C. Hughes

* In ICLR 2021 Workshop on Machine Learning for Preventing and Combating Pandemics 

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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

* Presented as a poster at the 2019 Machine Learning for Computational Biology Symposium, Vancouver, CA 

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POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning


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

* 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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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

* arXiv admin note: substantial text overlap with arXiv:1908.04494, arXiv:1711.06178 

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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

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 

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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

* Accepted poster at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/

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Beyond Sparsity: Tree Regularization of Deep Models for Interpretability


Nov 16, 2017
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

* To appear in AAAI 2018. Contains 9-page main paper and appendix with supplementary material 

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Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models


Jul 23, 2017
Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez


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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations


May 25, 2017
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez


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Supervised topic models for clinical interpretability


Dec 06, 2016
Michael C. Hughes, Huseyin Melih Elibol, Thomas McCoy, Roy Perlis, Finale Doshi-Velez

* Accepted poster presentation at NIPS 2016 Workshop on Machine Learning for Health (http://www.nipsml4hc.ws/

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Fast Learning of Clusters and Topics via Sparse Posteriors


Sep 23, 2016
Michael C. Hughes, Erik B. Sudderth


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Joint modeling of multiple time series via the beta process with application to motion capture segmentation


Nov 13, 2014
Emily B. Fox, Michael C. Hughes, Erik B. Sudderth, Michael I. Jordan

* Annals of Applied Statistics 2014, Vol. 8, No. 3, 1281-1313 
* Published in at http://dx.doi.org/10.1214/14-AOAS742 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: text overlap with arXiv:1111.4226 

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