Michael C. Hughes

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Finale Doshi-VelezProfessor, HarvardDirección de correo verificada de seas.harvard.edu
Erik B. SudderthProfessor of Computer Science, UC IrvineDirección de correo verificada de uci.edu
Zhe HuangPostdoc Associate, Cornell UniversityDirección de correo verificada de med.cornell.edu
Dr. Marzyeh GhassemiAssociate Professor, EECS/IMES, MITDirección de correo verificada de mit.edu
Mike WuStanford UniversityDirección de correo verificada de cs.stanford.edu
Tristan NaumannPrincipal Researcher, Microsoft Research Health FuturesDirección de correo verificada de microsoft.com
Thomas H McCoyMassachusetts General Hospital / Harvard Medical SchoolDirección de correo verificada de mgh.harvard.edu
Andrew RossPhD student, Harvard UniversityDirección de correo verificada de g.harvard.edu
Sonali ParbhooAssistant Professor, Imperial College LondonDirección de correo verificada de imperial.ac.uk
Roy PerlisMassachusetts General HospitalDirección de correo verificada de mgh.harvard.edu
Matthew B. A. McDermottPostdoctoral Researcher, Harvard Medical School, Department of Biomedical InformaticsDirección de correo verificada de hms.harvard.edu
Shuchin AeronElectrical and Computer Engineering, Tufts UniversityDirección de correo verificada de ece.tufts.edu
Eric MillerTufts UniversityDirección de correo verificada de tufts.edu
Gabriel HopeHarvey Mudd CollegeDirección de correo verificada de uci.edu
Emily B. FoxStanford UniversityDirección de correo verificada de stanford.edu
Melanie F. PradierMicrosoft ResearchDirección de correo verificada de microsoft.com
Bret NestorPostdoctoral Researcher, University of WashingtonDirección de correo verificada de cs.washington.edu
Soha HassounProfessor & Past Chair. Department of Computer Science, Tufts UniversityDirección de correo verificada de cs.tufts.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Jivko SinapovAssistant Professor, Tufts UniversityDirección de correo verificada de cs.tufts.edu

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Michael C. Hughes

Assistant Professor of Computer Science, Tufts University

Dirección de correo verificada de michaelchughes.com - Página principal

Machine Learning Clinical Informatics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations AS Ross, MC Hughes, F Doshi-Velez International Joint Conference on Artificial Intelligence, 2017	663	2017
Beyond sparsity: Tree regularization of deep models for interpretability M Wu, MC Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez Thirty-Second AAAI Conference on Artificial Intelligence, 2018	329	2018
MIMIC-Extract: A data extraction, preprocessing, and representation pipeline for MIMIC-III S Wang, MBA McDermott, G Chauhan, M Ghassemi, MC Hughes, ... Proceedings of the ACM Conference on Health, Inference, and Learning, 222-235, 2020	248	2020
The role of machine learning in clinical research: transforming the future of evidence generation EH Weissler, T Naumann, T Andersson, R Ranganath, O Elemento, Y Luo, ... Trials, 2021	237	2021
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks B Nestor, M McDermott, W Boag, G Berner, T Naumann, MC Hughes, ... Proceedings of the 4th Machine Learning for Healthcare Conference, 2019	152	2019
Memoized Online Variational Inference for Dirichlet Process Mixture Models MC Hughes, EB Sudderth Advances in Neural Information Processing Systems, 1133-1141, 2013	138	2013
Joint modeling of multiple time series via the beta process with application to motion capture segmentation EB Fox, MC Hughes, EB Sudderth, MI Jordan The Annals of Applied Statistics, 1281-1313, 2014	136	2014
Predicting intervention onset in the ICU with switching state space models M Ghassemi, M Wu, MC Hughes, P Szolovits, F Doshi-Velez AMIA Summits on Translational Science Proceedings 2017, 82, 2017	71	2017
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. MC Hughes, DI Kim, EB Sudderth AISTATS, 2015	54	2015
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning J Futoma, MC Hughes, F Doshi-Velez The 23rd International Conference on Artificial Intelligence and Statistics …, 2020	53	2020
Effective split-merge monte carlo methods for nonparametric models of sequential data MC Hughes, EB Fox, EB Sudderth Advances in Neural Information Processing Systems, 1295-1303, 2012	50	2012
Regional tree regularization for interpretability in deep neural networks M Wu, S Parbhoo, M Hughes, R Kindle, L Celi, M Zazzi, V Roth, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6413-6421, 2020	45	2020
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation B Nestor, M McDermott, G Chauhan, T Naumann, MC Hughes, ... arXiv preprint arXiv:1811.12583, 2018	45	2018
Predicting treatment dropout after antidepressant initiation MF Pradier, TH McCoy Jr, M Hughes, RH Perlis, F Doshi-Velez Translational Psychiatry 10 (1), 1-8, 2020	36	2020
The Nonparametric Metadata Dependent Relational Model DI Kim, MC Hughes, EB Sudderth The 29th International Conference on Machine Learning (ICML 2012), 2012	36	2012
Semi-Supervised Prediction-Constrained Topic Models MC Hughes, G Hope, L Weiner, TH McCoy Jr, RH Perlis, E Sudderth, ... International Conference on Artificial Intelligence and Statistics, 1067-1076, 2018	35	2018
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms Z Huang, G Long, B Wessler, MC Hughes Machine Learning for Healthcare, 2021	33	2021
Optimizing for interpretability in deep neural networks with tree regularization M Wu, S Parbhoo, MC Hughes, V Roth, F Doshi-Velez Journal of Artificial Intelligence Research 72, 1-37, 2021	32	2021
Enzyme promiscuity prediction using hierarchy-informed multi-label classification GM Visani, MC Hughes, S Hassoun Bioinformatics 37 (14), 2017-2024, 2021	28	2021
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models MC Hughes, WT Stephenson, EB Sudderth Advances in Neural Information Processing Systems, 2015	26	2015

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