Michael C. Hughes
Michael C. Hughes
Assistant Professor of Computer Science, Tufts University
Verified email at michaelchughes.com - Homepage
Cited by
Cited by
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
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
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 8 (3), 1281-1313, 2014
Memoized Online Variational Inference for Dirichlet Process Mixture Models
MC Hughes, EB Sudderth
Advances in Neural Information Processing Systems, 1133-1141, 2013
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
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
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process.
MC Hughes, DI Kim, EB Sudderth
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, ...
arXiv preprint arXiv:1908.00690, 2019
The Nonparametric Metadata Dependent Relational Model
DI Kim, MC Hughes, EB Sudderth
The 29th International Conference on Machine Learning (ICML 2012), 2012
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
MC Hughes, WT Stephenson, EB Sudderth
Advances in Neural Information Processing Systems, 2015
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
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
Nonparametric Discovery of Activity Patterns from Video Collections
MC Hughes, EB Sudderth
The Eighth IEEE Computer Society Workshop on Perceptual Organization in …, 2012
Bnpy: Reliable and scalable variational inference for Bayesian nonparametric models
MC Hughes, EB Sudderth
NIPS Probabilistic Programimming Workshop, 2014
Fast Learning of Clusters and Topics via Sparse Posteriors
MC Hughes, EB Sudderth
arXiv preprint arXiv:1609.07521, 2016
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
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
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
J Futoma, MC Hughes, F Doshi-Velez
arXiv preprint arXiv:2001.04032, 2020
Supervised topic models for clinical interpretability
MC Hughes, HM Elibol, T McCoy, R Perlis, F Doshi-Velez
arXiv preprint arXiv:1612.01678, 2016
Preserving Patient Confidentiality as Data Grow: Implications of the Ability to Reidentify Physical Activity Data
TH McCoy, MC Hughes
JAMA Network Open 1 (8), e186029-e186029, 2018
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