Michael C. Hughes
Michael C. Hughes
Assistant Professor of Computer Science, Tufts University
Adresă de e-mail confirmată pe michaelchughes.com - Pagina de pornire
Citat de
Citat de
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
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
Memoized Online Variational Inference for Dirichlet Process Mixture Models
MC Hughes, EB Sudderth
Advances in Neural Information Processing Systems, 1133-1141, 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
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
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
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
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
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process.
MC Hughes, DI Kim, EB Sudderth
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
J Futoma, MC Hughes, F Doshi-Velez
The 23rd International Conference on Artificial Intelligence and Statistics …, 2020
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
The Nonparametric Metadata Dependent Relational Model
DI Kim, MC Hughes, EB Sudderth
The 29th International Conference on Machine Learning (ICML 2012), 2012
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
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
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
MC Hughes, WT Stephenson, EB Sudderth
Advances in Neural Information Processing Systems, 2015
Nonparametric Discovery of Activity Patterns from Video Collections
MC Hughes, EB Sudderth
The Eighth IEEE Computer Society Workshop on Perceptual Organization in …, 2012
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
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
Sistemul nu poate realiza operația în acest moment. Încercați din nou mai târziu.
Articole 1–20