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ORCIDMichael C. Hughes
Person information
- affiliation: Tufts University, Department of Computer Science, Medford, MA, USA
- affiliation (PhD 2016): Brown University, Department of Computer Science, Providence, RI, USA
- affiliation (former): Olin College of Engineering, Needham, MA, USA
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Books and Theses
- 2016
[b1]Michael C. Hughes:
"Reliable and scalable variational inference for nonparametric mixtures, topics, and sequences". Brown University, USA, 2016
Journal Articles
- 2023
- [j6]Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes:
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. Trans. Mach. Learn. Res. 2023 (2023) - [j5]Kevin C. Cheng
, Eric L. Miller, Michael C. Hughes, Shuchin Aeron:
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations. IEEE Trans. Signal Process. 71: 3164-3178 (2023) - 2021
- [j4]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez:
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. J. Artif. Intell. Res. 72: 1-37 (2021) - 2020
- [j3]Gyan Tatiya, Ramtin Hosseini, Michael C. Hughes, Jivko Sinapov:
A Framework for Sensorimotor Cross-Perception and Cross-Behavior Knowledge Transfer for Object Categorization. Frontiers Robotics AI 7: 522141 (2020) - 2017
- [j2]Dae Il Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth:
Refinery: An Open Source Topic Modeling Web Platform. J. Mach. Learn. Res. 18: 12:1-12:5 (2017) - 2010
- [j1]Michael C. Hughes, Matthew C. Jadud, Ma. Mercedes T. Rodrigo:
String formatting considered harmful for novice programmers. Comput. Sci. Educ. 20(3): 201-228 (2010)
Conference and Workshop Papers
- 2023
- [c30]Zhe Huang, Mary-Joy Sidhom, Benjamin Wessler, Michael C. Hughes:
Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data. AISTATS 2023: 8373-8394 - [c29]Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes:
A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data. ML4H@NeurIPS 2023: 129-144 - [c28]Zhe Huang, Benjamin S. Wessler, Michael C. Hughes:
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning. MLHC 2023: 285-307 - 2022
- [c27]Preetish Rath, Michael C. Hughes:
Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint. AISTATS 2022: 4895-4914 - [c26]Gerardo Flores, George H. Chen, Tom J. Pollard, Ayah Zirikly, Michael C. Hughes, Tasmie Sarker, Joyce C. Ho, Tristan Naumann:
Conference on Health, Inference, and Learning (CHIL) 2022. CHIL 2022: 1-4 - [c25]Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes:
Easy Variational Inference for Categorical Models via an Independent Binary Approximation. ICML 2022: 23857-23896 - 2021
- [c24]Linfeng Liu, Michael C. Hughes, Soha Hassoun, Liping Liu:
Stochastic Iterative Graph Matching. ICML 2021: 6815-6825 - [c23]Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes:
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories. MLHC 2021: 567-613 - [c22]Zhe Huang, Gary Long, Benjamin Wessler, Michael C. Hughes:
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms. MLHC 2021: 614-647 - [c21]Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller:
Dynamical Wasserstein Barycenters for Time-series Modeling. NeurIPS 2021: 27991-28003 - [c20]Zhe Huang, Liang Wang, Giles Blaney, Christopher Slaughter, Devon McKeon, Ziyu Zhou, Robert J. K. Jacob, Michael C. Hughes:
The Tufts fNIRS Mental Workload Dataset & Benchmark for Brain-Computer Interfaces that Generalize. NeurIPS Datasets and Benchmarks 2021 - [c19]Liang Wang, Zhe Huang, Ziyu Zhou, Devon McKeon, Giles Blaney, Michael C. Hughes, Robert J. K. Jacob:
Taming fNIRS-based BCI Input for Better Calibration and Broader Use. UIST 2021: 179-197 - 2020
- [c18]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI 2020: 6413-6421 - [c17]Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez:
POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. AISTATS 2020: 3578-3588 - [c16]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, Tristan Naumann:
MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. CHIL 2020: 222-235 - [c15]Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller:
Optimal Transport Based Change Point Detection and Time Series Segment Clustering. ICASSP 2020: 6034-6038 - 2019
- [c14]Lily H. Zhang, Michael C. Hughes:
Rapid Model Comparison by Amortizing Across Models. AABI 2019: 1-11 - [c13]Gyan Tatiya, Ramtin Hosseini, Michael C. Hughes, Jivko Sinapov:
Sensorimotor Cross-Behavior Knowledge Transfer for Grounded Category Recognition. ICDL-EPIROB 2019: 1-6 - [c12]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. MLHC 2019: 381-405 - 2018
- [c11]Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. AAAI 2018: 1670-1678 - [c10]Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Semi-Supervised Prediction-Constrained Topic Models. AISTATS 2018: 1067-1076 - 2017
- [c9]Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Peter Szolovits, Finale Doshi-Velez:
Predicting intervention onset in the ICU with switching state space models. CRI 2017 - [c8]Geng Ji, Michael C. Hughes, Erik B. Sudderth:
From Patches to Images: A Nonparametric Generative Model. ICML 2017: 1675-1683 - [c7]Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez:
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. IJCAI 2017: 2662-2670 - 2015
- [c6]Michael C. Hughes, Dae Il Kim, Erik B. Sudderth:
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. AISTATS 2015 - [c5]Michael C. Hughes, William T. Stephenson, Erik B. Sudderth:
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. NIPS 2015: 1198-1206 - 2013
- [c4]Michael C. Hughes, Erik B. Sudderth:
Memoized Online Variational Inference for Dirichlet Process Mixture Models. NIPS 2013: 1133-1141 - 2012
- [c3]Michael C. Hughes, Erik B. Sudderth:
Nonparametric discovery of activity patterns from video collections. CVPR Workshops 2012: 25-32 - [c2]Dae Il Kim, Michael C. Hughes, Erik B. Sudderth:
The Nonparametric Metadata Dependent Relational Model. ICML 2012 - [c1]Michael C. Hughes, Emily B. Fox, Erik B. Sudderth:
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. NIPS 2012: 1304-1312
Informal and Other Publications
- 2024
- [i32]Michael T. Wojnowicz, Preetish Rath, Eric L. Miller, Jeffrey Miller, Clifford Hancock, Meghan O'Donovan, Seth Elkin-Frankston, Thaddeus Brunye, Michael C. Hughes:
Discovering group dynamics in synchronous time series via hierarchical recurrent switching-state models. CoRR abs/2401.14973 (2024) - [i31]Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes:
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis. CoRR abs/2403.06024 (2024) - [i30]Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes:
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning. CoRR abs/2403.10658 (2024) - 2023
- [i29]Zhe Huang, Benjamin S. Wessler, Michael C. Hughes:
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning. CoRR abs/2306.00003 (2023) - [i28]Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes:
Accuracy versus time frontiers of semi-supervised and self-supervised learning on medical images. CoRR abs/2307.08919 (2023) - [i27]Patrick Feeney, Michael C. Hughes:
SINCERE: Supervised Information Noise-Contrastive Estimation REvisited. CoRR abs/2309.14277 (2023) - [i26]Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes:
A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data. CoRR abs/2311.18025 (2023) - 2022
- [i25]Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes:
Easy Variational Inference for Categorical Models via an Independent Binary Approximation. CoRR abs/2206.00093 (2022) - [i24]Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes:
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. CoRR abs/2206.11736 (2022) - [i23]Zhe Huang, Mary-Joy Sidhom, Benjamin S. Wessler, Michael C. Hughes:
Fix-A-Step: Effective Semi-supervised Learning from Uncurated Unlabeled Sets. CoRR abs/2208.11870 (2022) - [i22]Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller:
Non-Parametric and Regularized Dynamical Wasserstein Barycenters for Time-Series Analysis. CoRR abs/2210.01918 (2022) - 2021
- [i21]Linfeng Liu, Michael C. Hughes, Liping Liu:
Modeling Graph Node Correlations with Neighbor Mixture Models. CoRR abs/2103.15966 (2021) - [i20]Alexandra Hope Lee, Panagiotis Lymperopoulos, Joshua T. Cohen, John B. Wong, Michael C. Hughes:
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach. CoRR abs/2104.09327 (2021) - [i19]Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes:
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories. CoRR abs/2105.00773 (2021) - [i18]Linfeng Liu, Michael C. Hughes, Soha Hassoun, Li-Ping Liu:
Stochastic Iterative Graph Matching. CoRR abs/2106.02206 (2021) - [i17]Patrick Feeney, Michael C. Hughes:
Evaluating the Use of Reconstruction Error for Novelty Localization. CoRR abs/2107.13379 (2021) - [i16]Zhe Huang, Gary Long, Benjamin Wessler, Michael C. Hughes:
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms. CoRR abs/2108.00080 (2021) - [i15]Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller:
Dynamical Wasserstein Barycenters for Time-series Modeling. CoRR abs/2110.06741 (2021) - 2020
- [i14]Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez:
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning. CoRR abs/2001.04032 (2020) - [i13]Gian Marco Visani, Michael C. Hughes, Soha Hassoun:
Hierarchical Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples. CoRR abs/2002.07327 (2020) - [i12]Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth:
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints. CoRR abs/2012.06718 (2020) - 2019
- [i11]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi:
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III. CoRR abs/1907.08322 (2019) - [i10]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. CoRR abs/1908.00690 (2019) - [i9]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Regional Tree Regularization for Interpretability in Black Box Models. CoRR abs/1908.04494 (2019) - [i8]Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez:
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. CoRR abs/1908.05254 (2019) - [i7]Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller:
Optimal Transport Based Change Point Detection and Time Series Segment Clustering. CoRR abs/1911.01325 (2019) - 2018
- [i6]Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation. CoRR abs/1811.12583 (2018) - 2017
- [i5]Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez:
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. CoRR abs/1703.03717 (2017) - [i4]Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. CoRR abs/1707.07341 (2017) - [i3]Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez:
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. CoRR abs/1711.06178 (2017) - [i2]Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Prediction-Constrained Topic Models for Antidepressant Recommendation. CoRR abs/1712.00499 (2017) - 2016
- [i1]Michael C. Hughes, Erik B. Sudderth:
Fast Learning of Clusters and Topics via Sparse Posteriors. CoRR abs/1609.07521 (2016)
Coauthor Index
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