default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j8]Shivam Goel, Panagiotis Lymperopoulos
, Ravenna Thielstrom, Evan A. Krause, Patrick Feeney, Pierrick Lorang, Sarah Schneider, Yichen Wei, Eric J. Kildebeck, Stephen A. Goss, Michael C. Hughes, Li-Ping Liu, Jivko Sinapov, Matthias Scheutz:
A neurosymbolic cognitive architecture framework for handling novelties in open worlds. Artif. Intell. 331: 104111 (2024)- [j7]Ethan Harvey, Mikhail Petrov, Michael C. Hughes:
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported. Trans. Mach. Learn. Res. 2024 (2024) - [c32]Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes:
Systematic comparison of semi-supervised and self-supervised learning for medical image classification. CVPR 2024: 22282-22293 - [c31]Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes:
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning. ICML 2024 - [i33]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) - [i32]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) - [i31]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) - [i30]Ethan Harvey, Mikhail Petrov, Michael C. Hughes:
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported. CoRR abs/2405.15583 (2024) - 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) - [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 - [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
- [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 - [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
- [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) - [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 - [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
- [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) - [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 - [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)
2010 – 2019
- 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 - [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
- [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 - [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
- [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) - [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 - [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
- [b1]Michael C. Hughes:
"Reliable and scalable variational inference for nonparametric mixtures, topics, and sequences". Brown University, USA, 2016 - [i1]Michael C. Hughes, Erik B. Sudderth:
Fast Learning of Clusters and Topics via Sparse Posteriors. CoRR abs/1609.07521 (2016) - 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 - 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)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:24 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
