Michael C. Hughes

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 62

Top Topics

Dirichlet Process

Top Venues

Publications

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 (2024)
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 (2024)
Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning. CoRR (2024)
Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis. CoRR (2024)
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported. Trans. Mach. Learn. Res. 2024 (2024)
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported. CoRR (2024)
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 (2023)
Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes
Accuracy versus time frontiers of semi-supervised and self-supervised learning on medical images. CoRR (2023)
Zhe Huang, Mary-Joy Sidhom, Benjamin Wessler, Michael C. Hughes
Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data. AISTATS (2023)
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)
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 (2023)
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)
Zhe Huang, Benjamin S. Wessler, Michael C. Hughes
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning. CoRR (2023)
Zhe Huang, Benjamin S. Wessler, Michael C. Hughes
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning. MLHC (2023)
Patrick Feeney, Michael C. Hughes
SINCERE: Supervised Information Noise-Contrastive Estimation REvisited. CoRR (2023)
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)
Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes
Easy Variational Inference for Categorical Models via an Independent Binary Approximation. CoRR (2022)
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 (2022)
Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes
Easy Variational Inference for Categorical Models via an Independent Binary Approximation. ICML (2022)
Zhe Huang, Mary-Joy Sidhom, Benjamin S. Wessler, Michael C. Hughes
Fix-A-Step: Effective Semi-supervised Learning from Uncurated Unlabeled Sets. CoRR (2022)
Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller
Non-Parametric and Regularized Dynamical Wasserstein Barycenters for Time-Series Analysis. CoRR (2022)
Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller
Dynamical Wasserstein Barycenters for Time-series Modeling. CoRR (2021)
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)
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 (2021)
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)
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)
Linfeng Liu, Michael C. Hughes, Soha Hassoun, Liping Liu
Stochastic Iterative Graph Matching. ICML (2021)
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)
Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller
Dynamical Wasserstein Barycenters for Time-series Modeling. NeurIPS (2021)
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 (2021)
Linfeng Liu, Michael C. Hughes, Soha Hassoun, Li-Ping Liu
Stochastic Iterative Graph Matching. CoRR (2021)
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 (2021)
Patrick Feeney, Michael C. Hughes
Evaluating the Use of Reconstruction Error for Novelty Localization. CoRR (2021)
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 (2021)
Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning. CoRR (2020)
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)
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)
Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez
POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. AISTATS (2020)
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)
Gian Marco Visani, Michael C. Hughes, Soha Hassoun
Hierarchical Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples. CoRR (2020)
Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints. CoRR (2020)
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 (2020)
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. CoRR (2019)
Lily H. Zhang, Michael C. Hughes
Rapid Model Comparison by Amortizing Across Models. AABI (2019)
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 (2019)
Gyan Tatiya, Ramtin Hosseini, Michael C. Hughes, Jivko Sinapov
Sensorimotor Cross-Behavior Knowledge Transfer for Grounded Category Recognition. ICDL-EPIROB (2019)
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 (2019)
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)
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 (2019)
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 (2019)
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)
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 (2018)
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)
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 (2017)
Geng Ji, Michael C. Hughes, Erik B. Sudderth
From Patches to Images: A Nonparametric Generative Model. ICML (2017)
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 (2017)
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. CoRR (2017)
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 (2017)
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. CoRR (2017)
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. IJCAI (2017)
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)
Michael C. Hughes, Erik B. Sudderth
Fast Learning of Clusters and Topics via Sparse Posteriors. CoRR (2016)
Michael C. Hughes, Dae Il Kim, Erik B. Sudderth
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. AISTATS (2015)
Michael C. Hughes, William T. Stephenson, Erik B. Sudderth
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. NIPS (2015)
Michael C. Hughes, Erik B. Sudderth
Memoized Online Variational Inference for Dirichlet Process Mixture Models. NIPS (2013)
Michael C. Hughes, Emily B. Fox, Erik B. Sudderth
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. NIPS (2012)
Dae Il Kim, Michael C. Hughes, Erik B. Sudderth
The Nonparametric Metadata Dependent Relational Model. ICML (2012)
Michael C. Hughes, Erik B. Sudderth
Nonparametric discovery of activity patterns from video collections. CVPR Workshops (2012)
Michael C. Hughes, Matthew C. Jadud, Ma. Mercedes T. Rodrigo
String formatting considered harmful for novice programmers. Comput. Sci. Educ. 20 (3) (2010)