Michael C. Hughes

86 papers A* 12A 5Misc 1Journal 50Unranked 17

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention. ICML Kyle Heuton, F. Samuel Muench, Shikhar Shrestha, Thomas J. Stopka, Michael C. Hughes
2025	J	`jnl`	Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention. CoRR Kyle Heuton, F. Samuel Muench, Shikhar Shrestha, Thomas J. Stopka, Michael C. Hughes
2025	J	`jnl`	Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models. Trans. Mach. Learn. Res. Michael T. Wojnowicz, Kaitlin Gili, Preetish Rath, Eric L. Miller, Jeffrey Miller, Clifford Hancock, Meghan O'Donovan, Seth Elkin-Frankston, Tad Brunyé, Michael C. Hughes
2025	J	`jnl`	Learning Hyperparameters via a Data-Emphasized Variational Objective. CoRR Ethan Harvey, Mikhail Petrov, Michael C. Hughes
2025	J	`jnl`	Partial VOROS: A Cost-aware Performance Metric for Binary Classifiers with Precision and Capacity Constraints. CoRR Christopher Ratigan, Kyle Heuton, Carissa Wang, Lenore Cowen, Michael C. Hughes
2025	—	`conf`	Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis. ISBI Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes
2025	J	`jnl`	Subgroup Validity in Machine Learning for Echocardiogram Data. CoRR Cynthia Feeney, Shane Williams, Benjamin S. Wessler, Michael C. Hughes
2025	J	`jnl`	Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning. CoRR Ethan Harvey, Dennis Johan Loevlie, Michael C. Hughes
2025	J	`jnl`	Using machine learning to measure evidence of students' sensemaking in physics courses. CoRR Kaitlin Gili, Kyle Heuton, Astha Shah, Michael C. Hughes
2024	J	`jnl`	A neurosymbolic cognitive architecture framework for handling novelties in open worlds. Artif. Intell. 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
2024	—	`conf`	Conference on Health, Inference, and Learning (CHIL) 2024. CHIL Tom J. Pollard, Edward Choi, Pankhuri Singhal, Michael C. Hughes, Elena Sizikova, Bobak Mortazavi, Irene Y. Chen, Fei Wang, Tasmie Sarker, Matthew B. A. McDermott, Marzyeh Ghassemi
2024	J	`jnl`	Discovering group dynamics in synchronous time series via hierarchical recurrent switching-state models. CoRR Michael T. Wojnowicz, Preetish Rath, Eric L. Miller, Jeffrey Miller, Clifford Hancock, Meghan O'Donovan, Seth Elkin-Frankston, Tad Brunyé, Michael C. Hughes
2024	A*	`conf`	InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning. ICML Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes
2024	J	`jnl`	InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning. CoRR Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes
2024	J	`jnl`	Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective. CoRR Ethan Harvey, Mikhail Petrov, Michael C. Hughes
2024	J	`jnl`	Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis. CoRR Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes
2024	A*	`conf`	Systematic comparison of semi-supervised and self-supervised learning for medical image classification. CVPR Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes
2024	J	`jnl`	Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported. CoRR Ethan Harvey, Mikhail Petrov, Michael C. Hughes
2024	J	`jnl`	Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported. Trans. Mach. Learn. Res. Ethan Harvey, Mikhail Petrov, Michael C. Hughes
2023	—	`conf`	A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data. ML4H@NeurIPS Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes
2023	J	`jnl`	A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data. CoRR Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes
2023	J	`jnl`	Accuracy versus time frontiers of semi-supervised and self-supervised learning on medical images. CoRR Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes
2023	—	`conf`	Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning. MLHC Zhe Huang, Benjamin S. Wessler, Michael C. Hughes
2023	J	`jnl`	Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning. CoRR Zhe Huang, Benjamin S. Wessler, Michael C. Hughes
2023	A	`conf`	Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data. AISTATS Zhe Huang, Mary-Joy Sidhom, Benjamin Wessler, Michael C. Hughes
2023	J	`jnl`	Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations. IEEE Trans. Signal Process. Kevin C. Cheng, Eric L. Miller, Michael C. Hughes, Shuchin Aeron
2023	J	`jnl`	NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. Trans. Mach. Learn. Res. Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes
2023	J	`jnl`	SINCERE: Supervised Information Noise-Contrastive Estimation REvisited. CoRR Patrick Feeney, Michael C. Hughes
2022	—	`conf`	Conference on Health, Inference, and Learning (CHIL) 2022. CHIL Gerardo Flores, George H. Chen, Tom J. Pollard, Ayah Zirikly, Michael C. Hughes, Tasmie Sarker, Joyce C. Ho, Tristan Naumann
2022	A*	`conf`	Easy Variational Inference for Categorical Models via an Independent Binary Approximation. ICML Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes
2022	J	`jnl`	Easy Variational Inference for Categorical Models via an Independent Binary Approximation. CoRR Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes
2022	J	`jnl`	Fix-A-Step: Effective Semi-supervised Learning from Uncurated Unlabeled Sets. CoRR Zhe Huang, Mary-Joy Sidhom, Benjamin S. Wessler, Michael C. Hughes
2022	J	`jnl`	Non-Parametric and Regularized Dynamical Wasserstein Barycenters for Time-Series Analysis. CoRR Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller
2022	J	`jnl`	NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. CoRR Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes
2022	A	`conf`	Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint. AISTATS Preetish Rath, Michael C. Hughes
2021	—	`conf`	A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms. MLHC Zhe Huang, Gary Long, Benjamin Wessler, Michael C. Hughes
2021	J	`jnl`	A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms. CoRR Zhe Huang, Gary Long, Benjamin Wessler, Michael C. Hughes
2021	—	`conf`	Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories. MLHC Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes
2021	J	`jnl`	Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories. CoRR Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes
2021	A*	`conf`	Dynamical Wasserstein Barycenters for Time-series Modeling. NeurIPS Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller
2021	J	`jnl`	Dynamical Wasserstein Barycenters for Time-series Modeling. CoRR Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller
2021	J	`jnl`	Enzyme promiscuity prediction using hierarchy-informed multi-label classification. Bioinform. Gian Marco Visani, Michael C. Hughes, Soha Hassoun
2021	J	`jnl`	Evaluating the Use of Reconstruction Error for Novelty Localization. CoRR Patrick Feeney, Michael C. Hughes
2021	J	`jnl`	Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach. CoRR Alexandra Hope Lee, Panagiotis Lymperopoulos, Joshua T. Cohen, John B. Wong, Michael C. Hughes
2021	J	`jnl`	Modeling Graph Node Correlations with Neighbor Mixture Models. CoRR Linfeng Liu, Michael C. Hughes, Liping Liu
2021	J	`jnl`	Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. J. Artif. Intell. Res. Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez
2021	A*	`conf`	Stochastic Iterative Graph Matching. ICML Linfeng Liu, Michael C. Hughes, Soha Hassoun, Liping Liu
2021	J	`jnl`	Stochastic Iterative Graph Matching. CoRR Linfeng Liu, Michael C. Hughes, Soha Hassoun, Li-Ping Liu
2021	A*	`conf`	Taming fNIRS-based BCI Input for Better Calibration and Broader Use. UIST Liang Wang, Zhe Huang, Ziyu Zhou, Devon McKeon, Giles Blaney, Michael C. Hughes, Robert J. K. Jacob
2021	—	`conf`	The Tufts fNIRS Mental Workload Dataset & Benchmark for Brain-Computer Interfaces that Generalize. NeurIPS Datasets and Benchmarks Zhe Huang, Liang Wang, Giles Blaney, Christopher Slaughter, Devon McKeon, Ziyu Zhou, Robert J. K. Jacob, Michael C. Hughes
2020	J	`jnl`	A Framework for Sensorimotor Cross-Perception and Cross-Behavior Knowledge Transfer for Object Categorization. Frontiers Robotics AI Gyan Tatiya, Ramtin Hosseini, Michael C. Hughes, Jivko Sinapov
2020	J	`jnl`	Hierarchical Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples. CoRR Gian Marco Visani, Michael C. Hughes, Soha Hassoun
2020	J	`jnl`	Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints. CoRR Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth
2020	—	`conf`	MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. CHIL Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, Tristan Naumann
2020	Misc	`conf`	Optimal Transport Based Change Point Detection and Time Series Segment Clustering. ICASSP Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller
2020	J	`jnl`	POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning. CoRR Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez
2020	A	`conf`	POPCORN: Partially Observed Prediction Constrained Reinforcement Learning. AISTATS Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez
2020	A*	`conf`	Regional Tree Regularization for Interpretability in Deep Neural Networks. AAAI Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
2019	—	`conf`	Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. MLHC Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi
2019	J	`jnl`	Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. CoRR Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi
2019	J	`jnl`	MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III. CoRR Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi
2019	J	`jnl`	Optimal Transport Based Change Point Detection and Time Series Segment Clustering. CoRR Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller
2019	J	`jnl`	Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. CoRR Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez
2019	—	`conf`	Rapid Model Comparison by Amortizing Across Models. AABI Lily H. Zhang, Michael C. Hughes
2019	J	`jnl`	Regional Tree Regularization for Interpretability in Black Box Models. CoRR Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
2019	—	`conf`	Sensorimotor Cross-Behavior Knowledge Transfer for Grounded Category Recognition. ICDL-EPIROB Gyan Tatiya, Ramtin Hosseini, Michael C. Hughes, Jivko Sinapov
2018	A*	`conf`	Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. AAAI Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
2018	J	`jnl`	Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation. CoRR Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi
2018	A	`conf`	Semi-Supervised Prediction-Constrained Topic Models. AISTATS Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
2017	J	`jnl`	Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. CoRR Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
2017	A*	`conf`	From Patches to Images: A Nonparametric Generative Model. ICML Geng Ji, Michael C. Hughes, Erik B. Sudderth
2017	—	`conf`	Predicting intervention onset in the ICU with switching state space models. CRI Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Peter Szolovits, Finale Doshi-Velez
2017	J	`jnl`	Prediction-Constrained Topic Models for Antidepressant Recommendation. CoRR Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
2017	J	`jnl`	Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. CoRR Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
2017	J	`jnl`	Refinery: An Open Source Topic Modeling Web Platform. J. Mach. Learn. Res. Dae Il Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth
2017	A*	`conf`	Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. IJCAI Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
2017	J	`jnl`	Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. CoRR Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
2016	—	`—`	"Reliable and scalable variational inference for nonparametric mixtures, topics, and sequences". Michael C. Hughes
2016	J	`jnl`	Fast Learning of Clusters and Topics via Sparse Posteriors. CoRR Michael C. Hughes, Erik B. Sudderth
2015	A	`conf`	Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. AISTATS Michael C. Hughes, Dae Il Kim, Erik B. Sudderth
2015	—	`conf`	Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. NIPS Michael C. Hughes, William T. Stephenson, Erik B. Sudderth
2013	—	`conf`	Memoized Online Variational Inference for Dirichlet Process Mixture Models. NIPS Michael C. Hughes, Erik B. Sudderth
2012	—	`conf`	Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. NIPS Michael C. Hughes, Emily B. Fox, Erik B. Sudderth
2012	—	`conf`	Nonparametric discovery of activity patterns from video collections. CVPR Workshops Michael C. Hughes, Erik B. Sudderth
2012	A*	`conf`	The Nonparametric Metadata Dependent Relational Model. ICML Dae Il Kim, Michael C. Hughes, Erik B. Sudderth
2010	J	`jnl`	String formatting considered harmful for novice programmers. Comput. Sci. Educ. Michael C. Hughes, Matthew C. Jadud, Ma. Mercedes T. Rodrigo