Hughes, Michael C.

31 publications

ICML 2025 Decision-Aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention Kyle Heuton, Frederick Muench, Shikhar Shrestha, Thomas J. Stopka, Michael C Hughes

TMLR 2025 Discovering Group Dynamics in Coordinated Time Series via Hierarchical Recurrent Switching-State Models Michael Wojnowicz, Kaitlin Gili, Preetish Rath, Eric Miller, Jeffrey W. Miller, Clifford Lee Hancock, Meghan O'Donovan, Seth Elkin-Frankston, Tad Brunye, Michael C Hughes

ICML 2024 InterLUDE: Interactions Between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C Hughes

NeurIPSW 2024 Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective Ethan Harvey, Mikhail Petrov, Michael C Hughes

CVPR 2024 Systematic Comparison of Semi-Supervised and Self-Supervised Learning for Medical Image Classification Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes

TMLR 2024 Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported Ethan Harvey, Mikhail Petrov, Michael C Hughes

MLHC 2023 Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning Zhe Huang, Benjamin S. Wessler, Michael C. Hughes

AISTATS 2023 Fix-a-Step: Semi-Supervised Learning from Uncurated Unlabeled Data Zhe Huang, Mary-Joy Sidhom, Benjamin Wessler, Michael C. Hughes

ICMLW 2023 Learning Where to Intervene with a Differentiable Top-K Operator: Towards Data-Driven Strategies to Prevent Fatal Opioid Overdoses Kyle Heuton, Shikhar Shrestha, Thomas Stopka, Michael C Hughes

TMLR 2023 NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds Cynthia Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C Hughes

ICMLW 2023 Semi-Supervised Ordinal Regression via Cumulative Link Models for Predicting In-Hospital Length-of-Stay Alexander Arjun Lobo, Preetish Rath, Michael C Hughes

CHIL 2022 Conference on Health, Inference, and Learning (CHIL) 2022 Gerardo Flores, George H Chen, Tom Pollard, Ayah Zirikly, Michael C Hughes, Tasmie Sarker, Joyce C Ho, Tristan Naumann

NeurIPSW 2022 Prediction-Constrained Markov Models for Medical Time Series with Missing Data and Few Labels Preetish Rath, Gabriel Hope, Kyle Heuton, Erik B. Sudderth, Michael C Hughes

MLHC 2021 A New Semi-Supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms Zhe Huang, Gary Long, Benjamin Wessler, Michael C. Hughes

MLHC 2021 Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes

NeurIPS 2021 Dynamical Wasserstein Barycenters for Time-Series Modeling Kevin Cheng, Shuchin Aeron, Michael C Hughes, Eric L Miller

JAIR 2021 Optimizing for Interpretability in Deep Neural Networks with Tree Regularization Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

ICML 2021 Stochastic Iterative Graph Matching Linfeng Liu, Michael C Hughes, Soha Hassoun, Liping Liu

AAAI 2020 Regional Tree Regularization for Interpretability in Deep Neural Networks Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo A. Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

MLHC 2019 Feature Robustness in Non-Stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

AAAI 2018 Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

AISTATS 2018 Semi-Supervised Prediction-Constrained Topic Models Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

ICML 2017 From Patches to Images: A Nonparametric Generative Model Geng Ji, Michael C. Hughes, Erik B. Sudderth

MLOSS 2017 Refinery: An Open Source Topic Modeling Web Platform Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth

IJCAI 2017 Right for the Right Reasons: Training Differentiable Models by Constraining Their Explanations Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez

AISTATS 2015 Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process Michael C. Hughes, Dae Il Kim, Erik B. Sudderth

NeurIPS 2015 Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models Michael C Hughes, William T Stephenson, Erik Sudderth

NeurIPS 2013 Memoized Online Variational Inference for Dirichlet Process Mixture Models Michael C Hughes, Erik Sudderth

NeurIPS 2012 Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data Michael C Hughes, Emily B. Fox, Erik B. Sudderth

CVPRW 2012 Nonparametric Discovery of Activity Patterns from Video Collections Michael C. Hughes, Erik B. Sudderth

ICML 2012 The Nonparametric Metadata Dependent Relational Model Dae Il Kim, Michael C. Hughes, Erik B. Sudderth