External links: [My Google Scholar Profile] • [My ORCID iD Profile]

This page contains select publications from my group. See the complete list in my Curriculum Vitae


Non-Parametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations

Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller

IEEE Transactions on Signal Processing (to appear), 2023

[IEEE official link] [PDF] [arXiv] [code on GitHub]

Approximate inference by broadening the support of the likelihood

Michael T. Wojnowicz, Martin D. Buck, Michael C. Hughes

Symposium on Advances in Approximate Bayesian Inference (AABI), 2023

[PDF] [OpenReview]

Learning where to intervene with a differentiable top-k operator: Towards data-driven strategies to prevent fatal opioid overdoses

Kyle Heuton, Shikhar Shrestha, Thomas J. Stopka, Michael C Hughes

Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023

[PDF] [OpenReview] [code on GitHub]

Semi-supervised Ordinal Regression via Cumulative Link Models for Predicting In-Hospital Length-of-Stay

Alexander Arjun Lobo, Preetish Rath, Michael C Hughes

Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023

[PDF] [OpenReview] • [code on GitHub]

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds

Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Li-Ping Liu, Matthias Scheutz, and Michael C. Hughes

Transactions on Machine Learning Research (TMLR), 03/2023

[OpenReview] [arXiv] [dataset:] [code on GitHub] [video on YouTube]

Automated Detection of Aortic Stenosis using Machine Learning

Benjamin S. Wessler, Zhe Huang, Gary Long, Stefano Pacifici, Nishant Prashar, Samuel Karmiy, Roman A. Sandler, Joseph Sokol, Daniel B. Sokol, Monica M. Dehn, Luisa Maslon, Eileen Mai, Ayan R. Patel, and Michael C. Hughes

Journal of the American Society of Echocardiography , 2023

[paper PDF]



Dynamical Wasserstein Barycenters for Time-series Modeling

Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, and Eric L. Miller

Neural Information Processing Systems (NeurIPS), 2021

[PDF in NeurIPS proceedings] [code on GitHub] [arXiv] [PDF on OpenReview] [video on SlidesLive]








bnpy: Reliable and scalable variational inference for Bayesian nonparametric models

Michael C. Hughes, Erik B. Sudderth

3rd NIPS Workshop on Probabilistic Programming

[paper PDF] [Python code: bnpy] [poster PDF]

Joint Modeling of Multiple Time Series via the Beta Process with Application to Motion Capture Segmentation.

Emily Fox, Michael C. Hughes, Erik B. Sudderth, Michael I. Jordan

Annals of Applied Statistics, Vol. 8(3), 2014.

[paper PDF] [supplement PDF] [Matlab code: NPBayesHMM]



Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data

Michael C. Hughes, Emily Fox, Erik B. Sudderth

Neural Information Processing Systems, 2012.

[paper PDF] [poster PDF] [supplement PDF]

The Nonparametric Metadata Dependent Relational Model

Dae Il Kim, Michael C. Hughes, Erik B. Sudderth

International Conference on Machine Learning, 2012.

[paper PDF] [poster PDF]

Nonparametric Discovery of Activity Patterns from Video Collections

Michael C. Hughes, Erik B. Sudderth

CVPR Workshop on Perceptual Organization in Computer Vision (POCV), 2012.

[paper PDF] [supplement ZIP] [Matlab code: NPBayesHMM]