Low-dimensional Representations Lab

This lab is developing methodologies that extract and exploit latent, low-dimensional structure when learning predictive models from high-dimensional data sources. The lab brings together tools from probability and statistics, geometry, topology, and computer science to study techniques such as variable selection, graphical modeling, classification, dimensionality reduction, matrix estimation, and manifold learning in concert with other projects and labs in CPCP.

Related CPCP Publications

Localizing differentially evolving covariance structures via scan statistics. Mehta R, Kim HJ, Wang S, Johnson S, Yuan M, Singh V. Quarterly of Applied Mathematics 77:357-398, 2019

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Combined hypothesis testing on graphs with applications to gene set enrichment analysis. Wang S, Yuan M. Journal of the American Statistical Association 114(527):1320-1338, 2018

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Utility of genetic testing in addition to mammography for determining risk of breast cancer depends on patient age. Feld S, Fan J, Yuan M, Wu Y, Woo K, Alexandridis R, Burnside E. Proceedings of the AMIA Informatics Summit, 2018

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Quantifying predictive capability of electronic health records for the most harmful breast cancer. Wu Y, Fan J, Peissig P, Berg R, Tafti P, Yin J, Yuan M, Page D, Cox J, Burnside E. Proceedings of SPIE Medical Imaging, 2018

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Statistical tests and identifiability conditions for pooling and analyzing multisite datasets. Zhou HH, Singh V, Johnson SC, Wahba G, and the Alzheimer’s Disease Neuroimaging Initiative. Proceedings of the National Academy of Sciences USA, 2018

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Lead

Ming Yuan

Investigators

Grace Wahba

Shulei Wang

Han Chen

Resources

CPCP 2018 Retreat: Graph Total Variation Regularization for fMRI Neural Decoding Symposium Video

RKColocal software

CPCP 2017 Retreat: Phenotype Models for Breast Cancer Screening Symposium Video