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

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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Automated and robust quantification of colocalization in dual-color fluorescence microscopy: A nonparametric statistical approach. Wang S, Arena E, Elicieri K, Yuan M. IEEE Transactions on Image Processing 27(2):622-636, 2018

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Screening rule for L1-regularized Ising model estimation. Kuang Z, Geng S, Page D. Advances in Neural Information Processing Systems Conference (NIPS), 2017

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Lead

Ming Yuan

Investigators

Grace Wahba

Shulei Wang

Han Chen

Resources

RKColocal software

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