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

Structure-leveraged methods in breast cancer risk prediction. Fan J, Wu Y, Yuan M, Page D, Liu J, Ong IM, Peissig P, Burnside E. Journal of Machine Learning Research 17:1-15, 2016

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Distance shrinkage and Euclidean embedding via regularized kernel estimation. Zhang L, Wahba G, Yuan M. Journal of the Royal Statistical Society B, doi:DOI: 10.1111/rssb.12138, 2016

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Comparing mammography abnormality features to genetic variants in the prediction of breast cancer in women recommended for breast biopsy. Burnside ES, Liu J, Wu Y, Onitilo AA, McCarty CA, Page CD, Peissig PL, Trentham-Dietz A, Kitchner T, Fan J, Yuan M. Academic Radiology 23(1):62–69, 2016

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Backward multiple imputation estimation of the conditional lifetime expectancy function with application to censored human longevity data. Kong J, Klein BEK, Klein R, Wahba G. Proceedings of the National Academy of Sciences USA 112(39):12069–12074, 2015

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Lead

Ming Yuan

Investigators

Grace Wahba

Shulei Wang

Han Chen