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Marten Wegkamp
Professor
Departments/Programs
 Mathematics
Graduate Fields
 Applied Mathematics
 Mathematics
 Statistics
Research
Mathematical statistics, empirical process theory, high dimensional statistics and statistical learning theory.
My main research effort concentrates on developing new methodology in statistics and machine learning.
Courses
Spring 2022
Fall 2022
 MATH 4900 : Supervised Research
 MATH 4901 : Supervised Reading
 MATH 7740 : Statistical Learning Theory: Classification, Pattern Recognition, Machine Learning
Publications

Interpolation under latent factor regression models: Generalization via lowdimensional adaptation (with Florentina Bunea and Seth StrimasMackey). arXiv:2002.02525.

Prediction in latent factor regression: Adaptive PCR and beyond (with Xin Bing, Florentina Bunea and Seth StrimasMackey). Journal of Machine Learning Research (2021) To appear.

Inference in Interpretable Latent Factor Regression Models (with Xin Bing and Florentina Bunea). Bernoulli (2021) To appear.

Optimal estimation of sparse topic models (with Xin Bing and Florentina Bunea). Journal of Machine Learning Research 21, 145 (2020).

A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics (with Xin Bing and Florentina Bunea). Bernoulli 26(3), 17651796 (2020).

Sparse Latent Factor Models with Pure Variables for Overlapping Clustering (Xin Bing, Florentina Bunea and Yang Ning). Annals of Statistics 48(4), 20552081 (2020).

Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models (with Xin Bing). Annals of Statistics, 47(6), 31573184 (2019).

Weak convergence of stationary empirical processes (with Dragan Radulovic). Journal of Statistical Planning and Inference, 149, 7584 (2018).

Weak convergence of empirical copula processes indexed by functions (with Dragan Radulovic and Yue Zhao). Bernoulli, 23(4B), 33463384 (2017).