108 citations · 179 across the 7 of their papers we have counts for
8 papers
Computational Sufficiency, Reflection Groups, and Generalized Lasso Penalties
Vincent Q. Vu
We study estimators with generalized lasso penalties within the computational sufficiency framework introduced by Vu (2018, arXiv:1807.05985). By representing these penalties as su…
Group Invariance and Computational Sufficiency
Vincent Q. Vu
Statistical sufficiency formalizes the notion of data reduction. In the decision theoretic interpretation, once a model is chosen all inferences should be based on a sufficient sta…
Sparsistency and agnostic inference in sparse PCA
Jing Lei, Vincent Q. Vu
The presence of a sparse "truth" has been a constant assumption in the theoretical analysis of sparse PCA and is often implicit in its methodological development. This naturally ra…
Squared-Norm Empirical Process in Banach Space
Vincent Q. Vu, Jing Lei
This note extends a recent result of Mendelson on the supremum of a quadratic process to squared norms of functions taking values in a Banach space. Our method of proof is a reduct…
Minimax sparse principal subspace estimation in high dimensions
Vincent Q. Vu, Jing Lei
We study sparse principal components analysis in high dimensions, where (the number of variables) can be much larger than (the number of observations), and analyze the prob…
Minimax Rates of Estimation for Sparse PCA in High Dimensions
Vincent Q. Vu, Jing Lei
We study sparse principal components analysis in the high-dimensional setting, where (the number of variables) can be much larger than (the number of observations). We prov…