On the Partial Convexification of the Low-Rank Spectral Optimization: Rank Bounds and Algorithms
Abstract A Low-rank Spectral Optimization Problem (LSOP) minimizes a linear objective
function subject to multiple two-sided linear inequalities intersected with a low-rank and …
function subject to multiple two-sided linear inequalities intersected with a low-rank and …
Optimal low-rank matrix completion: Semidefinite relaxations and eigenvector disjunctions
Low-rank matrix completion consists of computing a matrix of minimal complexity that
recovers a given set of observations as accurately as possible. Unfortunately, existing …
recovers a given set of observations as accurately as possible. Unfortunately, existing …
Variable selection for kernel two-sample tests
We consider the variable selection problem for two-sample tests, aiming to select the most
informative variables to distinguish samples from two groups. To solve this problem, we …
informative variables to distinguish samples from two groups. To solve this problem, we …
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
Optimal transport has been very successful for various machine learning tasks; however, it is
known to suffer from the curse of dimensionality. Hence, dimensionality reduction is …
known to suffer from the curse of dimensionality. Hence, dimensionality reduction is …