Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation
arXiv:2605.05446v1 Announce Type: new
Abstract: Nonconvex methods have emerged as a dominant approach for low-rank matrix estimation, a problem that arises widely in machine learning and AI for learning and representing high-dimensional data. Existing…