2 papers
cs.LG2019
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
Quanming Yao, Hangsi Yang, En-Liang Hu +1
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness o…
cs.LG2018
Provable Exactness for Asymmetric Low-Rank SDP Learning
Enliang Hu
Low-rank factorization is a standard way to make structured optimization problems in machine learning more tractable by replacing matrix variables with compact factors. For positiv…