We advance both the theory and practice of robust $\ell_p $-quasinorm regression for $ p\in
(0, 1] $ by using novel variants of iteratively reweighted least-squares (IRLS) to solve the …

Block coordinate descent is an optimization paradigm that iteratively updates one block of
variables at a time, making it quite amenable to big data applications due to its scalability …

Motivated by machine learning and computer vision applications, we formulate the problem
of Outlier-Robust Orthogonal Regression to find a point in a manifold that satisfies as many …