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math.NA2026
Debiasing Random Oblique Projections for Subsampled OLS and Fast CUR in High Dimensions
Chengmei Niu, Sachin Garg, Michał Dereziński +1
Random sampling is a fundamental tool in modern machine learning and numerical linear algebra for reducing the computational cost of large-scale matrix problems. Existing analyses,…
math.NA2019★ 5 cited
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Mojmír Mutný, Michał Dereziński, Andreas Krause
We analyze the convergence rate of the randomized Newton-like method introduced by Qu et. al. (2016) for smooth and convex objectives, which uses random coordinate blocks of a Hess…