Random orthonormal matrices are minimax optimal for sketched least squares and rotation-invariant embeddings for randomized SVD, yielding the sharpest error bounds.
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7 Pith papers cite this work. Polarity classification is still indexing.
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Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
Combines polynomial codes and randomized sketching into approximate distributed schemes that mitigate stragglers during optimization and machine learning tasks.
A unified randomized batch-sampling Kaczmarz framework yields scale-invariant expected linear convergence bounds for block methods solving linear systems.
Flexible GMRES stabilizes sketched GMRES through a new residual bound, producing a practical randomized solver with minimal tuning and robust non-increasing residual norms.
Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.
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Approximate Distributed Coded Computing: Polynomial Codes and Randomized Sketching
Combines polynomial codes and randomized sketching into approximate distributed schemes that mitigate stragglers during optimization and machine learning tasks.