Random orthonormal embeddings are minimax optimal for sketched least squares, and rotation-invariant embeddings are minimax optimal for randomized SVD, with sharp error formulas.
Xiangrui Meng and Michael W
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
representative citing papers
Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
FP16 SparseStack on GPUs shows embedding quality insensitive to rounding method, with sketch distribution as the primary accuracy driver across tested inputs.
A multilevel sketch-and-solve estimator reduces variance faster than averaging independent sketches but incurs slightly higher computational cost, making the approach unattractive for least squares problems.
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.
PEPSKit.jl is a Julia package that supplies high-level algorithms for ground-state, time-evolution and finite-temperature iPEPS simulations with symmetry support on various lattices.
citing papers explorer
-
Sharp analysis of sketched least squares and randomized low-rank approximation
Random orthonormal embeddings are minimax optimal for sketched least squares, and rotation-invariant embeddings are minimax optimal for randomized SVD, with sharp error formulas.
-
Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation
Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
-
Randomized Sketching is Robust to Low-Precision Rounding on GPUs
FP16 SparseStack on GPUs shows embedding quality insensitive to rounding method, with sketch distribution as the primary accuracy driver across tested inputs.
-
A multilevel sketch-and-solve method for overdetermined least squares problems
A multilevel sketch-and-solve estimator reduces variance faster than averaging independent sketches but incurs slightly higher computational cost, making the approach unattractive for least squares problems.
-
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.
-
Randomized batch-sampling Kaczmarz methods for solving linear systems
A unified randomized batch-sampling Kaczmarz framework yields scale-invariant expected linear convergence bounds for block methods solving linear systems.
-
Stabilizing randomized GMRES through flexible GMRES
Flexible GMRES stabilizes sketched GMRES through a new residual bound, producing a practical randomized solver with minimal tuning and robust non-increasing residual norms.
-
Randomized coupled decompositions
Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.
-
PEPSKit.jl: A Julia package for projected entangled-pair state simulations
PEPSKit.jl is a Julia package that supplies high-level algorithms for ground-state, time-evolution and finite-temperature iPEPS simulations with symmetry support on various lattices.