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Sketching as a Tool for Numerical Linear Algebra
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Spectral techniques applied to sparse random graphs
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On Data Structures and Asymmetric Communication Complexity
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A Fast Random Sampling Algorithm for Sparsifying Matrices
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Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods
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Nonparametric Goodness-of-Fit Testing Under Gaussian Models
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Linear and Nonlinear Programming
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Lectures on Discrete Geometry
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Moments of the Gaussian Chaos
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Lower Bounds for Oblivious Subspace Embeddings
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Introduction to Nonparametric Estimation
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Pseudo-skeleton approximations by matrices of maximal volume
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Approximation of zonoids by zonotopes
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Four algorithms for the the efficient computation of truncated pivoted QR approximations to a sparse matrix
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Dimensionality Reductions in ℓ2 that Preserve Volumes and Distance to Affine Spaces
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Fast Dimension Reduction Using Rademacher Series on Dual BCH Codes
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Incomplete Cross Approximation in the Mosaic-Skeleton Method
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Linear and nonlinear programming.
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The L1-norm best-fit hyperplane problem
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A pure L1 -norm principal component analysis
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Strong rank revealing LU factorizations
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A theory of pseudoskeleton approximations
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Improved bound for rank revealing LU factorizations
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Arbitrary-norm separating plane
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Finding frequent items in data streams
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Introduction to the non-asymptotic analysis of random matrices
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CUR matrix decompositions for improved data analysis
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A fast randomized algorithm for overdetermined linear least-squares regression
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Robust L₁ Norm Factorization in the Presence of Outliers and Missing Data by Alternative Convex Programming
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Improved Approximation Algorithms for Large Matrices via Random Projections
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Streaming Algorithms via Precision Sampling
inferredcites →https://doi.org/10.1109/focs.2013.21openalex-snapshot
OSNAP: Faster Numerical Linear Algebra Algorithms via Sparser Subspace Embeddings
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High frequency moments via max-stability
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Uncertainty Principles and Signal Recovery
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The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors
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Sampling Algorithms and Coresets for $\ell_p$ Regression
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Relative-Error $CUR$ Matrix Decompositions
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Blendenpik: Supercharging LAPACK's Least-Squares Solver
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Near-Optimal Column-Based Matrix Reconstruction
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Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition
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Non-asymptotic Theory of Random Matrices: Extreme Singular Values
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IMPROVED ANALYSIS OF THE SUBSAMPLED RANDOMIZED HADAMARD TRANSFORM
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Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems
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inferredcites →https://doi.org/10.1145/1147954.1147955openalex-snapshot
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inferredcites →https://doi.org/10.1145/1250790.1250881openalex-snapshot
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inferredcites →https://doi.org/10.1145/1250790.1250884openalex-snapshot
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inferredcites →https://doi.org/10.1145/1536414.1536445openalex-snapshot
Numerical linear algebra in the streaming model
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inferredcites →https://doi.org/10.1145/2488608.2488620openalex-snapshot
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inferredcites →https://doi.org/10.1145/2488608.2488621openalex-snapshot
Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
inferredcites →https://doi.org/10.1145/2488608.2488622openalex-snapshot
Sparsity lower bounds for dimensionality reducing maps
inferredcites →https://doi.org/10.1145/2488608.2488624openalex-snapshot
How robust are linear sketches to adaptive inputs?
inferredcites →https://doi.org/10.1145/2559902openalex-snapshot
Sparser Johnson-Lindenstrauss Transforms
inferredcites →https://doi.org/10.1145/2688073.2688113openalex-snapshot
Uniform Sampling for Matrix Approximation
inferredcites →https://doi.org/10.1145/2746539.2746541openalex-snapshot
Toward a Unified Theory of Sparse Dimensionality Reduction in Euclidean Space
inferredcites →https://doi.org/10.1145/2746539.2746569openalex-snapshot
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
inferredcites →https://doi.org/10.1184/r1/6609131openalex-snapshot
Robust subspace computation using L1 norm
inferredcites →https://doi.org/10.1201/b11822-37openalex-snapshot
Randomized Algorithms for Matrices and Data
inferredcites →https://doi.org/10.1214/009117906000000421openalex-snapshot
Estimates of moments and tails of Gaussian chaoses
inferredcites →https://doi.org/10.1214/aop/1176989118openalex-snapshot
Limit of the Smallest Eigenvalue of a Large Dimensional Sample Covariance Matrix
inferredcites →https://doi.org/10.1214/aos/1015957395openalex-snapshot
Adaptive estimation of a quadratic functional by model selection
inferredcites →https://doi.org/10.1561/2200000035openalex-snapshot
Randomized Algorithms for Matrices and Data
inferredcites →https://doi.org/10.1561/9781933019604openalex-snapshot
Data Streams: Algorithms and Applications
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Finite dimensional subspaces of $L_{p}$
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The best constants in the Khintchine inequality
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On the Sensitivity of Shape Fitting Problems
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Optimal Approximate Matrix Product in Terms of Stable Rank
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Faster Least Squares Approximation
inferredcites →https://doi.org/10.48550/arxiv.0803.0929openalex-snapshot
Graph Sparsification by Effective Resistances
inferredcites →https://doi.org/10.48550/arxiv.1008.0587openalex-snapshot
Row Sampling for Matrix Algorithms via a Non-Commutative Bernstein Bound
inferredcites →https://doi.org/10.48550/arxiv.1103.5453openalex-snapshot
Using a Non-Commutative Bernstein Bound to Approximate Some Matrix Algorithms in the Spectral Norm
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Improving CUR Matrix Decomposition and the Nyström Approximation via Adaptive Sampling
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Subspace Embeddings and $\ell_p$-Regression Using Exponential Random Variables
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Optimal CUR Matrix Decompositions
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Single Pass Spectral Sparsification in Dynamic Streams
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Subgradient and sampling algorithms for l1 regression
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Efficient subspace approximation algorithms
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An improved approximation algorithm for the column subset selection problem
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Coresets and sketches for high dimensional subspace approximation problems
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Fast approximation of matrix coherence and statistical leverage
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The fast cauchy transform and faster robust linear regression
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On sketching matrix norms and the top singular vector
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Pass efficient algorithms for approximating large matrices
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Variable selection using axis-aligned random projections for partial least-squares regression
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Randomized Spectral Clustering for Large-Scale Multi-Layer Networks
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Compressed dynamic mode decomposition for background modeling
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Efficient policy evaluation by matrix sketching
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Distance geometry and data science
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Towards efficient and accurate approximation: tensor decomposition based on randomized block Krylov iteration
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Accelerated Kaczmarz methods via randomized sketch techniques for solving consistent linear systems
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A unified linear convergence analysis of k-SVD
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On the theory of dynamic graph regression problem
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Pass-efficient truncated UTV for low-rank approximations
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Tracking tensor ring decompositions of streaming tensors
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Generative modeling via tree tensor network states
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Streaming statistical models via Merge & Reduce
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Community detection with a subsampled semidefinite program
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Salinity analysis based on multivariate nonlinear regression for web‐based visualization of oceanic data
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Far-field compression for fast kernel summation methods in high dimensions
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Analysis of the ratio of ℓ1 and ℓ2 norms in compressed sensing
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Randomized Kaczmarz with tail averaging
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A count sketch maximal weighted residual Kaczmarz method for solving highly overdetermined linear systems
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Two-sided randomized algorithms for approximate K -term t-SVD
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A generalized Nyström method with subspace iteration for low-rank approximations of large-scale nonsymmetric matrices
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Randomized algorithms for the low multilinear rank approximations of tensors
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Manifold reconstruction and denoising from scattered data in high dimension
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Low-rank approximation: Randomized QR with Column Pivoting and related methods using sparse projection and pass-efficient techniques
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High-precision randomized preconditioned iterative methods for the random feature method
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On the adaptively inexact sketched Newton-Raphson methods for nonlinear systems of equations
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A sketched finite element method for elliptic models
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Structure-preserving randomized quaternion full orthogonalization method for non-Hermitian quaternion linear systems
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Robust randomized preconditioned Newton solvers for nonlinear PDEs based on the random feature method
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Practical differentially private online advertising
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Gaussian random projections for Euclidean membership problems
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Accelerated Double-Sketching Subspace Newton
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An implicit dense sketching with lower complexity
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Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
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Pass-efficient methods for compression of high-dimensional turbulent flow data
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Spectral estimation from simulations via sketching
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Randomized methods for dynamical low-rank approximation
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Numerically safe Gaussian elimination with no pivoting
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Dimensionality reduction of SDPs through sketching
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Single-pass randomized algorithms for LU decomposition
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Guarantees for the Kronecker fast Johnson–Lindenstrauss transform using a coherence and sampling argument
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Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms
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M-IHS: An accelerated randomized preconditioning method avoiding costly matrix decompositions
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Fast randomized numerical rank estimation for numerically low-rank matrices
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Reflective block Kaczmarz algorithms for least squares
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Dominant subspace and low-rank approximations from block Krylov subspaces without a prescribed gap
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Error analysis of randomized symplectic model order reduction for Hamiltonian systems
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Two-sided preconditioned CGLS for the solution of factorized linear systems
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Gradient preconditioned mini-batch SGD for ridge regression
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A non-Euclidean gradient descent method with sketching for unconstrained matrix minimization
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Fast Fisher discriminant analysis with randomized algorithms
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Fast stochastic second-order method logarithmic in condition number
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Rethinking sketching as sampling: A graph signal processing approach
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Passive sampling in reproducing kernel Hilbert spaces using leverage scores
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Alternating minimization algorithm for unlabeled sensing and linked linear regression
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Sketched multi-view subspace clustering
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A consistency theorem for randomized singular value decomposition
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Sampling-based dimension reduction for subspace approximation with outliers
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Low-rank binary matrix approximation in column-sum norm
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Information-Theoretic Bounds on Sketching
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Basic tail and concentration bounds
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Uniform laws of large numbers
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Metric entropy and its uses
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Random matrices and covariance estimation
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Sparse linear models in high dimensions
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Principal component analysis in high dimensions
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Decomposability and restricted strong convexity
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Matrix estimation with rank constraints
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Graphical models for high-dimensional data
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Reproducing kernel Hilbert spaces
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Nonparametric least squares
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Localization and uniform laws
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Large-Scale Convex Optimization for C-RANs
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Randomized algorithms in numerical linear algebra
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Randomized numerical linear algebra: Foundations and algorithms
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Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
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Dictionary learning for integrative, multimodal and scalable single-cell analysis
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Dequantizing algorithms to understand quantum advantage in machine learning
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Exploring chemical and conformational spaces by batch mode deep active learning
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Fast deconvolution method for angular super‐resolution imaging based on sub‐space embedding
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Gaussian approximation potentials: Theory, software implementation and application examples
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Integration and transfer learning of single-cell transcriptomes via cFIT
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Density Regression with Conditional Support Points
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Data-adaptive binary classifiers in high dimensions using random partitioning
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Perturbation Analysis of Randomized SVD and its Applications to Statistics
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Scalable Community Detection in Massive Networks via Predictive Assignment
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An investigation of Newton-Sketch and subsampled Newton methods
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Compressed and Penalized Linear Regression
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A Distance-Preserving Matrix Sketch
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Stochastic linear regularization methods: random discrepancy principle and applications
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A guide to stochastic optimisation for large-scale inverse problems
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Unique reconstruction for discretized inverse problems: a random sketching approach via subsampling
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Equivariant test-time training with operator sketching for imaging inverse problems
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Enhanced reconstruction in magnetic particle imaging by whitening and randomized SVD approximation
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Randomized algorithms for fast computation of low rank tensor ring model
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Sketched and truncated polynomial Krylov subspace methods: Matrix Sylvester equations
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Dimensionality Reduction in Euclidean Space
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Determinantal Point Processes in Randomized Numerical Linear Algebra
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Randomized methods for matrix computations
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Probabilistic methods for approximate archetypal analysis
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Sketch-and-solve approaches to k-means clustering by semidefinite programming
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Universality laws for randomized dimension reduction, with applications
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Subspace embedding with random Khatri–Rao products and its application to eigensolvers
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Numerical algorithms for high-performance computational science
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Regularization by denoising sub-sampled Newton method for spectral CT multi-material decomposition
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Patch2Self denoising of Diffusion MRI with Self-Supervision and Matrix Sketching
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Quantum Principal Component Analysis Only Achieves an Exponential Speedup Because of Its State Preparation Assumptions
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Tensor-Reduced Atomic Density Representations
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