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Found 19 papers, 8 papers with code
Robust, randomized preconditioning for kernel ridge regression
This paper introduces two randomized preconditioning techniques for robustly solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$).
Efficient error and variance estimation for randomized matrix computations
Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning.
Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
The randomly pivoted partial Cholesky algorithm (RPCholesky) computes a factorized rank-k approximation of an N x N positive-semidefinite (psd) matrix.
Learning to Forecast Dynamical Systems from Streaming Data
This algorithm dramatically reduces the costs of training and prediction without sacrificing forecasting skill.
Tensor Random Projection for Low Memory Dimension Reduction
The TRP map is formed as the Khatri-Rao product of several smaller random projections, and is compatible with any base random projection including sparse maps, which enable dimension reduction with very low query cost and no floating point operations.
Inference of Black Hole Fluid-Dynamics From Sparse Interferometric Measurements
We are motivated by the task of imaging the stochastically evolving environment surrounding black holes, and demonstrate how flow parameters can be estimated from sparse interferometric measurements used in radio astronomical imaging.
Concentration for random product formulas
qDRIFT achieves a gate count that does not explicitly depend on the number of terms in the Hamiltonian, which contrasts with Suzuki formulas.
Quantum Physics Probability
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form.
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates.
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
This paper concerns a fundamental class of convex matrix optimization problems.
Practical sketching algorithms for low-rank matrix approximation
This paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch.
Universality laws for randomized dimension reduction, with applications
In the Euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to the data.
An Introduction to Matrix Concentration Inequalities
In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts.
Time--Data Tradeoffs by Aggressive Smoothing
This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization.
Paved with Good Intentions: Analysis of a Randomized Block Kaczmarz Method
The block Kaczmarz method is an iterative scheme for solving overdetermined least-squares problems.
Numerical Analysis Numerical Analysis 65F10, 65F20, 68W20, 41A65
Factoring nonnegative matrices with linear programs
The constraints are chosen to ensure that the matrix C selects features; these features can then be used to find a low-rank NMF of X.
Robust computation of linear models by convex relaxation
Consider a dataset of vector-valued observations that consists of noisy inliers, which are explained well by a low-dimensional subspace, along with some number of outliers.
Improved analysis of the subsampled randomized Hadamard transform
This paper presents an improved analysis of a structured dimension-reduction map called the subsampled randomized Hadamard transform.
Numerical Analysis Data Structures and Algorithms Probability 15B52
Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
These methods use random sampling to identify a subspace that captures most of the action of a matrix.
Numerical Analysis Probability
