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Found 11 papers, 1 papers with code
Error Estimation for Random Fourier Features
Three key advantages of this approach are: (1) The error estimates are specific to the problem at hand, avoiding the pessimism of worst-case bounds.
Randomized Algorithms for Scientific Computing (RASC)
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science.
Error Estimation for Sketched SVD via the Bootstrap
In order to compute fast approximations to the singular value decompositions (SVD) of very large matrices, randomized sketching algorithms have become a leading approach.
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting
When randomized ensemble methods such as bagging and random forests are implemented, a basic question arises: Is the ensemble large enough?
Estimating the Algorithmic Variance of Randomized Ensembles via the Bootstrap
Due to the fact that bagging and random forests are randomized algorithms, the choice of ensemble size is closely related to the notion of "algorithmic variance" (i. e. the variance of prediction error due only to the training algorithm).
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
As a more practical alternative, we propose a bootstrap method to compute a posteriori error estimates for randomized LS algorithms.
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication
In recent years, randomized methods for numerical linear algebra have received growing interest as a general approach to large-scale problems.
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
We study the residual bootstrap (RB) method in the context of high-dimensional linear regression.
Unknown sparsity in compressed sensing: Denoising and inference
This family interpolates between $\|x\|_0=s_0(x)$ and $\|x\|_1^2/\|x\|_2^2=s_2(x)$ as $q$ ranges over $[0, 2]$.
Estimating a sharp convergence bound for randomized ensembles
In the standard case when classifiers are aggregated by majority vote, the present work offers a way to quantify this convergence in terms of "algorithmic variance," i. e. the variance of prediction error due only to the randomized training algorithm.
A More Powerful Two-Sample Test in High Dimensions using Random Projection
We consider the hypothesis testing problem of detecting a shift between the means of two multivariate normal distributions in the high-dimensional setting, allowing for the data dimension p to exceed the sample size n. Specifically, we propose a new test statistic for the two-sample test of means that integrates a random projection with the classical Hotelling T^2 statistic.
