Miles E. Lopes | DeepAI

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∙ 09/08/2022

A Bootstrap Method for Spectral Statistics in High-Dimensional Elliptical Models

Although there is an extensive literature on the eigenvalues of high-dim...

0 Siyao Wang, et al. ∙

∙ 08/05/2022

Improved Rates of Bootstrap Approximation for the Operator Norm: A Coordinate-Free Approach

Let Σ̂=1/n∑_i=1^n X_i⊗ X_i denote the sample covariance operator of cent...

0 Miles E. Lopes, et al. ∙

∙ 04/19/2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence...

24 Aydin Buluc, et al. ∙

∙ 04/15/2021

Rates of Bootstrap Approximation for Eigenvalues in High-Dimensional PCA

In the context of principal components analysis (PCA), the bootstrap is ...

0 Junwen Yao, et al. ∙

∙ 09/13/2020

Central Limit Theorem and Bootstrap Approximation in High Dimensions with Near 1/√(n) Rates

Non-asymptotic bounds for Gaussian and bootstrap approximation have rece...

0 Miles E. Lopes, et al. ∙

∙ 07/02/2020

High-dimensional MANOVA via Bootstrapping and its Application to Functional and Sparse Count Data

We propose a new approach to the problem of high-dimensional multivariat...

0 Zhenhua Lin, et al. ∙

∙ 03/10/2020

Error Estimation for Sketched SVD via the Bootstrap

In order to compute fast approximations to the singular value decomposit...

0 Miles E. Lopes, et al. ∙

∙ 09/13/2019

Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching

Although the operator (spectral) norm is one of the most widely used met...

0 Miles E. Lopes, et al. ∙

∙ 08/04/2019

Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting

When randomized ensemble methods such as bagging and random forests are ...

0 Miles E. Lopes, et al. ∙

∙ 07/20/2019

Estimating the Algorithmic Variance of Randomized Ensembles via the Bootstrap

Although the methods of bagging and random forests are some of the most ...

0 Miles E. Lopes, et al. ∙

∙ 09/23/2018

On the Maximum of Dependent Gaussian Random Variables: A Sharp Bound for the Lower Tail

Although there is an extensive literature on the maxima of Gaussian proc...

0 Miles E. Lopes, et al. ∙

∙ 07/12/2018

Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional Data Analysis

In recent years, bootstrap methods have drawn attention for their abilit...

0 Miles E. Lopes, et al. ∙

∙ 03/21/2018

Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap

Over the course of the past decade, a variety of randomized algorithms h...

0 Miles E. Lopes, et al. ∙

∙ 08/06/2017

A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication

In recent years, randomized methods for numerical linear algebra have re...

0 Miles E. Lopes, et al. ∙

∙ 07/04/2016

A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs

We study the residual bootstrap (RB) method in the context of high-dimen...

0 Miles E. Lopes, et al. ∙

∙ 03/04/2013

A Sharp Bound on the Computation-Accuracy Tradeoff for Majority Voting Ensembles

When random forests are used for binary classification, an ensemble of t...

0 Miles E. Lopes, et al. ∙

∙ 04/19/2012

Estimating Unknown Sparsity in Compressed Sensing

In the theory of compressed sensing (CS), the sparsity ||x||_0 of the un...

0 Miles E. Lopes, et al. ∙

∙ 08/11/2011

A More Powerful Two-Sample Test in High Dimensions using Random Projection

We consider the hypothesis testing problem of detecting a shift between ...

0 Miles E. Lopes, et al. ∙