Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Computing optimal transport distances such as the earth mover's distance is a fundamental
problem in machine learning, statistics, and computer vision. Despite the recent introduction …
problem in machine learning, statistics, and computer vision. Despite the recent introduction …
Least squares after model selection in high-dimensional sparse models
A Belloni, V Chernozhukov - 2013 - projecteuclid.org
Supplementary material for Least squares after model selection in high-dimensional sparse
models. The online supplemental article 2 contains a finite sample results for the estimation …
models. The online supplemental article 2 contains a finite sample results for the estimation …
[BOOK][B] Introduction to high-dimensional statistics
C Giraud - 2021 - taylorfrancis.com
Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …
introduction to this active field of research.… it is arguably the most accessible overview yet …
Minimax Rates of Estimation for High-Dimensional Linear Regression Over -Balls
Consider the high-dimensional linear regression model y= X β*+ w, where y∈\BBR n is an
observation vector, X∈\BBR n× d is a design matrix with d>; n, β*∈\BBR d is an unknown …
observation vector, X∈\BBR n× d is a design matrix with d>; n, β*∈\BBR d is an unknown …
Bayesian linear regression with sparse priors
We study full Bayesian procedures for high-dimensional linear regression under sparsity
constraints. The prior is a mixture of point masses at zero and continuous distributions …
constraints. The prior is a mixture of point masses at zero and continuous distributions …
[BOOK][B] Oracle inequalities in empirical risk minimization and sparse recovery problems: École D'Été de Probabilités de Saint-Flour XXXVIII-2008
V Koltchinskii - 2011 - books.google.com
The purpose of these lecture notes is to provide an introduction to the general theory of
empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities …
empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities …
Estimation and inference for high-dimensional generalized linear models with knowledge transfer
Transfer learning provides a powerful tool for incorporating data from related studies into a
target study of interest. In epidemiology and medical studies, the classification of a target …
target study of interest. In epidemiology and medical studies, the classification of a target …
High-dimensional statistics
arxiv:2310.19244v1 [math.ST] 30 Oct 2023 Page 1 arxiv:2310.19244v1 [math.ST] 30 Oct
2023 High-Dimensional Statistics Lecture Notes Philippe Rigollet and Jan-Christian Hütter …
2023 High-Dimensional Statistics Lecture Notes Philippe Rigollet and Jan-Christian Hütter …
Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality
This paper considers estimation and prediction of a high-dimensional linear regression in
the setting of transfer learning where, in addition to observations from the target model …
the setting of transfer learning where, in addition to observations from the target model …
Sparse PCA: Optimal rates and adaptive estimation
Sparse PCA: Optimal rates and adaptive estimation Page 1 The Annals of Statistics 2013, Vol.
41, No. 6, 3074–3110 DOI: 10.1214/13-AOS1178 © Institute of Mathematical Statistics, 2013 …
41, No. 6, 3074–3110 DOI: 10.1214/13-AOS1178 © Institute of Mathematical Statistics, 2013 …