On the Improved Rates of Convergence for Matérn-type Kernel Ridge Regression, with Application to Calibration of Computer Models

1 January 2020

Papers citing "On the Improved Rates of Convergence for Matérn-type Kernel Ridge Regression, with Application to Calibration of Computer Models"

14 / 14 papers shown

Sampling Complexity of TD and PPO in RKHS

152

29 Sep 2025

Highly Adaptive Ridge

Alejandro Schuler

Alexander Hagemeister

Mark van der Laan

407

03 Oct 2024

Sobolev Calibration of Imperfect Computer Models

Qingwen Zhang

Wenjia Wang

31 Mar 2024

Asymptotic Theory for Linear Functionals of Kernel Ridge Regression

Rui Tuo

Lu Zou

239

07 Mar 2024

Random Smoothing Regularization in Kernel Gradient Descent Learning

Liang Ding

Tianyang Hu

273

05 May 2023

The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine LearningStatistical Science (Statist. Sci.), 2023

296

05 Mar 2023

Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation

Toni Karvonen

621

10 Mar 2022

Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High DimensionsManagement Sciences (MS), 2022

Wei Cao

Yanyuan Wang

Xiaowei Zhang

473

09 Jan 2022

Estimation of the Scale Parameter for a Misspecified Gaussian Process Model

Toni Karvonen

293

06 Oct 2021

Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression

Wei Cao

Bing-Yi Jing

368

20 Apr 2021

Fast Statistical Leverage Score Approximation in Kernel Ridge RegressionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021

Yifan Chen

Yun Yang

238

09 Mar 2021

Accumulation of Sub-Sampling Matrices with Applications to Statistical ComputationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021

Yifan Chen

Yun Yang

247

06 Mar 2021

Sample and Computationally Efficient Stochastic Kriging in High Dimensions

Liang Ding

Xiaowei Zhang

290

14 Oct 2020

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions

Simo Särkkä

348

29 Jan 2020