Optimal weighted least-squares methods

1 August 2016

Papers citing "Optimal weighted least-squares methods"

39 / 39 papers shown

Title
Verification and Validation for Trustworthy Scientific Machine Learning John D. Jakeman Lorena A. Barba J. Martins Thomas O'Leary-Roseberry AI4CE 220 1 0 21 Feb 2025
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning Nilo Schwencke Cyril Furtlehner 206 1 0 14 Dec 2024
Provably Accurate Shapley Value Estimation via Leverage Score Sampling Christopher Musco R. Teal Witter FAtt FedML TDI 148 7 0 02 Oct 2024
Operator Learning Using Random Features: A Tool for Scientific Computing Nicholas H. Nelsen Andrew M. Stuart 153 16 0 12 Aug 2024
Agnostic Active Learning of Single Index Models with Linear Sample Complexity Aarshvi Gajjar Wai Ming Tai Xingyu Xu Chinmay Hegde Yi Li Chris Musco 132 10 0 15 May 2024
On Fractional Moment Estimation from Polynomial Chaos Expansion Lukávs Novák Marcos Valdebenito Matthias Faes 81 6 0 04 Mar 2024
Sequential transport maps using SoS density estimation and $α$ -divergences Benjamin Zanger Tiangang Cui Martin Schreiber O. Zahm 95 1 0 27 Feb 2024
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification Himanshu Sharma Lukávs Novák Michael D. Shields AI4CE 134 13 0 23 Feb 2024
Optimal sampling for stochastic and natural gradient descent Robert Gruhlke A. Nouy Philipp Trunschke 86 3 0 05 Feb 2024
Weighted least-squares approximation with determinantal point processes and generalized volume sampling A. Nouy Bertrand Michel 97 3 0 21 Dec 2023
A unified framework for learning with nonlinear model classes from arbitrary linear samples Ben Adcock Juan M. Cardenas N. Dexter 119 3 0 25 Nov 2023
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency Sudeep Salgia Sattar Vakili Qing Zhao 119 12 0 23 Oct 2023
Signal reconstruction using determinantal sampling Ayoub Belhadji Rémi Bardenet P. Chainais 73 4 0 13 Oct 2023
Improved Active Learning via Dependent Leverage Score Sampling Atsushi Shimizu Xiaoou Cheng Chris Musco Jonathan Weare FedML 120 8 0 08 Oct 2023
Physics-Informed Polynomial Chaos Expansions Lukávs Novák Himanshu Sharma Michael D. Shields 92 22 0 04 Sep 2023
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions Ben Adcock Juan M. Cardenas N. Dexter 164 6 0 01 Jun 2023
Self-reinforced polynomial approximation methods for concentrated probability densities Tiangang Cui S. Dolgov O. Zahm 69 6 0 05 Mar 2023
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion Lukás Novák Michael D. Shields Václav Sadílek M. Vořechovský 68 9 0 31 Jan 2023
Sampling-based Nyström Approximation and Kernel Quadrature Satoshi Hayakawa Harald Oberhauser Terry Lyons 187 15 0 23 Jan 2023
Active Learning for Single Neuron Models with Lipschitz Non-Linearities Aarshvi Gajjar Chinmay Hegde Christopher Musco 155 12 0 24 Oct 2022
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning Ben Adcock Juan M. Cardenas N. Dexter 121 11 0 25 Aug 2022
Convergence bounds for local least squares approximation Philipp Trunschke 81 2 0 23 Aug 2022
A Stable Jacobi polynomials based least squares regression estimator associated with an ANOVA decomposition model Mohamed Jebalia Abderrazek Karoui 31 0 0 04 Aug 2022
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 132 11 0 25 Mar 2022
Gravitational wave surrogates through automated machine learning Damián Barsotti F. Cerino M. Tiglio Aarón Villanueva 66 9 0 17 Oct 2021
Convergence bounds for nonlinear least squares and applications to tensor recovery Philipp Trunschke 61 7 0 11 Aug 2021
A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression M. Götte R. Schneider Philipp Trunschke 57 7 0 29 Apr 2021
Randomized weakly admissible meshes Yiming Xu A. Narayan 34 13 0 11 Jan 2021
The Statistical Cost of Robust Kernel Hyperparameter Tuning R. A. Meyer Christopher Musco 87 2 0 14 Jun 2020
Fourier Sparse Leverage Scores and Approximate Kernel Learning T. Erdélyi Cameron Musco Christopher Musco 131 23 0 12 Jun 2020
The Random Feature Model for Input-Output Maps between Banach Spaces Nicholas H. Nelsen Andrew M. Stuart 168 154 0 20 May 2020
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark Nora Lüthen S. Marelli Bruno Sudret 169 171 0 04 Feb 2020
Boosted optimal weighted least-squares Cécile Haberstich A. Nouy G. Perrin 43 25 0 15 Dec 2019
`Regression Anytime' with Brute-Force SVD Truncation Christian Bender Nikolaus Schweizer 21 1 0 22 Aug 2019
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms H. Avron Michael Kapralov Cameron Musco Christopher Musco A. Velingker A. Zandieh 96 50 0 20 Dec 2018
Sequential sampling for optimal weighted least squares approximations in hierarchical spaces B. Arras M. Bachmayr A. Cohen 44 22 0 28 May 2018
Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal Design Paul Diaz Alireza Doostan Jerrad Hampton 102 102 0 29 Dec 2017
Least Squares Polynomial Chaos Expansion: A Review of Sampling Strategies M. Hadigol Alireza Doostan 87 134 0 23 Jun 2017
On the Sampling Problem for Kernel Quadrature François‐Xavier Briol Chris J. Oates Jon Cockayne W. Chen Mark Girolami 105 29 0 11 Jun 2017