Coherence Motivated Sampling and Convergence Analysis of Least-Squares Polynomial Chaos Regression

7 October 2014

Papers citing "Coherence Motivated Sampling and Convergence Analysis of Least-Squares Polynomial Chaos Regression"

29 / 29 papers shown

Title
Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization Ruda Zhang Negin Alemazkoor AI4CE 111 5 0 30 Oct 2024
Provably Accurate Shapley Value Estimation via Leverage Score Sampling Christopher Musco R. Teal Witter FAtt FedML TDI 148 7 0 02 Oct 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
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
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
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification Nuojin Cheng Osman Asif Malik Subhayan De Stephen Becker Alireza Doostan 122 11 0 25 May 2023
Self-reinforced polynomial approximation methods for concentrated probability densities Tiangang Cui S. Dolgov O. Zahm 69 6 0 05 Mar 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
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
Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification Kyriakos D. Kantarakias G. Papadakis 25 9 0 30 Jun 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
GenMod: A generative modeling approach for spectral representation of PDEs with random inputs Jacqueline Wentz Alireza Doostan 94 1 0 31 Jan 2022
Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification F. Newberry Jerrad Hampton K. Jansen Alireza Doostan 50 6 0 15 Apr 2021
Randomized weakly admissible meshes Yiming Xu A. Narayan 30 13 0 11 Jan 2021
Fourier Sparse Leverage Scores and Approximate Kernel Learning T. Erdélyi Cameron Musco Christopher Musco 127 23 0 12 Jun 2020
$Sparse Identification of Nonlinear Dynamical Systems via Reweighted $\ell_1$-regularized Least Squares$ Sparse Identification of Nonlinear Dynamical Systems via Reweighted $\ell_1$ -regularized Least Squares A. Cortiella K. Park Alireza Doostan 77 77 0 27 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
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
A preconditioning approach for improved estimation of sparse polynomial chaos expansions Negin Alemazkoor Hadi Meidani 72 9 0 22 Sep 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
Basis Adaptive Sample Efficient Polynomial Chaos (BASE-PC) Jerrad Hampton Alireza Doostan 100 44 0 03 Feb 2017
Optimal weighted least-squares methods A. Cohen G. Migliorati 119 212 0 01 Aug 2016
$On Polynomial Chaos Expansion via Gradient-enhanced $\ell_1$-minimization$ On Polynomial Chaos Expansion via Gradient-enhanced $\ell_1$ -minimization Jigen Peng Jerrad Hampton Alireza Doostan 120 80 0 01 Jun 2015
Efficient Bayesian experimentation using an expected information gain lower bound Panagiotis Tsilifis R. Ghanem P. Hajali 118 47 0 30 May 2015