Artigos com autorizações de acesso público - Ilse C. F. IpsenSaiba mais
20 artigos disponíveis publicamente
Eigenvector continuation with subspace learning
D Frame, R He, I Ipsen, D Lee, D Lee, E Rrapaj
Physical review letters 121 (3), 032501, 2018
Autorizações: US National Science Foundation, US Department of Energy, US Department of …
Randomized matrix-free trace and log-determinant estimators
AK Saibaba, A Alexanderian, ICF Ipsen
Numerische Mathematik 137 (2), 353-395, 2017
Autorizações: US Department of Defense
Structural convergence results for approximation of dominant subspaces from block Krylov spaces
P Drineas, ICF Ipsen, EM Kontopoulou, M Magdon-Ismail
SIAM Journal on Matrix Analysis and Applications 39 (2), 567-586, 2018
Autorizações: US National Science Foundation, US Department of Defense
A Bayesian conjugate gradient method (with discussion)
J Cockayne, CJ Oates, ICF Ipsen, M Girolami
Autorizações: US National Science Foundation, German Research Foundation, UK Engineering …
Low-rank matrix approximations do not need a singular value gap
P Drineas, ICF Ipsen
SIAM Journal on Matrix Analysis and Applications 40 (1), 299-319, 2019
Autorizações: US National Science Foundation
Probabilistic error analysis for inner products
ICF Ipsen, H Zhou
SIAM journal on matrix analysis and applications 41 (4), 1726-1741, 2020
Autorizações: US National Science Foundation, US National Institutes of Health
Probabilistic linear solvers: a unifying view
S Bartels, J Cockayne, ICF Ipsen, P Hennig
Statistics and Computing 29, 1249-1263, 2019
Autorizações: UK Engineering and Physical Sciences Research Council, European Commission
A probabilistic subspace bound with application to active subspaces
JT Holodnak, ICF Ipsen, RC Smith
SIAM Journal on Matrix Analysis and Applications 39 (3), 1208-1220, 2018
Autorizações: US National Science Foundation, US Department of Energy, US Department of …
Monte Carlo methods for estimating the diagonal of a real symmetric matrix
E Hallman, ICF Ipsen, AK Saibaba
SIAM Journal on Matrix Analysis and Applications 44 (1), 240-269, 2023
Autorizações: US National Science Foundation, US Department of Energy
Probabilistic iterative methods for linear systems
J Cockayne, ICF Ipsen, CJ Oates, TW Reid
Journal of machine learning research 22 (232), 1-34, 2021
Autorizações: US National Science Foundation, UK Engineering and Physical Sciences …
Precision-aware deterministic and probabilistic error bounds for floating point summation
E Hallman, ICF Ipsen
Numerische Mathematik 155 (1-2), 83-119, 2023
Autorizações: US National Science Foundation, US Department of Energy
Seagle: a scalable exact algorithm for large-scale set-based gene-environment interaction tests in biobank data
JT Chi, ICF Ipsen, TH Hsiao, CH Lin, LS Wang, WP Lee, TP Lu, JY Tzeng
Frontiers in genetics 12, 710055, 2021
Autorizações: US National Science Foundation, US National Institutes of Health
Randomized least squares regression: Combining model-and algorithm-induced uncertainties
JT Chi, ICF Ipsen
work 1, 34, 2018
Autorizações: US National Science Foundation
A projector-based approach to quantifying total and excess uncertainties for sketched linear regression
JT Chi, ICF Ipsen
Information and Inference: A Journal of the IMA 11 (3), 1055-1077, 2022
Autorizações: US National Science Foundation
Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms
JT Chi, ICF Ipsen
Linear Algebra and its Applications 624, 87-102, 2021
Autorizações: US National Science Foundation
A GEOMETRIC ANALYSIS OF MODEL-AND ALGORITHM-INDUCED UNCERTAINTIES 2 FOR RANDOMIZED LEAST SQUARES REGRESSION
JT CHI, ICF IPSEN
work 1, 39, 2019
Autorizações: US National Science Foundation
Statistical properties of BayesCG under the Krylov prior
TW Reid, ICF Ipsen, J Cockayne, CJ Oates
Numerische Mathematik 155 (3), 239-288, 2023
Autorizações: US National Science Foundation, US Department of Energy
Lecture 14: Randomized Algorithms for Least Squares Problems
ICF Ipsen
Autorizações: US National Science Foundation, US Department of Defense
special edition on probabilistic numerics
M Girolami, ICF Ipsen, CJ Oates, AB Owen, TJ Sullivan
Statistics and Computing 29, 1181-1183, 2019
Autorizações: US National Science Foundation
RandNLA, Pythons, and the CUR for Your Data Problems
E Gallopoulos, P Drineas, I Ipsen, MW Mahoney
Collections 49 (01), 2016
Autorizações: US National Science Foundation
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