Articles with public access mandates - Matthew DunlopLearn more
Available somewhere: 11
How deep are deep Gaussian processes?
MM Dunlop, MA Girolami, AM Stuart, AL Teckentrup
Journal of Machine Learning Research 19 (54), 1-46, 2018
Mandates: US National Science Foundation, US Department of Defense, UK Engineering and …
Hierarchical Bayesian level set inversion
MM Dunlop, MA Iglesias, AM Stuart
Statistics and Computing 27, 1555-1584, 2017
Mandates: US Department of Defense, UK Engineering and Physical Sciences Research Council
Iterative updating of model error for Bayesian inversion
D Calvetti, M Dunlop, E Somersalo, A Stuart
Inverse Problems 34 (2), 025008, 2018
Mandates: US National Science Foundation, US Department of Defense, UK Engineering and …
Large data and zero noise limits of graph-based semi-supervised learning algorithms
MM Dunlop, D Slepčev, AM Stuart, M Thorpe
Applied and Computational Harmonic Analysis 49 (2), 655-697, 2020
Mandates: US National Science Foundation, US Department of Defense, European Commission
Dimension-robust MCMC in Bayesian inverse problems
V Chen, MM Dunlop, O Papaspiliopoulos, AM Stuart
arXiv preprint arXiv:1803.03344, 2018
Mandates: US Department of Defense
MAP estimators for piecewise continuous inversion
MM Dunlop, AM Stuart
Inverse Problems 32 (10), 105003, 2016
Mandates: UK Engineering and Physical Sciences Research Council
Hyperparameter estimation in Bayesian MAP estimation: parameterizations and consistency
MM Dunlop, T Helin, AM Stuart
The SMAI journal of computational mathematics 6, 69-100, 2020
Mandates: US National Science Foundation, US Department of Defense, Academy of Finland
Stability of Gibbs posteriors from the Wasserstein loss for Bayesian full waveform inversion
MM Dunlop, Y Yang
SIAM/ASA Journal on Uncertainty Quantification 9 (4), 1499-1526, 2021
Mandates: US National Science Foundation, US Department of Energy
Reconciling Bayesian and perimeter regularization for binary inversion
ORA Dunbar, MM Dunlop, CM Elliott, VH Hoang, AM Stuart
arXiv, 2020
Mandates: US National Science Foundation, US Department of Defense, UK Engineering and …
New likelihood functions and level-set prior for Bayesian full-waveform inversion
M Dunlop, Y Yang
SEG Technical Program Expanded Abstracts 2020, 825-829, 2020
Mandates: US National Science Foundation, US Department of Energy
RECONCILING BAYESIAN AND TOTAL VARIATION REGULARIZATION METHODS FOR BINARY INVERSION
MM DUNLOP, CM ELLIOTT, VHA HOANG, AM STUART
arXiv preprint arXiv:1706.01960, 2017
Mandates: US Department of Defense, UK Engineering and Physical Sciences Research Council
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