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2020 – today
- 2024
[c3]Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber:
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels. AISTATS 2024: 1297-1305- [i3]Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber:
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels. CoRR abs/2402.03838 (2024) - 2023
- [c2]Clément Bénard, Brian Staber, Sébastien Da Veiga:
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization. NeurIPS 2023 - [c1]Fabien Casenave, Brian Staber, Xavier Roynard:
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability. NeurIPS 2023 - [i2]Fabien Casenave, Brian Staber, Xavier Roynard:
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability. CoRR abs/2305.12871 (2023) - 2022
- [i1]Brian Staber, Sébastien Da Veiga:
Quantitative performance evaluation of Bayesian neural networks. CoRR abs/2206.06779 (2022)
2010 – 2019
- 2015
- [j1]Brian Staber
, Johann Guilleminot:
Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models. SIAM/ASA J. Uncertain. Quantification 3(1): 599-621 (2015)
Coauthor Index
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