Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models. - Brian Staber; Johann Guilleminot (SIAM/ASA J. Uncertain. Quantification, 2015)

Quantitative performance evaluation of Bayesian neural networks. - Brian Staber; Sébastien Da Veiga (CoRR, 2022)

Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization. - Clément Bénard; Brian Staber; Sébastien Da Veiga (NeurIPS, 2023)

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability. - Fabien Casenave; Brian Staber; Xavier Roynard (NeurIPS, 2023)

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability. - Fabien Casenave; Brian Staber; Xavier Roynard (CoRR, 2023)

Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels. - Raphaël Carpintero Perez; Sébastien Da Veiga; Josselin Garnier; Brian Staber (AISTATS, 2024)

Learning signals defined on graphs with optimal transport and Gaussian process regression. - Raphaël Carpintero Perez; Sébastien Da Veiga; Josselin Garnier; Brian Staber (AISTATS, 2025)

Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning. - Fabien Casenave; Xavier Roynard; Brian Staber; William Piat; Michele Alessandro Bucci; Nissrine Akkari; Abbas Kabalan; Xuan Minh Vuong Nguyen; Luca Saverio; Raphaël Carpintero Perez; Anthony Kalaydjian; Samy Fouché; et al. (CoRR, 2025)

Scalable and adaptive prediction bands with kernel sum-of-squares. - Louis Allain; Sébastien Da Veiga; Brian Staber (CoRR, 2025)