Brian Staber

According to our database, Brian Staber authored at least 9 papers between 2015 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

Scalable and adaptive prediction bands with kernel sum-of-squares.

Louis Allain, Sébastien Da Veiga, Brian Staber

CoRR, May, 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, Thierry Gonon, Ghassan Najjar, Emmanuel Menier, Matthieu Nastorg, Giovanni Catalani, Christian Rey

CoRR, May, 2025

Learning signals defined on graphs with optimal transport and Gaussian process regression.

Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels.

Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

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

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization.

Clément Bénard, Brian Staber, Sébastien Da Veiga

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Quantitative performance evaluation of Bayesian neural networks.

Brian Staber, Sébastien Da Veiga

CoRR, 2022

2015

Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models.

Brian Staber, Johann Guilleminot

SIAM/ASA J. Uncertain. Quantification, 2015