Brian Staber

11 papers A* 2A 2Journal 7
YearRankTypeTitle / Venue / Authors
2025 A conf
Learning signals defined on graphs with optimal transport and Gaussian process regression.
AISTATS
Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber
2025 J jnl
Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning.
CoRR
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
2025 J jnl
Scalable and adaptive prediction bands with kernel sum-of-squares.
CoRR
Louis Allain, Sébastien Da Veiga, Brian Staber
2024 A conf
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels.
AISTATS
Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber
2024 J jnl
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels.
CoRR
Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber
2024 J jnl
Learning signals defined on graphs with optimal transport and Gaussian process regression.
CoRR
Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber
2023 A* conf
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization.
NeurIPS
Clément Bénard, Brian Staber, Sébastien Da Veiga
2023 J jnl
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability.
CoRR
Fabien Casenave, Brian Staber, Xavier Roynard
2023 A* conf
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability.
NeurIPS
Fabien Casenave, Brian Staber, Xavier Roynard
2022 J jnl
Quantitative performance evaluation of Bayesian neural networks.
CoRR
Brian Staber, Sébastien Da Veiga
2015 J jnl
Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models.
SIAM/ASA J. Uncertain. Quantification
Brian Staber, Johann Guilleminot