@article{AHPP22FurtherMLSVGD,title={Further Analysis of Multilevel Stein Variational Gradient Descent with an Application to the Bayesian Inference of Glacier Ice Models},author={Alsup, T. and Hartland, T. and Peherstorfer, B. and Petra, N.},journal={Advances in Computational Mathematics},year={2024},volume={50},issue={4},doi={https://doi.org/10.1007/s10444-024-10153-4},url={https://link.springer.com/article/10.1007/s10444-024-10153-4},}
2023
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
A. Maurais, T. Alsup, B. Peherstorfer, and Y. Marzouk
@article{MAPY23Reg,title={Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices},year={2023},journal={arXiv:2307.12438},author={Maurais, A. and Alsup, T. and Peherstorfer, B. and Marzouk, Y.},}
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry
A. Maurais, T. Alsup, B. Peherstorfer, and Y. Marzouk
In ICML’23: Proceedings of the 40th International Conference on Machine Learning, 2023
@inproceedings{MAPY23LEMF,title={Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry},author={Maurais, A. and Alsup, T. and Peherstorfer, B. and Marzouk, Y.},booktitle={ICML'23: Proceedings of the 40th International Conference on Machine Learning},year={2023},}
Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multi-Fidelity Importance Sampling and Bayesian Inverse Problems
T. Alsup, and B. Peherstorfer
SIAM/ASA Journal on Uncertainty Quantification, 2023
@article{AP20Context,title={Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multi-Fidelity Importance Sampling and Bayesian Inverse Problems},author={Alsup, T. and Peherstorfer, B.},journal={SIAM/ASA Journal on Uncertainty Quantification},year={2023},volume={11},issue={1},url={https://epubs.siam.org/doi/10.1137/21M1445594},doi={https://doi.org/10.1137/21M1445594},}
Trading off Deterministic Approximations and Sampling in Multifidelity Bayesian Inference
@phdthesis{thesis,author={Alsup, T.},title={Trading off Deterministic Approximations and Sampling in Multifidelity Bayesian Inference},school={New York University},year={2023},}
2021
Multilevel Stein Variational Gradient Descent with Applications to Bayesian Inverse Problems
T. Alsup, L. Venturi, and B. Peherstorfer
In Mathematical and Scientific Machine Learning (MSML) 2021, 2021
@inproceedings{AVP21MLSVGD,title={Multilevel Stein Variational Gradient Descent with Applications to Bayesian Inverse Problems},author={Alsup, T. and Venturi, L. and Peherstorfer, B.},year={2021},booktitle={Mathematical and Scientific Machine Learning (MSML) 2021},}
Expected Infomation Gain Estimates and Bayesian Optimal Experimental Design
T. Alsup, and T. Catanach
In Computer Science Research Institute Summer Proceedings 2021, 2021
@inproceedings{Sandia,title={Expected Infomation Gain Estimates and Bayesian Optimal Experimental Design},booktitle={Computer Science Research Institute Summer Proceedings 2021},author={Alsup, T. and Catanach, T.},note={Technical Report: SAND2022-0653R},institution={Sandia National Laboratories},year={2021},editor={Smith, J.D. and Galvan, E.},pages={269--282},}