Publications

  1. A. Maurais, T. Alsup, B. Peherstorfer, and Y. Marzouk. Multifidelity covariance estimation via regression on the manifold of symmetric positive definite matrices. arXiv:2307.12438, 2023. [link]
  2. A. Maurais, T. Alsup, B. Peherstorfer, and Y. Marzouk. Multi-fidelity covariance estimation in the log-Euclidean geometry. In International Conference on Machine Learning (ICML), 2023. [link]
  3. T. Alsup and B. Peherstorfer. Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems. SIAM/ASA Journal on Uncertainty Quantification, 2022. [link]
  4. T. Alsup, T. Hartland, B. Peherstorfer, and N. Petra. Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. arXiv:2122.03366, 2022. [link]
  5. T. Alsup, L. Venturi, and B. Peherstorfer. Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Mathematical and Scientific Machine Learning (MSML) 2021, 2021. [link]
  6. T. Alsup and T. Catanach. Expected information gain estimates and Bayesian optimal experimental design. In J.D. Smith and E. Galvan, editors, Computer Science Research Institute Summer Proceedings 2021, pages 269–282, 2021. Technical Report: SAND2022-0653R. [link]

Dissertation

T. Alsup. Trading off deterministic approximations and sampling in multifidelity Bayesian inference. New York University, 2023.