5 citations · 10 across the 5 of their papers we have counts for
6 papers
Cross-Market Meta-Learning for Electricity Price Forecasting
Delaram Pirhayatifard, Terrence Alsup
Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy: statistical and tree-based ba…
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer +1
We introduce a multifidelity estimator of covariance matrices formulated as the solution to a regression problem on the manifold of symmetric positive definite matrices. The estima…
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer +1
We introduce a multi-fidelity estimator of covariance matrices that employs the log-Euclidean geometry of the symmetric positive-definite manifold. The estimator fuses samples from…
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models
Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer +1
Multilevel Stein variational gradient descent is a method for particle-based variational inference that leverages hierarchies of surrogate target distributions with varying costs a…
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems
Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
This work presents a multilevel variant of Stein variational gradient descent to more efficiently sample from target distributions. The key ingredient is a sequence of distribution…
Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems
Terrence Alsup, Benjamin Peherstorfer
Multi-fidelity methods leverage low-cost surrogate models to speed up computations and make occasional recourse to expensive high-fidelity models to establish accuracy guarantees.…