A decomposed multi-fidelity covariance formulation allows Vecchia approximation on latent processes and GLS mean removal to deliver scalable, fully likelihood-based fusion of noisy low-fidelity and accurate high-fidelity spatio-temporal data.
Interpreting Mixed Membership Models: Implications of Erosheva's Representation Theorem , Year =
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BOOOM parametrizes Stiefel manifold optimization into Euclidean angle space using global Givens rotations and solves it with recursive modified pattern search for loss-agnostic black-box problems.
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A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models
A decomposed multi-fidelity covariance formulation allows Vecchia approximation on latent processes and GLS mean removal to deliver scalable, fully likelihood-based fusion of noisy low-fidelity and accurate high-fidelity spatio-temporal data.
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BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference
BOOOM parametrizes Stiefel manifold optimization into Euclidean angle space using global Givens rotations and solves it with recursive modified pattern search for loss-agnostic black-box problems.