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Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
We present a Bayesian approach to identify optimal transformations that ...
08/05/2020 ∙ by Panagiotis Tsilifis, et al. ∙
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Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
The challenges for non-intrusive methods for Polynomial Chaos modeling l...
12/23/2019 ∙ by Panagiotis Tsilifis, et al. ∙
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Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation...
01/06/2018 ∙ by Panagiotis Tsilifis, et al. ∙
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Efficient Bayesian experimentation using an expected information gain lower bound
Experimental design is crucial for inference where limitations in the da...
05/30/2015 ∙ by Panagiotis Tsilifis, et al. ∙
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Variational Reformulation of Bayesian Inverse Problems
The classical approach to inverse problems is based on the optimization ...
10/21/2014 ∙ by Panagiotis Tsilifis, et al. ∙
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Panagiotis Tsilifis
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University of Southern California
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