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Next-Generation Gaussian Processes for Function Approximation, Uncertainty Quantification, and Decision-Making

Lundi 4 mars 14:00 - Duree : 1 heure
Lieu : Science Building Seminar Room, SB-036, ILL, EPN Campus

Orateur : Markus NOACK (Lawrence Berkeley National Lab, Berkeley)

Résumé :

Gaussian processes (GPs) and Gaussian-related stochastic processes are powerful tools for function approximation, uncertainty quantification, global optimization, and autonomous data acquisition due to their robustness, analytical tractability, and natural inclusion of Bayesian uncertainty estimates. Even so, Gaussian processes are commonly criticized for poor approximation performance and neck-breaking computational costs in real-life applications. The reason for this gap, however, is not the methodology itself but rather a user-caused lack of flexibility and domain awareness of the underlying prior probability distribution, the likelihood, poor training, and insufficiently expressive utilization of the posterior for decision-making. In this talk, I want to address many challenges of GPs that might inhibit optimal performance, guiding the audience to “Next-Generation Gaussian Processes”. We will begin the journey by looking at a few motivating examples and some theoretical preliminaries before diving into commonly encountered challenges and what can be done to address them. We will discover hands-on solutions and tools that make implementation easy. The key takeaway for the audience will be a better understanding of Gaussian processes and ways to customize them for optimal performance.

Contact : tellier@ill.fr



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