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Kirk Public Lecture: On feasible set estimation with Bayesian active learning

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RCL - Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning

The general topic of this talk is Bayesian adaptive learning of excursion sets defined from a costly black-box model. This research field has received many attention in the last decades. During this talk, we will first review Gaussian Process Regression for feasible set estimation in the framework where the set to recover is defined from a numerical model with scalar values. We will exhibit that usual adaptive sampling criteria may lack of robustness, e.g., when the set to recover has several connex components. Then we will address more complex frameworks, such as the presence of uncertainties or the case of numerical models with vector outputs.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Webinar & LT2, Computer Laboratory, William Gates Building. Lecture Room 10, Cambridge University Engineering Department LT 2

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