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cs.AI2021
Accounting for Gaussian Process Imprecision in Bayesian Optimization
Julian Rodemann, Thomas Augustin
Bayesian optimization (BO) with Gaussian processes (GP) as surrogate models is widely used to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we p…
cs.AI2021
Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty
Christoph Jansen, Hannah Blocher, Thomas Augustin +1
In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on…