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Erik Orm Hellsten

2 accepted papers

2024

Vanilla Bayesian Optimization Performs Great in High Dimensions

ICML 2024poster

High-dimensional optimization problems have long been considered the Achilles' heel of Bayesian optimization algorithms. Spurred by the curse of dimensionality, a large collection of algorithms aim to make BO more performant in this setting, commonly by imposing various simplifying assumptions on th…

2023

Self-Correcting Bayesian Optimization through Bayesian Active Learning

NeurIPS 2023poster

Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full potential, and little effort is devoted to finding good hyperparameters in the literature. We demonstrate the impact of selec…

Cited by 16SourcePDFScholar