June 5, 2024, 4:45 a.m. | Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren

cs.LG updates on arXiv.org arxiv.org

arXiv:2307.03565v2 Announce Type: replace
Abstract: Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate models that suffer from scalability issues and are sensitive to observations with different scales and noise types across tasks. Moreover, they often overlook the uncertainty associated with task …

abstract arxiv bayesian box cs.lg faster free functions however knowledge likelihood meta meta-learning optimization popular replace scratch stat.ml tasks type while

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