BayMOTH unifies meta-Bayesian optimization with a usefulness-based fallback to lookahead, demonstrating competitive results on function optimization tasks even under low task relatedness.
As is shown in Figure 8, increasing M beyond M=1 yields only marginal performance improvements, with M=10 performing the best, without a clear monotonic trend
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BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH
BayMOTH unifies meta-Bayesian optimization with a usefulness-based fallback to lookahead, demonstrating competitive results on function optimization tasks even under low task relatedness.