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T1 - Correcting Boundary Over-Exploration Deficiencies In Bayesian Optimization With Virtual Derivative Sign Observations
SN - /
UR - http://hdl.handle.net/10138/310577
T3 - IEEE International Workshop on Machine Learning for Signal Processing
A1 - Siivola, Eero; Vehtari, Aki; Vanhatalo, Jarno; Gonzalez, Javier; Andersen, Michael
A2 - Pustelnik, N; Ma, Z; Tan, ZH; Larsen, J
PB - IEEE
Y1 - 2018
LA - eng
AB - Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of ℛ d , by using a Gaussian process (GP) as a surrogate model for the objective. Although currently available acquisition functions address this goal with different degree of success, an over-exploration effect of the contour of the search space is typically observed. However, in problems like the configuration of machine learning al...
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KW - 112 Statistics and probability; 113 Computer and information sciences; Bayesian optimization; Gaussian process; virtual derivative sign observation
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