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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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