High Dimensional Bayesian Optimization Using Dropout

Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited active variables or the additive form of the objective function... (read more)

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METHOD TYPE
Dropout
Regularization