Scalable Hyperparameter Optimization with Products of Gaussian Process Experts

ECML PKDD 2016 2016 Nicolas SchillingMartin WistubaLars Schmidt-Thieme

In machine learning, hyperparameter optimization is a challenging but necessary task that is usually approached in a computationally expensive manner such as grid-search. Out of this reason, surrogate based black-box optimization techniques such as sequential model-based optimization have been proposed which allow for a faster hyperparameter optimization... (read more)

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