On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

@inproceedings{Hoffman2014OnCA,
  title={On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning},
  author={Matthew D. Hoffman and Bobak Shahriari and Nando de Freitas},
  booktitle={AISTATS},
  year={2014}
}
We address the problem of finding the maximizer of a nonlinear function that can only be evaluated, subject to noise, at a finite number of query locations. Further, we will assume that there is a constraint on the total number of permitted function evaluations. We introduce a Bayesian approach for this problem and show that it empirically outperforms both the existing frequentist counterpart and other Bayesian optimization methods. The Bayesian approach places emphasis on detailed modelling… CONTINUE READING
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