Hyperparameter Tuning with Constrained Bayesian Optimization
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Description
The objective of the project is to determine the value of a model hyperparameter that maximizes the validation accuracy subject to a constraint on the average prediction speed. To this end, Bayesian Optimization with Expected Improvement (EI) and Upper Confidence Bound (UCB) acquisition functions are employed to implement the algorithm.