Saul Toscano-Palmerin

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning

Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck. Multi-fidelity optimization promises…