Speaker: Josif Grabocka
Josif Grabocka from the Representation Learning Research Group at Uni Freiburg will give a talk titled “Efficient Hyperparameter Optimization in the Age of Deep Learning“.
Hyperparameter Optimization (HPO) is essential in designing state-of-the-art Machine Learning systems. Unfortunately, existing HPO techniques are not efficient enough for Deep Learning, due to the high cost of evaluating multiple hyperparameter configurations in complex Deep Learning systems. In this talk, I will present three approaches for scaling HPO for Deep Learning along the lines of transfer learning, gray-box HPO, and pipeline optimization with pre-trained deep networks. The talk will end with a demonstration of the capability of HPO to achieve state-of-the-art predictive results on tabular datasets.