Deep learning has recently helped AI systems to achieve human-level performance in several domains, including speech recognition, object classification, and playing several types of games. The major benefit of deep learning is that it enables end-to-end learning of representations of the data on several levels of abstraction. However, the overall network architecture and the learning algorithms' sensitive hyperparameters still need to be set manually by human experts. In this talk, I will discuss extensions of Bayesian optimization for handling this problem effectively, thereby paving the way to fully automated end-to-end learning. I will focus on speeding up Bayesian optimization by reasoning over data subsets and initial learning curves, sometimes resulting in 100-fold speedups in finding good hyperparameter settings. I will also show competition-winning practical systems for automated machine learning (AutoML) and briefly show related applications to the end-to-end optimization of algorithms for solving hard combinatorial problems.
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