Paper:
In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture is done manually by the human expert through a slow trial and error process guided mainly by intuition. In this paper we describe a framework for automatically designing and training deep models. We propose an extensible and modular language that allows the human expert to compactly represent complex search spaces over architectures and their hyperparameters. The resulting search spaces are treestructured and therefore easy to traverse. Models can be automatically compiled to computational graphs once values for all hyperparameters have been chosen. We can leverage the structure of the search space to introduce different model search algorithms, such as random search, Monte Carlo tree search (MCTS), and sequential model-based optimization (SMBO). We present experiments comparing the different algorithms on CIFAR-10 and show that MCTS and SMBO outperform random search. We also present experiments on MNIST, showing that the same search space achieves near state-of-the-art performance with a few samples. These experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert. Code for our framework and experiments has been made publicly available.

Review:
Monte-Carlo Tree Search is a reasonable and promising approach to hyperparameter optimization or algorithm configuration in search spaces that involve conditional structure.
This paper must acknowledge more explicitly that it is not the first to take a graph-search approach. The cited work related to SMAC and Hyperopt / TPE addresses this problem similarly. The technique of separating a description language from the optimization algorithm is also used in both of these projects / lines of research. The [mis-cited] paper titled “Making a science of model search …” is about using TPE to configure 1, 2, and 3 layer convnets for several datasets, including CIFAR-10. SMAC and Hyperopt have been used to search large search spaces involving pre-processing and classification algorithms (e.g. auto-sklearn, autoweka, hyperopt-sklearn). There have been near-annual workshops on AutoML and Bayesian optimization at NIPS and ICML (see e.g. automl.org).
There is a benchmark suite of hyperparameter optimization problems that would be a better way to evaluate MCTS as a hyperparameter optimization algorithm: http://www.ml4aad.org/automl/hpolib/