text: 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/