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They bring two contributions to existing autoML methods: a meta-learning component, in which they use a list of past datasets to warm-start the bayesian optimizer, and a ensemble construction component, which reuses the ranking established by the bayesian optimizer to combine the best methods instead of just using the one found by the bayesian optimizer, for more robustness. They compare their system, auto-sklearn, to an existing autoML system, Auto-WEKA, and find that they outperform it in a majority of cases. They also compare variations of their system without (some of) the two novel components (meta-learning and ensemble construction) and show that the meta-learning component is the most helpful.