imitation learning strategies into [<span style="font-variant:small-caps;">Bustle</span>]{} is an interesting direction for future work.

Conclusion
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We have introduced [<span style="font-variant:small-caps;">Bustle</span>]{}, a technique for using machine learning to guide bottom-up search for program synthesis. [<span style="font-variant:small-caps;">Bustle</span>]{} exploits the fact that bottom-up search makes it easy to evaluate partial programs and it uses machine learning to predict the likelihood that a given intermediate value is a sub-expression of the desired solution. We have shown that [<span style="font-variant:small-caps;">Bustle</span>]{} improves over various baselines on human-written benchmarks, not only in terms of benchmarks solved per candidate program considered, but also in terms of wall-clock time. In fact, we view showing that learning-in-the loop can be made fast enough for program synthesis as perhaps the major contribution of this work; the idea of learning-in-the-loop, though novel as far as we are aware, is relatively obvious, but we were quite surprised to learn that it can be made efficient enough to provide overall end-to-end speedups.

Listing of Properties Used
==========================

[<span style="font-variant:small-caps;">Bustle</span>]{} computes two types of Property Signatures: the signature involving the inputs and the outputs, and the signature involving the intermediate state and the outputs. In this paper, the inputs and outputs are always strings, but 