Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, Adam Tauman Kalai
Citation: Proc. 30th Intíl Conf. Machine Learning (ICML), J. Machine Learning Research, Workshop and Conference Proceedings 28, 1 (June 2013), pp 187-195
Learning programs is a timely and interesting challenge. In Programming by Example (PBE), a system attempts to infer a program from input and output examples alone, by searching for a composition of some set of base functions. We show how machine learning can be used to speed up this seemingly hopeless search problem, by learning weights that relate textual features describing the provided input-output examples to plausible sub-components of a program. This generic learning framework lets us address problems beyond the scope of earlier PBE systems. Experiments on a prototype implementation show that learning improves search and ranking on a variety of text processing tasks found on help forums.