Synthesizing correct code for machine learning programs

Abstract

Success using machine learning (ML) in numerous fields has created a new class of users, who are not experts in the data science domain but want to use ML as a means to solve their inference problems. Various automatic machine learning (AutoML) approaches attempt to make ML solutions accessible to such users. In this work, we present a system that automatically synthesizes correct code within the context of the user’s data using sketching. In sketching, insight is determined through a partial program; a sketch expresses the high-level structure of implementation but leaves holes in place of the low-level details. We use meta-learning on meta-features to approximately solve holes. We observe that the sketch-based approach is more expressive, easier to implement, and easier to optimize than existing AutoML frameworks. Our initial results are very promising. Our approach uses fewer resources and still produces comparable results to existing techniques.