一句话：提出基于sketching的自动代码合成系统，用元学习填充程序洞，为机器学习任务生成正确代码。
本文关注缺乏数据科学专业知识的用户如何利用机器学习解决推断问题，而现有 AutoML 框架在可表达性、实现和优化方面仍有不足。作者提出一种在用户数据上下文中自动合成正确代码的系统，采用 sketching 范式：用户提供部分程序作为 sketch，刻画实现的高层结构，低层细节则留作待求解的洞；系统利用基于元特征的元学习来近似求解这些洞。作者认为该 sketch 方法比现有 AutoML 框架更具表达力、更易实现和优化。初步结果表明该方法很有前景：消耗更少资源，并能产生与现有技术可比的结果。
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.
DOI 原文 ·
@inproceedings{paperbot5813,
title = {Synthesizing correct code for machine learning programs},
author = {Joshua Gisi},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3418780}
}