AL: autogenerating supervised learning programs
Name
3360601.pdf
Description
Accepted version
Size
646.77 KB
Format
Adobe PDF
Checksum (MD5)
cc9fbc86d868130386ff6640204712d9
Author(s)
Cambronero, Jose
Rinard, Martin C
Date Issued
October 2019
Journal
Proceedings of the ACM on Programming Languages
Publisher
Association for Computing Machinery (ACM)
Citation
Cambroner, Jose P. and Martin C. Rinard. “AL: autogenerating supervised learning programs.” Proceedings of the ACM on Programming Languages, 3, OOPSLA (October 2019): 175 © 2019 The Author(s)
Version
Final published version
Abstract
We present AL, a novel automated machine learning system that learns to generate new supervised learning pipelines from an existing corpus of supervised learning programs. In contrast to existing automated machine learning tools, which typically implement a search over manually selected machine learning functions and classes, AL learns to identify the relevant classes in an API by analyzing dynamic program traces that use the target machine learning library. AL constructs a conditional probability model from these traces to estimate the likelihood of the generated supervised learning pipelines and uses this model to guide the search to generate pipelines for new datasets. Our evaluation shows that AL can produce successful pipelines for datasets that previous systems fail to process and produces pipelines with comparable predictive performance for datasets that previous systems process successfully.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3360601
