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SPLASH 2019
Sun 20 - Fri 25 October 2019 Athens, Greece
Fri 25 Oct 2019 14:00 - 14:22 at Attica - Synthesis

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 accuracy for datasets that previous systems process successfully.

This program is tentative and subject to change.

Fri 25 Oct

splash-2019-oopsla
14:00 - 15:30: OOPSLA - Synthesis at Attica
splash-2019-oopsla14:00 - 14:22
Talk
splash-2019-oopsla14:22 - 14:45
Talk
Benjamin MarianoUniversity of Maryland, College Park, Josh ReeseUniversity of Maryland, College Park, Siyuan XuPurdue University, ThanhVu NguyenUniversity of Nebraska, Lincoln, Xiaokang QiuPurdue University, Jeffrey S. FosterTufts University, Armando Solar-LezamaMassachusetts Institute of Technology
splash-2019-oopsla14:45 - 15:07
Talk
Rohan BavishiUC Berkeley, Caroline LemieuxUniversity of California, Berkeley, Roy FoxUC Berkeley, Koushik SenUniversity of California, Berkeley, Ion StoicaUC Berkeley
splash-2019-oopsla15:07 - 15:30
Talk
Anders MiltnerPrinceton University, Sumit GulwaniMicrosoft, Vu LeMicrosoft, Alan LeungMicrosoft, Arjun RadhakrishnaMicrosoft, Gustavo SoaresMicrosoft, Ashish TiwariMicrosoft, Abhishek UdupaMicrosoft
Pre-print Media Attached