Machine learning (ML) has become a dominate topic in the world of computing. This is driven by new algorithms, lots of data, hardware optimized for ML, and a seemingly constant stream of new applications. From natural language processing to self-driving cars, machine learning is changing the way we live-with computers. The impact of ML on software development, however, is largely untapped. We still write software by calling functions from an API or “from scratch” using our favorite programming languages with little change over the couple decades. We believe ML presents us with an opportunity to fundamentally change how we write software. Incredible research opportunities exist when combining machine learning and programming languages in novel ways. Now in its third year, the workshop on Machine Learning and Programming Languages (MAPL) is a forum for machine learning and programming systems researchers to join together and discuss how we will change the way we write software. MAPL will take place at PLDI in 2019 on Saturday, June 22, 2019. The call for papers is now available.

Call for Papers

Machine learning (ML) has become a dominate topic in the world of computing. This is driven by new algorithms, lots of data, hardware optimized for ML, and a seemingly constant stream of new applications. From natural language processing to self-driving cars, machine learning is changing the way we live-with computers.

The impact of ML on software development, however, is largely untapped. We still write software by calling functions from an API or “from scratch” using our favorite programming languages with little change over the couple decades. We believe ML presents us with an opportunity to fundamentally change how we write software. Incredible research opportunities exist when combining machine learning and programming languages in novel ways.

Now in its third year, the workshop on Machine Learning and Programming Languages (MAPL) is a forum for machine learning and programming systems researchers to join together and discuss how we will change the way we write software. MAPL will include a combination of peer-reviewed papers and invited events. The workshop will seek papers on a diverse range of topics related to programming languages and machine learning including (and not limited to):

  • Application of machine learning to compilation and run-time scheduling
  • Collaborative human / computer programming
  • Inductive programming
  • Infrastructure and techniques for mining and analyzing large code bases
  • Interoperability between machine learning frameworks and existing code bases 

  • Probabilistic programming
  • Programming language and compiler support for machine learning applications
  • Programming language support and implementation of deep learning frameworks

Evaluation Criteria

As in previous year, reviewers will evaluate each contribution for its significance, originality, and clarity to the topics listed above. Submissions should clearly state how they are novel and how they improve upon existing work.

This year we will be using double-blind reviewing. This means that author names and affiliations must be omitted from the submission. Additionally, if the submission refers to prior work done by the authors, that reference should be made in third person. These are firm submission requirements. If you have questions about making your paper double blind, please contact the Program Chair.

Paper Submissions

Submissions must be in English. papers should be in PDF and format and no more than 8 pages in standard two-column SIGPLAN conference format including figures and tables but excluding references. Shorter submissions are welcome. The submissions will be judged based on the merit of the ideas rather than the length. Submissions must be made through the online submission site.

All accepted papers will appear in the published proceedings and available on the ACM Digital Library. Authors will have the option of having their final paper accessible from the workshop website as well.

Authors must be familiar with and abide by SIGPLAN’s republication policy, which forbids simultaneous submission to multiple venues and requires disclosing prior publication of closely related work.