PLDI 2020
Mon 15 - Sat 20 June 2020 London, United Kingdom

Due to recent algorithmic and computational advances, machine learning has seen a surge of interest in both research and practice. However, the impact of these advances on programming languages remains mostly untapped. Yet, incredible research opportunities exist when combining machine learning and programming languages in novel ways.

This workshop seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in areas of mutual benefit. The workshop 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
  • Tools and techniques for mining and analyzing large code bases
  • Interoperability between machine learning frameworks and existing code bases
  • Probabilistic and differentiable programming
  • Programming language and compiler support for machine learning applications
  • Programming language support and implementation of deep learning frameworks
  • Intersection of cybersecurity and machine learning
  • Application of machine learning to code recommendation and autocompletion, test generation, program repair, and debugging, and vice-versa.
  • Novel structural representations of programming languages and machine learning

Call for Papers

The 4th Annual ACM SIGPLAN Machine Learning and Programming Languages Workshop (MAPL)

Call for Papers

Due to recent algorithmic and computational advances, machine learning has seen a surge of interest in both research and practice. From natural language processing to self-driving cars, machine learning is creating new possibilities that are changing the way we live and interact with computers. However, the impact of these advances on programming languages remains mostly untapped. Yet, incredible research opportunities exist when combining machine learning and programming languages in novel ways.

This workshop seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in areas of mutual benefit. The workshop 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
  • Tools and techniques for mining and analyzing large code bases
  • Interoperability between machine learning frameworks and existing code bases
  • Probabilistic and differentiable programming
  • Programming language and compiler support for machine learning applications
  • Programming language support and implementation of deep learning frameworks
  • Testing, debugging, and fault-localization support for deep learning frameworks
  • Intersection of cybersecurity and machine learning
  • Application of machine learning to code recommendation and autocompletion, test generation, program repair, and debugging
  • Novel structural representations of programming languages and machine learning

Evaluation Criteria

As in previous years, reviewers will evaluate each contribution for its significance, originality, and clarity to the topics listed above. Submissions should clearly state how their novelty 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 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 an 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.

Posters

Besides papers, MAPL this year will also have a poster session. We invite poster submissions that are related to the workshop topics. For each poster, please prepare a maximum 1 page abstract summarizing your project. All reasonable posters will be accepted. The poster titles will be posted on the workshop website but will not be included as part of the official proceedings, hence authors will be able to submit their work as a full paper to other venues. See below for submission deadlines. Note that posters should be submitted to a separate website. Use the ACM standard two-column SIGPLAN conference format for your poster abstract.

  • General Chair: Koushik Sen (UC Berkeley)
  • Program Chair: Mayur Naik (University of Pennsylvania)
  • Publicity Chair: Nesime Tatbul (Intel Labs / MIT)
  • Program Committee: TBD

Steering Committee:

  • Raj Barik (Uber)

  • Alvin Cheung (UC-Berkeley)

  • Stefano Ermon (Stanford University)

  • Justin Gottschlich (chair, Intel Labs)

  • Costin Iancu (Lawrence Berkeley National Lab)

  • Kunle Olukotun (Stanford University)

  • Tatiana Shpeisman (Google)

  • Armando Solar-Lezama (MIT)