ECOOP 2015
Sun 5 - Fri 10 July 2015 Prague, Czech Republic

Welcome to ML4PL, the first workshop on machine learning techniques applied to programming language-related applications. This workshop puts an emphasis on identifying open problem rather than presenting solution, and encourages discussion amongst the participants. Attendance will be limited to ensure that meeting retains an interactive character.

Tue 7 Jul

10:00 - 10:15: ML4PL - Welcome at Moravia III
ML4PL2015143625600000010:00 - 10:15
Day opening
10:15 - 11:00: ML4PL - Invited Talk at Moravia III
ML4PL2015143625690000010:15 - 11:00
11:00 - 12:30: ML4PL - Session 1 at Moravia III
ML4PL2015143625960000011:00 - 11:30
ML4PL2015143626140000011:30 - 12:00
ML4PL2015143626320000012:00 - 12:30

Call for Papers

Over the last few years, we have seen a rapid growth in the use of machine-learning technologies in programming languages and systems. This growth is driven by the need to design programming languages to analyze, detect patterns, and make sense of Big Data, along with the increasing complexity of programming language tools, including analyzers and compilers, and computer architectures. The scale of complexity in available unstructured data and system tools has reached a stage where simple heuristics and solutions are no longer feasible or do not deliver adequate performance. At the same time, statistical and machine learning techniques have become more mainstream.

This workshop is a broad forum to bring together researchers with interests in the intersection of programming languages and system tools with machine learning.

Topics of interest include (but are not limited to):

  • Program analysis + machine learning
  • Programming languages + machine learning
  • Compiler optimizations + machine learning
  • Computer architecture + machine learning
  • Probabilistic programming languages
  • Design space exploration

The workshop will feature a couple of longer talks, and the short problem statements.

Submissions should take the form of talk abstract or 2 page problem statements.