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.

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Tue 7 Jul
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10:00 - 10:15: ML4PL - Welcome at Moravia III
ML4PL201510:00 - 10:15
Day opening
10:15 - 11:00: ML4PL - Invited Talk at Moravia III
ML4PL201510:15 - 11:00
Martin VechevETH Zurich
11:00 - 12:30: ML4PL - Session 1 at Moravia III
ML4PL201511:00 - 11:30
Marc BrockschmidtMicrosoft Research
ML4PL201511:30 - 12:00
ML4PL201512:00 - 12:30
Emery BergerUniversity of Massachusetts, Amherst
13:45 - 15:45: ML4PL - Session 2 at Moravia III
ML4PL201513:45 - 14:15
Eran YahavTechnion
ML4PL201514:15 - 14:45
Miltiadis AllamanisUniversity of Edinburgh, Earl T. BarrUniversity College London, Christian BirdMicrosoft Research, Charles SuttonUniversity of Edinburgh
ML4PL201514:45 - 15:15
Kathleen FisherTufts University
ML4PL201515:15 - 15:45
James BornholtUniversity of Washington, Emina TorlakUniversity of Washington
16:10 - 18:10: ML4PL - Session 3 at Moravia III
ML4PL201516:10 - 16:40
Molham ArefLogicblox
ML4PL201516:40 - 17:10
Andrew D. GordonMicrosoft Research and University of Edinburgh
ML4PL201517:10 - 17:40
Pavel KordikCzech Technical University in Prague

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.