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Please visit https://sites.google.com/view/mapl2017/home for detailed information on the MAPL workshop.

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. MAPL seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in cross disciplinary research. The workshop will include a combination of peer-reviewed papers and invited events, such as invited talks, panels and/or town hall discussions.

MAPL seeks papers on a diverse range of topics related to programming languages and machine learning including:

  • Programming languages and compilers for machine learning
  • Deep learning frameworks
  • Machine learning for compilation and run-time scheduling
  • Improving programmer productivity via machine learning
  • Inductive programming
  • Formal verification of machine learning systems
  • Probabilistic programming
  • Collaborative human / computer programming
  • Interoperability of machine learning frameworks and existing code bases

Accepted Papers

Title
A Computational Model for TensorFlow (An Introduction)
MAPL
Combining the Logical and the Probabilistic in Program Analysis
MAPL
Debugging Probabilistic Programs
MAPL
Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
MAPL
Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools
MAPL
Verified Perceptron Convergence Theorem
MAPL

Call for Papers

MAPL paper submissions should be made through EasyChair (link to follow soon).

Papers must be submitted in PDF and be no more than 8 pages in standard two-column SIGPLAN conference format including figures and tables but not including 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 on-line submission site. Formal proceedings will be included in the ACM digital archive and available at the workshop.

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Sun 18 Jun

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09:15 - 09:30
OpeningMAPL at Vertex WS219
09:15
15m
Day opening
Introduction and Welcome
MAPL
Tatiana Shpeisman Intel Labs, Justin Gottschlich Intel Labs
09:30 - 10:30
KeynoteMAPL at Vertex WS219
09:30
60m
Talk
Programming by Examples: PL Meets ML
MAPL
Sumit Gulwani Microsoft Research
11:00 - 12:00
Languages and FrameworksMAPL at Vertex WS219
11:00
30m
Talk
A Computational Model for TensorFlow (An Introduction)
MAPL
Martin Abadi Google, Michael Isard Google, Derek Murray Google
11:30
30m
Talk
Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications
MAPL
Tim Vieira Johns Hopkins University, Matthew Francis-Landau The Johns Hopkins University, Nathaniel Wesley Filardo , Farzad Khorasani Rice University, Jason Eisner The Johns Hopkins University
12:00 - 12:30
Debugging, Analysis, and VerificationMAPL at Vertex WS219
12:00
30m
Talk
Debugging Probabilistic Programs
MAPL
Chandrakana Nandi University of Washington, USA, Dan Grossman University of Washington, Adrian Sampson Cornell University, Todd Mytkowicz , Kathryn S McKinley Microsoft Research
14:00 - 15:30
Debugging, Analysis, and Verification 2MAPL at Vertex WS219
14:00
30m
Talk
Combining the Logical and the Probabilistic in Program Analysis
MAPL
Xin Zhang Georgia Tech, Xujie Si , Mayur Naik Georgia Tech
14:30
30m
Talk
Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools
MAPL
Ugur Koc University of Maryland, College Park, Parsa Saadatpanah University of Maryland, Jeffrey S. Foster University of Maryland, College Park, Adam Porter University of Maryland
15:00
30m
Talk
Verified Perceptron Convergence Theorem
MAPL
Charlie Murphy Princeton University, Gordon Stewart Ohio University
16:00 - 16:45
Town Hall DiscussionMAPL at Vertex WS219
16:00
45m
Other
Town Hall Discussion
MAPL

16:45 - 17:00
ClosingMAPL at Vertex WS219
16:45
15m
Day closing
Concluding Remarks
MAPL
Tatiana Shpeisman Intel Labs, Justin Gottschlich Intel Labs