Welcome to ML4PL, a workshop on machine learning techniques applied to programming language-related research and development. 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.
Call for Submissions
Over the last 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
Submissions should take the form of talk abstract or 2-page problem statements. Materials of accepted talks will be published in ACM DL.
Wed 18 Jul Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna
|11:00 - 12:00|
|Inferring Input Structure for Machine LearningKeynote|
Andreas ZellerSaarland University
|12:00 - 12:30|
|On the Importance of Common Sense in Program Synthesis|
Hila PelegTechnion, Israel
|14:00 - 14:30|
|Buffer Overflow Detection for C Programs is Hard to Learn|
|14:30 - 15:00|
|Generating Software Adaptations using Machine Learning|
|15:00 - 15:30|
|Detecting anomalies in Kotlin code|
|16:00 - 16:30|
|Subtype Polymorphism à la carte via Machine Learning on Dependent Types|
|16:30 - 17:00|
|Can We Learn Some PL Theory? How To Make Use of a Corpus of Subtype Checks|
Artem PelenitsynCzech Technical University in Prague
|17:00 - 17:30|