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.
Conference DayTue 7 JulDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:15 - 11:00
|Machine Learning for Programming|
Martin VechevETH Zurich
11:00 - 12:30
|Learning to Decipher the Heap|
Marc BrockschmidtMicrosoft Research
|PPAML: Probabilistic Programming Advancing Machine Learning|
|Man vs. Machine: Challenges of Integrating Programming Languages and People|
Emery D. BergerUniversity of Massachusetts, Amherst
13:45 - 15:45
|Problems and opportunities — Program similarity|
|Inferring Coding Conventions with Machine Learning|
|Using topic models to understand programming languages literature|
Kathleen FisherTufts University
|Scaling Program Synthesis by Exploiting Existing Code|
16:10 - 18:10
|Problems and opportunities – Statistical modeling in (declarative) PLs|
|Bimodal Modelling of Source Code and Natural Language|
Andrew D. GordonMicrosoft Research and University of Edinburgh
|Machine learning for predictive modeling and recommender systems automation|
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.
- Submission site: https://easychair.org/conferences/?conf=ml4pl2015