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 Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:00 - 10:15 Day opening | Welcome and introductions ML4PL |
10:15 - 11:00 Talk | Machine Learning for Programming ML4PL Martin VechevETH Zurich |
11:00 - 11:30 Talk | Learning to Decipher the Heap ML4PL Marc BrockschmidtMicrosoft Research | ||
11:30 - 12:00 Talk | PPAML: Probabilistic Programming Advancing Machine Learning ML4PL Suresh JagannathanDARPA | ||
12:00 - 12:30 Talk | Man vs. Machine: Challenges of Integrating Programming Languages and People ML4PL Emery D. BergerUniversity of Massachusetts, Amherst |
13:45 - 14:15 Talk | Problems and opportunities — Program similarity ML4PL Eran YahavTechnion | ||
14:15 - 14:45 Talk | Inferring Coding Conventions with Machine Learning ML4PL Miltiadis AllamanisUniversity of Edinburgh, Earl T. BarrUniversity College London, Christian BirdMicrosoft Research, Charles SuttonUniversity of Edinburgh | ||
14:45 - 15:15 Talk | Using topic models to understand programming languages literature ML4PL Kathleen FisherTufts University | ||
15:15 - 15:45 Talk | Scaling Program Synthesis by Exploiting Existing Code ML4PL |
16:10 - 16:40 Talk | Problems and opportunities – Statistical modeling in (declarative) PLs ML4PL Molham ArefLogicblox | ||
16:40 - 17:10 Talk | Bimodal Modelling of Source Code and Natural Language ML4PL Andrew D. GordonMicrosoft Research and University of Edinburgh | ||
17:10 - 17:40 Talk | Machine learning for predictive modeling and recommender systems automation ML4PL 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