Due to recent algorithmic and computational advances, machine learning has seen a surge of interest in both research and practice. 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.
This workshop seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in areas of mutual benefit. The workshop will include a combination of peer-reviewed papers and invited events. The workshop will seek papers on a diverse range of topics related to programming languages and machine learning including (and not limited to):
- Application of machine learning to compilation and run-time scheduling
- Collaborative human / computer programming
- Inductive programming
- Tools and techniques for mining and analyzing large code bases
- Interoperability between machine learning frameworks and existing code bases
- Probabilistic and differentiable programming
- Programming language and compiler support for machine learning applications
- Programming language support and implementation of deep learning frameworks
- Intersection of cybersecurity and machine learning
- Application of machine learning to code recommendation and autocompletion, test generation, program repair, and debugging, and vice-versa.
- Novel structural representations of programming languages and machine learning
Tue 16 JunDisplayed time zone: Pacific Time (US & Canada) change
08:00 - 10:00 | |||
08:00 30mTalk | Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs MAPL Elizabeth Dinella University of Pennsylvania | ||
08:30 60mTutorial | A Gentle Tutorial on Graph Neural Networks and Its Application to Programming Languages MAPL Yizhou Sun UCLA | ||
09:30 30mTalk | LambdaNet: Probabilistic Type Inference using Graph Neural Networks MAPL Işıl Dillig University of Texas at Austin, USA | ||
10:00 - 10:30 | |||
10:30 - 11:30 | Deep Learning and Program VerificationMAPL at MAPL live stream Chair(s): Xujie Si McGill University, Canada | ||
10:30 30mTalk | An Abstraction-Based Framework for Neural Network Verification MAPL Guy Katz Hebrew University | ||
11:00 30mTalk | Generating Correctness Proofs with Neural Networks MAPL Alex Sanchez-Stern University of California, San Diego, Yousef Alhessi University of California, San Diego, Lawrence Saul University of California, San Diego, Sorin Lerner University of California at San Diego, USA | ||
11:30 - 12:30 | Compilers for Deep Learning FrameworksMAPL at MAPL live stream Chair(s): Charles Sutton Google Research | ||
11:30 30mTalk | On the Challenges in Programming Mixed-Precision Deep Neural Networks MAPL Ruizhe Zhao Imperial College London, Wayne Luk Imperial College London, Chao Xiong Corerain Technologies, Xinyu Niu Corerain Technologies, Kuen Hung Tsoi Corerain Technologies | ||
12:00 30mTalk | Semi-static Type, Shape and Symbolic Shape Inference for Dynamic Computation Graphs MAPL Momoko Hattori The University of Tokyo, Shimpei Sawada Preferred Networks, Shinichiro Hamaji Preferred Networks, Masahiro Sakai Preferred Networks, Shunsuke Shimizu Preferred Networks | ||
12:30 - 13:00 | |||
13:00 - 14:00 | |||
13:00 60mTalk | Program Optimization for Machine Learning MAPL Alex Aiken Stanford University, USA | ||
14:00 - 15:00 | Formal Methods and Reinforcement LearningMAPL at MAPL live stream Chair(s): Aws Albarghouthi University of Wisconsin-Madison, USA | ||
14:00 30mTalk | Learned Garbage Collection MAPL Lujing Cen MIT CSAIL, Ryan Marcus MIT CSAIL / Intel Labs, Hongzi Mao MIT CSAIL, Justin Gottschlich Intel Labs / Penn, Mohammad Alizadeh MIT CSAIL, Tim Kraska MIT CSAIL | ||
14:30 30mTalk | Trustworthy Autonomy through Program Synthesis MAPL Swarat Chaudhuri Rice University | ||
15:00 - 15:30 | |||
15:30 - 17:00 | |||
15:30 30mTalk | Neurosymbolic Reasoning and the Third Wave of Program Synthesis MAPL Armando Solar-Lezama Massachusetts Institute of Technology, USA | ||
16:00 30mTalk | Learning Quantitative Representation Synthesis MAPL Mayur Patil University of California, Riverside, Farzin Houshmand University of California, Riverside, Mohsen Lesani University of California, Riverside | ||
16:30 30mTalk | Towards Human-Like Program Synthesis MAPL Rishabh Singh Google Brain | ||
17:00 - 17:30 | |||
Accepted Papers
Call for Papers
The 4th Annual ACM SIGPLAN Machine Learning and Programming Languages Workshop (MAPL)
Call for Papers
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.
This workshop seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in areas of mutual benefit. The workshop will include a combination of peer-reviewed papers and invited events. The workshop will seek papers on a diverse range of topics related to programming languages and machine learning including (and not limited to):
- Application of machine learning to compilation and run-time scheduling
- Collaborative human / computer programming
- Inductive programming
- Tools and techniques for mining and analyzing large code bases
- Interoperability between machine learning frameworks and existing code bases
- Probabilistic and differentiable programming
- Programming language and compiler support for machine learning applications
- Programming language support and implementation of deep learning frameworks
- Testing, debugging, and fault-localization support for deep learning frameworks
- Intersection of cybersecurity and machine learning
- Application of machine learning to code recommendation and autocompletion, test generation, program repair, and debugging
- Novel structural representations of programming languages and machine learning
Evaluation Criteria
As in previous years, reviewers will evaluate each contribution for its significance, originality, and clarity to the topics listed above. Submissions should clearly state how their novelty and how they improve upon existing work.
This year we will be using double-blind reviewing. This means that author names and affiliations must be omitted from the submission. Additionally, if the submission refers to prior work done by the authors, that reference should be made in third person. These are firm submission requirements. If you have questions about making your paper double blind, please contact the Program Chair.
Paper Submissions
Submissions must be in English. Papers should be in PDF format and no more than 8 pages in standard two-column SIGPLAN conference format including figures and tables but excluding 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 an online submission site.
All accepted papers will appear in the published proceedings and available on the ACM Digital Library. Authors will have the option of having their final paper accessible from the workshop website as well.
Authors must be familiar with and abide by SIGPLAN’s republication policy, which forbids simultaneous submission to multiple venues and requires disclosing prior publication of closely related work.
Posters
Besides papers, MAPL this year will also have a poster session. We invite poster submissions that are related to the workshop topics. For each poster, please prepare a maximum 1 page abstract summarizing your project. All reasonable posters will be accepted. The poster titles will be posted on the workshop website but will not be included as part of the official proceedings, hence authors will be able to submit their work as a full paper to other venues. See below for submission deadlines. Note that posters should be submitted to a separate website. Use the ACM standard two-column SIGPLAN conference format for your poster abstract.
General Chair: Koushik Sen (UC Berkeley)
Program Chair: Mayur Naik (University of Pennsylvania)
Publicity Chair: Nesime Tatbul (Intel Labs / MIT)
Program Committee:
- Aws Albarghouthi, University of Wisconsin-Madison
- Satish Chandra, Facebook
- Sarah Chasins, University of California, Berkeley
- Dana Drachsler Cohen, Technion
- Vinod Grover, NVIDIA
- Ryan Marcus, Intel Labs and MIT CSAIL
- Ayman Nadeem, GitHub, Inc.
- Baishakhi Ray, Columbia University
- Rahul Sharma, Microsoft Research
- Xujie Si, University of Pennsylvania
- Charles Sutton, Google Research
- Ke Wang, Visa Research
Steering Committee:
- Raj Barik (Uber)
- Alvin Cheung (UC-Berkeley)
- Stefano Ermon (Stanford University)
- Justin Gottschlich (chair, Intel Labs / Penn)
- Costin Iancu (Lawrence Berkeley National Lab)
- Kunle Olukotun (Stanford University)
- Tatiana Shpeisman (Google)
- Armando Solar-Lezama (MIT)
| Tue 16 Jun 2020 Workshop |
| Sun 3 May 2020 Camera-Ready Due |
| Mon 27 Apr 2020 Author Notification |
| Tue 31 Mar 2020 Submission Deadline |
Aws Albarghouthi
University of Wisconsin-Madison, USA
Satish Chandra
Facebook
United States
Sarah E. Chasins
University of California, Berkeley
Dana Drachsler Cohen
Technion
Israel
Vinod Grover
NVIDIA
United States
Ryan Marcus
MIT CSAIL / Intel Labs
United States
Ayman Nadeem
GitHub, Inc.
United States
Baishakhi Ray
Columbia University, New York
United States
Rahul Sharma
Microsoft Research
India
Xujie Si
McGill University, Canada
Charles Sutton
Google Research
United States
Ke Wang
Visa Research
United States