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PLDI 2020
Mon 15 - Fri 19 June 2020

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
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Tue 16 Jun

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08:00 - 10:00
Graph Neural Networks for Program ReasoningMAPL at MAPL live stream
Chair(s): Ke Wang Visa Research
08:00
30m
Talk
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs
MAPL
Elizabeth Dinella University of Pennsylvania
08:30
60m
Tutorial
A Gentle Tutorial on Graph Neural Networks and Its Application to Programming Languages
MAPL
09:30
30m
Talk
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
30m
Talk
An Abstraction-Based Framework for Neural Network Verification
MAPL
Guy Katz Hebrew University
11:00
30m
Talk
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
30m
Talk
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
30m
Talk
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
Keynote TalkMAPL at MAPL live stream
Chair(s): Justin Gottschlich Intel Labs / Penn
13:00
60m
Talk
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
30m
Talk
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
30m
Talk
Trustworthy Autonomy through Program Synthesis
MAPL
Swarat Chaudhuri Rice University
15:00 - 15:30
15:30 - 17:00
Program SynthesisMAPL at MAPL live stream
Chair(s): Satish Chandra Facebook
15:30
30m
Talk
Neurosymbolic Reasoning and the Third Wave of Program Synthesis
MAPL
Armando Solar-Lezama Massachusetts Institute of Technology, USA
16:00
30m
Talk
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
30m
Talk
Towards Human-Like Program Synthesis
MAPL
Rishabh Singh Google Brain
17:00 - 17:30

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)