MAPS: The 5th Annual Symposium on Machine Programming

Aws Albarghouthi, Isil Dillig, Justin Gottschlich, Roopsha Samanta, Jesmin Jahan Tithi

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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 symposium seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in the areas of mutual benefit. The symposium will include a combination of rigorous peer-reviewed papers and invited events. The symposium 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 (i.e., conversational programming)
  • Deterministic and stochastic program synthesis
  • Infrastructure 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 machine learning frameworks
  • Neurosymbolic and intentional programming

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