MLP 2018: Workshop on Machine Learning for Programming
     part of FLOC 2018

Oxford, UK, July 18-19, 2018

Website:        http://ml4p.org/
Submission deadline: April 15, 2018

The two-day event will feature invited and contributed talks on improving software reliability and developer productivity by using machine learning, including deep learning. The techniques of interest include leveraging big code
repositories (such as GitHub) to build models of code and using them for
program analysis, synthesis, and repair techniques that advance the state of
the art.

# Submission Guidelines

We invite submissions of extended abstracts of at most 5 pages in length
(excluding references and appendices) for presentation at the workshop. We will consider original research contributions as well well-prepared surveys and vision statements. For instructions about how to submit see the workshop web
site: http://ml4p.org/

# List of Topics

* new learning algorithms, models, and architectures for the domain of programs; * machine learning methods for program suggestion, synthesis, debugging,
    and other programming tasks;
 * probabilistic extensions of conventional program analyses;
 * source code representations for learning;
 * applying natural language processing techniques to code, comments,
    documentation, and other software artifacts;
 * description and evaluations of new tools.

# Invited Speakers

  * Miltos Allamanis, Microsoft Research
  * Earl Barr, University College London
  * Swarat Chaudhuri, Rice
  * Prem Devanbu, UC Davis
  * Sergio Giro, Prodo.AI
  * Michel Pradel, TU Darmstadt
  * Rishabh Singh, Google
  * Dawn Song, UC Berkeley
  * Danny Tarlow, Google Brain
  * Eran Yahav, Technion
  * Martin Vechev, ETH Zurich
  * Jules Villard, Facebook

# Program Committee

  * Marc Brockshmidt, Microsoft Research
  * Aditya Kanade, IISc Bangalore
  * Viktor Kuncak (co-chair), EPFL
  * Bruno Marnette (co-chair), Prodo.AI
  * Sebastian Riedel, University College London
  * Charles Sutton (co-chair), University of Edinburgh and Google
  * Luke Zettlemoyer, University of Washington

# Organizing Committee

For any queries, please contact

 * Viktor KunĨak <vkun...@gmail.com>, EPFL
 * Bruno Marnette <br...@prodo.ai>, Prodo.AI
* Charles Sutton <csut...@inf.ed.ac.uk>, University of Edinburgh and Google

# Sponsors

* Main sponsor: ProdoAI, https://prodo.ai/
* Co-sponsors will be confirmed closer to the date of the event

# Student grants

* A limited number of student grants will be offered to contribute towards
registration and accommodation costs.
* More information will be provided at http://ml4p.org/ closer to the date
of the event

Charles Sutton * Reader in Machine Learning * University of Edinburgh
Fellow, The Alan Turing Institute * http://homepages.inf.ed.ac.uk/csutton/

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The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
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