Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications (2017)
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The :download:`paper <2017-mapl.pdf>` is available locally.

This is largely a machine learning paper; my contributions were rather
limited and largely confined to section 3, which discusses solver strategies
for Dyna.


Abstract:

  Declarative programming is a paradigm that allows
  programmers to specify what they want to compute, leaving
  how to compute it to a solver. Our declarative programming
  language, Dyna, is designed to compactly specify
  computations like those that are frequently encountered in
  machine learning. As a declarative language, Dyna's solver
  has a large space of (correct) strategies available to it.
  We describe a reinforcement learning framework for
  adaptively choosing among these strategies to maximize
  efficiency for a given workload. Adaptivity in execution is
  especially important for software that will run under a
  variety of workloads, where no fixed policy works well. We
  hope that reinforcement learning will identify good
  policies reasonably quickly—offloading the burden of
  writing efficient code from human programmers. 

BibTeX:

.. code-block:: none
    
    @InProceedings{vieira-et-al-2017,
      author  = {Tim Vieira and Matthew Francis-Landau and Nathaniel Wesley
                Filardo and Farzad Khorasani and Jason Eisner},
      title   = {Dyna: Toward a Self-Optimizing Declarative Language for Machine
                Learning Applications},
      booktitle={Proceedings of the First ACM SIGPLAN Workshop on Machine
                Learning and Programming Languages (MAPL)},
      year    = {2017},
      month   = {6},
      address = {Barcelona}
    }