Citation
Sebastian Riedel, Sameer Singh, Vivek Srikumar, Tim Rocktaschel, Larysa Visengeriyeva, Jan Noessner. "WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming". International Workshop on Statistical Relational AI (StarAI), 27, July, 2014.

Abstract
Existing modeling languages lack the expressiveness or efficiency to support many modern and successful machine learning (ML) models such as structured prediction or matrix factorization. We present WOLFE, a probabilistic programming language that enables practitioners to develop such models. Most ML approaches can be formulated in terms of scalar objectives or scoring functions (such as distributions) and a small set of mathematical operations such as maximization and summation. In WOLFE, the user works within a functional host language to declare scalar functions and invoke mathematical operators. The WOLFE compiler then replaces the operators with equivalent, but more efficient (strength reduction) and/or approximate (approximate programming) versions to generate low-level inference or learning code. This approach can yield very concise programs, high expressiveness and efficient execution.

Electronic downloads

• https://www.src.org/library/publication/p070326/. (Link to paper on SRC website.)
• http://svivek.com/research/publications/RSSRVN2014.pdf

• HTML

  Sebastian Riedel, Sameer Singh, Vivek Srikumar, Tim
  Rocktaschel, Larysa Visengeriyeva, Jan  Noessner. <a
  href="http://www.terraswarm.org/pubs/317.html"
  >WOLFE: Strength Reduction and Approximate Programming
  for Probabilistic Programming</a>, International
  Workshop on Statistical Relational AI (StarAI), 27, July,
  2014.

• Plain text

  Sebastian Riedel, Sameer Singh, Vivek Srikumar, Tim
  Rocktaschel, Larysa Visengeriyeva, Jan  Noessner.
  "WOLFE: Strength Reduction and Approximate Programming
  for Probabilistic Programming". International Workshop
  on Statistical Relational AI (StarAI), 27, July, 2014.

• BibTeX

  @inproceedings{RiedelSinghSrikumarRocktaschelVisengeriyevaNoessner14_WOLFEStrengthReductionApproximateProgrammingForProbabilistic,
      author = {Sebastian Riedel and Sameer Singh and Vivek
                Srikumar and Tim Rocktaschel and Larysa
                Visengeriyeva and Jan  Noessner},
      title = {WOLFE: Strength Reduction and Approximate
                Programming for Probabilistic Programming},
      booktitle = {International Workshop on Statistical Relational
                AI (StarAI)},
      day = {27},
      month = {July},
      year = {2014},
      abstract = {Existing modeling languages lack the
                expressiveness or efficiency to support many
                modern and successful machine learning (ML) models
                such as structured prediction or matrix
                factorization. We present WOLFE, a probabilistic
                programming language that enables practitioners to
                develop such models. Most ML approaches can be
                formulated in terms of scalar objectives or
                scoring functions (such as distributions) and a
                small set of mathematical operations such as
                maximization and summation. In WOLFE, the user
                works within a functional host language to declare
                scalar functions and invoke mathematical
                operators. The WOLFE compiler then replaces the
                operators with equivalent, but more efficient
                (strength reduction) and/or approximate
                (approximate programming) versions to generate
                low-level inference or learning code. This
                approach can yield very concise programs, high
                expressiveness and efficient execution.},
      URL = {http://terraswarm.org/pubs/317.html}
  }
  

Posted by Barb Hoversten on 22 May 2014.
Groups: services