International Workshop on Statistical Relational AI (StarAI), 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 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.

Links

Bib Entry 

@inproceedings{riedel2014wolfe,
  author = {Riedel, Sebastian and Singh, Sameer and Srikumar, Vivek and Rockt{\"a}schel, Tim and Visengeriyeva, Larysa and Noessner, Jan},
  title = {{WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming}},
  booktitle = {International Workshop on Statistical Relational AI (StarAI)},
  year = {2014}
}