StarAI 2014.


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.


Bib Entry

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