Bayesian synthesis of probabilistic programs for automatic data modeling

摘要

Somewhat overshadowed by neural networks (NN) is another thread in machine learning: the Bayesian-based approach. Less data hungry, it also has the promise of being closer to explainable artificial intelligence (XAI), although it hasn't had the spectacular successes achieved by NN. This isn't to say that tremendous advances haven't also been happening in Bayesian learning. One distinct advantage of probabilistic programming (PP) is that the tools from modern programming language theory and practice readily apply. The work here is an excellent example of the results of doing just that: by leveraging both a programming language (Venture, in this case) built expressly for PP and the power of embedded domain-specific languages (DSL) for the syntactic representation of models, one can start to infer classes of models instead of just parameters from a given model.