Title: Meta-Learning an Inference Algorithm for Probabilistic Programs. Abstract: We present a meta-algorithm for learning a posterior-inference algorithm for restricted probabilistic programs. Our meta-algorithm takes a training set of probabilistic programs that describe models with observations, and attempts to learn an efficient method for inferring the posterior of a similar program.
A key feature of our approach is the use of what we call a white-box inference algorithm that extracts information directly from model descriptions themselves, given as programs. Concretely, our white-box inference algorithm is equipped with multiple neural networks, one for each type of atomic command, and computes an approximate posterior of a given probabilistic program by analysing individual atomic commands in the program using these networks. The parameters of these networks are then learnt from a training set by our meta-algorithm.
We empirically demonstrate that the learnt inference algorithm generalises well to programs that are new in terms of both parameters and structures, and report cases where our approach
has advantages over alternative approaches such as HMC in terms of test-time efficiency.
The overall results show the promise as well as remaining challenges of our approach.