39 citations · 107 across the 30 of their papers we have counts for
4 papers · 1 filter
Bayesian Policy Search for Stochastic Domains
David Tolpin, Yuan Zhou, Hongseok Yang
AI planning can be cast as inference in probabilistic models, and probabilistic programming was shown to be capable of policy search in partially observable domains. Prior work int…
Probabilistic Programs with Stochastic Conditioning
David Tolpin, Yuan Zhou, Tom Rainforth +1
We tackle the problem of conditioning probabilistic programs on distributions of observable variables. Probabilistic programs are usually conditioned on samples from the joint data…
Stochastically Differentiable Probabilistic Programs
David Tolpin, Yuan Zhou, Hongseok Yang
Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs). However, the exis…
Stochastic Probabilistic Programs
David Tolpin, Tomer Dobkin
We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic…