64 citations · 119 across the 11 of their papers we have counts for
8 papers · 1 filter
Meta-Learning an Inference Algorithm for Probabilistic Programs
Gwonsoo Che, Hongseok Yang
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 th…
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…
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Wonyeol Lee, Hangyeol Yu, Xavier Rival +1
Differentiation lies at the core of many machine-learning algorithms, and is well-supported by popular autodiff systems, such as TensorFlow and PyTorch. Originally, these systems h…
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…
Differentiable Algorithm for Marginalising Changepoints
Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee +1
We present an algorithm for marginalising changepoints in time-series models that assume a fixed number of unknown changepoints. Our algorithm is differentiable with respect to its…