Search Results for author:
Found 3 papers, 2 papers with code
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pathwise gradient estimator, one of the most popular methods for posterior inference and model learning.
On Correctness of Automatic Differentiation for Non-Differentiable Functions
For these PAP functions, we propose a new type of derivatives, called intensional derivatives, and prove that these derivatives always exist and coincide with standard derivatives for almost all inputs.
Towards Verified Stochastic Variational Inference for Probabilistic Programs
In this paper, we analyse one of the most fundamental and versatile variational inference algorithms, called score estimator, using tools from denotational semantics and program analysis.
