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Found 6 papers, 4 papers with code
Program Analysis of Probabilistic Programs
The techniques analyse a probabilistic program and adapt it to make inference more efficient, sometimes in a way that would have been tedious or impossible to do by hand.
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph.
Conditional independence by typing
The resulting program can be seen as a hybrid inference algorithm on the original program, where continuous parameters can be drawn using efficient gradient-based inference methods, while the discrete parameters are inferred using variable elimination.
Automatic Reparameterisation of Probabilistic Programs
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the processes generating data.
Effect Handling for Composable Program Transformations in Edward2
Algebraic effects and handlers have emerged in the programming languages community as a convenient, modular abstraction for controlling computational effects.
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects.
