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Found 8 papers, 3 papers with code
Scaling Integer Arithmetic in Probabilistic Programs
Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for many of today's probabilistic programming languages (PPLs).
Type Prediction With Program Decomposition and Fill-in-the-Type Training
TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain.
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs
Many of today's probabilistic programming languages (PPLs) have brittle inference performance: the performance of the underlying inference algorithm is very sensitive to the precise way in which the probabilistic program is written.
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes
Central to this effort is the development of tractable probabilistic models (TPMs): models whose structure guarantees efficient probabilistic inference algorithms.
Dice: Compiling Discrete Probabilistic Programs for Scalable Inference
This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference.
Programming Languages
Generating and Sampling Orbits for Lifted Probabilistic Inference
A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable.
Sound Abstraction and Decomposition of Probabilistic Programs
Experimentally, we also illustrate the practical benefits of our framework as a tool to decompose probabilistic program inference.
Probabilistic Program Abstractions
Abstraction is a fundamental tool for reasoning about complex systems.
