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Probabilistic Programming

Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.

( Image credit: Michael Betancourt )

Papers

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Showing 101–125 of 273 papers

TitleStatusHype
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
From Probabilistic Programming to Complexity-based Programming—0
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
Bayesian Policy Search for Stochastic Domains—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
Hijacking Malaria Simulators with Probabilistic Programming—0
Efficient Search-Based Weighted Model Integration—0
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