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Learning Probabilistic Programs
We develop a technique for generalising from data in which models are sa...
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Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages
Probabilistic programming languages (PPLs) make it possible to reason un...
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Automatic Differentiable Monte Carlo: Theory and Application
Differentiable programming has emerged as a key programming paradigm emp...
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Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
We introduce a dynamic mechanism for the solution of analytically-tracta...
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PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Data cleaning can be naturally framed as probabilistic inference in a ge...
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Engineering a Fast Probabilistic Isomorphism Test
We engineer a new probabilistic Monte-Carlo algorithm for isomorphism te...
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Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs
Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
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Automatic Alignment of Sequential Monte Carlo Inference in Higher-Order Probabilistic Programs
Probabilistic programming is a programming paradigm for expressing flexible probabilistic models. Implementations of probabilistic programming languages employ a variety of inference algorithms, where sequential Monte Carlo methods are commonly used. A problem with current state-of-the-art implementations using sequential Monte Carlo inference is the alignment of program synchronization points. We propose a new static analysis approach based on the 0-CFA algorithm for automatically aligning higher-order probabilistic programs. We evaluate the automatic alignment on a phylogenetic model, showing a significant decrease in runtime and increase in accuracy.
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