From word models to world models: Translating from natural language to the probabilistic language of thought
How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …
meaning from language--and how can we leverage a theory of linguistic meaning to build …
Gen: a general-purpose probabilistic programming system with programmable inference
Although probabilistic programming is widely used for some restricted classes of statistical
models, existing systems lack the flexibility and efficiency needed for practical use with more …
models, existing systems lack the flexibility and efficiency needed for practical use with more …
Scaling exact inference for discrete probabilistic programs
Probabilistic programming languages (PPLs) are an expressive means of representing and
reasoning about probabilistic models. The computational challenge of probabilistic …
reasoning about probabilistic models. The computational challenge of probabilistic …
Detecting flaky tests in probabilistic and machine learning applications
Probabilistic programming systems and machine learning frameworks like Pyro, PyMC3,
TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training …
TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training …
Smcp3: Sequential monte carlo with probabilistic program proposals
This paper introduces SMCP3, a method for automatically implementing custom sequential
Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and …
Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and …
Termination of nondeterministic probabilistic programs
H Fu, K Chatterjee - … , Model Checking, and Abstract Interpretation: 20th …, 2019 - Springer
We study the termination problem for nondeterministic probabilistic programs. We consider
the bounded termination problem that asks whether the supremum of the expected …
the bounded termination problem that asks whether the supremum of the expected …
Probabilistic programming with programmable inference
We introduce inference metaprogramming for probabilistic programming languages,
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …
Automating involutive MCMC using probabilistic and differentiable programming
M Cusumano-Towner, AK Lew… - arXiv preprint arXiv …, 2020 - arxiv.org
Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes
many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels …
many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels …
Termination analysis of probabilistic programs with martingales
Probabilistic programs extend classical imperative programs with random-value generators.
For classical non-probabilistic programs, termination is a key question in static analysis of …
For classical non-probabilistic programs, termination is a key question in static analysis of …
[PDF][PDF] Automatic Alignment in Higher-Order Probabilistic Programming Languages.
Probabilistic Programming Languages (PPLs) allow users to encode statistical inference
problems and automatically apply an inference algorithm to solve them. Popular inference …
problems and automatically apply an inference algorithm to solve them. Popular inference …