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Found 8 papers, 3 papers with code
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as "uncertain evidence."
Assisting the Adversary to Improve GAN Training
Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator.
Attention for Inference Compilation
We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to current methods.
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness
Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded.
Amortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
