Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation

By Yura N. Perov, Frank D. Wood. 2016

8 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pushmeet Kohli
2. Rishabh Singh
3. Marc Brockschmidt
4. Daniel Tarlow
5. Alexander L. Gaunt
6. José Miguel Hernández-Lobato
7. Brooks Paige
8. Hongseok Yang

In Probabilistic & Bayesian Methods

1. Alexander L. Gaunt
2. Hongseok Yang
3. Daniel Tarlow
4. Marc Brockschmidt
5. Brooks Paige
6. Gwonsoo Che
7. José Miguel Hernández-Lobato
8. Pushmeet Kohli

Citations in the corpus

• Grammar Variational Autoencoder (2017, methodology): “Alternatives include running particle MCMC inference to estimate a Bayesian posterior over parse trees (Perov & Wood, 2016).”
• TerpreT: A Probabilistic Programming Language for Program Induction (2016, background): “like to create a belief propagation-based back-end for TerpreT. The problem of inducing samplers for probability distributions has also been cast as a problem of inference in a probabilistic program (Perov and Wood, 2016). Lake et al. (2015) induce probabilistic programs by performing inference in a”
• Meta-Learning an Inference Algorithm for Probabilistic Programs (2021, background): “One key objective of probabilistic programming is to automate reasoning about probabilistic models from diverse domains (Ritchie et al., 2015; Perov & Wood, 2016; Baydin et al., 2019; Schaechtle et al., 2016; Cusumano-Towner et al., 2017; Saad & Mansinghka, 2016; Kulkarni et al., 2015; Young et…”