Turing: Composable inference for probabilistic programming

By Hong Ge, Kai Xu, Zoubin Ghahramani. 2018

26 citations recorded by the source corpus.

How researchers see this paper

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

1. Hongseok Yang
2. Gwonsoo Che
3. Ulrich Schaechtle
4. Alexey Radul
5. Shivam Handa
6. Yutian Chen
7. Vikash K. Mansinghka
8. Martin C. Rinard

Citations in the corpus

• Foundation Posteriors for Approximate Probabilistic Inference (2022, neutral): “Many probabilistic programming languages (PPLs) have been developed [27, 45, 28, 44, 18, 58, 47, 53, 59, 12, 23, 17, 7, 57] each with inference approaches with strengths and weaknesses.”
• Meta-Learning an Inference Algorithm for Probabilistic Programs (2021, background): “This line of research has led to performant probabilistic programming systems (Goodman et al., 2008; Wood et al., 2014; Mansinghka et al., 2014; Minka et al., 2018; Narayanan et al., 2016; Salvatier et al., 2016; Carpenter et al., 2017; Tran et al., 2016; Ge et al., 2018; Bingham et al., 2018).”
• Probabilistic programming with programmable inference (2018, methodology): “After inference metaprogramming was introduced in Venture, other probabilistic languages, e.g. Turing [14] and PyMC3 [42], have added limited support for custom inference in terms of subproblems.”