Probabilistic Programming
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Push: Concurrent Probabilistic Programming for Bayesian Deep Learning. (Arxiv, 2023)
[preprint]
[Push]
Daniel Huang, Chris Camano, Jonathan Tsegaye, and Jonathan Austin Gale.
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An Application of Computable Distributions to the Semantics of
Probabilistic Programs. (Chapter in Foundations of Probabilistic Programming, 2020)
[chapter]
Daniel Huang, Bas Spitters, and Greg Morrisett.
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On Programming Languages for Probabilistic Modeling. (Dissertation, 2017)
[dissertation]
Daniel Huang.
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Compiling Markov Chain Monte Carlo Algorithms for Probabilistic
Modeling. (Programming Language Design and Implementation, 2017) [paper]
[augurv2]
Daniel Huang, Jean-Baptiste Tristan, and Greg Morrisett.
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An Application of Computable Distributions to the Semantics of
Probabilistic Programming Languages. (European Symposium on Programming, 2016, EAPLS Best
Paper) [paper]
Daniel Huang and Greg Morrisett.
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Augur: Data-parallel Probabilistic Modeling. (Neural Information Processing Systems, 2014, Spotlight)
[paper]
Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen Greene, and Guy Steele.