Understand
Algebraic effects and handlers have emerged in the programming languages community as a convenient, modular abstraction for controlling computational effects.
- They have found several applications including concurrent programming, meta programming, and more recently, probabilistic programming, as part of Pyro's Poutines library.
- We investigate the use of effect handlers as a lightweight abstraction for implementing probabilistic programming languages (PPLs).
- We interpret the existing design of Edward2 as an accidental implementation of an effect-handling mechanism, and extend that design to support nested, composable transformations.
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Adequacy for Algebraic Effects. In Foundations of Software Science and Computation Structures
Gordon Plotkin and John Power. 2001 · 2001
Earlier work this paper cites.
Handlers of Algebraic Effects. In Programming Languages and Systems
Gordon Plotkin and Matija Pretnar. 2009 · 2009
Earlier work this paper cites.
Automated variational inference in probabilistic programming
David Wingate and Theophane Weber. 2013 · 2013
Earlier work this paper cites.
Variational dropout and the local reparameterization trick. In Advances in Neural Information Processing Systems
Diederik P Kingma, Tim Salimans, and Max Welling. 2015 · 2015
Earlier work this paper cites.
Similar
An Introduction to Algebraic Effects and Handlers. Invited tutorial paper
Matija Pretnar. 2015 · 2015
Cited alongside, same era.
Effects in Bayesian inference
Adam Scibior and Ohad Kammar. 2015 · 2015
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Stan: A Probabilistic Programming Language
Bob Carpenter, Andrew Gelman, Matthew Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017 · 2017
Cited alongside, same era.
Joshua V Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, and Rif A Saurous. 2017 · 2017
Cited alongside, same era.
Then
Automatic differentiation variational inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei. 2017 · 2017
Later among the works it cites.
Pyro: A deep probabilistic programming language
Uber AI Labs. 2017 · 2017
Later among the works it cites.
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. In Submitted to Neural Information Processing Systems
Matthew D. Hoffman, Matthew Johnson, and Dustin Tran. 2018 · 2018
Closest in time.
Edward2: Simple, Distributed, Accelerated
Dustin Tran, Matthew D. Hoffman, Srinivas Vasudevan, Christopher Suter, Dave Moore, Alexey Radul, Matthew Johnson, and Rif A. Saurous. 2018 · 2018
Closest in time.
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