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Poster
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman · Matthew Johnson · Dustin Tran

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #16
in Thu Poster Session A »

Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates directly on Python functions that compute log-joint distribution functions. Autoconj provides support for conjugacy-exploiting algorithms in any Python-embedded PPL. This paves the way for accelerating development of novel inference algorithms and structure-exploiting modeling strategies. The package can be downloaded at https://github.com/google-research/autoconj.

Keywords: Graphical Models · Software Toolkits
[ Paper] 

Author Information

Matthew D. Hoffman (Google)
Matthew Johnson (Google Brain)

Matt Johnson is a research scientist at Google Brain interested in software systems powering machine learning research. He is the tech lead for JAX, a system for composable function transformations in Python. He was a postdoc at Harvard University with Ryan Adams, working on composing graphical models with neural networks and applications in neurobiology. His Ph.D. is from MIT, where he worked with Alan Willsky on Bayesian nonparametrics, time series models, and scalable inference.

Dustin Tran (Google Brain)

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