Deep Probabilistic Programming

Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

Nov 04, 2016 (modified: Mar 08, 2017) ICLR 2017 conference submission readers: everyone
  • Abstract: We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations—random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, we show on a benchmark logistic regression task that Edward is at least 35x faster than Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it is as fast as handwritten TensorFlow.
  • Conflicts: adobe.com, columbia.edu, google.com
  • Authorids: dustin@cs.columbia.edu, mathoffm@adobe.com, rif@google.com, ebrevdo@google.com, kpmurphy@google.com, david.blei@columbia.edu

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