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An intensive, twoday workshop on PROBABILISTIC PROGRAMMING, with contributed and invited talks, poster sessions, demos, and discussions.
Probabilistic models and inference algorithms have become standard tools for interpreting ambiguous, noisy data and building systems that learn from their experience. However, even simple probabilistic models can require significant effort and specialized expertise to develop and use, frequently involving custom mathematics, algorithm design and software development. Stateoftheart models from Bayesian statistics, artificial intelligence and cognitive science  especially those involving distributions over infinite data structures, relational structures, worlds with unknown numbers of objects, rich causal simulations of physics and psychology, and the reasoning processes of other agents  can be difficult to even specify formally, let alone in a machineexecutable fashion.
PROBABILISTIC PROGRAMMING aims to close this gap, making variations on commonlyused probabilistic models far easier to develop and use, and pointing the way towards entirely new types of models and inference. The central idea is to represent probabilistic models using ideas from programming, including functional, imperative, and logicbased languages. Most probabilistic programming systems represent distributions algorithmically, in terms of a programming language plus primitives for stochastic choice; some even support inference over Turinguniversal languages. Compared with representations of models in terms of their graphicalmodel structure, these representation languages are often significantly more flexible, but still support the development of generalpurpose inference algorithms.
The workshop will cover, and welcomes submissions about, all aspects of probabilistic programming. Some questions of particular interest include:
1. What realworld problems can be solved with probabilistic programming systems today? How much problemspecific customization/optimization is needed? Where is generalpurpose inference effective?
2. What does the probabilistic programming perspective, and in particular the representation of probabilistic models and inference procedures as algorithmic processes, reveal about the computability and complexity of Bayesian inference? When can theory guide the design and use of probabilistic programming systems?
3. How can we teach people to write probabilistic programs that work well, without having to teach them how to build an inference engine first? What programming styles support tractability of inference?
4. How can central ideas from software engineering  including debuggers, validation tools, style checkers, program analyses, reusable libraries, and profilers  help probabilistic programmers and modelers? Which of these tools can be built for probabilistic programs, or help us build probabilistic programming systems?
5. What new directions in AI, statistics, and cognitive science would be enabled if we could handle models that took hundreds or thousands of lines of probabilistic code to write?
Confirmed Keynote Speakers:
 Josh Tenenbaum (MIT)
 Stuart Russell (UC Berkeley)
 Christopher Bishop (Microsoft Research; University of Edinburgh)
Author Information
Vikash Mansinghka (Massachusetts Institute of Technology)
Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He served on DARPA’s Information Science and Technology advisory board from 20102012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation. He was an advisor to Google DeepMind and has cofounded two AIrelated startups, one acquired and one currently operational.
Dan Roy (Univ of Toronto & Vector)
Noah Goodman (Stanford University)
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