This is a reminder that contributions to the NIPS*2012 Workshop on Probabilistic Programming are due in one week on ~October 15, 2012. Please contact the organizers with any questions, comments, concerns, suggestions, or requests.
Confirmed keynote speakers include Chris Bishop, Stuart Russell and Josh Tenenbaum. Please consider pre-registering (http://goo.gl/yS3e0) and joining the probabilistic programming mailing list. More info at: http://probabilistic-programming.org/wiki/NIPS*2012_Workshop Regards, Vikash Mansinghka, Dan Roy and Noah Goodman <The Original Call> It is our pleasure to invite contributions to the NIPS*2012 Workshop on PROBABILISTIC PROGRAMMING: Foundations and Applications December 7-8, 2012 Lake Tahoe, Nevada, USA http://probabilistic-programming.org/wiki/NIPS*2012_Workshop Funded in part by Lyric Labs (part of Analog Devices). ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Important Dates: Submissions due . . . . . . . . . . . . Oct. 15, 2012 Notification of acceptance . . . . . . Nov. 01, 2012 NIPS Early Reg. deadline . . . . . . . Nov. 11, 2012 Workshop . . . . . . . . . . . . . . . Dec. 7-8, 2012 (two days) If you are at all interested in attending/participating, please pre-register for the workshop at http://goo.gl/yS3e0 (pre-registration form) By giving us your name, and answering a few additional questions, you will help us plan better. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Overview: An intensive, two-day 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. State-of-the-art 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 machine-executable fashion. PROBABILISTIC PROGRAMMING aims to close this gap, making variations on commonly-used 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 logic-based languages. Most probabilistic programming systems represent distributions algorithmically, in terms of a programming language plus primitives for stochastic choice; some even support inference over Turing-universal languages. Compared with representations of models in terms of their graphical-model structure, these representation languages are often significantly more flexible, but still support the development of general-purpose inference algorithms. The workshop will cover, and welcomes submissions about, all aspects of probabilistic programming. Some questions of particular interest include: 1. What real-world problems can be solved with probabilistic programming systems today? How much problem-specific customization/optimization is needed? Where is general-purpose 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: - Stuart Russell (UC Berkeley) - Chris Bishop (Microsoft Research; University of Edinburgh) - Josh Tenenbaum (MIT) Organizers: - Vikash Mansinghka (MIT) - Daniel Roy (Cambridge) - Noah Goodman (Stanford) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Submission instructions: Authors interested in presenting their work and ideas at the workshop should send an email with subject "NIPS 2012 Workshop Submission" to [email protected] and include: - a title - a list of authors and emails - an extended abstract (in NIPS 2012 format, maximum 3 pages, excluding references) Accepted contributions (whether as oral or poster presentation, or demo) will be made available shortly before the workshop, and will be linked online with the authors’ permission. For detailed instructions and background, see http://probabilistic-programming.org . </The Original Call> ---- Vikash K. Mansinghka MIT CSAIL _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
