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Generative probabilistic programming: applications and new ideas

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Probabilistic programming has recently attracted much attention in Computer Science and Machine Learning communities. I will briefly demonstrate two generative probabilistic graphics programs (models), which I contributed to develop. Then I will present ideas on two research directions I am interested in pursuing: a path to scaling up general-purpose approximate inference in probabilistic programs using parallelism, and a path to automatic programming via general-purpose approximate inference.

This is based on joint work with Frank Wood, Vikash Mansinghka, Tejas Kulkarni, Daniel Selsam, Joshua Tenenbaum, et al.

This talk is part of the Microsoft Research Cambridge, public talks series.

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