2.3k citations · 4.7k across the 88 of their papers we have counts for
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cs.AI2016★ 38 cited
Deep Amortized Inference for Probabilistic Programs
Daniel Ritchie, Paul Horsfall, Noah D. Goodman
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference i…
cs.AI2016★ 10 cited
Practical optimal experiment design with probabilistic programs
Long Ouyang, Michael Henry Tessler, Daniel Ly +1
Scientists often run experiments to distinguish competing theories. This requires patience, rigor, and ingenuity - there is often a large space of possible experiments one could ru…