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Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of(More)
We investigate the class of computable probability distributions and explore the fundamental limitations of using this class to describe and compute conditional distributions. In addition to proving the existence of noncomputable conditional distributions, and thus ruling out the possibility of generic probabilistic inference algorithms (even inefficient(More)
  • Jacob Beal, Jacob Stuart, Michael Beal, Gerald Jay Sussman, Joe Foley, Jade Wang +28 others
  • 2007
Human intelligence is a product of cooperation among many different specialists. Much of this cooperation must be learned, but we do not yet have a mechanism that explains how this might happen for the " high-level " agile cooperation that permeates our daily lives. I propose that the various specialists learn to cooperate by learning to communicate ,(More)
The collection and analysis of user data drives improvements in the app and web ecosystems, but comes with risks to privacy. This paper examines discrete distribution estimation under local privacy, a setting wherein service providers can learn the distribution of a categorical statistic of interest without collecting the underlying data. We present new(More)
  • Jacob Stuart, Michael Beal, Gerald Jay Sussman, Joe Foley, Jade Wang, Richard Tibbetts +27 others
  • 2007
Human intelligence is a product of cooperation among many different specialists. Much of this cooperation must be learned, but we do not yet have a mechanism that explains how this might happen for the " high-level " agile cooperation that permeates our daily lives. I propose that the various specialists learn to cooperate by learning to communicate ,(More)
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