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Probabilistic Programming

Another running theme is probabilistic programming, in which programs are used to represent complex, structured probability distributions. We are especially interested in using such programs to unite logical and probabilistic reasoning and perform complex generative modeling, and for causal inference and discovery. Our research studies a wide variety of technical problems in probabilistic programming, including the design of probabilistic programming languages, the development of methods to infer probabilities, independence relationships, and the effects of interventions and counterfactuals in probabilistic programs, and deriving algorithms for learning the structure and parameters of probabilistic programs.

Selected Publications

Samuel Anklesaria Calvin Smith,; Chaudhuri, Swarat

Deep Generative Logic Programming in Sherlog Journal Article

In: 0000.