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Smoothing Structured Decomposable Circuits

Abstract : We study the task of smoothing a circuit, i.e., ensuring that all children of a plus-gate mention the same variables. Circuits serve as the building blocks of state-of-the-art inference algorithms on discrete probabilistic graphical models and probabilistic programs. They are also important for discrete density estimation algorithms. Many of these tasks require the input circuit to be smooth. However, smoothing has not been studied in its own right yet, and only a trivial quadratic algorithm is known. This paper studies efficient smoothing for structured decomposable circuits. We propose a near-linear time algorithm for this task and explore lower bounds for smoothing decomposable circuits, using existing results on range-sum queries. Further, for the important case of All-Marginals, we show a more efficient linear-time algorithm. We validate experimentally the performance of our methods.
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Contributor : Antoine Amarilli <>
Submitted on : Tuesday, October 29, 2019 - 10:41:38 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:54 PM

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  • HAL Id : hal-02338336, version 1
  • ARXIV : 1906.00311


Andy Shih, Guy van Den Broeck, Paul Beame, Antoine Amarilli. Smoothing Structured Decomposable Circuits. NeurIPS 2019, Dec 2019, Vancouver, Canada. ⟨hal-02338336⟩



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