Work

Smoothing Structured Decomposable Circuits

Year: 2019

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1906.00311

Authors: Shih, Andy, Broeck, Guy Van den, Beame, Paul, Amarilli, Antoine

Institutions: University of California, Los Angeles, University of Washington, Télécom Paris, Laboratoire Traitement et Communication de l’Information

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Defense Advanced Research Projects Agency

Awards: G1037014486, BIGDATA: F: Open-World Foundations for Big Uncertain Data, CRII: RI: Inference for Probabilistic Programs: A Symbolic Approach, FMitF: Opening Up the Black Box of Probabilistic Program Inference, G6491371590

OpenAlex ID: W4288342136

Cites:

Cited by: 3

Related to: 10