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A Heuristic Independent Particle Approximation to Determinantal Point Processes

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Abstract

A determinantal point process is a stochastic point process that is commonly used to capture negative correlations. It has become increasingly popular in machine learning in recent years. Sampling a determinantal point process however remains a computationally intensive task. This note introduces a heuristic independent particle approximation to determinantal point processes. The approximation is based on the physical intuition of fermions and is implemented using standard numerical linear algebra routines. Sampling from this independent particle approximation can be performed at a negligible cost. Numerical results are provided to demonstrate the performance of the proposed algorithm.

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Authors and Affiliations

  1. Department of Mathematics, Stanford University, Stanford, CA, 94305, USA

    Lexing Ying

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  1. Lexing Ying
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Correspondence to Lexing Ying.

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The work of L.Y. is partially supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program and also by the National Science Foundation under award DMS-1818449.

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Ying, L. A Heuristic Independent Particle Approximation to Determinantal Point Processes. J Sci Comput 87, 57 (2021). https://doi.org/10.1007/s10915-021-01472-5

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  • DOI: https://doi.org/10.1007/s10915-021-01472-5

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