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On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes

2020
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International Conference on Machine Learning
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We consider the product of determinantal point processes (DPPs), a point process whose probability mass is proportional to the product of principal minors of multiple matrices as a natural, promising generalization of DPPs. We study the computational complexity of computing its normalizing constant, which is among the most essential probabilistic inference tasks. Our complexitytheoretic results (almost) rule out the existence of efficient algorithms for this task, unless input matrices are

dblp:conf/icml/OhsakaM20
fatcat:wnoawgl6lrgqxmziwayghjhyde