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Learning the Parameters of Determinantal Point Process Kernels.
ICML, (2014): 1224-1232
EI
Abstract
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing properties, such as efficient sampling, learning the parameters of a DPP is still considered a difficult problem due to the non-convex nature of the likelihood...More
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