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Determinantal point processes (DPPs) are well-suited for modeling repulsionand have proven useful in many applications where diversity is desired. WhileDPPs have many appealing properties, such as efficient sampling, learning theparameters of a DPP is still considered a difficult problem due to thenon-convex nature of the likelihood function. In this paper, we propose usingBayesian methods to learn the DPP kernel parameters. These methods areapplicable in large-scale and continuous DPP settings even when the exact formof the eigendecomposition is unknown. We demonstrate the utility of our DPPlearning methods in studying the progression of diabetic neuropathy based onspatial distribution of nerve fibers, and in studying human perception ofdiversity in images.

Learning the Parameters of Determinantal Point Process Kernels