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Approximate Inference in Continuous Determinantal Processes
Raja Hafiz Affandi · Emily Fox · Ben Taskar

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset selection from a discrete and finite base set. This discrete setting admits an efficient algorithm for sampling based on the eigendecomposition of the defining kernel matrix. Recently, there has been growing interest in using DPPs defined on continuous spaces. While the discrete-DPP sampler extends formally to the continuous case, computationally, the steps required cannot be directly extended except in a few restricted cases. In this paper, we present efficient approximate DPP sampling schemes based on Nystrom and random Fourier feature approximations that apply to a wide range of kernel functions. We demonstrate the utility of continuous DPPs in repulsive mixture modeling applications and synthesizing human poses spanning activity spaces.

Author Information

Raja Hafiz Affandi (University of Pennsylvania)
Emily Fox (University of Washington, Apple)
Ben Taskar (University of Washington)

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