Learning the Parameters of Determinantal Point Process Kernels

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Learning the Parameters of Determinantal Point Process Kernels

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Title: Learning the Parameters of Determinantal Point Process Kernels
Author: Affandi, Raja Hafiz; Fox, Emily; Adams, Ryan Prescott; Taskar, Ben

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Citation: Affandi, Raja Hafiz, Emily Fox, Ryan P. Adams, and Ben Taskar. 2014. "Learning the Parameters of Determinantal Point Process Kernels." In Proceedings of The 31st International Conference on Machine Learning, Beijing, China, June 22-24, 2014. Journal of Machine Learning Research: W&CP 32: 1224-1232.
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Abstract: Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in applications where diversity is desired. While DPPs have many appealing properties, learning the parameters of a DPP is diff cult, as the likelihood is non-convex and is infeasible to compute in many scenarios. Here we propose Bayesian methods for learning the DPP kernel parameters. These methods are applicable in large-scale discrete and continuous DPP settings, even when the likelihood can only be bounded. We demonstrate the utility of our DPP learning methods in studying the progression of diabetic neuropathy based on the spatial distribution of nerve fibers, and in studying human perception of diversity in images.
Published Version: http://jmlr.org/proceedings/papers/v32/affandi14.html
Other Sources: http://arxiv.org/pdf/1402.4862v1.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:17491846
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