Dynamic determinantal point processes

Takayuki Osogami, Rudy Raymond, Tomoyuki Shirai, Akshay Goel, Takanori Maehara

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)


The determinantal point process (DPP) has been receiving increasing attention in machine learning as a generative model of subsets consisting of relevant and diverse items. Recently, there has been a significant progress in developing efficient algorithms for learning the kernel matrix that characterizes a DPP. Here, we propose a dynamic DPP, which is a DPP whose kernel can change over time, and develop efficient learning algorithms for the dynamic DPP. In the dynamic DPP, the kernel depends on the subsets selected in the past, but we assume a particular structure in the dependency to allow efficient learning. We also assume that the kernel has a low rank and exploit a recently proposed learning algorithm for the DPP with low-rank factorization, but also show that its bottleneck computation can be reduced from O(M2 K) time to O(M K2) time, where M is the number of items under consideration, and K is the rank of the kernel, which can be set smaller than M by orders of magnitude.

ホスト出版物のタイトル32nd AAAI Conference on Artificial Intelligence, AAAI 2018
出版社AAAI Press
出版ステータス出版済み - 1月 1 2018
イベント32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, 米国
継続期間: 2月 2 20182月 7 2018


名前32nd AAAI Conference on Artificial Intelligence, AAAI 2018


会議32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CityNew Orleans

!!!All Science Journal Classification (ASJC) codes

  • 人工知能


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