Sadeghi, K;
Rinaldo, A;
(2019)
Markov Properties of Discrete Determinantal Point Processes.
In: Chaudhuri, K and Sugiyama, M, (eds.)
22nd International Conference On Artificial Intelligence And Statistics.
Microtome Publishing: Naha, Japan.
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Abstract
Determinantal point processes (DPPs) are probabilistic models for repulsion. When used to represent the occurrence of random subsets of a finite base set, DPPs allow to model global negative associations in a mathematically elegant and direct way. Discrete DPPs have become popular and computationally tractable models for solving several machine learning tasks that require the selection of diverse objects, and have been successfully applied in numerous real-life problems. Despite their popularity, the statistical properties of such models have not been adequately explored. In this note, we derive the Markov properties of discrete DPPs and show how they can be expressed using graphical models.
| Type: | Proceedings paper |
|---|---|
| Title: | Markov Properties of Discrete Determinantal Point Processes |
| Event: | 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) |
| Location: | Naha, JAPAN |
| Dates: | 16 April 2019 - 18 April 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://proceedings.mlr.press/v89/sadeghi19a.html |
| Language: | English |
| Additional information: | Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10094077 |
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