# Towards Deterministic Diverse Subset Sampling

arXiv: https://arxiv.org/abs/2105.13942 · HTML: https://mlsanity.com/paper/2105.13942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 4 of 4)

- [Fast DPP Sampling for Nystr"om with Application to Kernel Methods](https://mlsanity.com/paper/1603.06052/) (2016)
- [Provable Deterministic Leverage Score Sampling](https://mlsanity.com/paper/1404.1530/) (2014)
- [Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling](https://mlsanity.com/paper/1803.06010/) (2018)
- [Optimized Algorithms to Sample Determinantal Point Processes](https://mlsanity.com/paper/1802.08471/) (2018)

## Abstract (arXiv)

Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search. In this paper, we discuss a greedy deterministic adaptation of k-DPP. Deterministic algorithms are interesting for many applications, as they provide interpretability to the user by having no failure probability and always returning the same results. First, the ability of the method to yield low-rank approximations of kernel matrices is evaluated by comparing the accuracy of the Nystr"om approximation on multiple datasets. Afterwards, we demonstrate the usefulness of the model on an image search task.