# Efficient Sampling for k-Determinantal Point Processes

arXiv: https://arxiv.org/abs/1509.01618 · HTML: https://mlsanity.com/paper/1509.01618/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2015.

## Built on by (most cited first, 11 of 11)

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- [Diversity Boosted Learning for Domain Generalization with Large Number of Domains](https://mlsanity.com/paper/2207.13865/) (2022)
- [Composable Coresets for Determinant Maximization: Greedy is Almost Optimal](https://mlsanity.com/paper/2309.15286/) (2023)
- [VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs](https://mlsanity.com/paper/2512.12072/) (2025)
- [E2DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation](https://mlsanity.com/paper/2605.00159/) (2026)

## Abstract (arXiv)

Determinantal Point Processes (DPPs) are elegant probabilistic models of repulsion and diversity over discrete sets of items. But their applicability to large sets is hindered by expensive cubic-complexity matrix operations for basic tasks such as sampling. In light of this, we propose a new method for approximate sampling from discrete k-DPPs. Our method takes advantage of the diversity property of subsets sampled from a DPP, and proceeds in two stages: first it constructs coresets for the ground set of items; thereafter, it efficiently samples subsets based on the constructed coresets. As opposed to previous approaches, our algorithm aims to minimize the total variation distance to the original distribution. Experiments on both synthetic and real datasets indicate that our sampling algorithm works efficiently on large data sets, and yields more accurate samples than previous approaches.