# Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes

arXiv: https://arxiv.org/abs/2107.01622 · HTML: https://mlsanity.com/paper/2107.01622/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [Learning how to Active Learn: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1708.02383/) (2017)
- [Fair k-Center Clustering for Data Summarization](https://mlsanity.com/paper/1901.08628/) (2019)
- [Batch Active Learning Using Determinantal Point Processes](https://mlsanity.com/paper/1906.07975/) (2019)
- [Active Learning for Convolutional Neural Networks: A Core-Set Approach](https://mlsanity.com/paper/1708.00489/) (2017)
- [GDPP: Learning Diverse Generations Using Determinantal Point Process](https://mlsanity.com/paper/1812.00068/) (2018)
- [How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization](https://mlsanity.com/paper/1807.04219/) (2018)
- [Determinantal Point Processes for Mini-Batch Diversification](https://mlsanity.com/paper/1705.00607/) (2017)
- [Kronecker Determinantal Point Processes](https://mlsanity.com/paper/1605.08374/) (2016)

## Built on by (most cited first, 9 of 9)

- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios](https://mlsanity.com/paper/2207.01190/) (2022)
- [AutoAL: Automated Active Learning with Differentiable Query Strategy Search](https://mlsanity.com/paper/2410.13853/) (2024)
- [IDEAL-M3D: Instance Diversity-Enriched Active Learning for Monocular 3D Detection](https://mlsanity.com/paper/2511.19301/) (2025)
- [Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning](https://mlsanity.com/paper/2604.12303/) (2026)
- [An Interactive Visualization Tool for Understanding Active Learning](https://mlsanity.com/paper/2111.04936/) (2021)
- [Maximally Separated Active Learning](https://mlsanity.com/paper/2411.17444/) (2024)
- [Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation](https://mlsanity.com/paper/2602.02024/) (2026)

## Abstract (arXiv)

Active learning aims to achieve greater accuracy with less training data by selecting the most useful data samples from which it learns. Single-criterion based methods (i.e., informativeness and representativeness based methods) are simple and efficient; however, they lack adaptability to different real-world scenarios. In this paper, we introduce a multiple-criteria based active learning algorithm, which incorporates three complementary criteria, i.e., informativeness, representativeness and diversity, to make appropriate selections in the active learning rounds under different data types. We consider the selection process as a Determinantal Point Process, which good balance among these criteria. We refine the query selection strategy by both selecting the hardest unlabeled data sample and biasing towards the classifiers that are more suitable for the current data distribution. In addition, we also consider the dependencies and relationships between these data points in data selection by means of centroidbased clustering approaches. Through evaluations on synthetic and real-world datasets, we show that our method performs significantly better and is more stable than other multiple-criteria based AL algorithms.