# Batch Active Learning Using Determinantal Point Processes

arXiv: https://arxiv.org/abs/1906.07975 · HTML: https://mlsanity.com/paper/1906.07975/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 30 of 43)

- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Active Preference-Based Gaussian Process Regression for Reward Learning](https://mlsanity.com/paper/2005.02575/) (2020)
- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning](https://mlsanity.com/paper/2106.12059/) (2021)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels](https://mlsanity.com/paper/2206.12569/) (2022)
- [Uncertainty Herding: One Active Learning Method for All Label Budgets](https://mlsanity.com/paper/2412.20644/) (2024)
- [Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle](https://mlsanity.com/paper/2105.14559/) (2021)
- [APReL: A Library for Active Preference-based Reward Learning Algorithms](https://mlsanity.com/paper/2108.07259/) (2021)
- [The Effect of Diversity in Meta-Learning](https://mlsanity.com/paper/2201.11775/) (2022)
- [Machine Learning for a Sustainable Energy Future](https://mlsanity.com/paper/2210.10391/) (2022)
- [Active Reward Learning from Online Preferences](https://mlsanity.com/paper/2302.13507/) (2023)
- [Learning Perceptual Concepts by Bootstrapping from Human Queries](https://mlsanity.com/paper/2111.05251/) (2021)
- [Diversity-Aware Batch Active Learning for Dependency Parsing](https://mlsanity.com/paper/2104.13936/) (2021)
- [Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes](https://mlsanity.com/paper/2107.01622/) (2021)
- [Towards Balanced Active Learning for Multimodal Classification](https://mlsanity.com/paper/2306.08306/) (2023)
- [Time-Efficient Reward Learning via Visually Assisted Cluster Ranking](https://mlsanity.com/paper/2212.00169/) (2022)
- [Batch Active Learning of Reward Functions from Human Preferences](https://mlsanity.com/paper/2402.15757/) (2024)
- [Deep Active Learning for Computer Vision: Past and Future](https://mlsanity.com/paper/2211.14819/) (2022)
- [On the Opportunities of Green Computing: A Survey](https://mlsanity.com/paper/2311.00447/) (2023)
- [Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions](https://mlsanity.com/paper/2401.04305/) (2024)
- [Multi-Domain Active Learning: Literature Review and Comparative Study](https://mlsanity.com/paper/2106.13516/) (2021)
- [Neural Design for Genetic Perturbation Experiments](https://mlsanity.com/paper/2207.12805/) (2022)
- [Learning Preferences for Interactive Autonomy](https://mlsanity.com/paper/2210.10899/) (2022)
- [Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel](https://mlsanity.com/paper/2102.13382/) (2021)
- [Bayesian Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2408.01694/) (2024)
- [Deep Active Learning in the Open World](https://mlsanity.com/paper/2411.06353/) (2024)
- [WLD-Reg: A Data-dependent Within-layer Diversity Regularizer](https://mlsanity.com/paper/2301.01352/) (2023)
- [Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes](https://mlsanity.com/paper/2109.00727/) (2021)
- [Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios](https://mlsanity.com/paper/2207.01190/) (2022)

## Abstract (arXiv)

Data collection and labeling is one of the main challenges in employing machine learning algorithms in a variety of real-world applications with limited data. While active learning methods attempt to tackle this issue by labeling only the data samples that give high information, they generally suffer from large computational costs and are impractical in settings where data can be collected in parallel. Batch active learning methods attempt to overcome this computational burden by querying batches of samples at a time. To avoid redundancy between samples, previous works rely on some ad hoc combination of sample quality and diversity. In this paper, we present a new principled batch active learning method using Determinantal Point Processes, a repulsive point process that enables generating diverse batches of samples. We develop tractable algorithms to approximate the mode of a DPP distribution, and provide theoretical guarantees on the degree of approximation. We further demonstrate that an iterative greedy method for DPP maximization, which has lower computational costs but worse theoretical guarantees, still gives competitive results for batch active learning. Our experiments show the value of our methods on several datasets against state-of-the-art baselines.