# Hierarchical Subquery Evaluation for Active Learning on a Graph

arXiv: https://arxiv.org/abs/1504.08219 · HTML: https://mlsanity.com/paper/1504.08219/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2015.

## Built on by (most cited first, 21 of 21)

- [Influence Selection for Active Learning](https://mlsanity.com/paper/2108.09331/) (2021)
- [Multiple instance active learning for object detection](https://mlsanity.com/paper/2104.02324/) (2021)
- [Plug and Play Active Learning for Object Detection](https://mlsanity.com/paper/2211.11612/) (2022)
- [Exploring Active 3D Object Detection from a Generalization Perspective](https://mlsanity.com/paper/2301.09249/) (2023)
- [Boosting Active Learning via Improving Test Performance](https://mlsanity.com/paper/2112.05683/) (2021)
- [Deep Active Learning with Noise Stability](https://mlsanity.com/paper/2205.13340/) (2022)
- [Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types](https://mlsanity.com/paper/2112.11573/) (2021)
- [KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection](https://mlsanity.com/paper/2307.07942/) (2023)
- [Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention in Video Diffusion Model](https://mlsanity.com/paper/2505.17561/) (2025)
- [Scaling up instance annotation via label propagation](https://mlsanity.com/paper/2110.02277/) (2021)
- [Deep Active Learning for Computer Vision: Past and Future](https://mlsanity.com/paper/2211.14819/) (2022)
- [Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images](https://mlsanity.com/paper/2405.18078/) (2024)
- [Give me a hint! Navigating Image Databases using Human-in-the-loop Feedback](https://mlsanity.com/paper/1809.08714/) (2018)
- [Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography](https://mlsanity.com/paper/2102.12040/) (2021)
- [DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query](https://mlsanity.com/paper/2312.16931/) (2023)
- [Active Learning Guided by Efficient Surrogate Learners](https://mlsanity.com/paper/2301.02761/) (2023)
- [Table Detection with Active Learning](https://mlsanity.com/paper/2509.20003/) (2025)
- [Open-CRB: Towards Open World Active Learning for 3D Object Detection](https://mlsanity.com/paper/2310.10391/) (2023)
- [Boosting Active Learning with Knowledge Transfer](https://mlsanity.com/paper/2509.15805/) (2025)
- [Learnability-Driven Submodular Optimization for Active Roadside 3D Detection](https://mlsanity.com/paper/2601.01695/) (2026)
- [An Approximation Algorithm for Graph Label Selection](https://mlsanity.com/paper/2605.18623/) (2026)

## Abstract (arXiv)

To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction. Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning. Until now, it has also been prohibitively costly to compute for sizeable datasets. We demonstrate our highly practical algorithm, comparing it to other active learning measures on classification datasets that vary in sparsity, dimensionality, and size. Our algorithm is consistent over multiple runs and achieves high accuracy, while querying the human expert for labels at a frequency that matches their desired time budget.