# Exploring Representativeness and Informativeness for Active Learning

arXiv: https://arxiv.org/abs/1904.06685 · HTML: https://mlsanity.com/paper/1904.06685/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2019.

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

- [LLMaAA: Making Large Language Models as Active Annotators](https://mlsanity.com/paper/2310.19596/) (2023)
- [Deep Active Learning by Leveraging Training Dynamics](https://mlsanity.com/paper/2110.08611/) (2021)
- [Data Shapley Valuation for Efficient Batch Active Learning](https://mlsanity.com/paper/2104.08312/) (2021)
- [Information Gain Propagation: a new way to Graph Active Learning with Soft Labels](https://mlsanity.com/paper/2203.01093/) (2022)
- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [Neural Active Learning Beyond Bandits](https://mlsanity.com/paper/2404.12522/) (2024)
- [Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification](https://mlsanity.com/paper/2304.05578/) (2023)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [Out-of-Distribution Learning with Human Feedback](https://mlsanity.com/paper/2408.07772/) (2024)
- [Sample Noise Impact on Active Learning](https://mlsanity.com/paper/2109.01372/) (2021)
- [Data-Efficient Learning via Minimizing Hyperspherical Energy](https://mlsanity.com/paper/2206.15204/) (2022)
- [Active Learning of Quantum System Hamiltonians yields Query Advantage](https://mlsanity.com/paper/2112.14553/) (2021)
- [Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs](https://mlsanity.com/paper/2304.11989/) (2023)
- [Constructing Enhanced Mutual Information for Online Class-Incremental Learning](https://mlsanity.com/paper/2407.18526/) (2024)
- [AI Thinking: A framework for rethinking artificial intelligence in practice](https://mlsanity.com/paper/2409.12922/) (2024)
- [Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference](https://mlsanity.com/paper/2105.11492/) (2021)
- [Active learning for fast and slow modeling attacks on Arbiter PUFs](https://mlsanity.com/paper/2308.13645/) (2023)
- [Salutary Labeling with Zero Human Annotation](https://mlsanity.com/paper/2405.17627/) (2024)
- [Vendi Information Gain for Active Learning and its Application to Ecology](https://mlsanity.com/paper/2509.10390/) (2025)
- [A Critical Examination of Active Learning Workflows in Materials Science](https://mlsanity.com/paper/2601.05946/) (2026)
- [Multi-Agent LLMs for Adaptive Acquisition in Bayesian Optimization](https://mlsanity.com/paper/2603.28959/) (2026)

## Abstract (arXiv)

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified Best-versus-Second-Best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.