# Exponentiated Gradient Exploration for Active Learning

arXiv: https://arxiv.org/abs/1408.2196 · HTML: https://mlsanity.com/paper/1408.2196/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2014.

## Built on by (most cited first, 4 of 4)

- [Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection](https://mlsanity.com/paper/2406.00655/) (2024)
- [Active Reinforcement Learning for Robust Building Control](https://mlsanity.com/paper/2312.10289/) (2023)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)
- [RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation](https://mlsanity.com/paper/2312.17530/) (2023)

## Abstract (arXiv)

Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named EG-Active that can improve any Active learning algorithm by an optimal random exploration. Experimental results show a statistically significant and appreciable improvement in the performance of our new approach over the existing active feedback methods.