# Diameter-Based Active Learning

arXiv: https://arxiv.org/abs/1702.08553 · HTML: https://mlsanity.com/paper/1702.08553/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2017.

## Built on by (most cited first, 7 of 7)

- [Fast Rates in Pool-Based Batch Active Learning](https://mlsanity.com/paper/2202.05448/) (2022)
- [Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models](https://mlsanity.com/paper/2405.06724/) (2024)
- [Distribution Matching for Machine Teaching](https://mlsanity.com/paper/2105.13809/) (2021)
- [Targeted active learning for probabilistic models](https://mlsanity.com/paper/2210.12122/) (2022)
- [Towards Fundamental Limits for Active Multi-distribution Learning](https://mlsanity.com/paper/2506.17607/) (2025)
- [Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers](https://mlsanity.com/paper/2111.04915/) (2021)
- [Pool-based Active Learning as Noisy Lossy Compression: Characterizing Label Complexity via Finite Blocklength Analysis](https://mlsanity.com/paper/2602.05333/) (2026)

## Abstract (arXiv)

To date, the tightest upper and lower-bounds for the active learning of general concept classes have been in terms of a parameter of the learning problem called the splitting index. We provide, for the first time, an efficient algorithm that is able to realize this upper bound, and we empirically demonstrate its good performance.