# Para-active learning

arXiv: https://arxiv.org/abs/1310.8243 · HTML: https://mlsanity.com/paper/1310.8243/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2013.

## Built on by (most cited first, 2 of 2)

- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [Delay Complexity Dimension: Learning with Delayed and Censored Labels](https://mlsanity.com/paper/2610.18935/) (2026)

## Abstract (arXiv)

Training examples are not all equally informative. Active learning strategies leverage this observation in order to massively reduce the number of examples that need to be labeled. We leverage the same observation to build a generic strategy for parallelizing learning algorithms. This strategy is effective because the search for informative examples is highly parallelizable and because we show that its performance does not deteriorate when the sifting process relies on a slightly outdated model. Parallel active learning is particularly attractive to train nonlinear models with non-linear representations because there are few practical parallel learning algorithms for such models. We report preliminary experiments using both kernel SVMs and SGD-trained neural networks.