# Efficient Online Bootstrapping for Large Scale Learning

arXiv: https://arxiv.org/abs/1312.5021 · HTML: https://mlsanity.com/paper/1312.5021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2013.

## Built on by (most cited first, 1 of 1)

- [POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles](https://mlsanity.com/paper/2605.07775/) (2026)

## Abstract (arXiv)

Bootstrapping is a useful technique for estimating the uncertainty of a predictor, for example, confidence intervals for prediction. It is typically used on small to moderate sized datasets, due to its high computation cost. This work describes a highly scalable online bootstrapping strategy, implemented inside Vowpal Wabbit, that is several times faster than traditional strategies. Our experiments indicate that, in addition to providing a black box-like method for estimating uncertainty, our implementation of online bootstrapping may also help to train models with better prediction performance due to model averaging.