# Adaptive Monte Carlo via Bandit Allocation

arXiv: https://arxiv.org/abs/1405.3318 · HTML: https://mlsanity.com/paper/1405.3318/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2014.

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

- [ReVar: Strengthening Policy Evaluation via Reduced Variance Sampling](https://mlsanity.com/paper/2203.04510/) (2022)
- [Multiple-Prediction-Powered Inference](https://mlsanity.com/paper/2603.27414/) (2026)

## Abstract (arXiv)

We consider the problem of sequentially choosing between a set of unbiased Monte Carlo estimators to minimize the mean-squared-error (MSE) of a final combined estimate. By reducing this task to a stochastic multi-armed bandit problem, we show that well developed allocation strategies can be used to achieve an MSE that approaches that of the best estimator chosen in retrospect. We then extend these developments to a scenario where alternative estimators have different, possibly stochastic costs. The outcome is a new set of adaptive Monte Carlo strategies that provide stronger guarantees than previous approaches while offering practical advantages.