# Nonmyopic Multifidelity Active Search

arXiv: https://arxiv.org/abs/2106.06356 · HTML: https://mlsanity.com/paper/2106.06356/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Multi-fidelity Bayesian Optimisation with Continuous Approximations](https://mlsanity.com/paper/1703.06240/) (2017)
- [Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning](https://mlsanity.com/paper/1903.04703/) (2019)
- [Multi-Information Source Optimization](https://mlsanity.com/paper/1603.00389/) (2016)
- [The Multi-fidelity Multi-armed Bandit](https://mlsanity.com/paper/1610.09726/) (2016)
- [Discovering Valuable Items from Massive Data](https://mlsanity.com/paper/1506.00935/) (2015)

## Built on by (most cited first, 3 of 3)

- [Multi-Fidelity Active Learning with GFlowNets](https://mlsanity.com/paper/2306.11715/) (2023)
- [Vendi Information Gain for Active Learning and its Application to Ecology](https://mlsanity.com/paper/2509.10390/) (2025)
- [MICRO: Multi-Fidelity Active Search for Severe Error Discovery](https://mlsanity.com/paper/2609.26025/) (2026)

## Abstract (arXiv)

Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget. Previous work on active search has assumed access to a faithful (and expensive) oracle reporting experimental results. However, some settings offer access to cheaper surrogates such as computational simulation that may aid in the search. We propose a model of multifidelity active search, as well as a novel, computationally efficient policy for this setting that is motivated by state-of-the-art classical policies. Our policy is nonmyopic and budget aware, allowing for a dynamic tradeoff between exploration and exploitation. We evaluate the performance of our solution on real-world datasets and demonstrate significantly better performance than natural benchmarks.