# Near Optimal Bayesian Active Learning for Decision Making

arXiv: https://arxiv.org/abs/1402.5886 · HTML: https://mlsanity.com/paper/1402.5886/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2014.

## Built on by (most cited first, 10 of 10)

- [Bayesian Active Learning by Disagreements: A Geometric Perspective](https://mlsanity.com/paper/2105.02543/) (2021)
- [Active Cost-aware Labeling of Streaming Data](https://mlsanity.com/paper/2304.06808/) (2023)
- [Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing](https://mlsanity.com/paper/2112.13737/) (2021)
- [Efficient Online Decision Tree Learning with Active Feature Acquisition](https://mlsanity.com/paper/2305.02093/) (2023)
- [Loss-Driven Bayesian Active Learning](https://mlsanity.com/paper/2604.11995/) (2026)
- [Leveraging Experience in Lazy Search](https://mlsanity.com/paper/2110.04669/) (2021)
- [Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes](https://mlsanity.com/paper/2312.15357/) (2023)
- [Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation](https://mlsanity.com/paper/2604.03924/) (2026)
- [Distinguish or Homogenize: Last-Chance Policy Identification and Risk-Budgeted Recovery under Irreversible Resource Depletion](https://mlsanity.com/paper/2609.36741/) (2026)
- [Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback](https://mlsanity.com/paper/2610.17760/) (2026)

## Abstract (arXiv)

How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses. Instead of minimizing uncertainty per se, we consider a set of overlapping decision regions of these hypotheses. Our goal is to drive uncertainty into a single decision region as quickly as possible. We identify necessary and sufficient conditions for correctly identifying a decision region that contains all hypotheses consistent with observations. We develop a novel Hyperedge Cutting (HEC) algorithm for this problem, and prove that is competitive with the intractable optimal policy. Our efficient implementation of the algorithm relies on computing subsets of the complete homogeneous symmetric polynomials. Finally, we demonstrate its effectiveness on two practical applications: approximate comparison-based learning and active localization using a robot manipulator.