# Active Hypothesis Testing for Correlated Combinatorial Anomaly Detection

arXiv: https://arxiv.org/abs/2601.17430 · HTML: https://mlsanity.com/paper/2601.17430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Optimal Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/1602.04589/) (2016)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Simple Bayesian Algorithms for Best Arm Identification](https://mlsanity.com/paper/1602.08448/) (2016)
- [Sequential Experimental Design for Transductive Linear Bandits](https://mlsanity.com/paper/1906.08399/) (2019)
- [Optimal Best-arm Identification in Linear Bandits](https://mlsanity.com/paper/2006.16073/) (2020)
- [Fixed-Confidence Guarantees for Bayesian Best-Arm Identification](https://mlsanity.com/paper/1910.10945/) (2019)
- [Multi-Armed Bandits with Correlated Arms](https://mlsanity.com/paper/1911.03959/) (2019)
- [Contextual Bandits with Latent Confounders: An NMF Approach](https://mlsanity.com/paper/1606.00119/) (2016)
- [Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback](https://mlsanity.com/paper/2101.08534/) (2021)
- [Understanding Bandits with Graph Feedback](https://mlsanity.com/paper/2105.14260/) (2021)
- [A Fast Algorithm for the Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit](https://mlsanity.com/paper/2306.09202/) (2023)
- [Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (Extended Version)](https://mlsanity.com/paper/1805.01685/) (2018)
- [Towards High-Resolution Industrial Image Anomaly Detection](https://mlsanity.com/paper/2508.12931/) (2025)

## Abstract (arXiv)

We study the problem of identifying an anomalous subset of streams under correlated noise, motivated by monitoring and security in cyber-physical systems. This problem can be viewed as a form of combinatorial pure exploration, where each stream plays the role of an arm and measurements must be allocated sequentially under uncertainty. Existing combinatorial bandit and hypothesis testing methods typically assume independent observations and fail to exploit correlation for efficient measurement design. We propose ECC-AHT, an adaptive algorithm that selects continuous, constrained measurements to maximize Chernoff information between competing hypotheses, enabling active noise cancellation through differential sensing. ECC-AHT achieves optimal sample complexity guarantees and significantly outperforms state-of-the-art baselines in both synthetic and real-world correlated environments. The code is available on https://github.com/VincentdeCristo/ECC-AHT