# Online Algorithm for Unsupervised Sensor Selection

arXiv: https://arxiv.org/abs/1901.04676 · HTML: https://mlsanity.com/paper/1901.04676/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 7 of 7)

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- [Interaction-Grounded Learning](https://mlsanity.com/paper/2106.04887/) (2021)
- [Exploiting Correlated Auxiliary Feedback in Parameterized Bandits](https://mlsanity.com/paper/2311.02715/) (2023)
- [Censored Semi-Bandits for Resource Allocation](https://mlsanity.com/paper/2104.05781/) (2021)
- [Active Human Feedback Collection via Neural Contextual Dueling Bandits](https://mlsanity.com/paper/2504.12016/) (2025)

## Abstract (arXiv)

In many security and healthcare systems, the detection and diagnosis systems use a sequence of sensors/tests. Each test outputs a prediction of the latent state and carries an inherent cost. However, the correctness of the predictions cannot be evaluated since the ground truth annotations may not be available. Our objective is to learn strategies for selecting a test that gives the best trade-off between accuracy and costs in such Unsupervised Sensor Selection (USS) problems. Clearly, learning is feasible only if ground truth can be inferred (explicitly or implicitly) from the problem structure. It is observed that this happens if the problem satisfies the 'Weak Dominance' (WD) property. We set up the USS problem as a stochastic partial monitoring problem and develop an algorithm with sub-linear regret under the WD property. We argue that our algorithm is optimal and evaluate its performance on problem instances generated from synthetic and real-world datasets.