# Online Modeling and Monitoring of Dependent Processes under Resource Constraints

arXiv: https://arxiv.org/abs/2307.14208 · HTML: https://mlsanity.com/paper/2307.14208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms](https://mlsanity.com/paper/1407.8339/) (2014)
- [Impact of Representation Learning in Linear Bandits](https://mlsanity.com/paper/2010.06531/) (2020)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization](https://mlsanity.com/paper/2005.02934/) (2020)
- [Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms](https://mlsanity.com/paper/1708.03058/) (2017)
- [Factorization Bandits for Online Influence Maximization](https://mlsanity.com/paper/1906.03737/) (2019)

## Built on by (most cited first, 1 of 1)

- [FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning](https://mlsanity.com/paper/2405.20504/) (2024)

## Abstract (arXiv)

Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources in many healthcare and engineering systems. Examples include the risk-based disease screening and condition-based process monitoring. However, existing adaptive monitoring models either ignore the dependency among processes or overlook the uncertainty in process modeling. To design an optimal monitoring strategy that accurately monitors the processes with poor health conditions and actively collects information for uncertainty reduction, a novel online collaborative learning method is proposed in this study. The proposed method designs a collaborative learning-based upper confidence bound (CL-UCB) algorithm to optimally balance the exploitation and exploration of dependent processes under limited resources. Efficiency of the proposed method is demonstrated through theoretical analysis, simulation studies and an empirical study of adaptive cognitive monitoring in Alzheimer's disease.