# Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing

arXiv: https://arxiv.org/abs/2310.06306 · HTML: https://mlsanity.com/paper/2310.06306/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 84 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Poisoning the Unlabeled Dataset of Semi-Supervised Learning](https://mlsanity.com/paper/2105.01622/) (2021)
- [Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)](https://mlsanity.com/paper/1905.01719/) (2019)
- [AKM2D : An Adaptive Framework for Online Sensing and Anomaly Quantification](https://mlsanity.com/paper/1910.02119/) (2019)

## Built on by (most cited first, 5 of 5)

- [Survey: Multi-Armed Bandits Meet Large Language Models](https://mlsanity.com/paper/2505.13355/) (2025)
- [Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems](https://mlsanity.com/paper/2501.13139/) (2025)
- [FAIR: Facilitating Artificial Intelligence Resilience in Manufacturing Industrial Internet](https://mlsanity.com/paper/2503.01086/) (2025)
- [Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning](https://mlsanity.com/paper/2407.21503/) (2024)
- [Stream-Based Active Learning for Process Monitoring](https://mlsanity.com/paper/2411.12563/) (2024)

## Abstract (arXiv)

It is challenging but important to save annotation efforts in streaming data acquisition to maintain data quality for supervised learning base learners. We propose an ensemble active learning method to actively acquire samples for annotation by contextual bandits, which is will enforce the exploration-exploitation balance and leading to improved AI modeling performance.