# Online AutoML: An adaptive AutoML framework for online learning

arXiv: https://arxiv.org/abs/2201.09750 · HTML: https://mlsanity.com/paper/2201.09750/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [GAMA: a General Automated Machine learning Assistant](https://mlsanity.com/paper/2007.04911/) (2020)
- [Towards AutoML in the presence of Drift: first results](https://mlsanity.com/paper/1907.10772/) (2019)
- [Adaptation Strategies for Automated Machine Learning on Evolving Data](https://mlsanity.com/paper/2006.06480/) (2020)
- [ChaCha for Online AutoML](https://mlsanity.com/paper/2106.04815/) (2021)
- [Automated Adaptation Strategies for Stream Learning](https://mlsanity.com/paper/1812.10793/) (2018)

## Built on by (most cited first, 11 of 11)

- [Automated Imbalanced Learning](https://mlsanity.com/paper/2211.00376/) (2022)
- [A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining](https://mlsanity.com/paper/2309.04761/) (2023)
- [Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting](https://mlsanity.com/paper/2302.10347/) (2023)
- [Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets](https://mlsanity.com/paper/2211.13681/) (2022)
- [Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations](https://mlsanity.com/paper/2209.14598/) (2022)
- [AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers](https://mlsanity.com/paper/2410.17469/) (2024)
- [Help the machine to help you: an evaluation in the wild of egocentric data cleaning via skeptical learning](https://mlsanity.com/paper/2510.23635/) (2025)
- [OLR-WAA: Adaptive and Drift-Resilient Online Regression with Dynamic Weighted Averaging](https://mlsanity.com/paper/2512.12779/) (2025)
- [OLC-WA: Drift Aware Tuning-Free Online Classification with Weighted Average](https://mlsanity.com/paper/2512.12785/) (2025)
- [Robustness of AutoML on Dirty Categorical Data](https://mlsanity.com/paper/2602.00412/) (2026)
- [SCCM : Stream Cruise Control Method for Automated Drift Detection and Adaptation](https://mlsanity.com/paper/2609.09432/) (2026)

## Abstract (arXiv)

Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribution will evolve over time, and it is yet to be shown whether AutoML techniques can effectively design online pipelines in dynamic environments. This study aims to automate pipeline design for online learning while continuously adapting to data drift. For this purpose, we design an adaptive Online Automated Machine Learning (OAML) system, searching the complete pipeline configuration space of online learners, including preprocessing algorithms and ensembling techniques. This system combines the inherent adaptation capabilities of online learners with the fast automated pipeline (re)optimization capabilities of AutoML. Focusing on optimization techniques that can adapt to evolving objectives, we evaluate asynchronous genetic programming and asynchronous successive halving to optimize these pipelines continually. We experiment on real and artificial data streams with varying types of concept drift to test the performance and adaptation capabilities of the proposed system. The results confirm the utility of OAML over popular online learning algorithms and underscore the benefits of continuous pipeline redesign in the presence of data drift.