ASML: A Scalable and Efficient AutoML Solution for Data Streams

Nilesh Verma, Albert Bifet, Bernhard Pfahringer, Maroua Bahri
Proceedings of the Third International Conference on Automated Machine Learning, PMLR 256:11/1-26, 2024.

Abstract

Online learning poses a significant challenge to AutoML, as the best model and configuration may change depending on the data distribution. To address this challenge, we propose Automated Streaming Machine Learning (ASML), an online learning framework that automatically finds the best machine learning models and their configurations for changing data streams. It adapts to the online learning scenario by continuously exploring a large and diverse pipeline configuration space. It uses an adaptive optimisation technique that utilizes the current best design, adaptive random directed nearby search, and an ensemble of best performing pipelines. We experimented with real and synthetic drifting data streams and showed that ASML can build accurate and adaptive pipelines by constantly exploring and responding to changes. In several datasets, it outperforms existing online AutoML and state-of-the-art online learning algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v256-verma24a, title = {ASML: A Scalable and Efficient AutoML Solution for Data Streams}, author = {Verma, Nilesh and Bifet, Albert and Pfahringer, Bernhard and Bahri, Maroua}, booktitle = {Proceedings of the Third International Conference on Automated Machine Learning}, pages = {11/1--26}, year = {2024}, editor = {Eggensperger, Katharina and Garnett, Roman and Vanschoren, Joaquin and Lindauer, Marius and Gardner, Jacob R.}, volume = {256}, series = {Proceedings of Machine Learning Research}, month = {09--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v256/main/assets/verma24a/verma24a.pdf}, url = {https://proceedings.mlr.press/v256/verma24a.html}, abstract = {Online learning poses a significant challenge to AutoML, as the best model and configuration may change depending on the data distribution. To address this challenge, we propose Automated Streaming Machine Learning (ASML), an online learning framework that automatically finds the best machine learning models and their configurations for changing data streams. It adapts to the online learning scenario by continuously exploring a large and diverse pipeline configuration space. It uses an adaptive optimisation technique that utilizes the current best design, adaptive random directed nearby search, and an ensemble of best performing pipelines. We experimented with real and synthetic drifting data streams and showed that ASML can build accurate and adaptive pipelines by constantly exploring and responding to changes. In several datasets, it outperforms existing online AutoML and state-of-the-art online learning algorithms.} }
Endnote
%0 Conference Paper %T ASML: A Scalable and Efficient AutoML Solution for Data Streams %A Nilesh Verma %A Albert Bifet %A Bernhard Pfahringer %A Maroua Bahri %B Proceedings of the Third International Conference on Automated Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Katharina Eggensperger %E Roman Garnett %E Joaquin Vanschoren %E Marius Lindauer %E Jacob R. Gardner %F pmlr-v256-verma24a %I PMLR %P 11/1--26 %U https://proceedings.mlr.press/v256/verma24a.html %V 256 %X Online learning poses a significant challenge to AutoML, as the best model and configuration may change depending on the data distribution. To address this challenge, we propose Automated Streaming Machine Learning (ASML), an online learning framework that automatically finds the best machine learning models and their configurations for changing data streams. It adapts to the online learning scenario by continuously exploring a large and diverse pipeline configuration space. It uses an adaptive optimisation technique that utilizes the current best design, adaptive random directed nearby search, and an ensemble of best performing pipelines. We experimented with real and synthetic drifting data streams and showed that ASML can build accurate and adaptive pipelines by constantly exploring and responding to changes. In several datasets, it outperforms existing online AutoML and state-of-the-art online learning algorithms.
APA
Verma, N., Bifet, A., Pfahringer, B. & Bahri, M.. (2024). ASML: A Scalable and Efficient AutoML Solution for Data Streams. Proceedings of the Third International Conference on Automated Machine Learning, in Proceedings of Machine Learning Research 256:11/1-26 Available from https://proceedings.mlr.press/v256/verma24a.html.

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