ALF: An Active Learning Framework for Scientific Discovery

Abstract:Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to advance the field. Active learning (AL) offers promise wherever labelling requires expensive experiment, measurement, or simulation. Most existing tools cover only part of the data acquisition loop, and typically focus on either offline benchmarking or online deployment, but not both. We present ALF, a modular AL Framework that runs the full data acquisition loop via five modular components. One clear API for both settings: offline, against an existing dataset for controlled and reproducible experimentation; and online, against an oracle for acquiring new candidates in real-world deployments. ALF is open-source and available at this https URL.

Machine Learning (cs.LG)

arXiv:2609.31197 [cs.LG]

(or arXiv:2609.31197v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.31197 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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