<ul><li>Machine learning models are increasingly used for automated decision-making, requiring early detection of concept drift for optimal performance.</li><li>Current research on concept drift mainly focuses on supervised tasks with immediate access to true labels, posing challenges for large datasets without instant labels.</li><li>A new algorithm utilizing statistical process control in a label-less setting is proposed for efficient concept drift detection with improved statistical power.</li><li>Introduction of a novel drift detection framework enhances the algorithm's performance in detecting drift without labels, as demonstrated through numerical simulations.</li></ul>

Flexible and Efficient Drift Detection without Labels

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