Campus Units

Electrical and Computer Engineering, Computer Science

Document Type

Conference Proceeding


38th International Conference on Machine Learning

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Published Version

Publication Date


Journal or Book Title

Proceedings of the 38th International Conference on Machine Learning



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Conference Title

38th International Conference on Machine Learning

Conference Date

July 18-24, 2021


When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous drift-detection-based methods by incorporating drift detection into a broader stable-state/reactive-state process. The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives. The algorithm is generic in its base learner and can be applied across a variety of supervised learning problems. Our theoretical analysis shows that the risk of the algorithm is (i) statistically better than standalone drift detection and (ii) competitive to an algorithm with oracle knowledge of when (abrupt) drifts occur. Experiments on synthetic and real datasets with concept drifts confirm our theoretical analysis.


This proceeding is published as Tahmasbi, Ashraf, Ellango Jothimurugesan, Srikanta Tirthapura, and Phillip B. Gibbons. "DriftSurf: Stable-State/Reactive-State Learning under Concept Drift." In Proceedings of the 38th International Conference on Machine Learning, vol. 139, pp. 10054-10064. 2021. Posted with permission.

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