# Flexible and Efficient Drift Detection without Labels

arXiv: https://arxiv.org/abs/2506.08734 · HTML: https://mlsanity.com/paper/2506.08734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Revisiting Classifier Two-Sample Tests](https://mlsanity.com/paper/1610.06545/) (2016)
- [On the Reliable Detection of Concept Drift from Streaming Unlabeled Data](https://mlsanity.com/paper/1704.00023/) (2017)
- [Concept Drift Detection for Streaming Data](https://mlsanity.com/paper/1504.01044/) (2015)
- [Compress Then Test: Powerful Kernel Testing in Near-linear Time](https://mlsanity.com/paper/2301.05974/) (2023)

## Abstract (arXiv)

Machine learning models are being increasingly used to automate decisions in almost every domain, and ensuring the performance of these models is crucial for ensuring high quality machine learning enabled services. Ensuring concept drift is detected early is thus of the highest importance. A lot of research on concept drift has focused on the supervised case that assumes the true labels of supervised tasks are available immediately after making predictions. Controlling for false positives while monitoring the performance of predictive models used to make inference from extremely large datasets periodically, where the true labels are not instantly available, becomes extremely challenging. We propose a flexible and efficient concept drift detection algorithm that uses classical statistical process control in a label-less setting to accurately detect concept drifts. We show empirically that under computational constraints, our approach has better statistical power than previous known methods. Furthermore, we introduce a new semi-supervised drift detection framework to model the scenario of detecting drift (without labels) given prior detections, and show how our drift detection algorithm can be incorporated effectively into this framework. We demonstrate promising performance via numerical simulations.