arXiv:2602.13848v2 Announce Type: replace Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting. Existing CTM detectors construct test martingales by continually growing a reference set with each incoming sample, using it to assess how atypical the new sample is relative to past observations. While this design yields anytime-valid type-I error control, it suffers from test-time contamination: after a change, post-shift observations enter the reference set and dilute t
The paper addresses a known limitation in current sequential detection methods for distribution shifts, driven by the increasing need for robust AI systems in dynamic environments.
Improving the reliability of AI systems to detect distribution shifts is critical for their safe and effective deployment across various industries, from finance to autonomous systems.
This research provides a more robust and contamination-resistant method for AI systems to detect when their input data distributions change, enhancing their adaptability and trustworthiness.
- · AI developers
- · Autonomous systems manufacturers
- · Financial institutions using AI
- · Predictive maintenance industries
- · Systems with high false-positive rates for distribution shifts
AI models become more adaptable and reliable in real-world, non-stationary environments.
Increased adoption of AI in safety-critical applications due to enhanced trustworthiness in detecting changes.
Reduced need for constant human oversight in monitoring AI model performance, leading to greater automation efficiency.
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Read at arXiv cs.LG