arXiv Machine Learning July 20, 2026 By Athanasios Tziouvaras, Carolina Fortuna, George Floros, Kostas Kolomvatsos, Panagiotis Sarigiannidis, Marko Grobelnik, Bla\v{z} Bertalani\v{c}

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications

Read the original on arXiv Machine Learning →

arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Statistics ML

Sep 10

Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift: devices, subjects, and nearly three years underground

arXiv:2609.09257v1 Announce Type: cross Abstract: Sensor-based AI systems are rarely operated under the conditions under which they were trained: devices, personnel and recording epochs change, and e...

By Benny Platte (Mittweida University of Applied Sciences), Rico Thomanek (Mittweida University of Applied Sciences), Christian Roschke (Mittweida University of Applied Sciences), Marc Ritter (Mittweida University of Applied Sciences)

arXiv Machine Learning

Jul 21

When Drift Detectors cry Wolf: False Alarm Rates in continuous ML Monitoring

arXiv:2607. 17336v1 Announce Type: new Abstract: Drift detection is a core component of production machine learning monitoring systems, where detectors are used to compare incoming data with a reference distribution and trigger alerts when changes occur.

By Raj Shekhar Singh

safety

arXiv AI

Jun 19

Learner-based Concept Drift Detection: Analysis and Evaluation

arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.

By Md Moman Ul Haque Khan, Samira Sadaoui

arXiv AI

Aug 17

Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers

arXiv:2608. 14089v1 Announce Type: new Abstract: Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather than the deployer's desired policy, and their performance degrades as deployment traffic evolves.

By Thiago Sandoval, Ufuk Topcu

llms fine-tuning benchmarks safety

arXiv Machine Learning

Aug 18

Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling

arXiv:2608. 15465v1 Announce Type: cross Abstract: Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks.

By Shayan Azizi, Norihiro Okui, Masataka Nakahara, Ayumu Kubota, Gustavo Batista, Hassan Habibi Gharakaheili

arXiv Machine Learning

Jun 9

A Framework for Evaluating and Benchmarking Concept Drift Detection Methods

arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.

By Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden, Bernhard Pfahringer, Albert Bifet

benchmarks

One email a morning, machine-written

Related stories

Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift: devices, subjects, and nearly three years underground

When Drift Detectors cry Wolf: False Alarm Rates in continuous ML Monitoring

Learner-based Concept Drift Detection: Analysis and Evaluation

Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers

Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling

A Framework for Evaluating and Benchmarking Concept Drift Detection Methods