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cs.LG2024
SUDS: A Strategy for Unsupervised Drift Sampling
Christofer Fellicious, Lorenz Wendlinger, Mario Gancarski +2
Supervised machine learning often encounters concept drift, where the data distribution changes over time, degrading model performance. Existing drift detection methods focus on id…
cs.LG2024
DriftGAN: Using historical data for Unsupervised Recurring Drift Detection
Christofer Fellicious, Sahib Julka, Lorenz Wendlinger +1
In real-world applications, input data distributions are rarely static over a period of time, a phenomenon known as concept drift. Such concept drifts degrade the model's predictio…