Unlearning-based sliding window for continual learning under concept drift
Traditional machine learning assumes a stationary data distribution, yet many real-world applications operate on nonstationary streams in which the underlying concept evolves over time. This problem can also be viewed as task-free continual learning under concept drift, where a model must adapt sequentially without explicit task identities or task boundaries. In such settings, effective learning requires both rapid adaptation to new data and forgetting of outdated information. A common solution is based on a sliding window, but this approach is often computationally demanding because the model must be repeatedly retrained from scratch on the most recent data. We propose a different perspe...
| Authors | Michal Wozniak, Marek Klonowski, Maciej Maczynski et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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