Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2026
    Policy Gradient with Adaptive Entropy Annealing for Continual Fine-TuningYaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert BifetarXiv
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  2. 2025
    Rethinking Memory in Continual Learning: Beyond a Monolithic Store of the PastYaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert BifetTrans. Mach. Learn. Res.
  3. 2022
    Repeated Augmented Rehearsal: A Simple but Strong Baseline for Online Continual LearningYaqian Zhang, Bernhard Pfahringer, Eibe Frank … Yunzhe JiaNeurIPS
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  4. 2022
    A simple but strong baseline for online continual learning: Repeated Augmented RehearsalYaqian Zhang, Bernhard Pfahringer, Eibe Frank … Yunzhe JiaNeurIPS
  5. 2019
    Stochastic Gradient TreesHenry Gouk, Bernhard Pfahringer, Eibe FrankMachine Learning · University of Edinburgh · University of Waikato
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.