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.

7 papers of 11,817Sort Recent · Most cited
  1. 2026
    Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data StreamsJin Li, Kleanthis Malialis, Marios M. PolycarpouArtificial Intelligence Science and Engineering
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  2. 2026
    Drift-aware variational autoencoder-based anomaly detection with two-level ensemblingJin Li, Kleanthis Malialis, Christos G. Panayiotou, Marios M. PolycarpouNeurocomputing
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  3. 2024
    Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream ClassificationKleanthis Malialis, Jin Li, C. Panayiotou, Marios M. PolycarpouIEEE International Joint Conference on Neural Network
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  4. 2024
    Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous SequencesJin Li, Kleanthis Malialis, Marios M. PolycarpouIEEE International Joint Conference on Neural Network
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  5. 2022
    Unsupervised Unlearning of Concept Drift with AutoencodersAndré Artelt, Kleanthis Malialis, Christos G. Panayiotou … Barbara HammerIEEE Symposium Series on Computational Intelligence (SSCI) · Bielefeld University · University of Cyprus
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  6. 2023
    Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift AdaptationJin Li, Kleanthis Malialis, M. PolycarpouIEEE International Joint Conference on Neural Network
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  7. 2022
    A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream ClassificationKleanthis Malialis, Manuel Roveri, Cesare Alippi … Marios M. PolycarpouIEEE Symposium Series on Computational Intelligence (SSCI) · University of Cyprus · Politecnico di Milano
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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.