Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

17 papers of 6,984Sort Recent · Most cited
  1. 2026
    Learning Without Forgetting for Continual Learning in LLMs Through Adaptive LoRA RanksFuli Qiao, Mehrdad MahdaviMachine Learning · Pennsylvania State University
  2. 2026
    GE-PEFT: Gated Expandable Parameter-Efficient Fine-Tuning for Continual LearningJanna Omeliyanenko, Andreas Hotho, Daniel SchlörMachine Learning · University of Würzburg · Artificial Intelligence in Medicine (Canada)
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  3. 2026
    Class Incremental Learning and Auxiliary Unlabelled Data: The Importance of Neutral ExamplesIgor Sieradzki, Łukasz Struski, I. Podolak, Romuald Aleksander JanikMachine Learning · Jagiellonian University
  4. 2025
    Compression and restoration: exploring elasticity in continual test-time adaptationJingwei Li, Chengbao Liu, Xiwei Bai … Yu WangMachine Learning · Chinese Academy of Sciences · Shandong Institute of Automation · +2
  5. 2025
    Adaptive adapter routing for long-tailed class-incremental learningZhihong Qi, Da-Wei Zhou, Yiran Yao … De-Chuan ZhanMachine Learning · Nanjing Xiaozhuang University
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  6. 2024
    From MNIST to ImageNet and back: benchmarking continual curriculum learningKamil Faber, Dominik Żurek, Marcin Pietroń … Roberto CorizzoMachine Learning · Jagiellonian University · AGH University of Krakow · +2
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  7. 2023
    Continual variational dropout: a view of auxiliary local variables in continual learningNam Le Hai, T.T. Nguyen, Linh Ngo Van … Khoat ThanMachine Learning · FPT University · Hanoi University of Science and Technology · +1
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  8. 2023
    Overcoming catastrophic forgetting with classifier expanderXinchen Liu, Hongbo Wang, Ying-Jian Tian, Linyao XieMachine Learning
  9. 2022
    Hierarchically structured task-agnostic continual learningHeinke Hihn, Daniel BraunMachine Learning · Universität Ulm
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  10. 2022
    ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streamsAlberto Cano, Bartosz KrawczykMachine Learning · Virginia Commonwealth University
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  11. 2021
    Tensor decision trees for continual learning from drifting data streamsBartosz KrawczykMachine Learning · Virginia Commonwealth University
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  12. 2021
    Lifelong Learning with Sketched Structural RegularizationHaoran Li, Aditya Krishnan, Jingfeng Wu … Vladimir BravermanMachine Learning
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  13. 2019
    AutoML @ NeurIPS 2018 challenge: Design and ResultsHugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon … Qiang YangMachine Learning · Gleason (United States) · National Institute of Astrophysics, Optics and Electronics · +8
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  14. 2019
    Forward and Backward Knowledge Transfer for Sentiment ClassificationHao Wang, Bing Liu, Shuai Wang … Yan YangMachine Learning · Southwest Jiaotong University · University of Illinois Chicago
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  15. 2018
    Lifelong Machine Learning, Second EditionZhiyuan Chen, Bing LiuMachine Learning · Google (United States) · University of Illinois Chicago
  16. 2017
    Lifelong Machine LearningZhiyuan Chen, Bing LiuMachine Learning · Google (United States) · University of Illinois Chicago
  17. 1997
    CHILD: A First Step Towards Continual LearningMark RingMachine Learning · Center for Information Technology
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.