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.

8 papers of 11,817Sort Recent · Most cited
  1. 2025
    PPSEBM: An Energy-Based Model with Progressive Parameter Selection for Continual LearningXiaodi Li, Dingcheng Li, Rujun Gao … Latifur KhanBigData Congress [Services Society]
    PDF ↗
  2. 2025
    LSEBMCL: A Latent Space Energy-Based Model for Continual LearningXiaodi Li, Dingcheng Li, Rujun Gao … Latifur KhanDigital Signal Processing and Signal Processing Education…
    PDF ↗
  3. 2024
    ConfliLPC: Logits and Parameter Calibration for Political Conflict Analysis in Continual LearningXiaodi Li, Niamat Zawad, Patrick T. Brandt … Latifur KhanBigData Congress [Services Society]
  4. 2024
    Dynamic Environment Responsive Online Meta-Learning with Fairness AwarenessChen Zhao, Feng Mi, Xintao Wu … Feng ChenACM Transactions
    PDF ↗
  5. 2022
    Adaptive Fairness-Aware Online Meta-Learning for Changing EnvironmentsChen Zhao, Feng Mi, Xintao Wu … Feng ChenKDD · The University of Texas at Dallas · University of Arkansas at Fayetteville
    PDF ↗
  6. 2021
    CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream LearningYigong Wang, Zhuoyi Wang, Yu Lin … Dingcheng LiSIGIR · The University of Texas at Dallas · Amazon (United States)
  7. 2021
  8. 2019
    Robust High Dimensional Stream Classification with Novel Class DetectionZhuoyi Wang, Zelun Kong, Swarup Changra … Latifur KhanICDE · The University of Texas at Dallas
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.