Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    Convergence of Continual Learning in Homogeneous Deep NetworksMatan Schliserman, Gon Buzaglo, Itay Evron, Daniel SoudryCOLT
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  2. 2026
    Optimal L2 Regularization in High-dimensional Continual Linear RegressionGilad Karpel, Edward Moroshko, Ran Levinstein … Itay EvronarXiv
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  3. 2025
    Are Greedy Task Orderings Better Than Random in Continual Linear Regression?Matan Tsipory, Ran Levinstein, Itay Evron … Daniel SoudryNeurIPS
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  4. 2025
    Optimal Rates in Continual Linear Regression via Increasing RegularizationRan Levinstein, Amit Attia, Matan Schliserman … Itay EvronNeurIPS
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  5. 2025
    From Continual Learning to SGD and Back: Better Rates for Continual Linear ModelsItay Evron, Ran Levinstein, Matan Schliserman … Nathan SrebroarXiv
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  6. 2024PDF ↗
  7. 2023
    Continual Learning in Linear Classification on Separable DataItay Evron, Edward Moroshko, Gon Buzaglo … Daniel SoudryICML
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  8. 2022
    How catastrophic can catastrophic forgetting be in linear regression?Itay Evron, Edward Moroshko, Rachel Ward … Daniel SoudryCOLT
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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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.