mlsanityData to 14 Oct 2026

Regret Bounds for Lifelong Learning

arXiv 1610.08628 ↗First posted 27 Oct 2016stat.ML cs.LGcited by 19

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Abstract (arXiv)

We consider the problem of transfer learning in an online setting. Different tasks are presented sequentially and processed by a within-task algorithm. We propose a lifelong learning strategy which refines the underlying data representation used by the within-task algorithm, thereby transferring information from one task to the next. We show that when the within-task algorithm comes with some regret bound, our strategy inherits this good property. Our bounds are in expectation for a general loss function, and uniform for a convex loss. We discuss applications to dictionary learning and finite set of predictors. In the latter case, we improve previous O(1/√m) bounds to O(1/m) where m is the per task sample size.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Oct 2016.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 1; 2024: 1; 2025: 1.

19 papers.

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  1. 2021How Fine-Tuning Allows for Effective Meta-Learningcited by 41
  2. 2021Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stabilitycited by 30
  3. 2021An Online Learning Approach to Interpolation and Extrapolation in Domain Generalizationcited by 30
  4. 2021On the Power of Multitask Representation Learning in Linear MDPcited by 30
  5. 2021Joint Learning of Linear Time-Invariant Dynamical Systemscited by 16
  6. 2022Nearly Minimax Algorithms for Linear Bandits with Shared Representationcited by 16
  7. 2021Learning-to-learn non-convex piecewise-Lipschitz functionscited by 15
  8. 2021Provable Continual Learning via Sketched Jacobian Approximationscited by 15
  9. 2021A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learningcited by 12
  10. 2024A CMDP-within-online framework for Meta-Safe Reinforcement Learningcited by 12
  11. 2023Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayescited by 8
  12. 2021Meta-strategy for Learning Tuning Parameters with Guaranteescited by 6
7 more
  1. 2021Multitask Online Mirror Descentcited by 6
  2. 2022On the Sample Complexity of Representation Learning in Multi-task Bandits with Global and Local structurecited by 5
  3. 2022Multi-Environment Meta-Learning in Stochastic Linear Banditscited by 4
  4. 2022AdaTask: Adaptive Multitask Online Learningcited by 1
  5. 2022Prototypical quadruplet for few-shot class incremental learningcited by 1
  6. 2021On regret bounds for continual single-index learningcited by 0
  7. 2025Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measurecited by 0