November 8, 2025Open Access

Memory-Statistics Tradeoff in Continual Learning with Structural Regularization

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Authors

HLHaoran LiBeijing University of Posts and Telecommunications JWJingfeng WuShenzhen Institutes of Advanced Technology VBVladimir BravermanGoogle (United States)

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Implication

Analytical study reveals that structural regularization improves memory complexity and excess risk in continual learning with linear regression tasks, suggesting key implications for performance.

Key Points

Structural regularization enhances performance in continual learning by addressing memory complexity and statistical efficiency.
Analysis reveals that increasing vectors can improve excess risk, while reducing memory complexity.
Approach involves a structural regularization algorithm with generalized l2-regularization and Hessian insights.
Findings highlight the importance of curvature-aware regularization to mitigate catastrophic forgetting in continual learning.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/690e8b75a5b062d7a4e73866 https://doi.org/10.48550/arxiv.2504.04039

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Implication

Analytical study reveals that structural regularization improves memory complexity and excess risk in continual learning with linear regression tasks, suggesting key implications for performance.

Memory-Statistics Tradeoff in Continual Learning with Structural Regularization

Discussion

Member takes

Implication

Key Points

Cite This Study

Memory-Statistics Tradeoff in Continual Learning with Structural Regularization

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider