Multi-task learning analysis reveals regularization benefits and double descent mitigation

This paper analyzes the asymptotic behavior of multi-task learning formulations, specifically focusing on perceptron learning models. The research demonstrates that combining multiple related tasks is equivalent to a traditional formulation with added regularization terms, which enhances generalization performance. Furthermore, the study empirically shows that this task combination can delay and asymptotically mitigate the double descent phenomenon. AI

IMPACT Provides theoretical insights into improving model generalization and understanding the double descent phenomenon in machine learning.

RANK_REASON The item is an academic paper published on arXiv detailing theoretical and empirical analysis of a machine learning technique. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• Ayed Alrashdi
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• multi-task learning
• Perceptron learning models
• ScienceCast

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