New transfer learning framework for nonparametric regression with deep ReLU networks

This paper introduces a novel transfer learning framework designed for nonparametric regression tasks involving multiple data groups. The method estimates a common mean function by pooling data from all groups, then learns group-specific deviations to create additive estimators. The framework establishes theoretical bounds on L2 error and, when applied to deep ReLU networks, demonstrates the ability to overcome the curse of dimensionality by deriving explicit convergence rates under hierarchical composition models. The research also identifies conditions that facilitate positive transfer, leading to faster learning rates, and validates the approach through simulations and real-world experiments. AI

IMPACT Introduces a theoretical framework and practical validation for improving regression tasks with deep learning, potentially enhancing model performance in multi-group data scenarios.

RANK_REASON The cluster contains a single academic paper detailing a new methodology in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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