Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.
The assumed lower bound onλ then ensures that the remaining deterministic and stochastic terms are also bounded by1 2 λ
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Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.