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arXiv Machine Learning By Mahdi Mohammadigohari

Sample-Weighted End-to-End Trace-Norm Geometry for Multitask Learning

Read the original on arXiv Machine Learning →

The paper introduces a sample-weighted trace-norm geometry for multitask learning, focusing on the end-to-end map from task coefficients to input-space predictors. It derives the exact empirical Rademacher complexity for a fixed-radius class and demonstrates that this measure captures orientation and factorization effects that traditional product-based bounds miss. Experiments on 252 held-out comparisons show that weighted joint nuclear regularization consistently improves population excess error over unweighted nuclear regularization and other baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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