Filtering-based robust multi-task gradient descent matches minimax rates under task contamination and heterogeneity, removing the √d contamination barrier of regularization and score-based methods.
9th Innovations in Theoretical Computer Science Conference (ITCS 2018) , series =
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Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
Filtering-based robust multi-task gradient descent matches minimax rates under task contamination and heterogeneity, removing the √d contamination barrier of regularization and score-based methods.