An adaptive fused orthogonal estimator recovers latent clusters exactly with high probability and achieves pooled parametric rates plus asymptotic normality matching an oracle in semiparametric heterogeneous clustered multitask learning.
Advances in neural information processing systems , volume=
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A new first-order algorithm for multi-task learning with shared linear representation achieves near-optimal error rates in constant iterations, improving existing methods by a factor of k.
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Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
An adaptive fused orthogonal estimator recovers latent clusters exactly with high probability and achieves pooled parametric rates plus asymptotic normality matching an oracle in semiparametric heterogeneous clustered multitask learning.
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Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
A new first-order algorithm for multi-task learning with shared linear representation achieves near-optimal error rates in constant iterations, improving existing methods by a factor of k.