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.
The Annals of Statistics , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
Introduces a regularized estimator achieving optimal MSE rates under a new relative balancedness condition while providing safety guarantees that match independent learning when tasks are unrelated.
DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
citing papers explorer
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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.
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Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness and Safety
Introduces a regularized estimator achieving optimal MSE rates under a new relative balancedness condition while providing safety guarantees that match independent learning when tasks are unrelated.
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Distributionally Robust Multi-Objective Optimization
DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.