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.
arXiv preprint arXiv:1906.06268 , year=
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Harmonization works better than personalization for appearance-based domain shifts in federated medical imaging while personalization is superior for structural shifts, with both performing similarly when shifts are small.
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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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When To Adapt? Adapting the Model or Data in Federated Medical Imaging
Harmonization works better than personalization for appearance-based domain shifts in federated medical imaging while personalization is superior for structural shifts, with both performing similarly when shifts are small.