Distributed Multi-Task Learning with Shared Representation
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Abstract (arXiv)
We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available locally on the machine, and study communication-efficient methods for exploiting the shared structure.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Mar 2016.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
- 2021An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangianscited by 25
- 2022Provable and Efficient Continual Representation Learningcited by 14
- 2024Federated Representation Learning in the Under-Parameterized Regimecited by 7
- 2023Provable Pathways: Learning Multiple Tasks over Multiple Pathscited by 3
- 2024Federated Representation Learning in the Under-Parameterized Regime7 Jun 2024
- 2023Provable Pathways: Learning Multiple Tasks over Multiple Paths8 Mar 2023
- 2022Provable and Efficient Continual Representation Learning3 Mar 2022
- 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques19 Feb 2021
- 2021An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians10 Feb 2021