# Distributed Multi-Task Learning with Shared Representation

arXiv: https://arxiv.org/abs/1603.02185 · HTML: https://mlsanity.com/paper/1603.02185/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2016.

## Built on by (most cited first, 5 of 5)

- [Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques](https://mlsanity.com/paper/2102.09743/) (2021)
- [An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians](https://mlsanity.com/paper/2102.05320/) (2021)
- [Provable and Efficient Continual Representation Learning](https://mlsanity.com/paper/2203.02026/) (2022)
- [Federated Representation Learning in the Under-Parameterized Regime](https://mlsanity.com/paper/2406.04596/) (2024)
- [Provable Pathways: Learning Multiple Tasks over Multiple Paths](https://mlsanity.com/paper/2303.04338/) (2023)

## 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.