mlsanityData to 14 Oct 2026

Distributed Multitask Learning

arXiv 1510.00633 ↗First posted 2 Oct 2015stat.ML cs.LGcited by 11

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Abstract (arXiv)

We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space,where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2015.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 5; 2025: 2.

11 papers.

  1. 2022Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolutioncited by 54
  2. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  3. 2021An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangianscited by 25
  4. 2024Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Banditscited by 6
  5. 2025Shift is Good: Mismatched Data Mixing Improves Test Performancecited by 5
  6. 2023Less is More -- Towards parsimonious multi-task models using structured sparsitycited by 2
  7. 2024Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraintscited by 2
  8. 2024Online Parallel Multi-Task Relationship Learning via Alternating Direction Method of Multiplierscited by 2
  9. 2024Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learningcited by 1
  10. 2024Distributed Networked Multi-task Learningcited by 0
  11. 2025Adaptive Dual-Mode Distillation with Incentive Schemes for Scalable, Heterogeneous Federated Learning on Non-IID Datacited by 0