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2016 AISTATS AISTATS 2016

Distributed Multi-Task Learning

Abstract

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
📈 Trend Setter — Federated Learning
🐣 Hot Topic Early Bird — distributed learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jialei Wang , Mladen Kolar , Nathan Srerbo

Topics

Artificial Intelligence > Learning Paradigms > Federated Learning Machine Learning > Core Methods > Regression

Keywords

multi-task learning feature selection distributed learning high-dimensional regression
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