2012
NIPS
NeurIPS 2012
Multi-Task Averaging
Abstract
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task averages. We derive the optimal amount of regularization, and show that it can be effectively estimated. Simulations and real data experiments demonstrate that MTA both maximum likelihood and James-Stein estimators, and that our approach to estimating the amount of regularization rivals cross-validation in performance but is more computationally efficient.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— james-stein estimator
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Hot Topic Early Bird
— multi-task learning
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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
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Topic Pioneer
— Statistical Learning
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Statistical Learning
Mathematics & Optimization > Mathematics > Statistics
Mathematics & Optimization > Optimization > Continuous Optimization
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Core Methods > Statistical Learning