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Multi-Task Averaging
Sergey Feldman · Maya R Gupta · Bela A Frigyik

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

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.

Author Information

Sergey Feldman (Allen Institute for AI)
Maya R Gupta (University of Washington)
Bela A Frigyik (University of Washington)

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