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Sample Efficient Linear Meta-Learning by Alternating Minimization

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arxiv 2105.08306 v1 pith:BDU5ACZX submitted 2021-05-18 cs.LG cs.AImath.OCstat.ML

Kiran Koshy Thekumparampil , Prateek Jain , Praneeth Netrapalli , Sewoong Oh This is my paper · ORCID
classification cs.LGcs.AImath.OCstat.ML
keywords tasktasksnumbersamplesmeta-learningmllamsubspacealternating
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abstract

Meta-learning synthesizes and leverages the knowledge from a given set of tasks to rapidly learn new tasks using very little data. Meta-learning of linear regression tasks, where the regressors lie in a low-dimensional subspace, is an extensively-studied fundamental problem in this domain. However, existing results either guarantee highly suboptimal estimation errors, or require $\Omega(d)$ samples per task (where $d$ is the data dimensionality) thus providing little gain over separately learning each task. In this work, we study a simple alternating minimization method (MLLAM), which alternately learns the low-dimensional subspace and the regressors. We show that, for a constant subspace dimension MLLAM obtains nearly-optimal estimation error, despite requiring only $\Omega(\log d)$ samples per task. However, the number of samples required per task grows logarithmically with the number of tasks. To remedy this in the low-noise regime, we propose a novel task subset selection scheme that ensures the same strong statistical guarantee as MLLAM, even with bounded number of samples per task for arbitrarily large number of tasks.

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Cited by 4 Pith papers

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