• Corpus ID: 235417119

A Distribution-dependent Analysis of Meta Learning

  title={A Distribution-dependent Analysis of Meta Learning},
  author={Mikhail Konobeev and Ilja Kuzborskij and Csaba Szepesvari},
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a metalearner on a new task drawn from the unknown task distribution. In this paper, focusing on fixed design linear regression with Gaussian noise and a Gaussian task (or parameter) distribution, we give distribution-dependent lower bounds on the transfer risk of any algorithm, while we also show that a novel, weighted version of the so-called biased… 

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