# A Distribution-dependent Analysis of Meta Learning

@inproceedings{Konobeev2021ADA, title={A Distribution-dependent Analysis of Meta Learning}, author={Mikhail Konobeev and Ilja Kuzborskij and Csaba Szepesvari}, booktitle={ICML}, year={2021} }

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…

## One Citation

Meta Learning MDPs with Linear Transition Models

- Computer ScienceAISTATS
- 2022

It is proved that the proposed biased version of the UC-MatrixRL algorithm provides significant improvements in the transfer regret for task distributions of low variance and high bias compared to learning the tasks in isolation.

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