• Corpus ID: 243860726

# Turing-Universal Learners with Optimal Scaling Laws

@article{Nakkiran2021TuringUniversalLW,
title={Turing-Universal Learners with Optimal Scaling Laws},
author={Preetum Nakkiran},
journal={ArXiv},
year={2021},
volume={abs/2111.05321}
}
For a given distribution, learning algorithm, and performance metric, the rate of convergence (or datascaling law) is the asymptotic behavior of the algorithm’s test performance as a function of number of train samples. Many learning methods in both theory and practice have power-law rates, i.e. performance scales as n−α for some α > 0. Moreover, both theoreticians and practitioners are concerned with improving the rates of their learning algorithms under settings of interest. We observe the…

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