• Corpus ID: 243860726

Turing-Universal Learners with Optimal Scaling Laws

  title={Turing-Universal Learners with Optimal Scaling Laws},
  author={Preetum Nakkiran},
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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  • L. Devroye
  • Mathematics
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1982
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