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On Uniform Convergence and Low-Norm Interpolation Learning
[article]
2021
arXiv
pre-print
We consider an underdetermined noisy linear regression model where the minimum-norm interpolating predictor is known to be consistent, and ask: can uniform convergence in a norm ball, or at least (following Nagarajan and Kolter) the subset of a norm ball that the algorithm selects on a typical input set, explain this success? We show that uniformly bounding the difference between empirical and population errors cannot show any learning in the norm ball, and cannot show consistency for any set,
arXiv:2006.05942v3
fatcat:q6sday3xfzfb5fcertowseduii