Uniform generalization errors in binary linear classification concentrate around their expectation at O(1/sqrt(n)) rates under unbounded Lipschitz losses, via new log-Sobolev inequalities for (Y_i X_i, Y_i).
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Improved generalization bounds for binary linear classification via isoperimetry
Uniform generalization errors in binary linear classification concentrate around their expectation at O(1/sqrt(n)) rates under unbounded Lipschitz losses, via new log-Sobolev inequalities for (Y_i X_i, Y_i).