Establishes optimal dimension-free sampling complexities (k²/ε² for L2/k, k/ε² for L1/k, linear in k under bounded derivative) for regularized classification losses with matching lower bounds.
A similar assumption as Assumption [A2] is then implied by Jensen’s inequality since Ea∼P(∥a∥2)2 ≤ Ea∼P(∥a∥2 2)≤B
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Optimal Dimension-Free Sampling for Regularized Classification
Establishes optimal dimension-free sampling complexities (k²/ε² for L2/k, k/ε² for L1/k, linear in k under bounded derivative) for regularized classification losses with matching lower bounds.