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Particularly, when ϵ = O(1/√n) which matches the intrinsic excess error of a learning model and is sufficient for generalization, our complexity for quadratic and generic losses is respectively O (n0.5log2(n)) and O (n0.5log3(n)), which for the first time achieves optimal generalization in less than a single pass over data. Besides, we extend HSDMPG~to online strongly convex problems and prove its higher efficiency over the prior algorithms. Numerical results demonstrate the computational advantages of~HSDMPG.l_1-norm quantile regressio
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Divination With IFA
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