AI RESEARCH
Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm
arXiv CS.LG
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ArXi:2303.07287v3 Announce Type: replace-cross In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of paramount importance. However, directly estimating these parameters using the empirical moment generating function (MGF) is infeasible. To address this, we suggest using the sub-Gaussian intrinsic moment norm [Buldygin and Kozachenko, Theorem 1.3] achieved by maximizing a sequence of normalized moments.