dimensiond
Appendix
In this section, we provide further intuition about the proposed AdaQN method. In the next stage, with4m0 samples, we use the original Hessian inverse approximation 2Rm0(wm0) 1 and the new variablew2m0 for the BFGS updates. As Vn = O(1/n)(since n m0 = Ω(κ2logd)) and n = 2m, condition (38) is equivalent to (1/tn) tn (1/6.6). This parameter depends heavily on the variation/variance of the input features for linear models. Thus, we can focus on the diagonal components of these twomatrices only.
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Private data analysis suffers acostly curse of dimensionality.