Truncated kernel SGD with spherical RBFs projects stochastic gradients to a finite hypothesis space for minimax-optimal rates on optimization, generalization, and strong RKHS convergence across losses like least-squares and logistic.
Spherical regression models using projective linear transformations.Journal of the American Statistical As- sociation, 109(508):1615–1624
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Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
Truncated kernel SGD with spherical RBFs projects stochastic gradients to a finite hypothesis space for minimax-optimal rates on optimization, generalization, and strong RKHS convergence across losses like least-squares and logistic.