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.
Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates.Journal of Machine Learning Research, 16:3299–3340, 2015
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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.