Single-pass SGD with exponentially decaying steps is claimed to reach minimax-optimal excess risk in high-dimensional kernel regression for well-specified problems, with averaging handling misspecified problems.
Non-strongly-convex smooth stochastic approximation with convergence rate o (1/n)
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Learning Curves of Stochastic Gradient Descent in Kernel Regression
Single-pass SGD with exponentially decaying steps is claimed to reach minimax-optimal excess risk in high-dimensional kernel regression for well-specified problems, with averaging handling misspecified problems.