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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper studies the interesting question on online (stochastic) gradient descent in the unconstrained setting (sometime referred to non-strongly convexity or without explicit regularization). In the earlier work [33], it was proved that the last iterate of stochastic gradient descent with least-square loss in the unconstrained setting actually converges with explicit convergence rate by appropriately choosing the step sizes (or by a stopping early rule), which, however, needs to know the smoothness of the regression function. The paper proposed a kernel-based stochastic gradient descent algorithm without the need to perform model selection, which requires the loss function and its gradient are both Lipschitz. The proposed algorithm is mainly motivated by the recent studies [15,16] which involves a data-dependent regularization.