Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
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In the context of statistical supervised learning, the noiseless linear model assumes that there exists a deterministic linear relation Y = ⟨θ_*, X ⟩ between the random output Y and the random feature vector Φ(U), a potentially non-linear transformation of the inputs U. We analyze the convergence of single-pass, fixed step-size stochastic gradient descent on the least-square risk under this model. The convergence of the iterates to the optimum θ_* and the decay of the generalization error follow polynomial convergence rates with exponents that both depend on the regularities of the optimum θ_* and of the feature vectors Φ(u). We interpret our result in the reproducing kernel Hilbert space framework; as a special case, we analyze an online algorithm for estimating a real function on the unit interval from the noiseless observation of its value at randomly sampled points. The convergence depends on the Sobolev smoothness of the function and of a chosen kernel. Finally, we apply our analysis beyond the supervised learning setting to obtain convergence rates for the averaging process (a.k.a. gossip algorithm) on a graph depending on its spectral dimension.
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