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Conference Papers Year : 2020

Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model

Abstract

In the context of statistical supervised learning, the noiseless linear model assumes that there exists a deterministic linear relation $Y = \langle \theta_*, X \rangle$ between the random output $Y$ and the random feature vector $\Phi(U)$, 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 $\theta_*$ and the decay of the generalization error follow polynomial convergence rates with exponents that both depend on the regularities of the optimum $\theta_*$ and of the feature vectors $\Phi(u)$. 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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Dates and versions

hal-02866755 , version 1 (15-06-2020)
hal-02866755 , version 2 (26-10-2020)

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Raphaël Berthier, Francis Bach, Pierre Gaillard. Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model. NeurIPS '20 - 34th International Conference on Neural Information Processing Systems, Dec 2020, Vancouver, Canada. pp.2576--2586. ⟨hal-02866755v2⟩
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