Asymptotic approximation of population covariances by linearized recursions

Let Ω\Omega, Φ\Phi, and Ψ\Psi be the population covariances appearing in Theorem, and let ΩLlin\Omega_L^\mathrm{lin}, ΦL~lin\Phi_{\tilde{L}}^\mathrm{lin}, and ΨL~lin\Psi_{\tilde{L}}^\mathrm{lin} be the final iterates of the linear recursions from Definition. The Frobenius-norm errors satisfy

\normΩΩLlinF+\normΨΨL~linF+\normΦΦL~linF1.\norm{\Omega-\Omega_L^\mathrm{lin}}_F + \norm{\Psi-\Psi_{\tilde{L}}^\mathrm{lin}}_F + \norm{\Phi-\Phi_{\tilde{L}}^\mathrm{lin}}_F \lesssim 1.

Asymptotic covariance approximation conjecture. The population covariances can be asymptotically approximated by the last iterates of the corresponding linear recursions, with the displayed combined error bound. The source provides no resolution or further context establishing whether this claim is proved or remains open.

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Primary source

Dominik Schröder, Daniil Dmitriev, Hugo Cui and Bruno Loureiro, “Asymptotics of Learning with Deep Structured (Random) Features”, arXiv:2402.13999 (2024).

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