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3

Dropout Regularization Versus ℓ2\ell_2ℓ2​-Penalization in the Linear Model

18 June 2023
Gabriel Clara
S. Langer
Johannes Schmidt-Hieber
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Abstract

We investigate the statistical behavior of gradient descent iterates with dropout in the linear regression model. In particular, non-asymptotic bounds for the convergence of expectations and covariance matrices of the iterates are derived. The results shed more light on the widely cited connection between dropout and l2-regularization in the linear model. We indicate a more subtle relationship, owing to interactions between the gradient descent dynamics and the additional randomness induced by dropout. Further, we study a simplified variant of dropout which does not have a regularizing effect and converges to the least squares estimator

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