<ul><li>Learning a Single Index Model (SIM) from anisotropic Gaussian inputs using vanilla Stochastic Gradient Descent (SGD) is investigated.</li><li>The impact of the covariance matrix on the learning dynamics and sample complexity is analyzed.</li><li>Results show that vanilla SGD adapts to the data's covariance structure automatically.</li><li>Upper and lower bounds on the sample complexity are derived based on the covariance matrix, not the input data dimension.</li></ul>

Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent

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