Early-stopped gradient descent achieves the minimax-optimal classification error for Gaussian mixtures with label noise under fast-decaying covariance spectra, while interpolating classifiers can be exponentially worse.
Support vector machines and linear regression coincide with very high-dimensional features.Advances in Neural Information Processing Systems, 34: 4907–4918, 2021
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Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification
Early-stopped gradient descent achieves the minimax-optimal classification error for Gaussian mixtures with label noise under fast-decaying covariance spectra, while interpolating classifiers can be exponentially worse.