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We study benign overfitting in two-layer ReLU networks trained using gradient descent and hinge loss on noisy data for binary classification.

Moreover, the guarantees that they achieve in the heavy-tailed setting are weaker than those for sub-Gaussian distributions with known covariance.

Moreover, the guarantees that they achieve in the heavy-tailed setting are weaker than those for sub-Gaussian distributions with known covariance.

This requires balancing the plasticity for new knowledge with the stability for old [37].

In many real-world scenarios, it is crucial to be able to reliably and efficiently reason under uncertainty while capturing complex relationships in data.