Derives upper and lower generalization bounds for the student relative to the teacher using a new distillation divergence, plus a loss-sharpness-aware bound and a bias-variance-rank decomposition in the linear Gaussian case.
Assume a noisy linear label channel Y|X∼ MN(W ⋆X, I k, ν 2In), whereW ⋆ ∈R k×d is the ground-truth linear map andν >0is the noise level
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On the Generalization of Knowledge Distillation: An Information-Theoretic View
Derives upper and lower generalization bounds for the student relative to the teacher using a new distillation divergence, plus a loss-sharpness-aware bound and a bias-variance-rank decomposition in the linear Gaussian case.