The framework interprets MMD-balls as credal sets to derive MMD-parameterized PAC-Bayesian generalization bounds and separate epistemic from aleatoric uncertainty in test-time adaptation.
Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering
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MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation
The framework interprets MMD-balls as credal sets to derive MMD-parameterized PAC-Bayesian generalization bounds and separate epistemic from aleatoric uncertainty in test-time adaptation.