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Forget KL divergence – this work shows you *can* reliably evaluate generative models with finite samples, but only if you use the right metric (IPMs with bounded test classes).
Forget adversarial attacks – learning can be efficient even with counterexamples, as long as they're chosen symmetrically based on the difference between your model and the truth.
Learning to predict averages within neighborhoods, rather than individual labels, offers a new PAC learning framework with applications in explainability, fairness, and recommendation systems.
