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arXiv · 2410.01969

Which Algorithms Have Tight Generalization Bounds?

Michael Gastpar·Ido Nachum·Jonathan Shafer·Thomas Weinberger
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Abstract

We study which machine learning algorithms have tight generalization bounds. First, we present conditions that preclude the existence of tight generalization bounds. Specifically, we show that algorithms that have certain inductive biases that cause them to be unstable do not admit tight generalization bounds. Next, we show that algorithms that are sufficiently stable do have tight generalization bounds. We conclude with a simple characterization that relates the existence of tight generalization bounds to the conditional variance of the algorithm's loss.

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Michael Gastpar, Ido Nachum, Jonathan Shafer, Thomas Weinberger. 2024-10-02. Which Algorithms Have Tight Generalization Bounds?. https://arxiv.org/abs/2410.01969

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