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This paper establishes tight generalization bounds for multi-dimensional hyperparameter tuning in data-driven algorithm design, using real algebraic geometry and a multi-regime lower-bound framework to resolve theoretical gaps.
The paper proposes an attack-agnostic robustness metric based on the spectral norm of the Fisher Information Matrix, providing theoretical bounds and scalable evaluation methods for deep neural networks.