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Sharpness-Aware Minimization (SAM) trains models to perform well even when their weights are slightly perturbed, seeking flatter minima that generalize better.
The Minimax Theorem states that in zero-sum two-player games with suitable convexity and compactness, the best guaranteed payoff for the maximizer equals the worst-case loss for the minimizer.