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In statistical learning theory, Stability measures how much a learned predictor or its loss changes when the training sample is perturbed. Uniform stability of a learning algorithm imposes a bound valid at every test example.

This is a uniform version of Stability for learning algorithms. A learning algorithm has uniform stability $\beta$ for a loss when replacing one training example changes the loss at every test example by at most $\beta$.