AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work study the performance of OPTQ and a variant algorithm, stochastic OPTQ, in a generalization setting and derive bounds for the expected squared error accrued by the algorithm when a test point is drawn from a fixed distribution and proves two results.
A measure of regret is introduced for probabilistic classification tasks, which measures how the predictions of a model could change as a result of different training labels, and is used to show that regret is often higher for certain groups in the dataset and discuss potential applications of regret.
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