AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We study the PAC-learnability of function classes that map inputs to probability distributions over a finite space. The learner observes i.i.d. input-label pairs, with each label sampled from the conditional distribution of an unknown target hypothesis in the class. We measure learning through excess risk under a bound...
Clipping improves robustness in both studies, with gains varying across detectors and contamination settings, and at a target false-positive rate, clipping improves the log-likelihood--log-rank ratio detector's true-positive rate.
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