A no-regret procedure for safe online logistic classification that meets a target error rate with high probability using only O(sqrt(T)) excess tests over an oracle.
expert” model also releases an uncertainty level Ui about its prediction. The algorithmic challenge is to leverage the uncertainty levels to produce “PAC labels
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The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
A no-regret procedure for safe online logistic classification that meets a target error rate with high probability using only O(sqrt(T)) excess tests over an oracle.