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It is proved that PROBE is $\delta$-PAC and attains the known-correlation oracle sample complexity up to a constant multiplicative factor and a constant additive calibration cost and the guarantee extends to the $(\epsilon,\delta)$-PAC setting under minimal changes to the algorithm.
It is established that CGD converges to a neighborhood of the global optimum when the loss function exhibits sufficient strong convexity, and the derived bounds reveal a key insight: the strength of convexity in the loss function can compensate for the uncertainty introduced by decision-dependent effects.
To the authors' knowledge, AdmitOR is the first label-free admission mechanism designed around an explicitly calibrated false-discovery target, and the transfer failure identifies a necessary condition for extending it to wild streams.
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