A unified CMI generalization bound based on leave-m-out cross-validation that envelopes existing results, bridges MI/CMI gaps, and sharpens under bounded loss with empirical gains.
Shalev-Shwartz and S
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Derives adaptive generalization bounds {c_m / N^{1/(2∨m)}} for digital ML models via new concentration of measure results on finite metric spaces, with c_m = O(sqrt(m)).
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On Unified and Sharpened CMI Bounds for Generalization Errors
A unified CMI generalization bound based on leave-m-out cross-validation that envelopes existing results, bridges MI/CMI gaps, and sharpens under bounded loss with empirical gains.
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Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces
Derives adaptive generalization bounds {c_m / N^{1/(2∨m)}} for digital ML models via new concentration of measure results on finite metric spaces, with c_m = O(sqrt(m)).