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Aarhus University
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Every differentially private mechanism for continual counting faces a fundamental limit: an expected $\ell_\infty$ error of at least $惟(\log^{3/2} n)$.
Replicable PAC learning is harder than we thought: achieving it provably requires a sample complexity scaling as $(\log|H|)^{3/2}$, a significant hurdle for large hypothesis classes.
