AI RESEARCH
Optimal Rates for Differentially Private Hypothesis Testing with E-values
arXiv CS.LG
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ArXi:2605.28952v1 Announce Type: cross E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis testing is at the core of many of these applications, which can often involve private or sensitive data.