New minimax-quantile theory enhances privacy in statistical decision making

Researchers have developed a new minimax-quantile theory for interactive statistical decision making (ISDM) that addresses rare but significant failures, which traditional expectation-based criteria like minimax risk and regret do not capture. The theory establishes structural relationships between minimax quantiles, lower minimax quantiles, and minimax risk, including a conversion from quantiles to expectations and an equivalence between strict and lower minimax quantiles. The work also introduces converse tools for ISDM, such as high-probability interactive Fano's and Le Cam's methods, and demonstrates how mutual-information privacy can be integrated into this framework by constraining the admissible decision class. AI

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for statistical decision making. [lever_c_demoted from research: ic=1 ai=1.0]

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• Gaussian mean estimation
• Gaussian privatization
• Interactive Statistical Decision Making
• K-armed Gaussian bandit problem
• Minimax Quantile Lower Bounds for Interactive Statistical Decision Making with Privacy
• minimax risk
• Mutual-information privacy
• regret
• Two-armed Gaussian bandits

• paper
• safety

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