Characterizes the optimal e-power for ε-DP e-value hypothesis testing between P^n and Q^n, supplies a matching algorithm, and gives matching bounds on stopping times for private e-processes.
Nikolakakis, Dionysios S
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ACP-UCB1 achieves logarithmic upper-quantile regret in stochastic bandits by combining adaptive conformal quantile estimates with UCB-style optimism.
citing papers explorer
-
Optimal Rates for Differentially Private Hypothesis Testing with E-values
Characterizes the optimal e-power for ε-DP e-value hypothesis testing between P^n and Q^n, supplies a matching algorithm, and gives matching bounds on stopping times for private e-processes.
-
Conformal-Style Quantile Analyses for Stochastic Bandits
ACP-UCB1 achieves logarithmic upper-quantile regret in stochastic bandits by combining adaptive conformal quantile estimates with UCB-style optimism.