Multistage defer trees chain sparse decision trees with deferral to match complex ensemble accuracy while routing most samples through one or few interpretable trees.
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Gaussian mechanisms for Rényi Pufferfish Privacy under Gaussian and mixture priors deliver exact divergence derivations, closed-form sufficient conditions, and 48.9% less noise than additive baselines on statistical and model queries.
Venn-Abers predictors are extended to unbounded regression via conformal prediction, producing point regressors that modestly improve efficiency over standard methods for large datasets.
DECAF synthetic data generator best balances privacy and fairness while fairness pre-processing improves outcomes more on synthetic data than real data, though at some cost to predictive accuracy.
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R\'enyi Pufferfish Privacy with Gaussian-based Priors: From Single Gaussian to Mixture Model
Gaussian mechanisms for Rényi Pufferfish Privacy under Gaussian and mixture priors deliver exact divergence derivations, closed-form sufficient conditions, and 48.9% less noise than additive baselines on statistical and model queries.