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PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques
We propose PaloBoost, a Stochastic Gradient TreeBoost model that uses novel regularization techniques to guard against overfitting and is robust to parameter settings.
ACDC: $α$-Carving Decision Chain for Risk Stratification
The resulting chain of decision rules yields a pure subset of the minority class examples.
Perturbed Gibbs Samplers for Synthetic Data Release
We propose a categorical data synthesizer with a quantifiable disclosure risk.
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