Private gradient descent for linear regression: Tighter error bounds and instance-specific uncertainty estimation
We provide an improved analysis of standard differentially private gradient descent for linear
regression under the squared error loss. Under modest assumptions on the input, we …
regression under the squared error loss. Under modest assumptions on the input, we …
Better private linear regression through better private feature selection
Existing work on differentially private linear regression typically assumes that end users can
precisely set data bounds or algorithmic hyperparameters. End users often struggle to meet …
precisely set data bounds or algorithmic hyperparameters. End users often struggle to meet …
Differentially private and explainable boosting machine with enhanced utility
I Baek, YD Chung - Neurocomputing, 2024 - Elsevier
In this paper, we introduce DP-EBM*, an enhanced utility version of the Differentially Private
Explainable Boosting Machine (DP-EBM). DP-EBM* offers predictions for both classification …
Explainable Boosting Machine (DP-EBM). DP-EBM* offers predictions for both classification …
Revisiting differentially private XGBoost: are random decision trees really better than greedy ones?
E Wang, A Kolbeinsson, L Foschini, YX Wang - openreview.net
Boosted Decision Trees (eg, XGBoost) are one of the strongest and most widely used
machine learning models. Motivated by applications in sensitive domains, various versions …
machine learning models. Motivated by applications in sensitive domains, various versions …