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Found 2 papers, 1 papers with code
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
In this work, by leveraging tools from causal inference we attempt to increase the resilience of anomaly detection models to different kinds of distribution shifts.
S-GBDT: Frugal Differentially Private Gradient Boosting Decision Trees
For the Abalone dataset for $\varepsilon=0. 54$ we achieve $R^2$-score of $0. 47$ which is very close to the $R^2$-score of $0. 54$ for the nonprivate version of GBDT.
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