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Found 3 papers, 1 papers with code
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
This technique allows for public data to be included in a graphical-model-based mechanism.
The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning
Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space.
Identifying the Most Explainable Classifier
We introduce the notion of pointwise coverage to measure the explainability properties of machine learning classifiers.
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