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Found 2 papers, 1 papers with code
Provably Robust Model-Centric Explanations for Critical Decision-Making
We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be gleaned from LIME and SHAP, popular data-centric explanation tools in Artificial Intelligence (AI).
Scaling Active Search using Linear Similarity Functions
In this paper, we consider the problem of Active Search where we are given a similarity function between data points.
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