Prototypical Explanations

Learn about MMD-critic, a prototypical explanation algorithm that selects prototypes representing the data.

What are prototypical explanations?

Prototypical explanations are examples-based explanations (called prototypes) that represent all the data. Prototypes can be used independently from a machine learning model to describe the data, but they can also be used to create an interpretable model or to make a black-box model interpretable.

MMD-critic is a popular prototypical explanation algorithm that compares the actual data distribution D={X1,X2,....,XN}D = \{ X_1, X_2, ...., X_N \} and the selected prototypes' distribution P={P1,P2,...,Pm}P = \{P_1, P_2, ..., P_m \}. MMD-critic selects prototypes that minimize the discrepancy between the two distributions.

The figure below shows prototypes in a data distribution. The prototypes are data points that cover the data distribution.

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