Sobolev Independence Criterion

NeurIPS 2019 Youssef MrouehTom SercuMattia RigottiInkit PadhiCicero Dos Santos

We propose the Sobolev Independence Criterion (SIC), an interpretable dependency measure between a high dimensional random variable X and a response variable Y . SIC decomposes to the sum of feature importance scores and hence can be used for nonlinear feature selection... (read more)

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