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Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
We present a regularization approach to this problem that trades off predictive accuracy of the learned models (with respect to biased labels) for the fairness in terms of statistical parity, i. e. independence of the decisions from the sensitive covariates.
Remote Sensing Image Classification with Large Scale Gaussian Processes
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.
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