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Fairer and More Accurate Tabular Models Through NAS
Making models algorithmically fairer in tabular data has been long studied, with techniques typically oriented towards fixes which usually take a neural model with an undesirable outcome and make changes to how the data are ingested, what the model weights are, or how outputs are processed.
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
Even stronger, we prove that the kernel space is isometric to the space of causal ancestral graphs, so that distance between samples in the kernel space is guaranteed to correspond to distance between their generating causal structures.
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