Active Sampling for Min-Max Fairness. (arXiv:2006.06879v3 [stat.ML] UPDATED)

Web: http://arxiv.org/abs/2006.06879

We propose simple active sampling and reweighting strategies for optimizing
min-max fairness that can be applied to any classification or regression model
learned via loss minimization. The key intuition behind our approach is to use
at each timestep a datapoint from the group that is worst off under the current
model for updating the model. The ease of implementation and the generality of
our robust formulation make it an attractive option for improving model
performance on disadvantaged groups. For convex …

arxiv fairness ml sampling