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Active Sampling for Min-Max Fairness
Jacob Abernethy · Pranjal Awasthi · Matthäus Kleindessner · Jamie Morgenstern · Chris Russell · Jie Zhang

Tue Jul 19 08:55 AM -- 09:00 AM (PDT) @ Room 307
in Optimization: Convex »

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 learning problems, such as linear or logistic regression, we provide a fine-grained analysis, proving the rate of convergence to a min-max fair solution.

Keywords: OPT: Convex

Author Information

Jacob Abernethy (Georgia Institute of Technology)
Pranjal Awasthi (Google)
Matthäus Kleindessner (Amazon)
Jamie Morgenstern (U Washington)
Chris Russell (Amazon)
Jie Zhang (University of Washington)

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