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Optimizing Generalized Rate Metrics with Three Players
Harikrishna Narasimhan · Andrew Cotter · Maya Gupta

Thu Dec 12 10:05 AM -- 10:20 AM (PST) @ West Ballroom C

We present a general framework for solving a large class of learning problems with non-linear functions of classification rates. This includes problems where one wishes to optimize a non-decomposable performance metric such as the F-measure or G-mean, and constrained training problems where the classifier needs to satisfy non-linear rate constraints such as predictive parity fairness, distribution divergences or churn ratios. We extend previous two-player game approaches for constrained optimization to an approach with three players to decouple the classifier rates from the non-linear objective, and seek to find an equilibrium of the game. Our approach generalizes many existing algorithms, and makes possible new algorithms with more flexibility and tighter handling of non-linear rate constraints. We provide convergence guarantees for convex functions of rates, and show how our methodology can be extended to handle sums-of-ratios of rates. Experiments on different fairness tasks confirm the efficacy of our approach.

Author Information

Harikrishna Narasimhan (Google Research)
Andy Cotter (Google)
Maya Gupta (Google)

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