350 papers with code • 0 benchmarks • 11 datasets
This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem.
Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.
Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns.
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates.