Du, Zhuangjin2022-01-242022-01-242021-12Du_cornell_0058O_11340http://dissertations.umi.com/cornell:11340https://hdl.handle.net/1813/11079226 pagesFairness is an important concern in machine learning. One way to characterize the problem is to minimize some objective function, representing a quantity like loss, under constraints that represent fairness conditions like demographic parity. Previous papers show how a two-player approach to optimizing the Lagrangian can be computationally efficient and provably converge to a good, constraint-satisfying solution on convex objective functions. This work analyzes the convergence of the two-player framework in previous works on nonconvex functions, in particular those that satisfy the PL inequality.enConstrained OptimizationFairnessMachine Learningdissertation or thesishttps://doi.org/10.7298/nk0w-gt37