Constrained Optimization of Nonconvex Problems and its Applications in Fairness in Machine Learning
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Fairness 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.
Constrained Optimization; Fairness; Machine Learning
Townsend, Alex John
M.S., Computer Science
Master of Science
dissertation or thesis