Efficient Algorithms for Large-Scale Minimax Problems KU Leuven
Efficient algorithms for nonconvex minimax problems is the subject of investigation in this proposal. Such problems have recently emerged in a diverse range of applications related to robust learning and dynamical decision making in uncertain environments. For example, adversarial training that addresses the issue of sensitivity of machine learning models, or distributionally robust optimization that tackles decision making under uncertainty, ...