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Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization.

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Stability of Gradient Learning Dynamics in Continuous Games: Vector Action Spaces., , , , and . CoRR, (2020)Minimax Optimization with Smooth Algorithmic Adversaries., , , and . ICLR, OpenReview.net, (2022)Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games., , , and . AAMAS, page 860-868. International Foundation for Autonomous Agents and Multiagent Systems, (2020)Learning in Stochastic Monotone Games with Decision-Dependent Data., , , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 5891-5912. PMLR, (2022)An inverse correlated equilibrium framework for utility learning in multiplayer, noncooperative settings., , , , and . HiCoNS, page 9-16. ACM, (2013)Competitive Statistical Estimation with Strategic Data Sources., , , and . CoRR, (2019)Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games., and . CDC, page 6899-6904. IEEE, (2019)Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms., , , , and . AAAI, page 9217-9224. AAAI Press, (2022)Learning in Stochastic Stackelberg Games., , , , and . ACC, page 3557-3562. IEEE, (2024)Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization., , , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 6702-6734. PMLR, (2022)