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Efficient Regret Minimization in Non-Convex Games

Author(s): Hazan, Elad; Singh, Karan; Zhang, Cyril

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Abstract: We consider regret minimization in repeated games with non-convex loss functions. Minimizing the standard notion of regret is computationally intractable. Thus, we define a natural notion of regret which permits efficient optimization and generalizes offline guarantees for convergence to an approximate local optimum. We give gradient-based methods that achieve optimal regret, which in turn guarantee convergence to equilibrium in this framework.
Publication Date: 2017
Citation: Hazan, Elad, Karan Singh, and Cyril Zhang. "Efficient regret minimization in non-convex games." In Proceedings of the 34th International Conference on Machine Learning (2017): pp. 1433-1441.
ISSN: 2640-3498
Pages: 1433 - 1441
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the 34th International Conference on Machine Learning
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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