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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.|
|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.|
|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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