Efficient Regret Minimization in NonConvex Games
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Proceedings of the 34th International Conference on Machine Learning, PMLR 70:14331441, 2017.
Abstract
We consider regret minimization in repeated games with nonconvex 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 gradientbased methods that achieve optimal regret, which in turn guarantee convergence to equilibrium in this framework.
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