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Poster
Efficient Regret Minimization in Non-Convex Games
Elad Hazan · Karan Singh · Cyril Zhang
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.
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Author Information
Elad Hazan (Princeton University)
Karan Singh (Princeton University)
Cyril Zhang (Princeton University)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Talk: Efficient Regret Minimization in Non-Convex Games »
Mon. Aug 7th 03:30 -- 03:48 AM Room C4.1
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