Efficient Regret Minimization in Non-Convex Games
Elad Hazan ⋅ Karan Singh ⋅ Cyril Zhang
2017 Talk
Summary/NotesAbstract
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.
Chat is not available.
Successful Page Load