Skip to yearly menu bar Skip to main content


Poster

Efficient Regret Minimization in Non-Convex Games

Elad Hazan · Karan Singh · Cyril Zhang

Gallery #45

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.

Live content is unavailable. Log in and register to view live content