Timezone: »

Efficient Regret Minimization in Non-Convex Games
Elad Hazan · Karan Singh · Cyril Zhang

Sun Aug 06 08:30 PM -- 08:48 PM (PDT) @ C4.1

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.

Author Information

Elad Hazan (Princeton University)
Karan Singh (Princeton University)
Cyril Zhang (Princeton University)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors