16 search results - page 2 / 4 » No-regret learning in convex games |
ICML
2008
IEEE
14 years 6 months ago
2008
IEEE
Quite a bit is known about minimizing different kinds of regret in experts problems, and how these regret types relate to types of equilibria in the multiagent setting of repeated...
COLT
2010
Springer
13 years 3 months ago
2010
Springer
We study the regret of an online learner playing a multi-round game in a Banach space B against an adversary that plays a convex function at each round. We characterize the minima...
CORR
2011
Springer
13 years 13 days ago
2011
Springer
Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequenti...
ICML
2003
IEEE
14 years 6 months ago
2003
IEEE
Convex programming involves a convex set F Rn and a convex cost function c : F R. The goal of convex programming is to find a point in F which minimizes c. In online convex prog...
ATAL
2011
Springer
12 years 5 months ago
2011
Springer
Motivated by a machine learning perspective—that gametheoretic equilibria constraints should serve as guidelines for predicting agents’ strategies, we introduce maximum causal...