Computer Science – Learning
Scientific paper
2012-04-20
Computer Science
Learning
Scientific paper
We address online linear optimization problems when the possible actions of the decision maker are represented by binary vectors. The regret of the decision maker is the difference between her realized loss and the best loss she would have achieved by picking, in hindsight, the best possible action. Our goal is to understand the magnitude of the best possible (minimax) regret. We study the problem under three different assumptions for the feedback the decision maker receives: full information, and the partial information models of the so-called "semi-bandit" and "bandit" problems. Combining the Mirror Descent algorithm and the INF (Implicitely Normalized Forecaster) strategy, we are able to prove optimal bounds for the semi-bandit case. We also recover the optimal bounds for the full information setting. In the bandit case we discuss existing results in light of a new lower bound, and suggest a conjecture on the optimal regret in that case. Finally we also prove that the standard exponentially weighted average forecaster is provably suboptimal in the setting of online combinatorial optimization.
Audibert Jean-Yves
Bubeck Sébastien
Lugosi Gábor
No associations
LandOfFree
Regret in Online Combinatorial Optimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Regret in Online Combinatorial Optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Regret in Online Combinatorial Optimization will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-6263