Learning with Global Cost in Stochastic Environments

We consider an online learning setting where at each time step the decision maker has to choose how to distribute the future loss between k alternatives, and then observes the loss of each alternative, where the losses are assumed to come from a joint distribution. Motivated by load balancing and job scheduling, we consider a global cost function (over the losses incurred by each alternative), rather than a summation of the instantaneous losses as done traditionally in online learning. Specifically, we consider the global cost functions: (1) the makespan (the maximum over the alternatives) and

• Type: Journal
• Year: 2010
• Where: COLT
• Authors: Eyal Even-Dar, Shie Mannor, Yishay Mansour
• Keywords: COLT 2010, Decision Maker, Global Cost Functions, Instantaneous Losses, Machine Learning