Online Learning for Global Cost Functions

old_uid7352
titleOnline Learning for Global Cost Functions
start_date2009/09/21
schedule13h30
onlineno
summaryWe 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. 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. Such global cost functions include the makespan (the maximum over the
responsiblesBiau, Stoltz, Massart