Multitask Learning and Bandits via Robust Statistics

arXiv:2112.14233v4 Announce Type: replace-cross
Abstract: Decision-makers often simultaneously face many related but heterogeneous learning problems. For instance, a large retailer may wish to learn product demand at different stores to solve pricing or inventory problems, making it desirable to learn jointly for stores serving similar customers; alternatively, a hospital network may wish to learn patient risk at different providers to allocate personalized interventions, making it desirable to learn jointly for hospitals serving similar patient populations. Motivated by real datasets, we …

abstract arxiv cs.lg customers decision demand face hospital instance inventory learn makers making math.st multitask learning network pricing product robust solve statistics stat.ml stat.th stores type via