Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery

arXiv:2405.01994v1 Announce Type: cross
Abstract: This thesis aims to study some of the mathematical challenges that arise in the analysis of statistical sequential decision-making algorithms for postoperative patients follow-up. Stochastic bandits (multiarmed, contextual) model the learning of a sequence of actions (policy) by an agent in an uncertain environment in order to maximise observed rewards. To learn optimal policies, bandit algorithms have to balance the exploitation of current knowledge and the exploration of uncertain actions. Such algorithms have largely been …

abstract algorithms analysis applications arxiv challenges cs.lg decision making mathematics math.st modelling patients policy risk statistical stat.ml stat.th stochastic study surgery thesis type