Safe Start: Configuring Optimization Algorithms for Decision-Making under Extreme Risks
This work argues that, in the presence of a simulation model, natural attempts to integrate variance reduction into optimization, even executed in a reasonable adaptive fashion, encounter fundamental challenges in guaranteeing realistic runtime when using common stochastic gradient descent algorithms.