  In this paper, we formalise order-robust optimisation as an instance of
online learning minimising simple regret, and propose Vroom, a zero'th order
optimisation algorithm capable of achieving vanishing regret in non-stationary
environments, while recovering favorable rates under stochastic
reward-generating processes. Our results are the first to target simple regret
definitions in adversarial scenarios unveiling a challenge that has been rarely
considered in prior work.

Machine Learning;Machine Learning