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In this paper, we formalise order-robust optimisation as an instance ofonline learning minimising simple regret, and propose Vroom, a zero'th orderoptimisation algorithm capable of achieving vanishing regret in non-stationaryenvironments, while recovering favorable rates under stochasticreward-generating processes. Our results are the first to target simple regretdefinitions in adversarial scenarios unveiling a challenge that has been rarelyconsidered in prior work.

Derivative-Free & Order-Robust Optimisation