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We present FIESTA, a model selection approach that significantly reduces thecomputational resources required to reliably identify state-of-the-artperformance from large collections of candidate models. Despite being known toproduce unreliable comparisons, it is still common practice to compare modelevaluations based on single choices of random seeds. We show that reliablemodel selection also requires evaluations based on multiple train-test splits(contrary to common practice in many shared tasks). Using bandit theory fromthe statistics literature, we are able to adaptively determine appropriatenumbers of data splits and random seeds used to evaluate each model, focusingcomputational resources on the evaluation of promising models whilst avoidingwasting evaluations on models with lower performance. Furthermore, ouruser-friendly Python implementation produces confidence guarantees of correctlyselecting the optimal model. We evaluate our algorithms by selecting between 8target-dependent sentiment analysis methods using dramatically fewer modelevaluations than current model selection approaches.

FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms