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

ACL 2019 Henry B. MossAndrew MooreDavid S. LesliePaul Rayson

We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce unreliable comparisons, it is still common practice to compare model evaluations based on single choices of random seeds... (read more)

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