In decision theory and machine learning, competitive regret refers to a performance measure that evaluates an algorithm's regret relative to an oracle or benchmark strategy. Unlike traditional regret, which compares against the best fixed decision in hindsight, competitive regret compares against decision-makers with different capabilities—either with greater computational resources or access to additional information.
In decision theory and machine learning, competitive regret refers to a performance measure that evaluates an algorithm's regret relative to an oracle or benchmark strategy. Unlike traditional regret, which compares against the best fixed decision in hindsight, competitive regret compares against decision-makers with different capabilities—either with greater computational resources or access to additional information.
Source: Wikipedia "Competitive regret" · CC BY-SA 4.0
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