On Misspecification in Prediction Problems and Robustness via Improper Learning
Annie Marsden,
John Duchi,
Gregory Valiant
Abstract:We study probabilistic prediction games when the underlying model is misspecified, investigating the consequences of predicting using an incorrect parametric model. We show that for a broad class of loss functions and parametric families of distributions, the regret of playing a "proper" predictor-one from the putative model class-relative to the best predictor in the same model class has lower bound scaling at least as √ γn, where γ is a measure of the model misspecification to the true distribution in terms … Show more
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