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• W3119465765 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 $sqrt{gamma n}$, where $gamma$ is a measure of the model misspecification to the true distribution in terms of total variation distance. In contrast, using an aggregation-based (improper) learner, one can obtain regret $d log n$ for any underlying generating distribution, where $d$ is the dimension of the parameter; we exhibit instances in which this is unimprovable even over the family of all learners that may play distributions in the convex hull of the parametric family. These results suggest that simple strategies for aggregating multiple learners together should be more robust, and several experiments conform to this hypothesis." @default.
• W3119465765 created "2021-01-18" @default.
• W3119465765 creator A5047168403 @default.
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• W3119465765 date "2021-01-13" @default.
• W3119465765 modified "2023-09-27" @default.
• W3119465765 title "On Misspecification in Prediction Problems and Robustness via Improper Learning" @default.
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• W3119465765 hasPublicationYear "2021" @default.
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