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abstract: |
In a variety of applications, including recommender systems, anomaly detection and fraud detection, a “learning” predictor is deployed to make predictions of the outcome of a (potentially fast-changing) distribution of a data sample. In this setting, the success of the prediction is measured by the difference between the predicted outcome and the real outcome.

Given a data sample of cardinality $n$, we investigate the existence of optimal learning predictors in the minimax sense: for any two data-dependent predictors that agree with our learning predictor on all the data points (but that make incorrect predictions otherwise), we show how to construct a data-dependent predictor that achieves a higher difference between the expected outcome and the observed outcome. Further, we show that under certain well-behaved conditions, the minimax predictor is uniquely provided by a natural three-step heuristic.
author:
– |
Francesco Silvestri\
\
\
– |
David Bresler\
\
\
bibliography:
– ‘bib.bib’
title: ‘Learning to Learn: Optimal Prediction for Distributional Data’

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