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Stable Conformal Prediction Sets

by   Eugene Ndiaye, et al.
Georgia Institute of Technology

When one observes a sequence of variables (x_1, y_1), ..., (x_n, y_n), conformal prediction is a methodology that allows to estimate a confidence set for y_n+1 given x_n+1 by merely assuming that the distribution of the data is exchangeable. While appealing, the computation of such set turns out to be infeasible in general, e.g. when the unknown variable y_n+1 is continuous. In this paper, we combine conformal prediction techniques with algorithmic stability bounds to derive a prediction set computable with a single model fit. We perform some numerical experiments that illustrate the tightness of our estimation when the sample size is sufficiently large.


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