Stable Conformal Prediction Sets

• URL: http://arxiv.org/abs/2112.10224v1
• Date: Sun, 19 Dec 2021 18:53:32 GMT
• Title: Stable Conformal Prediction Sets
• Authors: Eugene Ndiaye
• Abstract summary: conformal prediction is a methodology that allows to estimate a confidence set for $y_n+1$ given $x_n+1$. While appealing, the computation of such set turns out to be infeasible in general. We combine conformal prediction techniques with algorithmic stability bounds to derive a prediction set computable with a single model fit.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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