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Conformal prediction uses past observations to build a region of confidence for future observations. It offers solid coverage guarantees with the only assumption that the data is exchangeable. However, for many regression problems, this method remains impractical. We present a parametric programming approach to follow the path of the solution in relation to sequential changes of observations. Our analysis presents computational complexity bounds as well as a numerical evaluation to estimate its efficiency.
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