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Conformal multi-target regression using neural networks

Abstract : Multi-task learning is a domain that is still not fully studied in the conformal prediction framework, and this is particularly true for multi-target regression. Our work uses inductive conformal prediction along with deep neural networks to handle multi-target regression by exploring multiple extensions of existing single-target non-conformity measures and proposing new ones. This paper presents our approaches to work with conformal prediction in the multiple regression setting, as well as the results of our conducted experiments.
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https://hal.archives-ouvertes.fr/hal-03029473
Contributor : Sébastien Destercke Connect in order to contact the contributor
Submitted on : Saturday, November 28, 2020 - 3:06:29 PM
Last modification on : Tuesday, November 16, 2021 - 4:30:58 AM

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  • HAL Id : hal-03029473, version 1

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Soundouss Messoudi, Sébastien Destercke, Sylvain Rousseau. Conformal multi-target regression using neural networks. 9th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2020), Aug 2020, Verone (virtual), Italy. pp.65-83. ⟨hal-03029473⟩

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