Conformal Prediction via Regression-as-Classification

Autor:	Guha, Etash, Natarajan, Shlok, Möllenhoff, Thomas, Khan, Mohammad Emtiyaz, Ndiaye, Eugene
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Zdroj:	International Conference of Learning Representations 2024
Druh dokumentu:	Working Paper
Popis:	Conformal prediction (CP) for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed by estimating a distribution over the output, but in reality, such approaches can be sensitive to estimation error and yield unstable intervals.~Here, we circumvent the challenges by converting regression to a classification problem and then use CP for classification to obtain CP sets for regression.~To preserve the ordering of the continuous-output space, we design a new loss function and make necessary modifications to the CP classification techniques.~Empirical results on many benchmarks shows that this simple approach gives surprisingly good results on many practical problems. Comment: International Conference of Learning Representations 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.08168 Zobrazit plný text záznamu View this record from Arxiv