Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning?
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-40953-0_26
Reference19 articles.
1. Kläs, M., Sembach, L.: Uncertainty wrappers for data-driven models – increase the transparency of AI/ML-based models through enrichment with dependable situation-aware uncertainty estimates. In: Romanovsky, A., Troubitsyna, E., Gashi, I., Schoitsch, E., Bitsch, F. (eds.) Computer Safety, Reliability, and Security. SAFECOMP 2019. Lecture Notes in Computer Science, vol. 11699, pp. 358–364. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26250-1_29
2. Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9(3), 371–421 (2008)
3. Angelopoulos, A.N., Bates, S.: Conformal prediction: A gentle introduction. Found. Trends Mach. Learn. 16(4), 494–591 (2023)
4. de Grancey, F., Jean-Luc, A., Alecu, L., Gerchinovitz, S., Mamalet, F., Vigouroux, D.: Object detection with probabilistic guarantees: a conformal prediction approach. In: WAISE (2022)
5. Lecture Notes in Computer Science;M Kläs,2018
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