$\left[\mathrm{PBC}^{+}20\right]$  Janis Postels, Hermann Blum, Cesar Cadena, Roland Siegwart, Luc Van Gool, and Federico Tombari. Quantifying aleatoric and epistemic uncertainty using density estimation in latent space. arXiv preprint arXiv:2012.03082, 2020. 9
[PBZ21] Tim Pearce, Alexandra Brintrup, and Jun Zhu. Understanding softmax confidence and uncertainty. arXiv preprint arXiv:2106.04972, 2021. 10
[PLMV23] Alexandre Perez-Lebel, Marine Le Morvan, and Gael Varouquaux. Beyond calibration: estimating the grouping loss of modern neural networks. In The Eleventh International Conference on Learning Representations, 2023. 10
[Ras04] Carl Edward Rasmussen. Gaussian processes in machine learning. In Summer school on machine learning, pages 63-71. Springer, 2004. 10
[RBSC19] Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel J Candès. With malice towards none: Assessing uncertainty via equalized coverage. arXiv preprint arXiv:1908.05428, 2019. 10
[Rip89] Lance J. Rips. Similarity, typicality, and categorization, page 21-59. Cambridge University Press, 1989. 1
[RM75] Eleanor Rosch and Carolyn B Mervis. Family resemblances: Studies in the internal structure of categories. Cognitive psychology, 7(4):573-605, 1975. 1
[RSC20] Yaniv Romano, Matteo Sesia, and Emmanuel Candes. Classification with valid and adaptive coverage. Advances in Neural Information Processing Systems, 33:3581-3591, 2020. 9
[RSS73] Lance J Rips, Edward J Shoben, and Edward E Smith. Semantic distance and the verification of semantic relations. Journal of verbal learning and verbal behavior, 12(1):1-20, 1973. 1
[SC19] Pragya Sur and Emmanuel J Candès. A modern maximum-likelihood theory for high-dimensional logistic regression. Proceedings of the National Academy of Sciences, 116(29):14516–14525, 2019. 6, 27, 33
[SV08] Glenn Shafer and Vladimir Vovk. A tutorial on conformal prediction. Journal of Machine Learning Research, 9(3), 2008. 8, 10
[TGZ+23] Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca, 2023. 4, 18
[TJ06] MTCAJ Thomas and A Thomas Joy. Elements of information theory. Wiley-Interscience, 2006. 3
[TK74] Amos Tversky and Daniel Kahneman. Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty. science, 185(4157):1124-1131, 1974. 1
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[VGS05] Vladimir Vovk, Alexander Gammerman, and Glenn Shafer. Algorithmic learning in a random world. Springer Science & Business Media, 2005. 23
$\left[\mathrm{WDS}^{+20}\right]$  Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierrick Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, 2020. 17