\begin{thebibliography}{68}
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Achilles, C., Bain, H.~P., Bellott, F., Boyd-Zaharias, J., Finn, J., Folger, J., Johnston, J., and Word, E.
\newblock {Tennessee's Student Teacher Achievement Ratio (STAR) project}, 2008.
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\bibitem[Ajroldi et~al.(2023)Ajroldi, Diquigiovanni, Fontana, and Vantini]{ajroldi2023conformal}
Ajroldi, N., Diquigiovanni, J., Fontana, M., and Vantini, S.
\newblock Conformal prediction bands for two-dimensional functional time series.
\newblock \emph{Computational Statistics \& Data Analysis}, 187:\penalty0 107821, 2023.

\bibitem[Alaa et~al.(2023)Alaa, Hussain, and Sontag]{alaa2023conformalized}
Alaa, A.~M., Hussain, Z., and Sontag, D.
\newblock Conformalized unconditional quantile regression.
\newblock In \emph{International conference on artificial intelligence and statistics}, pp.\  10690--10702. PMLR, 2023.

\bibitem[Angelopoulos et~al.(2020)Angelopoulos, Bates, Malik, and Jordan]{angelopoulos2020uncertainty}
Angelopoulos, A., Bates, S., Malik, J., and Jordan, M.~I.
\newblock Uncertainty sets for image classifiers using conformal prediction.
\newblock \emph{arXiv preprint arXiv:2009.14193}, 2020.

\bibitem[Angelopoulos \& Bates(2021)Angelopoulos and Bates]{angelopoulos2021gentle}
Angelopoulos, A.~N. and Bates, S.
\newblock A gentle introduction to conformal prediction and distribution-free uncertainty quantification.
\newblock \emph{arXiv preprint arXiv:2107.07511}, 2021.

\bibitem[Angelopoulos et~al.(2022)Angelopoulos, Kohli, Bates, Jordan, Malik, Alshaabi, Upadhyayula, and Romano]{angelopoulos2022image}
Angelopoulos, A.~N., Kohli, A.~P., Bates, S., Jordan, M., Malik, J., Alshaabi, T., Upadhyayula, S., and Romano, Y.
\newblock Image-to-image regression with distribution-free uncertainty quantification and applications in imaging.
\newblock In \emph{International Conference on Machine Learning}, pp.\  717--730. PMLR, 2022.

\bibitem[Angelopoulos et~al.(2023)Angelopoulos, Bates, et~al.]{angelopoulos2023conformal}
Angelopoulos, A.~N., Bates, S., et~al.
\newblock Conformal prediction: A gentle introduction.
\newblock \emph{Foundations and Trends{\textregistered} in Machine Learning}, 16\penalty0 (4):\penalty0 494--591, 2023.

\bibitem[Bai et~al.(2022)Bai, Mei, Wang, Zhou, and Xiong]{bai2022efficientdifferentiableconformalprediction}
Bai, Y., Mei, S., Wang, H., Zhou, Y., and Xiong, C.
\newblock Efficient and differentiable conformal prediction with general function classes, 2022.
\newblock URL \url{https://arxiv.org/abs/2202.11091}.

\bibitem[Barber et~al.(2023)Barber, Candes, Ramdas, and Tibshirani]{barber2023conformal}
Barber, R.~F., Candes, E.~J., Ramdas, A., and Tibshirani, R.~J.
\newblock Conformal prediction beyond exchangeability.
\newblock \emph{The Annals of Statistics}, 51\penalty0 (2):\penalty0 816--845, 2023.

\bibitem[Buza(2014)]{buza2014feedback}
Buza, K.
\newblock Feedback prediction for blogs.
\newblock In \emph{Data analysis, machine learning and knowledge discovery}, pp.\  145--152. Springer, 2014.

\bibitem[Cand{\`e}s et~al.(2023)Cand{\`e}s, Lei, and Ren]{candes2023conformalized}
Cand{\`e}s, E., Lei, L., and Ren, Z.
\newblock Conformalized survival analysis.
\newblock \emph{Journal of the Royal Statistical Society Series B: Statistical Methodology}, 85\penalty0 (1):\penalty0 24--45, 2023.

\bibitem[Cauchois et~al.(2021)Cauchois, Gupta, and Duchi]{cauchois2021knowing}
Cauchois, M., Gupta, S., and Duchi, J.~C.
\newblock Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction.
\newblock \emph{Journal of machine learning research}, 22\penalty0 (81):\penalty0 1--42, 2021.

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Cohen, J.~W., Cohen, S.~B., and Banthin, J.~S.
\newblock The medical expenditure panel survey: A national information resource to support healthcare cost research and inform policy and practice.
\newblock \emph{Medical Care}, 47:\penalty0 S44--S50, 2009.

\bibitem[Cresswell et~al.(2024)Cresswell, Sui, Kumar, and Vouitsis]{cresswell2024conformal}
Cresswell, J.~C., Sui, Y., Kumar, B., and Vouitsis, N.
\newblock Conformal prediction sets improve human decision making.
\newblock In \emph{Proceedings of the 41st International Conference on Machine Learning}, pp.\  9439--9457, 2024.

\bibitem[Dabah \& Tirer(2025)Dabah and Tirer]{dabah2025temperature}
Dabah, L. and Tirer, T.
\newblock On temperature scaling and conformal prediction of deep classifiers.
\newblock In \emph{International Conference on Machine Learning}, pp.\  11813--11845. PMLR, 2025.

\bibitem[De~Prado(2018)]{finance}
De~Prado, M.~L.
\newblock \emph{Advances in financial machine learning}.
\newblock John Wiley \& Sons, 2018.

\bibitem[Deng et~al.(2009)Deng, Dong, Socher, Li, Li, and Fei-Fei]{Deng2009ImageNetAL}
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.
\newblock Imagenet: A large-scale hierarchical image database.
\newblock \emph{2009 IEEE Conference on Computer Vision and Pattern Recognition}, pp.\  248--255, 2009.

\bibitem[Fanaee-T(2013)]{bike_sharing_275}
Fanaee-T, H.
\newblock {Bike Sharing}.
\newblock UCI Machine Learning Repository, 2013.
\newblock {DOI}: https://doi.org/10.24432/C5W894.

\bibitem[Feldman et~al.(2021)Feldman, Bates, and Romano]{feldman2021improving}
Feldman, S., Bates, S., and Romano, Y.
\newblock Improving conditional coverage via orthogonal quantile regression.
\newblock \emph{Advances in neural information processing systems}, 34:\penalty0 2060--2071, 2021.

\bibitem[Fisch et~al.(2022)Fisch, Schuster, Jaakkola, and Barzilay]{pmlr-v162-fisch22a}
Fisch, A., Schuster, T., Jaakkola, T., and Barzilay, D.
\newblock Conformal prediction sets with limited false positives.
\newblock In Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., and Sabato, S. (eds.), \emph{Proceedings of the 39th International Conference on Machine Learning}, volume 162 of \emph{Proceedings of Machine Learning Research}, pp.\  6514--6532. PMLR, 17--23 Jul 2022.
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\bibitem[Foygel~Barber et~al.(2021)Foygel~Barber, Candes, Ramdas, and Tibshirani]{foygel2021limits}
Foygel~Barber, R., Candes, E.~J., Ramdas, A., and Tibshirani, R.~J.
\newblock The limits of distribution-free conditional predictive inference.
\newblock \emph{Information and Inference: A Journal of the IMA}, 10\penalty0 (2):\penalty0 455--482, 2021.

\bibitem[Gibbs et~al.(2025)Gibbs, Cherian, and Cand{\`e}s]{gibbs2025conformal}
Gibbs, I., Cherian, J.~J., and Cand{\`e}s, E.~J.
\newblock Conformal prediction with conditional guarantees.
\newblock \emph{Journal of the Royal Statistical Society Series B: Statistical Methodology}, 87\penalty0 (4):\penalty0 1100--1126, 2025.

\bibitem[Guan(2023)]{guan2023localized}
Guan, L.
\newblock Localized conformal prediction: A generalized inference framework for conformal prediction.
\newblock \emph{Biometrika}, 110\penalty0 (1):\penalty0 33--50, 2023.

\bibitem[Guo et~al.(2017)Guo, Pleiss, Sun, and Weinberger]{guo2017calibration}
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K.~Q.
\newblock On calibration of modern neural networks.
\newblock In \emph{International conference on machine learning}, pp.\  1321--1330. PMLR, 2017.

\bibitem[H~Zargarbashi \& Bojchevski(2024)H~Zargarbashi and Bojchevski]{h2024conformal}
H~Zargarbashi, S. and Bojchevski, A.
\newblock Conformal inductive graph neural networks.
\newblock In \emph{International Conference on Learning Representations}, volume 2024, pp.\  55175--55197, 2024.

\bibitem[Han et~al.(2022)Han, Tang, Ghosh, and Liu]{han2022split}
Han, X., Tang, Z., Ghosh, J., and Liu, Q.
\newblock Split localized conformal prediction.
\newblock \emph{arXiv preprint arXiv:2206.13092}, 2022.

\bibitem[He \& Lam(2024)He and Lam]{he2024statistically}
He, S. and Lam, H.
\newblock Statistically optimal uncertainty quantification for expensive black-box models.
\newblock \emph{arXiv preprint arXiv:2408.05887}, 2024.

\bibitem[Hore \& Barber(2024)Hore and Barber]{hore2024conformalpredictionlocalweights}
Hore, R. and Barber, R.~F.
\newblock Conformal prediction with local weights: randomization enables local guarantees, 2024.
\newblock URL \url{https://arxiv.org/abs/2310.07850}.

\bibitem[Huang et~al.(2023)Huang, Bharti, Souza, Acerbi, and Kaski]{huang2023learningrobuststatisticssimulationbased}
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\newblock Learning robust statistics for simulation-based inference under model misspecification, 2023.
\newblock URL \url{https://arxiv.org/abs/2305.15871}.

\bibitem[Izbicki et~al.(2020)Izbicki, Shimizu, and Stern]{Flexible}
Izbicki, R., Shimizu, G., and Stern, R.
\newblock Flexible distribution-free conditional predictive bands using density estimators.
\newblock In \emph{International Conference on Artificial Intelligence and Statistics}, pp.\  3068--3077. PMLR, 2020.

\bibitem[Jensen et~al.(2024)Jensen, Bianchi, and Anfinsen]{Jensen_2024}
Jensen, V., Bianchi, F.~M., and Anfinsen, S.~N.
\newblock Ensemble conformalized quantile regression for probabilistic time series forecasting.
\newblock \emph{IEEE Transactions on Neural Networks and Learning Systems}, 35\penalty0 (7):\penalty0 9014–9025, July 2024.
\newblock ISSN 2162-2388.
\newblock \doi{10.1109/tnnls.2022.3217694}.
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\bibitem[Jin et~al.(2023)Jin, Ren, and Cand{\`e}s]{jin2023sensitivity}
Jin, Y., Ren, Z., and Cand{\`e}s, E.~J.
\newblock Sensitivity analysis of individual treatment effects: A robust conformal inference approach.
\newblock \emph{Proceedings of the National Academy of Sciences}, 120\penalty0 (6):\penalty0 e2214889120, 2023.

\bibitem[Kiyani et~al.(2024)Kiyani, Pappas, and Hassani]{kiyani2024length}
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\newblock Length optimization in conformal prediction.
\newblock \emph{Advances in Neural Information Processing Systems}, 37:\penalty0 99519--99563, 2024.

\bibitem[Kuleshov et~al.(2018)Kuleshov, Fenner, and Ermon]{kuleshov2018accurate}
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\newblock Accurate uncertainties for deep learning using calibrated regression.
\newblock In \emph{International conference on machine learning}, pp.\  2796--2804. PMLR, 2018.

\bibitem[Lei et~al.(2015)Lei, Rinaldo, and Wasserman]{lei2015conformal}
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\newblock A conformal prediction approach to explore functional data.
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\bibitem[Lei et~al.(2018)Lei, G’Sell, Rinaldo, Tibshirani, and Wasserman]{lei2018distribution}
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R.~J., and Wasserman, L.
\newblock Distribution-free predictive inference for regression.
\newblock \emph{Journal of the American Statistical Association}, 113\penalty0 (523):\penalty0 1094--1111, 2018.

\bibitem[Lei \& Cand{\`e}s(2021)Lei and Cand{\`e}s]{lei2021conformal}
Lei, L. and Cand{\`e}s, E.~J.
\newblock Conformal inference of counterfactuals and individual treatment effects.
\newblock \emph{Journal of the Royal Statistical Society Series B: Statistical Methodology}, 83\penalty0 (5):\penalty0 911--938, 2021.

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\newblock Revisiting the calibration of modern neural networks.
\newblock \emph{Advances in Neural Information Processing Systems}, 34:\penalty0 15682--15694, 2021.

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\newblock Uncertainty prediction for deep sequential regression using meta models.
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\newblock Normalized nonconformity measures for regression conformal prediction.
\newblock In \emph{Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2008)}, pp.\  64--69, 2008.

\bibitem[Papadopoulos et~al.(2011)Papadopoulos, Vovk, and Gammerman]{papadopoulos2011regression}
Papadopoulos, H., Vovk, V., and Gammerman, A.
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\bibitem[Rana(2013)]{physicochemical_properties_of_protein_tertiary_structure_265}
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\newblock {Physicochemical Properties of Protein Tertiary Structure}.
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\newblock Learning optimal conformal classifiers, 2022.
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\newblock Conformal prediction under covariate shift.
\newblock \emph{Advances in neural information processing systems}, 32, 2019.

\bibitem[Vovk(2012)]{pmlr-v25-vovk12}
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\newblock Conditional validity of inductive conformal predictors.
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\bibitem[Wang \& Ghosal(2023)Wang and Ghosal]{wang2023coverage}
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\newblock Conformal prediction interval for dynamic time-series.
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\newblock Conformal prediction sets for graph neural networks.
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\newblock Evaluating the utility of conformal prediction sets for ai-advised image labeling.
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\newblock Individual calibration with randomized forecasting.
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\end{thebibliography}
