\begin{thebibliography}{68}
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\bibitem[Achilles et~al.(2008)Achilles, Bain, Bellott, Boyd-Zaharias, Finn, Folger, Johnston, and Word]{DVN/SIWH9F_2008}
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.
\newblock URL \url{https://doi.org/10.7910/DVN/SIWH9F}.

\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.

\bibitem[Cohen et~al.(2009)Cohen, Cohen, and Banthin]{Cohen2009TheME}
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.
\newblock URL \url{https://proceedings.mlr.press/v162/fisch22a.html}.

\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}
Huang, D., Bharti, A., Souza, A., Acerbi, L., and Kaski, S.
\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}.
\newblock URL \url{http://dx.doi.org/10.1109/TNNLS.2022.3217694}.

\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}
Kiyani, S., Pappas, G., and Hassani, H.
\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}
Kuleshov, V., Fenner, N., and Ermon, S.
\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}
Lei, J., Rinaldo, A., and Wasserman, L.
\newblock A conformal prediction approach to explore functional data.
\newblock \emph{Annals of Mathematics and Artificial Intelligence}, 74:\penalty0 29--43, 2015.

\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.

\bibitem[Minderer et~al.(2021)Minderer, Djolonga, Romijnders, Hubis, Zhai, Houlsby, Tran, and Lucic]{minderer2021revisiting}
Minderer, M., Djolonga, J., Romijnders, R., Hubis, F., Zhai, X., Houlsby, N., Tran, D., and Lucic, M.
\newblock Revisiting the calibration of modern neural networks.
\newblock \emph{Advances in Neural Information Processing Systems}, 34:\penalty0 15682--15694, 2021.

\bibitem[Navratil et~al.(2020)Navratil, Arnold, and Elder]{navratil2020uncertainty}
Navratil, J., Arnold, M., and Elder, B.
\newblock Uncertainty prediction for deep sequential regression using meta models.
\newblock \emph{arXiv preprint arXiv:2007.01350}, 2020.

\bibitem[Papadopoulos et~al.(2008)Papadopoulos, Gammerman, and Vovk]{papadopoulos2008normalized}
Papadopoulos, H., Gammerman, A., and Vovk, V.
\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.
\newblock Regression conformal prediction with nearest neighbours.
\newblock \emph{Journal of Artificial Intelligence Research}, 40:\penalty0 815--840, 2011.

\bibitem[Rana(2013)]{physicochemical_properties_of_protein_tertiary_structure_265}
Rana, P.
\newblock {Physicochemical Properties of Protein Tertiary Structure}.
\newblock UCI Machine Learning Repository, 2013.
\newblock {DOI}: https://doi.org/10.24432/C5QW3H.

\bibitem[Romano et~al.(2019)Romano, Patterson, and Candes]{romano2019conformalized}
Romano, Y., Patterson, E., and Candes, E.
\newblock Conformalized quantile regression.
\newblock \emph{Advances in neural information processing systems}, 32, 2019.

\bibitem[Romano et~al.(2020)Romano, Sesia, and Candes]{aps}
Romano, Y., Sesia, M., and Candes, E.
\newblock Classification with valid and adaptive coverage.
\newblock In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H. (eds.), \emph{Advances in Neural Information Processing Systems}, volume~33, pp.\  3581--3591. Curran Associates, Inc., 2020.
\newblock URL \url{https://proceedings.neurips.cc/paper_files/paper/2020/file/244edd7e85dc81602b7615cd705545f5-Paper.pdf}.

\bibitem[Sadinle et~al.(2018)Sadinle, Lei, and Wasserman]{Sadinle_2018}
Sadinle, M., Lei, J., and Wasserman, L.
\newblock Least ambiguous set-valued classifiers with bounded error levels.
\newblock \emph{Journal of the American Statistical Association}, 114\penalty0 (525):\penalty0 223–234, June 2018.
\newblock ISSN 1537-274X.
\newblock \doi{10.1080/01621459.2017.1395341}.
\newblock URL \url{http://dx.doi.org/10.1080/01621459.2017.1395341}.

\bibitem[Sasaki et~al.(2022)Sasaki, Ura, and Zhang]{sasaki2022unconditional}
Sasaki, Y., Ura, T., and Zhang, Y.
\newblock Unconditional quantile regression with high-dimensional data.
\newblock \emph{Quantitative Economics}, 13\penalty0 (3):\penalty0 955--978, 2022.

\bibitem[Seedat et~al.(2023)Seedat, Jeffares, Imrie, and van~der Schaar]{seedat2023improving}
Seedat, N., Jeffares, A., Imrie, F., and van~der Schaar, M.
\newblock Improving adaptive conformal prediction using self-supervised learning.
\newblock In \emph{International Conference on Artificial Intelligence and Statistics}, pp.\  10160--10177. PMLR, 2023.

\bibitem[Shafer \& Vovk(2008)Shafer and Vovk]{shafer2008tutorial}
Shafer, G. and Vovk, V.
\newblock A tutorial on conformal prediction.
\newblock \emph{Journal of Machine Learning Research}, 9\penalty0 (3), 2008.

\bibitem[Singh(2015)]{facebook_comment_volume_363}
Singh, K.
\newblock {Facebook Comment Volume}.
\newblock UCI Machine Learning Repository, 2015.
\newblock {DOI}: https://doi.org/10.24432/C5Q886.

\bibitem[Smith(2024)]{smith2024uncertainty}
Smith, R.~C.
\newblock \emph{Uncertainty quantification: theory, implementation, and applications}.
\newblock SIAM, 2024.

\bibitem[Stankeviciute et~al.(2021)Stankeviciute, M~Alaa, and van~der Schaar]{stankeviciute2021conformal}
Stankeviciute, K., M~Alaa, A., and van~der Schaar, M.
\newblock Conformal time-series forecasting.
\newblock \emph{Advances in neural information processing systems}, 34:\penalty0 6216--6228, 2021.

\bibitem[Stutz et~al.(2022)Stutz, Krishnamurthy, Dvijotham, Cemgil, and Doucet]{stutz2022learningoptimalconformalclassifiers}
Stutz, D., Krishnamurthy, Dvijotham, Cemgil, A.~T., and Doucet, A.
\newblock Learning optimal conformal classifiers, 2022.
\newblock URL \url{https://arxiv.org/abs/2110.09192}.

\bibitem[Sullivan(2015)]{sullivan2015introduction}
Sullivan, T.~J.
\newblock \emph{Introduction to uncertainty quantification}, volume~63.
\newblock Springer, 2015.

\bibitem[Teng et~al.()Teng, Wen, Zhang, Bengio, Gao, and Yuan]{tengpredictive}
Teng, J., Wen, C., Zhang, D., Bengio, Y., Gao, Y., and Yuan, Y.
\newblock Predictive inference with feature conformal prediction.
\newblock In \emph{The Eleventh International Conference on Learning Representations}.

\bibitem[Teng et~al.(2021)Teng, Tan, and Yuan]{teng2021t}
Teng, J., Tan, Z., and Yuan, Y.
\newblock T-sci: A two-stage conformal inference algorithm with guaranteed coverage for cox-mlp.
\newblock In \emph{International conference on machine learning}, pp.\  10203--10213. PMLR, 2021.

\bibitem[Tibshirani et~al.(2019)Tibshirani, Foygel~Barber, Candes, and Ramdas]{tibshirani2019conformal}
Tibshirani, R.~J., Foygel~Barber, R., Candes, E., and Ramdas, A.
\newblock Conformal prediction under covariate shift.
\newblock \emph{Advances in neural information processing systems}, 32, 2019.

\bibitem[Vovk(2012)]{pmlr-v25-vovk12}
Vovk, V.
\newblock Conditional validity of inductive conformal predictors.
\newblock In Hoi, S. C.~H. and Buntine, W. (eds.), \emph{Proceedings of the Asian Conference on Machine Learning}, volume~25 of \emph{Proceedings of Machine Learning Research}, pp.\  475--490, Singapore Management University, Singapore, 04--06 Nov 2012. PMLR.
\newblock URL \url{https://proceedings.mlr.press/v25/vovk12.html}.

\bibitem[Vovk et~al.(2005)Vovk, Gammerman, and Shafer]{vovk2005algorithmic}
Vovk, V., Gammerman, A., and Shafer, G.
\newblock \emph{Algorithmic learning in a random world}, volume~29.
\newblock Springer, 2005.

\bibitem[Vovk et~al.(2016)Vovk, Fedorova, Nouretdinov, and Gammerman]{10.1007/978-3-319-33395-3_2}
Vovk, V., Fedorova, V., Nouretdinov, I., and Gammerman, A.
\newblock Criteria of efficiency for conformal prediction.
\newblock In Gammerman, A., Luo, Z., Vega, J., and Vovk, V. (eds.), \emph{Conformal and Probabilistic Prediction with Applications}, pp.\  23--39, Cham, 2016. Springer International Publishing.
\newblock ISBN 978-3-319-33395-3.

\bibitem[Wang \& Ghosal(2023)Wang and Ghosal]{wang2023coverage}
Wang, K. and Ghosal, S.
\newblock Coverage of credible intervals in bayesian multivariate isotonic regression.
\newblock \emph{The Annals of Statistics}, 51\penalty0 (3):\penalty0 1376--1400, 2023.

\bibitem[Wang \& Blei(2020)Wang and Blei]{wang2020variationalbayesmodelmisspecification}
Wang, Y. and Blei, D.~M.
\newblock Variational bayes under model misspecification, 2020.
\newblock URL \url{https://arxiv.org/abs/1905.10859}.

\bibitem[Xie et~al.(2024)Xie, Barber, and Cand{\`e}s]{xie2024boosted}
Xie, R., Barber, R.~F., and Cand{\`e}s, E.~J.
\newblock Boosted conformal prediction intervals.
\newblock \emph{Advances in Neural Information Processing Systems}, 37:\penalty0 71868--71899, 2024.

\bibitem[Xu \& Xie(2021)Xu and Xie]{xu2021conformal}
Xu, C. and Xie, Y.
\newblock Conformal prediction interval for dynamic time-series.
\newblock In \emph{International Conference on Machine Learning}, pp.\  11559--11569. PMLR, 2021.

\bibitem[Xu et~al.(2025)Xu, Ying, Guo, and Wei]{xu2025two}
Xu, Y., Ying, M., Guo, W., and Wei, Z.
\newblock Two-stage risk control with application to ranked retrieval.
\newblock In \emph{Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence}, pp.\  9104--9111, 2025.

\bibitem[Yeh(1998)]{concrete_compressive_strength_165}
Yeh, I.-C.
\newblock {Concrete Compressive Strength}.
\newblock UCI Machine Learning Repository, 1998.
\newblock {DOI}: https://doi.org/10.24432/C5PK67.

\bibitem[Zargarbashi et~al.(2023)Zargarbashi, Antonelli, and Bojchevski]{zargarbashi2023conformal}
Zargarbashi, S.~H., Antonelli, S., and Bojchevski, A.
\newblock Conformal prediction sets for graph neural networks.
\newblock In \emph{International Conference on Machine Learning}, pp.\  12292--12318. PMLR, 2023.

\bibitem[Zhang et~al.(2024)Zhang, Chatzimparmpas, Kamali, and Hullman]{zhang2024evaluating}
Zhang, D., Chatzimparmpas, A., Kamali, N., and Hullman, J.
\newblock Evaluating the utility of conformal prediction sets for ai-advised image labeling.
\newblock In \emph{Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems}, pp.\  1--19, 2024.

\bibitem[Zhao et~al.(2020)Zhao, Ma, and Ermon]{zhao2020individual}
Zhao, S., Ma, T., and Ermon, S.
\newblock Individual calibration with randomized forecasting.
\newblock In \emph{International Conference on Machine Learning}, pp.\  11387--11397. PMLR, 2020.

\end{thebibliography}