@book{vovk2005algorithmic,
  title={Algorithmic Learning in a Random World},
  author={Vovk, Vladimir and Gammerman, Alexander and Shafer, Glenn},
  year={2005},
  publisher={Springer},
  address={New York}
}

@article{vovk2012conditional,
  title={Conditional validity of inductive conformal predictors},
  author={Vovk, Vladimir},
  journal={Proceedings of the Asian Conference on Machine Learning (ACML), PMLR},
  volume={25},
  pages={475--490},
  year={2012}
}

@article{vovk2019nonparametric,
  title={Nonparametric predictive distributions based on conformal prediction},
  author={Vovk, Vladimir and Shen, Jieli and Manokhin, Valery and Xie, Min-ge},
  journal={Machine Learning},
  volume={108},
  number={3},
  pages={445--474},
  year={2019},
  publisher={Springer}
}

@article{lei2014distribution,
  title={Distribution-free prediction bands for non-parametric regression},
  author={Lei, Jing and Wasserman, Larry},
  journal={Journal of the Royal Statistical Society: Series B},
  volume={76},
  number={1},
  pages={71--96},
  year={2014},
  publisher={Wiley}
}

@article{lei2018distribution,
  title={Distribution-free predictive inference for regression},
  author={Lei, Jing and G'Sell, Max and Rinaldo, Alessandro and Tibshirani, Ryan J. and Wasserman, Larry},
  journal={Journal of the American Statistical Association},
  volume={113},
  number={523},
  pages={1094--1111},
  year={2018},
  publisher={Taylor \& Francis}
}

@article{barber2021limits,
  title={The limits of distribution-free conditional predictive inference},
  author={Foygel Barber, Rina and Cand{\`e}s, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
  journal={Information and Inference: A Journal of the IMA},
  volume={10},
  number={2},
  pages={455--482},
  year={2021},
  publisher={Oxford University Press}
}

@article{barber2023conformal,
  title={Conformal prediction beyond exchangeability},
  author={Barber, Rina Foygel and Cand{\`e}s, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
  journal={The Annals of Statistics},
  volume={51},
  number={2},
  pages={816--845},
  year={2023}
}

@inproceedings{tibshirani2019conformal,
  title={Conformal prediction under covariate shift},
  author={Tibshirani, Ryan J. and Foygel Barber, Rina and Cand{\`e}s, Emmanuel J. and Ramdas, Aaditya},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={32},
  year={2019}
}

@inproceedings{romano2019conformalized,
  title={Conformalized quantile regression},
  author={Romano, Yaniv and Patterson, Evan and Cand{\`e}s, Emmanuel J.},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={32},
  year={2019}
}

@inproceedings{gibbs2021adaptive,
  title={Adaptive conformal inference under distribution shift},
  author={Gibbs, Isaac and Cand{\`e}s, Emmanuel J.},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={34},
  pages={1660--1672},
  year={2021}
}

@article{gibbs2024conformal,
  title={Conformal inference for online prediction with arbitrary distribution shifts},
  author={Gibbs, Isaac and Cand{\`e}s, Emmanuel J.},
  journal={Journal of Machine Learning Research},
  volume={25},
  number={162},
  pages={1--36},
  year={2024}
}

@inproceedings{xu2021conformal,
  title={Conformal prediction interval for dynamic time-series},
  author={Xu, Chen and Xie, Yao},
  booktitle={International Conference on Machine Learning (ICML), PMLR},
  volume={139},
  pages={11559--11569},
  year={2021}
}

@inproceedings{zaffran2022adaptive,
  title={Adaptive conformal predictions for time series},
  author={Zaffran, Margaux and F{\'e}ron, Olivier and Goude, Yannig and Josse, Julie and Dieuleveut, Aymeric},
  booktitle={International Conference on Machine Learning (ICML), PMLR},
  volume={162},
  pages={25834--25866},
  year={2022}
}

@article{gneiting2007probabilistic,
  title={Probabilistic forecasts, calibration and sharpness},
  author={Gneiting, Tilmann and Balabdaoui, Fadoua and Raftery, Adrian E.},
  journal={Journal of the Royal Statistical Society: Series B},
  volume={69},
  number={2},
  pages={243--268},
  year={2007},
  publisher={Wiley}
}

@article{gneiting2007strictly,
  title={Strictly proper scoring rules, prediction, and estimation},
  author={Gneiting, Tilmann and Raftery, Adrian E.},
  journal={Journal of the American Statistical Association},
  volume={102},
  number={477},
  pages={359--378},
  year={2007},
  publisher={Taylor \& Francis}
}

@inproceedings{platt1999probabilistic,
  title={Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods},
  author={Platt, John},
  booktitle={Advances in Large Margin Classifiers},
  pages={61--74},
  year={1999},
  publisher={MIT Press}
}

@inproceedings{zadrozny2002transforming,
  title={Transforming classifier scores into accurate multiclass probability estimates},
  author={Zadrozny, Bianca and Elkan, Charles},
  booktitle={Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={694--699},
  year={2002}
}

@inproceedings{guo2017calibration,
  title={On calibration of modern neural networks},
  author={Guo, Chuan and Pleiss, Geoff and Sun, Yu and Weinberger, Kilian Q.},
  booktitle={International Conference on Machine Learning (ICML), PMLR},
  volume={70},
  pages={1321--1330},
  year={2017}
}

@inproceedings{kuleshov2018accurate,
  title={Accurate uncertainties for deep learning using calibrated regression},
  author={Kuleshov, Volodymyr and Fenner, Nathan and Ermon, Stefano},
  booktitle={International Conference on Machine Learning (ICML), PMLR},
  volume={80},
  pages={2796--2804},
  year={2018}
}

@inproceedings{angelopoulos2021uncertainty,
  title={Uncertainty sets for image classifiers using conformal prediction},
  author={Angelopoulos, Anastasios N. and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2021}
}

@article{angelopoulos2023gentle,
  title={Conformal prediction: A gentle introduction},
  author={Angelopoulos, Anastasios N. and Bates, Stephen},
  journal={Foundations and Trends in Machine Learning},
  volume={16},
  number={4},
  pages={494--591},
  year={2023}
}

@article{angelopoulos2025theoretical,
  title={Theoretical foundations of conformal prediction},
  author={Angelopoulos, Anastasios N. and Barber, Rina Foygel and Bates, Stephen},
  journal={arXiv preprint arXiv:2411.11824},
  year={2024}
}

@inproceedings{angelopoulos2024conformalrisk,
  title={Conformal risk control},
  author={Angelopoulos, Anastasios N. and Bates, Stephen and Fisch, Adam and Lei, Lihua and Schuster, Tal},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2024}
}

@article{bates2021distribution,
  title={Distribution-free, risk-controlling prediction sets},
  author={Bates, Stephen and Angelopoulos, Anastasios and Lei, Lihua and Malik, Jitendra and Jordan, Michael I.},
  journal={Journal of the ACM},
  volume={68},
  number={6},
  pages={1--34},
  year={2021}
}

@article{hanson2007logarithmic,
  title={Logarithmic market scoring rules for modular combinatorial information aggregation},
  author={Hanson, Robin},
  journal={The Journal of Prediction Markets},
  volume={1},
  number={1},
  pages={3--15},
  year={2007}
}

@article{dawid1984present,
  title={Present position and potential developments: Some personal views: Statistical theory: The prequential approach},
  author={Dawid, A. Philip},
  journal={Journal of the Royal Statistical Society: Series A},
  volume={147},
  number={2},
  pages={278--292},
  year={1984},
  publisher={Wiley}
}

@inproceedings{correia2024information,
  title={An Information Theoretic Perspective on Conformal Prediction},
  author={Correia, Alvaro H.C. and Massoli, Fabio Valerio and Louizos, Christos and Behboodi, Arash},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2024},
  note={arXiv:2405.02140}
}

@inproceedings{stutz2022learning,
  title={Learning Optimal Conformal Classifiers},
  author={Stutz, David and Dvijotham, Krishnamurthy and Cemgil, Ali Taylan and Doucet, Arnaud},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2022},
  note={arXiv:2110.09192}
}

@article{toccaceli2026crps,
  title={{CRPS}-Optimal Binning for Univariate Conformal Regression},
  author={Toccaceli, Paolo},
  journal={arXiv preprint arXiv:2603.22000},
  year={2026}
}

@article{braun2025conditional,
  title={Conditional Coverage Diagnostics for Conformal Prediction},
  author={Braun, Sacha and Holzm{\"u}ller, David and Jordan, Michael I. and Bach, Francis},
  journal={arXiv preprint arXiv:2512.11779},
  year={2025}
}

@article{laplante2026pivotal,
  title={A Post-Processing Conformal Prediction Approach for Conditional Coverage via Pivotal Scores},
  author={Laplante, F{\'e}lix},
  journal={arXiv preprint arXiv:2605.25852},
  year={2026}
}

@inproceedings{auer2023hopcpt,
  title={Conformal Prediction for Time Series with Modern Hopfield Networks},
  author={Auer, Andreas and Gauch, Martin and Klotz, Daniel and Hochreiter, Sepp},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2023},
  note={arXiv:2303.12783}
}

@inproceedings{bostrom2021mondrian,
  title={Mondrian conformal predictive distributions},
  author={Bostr{\"o}m, Henrik and Johansson, Ulf and L{\"o}fstr{\"o}m, Tuwe},
  booktitle={Conformal and Probabilistic Prediction and Applications (COPA), PMLR},
  volume={152},
  pages={24--38},
  year={2021}
}

@misc{bellotti2025trustworthy,
  title={Conformal Prediction and Trustworthy {AI}},
  author={Bellotti, Anthony and Zhao, Xindi},
  year={2025},
  howpublished={arXiv:2508.06885}
}

@article{pitfalls2025medical,
  title={Pitfalls of Conformal Predictions for Medical Image Classification},
  author={Mehrtens, Hendrik and Bucher, Tabea and Brinker, Titus J.},
  journal={arXiv preprint arXiv:2506.18162},
  year={2025}
}

@article{min2026coveragelength,
  title={Questioning the Coverage--Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better},
  author={Min, Yizhou and Lu, Yizhou and Li, Lanqi and Zhang, Zhen and Teng, Jiaye},
  journal={arXiv preprint arXiv:2601.21455},
  year={2026}
}

@misc{tds_allyouneed,
  title={All You Need Is Conformal Prediction},
  howpublished={Towards Data Science},
  note={\url{https://towardsdatascience.com/all-you-need-is-conformal-prediction-726f18920241/}}
}

@misc{tds_adduncertainty,
  title={How to Add Uncertainty Estimation to your Models with Conformal Prediction},
  howpublished={Towards Data Science},
  note={\url{https://towardsdatascience.com/how-to-add-uncertainty-estimation-to-your-models-with-conformal-prediction-a5acdb86ea05/}}
}

@misc{medium_reliable,
  title={Conformal Prediction: The Key to Reliable {AI} Confidence \& Uncertainty Estimation},
  author={Bijalwan, Vishwanath},
  howpublished={Medium},
  note={\url{https://medium.com/@vishwanath.bijalwan/conformal-prediction-the-key-to-reliable-ai-confidence-uncertainty-estimation-065978820c88}}
}

@misc{manokhin_fulldist,
  title={Predicting Full Probability Distributions with Conformal Prediction},
  author={Manokhin, Valeriy},
  howpublished={Medium},
  note={\url{https://valeman.medium.com/predicting-full-probability-distributions-with-conformal-prediction-1dd4c1f26973}}
}

@misc{cotton_timemachines,
  author={Cotton, Peter},
  title={timemachines: continuously evaluated online time-series prediction (skaters)},
  howpublished={\url{https://github.com/microprediction/timemachines}},
  year={2021}
}

@misc{recht2024cover,
  title={Cover songs; and From intervals to bands},
  author={Recht, Benjamin},
  howpublished={\emph{arg min} blog},
  year={2024},
  note={\url{https://www.argmin.net/}}
}

@book{cotton2022microprediction,
  author    = {Cotton, Peter},
  title     = {Microprediction: Building an Open AI Network},
  publisher = {MIT Press},
  year      = {2022}
}

@article{gibbs2025conditional,
  author  = {Gibbs, Isaac and Cherian, John J. and Cand{\`e}s, Emmanuel J.},
  title   = {Conformal Prediction with Conditional Guarantees},
  journal = {Journal of the Royal Statistical Society Series B},
  year    = {2025},
  note    = {arXiv:2305.12616}
}
