27 Referencias
Angelopoulos, A. N., & Bates, S. (2023). Conformal prediction: A
gentle introduction. Foundations and Trends in Machine
Learning. https://arxiv.org/abs/2107.07511
Angelopoulos, A. N., Bates, S., Candès, E. J., Jordan, M. I., & Lei,
L. (2025). Learn then test: Calibrating predictive algorithms to achieve
risk control. The Annals of Applied Statistics. https://doi.org/10.1214/24-AOAS1998
Angelopoulos, A. N., Bates, S., Fisch, A., Lei, L., & Schuster, T.
(2024). Conformal risk control. ICLR 2024. https://openreview.net/forum?id=33XGfHLtZg
Athey, S., & Wager, S. (2019). Estimating treatment effects with
causal forests: An application. Annals of Statistics. https://doi.org/10.1214/18-AOS1709
Ayari, N., Guetari, R., & Kraiem, N. (2026). ML powered
financial credit scoring: Systematic review. AI Review
(Springer).
Bai, Y., Mei, S., Wang, H., & Xiong, C. (2021). Don’t just blame
over-parametrization for over-confidence: Theoretical analysis of
calibration in binary classification. Proceedings of the 38th
International Conference on Machine Learning (ICML), 566–576.
Bairaktari, A. et al. (2025). Kandinsky
conformal prediction: Beyond class- and covariate-conditional coverage.
ICML 2025 (PMLR). https://proceedings.mlr.press/v267/bairaktari25a.html
Bao, Y., Hu, Y., Ren, H., Zhao, P., & Zou, C. (2025). Optimal
model selection for conformalized robust optimization
(CROMS). https://arxiv.org/abs/2507.04716
Bárcena Saavedra, M. J. et al. (2024). PD
for lifetime credit loss for IFRS 9 using ML
competing risks. Expert Systems with Applications.
Bates, S., Angelopoulos, A. N., Lei, L., Malik, J., & Jordan, M. I.
(2021). Distribution-free, risk-controlling prediction sets. arXiv
Preprint. https://arxiv.org/abs/2101.02703
Bellini, T. et al. (2024). Practical
credit risk and capital modeling. Springer.
Bertsimas, D., & Sim, M. (2004). The price of robustness.
Operations Research. https://doi.org/10.1287/opre.1030.0065
Board of Governors of the Federal Reserve System, & Office of the
Comptroller of the Currency. (2011). Supervisory guidance on model
risk management (SR 11-7 / OCC 2011-12). Federal Reserve. https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm
Boström, H. et al. (2021). Mondrian
conformal predictive distributions. COPA (PMLR V152). https://proceedings.mlr.press/v152/bostrom21a/bostrom21a.pdf
Botha, M., & Verster, T. (2026). Approaches for modelling
term-structure of default risk under IFRS 9.
International Journal of Data Science and Analytics. https://arxiv.org/abs/2501.04975
Capitaine, A., Haddouche, M., Moulines, E., Jordan, M. I., Boursier, E.,
& Durmus, A. (2026). Online decision-focused learning. ICLR
2026. https://doi.org/10.48550/arXiv.2505.13564
Causal inference for banking, finance, and insurance: A survey.
(2023). https://arxiv.org/abs/2307.16427
Chan, T. C. Y., Delage, E., & Lin, B. (2024). Conformal inverse
optimization. NeurIPS 2024. https://arxiv.org/abs/2402.01489
Chenreddy, A. et al. (2024). End-to-end
conditional robust optimization. UAI (PMLR V244). https://arxiv.org/abs/2403.04670
Chernozhukov, V. et al. (2018).
Double/debiased machine learning for treatment and structural
parameters. The Econometrics Journal. https://doi.org/10.1111/ectj.12097
Christoffersen, P. F. (1998). Evaluating interval forecasts.
International Economic Review, 39(4), 841–862. https://doi.org/10.2307/2527341
Conformal prediction: A data perspective. (2025). ACM Computing
Surveys. https://doi.org/10.1145/3736575
Defining and comparing SICR-events under IFRS
9. (2025). Annals of Operations Research.
Ding, T. et al. (2023). Class-conditional
conformal prediction with many classes. NeurIPS. https://arxiv.org/abs/2306.09335
Donti, P. L., Amos, B., & Kolter, J. Z. (2017). Task-based
end-to-end model learning in stochastic optimization. NeurIPS.
https://arxiv.org/abs/1703.04529
Efron, B., & Tibshirani, R. J. (1994). An introduction to the
bootstrap. Chapman; Hall/CRC.
Elmachtoub, A. N., & Grigas, P. (2022). Smart “predict, then
optimize.” Management Science, 68(1). https://doi.org/10.1287/mnsc.2020.3922
European Central Bank. (2024). IFRS 9 overlays and
model improvements for novel risks. ECB Banking Supervision.
Fantazzini, D. (2024). Adaptive conformal inference for computing market
risk measures. Journal of Risk and Financial Management.
Gibbs, I., & Candès, E. (2021). Adaptive conformal inference under
distribution shift. NeurIPS. https://arxiv.org/abs/2106.00170
Gibbs, I., & Cherian, J. J. (2024). Conformal prediction with
conditional guarantees. Journal of the Royal Statistical Society:
Series B. https://arxiv.org/abs/2305.12616
Gopalan, P., Luss, R., Sharan, V., & Urner, R. (2022).
Multicalibrated partitions for importance weights. arXiv Preprint
arXiv:2103.05853.
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On
calibration of modern neural networks. Proceedings of the 34th
International Conference on Machine Learning (ICML), 1321–1330.
Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity
in supervised learning. Advances in Neural Information Processing
Systems, 29.
Huangfu, Q., & Hall, J. A. J. (2018). Parallelizing the dual revised
simplex method. Mathematical Programming Computation,
10(1), 119–142. https://doi.org/10.1007/s12532-017-0130-5
IFRS Board. (2024). SICR feedback analysis. IFRS
Foundation.
Iutzeler, F., & Mazoyer, C. (2025). Risk-controlling prediction with
distributionally robust optimization. Transactions on Machine
Learning Research.
Johnstone, C., & Cox, B. (2021). Conformal uncertainty sets for
robust optimization. COPA (PMLR V152). https://proceedings.mlr.press/v152/johnstone21a.html
Kato, M. (2025). Conformal predictive portfolio selection. https://arxiv.org/abs/2410.16333
Kiyani, S. et al. (2025). Decision
theoretic foundations for conformal prediction. https://arxiv.org/abs/2502.02561
Kull, M., Silva Filho, T., & Flach, P. (2017). Beta calibration: A
well-founded and easily implemented improvement on logistic calibration
for binary classifiers. Proceedings of the 20th International
Conference on Artificial Intelligence and Statistics (AISTATS),
623–631.
Kumar, A., Liang, P., & Ma, T. (2019). Verified uncertainty
calibration. Advances in Neural Information Processing Systems
(NeurIPS), 32.
Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk
measurement models. The Journal of Derivatives, 3(2),
73–84. https://doi.org/10.3905/jod.1995.407942
Lessmann, S. et al. (2015). Benchmarking
state-of-the-art classification algorithms for credit scoring.
European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2015.05.030
Liu, T., Dobriban, E., & Orabona, F. (2026). Online conformal
prediction via universal portfolio algorithms. https://arxiv.org/abs/2602.03168
Lopez-Paz, D., & Oquab, M. (2018). Revisiting classifier two-sample
tests. arXiv Preprint arXiv:1610.06545. https://arxiv.org/abs/1610.06545
Mandi, J. et al. (2024). Decision-focused
learning: Foundations, state of the art, benchmark and future
opportunities. Journal of Artificial Intelligence Research. https://arxiv.org/abs/2307.13565
Manokhin, V. (2024). Awesome conformal prediction: A curated list of
resources. arXiv Preprint arXiv:2407.16613.
Murphy, A. H. (1973). A new vector partition of the probability score.
Journal of Applied Meteorology, 12(4), 595–600.
Noguer i Alonso, M. (2024a). Conformal portfolio optimization.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5011129
Noguer i Alonso, M. (2024b). Conformal prediction in finance.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4939336
Papadopoulos, H., Proedrou, K., Vovk, V., & Gammerman, A. (2002).
Inductive confidence machines for regression. In Machine learning:
ECML 2002 (Vol. 2430, pp. 345–356). Springer. https://doi.org/10.1007/3-540-36755-1_29
Patel, N., Rayan, O., & Tewari, A. (2024). Conformal contextual
robust optimization. AISTATS (PMLR V238). https://arxiv.org/abs/2310.10003
Plassier, V. et al. (2024).
Probabilistic conformal prediction with approximate conditional
validity. https://arxiv.org/abs/2407.00107
Platt, J. C. (1999). Probabilistic outputs for support vector machines.
In Advances in large margin classifiers. MIT Press.
Powell, W. B. (2026). Sequential decision analytics and modeling:
Modeling with Python (2nd ed.). Princeton University.
Romano, Y., Patterson, E., & Candès, E. J. (2019). Conformalized
quantile regression. Advances in Neural Information Processing
Systems (NeurIPS). https://arxiv.org/abs/1905.03222
Sun, C., Liu, L., & Li, X. (2024). Predict-then-calibrate: A new
perspective of robust contextual LP. arXiv Preprint
arXiv:2305.15686. https://arxiv.org/abs/2305.15686
Taquet, V. et al. (2025).
MAPIE: An open-source library for distribution-free
uncertainty quantification. arXiv Preprint.
Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic
learning in a random world. Springer. https://doi.org/10.1007/978-3-031-06649-8
Vovk, V., & Petej, I. (2014). Venn-Abers predictors.
UAI. https://arxiv.org/abs/1211.0025
Winkler, R. L. (1972). A decision-theoretic approach to interval
estimation. Journal of the American Statistical Association,
67(337), 187–191. https://doi.org/10.1080/01621459.1972.10481224
Xia, Y., Liu, C., Li, Y., & Liu, N. (2017). A boosted decision tree
approach using Bayesian hyper-parameter optimization for
credit scoring. Expert Systems with Applications, 78,
225–241.
Yang, Y., & Jin, Y. (2026). Multi-distribution robust conformal
prediction. https://doi.org/10.48550/arXiv.2601.02998
Yeh, C., Christianson, N., Wierman, A., & Yue, Y. (2025). Conformal
risk training: End-to-end optimization of conformal risk control.
NeurIPS 2025. https://arxiv.org/abs/2510.08748
Yeh, C., Christianson, N., Wu, A., Wierman, A., & Yue, Y. (2025).
End-to-end conformal calibration for optimization under uncertainty.
Transactions on Machine Learning Research. https://arxiv.org/abs/2409.20534
Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores
into accurate multiclass probability estimates. KDD. https://doi.org/10.1145/775047.775151
Zhao, L., Jiang, H., & Qi, W. (2026). Conformal robust
optimization and satisficing for prescriptive analytics with black-box
predictors. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5338354
Zhou, Y., & Sesia, M. (2024). Conformal classification with
equalized coverage for adaptively selected groups. NeurIPS
2024. https://arxiv.org/abs/2405.15106