El-Yaniv, R. and Wiener, Y. (2010). On the foundations of noise-free selective classification. Journal of Machine Learning Research, 11(5).
Fisher, A. and Kennedy, E. H. (2021). Visually communicating and teaching intuition for influence functions. The American Statistician, 75(2):162-172.
Gangrade, A., Kag, A., and Saligrama, V. (2021). Selective classification via one-sided prediction. In International Conference on Artificial Intelligence and Statistics, pages 2179-2187.
Geifman, Y. and El-Yaniv, R. (2017). Selective classification for deep neural networks. Advances in Neural Information Processing Systems, 30.
Geifman, Y. and El-Yaniv, R. (2019). SelectiveNet: A deep neural network with an integrated reject option. In Proceedings of the 36th International Conference on Machine Learning, volume 97, pages 2151-2159. PMLR.
Grgic-Hlača, N., Zafar, M. B., Gummadi, K. P., and Weller, A. (2017). On fairness, diversity and randomness in algorithmic decision making. arXiv preprint arXiv:1706.10208.
Hendrickx, K., Perini, L., Van der Plas, D., Meert, W., and Davis, J. (2021). Machine learning with a reject option: A survey. arXiv preprint arXiv:2107.11277.
Horvitz, D. G. and Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47(260):663-685.
Ju, C., Schwab, J., and van der Laan, M. J. (2019). On adaptive propensity score truncation in causal inference. Statistical Methods in Medical Research, 28(6):1741-1760.
Kalai, A. and Kanade, V. (2021). Towards optimally abstaining from prediction with ood test examples. Advances in Neural Information Processing Systems, 34:12774-12785.
Kennedy, E. H. (2019). Nonparametric causal effects based on incremental propensity score interventions. Journal of the American Statistical Association, 114(526):645-656.
Kennedy, E. H. (2022). Semiparametric doubly robust targeted double machine learning: A review. arXiv preprint arXiv:2203.06469.
Krizhevsky, A. (2009). Learning multiple layers of features from tiny images. Technical Report, University of Toronto.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436-444.
Léger, M., Chatton, A., Le Borgne, F., Pirracchio, R., Lasocki, S., and Foucher, Y. (2022). Causal inference in case of near-violation of positivity: Comparison of methods. Biometrical Journal, 64(8):1389-1403.
Lei, L., D'Amour, A., Ding, P., Feller, A., and Sekhon, J. (2021). Distribution-free assessment of population overlap in observational studies. Technical report, Stanford University.
Little, R. J. and Rubin, D. B. (2019). Statistical analysis with missing data, volume 793. John Wiley & Sons.
Madras, D., Pitassi, T., and Zemel, R. (2018). Predict responsibly: Improving fairness and accuracy by learning to defer. Advances in Neural Information Processing Systems, 31.
Pearl, J. (2000). Models, reasoning and inference. Cambridge University Press, 19(2).
Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., and Van Der Laan, M. J. (2012). Diagnosing and responding to violations in the positivity assumption. Statistical Methods in Medical Research, 21(1):31-54.
Pietraszek, T. (2005). Optimizing abstaining classifiers using ROC analysis. In Proceedings of the 22nd International Conference on Machine Learning, pages 665-672.