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Conformal prediction is a popular tool for providing valid prediction sets for classification and regression problems, without relying on any distributional assumptions on the data.
Distributional conformal prediction
Chernozhukov, V., Wüthrich, K., and Zhu, Y. (2019) · 1909
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Analyzing bagging
Bühlmann, P. and Yu, B. (2002) · 2002
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Inductive confidence machines for regression
Papadopoulos, H., Proedrou, K., Vovk, V., and Gammerman, A. (2002) · 2002
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Algorithmic learning in a random world
Vovk, V., Gammerman, A., and Shafer, G. (2005) · 2005
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Quantile regression forests
Meinshausen, N. (2006) · 2006
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Distribution-free prediction sets
Lei, J., Robins, J., and Wasserman, L. (2013) · 2013
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Conformal prediction for reliable machine learning: theory, adaptations and applications
Balasubramanian, V., Ho, S.-S., and Vovk, V. (2014) · 2014
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Aggregated conformal prediction
Carlsson, L., Eklund, M., and Norinder, U. (2014) · 2014
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Regression conformal prediction with random forests
Johansson, U., Boström, H., Löfström, T., and Linusson, H. (2014) · 2014
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Xsede: accelerating scientific discovery
Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., Hazlewood, V., Lathrop, S., Lifka, D., Peterson, G. D., et al. (2014) · 2014
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Bridges: A uniquely flexible hpc resource for new communities and data analytics
Nystrom, N. A., Levine, M. J., Roskies, R. Z., and Scott, J. R. (2015) · 2015
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Cross-conformal predictors
Vovk, V. (2015) · 2015
Cited alongside, same era.
Accelerating difficulty estimation for conformal regression forests
Boström, H., Linusson, H., Löfström, T., and Johansson, U. (2017) · 2017
Cited alongside, same era.
On the calibration of aggregated conformal predictors
Linusson, H., Norinder, U., Boström, H., Johansson, U., and Löfström, T. (2017) · 2017
Efficient conformal predictor ensembles
Linusson, H., Johansson, U., and Boström, H. (2019) · 2019
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Conformalized quantile regression
Romano, Y., Patterson, E., and Candes, E. (2019) · 2019
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The limits of distribution-free conditional predictive inference
Barber, F. R., Candès, E. J., Ramdas, A., and Tibshirani, R. J. (2020) · 2020
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Flexible distribution-free conditional predictive bands using density estimators
Izbicki, R., Shimizu, G., and Stern, R. (2020) · 2020
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Predictive inference is free with the jackknife+-after-bootstrap
Kim, B., Xu, C., and Barber, R. (2020) · 2020
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Adaptive, distribution-free prediction intervals for deep networks
Kivaranovic, D., Johnson, K. D., and Leeb, H. (2020) · 2020
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Distribution-free predictive inference for regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018) · 2018
Cited alongside, same era.
Combining p-values via averaging
Vovk, V. and Wang, R. (2018) · 2018
Cited alongside, same era.
A comparison of some conformal quantile regression methods
Sesia, M. and Candès, E. J. (2020) · 2020
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Predictive inference with the jackknife+
Barber, R. F., Candes, E. J., Ramdas, A., Tibshirani, R. J., et al. (2021) · 2021
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