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Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d.
The limits of distribution-free conditional predictive inference
Barber, R. F., Candès, E. J., Ramdas, A., and Tibshirani, R. J. (2019) · 1903
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Conformal prediction with localization
Guan, L. (2019) · 1908
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A comparison of some conformal quantile regression methods
Sesia, M. and Candès, E. J. (2019) · 1909
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Applied linear statistical models
Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996) · 1996
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Random forests
Breiman, L. (2001) · 2001
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Algorithmic learning in a random world
Vovk, V. et al. (2005) · 2005
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Quantile regression forests
Meinshausen, N. (2006) · 2006
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Inductive conformal prediction: Theory and application to neural networks
Papadopoulos, H. (2008) · 2008
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Decision theory: Principles and approaches
Parmigiani, G. and Inoue, L. (2009) · 2009
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On-line predictive linear regression
Vovk, V., Nouretdinov, I., Gammerman, A., et al. (2009) · 2009
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Conditional validity of inductive conformal predictors
Vovk, V. (2012) · 2012
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Distribution-free prediction bands for non-parametric regression
Lei, J. and Wasserman, L. (2014) · 2014
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Converting high-dimensional regression to high-dimensional conditional density estimation
Izbicki, R. and Lee, A. B. (2017) · 2017
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Flexible statistical inference for mechanistic models of neural dynamics
Lueckmann, J.-M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H. (2017) · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
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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
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Conformalized quantile regression
Romano, Y., Patterson, E., and Candès, E. J. (2019) · 2019
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Least ambiguous set-valued classifiers with bounded error levels
Sadinle, M., Lei, J., and Wasserman, L. (2019) · 2019
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