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This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data.
Neural networks for density estimation
M. Magdon-Ismail and A. Atiya · 1998
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A quantile regression neural network approach to estimating the conditional density of multiperiod returns
J. W. Taylor · 2000
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Algorithmic learning in a random world
V. Vovk, A. Gammerman, and G. Shafer · 2005
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Quantile regression forests
N. Meinshausen · 2006
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Power-law distributions in empirical data
A. Clauset, C. R. Shalizi, and M. E. Newman · 2009
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The medical expenditure panel survey: a national information resource to support healthcare cost research and inform policy and practice
J. W. Cohen, S. B. Cohen, and J. S. Banthin · 2009
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BART: Bayesian additive regression trees
H. A. Chipman, E. I. George, R. E. McCulloch, et al · 2010
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Conditional validity of inductive conformal predictors
V. Vovk · 2012
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Distribution-free prediction bands for non-parametric regression
J. Lei and L. Wasserman · 2014
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Nonparametric conditional density estimation in a high-dimensional regression setting
R. Izbicki and A. B. Lee · 2016
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Distribution-free predictive inference for regression
J. Lei, M. G’Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman · 2018
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https://archive.ics.uci.edu/ml/datasets/BlogFeedback
BlogFeedback data set · 2019
Cited alongside, same era.
https://archive.ics.uci.edu/ml/datasets/Facebook+Comment+Volume+Dataset
Facebook comment volume data set · 2019
Cited alongside, same era.
https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-181
Medical expenditure panel survey, panel 19 · 2019
Cited alongside, same era.
https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-181
Medical expenditure panel survey, panel 20 · 2019
Cited alongside, same era.
https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-192
Medical expenditure panel survey, panel 21 · 2019
Cited alongside, same era.
https://archive.ics.uci.edu/ml/datasets/Physicochemical+Properties+of+Protein+Tertiary+Structure
Making learning more transparent using conformalized performance prediction
M. J. Holland · 2020
Later among the works it cites.
CD-split: efficient conformal regions in high dimensions
R. Izbicki, G. Shimizu, and R. B. Stern · 2020
Later among the works it cites.
Predictive inference is free with the jackknife+-after-bootstrap
B. Kim, C. Xu, and R. F. Barber · 2020
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Adaptive, distribution-free prediction intervals for deep networks
D. Kivaranovic, K. D. Johnson, and H. Leeb · 2020
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With malice toward none: Assessing uncertainty via equalized coverage
Y. Romano, R. F. Barber, C. Sabatti, and E. Candès · 2020
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Physicochemical properties of protein tertiary structure data set · 2019
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Distributional conformal prediction
V. Chernozhukov, K. Wüthrich, and Y. Zhu · 2019
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The limits of distribution-free conditional predictive inference
R. Foygel Barber, E. Candès, A. Ramdas, and R. J. Tibshirani · 2019
Cited alongside, same era.
Nested conformal prediction and quantile out-of-bag ensemble methods
C. Gupta, A. K. Kuchibhotla, and A. K. Ramdas · 2019
Cited alongside, same era.
Flexible distribution-free conditional predictive bands using density estimators
R. Izbicki, G. T. Shimizu, and R. B. Stern · 2019
Cited alongside, same era.
Conformalized quantile regression
Y. Romano, E. Patterson, and E. Candès · 2019
Cited alongside, same era.
Conditional density estimation tools in python and r with applications to photometric redshifts and likelihood-free cosmological inference
N. Dalmasso, T. Pospisil, A. B. Lee, R. Izbicki, P. E. Freeman, and A. I. Malz · 2020
Cited alongside, same era.
Classification with valid and adaptive coverage
Y. Romano, M. Sesia, and E. Candès · 2020
Later among the works it cites.
A comparison of some conformal quantile regression methods
M. Sesia and E. Candès · 2020
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Predictive inference with the jackknife+
R. F. Barber, E. Candès, A. Ramdas, and R. J. Tibshirani · 2021
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Testing for outliers with conformal p-values
S. Bates, E. Candès, L. Lei, Y. Romano, and M. Sesia · 2021
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Knowing what you know: valid and validated confidence sets in multiclass and multilabel prediction
M. Cauchois, S. Gupta, and J. C. Duchi · 2021
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Learning multiple quantiles with neural networks
S. J. Moon, J.-J. Jeon, J. S. H. Lee, and Y. Kim · 2021
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Finite-sample efficient conformal prediction
Y. Yang and A. K. Kuchibhotla · 2021
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