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The field of distribution-free predictive inference provides tools for provably valid prediction without any assumptions on the distribution of the data, which can be paired with any regression algorithm to provide accurate and reliable predictive intervals.
Determination of sample sizes for setting tolerance limits
Samuel S Wilks · 1941
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An extension of wilks’ method for setting tolerance limits
Abraham Wald · 1943
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Random variables with maximum sums
Ludger Rüschendorf · 1982
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Algorithmic learning in a random world
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
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Concrete functional calculus
Richard M Dudley and Rimas Norvaiša · 2011
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Distribution-free prediction bands for non-parametric regression
Jing Lei and Larry Wasserman · 2014
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Bias reduction through conditional conformal prediction
Tuve Löfström, Henrik Boström, Henrik Linusson, and Ulf Johansson · 2015
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Cross-conformal predictors
Vladimir Vovk · 2015
Cited alongside, same era.
Discretized conformal prediction for efficient distribution-free inference
Wenyu Chen, Kelli-Jean Chun, and Rina Foygel Barber · 2018
Cited alongside, same era.
Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2018
Cited alongside, same era.
Conditional predictive inference for high-dimensional stable algorithms
Lukas Steinberger and Hannes Leeb · 2018
Cited alongside, same era.
Cross-conformal predictive distributions
Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, and Alexander Gammerman · 2018
Cited alongside, same era.
Fast exact conformalization of the lasso using piecewise linear homotopy
Jing Lei · 2019
Combining p-values via averaging
Vladimir Vovk and Ruodu Wang · 2020
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Distribution-free, risk-controlling prediction sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael Jordan · 2021
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PAC prediction sets under covariate shift
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani · 2021
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Finite-sample efficient conformal prediction
Yachong Yang and Arun Kumar Kuchibhotla · 2021
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Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2022
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Cited alongside, same era.
Adaptive, distribution-free prediction intervals for deep networks
Danijel Kivaranovic, Kory D Johnson, and Hannes Leeb · 2020
Cited alongside, same era.
Pac confidence predictions for deep neural network classifiers
Sangdon Park, Shuo Li, Insup Lee, and Osbert Bastani · 2020
Cited alongside, same era.
The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J Candès, Aaditya Ramdas, and Ryan J Tibshirani
Cited in the paper.
Predictive inference with the jackknife+
Rina Foygel Barber, Emmanuel J Candès, Aaditya Ramdas, and Ryan J Tibshirani
Cited in the paper.
Hongxiang Qiu, Edgar Dobriban, and Eric Tchetgen Tchetgen · 2022
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Doubly robust calibration of prediction sets under covariate shift
Yachong Yang, Arun Kumar Kuchibhotla, and Eric Tchetgen Tchetgen · 2022
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