Distribution‐free prediction bands for non‐parametric regression

By Jing Lei, Larry A. Wasserman. 2014

482 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/34bda2117ce50132a7c82a70185623048c3159d0

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Citations in the corpus

Conformal Prediction Under Covariate Shift (2019, background): “Moreover, see Lei and Wasserman (2014); Lei et al. (2018) for recent developments in nonparametric and high-dimensional regression.”
Distribution-Free Predictive Inference for Regression (2016, methodology): “Conformal prediction intervals (Vovk,Gammerman, and Shafer 2005; Vovk, Nouretdinov, and Gammerman 2009; Lei, Robins, and Wasserman 2013; Lei and Wasserman 2014) overcome the deficiencies of the naive intervals, and, somewhat remarkably, are guaranteed to deliver proper finite-sample coverage without”
Least Ambiguous Set-Valued Classifiers With Bounded Error Levels (2016, background): “ Finally, we notice 4 that although it would seem appealing to aim at controlling the conditional coverage PfY 2H(X)jX = xg1 , for all x2X, which Vovk (2013) calls \object validity," Lemma 1 of Lei and Wasserman (2014) unfortunately implies that if Xis continuous and Hbhas distribution-free con”
Uncertainty Sets for Image Classifiers using Conformal Prediction (2020, background): “We say that a set-valued predictor C : Distribution-free guarantees on conditional coverage are impossible (Vovk, 2012; Lei & Wasserman, 2014), but many algorithms try to satisfy it approximately (Romano et al., 2019; 2020; Cauchois et al., 2020).”
With Malice Towards None: Assessing Uncertainty via Equalized Coverage (2019, background): “Our contribution is nevertheless a little outside of classical conformal inference as we seek a form of conditional rather than marginal coverage guarantee [25–27].”
Conformal prediction with conditional guarantees (2023, unknown): “In particular, we show how our method can be used to satisfy two popular coverage desiderata: group-conditional coverage and coverage under covariate shift.”
Conditional validity of inductive conformal predictors (2012, background): “The theorem does not prevent the existence of efficient set predictors that are object conditionally valid in an asymptotic sense; indeed, the paper by Lei and Wasserman (2013) is devoted to constructing asymptotically efficient and asymptotically object conditionally valid set predictors in the cas”
Adaptive Conformal Inference Under Distribution Shift (2021, methodology): “Perhaps the most powerful and ﬂexible tools for solving this problem come from conformal inference [see e.g. 34, 16, 32, 22, 31, 15, 3] .”
Conformalized Quantile Regression (2019, background): “Nevertheless, existing methods yield conformal intervals of either fixed length or length depending only weakly on the predictors, as argued in [6, 15, 17].”
Localized Conformal Prediction: A Generalized Inference Framework for Conformal Prediction (2021, methodology): “…+1 , Y n +1 ) as the ( n + 1) th observation, CP achieves (1.1) under the assumption that Z n +1 is also independently generated from P XY , without additional distributional assumptions on P XY itself (Vovk et al. 2005, Shafer & Vovk 2008, Vovk et al. 2009, Lei & Wasserman 2014, Lei et al. 2018).”
Classification with Valid and Adaptive Coverage (2020, methodology): “We build upon conformal inference [12, 24, 26] and take inspiration from [3, 5, 8–10, 13, 18] which made conformal prediction for regression problems adaptive to heteroscedasticity, thus bringing it closer to conditional coverage [20].”
The limits of distribution-free conditional predictive inference (2019, background): “As another example, Vovk [2012], Lei and Wasserman [2014] consider a version of conformal prediction that guarantees coverage within each one of a finite number of subgroups, i.”