Inductive Confidence Machines for Regression

By Harris Papadopoulos, Kostas Proedrou, Vladimir G Vovk, A. Gammerman. 2002

690 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (2018, methodology): “Thus, we use an inductive variant of conformal prediction [83], [84] that does not require retraining the underlying classifier for each query because it assumes the existence of a calibration set—holdout data that does not overlap with the training or test data.”
Inductive Conformal Prediction: Theory and Application to Neural Networks (2008, methodology): “The experimental results detailed in Section 5 and in (Papadopoulos et al., 2002a; Papadopoulos et al., 2002b; Papadopoulos et al., 2007) show that the accuracy of ICPs is comparable to that of traditional methods, while the confidence measures they produce are useful in practice.”
Conformal Prediction Under Covariate Shift (2019, methodology): “A fast alternative, known as split conformal prediction (Papadopoulos et al., 2002; Lei et al., 2015), resolves this issue by taking the score function S to be defined using absolute residuals with respect to a fixed regression function, 4”
Conformal Risk Control (2022, methodology): “Split conformal prediction (Vovk et al., 2005; Papadopoulos et al., 2002), which we will refer to simply as “conformal prediction”, has been useful in areas such as computer vision (Angelopoulos et al., 2021b) and natural language processing (Fisch et al., 2021) to provide such a guarantee.”
Adaptive Conformal Inference Under Distribution Shift (2021, methodology): “Additionally, we remark that the method outlined above is often referred to as split or inductive conformal inference [27, 34, 26].”
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification (2021, background): “• the 2002 proof that in online conformal prediction, the probability of error is independent across time-steps [76]; • the 2002 development, along with Harris Papadopoulos and Kostas Proedrou, of split-conformal predictors [2]; • Glenn Shafer coins the term “conformal predictor” on December 1, 2003”
Conformalized Quantile Regression (2019, methodology): “In contrast, the method of split, or inductive, conformal prediction [2, 8] avoids this problem, at the cost of splitting the data.”
A comparison of some conformal quantile regression methods (2019, methodology): “Throughout this paper, we follow the split-conformal approach to conformal inference (Papadopoulos et al., 2002; Papadopoulos, 2008; Lei et al., 2018) adopted in Romano et al. (2019) and Kivaranovic et al. (2019), since it is computationally feasible even with large data.”
With Malice Towards None: Assessing Uncertainty via Equalized Coverage (2019, background): “The difference between CQR [24] and split conformal prediction [19] is that the former calibrates an estimated quantile regression interval Ĉinit(X), while the latter builds a prediction interval around an estimate of the conditional mean Ŷ = μ̂(X).”
Distribution-Free, Risk-Controlling Prediction Sets (2021, background): “A particularly convenient form of conformal prediction, known as split conformal prediction [10, 11], uses data splitting to generate prediction sets in a computationally efficient way; see also [12, 13] for generalizations that re-use data for improved statistical efficiency.”
Distribution-Free Predictive Inference for Regression (2016, methodology): “In our view, a simple, computationally efficient, and yet powerful method that seems to have been overlooked is split conformal inference (see Papadopoulos et al. 2002; Lei, Rinaldo, andWasserman 2015, or Section 2.2).”
Uncertainty Sets for Image Classifiers using Conformal Prediction (2020, methodology): “The result is not new—a special case of this result leveraging sample-splitting ﬁrst appears in the regression setting in Papadopoulos et al. (2002), and the core idea of conformal prediction was introduced even earlier; see Vovk et al. (2005).”
The limits of distribution-free conditional predictive inference (2019, methodology): “The split conformal prediction algorithm, introduced in [10,14] (under the name ‘inductive conformal prediction’) and studied further by [9,13,7], is a well-known method that achieves distribution-free marginal coverage guarantees.”
Least Ambiguous Set-Valued Classifiers With Bounded Error Levels (2016, methodology): “We consider generic plug-in estimators in Section 4, together with a technique called inductive conformal inference (Papadopoulos et al., 2002; Vovk et al., 2005; Shafer & Vovk, 2008; Vovk, 2013) or split-conformal inference (Lei et al., 2014), which we use to adjust the classiﬁers to have ﬁnite…”
Localized Conformal Prediction: A Generalized Inference Framework for Conformal Prediction (2021, methodology): “Similar problems are encountered in CP with data-dependent scores, and sample splitting is often used to reduce the computation cost (Papadopoulos et al., 2002; Lei et al., 2015).”