Transduction with Confidence and Credibility

By Craig John Saunders, A. Gammerman, Vladimir G Vovk. 1999

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Citations in the corpus

Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (2018, positive): “builds on an existing ML classifier to provide a probabilistically valid measure of confidence and credibility for predictions made by the underlying classifier [19], [20], [24].”
Deep Weighted Averaging Classifiers (2018, background): “Conformal methods refer to a broad set of ideas which aim to provide theoretical guarantees on error rates in classification or regression [26, 45, 47, 54].”
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification (2021, methodology): “The framework we now call conformal prediction was hatched by Vladimir Vovk, Alexander Gammerman, Craig Saunders, and Vladimir Vapnik in the years 1996-1999, first using e-values [74] and then with pvalues [5, 75].”
Conditional validity of inductive conformal predictors (2012, methodology): “1 Introduction This paper continues study of the method of conformal prediction, introduced in Vovk et al. (1999) and Saunders et al.”
Inductive Confidence Machines for Regression (2002, background): “: ECML, LNAI 2430, pp. 345–356, 2002. c© Springer-Verlag Berlin Heidelberg 2002 of TCM defined in Saunders et al. (1999), when coupled with a good underlying algorithm, can give confidence values as good as the universal values provided by the algorithmic theory of randomness (Nouretdinov et al.,…”
Inductive Conformal Prediction: Theory and Application to Neural Networks (2008, background): “Different variants of CPs are described in the papers (Saunders et al., 1999; Nouretdinov et al., 2001b; Proedrou et al., 2002; Papadopoulos et al., 2008).”