Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

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Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction (2019, result): “In contrast to the previous approaches, conformal prediction (Vovk et al., 2003; Shafer and Vovk, 2008; Balasubramanian et al., 2014) is a framework for reliable prediction that is rooted in classical frequentist statistics, more specifically in hypothesis testing.”
Conformal inference of counterfactuals and individual treatment effects (2020, methodology): “Conformal inference was introduced by Vladimir Vovk and his collaborators (e.g. Balasubramanian et al., 2014; Gammerman & Vovk, 2007; Shafer & Vovk, 2008; Vovk, 2012; Vovk, 2013; Vovk, 2015; Vovk et al., 2009).”
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (2019, background): “Conformal prediction (Vovk et al., 2003; Shafer and Vovk, 2008; Balasubramanian et al., 2014) is a framework for reliable prediction that is rooted in classical frequentist statistics, more specifically in hypothesis testing.”
Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction (2020, methodology): “In this example, we provide for completeness a comparison with the “Mondrian” conformal method Balasubramanian et al. (2014), which conformalizes the scores for each label, and hence provides a class-wise coverage guarantee of the form in contrast to feature-conditional coverage goal, harder to…”