Interplay between concentration, complexity and geometry in learning theory with applications to high dimensional data analysis

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All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, background): “This common scenario has been called the “Rashomon” effect of statistics (Breiman et al., 2001; see also Lecué, 2011; Statnikov et al., 2013; Tulabandhula and Rudin, 2014; Nevo and Ritov, 2017; Letham et al., 2016).”
A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning (2019, methodology): “The use of approximating sets, as used extensively in this paper, is used throughout the literature on learning theory [28, 31, 38, 33].”