Unsupervised Supervised Learning II: Margin-Based Classification without Labels

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Selective Classification Can Magnify Disparities Across Groups (2020, methodology): “As many margin distributions—in Figure 4 and in the broader literature (Balasubramanian et al., 2011; Lakshminarayanan et al., 2017)—resemble the distributions studied above (e.g., Gaussians and skewed Gaussians), we thus expect selective classification to improve average accuracy but worsen…”
Unsupervised Risk Estimation with only Structural Assumptions (2016, background): “Previous work required strong assumptions to circumvent this issue (e.g. that the class probabilities are distinct and known (Balasubramanian et al., 2011) or that the classifiers are correct on average (Jaffe et al., 2015)).”