Abstract:While a broad range of techniques have been proposed to tackle distribution shift, the simple baseline of training on an undersampled dataset often achieves close to state-of-the-art-accuracy across several popular benchmarks. This is rather surprising, since undersampling algorithms discard excess majority group data. To understand this phenomenon, we ask if learning is fundamentally constrained by a lack of minority group samples. We prove that this is indeed the case in the setting of nonparametric binary c… Show more
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