Title: Bias Amplification Improves Worst-Group Accuracy without Group Information. Abstract: Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels. Previous approaches based on worst-group loss minimization (\textit{e.g.} Group-DRO) are effective in improving worse-group accuracy but require expensive group annotations for all the training samples. In this paper, we focus on the more challenging and realistic setting where group annotations are only available on a small validation set or are not available at all. We propose \bam, a novel two-stage training algorithm: in the first stage, the model is trained using a \emph{bias amplification} scheme via introducing a learnable \emph{auxiliary variable} for each training sample together with the adoption of squared loss; in the second stage, we upweight the samples that the bias-amplified model misclassifies, and then continue training the same model on the reweighted dataset. Empirically, \bam leads to consistent improvement over its counterparts in worst-group accuracy, resulting in state-of-the-art performance in spurious correlation benchmarks in computer vision and natural language processing. Moreover, we find a simple stopping criterion that completely removes the need for group annotations, with little or no loss in worst-group accuracy. 