Review for NeurIPS paper: Self-Adaptive Training: beyond Empirical Risk Minimization

The paper focuses on the problem of learning from corrupted data (e.g. This objective can be interpreted as a self-training whereby the model's predictions are progressively averaged with the true (and possibly noisy labels) coupled with a sample weighting scheme which improves training stability. The authors show that this approach can be used for a variety of vision tasks, including classification under label noise, adversarial training, and selective classification. The reviewers appreciated the conceptual simplicity of the method, the clarity of the presentation, and the promising empirical results. The discussion phase focused on the following two drawbacks: - Theoretical justification: While the theoretical analysis is hard for the general case, it might be doable in the corrupted linear regression case, which could offer some valuable insights.