Research map / Label-efficient and robust learning / Test-time adaptation
Out-of-Distribution Generalization via Risk Extrapolation (REx)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use V-REx instead of ERM | Colored MNIST · Colored MNIST | test accuracy: 17.1 ± 0.6 -> 68.7 ± 0.9 | Helped |
| Use V-REx instead of IRM | Colored MNIST · Colored MNIST | test accuracy: 66.9 ± 2.5 -> 68.7 ± 0.9 | Helped |
| Use V-REx instead of MM-REx | Colored MNIST · Colored MNIST | test accuracy: 66.1 ± 1.5 -> 68.7 ± 0.9 | Mixed |
| Add V-REx risk-variance regularization | domain generalization models · Class, digit, and color imbalance CMNIST variants | accuracy | Helped |
| Add V-REx risk-variance regularization | domain generalization models · ColoredMNIST, VLCS, PACS, and OfficeHome | accuracy | No clear effect |
| Use REx in heteroskedastic tasks with differing intrinsic noise | domain generalization models · Toy structural equation models | performance | Hurt |
Closest papers by meaning
- Towards Understanding Extrapolation: a Causal Lens (NeurIPS 2024 · similarity 0.87)
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- Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization (NeurIPS 2024 · similarity 0.87)
- Not all distributional shifts are equal: Fine-grained robust conformal inference (ICML 2024 · similarity 0.87)
- Tolerant Algorithms for Learning with Arbitrary Covariate Shift (NeurIPS 2024 · similarity 0.86)
- Understanding Domain Generalization: A Noise Robustness Perspective (ICLR 2024 · similarity 0.86)
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