Research map / Label-efficient and robust learning / Recognition and long-tail
Covariate Shift Adaptation on Learning from Positive and Unlabeled Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add importance weighting to PU risk (PUc vs PU) | PU classification · MNIST positive-only shift | misclassification rate: 33.66 -> 31.68 | Helped |
| add importance weighting to PU risk | PU classification · MNIST negative-only shift | misclassification rate: 32.89 -> 30.66 | Helped |
| drop importance weighting and use unlabeled test data (PUc-te) in positive-only shift (ablation) | PUc · MNIST positive-only shift | misclassification rate: 31.68 -> 47.12 | Hurt |
| drop importance weighting and use unlabeled test data (PUc-te) in negative-only shift (ablation) | PUc · MNIST negative-only shift | misclassification rate: 30.66 -> 19.89 | Helped |
| add importance weighting to PU | PU classification · banana | misclassification rate: 26.4 -> 24.6 | Helped |
| add importance weighting to PU | PU classification · comp-talk | misclassification rate: 20.3 -> 15.9 | Helped |
| add importance weighting to PU | PU classification · susy | misclassification rate: 40.1 -> 40.1 | No clear effect |
| add importance weighting to supervised PN (PNc vs PN) | PN classification · comp-rec | misclassification rate: 6.9 -> 6.4 | Helped |
Closest papers by meaning
- Learning from Positive and Unlabeled Data with Arbitrary Positive Shift (NeurIPS 2020 · similarity 0.90)
- Double-Weighting for Covariate Shift Adaptation (ICML 2023 · similarity 0.89)
- Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms (ICLR 2026 · similarity 0.88)
- Positive-unlabeled AUC Maximization under Covariate Shift (ICML 2025 · similarity 0.87)
- Learning from Positive and Unlabeled Data with a Selection Bias (ICLR 2019 · similarity 0.87)
- Learning from positive and unlabeled examples -Finite size sample bounds (NeurIPS 2025 · similarity 0.87)
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