Research map / Label-efficient and robust learning / Recognition and long-tail
Partial-Label Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use least-loss candidate identification instead of the average-loss baselines | Partial-label regression · Seven UCI regression datasets; MLP and linear models | MSE | Helped |
| Use progressive soft weights rather than direct hard identification | IDent · Seven UCI regression datasets | MSE | Mixed |
| Increase the number of false candidate labels | Partial-label regression methods · Seven UCI regression datasets | MSE | Hurt |
| Train with more partial-label examples | IDent and PIDent · Concrete and Housing | test loss | Helped |
| Train with an MLP rather than a linear model | Partial-label regression methods · Seven UCI regression datasets | MSE | Helped |
| Use average loss over candidates rather than average candidate value | AVGL and AVGV baselines · Seven UCI regression datasets | MSE | Helped |
| Use MAE or Huber loss rather than MSE | AVGL and AVGV baselines · Seven UCI regression datasets | MSE | Mixed |
Closest papers by meaning
- Progressive Identification of True Labels for Partial-Label Learning (ICML 2020 · similarity 0.91)
- Leveraged Weighted Loss for Partial Label Learning (ICML 2021 · similarity 0.91)
- Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization (NeurIPS 2020 · similarity 0.90)
- Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning (NeurIPS 2025 · similarity 0.89)
- Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency (ICML 2024 · similarity 0.89)
- Controller-Guided Partial Label Consistency Regularization with Unlabeled Data (AAAI 2024 · similarity 0.88)
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