Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Estimating Accuracy from Unlabeled Data: A Bayesian Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add nonparametric coupling of domains via Dirichlet process (CBEE) to simple Bayesian error model (ablation) | BEE · all data samples and 10% of data (NELL and Brain) | MSE error rate / MAD label | No clear effect |
| Add hierarchical DP coupling of domains and classifiers (HCBEE) to the model (ablation) | CBEE/BEE · 10% of data samples (limited data) | MSE error rate / MAD label | Helped |
| Use the Gibbs-sampled Bayesian model with agreement-based inference instead of Dawid-Skene style methods (DW) | error rate estimation · NELL and Brain | MSE error rate | Mixed |
Closest papers by meaning
- Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach (NeurIPS 2017 · similarity 0.95)
- Evaluating multiple models using labeled and unlabeled data (NeurIPS 2025 · similarity 0.86)
- A Data-Driven Measure of Relative Uncertainty for Misclassification Detection (ICLR 2024 · similarity 0.85)
- Leveraging Uncertainty Estimates To Improve Classifier Performance (ICLR 2024 · similarity 0.85)
- How Does Unlabeled Data Provably Help Out-of-Distribution Detection? (ICLR 2024 · similarity 0.85)
- Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model (ICLR 2025 · similarity 0.85)
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