Research map / Label-efficient and robust learning / Noisy labels
Estimating the class prior and posterior from noisy positives and unlabeled data
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use AlphaMax-N rather than Elkan-Noto for class-prior estimation | Noisy positive-unlabeled learning · UCI real-life datasets | mean absolute difference | Helped |
| Use AlphaMax-N rather than AlphaMax | Noisy positive-unlabeled learning · UCI real-life datasets | mean absolute difference | Mixed |
| Use the univariate class-prior-preserving transform before estimation | MSGMM · UCI real-life datasets | mean absolute difference | Helped |
| Use the univariate class-prior-preserving transform before estimation | AlphaMax-N · UCI real-life datasets | mean absolute difference | Helped |
| Apply the Gaussian-mixture parametric approach to Laplace-distributed data | MSGMM · Synthetic Laplace data | class-prior estimation accuracy | Hurt |
| Use the nonparametric AlphaMax-N estimator across synthetic distributions | AlphaMax-N · Synthetic Gaussian and Laplace data | class-prior estimation accuracy | Helped |
| Use the shared-component two-sample Gaussian mixture rather than a single-sample GMM | MSGMM · Synthetic Gaussian data | class-prior estimation accuracy | Helped |
Closest papers by meaning
- Class Prior Estimation with Biased Positives and Unlabeled Examples (AAAI 2020 · similarity 0.91)
- Learning from Positive and Unlabeled Data with Arbitrary Positive Shift (NeurIPS 2020 · similarity 0.90)
- Recovering True Classifier Performance in Positive-Unlabeled Learning (AAAI 2017 · similarity 0.90)
- Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting (AAAI 2020 · similarity 0.89)
- Learning from Positive and Unlabeled Data without Explicit Estimation of Class Prior (AAAI 2020 · similarity 0.88)
- Learning from positive and unlabeled examples -Finite size sample bounds (NeurIPS 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.