Research map / Learning theory and algorithms / Fairness
Robust Fairness Under Covariate Shift
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 the fair robust log-loss predictor under covariate shift instead of the baselines | Binary classification under covariate shift · COMPAS | difference of equalized opportunity (DEO) | Helped |
| Use the fair robust log-loss predictor under covariate shift instead of the baselines | Binary classification under covariate shift · German | difference of equalized opportunity (DEO) | Helped |
| Use the fair robust log-loss predictor under covariate shift instead of the baselines | Binary classification under covariate shift · Drug | difference of equalized opportunity (DEO) | Helped |
| Use the fair robust log-loss predictor under covariate shift instead of the baselines | Binary classification under covariate shift · Arrhythmia | prediction error | Hurt |
| Use the fair robust log-loss predictor under covariate shift instead of the baselines | Binary classification under covariate shift · 12 dataset-sampling settings | difference of equalized opportunity (DEO): 10 out of 12 | Helped |
| Estimate density ratios with 2D KDE rather than 1D KDE | Fair robust log-loss predictor and RBA · Drug | consistency of results | Helped |
| Increase covariate-shift intensity | Hardt post-processing and fairLR · COMPAS and German | difference of equalized opportunity (DEO) | Hurt |
Closest papers by meaning
- Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples (AAAI 2024 · similarity 0.92)
- Empirical Likelihood for Fair Classification (ICLR 2024 · similarity 0.88)
- Improving Fair Training under Correlation Shifts (ICML 2023 · similarity 0.88)
- Navigating Towards Fairness with Data Selection (AAAI 2025 · similarity 0.87)
- Mitigating Label Bias in Machine Learning: Fairness through Confident Learning (AAAI 2024 · similarity 0.87)
- A Sequentially Fair Mechanism for Multiple Sensitive Attributes (AAAI 2024 · 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.