Research map / Learning theory and algorithms / Submodular and game algorithms
Envy-Free Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimize mixing weights eta after learning mixture components (ablation) | EF mixture of linear one-vs-all classifiers · synthetic data, varying number of individuals | average train and test loss | Helped |
| optimize mixing weights eta after learning mixture components (ablation) | EF mixture of linear one-vs-all classifiers · synthetic data, varying number of individuals | average envy | Helped |
| increase training set size | EF mixture of linear one-vs-all classifiers · synthetic data | generalization gap in loss and envy | Helped |
Closest papers by meaning
- Almost Envy-Free Allocations of Indivisible Goods or Chores with Entitlements (AAAI 2024 · similarity 0.86)
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- The Complexity of Fair Division of Indivisible Items with Externalities (AAAI 2024 · similarity 0.85)
- Individual Fairness In Strategic Classification (NeurIPS 2025 · similarity 0.85)
- Beyond the Half Approximation: Fair and Efficient Online Class Matching (NeurIPS 2026 · similarity 0.85)
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