Research map / Learning theory and algorithms / Fairness
Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the weight assigned to an objective in τsf | τsf · NYC yellow cabs | Competitive ratio for the weighted objective | Helped |
| Use driver- and rider-specific edge utilities instead of setting both utilities to 1 (ablation) | τsf · NYC yellow cabs | Competitive ratio | Helped |
| Use τsf (α = 1) instead of Greedy-O | NYC yellow cabs · NYC yellow cabs, |U| = 49, |V| = 172 | Profit: 0.431; 0.595 | Helped |
| Use τsf (β = 1) instead of Greedy-D | NYC yellow cabs · NYC yellow cabs, |U| = 49, |V| = 172 | Driver Fairness: 0.609; 0.571 | Hurt |
Closest papers by meaning
- Fairness and Efficiency in Online Class Matching (NeurIPS 2024 · similarity 0.91)
- Beyond the Half Approximation: Fair and Efficient Online Class Matching (NeurIPS 2026 · similarity 0.90)
- Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions (NeurIPS 2024 · similarity 0.90)
- Online Fair Division Meets Reordering Buffers (NeurIPS 2026 · similarity 0.89)
- Fast & Fair: A Collaborative Platform for Fair Division Applications (AAAI 2024 · similarity 0.87)
- Learning-Augmented Online Bipartite Fractional Matching (NeurIPS 2025 · similarity 0.87)
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