Research map / Learning theory and algorithms / Prediction and decision losses
Scalable Robust Kidney Exchange
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use edge-existence robustness against Γ edge failures instead of nominal matching | Kidney exchange matching · Real UNOS exchanges | standard deviation of ΔOPT: 1--2% lower | Helped |
| Use edge-existence robustness against Γ edge failures instead of nominal matching | Kidney exchange matching · Simulated exchanges | mean ΔOPT: 1% higher | Hurt |
| Use edge-existence robustness against Γ edge failures instead of nominal matching | Kidney exchange matching · Real and simulated exchanges | spread of ΔOPT outcomes: 1--2% lower | Helped |
| Use edge-weight robustness rather than maximizing expected edge weight | Kidney exchange matching · 64-vertex simulated exchanges and real UNOS exchanges | worst-case ΔOPT | Helped |
| Use edge-weight robustness rather than maximizing expected edge weight | Kidney exchange matching · 64-vertex simulated exchanges and real UNOS exchanges | median ΔOPT | Hurt |
| Increase edge-weight robustness by lowering the protection level ε | Robust edge-weight matching · 64-vertex simulated exchanges and real UNOS exchanges | protection against uncertain outcomes | Mixed |
| Use PI-TSP to enforce a minimum chain length | PI-TSP kidney exchange formulation · Real kidney exchange data | overall matching score | No clear effect |
Closest papers by meaning
- Improving Policy-Constrained Kidney Exchange via Pre-Screening (NeurIPS 2020 · similarity 0.94)
- Fast Optimal Clearing of Capped-Chain Barter Exchanges (AAAI 2016 · similarity 0.92)
- Small Representations of Big Kidney Exchange Graphs (AAAI 2017 · similarity 0.90)
- Fair and Welfare-Efficient Constrained Multi-Matchings under Uncertainty (NeurIPS 2024 · similarity 0.83)
- Improved Bandits in Many-to-One Matching Markets with Incentive Compatibility (AAAI 2024 · similarity 0.82)
- Stable Matching with Ties: Approximation Ratios and Learning (NeurIPS 2025 · similarity 0.81)
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