Research map / Learning theory and algorithms / Combinatorial and search optimization
Improving Policy-Constrained Kidney Exchange via Pre-Screening
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Greedy instead of exhaustive optimal edge selection | edge selection, budget 3 · Random graphs N=50/75/100 | max %OPT gap: 2.8 | No clear effect |
| Greedy vs Random edge selection | Greedy · UNOS Simple dist. | P50 Delta^MAX: 0.64 vs 0.10 | Helped |
| Greedy vs IIAB variable edge budget heuristic | Greedy · UNOS Simple dist. | P50 Delta^MAX: 0.64 vs 0.45 | Helped |
| Greedy vs Fail-Aware matching without queries | Greedy · UNOS KPD dist. | P90 Delta^MAX: 3.44 vs 2.17 | Helped |
| MCTS vs Greedy | MCTS · UNOS Simple dist. | P90 Delta^MAX: 1.11 vs 1.00 | No clear effect |
| MCTS vs Greedy on random graphs, single-stage | MCTS · Random graphs N=100 | P50 Delta^MAX: 0.33 vs 0.39 | Hurt |
| Larger edge budget up to 100 for Greedy | Greedy · UNOS Simple dist. | median Delta^MAX | No clear effect |
| Multi-stage instead of single-stage selection | Greedy · UNOS Simple dist. | Delta^MAX | Helped |
| Increase bootstrap samples from N=10 to N=1000 (ablation) | objective estimation · bootstrap std dev | normalized std dev: 0.10 -> 0.01 | Helped |
| Query edges at all (pre-screening ~10 edges) | Greedy/MCTS · UNOS graphs | Delta^MAX | Helped |
Closest papers by meaning
- Fast Optimal Clearing of Capped-Chain Barter Exchanges (AAAI 2016 · similarity 0.94)
- Scalable Robust Kidney Exchange (AAAI 2019 · similarity 0.94)
- Small Representations of Big Kidney Exchange Graphs (AAAI 2017 · similarity 0.91)
- Adapting a Kidney Exchange Algorithm to Align With Human Values (AAAI 2018 · similarity 0.89)
- Stable Matchings in Practice: A Constraint Programming Approach (AAAI 2024 · similarity 0.80)
- Stable Matching with Ties: Approximation Ratios and Learning (NeurIPS 2025 · similarity 0.80)
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