Research map / Learning theory and algorithms / Fairness
Equity Promotion in Online Resource Allocation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LP-based global sampling (SAMP) instead of GREEDY, UNIFORM, or RANKING | Online resource allocation · Minnesota COVID-19 vaccination data, over-demanded settings (ρ > 1) | competitive ratio | Helped |
| Use SAMP as minimum serving capacity b varies | Online resource allocation · Minnesota COVID-19 vaccination data, ρ = 2 | competitive ratio | Helped |
| Use SAMP to allocate scarce supply toward preset group targets | Online resource allocation · Minnesota COVID-19 vaccination data, ρ = 2 and ρ = 3 | ASR | Helped |
| Use SAMP when supply roughly meets demand | Online resource allocation · Minnesota COVID-19 vaccination data, ρ = 1 | ASR | No clear effect |
| Use SAMP in over-demanded settings | Online resource allocation · Minnesota COVID-19 vaccination data, ρ = 2 and ρ = 3 | RSR | Helped |
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- Evaluating Index-based Treatment Allocation in Underresourced Communities (AAAI 2025 · similarity 0.82)
- Online Allocation with Unknown Shared Supply (NeurIPS 2026 · similarity 0.80)
- Time Fairness in Online Knapsack Problems (ICLR 2024 · similarity 0.80)
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