Research map / Learning theory and algorithms / Submodular and game algorithms
Online Elicitation of Necessarily Optimal Matchings
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use set-compare queries to simulate serial dictatorship | NPO elicitation · Set-compare | competitive ratio: 1 | Helped |
| Use staged top-rank elicitation with hybrid rank and house queries | NPO elicitation · Hybrid-query | competitive ratio: \mathcal{O}(n^{\frac{1}{3} + \varepsilon}) | Helped |
| Use Dulmage–Mendelsohn decomposition to decide which agents to query | NRM elicitation · Next-best | competitive ratio: \frac{3}{2} | Helped |
| Use hybrid rank and house queries | NRM elicitation · Hybrid-query | competitive ratio: 6 | Helped |
| Use set-compare queries for NRM elicitation | NRM elicitation · Set-compare | competitive ratio: \frac{3}{2} to \mathcal{O}(n) | Mixed |
| Allow hybrid-query partial preferences for NPO existence | NPO existence decision · Hybrid-query | decision complexity: \np-complete | Hurt |
| Allow set-compare partial preferences for NPO existence | NPO existence decision · Set-compare | decision complexity: \np-complete | Hurt |
| Determine existence of an NRM matching from hybrid-query partial preferences | NRM existence decision · Hybrid-query | decision complexity: polynomial time | Helped |
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