Research map / Learning theory and algorithms / Prediction and decision losses
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use incomplete pairwise observations instead of full pairwise information | Rank aggregation methods · Synthetic PL data | Kendall distance | Hurt |
| Observe pairs from neighboring ranks | Rank aggregation methods · Synthetic PL data | Kendall distance | Hurt |
| Use BTL rather than the other compared methods | Rank aggregation methods · Synthetic PL data | Kendall distance | Helped |
| Use relative winning frequencies in BTL (BTL(R)) rather than absolute frequencies (BTL) | BTL · Synthetic PL data | Kendall distance | No clear effect |
| Use binary preferences in FAS (FAS(B)) rather than absolute or relative frequencies | FAS · Synthetic PL data | Kendall distance | Helped |
| Use FAS-based approaches as the number of items increases | FAS, FAS(R), and FAS(B) · Synthetic PL data | Kendall distance | Hurt |
| Replace the PL data-generating distribution with a Mallows distribution | BTL · Synthetic Mallows data | Kendall distance | Hurt |
| Replace PL-generated rankings with Mallows-generated rankings | Copeland · Synthetic Mallows data | Kendall distance | Helped |
| Use binary preferences in FAS (FAS(B)) rather than FAS or FAS(R) | FAS · Synthetic Mallows data | Kendall distance | Helped |
| Use random rank-pair coarsening rather than degenerate coarsening | Rank aggregation methods · Sushi data | Normalized Kendall distance | Helped |
| Apply rank-dependent coarsening to PL-generated rankings | Copeland · Pairwise preferences under rank-dependent coarsening | Consistency | Helped |
| Apply rank-dependent coarsening to PL-generated rankings | FAS, FAS(R), and FAS(B) · Pairwise preferences under rank-dependent coarsening | Consistency | Helped |
Closest papers by meaning
- Conditional independence and graphical models for rankings (NeurIPS 2026 · similarity 0.89)
- Learning to Rank from Incomplete Rankings (ICML 2026 · similarity 0.88)
- Learning Rich Rankings (NeurIPS 2020 · similarity 0.88)
- Optimal Sample Complexity of M-wise Data for Top-K Ranking (NeurIPS 2017 · similarity 0.88)
- Beyond Kemeny Medians: Consensus Ranking Distributions. Definition, Properties and Statistical Learning (NeurIPS 2026 · similarity 0.86)
- RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank (ICLR 2025 · similarity 0.86)
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